Article
Computer Science, Software Engineering
Zehua Zeng, Phoebe Moh, Fan Du, Jane Hoffswell, Tak Yeon Lee, Sana Malik, Eunyee Koh, Leilani Battle
Summary: This paper proposes an evaluation-focused framework for contextualizing and comparing visualization recommendation algorithms. It analyzes algorithmic performance through theoretical and empirical comparisons, suggesting the need for more rigorous formal comparisons to clarify the benefits of recommendation algorithms in different analysis scenarios.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Computer Science, Information Systems
Pierfrancesco Bellini, Luciano Alessandro Ipsaro Palesi, Paolo Nesi, Gianni Pantaleo
Summary: Fashion retail is popular and relevant, and improving customer relationship management solutions can enhance customer satisfaction and increase profitability for retailers. This paper proposes a recommendation system based on multi clustering approach to address the shortcomings of current marketing solutions and solve cold start problems using data mining techniques.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Biochemical Research Methods
Yichen Cheng, Yusen Xia, Xinlei Wang
Summary: We propose a drug recommendation model that integrates structured data and unstructured texts. The model is based on multitask learning and can predict review ratings for a given medicine. It incorporates topic modeling, sentiment analysis, and variable selection. The proposed method outperforms existing benchmark methods in accuracy and AUC.
Article
Computer Science, Information Systems
Badal Soni, Debangan Thakuria, Nilutpal Nath, Navarun Das, Bhaskarananda Boro
Summary: Anime is popular nowadays, especially among the younger generations. Building a recommendation engine for this relatively obscure entertainment medium is challenging due to insufficient knowledge on users' preferences and watching habits. In this study, we developed a novel hybrid recommendation system that can serve as both a recommendation system and a way to explore new anime genres and titles. Our solution utilizes deep autoencoders for predicting ratings and generating embeddings, and utilizes clusters formed by these embeddings to find similar anime titles liked or disliked by the user. We demonstrated the effectiveness of our approach and compared it to existing state-of-the-art techniques.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Mathematics, Interdisciplinary Applications
Yongkang Huo
Summary: This paper describes the main ideas and methods used in current recommendation systems and aims to build a personalized music recommendation system based on directed tags. It also introduces the collaborative filtering algorithm based on tags.
Article
Health Care Sciences & Services
Luis Fernando Granda Morales, Priscila Valdiviezo-Diaz, Ruth Reategui, Luis Barba-Guaman
Summary: This study developed a drug recommendation system for patients with diabetes based on collaborative filtering and clustering techniques. The system provides medication recommendations based on the characteristics of patients and drug information.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Yonis Gulzar, Ali A. A. Alwan, Radhwan M. M. Abdullah, Abedallah Zaid Abualkishik, Mohamed Oumrani
Summary: The e-commerce industry has gained popularity, providing great business opportunities. As society leans towards online shopping convenience, the challenge of choosing the best products from a vast selection arises. To address this, a new clustering technique called the Ordered Clustering-based Algorithm (OCA) was proposed to reduce the impact of cold-start and data sparsity issues in e-commerce recommendation systems. Through a comprehensive review of data clustering techniques, OCA utilizes collaborative filtering to cluster users based on their preferences. Experimental results confirmed that OCA outperforms previous approaches, achieving higher percentages of Precision, Recall, and F-measure.
Article
Computer Science, Information Systems
Hongwu Qin, Meng Zhao, Xiuqin Ma, HuanLing Sun, Weiyi Wei
Summary: This paper presents a new model called Best Matching Collaborator Recommendation (BMCR) to help scholars find suitable collaborators based on their academic level. Experimental results demonstrate that our model improves the feasibility of cooperation and achieves significant improvements in precision rate, recall rate, and F1 score.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Luis M. De Campos, Juan M. Fernandez-Luna, Juan F. Huete
Summary: This paper studies the problem of venue recommendation and proposes a method that combines clustering techniques and information retrieval to construct topic-based profiles, as well as utilizes authorship information to improve the recommendations.
