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  • 1.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
    Hernandez-Diaz, Kevin
    Halmstad University, School of Information Technology.
    Buades, Jose M.
    University of Balearic Islands, Palma, Spain.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Bigun, Josef
    Halmstad University, School of Information Technology.
    An Explainable Model-Agnostic Algorithm for CNN-Based Biometrics Verification2023In: 2023 IEEE International Workshop on Information Forensics and Security (WIFS), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper (Refereed)
    Abstract [en]

    This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting. LIME was initially proposed for networks with the same output classes used for training, and it employs the softmax probability to determine which regions of the image contribute the most to classification. However, in a verification setting, the classes to be recognized have not been seen during training. In addition, instead of using the softmax output, face descriptors are usually obtained from a layer before the classification layer. The model is adapted to achieve explainability via cosine similarity between feature vectors of perturbated versions of the input image. The method is showcased for face biometrics with two CNN models based on MobileNetv2 and ResNet50. © 2023 IEEE.

  • 2.
    Aslam, Muhammad Shamrooz
    et al.
    School of Automation, Guangxi University of Science and Technology, Liuzhou, P.R. China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Pandey, Hari Mohan
    Data Science and Artificial Intelligence, Department of Information and Computing, Bournemouth University, Bournemouth, United Kingdom.
    Band, Shahab S.
    Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan, Republic of China.
    Observer–Based Control for a New Stochastic Maximum Power Point tracking for Photovoltaic Systems With Networked Control System2023In: IEEE transactions on fuzzy systems, ISSN 1063-6706, E-ISSN 1941-0034, Vol. 31, no 6, p. 1870-1884Article in journal (Refereed)
    Abstract [en]

    This study discusses the new stochastic maximum power point tracking (MPPT) control approach towards the photovoltaic cells (PCs). PC generator is isolated from the grid, resulting in a direct current (DC) microgrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposed a Networked Control Systems (NCSs) beneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator's stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into the Takagi–Sugeno (T–S) fuzzy model is another option. Takagi–Sugeno (T–S) fuzzy model is presented in a unified framework, for which 1) the fuzzy observer–based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer–based controller can be created using this same premises be the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulating in the PC generator model for the realtime climate data obtained in China demonstrates the importance of our method. In addition, by using a new Lyapunov–Krasovskii functional (LKF) for combining to the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the T-P transformation, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators. © 2022 IEEE.

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  • 3.
    Aslam, Muhammad Shamrooz
    et al.
    China University of Mining And Technology, Xuzhou, China; Guangxi University of Science and Technology, Liuzhou, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Pandey, Hari Mohan
    Bournemouth University, Bournemouth, United Kingdom.
    Band, Shahab S.
    National Yunlin University of Science and Technology, Douliou, Taiwan.
    Robust stability analysis for class of Takagi-Sugeno (T-S) fuzzy with stochastic process for sustainable hypersonic vehicles2023In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 641, article id 119044Article in journal (Refereed)
    Abstract [en]

    Recently, the rapid development of Unmanned Aerial Vehicles (UAVs) enables ecological conservation, such as low-carbon and “green” transport, which helps environmental sustainability. In order to address control issues in a given region, UAV charging infrastructure is urgently needed. To better achieve this task, an investigation into the T–S fuzzy modeling for Sustainable Hypersonic Vehicles (SHVs) with Markovian jump parameters and H∞ attitude control in three channels was conducted. Initially, the reentry dynamics were transformed into a control–oriented affine nonlinear model. Then, the original T–S local modeling method for SHV was projected by primarily referring to Taylor's expansion and fuzzy linearization methodologies. After the estimation of precision and controller complexity was assumed, the fuzzy model for jump nonlinear systems mainly consisted of two levels: a crisp level and a fuzzy level. The former illustrates the jumps, and the latter a fuzzy level that represents the nonlinearities of the system. Then, a systematic method built in a new coupled Lyapunov function for a stochastic fuzzy controller was used to guarantee the closed–loop system for H∞ gain in the presence of a predefined performance index. Ultimately, numerical simulations were conducted to show how the suggested controller can be successfully applied and functioned in controlling the original attitude dynamics. © 2023 Elsevier Inc.

  • 4.
    Chavhan, Suresh
    et al.
    Indian Institute of Information Technology, Raichur, Karnataka, India.
    Kumar, Sachin
    South Ural State University, Chelyabinsk, Russian Federation.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Liang, Xueqin
    Xidian University, Xi'an, China.
    Lee, Ik Hyun
    Korea Polytechnic University, Siheung, South Korea.
    Muhammad, Khan
    Sungkyunkwan University, Seoul, South Korea.
    Edge-enabled Blockchain-based V2X Scheme for Secure Communication within the Smart City Development2023In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, no 24, p. 21282-21293Article in journal (Refereed)
    Abstract [en]

    As the high mobility nature of the vehicles results in frequent leaving and joining the transportation network, real-time data must be collected and shared in a timely manner. In such a transportation network, malicious vehicles can disrupt services and create serious issues, such as deadlocks and accidents. The blockchain is a technology that ensures traceability, consistency, and security in transportation networks. In this study, we integrated edge computing and blockchain technology to improve the optimal utilization of resources, especially in terms of computing, communication, security, and storage. We propose a novel, edge-integrated, blockchain-based vehicle platoon security scheme. For the vehicle platoon, we developed the security architecture, implemented smart contracts for practical network scenarios in NS-3, and integrated them with the SUMO TraCI API. We exhaustively simulated all the scenarios and analyzed the communication performance metrics, such as throughput, delay, and jitter, and the security performance metrics, such as mean squared error, communication, and computational cost. The performance results demonstrate that the developed scheme can solve security-related issues more effectively and efficiently in smart cities. © IEEE

  • 5.
    Deng, Dan
    et al.
    Guangzhou Panyu Polytechnic, Guangzhou, China.
    Li, Junxia
    Henan Polytechnic University, Jiaozuo, China.
    Jhaveri, Rutvij H.
    Pandit Deendayal Energy University, Gandhinagar, India.
    Tiwari, Prayag
    Halmstad University, School of Information Technology. Aalto University, Aalto, Finland.
    Ijaz, Muhammad Fazal
    University of Melbourne, Parkville, Australia.
    Ou, Jiangtao
    AI Sensing Technology, Foshan, China.
    Fan, Chengyuan
    AI Sensing Technology, Foshan, China.
    Reinforcement Learning Based Optimization on Energy Efficiency in UAV Networks for IoT2022In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, no 3, p. 2767-2775Article in journal (Refereed)
    Abstract [en]

    The combination of Non-Orthogonal Multiplex Access and Unmanned Aerial Vehicles (UAV) can improve theenergy efficiency (EE) for Internet-of-Things (IoT). On the condition of interference constraint and minimum achievable rate of the secondary users, we propose an iterative optimization algorithm on EE. Firstly, with given UAV trajectory, the Dinkelbach method based fractional programming is adopted to obtain theoptimal transmission power factors. By using the previous power allocation scheme, the successive convex optimization algorithmis adopted in the second stage to update the system parameters. Finally, reinforcement learning based optimization is introducedto obtain the best UAV trajectory. © 2022 IEEE

  • 6.
    Deshmukh, Shradha
    et al.
    Symbiosis Institute Of Technology, Pune, India.
    Behera, Bikash K.
    Bikash’s Quantum, Mohanpur, India.
    Mulay, Preeti
    Symbiosis Institute Of Technology, Pune, India.
    Ahmed, Emad A.
    Faculty Of Computers And Information, Qena, Egypt.
    Al-Kuwari, Saif
    College Of Science And Engineering, Doha, Qatar.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Farouk, Ahmed
    Faculty Of Computers And Information, Qena, Egypt.
    Explainable quantum clustering method to model medical data2023In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 267, p. 1-13, article id 110413Article in journal (Refereed)
    Abstract [en]

    Medical experts are often skeptical of data-driven models due to the lack of their explainability. Several experimental studies commence with wide-ranging unsupervised learning and precisely with clustering to obtain existing patterns without prior knowledge of newly acquired data. Explainable Artificial Intelligence (XAI) increases the trust between virtual assistance by Machine Learning models and medical experts. Awareness about how data is analyzed and what factors are considered during the decision-making process can be confidently answered with the help of XAI. In this paper, we introduce an improved hybrid classical-quantum clustering (improved qk-means algorithm) approach with the additional explainable method. The proposed model uses learning strategies such as the Local Interpretable Model-agnostic Explanations (LIME) method and improved quantum k-means (qk-means) algorithm to diagnose abnormal activities based on breast cancer images and Knee Magnetic Resonance Imaging (MRI) datasets to generate an explanation of the predictions. Compared with existing algorithms, the clustering accuracy of the generated clusters increases trust in the model-generated explanations. In practice, the experiment uses 600 breast cancer (BC) patient records with seven features and 510 knee MRI records with five features. The result shows that the improved hybrid approach outperforms the classical one in the BC and Knee MRI datasets. © 2023 Elsevier B.V.

