Article
Computer Science, Information Systems
Martin Pichl, Eva Zangerle
Summary: As music consumption has shifted towards music streaming platforms in the past decade, users are increasingly relying on music recommender systems to help them discover music they like due to the overwhelming amount of choices available.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Guoshuai Zhao, Zhidan Liu, Yulu Chao, Xueming Qian
Summary: In this paper, a Context-Aware Personalized Emoji Recommendation (CAPER) model is proposed, which fuses contextual and personal information to improve recommendation accuracy. Experimental results show better performance of the CAPER model compared to existing methods, demonstrating the effectiveness of considering contextual and personal factors.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Man-Ching Yuen, Irwin King, Kwong-Sak Leung
Summary: The study introduces a time-aware task recommendation framework for crowdsourcing systems, which combines worker preferences and performance history while considering constraints on the time dimension. The model is efficient, scalable, and the first of its kind to consider the time aspect of workers' preference on tasks.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zahra Abbasi-Moud, Saeed Hosseinabadi, Manoochehr Kelarestaghi, Farshad Eshghi
Summary: This paper proposes a context-aware fuzzy-ontology-based tourism recommendation system, which utilizes a fuzzy-weighted ontology and a new sentiment/emotion score scheme. By considering contextual information such as weather, location, and time, the system provides tourists with more accurate and high-quality recommendations, outperforming state-of-the-art tourism recommendation systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Reham Alabduljabbar, Halah Almazrou, Amaal Aldawod
Summary: With the increasing volume of news articles available on the internet, personalized news recommendations have become increasingly important for users to discover relevant and interesting news articles. However, traditional recommender systems often fail to capture the dynamic nature of users' preferences and the changing trends in news articles. To address this challenge, this paper proposes a context-aware personalized news recommendation system that incorporates contextual information to enhance the personalization of news recommendations.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
(2023)
Article
Computer Science, Information Systems
Tipajin Thaipisutikul, Ying-Nong Chen
Summary: In this paper, an improved deep sequential model for context-aware POI recommendation is proposed. This model captures both the short-term and long-term preferences of users, resulting in enhanced recommendation performance. Experimental results show significant improvement over several state-of-the-art baselines, and a case study demonstrates the model's ability to provide interpretable recommendation results to LSBN users.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Tianhui Wu, Fuzhen Sun, Jiawei Dong, Zhen Wang, Yan Li
Summary: A novel recommendation algorithm integrating session and contextual information is proposed, which maps contextual information into low-dimensional real vector features and fuses them into sessions for more accurate recommendations.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Xing Wu, Yisong Li, Jianjia Wang, Quan Qian, Yike Guo
Summary: The recommendation system is widely used in the digital economy to provide personalized services. Capturing the user-item relations efficiently is crucial, but it faces challenges in extracting complicated associations and integrating numerous item connections. To address these challenges, a User Behavior-Aware Recommendation method with knowledge graph (UBAR) is proposed. Experimental results on multiple datasets demonstrate the effectiveness and efficiency of the UBAR method.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yuanguo Lin, Fan Lin, Lvqing Yang, Wenhua Zeng, Yong Liu, Pengcheng Wu
Summary: This paper proposes a context-aware reinforcement learning method, named HRRL, for efficient course recommendation by utilizing recurrent reinforcement learning and attention mechanism. Experimental results demonstrate the superiority of this method over existing baselines.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Software Engineering
Junjie Wang, Ye Yang, Song Wang, Chunyang Chen, Dandan Wang, Qing Wang
Summary: Crowdsourced software testing, also known as crowdtesting, is a specialized form of crowdsourcing that requires skilled and dedicated crowdworkers. This paper addresses the issue of inappropriate task selection in crowdtesting, which leads to unpaid and wasted effort. The authors propose a context-aware personalized task recommendation approach called PTRec, which leverages a testing context model and a learning-based recommendation model to help crowdworkers make informed decisions. The evaluation of PTRec on a large crowdtesting platform demonstrates its potential in improving bug detection efficiency and increasing crowdworkers' earnings.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Information Systems
Youfang Leng, Li Yu
Summary: This paper proposes a Hierarchical Context-aware Recurrent Network (HiCAR) model to combine users' micro-interactions and two levels of context for more effective session-based recommendations. By experimenting on two real-world datasets, the HiCAR model outperformed state-of-the-art baselines in modeling users' sequential behaviors and contexts simultaneously.
