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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Duggal, Sonia Ather, Danish Yusupov, Ibrohimbek Sagar, Pinki Kumar, Raj Jain, Vishal Department of Computer Applications Faridabad India Amity University Department of It and Engineering Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Ibn Musa al-Khwarizm Department of Artificial Intelligence Tashkent Uzbekistan G.L. Bajaj Institute of Technology and Management Greater Noida India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

This paper aims at arguing the impact of digital technologies and system dynamics models towards the achievement of the United Nations - Sustainable Development Goals (UN-SDGs). Interrelated with numerous fields, this research focuses on the application on intelligent systems in big data environment and sensors networks to increase efficiency of resources, improve transparence of processes as well as to make sound decisions according to SDGs requirements. Based on findings of ten recent pieces of research, the paper integrates lessons and best practices with the aim of illustrating how technology can open new avenues for addressing complex problems. © 2024 IEEE.

关键词： Sustainable development goals

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From Sensor Data to Smart Decisions with an Integrated Framework for Next-Generation Farming Systems 4

From Sensor Data to Smart Decisions with an Integrated Frame...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Sagar, Pinki Ather, Danish Kumar, Raj Duggal, Sonia Yusupov, Ibrohimbek Jain, Vishal G.L. Bajaj Institute of Technology and Management Greater Noida India Amity University Department of It and Engineering Tashkent Uzbekistan Department of Computer Applications Faridabad India Tashkent University of Information Technologies Named after Muhammad ibn Musa al-Khwarizm Department of Artificial Intelligence Tashkent Uzbekistan Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

The advancements of internet of things sensors, artificial intelligence data analytics, and big data technologies have shifted Agriculture making new possibilities of productivity, sustainability, and decision making. They envisage an integrated system of these technologies that enhance the transformation of raw sensor data into usable information for next generation farming systems proposed in this paper. To increase efficiency, the framework is designed to bridge the existing gap between collecting data and applying it and the general concept will help to address the current challenges faced in post and present day farming. © 2024 IEEE.

关键词： Smart agriculture

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A Secure Stackelberg Game Framework for Profit Maximization in Vehicle-to-Grid Systems Using 5G

A Secure Stackelberg Game Framework for Profit Maximization ...

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IEEE International Conference on Communications (ICC)

作者： Aparna Kumari Anushka Nehra Pronaya Bhattacharya Sudeep Tanwar Rajesh Gupta Joel J. P. C. Rodrigues Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Thapar University Punjab India Department of Computer Science and Engineering Amity School of Engineering and Technology Research and Innovation Cell Amity University Kolkata India Amazonas State University Manaus AM Brazil

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In smart communities, Electric vehicles (EVs) have grown in popularity as a key component of the energy ecosystem where the focus has turned to the generation of clean, sustainable energy. The integration of EVs, charging stations (CS), and smart grids (SG), however, poses significant challenges in terms of energy trading (ET) optimization and profit maximization. Next, trust is another challenge in the ET ecosystem among the communicating entities (EVs, CS, and SG) to buy and sell energy. Recent studies have overlooked the fact of ET among CS and SG, and mostly have focused on ET by EVs. However, at peak loads, SG may experience bottlenecks in energy dissipation, and thus excess energy collected by CS from EVs might be traded to SG to manage loads during peak times. So, we propose a framework, StackGrid, that leverages the capabilities of Vehicle-to-Grid (V2G) systems over a blockchain network. We design a Stackelberg game between CS and SG for profit maximization of both parties and to obtain optimal payoff equilibria. The framework is powered over the 5G ultra-reliable low latency communications (uRLLC) service for real-time ET response and data exchange. To address blockchain scalability concerns, we incorporate Interplanetary File Systems (IPFS) as local off-chain ledgers, where only meta-information is stored on-chain to handle blockchain scaling issue. The framework is evaluated on metrics like 5G service latency, optimal payoff scenario, attack probability, and node throughput. The obtained results indicate StackGrid viability in real ET setups, with benefits for sustainable and efficient energy management.

关键词： Vehicle-to-grid 5G mobile communication Smart cities Scalability Ecosystems Games Ultra reliable low latency communication

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BanijyaPrayukti: e-commerce recommendation engine for sustainable products

BanijyaPrayukti: e-commerce recommendation engine for sustai...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Aditya Pradhan Amartya Ghosh Rintu Mondal Kumar Abhi Saxena P. K. Dutta Anirban Mitra Dept. of Computer Science and Engineering Amity School of Engineering and Technology Kolkata Kolkata India Dept. of Electronics and Communication Engineering Amity School of Engineering and Technology Kolkata Kolkata India

The rapid growth of e-commerce has revolutionized the way businesses operate and interact with customers. E- commerce has revolutionized the way businesses operate, making it easier than ever to reach customers and sell products. However, with this convenience comes a challenge: how can we promote sustainable products and encourage consumers to make eco- friendly choices? The primary objective of this project is to pro- mote sustainability by utilizing artificial intelligence techniques in recommending eco-friendly alternatives. Our goal is to provide users with recommendations on sustainable products based on their shopping history, behavior patterns, and demographic infor- mation. The system continuously learns from user interactions, enabling it to provide increasingly accurate and personalized recommendations over time.

