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An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

computer Systems science & engineering 2023年第1期44卷 143-156页

作者： M.Hema S.Kanaga Suba Raja Department of Information Technology Easwari Engineering CollegeChennai600089India Department of Computer Science and Engineering Easwari Engineering CollegeChennai600089India

In cloud data centers,the consolidation of workload is one of the phases during which the available hosts are allocated *** phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement(SLA).To consolidate the workloads,the hosts are segregated into three categories:normal hosts,under-loaded hosts,and over-loaded hosts based on their *** is to be noted that the identification of an extensively used host or underloaded host is challenging to ***-old values were proposed in the literature to detect this *** current study aims to improve the existing methods that choose the underloaded hosts,get rid of Virtual Machines(VMs)from them,andfinally place them in some other *** researcher proposes a Host Resource Utilization Aware(HRUAA)Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud *** mechanism presented in this study is contrasted with existing mechanisms *** results attained from the study estab-lish that numerous hosts can be shut down,while at the same time,the user's workload requirement can also be *** proposed method is energy-efficient in workload consolidation,saves cost and time,and leverages active hosts.

关键词： Workload consolidation energy consumption under load server resource utilization

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and Electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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Hybrid Kalman Filter and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

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Informatica (Slovenia) 2024年第23期48卷 121-132页

作者： Marzook, Ali K. Alkenani, Jawad University of Basrah College of Engineering Department of Electrical Engineering Iraq Department of Computer Science Shatt Al-Arab University College Basra Iraq

Several significant research studies have been done in distributed applications, database management systems, and information collecting in computer science concerning data mining and processing for wireless sensor networks (WSNs). As a result of WSNs' limited computation, networking, and data mining capabilities, the primary objective of creating WSN-based applications has proven to be extremely challenging: making decisions in real time. As such, typical data mining techniques are difficult to apply to sensor data due to their nature, peculiarities, and the constraints of wireless sensor networks. This work introduces a novel method for data mining and gathering and noise removal in wireless sensor networks (WSNs), dubbed KF-BA. This methodology increases network lifetime and efficiency by combining the Kalman Filter (KF) with the Bat Algorithm (BA). The recommended methods and BA contrast the provided techniques for raising energy consumption and prolonging network lifetime. The BA algorithm is not as effective as the KF-BA approach. The suggested strategy yields network longevity that is almost (57%) longer than BA in this instance, and that is after 2,000 packets are sent to two sensors spread over the network. The results were simulated using the MATLAB environment. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Kalman filters

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Load balancing routing in RPL for the internet of things networks: a survey

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International Journal of Wireless and Mobile Computing 2023年第3-4期24卷 243-257页

作者： Venugopal, Kala Basavaraju, T.G. Department of Computer Science and Engineering Government Engineering College Karnataka Hassan India

Presently, when the Internet of Things (IoT) makes virtually everything smart by improving every aspect of our life, continuous development in this area is imperative. As IoT deals with the Low-Power Lossy Networks (LLNs) with constrained resources, Routing in such constrained networks is considered an acute problem. The Internet engineering Task Force (IETF) has come out with a de facto routing protocol for IoT networks called the IPv6 Routing Protocol for Low-Power and Lossy networks (RPL). Unfortunately, though RPL is formulated with numerous salient characteristics, load balancing is a principal concern that is left unaddressed. Load balancing in IoT guarantees fair dissemination of traffic load amid the nodes in the network and affects the connectivity, stability, reliability and lifetime of LLNs. This paper elucidates the state-of-the-art load balancing routing protocols and issues of load balancing, aiding researchers to design efficient and reliable load balancing routing protocols for IoT networks. © 2023 Inderscience Enterprises Ltd.

关键词： Internet of things

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Weed Classification Using Particle Swarm Optimization and Deep Learning Models

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computer Systems science & engineering 2023年第1期44卷 913-927页

作者： M.Manikandakumar P.Karthikeyan Department of Computer Science and Engineering Thiagarajar College of EngineeringTamil NaduIndia Department of Information Technology Thiagarajar College of EngineeringTamil NaduIndia

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and *** and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of *** address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image *** this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed *** Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed *** effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification *** proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed *** work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

关键词： Deep learning convolutional neural network weed classification transfer learning particle swarm optimization evolutionary computing Algorithm 1:Metrics Evaluation

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Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

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computers, Materials & Continua 2024年第6期79卷 4603-4620页

作者： Mohammad Aldossary Hatem A.Alharbi Nasir Ayub Department of Computer Engineering and Information College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Al-Dawasir11991Saudi Arabia Department of Computer Engineering College of Computer Science and EngineeringTaibah UniversityMadinah42353Saudi Arabia Department of Creative Technologies Air University IslamabadIslamabad44000Pakistan

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer ***,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy *** paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research *** IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data *** sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for *** data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center *** the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT *** model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power ***,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and *** NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark *** findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive

