Article
Computer Science, Artificial Intelligence
Zhuangyan Fang, Shengyu Zhu, Jiji Zhang, Yue Liu, Zhitang Chen, Yangbo He
Summary: Learning causal structures represented by directed acyclic graphs (DAGs) in high-dimensional settings remains challenging, especially for non-sparse graphs. In this article, we propose using a low-rank assumption for the adjacency matrix of a DAG causal model to address this problem. By adapting existing low-rank techniques, we establish useful results connecting interpretable graphical conditions to the low-rank assumption. Our experiments demonstrate the utility of these low-rank adaptations, particularly for large and dense graphs, with comparable performance even when graphs are not restricted to be low rank.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Mehboob Hussain, Ming-Xing Luo, Abid Hussain, Muhammad Hafeez Javed, Zeeshan Abbas, Lian-Fu Wei
Summary: Cloud computing provides resources to users on a pay-as-you-go model, meeting their computing and storage needs. A private cloud is a cost-saving option with user-owned resources, while the public cloud is the only choice when private cloud resources are insufficient. In this study, a proposed algorithm called Deadline-constrained Cost-aware Workflow Scheduling (DCWS) addresses task scheduling problems in hybrid cloud environments. The algorithm optimizes task execution on the private cloud and utilizes the public cloud for unscheduled tasks, considering task precedence and deadline constraints.
SIMULATION MODELLING PRACTICE AND THEORY
(2023)
Article
Computer Science, Hardware & Architecture
Ranjit Rajak, Shrawan Kumar, Shiv Prakash, Nidhi Rajak, Pratibha Dixit
Summary: Cloud computing environment has become an important technology in communication, computing, and the Internet, and task scheduling is a challenging problem in this environment. DAG scheduling is widely applicable in different areas due to its importance. Developing a novel DAG scheduling model to optimize QoS parameters in the CCE platform has emerged as a critical issue.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Information Systems
Guoxin Liu, Haiying Shen, Haoyu Wang, Lei Yu
Summary: In this paper, a storage service called DGCloud is proposed to provide deadline guarantees for cloud storage systems. By building a mathematical model and incorporating three basic algorithms, including deadline-aware load balancing, workload consolidation, and data placement optimization, DGCloud aims to improve performance in terms of deadline guarantees and system resource utilization compared to previous methods. Further enhancement methods such as dynamic load balancing, data request queue improvement, and wakeup server selection are also introduced to improve DGCloud's performance.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
P. Rajasekar, Yogesh Palanichamy
Summary: Maximized deployment of workflows in research organizations has led to the emergence of multi-tenant environments that offer workflow deployment as a service. This study proposes a flexible deadline-driven resource provisioning and scheduling algorithm that leverages advanced virtual machine sharing protocol to minimize computing expenses and meet user-assigned deadlines.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Arabinda Pradhan, Sukant Kishoro Bisoy
Summary: This paper proposes a load balancing technique using modified PSO task scheduling to balance tasks in a cloud computing environment, aiming to minimize the makespan and maximize resource utilization.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Liang Wang, Zhiwen Yu, Qi Han, Dingqi Yang, Shirui Pan, Yuan Yao, Daqing Zhang
Summary: This paper discusses the background of mobile crowdsourcing and the importance of task graph scheduling in this context. The authors propose two heuristic approaches to solve the problem and demonstrate their superiority through experiments.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Computer Science, Information Systems
Anurina Tarafdar, Mukta Debnath, Sunirmal Khatua, Rajib K. Das
Summary: Cloud computing allows for various applications to be executed by users in a virtualized environment, but it also consumes significant energy; healthcare, scientific research, IoT tasks are deadline-sensitive, requiring efficient scheduling to reduce energy consumption; proposed approaches effectively address the trade-off between energy consumption and task completion time.
