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International Conference on Inventive Computation Technologies (ICICT)

作者： Prathmesh Waghmare Prateek Verma Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Chronic Diabetes Mellitus (DM) is typified by glucoregulatory system dysregulation, which results in hyperglycemia and long-term consequences that impact several organs. With over 425 million people affected globally, early diagnosis and effective management of diabetes are crucial. Recent advancements in artificial intelligence (AI) and machine learning (ML) provide encouraging ways to enhance the treatment of diabetes. The goal of AI is to develop systems that can handle complicated data, which is particularly useful for managing diabetes. As a subset off AI, ML enables the creation of tailored treatment plans and predictive algorithms that improve patient outcomes and diagnostic accuracy. AI and ML integration in the treatment of diabetes has led to significant innovations, such as automated insulin delivery systems and AI-driven clinical decision support tools. These technologies improve glucose management and patient empowerment by providing real-time feedback and personalized recommendations. Additionally, AI applications in retinal screening and genomics have advanced the diagnosis and understanding of diabetic complications and genetic predispositions. Despite these advancements, challenges remain, including data privacy concerns, technical barriers, and limitations in AI model design. Robust data management, device compatibility, and continual AI algorithm validation are necessary to overcome these challenges. All things considered, AI and ML have enormous potential to change the way diabetes is treated by facilitating early diagnosis, individualized therapy, and better patient self-management, all of which will eventually improve the quality of life for those who have the disease.

关键词： Technological innovation machine learning algorithms Reviews Medical treatment machine learning Retina Prediction algorithms Real-time systems Diabetes Insulin

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YouMatter: A Conversational AI Powered Mental Health Chatbot 15

YouMatter: A Conversational AI Powered Mental Health Chatbot

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

作者： Nazareth, Pradeep Nikhil, G.B. Chirag, G. Prathik, N.R. Alva's Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Karnataka Moodbidri India

ISBN: (纸本)9798350370249

In many regions globally, the shortage of trained mental health professionals leaves countless individuals without the support they desperately need. This deficiency, highlighted by the world health organization, underscores the pressing need for innovative solutions to bridge this gap. Chatbots emerged as a scalable intervention, offering an interactive platform for mental health solutions. These systems, adept in spoken, written, and visual languages, hold significant potential, particularly for individuals hesitant to seek traditional mental health services. Even though there are some chatbots already available, like SERMO and Aapka Chikitsak, they still have some problems such as the absence of analyzed reports and graphical data. This paper proposed a new chatbot referred to as YouMatter, to tackle these challenges. Developed on a foundation of advanced natural language processing, and Large Language Model (LLM), YouMatter serves as a mental health care chatbot, extending support and resources to those navigating mental well-being. Leveraging WebSockets, YouMatter facilitates real-time interactions, ensuring assistance is available around the clock. With the capability to analyze users' mental health trends and present graphical insights, YouMatter empowers individuals to track their progress and receive tailored support. Through personalized email tips and accessible chatbot assistance, YouMatter strives to democratize mental health care, removing barriers and fostering happier, healthier lives for all. © 2024 IEEE.

关键词： analyzed reports behavioral health graphical data LLM Model Mental health care chatbots natural language processing real-time interactions

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Visualization of Real-Time Smart Home Data Using PowerBI

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SN Computer Science 2024年第6期5卷 1-8页

作者： Narasimha, S Swamy Anna, Dheeraj Manirathnam Reddy, Akhil Vijayalakshmi, M.N. Raju, Kota Solomon Academy of Science and Industrial Research Department of Artificial Intelligence and Machine Learning RV College of Engineering CSIR-National Aerospace Laboratory

