Article
Computer Science, Artificial Intelligence
Guofang Ma, Yuexuan Wang, Xiaolin Zheng, Xiaoye Miao, Qianqiao Liang
Summary: This paper proposes a novel Trust-aware Latent Space Mapping approach (TLSM-CDR) for Cross-domain Recommendation, addressing the challenge of insufficient bridged users by considering users' trust relationships. Experimental results demonstrate that the TLSM-CDR model significantly outperforms several state-of-the-art methods on two real-world datasets.
Article
Computer Science, Artificial Intelligence
Omer Tal, Yang Liu, Jimmy Huang, Xiaohui Yu, Bushra Aljbawi
Summary: Neural attention is increasingly popular in recommender systems. We propose DARIA and SARAH models, which utilize different attention mechanisms to improve recommendation accuracy and reasoning. Various experiments demonstrate the significant improvement of SARAH and DARIA over baselines, showing the potential benefit of applying self-attention in different scenarios.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Yi-Hung Liu, Yen-Liang Chen, Po-Ya Chang
Summary: With the increasing number of mobile applications, it has become difficult for users to find the most suitable and interesting ones. This study proposes a better model for mobile app recommendation by combining matrix factorization, user reviews, and deep learning methods. Experimental results show that this model outperforms existing methods.
DECISION SUPPORT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Milad Ahmadian, Mahmood Ahmadi, Sajad Ahmadian
Summary: This paper proposes a trust-aware recommendation method based on deep sparse autoencoder to address challenges in deep learning based recommendation systems. Through the use of an effective probabilistic model and an implicit rating utilization mechanism, the method achieves significant improvements in generating latent features and providing accurate recommendations.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Haichi Huang, Sisi Luo, Xuan Tian, Shuo Yang, Xiaoping Zhang
Summary: The study proposes a neural explicit factor model (NEFM) to address the explainability issue of traditional recommendation systems and improve accuracy. NEFM enhances system performance by adding user-feature attention matrix and item-feature quality matrix, and utilizing neural networks to extract features from user, item, and item features.
Article
Computer Science, Artificial Intelligence
Zafar Ali, Guilin Qi, Khan Muhammad, Siddhartha Bhattacharyya, Irfan Ullah, Waheed Abro
Summary: The large number of research articles on the Web poses challenges for researchers to find related works, leading to the development of network representation learning-based citation recommendation models. Our proposed model effectively utilizes semantic relations and contextual information within bibliographic networks, demonstrating significant improvements compared to baseline models on DBLP datasets.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Zafar Ali, Guilin Qi, Khan Muhammad, Pavlos Kefalas, Shah Khusro
Summary: The study introduces a network embedding model called GCR-GAN for global citation recommendation, which shows promising results in generating personalized citation recommendations using HBN and learning semantic-preserving graph representations with SPECTER and Denoising Auto-encoder networks. Compared to baseline models, it achieves an improvement of nearly 11% and 12% in terms of MAP and nDCG metrics, respectively, and also performs well in addressing the network sparsity issue.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Zhengxiao Du, Jie Tang, Yuhui Ding
Summary: This study focuses on the personalized article recommendation issue when the user's preference data is missing or limited, known as the user cold-start problem in recommender systems. The proposal of POLAR++, an active recommendation framework utilizing Bayesian neural networks and one-shot learning, effectively addresses this problem. By designing an attention-based CNN to quantify the similarity between user preference and recommended articles, the model's effectiveness has been successfully validated.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Information Systems
Jirut Polohakul, Ekapol Chuangsuwanich, Atiwong Suchato, Proadpran Punyabukkana
Summary: The study proposes a real estate recommendation approach to solve the item cold-start problem, aiming to provide acceptable warm-start item recommendations for cold-start users.
