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Research on the Application Effects of AI technology in data Center Energy Saving 24

Research on the Application Effects of AI Technology in Data...

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2nd international conference on Electronics, Computers and communication technology, CECCT 2024

作者： Chen, Yaojia Ruan, Pan Wang, Guanghui Wuhan Vocational College of Software and Engineering Hubei Wuhan China Fiberhome Telecommunication Technologies Co. Ltd Hubei Wuhan China Hubei University of Automotive Technology Hubei Shiyan China

ISBN: (纸本)9798400710193

Current data centers face urgent demands for energy conservation and efficiency improvements. Integrating AI energy-saving systems into the data center infrastructure allows for the prediction of operational trends and enables second-level adjustments to the return air temperature setpoints of the air conditioning units. This proactive regulation of the AC equipment’s operational state results in real-time alignment of cooling capacity with the IT power consumption trends. The study selected an existing air-cooled micro-module data center and a chilled water terminal air-conditioned data center. After over six months of application, a substantial amount of data related to IT power consumption, air conditioning power consumption, and data center temperatures were collected. Comparative analyses were conducted on the energy consumption and PUE under conditions with and without AI technology during the operation of the data centers. The results indicate that AI technology can effectively reduce the energy consumption and PUE of the data *** energy-saving technology not only demonstrates efficacy in existing data centers but also holds significant potential for energy savings in newly constructed communication and data centers, offering high cost-effectiveness and considerable promotion value. © 2024 Copyright held by the owner/author(s).

关键词： Energy saving

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Millimetre Wave Self-Isolated MIMO Antenna with High Isolation and Radiation Efficiency 2

Millimetre Wave Self-Isolated MIMO Antenna with High Isolati...

引用

2nd international conference on Intelligent data communication Technologies and Internet of Things, IDCIoT 2024

作者： Thommandru, Raju Krishna, Ch Murali Suguna, N. Alamelu Alias Rajasree, S. Sathish, M. Kiran, K. Chalapathi Institute of Technology Department of Electronics and Communication Engineering Guntur India Rakon India Private Limited Bangalore India Vellore Institute of Technology Department of Electronics and Communication Engineering Vellore India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Rajalakshmi Engineering College Thandalam Department of Electronics and Communication Engineering Chennai India Vishnu Institute of Technology Department of Electronics and Communication Engineering Bhimavaram India

ISBN: (纸本)9798350327533

Four port multi element antenna geometry is proposed for 5G and millimetre (mm) wave frequencies. A key objective of the proposed antenna is to improve isolation and reduce mutual coupling between the antennas without the use of any additional approaches. The antenna is designed on 0.508mm Rogers 5870 substrate within compact size of 20mm*20mm. The designed antenna has the bandwidth of 10.27GHz from 33.76 to 44.03 GHz with fractional bandwidth (FBW) of 26.41% and isolation between antennas is greater than 43dB. The maximum gain and efficiency of the proposed antenna are 7.36 dB and 96.25%, respectively. Proposed Millimetre wave antenna is usable for Ka-band Q-band frequencies. © 2024 IEEE.

关键词： FBW High radiation efficiency Millimetre applications MIMO Self-Isolation Tapered fed

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A Secure Framework of Internet of Production for data Exchange 2

A Secure Framework of Internet of Production for Data Exchan...

引用

2nd international conference on Advances in Computation, communication and Information technology, ICAICCIT 2024

作者： Chakraborty, Mohuya Chakraborty, Subhalaxmi Palit, Sudip Kumar University of Engineering and Management Kolkata India University of Engineering and Management Kolkata India

ISBN: (纸本)9798331541217

The Internet of Production (IoP) is changing the manufacturing industry by helping companies work more efficiently, increase profits, and speed up product delivery. However, ensuring secure communication among different stakeholders in various supply chains is a major challenge that affects data sharing and collaboration. This study presents a strong framework that uses blockchain technology to improve secure communication among these stakeholders. The motivation for this research comes from the growing need for better security measures in the IoP, especially as the amount of data being exchanged increases. At the heart of this framework is a new algorithm designed to enhance both security and efficiency in data exchanges. Testing with the Automated Validation of Internet Security Protocols and Applications tool shows that the algorithm is safe from replay and man-in-the-middle attacks. Additionally, an analysis using the Burrows-Abadi-Needham method confirms that it provides mutual authentication among stakeholders. Performance analysis indicates that the algorithm achieves faster execution times (~18 milliseconds) compared to existing methods, while its communication time is transitional (~5152 bits), marking a significant improvement in the field. This work significantly contributes to the field by providing a secure and efficient solution for cross-domain communication in the IoP. © 2024 IEEE.

