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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Britto, Akash Ij Abhishek, S. Dhanush, S. Salim, Soja Soniya, B. Sct College of Engineering Dept. of Computer Science Engineering Thiruvananthapuram India

ISBN: (纸本)9798331507541

Autonomous vehicles are poised to revolutionize the transportation industry by offering safer and more efficient navigation in dynamic environments. A critical challenge is managing interactions with other vehicles, pedestrians, and road obstacles, which complicates framing autonomous driving as a supervised learning problem. This study leverages Deep Q-Networks (DQN), a reinforcement learning technique, to control steering, throttle, and braking in autonomous vehicles. The DQN approach enables vehicles to autonomously learn and improve driving strategies, thereby enhancing their ability to navigate complex traffic scenarios effectively. Our objective is to train a model using a Deep Reinforcement Learning (DRL) algorithm, guiding the vehicle to adhere to a predetermined itinerary while adapting to real-time traffic conditions. This work emphasizes the potential of reinforcement learning for enabling continuous learning and adaptation in complex traffic scenarios, laying the groundwork for more reliable and adaptive autonomous driving systems. © 2025 IEEE.

关键词： Deep reinforcement learning

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Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna Federated Learning 21

Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna...

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

ISBN: (纸本)9783903176553

We study joint downlink-uplink beamforming design for wireless federated learning (FL) with a multi-antenna base station. Considering analog transmission over noisy channels and uplink over-the-air aggregation, we derive the global model update expression over communication rounds. We then obtain an upper bound on the expected global loss function, capturing the downlink and uplink beamforming and receiver noise effect. We propose a low-complexity joint beamforming algorithm to minimize this upper bound, which employs alternating optimization to breakdown the problem into three subproblems, each solved via closed-form gradient updates. Simulation under practical wireless system setup shows that our proposed joint beamforming design solution substantially outperforms the conventional separate-link design approach and nearly attains the performance of ideal FL with error-free communication links. © 2023 IFIP.

关键词： Beamforming

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A Machine Learning Approach for Predicting Personality Types from Text Patterns Using Natural Language Processing(NLP) 8

A Machine Learning Approach for Predicting Personality Types...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Ganguly, Kinshuk Saha, Subhajit Mondal, Anupam Dey, Monalisa Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798331520762

This study explores the use of Natural Language Processing (NLP) approach to predict Myers-Briggs Type Indicator (MBTI) personality types from Twitter texts. Initially, we approached the problem as a 16-class classification task, but encountered significant challenges due to the lack of instances representing certain class labels adequately. To address this, we decomposed the MBTI types into four dimensions and trained individual classifiers for each dimension. Leveraging the best performing classifiers, we constructed a stacking ensemble model to predict personality classes corresponding to each dimension. The results demonstrate improved accuracy compared to the initial 16-class classification approach. Additionally, the performance of ensemble model is found to be better than the individual classifiers. Thus, ensemble approach paired with the separate dimension based classification method can be used for effective prediction of personality types for psychological profiling. © 2025 IEEE.

关键词： Natural language processing systems

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Enhancing Medical Image Analysis: Leveraging Transfer Learning in Convolutional Neural Networks for Tuberculosis Detection 9

Enhancing Medical Image Analysis: Leveraging Transfer Learni...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Dahiwadkar, Piyush Pankaj Bhamre, Hrishin Mandar Sahitya, Aarti Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350394474

Advancements in medical imaging have paved the way for image-based computer-aided diagnosis (CADx) systems, revolutionizing the detection of lung abnormalities, including lung nodules and diffuse lung diseases. This paper explores the potential of convolutional neural networks (CNNs) in image-based CADx without the reliance on image-feature extraction, presenting a powerful alternative to feature-based CADx methods. Additionally, we introduce an image-based computer-aided detection (CADe) algorithm employing regions with CNN features (R-CNN) for precise identification of lung abnormalities. We conduct comprehensive evaluations of our image-based CADx utilizing CNN and CADe employing R-CNN, demonstrating their effectiveness across various lung abnormalities. © 2024 IEEE.

关键词： computer aided diagnosis

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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment System 'Stationary 3-Axis Fire-Emergency Response Base' 8

Designing and Implementing an IoT-Based Long-Distance Fire-E...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kundu, Shyantan Dey, Swarajit Abdullha, Md. Mustafa Sen, Gobinda Gangopadhyay, Malay Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India Institute of Engineering & Management Dept. of Electronics & Communication Engineering Kolkata India

ISBN: (纸本)9798331520762

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named 'Stationary 3-Axis Fire-Emergency Response Base' (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management. © 2025 IEEE.

关键词： Fire hazards

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Smart Iridology: Deep Learning for Predictive Health Insights

Smart Iridology: Deep Learning for Predictive Health Insight...

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2024 IEEE International Conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Avhad, Vedika Bakal, Jagdish W. Dept. of Computer Engineering Maharashtra India

ISBN: (纸本)9798350388602

This study aims to develop a reliable, non-invasive method for predicting early health issues through iridology by leveraging computer vision and deep learning techniques. Iridology, which involves the analysis of iris patterns, is explored for its potential to provide early indications of health conditions. The methodology involved capturing high-quality iris images using a specialized camera, followed by processing and analysis using advanced computer vision techniques. A deep learning model was implemented to detect early stages of conditions such as diabetes, hypertension, and heart diseases. The model achieved an average accuracy of 93.7%, demonstrating superior performance in precision, sensitivity, and specificity compared to other leading machine learning approaches. These findings suggest that the proposed method could serve as a robust tool for early health issue detection, with implications for enhancing public health outcomes and reducing healthcare costs. © 2024 IEEE.

