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A Hybrid Strategy Integrating Ant Colony Optimization and Locust-Inspired Algorithm (HACO-LA) to Boost Efficiency and Performance in cloud Resource Management 3

A Hybrid Strategy Integrating Ant Colony Optimization and Lo...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Saraswathy, S. Malathi, J. Subiksha, N. LakshmiNarayanan, S. Kumar, K. Sathish Livinesh, L. Sri Sai Ram Engineering College Department of Computer Science and Business Systems Tamilnadu Chennai India Sri Sai Ram Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309409

Efficient cloud resource management is vital, as it ensures the accurate selection and allocation of resources to diverse workloads or applications. This entails real-time balancing of workload performance, compliance, and cost to achieve optimization. Since there are many cloud users involved in scheduling tasks, or cloudlets, the scheduling process becomes complex. It involves selecting appropriate data centers, servers (hosts), and virtual machines (VMs). There are several bioinspired algorithms that are effective and popular for solving the NP-complete problem of cloudlet scheduling. By using these algorithms, cloudlets can be efficiently allocated to achieve faster execution times, better resource utilization, and a shorter waiting time. This study presents a hybrid technique that combines the strengths of both ant colony optimization and locust-inspired algorithm (HACO-LA), which have been shown to outperform other bio-inspired algorithms in cloud computing environments. Implementing this technique will lead to a decrease in the average response time, while also increasing the utilization of VMs and servers. The method was evaluated using the Cloud Sim toolbox with authentic data. It was compared to the preexisting Artificial Bee Colony Optimization (ABC) algorithm and Particle Swarm Optimization (PSO) algorithm. The findings demonstrated that HACO-LA surpassed both methods, leading to notable enhancements in server utilization, increased reliability, and decreased average response time. © 2024 IEEE.

关键词： Ant colony optimization

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Exploring the Flexibility and Accuracy of Sentiment Scoring Models through a Hybrid KNN-RNN-CNN Algorithm and ChatGPT

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第2期4卷 315-326页

作者： Hariguna, Taqwa Ruangkanjanases, Athapol Department Information System Faculty of Computer Science Universitas Amikom Purwokerto Purwokerto Indonesia Chulalongkorn Business School Chulalongkorn University Bangkok Thailand

This study aimed to address the limitations of sentiment analysis by developing a more accurate and flexible sentiment scoring model using ChatGPT in combination with KNN, RNN, and CNN algorithms. To achieve this, primary data from ChatGPT and secondary data from Kaggle were utilized for training. The model's performance was evaluated, yielding an impressive accuracy rate of 88.17%. This research underscores ChatGPT's pivotal role in offering theoretical insights and precise data for diverse applications. The novelty of this study lies in its innovative approach of combining KNN, RNN, and CNN algorithms to create a more adaptable and accurate sentiment scoring model. Additionally, the primary data from ChatGPT greatly enhances the creation of precise and relevant training data across various topics and languages. Despite these achievements, there remains a need for further exploration of testing methods to mitigate the impact of data limitations on result generalizability. Moreover, it is acknowledged that the model's effectiveness may be diminished when applied to languages other than English. Nevertheless, this research provides a promising avenue for users seeking enhanced and precise sentiment analysis by integrating KNN, RNN, and CNN algorithms with ChatGPT. The findings of this study can serve as a solid foundation for future research endeavors in the advancement of sophisticated and effective sentiment analysis technologies. © 2023, Ital Publication. All rights reserved.

