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Diving into Brain Complexity: Exploring Functional and Effective Connectivity Networks 18

Diving into Brain Complexity: Exploring Functional and Effec...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Siddique, Abu Bakar Khalid, Hassan Bin Ali, Raja Hashim University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan

ISBN: (纸本)9798350328172

The human brain's intricate functions are under-pinned by a vast network of synapses that enable chemical impulses between neurons. Neuroscientists employ two key approaches, functional and effective connectivity, to understand the brain's complexity, focusing on its operations, cognition, and behavior. While both methods utilize graph theory for network analysis, functional connectivity, which investigates associated brain activity, has seen more substantial research efforts than effective connectivity, which examines information processing's inter-regional impacts. In this research paper, we aim to present an extensive examination of the emerging discipline that combines graph theory with the analysis of brain characteristics, shedding light on how these characteristics arise from the interactions among different groups of neurons. Our primary focus lies in exploring the diverse cognitive and neurological applications that leverage functional Magnetic Resonance Imaging (fMRI) as a tool. Additionally, we offer a comprehensive overview of the methods employed to construct brain networks based on functional and efficient connections. Throughout the discussion, we emphasize the advantages and limitations associated with these approaches. © 2023 IEEE.

关键词： Graph theory

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Early Prediction of Cardiovascular Diseases Using Feature Selection and Machine Learning Techniques 6

Early Prediction of Cardiovascular Diseases Using Feature Se...

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6th IEEE International Conference on Communication and Electronics Systems, ICCES 2021

作者： Rashme, Tamanna Yesmin Islam, Linta Jahan, Sohely Prova, Ayesha Aziz Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Barishal Dept. of Computer Science and Engineering Dhaka Bangladesh Central Women's University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665435871

cardiovascular disease is one of the most important diseases that affects the heart and blood vessels. The loss of lives is mostly linked to a lack of early disease detection, and a preemptive prediction of cardiovascular disease risk will greatly alleviate the situation. Due to the increasing amount of data growth in the health care industry, therefor Machine Learning techniques predict the disease depends on the severity of the patient's side effect. This research work proposes a model to perform early prediction of cardiovascular disease by using different machine learning algorithms, which are used for different prediction purposes. For feature selection purposes, Random Forest algorithm is used to select suitable attributes for the prediction process. The proposed model is assessed based on evaluation metrics;accuracy, precision, recall (sensitivity), f1- score, and specificity. In this exploration of predicting cardiovascular disease, the XGBoost machine learning classifier accomplished a higher rate of accuracy 75.10%. Also, this model provides a higher rate for other evaluation metrics for all the evaluation metrics 76.64%, 69.88%, 79.32%, 78.16%, and 72.84% for precision, sensitivity, specificity, and f1-score, respectively in the case of early cardiovascular diseases prediction. © 2021 IEEE.

关键词： Forecasting

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Enhancing data Transmission Reliability in Cluttered Environments using Adaptive Wireless Sensors 2

Enhancing Data Transmission Reliability in Cluttered Environ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Dinesh, R. Vanathi, P. Susitha, N. Priya, P. Ambikavathi, C. Ali, Syed Sumera Excel Engineering College Department of Ece Namakkal India Adhiyamaan College of Engineering Department of Information Technology Hosur India School of Arts and Science Vinayaka Mission's Chennai Campus Vinayaka Mission's Resarch Foundation Du Dept. of Computer Science India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur India School of Arts and Sciences Vinayaka Mission Research Foundation Department of Computer Science Tamilnadu Paiyanoor India Csmss Chh Shahu College of Engineering Electronics and Computer Engineering Department Maharashtra Aurangabad India

ISBN: (纸本)9798350369960

Wireless data transmission in complicated terrains like cities or deep woodlands sometimes raises dependability concerns. These issues are caused by barriers, multipath fading, and signal interference. Our study uses adaptive wireless sensors to improve data transmission reliability to solve this problem. These sensors are meant to dynamically modify gearbox parameters in real time, unlike standard sensors. This distinguishes them. The study uses key research methods. Dynamic Path Selection chooses the best transmission paths for data packets. Adaptive Modulation and Coding (AMC) alters modulation and coding rates based on real-time environmental information. The study also uses Spatial Diversity using Beamforming. This method concentrates energy using multi-antenna systems, increasing signal intensity and decreasing interference. Practical experiments in clutter-filled regions showed improved gearbox reliability. Compared to standard systems, adaptive sensors reduced packet loss by 45% and increased throughput by 30%. Adaptive wireless sensors increase connection reliability in difficult conditions. These sensors may be utilised for many applications, including urban IoT frameworks and autonomous navigation. © 2023 IEEE.

