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arXiv 2024年

作者： Yousefi, Milad Maleki, Shadi Farabi Jafarizadeh, Ali Youshanlui, Mahya Ahmadpour Jafari, Aida Pedrammehr, Siamak Alizadehsani, Roohallah Tadeusiewicz, Ryszard Plawiak, Pawel Faculty of Mathematics Statistics and Computer Sciences University of Tabriz Tabriz Iran Nikookari Eye Center Tabriz University of Medical Sciences Tabriz Iran Immunology Research Center Tabriz University of Medical Sciences Tabriz Iran Faculty of Design Tabriz Islamic Art University Tabriz Iran Deakin University VIC3216 Australia AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland

Thyroid cancer is an increasing global health concern that requires advanced diagnostic methods. The application of AI and radiomics to thyroid cancer diagnosis is examined in this review. A review of multiple databases was conducted in compliance with PRISMA guidelines until October 2023. A combination of keywords led to the discovery of an English academic publication on thyroid cancer and related subjects. 267 papers were returned from the original search after 109 duplicates were removed. Relevant studies were selected according to predetermined criteria after 124 articles were eliminated based on an examination of their abstract and title. After the comprehensive analysis, an additional six studies were excluded. Among the 28 included studies, radiomics analysis, which incorporates ultrasound (US) images, demonstrated its effectiveness in diagnosing thyroid cancer. Various results were noted, some of the studies presenting new strategies that outperformed the status quo. The literature has emphasized various challenges faced by AI models, including interpretability issues, dataset constraints, and operator dependence. The synthesized findings of the 28 included studies mentioned the need for standardization efforts and prospective multicenter studies to address these concerns. Furthermore, approaches to overcome these obstacles were identified, such as advances in explainable AI technology and personalized medicine techniques. The review focuses on how AI and radiomics could transform the diagnosis and treatment of thyroid cancer. Despite challenges, future research on multidisciplinary cooperation, clinical applicability validation, and algorithm improvement holds the potential to improve patient outcomes and diagnostic precision in the treatment of thyroid cancer. © 2024, CC BY-NC-SA.

关键词： Machine learning

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Impact of Expert academic Teaching Quality and its Performance Based on BiLSTM-Deep CNN Network

Impact of Expert Academic Teaching Quality and its Performan...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Tushar Dhar Shukla Purnendu Bikash Acharjee Chethan C Thulasimani T M Sindhu Sanjiv Sharma University Institute of Teachers Training & Research Chandigarh University Mohali India CHRIST University Pune India Department of Information Science and Engineering Sri Venkateshwara College of Engineering Bengaluru India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Tamilnadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of IT Madhav Institute of Technology and Science Gwalior Madhya Pradesh India

Undergraduate and postgraduate students from eight different departments at a UK institution participated in organized conversations about the impact of teachers' research activities on their education. In both samples, positive responses greatly outnumbered negative ones. There was an increase in positive feedback on professors' research when the overall quantity and quality of research in a specific field (as measured by research Assessment Exercise [RAE] ratings) improved. Undergraduate samples with higher RAE scores were more likely to have negative feedback on research than graduate student samples. Both graduate and undergraduate students agreed that lecturers' research increased the instructor's credibility, relevance, and knowledge, as well as piqued and maintained their own interest, engagement, and drive. Data processing, feature selection, and model training are the first steps in the proposed approach. The data are changed from their raw form into a form suitable for academic use during the data pre-processing phase. They are employing Information Gain and Symmetric Uncertainty for feature selection. Following the feature selection process, the models are trained using BiLSTM-CNN. Both the BiLSTM and the CNN methods are inferior to the proposed method.

