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Optimization Algorithms for Sidelobes SSL Reduction: A Comparative Study

Journal of computer and Communications 2024年第7期12卷 120-132页

作者： Mohsen Denden Aymen Alhamdan Department of Computer Science Higher Institute of Applied Sciences of Sousse Sousse University Sousse Tunisia Department of Electrical Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

The development of new technologies in smart cities is often hailed as it becomes a necessity to solve many problems like energy consumption and transportation. Wireless networks are part of these technologies but implementation of several antennas, using different frequency bandwidths for many applications might introduce a negative effect on human health security. In wireless networks, most antennas generate sidelobes SSL. SSL causes interference and can be an additional resource for RF power that can affect human being health. This paper aims to study algorithms that can reduce SSL. The study concerns typical uniform linear antenna arrays. Different optimum side lobe level reduction algorithms are presented. Genetic algorithm GA, Chebyshev, and Particle Swarm Optimization algorithm are used in the optimization process. A comparative study between the indicated algorithms in terms of stability, precision, and running time is shown. Results show that using these algorithms in optimizing antenna parameters can reduce SSL. A comparison of these algorithms is carried out and results show the difference between them in terms of running time and SSL reduction Level.

关键词： SSL Radio Wave Interference SAR Linear Antenna Arrays Genetic Algorithm Chebyshev Particle Swarm Optimization

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Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images

Comparative Analysis of Unsupervised and Supervised Autoenco...

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IEEE International Symposium on Biomedical Imaging

作者： Fatemeh Javadian Zahra Aminparast Johannes Stegmaier Abin Jose Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Institute of Medical Sciences Kermanshah University Medical School Kermanshah Iran

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

This study explores the application of supervised and unsupervised autoencoders (AEs) to automate nuclei classification in clear cell renal cell carcinoma (ccRCC) images, a critical diagnostic task that traditionally relies on subjective visual grading by pathologists. We evaluate various AE architectures, including standard AEs, contractive AEs (CAEs), and discriminative AEs (DAEs), as well as a classifier-based discriminative AE (CDAE), optimized using the hyperparameter tuning tool Optuna. Bhattacharyya distance is selected from several metrics to assess class separability within the latent space for different architectures, revealing challenges in distinguishing adjacent grades using fully unsupervised models. CDAE, integrating a supervised classifier branch, demonstrated superior performance in both latent space separation and classification accuracy. Given that CDAE-CNN achieved notable improvements in classification metrics, affirming the importance of supervised learning for enhancing class-specific feature extraction in ccRCC nuclei, F1 score was incorporated into the hyperparameter tuning process to achieve the best-fitting model. The results demonstrate distinct improvements in identifying more aggressive ccRCC grades by leveraging the classification capability of AE through latent space clustering followed by supervised fine-grained classification so that our model outperfotms state of the art, CHR-Network, in all evaluated metrics. Our findings suggest that not only does the inclusion of a classifier branch in AEs provide a promising approach for improving grading automation in ccRCC pathology compared to unsupervised clustering or supervised learning alone, but the use of neural architecture search combined with contrastive learning techniques to structure embeddings in the latent space of the AE architecture also leads to enhanced performance, particularly in identifying more aggressive grades, which are more influential in tumor grading. This advance

关键词： Visualization Accuracy Microprocessors Autoencoders Supervised learning computer architecture Neural architecture search Tuning Standards Tumors

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Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning

Resurrecting Old Classes with New Data for Exemplar-Free Con...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Dipam Goswami Albin Soutif-Cormerais Yuyang Liu Sandesh Kamath Bartłomiej Twardowski Joost Van De Weijer Department of Computer Science Universitat Autònoma de Barcelona Computer Vision Center Barcelona University of Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences IDEAS-NCBR

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Continual learning methods are known to suffer from catastrophic forgetting, a phenomenon that is particularly hard to counter for methods that do not store exemplars of previous tasks. Therefore, to reduce potential drift in the feature extractor, existing exemplar-free methods are typically evaluated in settings where the first task is significantly larger than subsequent tasks. Their performance drops drastically in more challenging settings starting with a smaller first task. To address this problem of feature drift estimation for exemplar-free methods, we propose to adversarially perturb the current samples such that their embeddings are close to the old class prototypes in the old model embedding space. We then estimate the drift in the embedding space from the old to the new model using the perturbed images and compensate the prototypes accordingly. We exploit the fact that adversarial samples are transferable from the old to the new feature space in a continual learning setting. The generation of these images is simple and computationally cheap. We demonstrate in our experiments that the proposed approach better tracks the movement of prototypes in embedding space and outperforms existing methods on several standard continual learning benchmarks as well as on fine-grained datasets. Code is available at https://***/dipamgoswami/ADC.

