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The Utilization of Google Cloud in the Healthcare Industry 2

The Utilization of Google Cloud in the Healthcare Industry

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kapse, Rishikesh Gourshettiwar, Palash Faculty of Engineering and Technology Dept. of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Dept. of Computer Science And Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331505745

This essay's primary focus is on Google Cloud's use in healthcare and how it affects data management, teamwork, and cost-effectiveness. With a focus on Google Cloud, which improves the processing, storing, and accessibility of medical data, the study examines the trends, advantages, and persistent challenges that come with the increasing usage of cloud computing technology. These consist of remote patient monitoring, 'artificial intelligence,' and 'machine learning' applications in diagnostics, diagnostics, and personalized medicine. Data privacy, security issues, and concerns with the HIPAA incident are among the problems that are discussed along with all the significant elements that could facilitate the cloud migration of healthcare. © 2025 IEEE.

关键词： Theranostics

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GolfPoseNet: Golf-Specific 3D Human Pose Estimation Network

GolfPoseNet: Golf-Specific 3D Human Pose Estimation Network

引用

2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Park, BoJeong Kim, JongWoo Mun, SeoYeong Choi, Younglim Kim, Hyunseok Dong-A University Dept. of Electric Engineering Busan Korea Republic of Dong-A University Dept. of Software Innovation Center Busan Korea Republic of Dong-A University Dept. of Computer Engineering Busan Korea Republic of

ISBN: (纸本)9798331510756

This paper aims to develop a robotic golf trainer using a wheeled robot and create a model that accurately recognizes the user's golf motion. Since existing 3D pose models show limitations in golf motion recognition [1], [2], we propose an optimized recognition model by combining various methods and preprocessing golf datasets to overcome these problems. We used HRNet to estimate 2D pose from video frames and fed these data into a transformer-based model to generate three hypotheses through residual linking and embedding layers. These were integrated to finally design a structure to predict 3D pose, and batch normalization, dropout, and LeakyReLU were used to improve the stability and nonlinearity handling of the model. Experimental results show that the proposed model effectively learns temporal information from multi-view 2D pose data without 3D annotation and performs well even when 3D data is scarce. The proposed method can be used in various applications such as human-robot interaction and sports motion analysis including golf, and further research on multiple datasets and real-world environments is expected to improve further and generalize the model's performance. © 2025 IEEE.

关键词： Human robot interaction

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An Extended Reality Platform Powered by Large Language Models: A Case Study on Teaching Dance Costumes 7

An Extended Reality Platform Powered by Large Language Model...

引用

7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Garzarella, Silvia Vallasciani, Giacomo Cascarano, Pasquale Hajahmadi, Shirin Cervellati, Elena Marfia, Gustavo University of Bologna Dept. of the Arts Bologna Italy University of Bologna Dept. of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798331521578

In this paper, we present an Extended Reality (XR) platform powered by Large Language Models (LLMs), designed to support education in dance history and cultural heritage. The platform here applied to a case study on a dance costume, allows users to interact with 3D models, annotate them, and explore their historical and cultural contexts collaboratively. Using features like image-to-LLM queries, users can gain insights into costume details and performance history, and the annotation tools enable uploading multimedia resources, thereby simulating an archival research environment. By integrating LLMs, the platform provides tailored information and context on demand, enriching the user experience with detailed explanations about objects, such as costume construction, usage, and cultural significance. © 2025 IEEE.

关键词： Hosiery manufacture

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Comparative Analysis of Machine Learning Algorithms for Binary Classification of Tea Leaf Diseases 5

Comparative Analysis of Machine Learning Algorithms for Bina...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Shekhar, Nidhi Megha, J.S. Varsha, J.S. Revathi, G.P. Pesu - Isfcr Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356236

India stands as one of the world's largest tea exporters. This requires for an implementation of an efficient and robust system for disease detection and prevention. The most important challenge faced by the tea industry is tea leaf diseases due to continuous pathogen infestation. These diseases can cause full-size crop losses, adversely affecting the economic system and the livelihood of tea farmers. The ability to detect these diseases early is essential. Early detection can cause timely intervention that can notably reduce harm to tea plants and keep them at their best possible quality. Unfortunately, traditional diagnostic techniques often depend on visible inspection and are hence inefficient as those methods can be gradual, labour in-dept. and vulnerable to human errors, reducing the effectiveness in larger fields. This research paper aims to classify whether a leaf is diseased or healthy through a two-step process involving feature extraction using a pretrained Convolutional Neural Network (CNN) and classification using four machine learning models - LightGBM, CatBoost, Autoencoders and Self Organized Maps (SOMs). This paper utilizes a pre-trained VGG16 model, which is trained on the ImageNet dataset. This model is capable of extracting high-level features from images. © 2025 IEEE.

