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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Reddy, N.V. Uma New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350300888

In most cases, a STATCOM is utilized to provide support for electrical networks in the event that as a result, the network's power factor decreases and voltage regulation issues become chronic. Because the STATCOM relies on a voltage source converter (VSC), its power supply must be behind a reactor. Adding a suitable energy storage device across the DC capacitor can improve the active power capabilities. This will allow for increased power output. The amount of reactive power present at the STATCOM terminals depends on how much power is supplied. When compared to an SVC, a STATCOM has a quicker response time. This is mostly because to the rapid switching speeds made possible by the voltage source converter's switches, which results in a shorter response time. In addition, compared to an SVC, the STATCOM provides improved support for reactive power even at low AC voltages. On the other hand, these devices can only provide shunt compensation. This research study suggests a novel technique centered on the reduction of line losses in loop distribution systems as a means of overcoming this restriction. The Unified Power Flow Controller, often known as the UPFC, is applied in order to experimentally accomplish these conditions of lowest loss. In contrast to the STATCOM, the UPFC can do series and shunt compensation, which enables the user to exercise control over both voltage and current. To meet this goal, this research developed a fuzzy logic controller-based implementation of a STATCOM and UPFC for reactive power compensation in distribution networks. © 2023 IEEE.

关键词： Static synchronous compensators

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Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions 3

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Yashas, D. Kashyap, M. Shivani Charan, P.R. Hemanth Kumar, G.P. Abhishek, B.K. Deekshith, C. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Dept of Information Science and Engineering Bengaluru India Bnm Institute of Technology Dept of Artificial Intelligence and Machine Learning Bengaluru India Siddaganga Institute of Technology Dept of Artificial Intelligence and Data Science Tumkur India Vidyavardhaka College of Engineering Dept of Computer Science and Engineering Mysore India

ISBN: (纸本)9798331515911

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field. © 2025 IEEE.

关键词： Convolutional neural networks

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Smart Agriculture: A Comprehensive Approach to Crop Disease Diagnosis using Offline Multilingual Technologies 6

Smart Agriculture: A Comprehensive Approach to Crop Disease ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ashok, K. Mattew, Jimsha K. Chandana, K.S. Maharathi, G.N. Abhilash, G.M. Somani, Nithish Computer Science and Engineering New Horizon College of Engineering Bengaluru India Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331522667

This research study presents a comprehensive multilingual approach to crop disease diagnosis aimed at transforming agricultural practices. The proposed system leverages advanced deep learning models such as ResNet, EfficientNet, and MobileNet to automate plant phenotyping using high-resolution images. Beyond disease identification, it provides intelligent plant parenting recommendations to enhance crop breeding strategies. The system is equipped with multilingual support and offline capabilities, ensuring global accessibility, especially in areas with limited internet access. Additionally, the model delivers outputs by detecting diseases and offering diagnosis in the user’s native language, facilitating better decision-making for farmers. Experimental results from real-world scenarios demonstrate the system’s high accuracy and potential to drive sustainable agriculture, accelerate genetic progress, and improve crop prediction. © 2025 IEEE.

关键词： Smart agriculture

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Gesture-Controlled Virtual Mouse and Finger Air Writing 14

Gesture-Controlled Virtual Mouse and Finger Air Writing

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Apoorva Shankar, Anushka Bondia, Anushree Rani, Ritu Jaiswal, Garima Sharma, Arun Computer Science Engineering-Artificial Intelligence Indira Gandhi Delhi Technical University for Women Delhi India Artificial Intelligence and Machine Learning Indira Gandhi Delhi Technical University for Women Delhi India Centre of Excellence-Artificial Intelligence Indira Gandhi Delhi Technical University for Women Delhi India Artificial Intelligence and Machine Learning Bennett University Noida India

ISBN: (纸本)9798350344837

The convergence of innovative strategies to human-computer interaction is evidently supported by two underlying research. One experiment involves controlling objects using hand motions, while the other involves identifying the symbols we write in the air. In the first, a ground-breaking method for manipulating the mouse cursor is introduced: gesture recognition. By fusing motion modelling, pattern recognition, and machine learning, this technique perfectly supports a range of mouse movements. The system, which was created in Python, effectively separates finger motions in order to accurately mimic mouse events. It is improved with coloured finger caps to guarantee the best contrast. The second research explores air-writing recognition, a novel technique that involves drawing letters and numbers by hand, in conjunction with the first. This is when individuals draw letters and numbers in the air with their hands. An approved CNN model is the result of extensive testing and sets new standards for recognising characters created in the air. It is quite effective at identifying the letters or numbers that someone is forming in the air. These research, which cover gesture-based control and progressive air-writing recognition and push the frontiers of technical interface development, collectively represent the evolution of human-computer interaction paradigms. Making computers and people interact in novel and fascinating ways is the main goal! © 2024 IEEE.

