Article
Computer Science, Artificial Intelligence
Zhaoliang Chen, Wei Zhao, Shiping Wang
Summary: This paper proposes a new neighborhood-based interpolation model that combines kernel function and similarity measurement to better approximate unknown ratings by computing impact weights of neighbors in a new Hilbert space. Experimental results demonstrate the effectiveness of the proposed method in improving the performance of the rating prediction task compared to traditional and state-of-the-art approaches.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Theory & Methods
M. Mehdi Afsar, Trafford Crump, Behrouz Far
Summary: Recommender systems have become an integral part of our daily lives, helping us find our favorite items, friends on social networks, and movies to watch. The recommendation problem was traditionally seen as a classification or prediction problem, but it is now widely agreed that formulating it as a sequential decision problem using reinforcement learning can better capture user-system interaction and long-term engagement.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaocong Chen, Lina Yao, Julian McAuley, Guanglin Zhou, Xianzhi Wang
Summary: This article provides a timely and comprehensive overview of recent trends in deep reinforcement learning (DRL) in recommender systems. It discusses the motivation for applying DRL in recommender systems, presents a taxonomy and summary of current DRL-based recommender systems, and explores emerging topics and open issues. The survey serves as an introductory material for readers from academia and industry and identifies notable opportunities for further research.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zeyu Cui, Feng Yu, Shu Wu, Qiang Liu, Liang Wang
Summary: Item representations in recommendation systems are traditionally done using single latent vectors, but utilizing attribute information has recently become popular for better item representations. This article proposes a fine-grained Disentangled Item Representation (DIR) method, representing items as separate attribute vectors for more detailed item information. Experimental results using the LearnDIR strategy show that models developed under DIR framework are effective and efficient, even outperforming state-of-the-art methods in cold-start situations.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
Summary: This survey provides a systematic summary of three categories of trust issues in recommender systems and focuses on the work based on deep learning techniques.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Haifeng Liu, Hongfei Lin, Wenqi Fan, Yuqi Ren, Bo Xu, Xiaokun Zhang, Dongzhen Wen, Nan Zhao, Yuan Lin, Liang Yang
Summary: In this paper, the authors propose a group rank fair recommender (GRFRec) method to address the issue of unfair recommendations caused by data bias in recommender systems. Through self-supervised learning and adversarial learning, the GRFRec algorithm enhances user representation and eliminates group-specific information to achieve fairness and improve recommendation accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Anchen Li, Bo Yang, Huan Huo, Farookh Khadeer Hussain
Summary: A novel recommendation method is proposed in this study, which enhances recommendation performance by mining implicit relations between users and items. Experimental results demonstrate that the method achieves superior performance in rating prediction and Top-k recommendation.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Yuxiang Han, Hong Chen, Tieliang Gong, Jia Cai, Hao Deng
Summary: Partially linear models (PLMs), which combine linear and nonlinear approximation, are effective in modeling complex data. However, most existing PLMs are limited to mean regression and are sensitive to non-Gaussian noises. To address this issue, this paper proposes a Robust Linear And Nonlinear Discovery algorithm (RLAND) that integrates modal regression and PLMs. The algorithm is supported by statistical analysis on generalization bound and structure discovery consistency, and it can be efficiently computed using half quadratic optimization and quadratic programming. Empirical evaluations on simulated and real-world data demonstrate the competitive performance of the proposed method.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Matteo Scandella, Mirko Mazzoleni, Simone Formentin, Fabio Previdi
Summary: In this paper, a non-parametric approach to infer a continuous-time linear model from data is proposed, which automatically selects a proper structure of the transfer function and guarantees system stability. Through benchmark simulation examples, the proposed approach is shown to outperform state-of-the-art continuous-time methods, even in critical cases where short sequences of canonical input signals are used for model learning.
INTERNATIONAL JOURNAL OF CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Shengyu Zhang, Fuli Feng, Kun Kuang, Wenqiao Zhang, Zhou Zhao, Hongxia Yang, Tat-Seng Chua, Fei Wu
Summary: The paper investigates the interactions between latent factors in recommender systems and proposes a personalized latent structure learning framework called PlanRec. It personalizes the universally learned dependencies through probabilistic modeling and balances shared knowledge and personalization through uncertainty estimation.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhenwen Ren, Quansen Sun
Summary: The study introduces a new multiple kernel learning method (SPMKC) that preserves the global and local structure of input data in kernel space through a new kernel weight strategy and a kernel adaptive local structure learning term.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yu Lei, Zhitao Wang, Wenjie Li, Hongbin Pei, Quanyu Dai
Summary: This paper proposes a method to address the issues of data sparsity and cold-start in recommender systems by leveraging social networks. Two algorithms based on this method are developed and the experimental results show their outstanding performance on real-world datasets with reasonable computation cost.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Xiaojian Ding, Yi Li, Shilin Chen
Summary: This research paper addresses the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. The proposed algorithms, which introduce a novel feature ranking criterion and an optimal feature subset evaluation algorithm, outperform current state-of-the-art methods.