关键词： Sentiment analysis

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Sentinel Shield DNS Filtering Reinvented with Threat Intelligence and AI/ML

Sentinel Shield DNS Filtering Reinvented with Threat Intelli...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Vignesh, S. Praveen Kumar, G. Santhosan, R. Sivachandran, S. K.S. Rangasamy College of Technology Department of Computer Science and Business Systems Tiruchengode Tamil Nadu Namakkal India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Traditional solutions of firewalls usually fail to keep pace with the emerging threats. For this purpose, it offers a new solution that integrates ML with a firewall system to dynamically identify malicious domain requests, such as a dataset consisting of 50 features and 100,000 records, which used authentic logs enriched with Open Source Intelligence carefully analyzed. Supervised algorithms were used, which resulted in very high accuracy levels of 90% to 98%, with very fast classification times ranging from 0.01 to 2.38 seconds. This research not only provides a valuable public dataset but also a methodology that can be exploited by researchers. Practically, the study provides the foundation for an in-band firewall that can proactively identify malicious domains in real time, thus enhancing organizational cybersecurity postures. Further optimization of the performance of the firewall can be made using ensemble learning techniques such as Random Forests and Gradient Boosting. Both these ensemble techniques use several models to improve predictive accuracy for complicated datasets and add robustness in general. Additionally, online learning algorithms should also be used. These are techniques used when new data are being presented one by one so that the model learns and adjusts from time to time, responding dynamically to evolving threats. Further performance improvements can be achieved by fine-tuning the hyperparameters of the chosen algorithms and experimenting with feature engineering. Updating the models with new data and monitoring their performance regularly is essential to keep them effective against changing threats. This paper opens up the transformative potential of ML-enhanced firewalls in modern cybersecurity frameworks and sets the stage for future developments in this critical domain. © 2025 IEEE.

关键词： Cybersecurity

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Evaluation the Impact Potentials of Materials and systems with Specific Criteria under Neutrosophic Set

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Neutrosophic Sets and systems 2023年 57卷 342-355页

作者： Abdel-aziem, Ayman H. Soliman, Tamer H. M. Ali, Ahmed M. Abdelhafeez, Ahmed Faculty of Information Systems and Computer Science October 6th University Cairo Egypt Faculty of Computers and Informatics Zagazig University Sharqiyah Zagazig44519 Egypt

Energy, healthcare, electronics, transportation, ecology, and infrastructure are just a few of the areas that might greatly benefit from the use of new materials and systems. This study delves into the factors that should be taken into account when determining the possible effect of a certain substance or system. This analysis takes into account ecological, monetary, technical, health and safety, regulatory, commercial, and cooperative factors. Stakeholders may make more well-informed decisions and ensure that materials and systems are used to their maximum capacity if they take these into account. This paper used the concept of multi-criteria decision-making (MCDM) to deal with various criteria and factors. The VIKOR method is used as an MCDM method to rank the materials according to various criteria. The VIKOR method is integrated with the single-valued neutrosophic set to overcome uncertain information. This paper used eight criteria and ten materials to select the best one. The results show the cost criteria is the height weight in all criteria. © (2023). All Rights Reserved.

关键词： Ecology

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Neutrosophic Hybrid MCDM Framework to Evaluate the Risks of Excavation System

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Neutrosophic Sets and systems 2023年 55卷 173-186页

作者： Abdel-Monem, Ahmed Abdelhafeez, Ahmed Faculty of Computers and Informatics Zagazig University Zagazig11544 Egypt Faculty of Information Systems and Computer Science October 6th University Cairo12585 Egypt

The building of excavations is an extremely dangerous job that incorporates a variety of different variables. It is possible to significantly lower the likelihood of an accident occurring by first accurately identifying high-risk variables and then taking appropriate preventative steps. Single-valued neutrosophic verbal sets (SVNVS) can effectively represent qualitative and vague information when used in the identification process for high-risk variables of excavation systems. In addition, the identification of highrisk elements associated with an excavation system is a multi-criteria decision-making (MCDM) issue. This issue may be resolved by using the multi-attribute border approximation area comparison (MABAC) technique. The MABAC method operates on the presumption that criteria are compensating. However, the identification process for high-risk variables of excavation systems may include characteristics that are not compensatory. Under conditions of single-valued neutrosophic sets, a MABAC approach is developed. The weights of the criterion are calculated using this approach, which uses the mean-squared deviation weight method. In addition to that, an illustrated example is carried out to demonstrate the process that is involved in the MABAC approach © 2023, Neutrosophic Sets and *** Rights Reserved.

