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Support Vector machines Based Predictive Seizure Care using IoT-Wearable EEG Devices for Proactive Intervention in Epilepsy

Support Vector Machines Based Predictive Seizure Care using ...

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Computer, Communication and Control (IC4), international conference on

作者： Porandla Srinivas M. Arulprakash M. Vadivel N Anusha G. Rajasekar C. Srinivasan Department of Information Technology Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chenagalpattu Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad Telangana India Department of IoT School School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation (DU) Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Epilepsy, a neurological illness that causes repeated seizures, can interfere with everyday life and needs prompt treatment. Internet of Things (IoT) wearable Electroencephalogram (EEG) devices and Support Vector machines (SVM) for predictive analytics are used in this study to suggest a unique strategy for proactive seizure treatment. Wearable EEG devices feed real-time brain activity data. It uses SVM, a strong machine learning method, to build a prediction model using historical EEG data to identify and predict seizures. The prediction software analyses EEG data in real-time to detect pre-seizure patterns and initiate preventive treatments. The seizure prediction method uses SVM’s capacity to handle high-dimensional data and catch complicated patterns to improve accuracy and reliability. Healthcare practitioners and caregivers may get timely warnings and react efficiently thanks to the IoT infrastructure’s seamless connectivity between wearable devices and a centralized monitoring system. It discusses the ethical and privacy issues of installing such a system, stressing user permission and data protection. Pilot investigations show promising prediction accuracy and reaction time. SVM with IoT-wearable EEG sensors for predictive seizure care offers a forward-looking technique for enhancing epilepsy patients’ quality of life by enabling individualized and proactive treatments.

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Yuj: Solace Amidst Distress

Yuj: Solace Amidst Distress

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Artificial Intelligence and machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： Karthiyayini J Sachin Mengji Satyajeet Kumar Shashi Kant Kumar S Shreyas N Information Science and Engineering (VTU) New Horizon College of Engineering (VTU) Bengaluru India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

A comprehensive software called "Yuj: Solace Amidst Distress" uses the age-old practice of yoga to achieve overall well-being. This project is comprised of four transformative modules designed to promote physical, mental, and emotional equilibrium. The word "Yuj" in Sanskrit means "yoga." A fundamental pillar is the "Yoga Postures" module, which hand overs users with animated movies that walk them through fundamental yoga poses. This module offers specialized techniques for a variety of needs and is further customized to address certain health concerns, such as orthopedics, diabetes and blood pressure, oncology, and women's health. The "Meditation" module offers a variety of Pranayamas and meditation techniques to take users on an inner exploration journey. This module not only promotes mindfulness but also cultivates mental clarity and memory enhancement. In the "Binaural Tones" module, users can immerse themselves in a sonic landscape of healing binaural beats. The "Time Management Techniques" module empowers users with effective tools for optimizing productivity and managing time efficiently, this module equips individuals with practical skills to steer their every day lifewith focus and purpose. "Yuj: Solace Amidst Distress" unites tradition with technology, offering a synergistic approach to well-being. Through its diverse modules, the platform invites users to embark on a transformative journey, fostering a harmonious union of mind, body, and spirit.

关键词： Productivity Hypertension Obesity Pain Pulmonary diseases Human factors Back

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Experimental Evaluation of BigData Analytics to Improve Communication Security and Privacy Enrichment using Cybersecurity Logics

Experimental Evaluation of BigData Analytics to Improve Comm...