Article
Telecommunications
Eyad Shtaiwi, Hongliang Zhang, Sriram Vishwanath, Moustafa Youssef, Ahmed Abdelhadi, Zhu Han
Summary: This paper investigates the channel estimation problem for RIS-aided MU-MIMO system, proposing an algorithm to estimate the composite channel, separate RIS-based channels, and direct channel by dividing the entire RIS surface into small sub-RISs and controlling the phase shifts for each sub-RIS unit. The simulation results confirm the efficiency and effectiveness of the proposed approach algorithm.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2021)
Article
Energy & Fuels
Wanjun Huang, Xinran Zhang, Weiye Zheng
Summary: A deep transfer learning approach based on Bi-directional Long Short-Term Memory (BiLSTM) is proposed to efficiently identify resilient network structures with better STVS performance in sub-transmission expansion planning (SEP). An improved Voltage Recovery Index (IVRI) is introduced to quantify the STVS of different network structures, and a BiLSTM-based STVS evaluation machine is devised to predict STVS without resorting to time-consuming time-domain simulations. The proposed approach has been verified effective through numerical tests on IEEE benchmarks and the real Guangdong Power Grid.
Article
Computer Science, Artificial Intelligence
Rida Ghafoor Hussain, Mustansar Ali Ghazanfar, Muhammad Awais Azam, Usman Naeem, Shafiq Ur Rehman
ARTIFICIAL INTELLIGENCE REVIEW
(2019)
Article
Computer Science, Information Systems
Salabat Khan, Amir Khan, Muazzam Maqsood, Farhan Aadil, Mustansar Ali Ghazanfar
JOURNAL OF GRID COMPUTING
(2019)
Article
Computer Science, Information Systems
Faria Nazir, Mustansar Ali Ghazanfar, Muazzam Maqsood, Farhan Aadil, Seungmin Rho, Irfan Mehmood
MULTIMEDIA TOOLS AND APPLICATIONS
(2019)
Article
Computer Science, Information Systems
Fouzia Jabeen, Muazzam Maqsood, Mustansar Ali Ghazanfar, Farhan Aadil, Salabat Khan, Muhammad Fahad Khan, Irfan Mehmood
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2019)
Article
Multidisciplinary Sciences
Mubbashir Ayub, Mustansar Ali Ghazanfar, Zahid Mehmood, Tanzila Saba, Riad Alharbey, Asmaa Mandi Munshi, Mayda Abdullateef Alrige
Article
Computer Science, Artificial Intelligence
Wasiat Khan, Usman Malik, Mustansar Ali Ghazanfar, Muhammad Awais Azam, Khaled H. Alyoubi, Ahmed S. Alfakeeh
Article
Computer Science, Artificial Intelligence
Mubbashir Ayub, Mustansar Ali Ghazanfar, Zahid Mehmood, Khaled H. Alyoubi, Ahmed S. Alfakeeh
Article
Computer Science, Artificial Intelligence
Wasiat Khan, Mustansar Ali Ghazanfar, Muhammad Awais Azam, Amin Karami, Khaled H. Alyoubi, Ahmed S. Alfakeeh
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2020)
Article
Computer Science, Information Systems
Muazzam Maqsood, Mustansar Ali Ghazanfar, Irfan Mehmood, Eenjun Hwang, Seungmin Rho
Summary: This study presents a new variant of adversarial attack that highlights the vulnerability of gait recognition systems by adding one-pixel adversarial noise in less perceptible locations. The study found that even when the noise is less imperceptible by the human naked eye, CNN-based gait recognition systems still face severe potential threats.
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
(2023)
Article
Multidisciplinary Sciences
Mubbashir Ayub, Mustansar Ali Ghazanfar, Tasawer Khan, Asjad Saleem
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2020)
Article
Computer Science, Information Systems
Faria Nazir, Muhammad Nadeem Majeed, Mustansar Ali Ghazanfar, Muazzam Maqsood
Article
Computer Science, Information Systems
Misbah Iqbal, Mustansar Ali Ghazanfar, Asma Sattar, Muazzam Maqsood, Salabat Khan, Irfan Mehmood, Sung Wook Baik
Proceedings Paper
Computer Science, Theory & Methods
Mubbashir Ayub, Mustansar Ali Ghazanfar, Muazzam Maqsood, Asjad Saleem
2018 32ND INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Usman Naeem, Abdel-Rahman Tawil, Ivans Semelis, Muhammad Awais Azam, Mustansar Ali Ghazanfar
PROCEEDINGS OF SAI INTELLIGENT SYSTEMS CONFERENCE (INTELLISYS) 2016, VOL 1
(2018)
Article
Computer Science, Information Systems
Arta Iftikhar, Mustansar Ali Ghazanfar, Mubbashir Ayub, Zahid Mehmood, Muazzam Maqsood
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)