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Automated Bengali abusive text classification: Using Deep Learning Techniques 1

Automated Bengali abusive text classification: Using Deep Le...

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1st IEEE International Conference on Advances in electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Titli, Showni Rudra Paul, Shimul National Institute of Technology Dept. Electronics and Communication Engineering Calicut India Ahsanullah University of Science and Technology Dept. Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350348057

In recent years, the breakdown of most human causes that hampers a large number of people is verbal abuse. The most noticeable fact is that most of the verbal abuse is made online by people behind the screen. While this problem has occurred with growing modern technologies, a modern solution is needed to solve the drawback. The growing rate of abusive comments and hate speech online is rapidly increasing on a large scale, and manual reports and corrections cannot help this critical situation. This proposed model introduces automation of the hate speech filtering process through deep learning. This work uses only Bengali datasets to find the real classification, as many Bengali and English mixed research works are available in this field. Using the intelligent automated classification of text comments in a limited resource-constrained language (example: Bengali) is critical for several reasons. This proposed system classifies 'Personal Offensive Text', 'Geographical Offensive Text', 'Religious Offensive text', 'Crime Offensive Text', 'Entertainment, Sports, Meme Tiktok, and others'. By combining two large datasets for this research and employing Bengali BERT, the best feasible outcome has been *** paper indicates the complete results of the combined two different datasets and classified into nine segments, while the proposed Bengali BERT models achieved the highest accuracy of 0.706 and a weighted f1 score of 0.705 in the identification and classification tasks. © 2023 IEEE.

关键词： Automation

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Comparison of Phase-Shifted and Phase-Disposition PWM Schemes in an Asymmetrical Cascaded H-Bridge Converter

Comparison of Phase-Shifted and Phase-Disposition PWM Scheme...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Lingom, Pascal Agamloh, Emmanuel Jouanne, Annette Von Yokochi, Alex Song-Manguelle, Joseph Unruh, Roland Baylor University Dept. Electrical and Computer Engineering WacoTX76706 United States Baylor University Dept. Mechanical Engineering WacoTX76706 United States University of Quebec Dept. Electrical and Computer Engineering Trois-RivieresQC76706 Canada Paderborn University Power Electronics/Electrical Drives Paderborn Germany

ISBN: (纸本)9798350376067

This paper presents a detailed and comprehensive performance comparison between phase-disposition (PD) and phase-shifted (PS) pulse-width-modulation (PWM) schemes. The comparison is carried out in a single-phase, two-cell asymmetrical cascaded H-bridge (ACHB) converter that uses DC-voltage sources chosen according to a geometric progression with a factor of two and three. The converter controlled by each PWM method is designed to generate an output voltage waveform with 7 and 9 levels under 2:1 and 3:1 DC voltage ratios, respectively. The paper analyzes the effects of PWM-induced harmonic components of each method on the converter's output current and voltage waveforms. The comparison is conducted on Typhoon HIL-based real-time simulation and lab prototype experimental results at different operating conditions. The comparative performance includes the spectra and THDs of the converter output voltages and currents, as well as the electromagnetic interference (EMI) noise emission. The single-phase ACHB demonstrates improved harmonic performance and 56% lower THD values when utilizing the PS-PWM technique compared to the PD-PWM method. The PS-PWM method operates at 6 times higher frequency than the PD-PWM method, leading to high switching losses, EMI noise emission, and lower efficiency. In contrast, the PD-PWM method lowers switch stress and reduces switching losses as less heat is generated. © 2024 IEEE.

关键词： Asymmetrical cascaded H-bridge converter Harmonic Phase Disposition PWM Phase Shift PWM Switching loss

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POCS-based Image Compression: An Empirical Examination 7

POCS-based Image Compression: An Empirical Examination

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7th International Conference on Green Technology and Sustainable Development, GTSD 2024

作者： Do, Truong-Dong Tran, Le-Anh Nguyen, Thanh-Dat Truong, Nghe-Nhan Park, Dong-Chul Le, My-Ha Sejong University Dept. of Aerospace Systems Engineering Seoul Korea Republic of Myongji University Dept. of Electronics Engineering Yongin Korea Republic of Dept. of Research and Development Ocst Co. Ltd. Seoul Korea Republic of Sejong University Dept. of Computer Science & Engineering Seoul Korea Republic of Hcmc University of Technology & Education Dept. of Electrical & Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350386073

This paper investigates the applicability of the Pro-jection onto Convex Set (POCS)-based clustering algorithm to image compression tasks. The POCS-based clustering approach treats all data points in a given dataset as non-intersecting convex sets and performs POCS-based parallel projections from each cluster prototype onto corresponding member data points to minimize an objective function and update cluster prototypes. The POCS-based clustering algorithm has been proven to be able to yield promising results against other prevailing clustering approaches in terms of convergence time and clustering error on general clustering tasks. In this study, a comparison of various clustering schemes for image compression applications has been conducted. The evaluations and analyses on various standard test images verify that the POCS-based clustering algorithm can perform competitively against other conventional clustering methods in image compression problems. © 2024 IEEE.

