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Efficiency beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in Data Centers

Efficiency beyond Limits: LSTM and k-NN Synergy for Forecast...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Kumar, C.H.N. Santhosh Devi, V. Chithra Gottumukkala, Prasanthi Satheesh Kumar, D. Sandip University School of Computer Science and Engineering Department of Computer Science & Application Maharashtra Nashik India Sandip University School of Computer Science and Engineering Department of Computer Science & Applications Maharashtra Nashik India Anurag Engineering College Department of CSE Telangana Kodada India PSNA College of Engineering and Technology Tamilnadu Dindigul India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350361537

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R2) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings. © 2024 IEEE.

关键词： k-nearest neighbors

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A Survey of Dynamic Exploration Symmetric Encryption with Backward & Forward Secrecy 3

A Survey of Dynamic Exploration Symmetric Encryption with Ba...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Singh, Satendra Bhagat, Samriti Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9798350399264

A sophisticated type of symmetric encryption is called Multiuser Dynamic Searchable Symmetric Encryption. It expands the capabilities of conventional symmetric encryption to enable actions such as adding and removing encrypted data as well as the ability for a verified set of information users to access their preferred encrypted data within the dynamic database. The latter can use using erased data and search results to comprehend the content, while the former allows attackers to discover by performing file injection attacks, keywords from the injected data were exposed. can do. To our knowledge, the available literature has not completely addressed these privacy problems for M-DSSE. In light of this, our attention is directed toward multiuser dynamic searchable symmetric encryption that satisfies the requirements for forwarding and backward security. It uses Inverted indexes are created and updated using random number generator Functions (RNG) and Authenticator primitives search tokens to suggest specific solutions. In the accidental model, the suggested approach is shown to be secure. Additionally, the performance study demonstrates that the suggested approach provides increased security assurances at a fair cost to efficiency. © 2023 IEEE.

关键词： Cryptography

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An In-Depth Analysis Of Local Mechanic Support For Immediately Apparent Repairs And Scheduled Maintenance Services

An In-Depth Analysis Of Local Mechanic Support For Immediate...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Rakhra, Manik Sarkar, Tiyas Sharma, Vikrant Angra, Prikshat Kumar Kumar, Ashwani Singh, Pritpal Lovely Professional University School of Computer Science and Engineering Phagwara India Graphic Era Hill University Grahic Era Deemed to be University School of Computer Science and Engineering Dehradun India Lovely Professional University School of Computer Application Phagwara India Lovely Professional University Mittal School of Business Phagwara India

ISBN: (纸本)9798350378092

This As technology advances, innovative systems are emerging to address the need for rapid auto repairs, especially in cases of unexpected vehicle breakdowns. This analysis delves into a sophisticated system designed to provide immediate on- site assistance by connecting vehicle owners with skilled mechanics. The system, developed using cutting-edge technologies such as Figma, React JS, Tailwindcss, Nodejs, Express JS and MongoDB, offers a seamless experience by incorporating features like real-time location tracking and efficient job assignment. These features significantly reduce wait times and alleviate the stress associated with roadside assistance. The article highlights the development and architecture of the application, focusing on its user-friendly interface and the strong technological framework that ensures its reliability and quick response to contemporary automotive challenges. © 2024 IEEE.

关键词： Scheduled maintenance

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Personalized Confederation Training for Smart Iot Systems: A Cloud - Delivered Structure 3

Personalized Confederation Training for Smart Iot Systems: A...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Katariya, Jitendra Kumar Nivesh Vivekananda Global University Department of Computer Science Engineering India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

The Internet of Things (IoT) has significantly impacted many facets of contemporary life, and several sophisticated IoT applications and services are already being developed. Recently, the concept of federated learning has been proposed in order to train a globally shared model on IoT devices utilising a large number of user-generated data samples while stopping data leak. Due to the widespread heterogeneities in device, statistics, and model in complex IoT contexts, traditional federated learning is not suitable for instant implementation. In this paper, we present a cloud-edge architecture for intelligent IoT applications that includes a customised federated learning framework. To address the heterogeneity worries in IoT conditions, we examine imaginative altered united learning calculations that could diminish the unfortunate results brought about by heterogeneities in various aspects. The advantages of edge registering empower smart IoT applications to meet the models for high handling pace and low inertness. At long last, we give a contextual investigation of human movement recognition in light of Web of Things to show the advantages of customisable combined learning for intelligent IoT applications. © 2023 IEEE.

