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Communication, Control and Intelligent Systems (CCIS)

作者： P. Balaji D. Devasena P. Rithick T. Karthi K. Rishmitha R. Roopa Dept. of. Electronics and Instrumentation Engineering Sri Ramakrishna Engineering College Coimbatore Tamilnadu Dept. of Robotics and Automation Engineering Sri Ramakrishna Engineering College Coimbatore Tamilnadu Dept. of Automobile Engineering Kongu Engineering College Erode India Dept. of Artificial Intelligence and Data Science Arjun College of Technology Coimbatore Tamilnadu

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

The detection of Glial Fibrillary Acidic Protein (GFAP) is a crucial biomarker for diagnosing neurological conditions such as traumatic brain injuries (TBI) and astrocytoma. This paper presents the design and analysis of a novel microfluidic Lab-on-a-Chip (LoC) platform for GFAP biomarker detection. The system integrates microfluidic channels with embedded gold nanoparticle-functionalized electrochemical sensors for high-sensitivity biomarker capture. The microfluidic chip is designed using soft lithography and PDMS (Polydimethylsiloxane), allowing for precise fluid handling and minimal sample consumption. The electrochemical sensors generate voltage signals corresponding to GFAP concentration levels, which are processed using digital filtering techniques to mitigate noise and improve signal quality. For accurate biomarker quantification, machine learning algorithms, including Random Forest ,KNN and Support Vector Machine (SVM), are employed to classify sensor output data into clinically significant concentration ranges. The model is trained on both simulated and experimentally collected sensor data, achieving an accuracy of over 93 percent in distinguishing low (below 50 ng/mL) and high (above 50 ng/mL) GFAP concentrations. The combination of microfluidic precision, sensitive electrochemical detection, and data analysis enables rapid and reliable detection of GFAP , providing a scalable and portable solution for early diagnosis of brain injuries and neurodegenerative diseases. This system highlights the potential of integrating microfluidics and machine learning in next-generation point-of-care diagnostic devices.

关键词： Support vector machines Soft lithography Machine learning algorithms Accuracy data analysis Lab-on-a-chip Sensor systems Biosensors Microfluidics Brain injuries

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Deep Learning based Chatbot Architecture for Medical Diagnosis and Treatment Recommendation

Deep Learning based Chatbot Architecture for Medical Diagnos...

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Advanced Computing Technologies and Applications (ICACTA), International Conference on

作者： Girish Rajani Khushi Ruparel Dept. of Artificial Intelligence and Data Science Thadomal Shahani Engineering College Mumbai India

With the exponential growth and rapid development in the fields of deep learning and neural networks, chatbots have gained a lot of popularity and have become a proven, and efficient tool to interact and provide service to users. Healthcare is one of the most promising fields where chatbots can be used more efficiently. This has become important, especially in the current medical landscape, where there is a shortage of doctors, and patients often have to wait long periods before getting any medical guidance. By using the power of transformer models and machine learning algorithms chatbots can help patients with personalized diagnoses and treatment recommendations, efficiently at ease and convenience. This helps the patients to access medical services anywhere, at any point in time. This paper proposes a well-planned systematic architecture for a medical chatbot that utilizes the potential of transformers, classification algorithms and machine-learning models. The architecture includes three main components: a Naïve Bayes Classifier, a Binary Tree classifier along with a Support Vector Classifier, and a sequence-to-sequence model. These algorithms are used to classify symptoms and determine the severity of a medical condition to provide patients with accurate medical diagnoses and treatment recommendations. Overall, the proposed architecture is built and designed to bridge the gap between doctors and patients by providing immediate access to medical advice, making it a promising tool for improving the quality and accessibility of healthcare services.

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Forecasting Model of Wind Speed and Direction by Convolutional Neural Network - Deep Convolutional Long Short Term Memory 8

Forecasting Model of Wind Speed and Direction by Convolution...

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8th IEEE Information Technology International Seminar, ITIS 2022

作者： Sari, Anggraini Puspita Prasetya, Dwi Arman Yasuno, Takashi Sihananto, Andreas Nugroho Al Haromainy, Muhammad Muharrom Saputra, Wahyu S.J. Universitas Pembangunan Nasional 'Veteran' Jawa Timur Informatics Dept. Surabaya Indonesia Universitas Pembangunan Nasional 'Veteran' Jawa Timur Data Science Dept. Surabaya Indonesia Tokushima University Electrical and Electronic Engineering Dept. Tokushima Japan