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Future Trends in HRM: The Role of Artificial Intelligence and Automation in Shaping the Workplace

Future Trends in HRM: The Role of Artificial Intelligence an...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Panicker, Aneesya Krishna, Somanchi Hari Garg, Shaifali Biswas, Prasanta Chatterjee Lourens, Melanie Kumar, K. Siva Gla University Institute of Business Management Uttar Pradesh Mathura India Vignana Bharathi Institute of Technology Department of Business Management Telangana State 501301 India Amity University Amity Business School Madhya Pradesh India Parul University Cdoe Vadodara India Durban University of Technology Deputy Dean Faculty of Management Sciences South Africa Bapatla Engineering College Computer Science and Engineering Andhra Pradesh Bapatla India

ISBN: (纸本)9798331508685

Artificial intelligence-based applications are integrated in the human resource management of firms for managing people within the region as well as international organisations. In the past years the growth of AI based applications proliferates the function of HR M that triggers to exciting new stream to study the effect of AI adoption on business level and individual outcomes. It evaluates the AI enabled HRM practises. The technology examines the firms with problem solving, decision making and utilisation of resources and opportunities for employees. The growing interest on AI based technologies for HRM for diffusion of innovation in large multinational enterprises. AI has the capability for reduction of administrative burden experienced by the HR Professional that allows them for precise decision making based on the data patterns. Adoption of artificial intelligence helps in effective employee management and decision making. So that employee of HR management can overcome the challenges. The study aims to analyse the future trends in HRM and the role of artificial intelligence and automation in shaping the workplace. © 2025 IEEE.

关键词： Resource allocation

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IOT based Machine Learning Algorithms for Data science on Quality Control Process

IOT based Machine Learning Algorithms for Data Science on Qu...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Kittusamy, Lingarasu Lakshmi, G. Prasanna Khatkale, Pravin B William, P. Ghige, Onkar Tingare, Bhagyashree Ashok Compunnel Inc Quality Engineering Lead MorganNJ United States Sandip University School of Computer Science and Engineering Maharashtra Nashik India Sanjivani University Maharashtra Kopargaon India Sanjivani College of Engineering Deaprtment of Information Technology Kopargaon India Amity University Dubai United Arab Emirates Sanjivani College of Engineering Department of Elecrical Engineering MH Kopargaon India D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350390049

Because of the combination of Internet of Things (IoT) technology and machine learning (ML) algorithms, quality control methods in the industrial sector are undergoing a revolution. This is because real-time monitoring, predictive maintenance, and anomaly detection are now entirely feasible. Conventional quality control methods, which often rely on random inspections and sampling, are being phased out in favour of these cutting-edge technologies, which provide continuous data collection and analysis. The purpose of this research is to get a better understanding of how it is possible for IoT and ML to enhance quality control by examining their applications, benefits, and challenges in industrial settings. In this work, we demonstrate how machine learning algorithms that are based on the Internet of Things (IoT) have the potential to significantly improve defect detection rates, reduce downtime, and optimise manufacturing processes by using a multidimensional research approach that combines empirical analysis and case studies. The findings illustrate how these technologies have a considerable and beneficial impact on decision-making in data-driven quality control processes, as well as on the efficiency of operations and the quality of products available. © 2024 IEEE.

关键词： Smart manufacturing

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Learning Based on Generative AI with Image Synthesis and Data Augmentation Techniques 11

Learning Based on Generative AI with Image Synthesis and Dat...

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11th IEEE International Conference on Social Networks Analysis, Management and Security, SNAMS 2024

作者： Jain, Alok Roy, Dharmendra Kumar Ayasrah, Firas Tayseer William, P. Lakshmi, G. Prasanna CA United States Hyderabad Institute of Technology and Management Hyderabad India College of Education Humanities and Science Al Ain University Al Ain United Arab Emirates Sanjivani College of Engineering Deaprtment of Information Technology Kopargaon India Amity University Dubai United Arab Emirates School of Computer Science and Engineering Sandip University Maharashtra Nashik India

ISBN: (纸本)9798331518349

Generative artificial intelligence enables improved image synthesis and data augmentation in computer vision. GANs and VAEs are essential. VAEs learn using data distributions, whereas GANs employ generator-discriminator architecture to generate realistic pictures. These technologies have improved data augmentation and produced a wide diversity of synthetic data. This study uses GANs to create a visual creative generative AI model. The study process includes data collection, model creation, participant training, and final evaluation. Generative artificial intelligence improves statistical model performance and durability during data augmentation. This study examines the scientific and ethical implications of this technological accomplishment. Generative artificial intelligence's effects on several fields are also examined. © 2024 IEEE.

