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共 20250 条记录，以下是151-160 订阅

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HFai: Deep Networks for Detecting Heart Failure Progression 3

HFai: Deep Networks for Detecting Heart Failure Progression

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Patnaik, Bipasha Kumar Nayak, Debasish Swapnesh Kumar Sahoo, Santanu FET-ITER Siksha 'o' Anusandhan University Department of Electronics and Communication Engineering Bhubaneswar India Centurion University of Technology and Management Soet Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798331528201

Heart Failure affects millions worldwide, and early identification is crucial to prevent serious complications. Current diagnostic approaches depend mostly on human interpretation and are frequently inaccurate. Artificial Intelligence models are efficient for estimating heart failure progression since they provide healthcare professionals with more accurate and automated tools. This study aims to train and evaluate DL models (1D-CNN, DNN, ANN) on raw and feature-engineered cardiac arrest detection datasets. The performance of this model was first evaluated using heart disease data. On the raw dataset, ANN outperformed with the highest Accuracy of 80% and AUC of 82.74%. After feature engineering using XGBoost, ANN's performance increased with an Accuracy of 95.08% and an AUC of 93.99%, indicating that feature engineering improves prediction. These findings emphasize the potential based on AI models in developing more reliable and automated diagnosis techniques for cardiac diseases. Future research will integrate real-time patient monitoring data with improved predictive models to detect developing health issues and allow for more accurate early diagnosis. This technique can transform heart failure therapy by providing personalized and more effective medicines, improving clinical decision-making and patient outcomes. © 2024 IEEE.

关键词： Personalized medicine

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Prediction of Insurance Claims for Health Sector using Machine learning Techniques 3

Prediction of Insurance Claims for Health Sector using Machi...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pragatheeswari, E. Kavitha, M.N. Saranya, S.S. Dhanushree, D. Jasmitha, S. Abiradhie, P.V. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science and Design Kongu Engineering College Perundurai Erode India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. Health insurance has become a vital aspect of people's lives. In this massive community, to access healthcare services such as insurance policies, LIC, ICICI, HDFC ERGO, Star Healthcare are benefits for claiming an amount for their medical expenses. The dataset encompasses three categories, such as generalized data, hospitalized data and claim data. Each record in the dataset represents an individual’s health insurance charges along with corresponding demographic and health-related characteristics. In this paper, Machine learning techniques are used to group the claim amount that has been adopted by the individuals based on the hospitalized expenditure and insurance charges paid by the individuals. In the health sector, machine learning techniques are used to forecast insurance claims by building predictive models from historical data on claims, patient demographics, treatment types, and results. By predicting future claims based on trends seen in the data, these models help insurance firms better manage risk and allocate resources. In case of a fresher to this concept, it feels challenging to understand whereas generating as a pattern can be more flexible to acquire knowledge about how much amount has been claimed. © 2025 IEEE.

关键词： Electronic health record

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intelligent Recognition Technology for Medical Laboratory Report Images Based on Deep learning 3

Intelligent Recognition Technology for Medical Laboratory Re...

引用

3rd international conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Cai, Qianzhuo Zheng, Sheng Liu, Jianke School of electrical and information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350365443

In the medical field, the accurate identification and utilization of laboratory report information is crucial for enhancing the efficiency and accuracy of diagnosis and treatment. This study employs deep learning technology to systematically research and develop techniques for extracting information from medical laboratory reports. We designed and implemented a series of algorithms that effectively convert image-based laboratory reports into structured data through layout analysis, text detection, text recognition, and table recognition. Experimental results show that the proposed methods achieve high levels of accuracy, significantly enhancing the automation of laboratory report information processing. This provides substantial technical support for medical institutions in handling large volumes of laboratory report data. © 2024 IEEE.

