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共 804 条记录，以下是171-180 订阅

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Personalized Healthcare: Utilizing Probabilistic Models for Disease-Drug Association and Treatment Prediction

Personalized Healthcare: Utilizing Probabilistic Models for ...

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conference on data science and machine learning applications (cdma)

作者： Sarah Abed Alsubhi Mashael Al-Luhaybi Rawabi Abod ALlihyani Raghad Abdulaziz Alharbi Futun Salem Alharbi Batool Mohammed Alharbi Computer Science Department JUC Umm Al-Qura University Makkah Saudi Arabia

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

the current healthcare system faces challenges in delivering treatment recommendations personalized to individual patient needs, leading to issues such as misdiagnosis, delayed treatment plans, and harmful drug interactions. In response to these challenges, personalized medicine has emerged as a critical component in healthcare, aiming to provide patient-specific clinical treatment recommendations based on individual conditions, thereby enabling more accurate diagnoses and treatments. machine learning (ML) has proven to be a crucial approach in advancing personalized medicine and healthcare. Numerous ML algorithms have been implemented to generate suitable recommendations tailored to individual patient conditions. However, most of these algorithms lack explain ability and reasoning behind their decisions, often relying on advanced black-box models. In this paper, we implement the Bayesian networks algorithm, which utilizes a probabilistic learning approach that is both explainable and effective due to its ability to learn, represent relationships, and exploit correlations between variables, thereby enabling ethically informed predictions of risks and side effects. Using graphical models, healthcare providers can deliver individualized care by proposing methods and treatment plans adapted to each patient's specific needs and conditions. this approach aims to minimize side effects and provide precise treatment recommendations, thereby enhancing overall patient care. We conducted three experiments to develop explainable predictive models, exploring three predictive classes: drug class, drug activity, and the side effects of drugs associated with diseases such as Allergies, Alzheimer's, Cancer, and Stroke, etc. the predictive models achieved high accuracy rates, ranging from 82% to 99%, and obtained very reasonable validation accuracies.

关键词： Drugs Adaptation models Accuracy machine learning algorithms Precision medicine Predictive models Prediction algorithms Probabilistic logic data models Bayes methods

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Predictive Maintenance in Industry 4.0: A Review of data Processing Methods 16

Predictive Maintenance in Industry 4.0: A Review of Data Pro...

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging data and Industry 4.0, EDI40 2025

作者： Ul Hassan, Ietezaz Panduru, Krishna Walsh, Joseph IMaR Research Centre Munster Technological University Co Kerry V92 CX88 Ireland

Industries require a revolution with the aim to improve productivity, sustainability, reduce costs, achieve customer satisfaction, and adopt emerging technologies. the current industrial revolution, known as Industry 4.0, is defined by smart technologies, which include robotics, big data, artificial intelligence (AI), and the internet of things (IoT). A breakdown in industrial process has consequences in terms of safety, productivity, and efficiency. Predictive maintenance is a cornerstone of Industry 4.0, which predicts the remaining useful life of equipment's, devices, and machines using condition monitoring, data analytics, and machine learning (ML). this article reviews a number of methods, including signal analysis, statistical analysis, and artificial intelligence-based methods, that can be used for performing predictive maintenance on the collected data from machines or equipment's. the signal processing and statistical analysis methods require domain knowledge, but the AI methods require labelled data (most of the data is healthy and lacks faulty data), which is a challenging task. © 2025 Elsevier B.V.. All rights reserved.

关键词： Condition monitoring Next generation heavy machinery Non-invasive testing Predictive maintenace Smart machines

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A Comparative Study of machine learning Models for Plant Disease Identification 19th

A Comparative Study of Machine Learning Models for Plant D...

