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Performance analysis of precise energy consumption algorithm for smart home using hybrid renewable energy

International Journal of Powertrains 2023年第1期12卷 1-14页

作者： Anguraj, Dinesh Kumar Sathya, C. Vinothini, C. Jayanthi, M. Rani, J. Vakula Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Computer Science and Engineering Dr. N.G.P. Institute of Technology Tamil Nadu Coimbatore India Department of MCA CMR Institute of Technology Karnataka Bengaluru India

Energy demand is increased day by day, so there is a need for energy management, and it plays a vital part in the 21st century. At present, non-renewable energy is utilised most of the time. The people are not aware of the wastage of energy when their home appliances turned on the whole day. So, hybrid renewable energy is an alternate source for supplying continuous power for their smart home in the future. The collected renewable energy is utilised for the smart home usages. To minimise power consumption and to predict energy consumption precisely is a stimulating task in the future. This work proposes an innovative precise energy consumption algorithm (PECA) that is used to calculate the smart home power consumption accurately. PECA utilises smart plug, smart gateway, and mobile app administration platform to build and deploy a deep learning model. The smart plug and smart gateway combined into the complete distributed sensor network is to analyse, improve, and expand the energy consumption efficiently. Artificial intelligence is used to predict the energy usage data, optimise resource consumption, reduce the cost and maximise renewable energy usage. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Electric power utilization

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Node Deployment Strategies and Challenges in Underwater Wireless Sensor Network 5

Node Deployment Strategies and Challenges in Underwater Wire...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Layolin Benisto, L.C. Sukumaran, Rajeev Agnel Shyam kumar, C. SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur India

ISBN: (纸本)9798350395235

Underwater Wireless Sensor Networks (UWSNs) are gaining importance for a wide range of applications, from environmental monitoring to underwater exploration. However, the challenging underwater environment poses significant obstacles to reliable data transmission. This study focuses on the role of deployment nodes in UWSNs, addressing the need for extended communication range, signal amplification, and data reliability. The study presents an in-depth exploration of node deployment, communication protocols, and the unique challenges faced in underwater environments. By strategically placing and managing nodes, UWSNs can overcome these challenges and improve overall network performance. UWSNs integrate a number of critical deployment strategies designed for land-based sensor networks. However, because to the unique 3-D properties of UWSNs, not all of these methodologies are immediately relevant. Underwater networks function in three dimensions, posing additional issues such as sensor node mobility driven by water currents and significant communication delays. The goal of this research is to look at various node deployment methodologies in order to shed light on the difficulty of sensor node deployment. The study intends to investigate various node deployment strategies in order to shed light on the complexities of sensor node deployment in UWSNs. Furthermore, the research performs an exhaustive literature analysis on node deployment, diving into difficulties and obstacles faced during deployment, and extensively analyzing the performance of various node deployment approaches. This research provides useful insights for optimizing node deployment in UWSNs, opening the path for increased communication efficiency in the difficult underwater realm. © 2024 IEEE.

关键词： Sensor nodes

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Experimental Analysis on Perceptual Image Quality Outcomes of Single Image Super Resolution Frameworks 2

Experimental Analysis on Perceptual Image Quality Outcomes o...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Venkatesh, S. Aravindhar, D. John Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

Numerous advancements in image super resolution in recent years have explored different deep learning approaches and frameworks based on Convolution Neural Network (CNN) to achieve enhanced perceptual results. In this article, detailed experimental and performance analysis on perpetual image quality results of Single Image Super Resolutions frameworks through multiple CNN architectures has carried out according to the reconstruction efficiency, perceptual and reconstruction accuracy. Initially background of the image super resolution is analyzed. Next, review of the image super resolution architecture based on deep learning architecture with different optimization approaches has been analyzed with respect to the benchmark dataset. Additionally different image quality assessment method has been discussed to highlight performance of the existing image super resolution frameworks. Finally these experimental findings provides the image results of basis of measures like Peak signal to noise ratio, structural similarity index and visual information fidelity to present the performance of the model. © 2024 IEEE.

关键词： Convolution Neural Network Deep learning Feedback learning Multi Scale features Single Image Super Resolutions

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Enhancing Cyber-Physical Systems Security: A Review of Deep Learning and Blockchain Integration 5

Enhancing Cyber-Physical Systems Security: A Review of Deep ...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Selvi, K. Dilip, Golda SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Vadapalani Campus Chennai India

ISBN: (纸本)9798350367171

Cyber-Physical Systems (CPS) combine physical and computational elements to produce intelligent systems communicating with their surroundings. By integrating digital and physical processes, CPS provides increased functionality in various industries, including industry, transportation, and healthcare. However, there are security, data integrity, and privacy issues brought on by the extensive integration of CPS across several domains. Therefore, addressing these issues is significant for an effective and reliable CPS model. This study presents a comprehensive review exploring the synergistic relationship between two cutting-edge technologies, Deep Learning (DL) and Blockchain, deployed to resolve these issues in CPS. This study explores the efficiency of leveraging DL and blockchain to enhance the reliability and effectiveness of the CPS models. Also, this study examines the challenges and limitations faced by those DL and blockchain approaches during their implementation across different CPS domains. This comprehensive review provides promising directions for future research in this dynamic and interdisciplinary field for mitigating the challenges in CPS models. It also aids researchers, practitioners, and policymakers by providing a deeper understanding of the possible effects, difficulties, and opportunities related to utilizing DL and Blockchain technologies to improve the security and functionality of CPS. © 2024 IEEE.