  • 7.
    Ding, Yijie
    et al.
    University of Electronic Science and Technology of China, Quzhou, China.
    Guo, Fei
    Central South University, Changsha, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Zou, Quan
    University of Electronic Science and Technology of China, Chengdu, China.
    Identification of Drug-Side Effect Association Via Multi-View Semi-Supervised Sparse Model2023In: IEEE Transactions on Artificial Intelligence, E-ISSN 2691-4581Article in journal (Refereed)
    Abstract [en]

    The association between drugs and side effects encompasses information about approved medications and their documented adverse drug reactions. Traditional experimental approaches for studying this association tend to be time-consuming and expensive. To represent all drug-side effect associations, a bipartite network framework is employed. Consequently, numerous computational methods have been devised to tackle this problem, focusing on predicting new potential associations. However, a significant gap lies in the neglect of the Multi-View Learning (MVL) algorithm, which has the ability to integrate diverse information sources and enhance prediction accuracy. In our study, we have developed a novel predictor named Multi-View Semi-Supervised Sparse Model (Mv3SM) to address the drug side effect prediction problem. Our approach aims to explore the distinctive characteristics of various view features obtained from fully paired multi-view data and mitigate the influence of noisy data. To test the performance of Mv3SM and other computational approaches, we conducted experiments using three benchmark datasets. The obtained results clearly demonstrate that our proposed method achieves superior predictive performance compared to alternative approaches. © IEEE

  • 8.
    Ding, Yijie
    et al.
    University of Electronic Science and Technology of China, Quzhou, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Guo, Fei
    Central South University, Changsha, China.
    Zou, Quan
    University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China, Chengdu, China.
    Multi-correntropy fusion based fuzzy system for predicting DNA N4-methylcytosine sites2023In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 100, p. 1-10, article id 101911Article in journal (Refereed)
    Abstract [en]

    The identification of DNA N4-methylcytosine (4mC) sites is an important field of bioinformatics. Statistical learning methods and deep learning have been applied in this direction. The previous methods focused on feature representation and feature selection, and did not take into account the deviation of noise samples for recognition. Moreover, these models were not established from the perspective of prediction error distribution. To solve the problem of complex error distribution, we propose a maximum multi-correntropy criterion based kernelized higher-order fuzzy inference system (MMC-KHFIS), which is constructed with multi-correntropy fusion. There are 6 4mC and 8 UCI data sets are employed to evaluate our model. The MMC-KHFIS achieves better performance in the experiment. © 2023

  • 9.
    Ding, Yijie
    et al.
    University of Electronic Science and Technology of China, Quzhou, PR China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Guo, Fei
    School of Computer Science and Engineering, Central South University, Changsha, PR China.
    Zou, Quan
    University of Electronic Science and Technology of China, Chengdu, PR China.
    Shared subspace-based radial basis function neural network for identifying ncRNAs subcellular localization2022In: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 156, p. 170-178Article in journal (Refereed)
    Abstract [en]

    Non-coding RNAs (ncRNAs) play an important role in revealing the mechanism of human disease for anti-tumor and anti-virus substances. Detecting subcellular locations of ncRNAs is a necessary way to study ncRNA. Traditional biochemical methods are time-consuming and labor-intensive, and computational-based methods can help detect the location of ncRNAs on a large scale. However, many models did not consider the correlation information among multiple subcellular localizations of ncRNAs. This study proposes a radial basis function neural network based on shared subspace learning (RBFNN-SSL), which extract shared structures in multi-labels. To evaluate performance, our classifier is tested on three ncRNA datasets. Our model achieves better performance in experimental results. © 2022 The Author(s)

  • 10.
    Guo, Xiaoyi
    et al.
    University Of Electronic Science And Technology Of China, Chengdu, China.
    Qian, Yuqing
    Suzhou University Of Science And Technology, Suzhou, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Zou, Quan
    University Of Electronic Science And Technology Of China, Chengdu, China.
    Ding, Yijie
    University Of Electronic Science And Technology Of China, Chengdu, China.
    Kernel Risk Sensitive Loss-based Echo State Networks for Predicting Therapeutic Peptides with Sparse Learning2022In: Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 / [ed] Adjeroh D.; Long Q.; Shi X.; Guo F.; Hu X.; Aluru S.; Narasimhan G.; Wang J.; Kang M.; Mondal A.M.; Liu J., Piscataway: IEEE, 2022, p. 6-11Conference paper (Refereed)
    Abstract [en]

    The detection of therapeutic peptides is usually a biochemical experimental method, which is time-consuming and labor-intensive. Lots of computational biology methods had been proposed to solve the problem of therapeutic peptide prediction. However, the existing methods did not consider the processing of noisy samples. We propose a kernel risk-sensitive mean p-power error-based echo state network with sparse learning (KRP-ESN-SL). An efficient iterative optimization algorithm is used to train the model. The KRP-ESN-SL has better performance than other methods. © 2022 IEEE.

  • 11.
    Guo, Xiaoyi
    et al.
    University of Electronic Science and Technology of China, Chengdu, PR China; University of Electronic Science and Technology of China, Quzhou, PR China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Zhang, Ying
    Beidahuang Industry Group General Hospital, Harbin, PR China.
    Han, Shuguang
    University of Electronic Science and Technology of China, Quzhou, PR China.
    Wang, Yansu
    University of Electronic Science and Technology of China, Chengdu, PR Chin.
    Ding, Yijie
    University of Electronic Science and Technology of China, Quzhou, PR China.
    Random Fourier features-based sparse representation classifier for identifying DNA-binding proteins2022In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 151, article id 106268Article in journal (Refereed)
    Abstract [en]

    DNA-binding proteins (DBPs) protect DNA from nuclease hydrolysis, inhibit the action of RNA polymerase,prevents replication and transcription from occurring simultaneously on a piece of DNA. Most of theconventional methods for detecting DBPs are biochemical methods, but the time cost is high. In recent years,a variety of machine learning-based methods that have been used on a large scale for large-scale screeningof DBPs. To improve the prediction performance of DBPs, we propose a random Fourier features-based sparserepresentation classifier (RFF-SRC), which randomly map the features into a high-dimensional space to solvenonlinear classification problems. And 𝐿2,1-matrix norm is introduced to get sparse solution of model. Toevaluate performance, our model is tested on several benchmark data sets of DBPs and 8 UCI data sets. RFF-SRCachieves better performance in experimental results. © 2022 Elsevier Ltd. 

  • 12.
    Guo, Xiaoyi
    et al.
    University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China, Quzhou, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Zou, Quan
    University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China, Quzhou, China.
    Ding, Yijie
    University of Electronic Science and Technology of China, Quzhou, China.
    Subspace projection-based weighted echo state networks for predicting therapeutic peptides2023In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 263, article id 110307Article in journal (Refereed)
    Abstract [en]

    Detection of therapeutic peptide is a major research direction in the current biopharmaceutical field. However, traditional biochemical experimental detection methods take a lot of time. As supplementary methods for biochemical experiments, the computational methods can improve the efficiency of therapeutic peptide detection. Currently, most machine learning-based therapeutic peptide identification algorithms do not consider the processing of noisy samples. We propose a therapeutic peptide classifier, called weighted echo state networks based on subspace projection (WESN-SP), which reduces the bias caused by high-dimensional noisy features and noisy samples. WESN-SP is trained by sparse Bayesian learning algorithm (SBL) and introduces a weight coefficient for each sample by kernel dependence maximization-based subspace projection. The experimental results show that WESN-SP has better performance than other existing methods. © 2023 The Author(s). Published by Elsevier B.V.

  • 13.
    Han, Ridong
    et al.
    Jilin University, Changchun, China.
    Peng, Tao
    Jilin University, Changchun, China.
    Wang, Benyou
    The Chinese University of Hong Kong, Shenzhen, China.
    Liu, Lu
    Jilin University, Changchun, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Wan, Xiang
    The Chinese University of Hong Kong, Shenzhen, China.
    Document-level Relation Extraction with Relation Correlations2024In: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 171, p. 14-24Article in journal (Refereed)
    Abstract [en]

    Document-level relation extraction faces two often overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into the document-level relation extraction task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of long-tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular datasets (i.e., DocRED and DWIE) are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. The data and code are released at https://github.com/RidongHan/DocRE-Co-Occur. © 2023 Elsevier Ltd

  • 14.
    Jaiswal, Amit Kumar
    et al.
    University Of Surrey, Guildford, United Kingdom.
    Liu, Haiming
    University Of Southampton, Southampton, United Kingdom.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Towards Subject Agnostic Affective Emotion Recognition2023In: CEUR Workshop Proceedings: Proceedings of the 2nd International Workshop on Multimodal Human Understanding for the Web and Social Media / [ed] Gullal S. Cheema; Sherzod Hakimov; Marc A. Kastner; Noa Garcia, Aachen: Rheinisch-Westfaelische Technische Hochschule Aachen , 2023, Vol. 3566, p. 47-61Conference paper (Refereed)
    Abstract [en]

    This paper focuses on affective emotion recognition, aiming to perform in the subject-agnostic paradigm based on EEG signals. However, EEG signals manifest subject instability in subject-agnostic affective Brain-computer interfaces (aBCIs), which led to the problem of distributional shift. Furthermore, this problem is alleviated by approaches such as domain generalisation and domain adaptation. Typically, methods based on domain adaptation confer comparatively better results than the domain generalisation methods but demand more computational resources given new subjects. We propose a novel framework, meta-learning based augmented domain adaptation for subject-agnostic aBCIs. Our domain adaptation approach is augmented through meta-learning, which consists of a recurrent neural network, a classifier, and a distributional shift controller based on a sum-decomposable function. Also, we present that a neural network explicating a sum-decomposable function can effectively estimate the divergence between varied domains. The network setting for augmented domain adaptation follows meta-learning and adversarial learning, where the controller promptly adapts to new domains employing the target data via a few self-adaptation steps in the test phase. Our proposed approach is shown to be effective in experiments on a public aBICs dataset and achieves similar performance to state-of-the-art domain adaptation methods while avoiding the use of additional computational resources. © 2023 Copyright for this paper by its authors.

