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8th International Conference on electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Abinaya, M. Duvvuri, Suneel Kumar Sathish Kumar, P.J. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Chengalpattu Department of Computer Science and Engineering Tamil Nadu India Rajahmundry Department of Computer Science and Applications Andhra Pradesh India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Chengalpattu Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331520762

Cardiomyopathy is the type of heart disease that results from diabetes that influences the heart muscles, leading to structural and functional abnormalities and these situations can weaken the heart muscle, loss its ability to supply blood effectively. Cardiomyopathy affects millions worldwide every year, with an estimated 1 in 500 people impacted. In the United States, it leads to approximately 50,000 deaths are reported annually. The existing method with Logistic Regression for the detection of Cardiomyopathy obtained inaccurate results with a high false alarm rate. Introduced a deep leaning convolution neural networks algorithm for the preprocessing, extraction of data selection of features and diagnosis of disease with accurate results in the early stage. As a result, got the accurate performance for accuracy, precision, sensitivity, and recall with 97%, 96%, 95.6%, and 96% respectively. © 2025 IEEE.

关键词： Deep neural networks

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Deep learning for diabetic retinopathy detection and classification Using Long Short-Term Memory (LSTM) 4

Deep learning for diabetic retinopathy detection and classif...

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4th IEEE International Conference on Data engineering and communication Systems, ICDECS 2024

作者： Prema Kumar, M. Srinivas Goud, D. Sankar, S. Sharath Kumar, S. Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India Vignana Bharathi Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Kavaraipettai Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350393354

Macular edema, diabetic retinopathy, and other apparent microvascular abnormalities in the human retina are common consequences of diabetes. The primary cause of blindness in the past ten years, diabetic retinopathy (DR), is the subject of this study. Preserving blood glucose control and preventing certain diabetic reactions require immediate or early therapy. Due of its intricacy and depth, DR is difficult to diagnose by laborious manual diagnostic procedures. This paper uses a novel methodology. Instead of manually creating features, we used the Long Short-Term Memory (LSTM) model to extract features for the Region-based Convolutional Neural Network (R-CNN) system. It is an R-CNN-LSTM model with two parts. The proposed R-CNN-LSTM system adds certain ground truth labels to training samples, which enables it to first identify the most informative patches and images before learning basic to complicated retinal properties. Using two independent public datasets, the suggested method investigates the retina vasculature and hard-exudate symptoms of diabetic retinopathy. The recommended illumination normalisation methodology improved the deep learning model and excelled other cutting-edge techniques in terms of accuracy, recall, and precision, according to extensive experimental results. © 2024 IEEE.

关键词： Long short-term memory

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Tuberculosis detection, an effective approach using convolutional neural network

Tuberculosis detection, an effective approach using convolut...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Balaji, L. Dhanalakshmi, A. Brindha, J. Pasupulati, Baburao Santhosh Krishna, B.V. Institute of Science and Technology Electronics & Communication Vel Tech Rangarajan Dr Sagunthala R & D TN Chennai India Panimalar Engineering College Electronics and Communication Engineering TN Chennai India Institute of Science and Technology Electronics & Electronics Engineering Vel Tech Rangarajan Dr Sagunthala R & D TN Chennai India New Horizon College of Engineering Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350381887

In most regions of the world, tuberculosis (TB) poses a major health risk and is spread through the air. A majority of diagnostic techniques takes a long time, are not always accurate, and were mostly created in the last century. The most popular technique for early tuberculosis detection in big populations is chest X-rays. The radiologist's experience and ability to interpret images will determine whether or not this procedure is successful. Because convolutional neural networks (CNNs) can learn both medium-level and high-level picture representations, they have become a popular deep learning technique. Several CNN models were employed in this work as Google Net models to categorize chest radiographs into TB-positive and TB-negative groups. A comparison of deep learning methods that can be used to process chest X-rays and identify tuberculosis is presented in this article. A publicly accessible dataset called the Tuberculosis (TB) Chest X ray Database is used to gauge the system's performance. © 2024 IEEE.

关键词： X ray radiography

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Document Ranking based on Similarity using Natural Language Processing Technique

Document Ranking based on Similarity using Natural Language ...