ISBN: (纸本)9798350398199

This paper serves forecast wind speed and direction using a convolutional neural network - deep convolutional long short term memory (CNN-DConvLSTM). The forecasting model merges merit between CNN and ConvLSTM to enhance forecasts accuracy of wind speed and direction. The input of the forecasting model is image data on two dimensions (2D) - coordinate, which expresses the time sequential image of the wind vector. The forecasting model consists of encoding and forecasting networks that forecast one hour later. The actual observed data was taken from the Automated Meteorological data Acquisition System (AMeDAS)in Japan. The forecasting model performance was evaluated to mean absolute error (MAE) between forecasted data and observed data. For confirming effectiveness, the forecasting model was compared to the persistent model and fully connected - long short term memory (FC-LSTM). The proposed forecasting model by CNN-DConvLSTM for one-year data can improve by 25% for wind speed and 19.36% for wind direction over the persistent model which is a more significant accuracy than the LSTM model. It indicates that the CNN-DConvLSTM model is the strongest forecast model. © 2022 IEEE.

关键词： Long short-term memory

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A Study on the Generation of Spectrogram for the Detection of Venous Needle Dislodgement by Image Recognition Using Machine Learning 22

A Study on the Generation of Spectrogram for the Detection o...

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22nd IEEE/ACIS International Conference on Software engineering Research, Management and Applications, SERA 2024

作者： Nakaya, Naofumi Koizumi, Mutsuki Watanabe, Satoshi Shirahama, Naruki Abe, Takayuki Watanabe, Akihiro Juntendo University Fac. of Medical Science Chiba Japan Tokyo Electronics College Dept. of Electronic Technology Tokyo Japan Coll. of Bioresource Sciences Nihon University Kanagawa Japan Shimonoseki City University Fac. of Data Science Yamaguchi Japan Tokyo Women's Medical University Hospital Dept. of Clinical Engineering Tokyo Japan Nihon Institute of Medical Science Fac. of Health Sciences Saitama Japan

ISBN: (纸本)9798350391343

The safety and quality of life for the approximately 350,000 dialysis patients in Japan are critically affected by the risk of unintentional needle dislodgement during hemodialysis, which represents a significant cause of medical accidents. This study introduces an innovative approach to detect needle dislodgement, focusing on the nonhemolytic detection of indwelling needle dislodgement. By analyzing the impedance change through capacitive coupling between the patient and a detection circuit, we developed a prototype system that generates spectrograms based on frequency and phase characteristics from 1 kHz to 1 MHz. These spectrograms capture the changes in electrical characteristics associated with needle dislodgement, with alterations in color indicating changes in gain and phase difference. Initial experiments demonstrated the feasibility of using spectrogram analysis to visually distinguish normal from venous needle dislodgement (VND) conditions. Our results emphasize the potential for applying machine learning algorithms to these spectrograms for accurate and automated detection of VND. This could significantly improve patient safety by enabling the early detection of dislodgement events under various conditions, including partial needle contact or leakage at the puncture site. Future research will focus on expanding the dataset of normal and VND state spectrograms for machine learning training, aiming to validate the effectiveness of this technology in clinical settings. © 2024 IEEE.

关键词： Hemodialyzers

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

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engineering Informatics, IEEE

作者： S. Aarthi Dharmita A Pragathi G S Elakkiya Dept. of Computer Science Engineering SRM Institute of Science and Technology Chennai India Dept. Computer Science Engineering specialization with Big Data Analytics SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350338522

ISBN: (纸本)9798350338539

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability.

关键词： Technological innovation Renewable energy sources data analysis Machine learning algorithms Wind energy Machine learning Feature extraction

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Exploring Cognitive Decline in Hypertension: A Deep Learning Approach to Meningeal Interleukin-17-Producing T Cells in Mice

Exploring Cognitive Decline in Hypertension: A Deep Learning...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Praveen Gupta, Priyanka Rajalakshmi, B. Nijhawan, Ginni Kumari, Neha Kyhoiesh, Hussein Ali Kadhim Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Science Bangalore India Lovely Professional University Phagwara India St. Martin's Engineering College Dept. of Cse Telangana Secunderabad India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

A deep learning approach that targets meningeal interleukin-17-producing T cells is used to research cognitive loss in high blood pressure patients. A detailed ablation study examined and rated each aspect of our recommended procedure. Including cellular activities was crucial since excluding them reduced accuracy. These elements are crucial to high blood pressure-related cognitive impairment. The deep learning model's layers worked effectively together to uncover complicated data patterns, as shown by removing some layers. Model complexity and computer time trade-offs were found, which can aid future improvements. In conclusion, our deep learning approach appears to be a promising tool to study how elevated blood pressure affects brain function. The ablation investigation proves the procedure works and reveals key components' responsibilities. This allows for more modifications and breakthroughs. This research helps us understand brain illnesses, including high blood pressure-related memory loss. It also sets the framework for innovative diagnosis and treatment methods. This study's deep learning and immunology discoveries might revolutionize high blood pressure-related cognitive impairment research and treatment. © 2024 IEEE.