关键词： Internet of things

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Using Neural Network-Based Time Series Analysis in Wireless Sensor Networks 2

Using Neural Network-Based Time Series Analysis in Wireless ...

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2nd International Conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Goyal, Manish Kumar Patil, Nagaraj Bhagat, Akash Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India ARKA JAIN University Department of Computer Science & IT Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

Neural community-primarily based time series analysis (NNTSA) is an effective technique for research records collected from wireless sensor networks (WSNs). NNTSA uses models stimulated by means of biological neural networks to capture complicated time-various sensor statistics. It requires minimum training of the enter facts and offers a high degree of modelling accuracy. This method has turned out to be increasingly famous for WSN facts evaluation when you consider that it is miles appropriate for streaming statistics and seizing complicated patterns that might not be effortlessly detected with the aid of other techniques. NNTSA fashions are commonly carried out the use of supervised gaining knowledge of processes, together with lower back-propagation, wherein education styles are fed to a neural network at the same time as its parameters are adjusted the use of a fixed of desired output values, together with an average temperature over some time. After the NNTSA c section is complete, new patterns can be evaluated with the trained model to analyze their temporal conduct. NNTSA techniques have been applied in diverse areas, inclusive of fitness monitoring, site visitors management, and weather tracking, with suitable result. © 2024 IEEE.

关键词： Time series analysis

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Investigation into a Medical Diagnostic System for Chronic Kidney Disease: Preparation and Execution 10

Investigation into a Medical Diagnostic System for Chronic K...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Sharma, Arvind Singh, Dalwinder School of Computer Application Lovely Professional University Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350382471

This abstract provides a concise overview of a study focused on the development and execution of a medical diagnostic system specifically designed for Chronic Kidney Disease (CKD). The primary objective of this study is to contribute to the advancement of early detection and intervention strategies for chronic kidney disease (CKD), a prevalent and potentially incapacitating condition. The inquiry process encompasses several sequential stages, including the initial planning phase and subsequent execution phase. During this stage, comprehensive literature studies are conducted to examine the various approaches used for diagnosing chronic kidney disease (CKD), as well as the risk factors associated with its development and the biomarkers used for its detection. This literature review aids in identifying the deficiencies within the diagnostic system and highlights the potential utility of advanced techniques such as machine learning and data analytics. The development and implementation of the diagnostic system occur during the execution phase. A comprehensive database is established by the consolidation of data from various sources, including patient records, laboratory findings, and diagnostic imaging. State-of-the-art algorithms and machine learning approaches are utilized to conduct comprehensive data analysis, pattern identification, and the construction of prediction models for the diagnosis of Chronic Kidney Disease (CKD). This article provides a comprehensive examination of various healthcare systems pertaining to the management of chronic kidney disease. Additionally, it presents a systematic review of recent research undertaken by multiple scientists during the past five years. © 2023 IEEE.

关键词： Biomarkers chronic kidney disease (CKD) Diagnosis Preparation Early detection Execution Literature review Machine learning Risk factors

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A Unified Framework for Image Caption Generation Using Deep Reinforcement Learning 15

A Unified Framework for Image Caption Generation Using Deep ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shree, Ritu Narmadha, T. Joseph, Vishakha Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Arka Jain University Department of Management Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

This paper introduces a unified framework for picture caption generation with the use of deep reinforcement mastering (RL) that combines convolution neural networks (CNNs) with RL. The paper first introduces a country representation as a remark for RL by means of combining CNN-primarily based photo representation and natural language processing (NLP) natural language representation. Then, the paper describes an RL-based reinforcement mastering (RL-RL) set of rules which uses the statement to generate captions. Eventually, the paper shows the experimental consequences of the proposed RL-RL set of rules on real-world datasets and shows that the RL-RL set of rules outperforms the present RL-NLP and CNN-RL algorithms. The paper additionally demonstrates the very best overall performance on the benchmark dataset COCO with a BLEU score of 0.645 and a METEOR score of 0.348. The proposed framework affords a sensible technique to generate interesting and affordable captions for photographs. © 2024 IEEE.

