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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Sarkar, Ripa Sarkar, Ratna R. BRAC University Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Savar Bangladesh

ISBN: (纸本)9798350324433

Sentiment analysis which involves identifying emotions in the text is a widely studied topic of natural language processing. Many researchers focus on social media content like posts, tweets and reviews for their studies. In this paper, a large number of data from comments about serials on social media and internet forums in Banglish is collected and analyzed. We use NLP techniques to do the sentiment analysis of the comments. This study explores how different kinds of serials have impacted society and concentrates on how they have influenced the creation of Banglish comments. This study aims to conduct a sentiment analysis of Banglish comments generated in response to different genres of serials and to analyze their potential societal implications. Along with Long Short-Term Memory (LSTM), we use several machine-learning algorithms such as Logistic Regression (LR), Multinomial Naive Bayes (MNB), Gaussian Naive Bayes, Decision Tree (DT), AdaBoost, Random Forest (RF) and Support Vector Machine (SVM) to create this model. The SVM gives the highest accuracy among all of the machine learning algorithms. © 2023 IEEE.

关键词： Sentiment analysis

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Optimizing DC-Stabilized Direct Antenna Modulation Transmitters for Electrically-Small Antennas

Optimizing DC-Stabilized Direct Antenna Modulation Transmitt...

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Mayekar, Miheer P. Dusenbury, Joseph K. Schab, Kurt R. Adams, Jacob J. Dept. of Electrical and Computer Engineering North Carolina State University RaleighNC United States Dept. of Electrical and Computer Engineering Santa Clara University Santa ClaraCA United States

ISBN: (纸本)9781946815200

Linear time-invariant (LTI) electrically small antennas have high radiation quality factors, leading to a challenging tradeoff between efficiency and bandwidth. Bandwidth limitations distort broadband signals, limiting overall data throughput. To circumvent the narrowband limit of LTI antennas, researchers have investigated non-LTI systems. In this paper, we study one time-varying antenna technology called direct antenna modulation (DAM), which is based on storing and releasing energy on a small, capacitive, dipole-like antenna synchronously with an input digital sequence. One challenge in DAM systems is parasitic oscillations that occur when the antenna is switched between states. This ringing significantly influences signal quality in DAM transmitters and leads to spurious radiation. To mitigate the ringing and ensure better transmitted signal quality, the use of an auxiliary DC source was recent proposed to stabilize the parasitic ringing during state transitions. In fact it has been shown that this DC-DAM technique can improve key signal quality metrics like EVM by as much as 10 dB. However, little work has been done to optimize the DC source for best performance in a variety of scenarios. © 2025 USNC-URSI.

关键词： Signal modulation

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Bridging the Modality Gap: Generative Adversarial Networks for T1-T2 MRI Image Translation 15

Bridging the Modality Gap: Generative Adversarial Networks f...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Mayank Kunda, Naga Sai Shreya Pranave, K.C. Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance College of Engineering and Design Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

CycleGAN, a significant deep learning model, has transformed medical imaging by providing a remedy for translating MRI images between different modalities. Its ability to transform T1-weighted to T2-weighted images is crucial in healthcare environments where obtaining multiple scans can be time-consuming and burdensome for patients. This can be achieved by utilizing CycleGAN, which generates images that contains vital information that will help the healthcare professionals to diagnose the patient without exposing them to further radiations. This unleashes an effective and safer way of diagnosing patients. The adaptability of CycleGAN also allows scalability and the ability to customize it for different types of medical images, where scanning can be achieved with less exposure to radiation, without loosing or compromising any important information. © 2024 IEEE.

关键词： Discriminator GAN Generator Magnetic Resonance Image T1 T2

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RGHT-Q: Reconfigurable GEMM Unit for Heterogeneous-Homogeneous Tensor Quantization

RGHT-Q: Reconfigurable GEMM Unit for Heterogeneous-Homogeneo...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Lee, Seungho Nam, Donghyun Park, Jeongwoo Sungkyunkwan University Dept. of Semiconductor Convergence Engineering Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9783982674100

The high computational demands of large language models (LLMs) are limited by the lack of GPU hardware support for heterogeneous quantization, which mixes integers and floating points. To address this limitation, we propose an LLM processing element (PE), RGHT-Q, which features reconfigurable general-matrix multiplication (GEMM) operations for both heterogeneous and homogeneous tensor quantization. The RGHT-Q introduces a novel design that leverages butterfly routing and multi-precision multipliers. As a result, we achieve significant performance improvements, offering 3.14× higher energy efficiency, and 1.56× better area efficiency compared to prior designs. © 2025 EDAA.

