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Microwaves Antennas and Propagation Conference (MAPCON)

作者： Farah Bilawal Fatemeh Babaeian Nemai Chandra Karmakar Dept. of Electrical and Computer Systems Engineering Monash University Melbourne Australia

ISBN: (数字)9798350379693

ISBN: (纸本)9798350379709

The goal of this research is to create an optimized hybrid coupler structure for a PIN diode reflection type phase shifter (RTPS) operating at microwave frequencies. To date, no phase shifter has been reported with the proposed technique, compactness, minimum phase error and balanced amplitude. Another limitation of designing such structures at such high frequency range, is that the $50 \Omega$ impedance lines’ width approaches its length for given substrate thickness, that is undesirable. This study introduces a new method of developing a module at $100 \Omega$ and translating it back to $50 \Omega$ at the input and output terminals. Moreover, the proposed design is highly resilient to manufacturing constraints owing to the addition of radial stubs instead of lumped components, as reflective loads. Its performance with respect to its bandwidth coverage is better with 15% bandwidth enhancement, insertion loss around 3 dB, and reflection co-efficient at all ports is better than $\mathbf{- 1 5 d B}$ over the whole operating bandwidth. The simulated and measured results validate the proposed design effectively.

关键词： PIN photodiodes Phase shifters Insertion loss Couplers Bandwidth Microwave theory and techniques Microwave frequencies Reflection Substrates Microwave propagation

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Visual Field Prediction for Fundus Image with Generative AI 18

Visual Field Prediction for Fundus Image with Generative AI

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Ko, Seonghyeon Kim, Juchan Le, Duc-Tai Bum, Junghyun Han, Jongchul Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of Ophthalmology Samsung Medical Center Seoul Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical Center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time. © 2024 IEEE.

关键词： Diagnosis

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Hate Speech Detection of Roman Urdu Using Transformer Models

Hate Speech Detection of Roman Urdu Using Transformer Models

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International Conference on Emerging Technologies, ICET

作者： Rehman Ullah Waseem Ullah Khan Safdar Nawaz Khan Marwat Jasim Ullah Fatima Irshad Dept. of Computer Systems Engg University of Engineering & Technology Peshawar Pakistan

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Hate speech means that giving discrimination about someone, such as a person, a group or any community on the base of religion, personality of someone, race, sexual harassment, gender, or on the base of other identities. To prevent hated speeches different laws has been made by the governments of different countries. Which has worked a lot for the citizen's safety of these countries and their citizens are safe from such hated speeches up to some extent. Too many researchers and scientists has worked a lot in NLP to prevent hated speeches throughout the world, and they been successes up to some extent in their aim. As we all see nowadays on different platforms of social media that hated speeches are detected. We have used some of transformer methods and some of non-transformer methods, such as BERT Bidirectional Encoder Representations from Transformers (BERT) in transformer method and DISTEL BERT in transformer method and transformer model in transformer method. And we have LSTM, LSTM + attention layer, CNN and LSTM in nontransformer method. And we have trained it by one of the roman Urdu datasets which consists 30000 thousand of roman (Urdu) tweets, moreover we will check or look for accuracy precession, F1 score and recall in it. Then we compared those models in which BERT has the best result that's transformer model.

关键词： Training Accuracy Social networking (online) Hate speech Bidirectional control Transformers Encoding Natural language processing Long short term memory Testing

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Language Independent Named Entity Recognition via Orthogonal Transformation of Word Vectors

arXiv

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arXiv 2025年

作者： Rakha, Omar E. Abbas, Hazem M. Dept. Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo11571 Egypt

Word embeddings have been a key building block for NLP in which models relied heavily on word embeddings in many different tasks. In this paper, a model is proposed based on using Bidirectional LSTM/CRF with word embeddings to perform named entity recognition for any language. This is done by training a model on a source language (English) and transforming word embeddings from the target language into word embeddings of the source language by using an orthogonal linear transformation matrix. Evaluation of the model shows that by training a model on an English dataset the model was capable of detecting named entities in an Arabic dataset without neither training or fine tuning the model on an Arabic language dataset. © 2025, CC BY.

