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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Thute, Trupti Saratkar, Saniya Chaudhari, Aarti Raut, Rohini Thakare, Gayatri Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This article explores the potential of machine learning (ML) in transforming healthcare. It provides an overview of ML and its various categories, highlighting how supervised and unsupervised learning algorithms can be utilized for tasks like disease diagnosis, treatment personalization, and resource allocation. The article delves into specific applications of ML in healthcare, including image analysis for early disease detection, visualization of complex medical data, and personalized medicine approaches. It further explores the exciting opportunities presented by ML, such as improved diagnostics and treatment, increased efficiency and cost reduction, and advancements in research and development. However, the article also acknowledges the challenges associated with implementing ML in healthcare, including data privacy concerns, real-time data requirements, and the complexity of certain models. Overall, the article emphasizes the multifaceted landscape of ML in healthcare, showcasing its potential to revolutionize the industry while acknowledging the hurdles that need to be addressed for successful integration. © 2025 IEEE.

关键词： Theranostics

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Adversarial dual decision-based model for event-related opinion sentence recognition

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Multimedia Tools and Applications 2024年 1-18页

作者： Xiang, Yan Qin, Yuan Yu, Zhengtao Huang, Yuxin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China

Event-related opinion sentences recognition aims to mine the valuable comments discussing about the specific event from the mass of microblogs comments. It’s difficult to label sufficient comments for a new microblogs event, and domain adaptation methods are suitable for this task, which can transfer the knowledge from the source domain(labeled event) to the target domain (unlabeled event). However, existing domain adaptation models do not make full use of the domain invariance to realize categories alignments of the two domains. Therefore, we propose a adversarial dual decision model for opinion sentences recognition, which takes advantage of the domain invariance that the opinion sentences are correlated with the event description. The proposed model has two generators to obtain different event-correlated features, and two classifiers to make dual decisions based on the two features. Through the adversarial process of generators and classifiers, the model completes the category feature alignment and realizes the opinion sentences recognition for the new event. The comparative experiment results show that our model outperforms the state-of-the-art models on six opinion sentences recognition tasks. In addition, we verify the effectiveness of each component of our model by ablation experiments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Adversarial machine learning

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural Computing and Applications 2025年 1-23页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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A Hybrid Model for Forecasting Stock Prices Using Bayesian and LSTM 11th

A Hybrid Model for Forecasting Stock Prices Using Bayesian a...

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Pinapatruni, Rohini Mohammed, Faizan Mohiuddin, Syed Anas Patel, Dheeraj Department of Data Science and Artificial Intelligence Faculty of Science and Technology ICFAI Foundation for Higher Education Hyderabad India

ISBN: (纸本)9789819967056

Forecasting stock price and intraday direction is the main problem in the area of Quantitative Finance. This paper explores the efficacy of Bayesian Long Short-Term Memory Neural Network Model (to be precise LSTM + BNN) in price forecasting. Performance was tested against ML models like Random Forest, XGBoost, and Vanilla LSTM. Public data on Indian stocks of five companies Reliance, Dr Reddy, Dmart, TCS and Hindunilvr is collected from YahooFinance. Performance of the proposed model is measured using root mean square error and mean absolute error. The model is deployed through a webapp, which displays the predictions for stocks along with statistical metrics such as level of confidence and uncertainty with respect to the predictions. Objective of the proposed research is to create a hybrid model B-LSTM and compare the result with existing models which outperforms with low error rate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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AI Based Criminal Detection and Recognition System for Public Safety and Security using novel CriminalNet-228 4th

AI Based Criminal Detection and Recognition System for Publi...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

