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Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

computers, Materials & Continua 2022年第4期71卷 1095-1109页

作者： R.Joshua Samuel Raj M.Varalatchoumy V.L.Helen Josephine A.Jegatheesan Seifedine Kadry Maytham N.Meqdad Yunyoung Nam Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Cambridge Institute of TechnologyBengaluru560036India Department of Computer Applications CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Swarnandhra College of Engineering and TechnologyNarasapur534280India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Science and Engineering Soonchunhyang University31538Korea

Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics.

关键词： Internet of things cloud computing task scheduling metaheuristics resource allocation

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An IoT Based Electromyography Signal Transmission from sEMG Electrodes to Client's Server with ICT Infrastructure 10

An IoT Based Electromyography Signal Transmission from sEMG ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Pavithra, P.M. Balakrishnan, M. Anandaraj, A. Ramya, M. Sudharsan, R. Raja Sri Shanmugha College of Engineering and Technology Department of Artificial intelligence and Data Science Salem India Dr. Mahalingam College of Engineering and Technology Department of Artificial intelligence and Data Science Pollachi India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Sri Shanmugha College of Engineering and Technology Department of Information Technology Salem India Sri Shanmugha College of Engineering and Technology Department of Biomedical Engineering Salem India

ISBN: (纸本)9798350384369

Bio-electric signal like Electromyography (EMG) can measure signal data from nerves and muscles separately in the particular region of affected nerve or muscles from a Human Being. For incorporating these measurements, can use a non-invasive technique. The Surface electrode can be used for measuring these problems in non-invasive fashion. Here, the idea of Internet of Things (IoT) can bring included advantage to applications with bio-electric signals to communicate to the doctors and patients such as human-PC cooperation by incorporating different advances for example, sensors, remote correspondence and information science. In this research, we worked on a remote bio-electric signals to another remote observing handling framework. A model with the contextual investigation of nerve and muscle signal acknowledgment utilizing 4-channel sEMG signals is executed. A Gaussian filter is prepared from one individual's sEMG signals with 4-nerve regions of human body such as ulnar, radial, median and radial. The introduced IoT application framework is actualized based on an 11-terminal bio-electric signal measuring device, with Arduino based Controller, Gateway, ESP8266 (Wifi) module, Server being the major mediums of remote transmission of signal data. The signal transmits at 35KB/s. In the framework, the ongoing sEMG signal from surface electrode is filtered from noise and other distortions using is given to the controller for performing the operation. © 2024 IEEE.

关键词： Gateways (computer networks)

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A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

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computers, Materials & Continua 2024年第11期81卷 2873-2894页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Naveed Ahmed Mohammed Ali Alshara School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadh11432Saudi Arabia

Image captioning has gained increasing attention in recent *** characteristics found in input images play a crucial role in generating high-quality *** studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption ***,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these ***,this leads to enhanced captioning network *** light of this,we present an image captioning framework that efficiently exploits the extracted representations of the *** framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language *** VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features ***,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative *** the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s *** the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve *** implementation code can be found here:https://***/althobhani/VFDICM(accessed on 30 July 2024).

关键词： Visual attention image captioning visual feature detector visual feature visual attention

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Multilingual Facial Emotion Decoding and Posture Recognition using Random Forest Classifier

Multilingual Facial Emotion Decoding and Posture Recognition...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Sangeetha, K. Balaji, V.S. Manju, M. Renuka Devi, S. Velayutham, P. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India

ISBN: (纸本)9798350374933

Effective communication is crucial for success in various interactions, including personal and online interviews. The work proposed is to refine the communication effectiveness and extend the understanding of interactions by incorporating new system functions of real-time emotion and body language capture. Facial and body landmarks are analyzed using Google’s Mediapipe to ensure accurate identification of the frequently performed emotions and frequently posed postures. The proposed system uses three classifiers, namely Random Forest, Gradient Boosting, and Ridge Classifier, all integrated in one pipeline;this makes our work distinct. In the testing phase the classifiers are checked to avoid misidentification of emotions or incorrect posture in different situations. One of the major novelties of this work is its ability to address users of different languages, thus it is relevant anywhere in the world. This dynamic and robust system provides accurate emotion analysis achieving the accuracy of 92%, enhancing understanding in various communication contexts and setting a new standard for interview and interaction analysis. © 2024 IEEE.

