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4th international conference on Electronics and Sustainable Communication systems, ICESC 2023

作者： Gampa, Sri Harshani Yellamma, Pachipala Ganta, Vanaja Siram, Chandana Rohith Sai Kamal, A. Brahma Rao, K. Koneru Lakshmaiah Education Foundation Computer Science and Engineering Guntur India

ISBN: (纸本)9798350300093

smart home automation is becoming more commercial and accessible. However, home automation systems have challenges in protecting data from unauthorized users. The proposed work is primarily focused on the design and development of a completely secure home automation application to allow users to operate home appliances from both inside and outside the house by utilizing wireless internet access. The fundamental purpose of the proposed system is to employ the cloud and cyber security for smart Home Automation (SHA). smart home automation may be improved by adding additional security measures and informing the user if a stranger enters the home. © 2023 IEEE.

关键词： Automation

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An Efficient smart Agriculture System Based on The Internet of Things Using Aeroponics Method 9

An Efficient Smart Agriculture System Based on The Internet ...

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9th international conference on Advanced computing and Communication systems, ICACCS 2023

作者： Dhanasekar, S. Abarna, V. Kaviya Gayathri, V. Valarmathi, G. Madhumita, D. Jeevitha, R. Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350397376

Agriculture has always been the largest industry in our country in terms of employment. The ongoing migration of people from rural to urban regions is hindering agriculture. The Internet of Things (IoT) has been evolving platform which aims to link various smart hardware elements with multi-domain upgrading. The Internet of Things is transforming the farming industry by giving farmers a wide collection of tools to handle a variety of difficulties they experience on the farm. The objective of smart farming is to improve agricultural efficiency and productivity by utilizing cutting-edge information technologies. Farmers can use IoT-enabled devices to connect to their farms practically anywhere and anytime. To efficiently adapt, soil-less agriculture is an alternative method in smart farming. This article discusses efficient smart agriculture based on IoT using the aeroponics method. The covered cultivation facilities, the hydroponic farming method, known as aeroponics, is becoming increasingly popular since it uses air as a growth medium and allows exposed roots to get enough oxygen. It also requires less maintenance because there is no chance of weed growth and no need to apply chemicals or pesticides. The water consumption for different plants with yield time comparison has been discussed using aeroponics and other traditional farming methods. © 2023 IEEE.

关键词： Hydroponics

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Consolidation of Cloud computing in smart and Sustainable Environment

Consolidation of Cloud Computing in Smart and Sustainable En...

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2023 international conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Sharma, Arnab Kumari, Anisha Sharma, Vandana Al-Farouni, Mohammed Mishra, Sushruta Deemed to Be University Kalinga Institute of Industrial Technology India Delhi NCR India The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350336474

Cloud computing has revolutionized IoT device data collection, administration, and analysis by offering a scalable and sustainable solution for managing vast amounts of data. The paper highlights cloud computing's benefits in data processing, device management, cost efficiency and scalability. However, challenges related to security, data ownership, and vendor lock-in require attention. A novel sustainable cloud-IoT model is presented by integrating smart computing with cloud infrastructure. It is observed that the model records promising performance. The mean response delay is 1.9 seconds and the 89.5% is the generated mean computational storage accuracy rate. In conclusion, the cloud computing empowered sustainable model can be used in organizations to gain insights from IoT data and make informed decisions, shaping future research in this rapidly evolving field. © 2023 IEEE.

关键词： Cloud computing architecture

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Optimization of Edge computing Task Offloading Based on Multi-Agent Game 3

Optimization of Edge Computing Task Offloading Based on Mult...

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3rd international conference on High Performance computing and Communication engineering, HPCCE 2023

作者： Yang, Bo Liu, Yusheng Li, Hao Chen, Yimin Deng, Xuegang School of Information Science Yunnan University Kunming650500 China School of Agricultural Economics and Rural Development Renmin University Beijing100000 China Yunnan University School of Information Science & Engineering Yunnan Engineering Rese Arch Center of Smart Tourism Kunming Kunming650500 China Yunnan Development and Reform Commission Kunming China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

In a multi-access edge computing environment, optimizing task offloading under various constraints is a complex challenge. Traditional methods often neglect constraint relationships, leading to uneven resource allocation and suboptimal system performance. Additionally, these methods struggle to adapt to dynamic demands in edge computing. This paper introduces a novel distributed task offloading algorithm based on multi-agent deep reinforcement learning. This approach coordinates multiple agents in making task offloading decisions, aiming to achieve optimized task allocation while respecting constraints, thereby enhancing system performance and reducing latency and energy consumption. Simulation experiments in edge computing scenarios validate the effectiveness and stability of the proposed method. Results demonstrate that the multi-agent deep reinforcement learning approach outperforms traditional methods significantly. It excels in reducing task completion time and lowering terminal device energy consumption, affirming its effectiveness. This research offers a fresh perspective on task offloading strategies in edge computing, addressing limitations in traditional methods and providing a foundation for further exploration in this domain. © 2024 SPIE.

