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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Subash, R. Prathaban, Banu Priya Ganeshkumar, N. Gopinathan, S. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chennai India Gitam School of Technology Gandhi Institute of Technology and Management Department of Computer Science and Engineering Karnataka Bengaluru562163 India Sri Sai Ram Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331508845

Efficient route optimization is a crucial concern in modern transportation systems, influencing travel time, resource consumption, and user satisfaction. By empowering users to select their priority parameters, such as safety or efficiency, this project enables travellers to make eco-conscious choices and select routes that minimize their environmental footprint. Real-time data integration ensures users can bypass traffic jams and accidents, reducing emissions and fuel consumption. Furthermore, the focus on safety factors helps prevent accidents, thereby fostering safer road environments. Existing systems often fall short, providing limited customization and overlooking safety and sustainability priorities. To bridge these gaps, this project introduces an advanced route planning system that empowers users to select their priority parameters, integrates real-time data for up-to- the-minute updates, and prioritizes road safety. It introduces a pioneering approach that enhances the Dijkstra Algorithm by integrating real-time accident rate and travel time data into the route optimization process. By considering these additional factors, the algorithm produces routes that not only minimize distance but also prioritize safety and time efficiency in dynamic real-world conditions. The algorithm's multi-factor optimization considers distance, accident rates, and travel times, enabling the creation of routes that balance these criteria and offer improved decision-making for users through a user- friendly interface. The algorithm's dynamic adaptability, multi- factor optimization, and practical implementation showcase its potential to revolutionize how routes are planned, leading to safer, faster, and more informed travel experiences. © 2024 IEEE.

关键词： Travel time

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Medical Imaging Analysis Using Machine Learning By Design Thinking Approach 4

Medical Imaging Analysis Using Machine Learning By Design Th...

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4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Devi, N. Ramya Revathi, K. Lekhaa, T.R. Aravindhan, K. Kavinkumar, K. Sarumathi, U. Sns College Of Engineering Department Of Information Technology Tamil Nadu Coimbatore India Reva University Department Of Computing And Information Technology Bangalore India Sns College Of Engineering Department Of Artificial Intelligence And Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384628

Breast cancer is a significant global health concern and a leading cause of cancer-related morbidity and mortality among women. Early detection and accurate prediction of breast cancer are crucial for improving outcomes and reducing the impact of this disease. The development of predictive models and risk assessment tools has become an important area of research, as they can help healthcare professionals identify individuals at a higher risk of developing breast cancer and tailor screening and prevention strategies accordingly. This introduction provides an overview of breast cancer prediction, highlighting the importance of the topic and the motivation behind predictive models. It sets the stage for a more comprehensive exploration of the subject and the various methods, technologies, and factors involved in breast cancer prediction. © 2023 IEEE.

关键词： Machine learning

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Fetal Brain Component Segmentation Using 2-Way Ensemble U-Net 7th

Fetal Brain Component Segmentation Using 2-Way Ensemble U-Ne...

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7th International Conference on data Management, Analytics and Innovation, ICDMAI 2023

作者： Halder, Shinjini Gangopadhyay, Tuhinangshu Dasgupta, Paramik Chatterjee, Kingshuk Ganguly, Debayan Sarkar, Surjadeep Roy, Sudipta Government College of Engineering and Leather Technology Kolkata700106 India Asian Institute of Technology Khlong Nueng Thailand Government College of Engineering and Ceramic Technology Kolkata700010 India Artificial Intelligence and Data Science Jio Institute Navi Mumbai410206 India

ISBN: (纸本)9789819914135

Fetal brain segmentation has been a field of interest since a long time. However, it is a challenging task as well for reasons, like blurred images due to fetal motion. Recently, deep learning has been successful in performing this task with good accuracy. In this paper, we developed 2-way Ensemble U-Net model, a convolutional neural network architecture for performing segmentation on the fetal brain image to divide it into its seven components: intracranial space and extra-axial cerebrospinal fluid spaces, gray matter, white matter, ventricles, cerebellum, deep gray matter, and brainstem and spinal cord. The fetal brain image can be obtained by segmenting it from the fetal magnetic resonance images using any of the previous works on fetal brain segmentation, which presents our work as an extension of the already existing segmentation works. The Jaccard similarity and Dice score for this task are 83% and 88%, respectively. This is higher than that returned by any of the previous models, when trained for the same task, thus showing the potential of our model in segmentation related tasks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cerebrospinal fluid

