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3rd International Conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Sathishkumar, S. Santhoshkumar, S.P. Kumar, Satyam Banupriya, N. Hariharasudhan, S. Gajalakshmi, S. Hindustan Institute of Technology Department of Computer Science and Engineering Coimbatore India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi India School of Cs and It Mahatma Gandhi Central University Motihari India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore India Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331508456

Masses of people around the world are affected by Parkinson's disease (PD), a progressive neurological disorder. Timely and precise diagnosis is critical for effective management. Yet conventional diagnostic methods often lack compassion and specificity. Advances in machine learning and cerebral imaging offer promising approaches to improve PD diagnosis. This investigation investigates an ensemble machine learning move toward to enhance the identification of Parkinson's disease using cerebral imaging data. By integrating models such as decision trees (DT), support vector machines (SVM), and neural networks, the ensemble method combines their strengths to create a reliable diagnostic system. Feature extraction and preprocessing further enhance interpretability and performance. Compared to individual models, the ensemble approach significantly improves diagnostic accuracy, sensitivity, and specificity. Brain imaging aids differential diagnosis in Parkinsonism, but no Cerebral imaging methods is currently recommended for routine clinical practice despite their proven utility. Larger trial are needed to validate the medical value and long-standing cost-effectiveness of Cerebral imaging for managing patients within healthcare systems like the NHS. © 2025 IEEE.

关键词： Neurodegenerative diseases

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SMER: A Novel Medical Entity Recognition Technique based on ScispaCy(BC5CDR) and Med7 23

SMER: A Novel Medical Entity Recognition Technique based on ...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Rashmi Gupta Tushar Agarwal Aviral Mehlawat Deeksha Mathur Devendra Kumar Somwanshi Anil Kumar CSE Amity University Haryana India Amity School of Engineering and Technology Amity University Haryana India Electronics & Communication Engineering Poornima College of Engineering India Department of Computer Engineering Poornima Institute of Engineering and Technology India

ISBN: (纸本)9798400709418

Named Entity Recognition (NER) is a stream of Natural language pro-cessing playing a pivotal role in the KDD process of data mining. The Last few years have seen a sudden surge in research about NER models, however, the scope of this research is often restricted to general text and speech. This paper thus talks about a specific stream the Medical NER. In the scope of this paper, a novel technique is proposed which utilizes a combination of two state-of-the-art NER models, Sciscapcy ( BC5CDR) and Med7 to achieve optimal results. Scis-pacy is a machine learning model trained on a sizeable biomedical corpus while Med7 is a rule-based system that specializes in detecting medical concepts by utilizing its large medical pattern vocabulary. The model is evaluated on bench-mark datasets and showcases better susceptibility to medical terms in a text. The model is able to distinguish between a wide range of medical entities including drug name, disease name, drug quantity, clinical procedures, and pathogen name. The paper highlights the potential of combining rule-based systems with machine learning models in developing robust models better suited for domain NER. By fetching strengths from both Scispacy(BC5CDR) and Med7, the paper proposes “SMER – ScispaCy – Med7 Entity Recogniser”, an adaptable solution that con-tributes to more accurate and comprehensive medical entity recognition model that can further be used in automatic medical record-keeping, patient medicine orders and facilitate medical practioners.

关键词： Biomedical NLP

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EEG-based AI-BCI Wheelchair Advancement: A Brain-computer Interfacing Wheelchair System Using Deep Learning Approach

arXiv

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arXiv 2024年

作者： Paneru, Biplov Paneru, Bishwash Thapa, Bipul Poudyal, Khem Narayan Department of Electronics and Communication Nepal Engineering College Pokhara University Bhaktapur Nepal Department of Applied Sciences and Chemical Engineering Institute of Engineering Tribhuvan University Pulchowk Campus Lalitpur Nepal Department of Computer Science and Engineering Kathmandu University Nepal

This study offers a revolutionary strategy to developing wheelchairs based on the Brain-computer Interface (BCI) that incorporates Artificial Intelligence (AI) using a The device uses electroencephalogram (EEG) data to mimic wheelchair navigation. Five different models were trained on a pre-filtered dataset that was divided into fixed-length windows using a sliding window technique. Each window contained statistical measurements, FFT coefficients for different frequency bands, and a label identifying the activity carried out during that window that was taken from an open-source Kaggle repository. The XGBoost model outperformed the other models, CatBoost, GRU, SVC, and XGBoost, with an accuracy of 60%. The CatBoost model with a major difference between training and testing accuracy shows overfitting, and similarly, the best-performing model, with SVC, was implemented in a tkinter GUI. The wheelchair movement could be simulated in various directions, and a Raspberry Pi-powered wheelchair system for brain-computer interface is proposed here. © 2024, CC BY.

