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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Santhosh, Madhav Robert, Noel John Varghese, Reuben Vinod John, Rohaan Renu Mathew, Tessy Mar Baselios College of Engineering and Technology Department of Computer Science Engineering Trivandrum India

ISBN: (纸本)9798350369083

Dhwani is an innovative app designed to assist people with Autism, Intellectual disability, and adult language disorders in their communication. The project utilizes Augmen- tative and Alternative Communication (AAC) software to provide a user-friendly interface for individuals who display difficulties understanding and using language, following directions, and *** project has various modules, with the focus being on Picture tiles, which enable users to form sentences by selecting and arranging images. Once a sentence is formed, the project provides audible feedback, allowing the user to communicate their thoughts and ideas effectively. This system takes into account the user's preferences and needs to provide a customized experience. User information is collected through a questionnaire, which is used to recommend initial tiles to the user. This feature ensures that the project is tailored to the individual's specific communication ***, Dhwani provides a powerful tool for individuals with speech disabilities with its personalized recommendation system making it an effective and user-friendly option for enhancing communication. © 2024 IEEE.

关键词： Speech enhancement

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Cyber-Physical Systems and IoT Intrusion Detection: A Hybrid Deep Learning Perspective 3

Cyber-Physical Systems and IoT Intrusion Detection: A Hybrid...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Prakash, S. Jaya Devi, D. Muthukumaran, N. Romila, R. Idhaya Engineering College for Women Department of Computer Science and Engineering Tamilnadu Chinnasalem India Maha Barathi Engineering College Department of Computer Science and Engineering Tamilnadu Chinnasalem India A.R. Engineering College Department of Computer Science and Engineering Tamilnadu Villupuram India Sri Muthukumaran Institute of Technology Department of Information Technology Tamilnadu Chennai India

ISBN: (纸本)9798331509675

Several vital resources are increasingly being protected by cyber-physical systems (CPSs), makes the detection of incidents on these systems critical. CPSs along with other domains, such as the Internet of Things (IoT), frequently use machine learning (ML) and deep learning (DL) approaches to combat intrusion detection. Existing approaches, however, frequently favor the deployment of advanced detection models over their usefulness in actual operations. This research work develops a hybrid intrusion detection technique by combining Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM). This work analyzes the experimental results to LSTM and CNN to develop an effective comparative approach. The experiments are conducted on the two datasets. The efficiency indicators examined are intrusion detection results such as accuracy, precision, recall, and f1score. This comparison indicates that the proposed hybrid algorithm is the preferable approach. © 2025 IEEE.

关键词： Network intrusion

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Attention Contours: A Comparative Study on 2D and Virtual Reality Attention Profile Assessment Tests 5

Attention Contours: A Comparative Study on 2D and Virtual Re...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rajarajeswari, P. Madhubala, R. Gomathi, V. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350389609

Virtual Reality (VR) technology in health-care has emerged as a valuable tool for advancing diagnostic techniques and enhancing patient care. This research explores the application of VR in attention profile assessments, with a particular focus on diagnosing Attention Deficit/Hyperactivity Disorder (ADHD). ADHD poses challenges in maintaining attention, and traditional diagnostic approaches often lack real-world relevance. This study examines the efficacy of Attention Contours - a VR-based Continuous Performance Task (CPT) - in comparison to its conventional 2D (two-dimensional) counterpart for assessing attention profiles. Attention Contours VR incorporates varied virtual settings and language-neutral stimuli to improve ecological validity. User trials reveal greater engagement and fewer distractions in the VR environment, highlighting its potential for more accurate attention assessment. Participants showed a strong preference for the VR format, praising its immersive experience and enhanced usability. The findings emphasize the impact of VR in advancing attention assessment tools and suggest its promising role in ADHD assessment. © 2024 IEEE.

关键词： Virtual environments

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A Comprehensive Review of Driver Drowsiness Detection Systems and Applied Technologies 5

A Comprehensive Review of Driver Drowsiness Detection System...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Gupta, Ansh Tiwari, Abhinav Chauhan, Atul Singh Gupta, Sachi Galgotias College of Engineering & Technology Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

Drowsiness among drivers is a significant contributing factor to traffic injuries, leading to a yearly toll of severe injuries and fatalities. This study reviews the literature in-depth on driving force sleepiness detection systems, considering physiological signals, facial expressions, and riding habits. It not only describes the current approaches for each class but also offers a comparative analysis of recently published studies, closely contrasting correctness, dependability, hardware specifications, and intrusiveness. The research also examines the advantages and disadvantages of each technique's brilliance, highlighting the need for a hybrid machine that builds on each one's advantages to guarantee performance, resilience, accuracy and real-time use. This crucial issue requires attention and creativity, as demonstrated by a different investigation looking for ways to reduce driving induce sleepiness by strengthening deeply ingrained patterns. This paper investigates several Convolution Neural network configurations, visual Geometry Group architectures (VGG16 and VGG19), a Generative antagonistic network (GAN), and a Multi-Layer Perceptron (MLP) and offers enhancing statistics to model real-world scenarios. Using eye photo datasets, general performance criteria including as accuracy, precision, F1-score, recall, and specificity are assessed. The findings highlight the ability for extensive makeup in fashions with accuracy and an F1-score of © 2024 IEEE.

