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26th International Conference on computer and Information technology, ICCIT 2023

作者： Rafi Imam, M.D. Jyoti, Oishi Afrin, Zakia Munawar Hossain, Md. Hossain Mou, Tamanna Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Our study harnesses the power of NLP to contribute to the identification of suicidal ideation in text data. In the realm of mental health, identification of suicidal ideation at an early stage is of paramount importance for suicide prevention efforts. This paper presents a novel approach to suicide ideation detection using Natural Language Processing (NLP) techniques. Faced with a dearth of publicly available datasets for this critical task, we have contributed a valuable resource by curating a dataset from the 'SuicideWatch' and 'depression' subreddits on the Reddit platform, collected via the Pushshift API. Specifically, we employ the LSTM and a Random Forest classifier separately to achieve promising results in this vital area of research. This work not only advances the field of NLP-based suicide ideation detection but also contributes a valuable dataset for future investigations, potentially saving time and resources for researchers and professionals dedicated to the prevention of suicide and the improvement of mental health. We could achieve up to 93% accuracy in suicidal thought analysis using NLP techniques. © 2023 IEEE.

关键词： API LSTM NLP Pushshiftf Random Forest Classifier Reddit

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Design of All Textile Ultra-Wideband Monopole Antenna for Wearable Applications 12

Design of All Textile Ultra-Wideband Monopole Antenna for We...

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12th International electrical engineering Congress, iEECON 2024

作者： Thaiwirot, Wanwisa Ardhan, Jariyanee Prengsri, Saowarat Akkaraekthalin, Prayoot King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

This paper proposes the design of all textile ultra-wideband (UWB) monopole antenna. The textile antenna structure includes of the octagonal-shaped patch and modified ground plane. Conventional rectangular ground plane is modified as curve-shaped ground plane with circular parasitic patch. By using this ground plane structure, it can shift the lowest edge of impedance bandwidth to lower frequency and enhances the directivity. The textile antenna can provide a compact size having dimension of 35 × 40 × 1 mm3. Based on simulation, the impedance bandwidth of antenna is 138% (2.36-12.78 GHz) and maximum gain is 5.19 dBi at 7 GHz. The textile antenna achieves very wide bandwidth covering ISM band and UWB which is suitable for wearable applications. © 2024 IEEE.

关键词： Slot antennas

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FPMT: Enhanced Semi-supervised Model for Traffic Incident Detection 27th

FPMT: Enhanced Semi-supervised Model for Traffic Incident De...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Lu, Xinying Xiao, Jianli School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9783031781063

For traffic incident detection, the acquisition of data and labels is notably resource-intensive, rendering semi-supervised traffic incident detection both a formidable and consequential challenge. Thus, this paper focuses on traffic incident detection with a semi-supervised learning way. It proposes a semi-supervised learning model named FPMT within the framework of MixText. The data augmentation module introduces Generative Adversarial Networks to balance and expand the dataset. During the mix-up process in the hidden space, it employs a probabilistic pseudo-mixing mechanism to enhance regularization and elevate model precision. In terms of training strategy, it initiates with unsupervised training on all data, followed by supervised fine-tuning on a subset of labeled data, and ultimately completing the goal of semi-supervised training. Through empirical validation on four authentic datasets, our FPMT model exhibits outstanding performance across various metrics. Particularly noteworthy is its robust performance even in scenarios with low label rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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ACDD: Automated COVID Detection using Deep Neural Networks

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IEIE Transactions on Smart Processing and Computing 2023年第6期12卷 518-525页

作者： Raza, Ghulam Musa Shoaib, Muhammad Kim, Byung-Seo Department of Software and Communications Engineering Hongik University Sejong Korea Republic of School of Electrical Engineering and Computer Science National University of Science and Technology Islamabad Pakistan