关键词： Embeddings

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Evaluating Neural Word Embeddings for Sanskrit 18

Evaluating Neural Word Embeddings for Sanskrit

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18th World Sanskrit Conference

作者： Sandhan, Jivnesh Paranjay, Om Adideva Komal, Digumarthi Behera, Laxmidhar Goyal, Pawan Dept. of Electrical Engineering IIT Kanpur India Tata Consultancy Services India Dept. of Electrical and Systems Engineering University of Pennsylvania United States Dept. of Computer Science and Engineering IIT Kharagpur India

ISBN: (纸本)9781950737710

Recently, the supervised learning paradigm's surprisingly remarkable performance has garnered considerable attention from Sanskrit Computational Linguists. As a result, the Sanskrit community has put laudable efforts to build task-specific labeled data for various downstream Natural Language Processing (NLP) tasks. The primary component of these approaches comes from representations of word embeddings. Word embedding helps to transfer knowledge learned from readily available unlabelled data for improving task-specific performance in low-resource setting. Last decade, there has been much excitement in the field of digitization of Sanskrit. To effectively use such readily available resources, it is essential to perform a systematic study on word embedding approaches for the Sanskrit language. In this work, we investigate the effectiveness of word embeddings. We classify word embeddings in broad categories to facilitate systematic experimentation and evaluate them on four intrinsic tasks. © 2022 Proceedings of the Computational Sanskrit and Digital Humanities at the 18th World Sanskrit Conference. All rights reserved.

关键词： Embeddings

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting 17

An Image Analysis-Based Automated Method using Deep Learning...

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17th IEEE International Conference on Industrial and Information systems, ICIIS 2023

作者： Ajikaran, Ramesh Hewarathna, Ashen Iranga Palanisamy, Vigneshwaran Joseph, Charles Thuseethan, Selvarajah Sabaragamuwa University of Sri Lanka Dept. of Computing and Infromation Systems Sri Lanka Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sri Lanka Fau Dept. of Computer Science Germany School of Information Technology Deakin University Australia

ISBN: (纸本)9798350323634

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst. © 2023 IEEE.

关键词： Grain (agricultural product)

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A Systematic Review of Computational Intelligence Approaches for Prediction of Chronic Kidney Disease

A Systematic Review of Computational Intelligence Approaches...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： M.B.L. Prasanna G. Lavanya Devi Dept. of Computer Science and Systems Engineering Andhra University College of Engineering Visakhapatnam India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

CKD is a global health issue with rising prevalence and severe morbidity and mortality. Detecting and addressing diseases early is crucial to minimize disease progression and enhance patient outcomes. In the field of chronic kidney disease (CKD) prediction, computational intelligence (CI) algorithms have shown significant potential as effective tools, with the assurance of non-invasive and precise diagnosis. The use of CI methodologies, such as machine learning and deep learning techniques, for the prediction of chronic kidney disease (CKD) is examined in this systematic review. The advancement of CKD is linked to a number of significant effects such as an increased incidence of various ailments, Anemia, and death. There is a risk of hyperlipidaemia, nerve damage, pregnancy difficulties, and possibly renal failure. It kills millions of individuals each year. Diagnosis of CKD is difficult because there are no obvious symptoms to diagnose the illness. However, some results may be overlooked when the diagnosis is chronic. This study investigated CI approaches for CKD prediction using clinical data, biomarkers, or imaging aspects to diagnose CKD. Age, glucose level, number of red blood cells, and other symptoms were included when training the algorithm with 500 participants. The experimental results show that the suggested model excels at categorization. The objective is to develop computational intelligence (CI) algorithms for learning from attribute reports in datasets and effectively detect CKD that is more accessible. Eligible studies used CI to predict CKD based on clinical data, biomarkers, or imaging characteristics. This systematic investigation contributes to a better understanding of the landscape of CI applications in predicting CKD and provides light on potential future advances in the field.