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Emerging Trends in engineering and Medical Sciences (ICETEMS), international conference on

作者： S Agnes Shifani M Swarna S Sangeetha T Jayakumar Rajkumar Chadge Ahmad Abdelhafiz Ali Samhan Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of ECE Dr. MGR Educational and Research Institute Chennai Tamil Nadu India Department of IT Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Department of Mechatronics Engineering KCG College of Technology Chennai Tamil Nadu India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

The massive volume of data generated by the rapid expansion of digital communication necessitates a sufficient degree of security and privacy. Big data analytics is demonstrated to be helpful in enhancing privacy and security of communications in one such study. The aim is to use advanced analytics to identify and mitigate security risks from large databases. In processing large-scale disorganized and structured or partially organized information, the major benefits of the proposed approach, BigData enabled Communication Security Protocol (BCSP), are cross-verified against an existing approach, Secured BigData Analyzer (SBA). Enhancing utilizing big data analytics, communication security is employed to analyze data obtained from a company's network, user behavior data, or even threat-related information. The data analyzed is then utilized to improve the digital communication security of the business using the conclusions the analysis produces. This study offers an integrated approach to security and privacy, including pre-processing and feature engineering, and presents a framework for designing effective machine learning systems that integrate data collecting.

关键词： Data privacy Privacy Protocols Databases machine learning Big Data Digital communication Market research Security Logic

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Optical Character Recognition (OCR) in Handwritten Characters Using Convolutional Neural Networks to Assist in Exam Reader System

Optical Character Recognition (OCR) in Handwritten Character...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： P L Lekshmy S. Velmurugan Indra Kumari S. Kayalvili B. Teja Sree P. Karthik Kumar Department of Computer Science and Engineering LBS Institute of Technology for Women Kerala India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India Department of Machine Learning Data Research Applied AI Korea National University of Science and Technology (UST) Daejeon South Korea Department of Artificial Intelligence Kongu Engineering College (Autonomous) Erode Tamilnadu India Department of Information Technology S.R.K.R. Engineering College Chinaamiram Bhimavaram Andhra Pradesh India Department of Computer Science and Engineering (Cyber Security) Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

This work aimed to develop a character recognition method to facilitate the correction of answer cards in the Multiprova software through the development of a response card analysis flow that would culminate in the recognition of letters written by students and automatic correction of the tests. To isolate and identify the answers written on the answer cards, image segmentation techniques were used based on fixed marks printed on the cards. To recognize the letters and numbers written on the cards, trained three convolutional neural networks (for digits, letters and true or false). The results achieved (98.84% accuracy for digit CNN, 98.38% accuracy for letter CNN and 99.89% accuracy for true or false CNN) point out a great average success rate.

关键词： Training Image segmentation Image recognition Codes Optical character recognition Neural networks Streaming media

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Industrial informatics in optimizing software-defined vehicles for logistics

Industrial informatics in optimizing software-defined vehicl...

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Innovations in High Speed Communication and Signal Processing (IHCSP), international conference on

作者： Bhavuk Samrat Thomas P K Santosh Kumar Benila A Rishabh Bhardwaj M. Vigenesh Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Adjunct Faculty School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka India AI & Ds Vishwakarma Institute of Technology Pune India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Integration of industrial informatics and software-defined vehicle optimization is revolutionizing logistics. This study discusses how this integration may improve logistics safety, sustainability, and efficiency. Logistics are the backbone of international companies; thus, they must run smoothly. Industrial informatics and optimization studies yielded new findings. This powerful technology combines advanced machine learning algorithms, data analytics, and real-time monitoring to change logistics. Data-driven decisions are the foundation of industrial informatics. This research determines the best data analytics methods for gas and labor savings. Real-time monitoring keeps logistical operations adaptive to changing circumstances, while predictive maintenance and energy-efficient driving boost system efficiency. Nearest Neighbor, Branch and Bound, Particle Swarm Optimization, Monte Carlo Simulation, and Genetic Algorithms reveal that the suggested technique is superior. This study shows the indicated approach's considerable effect. Its cheaper pricing, faster delivery, and lesser carbon emissions make it better than conventional solutions. Logistics operations are safer with the proposed method, which has a higher safety score and fewer accidents and incidents. The suggested method benefits from its flexibility, scalability, and adaptability, which are crucial in a fast-changing firm. Expense savings: Save $250,000 using the proposed technique. The new method results in a time savings of 15.79%. The suggested approach reduces carbon emissions by 3,000 kg (26.09%). The suggested method improves safety ratings by 10—11.36%.