  • 15.
    Jannu, Srikanth
    et al.
    Vagdhevi Engineering College, Dhanbad, India.
    Dara, Suresh
    B V Raju Institute of Technology, Narsapur, India.
    Thuppari, Chaitanya
    Vagdhevi Engineering College, Hyderabad, India.
    Vidyarthi, Ankit
    Jaypee Institute of Information Technology, Noida, India.
    Ghosh, Debjani
    Bennett University, Greater Noida, India.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Muhammad, Ghulam
    College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
    Energy Efficient Quantum-Informed Ant Colony Optimization Algorithms for Industrial Internet of Things2022In: IEEE Transactions on Artificial Intelligence, E-ISSN 2691-4581, p. 1-10Article in journal (Refereed)
    Abstract [en]

    One of the most prominent research areas in information technology is the Internet of things (IoT) as its applications are widely used such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-the-art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics such as residual energy of the network, network lifetime, and the number of live IoT nodes. © 2022 IEEE

  • 16.
    Karami, Saeed
    et al.
    Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran.
    Saberi-Movahed, Farid
    Graduate University of Advanced Technology, Kerman, Iran.
    Tiwari, Prayag
    Halmstad University, School of Information Technology. Aalto University, Espoo, Finland.
    Marttinen, Pekka
    Aalto University, Espoo, Finland.
    Vahdati, Sahar
    Nature-Inspired Machine Intelligence-InfAI, Dresden, Germany.
    Unsupervised feature selection based on variance–covariance subspace distance2023In: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 166, p. 188-203Article in journal (Refereed)
    Abstract [en]

    Subspace distance is an invaluable tool exploited in a wide range of feature selection methods. The power of subspace distance is that it can identify a representative subspace, including a group of features that can efficiently approximate the space of original features. On the other hand, employing intrinsic statistical information of data can play a significant role in a feature selection process. Nevertheless, most of the existing feature selection methods founded on the subspace distance are limited in properly fulfilling this objective. To pursue this void, we propose a framework that takes a subspace distance into account which is called “Variance–Covariance subspace distance”. The approach gains advantages from the correlation of information included in the features of data, thus determines all the feature subsets whose corresponding Variance–Covariance matrix has the minimum norm property. Consequently, a novel, yet efficient unsupervised feature selection framework is introduced based on the Variance–Covariance distance to handle both the dimensionality reduction and subspace learning tasks. The proposed framework has the ability to exclude those features that have the least variance from the original feature set. Moreover, an efficient update algorithm is provided along with its associated convergence analysis to solve the optimization side of the proposed approach. An extensive number of experiments on nine benchmark datasets are also conducted to assess the performance of our method from which the results demonstrate its superiority over a variety of state-of-the-art unsupervised feature selection methods. The source code is available at https://github.com/SaeedKarami/VCSDFS. © 2023 The Author(s)

  • 17.
    Khan, Hameed Ullah
    et al.
    Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
    Raza, Basit
    Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
    Shah, Munawar Hussain
    Pathology Department, Nishtar Medical University, Multan, Pakistan.
    Usama, Syed Muhammad
    Post Graduate Resident Surgeon at College of Physicians and Surgeons Pakistan (CPSP), Karachi, Pakistan.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Band, Shahab S.
    Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Yunlin, Douliou, Taiwan.
    SMDetector: Small mitotic detector in histopathology images using faster R-CNN with dilated convolutions in backbone model2023In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 81, article id 104414Article in journal (Refereed)
    Abstract [en]

    Breast cancer is one of the most common cancer types among women, and it is a deadly disease caused by the uncontrolled proliferation of cells. Pathologists face a challenging issue of mitotic cell identification and counting during manual detection and identification of cancer. Artificial intelligence can help the medical professional with early, quick, and accurate diagnosis of breast cancer. Consequently, the survival rate will be improved and mortality rate will be decreased. Different deep learning techniques are used in computational pathology for cancer diagnosis. In this study, the SMDetector is proposed which is a deep learning model for detecting small objects such as mitotic and non-mitotic nuclei. This model employs dilated layers in the backbone to prevent small objects from disappearing in the deep layers. The purpose of the dilated layers in this model is to reduce the size gap between the image and the objects it contains. Region proposal network is optimized to accurately identify small objects. The proposed model yielded overall average precision (AP) of 50.31% and average recall (AR) of 55.90% that outperforms the existing standard object detection models on ICPR 2014 (Mitos-Atypia-14) dataset. To best of our knowledge the proposed model is state-of-the-art model for precision and recall of mitotic object detection on ICPR 2014 (Mitos-Atypia-14) dataset. The proposed model has achieved average precision for mitotic nuclei 68.49%, average recall for mitotic nuclei 59.86% and f-measure 63.88%. © 2022 The Authors

  • 18.
    Kumar, Vivek
    et al.
    University of Cagliari, Cagliari, Italy.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Singh, Sushmita
    Liverpool John Moores University, Liverpool, United Kingdom.
    VISU at WASSA 2023 Shared Task: Detecting Emotions in Reaction to News Stories Using Transformers and Stacked Embeddings2023In: Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis / [ed] Jeremy Barnes; Orphée De Clercq; Roman Klinger, Stroudsburg, PA: Association for Computational Linguistics, 2023, p. 581-586Conference paper (Refereed)
    Abstract [en]

    Our system, VISU, participated in the WASSA 2023 Shared Task (3) of Emotion Classification from essays written in reaction to news articles. Emotion detection from complex dialogues is challenging and often requires context/domain understanding. Therefore in this research, we have focused on developing deep learning (DL) models using the combination of word embedding representations with tailored prepossessing strategies to capture the nuances of emotions expressed. Our experiments used static and contextual embeddings (individual and stacked) with Bidirectional Long short-term memory (BiLSTM) and Transformer based models. We occupied rank tenth in the emotion detection task by scoring a Macro F1-Score of 0.2717, validating the efficacy of our implemented approaches for small and imbalanced datasets with mixed categories of target emotions. © 2023 Association for Computational Linguistics.

  • 19.
    Lakhan, Abdullah
    et al.
    Dawood University of Engineering and Technology, Karachi, Pakistan.
    Mohammed, Mazin Abed
    University of Anbar, Anbar, Iraq.
    Nedoma, Jan
    VSB-Technical University of Ostrava, Ostrava, Czech Republic.
    Martinek, Radek
    VSB-Technical University of Ostrava, Ostrava, Czech Republic.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Kumar, Neeraj
    Thapar Institute of Engineering and Technology, Punjab, India.
    Blockchain-Enabled Cybersecurity Efficient IIOHT Cyber-Physical System for Medical Applications2022In: IEEE Transactions on Network Science and Engineering, E-ISSN 2327-4697, Vol. 10, no 5, p. 2466-2479Article in journal (Refereed)
    Abstract [en]

    Cybersecurity issues such as malware, denial of service attacks, and unauthorized access to data for different applications are growing daily. The Industrial Internet of Healthcare Things (IIoHT) has recently been a new healthcare mechanism where many healthcare applications can run on hospital servers for remote medical services. For instance, cloud medical applications offer different services remotely from home. However, the existing IIoHT mechanisms can not handle critical cybersecurity issues and incur many medical care application processing and data security costs. The processing costs associated with security and deadline are the main findings of this proposed work. This work devises a cost-efficient blockchain task scheduling (CBTS) cyber-physical system (CPS) with different heuristics. All tasks are sorted, scheduled, and stored in a secure form in the IIoHT network. The performance evaluation proves that the CBTS framework outperforms the simulation results for the IIoHT application and reduces the cost by 50% of security execution and 33% of cybersecurity data validation blockchain costs compared to existing scheduling and blockchain schemes. © Copyright 2022 IEEE

  • 20.
    Lakhan, Abdullah
    et al.
    Dawood University Of Engineering And Technology, Karachi, Pakistan; Vsb-technical University Of Ostrava, Ostrava, Czech Republic; Vsb-technical University Of Ostrava, Ostrava, Czech Republic.
    Mohammed, Mazin Abed
    University Of Anbar, Ramadi, Iraq; Vsb-technical University Of Ostrava, Ostrava, Czech Republic; Vsb-technical University Of Ostrava, Ostrava, Czech Republic.
    Nedoma, Jan
    Vsb-technical University Of Ostrava, Ostrava, Czech Republic.
    Martinek, Radek
    Vsb-technical University Of Ostrava, Ostrava, Czech Republic.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Kumar, Neeraj
    Thapar University, Patiala, India; University Of Petroleum And Energy Studies, Dehradun, India; Asia University, Taichung, Taiwan.
    DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system2023In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, p. 1-15, article id 4124Article in journal (Refereed)
    Abstract [en]

    Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network. © 2023, The Author(s).