关键词： Unsupervised learning

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A Cluster-based Virtual Edge Computation Offloading Scheme for MEC-enabled Vehicular Networks 26

A Cluster-based Virtual Edge Computation Offloading Scheme f...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Sotiriadis, Leontios Mamalis, Basilis Pantziou, Grammati University of West Attica Dept. of Informatics and Computer Engineering Greece

ISBN: (纸本)9781450398541

Internet of Vehicles (IoV) has received a great deal of attention in recent years from many researchers. Recently, vehicular edge computing has been a new paradigm to support computation-intensive and latency-sensitive services in IoV. Moreover, with the cellular-vehicle to everything technology, many tasks and applications can be efficiently offloaded to another node for processing. In this paper a novel cluster-based virtual edge computation offloading scheme is proposed, which has as its main objective to efficiently find the most suitable multi-hop neighbor to act as a virtual edge computing (VEC) server for task offloading. The proposed scheme is initially based on the formation and maintenance of multi-hop clusters with high stability, whereas its efficiency is further enhanced by the local/distributed computations taking place where it's possible. © 2022 ACM.

关键词： Computation offloading

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Helmet Detection of Motobike Riders in Real-World Scenarios

Helmet Detection of Motobike Riders in Real-World Scenarios

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2024 IEEE International Conference on Consumer electronics-Asia, ICCE-Asia 2024

作者： Kim, Byeong-Il Ko, Byoung Chul Jang, In-Su Kim, Kwang-Ju Keimyung University Dept. of Computer Engineering Daegu Korea Republic of Electronics and Telecommunications Research Institute Daegu Korea Republic of

ISBN: (纸本)9798331530839

Automated video surveillance systems for detecting helmet-wearing by motorbike riders and passengers play a critical role in enhancing road safety and increasing the efficiency of law enforcement. However, accurately detecting small objects, such as helmets, remains a technical challenge under varying environmental conditions. In this paper, we propose an ensemble strategy that combines a CNN-based model (YOLOv10) with a transformer-based model (DE-DETRs) to detect violations of motorcycle helmet-wearing regulations. The DE-DETRs model utilizes multi-scale features to detect small objects, while YOLOv10 offers real-time performance without the need for Non-Maximum Suppression (NMS). To combine the predictions from these two models, we apply the Weighted Box Fusion (WBF) technique, enabling more precise object detection. Additionally, to address the issue of data imbalance, we employ Curriculum of Data Augmentation (CUDA)-based data augmentation techniques to optimize the model's training performance. Experimental results demonstrate that the proposed ensemble model outperforms individual models, and WBF -based post-processing effectively improves detection accuracy across various scenarios. © 2024 IEEE.

关键词： Security systems

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Inter-bridge Power-Sharing Capabilities in a Single-Phase 5-level Cascaded H-Bridge Inverter

Inter-bridge Power-Sharing Capabilities in a Single-Phase 5-...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Lingom, Pascal Agamloh, Emmanuel Von Jouanne, Annette Yokochi, Alex Song-Manguelle, Joseph Unruh, Roland Baylor University Dept. Electrical and Computer Engineering WacoTX76706 United States Baylor University Dept. Mechanical Engineering WacoTX76706 United States University of Quebec Dept. Electrical and Computer Engineering Trois-RivieresQC76706 Canada Paderborn University Power Electronics/Electrical Drives Paderborn Germany

ISBN: (纸本)9798350376067

This paper investigates inter-bridge power-sharing capabilities for a 5-level cascaded H-bridge inverter (CHBI) operation using different redundant state combinations with the conventional phase-disposition pulse-width-modulation (PD-PWM) technique. It is demonstrated that redundant switching states can be chosen to distribute the power among different modules evenly or unevenly (50% imbalance is possible). As a result, the desired power distribution between the modules with the conventional PD-PWM method is achievable by choosing appropriate redundant switching states without tuning the modulation stage. The proposed method combines balanced power and switching loss distribution across all modulation indices in a single fundamental period. Theoretical analyses of the power balance capability of a 5-level CHBI under the conventional PD-PWM method, followed by PLECS-based simulation results, are presented to demonstrate the advantages of the proposed switching method. Finally, experimental results were provided from a 5-level SiC MOSFET-based CHB inverter Lab setup controlled by a Typhoon HIL 402 device to validate the balance power-sharing method. © 2024 IEEE.

关键词： Pulse width modulation

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Impact of Image Augmentation on Deep Learning-Based Classification of Granite Tiles 9

Impact of Image Augmentation on Deep Learning-Based Classifi...