The recent surge in public space criminal activities underscores the need for an efficient system to promptly detect, recognize, and track criminals. Existing AI-based criminal detection literature, while insightful, has limitations such as the complexity to analyze video data, time, and speed of accuracy when it comes to training the algorithms that call for further advancements. Hence proposed AI-driven system addresses this demand, aiming to automate criminal identification, equipping law enforcement with a potent tool for proactive crime prevention and resolution. The system utilizes the innovative "CriminalNet-228" Convolutional Neural Network (CNN) architecture, meticulously trained on a vast criminal image dataset for enhanced detection accuracy. To bolster face detection, additional computational resources like parallel processing and distributed computing are employed, enabling real-time analysis of extensive CCTV footage. Notably, the system tracks identified criminals, providing law enforcement with real-time situational awareness. CriminalNet-228 achieves an impressive overall Mean Average Precision (mAP) of 0.65 and excels in detecting facial features as small as 4 × 4 pixels, demonstrating its detail-oriented recognition capabilities. When evaluating Proposed CriminalNet-228 in comparison to established state-of-the-art techniques like Fast-RCNN, Yolov7, and AlexNet, it surpassed them in terms of precision, recall, f-measure, and accuracy, achieving an impressive accuracy rate of 99.2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Efficient Image Captioning Based on Vision Transformer Models

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Computers, Materials & Continua 2022年第10期73卷 1483-1500页

作者： Samar Elbedwehy T.Medhat Taher Hamza Mohammed F.Alrahmawy Department of Data Science Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Department of Electrical Engineering Faculty of EngineeringKafrelsheikh UniversityEgypt Department of Computer Science Faculty of Computer and Information ScienceMansouraEgypt

Image captioning is an emerging field in machine *** refers to the ability to automatically generate a syntactically and semantically meaningful sentence that describes the content of an *** captioning requires a complex machine learning process as it involves two sub models:a vision sub-model for extracting object features and a language sub-model that use the extracted features to generate meaningful ***-based vision transformers models have a great impact in vision field *** this paper,we studied the effect of using the vision transformers on the image captioning process by evaluating the use of four different vision transformer models for the vision sub-models of the image captioning The first vision transformers used is DINO(self-distillation with no labels).The second is PVT(Pyramid Vision Transformer)which is a vision transformer that is not using convolutional *** third is XCIT(cross-Covariance Image Transformer)which changes the operation in self-attention by focusing on feature dimension instead of token *** last one is SWIN(Shifted windows),it is a vision transformer which,unlike the other transformers,uses shifted-window in splitting the *** a deeper evaluation,the four mentioned vision transformers have been tested with their different versions and different configuration,we evaluate the use of DINO model with five different backbones,PVT with two versions:PVT_v1and PVT_v2,one model of XCIT,SWIN *** results show the high effectiveness of using SWIN-transformer within the proposed image captioning model with regard to the other models.

关键词： Image captioning sequence-to-sequence self-distillation transformer convolutional layer

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Revolutionizing Influenza A Detection in Humans: Unleashing the Power of XGBoost Classification 2

Revolutionizing Influenza A Detection in Humans: Unleashing ...

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2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Nikhil, V. Sahaaya Arul Mary, S.A. Natarajan, B. Bajulunisha, A. Anuradha, R.S. Venkatraman, K. Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VIT University School of Computer Science Engineering Chennai India M.A.M College of Engineering and Technology Department of Artificial Intelligence & Data Science Tiruchirappalli621105 India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350383287

Influenza A, a zoonotic virus potentially affecting and infecting humans, poses a significant global health threat. This research paper presents a comprehensive study on predicting Influenza A outbreaks by applying the XGBoost classification algorithm. The study utilizes two substantial datasets: one from the World Health Organization (WHO) containing global Influenza A epidemiological data from 2003 to 2023 and the other encompassing Influenza A infections in New York counties from 2009 to 2019. Both datasets underwent pre-processing, with essential features selected to develop an XGBoost Classification model for forecasting Influenza A cases in humans. The models exhibited impressive accuracy and performance metrics. Specifically, the WHO dataset demonstrated an AUC of 0.996 and an accuracy score of 0.99, while the New York dataset displayed an AUC of 0.992 and an accuracy score of 0.978. These outcomes underscore the model's capability to differentiate between positive and negative cases, showcasing high precision, accuracy, and F1 scores. The implications of this research extend to various domains, including the early detection of outbreaks, informed decision-making processes, and the implementation of public health interventions. The findings emphasize the pivotal role of high-quality and comprehensive datasets in training effective predictive models. Furthermore, the study points towards potential future avenues, such as personalized predictions, geo-spatial analysis, and integrating these predictive models with healthcare systems. © 2024 IEEE.

