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  • 1.
    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.

  • 2.
    Galozy, Alexander
    et al.
    Halmstad University, School of Information Technology.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology.
    Information-gathering in latent bandits2023In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 260, article id 110099Article in journal (Refereed)
    Abstract [en]

    In the latent bandit problem, the learner has access to reward distributions and – for the non-stationary variant – transition models of the environment. The reward distributions are conditioned on the arm and unknown latent states. The goal is to use the reward history to identify the latent state, allowing for the optimal choice of arms in the future. The latent bandit setting lends itself to many practical applications, such as recommender and decision support systems, where rich data allows the offline estimation of environment models with online learning remaining a critical component. Previous solutions in this setting always choose the highest reward arm according to the agent’s beliefs about the state, not explicitly considering the value of information-gathering arms. Such information-gathering arms do not necessarily provide the highest reward, thus may never be chosen by an agent that chooses the highest reward arms at all times.

    In this paper, we present a method for information-gathering in latent bandits. Given particular reward structures and transition matrices, we show that choosing the best arm given the agent’s beliefs about the states incurs higher regret. Furthermore, we show that by choosing arms carefully, we obtain an improved estimation of the state distribution, and thus lower the cumulative regret through better arm choices in the future. Through theoretical analysis we show that the proposed method retains the sub-linear regret rate of previous methods while having much better problem dependent constants. We evaluate our method on both synthetic and real-world data sets, showing significant improvement in regret over state-of-the-art methods. © 2022 The Author(s). 

  • 3.
    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.

  • 4.
    Kruusmaa, Maarja
    et al.
    Department of Mechatronics, Tallinn Technical University, Tallinn, Estonia.
    Willemson, Jan
    Tartu University, Department of Computer Science, Tartu, Estonia.
    Covering the path space: a casebase analysis for mobile robot path planning2003In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 16, no 5-6, p. 235-242Article in journal (Refereed)
    Abstract [en]

    This paper presents a theoretical analysis of a casebase used for mobile robot path planning in dynamic environments. Unlike other case-based path planning approaches, we use a grid map to represent the environment that permits the robot to operate in unstructured environments. The objective of the mobile robot is to learn to choose paths that are less risky to follow. Our experiments with real robots have shown the efficiency of our concept. In this paper, we replace a heuristic path planning algorithm of the mobile robot with a seed casebase and prove the upper and lower bounds for the cardinality of the casebase. The proofs indicate that it is realistic to seed the casebase with some solutions to a path-finding problem so that no possible solution differs too much from some path in the casebase. This guarantees that the robot would theoretically find all paths from start to goal. The proof of the upper bound of the casebase cardinality shows that the casebase would in a long run grow too large and all possible solutions cannot be stored. In order to keep only the most efficient solutions the casebase has to be revised at run-time or some other measure of path difference has to be considered.

  • 5.
    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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  • 6.
    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)

  • 7.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Assessing print quality by machine in offset colour printing2013In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 37, p. 70-79Article in journal (Refereed)
    Abstract [en]

    Information processing steps in printing industry are highly automated, except the last one print quality assessment, which usually is a manual, tedious, and subjective procedure. This article presents a random forests-based technique for automatic print quality assessment based on objective values of several printquality attributes. Values of the attributes are obtained from soft sensors through data mining and colour image analysis. Experimental investigations have shown good correspondence between print quality evaluations obtained by the technique proposed and the average observer. (C) 2012 Elsevier B.V. All rights reserved.

