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Performance Analysis of a Scalable Federated learning-Based Agricultural Network 4

Performance Analysis of a Scalable Federated Learning-Based ...

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4th IEEE international conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Chakraborty, Shatanik Sarkar, Maharnab Gayen, Avijit Jana, Angshuman Chakraborty, Nilotpal Meghnad Saha Institute of Technology Dept. of Computer Science & Engg. Kolkata India Govt. College Of Engineering & Textile Technology Dept. of Computer Science & Engg. Berhampore India Indian Institute of Information Technology Dept. of Computer Science & Engg. Guwahati India Techno India University Dept. of Computer Science & Engg. West Bengal India

ISBN: (纸本)9798350387063

Although agriculture is a substantial part of the Indian economy, it encounters several challenges. Precision agri-culture is a farming management strategy that aims to overcome these challenges. It optimizes several components of the farming process using data and technology, resulting in increased crop yields while reducing waste and raising total resource use effi-ciency. thus, in its context, we present a study on the development of a Federated learning-based Agricultural Network consisting of a popular federated learning algorithm FedAvg to perform image classification on an open dataset, Plant Village. In this work, we specifically analyze the performance of the proposed Federated learning-based Agricultural Network in terms of its scalable deployment. the proposed network yields an average classification accuracy of 97.52 % on test data (C ≈ 70), and provides a host of other benefits it derives from its federated nature, such as upholding the privacy of local client data as only the model updates are shared between the clients and the aggregation server. the prime objective of our work has been to study and explore the possibility of applying federated learning in the agriculture sector so that more efficient and privacy- preserving smart farming networks could be developed. © 2024 IEEE.

关键词： Agribusiness

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Emotion Analysis in smart Journals: Bridging ML and NLP for Holistic Mental Health Monitoring 4

Emotion Analysis in Smart Journals: Bridging ML and NLP for ...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Vaidya, Soumya Bhatnagar, Anushka Patel, Shobhit Tiwari, Manisha Khare, Pragati NMIMS University Computer Engineering Department Mumbai India

ISBN: (纸本)9798350353266

With the growing awareness of mental health, effective tools to monitor emotional well-being are increasingly needed. Concerns about stress, anxiety, and depression highlight the importance of tracking emotions. Traditional methods like journaling have long been therapeutic for self-reflection and emotional expression but lack modern technology's depth of analysis. In today's digital age, natural language processing (NLP) and machine learning (ML) offer new opportunities to enhance these practices. Digitizing journaling and combining it with these techniques can extract valuable insights from textual data, helping users determine common or dominant emotions over time. Utilizing the CARER Dataset for emotion recognition, the research focuses on data collection, exploratory data analysis, and model development. the developed models, including Extra Trees Classifier, SGD, and Random Forest, demonstrate robust performance in detecting and categorizing emotions in journal entries. By identifying and tracking emotions in journal entries, this study aims to offer a powerful tool for self-awareness, emotional regulation, and mental health management. © 2024 IEEE.

关键词： Self-supervised learning

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Engineering Brain Technology Based on Artificial Intelligence and Federated learning 4

Engineering Brain Technology Based on Artificial Intelligenc...

引用

4th international conference on Machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Li, Qiang Zhao, Feng Tao, Jun Zhou, Wei Yu, Chengchen State Grid Information & Telecommunication Group Co. Ltd Beijing China Anhui Jiyuan Software Co. Ltd Hefei China

the digital wave has given birth to the arrival of smart power and smart cities, a large number of sensor systems deployed for traditional smart power sites have high fragmentation, large and scattered data collection, difficult system integration, and unsatisfactory application effects. A key technical solution for engineering brain based on artificial intelligence and joint learning in intelligent power plant power has been proposed. At the same time, under the premise of data not leaving islands, achieve a learning mode from the model side to multiple data sides, achieving the effect of data being available but not available. the experimental results indicate that: Although federated data governance technology increases model training time due to encrypted data transmission, with the increase of data silos, it can gradually approach data aggregation mode in testing accuracy, greatly improving the problem of data silos. the key technology solutions for the power of engineering brain based on artificial intelligence and federated learning have been demonstrated in practice. the use of digital twin technology to improve automation collection capabilities, the use of artificial intelligence technology to upgrade automation collection devices, and the use of federated learning technology to enhance the intelligence level of engineering power can effectively improve the overall level of intelligent power. In terms of engineering safety supervision, quality monitoring cost control and other aspects can provide reference for similar projects. © 2024 the Authors.

关键词： Artificial intelligence collaborative learning data remains on the island engineering brain intelligent power station

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Detecting Inattentive and Aggressive Driver Behavior Using Deep learning: Recent Advances, Challenges with Performance Evaluation 4th

Detecting Inattentive and Aggressive Driver Behavior Using D...