关键词： Classification (of information)

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A Real-Time Framework for Automatic Sarcasm Detection Using Proposed Tensor-DNN-50 Algorithm 4th

A Real-Time Framework for Automatic Sarcasm Detection Using ...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

Social media platforms such as Twitter, Facebook, and Instagram are vast repositories of trending global news. They generate an enormous amount of data, offering a valuable resource for both academic researchers and IT marketing teams. These data can be harnessed for various insights and applications. A prominent and pressing research area within this data is the detection of sarcasm and fake news. The internet is rife with misleading and false information, often causing widespread confusion. Current research primarily categorizes news as either real or fake, using machine learning algorithms. However, this binary approach struggles with news that is partially true yet contains elements that could be deemed fake. To address this issue, our work introduces a real-time framework for sarcasm detection, utilizing an innovative deep learning technique called Tensor-DNN-50 for multiclass classification. The TDNN-50 model not only classifies news articles based on authenticity but also quantifies the level of sarcasm within them. Comparative analysis against leading techniques like SVM, att-RNN, and EANN demonstrates the superiority of our proposed algorithm. It achieves an impressive accuracy of 96.4% for two-class classification, 94.32% for four-class classification, and an astonishing 99.1% for seven-class classification. These results underscore the potential of our Tensor-DNN-50 Model in effectively addressing the intricate challenges of sarcasm detection and news classification with unparalleled accuracy across multiple classes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Prediction of Cost for Medical Care Insurance by Using Regression Models 8th

Prediction of Cost for Medical Care Insurance by Using Regre...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Sandra, J. Ruth Joshi, Sanjana Ravi, Aditi Kodipalli, Ashwini Rao, Trupthi Kamal, Shoaib Department of Information Science and Engineering Global Academy of Technology Bangalore India Department of Artificial Intelligence and Data Science Global Academy of Technology Bangalore India

ISBN: (纸本)9789819976324

Medical care is an essential part of every individual and will be utilized during their lifetime;to obtain the healthcare facilities, the expenditure plays a major role. An estimate of 514 million people was assured health insurance plans in India in the year 2021. As the number of in-patient days increases, there is an incremental rise in the medical expense. Insurance ensures protection and avoids risk of a financial loss, by aggregating a sum of money by an individual for a particular cause;therefore, medical insurance can offer financial security during a medical emergency. The objective of this paper is to build a regression-based machine learning model to analyse and predict medical expenses based on factors of age, smoking habits, BMI, gender, and region. In this research, several traditional regression models’ performances were studied for the healthcare insurance data. Random forest regressor performed the best among all the algorithms with the R-squared score of 0.8643297. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Health care

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A Real-Time Computer Vision-Based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System

A Real-Time Computer Vision-Based Green Intensity Estimation...

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

作者： Nagaraja Hebbar, N. Sandeep, Bhat Artificial Intelligence and Data Science Department Srinivas Institute of Technology Mangaluru India Computer Science and Engineering Department Srinivas Institute of Technology Mangaluru India

ISBN: (纸本)9789819746569

In India, one of the commercial crops is arecanut. The majority of arecanut growers depend on the arecanut production. However, they are also having a great deal of difficulty finding skilled workers to do pesticide spraying jobs. This effort uses computer vision technologies to address the aforementioned problem. This work compares the HSV estimation with the green pixel estimate approach. This makes it easier to position the robot at the right height, where the pesticide spray will work best. In this case, the HSV estimation identified a better way to manage the robot’s motion. Using a wireless camera, the experimental setup achieves an incredibly innovative performance to capture an image of the arecanut crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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