Article
Engineering, Electrical & Electronic
Yuanqian Ma, Xiaolong Ma, Hanzhong Chen, Yi Lei
Summary: In this paper, a method for recommending electricity sales packages based on unbalanced evaluation and incomplete weight multi-granularity fuzzy language sets is proposed. The method improves the accuracy of the recommendation results by addressing the issue of fuzzy user evaluation information that existing methods ignore.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Review
Computer Science, Information Systems
Ying-Chia Hsieh, Long-Chuan Lu, Yi-Fan Ku
Summary: This paper proposes a novel approach to evaluate the trustworthiness of reviews in online travel communities. The approach considers the sentiment similarity of reviewers, features of the reviews, and behaviors of the reviewers.
Article
Computer Science, Artificial Intelligence
Zhu Sun, Hui Fang, Jie Yang, Xinghua Qu, Hongyang Liu, Di Yu, Yew-Soon Ong, Jie Zhang
Summary: Recently, the lack of effective benchmarks for rigorous evaluation in recommender systems has become a critical issue, resulting in unreproducible evaluation and unfair comparison. To address this, we conducted both theoretical and experimental studies to benchmark recommendation for rigorous evaluation. The theoretical study systematically analyzed hyper-factors affecting recommendation performance throughout the evaluation chain, while the experimental study released DaisyRec 2.0 library to integrate these hyper-factors and conducted empirical research on their impacts. With the support of both studies, we proposed standardized procedures and provided performance benchmarks for later study.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Hardware & Architecture
Nayan Ranjan Das, Imon Mukherjee, Anubhav D. Patel, Goutam Paul
Summary: Player selection is crucial in team-based sports like cricket due to various situations that may require substitution. We propose a knowledge-based intelligent framework for substitute suggestions utilizing clustering techniques such as DBSCAN and Spectral clustering. Our results show a significant similarity between the suggestions generated using Spectral clustering and the actual substitutions made during real-time team selection. Additionally, our framework outperforms existing state-of-the-art works that use K-means clustering. Moreover, we present an intelligent framework for team selection using similarity measures like Euclidean distance, Cosine Similarity, Manhattan distance, and Pearson Correlation Coefficient, achieving a maximum accuracy of 77.50% and providing diverse directions for team line-up formation.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Emre Yalcin, Alper Bilge
Summary: This study proposes novel automatic user grouping approaches by constructing a binary decision tree via bisecting k-means clustering for enhanced group formation and group size restriction, and adopting a genre-based mapping of user ratings to represent users, which improves computation time and eliminates adverse effects of sparsity. Furthermore, the combination of demographic characteristics and genre-based similarities is introduced to achieve a more homogeneous automatic group formation.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Hong -Yu Wang, Jie-Sheng Wang, Guan Wang
Summary: This paper reviews fuzzy clustering validity functions and combined fuzzy clustering validity evaluation methods, and analyzes their research status and construction strategies. The accuracy and stability of each evaluation method are analyzed through comparative experiments. Finally, the paper summarizes the shortcomings and advantages of the current research on fuzzy clustering validity, and looks forward to future research directions and improved methods.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Shahla Asadi, Mehrbakhsh Nilashi, Rabab Ali Abumalloh, Sarminah Samad, Ali Ahani, Fahad Ghabban, Salma Yasmin Mohd Yusuf, Eko Supriyanto
Summary: The spectrum of diseases caused by infections is rapidly evolving due to recent changes in social and environmental conditions. The global outbreak of COVID-19 has significantly impacted lives, families, and societies, prompting action from governments, including Malaysia, to prevent the spread of the virus. The Malaysian government has focused on measures recommended by the World Health Organization to address the public health threat posed by COVID-19 and has identified key factors, such as movement control orders and travel restrictions, to prevent the transmission of infections.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Engineering, Environmental
Shahla Asadi, Mehrbakhsh Nilashi, Mohammad Iranmanesh, Morteza Ghobakhloo, Sarminah Samad, Abdullah Alghamdi, Ahmed Almulihi, Saidatulakmal Mohd
Summary: The study highlights that environmental concern, trust in EVs, personal norms, price value, attitudes towards EVs, and subjective norms are the most important factors influencing the adoption of EVs by consumers in Malaysia.