关键词： Blockchain technologies Cyber-Physical Systems Deep Learning Internet of Things

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Walmart Sales Forecasting Using Time Series Analysis

Walmart Sales Forecasting Using Time Series Analysis

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

作者： Shrieenidhi, A.M. Ruba Roshini, S. Ranjana, P. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The field of retail analytics concentrates on Walmart's challenging issue of forecasting sales by utilizing advanced time series analysis. Walmart is attempting to leverage time series models because it understands how important precise sales projections are for revenue optimization, inventory control, and customer pleasure. By the usage of EDA and Feature engineering, which is capable of capturing underlying trends, temporal patterns, seasonality (Holiday, Weekly Sales, CPI), the impact of external factors (Prominent Holidays) and aiming to improve sales, optimize budgeting and resource allocation, inventory management, business planning is achieved. The principal aim is to provide Walmart a future forecasted sales using traditional time series model such ARIMA & SARIMA. Statistical metrics such as MAE, MSE, RMSE, WMAE have been adopted to evaluate the quality of the model. By refining sales forecasting through advanced time series models and meticulous evaluation, Walmart anticipates not only enhanced operational efficiency but also an elevated customer experience, reinforcing its position as a retail industry leader. © 2024 IEEE.

关键词： Sales

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Datasets for Aquaculture: A Deep Learning Perspective 8

Datasets for Aquaculture: A Deep Learning Perspective

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Kabitha, P. Nandini, D. Usha Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

Aquaculture is being used to a greater extent for conservation goals, such as species recovery, habitat restoration, and mitigating the effects of wild capture on harvested species that are vulnerable. Deep Learning (DL) models are in use for training datasets of aquatic organisms, to provide solutions to various problems in aquaculture farming. However, there is still motivation, awareness, and knowledge that are found necessary to encourage researchers to explore the potential unmet. In this work, the various aquaculture-associated datasets used in DL for image processing procedures have been elucidated. It addresses the challenges of these datasets to pinpoint areas for improvement, ultimately paving the way for future advancements in the application of DL within aquaculture. © 2024 IEEE.

关键词： Aquaculture Convolutional Neural Networks datasets Deep Learning image processing Marine science smart fish farming

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Exploring Sign Language Recognition using Convolutional Neural Network 7

Exploring Sign Language Recognition using Convolutional Neur...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Edwin Samuel, M. Samuel Christopher, S. Prem Jacob, T. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

The recognition of sign language holds significant utility in facilitating communication for the hearing-impaired, aiding in robotic interactions, and connecting with non-verbal communities. This field has evolved from early electric signal-based methods to contemporary approaches utilizing machine and machine learning techniques. In this project, the primary objective is to implement sign language recognition (SLR) based on key point detection. The model is trained using a variety of machine learning algorithms, such as random forest, support vector machine, and k-nearest neighbor. The optimal model is determined through testing and evaluation metrics such as f1 score, precision, and recall. Leveraging Media Pipe for hand tracking and key point detection enhances the project's capabilities. The video is captured through a webcam using Open CV, and the model predicts gestures, allowing the robot to respond accordingly based on the recognized sign language gestures. © 2024 IEEE.

关键词： Convolutional neural networks

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DeepRetina: A Deep Learning Approach for Diabetic Retinopathy Detection and Stage Classification

DeepRetina: A Deep Learning Approach for Diabetic Retinopath...

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

作者： Nithishvikraman, H. Sudharsan, G. Anand Joseph Daniel, D.I. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Using color fundus imaging to identify diabetic retinopathy (DR) is a tough process that requires skilled doctors to comprehend the existence and significance of certain small characteristics. This effort is further complicated by a complex categorization system. This research aims to use convolution neural network (CNN) to consistently diagnose diabetic retinopathy and grade patients into five groups or stages. An automatically generated diagnostic may be provided by a data-enhanced CNN architecture network that can identify the complex components involved in the class task, as well as exudates, hemorrhages, and micro-aneurysms in the retina, without requiring human input. This work trained CNN using data that was made accessible to the public. When compared to other algorithms on the same dataset, it demonstrated an astounding performance. It was used to the global dataset and obtained a great accuracy of 97% on the validation image. © 2024 IEEE.

关键词： Complex networks

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Finger Vein Extraction and Authentication for Security Purpose Based on CNN

Finger Vein Extraction and Authentication for Security Purpo...

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

作者： Nathan, R. Sakthivel, S. Selvi Rajendran, P. Hindustan Institute Of Technology And Science Department Of Computer Science &engineering Chennai India

ISBN: (纸本)9798350364828

Finger Vein ID is a biometric verification toolthat compares a person's finger vascular pattern to previously gathered data. Numerous applications, such as automated teller machines, computer and network authentication, end point security, and fingerprint recognition-one of the numerous biometrics used to identify people-are now being developed or deployed for this technology. Individuals and validate their identities that uses images of the vein patterns in human fingers that lie beneath the skin's surface employeetime and attendance tracking, credit card authentication, and vehicle security. With the advancement of electronics, there has been a steady rise in the need for easy-to-use, convenient, and highly secure authentication solutions to safeguard personal data held in mobile devices. One of the most effective biometric identification methods for user authentication is the finger vein recognition technique. due to its security and singularity. Since the dawn of technology, created for the detection of finger veins;however, the majority of these techniques relied on manually constructed characteristics, which have limitations with regard to image quality, finger location, etc. Since neural networks initially appeared, numerous identity verification techniques created. This research identifies the variables that may impact the outcome and surveys many finger vein verification methods utilizing CNN. The experimental examination of these methods uses both user- designed datasets with different qualities and publicly available datasets from finger veins. Biometrics, a method of protecting personal information, employs physiological or behavioral traits of humans to uniquely identify them. We send over SMS using AVR technology to deliver the SMS to a verified individual. © 2024 IEEE.

关键词： Biometrics

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