关键词： Benchmark Consequences Convolution Photographs Reinforcement Representation

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A Bayesian Optimization Framework for Adaptive Node Deployment in Wireless Sensor Networks 14

A Bayesian Optimization Framework for Adaptive Node Deployme...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Priyadarshi, Rahul Rout, Jayant Kumar Vishwakarma, Anish Kumar Dhengre, Nikhil Ranjan, Rakesh Verma, Amit Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India School of Computer Science UPES Uttarakhand Dehradun India Department of ECE IIIT Maharashtra Nagpur India

ISBN: (纸本)9798331531935

Wireless Sensor Networks (WSNs) require efficient node deployment strategies to optimize network longevity, energy consumption, and coverage. This paper introduces a novel approach for strategic node deployment in WSNs using Bayesian Optimization (BO). A BO-based framework that iteratively modifies node positions to maximize network performance metrics, such as energy efficiency and coverage ratio, while minimizing execution time is proposed. Through extensive simulations across diverse deployment scenarios, the proposed method is evaluated and demonstrates superior performance compared to both traditional and machine learning (ML)-based techniques. The results indicate significant improvements in coverage and energy efficiency, alongside competitive execution times. These findings highlight the potential of BO to enhance WSN deployment strategies, offering a scalable and adaptive solution for real-world applications. Future research will focus on integrating BO with other optimization techniques and extending the approach to dynamic environments and varied network configurations. © 2025 IEEE.

关键词： Bayesian Optimization Coverage Ratio Energy Efficiency Node Deployment Wireless Sensor Networks

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Exploring the Benefits of Interactive Communication Media Algorithms for Networking applications 3

Exploring the Benefits of Interactive Communication Media Al...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Ahmed, Zahid Beemkumar, N. Ghosh, Debanjan Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India

ISBN: (纸本)9798350381931

Interactive communication media algorithms are useful tools for creating efficient and effective networking applications. Such algorithms enable the application to accurately predict user behaviour and preferences, use natural language processing to automate conversations, and integrate with other systems. This paper investigates the benefits of using interactive communication media algorithms for developing networking applications, including improved user experience, scalability, and quicker response times. Additionally, this paper will address potential limitations, such as the need for significant data collection and availability of relevant computing resources. Finally, this paper will conclude with a discussion of the potential implications of further investigations into interactive communication media algorithms for networking applications. © 2024 IEEE.

关键词： Behavioral research

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Elevating Cybersecurity in Defence: A Collaborative and Technologically Driven Approach 6

Elevating Cybersecurity in Defence: A Collaborative and Tech...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ansar, Syed Anas Aggarwal, Shruti Arya, Swati Sayeed, Mohd Asim Soni, Nupur Pathak, Prabhash Chandra School of Computer Application Babu Banarasi das University Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350304480

As the world enters the 21st century, the imperative of safeguarding cybersecurity has intensified. Considering the expanding dependence on information technology (IT) systems for a broad spectrum of military operations, the stakes have never been higher for thwarting successful cyberattacks. Conventional security protocols have become increasingly precarious in the face of evolving cyber threats. This necessitates the adoption of avant-garde and nascent technologies to bolster the military's cybersecurity stance. The paper delves into an array of incipient technologies that proffers the prospect of enhancing cybersecurity in the realm of defence. These include artificial intelligence (AI), machine learning (ML), blockchain, and quantum computing. AI can be leveraged to automate numerous cybersecurity functions, such as threat detection and response. ML can augment the precision of threat detection and response systems. Blockchain, a distributed ledger technology, can safeguard data and transactions. Quantum computing, a nascent technology, harbours the potential to revolutionize computation. It could be utilized to breach present-day encryption standards, thereby rendering data vulnerable to attackers. Moreover, the paper appraises the exigencies and possibilities in defence cyber security and scrutinizes the policy and practice ramifications of these emergent technologies and best practices. © 2023 IEEE.

关键词： artificial intelligence blockchain cybersecurity machine learning national security quantum computing zero-trust security

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