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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Vijaya Shetty, S. Hemantha, N.A. Devaganamath, Anand Ananth, R. Alur, Abhishek M Nitte Meenakshi Institute of Technology Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350323795

Plant diseases can have a significant influence on agricultural productivity, quality, and profitability. Early identification and precise diagnosis of plant diseases are essential for successful disease control, which may reduce the use of pesticides and minimize crop losses. In this study, a machine learning-based approach is developed to identify the diseases of various plants using leaf images. Dataset of leaf images of 14 different crops are collected, including apple, pepper, potato, raspberry, blueberry, cherry, peach, soybean, squash, strawberry, corn, grape, orange, and tomato. The images are preprocessed using techniques such as normalization, resizing, and augmentation, to reduce noise and increase the diversity of the dataset. Convolution neural network (CNN) model is used as the back-end and Flask for the front-end to create a user-friendly web application. An interpretability analysis is conducted to understand the features learned by the model, which can provide insights into the underlying causes of the diseases. The study shows the potential of machine learning approaches for early diagnosis and identification of plant diseases, which can assist farmers in taking prompt action and minimizing crop losses. This approach can be extended to other crops and can be integrated with other technologies, such as drones and sensors, for remote and real-time monitoring of crop health. © 2023 IEEE.

关键词： Crops

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Mobile Device Security in Wireless Networks: Challenges, issues and Solutions 15

Mobile Device Security in Wireless Networks: Challenges, iss...

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

作者： Gopalakrishna, K. Sahoo, Laxman Arunadevi, R. Wasatkar, Namrata N. Sahoo, Girija Shankar Singh, Dhiraj Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Ramnagar District562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Mobile device protection has emerged as an increasingly important difficulty as more and more customers depend upon wireless networks for everyday conversation and other duties. Wi-Fi networks offer convenience and versatility for users;however, they also come with safety worries. This paper presents an overview of the safety-demanding situations related to Wi -Fi networks, discusses safety issues related to cellular devices, and offers more than a few answers for creating cozy cell surroundings. It focuses on defensive person identification, privacy, statistics, and defending against possible malicious activities. Primarily, the paper explores security solutions, encryption, authentication, community access control, and tool control. Moreover, it appears on the role of service carriers in cell tool protection and shows an aggregate of quality practices and generation to sell secure usage. Finally, the paper recommends persevering with studies to identify new threats and answers to decorate mobile safety similarly. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Increasing Hosting Capacity of Uncontrollable Distributed Energy Resources in Isolated Power Systems

Increasing Hosting Capacity of Uncontrollable Distributed En...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Therapontos, Phivos Stavrou, Andreas Aristidou, Petros Electricity Authority of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Dept. of Electrical and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9798350396782

Due to the push for more sustainable generation, small-scale distributed energy resources (DERs) penetration has increased significantly. Most countries have no remote monitoring and control requirements for DERs below a certain size. Therefore, they can be labeled as uncontrollable DERs (UDERs). In isolated systems, the UDERs hosting capacity is limited due to frequency stability concerns, as it is impossible to modify or curtail their generation in a centralized manner. In this paper, we propose the utilization of the active power over-frequency reduction curve for increasing the hosting capacity of UDERs in isolated power systems. Cyprus’s power system has been used to evaluate the proposed solution’s performance. The optimal settings of the curve have been identified, and it was found that UDERs hosting capacity can be increased significantly with the proposed methodology. © 2023 IEEE.

关键词： frequency stability grid code requirements hosting capacity islanded systems uncontrollable distributed energy resources

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Implementation of Industry 4.0 in the Energy Sector 2

Implementation of Industry 4.0 in the Energy Sector

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Prasad, Gunji Prem Kathrine, G. Jaspher W. Kuruvilla, Jemimah M. Mohammed Moosa Razeek, J. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350347579

This research study intends to explore various challenges that humans have encountered as well as any that may arise in the near future. Poor sanitation facility is one of the main causes of malnutrition and it leads to a number of diseases, which also indirectly impacts other sectors. Excessive use of resources that are not renewable is another considerable issue. There is a lot of energy resource wastage;for example, streetlights in cities and on highways are often left ON during the day, this may cause energy wastage. Also, it is already known fact that the non-renewable energy resources are very limited. Therefore, an alternative energy source such as renewable energy like wind, solar power, and others are required. In future there might be a rise in the electricity consumption, therefore some innovative ways should be developed to fulfill the requirements. Through innovative technologies, energy can be efficiently utilized. IIoT aims to automate system-based monitoring and control of devices. Integrating the use of renewable energy and leveraging technology in the sanitation sector will promote social development. © 2023 IEEE.

