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

作者： Kavitha, T. Hemalatha, S. Raja, B. Saran Sripriya, R. Yokeshwaran, D. Kongu Engineering College Department of Computer Applications Perundurai Erode India

ISBN: (纸本)9798350372748

The adoption of face mask stands as a pivotal preventive measure in mitigating the transmission of the covid-19 pandemic and also the process of identifying the face if the person is wearing the mask. The increasing need to wear mask in public spaces has resulted in a rising demand for automated solutions to detect face masks. This research presents a novel face mask identification system leveraging Convolutional Neural Networks (CNNs) to accurately detect individuals wearing masks. The proposed CNN-based approach incorporates a deep learning architecture, facilitating the extraction of distinctive facial features for face mask recognition. Training the proposed algorithm involves a substantial dataset comprising diverse images depicting individuals both with and without masks. The deployment of the developed face mask detection technology holds a significant promise for enhancing security, healthcare, and public safety. Its application extend to enforcing mask-wearing regulations, ensuring the well-being of individuals during pandemic situations, and finding utility in real-world scenarios such as airports, hospitals, schools, and public transportation. © 2024 IEEE.

关键词： Convolutional neural networks

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Upgraded Machine Learning Techniques to Forecast Obesity Hazards 3

Upgraded Machine Learning Techniques to Forecast Obesity Haz...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sukanya, N.S. Asha, V. Vasumathi, M.T. Chakraborty, Sumit Sushanth, S.M. Kumari, Supriya Department of Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331527549

In recent years, investigators have prolonged their research on predicting obesity risks using Machine Learning (ML) techniques. Obesity is an intricate condition with multiple contributing factors, such as genetics, behaviour, environment, and social factors. Predicting obesity risk through ML can aid recognise individuals who may be at risk also aids in early identification to prevent or manage the condition. There is a need for these predictive models which is a combination of various factors like age, medical history, and lifestyle and these are the probability of increasing obesity among individuals. The model aims to accurately predict the obesity risks as early as possible. Detecting obesity at an initial stage is a stimulating procedure. This paper aims to use ML models such as XGBoost, CatBoost and LightGBM to predict obesity at a primary stage. To predict the obesity, Nobeyesdad dataset from Kaggle is used in this study. The obesity prediction model classifies the data into multiple categories like normal, insufficient weight, overweight and obesity individuals. The results of the enhanced obesity prediction model generates ideal results by classifying the datasets which aids the specialists to make decisions easily. © 2025 IEEE.

关键词： Prediction models

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Software Development Life Cycle for Web Application by Using Traditional Methodology vs Agile Methodology 9

Software Development Life Cycle for Web Application by Using...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Krishna, V. Vamsi Gopinath, G. School of Computer Science Engineering & Applications Bharathidasan University Tiruchirappalli India

ISBN: (纸本)9798350365092

Life cycle software development is concerned in the software development process (SDP), which inspects the software development region. Life cycle of software development (SDLC) is a technique that assures that the software abides by the recognized necessities. In this methodologies, to create the method more effective and also conventional it applies numerous grades in the SDP. In this work, the heavy class methodology (traditional SDLC) and as well the Lightweight Methodology (Agile SDLC) are explicated and also conducted a correlation betwixt the policy of Method. © 2024 IEEE.

关键词： Life cycle

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Deep Learning for Real-Time Drowsiness Onset Detection 2

Deep Learning for Real-Time Drowsiness Onset Detection

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Govindaraj, M. Asha, V. Katingeri, Mohammed Mazil Manvanth, L.J. Ch, Mouli Raghavendra Prashanth Mathew, Jincy C New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350317558

Drowsiness detection is a pivotal element in ensuring the well-being of both drivers' and passengers safety, as it helps prevent accidents caused by tired or drowsy individuals behind the wheel. This research introduces an advanced hybrid model to effectively and swiftly identify drowsiness in real-time. The process commences with data pre-processing, involving the capture and examination of facial images of the driver for analysis. These images go through different pre-processing stages, including resizing, converting to grayscale, and reducing noise through a blend of filters, such as Weiner and Median filters. Our model similar to ResNet, is meticulously fine-tuned and optimized using sophisticated techniques, including Aquila optimization, to enhance its performance. The primary objective of this model is to ascertain whether the driver is in a drowsy or alert state. The model's performance is rigorously assessed and benchmarked against previous research in the field. The attained outcomes by our proposed drowsiness detection model are truly impressive, boasting an F1 score of 98.98%, an accuracy rate of 99.2%, a sensitivity rate of 98.9%, a specificity rate of 99.1%, and a precision rate of 99%. These exceptional metrics underscore the model's effectiveness in accurately identifying drowsiness, underscoring its importance in promoting road safety. © 2024 IEEE.

