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Benefits of Internet of Things (IoT) Applications in Health care - An Overview 1

Benefits of Internet of Things (IoT) Applications in Health ...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Chaudhary, Preeti Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Nicosia Artificial Intelligence Engineering Dept. Mersin10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty OfEngineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

In a nutshell, the IoT refers to the idea of integrating high-tech technology in the form of "smart"gadgets and sensors into a single setting to gather data. There are still numerous areas in which machines cannot compete favorably with people, but data collection and analysis are undeniably two of their strongest points. The Internet of Things makes it possible to fully or partially automation data, it involves related activities, which is obviously very important for the healthcare industry. What exactly is the Internet connected medical things, and how exactly might it make a significant impact on the sector? By having to apply the concept of the internet of things (IoT) to the field of medicine, several significant advancements have become possible, including the ability to automatically control the thermostat of containers while transmitting inoculations and antibiotics, the accurate tracking of the ailments of a lot of illnesses without a need for additional meetings with a general practitioner, the correct and most useful use of meds, and a great deal more. All of this is realizable because of the capability of MedTech gadgets to carry out continuous monitoring system. The potential applications of internet of things technology in healthcare are very interesting. Let's review the ways in which this technology. © 2023 IEEE.

关键词： Medical applications

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Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video Processing 3

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kumar, M. Ashok Jasim, Laith H. Sudhakar, K. Reddy, R. Archana Saranya, N. Naga Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India School Of Computer Science And Engineering Sr University Warangal India Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%). © 2024 IEEE.

关键词： Convolutional neural networks

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On the Necessity of Collaboration for Online Model Selection with Decentralized Data 38

On the Necessity of Collaboration for Online Model Selection...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Junfan Wu, Zheshun Xu, Zenglin King, Irwin School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Pengcheng Lab China Institute Fudan University China Shanghai Academy of AI for Science China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

We consider online model selection with decentralized data over M clients, and study the necessity of collaboration among clients. Previous work proposed various federated algorithms without demonstrating their necessity, while we answer the question from a novel perspective of computational constraints. We prove lower bounds on the regret, and propose a federated algorithm and analyze the upper bound. Our results show (i) collaboration is unnecessary in the absence of computational constraints on clients;(ii) collaboration is necessary if the computational cost on each client is limited to o(K), where K is the number of candidate hypothesis spaces. We clarify the unnecessary nature of collaboration in previous federated algorithms for distributed online multi-kernel learning, and improve the regret bounds at a smaller computational and communication cost. Our algorithm relies on three new techniques including an improved Bernstein's inequality for martingale, a federated online mirror descent framework, and decoupling model selection and prediction, which might be of independent interest. © 2024 Neural information processing systems foundation. All rights reserved.

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Selective Kernel Spatial Feature Extraction based Deep Learning Approach for Identification and Classification of Archaeological Sites

Selective Kernel Spatial Feature Extraction based Deep Learn...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sajja, Sri Lavanya Alsalami, Zaid Sunil, G. Sunil Kumar, V. Mohamad Ramadan, Ghazi Malla Reddy Engineering College for Women Secunderabad India The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Dept. of Ai and Ml Bengaluru India Ahl Al Bayt University College of Mlt Karbala Iraq

ISBN: (纸本)9798350360660

Deep Learning (DL) based identification and classification of archaeological sites are being used progressively from satellite images obtained from Geographic Information System (GIS). Even though, there were promising results from the existing researches, there is still need to enhance the classification with distinct features of the satellite images. The methods found challenging to accurately or partially identify an archaeological feature especially in a complex landscape due to lack of distinction between archaeological and non-archaeological features. For this, the geo-referenced satellite images are considered in this research, which are pre-processed using min-max normalization for efficient data analysis and to remove unwanted data. The Selective kernel spatial Feature Extraction (SFE) module is used to improve the feature extraction process where the spatial features are extracted. The extracted features are then classified using Fully Convoluted Deep Neural Network (FCDNN) for effective identification and classification of archaeological sites. The results showed that the proposed SFE-FCDNN achieved better classification accuracy with 98.83%, precision of 97.41%, and an error rate of 1.71 when compared to the existing methods such as Neural Network (NN), and Backpropagation Neural Network (BNN). © 2024 IEEE.