关键词： Emotion Recognition

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd Science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

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computers, Materials & Continua 2024年第11期81卷 2819-2843页

作者： Mehrdad Shoeibi Mohammad Mehdi Sharifi Nevisi Sarvenaz Sadat Khatami Diego Martín Sepehr Soltani Sina Aghakhani The WPI Business School Worcester Polytechnic InstituteWorcesterMA 01609-2280USA Department of Computer Science Escuela de Ingeniería Informática de SegoviaUniversidad de ValladolidSegovia40005Spain Department of Data Science Engineering University of HoustonHoustonTX 77204USA Department of Industrial Engineering College of EngineeringUniversity of HoustonHoustonTX 77204USA Department of Industrial and Manufacturing Systems Engineering Iowa State UniversityAmesIA 50011USA

In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy ***,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of *** by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the *** integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization *** can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the *** compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network *** proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence *** our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation *** was accompanied by the lowes

关键词： Smart grid communication secrecy rate optimization reinforcement learning improved chimp optimization algorithm

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ENERGY-EFFICIENT ANT ROUTING ALGORITHM FOR OPTIMIZED PATH SELECTION NETWORK LONGEVITY IN WIRELESS SENSOR NETWORKS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第2期84卷 21-42页

作者： Zelani, Shaik Khader Babu, D. Nagabhushana Surendra, Doddaga Rahman, Mohammad Zia Ur Department of Electronics and Communication Engineering Sri Vasavi Institute of Engineering and Technology-Autonomous Pedana India Department of Data Science Malla Reddy University Telangana Hyderabad India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India

The exponential growth of wireless sensor networks (WSNs) in various applications underscores the critical need for energy-efficient routing protocols to extend network lifespan. This paper introduces the Energy-Efficient Ant Routing Algorithm, a novel bioinspired routing strategy derived from ant colony optimization (ACO), which integrates traditional pheromone mechanisms with dynamic energy-aware adaptations to prioritize routes through nodes with higher residual energy levels, thereby balancing load and prolonging network functionality. The model’s adaptive pheromone updating mechanism, which considers both the cumulative path energy cost and the minimum residual energy of nodes along the path, encourages the use of energy-efficient routes and mitigates rapid energy depletion in critical network areas. This approach not only extends operational network lifetime but also supports higher data reliability and throughput in challenging WSN scenarios. The results of the proposed model demonstrated significant improvements over traditional ACO and other common energy-aware routing protocols. The network lifespan is extended by ~ 25% compared to standard ACO and by 15% compared to the best-performing conventional energy-aware routing protocol under the same conditions. Furthermore, EEARA achieved a 20% reduction in average energy consumption per node and enhanced the data delivery ratio by 10% due to more stable routing paths. The results underline the model’s potential as a scalable, efficient routing solution for energy-limited networks, promising substantial improvements in sustainable network operations. © 2025 by Begell House, Inc.

关键词： Ant colony optimization

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Investigating customer churn in banking:a machine learning approach and visualization app for data science and management

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data Science and Management 2024年第1期7卷 7-16页

作者： Pahul Preet Singh Fahim Islam Anik Rahul Senapati Arnav Sinha Nazmus Sakib Eklas Hossain Institute for Artificial Intelligence and Data Science University at BuffaloThe State University of New YorkAmherst14068United States Department of Mechanical Engineering Khulna University of Engineering&TechnologyKhulna9203Bangladesh Department of Information Technology Kennesaw State UniversityMarietta30067United States Department of Electrical and Computer Engineering Boise State UniversityBoise83725United States

Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and,after that,end their connection with the ***,customer retention is essential in today’s extremely competitive banking ***,having a solid customer base helps attract new consumers by fostering confidence and a referral from a current *** factors make reducing client attrition a crucial step that banks must *** our research,we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying *** use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation *** addition,we developed a data Visualization RShiny app for data science and management regarding customer churn *** this data will help the bank indicate the trend and then try to retain customers on the verge of attrition.

关键词： Bank customer attrition Churn prediction Machine learning XGboost Random forest

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Predicting Flight Delays and Error Calculation Using Machine Learning Classifiers 5

Predicting Flight Delays and Error Calculation Using Machine...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Rajeshkumar, G. Vijaya, K. Sadesh, S. Arunkumar, P.M. Manoj Praveen, V. Kumar, M. Senthil Bannari Amman Institute of Technology Dept of Cse Sathyamangalam India Kongu Engineering College Department of Csd Perunduarai Erode India Velalar College of Engineering and Technology Department of Artificial Intelligence and Data Science Erode India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore641032 India Erode Sengunthar Engineering Collge Department of Artificial Intelligence and Data Science Perundurai India

ISBN: (纸本)9798350379945

Flight delays are a significant concern in the aviation industry, causing inconvenience for passengers and financial losses for airlines. Predicting flight delays accurately can help mitigate these issues by allowing both airlines and passengers to proactively manage schedules and resources. The delay might depend on weather, crew management, and resource planning. Existing prediction methods often lack accuracy or rely on historical data and statistical models, which may not capture the complex interactions and patterns present in flight operations to predict delays. This study proposes a novel approach for flight delay prediction using advanced machine learning techniques and tools. Specifically, machine-learning classifiers, such as Support Vector Machines (SVM) and Decision Tress, are employed to analyze a comprehensive live streaming dataset comprising various incorporated factors like flight operations, weather conditions, air traffic congestion, and aircraft performance. Proposed model is trained on a large dataset encompassing diverse routes and airlines and has achieved an accuracy of 95% in predicting delays by outperforming existing methods. © 2024 IEEE.

关键词： Traffic congestion

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