关键词： Reinforcement learning

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Two-dimensional convolution-based power system reliability assessment

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ENERGY REPORTS 2023年 9卷 472-478页

作者： Zhu, Lewei Long, Kewen Dong, Ziheng Hou, Kai Tianjin Univ Technol Tianjin 300384 Peoples R China Tianjin Univ Key Lab Smart Energy & Informat Technol Tianjin M Tianjin 300072 Peoples R China

With the rise in carbon emissions, it has become fashionable to construct a new power system using new energy as the primary raw material for power generation. This, however, might result in a significant rise in the number of system states and frequent changes in the system state. These issues raise the bar for the effectiveness of system dependability evaluation. Traditional evaluation procedures necessitate computing Optimal Power Flow (OPF) for many systems states one by one, which is unquestionably time-consuming. For that purpose, this research proposes a reliability evaluation approach based on two-dimensional convolutional networks. Branch circuit fault messages are analyzed with generator fault messages. Multiple channel state data from power system node admittance matrices, power generation, and energy demand are fed into a two-dimensional convolutional neural network. This is used to determine the regression connection between load curtailments and system status data and to forecast load curtailments. Finally, it is applied to the RTS-79 system, and experimental results demonstrate that the technique has certain advantages in terms of computing speed and accuracy. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Reliability assessment Power system Convolutional neural networks Multiple channels

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An Exploration of the Use Cases for Artificial Intelligence in Data Access Optimization 3

An Exploration of the Use Cases for Artificial Intelligence ...

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3rd international conference on smart Generation computing, Communication and Networking, smart GENCON 2023

作者： Sharmila, P. Rathi, Snehal R. Singh, Manpreet Naval, Preeti Batri, Krishnan Agnihotri, Bhavesh Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

This paper provides an exploration of the use cases for artificial intelligence (AI) in information access optimization. AI-based tactics to statistics optimization have the ability to enhance performance and reduce cost drastically. The paper discusses the characteristics of AI-pushed optimization techniques, together with fusion of heterogeneous facts assets, predictive modeling, forecasting, anomaly detection, and clustering. It also explores the numerous statistics assets that can be optimized using AI, which includes conventional database systems, disbursed information lakes, and streaming event statistics. Moreover, the paper considers the demanding situations related to AI-enabled facts get admission to optimization, such as loss of availability of labeled data and issue in imposing commercial enterprise good judgment. Finally, excellent practices and considerations for implementation are mentioned. Through exploring the use instances for AI in records get entry to optimization, this paper provides important insights for builders and businesses considering the usage of AI for data get entry to optimization. © 2023 IEEE.

关键词： businesses commercial data exploring optimization

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Trust in Human-AI Interaction: Review of Empirical research on Trust in AI-Powered smart Home Ecosystems

Trust in Human-AI Interaction: Review of Empirical Research ...

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ASCE international conference on computing in Civil engineering (I3CE)

作者： He, Tianzhi Jazizadeh, Farrokh Virginia Tech Dept Civil & Environm Engn Blacksburg VA 24061 USA

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

The growing socio-technical influence of artificial intelligence (AI) is increasing in various dimensions of our daily lives. The monitoring and decision-making role of smart home AI agents could go beyond simple operations, and a lack of users' trust could lead to the underuse or misuse of their capabilities. As such, lying at the intersection of trust in human-AI interactions and smart home ecosystems, this paper attempts to explore the driving factors of building trustworthy AI-powered smart home ecosystems. To this end, through a systematic literature review, we have identified the characteristics of human-AI trust in intelligent environments, factors that can influence users' trust in AI, features that can be implemented to improve users' trust, and challenges and opportunities that need to be considered. The findings can help develop a pathway for building long-term users' trust in AI-powered smart homes for integrated human-AI interactions.

关键词： Ecosystems

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An Appliance of Energy Efficient Mechanism to Design smart Systematic Street Light Controlling System using Internet of Things

An Appliance of Energy Efficient Mechanism to Design Smart S...

