Article
Computer Science, Information Systems
Tapas Bhadra, Sanghamitra Bandyopadhyay
Summary: The paper proposes a novel supervised feature selection approach based on dense subgraph discovery. The algorithm proceeds in two phases to select features with maximal average class relevance, minimal average pairwise redundancy, and good discriminating power. Experimental results show the proposed approach is competitive with conventional and state-of-the-art algorithms in supervised feature selection.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Kanchan Jha, Sriparna Saha
Summary: The work introduces a new feature selection technique that combines multimodal multiobjective optimization and filter-based feature selection, aiming to generate diverse feature subsets and evaluate their quality using different measurements. By employing multiobjective PSO and non-dominated sorting with special crowding distance, the approach achieves the objectives of identifying a large number of Pareto-optimal solutions and selecting feature subsets with minimal redundancy and high correlation. Experimental results demonstrate that the multimodal PSO based feature selection approach outperforms its simple PSO counterpart in finding more feature subsets in multiobjective environment.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Pei Huang, Xiaowei Yang
Summary: Unsupervised feature selection is an important topic in the fields of machine learning, pattern recognition and data mining. A novel method called AGDS is proposed to address the issues in feature selection by utilizing adaptive graph and dependency score.
PATTERN RECOGNITION
(2022)
Article
Biochemical Research Methods
Tapas Bhadra, Saurav Mallik, Amir Sohel, Zhongming Zhao
Summary: This article proposes a novel unsupervised feature selection method by combining hierarchical feature clustering with singular value decomposition. The experimental results demonstrate that the proposed algorithm performs well against several state-of-the-art methods of feature selection in terms of various evaluation criteria. The analysis of cancer genomics data identifies a candidate gene-marker, EREG, which is important for biomarker discovery in precision medicine.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Computer Science, Information Systems
Sadegh Asghari, Hossein Nematzadeh, Ebrahim Akbari, Homayun Motameni
Summary: This paper proposes a new model BC-NMIQ to rank the best features and improves it with the IAMB feature selection method. The experiments on multiple datasets show that BC-NMIQ-IAMB significantly improves the average accuracy of existing binary and multi-class algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Tapas Bhadra, Ujjwal Maulik
Summary: In this article, an unsupervised feature selection algorithm based on the SEA algorithm is proposed. The algorithm identifies dense subgraphs within a weighted graph and presents the feature selection problem as obtaining dense sub-feature spaces. It consists of two stages, finding dense feature subgraphs and identifying representative features. The algorithm does not require the user to provide the number of features to be selected and achieves high accuracy scores.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2022)
Article
Energy & Fuels
Xu Ping, Fubin Yang, Hongguang Zhang, Chengda Xing, Wujie Zhang, Yan Wang
Summary: This study systematically analyzes the relationship between operating parameters and performance of the organic Rankine cycle (ORC) system using unsupervised learning and bilinear interpolation algorithm. A hybrid algorithm is proposed to simultaneously remove outliers and reduce the dimensionality of features in the ORC system. The hybrid algorithm improves the accuracy and reduces the time cost of the ORC system prediction model.
Article
Computer Science, Artificial Intelligence
Zhong Yuan, Hongmei Chen, Pengfei Zhang, Jihong Wan, Tianrui Li
Summary: This paper proposes a novel feature selection approach based on fuzzy mutual information in fuzzy rough set theory to effectively select relevant features from heterogeneous data without decision. The fuzzy relevance and fuzzy conditional relevance of each feature are defined using fuzzy mutual information. The evaluation index of feature importance is obtained using the idea of unsupervised minimum redundancy and maximum relevance. The proposed algorithm selects fewer heterogeneous features while maintaining or improving the performance of learning algorithms.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Anurag Tiwari, Amrita Chaturvedi
Summary: The widespread use of feature selection in various fields emphasizes its importance in expert and intelligent systems. Conventional methods suffer from poor classification accuracy and high computational cost, while hybrid methods offer better efficiency and scalability. A new hybrid feature selection method, IFS-DBOIM, was introduced to address these issues, showing improved performance in classification accuracy with fewer features.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Utkarsh Agrawal, Vasudha Rohatgi, Rahul Katarya
Summary: The problem of feature selection involves selecting the most informative subset of features that have the most impact in classification. This paper proposes a novel variant of the Equilibrium Optimizer called Normalized Mutual Information-based equilibrium optimizer (NMIEO) for feature selection. The proposed method incorporates a local search strategy based on Normalized Mutual Information and utilizes Chaotic maps for population initialization. Experimental results demonstrate the superior performance of NMIEO compared to other competitive methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Hossein Hassani, Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
Summary: This study introduces a feature selection method CFMI based on mutual information to enhance fault diagnosis and network attack detection in power systems. Results indicate that current and voltage features are more informative for diagnostic systems, and features from generation buses have higher priority for diagnostic systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Rohit Kundu, Rammohan Mallipeddi