关键词： Blood pressure

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Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

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Energy, Materials and Communication engineering (ICEMCE), International Conference on

作者： M. S. Minu Rohit Suryaa S Jovan Titus J Sharan S Dept. of Computer Science Engineering SRM Institute of Science and Technology Chennai India Dept. Computer Science Engineering Specialization With Big Data Analytics SRM Institute of Science and Technology Chennai India

The growing prominence of wind energy as an eco-friendly power alternative amplifies the need for optimal turbine performance. Central to this is the ability to accurately predict and modulate the wind turbine pitch. Our research focuses on integrating adaptive control strategies with advanced power generation analytics to enhance pitch prediction precision. We have processed and analyzed vast amounts of wind data using state-of-the-art data analytics to discern patterns in wind velocity changes accurately. The continuous stream of real-time data is the backbone of our adaptive controls, ensuring perpetual pitch adjustments that optimize energy capture. In tandem with this, we present a thorough analysis linking momentary wind shifts to power production, ensuring consistent and efficient power output. The culmination of these methodologies offers a breakthrough solution in wind energy optimization. Preliminary results highlight the paramount importance of real-time data analytics in revolutionizing green energy advancements.

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Explainable AI for Enhanced Intrusion Detection Systems

Explainable AI for Enhanced Intrusion Detection Systems

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Advancement in Technology (ICONAT), International Conference for

作者： G Ananya N Siri Ashwini Kodipalli Trupthi Rao Dept. of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India Dept. of Electronic and Communication Engineering Global Academy of Technology Bengaluru Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bengaluru Karnataka India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection System (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection Systems. To achieve this, we have used the Coburg Intrusion Detection dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of $\mathbf{9 9. 9 9 \%}$, a precision of $\mathbf{9 9. 9 9 \%}$, and an F1-score of $\mathbf{9 9. 9 9 \%}$. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems

关键词： Support vector machines Machine learning algorithms Accuracy Explainable AI Computational modeling Intrusion detection Nearest neighbor methods Turning Reliability Random forests

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Sliced-Wasserstein-based Anomaly Detection and Open dataset for Localized Critical Peak Rebates

arXiv

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arXiv 2024年

作者： Pallage, Julien Scherrer, Bertrand Naccache, Salma Bélanger, Christophe Lesage-Landry, Antoine Dept. of Electrical Engineering Polytechnique Montréal GERAD Canada Open Data | Data and Analytics Hydro-Québec Canada Data Science Hilo Hydro-Québec Canada

In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e.g., energy, as it offers a conservative data selection. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset. © 2024, CC BY.

关键词： Adversarial machine learning

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Federated Learning for Real-Time Decentralized Smile Detection in Virtual Reality Environments

Federated Learning for Real-Time Decentralized Smile Detecti...

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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Salabat Khan Anwar Ghani Syed Shehreyar Ali Naqvi Murad Ali Khan Muhammad Faseeh Do Hyeun Kim Muhammad Sharif Dept. of Computer Engineering Big Data Research Center Jeju National University South Korea Dept. of Computer Engineering Jeju National University South Korea Dept. of Computer Science Comsats University Islamabad Pakistan

ISBN: (数字)9798331515997

ISBN: (纸本)9798331516000

Facial expression detection is paramount in real-world applications, including patient monitoring, photo selection, and facial protection within the metaverse and virtual reality environments. Among various expressions, detecting smiles poses a significant challenge. Traditional methods relying on low-level face descriptors are being superseded by advancements leveraging Convolutional Neural Networks (CNNs) to enhance smile detection performance. In this research, we introduce and implement three CNN architectures tailored for smile detection from facial images. Our proposed model incorporates a fully convolutional neural network featuring two deep convolutional layers dedicated to smile detection. Additionally, pre-trained LeNet and DenseNet CNNs are employed for this task. The effectiveness of our approach is demonstrated through extensive evaluations of the Hromada dataset, encompassing images with and without smiles within the virtual reality environment. Moreover, we delve into detailed discussions on the practicality and efficacy of CNNs in facial expression detection, explicitly focusing on smile detection in real-time scenarios. Our experiments are conducted within a federated learning framework, enabling model inference and optimization for privacy preservation and scalability of the smile detection system in large-scale virtual reality applications. Through this decentralized approach, we address challenges related to real-time detection, model inference, and collaboration in the metaverse.

关键词： Training Solid modeling Federated learning Metaverse Scalability Collaboration Virtual reality Real-time systems Convolutional neural networks Faces

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