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Prediction of Heart Disease Risk in Early Ages with Boosting Techniques 1

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Yadav, Jyotsna Rahman, Habib Ur Department of Computer Science and Engineering Kanpur Institute of Technology Kanpur India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

Heart disease is one of the top causes of mortality globally. Early detection can save a person's life. In previous five years, it is found that young individuals are also experiencing heart disease. In order to automate the medical industry and to forecast cardiac illness, artificial intelligence (AI) provides a prediction model. We could employ a number of AI algorithms to predict the risk of heart disease as in previous work, done in this field;generally, authors have used single classifiers. In this work, we have used two approaches of AI. In the first approach, single or base classifiers like logistical regression (LR), support vector machine (SVM) and decision tree (DT) are examined. In the second approach, boosting classifiers such as adaptive boost (AdaBoost), extreme gradient boost (XGBoost) and gradient boost are analyzed, and then comparison is done between single classifiers and boosting classifiers and found that boosting model achieved highest accuracy and the most accurate model as compared with single classifiers in predicting heart disease risk in youngers. So a type of ensemble model called boosting model is employed to prevent overfitting and to offer the highest accuracy as compared to single models. With the XGBoost classifier, we obtained 96.29% accuracy, with AdaBoost classifier, we obtained 91.35% accuracy, and with the gradient boost classifier, we achieved the highest accuracy of 98.76%, which is best in prediction of heart disease ages between 28 and 50. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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MEC-EnergySaver: Unleashing Efficiency Through D2D and Data Compression 16

MEC-EnergySaver: Unleashing Efficiency Through D2D and Data ...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Ghosh, Anindita Mukherjee, Poulomi De, Tanmay National Institute of Technology Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350383119

The shift from basic cell phones to smartphones has ushered in intelligent devices, alongside a surge in mobile apps that reshape daily life. Simultaneously, technologies like data analytics, AI and IoT have enabled smart homes and advanced transport systems, demanding substantial computational power from mobile devices. To tackle this challenge, mobile-edge computing (MEC) has emerged as a solution. Unlike remote cloud computing, MEC offers computing services at the network's edge, allowing mobile users to transfer their computing tasks to MEC servers located nearby. However, MEC systems face challenges in efficient computation offloading and distributing resources in a way that reduces energy use and processing times. The evolution of mobile devices and the rise of data-intensive applications have led to the need for efficient computing solutions like MEC, with the potential to integrate data compression for energy savings. This paper introduced a two-phase approach for request servicing in MEC systems. The first phase prioritizes Device-to-Device (D2D) connections based on device proximity, efficiently pairing devices for data exchange. Unpaired devices are directed to the nearest MEC node. In the second phase, requests not fulfilled via D2D connections are handled by strategically positioned MEC nodes, ensuring comprehensive coverage and minimizing network energy consumption. © 2024 IEEE.

关键词： D2D communication data compression energy efficiency MEC

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Priority-Based MPTCP Scheduler for 5G and Beyond Heterogeneous Links 16

Priority-Based MPTCP Scheduler for 5G and Beyond Heterogeneo...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Gumasthi, Nikhil B. Baswade, Anand M. Indian Institute of Technology Bhilai Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Modern cellular devices often have dual interfaces for providing high-speed data transfer. This dual-interface design enhances versatility and user convenience. Dual interface technology allows 5G NR devices to connect to the 5G core using two separate base stations. One base station operates in the mmWave frequency range, and the other operates in the sub-6 GHz frequency range. However, millimeter-wave (mmWave) signals face challenges in terms of limited penetration through obstacles, such as buildings, and susceptibility to atmospheric absorption, leading to reduced coverage and potential signal degradation in adverse weather conditions. On the other hand, sub-6 GHz waves offer better penetration through obstacles and a broader scope compared to mmWave, making them suitable for providing reliable and expansive wireless connectivity in urban and indoor environments. Both mmWave and sub-6 GHz frequencies have distinct advantages and limitations;mmWave provides high data rates but has limited coverage, while sub-6 GHz offers a broader range but at lower data rates. However, in a heavy data transfer scenario, frequent loss in the mmWave link while transmitting packets can lead to packet drops (wireless medium), reducing the congestion window size and increasing the flow completion time. To mitigate this, we propose to use analytical hierarchy process (AHP) and grey rational analysis (GRA) based priority allocation mechanism to schedule packets on the most reliable link, thereby reducing the packet drops and flow completion time. © 2024 IEEE.