关键词： Fast Fourier transforms

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Advancements in Brain Disease Diagnosis for Comparing Traditional Methods and Deep Learning Approaches in Medical Imaging

Advancements in Brain Disease Diagnosis for Comparing Tradit...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Sunita Kumari Laxman Singh Moghal Irfan Pasha M. Kumaraswamy Amar Choudhary Monika Agarwal Department Computer Science and Engineering ABES Institute of Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE-AI&ML CVR College of Engineering Hyderabad India Department of CST Sasi Institute of Technology and Engineering Tadepalligudem India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

This work explores improvements in the diagnosis of brain disease through traditional vs deep learning base techniques with respect to Medical Imaging. The dataset containing 3,000 images of brain tumors was preprocessed to resize (224×224), normalize and augmentation techniques like rotation, flipping were further implemented. The dataset are splitted into70% training and 30%. testing across four deep learning models, Convolutional Neural Networks (CNN), VGG16. The models were trained to classify brain tumor images into more or less complex categories. After training VGG19 gave top accuracy of 98.6 per cent, followed byVVG16 at second place with an accuracy rate of 96.75%, while the ResNet50 and CNN were rated third and fourth in terms of their accuracies; as neither performed well with just 92.3% and 89.3%. VGG19 showed the best precision (98.8%), recall(98.5%) and F1-score(98.65) among metrics of all models utilised in this research(which ever they might be). VGG19 had low false positives and negatives which are important for accurate diagnosis as can be seen in the confusion matrices. The type of research exemplifies how deep learning can improve the accuracy and certainty within medical imaging diagnostics, particularly in difficult tasks such as brain tumor classification. This study highlights the strong possibilities deep learning could lead to transformative improvements in clinical neuroscience by employing advanced neural network architectures and meticulous preprocessing strategies.

关键词： Deep learning Training Accuracy Brain tumors Brain modeling Feature extraction Convolutional neural networks Medical diagnosis Biomedical imaging Residual neural networks

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Quantifying TRM by modified DCQ load flow method

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Revue des Composites et des Materiaux Avances 2021年第2期23卷 157-163页

作者： Nadia, Awatif Hossain, Md Sanwar Hasan, Md Mehedi Islam, Khondoker Ziaul Miah, Shahajan Department of Electrical Electronic & Communication Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Bangladesh University of Business and Technology Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Green University of Bangladesh Dhaka1216 Bangladesh Information Technology Murdoch University 90 South Street MurdochWA6150 Australia

In the integrated power system network uncertainty can occur at any time. The transmission reliability (TRM) margin is the amount of transmission capacity that guarantees that the transmission network is protected from instability in the operating state of the system. The calculation of the available transfer capacity (ATC) of the transmission reliability margin should be included in a deregulated power system to ensure that the transmission network is safe within a fair range of uncertainties that arise during the power transfer. However, the TRM is conserved as a reliability margin to reflect the unpredictability of the operation of the electric system. Besides, the system operator (SO) utilizes the TRM value during unreliability by adjusting the ATC value some amount up or down to account for errors in data and uncertainty in the model. This paper describes a technique for TRM estimation by modified DCQ load flow method considering VAR transfer distribution factor. The main focus of this study is to get a new approach to determine TRM by incorporating with ATCQ considered reactive power and sensitivity w.r.t ATC considered voltage magnitude. This technique is applied to the IEEE 6 bus system, and results are compared with previous results for validation. The technique leads to more exact and secure estimates of transmission reliability margin. © 2021 Lavoisier. All rights reserved.

关键词： Reactive power

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Adaptive Voltage Regulation System with GSM Integration and Enhanced Disaster Mitigation Strategies

Adaptive Voltage Regulation System with GSM Integration and ...