关键词： Continuing education Learning systems Tracking Memory management Prototypes Estimation Benchmark testing

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Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

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computers, Materials & Continua 2022年第11期73卷 3419-3432页

作者： Fadwa Alrowais Saud S.Alotaibi Sami Dhahbi Radwa Marzouk Abdullah Mohamed Anwer Mustafa Hilal Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia University of Tunis EL Manar Higher Institute of ComputerResearch Team on Intelligent Systems in Imaging and Artificial Vision(Siiva)–Lab LimticAryanah2036Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Sign language recognition can be considered as an effective solution for disabled people to communicate with *** helps them in conveying the intended information using sign languages without any *** advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective *** optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters,used in Deep Learning(DL)models as the latter considerably impacts the classification *** this motivation,the current study designs the Optimal Deep Transfer Learning Driven Sign Language Recognition and Classification(ODTL-SLRC)model for disabled *** aim of the proposed ODTL-SLRC technique is to recognize and classify sign languages used by disabled *** proposed ODTL-SLRC technique derives EfficientNet model to generate a collection of useful feature *** addition,the hyper parameters involved in EfficientNet model are fine-tuned with the help of HGSO ***,Bidirectional Long Short Term Memory(BiLSTM)technique is employed for sign language *** proposed ODTL-SLRC technique was experimentally validated using benchmark dataset and the results were inspected under several *** comparative analysis results established the superior performance of the proposed ODTL-SLRC technique over recent approaches in terms of efficiency.

关键词： Sign language image processing computer vision disabled people deep learning parameter tuning

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Secure multimedia communication: advanced asymmetric key authentication with grayscale visual cryptography

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Mathematical Biosciences and Engineering 2024年第3期21卷 4762-4778页

作者： Liu, Tao Vairagar, Shubhangi Adagale, Sushadevi Karthick, T. Karunya, Catherine Esther Blesswin, A. John Mary, G. Selva Tianjin Sino-German University of Applied Sciences Tianjin300350 China Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune411018 India Department of Computer Engineering KJEI's Trinity Academy of Engineering Pune411048 India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Directorate of Learning and Development SRM Institute of Science and Technology Kattankulathur603203 India

The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and forgery, thereby bolstering the foundation for informed decision-making, whether managing traffic, enhancing public safety, or monitoring environmental conditions. Conventional visual cryptographic protocols offer solutions, particularly for color images, though they grapple with challenges such as high computational demands and reliance on multiple cover images. Additionally, they often require third-party authorization to verify the image integrity. On the other hand, visual cryptography offers a streamlined approach. It divides images into shares, where each pixel represented uniquely, thus allowing visual decryption without complex computations. The optimized multi-tiered authentication protocol (OMTAP), which is integrated with the visual sharing scheme (VSS), takes secure image sharing to the next level. It reduces share count, prioritizes image fidelity and transmission security, and introduces the self-verification of decrypted image integrity through asymmetric key matrix generators, thus eliminating external validation. Rigorous testing has confirmed OMTAP's robustness and broad applicability, thereby ensuring that decrypted images maintain their quality with a peak signal-to-noise ratio (PSNR) of 40 dB and full integrity at the receiver's end. © 2024 American institute of Mathematical sciences. All rights reserved.

关键词： Medical applications

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Evaluating the Effectiveness of Large Language Models in Automated News Article Summarization

arXiv

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arXiv 2025年

作者： Houamegni, Lionel Richy Panlap Gedikli, Fatih Institute of Computer Science University of Applied Sciences Ruhr West Mülheim an der Ruhr Germany

The automation of news analysis and summarization presents a promising solution to the challenge of processing and analyzing vast amounts of information prevalent in today’s information society. Large Language Models (LLMs) have demonstrated the capability to transform vast amounts of textual data into concise and easily comprehensible summaries, offering an effective solution to the problem of information overload and providing users with a quick overview of relevant information. A particularly significant application of this technology lies in supply chain risk analysis. Companies must monitor the news about their suppliers and respond to incidents for several critical reasons, including compliance with laws and regulations, risk management, and maintaining supply chain resilience. This paper develops an automated news summarization system for supply chain risk analysis using LLMs. The proposed solution aggregates news from various sources, summarizes them using LLMs, and presents the condensed information to users in a clear and concise format. This approach enables companies to optimize their information processing and make informed decisions. Our study addresses two main research questions: (1) Are LLMs effective in automating news summarization, particularly in the context of supply chain risk analysis? (2) How effective are various LLMs in terms of readability, duplicate detection, and risk identification in their summarization quality? In this paper, we conducted an offline study using a range of publicly available LLMs at the time and complemented it with a user study focused on the top performing systems of the offline experiments to evaluate their effectiveness further. Our results demonstrate that LLMs, particularly Few-Shot GPT-4o mini, offer significant improvements in summary quality and risk identification. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk management

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Using Autoencoder as Feature Extractor for Malware Detection

Using Autoencoder as Feature Extractor for Malware Detection

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IT and Industrial Technologies (ICIT), International Conference on

作者： Umm-e-Hani Tayyab Faiza Babar Khan Muhammad Hanif Durad Mureed Hussain Department of Computer Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Pakistan CESAT Islamabad Pakistan

ISBN: (数字)9798331504465

ISBN: (纸本)9798331504472

Malware detecting approaches suffer many limitations, and all anti-malware solutions have failed to be reliable enough for detecting zero day malware. Signature based solutions depend upon the signatures generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI based anti-malware solutions that have been trained with enough samples targeted a selected feature vector thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware’s processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier mentioned shortcomings. Our proposed framework, which is based upon automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54

