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

作者： Arora, Drishti Asif, Farhan Amity University Dept. of Computer Science & Engineering Uttar Pradesh India

ISBN: (纸本)9798350383522

Cardiovascular diseases encompass a range of medical conditions that impact the heart and blood vessels The prediction of heart disease pose considerable complexity and present a formidable challenge within the medical field According to the WHO, heart diseases are the leading cause of death worldwide, claiming approximately 17.9 million lives each year, accounting for around 31% of all global deaths To address the need for accurate heart disease prediction, extensive research has employed advanced machine learning models This study conducted a comprehensive evaluation of seven distinct classification models, combining various physiological factors with well-known machine learning algorithms The model employed in this study includes Naïve Bayes, Logistic Regression, Decision Trees, Random Forest, XGBoost, CatBoost, and Voting Classifiers Using these models, a robust heart disease prediction system is designed, facilitating precise evaluation of an individual's risk The observation shows that through meticulous evaluation and comparison, the Random Forest algorithm, which is a bagging technique, outperforms the existing state-of-the-art methods It exhibited remarkable accuracy, yielding prediction results of approximately 90.16% This exceptional accuracy establishes the random forest algorithm as the pre-eminent model for precise and reliable heart disease prediction within the scope of this study. © 2024 IEEE.

关键词： Cardiovascular Disease Machine Learning Classifiers Performance Evaluation Predictive Analysis

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A Review on Deep Learning Based Question Answering with Natural Language Processing in Healthcare

A Review on Deep Learning Based Question Answering with Natu...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Javeed, Abdul Saleem Gopala Krishnan, C. Gitam School of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350359688

Over the Internet, an efficient approach and promising solution to retrieve significant information envisages the beginning of Question Answering Systems (QAS). Because of data sources availability, the deep learning model based on open domain QAS focuses a substantial quantity of research work. Because of the lack of medical datasets, less attention is received in the medical domain. The deep learning (DL) utility increases the development of huge data in the healthcare domain. In the past years, popularity has grown in hardware parallelism and quick data storage availability. The challenge, pipelines, and tasks involved in medical NLP and medical imaging deploy the existing review of research in this work. The medical NLP and imaging fields deploy DL structures with its extensive survey is presented here. Over all 39 research papers are collected for this survey analysis from the years 2017 to 2023. The diagnosis is enhanced with medical imaging, Natural language processing, and DL combination to detect the appropriate combination. Each of the outcomes is shown graphically. The information provided in this survey is quite useful for newcomers carrying out research in medical informatics. © 2024 IEEE.

关键词： and Medical data Deep Learning Healthcare Natural Language Processing Question Answering

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Streamlining Livestock Surveillance: YOLOv8 Approach to Cattle Body Segmentation

Streamlining Livestock Surveillance: YOLOv8 Approach to Catt...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chugh, Bharti Kumar, Pushepndra Mishra, Upendra Chaudhary, Himanshi Kiet Group of Institutions Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350375237

This research paper introduces an innovative approach to optimize livestock surveillance by employing YOLOv8 for cattle body segmentation. The study attained a mAP(50), mean Average Precision of 0.598 and 0.464 and mAP(50-95) on a dataset comprising 704 images. Preceding model training, extensive preprocessing and image augmentation techniques were implemented, resulting in a dataset of 1476 training, 171 validation, and 71 testing images. The developed model demonstrates considerable promise in enabling real-time mon-itoring of cattle and their health status. Additionally, to enhance its suitability for edge computing environments, quantization-aware training (QAT) abd weight compression techniques were employed, ensuring efficient deployment on resource-constrained devices. The results highlight the effectiveness and practicality of employing YOLOv8-based segmentation for livestock surveillance, with notable implications for precision agriculture and animal welfare. However, further enhancements are warranted, particularly in achieving higher mAP scores across a broader range of Intersection over Union (IoU) thresholds, to fully address the segmentation challenges in this domain. © 2024 IEEE.

关键词： computer vision

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Deep Learning Models for Solar Wind Speed Predictions Using Univariate Time Series dataset 2

Deep Learning Models for Solar Wind Speed Predictions Using ...