关键词： Cluttered Environments data Transmission Dynamic Path Selection Wireless Sensors

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Exploration and Evaluation of Machine Learning Methods for Detecting Oil Spills in Open Seas

Exploration and Evaluation of Machine Learning Methods for D...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Sibai, Fadi N. Sibai, Ahmad El-Moursy, Ali GUST Engineering & Applied Innovation Research Center Gulf Univ. for Science and Tech. West Mishref Hawalli Kuwait Univ. of South Florida Computer Science & Engg. Dept. TampaFL United States Univ. of Sharjah Comp. Engg. Dept. Sharjah United Arab Emirates

ISBN: (纸本)9798350369106

Detecting oil spills in open seas is important for the early containment of the pollution diffusion and avoidance of further ecological catastrophes. Herein, we explore and assess the application of several machine learning (ML) methods and image processing techniques for the detection of oil spills in images of open seas captured at the elevation of planes, helicopters, or drones. The image dataset used contains 24 clean sea images and 24 dirty sea images with oil slicks. The machine learning methods experimented with include neural networks, SVM, random forest, decision tree, naïve Bayes, AdaBoost, and logistic regression. We also evaluated these ML methods with and without the application of Sobel Feldman edge detection and Vintage filtering image processing techniques. Results indicate the superiority of the in and neural networks methods over the other explored ML methods, achieving perfect and near-perfect classification, respectively. © 2024 IEEE.

关键词： Oil spills

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Non-intrusive Emotional Development Monitoring of Pre-Primary Children Using Image Processing and Machine Learning Model

Non-intrusive Emotional Development Monitoring of Pre-Primar...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： Anilloy Frank Mrinal Raj Dept. of Electronics and Communication Engineering Presidency University Bangalore Dept. of Computer Science Engineering Presidency University Bangalore

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

The development of emotions begins in a child from infancy and continues into adulthood. The pre-primary experiences are unique to each child and the learning environments in early life often serve as the source for behavioral outcomes as they grow. The study is mainly based on detecting the emotions of children to have an insight of their behavior with respect to the external environment. The approach aligns with the Goal-3 Good health and well-being of the United Nations Sustainable Development Goals. It provides with the behavior pattern in better understanding of the child emotions both by parents and teachers of the student. Emotion recognition model using image processing enable understand how the emotions of children change and affects their lives with the human interaction. The model uses Transfer Learning model. Many students sit in front of their computers and can easily analyze their interaction with the system. The technology is evolving and using this model, can analyze human interaction and their behavior patterns in a given particular situation.

关键词： Training Pediatrics Analytical models Technological innovation Accuracy Computational modeling Image processing Transfer learning Sustainable development Monitoring

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TotCareBot: A Comprehensive Approach Towards Enhancing Early Childhood Education through Robotics

TotCareBot: A Comprehensive Approach Towards Enhancing Early...

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International Conference on Circuits, Communication, Control and Computing

作者： Manjula R Chougala Sushma B Akshata S Bhayyar Abhay Bhandarkar P N Pavithra Dept. of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Robots have emerged as versatile tools with significant potential to enhance teaching and learning environments, including classrooms, laboratories, play homes, crèches, and even at home. Their engaging nature can captivate young children, boosting motivation and interest. This paper proposes a methodology for an Educational Robot (ER) designed for play homes. The ER is capable of moving around the classroom, monitoring children, analyzing their emotions, responding appropriately, and alerting the teacher when necessary. The proposed system aims to leverage the positive impact of expressive behaviors in robots, such as storytelling and pampering. Our findings indicate that expressive robot narration is as effective as that of a human displaying similar behaviors and even surpasses a static, inexpressive human narrator. Furthermore, the robot excels in conveying educational content, although its limited emotional expression slightly reduces its effectiveness in narrating emotionally rich stories. This research underscores the potential of ERs to create dynamic and interactive learning experiences for young children, fostering both their cognitive and emotional development.

关键词： Educational robots Education Laboratories Circuits Boosting Behavioral sciences Robots Erbium Monitoring

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Enhancing Security with Real-Time Animal Intruder Detection: A Deep Learning Approach for Automated Alerts and Notifications

Enhancing Security with Real-Time Animal Intruder Detection:...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Naveen Kumar M M Nixon Emmanuel J S. Shanthini Dept. of Computer Science and Engineering St. Joseph’s College of Engineering OMR Chennai

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This paper develops an animal real-time intrusion detection system based on the advanced techniques of deep learning. The proposed system makes use of CNN with transfer learning and has achieved an accuracy level of more than 90 percent in distinguishing among various animal species. This high degree of precision will allow any probable intrusion to be detected with full reliability and in real-time at the monitored site. The system is continuously processing video feeds, where animals are being identified based on unique features. The solution is to fit smoothly in existing security architecture and increase safety for agricultural fields, residential sites, and wildlife management. It offers immediate alarms whereby quick actions can be done to decrease the risks made by animal invasions. The real-time capability minimizes responses between detection and intervention. And aside from the accuracy, the whole system is very adaptable-very flexible-and therefore applicable to other settings. Be it on small farms in the countryside, suburban environments, or in large settings for wildlife conservation, such a system would scale effectively to meet diverse needs that different environments might have. With such design, it is easy to customize for specific user needs, providing a flexible yet versatile solution for animal monitoring and intrusion prevention. What follows are the architecture and methodologies of the system itself-evaluation in real-world conditions.