关键词： Data analysis machine learning algorithms Scalability Carbon dioxide Real-time systems Safety Informatics Sustainable development Optimization Logistics

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PV Output Power Prediction Using Regression Methods 2nd

PV Output Power Prediction Using Regression Methods

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2nd international conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Aboumadi, Abdulhameed Arslan, Hilal Department of Electrical and Electronic Engineering Ankara Yıldırım Beyazıt Univesity Ankara Turkey Department of Software Engineering Ankara Yıldırım Beyazıt University Ankara Turkey

ISBN: (纸本)9783031509193

Over the past few years, the general public has become increasingly aware of climate change and the role of greenhouse gas emissions, especially carbon dioxide, in contributing to it. Therefore, individuals, businesses, and governments around the world have taken steps to reduce their emissions. One of these steps is to increase adoption of renewable energy sources, such as solar power which provides clean energy, in addition to low building and operation costs and minimal maintenance requirements. Accurate estimation of solar energy production is crucial to ensure the stability of electrical networks as the transition to renewable energy sources such as solar power increases. In this study, machine learning regression algorithms including artificial neural networks, support vector regression, regression trees, and k-nearest neighbor are performed to estimate hourly solar energy production of one month using historical production data and various meteorological parameters. The models are optimized using grid search and validated using K-fold cross validation method. The performance of the models is evaluated using the RMSE, MAE, and R2 evaluation metrics. The results showed that the k-nearest neighbor regression model achieves the highest performance with an R2 score of 0.9715. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning regression Photovoltaic Power prediction

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Non-Linear Time Sequence Problems Based on GA-BP Neural Network Prediction Model

Non-Linear Time Sequence Problems Based on GA-BP Neural Netw...

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Image Processing and Computer Applications (ICIPCA), IEEE international conference on

作者： Teng Wang Yuxuan Chen College of Technology and Data Yantai Nanshan University Yantai China School of Software Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In the fields of image recognition and computer vision, data mining and predictive analytics, BP neural network as a kind of simple structure and powerful model has been widely concerned by scholars, and the improvement of BP neural network is very important. In this paper, we used genetic algorithm to optimize BP neural network, and optimize the selection of weights and thresholds of BP neural network through the characteristics of local optimization of genetic algorithm. Compared with the single BP neural network, the root mean square error (RMSE) of the optimized BP neural network had been reduced by 0.82%, the mean absolute error (MAE) had been reduced by 0.85%, and the coefficient of determination had been improved by 1.34%• The experimental results have shown that the optimization of BP neural network with genetic algorithm is more advantageous than the single BP neural network, and the optimized BP neural network has a strong ability to deal with the complex problems with high dimensions and multi-objectives.

关键词： machine learning algorithms Image recognition Image coding Neural networks Predictive models Genetics Root mean square Predictive analytics Optimization Genetic algorithms

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GAN-Based Intrusion Detection System for IoT Services

GAN-Based Intrusion Detection System for IoT Services

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Computer Network Technology and Electronic and Information engineering (CNTEIE), international conference on

作者： Jingjing Zhang Kai Zhang Libo Ma Beijing Institute of Tracking and Communication Technology Beijing China

ISBN: (数字)9798331524432

ISBN: (纸本)9798331524449

With the great development of modern technology, the Internet of things are gradually changing the way we live. Many industry experts believe it will be the biggest technological shock since cloud computing because plenty of data can be collected, stored and analyzed than ever before. Thus, as an important technology, IoT services have been extensively used in not only our daily life but also Industry and agriculture. It is worth nothing that attackers are increasingly turning their attention to the public or personal IoT services. Hence, an effective detection scheme should be conducted as soon as possible. Generating Adversarial Network (GANs) can dispose the dataset of real life which is complex high-dimensional distribution, making it very suitable for the detection of abnormal state. However, at present, GANs commonly used for pattern recognition and speech recognition but rarely used in the field of information safety. In this paper, we used the an improved GANs model try to perform intrusion detection in IoT services. In the experiment, we improved the architecture of GANs to achieve the most advanced performance and shorten the training time when being compared with the present published other methods.

