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Beamforming and Reflection Design for Short Packet ISAC With Non-Ideal RIS: An A3C-Based Approach

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8379-8384页

作者： Mahmoudi, Behrad Khonsari, Ahmad Zeinali, Farshad Mili, Mohammad Robat Mashhadi, Mahdi Boloursaz Xiao, Pei University of Tehran Department of Electrical and Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran School of Computer Science in the Institute for Research in Fundamental Sciences Tehran Iran United Kingdom Institute for Advanced Innovative Solutions Tehran Iran

Integrated sensing and communication (ISAC) is a promising solution to mitigate the increasing congestion of the wireless spectrum. In this paper, we investigate the short packet communication regime within an ISAC system assisted by a reconfigurable intelligent surface (RIS) to meet the low latency ultra-reliable requirements in the next-generation wireless networks. We consider a non-ideal RIS model that captures effects of the phase-dependent amplitude variations in the reflection coefficients, and we have incorporated the near-field model into the channels between the RIS and the users or targets. In this setup, we jointly design the transmit beamforming and the RIS phase shifts to maximize the sum rate while satisfying the sensing signal-to-noise ratio (SNR) requirement. The system simultaneously carries out multitarget sensing and multi-user short packet communications with the help of the RIS. Considering the non-convex and dynamic nature of the resulting optimization problem, we propose an asynchronous advantage actor-critic (A3C) based method for beamforming and reflection design in this setup. Numerical results demonstrate the superiority of the proposed scheme over the benchmarks. © 1967-2012 IEEE.

关键词： Beamforming

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Detection of lung and colon cancer from histopathological images using deep learning 4th

Detection of lung and colon cancer from histopathological im...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Yadav, Vikas Singh, Jagrati Department of Computer Science and Engineering Centre for Advanced Studies Uttar Pradesh Lucknow India Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women Delhi India

ISBN: (纸本)9781032911571

Lung cancer is the most life-threatening cancer worldwide, emphasizing the critical importance of its timely detection and treatment for patient recovery. Colon cancer is also a very hazardous cancer if not treated timely because of its diagnosis at later stage. The cornerstone of diagnosis for these cancers relies on histopathological images obtained from biopsied tissues in potentially affected lung and colon regions. However, accurately and efficiently diagnosing specific types of lung and colon cancer presents challenges, consuming valuable time in the process. Diagnostic modalities have also evolved significantly, with the integration of advanced imaging techniques, liquid biopsy, and molecular profiling contributing to more accurate and personalized diagnosis. Additionally, advancements in radiomics and artificial intelligence have shown promise in improving the efficiency and precision of these types of cancer detection. In response to these challenges, Convolutional networks (CNNs) have emerged as a promising solution. In this paper, CNN model is used for prediction of above-stated two types of cancers which has demonstrated remarkable results, achieving accuracy of 99.27%. © 2025 the Author(s).

关键词： Lung cancer

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Ensemble of Deep Learning with Crested Porcupine Optimizer Based Autism Spectrum Disorder Detection Using Facial Images

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computers, Materials & Continua 2025年第5期83卷 2793-2807页

作者： Jagadesh Balasubramani Surendran Rajendran Mohammad Zakariah Abeer Alnuaim Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105Tamil NaduIndia Department of Computer Science and Engineering College of Applied Studies and Community ServiceKing Saud UniversityP.O.Box 22459Riyadh11495Saudi Arabia

Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several *** all autistic children remain undiagnosed before the age of *** problems affecting face features are often associated with fundamental brain *** facial evolution of newborns with ASD is quite different from that of typically developing *** recognition is very significant to aid families and parents in superstition and *** facial features from typically developing children is an evident manner to detect children analyzed with ***,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic *** study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)*** overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal *** the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed *** the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)*** extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI *** simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.

关键词： Autism spectrum disorder ensemble learning crested porcupine optimizer facial images computeraided diagnosis

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Lung Cancer Analysis and Classification using Enhanced Efficient Net V3 6

Lung Cancer Analysis and Classification using Enhanced Effic...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ramya, J. Poongodi, A. Department of Software Applications Agurchand Manmull Jain College Chennai India Department of Computer Sciences Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Lung cancer is one of the main causes of death globally and adds to the burden of disease and mortality. Early detection of lung cancer may reduce the chance of developing lung cancer. Artificial intelligence (AI) in diagnostic imaging has garnered a lot of attention in recent years, particularly for the detection of lung cancer. On the other hand, manual CT scan analysis takes a lot of time and is inaccurate or prone to mistakes. Given these limitations, the accurate categorization of CT images as either malignant or non-cancerous is accelerated by the application of computer approaches, including machine learning and deep learning algorithms. In this work, we provide a new and improved CNN model architecture with FCN and Deeplab V3+ for segmentation and classification using hybrid efficient net B0 to investigate lung cancer. The suggested model achieves superior results in both segmentation and classification. © 2025 IEEE.