Article
Computer Science, Artificial Intelligence
Jiajia Chen, Xin Xin, Xianfeng Liang, Xiangnan He, Jun Liu
Summary: Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. Existing graph-based methods fail to consider the bias offsets of users (items). We propose Graph-Based Decentralized Collaborative Filtering for Social Recommendation (GDSRec) which treats biases as vectors and incorporates them into the learning process of user and item representations. Experimental results show that GDSRec achieves superior performance compared with state-of-the-art related baselines.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Review
Computer Science, Artificial Intelligence
Wu-Dong Xi, Ling Huang, Chang-Dong Wang, Yin-Yu Zheng, Jian-Huang Lai
Summary: Many recommender systems utilize review text as auxiliary information to enhance recommendation quality, but existing models typically use ratings as the ground truth for error backpropagation, potentially resulting in the loss of valuable review information. This article introduces a novel deep model DRRNN, which considers both target ratings and reviews as ground truth for error backpropagation, allowing for the retention of more semantic information in rating predictions. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DRRNN model in terms of rating prediction.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Review
Computer Science, Artificial Intelligence
Parisa Abolfath Beygi Dezfouli, Saeedeh Momtazi, Mehdi Dehghan
Summary: Users' reviews are valuable for recommendation systems and can help alleviate data sparsity issues. The MatchPyramid Recommender System (MPRS) presented in this paper leverages review texts to predict user ratings for items, treating recommendation as a text matching problem. Experimental results show relative improvements compared to existing models on different datasets, demonstrating the effectiveness of leveraging review texts in recommendations.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Juan Ni, Zhenhua Huang, Chang Yu, Dongdong Lv, Cheng Wang
Summary: Recent studies have shown that attention mechanisms are crucial for accurately capturing user interests in recommender systems. The proposed CCDMA model achieves higher accuracy in extracting user and item latent feature vectors, considering both self-attention and cross-attention, and optimizing a comparative learning framework, leading to significant improvements in various evaluation metrics.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Lei Chen, Guixiang Zhu, Weichao Liang, Youquan Wang
Summary: Trip recommendation is an intelligent service that offers personalized itinerary plans to tourists in unfamiliar cities, considering temporal and spatial constraints. In this article, we propose MORL-Trip, a Multi-Objective Reinforcement Learning approach, to address the challenges of capturing users' dynamic preferences and enhancing the diversity and popularity of personalized trips. MORL-Trip models the recommendation as a Markov Decision Process and incorporates sequential, geographic, and order information to learn user's context. It also introduces a composite reward function to reinforce accuracy, popularity, and diversity as principal objectives.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Agung Hadhiatma, Azhari Azhari, Yohanes Suyanto
Summary: Personalized PageRank, a variant of PageRank, is widely used for citation recommendation. However, it still results in information overload when working with vast and rich scholarly data. This research proposes a framework of scientific citation recommendation with serendipitous perspectives to find influential papers with similar and related topics.
Review
Computer Science, Theory & Methods
Fath U. Min Ullah, Mohammad S. Obaidat, Amin Ullah, Khan Muhammad, Mohammad Hijji, Sung Wook Baik
Summary: Recent advancements in intelligent surveillance systems for video analysis have attracted significant attention in the research community. Automatic violence detection systems using artificial neural networks and machine intelligence are in high demand in heavily crowded areas to ensure safety and security in smart cities. Extensive literature on violence detection has been published, but existing surveys are limited in scope. To address this, we conduct a comprehensive survey and analysis of the literature, examining machine learning strategies, neural network-based analysis, limitations, and datasets. We also discuss evaluation strategies, metrics, and provide recommendations for future research in violence detection.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Arun Kumar Sangaiah, Khan Muhammad
Summary: Video capsule endoscopy (VCE) is a revolutionary technology for early diagnosis of gastric disorders, but manual interpretation of VCE videos is time-consuming due to the high redundancy and subtle manifestation of anomalies. Several machine learning methods have been adopted to improve VCE analysis, but their clinical impact is yet to be explored. This survey aimed to bridge the gap between existing ML-based research and clinically significant rules established by gastroenterologists. A framework for interpreting raw frames and merging findings with meta-data was proposed. The challenges and opportunities for VCE analysis were discussed, and the importance of maximizing the discriminative power of features, creating large datasets, and ensuring explainability and reliability of ML-based diagnostics in VCE was emphasized.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Kit Yan Chan, Bilal Abu-Salih, Khan Muhammad, Vasile Palade, Rifai Chai
Article
Computer Science, Information Systems
Khan Muhammad, Hayat Ullah, Mohammad S. Obaidat, Amin Ullah, Arslan Munir, Muhammad Sajjad, Victor Hugo C. de Albuquerque
Summary: This article proposes an efficient deep-learning-based framework for multiperson salient soccer event recognition in the IoT-enabled FinTech. The framework performs event recognition through frames preprocessing, frame-level discriminative features extraction, and high-level events recognition in soccer videos. The results validate the suitability of the proposed framework for salient event recognition in Nx-IoT environments.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Muhammad Irfan, Khan Muhammad, Muhammad Sajjad, Khalid Mahmood Malik, Faouzi Alaya Cheikh, Joel J. P. C. Rodrigues, Victor Hugo C. de Albuquerque
Summary: This article discusses the significant bandwidth consumption of immersive videos in industry 4.0 and proposes a solution using convolutional neural networks to select the user's region of interest and reduce bandwidth usage.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Mathematics
Senthil Kumar Jagatheesaperumal, Khan Muhammad, Abdul Khader Jilani Saudagar, Joel J. P. C. Rodrigues
Summary: Fire accidents cause a high number of casualties and manually extinguishing the fire is risky. The development of fire-extinguishing robots with advanced functionalities is ongoing, however, early detection of fire is lacking in most systems. This study introduces a deep learning-based automatic fire extinguishing mechanism utilizing convolutional neural networks for fire detection and human presence in fire locations. Experimental results show that the best combination of neural network parameters is an Adam optimizer with softmax activation and a learning rate of 0.001. The proposed model was tested using a mobile robotic system in automatic and wireless control modes, successfully extinguishing fires.
