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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Shifa Tasmiah Tisha Sharmistha Chanda Tista Md Nazmul Hoq Dept. of Computer Science and Engineering Chittagong University of Engineering & Technology Chattagram Bangladesh Dept. of Computer Science and Engineering Concordia University Montreal Canada

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetic Retinopathy (DR) is a common consequence of diabetes mellitus, resulting in retinal lesions that can adversely affect vision. If undetected and mistreated, diabetic retinopathy can ultimately result in blindness. This study concentrates on creating an enhanced model for the precise classification of retinal illnesses, particularly Diabetic Retinopathy. The suggested model increases the precision and effectiveness of disease classification by utilising deep learning methods and attention mechanisms. The study analyses the performance of many pre-trained models, such as ResNet152, EfficientNetB7, and MobileNetV3Large, using a varied dataset of retinal pictures. The ResNet152 model, enhanced with the Spatial Attention Module, achieved the best results in retinal disease classification, with an accuracy of 87.65(%), precision of 89.88(%), recall of 90.69(%), and F1-score of 88.58(%). This performance surpasses other models and demonstrating the effectiveness of attention modules in improving classification accuracy and robustness. The study also highlights the competitive performance of the suggested model by contrasting it with earlier studies in the field. All things considered, this research advances the classification of retinal diseases and may improve patient treatment and ophthalmology diagnostic precision.

关键词： Deep learning Diabetic retinopathy Accuracy Transfer learning Retina Software Robustness Medical diagnostic imaging Diseases Residual neural networks

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Animated Virtual Therapist in Virtual Reality Environment for People with Parkinson's Disease 7

Animated Virtual Therapist in Virtual Reality Environment fo...

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7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Li, Wenting Agada, Ruth Yan, Jie Washington University Biomedical Engineering Dept. St. Louis United States Bowie State University Department of Computer Science Bowie United States

ISBN: (纸本)9798331521578

This study investigates the effectiveness of virtual rehabilitation using a 3D animated virtual therapist delivering the LSVT-BIG program for people with Parkinson's disease (PwPD). Integrating an animated virtual therapist in a VR environment aims to overcome accessibility barriers, especially for individuals in remote areas. This VR-enhanced system provides immersive experiences, real-time feedback, and posture correction through computer vision, along with progress-tracking via personalized reports. This approach offers potential improvements in accessibility, engagement, and motivation, which may contribute to enhanced quality of life for individuals with Parkinson's disease. © 2025 IEEE.

关键词： Virtual environments

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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

arXiv

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arXiv 2025年

作者： Khawaja, Wahab Jacobsen, Rune H. Hussain, Sajid Guvenc, Ismail Dept. Electrical and Computer Engineering Aarhus University Aarhus8000 Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology AJK Mirpur10250 Pakistan

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario. © 2025, CC BY.

关键词： Markov chains

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Unique MS Lesion Identification from MRI

Unique MS Lesion Identification from MRI

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Medical Imaging 2025: Image Processing

作者： Rivas, Carlos A. Zhang, Jinwei Wei, Shuwen Remedios, Samuel W. Carass, Aaron Prince, Jerry L. Image Analysis and Communications Laboratory Dept. of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Dept. of Computer Science Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781510685901

Unique identification of multiple sclerosis (MS) white matter lesions (WMLs) is important to help characterize MS progression. WMLs are routinely identified from magnetic resonance images (MRIs) but the resultant total lesion load does not correlate well with EDSS∗;whereas mean unique lesion volume has been shown to correlate with EDSS. Our approach builds on prior work by incorporating Hessian matrix computation from lesion probability maps before using the random walker algorithm to estimate the volume of each unique lesion. Synthetic images demonstrate our ability to accurately count the number of lesions present. The takeaways, are: 1) that our method correctly identifies all lesions including many that are missed by previous methods;2) we can accurately separate confluent lesions;and 3) we can accurately capture the total volume of WMLs in a given probability map. This work will allow new more meaningful analyses to be computed from WMLs in brain MRIs. © 2025 SPIE.