JOURNAL OF GRID COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
David M. Walker, Debora C. Correa, Shannon D. Algar
Summary: Phase space networks based on the proximity of reconstructed state space points from time series are valuable for system identification and characterization. A new network is introduced to capture the evolution of induced proximity network subgraphs over time. This network has the potential to detect dynamical changes and the evolving geometry of state space.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Sampa Sahoo, Bibhudatta Sahoo, Ashok Kumar Turuk
Summary: Scheduling deadline sensitive tasks in cloud computing systems is challenging, requiring a balance between minimizing energy consumption and ensuring satisfactory service delivery.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2021)
Article
Computer Science, Theory & Methods
Junyan Hu, Kenli Li, Chubo Liu, Jianguo Chen, Keqin Li
Summary: In order to reduce costs, a group of customers with specific needs collaboratively purchase resources, with a mechanism designed to ensure each customer pays the lowest cost possible. The algorithm aims to find an optimal solution while satisfying individual and group stability.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2021)
Article
Computer Science, Information Systems
Mohammed Maray, Ehzaz Mustafa, Junaid Shuja, Muhammad Bilal
Summary: Edge computing is a revolutionary paradigm in IoT that improves job completion time and optimizes task dependencies with deadline constraints. The proposed strategy outperforms other offloading methods in terms of throughput and task satisfaction rate.
INTERNET OF THINGS
(2023)
Article
Computer Science, Information Systems
Kaushik Mishra, Jharashree Pati, Santosh Kumar Majhi
Summary: This article discusses the importance of handling dynamic workloads in datacenters and the challenges that can lead to server imbalance. To address the fluctuating resource provisioning needs, a method based on binary JAYA is proposed for load scheduling and load balancing, aiming to improve resource utilization and reduce energy consumption and makespan.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Guangshun Yao, Qian Ren, Xiaoping Li, Shenghui Zhao, Ruben Ruiz
Summary: The article proposes a Hybrid Fault-Tolerant Scheduling Algorithm (HFTSA) for independent tasks with deadlines in virtualized Cloud systems. The algorithm integrates both resubmission and replication techniques to provide an efficient fault-tolerant scheduling strategy with high resource utilization for deadline-constrained tasks.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Information Systems
Jiawen Chen, Yajun Yang, Chenyang Wang, Heng Zhang, Chao Qiu, Xiaofei Wang
Summary: This article focuses on the problem of dependent task offloading in multiuser scenarios and proposes the ACED algorithm. By modeling the problem as an MDP and considering both the topology of the application and the channel interference between users, the paper achieves multiple dependent tasks computation offloading.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Longxin Zhang, Lan Wang, Mansheng Xiao, Zhicheng Wen, Cheng Peng
Summary: This study proposes an energy minimization whale optimization algorithm (EM_WOA) that reduces energy consumption in the cloud under budget constraints. Experimental results show that EM_WOA is more efficient and competitive than other state-of-the-art meta-heuristic algorithms.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Mervat Hashem, Kenli Li, Ahmad Salah
Summary: This study proposes a blend rotation algorithm that combines the merits of three different rotation algorithms, which is parallel-friendly and in-place. Additionally, a set of implementations for parallel in-place sequence rotation is proposed. The performance of these implementations is examined through experiments, showing that the blend and reversal rotations are the fastest parallel implementations, outperforming Intel parallel STL's parallel rotation function.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Khalid M. Hosny, Amal Magdi, Nabil A. Lashin, Osama El-Komy, Ahmad Salah
Summary: This article discusses the implementation of a color image watermarking algorithm on the Raspberry Pi platform to protect and ensure the copyright of digital images. The use of embedded hidden information to prove copyright, along with parallel computing and the C++ programming language, accelerates the execution time.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Multidisciplinary Sciences
Islam Mohamed, Ibrahim Elhenawy, Ahmed W. Sallam, Andrew Gatt, Ahmad Salah
Summary: This article focuses on CF-based visual object trackers and evaluates existing trackers using a set of new video sequences. The evaluation shows that some existing trackers perform poorly on certain sequences, indicating the need for improvement.
Article
Computer Science, Artificial Intelligence
Longxin Zhang, Yang Hu, Jingsheng Chen, Chuang Li, Keqin Li
Summary: A novel one-stage object detection method based on YOLOv4, called the MSSIF-Net, is proposed for the fault detection of freight train parts. It achieves high detection accuracy and speed, outperforming other traditional methods. Furthermore, the MSSIF-Net demonstrates favorable anti-interference ability.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ahmed Fathalla, Ahmad Salah, Mahmoud Bekhit, Esraa Eldesouky, Ahmed Talha, Abdalla Zenhom, Ahmed Ali
Summary: In sports science, the need for automation in performance analysis and assessment is urgent. Existing methods, either manual or using motion analysis software, only assess one side of a subject. Therefore, we propose an automated system that can be used for any human movement. The system involves three stages: data collection, joint angle curve extraction, and performance curve summarization. The system's results are identical to expert decisions and suitable for real-time applications.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Mathematics
Marwa F. Mohamed, Mohamed Meselhy Eltoukhy, Khalil Al Ruqeishi, Ahmad Salah
Summary: With the advancement of information technology and economic globalization, the problem of supplier selection is gaining popularity, particularly in the healthcare industry. This study proposes a mathematical model and a multi-objective algorithm to address the optimal healthcare supplier selection problem. The results demonstrate that the adapted NSGA-III algorithm outperforms other methods in terms of reducing transportation cost, delivery time, and the number of damaged items.