Article
Computer Science, Information Systems
Dan Wang, Bo Li, Bin Song, Yingjie Liu, Khan Muhammad, Xiaokang Zhou
Summary: In this article, a novel blockchain-supported hierarchical digital twin IoT (HDTIoT) framework is proposed to achieve secure and reliable real-time computation. The framework combines digital twin with edge network and adopts blockchain technology. By utilizing a data and knowledge dual-driven learning solution, the communication and computation efficiency is improved. Experimental results demonstrate the efficiency and reliability of the proposed resource allocation scheme in the HDTIoT system.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Review
Engineering, Multidisciplinary
Muhammad Sajjad, Fath U. Min Ullah, Mohib Ullah, Georgia Christodoulou, Faouzi Alaya Cheikh, Mohammad Hijji, Khan Muhammad, Joel J. P. C. Rodrigues
Summary: Facial expression recognition (FER) is a complex research topic with applications in various fields, such as healthcare and security. Computational FER mimics human facial expression coding skills to assist human-computer interaction. This study thoroughly analyzes and surveys the existing literature on FER, highlights the working flow of FER methods, discusses limitations in existing surveys, investigates FER datasets, and comprehensively discusses measures to evaluate FER performance.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Mathematics
Mohammad Hijji, Hikmat Yar, Fath U. Min Ullah, Mohammed M. Alwakeel, Rafika Harrabi, Fahad Aradah, Faouzi Alaya Cheikh, Khan Muhammad, Muhammad Sajjad
Summary: Nowadays, people prefer to use private transport due to its low cost, comfortable ride, and personal preferences, resulting in a reduction in the use of public transportation. However, the use of personal transport has led to numerous road accidents due to drivers' conditions such as drowsiness, stress, tiredness, and age. To address this issue, an efficient deep learning-assisted intelligent fatigue and age detection system (FADS) was proposed to detect and identify different states of the driver. The system outperformed state-of-the-art techniques in experiments conducted on custom and publicly available datasets.
Article
Engineering, Electrical & Electronic
Samee Ullah Khan, Noman Khan, Tanveer Hussain, Khan Muhammad, Mohammad Hijji, Javier Del Ser, Sung Wook Baik
Summary: This article proposes a multi-scale pyramid attention model for person re-identification (P-ReID) that leverages the complementarity between semantic attributes and visual appearance. The proposed model consists of three steps, including individual training of backbone model and appearance/attribute networks, fusion of dual network features, and re-training for P-ReID.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Tanveer Hussain, Fath U. Min Ullah, Samee Ullah Khan, Amin Ullah, Umair Haroon, Khan Muhammad, Sung Wook Baik, Victor Hugo C. de Albuquerque
Summary: Video summarization is important for suppressing high-dimensional video data. However, prior research has not focused on the need for surveillance video summarization, and mainstream techniques lack event occurrence detection. Therefore, we propose a two-fold 3-D deep learning-assisted framework for video summarization.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Parsa Sarosh, Shabir Ahmad Parah, Bilal Ahmad Malik, Mohammad Hijji, Khan Muhammad
Summary: This article presents a cybersecurity framework for medical images in a smart healthcare system. It introduces two novel two-dimensional chaotic maps that generate highly robust cipher images, protecting medical data against cyberattacks. The proposed solution ensures data privacy and a seamless treatment experience.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Khan Muhammad, Hayat Ullah, Salman Khan, Mohammad Hijji, Jaime Lloret
Summary: This paper proposes an efficient and lightweight CNN architecture for early fire detection and segmentation. By utilizing depth-wise separable convolution, point-wise group convolution, and a channel shuffling strategy, the model size and computation costs are significantly reduced. Extensive experiments validate the effectiveness and robustness of the proposed method in fire segmentation.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Mathematics
Senthil Kumar Jagatheesaperumal, Varun Prakash Rajamohan, Abdul Khader Jilani Saudagar, Abdullah Altameem, Muhammad Sajjad, Khan Muhammad
Summary: The aim of this study is to develop a cost-effective and efficient mobile robotic manipulator designed for decluttering objects. Through the use of deep learning, inverse kinematics, and the innovative MoMo algorithm, accurate object detection, precise positioning, and optimized grasp planning can be achieved. The experiment results demonstrate impressive performance in path planning and efficiency.
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)