关键词： Deep learning

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Prognostic Evaluation of Liver Tumors through Computed Tomography Imaging 3

Prognostic Evaluation of Liver Tumors through Computed Tomog...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Devi, A.R. Karthik, S. Mohan, B. Harikrishnan, P. Vigneshwaran, P. Rengaraj, T. Sarathkumar, D. P.S.R. Engineering College Department of Biomedical Engineering Tamilnadu Sivakasi India P.S.R. Engineering College Department of Ece Tamilnadu Sivakasi India Kalasalingam Academy of Research and Education Department of Ece Tamilnadu Krishnankoil India Kongu Engineering College Department of Electrical and Electronics Engineering Perundurai Tamilnadu Erode638 060 India

ISBN: (纸本)9798331515911

Liver tumors present a significant challenge in the realm of public health, requiring early identification and precise prognostication to enhance patient outcomes. The current investigation delves into the utilization of machine learning methodologies to improve the accuracy of liver tumor prediction. A holistic approach is recommended, involving the gathering of medical imaging data such as CT scans and MRI images from a diverse cohort of patients. These images are subjected to preprocessing steps to extract relevant features, establishing a resilient dataset for further analysis. The results of the study showcase the effectiveness of machine learning algorithms in precisely forecasting the presence of liver tumors. The developed predictive models demonstrate elevated levels of sensitivity and specificity, emphasizing their potential as valuable resources for early detection and treatment strategizing. This research emphasizes the potential of machine learning in the realm of medical imaging, illuminating its ability to improve clinical decision-making processes and patient care. © 2025 IEEE.

关键词： Image segmentation

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data-Driven Approaches for Personalized Sleep Disorder Diagnosis 3

Data-Driven Approaches for Personalized Sleep Disorder Diagn...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Asmita, R. Hrithik, T.H. Akhil, K.H. Mishra, Nisha Kirthika Devi, V.S. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

These days, sleeping disorders are a concern for young people and people with a variety of health issues. It is important to keep in mind that sleep problems are not a recent occurrence and should not be sought after or accepted as a trend, even though it is true that issues related to sleep have garnered more attention and discussion in recent years due to growing knowledge and research. Numerous problems, such as anxiety, depression, and mental health strain, can be brought on by this illness. Machine learning (ML) can handle a wide range of tasks, including classification, prediction, and decision-making. To analyze and identify sleeping disorders using ML and can also be employed to gauge a person’s level of stress. This paper presents the DL models and ML algorithms that have been deployed to sleep stage classification in the literature to date. The effectiveness of Adaboost models is investigated in this work. These models’ research findings suggest the ideal parameter combination for improved performance. © 2025 IEEE.

关键词： Sleep research

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Automail AI: intelligent Voice-Controlled Email Generator 3

Automail AI: Intelligent Voice-Controlled Email Generator

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Jayaram, Kavitha Mandagere, Samarth Gopisetti, Sampriti Sahit, S. Bnm Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

The complexity of contemporary communication further emphasizes the need to automate monotonous work to increase efficiency and effectiveness. This paper introduces a new advance, voice-controlled Automail AI, in the form of an email generation tool that automatically sends an email according to end-user-specified keywords. Automail AI utilizes NLP, ML, and Speech Recognition to differentiate formal emails from informal ones and draft appropriate responses as the situation demands. It has been tuned with Naive Bayes Classifier that yields a better level of accuracy as compared to models such as Decision Tree, SVM, and Logistic Regression. Setting the benchmark for automated Communication tools, Automail AI combines diverse technologies like SMTP to guarantee secure Email delivery, Google Text-to-Speech or GTTS for auditory feedback, and a module providing an interface for speech recognition that offers the possibility to use the tool with no hands. © 2025 IEEE.

关键词： Automail AI email generation GTTS machine learning Naive Bayes natural language processing SMTP speech recognition

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WildGuard: automated Barrier System for Animal Attack Prevention 3

WildGuard: Automated Barrier System for Animal Attack Preven...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Begum, S. Ariffa Subbulakshmi, N. Reddy, A. Balakrishna Kumar, Battu Aravind Reddy, B. Sandeep Kumar Reddy, B. Manikanta Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Taminadu Krishnan Koil India

ISBN: (纸本)9798331532420

In areas that are close to forests, human-wildlife conflict poses serious threats to both human life and property. In order to detect and categorise animals in real-time CCTV camera video feeds, this study presents an intelligent system that makes use of deep learning techniques, particularly Convolutional Neural Networks (CNNs). Using Internet of Things components, the system classifies animals as either harmful or non-threatening and initiates automated reactions for the latter. A red warning sign is triggered in the village, a sound-based trap is set up to draw the animal within the camera's field of view, and notifications are sent to the appropriate authorities when a dangerous animal is spotted. By improving safety by early detection and real-time response, this proactive system seeks to safeguard communities that are close to woods. © 2024 IEEE.