关键词： Blockchain

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Efficient and Secure Deep Learning Asynchronous Training in Serverless P2P Networks 2

Efficient and Secure Deep Learning Asynchronous Training in ...

引用

2nd international conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Manikanadan, S.P. Meenakshi Sundaram, B. Gurajala, Toshit Rai, Sarthak Swaran, Apeksha Sahoo, Abhishek New Horizon College of Engineering Department of Computer Engineering Bengaluru India

ISBN: (纸本)9798350328202

In this work, we leverage the power of AWS Lambda and S3 buckets to propose a revolutionary method for peer-to- peer deep learning. Unlike traditional synchronous approaches, ours adopts asynchronous training, enabling every peer to advance independently. By guaranteeing the integrity and confidentiality of transferred data, encryption techniques improve data security during communication. By reducing data transfer overhead and maximizing parallelism, dataset partitioning makes effective model training possible. The peer-to-peer deep learning landscape is redefined by this decentralized, secure, and scalable architecture, which promises significant gains in efficiency and model convergence. © 2024 IEEE.

关键词： Cryptography

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Automated Audit Methods Supported by Blockchain technology 2

Automated Audit Methods Supported by Blockchain Technology

引用

2nd IEEE international conference on Integrated Intelligence and communication Systems, ICIICS 2024

作者： Li, Lina School of Management Engineering Jilin Communications Polytechnic Jilin Changchun China

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504960

With the rapid development of information technology, especially the widespread application of blockchain technology in various industries, the auditing industry is facing challenges and opportunities in the integration of traditional methods and modern technology. This study aims to explore how blockchain technology can enhance data transparency and security in automated auditing, thereby improving audit efficiency and reducing human errors. This study adopts the distributed ledger feature of blockchain to store audit evidence, ensuring that data is tamper proof and easy to trace, and automatically executes standard audit tasks through smart contracts, such as verifying the consistency and integrity of financial statements. The total number of transactions gradually increased from 10000 in January to 16500 in December, indicating that the system's processing power has strengthened over time. The value of this study lies in providing a new technological solution for the auditing industry, and provide theoretical and practical support for promoting the application of blockchain technology in the auditing field. © 2024 IEEE.

关键词： Smart contract

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Intelligent Financial Forecasting: A Fusion of Computer engineering and AI in Accounting 2

Intelligent Financial Forecasting: A Fusion of Computer Engi...

引用

2nd IEEE international conference on IoT, communication and Automation technology, ICICAT 2024

作者： Ibrokhim, Ismanov Khojaev, Azizkhon Akhmadalieva, Zebo Mekhmonaliev, Ulugbek Fergana Polytechnic Institute Department of Accounting and Auditing Fergana Uzbekistan

ISBN: (纸本)9798350368109

The advancement in the field of finance, taking into account predictability and efficiency to a next level expectation, by integration through computer engineering collaboration with artificial intelligence AI techniques is valued may create new pathways for accounting practices in this article, we explore the design and deployment of an automated financial forecasting model powered by state-of-the-art machine learning methodologies and sophisticated computational algorithms. That's in order to deliver a holistic approach to the challenges and variability of financial data, which can result in better forecasting accuracy for financial analysts and accountants. Our approach is an ensemble model which integrates methods from deep learning architectures (RNNs, CNN) as well as traditional statistical methods (ARIMA/Exponential smoothing). A model trained this way will be able to catch both linear and nonlinear patterns in the data, making its forecasts more accurate as well as improving stability. So, we researched the actual financial data from different areas such as retail, technology and manufacturing sectors to test our Intelligent forecasting model. © 2024 IEEE.

关键词： Decentralized finance

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Infectious Disease Detection and Drugs Recommendation Using Machine Learning 2

Infectious Disease Detection and Drugs Recommendation Using ...

引用

2nd international conference on Advances in Computation, communication and Information technology, ICAICCIT 2024

作者： Archana, T. Gupta, Sandeep Kr Hembrom, Kankan Mandal, Sayantan Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

This paper introduces a machine learning system that combines Logistic Regression, Decision Tree, and Random Forest algorithms to analyze patient data, focusing on symptoms and case histories to predict likely diseases and recommend effective treatments. The objective is to improve early disease detection and personalized medication recommendations, reducing side effects and enhancing clinical judgment. Extensive medical data trains these models, enabling integration with electronic health records to support clinical decisions, which strengthens healthcare delivery. This work contributes to the field by providing a model that increases diagnostic accuracy and offers a tailored treatment plan for individual patients, marking a step towards enhanced personalized healthcare. © 2024 IEEE.

