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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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Joint Rain Streaks & Haze Removal Network for Object Detection

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computers, Materials & Continua 2024年第6期79卷 4683-4702页

作者： Ragini Thatikonda Prakash Kodali Ramalingaswamy Cheruku Eswaramoorthy K.V Department of Electronics and Communication Engineering National Institute of TechnologyWarangal506004India Department of Computer Science and Engineering National Institute of TechnologyWarangal506004India Department of Electronics and Communication Engineering Indian Institute of Information TechnologyDesign and ManufacturingKurnool518008India

In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant *** emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior ***,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational *** response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature *** methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map *** the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets.

关键词： Image deraining Selective Dense Residual Module(SDRM) Selective Kernel Fusion Module(SKFM) Selective KernelDense Residual M-Shaped Network(SKDRMNet)

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Finding Friendly Neighborhood: Optimal D2D Relaying in mmWave IoT Networks

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IEEE Access 2025年 13卷 89193-89207页

作者： Mahendran, V. Badarla, Venkataramana Krishna, Vamshi Vijay Indian Institute of Technology Department of Computer Science and Engineering Tirupati517619 India

Device-to-Device (D2D) relaying helps improve the coverage range and throughput of the millimeter Wave (mmWave) networks. This work focuses on improving the uplink throughput in a single-cell mmWave-based Internet-of-Things (IoT) network. The key factors involve the placement of relays and neighbor selection (NS)(, i.e., the process of selecting source IoTs within the relay node's coverage range). The problem is formulated using model-based bandits, and the associated structural results are analyzed. Considering the resource-constrained nature of IoTs, we have developed a simple rule-based NS as an alternative to computationally intensive learning-based neighbor-selection processes from state of the art. By utilizing the rule-based NS, we show that there exists a conducive coverage range (called a friendly neighborhood) for the relay node to choose the neighbors. Accordingly, a geometry-based relay placement mechanism is proposed to maximize the uplink throughput. The optimality of the proposed solutions is shown analytically, and their performance concerning the state of the art is evaluated using extensive simulation. © 2013 IEEE.

关键词： Beamforming

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Wide field of view large aperture meta-doublet eyepiece

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Light(science & Applications) 2025年第1期14卷 167-176页

作者： Anna Wirth-Singh Johannes E.Fröch Fan Yang Louis Martin Hanyu Zheng Hualiang Zhang Quentin T.Tanguy Zhihao Zhou Luocheng Huang Demis DJohn Biljana Stamenic Juejun Hu Tian Gu Arka Majumdar Department of Physics University of WashingtonSeattleWAUSA Department of Electrical and Computer Engineering University of WashingtonSeattleWAUSA Department of Materials Science and Engineering Massachusetts Institute of TechnologyCambridgeMAUSA Department of Electrical and Computer Engineering University of MassachusettsLowellMAUSA Department of Electrical and Computer Engineering University of CaliforniaSanta BarbaraCAUSA

Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of view,leading to limited performance and increased size and *** particular,simultaneously achieving a wide field of view and large aperture for light collection is desirable but challenging to realize in a compact ***,we demonstrate a wide field of view(greater than 60°)meta-optic doublet eyepiece with an entrance aperture of 2.1 *** the design wavelength of 633 nm,the meta-optic doublet achieves comparable performance to a refractive lens-based eyepiece *** meta-doublet eyepiece illustrates the potential for meta-optics to play an important role in the development of high-quality monochrome near-eye displays and night vision systems.

