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Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

computer Systems Science & engineering 2022年第2期40卷 581-592页

作者： K.Rajakumari M.Vinoth Kumar Garima Verma S.Balu Dilip Kumar Sharma Sudhakar Sengan Department of Computer Science and Engineering School of EngineeringAvinashlingam Institute for Home Science and Higher Education for WomenCoimbatore641043Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India School of Computing DIT UniversityDehradun248009UttarakhandIndia Department of Computer Science and Engineering Paavai Engineering CollegePachal637018Tamil NaduIndia Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226M.P.India Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Cloud computing is an information technology deployment model established on *** scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing ***,task scheduling challenges such as optimal task scheduling performance solutions,are addressed in cloud ***,the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin *** recommended DWRR algorithm improves the task scheduling performance by considering resource competencies,task priorities,and ***,a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task *** the end,a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment.A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the ***,the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time,system throughput,and maximizing resource utilization.

关键词： Cloud Computing scheduling ant colony optimization fuzzy logic

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A Multi-objective Smart Grid Optimization: Integrating Swarm Intelligence with Domain-Specific Heuristics for Superior Energy Efficiency 3rd

A Multi-objective Smart Grid Optimization: Integrating Swarm...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Ammasai, Viswanathan Katbar, Nek Muhammad Kothalanka, Amarendra Vandadi, Venkata Ramana Udhaya Shankar Latha, Sindhu Sharma, Dilip Kumar Sengan, Sudhakar School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India School of Mathematics and Statistics Central South University Hunan Changsha410083 China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering KSRM College of Engineering Andhra Pradesh Kadapa516003 India Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli62745 India

ISBN: (纸本)9789819783540

The realm of Smart Grid Systems (SGS) has witnessed unprecedented progress, driven by increasing energy demands and the integration of Renewable Energy Sources (RES). While these grids promise enhanced efficiency and sustainability, their involved and dynamic nature poses significant optimization challenges. Existing solutions, although innovative, often overlook the intricate interplay between diverse factors, leaving room for potential improvements in optimization approaches. Recognizing this gap, the current study delves into developing a novel method that integrates Ant Colony Optimization (ACO) with domain-specific heuristics. By focusing on analytical sections like Load Forecasting, Renewable Energy Integration, Peak Shaving, and Grid Resilience, this method attempts to bridge the divide that existing approaches miss. The proposed method not only ensures optimal energy distribution but also tailors the solution to address the unique features and requirements of modern SGS. A more reliable, effective, and Renewable Energy (RE) future is within reach according to the outcomes of this investigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

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A hybrid model: PNM for improving prediction capability of classifier

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International Journal of information technology (Singapore) 2024年第1期16卷 483-491页

作者： Mehrotra, Shashi Muttum, Vinay Kumar Krishna, Redrouthu Vamsi Kumar, Vinod Varish, Naushad College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad 244001 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur 522302 India School of Computing Science and Engineering GALGOTIAS University Greater Noida 203201 India Department of Computer Science and Engineering GITAM (Deemed to be University) Rudraram Telangana Hyderabad 502329 India

In recent years, the COVID-19 and its variant are more dangerous for people with some health complexity, such as breast cancer, diabetes, and heart disease. Diagnosis at the early stage of these diseases may save people from deadly diseases and infections. However, often these diseases are diagnosed very late. Classification algorithms are in use for the diagnosis of several diseases. Classification result significance is a major issue in many real-time domains which deal with class imbalance data. Class imbalance data is one of the primary challenges for the performance of classification models. Most sensitive and essential real-time domain data are class imbalances, resulting in poor classification performance. Due to the dominancy of the major class, the classification of the minor class may show high accuracy. Misclassification can not be accepted in medical cases, which can cost human life as well. The paper presents a hybrid model named PNM to address the class imbalance issue and thus improve the performance of the classifiers. This proposed sampling model integrates Principal Component Analysis and the Near-Miss sampling method. Experimental results show the efficiency of the PNM model for the class imbalance data. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Class imbalance Classification K-Nearest Neighbor Near-Miss Principal component analysis Support vector machine

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Harmonious Music-driven Group Choreography with Trajectory-Controllable Diffusion 39

Harmonious Music-driven Group Choreography with Trajectory-C...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dai, Yuqin Zhu, Wanlu Li, Ronghui Ren, Zeping Zhou, Xiangzheng Ying, Jixuan Li, Jun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)157735897X