关键词： Solar energy

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Enhancing Human Action Recognition through advanced Motion Descriptors and Machine Learning 1

Enhancing Human Action Recognition through advanced Motion D...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Ramalingam, Vijay Saravanan, D. Vignesh, S. Rahin Batcha, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328042

Understanding human activities from videos presents a formidable challenge in computer Vision. Intelligent video systems primarily specialize in automatically recognizing human actions within video sequences and annotating them accordingly;human action identification involves recognizing actions associated with one or more objects based on analysis of their activities and context. As technology has proliferated rapidly, huge amounts of human-centric video data have accumulated;it has become an obstacle in contemporary video processing systems due to identifying all these actions efficiently. Complex backgrounds compound these challenges, creating error margins that severely hamper many object detection algorithms and creating error margins that compromise performance - particularly with regard to occlusion and distortion issues. This work addresses these obstacles using Apache Spark with in-memory computing and distributed environment for improved recognition of human actions;specifically, a Local Motion Descriptor is employed for feature extraction that calculates intensities between frames that include both motion and appearance elements. Spark MLlib employs random forest to effectively recognize human behavior. Experimental studies are performed to evaluate its activity recognition capabilities and results are benchmarked against popular supervised classification techniques like Decision Trees;results demonstrate that our proposed model outshone other classification techniques, confirming its efficacy in activity recognition. © 2023 IEEE.

关键词： Decision trees

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Convergence of Geo-IoT with Advanced Technologies

Convergence of Geo-IoT with Advanced Technologies

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Sinha, Monalisha Shalini Thejaswini, M. Indian Institute of Information Technology Department of Computer Science and Engineering Bhagalpur India

ISBN: (纸本)9798350323887

IoT is an emerging digital technology where every physical object is connected to one another via the internet. IoT devices embedded with GPS sensors are called Geo-IoT systems where spatial data is the most prominent requirement in developing any IoT services and applications. Geo-IoT data can be used in various IoT applications for optimizing routes, tracking assets, real-time traffic notification, auto-driving, precision agriculture, anti-theft prevention, etc. This paper provides a review of the convergence of Geo-IoT with advanced technologies such as artificial intelligence, machine learning, and blockchain technology. This paper mainly discusses the current status and applicability of artificial intelligence and machine learning methods in solving and computing location-based IoT data for developing new advanced Geo-IoT applications, routing protocols, and security issues. © 2023 IEEE.

关键词： Blockchain

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Content recommendation based on recognised Emotion

Content recommendation based on recognised Emotion

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2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Challa, Sireesha Gowri, Manohari V. Roobini, M.S. Nandhini, P. Daphine Desona Clemency, A.C. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350337426

E-commerce, video websites, and social networking sites are just a few examples of where recommendation algorithms are currently being used. As the amount and variety of data used in recommendation systems grows, more and more of it will be unstructured multimodal data, including text, images, and videos. Yet, it is difficult to employ unstructured multimodal data to enhance the effectiveness of recommendation schemes because of the illustration gap between different modalities. In this paper, we present a new multimodal framework for user-driven ensemble modelling based on the recommendation of mood descriptions. In contrast to standard approaches, our video reference formulation makes use of not just the audio stream but also the face (emotion) and textual modalities that together make up a video document's elementary data. Audio data is used as input by algorithms like Gaussian Naïve Bayes (GNB), Logistic Regression (LR), and Convolutional Neural Network (CNN), whereas text data is used by methods like, LR, Long-short Term Memory (LSTM), and Naive Bayes. The user's current disposition is used to inform recommendations for appropriate music, quotes, etc. generated by these ensemble models. Comprehensive experiments on real-world datasets demonstrate that, across a wide range of assessment measures, the proposed ensemble model greatly outperforms the state-of-the-art baselines. © 2023 IEEE.