关键词： Deep learning Accuracy Wildlife Transfer learning Streaming media Real-time systems Safety Security Reliability Monitoring

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A simplified hybrid model for state of charge estimation using 1-D Kalman filter and Coulomb counting

A simplified hybrid model for state of charge estimation usi...

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Annual Conference of Industrial Electronics Society

作者： Kritika Garg Rahul Sarker Aaqib Ahmad Mehakpreet Kaur A. V. Ravi Teja Dept. of Electrical Engineering IIT Ropar Rupnagar India Dept. of Computer Science Engineering PU Patiala India

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This document is a model with an accurate estimation of the battery’s State Of Charge (SOC), which is pivotal for prime performance and optimal lifespan of the rechargeable batteries. Though traditional methods, like Open Circuit Voltage (OCV) and Coulomb Counting (CC), are advantageous, they suffer from several limitations, such as sensitivity to temperature changes, frequent calibration, ageing effects, and measurement inaccuracies. This paper proposes 1-D Kalman Filter (1-D HKF) with an approach that incorporates the OCV and CC results into the state space model for better and more accurate SOC estimation. So, the model is called 1-D HKF or Hybrid Kalman filter that, influenced the long-term stability and estimation accuracy of CC and the accuracy of OCV at specific points to get over the individual limitations. The model trained by 1-D HKF adapts itself and adjusts the gain based on the uncertainties of each measured value to ensure optimal value information. So, We evaluate the performance of the 1-D HKF versus the traditional methods Kalman Filter using examples and exploratory data. The proposed approach illustrates significantly improved accuracy and robustness across various operating conditions, including varying temperatures and discharge rates.

关键词： Temperature measurement Adaptation models Accuracy Voltage measurement Estimation Mathematical models data models State of charge Kalman filters Integrated circuit modeling

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Identification of Affective States from Malayalam Audios Based on Valence Arousal Prediction

Identification of Affective States from Malayalam Audios Bas...

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Brain computer Interface & Healthcare Technologies (iCon-BCIHT), International Conference on

作者： Talit Sara George Jini George Pournami P N Dept. of Computer Science and Engg. National Institute of Technology Calicut India Dept. of Computer Science and Engg. Providence College of Engineering Chengannur India

ISBN: (数字)9798331540067

ISBN: (纸本)9798331540074

Affective state plays a key role in emotion recognition, influencing various applications from mental health evaluation to human-machine interaction. These states can be described using two primary aspects: valence, which indicates the pleasantness of the speech, and arousal, which relates to the intensity of the emotion. This study aims to automate the prediction of valence and arousal values from speech data, addressing the limitations of manual annotation and human error. The IIITSAINT-EMOMDB dataset, created from Malayalam films, uses the Circumplex Model of Affect to map emotions such as happy, sad, angry, and calm. The approach leverages machine learning models, including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), for prediction. Experimental results demonstrate that the traditional models like SVR and ANN are outperformed by the CNN model, which achieved a mean squared error (MSE) of 0.3 for valence and 0.1 for arousal. These findings highlight the power of automated emotion recognition in large-scale audio datasets, offering promising applications in affective computing and psychological analysis.

关键词： Emotion recognition Affective computing Accuracy Annotations Computational modeling Machine learning Predictive models data models Convolutional neural networks Cultural differences

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Efficiency Redefined: The Document Scanner App for Precise Entity Identification with Customized NER

Efficiency Redefined: The Document Scanner App for Precise E...

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Delhi Section Conference (DELCON), IEEE

作者： Sajid Javid Mohammad Saim Saiful Islam Dept. of Computer Science & Engineering Indraprastha Institute of Information Technology Delhi Delhi India Dept. of Computer Science & Engineering University of Cincinnati Cincinnati OH USA Dept. of Computer Engineering Zakir Husain College of Engineering & Technology Aligarh Muslim University Aligarh India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

In the era of rapid advancements in technology, the efficient digitization of paper-based documents remains a crucial challenge across various domains. The traditional approach to document scanning often struggles with accurate extraction of critical information from unstructured or diverse document formats. This paper presents the development of a web-based document scanner application that leverages Named Entity Recognition (NER) to extract entities from scanned business cards efficiently. The study aims to address the challenge of digitizing and organizing physical documents, with a focus on precise entity extraction. The methodology involves creating a custom dataset of scanned business cards, preprocessing the data, and training an NER model using machine learning techniques. The application is built using the Flask framework and integrates OpenCV for text extraction and SpaCy for NER. The key findings include the model's high F1 score in identifying entities such as names, organizations, designations, emails, and contact details. The application streamlines document digitization by automatically extracting and tagging relevant metadata, minimizing the need for manual data entry. This research contributes to enhancing document management and demonstrates broader applicability for other document types apart from business cards. The implications highlight improvements in workflow automation, data accuracy, and the potential for integrating with larger document management systems.

关键词： Training Accuracy Standards organizations Pipelines Named entity recognition Manuals Machine learning Tagging data mining Character recognition

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