关键词： Analysis of Driving Patterns Assessment of Facial Capabilities Assessment of Physiological Alarms Dlib Motive Force Detection of Sleepiness Safe Riding

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Brain Tumor Retrieval in MRI Images with Integration of Optimal Features

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Journal of Harbin Institute of technology(New Series) 2024年第6期31卷 71-83页

作者： N V Shamna B Aziz Musthafa Department of Computer Science and Engineering PA College of EngineeringMangaluru 574153KarnatakaIndia Department of Computer Science and Engineering Bearys Institute of TechnologyMangaluru 5754199KarnatakaIndia

This paper presents an approach to improve medical image retrieval, particularly for brain tumors, by addressing the gap between low-level visual and high-level perceived contents in MRI, X-ray, and CT scans. Traditional methods based on color, shape, or texture are less effective. The proposed solution uses machine learning to handle high-dimensional image features, reducing computational complexity and mitigating issues caused by artifacts or noise. It employs a genetic algorithm for feature reduction and a hybrid residual UNet(HResUNet) model for Region-of-Interest(ROI) segmentation and classification, with enhanced image preprocessing. The study examines various loss functions, finding that a hybrid loss function yields superior results, and the GA-HResUNet model outperforms the HResUNet. Comparative analysis with state-of-the-art models shows a 4% improvement in retrieval accuracy.

关键词： medical images brain MRI machine learning feature extraction and reduction content-based image retrieval(CBIR)

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A comprehensive review on detection of depression through social media analysis 4th

A comprehensive review on detection of depression through so...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Dogra, Shriya Kaur, Parneet Sharma, Tejpal Kumari, Nisha Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781032911571

Social media platforms like Instagram, Twitter, and Facebook have completely changed our world. People today exhibit a kind of digital character and are more linked than ever. While social media undoubtedly offers many noteworthy advantages, there are also unavoidable drawbacks. Later Research has demonstrated a link between elevated use of social media platforms and heightened levels of depression. The goal of the current study is to study the existing machine learning approaches to identify potentially sorrowful Twitter users based on their tweets and network behaviour. To accomplish this, we assessed classifiers that identify whether or not a person is depressed based on information extracted from their tweets and network behaviour. It is observed that as a greater number of features are incorporated, the F-score and accuracy in classifying depressed persons also improve. This technique is a predictive, data-driven tool for early mental health diagnosis of depression or any type of psychiatric ailments. The main contribution of the article is the examination of the traits and how they affect the ability to gauge depression severity. © 2025 the Author(s).

关键词： Tweets

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An Automated System Pre-Trained to Identify Cardiovascular Disorders from ECG Pictures 2

An Automated System Pre-Trained to Identify Cardiovascular D...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Bhaskar, Nuthanakanti Aelgani, Vivekanand Kumar, Sheo Devi, Bigul Sunitha Kumar, Voruganti Naresh Divya, G. CMR Technical Campus Department of Computer Science and Engineering Hyderabad India CMR College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India CMR Engineering College Department of Computer Science and Engineering Hyderabad India CMR Institute of Technology Department of Computer Science and Engineering Hyderabad India CMR Technical Campus Department of Information Technology Hyderabad India

ISBN: (纸本)9798331519056

Globally, heart disorders, often recognized as cardiovascular diseases, are among the major causes of death. Additional lives might be protected the earlier they are identified and anticipated. Cardiovascular disease can be identified with an electrocardiogram (ECG), a simple, low-cost, non-invasive method of detecting the electrical functioning of the heart. Employing a public dataset of ECG images from heart patients, deep learning algorithms were utilized in this study to calculate the four main cardiac abnormalities: abnormal heartbeat, myocardial infarction history, coronary artery disease, and typical individual categories. Initially, the transfer method for learning was studied using the previously learned deep neural networks, VGG16, RESNET50, and InceptionV3. Classical methods of machine learning such as Support Vector Machine, K-nearest relatives, Random Forest, and Naïve Bayes were able to identify features employing the models that were already trained previously stated. For the study, ECG images from people who had or did not have a diagnosis of CVD were collected. Using these preprocessed pictures, relevant features including time-domain, frequency-domain, including nonlinear features were extracted. The collected features were then used to train various machine learning computations, which helped identify the prevalence of CVD. The efficiency of each algorithm was evaluated using metrics such as precision, specificity, and consciousness. In addition, the research employed Flask technology in Python to create an online user experience for the model, facilitating patients' and healthcare practitioners' easy access. ECG pictures can be uploaded through the interface, and the model analyzes them to determine whether CVD is present. These algorithms can also offer a reliable diagnosis, which lowers the possibility of human error. © 2024 IEEE.