Article
Computer Science, Artificial Intelligence
Wei Wang, Guoqiang Sun, Siwen Zhao, Yujun Li, Jianli Zhao
Summary: Tensor decomposition is widely used in context-aware recommendation, but current models have limitations such as fewer parameters in CP decomposition and high computational complexity in Tucker decomposition. To address these issues, we propose a bias Tensor Ring decomposition framework for context-aware recommendation, which achieves a better balance between recommendation performance and computational complexity.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Wenjie Wang, Ling-Yu Duan, Hao Jiang, Peiguang Jing, Xuemeng Song, Liqiang Nie
Summary: This study introduces Market2Dish, which aims to achieve personalized health-aware food recommendation through recipe retrieval, user health profiling, and health-aware food recommendation. By capturing health-related information from social networks and utilizing a deep model to learn the correlations between users and recipes, the proposed scheme offers better food recommendations.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Haoyang Li, Xin Wang, Ziwei Zhang, Jianxin Ma, Peng Cui, Wenwu Zhu
Summary: This paper presents the ISRec method, which aims to capture user intentions to improve recommendation system performance. By extracting intentions from user's historical interaction behaviors and using a message-passing mechanism on an intention graph, the method predicts future user behaviors more accurately and provides transparent and explainable recommendations.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Hardware & Architecture
Jinrong Chen, Lin Liu, Rongmao Chen, Wei Peng, Xinyi Huang
Summary: This article introduces a method for preserving privacy in a context-aware recommendation system in a two-cloud model. The author adjusts the additive secret sharing scheme and designs secure comparison and division protocols to propose a secure and efficient recommendation system. Experimental results demonstrate the effectiveness of the scheme.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
Sara Sajid, Muhammad Jawad, Kanza Hamid, Muhammad U. S. Khan, Sahibzada M. Ali, Assad Abbas, Samee U. Khan
Summary: The paper investigates the energy consumption cost optimization problem in cloud data centers and proposes a blockchain-based decentralized workload distribution and management model, which can effectively reduce power consumption costs.
SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Samee Ullah Khan, Ijaz Ul Haq, Noman Khan, Khan Muhammad, Mohammad Hijji, Sung Wook Baik
Summary: Person reidentification (P-Reid) is a growing research field aiming to identify individuals in multiview surveillance videos; current fully supervised learning techniques face scalability issues due to overfitting in real-world scenarios; LR-Net framework proposes a solution through fine-tuning, siamese network, and fusion strategy, achieving significant improvements in handling unlabeled data.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Chemistry, Analytical
Samee Ullah Khan, Ijaz Ul Haq, Zulfiqar Ahmad Khan, Noman Khan, Mi Young Lee, Sung Wook Baik
Summary: This study proposes an intelligent deep learning framework that integrates different neural network layers to match power demand with supply, accurately predict short-term energy demand, and provide effective communication methods. Through data acquisition, preprocessing, and feature extraction, the sequential learning model is utilized to optimize energy management, achieving better performance compared to existing approaches.
Article
Mathematics
Noman Khan, Ijaz Ul Haq, Fath U. Min Ullah, Samee Ullah Khan, Mi Young Lee
Summary: The proposed CL-Net architecture based on ConvLSTM and LSTM significantly reduces root mean squared error (RMSE) on the NASA battery and IHEPC datasets compared to the state-of-the-arts, providing robust and accurate SOH and power consumption forecasting.
Article
Mathematics
Noman Khan, Fath U. Min Ullah, Ijaz Ul Haq, Samee Ullah Khan, Mi Young Lee, Sung Wook Baik
Summary: The article introduces a new architecture AB-Net for intelligent matching of renewable energy generation and consumption for efficient energy management. This method combines an autoencoder and bidirectional long short-term memory network in steps such as data acquisition, preprocessing, and feature extraction, achieving good prediction results.