Summary: This paper proposes a hybrid filter multi-objective evolutionary algorithm (HFMOEA) based on the non-dominated sorting genetic algorithm (NSGA-II) coupled with filter-based feature ranking methods. The HFMOEA aims to select an optimal trade-off solution set by minimizing the number of selected features and maximizing the classification accuracy. The proposed method has been evaluated on various datasets to demonstrate its effectiveness.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Automation & Control Systems
Mohsen Rahmanian, Eghbal G. Mansoori
Summary: Gene expression data analysis is challenging due to complex and high-dimensional samples and genes. This paper proposes an unsupervised gene selection scheme based on information theoretic measures to reduce the risk of overfitting and improve the predictability and readability of genetic data. Experimental results demonstrate the effectiveness of this approach in both unsupervised and supervised scenarios.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Abdolreza Rashno, Milad Shafipour, Sadegh Fadaei
Summary: This paper introduces a novel multi-objective particle swarm optimization feature selection method. It decodes feature vectors as particles and ranks them in a two-dimensional optimization space. The proposed method incorporates feature ranks to update particle velocity and position during the optimization process. Experimental results demonstrate the effectiveness of the method in finding Pareto Fronts of the best particles in multi-objective optimization space.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Junyu Li, Jiazhou Chen, Fei Qi, Tingting Dan, Wanlin Weng, Bin Zhang, Haoliang Yuan, Hongmin Cai, Cheng Zhong
Summary: Unsupervised feature selection is an important and challenging task, and 2D feature selection methods have shown good performance in image analysis. We propose two different strategies for feature selection and extensive experiments demonstrate that our methods outperform state-of-the-art methods in terms of performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Gaurang Panchal, Debasis Samanta, Subhas Barman
MULTIMEDIA TOOLS AND APPLICATIONS
(2019)
Article
Computer Science, Information Systems
S. R. Sreeja, Himanshu, Debasis Samanta
MULTIMEDIA TOOLS AND APPLICATIONS
(2020)
Article
Computer Science, Information Systems
Fagul Pandey, Priyabrata Dash, Debasis Samanta, Monalisa Sarma
Summary: The study introduces a robust Singular Value Decomposition-based fingerprint alignment method that improves accuracy in fingerprint recognition without relying on image quality or reference images.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
Fagul Pandey, Priyabrata Dash, Debasis Samanta, Monalisa Sarma
Summary: This paper presents a software-based approach for generating private keys using user-provided question-answer pairs and email-Id as input. By constructing triplets and defining unique parameters, a seed is generated and a unique key is generated using cyclic idiosyncratic architecture. The method has been tested for correctness, randomness, dissimilarity, information entropy, reliability, and resilience against various security threats.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Proceedings Paper
Computer Science, Hardware & Architecture
Sricheta Parui, Debasis Samanta, Nishant Chakravorty
Summary: This study aims to improve the healthcare system through the collaboration of Brain-Computer Interface and the Internet of Things to create a smart system for controlling smart homes. The experiment findings indicate that the speed of IoT is sufficient for a real-time BCI system.
PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING AND NETWORKING, ICDCN 2023
(2023)
Article
Computer Science, Information Systems
Gaurang Panchal, Debasis Samanta, Ashok Kumar Das, Neeraj Kumar, Kim-Kwang Raymond Choo
Summary: In this article, a biometric-based authentication protocol is designed to provide secure access to a remote server. The protocol generates a private key from the user's biometric data and a session key using two biometric templates, and it is shown to resist multiple known attacks.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Computer Science, Information Systems
Subhas Barman, Samiran Chattopadhyay, Debasis Samanta, Sayantani Barman
Summary: This paper proposes a blockchain-based approach to secure electronic health records and addresses common issues of blockchain. It utilizes elliptic curve cryptography and a biometric-based commitment scheme to ensure data confidentiality and integrity. The security of the proposed scheme is verified using the Random Oracle model and compared with existing approaches.
SECURITY AND PRIVACY
(2022)
Article
Information Science & Library Science
Arpita Chaudhuri, Nilanjan Sinhababu, Monalisa Sarma, Debasis Samanta
Summary: The design of a research paper recommendation system is crucial for researchers, and the introduction of indirect features provides a new perspective for paper recommendations, improving the accuracy of recommendations. Experimental results show that the proposed features can better define research articles, enabling real-time filtering of a large number of papers.
INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES
(2021)
Article
Computer Science, Interdisciplinary Applications
Debasis Samanta, Tuhin Chakraborty
ACM TRANSACTIONS ON ACCESSIBLE COMPUTING
(2020)
Proceedings Paper
Engineering, Biomedical
S. R. Sreeja, Himanshu, Debasis Samanta, Monalisa Sarma
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Arpita Chaudhuri, Monalisa Sarma, Debasis Samanta
PROCEEDINGS OF THE 2019 IEEE REGION 10 CONFERENCE (TENCON 2019): TECHNOLOGY, KNOWLEDGE, AND SOCIETY
(2019)
Proceedings Paper
Acoustics
Tauheed Ahmed, Monalisa Sarma, Debasis Samanta
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
(2019)
Proceedings Paper
Computer Science, Software Engineering
E. S. F. Najumudheen, Rajib Mall, Debasis Samanta
PROCEEDINGS OF THE 12TH INNOVATIONS ON SOFTWARE ENGINEERING CONFERENCE (ISEC)
(2019)
Article
Computer Science, Information Systems
Subhas Barman, Hubert P. H. Shum, Samiran Chattopadhyay, Debasis Samanta
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)