  • 21.
    Li, Dayi
    et al.
    Dalian Maritime University, Dalian, China.
    Zhou, Jingchun
    Dalian Maritime University, Dalian, China.
    Wang, Shiyin
    Dalian Maritime University, Dalian, China.
    Zhang, Dehuan
    Dalian Maritime University, Dalian, China.
    Zhang, Weishi
    Dalian Maritime University, Dalian, China.
    Alwadai, Raghad
    General Directorate Of Health Affairs, Madinah, Saudi Arabia.
    Alenezi, Fayadh
    Jouf University, Sakakah, Saudi Arabia.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Shi, Taian
    Dalian Maritime University, Dalian, China.
    Adaptive weighted multiscale retinex for underwater image enhancement2023In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 123, article id 106457Article in journal (Refereed)
    Abstract [en]

    Vision-dependent underwater vehicles are widely used in seabed resource exploration. The visual perception system of underwater vehicles relies heavily on high-quality images for its regular operation. However, underwater images taken underwater often have color distortion, blurriness, and poor contrast. To address these degradation issues, we develop an adaptive weighted multiscale retinex (AWMR) method for enhancing underwater images. To utilize the local detail features, we first divide the image into multiple sub-blocks and calculate the detail sparsity index for each one. Then, we combine the global detail sparsity index with the local detail sparsity indices to determine the optimal scale parameter and corresponding weights for each sub-block. We apply retinex processing to each sub-block using these parameters and then subject the processed sub-blocks to detail enhancement, color correction, and saturation correction. Finally, we use a gradient domain fusion method based on structure tensors to fuse the corrected and enhanced sub-blocks and obtain the final output image. Our approach improves underwater images through comparisons with current state-of-the-art (SOTA) techniques on several open-source datasets, both quality, and performance. © 2023 Elsevier Ltd

  • 22.
    Li, Jianquan
    et al.
    The Chinese University of Hong Kong, Shenzhen, China.
    Wu, Xiangbo
    The Chinese University of Hong Kong, Shenzhen, China.
    Liu, Xiaokang
    The Chinese University of Hong Kong, Shenzhen, China.
    Xie, Qianqian
    University of Manchester, Manchester, United Kingdom.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Wang, Benyou
    The Chinese University of Hong Kong, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen, China.
    Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk2023In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) / [ed] Anna Rogers; Jordan Boyd-Graber; Naoaki Okazaki, Stroudsburg, PA: Association for Computational Linguistics, 2023, Vol. 1, p. 7581-7596Conference paper (Refereed)
    Abstract [en]

    Language is the principal tool for human communication, in which humor is one of the most attractive parts. Producing natural language like humans using computers, a.k.a, Natural Language Generation (NLG), has been widely used for dialogue systems, chatbots, text summarization, as well as AI-Generated Content (AIGC), e.g., idea generation, and scriptwriting. However, the humor aspect of natural language is relatively under-investigated, especially in the age of pre-trained language models. In this work, we aim to preliminarily test whether NLG can generate humor as humans do. We build the largest dataset consisting of numerous Chinese Comical Crosstalk scripts (called C3 in short), which is for a popular Chinese performing art called 'Xiangsheng' or '相声' since 1800s. We benchmark various generation approaches including training-from-scratch Seq2seq, fine-tuned middle-scale PLMs, and large-scale PLMs with and without fine-tuning. Moreover, we also conduct a human assessment, showing that 1) large-scale pretraining largely improves crosstalk generation quality; and 2) even the scripts generated from the best PLM is far from what we expect. We conclude humor generation could be largely improved using large-scale PLMs, but it is still in its infancy. The data and benchmarking code are publicly available in https://github.com/anonNo2/crosstalk-generation. © 2023 Association for Computational Linguistics.

  • 23.
    Li, Ping
    et al.
    College of Intelligence and Computing, Tianjin University, Tianjin, PR China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Xu, Junhai
    College of Intelligence and Computing, Tianjin University, Tianjin, PR China.
    Qian, Yuqing
    College of Intelligence and Computing, Tianjin University, Tianjin, PR China.
    Ai, Chengwei
    College of Intelligence and Computing, Tianjin University, Tianjin, PR China.
    Ding, Yijie
    Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, PR China.
    Guo, Fei
    School of Computer Science and Engineering, Central South University, Changsha, PR China.
    Sparse regularized joint projection model for identifying associations of non-coding RNAs and human diseases2022In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 258, article id 110044Article in journal (Refereed)
    Abstract [en]

    Current human biomedical research shows that human diseases are closely related to non-coding RNAs, so it is of great significance for human medicine to study the relationship between diseases and non-coding RNAs. Current research has found associations between non-coding RNAs and human diseases through a variety of effective methods, but most of the methods are complex and targeted at a single RNA or disease. Therefore, we urgently need an effective and simple method to discover the associations between non-coding RNAs and human diseases. In this paper, we propose a sparse regularized joint projection model (SRJP) to identify the associations between non-coding RNAs and diseases. First, we extract information through a series of ncRNA similarity matrices and disease similarity matrices and assign average weights to the similarity matrices of the two sides. Then we decompose the similarity matrices of the two spaces into low-rank matrices and put them into SRJP. In SRJP, we innovatively use the projection matrix to combine the ncRNA side and the disease side to identify the associations between ncRNAs and diseases. Finally, the regularization term in SRJP effectively improves the robustness and generalization ability of the model. We test our model on different datasets involving three types of ncRNAs: circRNA, microRNA and long non-coding RNA. The experimental results show that SRJP has superior ability to identify and predict the associations between ncRNAs and diseases. © 2022 The Author(s)

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  • 24.
    Liang, Guojun
    et al.
    Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China.
    U, Kintak
    Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China.
    Ning, Xin
    Laboratory of Artificial Neural Networks and High Speed Circuits, Institute of Semiconductors, Chinese Academy of Sciences, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology.
    Kumar, Neeraj
    School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India; Lebanese American University, Beirut, Lebanon; King Abdulaziz University, Jeddah, Saudi Arabia.
    Semantics-aware Dynamic Graph Convolutional Network for Traffic Flow Forecasting2023In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 72, no 6, p. 7796-7809Article in journal (Refereed)
    Abstract [en]

    Traffic flow forecasting is a challenging task due to its spatio-temporal nature and the stochastic features underlying complex traffic situations. Currently, Graph Convolutional Network (GCN) methods are among the most successful and promising approaches. However, most GCNs methods rely on a static graph structure, which is generally unable to extract the dynamic spatio-temporal relationships of traffic data and to interpret trip patterns or motivation behind traffic flows. In this paper, we propose a novel Semantics-aware Dynamic Graph Convolutional Network (SDGCN) for traffic flow forecasting. A sparse, state-sharing, hidden Markov model is applied to capture the patterns of traffic flows from sparse trajectory data; this way, latent states, as well as transition matrices that govern the observed trajectory, can be learned. Consequently, we can build dynamic Laplacian matrices adaptively by jointly considering the trip pattern and motivation of traffic flows. Moreover, high-order Laplacian matrices can be obtained by a newly designed forward algorithm of low time complexity. GCN is then employed to exploit spatial features, and Gated Recurrent Unit (GRU) is applied to exploit temporal features. We conduct extensive experiments on three real-world traffic datasets. Experimental results demonstrate that the prediction accuracy of SDGCN outperforms existing traffic flow forecasting methods. In addition, it provides better explanations of the generative Laplace matrices, making it suitable for traffic flow forecasting in large cities and providing insight into the causes of various phenomena such as traffic congestion. The code is publicly available at https://github.com/gorgen2020/SDGCN. © 2023 IEEE.

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  • 25.
    Liu, Junkai
    et al.
    Suzhou University of Science and Technology, Suzhou, China; University Of Electronic Science and Technology of China, Quzhou, China.
    Guan, Shixuan
    University of Electronic Science and Technology of China, Quzhou, China; University of Tsukuba, Tsukuba, Japan.
    Zou, Quan
    University of Electronic Science and Technology of China, Quzhou, China.
    Wu, Hongjie
    Suzhou University of Science and Technology, Suzhou, China; University Of Electronic Science and Technology of China, Quzhou, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Ding, Yijie
    University of Electronic Science and Technology of China, Quzhou, China.
    AMDGT: Attention aware multi-modal fusion using a dual graph transformer for drug–disease associations prediction2024In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 284, article id 111329Article in journal (Refereed)
    Abstract [en]

    Identification of new indications for existing drugs is crucial through the various stages of drug discovery. Computational methods are valuable in establishing meaningful associations between drugs and diseases. However, most methods predict the drug–disease associations based solely on similarity data, neglecting valuable biological and chemical information. These methods often use basic concatenation to integrate information from different modalities, limiting their ability to capture features from a comprehensive and in-depth perspective. Therefore, a novel multimodal framework called AMDGT was proposed to predict new drug associations based on dual-graph transformer modules. By combining similarity data and complex biochemical information, AMDGT understands the multimodal feature fusion of drugs and diseases effectively and comprehensively with an attention-aware modality interaction architecture. Extensive experimental results indicate that AMDGT surpasses state-of-the-art methods in real-world datasets. Moreover, case and molecular docking studies demonstrated that AMDGT is an effective tool for drug repositioning. Our code is available at GitHub: https://github.com/JK-Liu7/AMDGT. © 2023 The Author(s)

  • 26.
    Liu, Yizhong
    et al.
    Beihang University, Beijing, China.
    Liu, Andi
    Beihang University, Beijing, China.
    Xia, Yu
    Beihang University, Beijing, China.
    Hu, Bin
    Beihang University, Beijing, China.
    Liu, Jianwei
    Beihang University, Beijing, China.
    Wu, Qianhong
    Beihang University, Beijing, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    A Blockchain-Based Cross-Domain Authentication Management System for IoT Devices2024In: IEEE Transactions on Network Science and Engineering, E-ISSN 2327-4697, Vol. 11, no 1, p. 115-127Article in journal (Refereed)
    Abstract [en]

    With the emergence of the resource and equipment sharing concept, many enterprises and organizations begin to implement cross-domain sharing of devices, especially in the field of the Internet of Things (IoT). However, there are many problems in the cross-domain usage process of devices, such as access control, authentication, and privacy protection. In this paper, we make the following contributions. First, we propose a blockchain-based cross-domain authentication management system for IoT devices. The sensitive device information is stored in a Merkle tree structure where only the Merkle root is uploaded to the smart contract. Second, a detailed security and performance analysis is given. We prove that our system is secure against several potential security threats and satisfies validity and liveness. Compared to existing schemes, our schemes realize decentralization, privacy, scalability, fast off-chain authentication, and low on-chain storage. Third, we implement the system on Ethereum with varying parameters known as domain number, concurrent authentication request number, and Merkle tree leaf number. Experimental results show that our solution supports the management of millions of devices in a domain and can process more than 10,000 concurrent cross-domain authentication requests, consuming only 5531 ms. Meanwhile, the gas costs are shown to be acceptable. © IEEE

  • 27.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology.
    Hashemi, Atiye Sadat
    Halmstad University, School of Information Technology.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Explainable Graph Neural Networks for Atherosclerotic Cardiovascular Disease2023In: Caring is sharing - exploiting the value in data for health and innovation: [33rd Medical Informatics Europe Conference, MIE2023, held in Gothenburg, Sweden, from 22 to 25 May, Amsterdam: IOS Press, 2023, Vol. 302, p. 603-604Conference paper (Refereed)
    Abstract [en]

    Understanding the aspects of progression for atherosclerotic cardiovascular disease and treatment is key to building reliable clinical decision-support systems. To promote system trust, one step is to make the machine learning models (used by the decision support systems) explainable for clinicians, developers, and researchers. Recently, working with longitudinal clinical trajectories using Graph Neural Networks (GNNs) has attracted attention among machine learning researchers. Although GNNs are seen as black-box methods, promising explainable AI (XAI) methods for GNNs have lately been proposed. In this paper, which describes initial project stages, we aim at utilizing GNNs for modeling, predicting, and exploring the model explainability of the low-density lipoprotein cholesterol level in long-term atherosclerotic cardiovascular disease progression and treatment.