引用

4th international conference on smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Debbarma, Tina Pal, Tannistha Debbarma, Nikhil Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (纸本)9789819751990

Road safety faces a grave threat from the prevalence of aggressive and inattentive driving behaviors, contributing significantly to the global rise in traffic accidents and fatalities. Identifying, tracking, and proactively addressing instances of aggressive and inattentive driving are pivotal steps toward mitigating accidents and enhancing road safety. this research project seeks to explore the potential of deep learning methods for analyzing human driving behavior, with a specific focus on identifying inattentive and aggressive driving behaviors. the ultimate goal is to develop a robust and reliable system capable of accurately recognizing and categorizing such behaviors. To achieve this objective, we harness the power of deep learning algorithm. this paper also discusses the identification and categorization of aggressive and distracted driving behaviors among human drivers, using deep learning methodologies which offer substantial promise for elevating traffic safety. the literature review uncovered several noteworthy techniques, including CNN, MTCNN, transformer-based action detection systems, and fusion models inspired by densely connected convolution networks. Comparative analysis revealed improvements in accuracy and precision through the utilization of these techniques. However, the pursuit of larger and more diverse datasets, real-time implementation capabilities, and robustness in various environmental conditions remain essential requirements. Despite these challenges, deep learning-based systems offer the potential for automatic and precise analysis of intricate driving patterns, pivotal in accident prevention and overall road safety enhancement. Future research should focus on surmounting current limitations and advancing the development of more intricate deep learning models for accurate recognition and categorization of human driving behaviors. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Preparing Islanding threshold Data for Machine learning Algorithm 4

Preparing Islanding Threshold Data for Machine Learning Algo...

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4th IEEE international conference in Power Engineering Applications, ICPEA 2024

作者： Rahman, Md Siddikur Hasan, Khairul Nisak Binti Md Romlie, Mohd Fakhizan Bin Abdullah, Mohd Faris Bin Universiti Teknologi PETRONAS Dept. of Electrical and Electronics Engineering Perak Seri Iskandar32610 Malaysia Xiamen University Malaysia Selangor Darul Ehsan Sepang43900 Malaysia

ISBN: (纸本)9798350344578

Under the smart grid paradigm, numerous independent operators oversee distribution systems;therefore, for situational awareness and protection, all dispatchers need to have a comprehensive picture of the network. this visualization is made possible by a protection type micro-phasor measurement unit (μ-PMU) mounted on the relevant distributed generation (DG) bus. the potential for accidental islanding, which jeopardizes utility personnel and hinders orderly reconnection, is one of the biggest issues with DGs. the heavily sampled 'Big Data' captured by phasor measurement units (PMUs) contains important information about the system's health. Most power system challenges, such as voltage stability, power system modelling, fault event monitoring, unintended islanding, state estimate, and so on, may be addressed by efficient and timely analysis of this data. In light of this, this paper recommends using PMU to detect inadvertent islanding in real time and presents a methodology for islanding threshold data preparation for artificial intelligence (e.g., machine learning) algorithm. the discrete Fourier transform voltage and current phasors obtained from these PMUs are then processed using the Fortescue transform to compute the angle of sequence components. Under islanded conditions, the absolute angle difference between positive and zero components are employed to initiate signal for islanding. A modified IEEE 30 bus system is simulated by using Power World Simulator for data generation. © 2024 IEEE.

关键词： Phasor measurement units

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Reinforcement learning-Based Energy Optimization for a Fuel Cell Electric Vehicle 4

Reinforcement Learning-Based Energy Optimization for a Fuel ...

引用

4th international conference on smart Power and Internet Energy Systems (SPIES)

作者： Hou, Shengyan Liu, Xuan Yin, Hai Gao, Jinwu Jilin Univ Dept Control Sci & Engn Changchun Jilin Peoples R China Jilin Univ State Key Lab Automot Simulat & Control Changchun Jilin Peoples R China

ISBN: (纸本)9781665489577

Fuel cell electric vehicles are one of the important ways to solve the problems of energy shortage and environmental pollution. Energy management strategy, as a key technology for fuel cell electric vehicles, reduces hydrogen consumption and improves vehicle hydrogen economy by optimizing the energy distribution among multiple energy sources. this paper studies the control strategy of fuel cell electric vehicle powertrain system, and proposes a reinforcement learning-based energy management strategy. Based on the modeling of powertrain of a fuel cell electric vehicle, the framework construction and parameter updating process of reinforcement learning-based energy management algorithm are completed, and it is applied to fuel cell electric vehicles. the simulation and experiment results show that the proposed algorithm can minimizing the hydrogen consumption and keeping battery SOC stable.

关键词： energy management strategy fuel cell electric vehicles markov decision process reinforcement learning

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Implementation of IoT and Data Analytics in smart Agricultural System 4th

Implementation of IoT and Data Analytics in Smart Agricultur...