关键词： Training machine learning algorithms Intrusion detection Stochastic processes Speech recognition Turning NSL-KDD software Safety Internet of Things

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Optimizing Cloud IoT Applications with machine learning Driven Hybrid Key Management Protocols for WSN Performance

Optimizing Cloud IoT Applications with Machine Learning Driv...

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international conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Simhadri Mallikarjuna Rao B. Adinarayana Kasani Vaddikasulu Satya Prasad Dontamsetti Peesapati Surya Venkata Ranganadh Research Scholar Department of Computer Science and Engineering Aacharya Nagarjuna University Guntur Andhra Pradesh India R &D Department of EEE & CSE Ravi Technology Vishakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Eluru college of engineering and technology Eluru Andhra Pradesh India Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Bhimavaram Andhra Pradesh India Software Engineer USA

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

AI-IoT integration is significant, changing the future of networking, particularly in terms of performance and security enhancements. The collection and transmission of sensitive data across secure networks is crucial for the IoT, which combines embedded systems and Networks of Wireless Sensors (WSNs). Ensuring the efficacy and security of these networks is crucial. In this research, a novel hybrid key Hybrid management protocol for Internet of Things applications using WSNs is proposed. The maximizes network performance of efficiency and security by utilizing cloud services and machine learning, specifically XGBoost. The suggested protocol creates a secure communication framework for WSNs by combining symmetric and asymmetric key management strategies. Furthermore, the centralized functions and scalability of cloud-based services enable network-wide updates and effective key distribution. The protocol offers anomaly detection, intelligent network load balancing, dynamic key management, predictive key distribution, and intelligent network load balancing by employing XGBoost-based machine learning algorithms. By using network behavior modelling and historical data analysis, the system forecasts the optimal times and locations for important updates, reducing overhead and enhancing security. Additionally, anomaly detection based on XGBoost improves the protocol's ability to recognize anomalies and potential security risks. This hybrid architecture creates a highly flexible and effective protocol for IoT-enabled WSNs by combining cloud integration, centralized key management, and AI-driven intelligence. The proposed approach performs better on the network due to proven methods and algorithms.

关键词： Support vector machines Wireless sensor networks Cloud computing Protocols Accuracy machine learning algorithms Scalability Internet of Things Security Anomaly detection

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A multimodal data-driven framework and system for evaluating the ideological and political education of courses under the Chinese new engineering science 24

A multimodal data-driven framework and system for evaluating...

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Proceedings of the 2024 2nd international conference on Information Education and Artificial Intelligence

作者： Jun Zhang Shihan Zhang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (纸本)9798400711732

To address the existing challenges in the teaching ideological and political assessment system within Chinese universities, particularly in the context of New engineering disciplines, this study proposes a new six-dimensional teaching ideological and political evaluation framework. This framework is designed to provide a comprehensive, systematic, and scientifically grounded approach to evaluating the integration of ideological and political education into engineering curricula. By focusing on six key dimensions—socialist core values, Chinese excellent traditional culture, professional ethics and engineering ethics, craftsmanship and creative awareness, teamwork and communication skills, and social responsibility and patriotism—the framework ensures that ideological and political education is not only embedded in the curriculum but also effectively assessed and continuously improved. To operationalize this framework, a teaching ideological and political evaluation system was designed and implemented, leveraging multimodal data-driven algorithms. This system integrates diverse data types, such as text, images, audio, video, and behavioral data, to provide a holistic and objective assessment of teaching effectiveness. The use of advanced data mining and machine learning techniques enables the system to analyze complex datasets, extract meaningful insights, and generate actionable feedback for educators and administrators. The introduction of this pedagogical and political evaluation system provides a scientific and efficient evaluation system for educational administrators to assess potentially valuable ideological and political education in the teaching and learning process and apply it to the management of ideological and political work.

关键词： Curriculum Ideological and Political Education

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