  • 28.
    Manikandan, Ramachandran
    et al.
    Sastra University, Thanjavur, India.
    Indu, .
    Galgotias University, Greater Noida, India; Instituto Federal De Educação, Ciência E Tecnologia Do Ceará, Fortaleza, Brazil.
    de Albuquerque, Victor Hugo C.
    Universidade Federal Do Ceará, Fortaleza, Brazil.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    AlQahtani, Salman Ali
    King Saud University, Riyadh, Saudi Arabia.
    Hossain, M. Shamim
    King Saud University, Riyadh, Saudi Arabia.
    Quality of Service-Aware Resource Selection in Healthcare IoT Using Deep Autoencoder Neural Networks2022In: Human-centric Computing and Information Sciences, E-ISSN 2192-1962, Vol. 12, no 36, p. 1-16Article in journal (Refereed)
    Abstract [en]

    Heterogeneous network and device-to-device communication are two possible solutions for improving wireless network spectral efficiency. Techniques based on the Internet of Things (IoT) can interact between a large number of smart devices as well as heterogeneous networks. The goal of this study is to investigate proposed quality of service-aware resource selection in an IoT network for healthcare data using a deep auto encoder neural network with spectrum reuse utilizing mixed integer nonlinear programming (MINLP). The suggested MINLP spectrum reuse was used to address the optimization problem, and the spectrum allocation was done using fast Fourier transform based reinforcement Q-learning. Increased transmission repetitions have been identified as a promising strategy for improving IoT coverage by up to 164 dB in terms of maximum coupling loss for uplink transmissions, which is a significant improvement over traditional LTE technology, particularly for serving customers in deep coverage. Based on a comparison of existing methodologies, the experimental study is performed using parameters such as bit error rate of 40%, signal-to-interference plus noise ratio of 72%, sum rate of 88%, and spectral efficiency of 98% © 2022. Human-centric Computing and Information Sciences. All Rights Reserved.

  • 29.
    Miao, Junfeng
    et al.
    University of Science and Technology Beijing, Beijing, China.
    Wang, Zhaoshun
    University of Science and Technology Beijing, Beijing, China.
    Wu, Zeqing
    Xinxiang Medical University, Xinxiang, China.
    Ning, Xin
    Chinese Academy of Sciences, Beijing, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    A blockchain-enabled privacy-preserving authentication management protocol for Internet of Medical Things2024In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 237, Part A, article id 121329Article in journal (Refereed)
    Abstract [en]

    Over the last decade, with the increasing popularity and usage of the internet of things worldwide, Internet of Medical Things (IoMT) has emerged as a key technology of the modern era. IoMT uses Artificial Intelligence, 5G, big data, edge computing, and blockchain to provide users with electronic medical services. However, it may face several security threats and attacks over an insecure public network. Therefore, to protect sensitive medical data in IoMT, it is necessary to design a secure and efficient authentication protocol. In this study, we propose a privacy-preserving authentication management protocol based on blockchain. The protocol uses a blockchain to store identities and related parameters to assist communication entities in authentication. In addition, the protocol adopts a three-factor authentication method and introduces Chebyshev chaotic map to ensure the security of user login and authentication. Formal security proof and analysis using the random oracle model and Burrows-Abadi-Needham logic show that the proposed protocol is secure. Moreover, we use informal security analysis to demonstrate that the protocol can resist various security attacks. The functional comparison shows that the protocol has high security. Through performance analysis and comparison with other protocols, the proposed protocol can increase computation overhead, communication overhead, and storage overhead by up to 39.8%, 93.6%, and 86.7%,respectively. © 2023 Elsevier Ltd

  • 30.
    Mohammed, Mazin Abed
    et al.
    University Of Anbar, Ramadi, Iraq; Vsb-technical University Of Ostrava, Ostrava, Czech Republic; Vsb-technical University Of Ostrava, Ostrava, Czech Republic.
    Lakhan, Abdullah
    Vsb-technical University Of Ostrava, Ostrava, Czech Republic; Vsb-technical University Of Ostrava, Ostrava, Czech Republic; Dawood University Of Engineering And Technology, Karachi, Pakistan.
    Zebari, Dilovan Asaad
    Nawroz University, Duhok, Iraq.
    Abdulkareem, Karrar Hameed
    Al-muthanna University, Samawah, Iraq; University Of Warith Al-anbiyaa, Karbala, Iraq.
    Nedoma, Jan
    Vsb-technical University Of Ostrava, Ostrava, Czech Republic.
    Martinek, Radek
    Vsb-technical University Of Ostrava, Ostrava, Czech Republic.
    Tariq, Usman
    Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia.
    Alhaisoni, Majed
    University Of Hail, Hail, Saudi Arabia.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Adaptive secure malware efficient machine learning algorithm for healthcare data2023In: CAAI Transactions on Intelligence Technology, ISSN 2468-2322Article in journal (Refereed)
    Abstract [en]

    Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices. On the other hand, IoT-based heartbeat digital healthcare applications have been steadily increasing in popularity. These applications make a lot of data, which they send to the fog cloud to be processed further. In healthcare networks, it is critical to examine healthcare data for malicious intent. The malware is a peace code with polymorphic and metamorphic attack forms. Existing malware analysis techniques did not find malware in the content-aware heartbeat data. The Adaptive Malware Analysis Dynamic Machine Learning (AMDML) algorithm for content-aware heartbeat data in fog cloud computing is described in this article. Based on heartbeat data from health records, an adaptive method can train both pre- and post-train malware models. AMDML is based on a rule called ‘federated learning,’ which says that malware analysis models are made at both the local fog node and the remote cloud to meet the performance workload safely. The simulation results show that AMDML outperforms machine learning malware analysis models in terms of accuracy by 60%, delay by 50%, and detection of original heartbeat data by 66% compared to existing malware analysis schemes. © 2023 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.

  • 31.
    Mumtaz, Nadia
    et al.
    Iqra University, Islamabad, Pakistan.
    Ejaz, Naveed
    Iqra University, Islamabad, Pakistan.
    Habib, Shabana
    Qassim University, Buraidah, Saudi Arabia.
    Mohsin, Syed Muhammad
    COMSATS University, Islamabad, Pakistan.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Band, Shahab S.
    National Yunlin University of Scilogy, Douliou, Taiwan.
    Kumar, Neeraj
    Deemed University, Patiala, India; Lebanese American University, Beirut, Lebanon; University of Petroleum and Energy Studies, Dehradun, India; King Abdul Aziz University, Jeddah, Saudi Arabia.
    An overview of violence detection techniques: current challenges and future directions2023In: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462, Vol. 56, p. 4641-4666Article in journal (Refereed)
    Abstract [en]

    The Big Video Data generated in today’s smart cities has raised concerns from its purposeful usage perspective, where surveillance cameras, among many others are the most prominent resources to contribute to the huge volumes of data, making its automated analysis adifcult task in terms of computation and preciseness. Violence detection (VD), broadly plunging under action and activity recognition domain, is used to analyze Big Video data for anomalous actions incurred due to humans. The VD literature is traditionally basedon manually engineered features, though advancements to deep learning based standalone models are developed for real-time VD analysis. This paper focuses on overview of deepsequence learning approaches along with localization strategies of the detected violence.This overview also dives into the initial image processing and machine learning-based VD literature and their possible advantages such as efciency against the current complex models. Furthermore,the datasets are discussed, to provide an analysis of the current models, explaining their pros and cons with future directions in VD domain derived from anin-depth analysis of the previous methods. © The Author(s), under exclusive licence to Springer Nature B.V. 2022.

  • 32.
    Ning, Enhao
    et al.
    Chinese Academy of Sciences, Beijing, China.
    Wang, Changshuo
    Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
    Zhang, Huang
    Xinjiang University, Xinjiang, China.
    Ning, Xin
    Chinese Academy of Sciences, Beijing, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Occluded person re-identification with deep learning: A survey and perspectives2024In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 239, article id 122419Article, review/survey (Refereed)
    Abstract [en]

    Person re-identification (Re-ID) technology plays an increasingly crucial role in intelligent surveillance systems. Widespread occlusion significantly impacts the performance of person Re-ID. Occluded person Re-ID refers to a pedestrian matching method that deals with challenges such as pedestrian information loss, noise interference, and perspective misalignment. It has garnered extensive attention from researchers. Over the past few years, several occlusion-solving person Re-ID methods have been proposed, tackling various sub-problems arising from occlusion. However, there is a lack of comprehensive studies that compare, summarize, and evaluate the potential of occluded person Re-ID methods in detail. In this review, we commence by offering a meticulous overview of the datasets and evaluation criteria utilized in the realm of occluded person Re-ID. Subsequently, we undertake a rigorous scientific classification and analysis of existing deep learning-based occluded person Re-ID methodologies, examining them from diverse perspectives and presenting concise summaries for each approach. Furthermore, we execute a systematic comparative analysis among these methods, pinpointing the state-of-the-art solutions, and provide insights into the future trajectory of occluded person Re-ID research.