关键词： Lung cancer

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Denoising Diffusion Models for High-Resolution Microscopy Image Restoration

Denoising Diffusion Models for High-Resolution Microscopy Im...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Osuna-Vargas, Pamela Wehrheim, Maren H. Zinz, Lucas Rahm, Johanna Balakrishnan, Ashwin Kaminer, Alexandra Heilemann, Mike Kaschube, Matthias Frankfurt Institute for Advanced Studies Germany Goethe University Frankfurt Department of Computer Science and Mathematics Germany Institute of Physical and Theoretical Chemistry Goethe University Frankfurt Germany

ISBN: (纸本)9798331510831

Advances in microscopy imaging enable researchers to visualize structures at the nanoscale level thereby unraveling intricate details of biological organization. However, challenges such as image noise, photobleaching of fluo-rophores, and low tolerability of biological samples to high light doses remain, restricting temporal resolutions and experiment durations. Reduced laser doses enable longer measurements at the cost of lower resolution and increased noise, which hinders accurate downstream analyses. Here we train a denoising diffusion probabilistic model (DDPM) to predict high-resolution images by conditioning the model on low-resolution information. Additionally, the probabilistic aspect of the DDPM allows for repeated generation of images that tend to further increase the signal-to-noise ratio. We show that our model achieves a performance that is better or similar to the previously best-performing methods, across four highly diverse datasets. Importantly, while any of the previous methods show competitive performance for some, but not all datasets, our method consistently achieves high performance across all four data sets, suggesting high generalizability. Our code and datasets are available at https://githubcom/kaschube-lab/ddpm_highres_microscopy, https://***/10.5281/zenodo.14178277, https://***/10.5281/zenodo.14215837. © 2025 IEEE.

关键词： Fluorescence microscopy

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A class imbalance aware hybrid model for accurate rice variety classification

International Journal of Cognitive Computing in Engineering

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International Journal of Cognitive Computing in Engineering 2025年 6卷 170-182页

作者： Vaidya, Hanumesh Prasad, K.V. Rajashekhar, C. Tripathi, D. S, Renuka Shetty, Jyoti Swamy, Kumar Y, Shobha Department of Studies in Mathematics Vijayanagara Sri Krishnadevaraya University Karnataka Ballari India Department of Mathematics Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India Department of Studies in Computer Science Vijayanagara Sri Krishnadevaraya University Karnataka Ballari India Karnataka Ballari India Department of Studies in Computer Science Kishkinda University Karnataka Ballari India Department of Mathematics National Institute of Technology Uttarakhand Srinagar India

Rice classification plays a critical role in global agriculture, impacting the food security of over 3.5 billion people worldwide. As the complexity of this task has grown, advanced Machine Learning (ML) and Deep Learning (DL) models have emerged as powerful tools for accurate variety identification. These sophisticated algorithms excel in processing extensive datasets, leveraging key characteristics such as grain size, color, and texture to make precise predictions. However, a persistent challenge in this field is a class imbalance, where certain rice varieties are significantly underrepresented in datasets. This imbalance can severely impact model performance, particularly for minority classes, leading to biased predictions that favor more abundant varieties. Many existing models struggle to effectively address this issue, often prioritizing majority classes at the expense of overall generalization across all rice types. To tackle this critical gap in rice classification, we introduce a novel hybrid model: the XGBoost Multi-Layer 33Perceptron (XGB-MLP). This innovative approach is specifically designed to handle class imbalance, ensuring fair and accurate classification across all rice varieties, regardless of their representation in the dataset. Our model demonstrates remarkable versatility, effectively accommodating binary and multi-class scenarios while maintaining robust performance in the face of imbalanced data. As part of our contribution, we have also developed a new dataset intentionally incorporating class imbalance. This dataset serves as a rigorous benchmark for evaluating our model's performance against existing works in the field. The results of our comprehensive evaluation are compelling, with the XGB-MLP model achieving outstanding accuracy across various classification tasks: 99.86 % for binary class, 99.95 % for multi-class, and 98.46 % for a challenging multi-class scenario using a merged dataset. These impressive results not only surpass the pe

关键词： Adversarial machine learning

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The Impact of SMS Phishing using Machine Learning Classifiers with Innovative Techniques

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Procedia computer science 2025年 260卷 608-615页

作者： Anisha Asirvatham C. Meenakshi Research Scholar Department of Computer Science Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Associate Professor Department of Computer Science Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