关键词： Cloud computing energy efficiency data center optimization internet of things(IoT) hybrid models

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An efficient integrity based multi-user blockchain framework for heterogeneous supply chain management applications

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International Journal of computers and Applications 2023年第4期45卷 337-351页

作者： Karumanchi, Mani Deep Sheeba, J.I. Devaneyan, S. Pradeep Department of Computer Science and Engineering Bapatla Engineering College Bapatla India Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Mechanical Engineering Sri Venkateshwaraa College of Engineering and Technology Puducherry India

Most of the traditional cloud-based applications are insecure and difficult to compute the data integrity with variable hash size on heterogeneous supply chain datasets. Also, cloud storage systems are independent of integrity computational and data security due to structured data and computational memory. As the size of the cloud data files is increasing in the public and private cloud servers, it is difficult to provide strong data security due to file format and high dimensionality. The computational time and storage space of the conventional attribute-based encryption and decryption models are high during the data integrity verification in the traditional blockchain frameworks. This paper implements a hybrid variable size data integrity algorithm on the heterogeneous cloud supply chain data files for the strong data encryption and decryption process. This work implements an optimized blockchain framework using the advanced heterogeneous integrity computation approach and integrity policy-based attribute encryption and decryption approach for better cloud data security. Experimental results proved that the proposed integrity-based encryption model has better efficiency than the traditional integrity-based encryption frameworks on cloud heterogeneous data types. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

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Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第1期35卷 853-866页

作者： S.Sakthivel V.Vivekanandhan M.Manikandan Department of Computer Science and Engineering Sona College of TechnologySalem 636005TamilnaduIndia Department of Computer Science and Engineering Adhiyamaan College of EngineeringHosur 635109TamilnaduIndia

Irrigation plays a significant role in various agricultural cropping methods deployed in semiarid and arid regions where valuable water applications and managing are considered crucial *** factors such as weather,soil,water,and crop data need to be considered for irrigation maintenance in an efficient besides uniform manner from multifaceted and different information-based systems.A Multi-Agent System(MAS)has been proposed recently based on diverse agent subsystems with definite objectives for attaining global MAS objective and is deployed on Cloud Computing paradigm capable of gathering information from Wireless Sensor Networks(WSNs)positioned in rice,cotton,cassava crops for knowledge discovery and decision *** radial basis function network has been used for irrigation ***,in recent work,the security of data has not focused on where intruder involvement might corrupt the data at the time of data transferring to the cloud,which would affect the accuracy of decision *** handle the above mentioned issues,an efficient method for irrigation prediction is used in this *** factors considered for decision making are soil moisture,temperature,plant height,root *** above-mentioned data will be gathered from the sensors that are attached to the *** data will be forwarded to the local server,where data encryption will be performed using Adaptive Elliptic Curve Cryptography(AECC).After the encryption process,the data will be forwarded to the *** the data stored in the cloud will be decrypted key before being given to the deci-sion-making ***,the uniform distribution-based fuzzy neural network is formulated based on the received data information in the decisionmaking *** decision regarding the level of water required for cropfields would be *** on this outcome,the water volve opening duration and the level of fertilizers required will be *** results demons

关键词： Irrigation multi-agent system precision irrigation accuracy elliptic curve cryptography encryption wireless sensor networks fertilizers

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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An Intelligent Vehicle Control System for Enhancing Road Safety Using Optimal Visible Light Communication Network

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Journal of Optical Communications 2023年第1期44卷 81-94页

作者： Begum, Shaeista Patil, Nagaraj B. Department of Computer Science and Engineering Government Engineering College Karnataka Raichur India

A visible light communication (VLC) provides potential and effective communication paradigm due to the demand of high data-rate applications. VLC networks, consisting of multiple light emitting diodes (LEDs) and it provides the low-cost high data-rate transmission to multiple users simultaneously in indoor environments. VLC has been recently introduced as a secure directional data transmission in vehicle to vehicle to provide an intelligent vehicle control system. However, the performance of this system is mostly affected by the collision of data transmission between different users. In this paper, we introduce an optimal visible light communication (OVLC) network that allows vehicles which have provides collision aware data transmission to improve the chance of transmitting information successively according to the network condition. Firstly, the next forwarding node is selected by the chaotic fish swarm optimization (CFSO) algorithm with the help of vehicle information's such as intensity of light, the distance and speed of neighboring vehicles. The second contribution is to illustrate the congestion control (CC) system for avoiding extra time due to the control packets exchange process. The optimal result is then forward to the source vehicle equipped device, which helps the driver to make a healthy to control vehicle and efficiently avoid or prevent road accidents under different circumstances. The results show that the proposed OVLC network performs very efficient than existing network in terms of quality metrics, such as throughput, delay, packet loss rate, energy consumption and fairness index. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Visible light communication

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