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19th international conference on Soft Computing Models in Industrial and Environmental applications, SOCO 2024

作者： Mara, Măcelaru Pop, Petrică Barata, José Department of Mathematics and Computer Science North University Center at Baia Mare Technical University of Cluj-Napoca Cluj-Napoca Romania Electrical and Computer Engineering Department School of Sciences and Technology NOVA University of Lisbon Lisbon Portugal

ISBN: (纸本)9783031750090

Early identification of plant diseases plays an important role in reducing the financial losses for farmers and enhancing the productivity and quality of crops. the present work proposes five machine learning (ML) algorithms: K-Nearest Neighbors, Random Forest, Iterative Dichotomizer 3, Adaptive Boosting and Logistic Regression for plant disease identification. In order to test the proposed ML algorithms, we used a large data set containing 74798 images mainly from PlantVillage dataset. the achieved experimental results show that the proposed ML techniques can be used to reliably identify various plant diseases. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Leveraging LSTM for Predictive Modeling of Satellite Clock Bias

Leveraging LSTM for Predictive Modeling of Satellite Clock B...

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conference on data science and machine learning applications (cdma)

作者： Ahan Bhatt Ishaan Mehta Pravin Patidar Space Applications Centre ISRO Ahmedabad Gujarat

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

Satellite clock bias prediction plays a crucial role in enhancing the accuracy of satellite navigation systems. In this paper, we propose an approach utilizing Long Short-Term Memory (LSTM) networks to predict satellite clock bias. We gather data from the PRN 8 satellite of the Galileo and preprocess it to obtain a single difference sequence, crucial for normalizing the data. Normalization allows resampling of the data which ensures that the predictions are equidistant and complete. Our methodology involves training the LSTM model on varying lengths of datasets, ranging from 7 days to 31 days. We employ a training set consisting of two days' worth of data in each case. Our LSTM model exhibits exceptional accuracy, with a Root Mean Square Error (RMSE) of 2.11 × 10 −11 . Notably, our approach outperforms traditional methods which are used for similar time-series forecasting projects, being 170 times more accurate than RNN, 2.3 × 10 7 times more accurate than MLP, and 1.9×10 4 times more accurate than ARIMA. this study holds significant potential in enhancing the accuracy and efficiency of low-power receivers used in various devices, particularly those requiring power conservation. By providing more accurate predictions of satellite clock bias, the findings of this research can be integrated into the algorithms of such devices, enabling them to function with heightened precision while conserving power. Improved accuracy in clock bias predictions ensures that low-power receivers can maintain optimal performance levels, thereby enhancing the overall reliability and effectiveness of satellite navigation systems. Consequently, this advancement holds promise for a wide range of applications, including in remote areas, IoT devices, wearable technology, and other devices where power efficiency and navigation accuracy are paramount.

关键词： Training Satellites Accuracy Receivers Satellite navigation systems data models Reliability Root mean square Long short term memory Clocks

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / the 8th international conference on Emerging data and Industry 4.0, EDI40 2025

16th International Conference on Ambient Systems, Networks a...

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging data and Industry 4.0, EDI40 2025

the proceedings contain 166 papers. the topics discussed include: smartphones and syntax: a quantitative study on harnessing the role of mobile-assisted language learning in the digital classroom and applications for language learning;context-driven service provisioning for ubiquitous systems;building heat-resilient communities: a collaborative approach to beat the heat;risk management in transportation systems: how big data can help predict behaviors and events;customized contraction hierarchies with flow-based natural cut heuristic;integrating establishments in an agent-based modeling framework for urban parcel shipments on the first and last mile;exploring the adoption and innovation of digital twins in healthcare;agent-based modeling of on-street parking supply and demand;and enhancing printed Lingala script recognition using deep learning techniques.

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44th SGAI international conference on Innovative Techniques and applications of Artificial Intelligence, SGAI 2024

44th SGAI International Conference on Innovative Techniques ...

