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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Mutholib, Abdul Rahim, Nadirah Abdul Gunawan, Teddy Surya Ahmarofi, Ahmad Afif International Islamic University Malaysia Faculty of Engineering ECE Department Kuala Lumpur Malaysia UIN Syarif Hidayatullah Jakarta Faculty of Science and Technology Jakarta Indonesia Kedah Department of Computer Technology and Networking Merbok Malaysia

ISBN: (纸本)9798350365283

Satellites are critical components of modern infrastructure, supporting countless applications in communication, navigation, and observation. However, ensuring their functionality and safety within complex space environments can be challenging. The satellite experiences the highest loss in the space industry caused by anomalies. Hence, it needs early detection so that the loss can be avoided immediately. With the advancement of technology, satellite anomalies diagnosis and detection can be done with trade-space exploration (TSE) and Artificial Intelligence (AI) models based on satellite data. The problem is that in satellite data preprocessing step, the data can be too large and sometimes there are some missing values encountered which leads to outliers. To mitigate these problems, efficient data preprocessing is needed so that the accuracy can be leveraged and requires only minimal computation resources. This paper presents the examination of the data preprocessing performance from the combination of both data cleansing and data normalization methods. Elimination, Imputation, Feature of Missing and Imperative Imputation methods are involved in data cleansing. While for the data normalization presented, Min Max, Z-Score using Standard Scalar, Robust Scaling, Vector Normalization and Power Transformation methods are used. As for the AI model classification, it is using Support Vector Machines (SVMs). The test was conducted using data from Satellite Database and Space Market Analysis (Seradata) consisting of approximately 4,455 data. The result shows that the accuracy of the Elimination and the Power Transformation normalization is the highest in training accuracy with 60%. While the Elimination and the Min Max or the Z-Score methods are the top in the testing accuracy with 60%. © 2024 IEEE.

关键词： Support vector machines

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A Novel Approach to Feature Extraction in MI - Based BCI Systems 8

A Novel Approach to Feature Extraction in MI - Based BCI Sys...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Arun Kumar, S. Anand, L. Kannur, Anil School of Computing Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Kattankulathur India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India BLDEA'S V.P. Dr. P.G. Halakatti College of Engineering and Technology Department of Computer Science and Engineering Vijayapura India

ISBN: (纸本)9798331505462

Brain-computer interfaces based on motor imagery (MI-BCI) enable communication between the brain and the outside world. It accomplishes this by capturing and examining the EEG data generated when a person visualizes moving a certain limb. Based on this investigation;to enhance categorization precision, we enhance the attribute extraction process. Once the EEG signals have been pre-processed and deconstructed into distinct bands of frequencies, we apply similar spatial algorithms for every sub-band. After that, we use Blind Source Separation (BSS) and spatial filter techniques to extract attributes directly from the sources of the brain instead of only from channels. Ultimately, by choosing the most relevant attributes as inputs for the categorization stage, we distinguish between the envisioned movements. To assess our recommended approach, we utilized data set IVa from the BCI competition III. The outcomes from diverse experiments indicate that the system achieves an average accuracy of 98.8%, with 100% sensitivity across all subjects. These results illustrate that the suggested approach effectively improves MI-based BCI. © 2024 IEEE.

关键词： Blind source separation

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Recognition of Heart Disease Based on Primitive Parameters Using Machine Learning Model 7

Recognition of Heart Disease Based on Primitive Parameters U...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Digumarthi, Jyothirmai Gayathri, V.M. Pitchai, R. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana State Narsapur India

ISBN: (纸本)9798350340600

Cardiovascular diseases (CVDs) have a significant worldwide impact, affecting millions of victims. The complex nature of these conditions requires modern treatments to address them effectively. In order to forecast certain diseases, machine learning algorithms take into account factors like age, gender, and blood pressure, amongst other characteristics. To enhance the usefulness of these algorithms, it is necessary to carefully choose their characteristics in an ideal manner. Various techniques, in order to identify the necessary traits, many techniques, including Recursive Feature Elimination, the least absolute shrinkage and selection operator, Relief approach, and mutual data, are being applied. Methods from the field of machine learning (ML) such as random forest, logistical regression, and K-nearest neighbours (KNN) are frequently employed in the process of predicting cardiovascular disease (CVD). These approaches have demonstrated beneficial effects and may be used to enhance the health of patients through early diagnosis. This study proposes an integrated approach that blends feature selection approaches with machine learning techniques in order to enhance cardiovascular disease prediction. Several methodologies have been evaluated in order to determine the most effective approach for predicting cardiovascular illnesses. © 2023 IEEE.