引用

4th international conference on Recent Trends in Machine learning, IoT, smart Cities, and Applications, ICMISC 2023

作者： Rashmi, K.T. Tigadi, Sanjeev S. Nikhil, K.S. Swamy, Dharnesh Narayana Darshan Nagesh, S. Department of ECE Don Bosco Institute of Technology Bengaluru India

ISBN: (纸本)9789819994410

Headway in innovation is impacting the world at extraordinary speed. New techniques and frameworks are being created all through the world in various application regions. this headway can demonstrate as a help to agriculture industry to fulfill the rising needs of food and feed all over the planets. this paper proposes a non-contact plant development observing framework utilizing Machine learning and IOT. A mechanized water system framework was created for improved utilization of water in farming. the framework has a conveyed remote organization of soil wetness, mugginess, and temperature sensors set at the root zone of plants. Arduino microcontroller gets the sensor data and sets off the result gadgets. An instrument was created relying upon the edge upsides of the dirt dampness, mugginess and temperature sensor, and sort of plant. Customized into Arduino to control the temperature of water took care of to the water system field. Sensors and Arduino are associated with one another with wire. As a result of low-energy utilization and cost, the framework can possibly be helpful in water-restricted geologically disconnected regions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data Analytics

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Hybrid Machine learning Method for Product Sales Forecasting in E-Commerce 4

Hybrid Machine Learning Method for Product Sales Forecasting...

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4th international conference on smart Electronics and Communication, ICOSEC 2023

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

ISBN: (纸本)9798350300888

the term 'e-commerce' refers to the practice of buying and selling products using the internet. E-commerce was created so that consumers wouldn't have to leave the comfort of their homes to purchase something. So, they can buy it online and have it delivered to their house in a few days. Online retailing has recently developed in a favorable environment because of the robust backing of national legislation. this year's pandemic has made the e-commerce sector's contribution to the growth of the country's economy more noticeable. the quantity and effectiveness of e-commerce networks and e-commerce businesses are growing under such circumstances. A network must've been able to better match user demands and perform admirably in all areas of collaboration and administration if it hopes to keep its competitive edge. Accurately predicting the sales volume of e-commerce networks at this time is crucial. this article presents a novel hybrid machine learning (ML) algorithm to accurately predict revenue growth for an online store. A hybrid cat-XGBoost (HC-XG) algorithm is suggested to predict the sale. the main goal of the proposed work is to forecast e-commerce sales, thus i the final selling value is included in the solution since it has a significant impact on sales in extremely competitive and price-sensitive contexts like e-commerce. We next continue with the development of the suggested solution and evaluate it for accuracy, error, and overall efficiency, and compare it to some competitor solutions. thus, the proposed approach is compared with other existing models for sales forecasting and demonstrate that it provides the best overall performance in product sales forecasting. © 2023 IEEE.

关键词： Sales

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Alive in smart Countryside 5th

Alive in Smart Countryside

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5th international conference on smart learning ecosystems and regional development (SLERD)

作者： Lindblom, Pekko Nygren, Eeva Heikkonen, Jukka Sutinen, Erkki Univ Turku Dept Future Technol Turku 20014 Finland

ISBN: (纸本)9789811573835;9789811573828

We suggest that the rapidly desolating Finnish countryside can become a creative zone for education and development where new ideas can be conceptualized, piloted, and tested, and new technologies developed more freely than in urban areas. We used design science as a research approach to develop the "Alive in smart Countryside" concept and business development process involving user communities and SMEs not only as observed subjects but especially as co-designers. Faculty and students from the University of Turku created a Living Lab "Alive in smart Countryside" to the rural municipalities Juuka and Kitee, located in North Karelia, Finland. Living Lab promoted disruptive innovation in order to design solutions toward smart countryside. In addition to analyzing and examining the design endeavor, we discuss the project as an informal learning initiative. Having a folk high school as the coordinator of the project brought in the pragmatic orientation of informal adult education and connected different actors in the region.

关键词： smart countryside Co-design regional development Small and medium enterprises Informal learning Design science

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Programming (and learning) Self-Adaptive & Self-Organising Behaviour with ScaFi: for Swarms, Edge-Cloud ecosystems, and More 4

Programming (and Learning) Self-Adaptive & Self-Organising B...

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4th IEEE international conference on Automatic Computing and Self-Organizing Systems Companion, ACSOS-C 2023

作者： Casadei, Roberto Aguzzi, Gianluca Pianini, Danilo Viroli, Mirko University of Bologna Cesena Italy

ISBN: (纸本)9798350337464

Large-scale and fully distributed cyber-physical systems (CPS), such as swarm robotics or IoT systems, pose significant challenges for programming and design. these challenges include promoting the desired (emergent) collective and self-organising behaviour, dealing with failures, enacting decentralised coordination, and deploying efficient executions. Aggregate computing is a promising approach that aims to simplify the design of such systems by providing a high-level abstraction for describing collective and self-organising behaviours. In this tutorial, we introduce a toolchain that supports the development of aggregate computing applications, based on ScaFi (a Scala-based language and toolkit for aggregate computing) and Alchemist (a simulator for CPS scenarios). We will showcase the toolchain by means of a series of examples, ranging from simple collective behaviours to more complex self-adaptive and self-organising ones. Finally, we provide several pointers to research opportunities (e.g., related to learning collective behaviours and adaptive large-scale deployments) and applications (e.g., in swarm robotics, edge-cloud ecosystems, and more). © 2023 IEEE.

关键词： Aggregates

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