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2nd IEEE International Conference on data science and Network Security, ICDSNS 2024

作者： Raaz, Hemant Kumar, Vipin Mahatma Gandhi Central University Dept. Computer Science and It Motihari India

ISBN: (纸本)9798350373110

The problem of solar wind speed prediction in time series involves forecasting future values of solar wind velocity based on historical data, which is crucial for space weather forecasting and understanding the dynamics of the solar-terrestrial environment. This study tackles the challenge using deep learning models such as LSTM, BiLSTM, CNN, RNN, and GRU, trained on historical data to capture temporal dynamics and correlations. The findings reveal that deep learning effectively forecasts solar wind speed, with the LSTM model achieving RMSE, MSE, MAPE, and MAE of 3.93, 15.42, 0.65, and 2.42, respectively. The BiLSTM model's metrics were 47.59, 2264.81, 11.11, and 40.38;the CNN model had 49.80, 2480.11, 11.53, and 41.86;the RNN model achieved 3.27, 10.70, 0.55, and 2.05;and the GRU model recorded 3.32, 11.01, 0.53, and 1.96. The results highlight the potential of advanced neural networks to improve prediction accuracy and advance space weather forecasting models. © 2024 IEEE.

关键词： Wind forecasting

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The SNS based E mentoring and Development of Computational Thinking for Undergraduate Students in an Online Course

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Educational Technology and Society 2023年第2期26卷 147-164页

作者： Jang, Yeonju Choi, Seongyune Kim, Seonghun Kim, Hyeoncheol Dept. of Computer Science and Engineering The Graduate School of Korea University South Korea Dept. of Education Gachon University South Korea Dept. of Computer Science and Engineering Korea University South Korea

Given the importance of digital technology in daily life, computational thinking (CT) has become a necessary skill for everyone, not just for computer scientists. For CT development, students need to receive appropriate social learning support. However, instructors find it difficult to provide such support to many students in online courses. This study aimed to examine the effectiveness of e-mentoring via social network services (SNS) in developing students’ CT during large-scale online courses. A total of 327 undergraduate students volunteered to participate in this study, which included 16 weeks of lectures aimed at developing CT. The effects of SNS-based e-mentoring on CT development, the influences of each e-mentoring activity on CT development, and gender differences were analyzed using data on participants’ CT assistance scores and their utilization of e-mentoring activities. The findings indicated that SNS-based e-mentoring was effective in developing the CT of undergraduate students’ engagement in a large-scale online course. The most influential e-mentoring activities for students’ CT development were informational and technical support in a group and informational support in a private environment. Female students benefited more from SNS-based e-mentoring than male students, and they also engaged in more types of e-mentoring activities than male students. Participation in SNS-based e-mentoring was found to lower the gap in CT between students with and without prior learning experience. Our study findings can be used by educational institutions and instructors when designing courses for students’ CT development in large-scale online courses or when developing strategies to close the gender gap in CT ability. © 2023,Educational Technology and Society. All Rights Reserved.

关键词： Computational thinking Computational thinking and prior learning experience e-Mentoring Gender difference Social Network Service (SNS)

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'RNN-Driven Prognosis of Heart Disease Based on Health Parameters' 2

'RNN-Driven Prognosis of Heart Disease Based on Health Param...

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

作者： Rahi, Parvez Singh, Inderjeet Singh, Ajay Pal Kumar, Alok Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798331519056

Cardiovascular diseases (CVD) are the leading cause of death globally and are estimated to affect 17. 9 million deaths annually. Early diagnosis is very important if one is to prevent adverse consequences. However, traditional diagnostic methods can take time to complete and miss subtle trends in patient information. This research presents an innovative approach utilizing a Recurrent Neural Network (RNN) model, optimized through Stochastic Gradient Descent (SGD), for the prediction of seven distinct types of heart disease: CAD, Heart Failure, AFib, Hypertensive Heart Disease, Congenital Heart Disease, Cardiomyopathy, and PAD. Their architecture is specifically suited for direct modeling of sequential patterns of the health related data that in turn helps to detect temporal patterns and changes in them. This model use ReLU activation function in their hidden layers, dropout in preventing overfitting and batch normalization for stable learning. Moreover, the feature augmentation is used in the model making it use various psychological and physiological parameters that help in making comprehensive predictions on the risks of heart disease. After training the RNN with a Kaggle dataset and properly tuning it, the model obtained an accuracy level of 91%, which means that is highly useful in identifying risk factors of cardiovascular diseases and could be used for early diagnosis and risk factors identification. © 2024 IEEE.

关键词： Recurrent neural networks

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Rough Set Based Recipe Clustering Approach 10

Rough Set Based Recipe Clustering Approach

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Das, Priyanka Mukherjee, Chandrani Gupta, Samiksha Roy, Sayan Shah, Khushi Singh, Shreya Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350384369

Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categorical data sets is a crucial task. Conventional clustering analysis algorithms frequently fail to address the uncertainty surrounding the exact value of features found in these categorical data sets. Thus to aid in decision-making, rough set based clustering is frequently used. This study aims to identify the optimal clustering feature from the recipe data that will result in the grouping of related food products by applying the rough purity notion. The recipe data has been regarded as a categorical valued information system in this instance, and information theoretic attribute purity is taken into account using the rough purity technique. © 2024 IEEE.