关键词： Palmprint recognition

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Predicting Agricultural Crop Damage Caused by Unexpected Rainfall Using Deep learning

Predicting Agricultural Crop Damage Caused by Unexpected Rai...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Fulkar, Bhushan Patil, Pawan Srivastav, Gaurav Mahale, Promod Maharashtra Wardha India Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350375237

Efficient allocation of resources and timely agricultural interventions depend on the precise identification of crop loss at the field parcel level. Using recent data from 2018 to 2023, this study investigates the integration of deep learning techniques with real-time field photography to classify agricultural field parcels into those experiencing crop loss and those not. We build and assess deep learning models specifically for crop loss classification using a dataset from the Finnish Food Authority (FFA) that combines on-the-ground photos taken during field visits with comprehensive field parcel information. In this work, we use convolutional neural networks (CNNs) to examine the visual characteristics that we have extracted from field photos in order to identify the subtleties that may indicate crop health or possible loss. Our goal is to improve the model's generalization capabilities over a variety of crop types and agricultural landscapes by training it on a combination of field parcel data and corresponding photographs. © 2024 IEEE.

关键词： Image segmentation

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SightAssist: A Multi-facility machine learning Approach for Empowering the Visually Impaired 2nd

SightAssist: A Multi-facility Machine Learning Approach for...

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2nd International Conference on Data Science and Network engineering, ICDSNE 2024

作者： Bandopadhyay, Saikat Dutta, Jhalak Das, Smita Datta, Soumyajit Ghosh, Debaditya Dey, Rohit Kumar Nandigrami, Jeet Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Computer Science and Engineering National Institute of Technology Agartala India Department of Artificial Intelligence and Machine Learning Heritage Institute of Technology Kolkata India

ISBN: (纸本)9789819783359

Blindness or visual impairment ranks among the top ten disabilities affecting both men and women, impacting over 35 million individuals of all ages in India. Access to visual information is crucial for enhancing the independence and safety of blind and visually impaired individuals. There exists a compelling imperative to develop assistive technologies that can significantly improve their quality of life. This paper presents SightAssist, a multilingual software solution designed to address the challenges faced by the visually impaired community in India. SightAssist utilizes a camera-based approach to serve as a digital prosthetic eye and virtual assistant, capable of currency detection, facial recognition, and providing various functionalities to assist with daily activities. Initially, a custom-labeled dataset has been prepared using a pre-trained YOLO model. Gradually, dataset pre-processing, normalization, and semantic segmentation are employed to optimize model performance. Additionally, voice-based accessibility features have been integrated using the Web Speech API, enabling users to interact with the application through voice commands. The experimental results show the successful development of SightAssist and its potential to significantly improve the lives of visually impaired people. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Assistive technology

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Automated Plant Disease Detection: CNN for Corn Maize, Tomato, and Potato 7th

Automated Plant Disease Detection: CNN for Corn Maize, Tomat...

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7th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Angeline, R. Aruneshwaran, S. Department of Computer Science and Engineering with Specialization in Artificial Intelligence and Machine Learning SRM Institute of Science and Technology Ramapuram India

ISBN: (纸本)9789819720880

Plant ailments pose present a significant challenge to the worldwide food security and the agricultural sector. Swift and precise detection of these diseases is pivotal for effectively managing them and preventing crop yield reductions. Lately, advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs), have exhibited encouraging outcomes across various tasks involving image recognition. This undertaking strives to create and execute a model founded on CNNs to prognosticate plant diseases through leaf images. The proposed strategy encompasses three main phases: compiling and preparing the data, developing the model architecture, and assessing performance. Initially, an extensive dataset of plant leaf images, encompassing leaves afflicted by diverse diseases, is assembled. The images undergo preprocessing to heighten quality and eliminate disturbances, ensuring a dependable model training process. Subsequently, a CNN structure is devised and trained to employ the dataset. The chosen CNN model adheres to a sequential design, where each layer possesses precisely one input and output. These layers are arranged sequentially to construct the entire network and incorporate multiple convolutional layers such as Conv2D, MaxPooling2D, Flatten, and Dense, enabling the learning of features from the input images. The findings underscore that the CNN-centered model for forecasting plant diseases attains remarkable training precision of 99.65%, accompanied by a testing precision of 99.44% and a validation precision of 98.61%, proficiently identifying prevalent ailments like common rust disease in corn plants, bacterial spot infection in tomato crops, and the early blight ailment in potato plants. In conclusion, the proposed CNN-driven prognostic model for plant diseases manifests encouraging outcomes in precisely recognizing these diseases from leaf images. The efficacious application of this model can assist farmers and agricultural specialists in inform