关键词： Electronic health record

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Intelligent Solar Tracking System with Fuzzy Logic Control and IoT Integration 2

Intelligent Solar Tracking System with Fuzzy Logic Control a...

引用

2nd international conference on Computer, communication and Control, IC4 2024

作者： Rathika, N. Raman, Ramakrishnan Sutaria, Kamal Kishorbhai Dattatreya, Shirish Reddy, Ramireddy Sasidhar Muthulekshmi, M. Mahendra Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Tiruchengode India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Maharashtra Pune India Mother Theresa Institute of Engineering and Technology Department of Mechanical Engineering Andhra Pradesh Palamaner India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

The amount of UltraViolet (UV) rays and the surrounding temperature have a general effect on the amount of energy produced by PV panels. In the case of optimal electrical power generation, PVs are angled perpendicular to the sun. A smart Solar Panel Tracking System (SPTS) that uses Fuzzy Logic Control (FLC) and the Internet of Things (IoT) is proposed to increase solar panels' output. FLCs analyze data to determine the best possible orientation for the solar systems. The goal is to position the solar panels such that they get the most sunlight available while receiving the least amount of shade possible and experiencing no obstacles. The system incorporates the IoTs that allow for easier communication and control. The IoT module allows for the collection of data from a wide range of sensors, including devices that monitor light, temperature, and weather. The FLC receives this data and continually evaluates the environmental parameters to determine the optimal panel position. © 2024 IEEE.

关键词： Solar panels

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Secured IoT-Based Neighborhood Area Network for Real-Time Energy data Management in Smart Grids 2

Secured IoT-Based Neighborhood Area Network for Real-Time En...

引用

2nd international conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Prabhakar, G. Kaviya, S. Department of Electronics and Communication Engineering Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9798350328202

In modern smart grid systems, seamless communication between residential areas and central monitoring systems is essential for effective energy management and grid optimization. However, existing communication methods may lack scalability and efficiency. This research proposes an innovative IoT solution for smart grid communication within the Home Area Network (HAN). Voltage and current sensors, serving as smart meters, transmit electronic appliance usage data to NodeMCU devices, selected for cost-effectiveness and versatility in the HAN. Leveraging LoRaWAN for its long-range and low-power communication, a scalable Neighborhood Area Network (NAN) is established in residential areas. The system employs Advanced Encryption Standard (AES) for secure data transmission, addressing smart grid and metering challenges. A distributed metering infrastructure with bidirectional communication, self-configuration, and auto-update capabilities is introduced. Chirp spread spectrum (CSS) modulation ensures efficient data transfer for battery-operated NodeMCUs, exhibiting resistance to interference and multipath fading. The base station, a central communication hub, decrypts AES-encrypted data, processes information from various sources, and acts as the interface between the smart grid and consumers, offering personalized data and services. This comprehensive IoT solution enhances communication efficiency, security, and consumer interaction in smart grid environments, contributing to sustainable and reliable energy management. © 2024 IEEE.

关键词： Cryptography

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Generative AI: A Transformative Force in Business Intelligence 2

Generative AI: A Transformative Force in Business Intelligen...

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2nd international conference on Intelligent data communication Technologies and Internet of Things, IDCIoT 2024

作者： Thiruneelakandan, A. Umamageswari, A. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

In an era where data is the cornerstone of strategic decision-making, Generative Artificial Intelligence (Gen AI) is revolutionizing the Business Intelligence (BI) landscape. This study investigates the multifaceted role of Generative AI in BI, encompassing data generation and augmentation, predictive analytics, reporting automation, product and service innovation, and personalization of customer experiences. The work employs a combination of synthetic data generated by Generative Adversarial Networks (GANs) and Seasonal Autoregressive Integrated Moving Average (SARIMA) predictive modeling techniques to demonstrate the potent capabilities of Generative AI in enhancing data-driven insights. The findings reveal that Generative AI not only significantly improves the accuracy of predictive models, especially in scenarios with limited historical data but also streamlines reporting processes and catalyzes innovation by uncovering latent customer needs. The research underscores the transformative impact of Generative AI in BI, while also addressing the attendant ethical considerations and the necessity for rigorous data governance. Through this exploration, the study provides a roadmap for businesses to harness the power of Generative AI, paving the way for smarter, more agile, and more personalized business strategies. © 2024 IEEE.

关键词： data Augmentation Ethical Artificial Intelligence Generative Adversarial Networks Generative Artificial Intelligence Reporting Automation

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