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2025 International Conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Jasmine Ananthi, M. Baby Shamini, P. Kalaivani, S. Santhosh, S. Jency, A. Rishik, P. DMI College of Engineering Electronics and Communication Engineering Chennai India RMK Engineering College Computer Science and Engineering Kavaraipettai India Rajalakshmi Institute of Technology Electronics and Communication Engineering Chennai India St.Joseph College of Engineering Electronics and Communication Engineering India R.M.K College of Engineering and Technology Artificial Intelligence and Data Science India RMK Engineering College Computer Science and Engineering TN Kavaraipettai India

ISBN: (纸本)9798331512088

As a result of the growth of the internet, Information was digitalized as files and documents. Information retrieval is the process of obtaining the required documents from vast databases. As the number of documents multiplies every five years, It is crucial to retrieve information effectively. Therefore, automatic indexing of documents is necessary for efficient information retrieval. In this research, an unsupervised model using genism was introduced to solve the problem. However, in the related work, the challenges of Information retrieval such as classification of similar documents, extraction of keywords, scientific documents clustering and evaluating the similarity are addressed using Natural Language Methods. The research provides an unsupervised model using genism to rank the documents based on the user query. The proposed model preprocesses the documents, generates the similarity score according to the user's query and ranks the documents based on the similarity score. The proposed methodology makes it easier to quickly retrieve the relevant documents based on the user's needs. To evaluate the performance of the proposed method, we collected the documents from different domains on the internet. The proposed method obtains comparatively more relevant documents than the traditional methods. © 2025 IEEE.

关键词： Document Ranking Information retrieval Similarity and Natural language processing

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IoT-Based Data Logging System for Authenticated Bank Locker Access to Ensuring Security

IoT-Based Data Logging System for Authenticated Bank Locker ...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sabeenian, R.S. Kawin, M. Akshayasri, S. Dinesh, P.M. Alagarsamy, Manjunathan Lakshmi, G. Prasanna Sona College of Technology Electronics and Communication Engineering Salem India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621112 India Sandip University School of Computer Science and Engineering Maharashtra Nashik India

ISBN: (纸本)9798350390049

In the conventional method of the banking system, whenever someone visits a bank to access the lockers, there will be a security guard appointed by the bank to ensure that the customer accessing his/her locker is authorized and only accessing his/her locker. Before entering the locker room to access their lockers, the person needs to enter the logbook available at the bank. The proposed system is a data logging system with Near Field communication (NFC) capability. NFC is used to ensure a person's authentication and logging details. The NFC reader is installed at the entrance to bank lockers. Each customer who wants to utilize a locker is given an NFC tag, and the suggested system saves the customer's basic information as well as the unique NFC tag id assigned to each customer in a MySQL database. The authenticated individual must display the NFC tag near the reader. The reader detects the tag value and compares it to the database. If the customer is authenticated, the electromagnetic locking system will unlock the locker room door. Along with the NFC tag id, additional logging data such as the check-in time and date, as well as the customer's basic information, are simultaneously and automatically logged into a separate database table. Another reader can be installed inside the locker room to record the customers' check-out date and time in a manner similar to the one described above. Automated, authenticated-based entries are created by the system. The customer access time in the locker room can also be calculated with its help. © 2024 IEEE.

关键词： Sales

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An Analysis of Various Cyber Threat Modeling 3

An Analysis of Various Cyber Threat Modeling

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Balamurugan, K. Sudalaimuthu, T. Sherlin Solomi, V. Hindustan Institute of Technology and Science Computer Science and Engineering Chennai India Hindustan Institute of Technology and Science Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665462167

Cyber security plays a major concern in various types of organizations. The security of software systems creates a threat in terms of software threat and design threats. The process involves relevant mitigation approaches, predicting security susceptibilities to software assets and processes. The security necessities of software assets and processes are identified and numerous methodologies have been developed. Threat modelling is a technique for determining security requirements and implementing them into the design of software systems. Software developers can add mitigations to their designs to make them more dependable and safe by using threat modeling to detect all potential dangers to software systems. Since its inception, numerous threat modelling strategies have been created. The proposed approach gives a summary of the several threat modeling methods that are suitable for various environment. Models like "STRIDE, PASTA, OCTAVE, Attack trees, Security Cards, and CVSS"are included in the proposed study. © 2023 IEEE.

关键词： Cybersecurity

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RESNET-50-Based Feature Extraction and Classification Model for Alzheimer’s Disease Detection 11th

RESNET-50-Based Feature Extraction and Classification Model ...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Rani, K. Emily Esther Baulkani, S. Department of Computer Science and Engineering Jayaraj Annapackiam CSI College of Engineering Tamil Nadu Nazareth India Department of Electronics and Communication Engineering Government College of Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9789819717231

Alzheimer disease (AD) is a brain disease that affects any senior people of the country. The burden of care givers can be reduced by detecting the disease at early stage itself. In neuro imaging, deep learning creates new intuitions in finding brain cell variations for detecting various brain diseases such as schizophrenia and Alzheimer’s disease. In this paper, Resnet-50 based deep learning model is constructed for feature extraction and classification to diagnose AD. To do this, we have collected resting state functional magnetic resonance image (rsFMRI) dataset from ADNI 3 database. Initially, the input rsFMRI images are preprocessed and augmented. Then, the dataset is divided into training set and testing set. After that, the RESNET-50-based deep learning feature extraction and classification model is constructed to extract features from the training set and the pre trained model is used to classify AD patients from the test dataset. The experimental results show that this model diagnoses different AD stages with the classification accuracy of 95%, and it also outperforms some recent methods for diagnosing AD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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Optimization of IoT Devices in Smart Home to Minimize Cost Energy Conservation Based on Human Detector 1st

Optimization of IoT Devices in Smart Home to Minimize Cost E...