关键词： Nearest neighbor search

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Detection of Retinal Diseases in Retinal Optical Coherence Tomography Images 2

Detection of Retinal Diseases in Retinal Optical Coherence T...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Anand, M. Sheeba, T.M. Princella, A.S.Anshy SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Valliammai Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350397635

In biomedicine, disease image classification is a laborious, manual process. However, recent developments in computer vision have made applying artificial intelligence for the detection and categorization of medical images a more practical option. It is essential to analyse retinal illnesses utilising Optical Coherence Tomography (OCT) scans. For patients to survive, these disorders must be accurately detected and classified. The analysis of retinal illnesses is currently done by doctors on a regular basis by looking at the OCT pictures. The manual diagnosing process is laborious, though. In order to aid clinicians in their diagnosis, an automatic detection and classification technique for retinal illnesses has been developed in this work. Utilizing OCT scans, a deep multilayered Artificial Neural Network (ANN) has been employed to identify and categorise the retinal anomalies. On a 150 image open source retinal OCT dataset, the suggested technique has been used. © 2023 IEEE.

关键词： Optical tomography

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Age-Based Automatic Voice Conversion Using Blood Relation for Voice Impaired

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computers, Materials & Continua 2022年第2期70卷 4027-4051页

作者： Palli Padmini C.Paramasivam G.Jyothish Lal Sadeen Alharbi Kaustav Bhowmick Department of Electronics&Communication Engineering Amrita School of EngineeringBengaluruAmrita Vishwa VidyapeethamIndia Center for Computational Engineering and Networking(CEN) Amrita School of EngineeringCoimbatoreAmrita Vishwa VidyapeethamIndia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Electronics and Communication Engineering PES UniversityBengaluruIndia

The present work presents a statistical method to translate human voices across age groups,based on commonalities in voices of blood *** age-translated voices have been naturalized extracting the blood relation features e.g.,pitch,duration,energy,using Mel Frequency Cepstrum Coefficients(MFCC),for social compatibility of the *** system has been demonstrated using standard English and an Indian *** voice samples for resynthesis were derived from 12 families,with member ages ranging from 8–80 *** voice-age translation,performed using the Pitch synchronous overlap and add(PSOLA)approach,by modulation of extracted voice features,was validated by perception *** translated and resynthesized voices were correlated using Linde,Buzo,Gray(LBG),and Kekre’s Fast Codebook generation(KFCG)*** translated voice targets,a strong(θ>∼93%andθ>∼96%)correlation was found with blood relatives,whereas,a weak(θ<∼78%andθ<∼80%)correlation range was found between different families and different gender from same *** study further subcategorized the sampling and synthesis of the voices into similar or dissimilar gender groups,using a support vector machine(SVM)choosing between available voice ***,∼96%,∼93%,and∼94%accuracies were obtained in the identification of the gender of the voice sample,the age group samples,and the correlation between the original and converted voice samples,*** results obtained were close to the natural voice sample features and are envisaged to facilitate a near-natural voice for speech-impaired easily.

关键词： Blood relations KFCG LBG MFCC vector quantization correlation speech samples same-gender dissimilar gender voice conversion PSOLA SVM

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A Real-Time Oral Cavity Gesture Based Words Synthesizer Using Sensors

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computers, Materials & Continua 2022年第6期71卷 4523-4554页

The present system experimentally demonstrates a synthesis of syllables and words from tongue manoeuvers in multiple languages,captured by four oral sensors *** an experimental demonstration of the system used in the oral cavity,a prototype tooth model was *** on the principle developed in a previous publication by the author(s),the proposed system has been implemented using the oral cavity(tongue,teeth,and lips)features alone,without the glottis and the *** positions of the sensors in the proposed system were optimized based on articulatory(oral cavity)gestures estimated by simulating the mechanism of human *** system has been tested for all English alphabets and several words with sensor-based input along with an experimental demonstration of the developed algorithm,with limit switches,potentiometer,and flex sensors emulating the tongue in an artificial oral *** system produces the sounds of vowels,consonants,and words in English,along with the pronunciation of meanings of their translations in four major Indian languages,all from oral cavity *** experimental setup also caters to gender mapping of *** sound produced from the hardware has been validated by a perceptual test to verify the gender and word of the speech sample by listeners,with∼98%and∼95%accuracy,*** a model may be useful to interpret speech for those who are speech-disabled because of accidents,neuron disorder,spinal cord injury,or larynx disorder.

关键词： English vowels and consonants oral cavity proposed system sensors speech-disabled speech production vocal tract model

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Underwater Image Enhancement and Restoration Using Cycle GAN 1

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Spandana, Chereddy Srisurya, Ippatapu Venkata Priyadharshini, A.R. Krithika, S. Nandhini, S. Aasha Kumar, R. Prasanna Mohan, G. Bharathi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattangulathur India