关键词： Generative adversarial networks

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Production prediction using machine learning

Production prediction using machine learning

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Rakshit Batra Prateek Abbi Rohan Sharma Hardik Agarwal Paurush Bhulania Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Gautam Budh Nagar Uttar Pradesh India

Our project is inspired from the problems faced by Micro and Small-scale industry owners who are into food production and have to manufacture their goods on daily basis of demand and sale. To plan the production these business owners, predict their production based on several parameters, Day of week, festival, weather etc. but sometimes their prediction goes wrong. So, we are developing a machine model which will be trained on various parameters such as weather, festival, etc. and it will return production quantity as prediction. We are not only developing machine model, but also a full stack application in which a Micro and Small-Scale industry owner can feed the details and according to his/her details, machine will predict the quantity of the product and that predicted quantity will be visible to owner.

关键词： Industries Training Full stack Production Machine learning Predictive models Signal processing

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Enhancing Patient Care: A Data-Driven Approach Using Decision Tree-ELM Models for Improved Safety and Quality

Enhancing Patient Care: A Data-Driven Approach Using Decisio...

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Advancement in technology (ICONAT), International Conference for

作者： Supriya Shrikant Telsang Akhya Sanjay Singh Simarpreet Kaur Bramah Hazela Shikha Singh Hemanand Chittapragada Vishwakarma Institute of Technology Bibwewadi Pune Maharashtra India Computer Engineering (AI & ML) Shah & Anchor Kutchhi Engineering College Mumbai India Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

In recent years, educational initiatives that seek to combine teaching medical students about patient safety and quality improvement (QI) have received funding from both the World Health Organization and the American Association of Medical Colleges. When developing curriculum, it is important to keep in mind the interests and values of the target audience. The proposed used a poll to find out how students felt about lessons on patient safety and quality improvement. It would be more efficient to incorporate patient safety and quality improvement into clinical education rather than teach it in separate computer modules or during pre-clinical lectures. This proposed approach consists of three stages, which are feature selection, model training, and preprocessing. Before doing thorough text processing, stop words are typically filtered out in conventional methods of natural language processing and information retrieval. Unigram and bigram selection are part of feature selection. During the entire model training process, a Decision Tree-ELM Ensemble was utilized. This innovative approach outperforms ELM and Decision Tree with an average accuracy of ${9 4 . 3 6 \%}$.

关键词： Training Accuracy Medical services Feature extraction Information filters Natural language processing Safety Decision trees Systems thinking Text processing

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Adaptive Varying Contention Window MAC Protocol Based on Underwater Acoustic Propagation Delay

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IEEE Sensors Journal 2023年第13期23卷 15019-15031页

作者： Guo, Yongan Obaidat, Mohammad S. Guo, Jing Wu, Tin-Yu Tey, Fu Jie Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education College of Telecommunications and Information Engineering Nanjing210003 China The King Abdullah II School of Information Technology University of Jordan Amman11942 Jordan The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Amity University UP Noida201301 India Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China National Pingtung University of Science and Technology Department of Management Information Systems Pingtung912301 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106335 Taiwan

Underwater acoustic wave propagation time delay and fluctuations are much greater than radio waves. However, many of the protocols used for underwater acoustic networking and communication are designed according to the principles of terrestrial wireless communication networks, failing to give full consideration to utilizing the underwater acoustic propagation and its time delay variation. For this reason, this article proposes an adaptive varying contention window media access control (AVCW-MAC) protocol based on propagation delay. The protocol uses AVCW random backoff mechanism to differently set up the CW of each node based on the different propagation delays from each underwater sensor node to the cluster head node to improve the channel utilization. In addition, to avoid excessive difference in the CW of each node, the backoff factors K1 and K2 are introduced in the AVCW random backoff mechanism to improve the fairness of nodes' accessing the channel. Simulation results show that for the same amount of sensor nodes, the AVCW-MAC protocol has significantly improved network throughput and decreased average end-to-end latency when compared to legacy CW-MAC protocols. © 2001-2012 IEEE.

关键词： Underwater acoustics

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