Article
Chemistry, Analytical
Sagheer Ahmed Jan, Noor Ul Amin, Junaid Shuja, Assad Abbas, Mohammed Maray, Mazhar Ali
Summary: This paper proposes an efficient and secure lightweight anonymous mutual authentication and key establishment (SELWAK) scheme for IoT-based VANETs. Performance evaluation and security analysis demonstrate its effectiveness in terms of computational cost and communication overhead.
Article
Agronomy
Aqeel Iftikhar Jajja, Assad Abbas, Hasan Ali Khattak, Gniewko Niedbala, Abbas Khalid, Hafiz Tayyab Rauf, Sebastian Kujawa
Summary: Cotton, as an economically significant agricultural product, is susceptible to pest and virus attacks. This study proposes a Compact Convolutional Transformer (CCT)-based approach for classifying cotton leaf images, achieving higher accuracy compared to existing models.
Article
Computer Science, Information Systems
Zeyad A. Al-Odat, Samee U. Khan, Eman Al-Qtiemat
Summary: This paper introduces an improved version of the secure hash algorithms SHA-1 and SHA-2. The proposed design strengthens the security of the algorithms by combining the standards of SHA-1 and SHA-2, and it has been verified through various test cases.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2022)
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)
Proceedings Paper
Computer Science, Artificial Intelligence
Qikai Zhang, Fan Zhang, Samee U. Khan
Summary: In this paper, we propose a method to co-prune the validation dataset and the training dataset by mining the most influential training data, which can effectively reduce training time and model complexity while maintaining accuracy.
2022 IEEE 34TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, ICTAI
(2022)
Proceedings Paper
Computer Science, Information Systems
Yuyang Wang, Fan Zhang, Samee U. Khan
Summary: This paper presents a customized Kubernetes controller called HCA Operator, which can auto-scale microservice applications in a hybrid cloud environment. By load balancing, monitoring, and autoscaling, it achieves automatic expansion between destination clusters in different clouds.
2022 18TH INTERNATIONAL CONFERENCE ON MOBILITY, SENSING AND NETWORKING, MSN
(2022)
Proceedings Paper
Computer Science, Information Systems
Kai Cui, Guoting Zhang, Fan Zhang, Samee U. Khan
Summary: This paper discusses the deployment of facial expression recognition system on a distributed edge-cloud infrastructure and introduces a solution. By executing different algorithms on the edge and cloud separately, the performance can be improved and network overhead can be reduced.
2022 IEEE CLOUD SUMMIT
(2022)
Proceedings Paper
Computer Science, Information Systems
Bo Yang, Fan Zhang, Samee U. Khan
Summary: This article proposes an assessment method for cloud elasticity based on fuzzy hierarchical analysis, aiming to quantify and compare the elastic features among different public cloud providers. The effectiveness of the method is verified through case studies and performance metric evaluations.
2022 IEEE CLOUD SUMMIT
(2022)
Article
Computer Science, Information Systems
Waleed Afandi, Syed Muhammad Ammar Hassan Bukhari, Muhammad U. S. Khan, Tahir Maqsood, Samee U. Khan
Summary: Recently, the increased traffic from video streaming services like YouTube, Twitch, and Facebook raises concerns about the transmission of unwanted and inappropriate content to minors or individuals at workplaces. To address this issue, researchers propose a fingerprinting method that accommodates for Variable Bit-Rate (VBR) inconsistencies, and optimize a Convolutional Neural Network (CNN) to detect YouTube streams in various network traffic conditions.
Article
Computer Science, Hardware & Architecture
Panagiotis Oikonomou, Nikos Tziritas, Thanasis Loukopoulos, Georgios Theodoropoulos, Masatoshi Hanai, Samee U. Khan
Summary: This paper discusses the online version of the interval scheduling problem and proposes a novel algorithm for preprocessing bin packing schemes and providing job allocation recommendations based on job overlaps. Experimental results show the advantages of this approach compared to existing algorithms.
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
(2022)