IoT has experienced tremendous advancement over the last decade;this is due to the benefits like data acquisition, data processing, and communication capabilities. Smart Home (SH) takes the key benefits of the IoT to increase the resident’s quality of life. In SH most of the devices ranging from toothbrushes to television are connected to the internet;these devices generate the massive amount of data and that needs to be analyzed. This article presents the real-time streaming analysis of smart home data. The proposed work comprises of three phases: (1) Data acquisition, (2) Data Processing, and (3) Data Analytics. In this work, the RasberryPi3 B models are used to collect data from the smart home environment. RaspberryPi3 is used as an edge node, and which is used to accomplish the data processing task. The processed data is used to create visuals, which helps in understanding the data, and to impart better decisions. This work uses PowerBI services to create visuals and notifications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud service Edge computing Internet of things MQTT PowerBI Smart homes

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Autonomous System Enabling Node and Edge Detection, Path Optimization, and Effective Color-Coded Box Management in Diverse Robotic Environments

Autonomous System Enabling Node and Edge Detection, Path Opt...

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International Conference on Data Science, Computation and Security, IDSCS 2023

作者： Jayanth, Deeksha Mundra, Chetna Sowmya, H.K. Dsouza, Sonia Maria Department of Machine Learning BMS College of Engineering Karnataka Bengaluru560019 India Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru560103 India

ISBN: (纸本)9789819709748

This research paper introduces an innovative system for optimizing operations in industrial warehouses and similar environments. The system utilizes visual input, capturing the warehouse layout and designated pick-up and drop-off zones tailored to specific industry requirements. Through image analysis, the system extracts critical data and employs a shortest-path planning algorithm to determine the most efficient sequence of pick-up, drop-off, and navigation tasks that align with the given specifications within the designated setup. Once the optimal path is determined, the system generates a sequence of precise instructions to guide a robot through the required operations. These instructions are comprehensible to the robot, which executes them as it encounters nodes, junctions, dead ends, or obstacles in its path. This research presents a powerful tool for streamlining warehouse operations, enhancing productivity, and ensuring efficient task execution in complex industrial settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Edge detection

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Image Super Resolution using U-Net architecture and SRGAN: Comparative Analysis 2

Image Super Resolution using U-Net architecture and SRGAN: C...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Dandekar, Pranali Bhojwani, Bhavika Balpande, Aarya Zanwar, Sanskar Deb, Ankan Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering [Artificial Intelligence and Machine Learning Nagpur India

ISBN: (纸本)9798350387933

Super-resolution is an important image processing task that improves the resolution of high-resolution images. Low resolution is used in many different fields, including medical imaging, satellite imagery, and computer vision. This paper presents a new approach to ultra-high resolution using the U-Net architecture, a deep learning framework known for its success in image segmentation and restoration *** this study, we propose an adaptive U-Net model specifically designed for ultra-high-resolution tasks. The architecture includes an encoder-decoder network with bypass connections, enabling multi-scale feature extraction and high-resolution detail reconstruction. Our model is trained on a diverse dataset of low- and high-resolution image pairs, allowing it to learn complex relationships and patterns in *** evaluate the performance of the U-Net based superresolution method using standard image quality metrics and qualitative visual evaluation. Test results show a significant improvement in image quality, with improved sharpness, texture, and detail recovery. Furthermore, our model outperforms state-of-the-art super-resolution methods in terms of signal-to-noise ratio (PSNR) and structural similarity index (SSIM). © 2024 IEEE.

关键词： Image segmentation

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Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics 3

Enhancing Interpretability: The Role of Explainable AI in He...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Zade, Nikita Langote, Meher Verma, Prateek Faculty of Engineering & Technology Department of Artificial Intelligence & Data Science Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Artificial Intelligence & Machine Learning Maharashtra Sawangi442001 India

ISBN: (纸本)9798331509675

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare. © 2025 IEEE.