关键词： Ferrimagnetic resonance

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Calculating Critical Inertia of a Power System

Calculating Critical Inertia of a Power System

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Sneha, Fariha Hakim Liu, Yilu Martinez, Rocio Uria Dong, Jiaojiao University of Tennessee Dept. of Electrical Engineering and Computer Science Knoxville United States Oak Ridge National Laboratory United States

ISBN: (纸本)9798331504847

The increasing integration of renewable energy sources in modern power systems has led to a decline in system inertia, raising concerns about frequency stability following large disturbances. Determining critical inertia is essential to prevent excessive frequency decline and ensure grid stability. This paper evaluates four different methods for calculating critical inertia, using the Electric Reliability Council of Texas (ERCOT) system as a test case. The results highlight the importance of employing multiple methodologies to capture the full spectrum of inertia requirements. © 2025 IEEE.

关键词： Frequency stability

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Banknote Fitness Judgment System Using MemSeg Based on K-Means Memory Update

Banknote Fitness Judgment System Using MemSeg Based on K-Mea...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Bae, Hui-Jae Jung, Dae-Sik Kang, Byung-Jun Sangmyung University Dept. of Computer Science Seoul Korea Republic of College of Convergence Engineering Sangmyung University Dept. of Faculty of SW Convergence Seoul Korea Republic of LG AI Research Seoul Korea Republic of

ISBN: (纸本)9798331510756

In general, determining the authenticity of damaged banknotes can often be challenging. To address this, the Bank of Korea exchanges banknotes that are not suitable for circulation due to damage or wear. In this study, we propose a banknote authenticity judgment system using MemSeg based on K-means memory update. This system identifies damage to banknotes more objectively and quickly. When memory update is performed using K-means, normal images with various feature patterns are updated uniformly in the memory. The experimental results show that the image-AUROC is improved by an average of 4.27% compared to the existing method. This proves that the proposed method is more objective. © 2025 IEEE.

关键词： Electronic health record

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An Integrated Approach Using Ensemble Machine Learning and Deep Learning for Loan Approval Prediction

An Integrated Approach Using Ensemble Machine Learning and D...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Umme Ara Mahinur Istia Al-Amain Khandaker Mohammad Mohi Uddin Md. Tahzib Ul Islam Md. Abdul Based Dept. of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The suggested loan prediction system overcomes the drawbacks of conventional selection processes by utilizing machine learning (ML) to enhance the identification of creditworthy borrowers. A Random Forest (RF) model to predict loan is evaluated in our study along with data pretreatment using the Synthetic Minority Over-Sampling Technique (SMOTE) to solve the class imbalance. Nine ML models in all, including Deep Learning (DL) approaches, ensemble learning strategies, and conventional classifiers, were used in this work to reliably and successfully forecast loan acceptance results. According to a number of characteristics, the dataset utilized in this study aims to predict whether a loan application will either be approved or denied. The RF model obtained 94.10% of F1-Score, an Accuracy of 94.12%, Precision and Recall values of 94.24% and 94.12%, respectively, following the use of SMOTE for maintaining equilibrium of *** findings demonstrate RF's effectiveness in managing unbalanced datasets and its capacity to forecast loan approval outcomes. Because they automate the approval process, lower human error, and increase decision-making efficiency, loan prediction models are essential to financial organizations.

关键词： Deep learning Accuracy Computational modeling Organizations Predictive models Ensemble learning Reliability Random forests Long short term memory Classification tree analysis

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Heart Disease Prediction Using ML and Ensemble Models: A Comprehensive Analysis