关键词： Deep learning Machine learning algorithms Computational modeling Signal processing algorithms Imaging Prediction algorithms Chronic kidney disease Computational intelligence Diseases Systematic literature review

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Privacy Protection for Recommendation systems using Federated Learning and ADMM

Privacy Protection for Recommendation Systems using Federate...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Gowri Priya Thota Lavanya Devi G Dept. of Computer Science and Systems Engineering Andhra University College of Engineering Visakhapatnam India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Federated Learning (FL) represents a significant advancement in machine learning, focusing on collaborative data processing across decentralized systems while ensuring user privacy. This research explores the application of an inexact Alternating Direction Method of Multipliers (ADMM) algorithm within the context of movie recommendation systems. The inexact ADMM method improves computational efficiency and scalability by easing the strict convergence criteria. Our results demonstrate that this approach maintains high recommendation accuracy and significantly reduces communication overhead and computational costs. Additionally, we compare the inexact ADMM with the exact ADMM, highlighting the advantages and trade-offs of each method. Our study illustrates that the inexact ADMM method effectively balances local computation and global aggregation, promoting the development of more efficient and user-centric recommendation systems.

关键词： Privacy Accuracy Federated learning Signal processing algorithms Signal processing Motion pictures Convex functions Computational efficiency Protection Recommender systems

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Learning Adaptive Safety for Multi-Agent systems

Learning Adaptive Safety for Multi-Agent Systems

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Luigi Berducci Shuo Yang Rahul Mangharam Radu Grosu Institute of Computer Engineering TU Wien Dept. of Electrical and Systems Engineering University of Pennsylvania

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents. Control Barrier Functions (CBFs) are showing promise for safety assurance but current methods make strong assumptions about other agents and often rely on manual tuning to balance safety, feasibility, and performance. In this work, we delve into the problem of adaptive safe learning for multi-agent systems with CBF. We show how emergent behaviour can be profoundly influenced by the CBF configuration, highlighting the necessity for a responsive and dynamic approach to CBF design. We present ASRL, a novel adaptive safe RL framework, to fully automate the optimization of policy and CBF coefficients, to enhance safety and long-term performance through reinforcement learning. By directly interacting with the other agents, ASRL learns to cope with diverse agent behaviours and maintains the cost violations below a desired limit. We evaluate ASRL in a multi-robot system and competitive multi-agent racing, against learning-based and control-theoretic approaches. We empirically demonstrate the efficacy of ASRL, and assess generalization and scalability to out-of-distribution scenarios.

关键词： Adaptation models Costs Uncertainty Scalability Reinforcement learning Safety Observability

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FORTUNATE: Decrypting and Classifying Malware by Variable Length Instruction Sequences 13

FORTUNATE: Decrypting and Classifying Malware by Variable Le...

引用

13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Andrade, Cesar A. B. Rocha Filho, Geraldo P. Meneguette, Rodolfo I. Maranhao, Joao Paulo A. Sant'ana, Ricardo Duarte, Julio Cesar Serrano, Andre Luiz M. Goncalves, Vinicius P. University of Brasília Electrical Engineering Dept. Brasília Brazil State University of Southwest Bahia Exact and Technological Sciences Dept. Vitória da Conquista Brazil Institute of Mathematics and Computer Science Universityof São Paulo São Carlos Brazil Brazilian Army Systems Development Center Brasília Brazil Military Institute of Engineering Defense Engineering Dept. Rio de Janeiro Brazil Military Institute of Engineering Computing Engineering Dept. Rio de Janeiro Brazil University of Brasília Production Engineering Dept. Brasília Brazil

ISBN: (纸本)9798350376562

After identifying an artifact as malware, the additional challenge arises of correctly classifying it into a specific family. This raises the challenge of classifying malware amid growing complexity and the increasing volume of cyber threats. Therefore, we introduce FORTUNATE, a framework that employs variable-length instruction sequences to classify malware more efficiently and accurately, focusing on real and active malware. By introducing effective methods for extracting opcodes and their representation in the smallest possible vectors, FORTUNATE surpasses the limitations of previous approaches, enabling the correct identification of malware in their respective families, while avoiding redundancies in the data. The results achieved with FORTUNATE demonstrate advances in both precision and computational efficiency, consolidating it as a significant contribution to the field of cybersecurity. © 2024 IEEE.

关键词： Long short-term memory

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