  • 33.
    Ning, Xin
    et al.
    Chinese Academy Of Sciences, Beijing, China.
    Yu, Zaiyang
    Chinese Academy Of Sciences, Beijing, China; University Of Chinese Academy Of Sciences, Beijing, China.
    Li, Lusi
    Old Dominion University, Norfolk, United States.
    Li, Weijun
    Chinese Academy Of Sciences, Beijing, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    DILF: Differentiable rendering-based multi-view Image–Language Fusion for zero-shot 3D shape understanding2024In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 102, p. 1-12, article id 102033Article in journal (Refereed)
    Abstract [en]

    Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has shown promising open-world performance in zero-shot 3D shape understanding tasks by information fusion among language and 3D modality. It first renders 3D objects into multiple 2D image views and then learns to understand the semantic relationships between the textual descriptions and images, enabling the model to generalize to new and unseen categories. However, existing studies in zero-shot 3D shape understanding rely on predefined rendering parameters, resulting in repetitive, redundant, and low-quality views. This limitation hinders the model's ability to fully comprehend 3D shapes and adversely impacts the text–image fusion in a shared latent space. To this end, we propose a novel approach called Differentiable rendering-based multi-view Image–Language Fusion (DILF) for zero-shot 3D shape understanding. Specifically, DILF leverages large-scale language models (LLMs) to generate textual prompts enriched with 3D semantics and designs a differentiable renderer with learnable rendering parameters to produce representative multi-view images. These rendering parameters can be iteratively updated using a text–image fusion loss, which aids in parameters’ regression, allowing the model to determine the optimal viewpoint positions for each 3D object. Then a group-view mechanism is introduced to model interdependencies across views, enabling efficient information fusion to achieve a more comprehensive 3D shape understanding. Experimental results can demonstrate that DILF outperforms state-of-the-art methods for zero-shot 3D classification while maintaining competitive performance for standard 3D classification. The code is available at https://github.com/yuzaiyang123/DILP. © 2023 The Author(s)

  • 34.
    Qu, Zhiguo
    et al.
    Nanjing University of Information Science and Technology, Nanjing, China.
    Chen, Zhixiao
    Nanjing University of Information Science and Technology, Nanjing, China.
    Ning, Xin
    Chinese Academy of Sciences, Beijing, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    QEPP: A Quantum Efficient Privacy Protection Protocol in 6G-Quantum Internet of Vehicles2023In: IEEE Transactions on Intelligent Vehicles, ISSN 2379-8858Article in journal (Refereed)
    Abstract [en]

    The increasing popularity of 6G communication within the Internet of Vehicles (IoV) ecosystem is expected to induce a surge in both user numbers and data volumes. This expansion will cause substantial challenges in ensuring network security and privacy protection, as well as in addressing the associated issue of inadequate cloud computing resources. In this article, we propose a Quantum Efficient Privacy Protection (QEPP) protocol that leverages reversible information hiding in quantum point clouds. This protocol utilizes quantum communication technology in edge-to-cloud communication of the IoV to transmit sensitive information embedded in quantum state data, thereby ensuring privacy protection. It employs quantum error-correction coding and efficient coding techniques to extract information and recover the carriers. In addition, the protocol utilizes an improved quantum Grover algorithm in the cloud to accelerate the processing speed of quantum data. By addressing security vulnerabilities and improving cloud-computing capabilities, the QEPP can effectively accommodate critical requirements, including precision, timeliness, and robust privacy protection. © IEEE

  • 35.
    Qu, Zhiguo
    et al.
    Nanjing University Of Information Science And Technology, Nanjing, China.
    Li, Yang
    Nanjing University Of Information Science And Technology, Nanjing, China.
    Liu, Bo
    Hubei University Of Science And Technology, Xianning, China.
    Gupta, Deepak
    Maharaja Agrasen Institute Of Technology, New Delhi, India.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    DTQFL: A Digital Twin-Assisted Quantum Federated Learning Algorithm for Intelligent Diagnosis in 5G Mobile Network2023In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, p. 1-10Article in journal (Refereed)
    Abstract [en]

    Smart healthcare aims to revolutionize med-ical services by integrating artificial intelligence (AI). The limitations of classical machine learning include privacy concerns that prevent direct data sharing among medical institutions, untimely updates, and long training times. To address these issues, this study proposes a digital twin-assisted quantum federated learning algorithm (DTQFL). By leveraging the 5G mobile network, digital twins (DT) of patients can be created instantly using data from various Internet of Medical Things (IoMT) devices and simultane-ously reduce communication time in federated learning (FL) at the same time. DTQFL generates DT for patients with specific diseases, allowing for synchronous training and updating of the variational quantum neural network (VQNN) without disrupting the VQNN in the real world. This study utilized DTQFL to train its own personalized VQNN for each hospital, considering privacy security and training speed. Simultaneously, the personalized VQNN of each hospital was obtained through further local iterations of the final global parameters. The results indicate that DTQFL can train a good VQNN without collecting local data while achieving accuracy comparable to that of data-centralized algorithms. In addition, after personalized train-ing, the VQNN can achieve higher accuracy than that with-out personalized training.

  • 36.
    Qu, Zhiguo
    et al.
    Nanjing University of Information Science and Technology, Nanjing, China; Nanjing University of Information Science and Technology, Nanjing, China.
    Li, Yang
    Nanjing University of Information Science and Technology, Nanjing, China; Nanjing University of Information Science and Technology, Nanjing, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    QNMF: A quantum neural network based multimodal fusion system for intelligent diagnosis2023In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 100, article id 101913Article in journal (Refereed)
    Abstract [en]

    The Internet of Medical Things (IoMT) has emerged as a significant research area in the medical field, enabling the transmission of various types of data to the cloud for analysis and diagnosis. Fusing data from multiple modalities can enhance accuracy but requires substantial computing power. Theoretically, quantum computers can rapidly process large volumes of high-dimensional medical data. Despite accelerated developments in quantum computing, research on quantum machine learning (QML) for multimodal data processing remains limited. Considering these factors, this paper presents a quantum neural network-based multimodal fusion system for intelligent diagnosis (QNMF) that can process multimodal medical data transmitted by IoMT devices, fuse data from different modalities, and improve the performance of intelligent diagnosis. This system employs a quantum convolutional neural network (QCNN) to efficiently extract features from medical images. These QCNN-based features are then fused with other modality features (such as blood test results or breast cell slices), and used to train an effective variational quantum classifier (VQC) for intelligent diagnosis. The experimental results demonstrate that a QCNN can effectively extract image data features. Furthermore, QNMF achieved an accuracy of 97.07% and 97.61% on breast cancer diagnosis and Covid-19 diagnosis experiments, respectively. In addition, the QNMF exhibits strong quantum noise robustness. © 2023 Elsevier B.V.

  • 37.
    Qu, Zhiguo
    et al.
    Nanjing University of Information Science and Technology, Nanjing, China.
    Meng, Yunyi
    Nanjing University of Information Science and Technology, Nanjing, China.
    Liu, Bo
    Hubei University of Science and Technology, Xianning, China.
    Muhammad, Ghulam
    King Saud University, Riyadh, Saudi Arabia.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    QB-IMD: A secure medical data processing system with privacy protection based on quantum blockchain for IoMT2024In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 11, no 1, p. 40-49Article in journal (Refereed)
    Abstract [en]

    Security and privacy are issues that cannot be ignored when collecting and processing medical data in the Internet of Medical Things (IoMT). Blockchain technology is a decentralized ledger system that has diverse application scenarios in the medical field. Blockchain technology relies on traditional cryptography to ensure data integrity and verifiability, but the creation of quantum computing has made it possible to break traditional encryption and signature methods. Therefore, quantum blockchain can provide a higher level of security for handling medical data. This paper innovatively designs a new medical data processing system based on quantum blockchain (QB-IMD). In QB-IMD, a quantum blockchain structure and a novel electronic medical record algorithm (QEMR) are proposed to ensure that the processed data is legitimate and tamper-proof. QEMR combines quantum signature and quantum identity authentication to avoid the potential security risks of digital signatures. In addition, through delegated computing by quantum cloud, medical diagnostic data can be computed without leaking to quantum cloud servers, thus protecting user privacy. Through mathematical proof, theoretical analysis and simulation, it is demonstrated that our scheme can resist six attacks and is feasible to protect user privacy. © IEEE

  • 38.
    Qu, Zhiguo
    et al.
    Nanjing University of Information Science and Technology, Nanjing, China.
    Meng, Yunyi
    Nanjing University of Information Science and Technology, Nanjing, China.
    Muhammad, Ghulam
    King Saud University, Riyadh, Saudi Arabia.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    QMFND: A quantum multimodal fusion-based fake news detection model for social media2024In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 104, article id 102172Article in journal (Refereed)
    Abstract [en]

    Fake news is frequently disseminated through social media, which significantly impacts public perception and individual decision-making. Accurate identification of fake news on social media is usually time-consuming, laborious, and difficult. Although the leveraging of machine learning technologies can facilitate automated authenticity checks, the time-sensitive and voluminous nature of the data brings considerable challenge for fake news detection. To address this issue, this paper proposes a quantum multimodal fusion-based model for fake news detection (QMFND). QMFND integrates the extracted images and textual features, and passes them through a proposed quantum convolutional neural network (QCNN) to obtain discriminative results. By testing QMFND on two social media datasets, Gossip and Politifact, it is proved that its detection performance is equal to or even surpasses that of classical models. The effects of various parameters are further investigated. The QCNN not only has good expressibility and entangling capability but also has good robustness against quantum noise. The code is available at © 2023 Elsevier B.V.