关键词： 5G NR Multipath TCP (MPTCP) network simulator-3 (NS-3) network simulator-3-mmWave priority scheduling

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MediMind: A Comprehensive Health Prediction and Record-Keeping Platform 6th

MediMind: A Comprehensive Health Prediction and Record-Keep...

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6th International Conference on Information Systems and Management Science, ISMS 2023

作者： Naik, Darshana A. Sharma, Aditya Mahajan, Danish Khandelwal, Dhruv Gajya, Arpit Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9783031707889

The "MediMind" project signifies a revolutionary fusion of predictive analytics, centered on diabetes and heart disease risk assessment, coupled with state-of-the-art skin disease prediction and robust Electronic Health Record (EHR) management. By employing advanced machine learning models like Logistic Regression and Support Vector Machines (SVM) for diabetes and heart disease predictions, alongside the cutting-edge Convolutional Neural Network (ResNet-50) for precise skin disease classification, "MediMind" achieves unparalleled accuracy and applicability. The predictive models exhibit exceptional performance metrics, boasting high accuracy, precision, recall, and F1-score. Seamless integration into Electronic Health Records (EHR) ensures immediate implementation in clinical decision-making, providing healthcare professionals with precise insights into diabetes and heart disease risks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electronic health record

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NLP Based Video SparkNotes Assistant 4

NLP Based Video SparkNotes Assistant

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Bhilare, Amol Gaikwad, Namrata Kurapati, Aditya Dabade, Ronak Saklani, Daksh Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798331515720

This research presents a novel method of automatic video summarising and note-generating using Natural Language Processing (NLP) and audio recognition techniques. The exponential rise in internet video footage has increased demand for effective summarising methods. The proposed approach uses speech recognition to transcribe audio content, which is further processed with natural language processing (NLP) algorithms to provide succinct summaries. Furthermore, the system creates notes based on the main ideas that are taken from the transcript of the movie. The spoken words in the video are converted to text using voice recognition technology. After that, this study interpret and condense this material using specialized computer techniques, making it more concise yet nonetheless useful. Furthermore, our technology creates notes that highlight significant scenes or topics from the video. © 2024 IEEE.

关键词： Natural language processing systems

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Interpreting Fake Reviews Using Machine Learning and Deep Learning

Interpreting Fake Reviews Using Machine Learning and Deep Le...

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World Conference on Information Systems for Business Management, ISBM 2023

作者： Bhat, Mohammad Qazim Jayalakshmi, D.S. Mallegowda, M. Geetha, J. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819983452

The issue of fake reviews on online platforms has increasingly become a huge concern in recent years, with the potential to mislead consumers and negatively impact businesses. In this paper, we present a comprehensive approach to detecting fake reviews using both supervised and unsupervised learning techniques. Our approach includes classic machine learning algorithms, deep learning techniques such as RNN and attention networks, as well as state-of-the-art models like BERT and GPT. We leverage a labeled dataset of restaurant reviews from *** to train and evaluate our models. We also compare the performance of supervised and unsupervised learning techniques and identify the most effective and explainable models for detecting fake reviews. Our results show that our approach achieves high accuracy in detecting fake reviews, and the interpretation of our models offers valuable insights into the factors that contribute to the identification of fake reviews. We believe our work contributes to the ongoing effort of combating fake reviews and provides a practical and effective solution for businesses and consumers to identify trustworthy reviews. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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MedHAI: Improved Framework for medical diagnosis using human experts 5

MedHAI: Improved Framework for medical diagnosis using human...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Gunika Sangal, A.L. National Institute of Technology Jalandhar Department of Computer Science and Engineering India

ISBN: (纸本)9798350376753

While machine learning (ML) models can be trained on large amounts of data, their applicability in critical domains such as cyber-attacks and clinical applications is still limited. Recent research suggests that ML models' predictions when combined with those of human experts, a technique known as human-in-the-loop improves model performance. Due to their complementary strengths, combining them for critical areas like medical, self-driving cars proves beneficial. Also, human experts are scarce and costly, hence it is not possible to take expert advice or annotation for every instance. We propose an approach, which we call MedHAI (Medical Human in AI loop) to reduce reliance on human experts by deferring decisions only when confidence levels for machine learning models' prediction are low and when probabilities fall beyond dynamically determined threshold ranges. The pretrained AI model produces calibrated probabilistic outputs and dynamically determines whether to accept the classifier's output or defer to human expertise based on probability thresholds and calibrated confidence levels. The expert advice along with image are then used to predict the output. This combined model using human-in-the-loop uses calibration and improves reliability in terms of accuracy by about 6% on ChestX-ray8 dataset. © 2024 IEEE.