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1st International Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Muthuvel, P. Raja Sekar, R. Rajkumar, T. Dhiliphan Mari Selvan, R. Department of Electronics and Communication Engineering Saveetha Engineering College Saveetha Nagar Thandalam India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India

ISBN: (纸本)9789819776153

Smart home systems are becoming an exciting new paradigm among researchers and homeowners. In modern society, the use of electrical appliances is an essential element for a comfortable life. However, in many classrooms or dorms, students may waste energy by leaving lighting and air conditioning systems on even when not in use and an optimized distribution of the used sensors is required for the resource-efficient (indoor) location of mobile humans. Indoor positioning, or the location of beacons or sensors inside buildings, may be a challenging and time-consuming process, particularly when done by hand. This topic has already been attempted to be solved by a number of researchers under various use cases and using various approaches. Nevertheless, none of the approaches that are now available use Boolean variables alone to do constraint programming. To address this issue, we are creating a smart IoT system that saves energy by turning on lights and fans when motion is detected in a specific part of a room. To be more precise, we want to optimize the beacons for an indoor locating system that uses trilateration. According to this method, three beacons have to be within range of each potential location inside the structure. The initially determined beacon placements are then further optimized in a subsequent stage. In order to do this, fewer beacons are used. Next, we assess the beacon placements’ quality by comparing them to a manually optimized beacon placement and assessing the quality of many sample places in an existing building. This system uses a PIR sensor for motion detection which makes it cost efficient. The electrical switch is controlled by a5V-1 channel relay connected to an Arduino board in the switchboard. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart homes

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An Intelligent Fault Detection Framework for HVAC Systems with Alert Generation

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SN computer science 2023年第5期4卷 616页

作者： Sinha, Aparna Pandaw, Akanksha Singh Das, Debanjan Department of Electronics and Communication Engineering IIIT Naya Raipur Raipur India Department of Computer Science and Engineering IIIT Naya Raipur Raipur India

The HVACs used in commercial buildings are one of the major electricity consumers. They undergo periodic inspections to prevent discomfort and potential hazards to the occupants. The existing technologies for fault detection are either based on personnel expertise or condition monitoring. Prediction of possible faults in the system gives time to take proper measures to act upon those faults and reduce the risk of system breakdown. Machine Learning (ML) models are able to analyze and predict a vast dataset in a short time, which makes them dependable assistance for diagnosing faults in HVAC systems. Thus, there is a need to develop a reliable and robust system that could predict working conditions and provide necessary alerts about faults on time. This study proposes a predictive health maintenance system that predicts upcoming faults and generates alert messages with their root cause analysis. The Automated ML (AutoML) detected different faults with the highest accuracy of 98.32% by choosing the random forest as the classifier. The root cause analysis provided with the alert messages saves time and resources for rectifying the faults and taking preventive measures. Hence, fault diagnosis using ML algorithms can help increase the HVAC’s lifespan and effectiveness. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AutoML Boilers Fault diagnosis Machine learning Predictive health maintenance

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Realm of Medical Image Processing Under the Light of Artificial Intelligence 1

Realm of Medical Image Processing Under the Light of Artific...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Tamboli, Asif I. Shah, Samarth Saxena, Gaurav Wagh, S.J. Prashanth, Addagatla Diallo, Mamadou Yero Department of Radio-Diagnosis Karad India Chandigarh University Department of Computer Science Mohali India Government Engineering College Department of Computer Science Karad India Institute of Aeronautical Engineering Electronics and Communication Engineering Hyderabad India Sharda University Computer Science & Engineering Gr.Noida India

ISBN: (纸本)9798350360707

Over the previous century, a fundamental breakthrough in medical imaging processing as well as storage occurred, with the switch from traditional detectors in radiography to digital detectors. As a result, medical imaging digitalization constitutes a big achievement and promises considerable breakthroughs in cancer. Thus, goal of study is to analyze the advanced imaging process in medical field using artificial intelligence (AI). AI, on the other hand, has the potential to go beyond these applications by producing picture-based biomarkers that are able to accurately predicting survival or response to therapy, thereby extracting far more information from photographic images than used to be conceivable. Such biomarkers might be incorporated into existing prognostic as well as predictive models used in clinical treatment, functioning as integrative biomarkers. So, the study helps in understanding importance of medical imaging processes and further helps in diagnosing minor diseases and eliminates it without human intervention with help of AI. © 2024 IEEE.

关键词： Medical image processing

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