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44th SGAI international conference on Innovative Techniques and applications of Artificial Intelligence, SGAI 2024

ISBN: (纸本)9783031779176

the proceedings contain 52 papers. the special focus in this conference is on Innovative Techniques and applications of Artificial Intelligence. the topics include: Semantic Segmentation for Landslide Detection Using Segformer;vision-Based Human Fall Detection Using 3D Neural Networks;Drone-Assisted Infrared thermography and machine learning for Enhanced Photovoltaic Defect Detection: A Comparative Study of Vision Transformers and YOLOv8;evaluating Algorithms for Missing Value Imputation in Real Battery data;using Pseudo Cases and Stratified Case-Based Reasoning to Generate and Evaluate Training Adjustments for Marathon Runners;Emotion Detection in Hindi Language Using GPT and BERT;Classification and Recommendation of Mental Health Assistance Events Using an RNN-LSTM, Fast-And-Frugal Trees and Weighted Sum System;digit Detection: Localizing and Convoluting;djinn—data Journalism Interface for Newsgathering and Notifications;advancing Financial Text Sentiment Analysis with Deep learning and Ensemble Models;explaining a Staff Rostering Problem Using Partial Solutions;formalise Regulations for Autonomous Vehicles with Right-Open Temporal Deontic Defeasible Logic;SLANGO - the Initial Blueprint of Privacy-Oriented Legal Query Assistance: Exploring the Potential of Retrieval-Augmented Generation for German Law Using SPR;an Ensemble Modelling of Feature Engineering and Predictions for Enhanced Fake News Detection;a Child-Robot Interaction Experiment to Analyze Gender Stereotypes in the Perception of Mathematical Abilities;reinforcement learning for Patient Scheduling with Combinatorial Optimisation;nursing Activity Recognition for Automated Care Documentation in Clinical Settings;exploring Efficient Job Shop Scheduling Using Deep Reinforcement learning;respiratory Disease Detection Using Deep Convolutional Transformer Models;Evaluating the Performance of LLMs When Translating Saudi Arabic as Low Resource Language;Bi-directional LSTM Applied to the Maritime Target Motion Ana

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44th SGAI international conference on Innovative Techniques and applications of Artificial Intelligence, SGAI 2024

44th SGAI International Conference on Innovative Techniques ...

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44th SGAI international conference on Innovative Techniques and applications of Artificial Intelligence, SGAI 2024

ISBN: (纸本)9783031779145

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Retinal Image Analysis for Detection of Diabetic Macular Edema Using Transfer learning

Retinal Image Analysis for Detection of Diabetic Macular Ede...

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conference on data science and machine learning applications (cdma)

作者： Tayyaba Asim Shahzad Akbar Usama Shahzore Amjad Rehman Tanzila Saba Noor Ayesha Riphah College of Computing Riphah International University Faisalabad Pakistan AIDA Lab. CCIS Prince Sultan University Riyadh Saudi Arabia Zhengzhou University Zhengzhou China Henan RP China

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

Diabetic Macular Edema (DME) is a disease of the eye's retina and it's a major factor of causing vision problems and leads towards blindness if it is undiagnosed. Early detection of DME can prevent vision loss and may reduce diabetic-related problems like cardiovascular issues. therefore, this study presents a method for detecting DME through Optical Coherence Tomography (OCT) and fundus images using transfer learning. the proposed method is based on four stages: Pre-processing, augmentation, segmentation through DeepLabV3+, and binary classification of DME by using two (02) publicly available datasets; the first dataset is Messidor-2, which contains fundus images, and the second dataset is Retinal Images of Optical Coherence Tomography (OCT). In the Messidor-2 dataset, the total number of images is 1744, and Retinal OCT Images dataset consists of 84,495 images total, separated into four categories (CNV, DME, DRUSEN, NORMAL). In the proposed method, Convolutional Neural Networks (CNN) architectures ResNet50 and VGG-19 have been used for the detection of the DME. Convolutional Neural Networks (CNNs) have been extensively used in medical imaging analysis and classification. Using the well-known ResNet50 architecture for classification of each dataset, the proposed model yielded an accuracy of 98.79%, 99% of F1 Score, 98.43% of Precision, and recall of 98.89%. By using VGG-19, the proposed model gives an accuracy of 98.81%, 98.94% of Fl Score, 98.1 % of Precision and recall of 98.73%. When both models (ResNet50 and VGG-19) were compared, the VGG-19 gave the best accuracy.

