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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

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computers, Materials & Continua 2025年第2期82卷 2495-2520页

作者： Jeng-Shyang Pan Na Yu Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan College of Electronics Communication and PhysicsShandong University of Science and TechnologyQingdao266590China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.

关键词： Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy

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Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

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computer Systems science & engineering 2023年第5期45卷 1655-1677页

作者： G.Nallasivan V.Ramachandran Roobaea Alroobaea Jasem Almotiri Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21974Saudi Arabia

An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver *** of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s *** changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis,suggesting that the cause may be alcohol instead of liver *** Two-Dimensional(2D)ultrasound data sets contain an accuracy rate of 85.9%and a 2D Computed Tomography(CT)data set of 91.02%.The most recent work on designing a Three-Dimensional(3D)ultrasound imaging system in or close to real-time is *** this article,a Deep Learning(DL)model is implemented and modified to fit liver CT segmentation,and a semantic pixel classification of road scenes is *** architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers.A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training *** the normal class,we obtained 100%precision for chronic cirrhosis hepatitis(73%),offset cirrhosis(59.26%),and offensive cirrhosis(91.67%)for chronic hepatitis or cirrhosis(73,0%).The aim is to develop a computer-Aided Detection(CAD)screening tool to detect *** results proved 98.33%exactness,94.59%sensitivity,and 92.11%case with Convolutional Neural Networks(CNN)*** the classifier’s performance did not differentiate so clearly at this level,it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve(AUC)and accuracy.

关键词： Deep learning liver tumor CNN CT accuracy

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CompressionShield: Architectural Adaptations towards Enhancing InceptionV3 for Image Forgery Detection

CompressionShield: Architectural Adaptations towards Enhanci...

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Bhowal, Arundhati Naskar, Ruchira Neogy, Sarmistha Jadavpur University Computer Science and Engineering Kolkata India Indian Institute of Engineering Science and Technology Information Technology Shibpur India

ISBN: (纸本)9798331516666

The most popular methods for tampering with visual data integrity are copy-move forgery and splicing. These techniques can be used for several malicious purposes, including the production of false or misleading images and videos. Forgery detection becomes challenging when useful data is lost while maintaining fine details, such as during image compression. Therefore, it is crucial to create reliable techniques that can recognize forgery in compressed images with accuracy. This work proposes a deep learning-based transfer learning technique to detect manipulated images after compression. The proposed approach has the advantage of extracting appropriately nontrivial visual features, as it can quickly adjust to the forgery detection task by using a pre-trained transfer learning model named Inception-V3. Experimental results show that the proposed method can successfully identify forgeries in compressed images with superior accuracy. © 2024 IEEE.

关键词： Image compression

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Chinese Relation Extraction Based on Cross-Attention and Multi-feature Perception

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1837-1853页

作者： Xu, Shiao Sun, Shuihua Zhang, Zhiyuan Zhou, Huan School of Electronics and Information Engineering Jingchu University of Technology Jingmen448000 China School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

Relation extraction aims at extracting semantic relations between given pairs of entities from unstructured textual data and is a critical task in information extraction. A relation extraction model based on cross-attention and multi-feature perception is proposed to address the limitations of Chinese relation extraction models that rely solely on characters, which struggle to capture rich semantic information, and the inability to ful ly utilize the textual structural information during neural network feature extraction. First, the model uses BERT to encode sentences and entities to obtain character vector sequences and word vector sequences of sentences, and uses word cross-attention mechanism to capture the key word and word information in sentences to form character and word cross-fused sentence sequences. Second, uses graph convolutional network and deep separable convolutional network to capture syntactic information between nodes and multi-granularity local semantic information in sentence sequences, respectively, and forms sentence features for classification by attention mechanism. Final ly, combines with Softmax classifier for relation classification. Experimental results on the Chinese SanWen dataset demonstrate the superiority of the proposed method over mainstream neural network-based relation extraction methods, with an F1-score of 71.57%. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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A Lightweight, Secure, and Scalable Blockchain-Fog-IoMT Healthcare Framework with IPFS Data Storage for Healthcare 4.0

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SN computer science 2024年第1期5卷 198页

作者： Mallick, Soubhagya Ranjan Lenka, Rakesh Kumar Tripathy, Pradyumna Kumar Rao, D. Chandrasekhar Sharma, Suraj Ray, Niranjan Kumar Computer Science and Engineering International Institute of Information Technology Odisha Bhubaneswar 751003 India Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar 751024 India Information Technology Veer Surendra Sai University of Technology Odisha Burla 768018 India Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur 495009 India School of Computer Science and Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar 751024 India