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9th International Conference on computer Science and engineering, UBMK 2024

作者： Bartos, Gaye Ediboglu Ünaldi, Sibel Yalçin, Nesibe Alba Regia Technical Faculty Obuda University Szekesfehervar Hungary Bilecik Şeyh Edebali University Dept. of Electrical and Electronics Engineering Bilecik Turkey Erciyes University Dept. of Computer Engineering Kayseri Turkey

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365887

Automated image classification of granite tiles presents challenges due to the variable textures present in the images. This study explores the impact of image augmentation on the classification performance of granite tile images using the pre-trained VGG16 Convolutional Neural Network (CNN) architecture. Contrary to the expected benefits of image augmentation in enhancing model robustness and generalization, our experiments highlight potential drawbacks when applied to texture classification. Using transfer learning, the baseline model, trained without augmentation, achieved the highest accuracy of 97.1%. However, most models trained with augmented data exhibited reduced accuracies, with the exception of flipping technique reaching up to 98.5%. Notably, common augmentation techniques, including rotation and scaling, demonstrated varying degrees of performance degradation, suggesting that these techniques may distort essential textural patterns crucial for accurate classification. These findings underscore the importance of employing dataset-specific augmentation strategies tailored to the unique characteristics of the dataset, particularly in texture-sensitive applications like granite tile classification. © 2024 IEEE.

关键词： Convolutional neural networks

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A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 Pandemic 16

A Review and Implementation of Object Detection Models and O...

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16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022

作者： Gogou, Ioanna C. Koutsomitropoulos, Dimitrios A. University of Patras Dept. of Computer Engineering and Informatics Patras Greece

ISBN: (数字)9781665498104

ISBN: (纸本)9781665498104

Convolutional Neural Networks (CNN) are commonly used for the problem of object detection thanks to their increased accuracy. Nevertheless, the performance of CNN-based detection models is ambiguous when detection speed is considered. To the best of our knowledge, there has not been sufficient evaluation of the available methods in terms of the speed/accuracy trade-off in related literature. This work assesses the most fundamental object detection models on the Common Objects in Context (COCO) dataset with respect to this trade-off, their memory consumption, and computational and storage cost. Next, we select a highly efficient model called YOLOv5 to train on the topical and unexplored dataset of human faces with medical masks, the Properly-Wearing Masked Faces Dataset (PWMFD), and analyze the benefits of specific optimization techniques for real-time medical mask detection: transfer learning, data augmentations, and a Squeeze-and-Excitation attention mechanism. Using our findings in the context of the COVID-19 pandemic, we propose an optimized model based on YOLOv5s using transfer learning for the detection of correctly and incorrectly worn medical masks that surpassed more than two times in speed (69 frames per second) the state-of-the-art model SE-YOLOv3 on the PWMFD dataset while maintaining the same level of mean Average Precision (67%). © 2022 IEEE.

关键词： Object detection

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Precision Localization: An Experimental Study on BLE Fingerprinting and Trilateration with ESP32 1

Precision Localization: An Experimental Study on BLE Fingerp...

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1st IEEE International Conference on Advances in electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Elhusseiny, Ahmed H. Zamzam, Marwa Zaghloul, Yasmine The German International University Dept. of Electrical Engineering Cairo Egypt The German International University Dept. of Informatics and Computer Science Cairo Egypt

ISBN: (纸本)9798350348057

This paper presents a method for indoor localization using two complementary techniques: Fingerprinting (FP) and Trilateration. The system is based on Bluetooth Low Energy (BLE) technology and a network of wireless anchors to determine the location of a mobile device. The FP approach collects Received Signal Strength Indicator (RSSI) data from BLE anchors in various indoor locations and creates a map of the environment in an offline stage. During the online stage, the device's location is determined by comparing its current RSSI measurements to the map. The FP technique showed an accuracy of 79.31% without positions on the other side of a separating concrete wall and 71.88% including those positions. In contrast, trilateration calculates the position of the device using distance measurements from multiple anchors, achieving an accuracy of 44.44%. Both techniques were tested in various environments and layouts, and the results indicate that FP is more suitable for indoor localization, particularly in large buildings with many rooms. This paper highlights the potential of these two techniques for practical indoor localization applications. Moreover, one of the experimental setups for the fingerprinting technique was tested in multiple rooms separated by a concrete wall to provide information about the potential scalability of the technique. © 2023 IEEE.

关键词： Bluetooth

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An Indicative Demanding Teaching Scenario for Primary Education with the Support of a Multi-layer Fog Computing Architecture 26

An Indicative Demanding Teaching Scenario for Primary Educat...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Giannou, Vasiliki Mamalis, Basilis University of West Attica Dept. of Informatics and Computer Engineering Greece

ISBN: (纸本)9781450398541

Fog computing is a new, modern type of computing that distributes some of the storage, estimation, and processing constraints of cloud computing closer to the edge of the network. The impact of Cloud and Fog Computing in an educational context is now more important than ever. This paper presents innovative designed services of fog computing techniques in the educational sector. An original educational scenario is described, based on innovative fog computing techniques and the appropriate use of online collaborative learning tools. The aim of the paper is to propose an appropriate fog-based architecture to support education in Primary and Secondary Education as well as to provide appropriate recommendations and present some indicative education scenarios implemented based on the use of cloud and fog computing. The proposed scenario is properly documented, while specific implementation issues are presented and analyzed. © 2022 ACM.

关键词： Fog computing

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