Heart Disease Prediction Using ML and Ensemble Models: A Com...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Mirza Nadim Saad Umme Ara Mahinur Istia Al-Amain Md. Habibullah Belali Khandaker Mohammad Mohi Uddin Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Computer Science and Engineering Dhaka International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The leading cause of death worldwide is still heart disease, highlighting the urgent need for improved diagnostics. This research included a range of ML techniques. Employing SVM, RF, DT, KNN, and LGBM to predict heart illness. The assessment metrics consist of F1 score, recall, accuracy, ROC, and precision. To ensure accurate analysis, data pretreatment techniques including reduction, integration, transformation, and purification were applied. Hyperparameter adjustment improved the model's performance even further. Among the models analyzed, Naïve Bayes performed the worst, with an F1 score of 98%, accuracy of 69.63%, precision of 93%, and recall of 98%. Three distinct datasets were employed in the study, and their strengths were combined to enhance model discrimination, reduce overfitting, and raise overall accuracy. We combined DT, LGNM, and LGBM to create a number of models. From RF we are able to achieve an accuracy of up to 98.89%, Precision 99.00%, Recall 98.00% and F1-Score 99.00%. These results show how well ensemble methods and data integration may enhance machine learning-based heart disease prediction abilities.

关键词： Heart Measurement Analytical models Accuracy Medical services Predictive models Ensemble learning Diseases Classification tree analysis Overfitting

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Hybrid Offline Passive Grammatical Inference and Online Planning for Non-Markovian Tasks

Hybrid Offline Passive Grammatical Inference and Online Plan...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mahyar Alinejad Alvaro Velasquez Yue Wang George Atia Dept. of Electrical and Computer Engineering University of Central Florida Orlando Florida Dept. of Computer Science University of Colorado Boulder Colorado Dept. of Electrical and Computer Engineering Dept. of Computer Science University of Central Florida Orlando Florida

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Planning in non-Markovian environments often requires inferring task structures, such as reward machines, through interactions with the environment. Traditional active grammatical inference methods, like Angluin’s L * algorithm, depend on continuous querying to learn task structures for the underlying planning objectives. In contrast, we propose a hybrid approach that combines passive grammatical inference, using the Regular Positive and Negative Inference (RPNI) algorithm, with online planning. By leveraging pre-collected positive and negative trajectories, RPNI learns a deterministic finite automaton (DFA) that captures the task structure, significantly reducing the need for real-time interactions. Subsequently, online planning is conducted over the product MDP, which integrates the environment with the learned DFA. This hybrid methodology minimizes the cost of online interactions and improves learning efficiency in complex environments. Our approach outperforms baseline algorithms in terms of runtime and sample complexity, and is well-suited for real-world scenarios where task structures are implicit, and interactions with the environment are expensive.

关键词： Runtime Learning automata Signal processing algorithms Signal processing Inference algorithms Real-time systems Planning Complexity theory Trajectory Speech processing

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A Hybrid Machine Learning Approach Utilizing CNN Feature Extraction with Traditional Classifier to Identify Strawberry Leaf Diseases

A Hybrid Machine Learning Approach Utilizing CNN Feature Ext...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Sabbir Hossen Pabon Shaha Md. Saiduzzaman Dept. of Computer Science & Engineering Bangladesh University Dhaka Bangladesh Dept. of Computer Science & Engineering Mawlana Bhashani Science & Technology University Tangail Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Strawberry production is globally significant because it contains high nutrients. Strawberry leaf disease shapes a significant barrier to strawberry cultivation worldwide. Numerous strawberry leaf diseases can impede the growth of strawberry plants. Leaf Scorch is one of the dangerous strawberry leaf diseases caused by the fungus "Diplocarpon Ear-liana." Strawberry leaf disease diagnosis by traditional methods requires a lot of effort and time and is also challenging to identify accurately. In order to increase strawberry production, it is therefore essential to automatically detect strawberry leaf diseases. Machine learning and Deep learning have the potential to detect strawberry leaf diseases automatically. In this paper, we proposed a hybrid Machine Learning methodology using CNN feature extraction with the traditional ML classifier to detect Strawberry Leaf Diseases. For this instance, we used the strawberry leaf dataset from the PlantVillage Updated dataset. Based on our research, ResNet50 with Support Vector Machine diagnoses strawberry leaf scorch with 99.80% accuracy. This research assured sustainable advancements in strawberry production by integrating and offering an effective way to detect strawberry leaf diseases.

关键词： Deep learning Support vector machines Shape Production Feature extraction Nutrients Convolutional neural networks Medical diagnosis Diseases Residual neural networks

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