关键词： Excavation

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Metaheuristics for Financial Investment Strategies: Applications Survey

Metaheuristics for Financial Investment Strategies: Applicat...

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2023 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Bousbaa, Zineb Bencharef, Omar Cady Ayyad University Computer and Systems Engineering Laboratory Faculty of Sciences and Technology Computer Science Department Marrakesh Morocco

ISBN: (纸本)9798350322972

Machine learning has been widely used as part of financial markets investment strategies, whether for forecasting the financial assets exchange rate, managing market volatility, or solving different classification problems that help with decision-making. Building an investment strategy using a scientific approach requires a massive amount of data, good computational power, and some expertise in the finance industry. Machine learning applications to the financial field, such as price exchange rate prediction, market pattern recognition, or other trading strategy tasks, are considered optimization problems. As they require an efficient algorithm dedicated to finding a global optimum, they can be solved using metaheuristics. In this survey, we study how metaheuristic optimization techniques contribute to building a robust learning model dedicated to financial investment strategy applications. © 2023 IEEE.

关键词： Financial markets

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Choice of Suitable Referral Hospital to Improve the Financial Result of Hospital Operations and Quality of Patient Care under a Neutrosophic Environment

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Neutrosophic Sets and systems 2023年 56卷 351-360页

作者： Abdelhafeez, Ahmed Abdel-Monem, Ahmed Aziz, Alber S Tantawy, Alshaimaa A. Faculty of Information Systems and Computer Science October 6th University Cairo Egypt Faculty of Computers and Informatics Zagazig University Sharqiyah Zagazig44519 Egypt

The referral cooperation hospital choice has been studied to better rationalize the allocation of healthcare assets and enhance the efficacy of resource utilization. Choosing hospitals to work within a collaborative referral arrangement is a crucial step in the patient-referral process. A referral cooperative hospital is chosen after careful consideration of these aspects to guarantee that patients will get the kind of detailed treatment that is appropriate for their condition. The concept of multi-criteria decision-making (MCDM) is used due to various criteria. The VIKOR method is the MCDM method used in this paper to rank the referral cooperation hospitals. The VIKOR method is integrated with the single-valued neutrosophic set to overcome uncertain information. The single-valued neutrosophic VIKOR method is applied to a case study in Egypt. We achieved quality of care as the best criterion from the eleven criteria used. © (2023). All Rights Reserved.

关键词： Hospitals

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Using Machine Learning Techniques to Maximize Profitability in the Hospitality Industry 6

Using Machine Learning Techniques to Maximize Profitability ...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Shallan, Mohammed S. Moawad, Ibrahim F. El Naggar, Rasha Montasser, Hend Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University Cairo Egypt

ISBN: (纸本)9798350373875

In the highly competitive hospitality industry, customer satisfaction is critical, and online reviews and ratings are instrumental in influencing hotel booking decisions. Tourists prioritize hotels offering exceptional services, making it vital for hospitality businesses to understand and address customer concerns promptly and effectively. Machine learning algorithms have become essential tools for analyzing online reviews and ratings. This paper analyzes over 70,000 reviews from TripAdvisor, focusing particularly on negative reviews with low ratings. The proposed methodology encompasses both unsupervised and supervised learning Methods. The unsupervised learning phase utilizes the K-Means algorithm to cluster similar reviews and identify prevalent themes and issues. The supervised learning phase employs five distinct algorithms: Support Vector Machine (SVM), Naive Bayes, XGBoost, Logistic Regression, and Random Forest, to classify reviews. These algorithms are evaluated on accuracy, precision, recall, and F-score metrics. Among them, the SVM algorithm demonstrates superior performance, achieving an accuracy rate of 76%%. The findings reveal that SVM is highly effective in analyzing online reviews, offering valuable insights for strategic decision-making in the hospitality sector. The results suggest practical implications for hospitality businesses in enhancing service quality and customer satisfaction. © 2024 IEEE.

关键词： Hotels

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