关键词： optics refractive illustrate

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Performance Evaluation of Natural Language Processing Algorithms for Sentiment Analysis

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SN computer science 2024年第6期5卷 724页

作者： Silviya, S. H. Annie Faith, S. Julia Seetha, R. Hemalatha, M. Department of Computer Science and Engineering Rajalakshmi Institute of Technology (RIT) Department of Information Technology S.A.Engineering College Department of Information Technology Vellore Institute of Technology Department of Computer Science and Engineering RMD Engineering College

Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, computer science, and artificial intelligence called “natural language processing” studies how computers and human language interact, with a focus on how to teach computers to process and analyze vast volumes of natural language data. Sentiment analysis, sometimes referred to as opinion mining or emotion artificial intelligence, is the methodical identification, extraction, quantification, and study of affective states and subjective data using natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is frequently used in online and social media platforms, voice of the customer materials (such as reviews and survey replies), and healthcare materials for marketing, customer service, and clinical medicine purposes. This paper discusses different sentiment analysis methods and selects the best approach for a certain task. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics Computational linguistics Natural language processing Sentiment analysis Text analysis

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Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

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computer Systems science & engineering 2023年第4期45卷 715-736页

作者： Kandasamy Sellamuthu Vishnu Kumar Kaliappan Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407Tamil NaduIndia

Pesticides have become more necessary in modern agricultural ***,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the *** to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to *** residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly *** residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in *** is an obligation to provide a warning about this amount of pesticide use in *** technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential *** analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and ***,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.

关键词： Pesticide contamination complex event processing recurrent neural network Q learning multi residual level and contamination level

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Smart Railway Platform Bridge Design with Hall Effect Sensor 2nd

Smart Railway Platform Bridge Design with Hall Effect Sensor

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2nd Congress on Control, Robotics, and Mechatronics, CRM 2024

作者： Sujatha, Canavoy Narahari Reddy, Ch. Sai Ganesh Yoginath, G. Kumar, K. V. N. S. Revanth Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India

ISBN: (纸本)9789819768097

The railways play a crucial role in transportation, and stations are improving for passenger convenience. Escalators and lifts are available in main stations, but not everywhere due to high costs of implementation and maintenance. Some passengers illegally cross railway tracks between platforms, risking their safety and causing inconvenience to railway staff. To address this, Smart Railway Platform Bridges (SRPBs) can be built at railway stations upon request, providing safe passage. This falls under the subject of smart railway stations, where SRPBs are designed to connect adjacent platforms automatically when no trains are present. This is especially helpful for passengers carrying heavy luggage or with mobility issues. This proposed idea also benefits senior citizens. The SRPB uses Hall Effect sensors, which are placed at the opposite ends of both sides of the platform at a specific distance apart, to detect train arrivals and departures at the station. In view of passengers’ safety, a voice announcement is in SRPB. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Hall effect transducers

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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Study Influencing Factors of Maternal Health and the Role of Internet of Things (IoT) to Improve Maternal Care

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SN computer science 2024年第6期5卷 778页

作者： Rani, Deepa Kumar, Rajeev Chauhan, Naveen Department of Computer Science and Engineering National Institute of Technology

Maternal health during pregnancy is influenced by various factors that significantly impact pregnancy outcomes. This paper aims to highlight these critical factors, promote awareness, and advocate proactive self-care among women. By analyzing various works, a taxonomy of influencing factors is proposed to provide an understanding of their effects on maternal and fetal health. The taxonomy reveals multiple factors, including individual characteristics, social determinants, and environmental factors, that significantly affect maternal and fetal health. Therefore, by thoroughly reviewing reputed articles in the field of IoT technology in maternal care, a list of sensors and devices capable of capturing health-related parameters and various applications of IoT in maternal care is presented. IoT technology and promoting self-care can enhance maternal care and improve outcomes for both mother and fetus. Therefore, this research emphasizes the factors influencing pregnancy and the benefits of the adoption of IoT technology to enable a patient-centric approach for prompt detection of potential risks in maternal care. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： IoT Maternal care Remote monitoring Risk monitoring Self-care

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Classification Model for IDS Using Auto Cryptographic Denoising Technique

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computer Systems science & engineering 2023年第4期45卷 671-685页

作者： N.Karthikeyan P.Sivaprakash S.Karthik Department of Computer Science&Engineering PPG Institute of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the *** are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting *** algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of *** this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam *** classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and *** neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 *** their power and limitations in the proposed methodology that could be used in future works in the IDS area.

关键词： Auto cryptographic denoising(ACD) classifier intrusion detection system(IDS) optimizer performance measures

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