关键词： Deep neural networks

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AI-Driven Retention: A Hybrid Approach to Employee Turnover Prediction 9

AI-Driven Retention: A Hybrid Approach to Employee Turnover ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Ponmalar, S. Affrin Fowmiya, N. Nandhini, C. Velammal College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798350377972

Predictive analytics by combining ensemble learning and deep learning techniques. The first step normalizes the feature space with MinMaxScaler, which improves the performance of the algorithms. To tackle class imbalances, the Synthetic Minority Over-sampling Technique (SMOTE) is used, ensuring strong predictive modeling. An empirical study shows the effectiveness of various algorithms, including ensemble methods like CatBoost, AdaBoost, LightGBM, and Gradient Boosting Machine (GBM), whicheffectively capture complex interactions and nonlinear relationships in employee data. Furthermore, Long Short- Term Memory (LSTM) networks are employed to identify temporal dependencies in longitudinal records. The findings emphasize the approach's capability to detect subtle signals of potential turnover. By integrating these methodologies, this research provides HR professionals with valuable tools for proactive retention strategies. Ultimately, it advances HR analytics, assisting organizations in reducing attrition risks and promoting an engaged workforce. © 2024 IEEE.

关键词： Predictive analytics

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Climate Change Impacts on Vaitarna River Basin Hydrology Using Downscaling Machine Learning Technique 4th

Climate Change Impacts on Vaitarna River Basin Hydrology Usi...

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4th International Conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Deshmukh, M.K. Department of Computer Science and Engineering College of Engineering and Technology Maharashtra Akola444104 India

ISBN: (纸本)9789819706990

Climate change's impact on hydrology is critical since it affects agriculture, vegetation, and livelihood. This investigation aims to determine whether climate change is possible in the Vaitarna River basin. The IPCC has studied temperature extremes and their effects on ecosystems (IPCC). Temperature variations are attributed to changes in solar energy, biological processes, and human activities. Climate models replicate how the climate's drivers interact using ocean, atmospheric, ice, and land surface models. Impact evaluation uses several climate models. Global Climate Models (GCMs) and GCMs are used to analyze changes in global temperature generated by doubling CO2 concentrations. In this experiment, free software SDSM 4.2 was employed. It's a blend of regression-based and stochastic weather generators in which local and large-scale data are connected. CCIS proposes the Statistical Downscaling Model as a downscaling technique. Downscaling uses GCMs to anticipate local conditions. GCMs are accurate atmospheric simulations based on large-scale spatiotemporal models. GCMs can't predict local and regional climate factors or their intensity. Dynamic downscaling didn't surpass statistical downscaling, according to a thorough study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rivers

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Remote Sensing–Based UAV Imaging in Heat Pattern Analysis Impact on Climate Change Detection Using Fuzzy Stacked Lasso Elastic-Net Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 699-708页

作者： Sailaja, M. Kumar, M. Prema Jyothi, B. Swarna Vanguri, G. L. Narasamba Manjula, S. Priya, D. Divya Department of Computer Science and Engineering Ravindra College of Engineering for Women Andhra Pradesh Kurnool India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Information Technology Aditya College of Engineering & amp Technology(A) Andhra Pradesh Surampalem 533437 India Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat islands raise surface temperatures, which has an effect on city dwellers’ health and welfare. Urbanisation-related changes to the land surface, which are especially notable right after sunset, have an impact on radiative forcing. Recently, there has been a surge in the usage of unmanned aerial vehicle (UAV) technology, particularly for precision agriculture and plant phenotyping. This is due to the declining cost and increased accessibility of both UAVs and thermal imaging sensors. This interruption affects subsequent operations like target item categorisation and water quality parameter inversion. The goal of this study was to evaluate the potential for combining machine learning (ML) with unmanned aerial vehicle (UAV)–based imaging technology. In this research, the novel technique in UAV image–based temperature pattern detection of various regions of earth and their sea level rise analysis using image classification by machine learning model. Here, the input is collected as UAV images which have been processed for noise removal and normalisation. These image features have been extracted using convolutional active contour–based kernel component analysis and classified using region-based fuzzy stacked lasso elastic-net model. The classified output shows temperature analysis with sea level rise based on image analysis. The experimental analysis has been carried out for various UAV image dataset in terms of detection accuracy, RMSE, F-measure, mean average precision (MAP), and recall. Proposed technique F-measure of 89%, detection accuracy of 96%, MAP of 94%, recall of 92%, and RMSE of 60%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional active contour Heat pattern detection Kernel component analysis Lasso elastic-net model Remote sensing

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