关键词： Satellite imagery

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Implementing IoT and Deep Learning-Based Educational Systems for Assessing Practical Learning with Cloud Computing Applications

Implementing IoT and Deep Learning-Based Educational Systems...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Hardeo Kumar Thakur Minal Bafna Jagendra Singh Usikela Naresh Susmi Biswas Saggurthi Kiran School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Physics Agrawal PG College Jaipur India Department of Computer Science and Engineering(AI&ML) CVR College of Engineering Rangareddy India Department of Management Science Haldia Institute of Management MAKAUT West Bengal India Department of CSE (AI&ML) CMR Technical Campus (Autonomous) Hyderabad India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

In this study, the practical application of technologies related to IoT and machine learning in the robotics education context is examined based on the use of a robot with two proximity sensors. The trial system was designed to leverage the real-time sensor data and monitor and optimize student interactions with the robot in the course of performing such tasks as object manipulation and palletization. The sensor data is used to critically track the accuracy of student actions and help mentors provide feedback on the issue. The machine learning models used in the study, which are Recurrent Neural Network (RNN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM), are applied to analyze the sensor data and predict and classify the student's actions so that corrective measures can be taken in time. The RNN model has the highest accuracy and efficiency for the task, as it reaches the level of 96.7 % compared to 80% for KNN, 71% for SVM, and 75% for LSTM. This advantage is explained by the capability to model temporal dependencies or otherwise capture with its memory the sequences present in the sensor data. The data is transmitted securely and efficiently to the cloud platforms and back due to the implementation of the IoT, which also helps to deploy the model more effectively. Overall, it is concluded that the integration of IoT and machine learning is beneficial and transformative in educational contexts as it provides students with personalized real-time feedback. In this way, the level of proficiency of the students in dealing with robotic and IoT-based systems is significantly enhanced, preparing them to successfully address the future challenges of industry and academia in the sphere.

关键词： Support vector machines Recurrent neural networks Education Machine learning Nearest neighbor methods Robot sensing systems Data models Real-time systems Sensors Long short term memory

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LLM-based Methodology for Expanding 3D Metadata in Point Cloud 27

LLM-based Methodology for Expanding 3D Metadata in Point Clo...

引用

27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Kim, Geon-Woo Kim, Joo-Chang Department of AI Computer Science and Engineering Kyonggi University Suwon16227 Korea Republic of Contents Convergence Software Research Institute Kyonggi University Suwon16227 Korea Republic of

ISBN: (纸本)9791188428137

This paper proposes a method for expanding the metadata of three-dimensional point cloud data using Large Language Models (LLMs). Currently, point cloud data plays a crucial role in various fields such as autonomous driving and medical image reconstruction, necessitating the expansion of metadata for efficient processing. Traditionally, metadata construction has relied on manual input, which is prone to errors. In this study, we propose a method that utilizes LLMs, particularly the Llama 3.1 model, to extract the center points of each class in the point cloud data and expand the metadata by adding these center points to the annotation files. By using center points, computational costs are reduced, and the performance of segmentation and detection models based on this data is improved. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Data assimilation

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Research on Optimization Algorithm of Real Estate Enterprise Value Evaluation Based on BP Neural Network 2

Research on Optimization Algorithm of Real Estate Enterprise...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Niranjanamurthy, M. Saravanan, S. Mahaveerakannan, R. Surendran, S. Nakrani, Tulsidas Arun, M.R. Department of AI and ML BMS Institute of Technology and Management Avalahalli Karnataka Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Tamilnadu Chennai India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu Chennai India Department of Computer Application Sankalchand Patel College of Engineering Sankalchand Patel University Gujarat Visnagar India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India

ISBN: (纸本)9798350305418

Because of its professional skills and expertise, it is able to persuade both the buyer and the selling and make the transaction easier. An optimal design scheme for real estate businesses' worth appraisals is proposed here, and it is based on BPNN (BP neural network). A worth appraisal system is developed by doing an analysis of several worth indicators pertaining to real estate companies, and then the system is optimized with the use of BPNN. In the final step, an analysis of the simulation test is performed. According to the findings of the simulation, the arithmetic has a certain accuracy, which is 8.34% higher than the accuracy of traditional arithmetic. On the premise of ensuring the efficient processing of data in batches and finding the rules therein, reduce the impact of human subjectivity, improve the accuracy and scientificity of real estate appraisal, and provide certain reference for the national and local governments to reasonably regulate house prices, so as to promote the stable operation and healthy development of the real estate market. In doing so, you will be able to promote the growth of the real estate market in a healthy manner. © 2023 IEEE.