ISBN: (数字)9789819930104

ISBN: (纸本)9789819930098

Underwater image augmentation is a technique for recovering low-resolution underwater photographs to produce equivalently high-resolution images. Deep learning-based techniques for enhancing photos commonly use paired data to train the model. Another crucial issue is how to effectively keep the fine details in the improved image. In order to address these problems, providing a revolutionary unpaired underwater picture-enhancing technique that fixes the underwater photos using a cycle generative adversarial network. At last test results on two datasets of unpaired underwater image datasets showed the recommended model’s utility by surpassing cutting-edge image enhancement methods. The CycleGAN generator includes a content loss regularizer that retains the important information of one low-resolution image in the corresponding clear image that is generated. The results are given based on mean square error and mean absolute error after the findings have been optimized using the ADAM optimizer. This architecture has a 4 × 4 kernel and a 2 × 2 stride, using Adam as the optimizer and a learning rate of 0.002. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Comparative Analysis of Machine Learning Algorithms in Thyroid Disease Prediction 23th

Comparative Analysis of Machine Learning Algorithms in Thyro...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Preethiya, T. Tyagi, Amit Kumar Pandiarajan, T. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Kanchipuram 603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India Department of Computer Science Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9783031648120

Thyroid disease is estimated to be the most common illness in the twenty-first century. The thyroid hormones are produced by a butterfly-shaped gland in the throat. The crucial function these hormones play in bodily metabolism, mental health, and general well-being. In this study, we present a method for accurately analysing thyroid function in an unbalanced dataset using Machine Learning (ML) algorithms. This dataset's accuracy score is ambiguous because most target variable classes are 1. Performance measurements are accuracy and recall, but when class distribution is asymmetrical, the F1-score balances the other parameters. The performance is evaluated using the F1-score, which is one of the best output metrics for unbalanced classification tasks. Simulation results shows that the Decision Tree (DT) algorithm outperforms the other algorithms with an F1-score of 0.9957, the Area Under Curve (AUC) score of 0.9917 and the kappa coefficient of 0.978 in the disease prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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IoT-Enabled Black Box for Driver Behavior Analysis Using Cloud Computing

IoT-Enabled Black Box for Driver Behavior Analysis Using Clo...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Jegan, J. Suguna, M. Raja Shobana, M. Azath, H. Murugan, S. Rajmohan, M. School Of Technology The Apollo University Department Of Computer Science And Engineering Andhra Pradesh Chittoor India Rmk College Of Engineering And Technology Department Of Computer Science And Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Department Of Networking And Communication Chennai Tamil Nadu Kattankulathur India School Of Computing Science And Engineering Vit Bhopal University Bhopal India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Computer Science And Engineering Tamil Nadu Chennai India Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

The Internet of Things (IoT)-powered black box for advanced driver behavior analysis and vehicle safety is a revolutionary strategy for improving driver behavior and road safety via the use of black boxes powered by the Internet of Things. This project captures realtime data on driving behavior, encompassing characteristics such as speed, acceleration, braking, and adherence to lanes via the seamless integration of IoT technologies into the vehicle's black box. It does this by providing in-depth insights into drivers' behaviors, which encourages adopting safe driving practices thanks to the advanced data analytics it employs. In addition to its behavior monitoring capabilities, the IoT-driven black box enhances the vehicle's safety by including collision detection technologies. If an accident occurs, the system immediately sends an alert, communicating the severity of the collision and the exact GPS locations to the emergency services, which speeds up the process of responding to the incident. This attempt combines cutting-edge Internet of Things capabilities with vehicle safety to develop responsible driving behaviors and, eventually, a safer highway environment for all parties involved. This invention can modify driving norms thanks to its comprehensive approach, highlighting the potential of technology-driven solutions in promoting safer roadways. © 2024 IEEE.

关键词： Vehicle safety

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Enhancing Road Safety with Real-Time Helmet Detection and E-Challan Issuance using YOLO and OCR 1

Enhancing Road Safety with Real-Time Helmet Detection and E-...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Deepa, S. Vinoth Kumar, C.N.S. Duraivelu, Vivek Easwaran, Balamurugan Suguna, R. Aparna, H. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Srm Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Tamilnadu Chennai India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur502313 India Texila American University School of It Zambia Bannari Amman Institute of Technology Department of Computer Science and Business Systems Tamilnadu Sathyamangalam India Kongu Engineering College Perundurai Tamilnadu Erode638 060 India

ISBN: (纸本)9798350372748

This study proposes an integrated system combining Optical Character Recognition (OCR) and YOLO (You Only Look Once) for enforcing helmet laws and improving traffic safety. A user-friendly PyQT GUI facilitates vehicle registration, creating an organized CSV database for future reference. YOLOv5, an advanced object detection algorithm, identifies key elements like motorcycles, helmets, and number plates from input images. The primary objective is detecting motorcyclists without helmets, followed by OCR extraction of text from license plates. The extracted text is compared with registered bike information in the CSV file, generating e-challans and fines for matches. Automatic email notifications ensure timely communication and compliance with traffic regulations. The system's implementation not only aids helmet enforcement but also acts as a deterrent for future violations, fostering a safer driving environment. With scalability and practicality, this approach can integrate seamlessly into existing traffic control systems, leveraging AI technologies like YOLOv5 and OCR for effective problem-solving and enhanced road safety. © 2024 IEEE.

关键词： Object detection

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