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Industry 5.0: Enhancing Human-Robot Collaboration through Collaborative Robots – A Review

Industry 5.0: Enhancing Human-Robot Collaboration through Co...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Raffik R Rithish Ramamoorthy Sathya Vaishali V Balavedhaa S Jyothi Lakshmi N Mechatronics Engineering Kumaraguru College of Technology Coimbatore India Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore India Artificial Intelligence and Data Science Engineering Kumaraguru College of Technology Coimbatore India

Modern production systems are currently changing, moving from mass production to mass customization logic, as described in Industry 5.0. The aim of Industry 5.0 is to outperform conventional manufacturing techniques by creating interconnected systems that include everything from machinery, robots, and workers to goods and consumers. This is accomplished by fully integrating the physical and virtual worlds. It focuses on building intelligent hybrid workspaces where robots share labour-intensive duties to increase productivity and safety in a range of industrial sectors. Cobots, or collaborative robots, are introduced in this. Cobots are becoming more prevalent in modern industrial systems because they combine the adaptability and competence of manual labour with the efficiency of automated systems. To maintain consistent human safety and effective collaborative productivity, communication between a human operator and the robot in these collaborative contexts must be precise and seamless. In more specific words, the framework can be used to operate an industrial robot securely and remotely in real-time while still protecting the safety of the technician. The objective of this article is to offer a clear overview of Industry 5.0's major ideas and prospective applications. It focuses on the supporting technologies of Industry 5.0 such as the Internet of Things (IoT), 6G systems, Blockchain technology (BT), Digital Twin (DT), artificial intelligence (AI), Cyber-physical systems, and Extended Reality (XR) technologies, for seamless connectivity and data exchange. Furthermore, it also elaborates on the application of Cobots in various industrial fields.

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The Utilization of Google Cloud in the Healthcare Industry

The Utilization of Google Cloud in the Healthcare Industry

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Rishikesh Kapse Palash Gourshettiwar Dept. of Artificial Intelligence and Data Science Faculty of Engineering and Technology DMIHER (DU) Sawangi(M) Wardha Maharashtra Dept. of Computer Science And Medical Engineering Faculty of Engineering and Technology DMIHER (DU) Sawangi(M) Wardha Maharashtra

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

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.

关键词： Industries Cloud computing data privacy Technological innovation Medical services Machine learning Teamwork Security Predictive analytics Medical diagnostic imaging

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Optimizing Medication Compliance through Machine Learning and IoT for Personalized Health Management 9

Optimizing Medication Compliance through Machine Learning an...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Balaji, P. Mohitha, I.K. Prashanthini, K. Sivaprakash, K. Rithick, P. Kasthuri, S. Sri Ramakrishna Engineering College Department of Electronics and Instrumentation Engineering Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Sri Ramakrishna Engineering College Department of Robotics and Automation Engineering Coimbatore India Sns College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350377972

Machine learning (ML) and Internet of Things (IoT) technologies have revolutionized the healthcare industry, particularly in the field of medication administration. Inadequate adherence to medication is a serious worldwide issue that can result in poor health and higher medical expenses. Through continuous monitoring and analysis of patient-specific parameters, such as vital signs, medication history, and lifestyle factors, the system dynamically adjusts medication schedules and dosages to optimize therapeutic efficacy while minimizing adverse effects. The system tracks medicine usage, vital signs, and ambient variables using data gathered from Internet of Things (IoT) devices like wearable devices and smart pill bottles. An ML model, trained to anticipate potential adherence problems, is then provided with this preprocessed data. Personalized interventions, like medicine reminders, educational content, and progress reports, are delivered by the Intelligent Medication Compliance System (IMAC) based on these predictions. The integration of IoT-enabled smart sensors and connectivity allows for smooth communication between patients, healthcare professionals, and medicine dispensers, permitting prompt interventions, prescription reminders, and remote monitoring. Additionally, the system makes it easier for patients and medical professionals to communicate, allowing for prompt modifications to treatment programs. Serving as an anticipatory system, the IMAC provides personalized interventions. Predicting possible adherence problems, such as missing doses or inadvertent drug interactions, is done by the model through data analysis. The proposed IMAS seeks to empower patients, personalized medication management, and eventually improve medication adherence by fusing real-time IoT data with ML-driven insights. © 2024 IEEE.