Article
Mathematics
Ahmed Fathalla, Ahmad Salah, Ahmed Ali
Summary: This article introduces a service for predicting the prices of categorical products in e-commerce. By applying two unique data transformations, the regression analysis on categorical data is improved with improvements ranging from 1.98% to 8.91% for the evaluation metrics.
Article
Mathematics
Walaa H. El-Ashmawi, Ahmad Salah, Mahmoud Bekhit, Guoqing Xiao, Khalil Al Ruqeishi, Ahmed Fathalla
Summary: The BPPC is a less-studied variation of the classic combinatorial optimization problem. This work proposes an improved jellyfish metaheuristic algorithm to solve the BPPC by defining jellyfish operations. The proposed method outperforms other comparison methods in terms of the number of bins and the average bin utilization.
Article
Computer Science, Interdisciplinary Applications
Longxin Zhang, Yang Hu, Tianyu Chen, Hong Wen, Peng Zhou, Wenliang Zeng
Summary: This study proposes a multi-class freight train fault recognition model, which accurately recognizes typical faults by designing an object detection model and a fault classification model. Experimental results show that the model outperforms traditional machine learning and state-of-the-art deep learning methods in terms of accuracy for typical faults, and has good anti-interference ability.
INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Longxin Zhang, Peng Zhou, Miao Wang, Chengkang Weng, Xiaojun Deng
Summary: FCAODNet is a lightweight object detection model proposed for the fault detection of freight train images. It consists of four modules that work together to improve detection accuracy and speed. Experimental results show that FCAODNet outperforms other models in detection speed and exhibits good accuracy and robustness.
INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Ahmed A. Khalil, Zaiming Liu, Ahmad Salah, Ahmed Fathalla, Ahmed Ali
Summary: Insolvency is a crucial problem for insurance companies, and this study explores the prediction of insurance company insolvency using ensemble learning methods in the Egyptian market. A dataset of 11 Egyptian insurance companies was collected, and different evaluation metrics were used to assess the proposed models.
Proceedings Paper
Computer Science, Artificial Intelligence
Basant Adel, Asmaa Badran, Nada E. Elshami, Ahmad Salah, Ahmed Fathalla, Mahmoud Bekhit
Summary: In a typical HAR system, human activities are recognized using data collected from sensors and labeled with the activities. Recent advancements in HAR systems focus on sports science, healthcare, and security domains to explore DL architectures and achieve high accuracy rates.
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INNOVATIONS IN COMPUTING RESEARCH (ICR'22)
(2022)
Article
Computer Science, Information Systems
Longxin Zhang, Miao Wang, Ke Liu, Mansheng Xiao, Zhihua Wen, Junfeng Man
Summary: In this paper, a object detection model called BD-YOLO is proposed for automatic fault detection of freight train image. The model consists of four steps which includes feature extraction, multi-scale feature fusion, prediction across scale modules, and decoding of prediction. The model is trained using mosaic data enhancement and K-means clustering algorithm to improve detection accuracy and speed. Experimental results demonstrate that BD-YOLO model outperforms state-of-the-art object detection models with an average improvement of 17.57% in mean average precision on four types of datasets. The BD-YOLO model can accurately detect three typical faults of freight trains.
Article
Computer Science, Information Systems
Mohamed Ali Mohamed, Ibrahim Mahmoud El-Henawy, Ahmad Salah
Summary: Price prediction of goods is an important research topic, particularly for seasonal goods. This study utilizes machine learning and statistical models to predict prices of Christmas gifts, and evaluates the performance of these models.
CMC-COMPUTERS MATERIALS & CONTINUA
(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)