关键词： Training Deep learning Visualization Analytical models Autoencoders Feature extraction Malware Vectors Real-time systems Reliability

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Recent Advances in Analysis and Detection of Tuberculosis System in Chest X-Ray Using Artificial Intelligence (AI) Techniques: A Review

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Current Materials Science 2023年第1期16卷 43-51页

作者： Suchitra, Shanmugam Ibrahim, S. Jafar Ali Sathya, Mariappan Sahini, Varsha Chakravarthy, N. Surya Kalyan Kumar, Vaneet Saruchi Department of Computer Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT SCOPE Vellore Institute of Technology Tamilnadu Vellore India AMC Engineering College Karnataka Bengaluru India Department of Computer Science Engineering CT Institute of Engineering Management and Technology Punjab Jalandhar India Data Science QIS College of Engineering and Technology Andhra Pradhesh Ongole India Department of Applied Sciences CT Institute of Engineering Management and Technology Shahpur Campus Punjab Jalandhar India Department of Biotechnology CT Institute of Pharmaceutical Sciences Shahpur Campus Punjab Jalandhar India

Mycobacterium tuberculosis causes tuberculosis (TB), a bacterial illness. Although germs are most typically found in the lungs, they can affect other sections of the body as well. Tuberculosis is one of the primary causes of mortality in both developed and developing nations, necessitating worldwide attention. Even though TB may be prevented in the majority of instances if discovered and treated early, the number of deaths caused by the disease is quite high. There has been a significant increase in interest and research activity in TB detection in recent years. The new advancement in the field of AI Technology may be able to assist them in overcoming these development gaps. computer-Aided Detection and Diagnosis (CADD) aids in the diagnosis of diseases by analysing symptoms and X-ray images of patients. Many solutions are currently be-ing developed to improve the effectiveness of TB diagnosis classification using AI and DL ap-proaches. Although a variety of TB detection techniques have been developed, there is no common-ly acknowledged method. The purpose of this study is to give a survey on Tuberculosis Detection. It also emphasises the difficulty and complexity of the Tuberculosis Detection System's design. © 2023 Bentham Science Publishers.

关键词： computer vision

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Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards

arXiv

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arXiv 2023年

作者： van Meekeren, Alexander Gavari, Maya Aghaei Dijkstra, Klaas The NHL Stenden University of Applied Sciences Netherlands The NHL Stenden Professorship in Computer Vision & Data Science Netherlands

Deep object detection models have achieved notable successes in recent years, but one major obstacle remains: the requirement for a large amount of training data. Obtaining such data is a tedious process and is mainly time consuming, leading to the exploration of new research avenues like synthetic data generation techniques. In this study, we explore the usability of Stable Diffusion 2.1-base for generating synthetic datasets of apple trees for object detection and compare it to a baseline model trained on real-world data. After creating a dataset of realistic apple trees with prompt engineering and utilizing a previously trained Stable Diffusion model, the custom dataset was annotated and evaluated by training a YOLOv5m object detection model to predict apples in a real-world apple detection dataset. YOLOv5m was chosen for its rapid inference time and minimal hardware demands. Results demonstrate that the model trained on generated data is slightly underperforming compared to a baseline model trained on real-world images when evaluated on a set of real-world images. However, these findings remain highly promising, as the average precision difference is only 0.09 and 0.06, respectively. Qualitative results indicate that the model can accurately predict the location of apples, except in cases of heavy shading. These findings illustrate the potential of synthetic data generation techniques as a viable alternative to the collection of extensive training data for object detection models. Copyright © 2023, The Authors. All rights reserved.

关键词： Data acquisition

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Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter 24

Unmasking the Web of Deceit: Uncovering Coordinated Activity...

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33rd ACM Web Conference, WWW 2024

作者： Luceri, Luca Pantè, Valeria Burghardt, Keith Ferrara, Emilio University of Southern California Information Sciences Institute Marina del ReyCA United States University of Southern California Thomas Lord Department of Computer Science Los AngelesCA United States University of Applied Sciences and Arts of Southern Switzerland Switzerland

ISBN: (纸本)9798400701719

Social media platforms, particularly Twitter, have become pivotal arenas for influence campaigns, often orchestrated by state-sponsored information operations (IOs). This paper delves into the detection of key players driving IOs by employing similarity graphs constructed from behavioral pattern data. We unveil that well-known, yet underutilized network properties can help accurately identify coordinated IO drivers. Drawing from a comprehensive dataset of 49 million tweets from six countries, which includes multiple verified IOs, our study reveals that traditional network filtering techniques do not consistently pinpoint IO drivers across campaigns. We first propose a framework based on node pruning that emerges superior, particularly when combining multiple behavioral indicators across different networks. Then, we introduce a supervised machine learning model that harnesses a vector representation of the fused similarity network. This model, which boasts a precision exceeding 0.95, adeptly classifies IO drivers on a global scale and reliably forecasts their temporal engagements. Our findings are crucial in the fight against deceptive influence campaigns on social media, helping us better understand and detect them. © 2024 ACM.

关键词： Social networking (online)

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