These days, machine learning has an amazing ability to present the results efficiently. Network technology computing systems have long served as a critical framework to supply machine learning with deterministic computing resources. This technique can be advantageous to networking. The application of machine learning in detection of phishing of SMS in a network is the main topic of this article. This not only stimulates new network applications but also aids in the resolution of certain obsolete network issues. The fundamental approach to describe how machine learning technology works in network model is summarized in this article. The SMS spam can be detected using the proposed system, where the feature extraction can be done using stemming and the different machine learning classifiers are used to evaluate the accuracy of the proposed system. The different machine learning classifiers used in this system are Logistic regression, linear regression, K Nearest Neighbors and Naïve Bayes. The new system SpamSMS is the ensemble method using Naïve Bayes and Stacking Algorithm which gives the maximum accuracy towards the provided dataset to check for the spam messages.

关键词： Network Intrusion Detection Neural Network Artificial Intelligence computer Network Machine learning classifiers

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Social Argumentation Systems

Journal of Applied Logics

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Journal of Applied Logics 2025年第3期12卷 251-294页

作者： Bikakis, Antonis Flouris, Giorgos Leite, Joao Patkos, Theodore Department of Information Studies University College London United Kingdom Institute of Computer Science FORTH Heraklion Greece NOVA LINCS NOVA University Lisbon Portugal

While a lot of research has been conducted on understanding and formalising the interplay of arguments within the context of Computational Argumentation, this research is not fully applicable to the types of arguments that populate the Social Web. In that context, arguments usually have the form of comments, opinions or reviews, and are the main ingredients of online discussion forums, social networks, online rating and review sites, debate portals and other online communities-the electronic version of word-of-mouth communication. As a result, voting and other forms of reaction to the provided comments or arguments (other than just "attacks") are allowed, features that are not normally considered in the classical literature on Computational Argumentation. In this chapter, we study extensions of argumentation frameworks that have been proposed to describe and understand the more complex types of interactions among arguments that can be found in the Social Web, and present the current state-of-the-art, as well as open problems. © 2025, College Publications. All rights reserved.

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Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation 31

Extending LLMs to New Languages: A Case Study of Llama and P...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Sani, Samin Mahdizadeh Sadeghi, Pouya Vu, Thuy-Trang Yaghoobzadeh, Yadollah Haffari, Gholamreza Department of Electrical and Computer Engineering University of Tehran Iran Tehran Institute for Advanced Studies Khatam University Iran Department of Data Science & AI Monash University Australia

ISBN: (纸本)9798891761964

Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited understanding of Persian) using parameter-efficient fine-tuning. We employ a multi-stage approach involving pretraining on monolingual Persian data, aligning representations through bilingual pretraining and instruction datasets, and instruction-tuning with task-specific datasets. We evaluate the model's performance at each stage on generation and classification tasks. Our findings suggest that incorporating the Persian language, through bilingual data alignment, can enhance classification accuracy for Persian tasks, with no adverse impact and sometimes even improvements on English tasks. Additionally, the results highlight the model's initial strength as a critical factor when working with limited training data, with cross-lingual alignment offering minimal benefits for the low-resource language. Knowledge transfer from English to Persian has a marginal effect, primarily benefiting simple classification tasks. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Revolutionizing Learning: The Role of AR/VR Technologies in Educational Delivery and Student Engagement in Higher studies 2

Revolutionizing Learning: The Role of AR/VR Technologies in ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Patel, Krisha R. Brahmbhatt, Vishva B. Dubey, Nilesh Jayswal, Hardik Devang Patel Institute of Advanced Technology and Research Department of Computer Engineering Vadodara India Devang Patel Institute of Advanced Technology and Research Department of Computer Science and Engineering Vadodara India Devang Patel Institute of Advanced Technology and Research Department of Infromation Technology Vadodara India

ISBN: (纸本)9798331505745

Augmented Reality (AR) and Virtual Reality (VR) are emerging technologies that combine digital and physical worlds to provide immersive and interactive experiences. Their use in education has been revolutionary, particularly in terms of increasing student engagement, grasp of complicated concepts, and encouraging experiential learning. AR superimposes virtual things on real-world surroundings, whereas VR immerses viewers in a completely digital environment. These tools gained traction during the COVID-19 epidemic, demonstrating their potential for remote learning and increasing student enthusiasm and information retention. While AR and VR are commonly employed in higher education, particularly in sectors such as healthcare and engineering, there are still barriers to their complete adoption, including cost, accessibility, and ethical concerns. This study explores the impactful uses of AR and VR in education, shedding light on their potential to modernize traditional teaching methods and improve student outcomes. © 2025 IEEE.

关键词： Students

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