关键词： Fruits

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Hybrid Design for Privacy Preserved Image Representation in a Cloud Environment 12th

Hybrid Design for Privacy Preserved Image Representation in ...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Vijay, K. Sorna Shanthi, D. Jaeyalakshmi, M. Vignesh, P. Yuvaraja, M. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The digital era has made seamless sharing and keeping of media such as images on cloud platforms an integral part of our lives. Still, there is a big issue about user privacy and data security in these repositories. We have thus created a very strong system that is able to do image encryption and decryption so as to protect the privacy of users while at the same time making their pictures accessible. Our system uses hybrid encryption where we try to prevent even cloud service providers from accessing the original files hence ensuring that only the owner who has been authenticated through password based authentication can decrypt or access them. In this way, it allows maintaining high privacy standards without sacrificing user convenience. It starts with preparing photos for encryption so that they are kept safe during transmission and storage. When needed by a user, images are decrypted by our system and become available only to authorized users. Consequently, users can comfortably leave their private photo albums on cloud storage systems knowing too well that their friends cannot access them thereby increasing trust in using cloud services for storing sensitive information. The aim of this work is to increase user privacy and data security in cloud repositories through employing this encryption technique. The users are guaranteed that their private details are not exposed to others enabling them to use cloud storage efficiently without interfering with their privacy at all. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Revamping Primary Programming Education with Augmented Reality Teaching via EnvisionAR 5

Revamping Primary Programming Education with Augmented Reali...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Annie Silviya, S.H. Anitha, R. Gayathri, P. Hiranmaie, N. Harini, R. Hareene, D. Rajalakshmi Institute of Technology Computer Science and Engineering Chennai India Rajalakshmi Engineering College Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350385649

This study introduces a transformative approach to primary programming education, leveraging Augmented Reality (AR) teaching methodologies through the innovative EnvisionAR platform. In response to the dynamic technological landscape, this research addresses the need for engaging, immersive learning experiences for young learners. The EnvisionAR platform, a pioneering fusion of AR technology and tailored curriculum, caters to the cognitive development stages of primary school students. By seamlessly integrating virtual elements into the physical environment, students engage in interactive, hands-on learning that cultivates creativity, problem solving skills, and a profound understanding of programming concepts. This research signifies a broader potential for AR integration in diverse educational disciplines. © 2024 IEEE.

关键词： Augmented reality

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Polyhouse Agriculture with AI: Strategies for Climate Control, Energy Efficiency and Yield Improvement

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SN Computer Science 2024年第8期5卷 1119页

作者： Mayuri, K.P. Kathavate, Sheela Niranjanamurthy, M. Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management (Affiliated to VTU -Belgaum) Karnataka Bengaluru India Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Karnataka Bengaluru India

artificial intelligence is applied to an extensive array of climate change responses, from data collection and classification to conservation decision-making and rule enforcement, leading to improved understanding and mitigation efforts. Greenhouse and polyhouse farming practices could significantly add to the carbon footprint, posing a challenge to be addressed if sustainable and environmentally friendly solutions are to be assured. This study describes the use of artificial intelligence in polyhouse agriculture to achieve better yields, save energy and advocate for sustainable farming. This study further elucidates the impact of AI-driven climate control systems on the quality of crops, resource use efficiency and productivity. The findings demonstrated the enormous potential of AI technology to improve plant growth, lower greenhouse gas emissions and increase sustainability in polyhouse agriculture. AI-driven climate control systems optimize the growing environment and reduce energy use with reduced emissions of GHGs, thus contributing to sustainable farming. However, this study also emphasizes the need for detailed research into the deployment and consequences of AI technology within polyhouse operations. The key bottlenecks are availability, security and regulatory compliance related to the generation of data. Therefore, need for a holistic approach to the design phase of AI-driven climate control systems in polyhouses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： AI-driven climate control Crop yield Greenhouse gas emissions Polyhouse agriculture Resource efficiency Sustainable farming

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