关键词： Accident prevention Convolutional Neural Networks Eye tracking Onset Detection Real-time Detection time cumulative effect

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Enhanced Machine Learning Algorithm to Predict Brain Stroke 7

Enhanced Machine Learning Algorithm to Predict Brain Stroke

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kavitha, T. Hemalatha, S. Elakkiya, P. Kaviya, K. Kousika, M. Kongu Engineering College Department of Computer Applications Erode Perundurai India

ISBN: (纸本)9798350359299

A stroke is a condition when there is insufficient blood flow to the brain, which ultimately leads to cell death. It is nowadays a leading cause of dying all over the world. An analysis of those experiencing strokes determined a multitude of medical conditions that have been designated as possibly associated with the root cause of the stroke. Although many individuals have died from strokes, as much as 85 percent of strokes can be avoided by discovering or detecting them in their early stages. Leveraging these risk factors, numerous research studies have been conducted to predict the conditions linked to stroke. The majority of these predictive models are constructed using machine learning techniques. In previous research, they have trained the models for accurate prediction using machine learning methods including Logistic Regression Algorithm, Decision Tree Classification Algorithm, Random Forest Classification Algorithm, K-Nearest Neighbors Algorithm, Support Vector Machine Algorithm, and Naive Bayes Classification Algorithm. A textual (.csv) training dataset was used for this work. The algorithm that performed this task most accurately was Support Vector Machine, which achieved an accuracy of almost 94.6%. In this proposed analysis, the stroke prediction process has been advanced by employing Random Forest Classification and XGBoost Algorithm methods. These approaches are augmented with feature selection techniques like Chi-Square and Information Gain to elevate the accuracy of the predictive models. The textual dataset is used in this study. Both Random Forest Classification and XGBoost Algorithm, which achieved an accuracy of about 98%, was the algorithm that performed these tasks best. © 2024 IEEE.

关键词： Forecasting

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IoT-based Smart Pill Reminding System

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Recent Advances in computer Science and Communications 2024年第2期17卷 13-19页

作者： Nagchandi, Akul Kher, Pradyot Gudipalli, Abhishek Tiwari, Amit School of Electrical Engineering Vellore Institute of Technology Tamil Nadu India Department of Computer Applications ABES Engineering College UP Uttar Pradesh Ghaziabad India

Background: The concept of pill reminders has been discussed and developed throughout the decade. It varies from cascaded plastic pill boxes to complicated robust dispensers. This proposed smart pill reminding system based on IoT is being designed by considering ease-to-use and cost-effectiveness. Method: A smart pill reminding system is a system that will alert the patient to take their respective pill at the desired time. It will also track the motion of the patient’s hand while taking the pill and will also display the pill count on an LCD Screen. In case a patient forgets/ignores the reminder provided by the system, the system will automatically display the status on the application that will be installed in the relative/caretaker’s phone and through an email on the patient's relative/caretaker’s email address to take subsequent action. The system will monitor the real-time using an RTC module, and as and when the current time matches the medicines time, it will activate its mechanism, and the patient will have a buffer time to take their medicine. In case a patient does take the medicine in the buffer time provided by the system, then one mechanism of the system will be activated. In another case, if a patient does not take the medicine in the stipulated time, further actions will be initiated by the system to benefit the patient. Results: It was tested and found that out of ten times, the system worked accurately nine times, with calculated accuracy as high as 90%. Initially, the Blynk application will display "Welcome Patient" and "You will be updated". Once the RTC matches the scheduled time to take medicine, the buzzer starts buzzing. If the IR sensor detects the movement of the user’s hand, the LCD will update the pill count, and the pill count is reduced by one. The LCD will also display the message "Medicine Taken". If the IR sensor does not detect the movement of the user’s hand, the LCD will display the same pill count. The LCD will also display the me

关键词： Liquid crystal displays

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Predictive Autoscaling for Containerized applications Using Machine Learning 1

Predictive Autoscaling for Containerized Applications Using ...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. School of Computer-Science and Engineering Sandip University Department of Computer Science & Applications Nashik India

ISBN: (纸本)9798350361780

As containerization continues to gain prominence in modern application deployment, the need for efficient autoscaling mechanisms becomes paramount to ensure optimal resource utilization and adherence to service level objectives. This research paper proposes a Predictive Auto-scaling framework leveraging machine-learning(ML) techniques to anticipate and proactively adjust the resources allotted to containerized applications. The aim is to enhance the scalability and responsiveness of applications running in dynamic and cloud-native environments. An auto-scaling technique with burst awareness for containerized applications is proposed in this research. This innovative method optimizes container utilization while meeting the Quality-of-Service requirement by taking into account a mix of both vertical and horizontal skills. The proposed proactive autoscaling strategy addresses the challenges posed by unpredictable workloads and varying application demands, fostering adaptability to dynamic environments. By highlighting the significance of machine learning in improving the predictability and effectiveness of resource scaling for containerized systems, this study adds to new work of research on auto-scaling methodologies. © 2024 IEEE.