关键词： Recommender systems

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Analysis of COVID-19 Datasets Using Statistical Modelling and Machine Learning Techniques to Predict the Disease

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SN computer Science 2024年第1期5卷 1-18页

作者： Nramban Kannan, Senthil Kumar Kolla, Bhanu Prakash Sengan, Sudhakar Muthusamy, Rajendiran Manikandan, Raja Patel, Kanubhai K. Dadheech, Pankaj Department of Computer Science and Engineering School of Computing Institute of Science and Technology (Deemed to Be University) Vel Tech Rangarajan Dr. Sagunthala R&D Tamil Nadu Chennai 600062 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India Department of Computer Science and Engineering Panimalar Engineering College Chennai 600123 India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Tamil Nadu Trichy 621112 India Charotar University of Science and Technology Gujarat 388421 India Department of Computer Science and Engineering Management and Gramothan (SKIT) Swami Keshvanand Institute of Technology Rajasthan Jaipur 302017 India

Sustainable development is crucial for a prosperous future, but epidemic diseases like Coronavirus Disease 2019 (COVID-19) pose real and complex challenges. The global pandemic, declared by the WHO on March 11, 2020, has led to significant loss of life and challenges in public health, economy, food systems, and social life. This research article aims to develop a Machine Learning (ML) model to predict a patient’s COVID-19 diagnosis, as early diagnosis is fundamental for controlling the spread of COVID-19. An investigation of the literature and a research study is conducted to find suitable mathematical methods and measure how they impact prediction models. This paper analyses COVID-19 pandemic deaths, confirmed cases, and recovered individuals using Time Series Analysis (TSA) to study the disease's impacts and understand the TAS. A forecasting model can predict future COVID cases by analyzing trends in time-series and connecting global changes with government restrictions. Since higher predictive accuracy is the limitation of ensemble learning algorithms, better ML approaches are proposed here. Autocorrelation plots clearly showed the results executed for the considered objectives. The hybrid ARIMA algorithm proposed in this work proved adequate results. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Analysis and visualization Autocorrelation COVID-19 Machine learning Time-series analysis Time-series forecasting

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Detection of Stress through Sleeping Habits 14

Detection of Stress through Sleeping Habits

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Nirmala, M.B. Chandana, H.S. Khan, Amnah Rafeeq Kumar, Bhavana S. Department of Computer Science and Engineering Siddaganga Institute of Technology Karnataka Tumakuru India

Considering today’s lifestyle, people tend to neglect their health, specifically when it comes to their sleep. Sleep quality at night will affect our day-to-day activities. Physiological and non-physiological parameters tend to vary during sleep and can affect the quality of sleep, which in turn can build up stress. Insufficient sleep or bad sleep can build up stress in people. Advancement in technology especially in the IoMT sector has provided us with designed tools which can help us in calculating stress. The proposed system represents the relation between stress and sleep and gives an idea on how the former affects the latter. It takes in consideration the various parameters like bodily movements that indicate the level of stress. A user interface is inculcated for user accessibility. The accuracy level of the proposed system is as much as 96%. © Grenze Scientific Society, 2023.

关键词： Machine learning

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Blockchain and DL-based Communicable Disease Spread Control Framework by Monitoring Quarantine Activities 20

Blockchain and DL-based Communicable Disease Spread Control ...

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20th IEEE India Council International Conference, INDICON 2023

作者： Shah, Harshit Shaha, Kanisha Jadav, Nilesh Kumar Gupta, Rajesh Tanwar, Sudeep Bodkhe, Umesh Nirma University Institute of Technology Department of Computer Science and Engineering Ahmedabad India

ISBN: (纸本)9798350305593

In the face of global health crises, ensuring compliance with quarantine regulations is paramount for effectively controlling the spread of infectious diseases. This paper presents a novel system for monitoring activities among quarantined patients using wearable devices, artificial intelligence (AI) models, and blockchain technology. Our system enables continuous monitoring of patients' activities and provides real-time predictions of their compliance status while securely storing and utilizing data on a blockchain. By leveraging wearable devices and employing AI techniques, our system offers an automated and efficient solution for quarantine enforcement. The system's effectiveness is demonstrated through its ability to accurately predict compliance and trigger necessary interventions when deviations from designated areas occur. Our approach is able to achieve a 98.92% accuracy score to predict the activity of the subject. We also demonstrate the effect of different optimizers and their performance. The proposed approach, enhanced by blockchain technology, holds promise for reducing manual efforts, enhancing healthcare workers' safety, and ensuring the integrity and privacy of patient data during the monitoring process. © 2023 IEEE.

关键词： Artificial Intelligence Blockchain Communicable diseases Healthcare systems Quarantine activities Wearable devices

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