关键词： Self organizing maps

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Stomach Cancer Prediction and Detection using Deep Learning: A Review 2

Stomach Cancer Prediction and Detection using Deep Learning:...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kaur, Sharandeep Goel, Paurav Kapoor, Nitika Chandigarh University dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798331505745

Stomach cancer, sometimes referred to as gastric cancer, is still one of the most common and widespread deadly disease worldwide offers a significant challenge in oncology due to late detection and high mortality rates. The advent of deep learning has revolutionized stomach cancer prediction by leveraging sophisticated models such as convolutional neural net-works (CNNs), multimodal learning and hybrid frameworks that integrate multiple data sources like medical imaging, histological slides, clinical records and genetic profiles and provide better performance with high accuracy. This review comprehensively ex-plores recent advances in deep learning applications for the detection of stomach cancer, highlighting their performance, accuracy, and clinical implications. Comparative analysis demonstrates that different deep learning models, such as ResNet50, DenseNet121, EfficientNetB5, and MobileNetV2, provide impressive results with accuracy up to 99% and higher sensitivity and specificity. It also indicates that the system accuracy needs to be improved to 100% in order to detect stomach cancer as early as possible. Early detection helps in the stoppage of further spreading of the disease and lowers the death rates. © 2025 IEEE.

关键词： Convolutional neural networks

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Plant Disease Detection using Image Processing and Generative Adversarial Network 14

Plant Disease Detection using Image Processing and Generativ...

引用

14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Teresha, Y. Shaji, Nikhil John Jarial, Manav Mirjankar, Ved Vishal Patil, Swetha Computer Science Engineering Dept. PES University Bengaluru India

ISBN: (纸本)9798331531935

Agriculture is a key contributor to the GDP of our country, India. Plants play a very important role in the ecosystem and plant diseases can negatively impact the agricultural industry by causing huge losses to the food supply. Plant leaves are prone to a variety of diseases, including blights, leaf spots, and other infections. Therefore, identifying diseases efficiently is crucial to pave the way for sustainable agriculture. In the field of agriculture, traditional and manual methods for detecting diseases in plants have sometimes been found to be inefficient and error prone. We therefore present a novel solution to these issues by introducing a method to automate plant disease detection in the Tomato plant leaf by using advanced Convolutional Neural Network (CNN) frameworks, image processing libraries and Generative Adversarial Networks (GANs). The pipeline of our work involves preprocessing plant image datasets, generating synthetic images using GANs, segmenting region of interests via a U-Net model, and classifying diseases with a CNN-based classifier. A deployed model ensures accurate disease identification from user input. © 2025 IEEE.

关键词： Photointerpretation

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PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier 3

PhishVQC: Optimizing Phishing URL Detection with Correlation...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Shahriyar, Farhan Tanbhir, Gazi Raihan Chy, Abdullah Md Tanzin, Mohammed Abdul Al Arafat Mashrafi, Jisan World University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Southern University Bangladesh Dept. of Computer Science and Engineering Chittagong Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331523893

Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive information. Traditional machine learning techniques struggle to perform well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and variational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows. © 2025 IEEE.

关键词： Correlation Based Feature Selection Cybersecurity Feature Selection Phishing Detection QML Quantum Classical Hybrid Model Quantum Computing Quantum Machine Learning URL Detection Variational Quantum Classifier VQC

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Smart Agriculture: A Crop Monitoring System to Empower Agricultural Field Using IOT

Smart Agriculture: A Crop Monitoring System to Empower Agric...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Madhu, Surya Sujith Kumar, P.S. Supriya, L.P. Sree Buddha College of Engineering Dept. of Computer Science and Engineering Kerala India