关键词： Invertebrates

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Comparative Analysis of ResNet for Offline Signature Verifcation using Various Activation Functions 3

Comparative Analysis of ResNet for Offline Signature Verifca...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pandey, Gaurav Kumar Saxena, Pankaj Singh, Poonam Srivastava, Sumit Computer Science and Engineering Department Birla Institute of Technology Mesra Ranchi India MCA Department Raja Balwant Singh Management and Technical Campus Agra India Computer Science and Engineering Department Hindustan College of Science and Technology Mathura India

ISBN: (纸本)9798331527549

Signature verification is an essential function in biometric authentication, especially for safeguarding document integrity and confirming identity. This research investigates the utilization of ResNet, a deep convolutional neural network, for offline signature verification. We conduct a comparative analysis of ResNet-18 utilizing various activation functions, emphasising ReLU (Rectified Linear Unit) and ELU (Exponential Linear Unit). The selection of an activation function can profoundly affect model performance, influencing both convergence speed and total accuracy. We utilize a publicly accessible signature dataset (CEDAR) to assess the models’ performance, assessing critical metrics such as accuracy. Extensive investigation reveals that selecting activation function is crucial in achieving a balance between learning capacity and generalization. Our findings indicate that ResNet-18 with the SWISH activation function surpasses both ELU and ReLU in specific contexts, especially in managing the subtleties of signature variations. This research provides significant insights into enhancing deep learning models for biometric applications, offering a framework for advancing signature verification systems. © 2025 IEEE.

关键词： Authentication Protocol

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A Survey on Predicting Depression Among Teachers and Students at Metropolitan University (Sylhet, Bangladesh) Using Machine learning 3

A Survey on Predicting Depression Among Teachers and Student...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Tabassum, Anika Ema, Yasmin Firuza Rafee, Md. Mushtaq Shahriyar Limon, Md Shamihul Islam Khan Metropolitan University Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798331515911

Depression, a psychiatric condition marked by profound sadness and apathy, influences cognitive processes, emotions, and behaviors, causing psychological and physiological challenges. Everyday tasks become arduous, life loses vibrancy, and in extreme cases, may lead to suicidal tendencies. University students face unique stressors due to academic, social, and personal pressures, underscoring the need for mental health support. This thesis investigates depression diagnosis among Metropolitan University teachers and students using machine learning techniques. Survey data, comprising 516 anonymized responses covering mental well-being, mood, and behavior, form the basis. The researchers preprocess data, visualize it, and train models to detect depression indicators, evaluating techniques like Logistic Regression, Decision Trees, and Gradient Boosting for effectiveness. The research highlights that the reasons for depression differ between females and males. The aim is to enhance early detection and support mechanisms, fostering proactive intervention within the university community. © 2025 IEEE.

关键词： Students

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intelligent Recognition of Traffic Safety Facilities Using DINO Algorithm in Deep learning 3

Intelligent Recognition of Traffic Safety Facilities Using D...

引用

3rd international conference on Electronic Information engineering and Computer, EIECT 2023

作者： Lu, Lingxin Xiao, Zhonglin Zhou, Lu Southwest University College of Artificial Intelligence Chongqing China

ISBN: (纸本)9798350357707

In an era of rapid technological advancement, the deep integration of computer technology with various fields, including traffic engineering, has become increasingly profound. Traditional methods of facility inspection and management suffer from inefficiencies and incomplete information, challenges that are effectively addressed by the intelligent and automated capabilities of computer technology. This pioneering study employs the DINO deep learning algorithm, focusing on comprehensive facility inspection and proposing an efficient intelligent recognition model. The model achieves an outstanding mean Average Precision (mAP) of 80.6%. Simultaneously, the article compares the training results of DINO with its predecessors, DN-DETR and Deformable DETR, highlighting the significant performance advantage of DINO. In comparison to previous research, this study places greater emphasis on the identification of various types of traffic safety facilities, further significantly enhancing the efficiency of traffic facility management and road safety. © 2023 IEEE.

关键词： Accident prevention

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