关键词： Digital arithmetic

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Brain Cancer Detection and Classification using Transfer Learning 3

Brain Cancer Detection and Classification using Transfer Lea...

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3rd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Gadaokar, Varad More, Piyush Wakode, Madhuri Kale, Geetanjali Pune Institute of Computer Technology Dept. of Computer Engineering Pune India Pune Institute of Computer Technology Dept. of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798331521691

Brain tumors are abnormal cells that grow inside the brain. If not diagnosed or treated early, such incidents can be fatal. Types of brain tumors include meningioma, glioma, and pituitary tumors. Magnetic resonance imaging (MRI) is an essential resource in diagnosis due to the clear expression of brain tissues, enabling professionals to analyze abnormal developments in the brain. The detection and classification of brain tumors using MRIs can be automated using machine learning techniques that can help to identify and diagnose brain tumors in their early stages, and subsequent treatment may result in a lower death rate. Transfer Learning allows the transfer of knowledge learned from large-scale datasets to other smaller datasets, thus avoiding wide-ranging training requirements with high accuracy and making the system converge much faster. This substantially minimizes computational resources and time in the training process. Transfer Learning also enables the effective MRI image abstraction of complex features, improved accuracy in tumor detection and classification, minimizes overfitting, especially with small amounts of medical data. In this work, we have used Transfer Learning with two pre-trained deep models, VGG16 and ResNet-50 on 3064 images available in the dataset to classify brain tumors. Fine-tuning for VGG16 and ResNet-50, we achieved validation accuracies of 98.85% and 97.72%, respectively. The results show that transfer learning can be used for the classification of brain tumors to enhance efficiency and precision for early detection and treatment. © 2025 IEEE.

关键词： Brain Tumors Convolutional Neural Networks Medical Image Processing ResNet-50 Transfer Learning VGG16

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Another Software for Designing Electric Transformers 21

Another Software for Designing Electric Transformers

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21st IEEE/ACIS International Conference on Software engineering Research, Management and Applications, SERA 2023

作者： Jo, Jangwu Park, Junhyun Jang, Ayoung Chon, Youngsu Kang, Byeongdo Dong-A University Dept. of Computer Engineering Busan Korea Republic of Daegu University Dept. of Computer Engineering Daegu Korea Republic of

ISBN: (纸本)9798350345889

The article discusses the need for a transformer design software program that can be easily used by beginners in the field without the need for trial and error. Most small-sized transformer manufacturers in South Korea do not use such software due to the high price, instead relying on spreadsheets like Excel. The present study aims to overcome the problems of conventional methods by finding a design method that does not require expert knowledge and obtaining all possible designs to avoid missing optimal values. The developed software program is introduced and its usefulness is demonstrated experimentally in various chapters of the paper. © 2023 IEEE.

关键词： Optimal systems

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Investigating the Efficacy of Diverse Machine Learning Classifiers for Parkinson's Disease Detection 15

Investigating the Efficacy of Diverse Machine Learning Class...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Roy, Sourabarna Debbarma, Tina Pal, Tannistha Duraisamy, Prakash National Institute of Technology Agartala Dept. of Computer Science and Engineering Agartala India University of Wisconsin- Green Bay Dept. of Computer Science and Engineering WI54311 United States

ISBN: (纸本)9798350370249

To detect Parkinson's disease, we compare the effectiveness of K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) algorithms. Utilizing a dataset with clinical and biomedical features, we preprocess the data to handle missing values and standardize the features. Subsequently, we train each algorithm with the preprocessed data and evaluate their performance using metrics like accuracy, precision, recall, and F1-score. Our results indicate that all four algorithms achieve excellent accuracy in diagnosing Parkinson's disease, with KNN slightly outperforming the others. However, the selection of the algorithm may depend on specific needs such as interpretability and computational efficiency. Additionally, we conduct a feature importance analysis to identify the most relevant features for Parkinson's disease identification, offering insights that can aid in early diagnosis and disease management. © 2024 IEEE.