Creating group choreography from music is crucial in cultural entertainment and virtual reality, with a focus on generating harmonious movements. Despite growing interest, recent approaches often struggle with two major challenges: multi-dancer collisions and single-dancer foot sliding. To address these challenges, we propose a Trajectory-Controllable Diffusion (TCDiff) framework, which leverages non-overlapping trajectories to ensure coherent and aesthetically pleasing dance movements. To mitigate collisions, we introduce a Dance-Trajectory Navigator that generates collision-free trajectories for multiple dancers, utilizing a distance-consistency loss to maintain optimal spacing. Furthermore, to reduce foot sliding, we present a footwork adaptor that adjusts trajectory displacement between frames, supported by a relative forward-kinematic loss to further reinforce the correlation between movements and trajectories. Experiments demonstrate our method’s superiority. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Functional overview of integration of AIML with 5G and beyond the network

Functional overview of integration of AIML with 5G and beyon...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Srinivas, Kolli Aswini, J. Patro, Pramoda Kumar, Devendra VNR Vignana Jyothi Institute of Engineering & Technology Department of Information Technology Hyderabad India Saveetha Engineering College Department of AIML Chennai India Koneru Lakshmaiah Education Foundation Department of Engineering Mathematics College of Engineering Hyderabad India ABES Engineering College Department of Computer Applications Ghaziabad India

ISBN: (纸本)9798350348217

4 G/LTE mobile networks resolved this issue. Strong physical layer and adaptable network design enable high-capacity mobile broadband Internet. Despite this, the prevalence of bandwidth-intensive technologies like virtual reality, augmented reality, and others has grown. In addition, the rising popularity of new services places astrain on mobile infrastructure. Applications requiring high availability and low latency, such Internet-of-Vehicles or communications between vehicles (IoV). With the advent of the new 5G technology with its massive MIMO radio interface, these problems are no longer a concern. Networks protected by software-defined networking (SDN)and NFV have added a new level of flexibility that allows network operators to serve services with very high requirements across several industries. Network operators must increase and diversify their intelligence to fully comprehend the operational environment, user behaviours, and user demands. A further goal is to become(self-) networkable proactively and effectively. This chapter will look at how AI may help us in the modern world. Next-generation mobile networks that are both efficient and adaptable may benefit greatly from machine learning in the 5G era and beyond. The evolution of AI and ML in network applications. © 2023 IEEE.

关键词： Wireless networks

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Prediction of Rice Leaf Diseases at an Early Stage Using Deep Neural Networks 1

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8th International Conference on computers, Management and Mathematical Sciences, ICCM 2022

作者： Mahanty, Mohan Vamsi, Bandi Srilatha, Y. Doppala, Bhanu Prakash Department of Computer Science and Engineering Vignan’s Institute of Information Technology-Duvvada Andhra Pradesh Visakhapatnam India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Data Analytics Generation Australia SydneyNSW Australia

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

Rice is the major crop in India, and India has been the biggest exporter and the second-largest producer in the entire world, so it is heavily reliant on rice for its economy and food supply. There has been an increase in rice production from 53.6 million tons in fiscal to 120 million tons over the last 40 years. Healthy and proper rice plant growth is required to maintain food production and supply in accordance with people’s needs and demands. The major diseases affecting these rice plants are leaf blast, hispa, and brown spot. The advancement of deep learning paved the way for these diseases to be detected using computer vision. Many researches have been conducted in the detection of rice leaf diseases, where the rice leaves have been deeply affected at a later stage. In this chapter, we propose an effective deep learning model for an early-stage disease detection of rice leaves. The proposed model was developed by an ensemble of the pretrained DenseNet-201 with the Naïve Inception module. The model was trained and tested over a Rice Diseases Image dataset;it attained an overall disease detection accuracy of 87.69% and surpasseed the existing models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Food supply

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Legal Judgment Prediction with LLM and Graph Contrastive Learning Networks 24

Legal Judgment Prediction with LLM and Graph Contrastive Lea...