  • 39.
    Qu, Zhiguo
    et al.
    Nanjing University of Information Science and Technology, Nanjing, China.
    Shi, Wenke
    Nanjing University of Information Science and Technology, Nanjing, China.
    Liu, Bo
    Hubei University of Science and Technology, Xianning, China.
    Gupta, Deepak
    Maharaja Agrasen Institute of Technology, New Delhi, India; Chandigarh University, Mohali, India.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    IoMT-based smart healthcare detection system driven by quantum blockchain and quantum neural network2023In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208Article in journal (Refereed)
    Abstract [en]

    Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things (IoMT). The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system named QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network nameded HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack. © IEEE

  • 40.
    Qu, Zhiguo
    et al.
    Nanjing University Of Information Science And Technology, Nanjing, China.
    Shi, Wenke
    Nanjing University Of Information Science And Technology, Nanjing, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Quantum conditional generative adversarial network based on patch method for abnormal electrocardiogram generation2023In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 166, p. 1-13, article id 107549Article in journal (Refereed)
    Abstract [en]

    To address the scarcity and class imbalance of abnormal electrocardiogram (ECG) databases, which are crucial in AI-driven diagnostic tools for potential cardiovascular disease detection, this study proposes a novel quantum conditional generative adversarial algorithm (QCGAN-ECG) for generating abnormal ECG signals. The QCGAN-ECG constructs a quantum generator based on patch method. In this method, each sub-generator generates distinct features of abnormal heartbeats in different segments. This patch-based generative algorithm conserves quantum resources and makes QCGAN-ECG practical for near-term quantum devices. Additionally, QCGAN-ECG introduces quantum registers as control conditions. It encodes information about the types and probability distributions of abnormal heartbeats into quantum registers, rendering the entire generative process controllable. Simulation experiments on Pennylane demonstrated that the QCGAN-ECG could generate completely abnormal heartbeats with an average accuracy of 88.8%. Moreover, the QCGAN-ECG can accurately fit the probability distribution of various abnormal ECG data. In the anti-noise experiments, the QCGAN-ECG showcased outstanding robustness across various levels of quantum noise interference. These results demonstrate the effectiveness and potential applicability of the QCGAN-ECG for generating abnormal ECG signals, which will further promote the development of AI-driven cardiac disease diagnosis systems. The source code is available at github.com/VanSWK/QCGAN_ECG. © 2023 The Author(s)

  • 41.
    Qu, Zhiguo
    et al.
    Nanjing University of Information Science and Technology, Nanjing, China.
    Tang, Yang
    Nanjing University of Information Science and Technology, Nanjing, China.
    Muhammad, Ghulam
    King Saud University, Riyadh, Saudi Arabia.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Privacy protection in intelligent vehicle networking: A novel federated learning algorithm based on information fusion2023In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 98, article id 101824Article in journal (Refereed)
    Abstract [en]

    Federated learning is an effective technique to solve the problem of information fusion and information sharing in intelligent vehicle networking. However, most of the existing federated learning algorithms generally have the risk of privacy leakage. To address this security risk, this paper proposes a novel personalized federated learning with privacy preservation (PDP-PFL) algorithm based on information fusion. In the first stage of its execution, the new algorithm achieves personalized privacy protection by grading users’ privacy based on their privacy preferences and adding noise that satisfies their privacy preferences. In the second stage of its execution, PDP-PFL performs collaborative training of deep models among different in-vehicle terminals for personalized learning, using a lightweight dynamic convolutional network architecture without sharing the local data of each terminal. Instead of sharing all the parameters of the model as in standard federated learning, PDP-PFL keeps the last layer local, thus adding another layer of data confidentiality and making it difficult for the adversary to infer the image of the target vehicle terminal. It trains a personalized model for each vehicle terminal by “local fine-tuning”. Based on experiments, it is shown that the accuracy of the proposed new algorithm for PDP-PFL calculation can be comparable to or better than that of the FedAvg algorithm and the FedBN algorithm, while further enhancing the protection of data privacy. © 2023 Elsevier B.V.

  • 42.
    Qu, Zhiguo
    et al.
    Nanjing University Of Information Science And Technology, Nanjing, China.
    Zhang, Zhexi
    Nanjing University Of Information Science And Technology, Nanjing, China.
    Liu, Bo
    Hubei University Of Science And Technology, Xianning, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Ning, Xin
    Chinese Academy Of Sciences, Beijing, China.
    Muhammad, Khan
    Sungkyunkwan University, Seoul, South Korea.
    Quantum detectable Byzantine agreement for distributed data trust management in blockchain2023In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 637, article id 118909Article in journal (Refereed)
    Abstract [en]

    No system entity within a contemporary distributed cyber system can be entirely trusted. Hence, the classic centralized trust management method cannot be directly applied to it. Blockchain technology is essential to achieving decentralized trust management, its consensus mechanism is useful in addressing large-scale data sharing and data consensus challenges. Herein, an n-party quantum detectable Byzantine agreement (DBA) based on the GHZ state to realize the data consensus in a quantum blockchain is proposed, considering the threat posed by the growth of quantum information technology on the traditional blockchain. Relying on the nonlocality of the GHZ state, the proposed protocol detects the honesty of nodes by allocating the entanglement resources between different nodes. The GHZ state is notably simpler to prepare than other multi-particle entangled states, thus reducing preparation consumption and increasing practicality. When the number of network nodes increases, the proposed protocol provides better scalability and stronger practicability than the current quantum DBA. In addition, the proposed protocol has the optimal fault-tolerant found and does not rely on any other presumptions. A consensus can be reached even when there are n−2 traitors. The performance analysis confirms viability and effectiveness through exemplification. The security analysis also demonstrates that the quantum DBA protocol is unconditionally secure, effectively ensuring the security of data and realizing data consistency in the quantum blockchain. © 2023 The Authors

  • 43.
    Ran, Hang
    et al.
    Chinese Academy Of Sciences, Beijing, China; University Of Chinese Academy Of Sciences, Beijing, China.
    Li, Weijun
    Chinese Academy Of Sciences, Beijing, China; University Of Chinese Academy Of Sciences, Beijing, China.
    Li, Lusi
    Old Dominion University, Norfolk, United States.
    Tian, Songsong
    Chinese Academy Of Sciences, Beijing, China; University Of Chinese Academy Of Sciences, Beijing, China.
    Ning, Xin
    Chinese Academy Of Sciences, Beijing, China; University Of Chinese Academy Of Sciences, Beijing, China; Cognitive Computing Technology Joint Laboratory, Beijing, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Learning optimal inter-class margin adaptively for few-shot class-incremental learning via neural collapse-based meta-learning2024In: Information Processing & Management, ISSN 0306-4573, E-ISSN 1873-5371, Vol. 61, no 3, article id 103664Article in journal (Refereed)
    Abstract [en]

    Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes incrementally with a limited number of samples per class. It faces issues of forgetting previously learned classes and overfitting on few-shot classes. An efficient strategy is to learn features that are discriminative in both base and incremental sessions. Current methods improve discriminability by manually designing inter-class margins based on empirical observations, which can be suboptimal. The emerging Neural Collapse (NC) theory provides a theoretically optimal inter-class margin for classification, serving as a basis for adaptively computing the margin. Yet, it is designed for closed, balanced data, not for sequential or few-shot imbalanced data. To address this gap, we propose a Meta-learning- and NC-based FSCIL method, MetaNC-FSCIL, to compute the optimal margin adaptively and maintain it at each incremental session. Specifically, we first compute the theoretically optimal margin based on the NC theory. Then we introduce a novel loss function to ensure that the loss value is minimized precisely when the inter-class margin reaches its theoretically best. Motivated by the intuition that “learn how to preserve the margin” matches the meta-learning's goal of “learn how to learn”, we embed the loss function in base-session meta-training to preserve the margin for future meta-testing sessions. Experimental results demonstrate the effectiveness of MetaNC-FSCIL, achieving superior performance on multiple datasets. The code is available at https://github.com/qihangran/metaNC-FSCIL. © 2024 The Author(s)

  • 44.
    Ran, Hang
    et al.
    Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
    Ning, Xin
    Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Cognitive Computing Technology Joint Laboratory, Wave Group, Beijing, China.
    Li, Weijun
    Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Beijing Key Laboratory Of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing, China.
    Hao, Meilan
    Chinese Academy of Sciences, Beijing, China; Hebei University of Engineering, Handan, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    3D human pose and shape estimation via de-occlusion multi-task learning2023In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 548, article id 126284Article in journal (Refereed)
    Abstract [en]

    Three-dimensional human pose and shape estimation is to compute a full human 3D mesh given a single image. The contamination of features caused by occlusion usually degrades its performance significantly. Recent progress in this field typically addressed the occlusion problem implicitly. By contrast, in this paper, we address it explicitly using a simple yet effective de-occlusion multi-task learning network. Our key insight is that feature for mesh parameter regression should be noiseless. Thus, in the feature space, our method disentangles the occludee that represents the noiseless human feature from the occluder. Specifically, a spatial regularization and an attention mechanism are imposed in the backbone of our network to disentangle the features into different channels. Furthermore, two segmentation tasks are proposed to supervise the de-occlusion process. The final mesh model is regressed by the disentangled occlusion-aware features. Experiments on both occlusion and non-occlusion datasets are conducted, and the results prove that our method is superior to the state-of-the-art methods on two occlusion datasets, while achieving competitive performance on a non-occlusion dataset. We also demonstrate that the proposed de-occlusion strategy is the main factor to improve the robustness against occlusion. The code is available at https://github.com/qihangran/De-occlusion_MTL_HMR. © 2023