关键词： calibration deep learning disease detection Human AI human-in-the-loop

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Detection of Skin Cancer using CNN 8

Detection of Skin Cancer using CNN

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8th International Conference on Trends in Electronics and Informatics, ICOEI 2025

作者： Ronald Doni, A. Varun, Mahanthi Kumar, Mannepalli Manoj Nagarajan, G. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331544607

Skin diseases have been on the rise in human beings over the past few years. Ringworm, yeast infections, and allergies are typically caused by germs or infections. As infections can easily spread, it is imperative to detect them early to avoid further transmission. Clinical signs are necessary for the effective diagnosis of such illnesses. Various skin diseases affect individuals at different ages. For instance, children between the ages of 0 and 5 tend to have dermatitis, whereas those between the ages of 6 and 11 are most affected by acne. Teenagers between the ages of 12 and 16 are most susceptible to vulgaris. Children between the ages of 5 and 15 can also suffer from dermatomyositis. Adults aged between 40 and 60 also experience different skin diseases. Early detection and proper treatment are essential for tracking and containing the spread of these diseases. © 2025 IEEE.

关键词： Dermatitis

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Tolerant Testing and Distance Estimation for Distributions Under Memory Constraints 50th

Tolerant Testing and Distance Estimation for Distributions...

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50th International Conference on Current Trends in Theory and Practice of computer Science, SOFSEM 2025

作者： Roy, Sampriti Vasudev, Yadu Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9783031826962

We investigate tolerant testing and distance estimation problems in distribution testing when the amount of memory is limited. In particular, our aim is to provide a good estimate of the distance measure (in total variation distance) using constant memory words. Though the problems are well understood in the standard access model with an Ω(nlogn) lower bound, it has not been explored under memory constraints. We propose algorithms that require O(nϵ7) queries for estimating distance to a known distribution and O(nϵ6) queries (where ϵ∈(0,1) is an error parameter) for estimating distance to uniformity that use only O(1) memory words. Our algorithms match the Ω(nϵ4) lower bound established by [19] in terms of the dependence on n. Additionally, we present an (ϵ,cϵ) tolerant closeness tester in terms of 2 distance for all c≥3, which requires O(n3mϵ2) samples and O(m) bits of memory for logn/ϵ4≤m≤n/ϵ2. Furthermore, we outline an O(n2ϵ12mϵ24) trade-off for (ϵ1,ϵ2) tolerant closeness testing problem (0121 distance, where logn≤m≤nlogn is the available memory in bits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distribution testing Property testing Streaming

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An enhanced AMBTC for color image compression using color palette

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Multimedia Tools and Applications 2024年第11期83卷 31783-31803页

作者： Xiong, Lizhi Zhang, Mengtao Yang, Ching-Nung Kim, Cheonshik School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Computer Engineering Sejong University Seoul Korea Republic of

Digital image has been used in various fields as an essential carrier. Many color images have been constantly produced since their more realistic description, which takes up much storage space and network bandwidth. Thus, color image compression has become an essential key technology. Absolute Moment Block Truncation Coding (AMBTC) has been widely studied as one of the classical image compression methods. However, in the existing methods, the visual quality of the reconstructed images and the compression rate are all relatively low. Therefore, this paper proposes an enhanced AMBTC for color image compression using a color palette. In the proposed method, the K-means clustering algorithm is utilized for training the image's palette pattern. The color palette obtained by K-mean will be more suitable for reconstructing this image than the standard color palette, and the visual quality will be higher. The six clustered central pixels are matched with the palette through a color difference formula, and the obtained index values are used as the quantization levels. Huffman coding is used to build a bitmap to achieve a higher compression rate, that is, a lower bit rate. At last, a block of a color image can be represented by six index values and a bitmap. Experimental results and theoretical analysis demonstrate that the proposed method has better visual quality and bit rate than similar schemes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

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