关键词： Food chemistry

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Assessment Technology for Physiotherapy Practices using Deep Learning 2

Assessment Technology for Physiotherapy Practices using Deep...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Vineeth, Nandhini Darshan, P.M. Deepu, K. Daragaj, Arihant Prabhu, Ananth G. Bms College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370188

The innovative technology for physiotherapy assessment employing deep learning aims to offer a precise and streamlined approach to evaluating physiotherapy needs. Conventional manual assessment techniques utilized in physiotherapy often consume time and are susceptible to human inaccuracies, resulting in erroneous diagnoses and treatment strategies. This technology tackles these challenges by employing sophisticated deep learning algorithms that identify angles and offer auditory guidance to patients based on their posture. The process commences with capturing the patient's video through a webcam and extracting frames utilizing OpenCV. Following this, the frames undergo processing via a media pipe library, a sophisticated tool designed for body key point detection. These identified key points are subsequently utilized to connect essential body segments for a particular exercise and compute the angular measurements between them. Using these measurements, the system assesses the correctness of the posture, offering audio feedback with repetition counts for accurate postures and tailored guidance for incorrect ones. The audio cues are exercise-specific, delivering precise instructions to aid the patient in refining their posture. The system also keeps track of patient's progress over time and provides a visual display of their improvement. This helps the patient monitor their progress and provides motivation for them to continue with their therapy. The use of learning algorithms and the media pipe library makes this solution an accurate, efficient, and cost-effective method for physiotherapy assessments. © 2024 IEEE.

关键词： Deep learning

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Simulations and Advancements in MRI-Guided Power-Driven Ferric Tools for Wireless Therapeutic Interventions 3

Simulations and Advancements in MRI-Guided Power-Driven Ferr...

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3rd IEEE International Conference on AI in Cybersecurity, ICAIC 2024

作者： Chu, Wenhui Jin, Aobo Gohel, Hardik A. University of Houston-Victoria Dept. of Computer Science Victoria United States

ISBN: (纸本)9798350381856

Designing a robotic system that functions effectively within the specific environment of a Magnetic Resonance Imaging (MRI) scanner requires solving numerous technical issues, such as maintaining the robot's precision and stability under strong magnetic fields. This research focuses on enhancing MRI's role in medical imaging, especially in its application to guide intravascular interventions using robot-assisted devices. A newly developed computational system is introduced, designed for seamless integration with the MRI scanner, including a computational unit and user interface. This system processes MR images to delineate the vascular network, establishing virtual paths and boundaries within vessels to prevent procedural damage. Key findings reveal the system's capability to create tailored magnetic field gradient patterns for device control, considering the vessel's geometry and safety norms, and adapting to different blood flow characteristics for finer navigation. Additionally, the system's modeling aspect assesses the safety and feasibility of navigating pre-set vascular paths. Conclusively, this system, based on the Qt framework and C/C++, with specialized software modules, represents a major step forward in merging imaging technology with robotic aid, significantly enhancing precision and safety in intravascular procedures. © 2024 IEEE.

关键词： Magnetic resonance imaging

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An algorithm frequently mines a set of items for big data to enhance efficiency

An algorithm frequently mines a set of items for big data to...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Sonkar, Sanjay Kumar Singh, Balkar SunRise University Dept. of Computer Science and Engineering Rajasthan Alwar India

ISBN: (纸本)9798350382723

In the landscape of rapidly expanding data streams, deriving meaningful insights, particularly frequent itemsets, within massive datasets at a swift pace poses a significant challenge. Addressing this challenge, various methodologies for Frequent Itemset Mining have been proposed, yet they struggle with low support counts and efficiency concerns in handling large datasets. In response, our research introduces Jagged Itemset Counting (JIC) methodologies, aiming to effectively mine Frequent Itemsets from extensive data. The core objective revolves around devising a robust algorithm capable of identifying all Frequent Itemsets, irrespective of database size or the nature of the itemset. Central to this approach is the introduction of a straightforward label representation, GPLN (Geometric Progression Label Number), assigned to each frequent item. Utilizing CGPLN (Cumulative Geometric Progression Label Number), derived from the arithmetic sum of GPLNs within transaction subsets, forms the CGPLN-Label representation for each transaction subset (itemset). Comparative analysis reveals superior performance of JIC over Apriori and Eclat algorithms for small and medium-sized datasets, exhibiting efficiency even at minimal support thresholds. In the realm of Big data, where FP-Growth and Eclat falter, the proposed technique shines with faster execution times, optimized main memory utilization, and efficient disc memory usage. © 2024 IEEE.

关键词： data mining

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