关键词： Image segmentation Accuracy Optical coherence tomography Transfer learning Computer architecture Retina Diabetes Topology Convolutional neural networks Residual neural networks

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machine learning Approach of Stent Placement for Coronary Artery Disease Patients—A Hypothetical Approach 8th

Machine Learning Approach of Stent Placement for Coronary Ar...

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8th international conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Ramakrishna, B. Sekhar, B.V.D.S. Lakshmi, Sripada V. S. S. Murthy, K. Sreerama Swarnandhra College of Engineering andTechnology Narsapur India S R K R Engineering College Chinamiram Rural India Srinidhi Institute of Science and Technology Ghatkesar India

ISBN: (纸本)9789819784219

Heart disease is a major cause of mortality worldwide, and stent placement is an effective treatment for many heart-related conditions. In this paper, we propose a novel robotic stent operation technique that uses an angiogram artery to identify and treat blocked areas in the heart. the images are real time images from an angiogram procedure. these images need to be obtained from the angiogram video, converting them into frames and images thus obtained are preprocessed [1, 2, 3, 4, 5–6]. the technique involves converting angiogram images into a tree data structure and using the shortest path algorithm to reach the blocked area. Motor-driven stent placement ensures precise and accurate placement, saving time and improving patient safety. machine learning algorithms can enhance the identification of nodes and branches in the angiogram image, and predictive models can be developed to assess stent placement success. Additionally, incorporating shortest path algorithms into robotic-assisted stent placement can further improve the precision and efficiency of the procedure, ultimately leading to better patient outcomes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Trees (mathematics)

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A Vision Transformer-Based Framework for Detection and Classification of Soybean Leaf Diseases

A Vision Transformer-Based Framework for Detection and Class...

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conference on data science and machine learning applications (cdma)

作者： Usama Shahzore Shahzad Akbar Ijaz Ali Shoukat Amjad Rehman Tanzila Saba Tanjin Sikder Riphah College of Computing Riphah International University Faisalabad Pakistan AIDA Lab. CCIS Prince Sultan University Riyadh Saudi Arabia Department of Engineering and IT Ajman University Ajman UAE

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

Soybean is among most cultivated oil seed crop worldwide. Leaf diseases pose a serious threat to the yield of soybean crops which could lead to huge economic losses. It is essential to diagnose these diseases for effective management and mitigation. Traditional methods involved physical inspection of leaves which often led to errors and inconsistencies. Various deep learning models were developed using Convolutional Neural Networks but lacked comprehensive feature extraction and attention mechanisms. this research established a methodology for detecting and classifying soybean leaf diseases using pre-trained Vision Transformer. Research was performed using the Auburn Soybean Disease Image dataset, Soybean Diseased Leaf and SoyNet. the datasets were divided into 70% training, 10% validation and 20% testing sets and evaluated using standard performance metrics. For the Auburn Soybean Disease Image dataset, the proposed model achieved an accuracy of 98.7 % with precision, recall and F1-score values of 99.4%, 99.2% and 99.3% respectively. On the Soybean Diseased Leaf dataset, the proposed model achieved an accuracy of 98.9% with a 98.6% recall precision of 98.4% and an F1-score of 98.5%. For the SoyNet dataset, despite a larger and more complex dataset, the model achieved an accuracy of 97.4%, with precision, recall and F1-score values of 97.0%, 97.2% and 97.1 % respectively. the training and validation losses of model decreased consistently across epochs, indicating effective learning and classification. the proposed model will help farmers classify soybean leaf diseases with potential applications in precision agriculture and plant health monitoring.

关键词： Training Productivity Accuracy Biological system modeling Prevention and mitigation Transformers Seeds (agriculture) Standards Diseases Testing

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