关键词： Containers

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Parkinson’s disease Severity Detection Based on Fused features with tuned Residual Deep Convolutional Neural Network

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Multimedia Tools and applications 2025年第14期84卷 13111-13131页

作者： Vishwakarma, Gagan Nandanwar, Amit Kumar Thakur, Ghanshyam Singh Computer Science & Engineering Indian Institute of Information Technology MP Bhopal India Computer Science & Engineering Maulana Azad National Institute of Technology MP Bhopal India Department of Computer Applications Maulana Azad National Institute of Technology MP Bhopal India

Parkinson's disease (PD) diagnosis involves the assessment of a variety of motor and non-motor symptoms. To accurately diagnose PD, it is necessary to differentiate its symptoms from those of other conditions. During the diagnosis process, clinicians must consider gait abnormalities as one of the key symptoms. However, relying solely on clinical judgment for the diagnosis of PD may have limitations. In this work, we have proposed a Multikernel SVM (MK-SVM) based Parkinson's severity analysis method. Initially, an adaptive median filter is used to reduce the noise in the gait image. Then, to extract the features, we propose a dual-layer method that employs stacked autoencoders (SAEs) for local feature extraction and residual deep convolution neural networks (RDCNN) for global feature extraction. The hyper-parameters from the RDCNN are optimized using the Enhanced walrus optimization algorithm (EWO). In the final step, we use MK-SVM to classify PD according to severity. This model is evaluated using the GAIT-IT and GAIT-IST datasets. With the help of Python, we evaluated the proposed model in terms of precision, accuracy, recall, specificity, and F-scores, and the model achieved a maximum accuracy of 97.3%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A Novel Object Detection and Tracking Approach Using LSTM Networks and DSOD for UAV 4

A Novel Object Detection and Tracking Approach Using LSTM Ne...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kumar, Ankit Ray, Kumar Sankar Gla University Department of Computer Engineering & Applications UP Mathura India

ISBN: (纸本)9798350364729

Unmanned Aerial Vehicles (UAVs) are being used in traffic control, disaster response, and logistics. UAVs provide very detailed monitoring and object tracking, surpassing traditional surveillance systems in high-resolution data collecting and access to inaccessible regions. We develop a new object detection and tracking method utilizing LSTM networks and Deeply Supervised Object Detector in this paper. This method takes use of UAVs' mobility and high altitude. In dynamic environments, it improves object tracking. The technique relies on object labeling for precise position identifiers, Deeply Supervised Object Detector (DSOD) training on frames with improved object recognition, and LSTM for advanced processing of visual characteristics and bounding box coordinates from DSOD. Integrating the DSOD unit with the LSTM corrects the vanishing gradient problem, improving gradient propagation to capture crucial temporal correlations across frames. Occlusion, size and light variations, and camera motion are in the UAV123 dataset, which we used for our experiments. The recommended strategy outperforms all previous static camera data-based training methods in accuracy, recall, and F-scores, obtaining 97.42%, 96.13%, and 96.21%, respectively. This shows the need of training object detectors using UAV data. DSOD and LSTM provide a scalable and efficient framework for real-time object detection and tracking, improving UAV surveillance and autonomous systems. The findings suggest that deep learning methods may improve object recognition and tracking, particularly in complicated scenarios. © 2024 IEEE.

关键词： Object tracking

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Autism Spectrum Disorder Using Convolutional Neural Networks 2

Autism Spectrum Disorder Using Convolutional Neural Networks

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Mathew, Jincy C Asha, V. Pravallika, S. Kumbhar, Omkar S RaviTeja, T. Rohit, P. New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350317558

The medical and health research domain continues to grapple with ongoing obstacles in promptly diagnosing ASD. Swift recognition of ASD holds paramount importance for proficient condition management and the early deployment of intervention tactics. Encouragingly, advancements in deep learning, notably in CNN, present hopeful paths toward overcoming these diagnostic hurdles. This research concentrates on crafting a tailored dataset for deep learning, specifically leveraging CNN, to improve the early detection of ASD. The dataset underwent thorough curation, incorporating diverse tasks encompassing written and visual components, administered to individuals spanning ASD and non-ASD profiles. The objective was to construct a robust dataset that encapsulates the intricacies of handwriting challenges linked to ASD. Following this, the dataset was utilized to devise an automated ASD diagnosis technique employing transfer learning with the GoogleNet architecture. Each handwritten task within the dataset underwent scrutiny and classification via the trained CNN model. This method endeavors to harness the capabilities of deep learning in identifying patterns and characteristics in the handwriting of individuals with ASD, thus advancing towards more precise and streamlined diagnostic procedures. © 2024 IEEE.

关键词： and autism spectrum disorder GoogleNet computer vision Convolutional neural networks deep learning networks

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