关键词： BP neural network Enterprise worth appraisal Real estate

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Context-Aware Summarization for PDF Documents using Large Language Models

Context-Aware Summarization for PDF Documents using Large La...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Akshara Ramprasad P. Sivakumar B. Tech Computer Science with specialization in AI & ML School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

In an era characterized by the overflow of textual information, the demand for effective text summarization techniques has become increasingly evident. This research study presents a novel solution to address this demand: the development of a PDF Summarizer Generative ai Platform for context-aware text and document summarization. The developed platform empowers users to generate informative summaries from lengthy textual documents. It features an interface powered by Gradio for enabling user-friendly accessibility. Equipped with essential components such as Openai's language models for text comprehension, Chroma vector enables document embedding and conversational retrieval chains for contextual understanding, where the platform offers a comprehensive solution. Users can effortlessly upload documents in various formats, including PDFs, and interactively engage with the system to obtain concise summaries tailored to their specific requirements.

关键词： Generative ai Databases Large language models Text summarization Portable document format Vectors Context modeling

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Detection of the Crown of Thorns starfish using YOLOv5 8

Detection of the Crown of Thorns starfish using YOLOv5

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Kunekar, Pankaj Patil, Pradnya Vishal Bbhagat, Babeetta Patil, Tejal H. Rajas, Neha Sawant, Pratiksha Yuvraj Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India Pillai Hoc College of Engineering Computer Engineering Rasayani India Mit School of Computing Maharashtra Pune India Sinhgad College of Engineering Department of Ai&ds Maharashtra Pune India Vishwakarma Institute of Technology Department of Ai&ds Maharashtra Pune India PVG's College of Science and Commerce Department of Computer Science Maharashtra Pune India

ISBN: (纸本)9798350367904

The invasive Crown-of-Thorns starfish (COTS) poses a significant threat to coral reef ecosystems. Early detection and removal are crucial to mitigating their impact. This research proposes a YOLOv5-based object detection system to accurately identify and locate COTS in underwater images. By fine-tuning the YOLOv5 model with a dataset of COTS images, the system can effectively detect and classify COTS with high precision and recall. The output of the system includes bounding boxes and confidence scores for each detected COTS, enabling timely intervention and removal efforts. This research contributes to the conservation of coral reefs by providing a valuable tool for monitoring and controlling COTS populations. The proposed system can be integrated into underwater drones or diveroperated devices to facilitate efficient and accurate COTS detection and removal. © 2024 IEEE.

关键词： Reefs

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Enhancing Cloud Image Retrieval Efficiency through Secure Optimization

Enhancing Cloud Image Retrieval Efficiency through Secure Op...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Balakrishna, N. Sakthivel, M. Ushasri, G.Sai Thanmai, J.Naga Kumar, K.Bharath Swamy, G.Venkaiah Mohan Babu University School of Computing Department of AI/ML Data Science AndhraPradesh Tirupati India Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence Salem Tamilnadu Sankari637304 India Sree Vidyanikethan Engineering College Department of Cse Sree Andhra pradesh Tirupati India

ISBN: (纸本)9798350359688

There is a growing trend of using public clouds for image search outsourcing. However, privacy issues arise when image datasets are directly outsourced to untrusted clouds. Recently, a number of secure image retrieval techniques have been put forth. Nevertheless, the majority of them demand input from image owners in order to create secure indexes, wasting a significant amount of their computational power. Numerous approaches are put forth to address this issue;however, their search accuracy is poor when applied to large datasets. We present the first secure image retrieval scheme in this paper that addresses these two issues at the same time to achieve more precise search results. To reduce search time and image owner costs, we allow cloud servers to locally build a reliable hierarchical index graph using the encrypted image features. Moreover, parallel construction and updating of the secure index is possible. We offer a security analysis of the suggested plan. © 2024 IEEE.

关键词： Cloud security

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