ISBN: (纸本)9798331507541

Agriculture has long been the cornerstone of India's economy and way of life, contributing significantly to the nation's GDP and employment. With a diverse range of crops and climatic conditions, India's agricultural sector is both vast and complex, demanding innovative approaches to meet the growing needs of its population. The sector faces numerous challenges, including climate variability, soil degradation, water scarcity, and pest infestations, which impact productivity and sustainability. In response to these challenges, modern agricultural system in India increasingly focuses on integrating technology to enhance efficacy and eco-friendly. One such initiative is the development of smart agricultural systems that leverage advanced technologies. This paper presents an advanced Internet of Things (IoT) based crop monitoring system designed to enhance agricultural efficacy and conciliatory. The system integrates multiple sensors to monitor critical environmental parameters, including pH levels, soil properties, and temperature. By continuously measuring these variables, the system provides real-time data to optimize crop health and growth conditions. Additionally, the paper incorporates pest control mechanisms, leveraging IoT technology to detect and manage pest populations effectively. This comprehensive approach not only ensures the optimal condition for crops but also minimizes the need for manual intervention and reduces the reliance on chemical treatments. The data collected through this system is analysed and visualized through a user-friendly interface, offering farmers actionable insights and enabling timely decision-making. The integration of these technologies aims to promote sustainable agricultural practices, improve crop yield, and reduce environmental impact, demonstrating the potential of IoT in modernizing agricultural operations. © 2025 IEEE.

关键词： Insect control

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AI-Driven Solutions with CNN and IoT for Enhancing Visual Impairments 5

AI-Driven Solutions with CNN and IoT for Enhancing Visual Im...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Prasad, M. Lakshmi Varshitha, Veeramalli Goud, A. Praneeth Bindu, R. Srinivasulu, Ch. Rao, S. Janardhana Institute of Aeronautical Engineering Dept. of Computer Science and Engineering Dundigal Hyderabad India

ISBN: (纸本)9798331522100

Visual impairment is a problem that affects millions of people worldwide, preventing them from walking independently and performing daily tasks. Some of the challenges that blind people face include crossing the street, reading, driving, and socializing. Despite the scientific community's continuous efforts to treat the eye, these solutions have not yet been found. Traditional aids such as canes are useful but limited. Get in the groove. The main focus of the project is to integrate the YOLOv5 object detection model with a convolutional neural network (CNN). Known for its fast and accurate target detection, YOLOv5, together with CNN, improves the physical ability to detect and classify targets in difficult areas with high accuracy. The eye uses the camera module to continuously monitor the environment and identify various objects in real time;the detected data is converted into speech by text-to-speech (TTS) converters such as Google Text- to-Speech (gTTS) and pyttsx3 This description allows users to get an instant description of their surroundings, unlike helping the blind, it improves their ability to navigate independently rather than just providing references, but there are also good ways to work with better and more reliable assistive technologies. © 2025 IEEE.

关键词： Convolutional neural networks

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Implementing HR Management System with an AI Powered ChatBot 3

Implementing HR Management System with an AI Powered ChatBot

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susitra, D. Pratheesh, G. Karthiga, J.D. Dhanalakshmi, K. Shamreen Ahamed, B. Sylvia Grace, J. Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

Human Resource (HR) management is one important section that increases the success of an organization to handle some important functions like employee data manipulation, attendance tracking, payroll processing, and performance evaluation. With the management of these processes becoming key to the success of small-scale startups, it poses a very big challenge for many of them to be able to manage these processes efficiently and cost efficient. By contrast, the old era of traditional HR systems was based on database driven architectures that are expensive in terms of operational cost, require storage space and complex infrastructural needs. These limitations often make traditional HR systems impossible for a startup that would prefer lightweight, affordable, and scalable solutions. Startups need robust platforms to streamline HR work streams, but often lack with financial resources for building complex database driven systems. In this paper, an AI powered HR management system is developed to simplify employee management processes for small scale startups. In this system. cache memory is used instead of traditional databases, thereby the operational cost of the system will be reduced for storage and retrieval of employee details. Automated Attendance Management enhanced with Face Recognition technology, Payroll Management, Leave Management, Feedback Submission modules are included. It is integrated with an AI powered chatbot, which facilitates a smooth interaction by the employees with query for information, submit leave requests and pull up all payroll information etc. A user-friendly dashboard for a consolidated view of employee profiles, attendance records, leave approvals, and payroll data can be served over an optimized and time efficient HR workflow. This system addresses the needs of the startups such as flexible usage, cost effective operations and improved resource management. © 2025 IEEE.

关键词： AI powered chatbot for Small-scale startups using cache memory attendance management face recognition HR Management System leave management payroll management

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