  • 8.
    Mahdavi, Ehsan
    et al.
    Isfahan University of Technology, Isfahan, Iran.
    Fanian, Ali
    Isfahan University of Technology, Isfahan, Iran.
    Mirzaei, Abdolreza
    Isfahan University of Technology, Isfahan, Iran.
    Taghiyarrenani, Zahra
    Halmstad University, School of Information Technology.
    ITL-IDS: Incremental Transfer Learning for Intrusion Detection Systems2022In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 253, article id 109542Article in journal (Refereed)
    Abstract [en]

    Utilizing machine learning methods to detect intrusion into computer networks is a trending topic in information security research. The limitation of labeled samples is one of the challenges in this area. This challenge makes it difficult to build accurate learning models for intrusion detection. Transfer learning is one of the methods to counter such a challenge in machine learning topics. On the other hand, the emergence of new technologies and applications might bring new vulnerabilities to computer networks. Therefore, the learning process cannot occur all at once. Incremental learning is a practical standpoint to confront this challenge. This research presents a new framework for intrusion detection systems called ITL-IDS that can potentially start learning in a network without prior knowledge. It begins with an incremental clustering algorithm to detect clusters’ numbers and shape without prior assumptions about the attacks. The outcomes are candidates to transfer knowledge between other instances of ITL-IDS. In each iteration, transfer learning provides target environments with incremental knowledge. Our evaluation shows that this method can combine incremental and transfer learning to identify new attacks. © 2022 Published by Elsevier B.V.

  • 9.
    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)

  • 10.
    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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  • 11.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, M.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Dosinas, A.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Bartkevicius, V.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, A.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Vaitkunas, M.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Lipnickas, A.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    An intelligent system for tuning magnetic field of a cathode ray tube deflection yoke2003In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 16, no 3, p. 161-164Article in journal (Refereed)
    Abstract [en]

    This short communication concerns identification of the number of magnetic correction shunts and their positions for deflection yoke tuning to correct the misconvergence of colours of a cathode ray tube. The misconvergence of colours is characterised by the distances measured between the traces of red and blue beams. The method proposed consists of two phases, namely, learning and optimisation. In the learning phase, the radial basis function neural network is trained to learn a mapping: correction shunt position→changes in misconvergence. In the optimisation phase, the trained neural network is used to predict changes in misconvergence depending on a correction shunt position. An optimisation procedure based on the predictions returned by the neural net is then executed in order to find the minimal number of correction shunts needed and their positions. During the experimental investigations, 98% of the deflection yokes analysed have been tuned successfully using the technique proposed.

  • 12.
    Yu, Zaiyang
    et al.
    Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Hou, Luyang
    Beijing University of Posts and Telecommunications, Beijing, China.
    Li, Lusi
    Old Dominion University, Norfolk, United States.
    Li, Weijun
    Chinese Academy of Sciences, Beijing, China.
    Jiang, Limin
    Chinese Academy Of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
    Ning, Xin
    Chinese Academy of Sciences, Beijing, China.
    MV-ReID: 3D Multi-view Transformation Network for Occluded Person Re-Identification2024In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 283, article id 111200Article in journal (Refereed)
    Abstract [en]

    Re-identification (ReID) of occluded persons is a challenging task due to the loss of information in scenes with occlusions. Most existing methods for occluded ReID use 2D-based network structures to directly extract representations from 2D RGB (red, green, and blue) images, which can result in reduced performance in occluded scenes. However, since a person is a 3D non-grid object, learning semantic representations in a 2D space can limit the ability to accurately profile an occluded person. Therefore, it is crucial to explore alternative approaches that can effectively handle occlusions and leverage the full 3D nature of a person. To tackle these challenges, in this study, we employ a 3D view-based approach that fully utilizes the geometric information of 3D objects while leveraging advancements in 2D-based networks for feature extraction. Our study is the first to introduce a 3D view-based method in the areas of holistic and occluded ReID. To implement this approach, we propose a random rendering strategy that converts 2D RGB images into 3D multi-view images. We then use a 3D Multi-View Transformation Network for ReID (MV-ReID) to group and aggregate these images into a unified feature space. Compared to 2D RGB images, multi-view images can reconstruct occluded portions of a person in 3D space, enabling a more comprehensive understanding of occluded individuals. The experiments on benchmark datasets demonstrate that the proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks. These results also suggest that our approach has the potential to solve occlusion problems and contribute to the field of ReID. The source code and dataset are available at https://github.com/yuzaiyang123/MV-Reid. © 2023 Elsevier B.V.

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