December 2019 witnessed the outbreak of a novel coronavirus, thought to have started in the Chinese city of Wuhan. The situation worsened owing to its quick spread across the globe, leading to a worldwide pandemic that became known as COVID-19. To suppress the pandemic, early detection of positive COVID-19 patients has become highly important. There is a lack of precise automated tool kits available for use in diagnosing medical conditions, so auxiliary diagnostic tools are in high demand. Important information about this virus can be extracted from X-ray images, which can be used in conjunction with advanced artificial intelligence. This study addresses the unavailability of physicians in remote areas, and the complex algorithm proposed in this study can find potential matches for patients in rural areas who need care. This could help to improve access to care for those who need it most. The purpose of this study is to develop a novel model that can automatically detect COVID-19 by utilizing chest X-ray images. The proposed model incorporates binary and multi-class classification and can be employed by radiologists for timely detection of the COVID-19 virus in an effective manner. © 2023 The Institute of Electronics and Information Engineers.

关键词： COVID-19

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Time-Invariant Beampattern Design in Frequency Diverse Array Radar: Some New Results 4

Time-Invariant Beampattern Design in Frequency Diverse Array...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Shaiza Ahmed, Obaidullah Ali, Hussain Zaib, Alam Ahmed, Sajid National University of Sciences and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan National University of Sciences and Technology Department of Electrical Engineering Military College of Signals Islamabad Pakistan COMSATS University Islamabad Department of Electrical and Computer Engineering Islamabad Abbottabad22060 Pakistan King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences and Engineering Division Thuwal23955-6900 Saudi Arabia

ISBN: (纸本)9798350391862

A new frequency diverse array (FDA) radar system is proposed for range estimation of a target using time-invariant beampattern. To illuminate a desired range, the transmit beampattern is optimized using the inverse fast Fourier transform (IFFT) technique. This allows for a longer dwell period, which improves the accuracy of weak target identification. To lessen noise effects in range estimation, the minimum power distortionless response (MPDR) beamformer is employed. The precision of range estimation is derived using the Cramér-Rao lower bound (CRLB). Based on simulation data, the FDA design that has been proposed performs better than current approaches as measured by the root-mean-squared error (RMSE). The proposed FDA radar shows superior target localization performance compared to existing techniques, achieving the CRLB at a signal-to-noise (SNR) value of -5 dB. © 2024 IEEE.

关键词： Mean square error

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Real Time computer Vision Based Robotic Arm Controller with ROS and Gazebo Simulation Environment 14

Real Time Computer Vision Based Robotic Arm Controller with ...

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14th International Conference on electrical and Electronics engineering, ELECO 2023

作者： Aksoy, Egemen Çakir, Arif Dorukan Erol, Berat Alper Gumus, Abdurrahman Izmir Institute of Technology Electrical and Electronics Engineering Turkey Izmir Institute of Technology Computer Engineering Turkey

ISBN: (纸本)9798350360493

Robotic arms are widely prevalent and find utility in a variety of applications. However, a significant and widespread challenge faced by these arms is their inability to replicate the intricate functionalities of a human hand, primarily due to the distinct structure of the hand. The primary objective of this work is to create a simulation of a robotic arm that can replicate the movements and functions of a human hand in real-time. Data obtained from hand and arm gestures created with Mediapipe will be transferred in real-time to the robotic arm that is visualized and simulated on ROS and Gazebo. Thus, the hand and arm movements of the user in front of the camera will be effective in real-time on the manipulable joints. This advancement holds the potential to facilitate the construction of a robot capable of emulating both the hand and the arm of humans with high fidelity, thereby enabling comprehensive control over the robotic arm's actions in real-time. © 2023 IEEE.

关键词： Robotic arms

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Explainable AI Based Malaria Detection Using Lightweight CNN

Explainable AI Based Malaria Detection Using Lightweight CNN

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Faruq Goni, Md. Omaer Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350316001