  • 45.
    Rani, Sita
    et al.
    Guru Nanak Dev Engineering College, Ludhiana, Punjab, India.
    Kataria, Aman
    CSIR-Central Scientific Instruments Organisation, Chandigarh, India.
    Kumar, Sachin
    South Ural State University, Chelyabinsk, Russian Federation.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review2023In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 274, article id 110658Article, review/survey (Refereed)
    Abstract [en]

    Recent developments in the Internet of Things (IoT) and various communication technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into various medical devices and equipment, leading to the progression of the Internet of Medical Things (IoMT). Therefore, various IoMT-based healthcare applications are deployed and used in the day-to-day scenario. Traditionally, machine learning (ML) models use centralized data compilation and learning that is impractical in pragmatic healthcare frameworks due to rising privacy and data security issues. Federated Learning (FL) has been observed as a developing distributed collective paradigm, the most appropriate for modern healthcare framework, that manages various stakeholders (e.g., patients, hospitals, laboratories, etc.) to carry out training of the models without the actual exchange of sensitive medical data. Consequently, in this work, the authors present an exhaustive survey on the security of FL-based IoMT applications in smart healthcare frameworks. First, the authors introduced IoMT devices, their types, applications, datasets, and the IoMT security framework in detail. Subsequently, the concept of FL, its application domains, and various tools used to develop FL applications are discussed. The significant contribution of FL in deploying secure IoMT systems is presented by focusing on FL-based IoMT applications, patents, real-world FL-based healthcare projects, and datasets. A comparison of FL-based security techniques with other schemes in the smart healthcare ecosystem is also presented. Finally, the authors discussed the challenges faced and potential future research recommendations to deploy secure FL-based IoMT applications in smart healthcare frameworks. © 2023 The Author(s)

  • 46.
    Saberi-Movahed, Farid
    et al.
    Graduate University of Advanced Technology, Kerman, Iran.
    Rostami, Mehrdad
    University of Oulu, Oulu, Finland.
    Berahmand, Kamal
    Queensland University of Technology (QUT), Organization, Brisbane, Australia.
    Karami, Saeed
    Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Oussalah, Mourad
    University of Oulu, Oulu, Finland.
    Band, Shahab S.
    National Yunlin University of Science and Technology, Douliu, Taiwan.
    Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy with application in gene selection2022In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 256, article id 109884Article in journal (Refereed)
    Abstract [en]

    Gene expression data have become increasingly important in machine learning and computational biology over the past few years. In the field of gene expression analysis, several matrix factorization-based dimensionality reduction methods have been developed. However, such methods can still be improved in terms of efficiency and reliability. In this paper, an innovative approach to feature selection, called Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy (DR-FS-MFMR), is introduced. The major focus of DR-FS-MFMR is to discard redundant features from the set of original features. In order to reach this target, the primary feature selection problem is defined in terms of two aspects: (1) the matrix factorization of data matrix in terms of the feature weight matrix and the representation matrix, and (2) the correlation information related to the selected features set. Then, the objective function is enriched by employing two data representation characteristics along with an inner product regularization criterion to perform both the redundancy minimization process and the sparsity task more precisely. To demonstrate the proficiency of the DR-FS-MFMR method, a large number of experimental studies are conducted on nine gene expression datasets. The obtained computational results indicate the efficiency and productivity of DR-FS-MFMR for the gene selection task. © 2022 The Author(s)

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  • 47.
    Saeed, Uzair
    et al.
    Beijing Institute of Technology, Beijing, China.
    Armghan, Ammar
    College of Engineering, Jouf University, Sakaka, Saudi Arabia.
    Quanyu, Wang
    Beijing Institute of Technology, Beijing, China.
    Alenezi, Fayadh
    College of Engineering, Jouf University, Sakaka, Saudi Arabia.
    Yue, Sun
    Beijing Institute of Technology, Beijing, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    One-shot many-to-many facial reenactment using Bi-Layer Graph Convolutional Networks2022In: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 156, p. 193-204Article in journal (Refereed)
    Abstract [en]

    Facial reenactment is aimed at animating a source face image into a new place using a driving facial picture. In a few shot scenarios, the present strategies are designed with one or more identities or identity-sustained suffering protection challenges. These current solutions are either developed with one or more identities in mind, or face identity protection issues in one or more shot situations. Multiple pictures from the same entity have been used in previous research to model facial reenactment. In contrast, this paper presents a novel model of one-shot many-to-many facial reenactments that uses only one facial image of a face. The proposed model produces a face that represents the objective representation of the same source identity. The proposed technique can simulate motion from a single image by decomposing an object into two layers. Using bi-layer with Convolutional Neural Network (CNN), we named our model Bi-Layer Graph Convolutional Layers (BGCLN) which utilized to create the latent vector’s optical flow representation. This yields the precise structure and shape of the optical stream. Comprehensive studies suggest that our technique can produce high-quality results and outperform most recent techniques in both qualitative and quantitative data comparisons. Our proposed system can perform facial reenactment at 15 fps, which is approximately real time. Our code is publicly available at https://github.com/usaeed786/BGCLN

  • 48.
    Samareh-Jahani, Mahsa
    et al.
    Shahid Bahonar University of Kerman, Kerman, Iran.
    Saberi-Movahed, Farid
    Graduate University of Advanced Technology, Kerman, Iran.
    Eftekhari, Mahdi
    Shahid Bahonar University of Kerman, Kerman, Iran.
    Aghamollaei, Gholamreza
    Shahid Bahonar University of Kerman, Kerman, Iran; Shahid Bahonar University of Kerman, Kerman, Iran.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Low-Redundant Unsupervised Feature Selection based on Data Structure Learning and Feature Orthogonalization2024In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 240, article id 122556Article in journal (Refereed)
    Abstract [en]

    An orthogonal representation of features can offer valuable insights into feature selection as it aims to find a representative subset of features in which all features can be accurately reconstructed by a set of features that are linearly independent, uncorrelated, and perpendicular to each other. In this paper, a novel feature selection method, called Low-Redundant Unsupervised Feature Selection based on Data Structure Learning and Feature Orthogonalization (LRDOR), is presented. In the first stage, the suggested LRDOR method makes use of the QR factorization over the whole set of features to find the orthogonal representation of the feature space. Then, LRDOR utilizes the directional distance based on the matrix factorization in order to determine the distance among the set of considered features and the orthogonal set obtained from the original features. Moreover, LRDOR simultaneously takes into account the local correlation of features and the data manifold as dual information into the feature selection process, which can lead to a low level of redundancy and maintain the geometric data structure when reducing the data dimension. In addition to providing a proficient iterative algorithm, the convergence analysis is also included to solve the objective function of LRDOR. The results of the experiments demonstrate that for clustering purposes, LRDOR works better than other related state-of-the-art unsupervised feature selection methods on ten real-world datasets. © 2023 Elsevier Ltd

  • 49.
    Shamrooz Aslam, Muhammad
    et al.
    China University of Mining and Technology, Xuzhou, China; Guangxi University of Science and Technology, Liuzhou, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Pandey, Hari Mohan
    Bournemouth University, Poole, United Kingdom.
    Band, Shahab S.
    National Yunlin University of Science and Technology, Douliou, Taiwan.
    El Sayed, Hesham
    United Arab Emirates University, Abu Dhabi, United Arab Emirates.
    A delayed Takagi–Sugeno fuzzy control approach with uncertain measurements using an extended sliding mode observer2023In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 643, article id 119204Article in journal (Refereed)
    Abstract [en]

    In this study, a sliding mode observer (SMO) is implemented on a T–S fuzzy system with multiple time–varying delays over continuous time. Because state data may not be fully available in practice, state observers are used to estimate state information. A system based on observers is implemented with non–parallel distribution compensation (N-PDC). Moreover, the concept of dissipative control provides a framework for analyzing the performance of H∞, L2L∞, and dissipativeness. In order to design two sliding surfaces using the SMO gain matrix, first two integral–type sliding surfaces must be constructed. Then, we define a few additional parameters using fuzzy Lyapunov stability and SMO theory, resulting in asymptotically stable closed–loop performances. On the basis of the new error system, convex optimization is used to generate the sliding mode controller and the gained weight matrices. Following is an example of the power system (ship electric propulsion) to demonstrate the potential scheme. © 2023 Elsevier Inc.

  • 50.
    Singh, Ram
    et al.
    Baba Ghulam Shah Badshah University Rajouri, Rajouri, India.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Band, Shahab S.
    Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan.
    Rehman, Attiq U.
    Baba Ghulam Shah Badshah University Rajouri, Rajouri, India.
    Mahajan, Shubham
    Ajeenka D Y University, Pune, Maharashtra, India; iNurture Education Solutions Pvt. Ltd., Bangalore, India; School of Electronic and Communication, Shri Mata Vaishno Devi University, Katra, India.
    Ding, Yijie
    Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
    Liu, Xiaobin
    Department of Nephrology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China.
    Pandit, Amit Kant
    School of Electronic and Communication, Shri Mata Vaishno Devi University, Katra, India.
    Impact of quarantine on fractional order dynamical model of Covid-192022In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 151, Part A, article id 106266Article in journal (Refereed)
    Abstract [en]

    In this paper, a Covid-19 dynamical transmission model of a coupled non-linear fractional differential equation in the Atangana-Baleanu Caputo sense is proposed. The basic dynamical transmission features of the proposed system are briefly discussed. The qualitative as well as quantitative results on the existence and uniqueness of the solutions are evaluated through the fixed point theorem. The Ulam-Hyers stability analysis of the suggested system is established. The two-step Adams-Bashforth-Moulton (ABM) numerical method is employed to find its numerical solution. The numerical simulation is performed to accesses the impact of various biological parameters on the dynamics of Covid-19 disease. © 2022 The Author(s). Published by Elsevier Ltd. All rights reserved.

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