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International conference of electronics, communication and Aerospace Technology (ICECA)

作者： A. Shanmugapriya A. Sangeetha Devi M. Gomathi G. Kiruthika Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu IN Departmentof Science and Humanities Nehru Institute of Engineeringand Technology Coimbatore Tamilnadu IN Department of Computer Science and Engineering Dr.Mahalingam College of Engineering and Technology Tamilnadu IN Department of Electrical and Electronics Engineering Pollachi Institute of Engineering and Technology Tamilnadu IN

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

An innovative device called the PV-VVR is made to guard against power outages and variations in voltage in low-voltage home distribution systems. When the photovoltaic system generates enough electricity to meet or exceed residential demand, usually during the day, it disconnects from the utility grid, ensuring stable voltage levels and enhancing energy independence. This innovation reduces emissions, which not only increases power supply dependability but also advances environmental sustainability. The PV-VVR is an affordable option for incorporating renewable energy into home systems because it also helps to reduce panel tariff expenses.

关键词： Photovoltaic systems Technological innovation Renewable energy sources Low voltage Power supplies Prevention and mitigation Green products Tariffs Power system reliability Voltage control

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Deploying Faster R-CNN for Real-Time Tiger Monitoring in Wildlife Conservation

Deploying Faster R-CNN for Real-Time Tiger Monitoring in Wil...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： S. Saravanan J. Relin Francis Raj G. Vinoth Rajkumar C. Antony Vasantha Kumar N Soundiraraj K. Bharathi Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Rohini College of Engineering and Technology Kanyakumari India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This study introduces a real-time tiger monitoring system built on the Faster R-CNN deep learning framework, designed to support wildlife conservation through accurate tiger detection and tracking. The primary aim is to effectively identify and monitor tigers across various environments, using an extensive dataset drawn from Google’s Open Images Dataset and further enriched with synthetic images created by Generative Adversarial Networks (GANs). These synthetic images simulate challenging conditions, such as low lighting and uncommon camera angles, to improve the model’s robustness and adaptability in field settings. The model is initialized with a pre-trained ResNet50 backbone, utilizing transfer learning to accelerate training while ensuring precise detection. Advanced techniques such as dynamic data augmentation and hard negative mining have been integrated to minimize false positives and increase model resilience. Hyperparameter tuning, including cyclical learning rate scheduling and anchor box optimization, is applied to maximize both accuracy and recall, refining the model's ability to capture tiger-specific features under diverse conditions. To facilitate real-time monitoring, the model is deployed on NVIDIA Jetson devices, supporting edge processing without relying on cloud-based infrastructure. This deployment strategy ensures efficient data processing in remote areas, where stable network connectivity may be limited. Non-Maximum Suppression is used to reduce duplicate detections, while a Kalman Filter maintains tracking stability across video frames, ensuring seamless observation of tigers in motion. This optimized Faster R-CNN-based approach enables high-accuracy, low-latency tiger detection, equipping conservationists with reliable data on tiger presence and movement patterns. By enhancing detection and tracking capabilities, this system aims to support informed decision-making in wildlife conservation and habitat management.

关键词： Performance evaluation Adaptation models Accuracy Tracking Wildlife Transfer learning Lighting Real-time systems Environmental management Monitoring

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Cognitive Radio Technique for Blockchain-based Vehicles with IoT 5

Cognitive Radio Technique for Blockchain-based Vehicles with...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Veerraju, Sampenga Ghantasala, G S Pradeep Verma, Ekta Kumar, Ajay Akram, Shaik Vaseem Pant, Kumud Dadi Institute of Engineering and Technology Department of Electronics and Communication Engineering Andhra Pradesh Anakapalle India Chitkara Institute of Engineering and Technology Department of Computer Science and Engineering Chitkara University Punjab India Scientific Officer National Informatics Center New Delhi India JECRC University Computer Science and Engineering Rajasthan Jaipur India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