关键词： Contrastive Learning

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Deep Vision: Iris Segmentation for Diagnostic Insights in Iridology 1

Deep Vision: Iris Segmentation for Diagnostic Insights in Ir...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Avhad, Vedika Bakal, Jagdish W. Dept. of Computer Engineering Maharashtra India

ISBN: (纸本)9798350353648

Ocular diseases, particularly diabetic retinopathy, are increasingly prevalent and necessitate precise detection methods. The proposed method, 'Deep Vision: Iris Segmentation for Diagnostic Insights in Iridology,' leverages deep learning for enhanced prediction of diabetes-related ocular conditions. Data from the diabetes-iridology dataset is pre-processed with Contrast Limited Adaptive Histogram Equalization (CLAHE) [1]. The Attenuation-based U-Net model [2] ensures precise iris segmentation. For improved classification, DenseNet [3], EfficientNet [4], and Capsule Networks (CapsNet) [5] are evaluated. The Python-based implementation achieves an impressive accuracy rate of 95.87% [6], demonstrating a substantial improvement over conventional method. This approach not only advances diagnostic performance but also sets a new benchmark for precision in ocular disease prediction. © 2024 IEEE.

关键词： accuracy rate Attenuation-based U-Net benchmark Capsule Networks (CapsNet) Contrast Limited Adaptive Histogram Equalization (CLAHE) deep learning DenseNet diabetic retinopathy diagnostic performance EfficientNet iris segmentation Ocular diseases Python

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Calorie Burn Estimator on Stationary Bike using Human Body Pose Detector 24

Calorie Burn Estimator on Stationary Bike using Human Body P...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Batan, Iwap Saputra Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Ramadhani, Ahmad Dept. of Electrical Engineering Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350313956

During the pandemic, we must maintain our body's immunity at best. Outdoor activities have been closed;therefore, there are limitations to activities that can be done. A solution to staying healthy during the pandemic is indoor exercise. One type of indoor exercise is regularly riding stationary bikes. We maintain the body's fitness and burn calories by riding stationary bikes. However, some stationary bikes could not show accurate calories burned;they calculated it only based on speed, resistance, and duration, so people with different weights seem to burn the same amount of calories. Therefore, this paper proposed an additional parameter essential in calorie burning: body weight. We used the human body pose estimator to detect the activity of riding the stationary bike and calculated it by adding the person's weight. The result shows a difference between the estimated calorie burned and the data directly from the stationary bike. The difference is between 0.0225 to 2.8775. The heavier the person, the more differences in the calories burned. The proposed method helped prove that a person's weight affects the calories burned while doing an activity, especially riding a stationary bike. © 2023 IEEE.

关键词： Bicycles

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Study on the Analysis and Reduction Method for AC Copper Loss in Electric Vehicle Traction Motor

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Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 295-301页

作者： Wang, Chang-Hyeon Kim, Nam-Ho Choi, Ho-Yong Jung, Sang-Yong Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Special Motor Engineering 1 Team Hyosung Heavy Industies Korea Republic of

The advancements in electrified drive systems have enabled the design of high-power density electric vehicle (EV) motors. However, the increased operating frequency necessary for achieving high-power density amplifies AC copper losses, primarily due to skin and proximity effects. This study focuses on reducing AC copper losses in an 8-pole, 48-slot Interior Permanent Magnet Synchronous Motor (IPMSM) using 2D finite element analysis (FEA). The analysis evaluates the contributions of skin and proximity effects to total copper losses and investigates the influence of slot leakage flux. Additionally, the impact of slot and conductor geometries on leakage flux, its variation over time, and the resulting AC copper losses were systematically analyzed. By balancing manufacturability and performance, optimal design parameters were derived to minimize AC copper losses. The results demonstrate that adjustments in slot width, slot dept., and conductor placement significantly affect leakage flux and copper losses. The proposed design strategies improve motor efficiency and thermal performance under high-speed operating conditions, contributing to the development of high-performance EV motors. Copyright © The Korean Institute of Electrical Engineers.

关键词： Permanent magnets

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Enhanced Classification of Alzheimer's Disease Using Deep Learning and Machine Learning Models with MRI Scans

Enhanced Classification of Alzheimer's Disease Using Deep Le...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Salahudin, Amna Qadeer, Eeman Ahmed, Ayesha Fahad, Labiba Gillani Dept. of Software Engineering FAST National University of Computer and Emerging Sciences Islamabad Pakistan Dept. of Computer Science FAST National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (纸本)9798331510503

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disease, which necessitates early and accurate diagnosis for effective management. This research presents a comprehensive approach to the classification of Alzheimer's disease using Magnetic Resonance Imaging (MRI) scans, focusing on four distinct classes: nondemented, mild, very mild, and moderate demented. Our main goals are to combine diverse datasets to improve the robustness and adaptability of the model and to fully investigate Alzheimer's disease in all four stages. To achieve these goals, we employed a combination of machine learning and deep learning models. The models utilized include Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM), Gabor filters with SVM, Gabor-Gray Level Co-occurrence Matrix (GLCM) with SVM, ResNet50, AlexNet, and InceptionV3. Among these, the InceptionV3 model achieved the highest accuracy of 96.20% using 5-fold cross-validation followed the Resnet50 model achieving accuracy of 95.43%. Our approach addresses key challenges such as class imbalance, high intra-class variation, inter-class similarities, and dataset variability, demonstrating significant potential in improving Alzheimer's disease classification and detection. © 2024 IEEE.

关键词： Gabor filters

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