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2023 international conference on Innovative computing, Intelligent Communication and smart Electrical systems, ICSES 2023

作者： Pavaiyarkarasi, R. Lakshmi, R. Ramesh, S. Krishnamoorthy, N.V. Rajeswari, V. R.M.K. Engineering College Department of ECE Chennai India Siddharth Institute of Engineering and Technology Department of EEE Andhra Pradesh Puttur India Siddhartha Institute of Science and Technology Department of EEE Andhra Pradesh Puttur India Sri Krishna College of Engineering and Technology Department of Mechanical Engineering Coimbatore Kuniyamuthur India Panimalar Institute of Technology Department of IT Chennai India

ISBN: (纸本)9798350319200

In an era marked by rapid urbanization and the pursuit of energy-efficient solutions, the integration of Internet of Things (IoT) technologies has emerged as a transformative approach to enhance public infrastructure. This study delves into the development and implementation of a cutting-edge smart Street Light System that leverages Light Dependent Resistor (LDR) sensors and Passive Infrared (PIR) sensors. The system's core objective is to dynamically regulate street lighting based on real-time environmental conditions and the presence of vehicular or pedestrian traffic. LDR sensors monitor ambient light levels to ensure optimal energy utilization, while PIR sensors detect motion, thereby illuminating the path of passersby and vehicles. This system offers a cost-effective, eco-friendly, and technologically advanced solution to address the pressing issues of energy conservation and enhanced public safety. This study investigates the behavior of an LED lighting system in response to varying sensor values, specifically 245, 278, and 365, within the context of an environmental monitoring application. Notably, the analysis reveals a consistent LED activation time of 2.0 seconds for these higher sensor values, which signify the least sensitivity. The research explores the technical intricacies, benefits, and practical implications of this IoT-based smart Street Light System, highlighting its potential to redefine urban lighting infrastructure in the context of modern smart cities. © 2023 IEEE.

关键词： Internet of Things IoT LDR Sensor PIR Sensor Street Light Controlling

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Comparative Performance Analysis of Cardiovascular Disease Prediction by Machine Learning Techniques

Comparative Performance Analysis of Cardiovascular Disease P...

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2023 international conference on Innovative computing, Intelligent Communication and smart Electrical systems, ICSES 2023

作者： Singh, Amanveer Chitkara University Institute of Engineering and Technology Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319200

Globally, the prevalence of cardiovascular disease as a cause of death has risen in recent years. Variations in people's lifestyles, dietary preferences, and workplace cultures, among other factors, have affected the severity of this alarming global problem. Establishing a method that can identify early symptoms and save numerous lives is the most effective method for preventing this condition. Machine learning (ML) methods for cardiovascular disease identification and prediction are exceedingly difficult to develop, despite their potential clinical utility. The development of precise and efficient initial-stage cardiac disease prediction using computational assistance of clinical decision-making utilising electronic patient records could address the dearth of cardiac knowledge and the high rate of incorrectly diagnosed cases that plagues nations. This investigation intended to investigate the architecture to predict cardiovascular disease using ML and also on the evaluation matrices for the performance analysis. As a consequence, this study discovered that a relatively simple supervised ML technique can be used to predict cardiovascular disease with high accuracy and considerable potential value. © 2023 IEEE.

关键词： accuracy and sensitivity Cardiovascular disease machine learning prediction

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Review Based Analysis with Applications of Blockchain in Food Supply Chain management 10

Review Based Analysis with Applications of Blockchain in Foo...

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17th INDIACom;2023 10th international conference on computing for Sustainable Global Development, INDIACom 2023

作者： Sharma, Deepti Singh, Gurpreet Kaur, Amanpreet Giani Zail Singh Campus College of Engineering and Tecnology Mrsptu Department of Computer Science and Engineering Punjab Bathinda India Punjab Institute of Technology Rajpura Mrsptu Department of Computer Science and Engineering Punjab Bathinda India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9789380544472

The main aim of food supply chains is to ensure the availability of safe and good quality food the consumers across the world. With the advancement in technology the food supply chains are experiencing significant changes. Block chain technology is attracting interest as its provides benefits of transparency, traceability, smart contracts, decentralization and better sustainability. The ubiquitous technology of block chain has the potential to improve traditional food supply system. It can guide the current food processing towards better food sustainability and efficiency. This paper presents a literature review on the use of block chain technology in food supply systems. We discuss the benefits of blockchain technology. We also discuss the challenges faced by the users of block chain-based food supply systems. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： smart contract

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