In the In the Internet of Vehicles (IoV), intelligent decision-making is performed by objects with cognition. by comprehending both the social and physical worlds, Cognition Radio Network (CRN) is seen as a workable option. The existence of dangerous devices (MD) further impairs network performance, although For services to be improved and safety metrics to be driven, spectrum availability and information exchange among vehicles are crucial. Recently, a blockchain approach has been added to CRN-based IoV to stop data modification from these Entities and enable the cars to monitor both legal and illegitimate network activity. In this paper, we provide security to IoV throughout spectrum sensors and details transfer using CRN by sensing the paths through with a decision-making technique called Method for Order Personal taste by Similarity to a Solution (topsis (TOPSIS), a technique that inspires the confidence of its Behavioral Users (CU) by analysing certain predefined attributes. The network also maintains a blockchain to track every action involving information that has been saved. Using different frequency sensing and security settings in comparison to an IoV baseline solution, the suggested method is thoroughly evaluated against a number of security criteria. Numerous simulations indicate that when compared to the baseline system, our suggested approach is around 70% more effective at identifying malicious nodes and DoS threats. © 2022 IEEE.

关键词： Internet of things

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Development of Mutal Recognition of Education Approach in Advanced computer-Oriented engineering Technologies in the Baltic Region 64

Development of Mutal Recognition of Education Approach in Ad...

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64th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical University, RTUCON 2023

作者： Kunicina, Nadezhda Rassolkin, Anton Bruzgiene, Rasa Plonis, Darius Caiko, Jelena Riga Technical University Institute of Electrical Engineering and Electronics Feee Riga Latvia Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia Kaunas University of Technology Faculty of Informatics Department of Computer Sciences Kaunas Lithuania Vilnius Gediminas Technical University Department of Electronic Systems Vilnius Lithuania

ISBN: (纸本)9798350317732

The continued improvement and development of study programmes content for mutual recognition of study programmes among the largest technical universities in the Baltic region provides a very good opportunity for regional development as it brings a good advancement in academics and research at the involved universities, allows for more flexible and reliable collaboration with industrial partners, and provides an opportunity to develop new technologies and implement them for regional support. The goal of the development is to promote the involvement of students in the design of adaptive systems by providing them with the necessary set of skills that will allow them to immediately engage in the design of innovative adaptive systems and successfully solve technological challenges that have been identified through the cooperation of educational and research institutions. The adoption of an innovative educational strategy will aid in the creation of new technology and creative solutions even before students begin their studies. Collaboration between academic personnel and industry will help students build their abilities. © 2023 IEEE.

关键词： Students

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Adaptive Real-Time Vehicle Usage Patterns with Cloud-Enabled Bayesian Networks

Adaptive Real-Time Vehicle Usage Patterns with Cloud-Enabled...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Y M Blessy Raveendra N Amarnath D. Antony Joseph Rajan R Dillibai. S.T Aarthy. T. R. GaneshBabu Department of Electornics and Communication Engineering R.M.K. Engineering College Chennai Tamil Nadu India Mphasis Campbell Dr Melissa Texas USA Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In this research, we provide a new real-time method for managing vehicle utilization patterns using cloud-enabled Bayesian networks (BN). Traditional solutions often employ static models and batch processing to account for the ever-changing nature of vehicle utilization. The objective is to use cloud-enabled BN to improve real-time vehicle utilisation monitoring, optimise fleet management, and make better decisions. To improve the precision and responsiveness of consumption predictions, businesses are increasingly turning to cloud computing, enabling continuous, real-time data stream processing. To weather, traffic patterns, and historical data, among other elements that impact vehicle utilization, BN provide a strong foundation for modelling complicated connections. To increase the accuracy of its adaptive use pattern forecasts, the system updates probabilistic associations in real time using incoming data. Fleet managers may optimize resource allocation, improve service delivery, and achieve overall operational efficiency via incorporating cloud computing, enabling scalable and efficient processing. In metropolitan areas, where use patterns might change quickly, this method helps manage vehicle fleets more adaptively and responsively. It showcases the practicality and advantages of our method in simulations and case studies, showing how it can change the game for fleet management techniques. It highlights the revolutionary power of BN offered by the cloud in improving the efficiency and responsiveness of fleet operations.

关键词： Cloud computing Adaptation models Adaptive systems Computational modeling Biological system modeling Transportation Real-time systems Bayes methods Resource management Vehicle dynamics

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