RESOURCES CONSERVATION AND RECYCLING
(2022)
Article
Engineering, Multidisciplinary
Mehrbakhsh Nilashi, Rabab Ali Abumalloh, Mesfer Alrizq, Ahmed Almulihi, O. A. Alghamdi, Murtaza Farooque, Sarminah Samad, Saidatulakmal Mohd, Hossein Ahmadi
Summary: Travel recommendation agents are helpful tools for travelers, but the COVID-19 outbreak has caused data sparsity to become a major issue. Machine learning is effective in dealing with sparsity, and developing new algorithms can help solve this problem.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Article
Social Issues
Masoumeh Zibarzani, Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Sarminah Samad, O. A. Alghamdi, Fatima Khan Nayer, Muhammed Yousoof Ismail, Saidatulakmal Mohd, Noor Adelyna Mohammed Akib
Summary: This research explores customers' satisfaction and preferences of restaurants' services during the COVID-19 crisis using online reviews. It also investigates the moderating impact of COVID-19 safety precautions on restaurants' quality dimensions and satisfaction. The study applies a hybrid approach combining clustering, supervised learning, and text mining techniques to analyze data collected from the TripAdvisor platform. The findings contribute to understanding customer satisfaction in the tourism and hospitality sectors during the COVID-19 crisis.
TECHNOLOGY IN SOCIETY
(2022)
Article
Biology
Mehrbakhsh Nilashi, Rabab Ali Abumalloh, Salma Yasmin Mohd Yusuf, Ha Hang Thi, Mohammad Alsulami, Hamad Abosaq, Sultan Alyami, Abdullah Alghamdi
Summary: In this research, a combined approach using Deep Belief Network (DBN) and Neuro-Fuzzy methods is proposed for Parkinson's disease diagnosis. Large datasets are handled using the Expectation-Maximization (EM) clustering approach. Principle Component Analysis (PCA) is used for noise removal. The approach is assessed on a real-world PD dataset, showing improved UPDRS prediction accuracy and lower time complexity in handling large datasets compared to previous methods.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2023)
Article
Business
Behzad Foroughi, Pham Viet Nhan, Mohammad Iranmanesh, Morteza Ghobakhloo, Mehrbakhsh Nilashi, Elaheh Yadegaridehkordi
Summary: This study identifies the major determinants of intention to adopt autonomous vehicles, including trust, hedonic motivation, social influence, compatibility, and effort expectancy. The findings are valuable for devising effective strategies in the development and adoption of autonomous vehicles.
JOURNAL OF RETAILING AND CONSUMER SERVICES
(2023)
Article
Business
Morteza Ghobakhloo, Mohammad Iranmanesh, Manuel E. Morales, Mehrbakhsh Nilashi, Azlan Amran
Summary: This study addresses the knowledge gap in understanding how Industry 5.0 can deliver sustainable transformation. It identifies 11 actions and approaches that enable Industry 5.0 transformation and develops a strategy roadmap for sustainable development. The results highlight the significance of stakeholder integration and collaboration, as well as proactive governmental support, in driving Industry 5.0. Additionally, eco-innovation and sustainable value network reformation are identified as complex and challenging enablers. The study provides implications for policymakers and practitioners in terms of functionality and optimal development sequence.
CORPORATE SOCIAL RESPONSIBILITY AND ENVIRONMENTAL MANAGEMENT
(2023)
Article
Information Science & Library Science
Mehrbakhsh Nilashi, Rabab Ali Abumalloh, Saidatulakmal Mohd, Sharifah Nurlaili Farhana Syed Azhar, Sarminah Samad, Ha Hang Thi, O. A. Alghamdi, Abdullah Alghamdi
Summary: The COVID-19 crisis has had a significant impact on the world, particularly on the implementation of SDGs. This study analyzes the implications of the COVID-19 crisis on SDGs using a bibliometric analysis approach and a SWOT analysis approach, with a focus on Malaysia. The study reveals unprecedented challenges faced by countries in terms of implementation, coordination, decision-making, and regional issues.