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8th International Conference on computer Science and Artificial Intelligence, CSAI 2024

作者： Xia, Yangbin Luo, Xudong School of Computer Science and Engineering Guangxi Normal University Guangxi Guilin China Guangxi Key Lab of Multi-Source Information Mining Guangxi Normal University Guangxi Guilin China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9798400718182

Legal judgment prediction is a complex task that involves analysing case descriptions and relevant facts to predict outcomes. This paper presents LLG-Judger, a novel framework combining Large Language models and Graph neural networks for LJP. LLG-Judger integrates the strengths of rule-based divisions, Large Language Models (LLMs) for initial case predictions, and graph neural networks for structured fact representation. The proposed model first constructs a fact graph from case descriptions and then applies a graph encoder to capture syntactic and semantic relationships within the text. To enhance prediction accuracy, we employ a contrastive learning module that improves feature representation by focusing on both syntactic and global semantic neighbours. Additionally, we integrate ChatGPT’s predictions with the graph model’s results using KL divergence to optimise the final prediction. Experimental evaluations demonstrate the effectiveness of LLG-Judger, achieving superior performance over various baselines, especially in the legal domain. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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Employing a Machine Learning-Based Approach for Enhanced Big Data Analysis in Supply Chain Management

Employing a Machine Learning-Based Approach for Enhanced Big...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Pradeepa, M. Sumathi, V. Ponnusamy, Sivakumar Suneetha, G. Singh, Gurjeet Kruthika, P. School Of Computer Science Engineering And Information Systems Vellore Institute Of Technology Tamil Nadu Vellore632 014 India Sri Sai Ram Engineering College Department Of Mathematics West Tambaram Tamil Nadu Chennai44 India Ksr College Of Engineering Department Of Computer Science And Engineering Namakkal TamilNadu Tiruchengode India Koneru Lakshmaiah Education Foundation Department Of Cse Andhra Pradesh Vaddeswaram India Ies College Of Technology Ies University Bhopal Department Of Mechanical Engineering Madhya Pradesh India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India

ISBN: (纸本)9798350364828

This study uses machine learning models as potent analytical tools to look into the issue of inventory cost and profit prediction in the automobile industry. Throughout a ten-year dataset from 2012 to 2023, the study carefully analyses several cost components and profit data, illuminating the complex relationships between these elements. To provide insights into inventory dynamics, the Convolutional Neural Network (CNN), k-nearest Neighbours (KNN), and Support Vector Machine (SVM) models are rigorously trained and tested. An impressive feat is achieved by the CNN model, which achieves a phenomenal accuracy of 98.7%, followed by KNN at 96.7% and SVM at 93.4%. Additional proof of the models' propensity to make accurate predictions comes from their precision, recall, F1 score, and confusion matrices. The research provides data-driven insights that players in the dynamic vehicle industry can use to improve inventory management procedures and financial results. This work serves as an example of how machine learning may fundamentally alter how people make decisions in this difficult domain. © 2024 IEEE.

关键词： Forecasting

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Diagnosis of Diabetic Retinopathy Using Hierarchical Feature Learning and Modified CNN with ACAF 4

Diagnosis of Diabetic Retinopathy Using Hierarchical Feature...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sankarananth, S. Uma Maheswari, G. Arun Kumar, B. Nagasuresh, M. Arun Kumar, R. Karthiga, M. Bamnari Amman Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Sathyamangalam India Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Karpagam Academy of Higher Education Department of Artificial intelligence and Data Science Tamilnadu Coimbatore India Karpagam Institute of Technology Department of Information Technology Tamilnadu Coimbatore India V.S.B Engineering College Department of Electrical and Electronics Engineering Tamilnadu Karur India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Diabetic retinopathy (DR) remains one of the leading causes of vision loss and blindness today, making the need for accurate and reliable detection techniques paramount. This work presents a new method for feature extraction through combining two methods namely;vSLM and Wavelet CNN. The approach is complemented by a modified CNN model that is equipped with an AdaptiveConvolutional ActivationFunction (ACAF) for classification. As it was mentioned before, the vSLM technique creates detailed retinal maps, and thus can be effective for the detection of critical DR indicators such as microaneurysms, haemorrhages, and exudates. Wavelet CNN enhances the feature extraction procedure, taking into consideration both spatial and spectral features of the retinal images. The resolution provided by the proposed CNN model with ACAF demonstrates outstanding classification results with accuracy rates of 97.12% on DiaretDB0 dataset, 99. 12% on the public EyePACS Kaggle dataset and 99.15% on the DRIVE dataset and 98.12% on the CHASE dataset. These findings highlight the importance of using the feature extraction and classification approach for improving the accuracy of DR detection and classification. The findings point to severe advancements in the diagnostic tools of DR and the effect of these enhancements in the pragmatic aspect of identify and handling of DR. © 2024 IEEE.

关键词： Convolutional neural networks

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