关键词： Personalized medicine

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Intelligent diagnosis of Alzheimer’s disease based on BP neural network 3

Intelligent diagnosis of Alzheimer’s disease based on BP ne...

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3rd Guangdong-Hong Kong-Macao Greater Bay Area artificial intelligence and Big data Forum, AIBDF 2023

作者： Zhao, Jia Huang, Jilin Cheng, Jiejia Cai, Shiyuan Faculty of Information Engineering Guangxi Polytechnic of Construction Nanning530007 China School of Arts and Sciences China University of Petroleum-Beijing At Karamay Karamay834000 China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300401 China

ISBN: (纸本)9798400716362

Alzheimer’s disease (AD) is a progressive neurodegenerative disease, and its onset is unknown. It usually occurs in the elderly and dies due to complications 10 to 20 years after onset. Therefore, early and accurate diagnosis of AD and mild cognitive impairment is of great significance. Firstly, preprocess the data, manually remove the data with no specific information, and then use Python to remove duplicate feature variables with the same name and data with more than 10% missing features. Fill in the remaining data with missing values and fill in the average values, leaving 16 sample labels;Then, the processed data is divided into training and testing sets to train a random forest model, and the correlation scores between data features and Alzheimer’s disease diagnosis are calculated separately. Some results are as follows: LDELTOTAL_ BL-18.81%, PACCDigit 14.30%, and combined with the existing dataset with nonlinear data features for comprehensive analysis. Based on the advantages of BP neural network nonlinear mapping function and existing feature labels, a model is established to intelligently diagnose Alzheimer’s disease. Through testing and analysis, when the number of hidden neurons is 9 and the model training method is Bayesian regularization, the solution effect is better. Some of the optimal intelligent diagnosis schemes are: FAQ: 0.0-30.0;ADAS13:0.0-54.68;AGE: 50.4-91.4;MMSE: 16.0-30.0. © 2023 Copyright held by the owner/author(s).

关键词： Diagnosis

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Cloud-Based Oxygen Management System with Seamless HTTP Protocol Integration 4

Cloud-Based Oxygen Management System with Seamless HTTP Prot...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Hemalatha, S. Ramasamy, Jayabharathi Selvi, K. Anandan, P. Meenakshi, R. Maniraj, S.P. Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India S. A. Engineering College Faculty of Management Studies Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management. © 2024 IEEE.

关键词： HTTP

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Exploring Diabetic Retinopathy Diagnosis: Insights from a Comprehensive Literature Review and Initial Fundus Image Analysis

Exploring Diabetic Retinopathy Diagnosis: Insights from a Co...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar Reddy, K Uday Malleswari, M. Kumar Reddy, P Phanindra Karimullah, Shaik Shaik, Fahimuddin Hariobulesu, Peddapullaiahgari Annamacharya University Artificial Intelligence and Data Science Department Rajampet India Sri Annamacharya Institute of Technology and Sciences Computer Science and Engineering Department India Annamacharya University AI&DS Department Rajampet India Annamacharya University ECE Department Rajampet India

ISBN: (纸本)9798350378092

This article looks at recent advances in diabetic retinopathy (DR) detection with retinal fundus and optical coherence tomography angiography (OCTA) pictures. Key studies have investigated a variety of methods, including combining Multiscale Retinex with Chromaticity Preservation (MSRCP) and Bi-LSTM networks for high accuracy, integrating the Internet of Things (IoT) with deep learning for increased sensitivity, and using Multi-Stream Deep Neural Networks to classify DR severity. Innovations like the PMNPDR system for microaneurysm detection and deep learning ensembles for OCT images have also performed well. Despite these gains, issues like as data transfer latency, model complexity, and reliance on tiny datasets remain. Overall, while current models show great potential, they require further modification and validation to assure their efficacy and applicability in clinical situations. © 2024 IEEE.

关键词： Optical coherence tomography

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