The gathering, processing, transmission, sharing, and storage of healthcare data was the core idea behind Healthcare 4.0. Currently, most of the existing solutions for offering smart healthcare services rely on cloud-based platforms. The main issues with current healthcare systems include storage overhead, processing speed, scalability, single points of failure, bandwidth requirements, and device connectivity. The recent challenges in the healthcare system motivated us to develop a system by integrating the Internet of Medical Things (IoMT), Blockchain, fog computing, and InterPlanetary File System (IPFS) to achieve decentralization, scalability, security, immutability, and data privacy. In this paper, we propose a novel IoMT and Blockchain framework with fog node computing to reduce latency and speed up processing, as well as to reduce the network congestion, bandwidth requirements, and the main Blockchain network overload. Untrusted devices are connected to the system using a proxy monitor, which records the device’s activity. In addition, the interplanetary file system (IPFS) is integrated with the Blockchain network to store patient data and files in order to ensure the system’s decentralization, scalability, security, and privacy. Moreover, we evaluate the proposed framework in terms of security, scalability, latency, storage, efficiency, and performance. The experimental analysis shows that the proposed framework makes access, searches, uploads, and downloads faster and more secure. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Efficiency Fog computing IoMT IPFS Scalability Security

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Preserving friendly stacking and weighted shadows in selective scalable secret image sharing

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Multimedia Tools and Applications 2023年第19期82卷 29283-29305页

作者： Lee, Jung-San Chen, Yin-Chin Hsieh, Yun-Hao Chang, Shih-Hao Huynh, Ngoc-Tu Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

With advancement of technology, the technique of selective scalable secret image sharing (SSSIS) was first proposed in 2017 as a new alternative of secret image sharing. The region of interest (ROI) in the protected secret could be revealed progressively once the number of collected shadows has reached a predefined threshold. Accordingly, the ROIs of the secret image increase based on the number of collected shadows. Nonetheless, players without sufficient shadows may be able to learn the location and contour of hidden secret to perform advanced guessing attacks. Thus, we aim to introduce friendly stacking to yield a genuine outcome of mitigating this risk during the recovery process. Furthermore, the weighting concept is brought into constructing the meaningful shadow so that the priority of a specific participant can be emphasized to approach real applications. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Image segmentation

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Prior detection of Melanoma skin cancer using Deep Learning 4

Prior detection of Melanoma skin cancer using Deep Learning

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kathuria, Kajol Sahoo, Anita Jain, Chakresh Kumar Jaypee Institute of Information Technology Computer Science & Engineering and Information Technology Noida India Biotechnology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798350364729

Anomalous growth of skin cells caused due to harmful Sun's rays is predominant form of cancer. According to WHO skin cancer is the fifth most common cancer across the world. In 2020, 324635 new cases were accounted of both sexes world wide & 57043 deaths have been reported by Globocan 2020. Skin cancer is categorized in to three: Basal Cell carcinoma, Melanoma & Squamous Cell Carcinoma. Furthermore Melanoma is the most lethal malignancies in the world with very less chances of Survival. Early detection of skin cancer is crucial as it provides treatment options & better survival rates, conversely it may spread to other organs & can access lymphatic vessels. Assuming the danger of skin cancer, researchers have developed many early detection methods. Colour, symmetry, form, size, & other lesion criteria are utilised to detect & distinguish between benign & malignant skin cancers. Convolutional neural networks (CNN) are one of the most effective approaches for diagnosing melanoma today. This paper provides an overview of deep learning algorithms for the early diagnosis of skin cancer. The topic of skin cancer diagnosis-related research articles from credible publishers was investigated. The table shows a comparative examination of skin cancer detection using deep learning algorithms. © 2024 IEEE.

关键词： Lung cancer

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Asynchronous Energy-to-peak Control for 2D Roesser-type Markov Jump Systems

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第11期51卷 1761-1768页

作者： Chen, Qing Zhang, Yu Han, Chengyi Chen, Ling Zhou, Jianping The School of Computer Science and Technology Anhui University of Technology Ma’anshan243032 China The School of Electrical and Information Engineering Wanjiang University of Technology Ma’anshan243032 China

This paper studies asynchronous energy-to-peak control for 2D Roesser-type Markov jump systems (RTMJSs). Given the practical challenge of obtaining the system state, output-feedback is utilized for closing the control loop instead of state feedback. The asynchronous behavior between the controlled plant and the controller is represented by a hidden Markov model. A sufficient condition for the stochastic stability and energy-to-peak disturbance attenuation (EPDA) of the closed-loop RTMJS is established using a Lyapunov function and Schur’s complement. Under fixed conditional probability, a scheme for asynchronous output-feedback controller design is proposed, employing variable replacement and matrix transformation to cope with the nonlinear terms. The present work is then expanded to the uncertain conditional probability scenario and a related controller design method is developed. Finally, the effectiveness of the proposed design methods is demonstrated through a thermal process. © (2024), (International Association of Engineers). All rights reserved.

关键词： Lyapunov functions

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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