A customized lightweight Convolutional Neural Network (CNN) for the fastest malaria detection from RBC images with explainability is presented in this study. Accuracy, precision, recall, f1-score and number of model parameters are considered for evaluating the proposed method. It is called lightweight as it has a small number of parameters (0.17 M). A small number of model parameters can reduce the processing time generally. It's performance is compared with the well-known transfer-learning (TL) model's performance. Additionally, the proposed method is also compared with state-of-the-arts (SOTA) models. In both the cases, the proposed method performed similarly or better in every performance criterion as mentioned earlier. The proposed CNN model achieves 99.45%, 99.75%, 99.17% and 99.46% scores on accuracy, precision, recall and f1-score respectively. Explainable Artificial Intelligence (XAI) tool 'SHAP' (Shapley Additive Explanations) is used to explain the decision of the proposed model. The results show that the proposed lightweight CNN is an efficient model in terms of performance parameters as mentioned for malaria detection. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhancing Grid Resilience Through Hybrid Renewable Energy Systems: Challenges, Control Strategies, and Future Directions: A Review 33

Enhancing Grid Resilience Through Hybrid Renewable Energy Sy...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Lumina, Sampi Denis Elvis Mnguni, Mkhululi Tshemese-Mvandaba, Nomzamo Cape Peninsula University of Technology Electrical Electronic and Computer Engineering Cape Town South Africa

ISBN: (纸本)9798331535162

Expanding renewable energy integration in a traditional grid is a vital means to strengthen the contribution of renewable resources in the energy landscape, aligning with the maintenance of adequate power systems and carbon peaking objectives, especially when power systems networks look into providing reliable, efficient and safe voltage to loads end consumption. As electricity demand keeps growing, and technologies evolve, traditional power systems are at some stage no longer able to provide sufficient power to the load. Emerging in this regard is the idea of incorporating new technology in the traditional grid to improve the generation capacity. Hybrid Renewable Energy appears to be a suitable solution for improving grid resilience and rentability, creating a primacy for power system performance benchmarking to its objectives. Thus, call to look at some contributions in the field of Hybrid Renewable Energy influence in power systems. © 2025 IEEE.

关键词： Hybrid power

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Enhancing Stroke Prediction through Interpretable AI: Distinguishing Stroke Cases from Non-Stroke Cases 6

Enhancing Stroke Prediction through Interpretable AI: Distin...

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6th International Conference on electrical Information and Communication technology, EICT 2023

作者： Islam, Nazmin Kibria, Hafsa Binte Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350370836

A sudden interruption of blood supply to part of the brain can result in a stroke, causing impairment and possible fatalities due to damage to brain cells. Rapid identification and intervention are crucial for preventing strokes and minimizing severe brain damage. The integration of Artificial Intelligence (AI) and machine learning (ML) shows promise in improving healthcare practitioners' ability to predict strokes swiftly and accurately. This study focuses on creating and assessing a stroke prediction model using a Random Forest (RF) classifier, supported by various performance metrics like AUC, precision, recall, F1-score, and accuracy, achieving a commendable accuracy rate of 94.6%. To address the class imbalance in the training set, a Random Over-Sampler was employed. Moreover, interpretability is enhanced through Explainable AI (XAI) techniques, specifically Shapley Additive Values (SHAP) and Local Interpretable Modelagnostic Explanations (LIME). By establishing an automated screening framework, this research aims to provide healthcare professionals with tools to offer more personalized and efficient care, potentially transforming stroke prevention and treatment. © 2023 IEEE.

关键词： Lime

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Analog High-Level Synthesis for Field Programmable Analog Arrays

Analog High-Level Synthesis for Field Programmable Analog Ar...

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2024 IEEE Opportunity Research Scholars Symposium, ORSS 2024

作者： Hanks, Luke Lonergan, Cullen Richardson, Karsten Hasler, Jennifer Mathews, Pranav Ige, Afolabi Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9798350390698

In this paper, we describe our effort to extend the development of a standard framework for analog computing through further developing and integrating an existing high level synthesis (HLS) tool for analog system design. These Python and Scilab based tools allow designers to design and implement reconfigurable systems on field-programmable analog arrays (FPAA). In doing this, we can provide a way to have the same ease of development that digital integrated circuits (ICs) have with the field-programmable gate-array (FPGA). We describe the importance of analog computing, the state of the old tool flow, our contributions to upgrading the tool flow, and our demonstration of the working tools. © 2024 IEEE.

关键词： High level synthesis

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