关键词： Logistic regression

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A systematic review of decentralized finance protocols

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International Journal of Intelligent Networks 2023年第1期4卷 171-181页

作者： Shah, Kaushal Lathiya, Dhruvil Lukhi, Naimish Parmar, Keyur Sanghvi, Harshal Dept. of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar382007 India Dept. of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India Dept. of CEECS Florida Atlantic University Boca Raton United States

Decentralized finance (DeFi) has revolutionized the financial industry in recent years. Industries such as banking, insurance, and investment are experiencing a significant shift due to the growth of DeFi. The decentralized finance market is expanding exponentially, emphasizing the potential of digital currencies and decentralized platforms in providing an alternative to the traditional finance paradigm. It eliminates the need for costly intermediaries, reduces transaction fees, and increases accessibility to financial services for everyone, regardless of their geographic location or economic status. With the enormous increase in cryptocurrency investment, individuals and institutions have started to use DeFi. In this context, understanding the architecture and working mechanisms of different DeFi protocols becomes crucial in creating new and innovative products. This review paper explores various DeFi protocols, including derivatives, decentralized exchanges (DEX), lending and borrowing, asset management, and stablecoins. It analyses their internal structure and composability, providing insights into how these protocols can be combined to create new and innovative DeFi products and explore the potential of DeFi in providing an alternative to the traditional finance paradigm. © 2023 The Authors

关键词： Asset management

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Design of Digitized Visual Search Task for Assessment of Aging Effects 2

Design of Digitized Visual Search Task for Assessment of Agi...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Navalyal, Geeta Navalyal, Apoorva Naik, Sayali Dept. of Computer Science & Engineering Uttarakhand Dehradun India University of Florida Dept. of Electrical & Computer Engineering GainesvilleFL United States Grant Thornton Karnataka Bengaluru India

ISBN: (纸本)9798350372847

Mild cognitive impairments (MCI) develop in adults due to the normal aging process and may sometimes progress to a more serious cognitive decline stage resulting in Dementia. If the cognitive decline is identified at the MCI stage, it can be delayed or prevented from progressing by different interventions. A typical indicator of cognitive impairments that might be caused due to an injury, aging, or other factors is found to be a decreased search efficiency in visual search tasks. Here we propose a model to assess this efficiency by designing a digitized, easy-to-use visual search task capable of incorporating multiple variables in the visual stimulus, affecting the search capabilities. The variables under consideration include the number of distracting elements, target size, target distance from the center of the stimulus area, and habituation effects. This designed stimulus is tested on participants from different age groups. © 2024 IEEE.

关键词： aging cognitive impairments Visual search task

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Investigating Requirements and Expectations of Wearable Telexistence Robotic systems 29

Investigating Requirements and Expectations of Wearable Tele...

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29th IEEE International Conference on Embedded and Real-Time Computing systems and Applications, RTCSA 2023

作者： Iskandar, Abdullah Al-Sada, Mohammed Halabi, Osama Nakajima, Tatsuo Waseda University Dept. of Computer Science and Engineering Tokyo Japan Qatar University Dept. of Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350337860

Telexistence refers to various technologies that enable a high sense of embodiment and interaction capabilities with remote environments. Although numerous telexistence systems have been explored in previous works of literature, no prior studies have investigated the usability of telexistence systems for daily use. Therefore, this study focuses on Piton, which is a novel wearable telexistence robot which has a high degree of freedom in a snake-shaped design. Piton offers a unique opportunity for remote interaction and manipulation in diverse environments, owing to its flexible movement, which enables users to adapt to various scenarios with ease. The robot's wearability is also an essential feature that allows the user to wear it as a backpack. However, despite its promising features, deeper investigation is necessary to explore potential use cases for Piton in users' daily lives and the challenge of Piton. © 2023 IEEE.

关键词： Robots

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