关键词： Adversarial machine learning

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Design of Weed Detection Technique Using Yolo Models

Design of Weed Detection Technique Using Yolo Models

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Sreehari, M. Jain, Ankur Vellore Institiute of Technology Artificial intelligence and Machine Learning Department of computer science engineering specialization Bhopal India

ISBN: (纸本)9798350374711

The use of the ADAM (Adaptive Moment Estimation) and SGD (Stochastic Gradient Descent) algorithms to optimize the YOLOv7(You Only Look Once), YOLOv8, and YOLO-NAS models for weed detection in agricultural landscapes is investigated in this study. Offering a thorough comparative study based on mean Average Precision (mAP), recall, and precision measures is the goal. When compared to YOLOv7, YOLOv8 regularly performs better, exhibiting balanced recall and higher precision. With remarkable mAP scores at IOU (Intersection Over Union) 0.50, YOLOv8 ADAM in particular sticks out, demonstrating strong weed identification abilities. The YOLOv7 ADAM exhibits a precision-recall trade-off, with precision being slightly sacrificed for recall. Model superiority is not solely determined by the optimization algorithm used (ADAM vs. SGD);YOLOv8 variations perform admirably. For real-world implementation, practical factors like processing efficiency and deployment viability are even more important than quantitative criteria. This work establishes the foundation for comprehending the complex trade-offs and advantages that are present in every model and algorithm, in addition to helping with the educated selection of the best YOLO models for weed detection. Future research directions in object detection in agriculture, including ensemble techniques and hyperparameter tuning, promise more advancements in this subject as technology develops. © 2024 IEEE.

关键词： Stochastic systems

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Web-Based Smart Healthcare System 2

Web-Based Smart Healthcare System

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Daigavhane, Mansvi Kishorrao Gundewar, Swapnil Gadkari, Ayush Pravin Anpan, Sakshi Prakash Faculty of Engineering and Technology Wardha India Faculty of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Wardha India

ISBN: (纸本)9798331528713

In today's healthcare sector, utilizing information technology is essential for improving hospital operations. This study presents the creation and deployment of a web-based Smart Healthcare system (SHS) aimed at optimizing various administrative and clinical tasks within a healthcare setting. Some of the components are patient portal for signing in, practice calendar, assessment tools, invoicing, and reporting using webbased platform. Web based technologies and keeping a centralized database leads to secure data management, real time information and effective communication between different departments. This paper focuses on the description of the system's layout, structure and basic operations identifying it as a tool for raising organizational productivity, improving data accuracy and promoting patient care benefits. © 2024 IEEE.

关键词： Electronic health record

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DocAssist: Signature Perception System using Deep learning

DocAssist: Signature Perception System using Deep Learning

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2024 International Conference on Computational intelligence and Computing Applications, ICCICA 2024

作者： Sharma, Pooja Miglani, Ishika Sharma, Lucky Panipat Institute Of Engineering And Technology Samalkha India Panipat Institute Of Engineering And Technology Artificial Intelligence And Machine Learning Samalkha India

ISBN: (纸本)9798350306446

All In the realm of communication, individuals employ two primary modes: written and spoken language. Handwriting, in particular, serves as a powerful tool for conveying information and emotions across various contexts, including healthcare. Despite its widespread use, deciphering handwritten text remains a challenge due to variations in individual writing styles and the lack of standardization. This challenge is particularly pronounced in the medical field, where handwritten prescriptions can lead to serious consequences if misinterpreted. Our research addresses this issue by proposing DocAssist, a novel model that utilizes Convolutional Neural Networks to achieve an impressive 81% accuracy in recognizing doctors' handwriting. By leveraging machine learning and image recognition technologies, DocAssist aims to enhance prescription legibility, mitigate medication errors, and ultimately improve patient safety and healthcare outcomes. Through rigorous experimentation with the EMNIST dataset, our study demonstrates the effectiveness of DocAssist in accurately interpreting handwritten prescriptions, highlighting its potential to revolutionize prescription processing and alleviate the burden on healthcare professionals and pharmacists. © 2024 IEEE.

关键词： Image enhancement

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Improved Performance Analysis and Reactive Power Compensation of FACTS Devices Utilizing Fuzzy Logic 4

Improved Performance Analysis and Reactive Power Compensatio...

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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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