TELEMATICS AND INFORMATICS
(2023)
Article
Information Science & Library Science
Mehrbakhsh Nilashi, Rabab Ali Abumalloh, Sarminah Samad, Behrouz Minaei-Bidgoli, Ha Hang Thi, O. A. Alghamdi, Muhammed Yousoof Ismail, Hossein Ahmadi
Summary: Recommender systems play a vital role in online retailing and customer decision-making. Conventional Collaborative Filtering (CF) approaches rely on individual customer ratings, while Multi-criteria CF (MCCF) approaches provide more reliable and effective recommendations on retailing websites. However, these approaches need improvement in terms of accuracy, addressing sparsity issues, and incorporating criteria ratings.
TELEMATICS AND INFORMATICS
(2023)
Article
Operations Research & Management Science
Mehrbakhsh Nilashi, Abdullah M. Baabdullah, Rabab Ali Abumalloh, Keng-Boon Ooi, Garry Wei-Han Tan, Mihalis Giannakis, Yogesh K. Dwivedi
Summary: Big data and predictive analytics (BDPA) techniques have been used to enhance individuals' quality of living and business performance in various research areas. The emergence of big data has made recycling and waste management easier and more efficient. This study examines the impact of BDPA on the performance and competitive advantage of the food waste and recycling industry.
ANNALS OF OPERATIONS RESEARCH
(2023)
Review
Clinical Neurology
Sorayya Rezayi, Meysam Rahmani Katigari, Leila Shahmoradi, Mehrbakhsh Nilashi
Summary: This study aims to evaluate the vulnerability of Parkinson's disease patients to COVID-19 and its consequences. A systematic review found that Parkinson's disease patients experienced worsened symptoms during the pandemic, and Parkinson's disease was also found to be a risk factor for more severe COVID-19 disease.
PARKINSONS DISEASE
(2023)
Article
Neurosciences
Mehrbakhsh Nilashi, Rabab Ali Abumalloh, Sultan Alyami, Abdullah Alghamdi, Mesfer Alrizq
Summary: This paper aims to develop a new method for PD diagnosis using supervised and unsupervised learning techniques. The authors utilize the Laplacian score, Gaussian process regression, and self-organizing maps for modeling and predicting UPDRS scores in a PD dataset. The study finds that the combination of SOM, Laplacian score, and Gaussian process regression with the exponential kernel provides the best results in predicting UPDRS scores.
Article
Operations Research & Management Science
Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Keng Boon Ooi, Garry Wei-Han, Tat-Huei Cham, Yogesh K. Dwivedi, Laurie Hughes
Summary: This study explores the factors affecting the adoption of the metaverse in the retail industry and proposes a new model based on the Resource-Based View theory. The research findings indicate that product innovation through the use of the metaverse positively impacts the sustainable competitive advantage of retail companies. The intention to use the metaverse is also found to drive product innovation in retail companies.
ANNALS OF OPERATIONS RESEARCH
(2023)
Review
Multidisciplinary Sciences
Mehrbakhsh Nilashi, Rabab Ali Abumalloh, Hossein Ahmadi, Sarminah Samad, Mesfer Alrizq, Hamad Abosaq, Abdullah Alghamdi
Summary: This study investigates customer satisfaction with CRM systems through online reviews. The dimensions of information quality, system quality, and service quality were extracted and their relationship with customer satisfaction was assessed using ANFIS.
Article
Environmental Sciences
Mahmood Safaei, Elankovan A. Sundararajan, Shahla Asadi, Mehrbakhsh Nilashi, Mohd Juzaiddin Ab Aziz, M. S. Saravanan, Maha Abdelhaq, Raed Alsaqour
Summary: Obesity and its complications are a major global issue, and Malaysia ranks sixth among Asian countries in terms of adult obesity. This study aimed to investigate and assess the risk factors associated with obesity and overweight in Malaysia. The findings revealed that lack of physical activity, unhealthy diet, insufficient sleep, genetics, and perceived stress were the most significant risk factors for obesity.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)