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Mamba vision models: Automated American sign language recognition

Franklin Open

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Franklin Open 2025年 10卷

作者： Altaher, Ali Salem Bang, Chiron Alsharif, Bader Altaher, Ahmed Alanazi, Munid Altaher, Hasan Zhuang, Hanqi Department of Electrical Engineering and Computer Science Florida Atlantic University 777 Glades Road Boca Raton 33431 FL United States College of Medicine Ibn Sina University of Medical and Pharmaceutical Science Baghdad Iraq Department of Computer Science and Engineering College of Telecommunication and Information Technical and Vocational Training Corporation Riyadh 12464 Saudi Arabia Electronic Computer Center Al-Nahrain University Jadriya Baghdad Iraq Business Informatics Department College of Business King Khalid University Abha Saudi Arabia Information and Communication Technology Department Baghdad Institute of Technology Middle Technical University Baghdad Iraq

Historically, individuals with hearing impairments have faced significant challenges in effective communication due to the lack of adequate resources. Recent technological advancements have spurred the development of innovative tools aimed at enhancing the quality of life for those with hearing disabilities. This research focuses on the application of Vision Mamba Models for classifying hand gestures representing the American Sign Language (ASL) alphabet, with a detailed comparative analysis of its performance against 13 deep learning architectures. The Vision Mamba Models, namely, Vision Mamba and Remote Sensing Mamba, were trained on a substantial dataset comprising 87,000 images of ASL hand gestures, and through iterative fine-tuning of their architectural parameters, the models’ accuracy and performance were optimized. Experimental results demonstrated that the Mamba Vision Models outperformed all other models previously examined in this context scoring exceptional accuracy rate that exceeded 99.98%, with less architectural complexity. These findings highlight the potential of deep learning technologies, particularly the Mamba vision models, in advancing assistive technologies, offering a sophisticated and highly accurate tool for interpreting ASL hand gestures and promising improved communication and accessibility for individuals with hearing impairments. © 2025 The Authors

关键词： American sign language Deep learning Remote sensing mamba Transfer learning Vision Mamba.

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Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information

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Cyber-Physical Systems 2025年

作者： Byeon, Haewon AlMahadin, Ghayth Bsoul, Qusay Quraishi, Aadam Soni, Mukesh Khan, Shahi Raza Shabaz, Mohammad Department of AI and Software Inje University Gimhae Korea Republic of Data Science Department College of Information Technology Mutah University Mutah Jordan Department of Cybersecurity College of Computer Sciences and Informatics Amman Arab University Amman Jordan M.D. Research Intervention Treatment Institute HoustonTX United States Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune Pune India MJMC Centre for Media and Mass Communication Jamia Hamdard Delhi India Model Institute of Engineering and Technology Jammu India

This paper proposes an improved cyber-physical false information detection model based on cross-modal ambiguous learning. This work introduced the IC2 LFD model with an emphasis on ‘text-image’ ambiguity, focusing on the main goal of intelligent multi-cross modal data fake information identification. The experiment revealed that the significance of text and images differs in intelligent multi-cross modal data fake information detection. The effectiveness of the model is verified on the twitter dataset, showing a 6% accuracy improvement compared to the baseline model and a 1.6% performance improvement over detection methods without dynamic weight allocation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adversarial machine learning

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Scribble2label: Scribble-supervised cell segmentation via self-generating pseudo-labels with consistency 23rd

Scribble2label: Scribble-supervised cell segmentation via se...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Lee, Hyeonsoo Jeong, Won-Ki School of Electrical and Computer Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of College of Informatics Department of Computer Science and Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9783030597092

Segmentation is a fundamental process in microscopic cell image analysis. With the advent of recent advances in deep learning, more accurate and high-throughput cell segmentation has become feasible. However, most existing deep learning-based cell segmentation algorithms require fully annotated ground-truth cell labels, which are time-consuming and labor-intensive to generate. In this paper, we introduce Scribble2Label, a novel weakly-supervised cell segmentation framework that exploits only a handful of scribble annotations without full segmentation labels. The core idea is to combine pseudo-labeling and label filtering to generate reliable labels from weak supervision. For this, we leverage the consistency of predictions by iteratively averaging the predictions to improve pseudo labels. We demonstrate the performance of Scribble2Label by comparing it to several state-of-the-art cell segmentation methods with various cell image modalities, including bright-field, fluorescence, and electron microscopy. We also show that our method performs robustly across different levels of scribble details, which confirms that only a few scribble annotations are required in real-use cases. © Springer Nature Switzerland AG 2020.

关键词： Deep learning

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The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms

arXiv

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arXiv 2022年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Saadatfar, Hamid Nodehi, Issa Fazl, Fatemeh Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Dipu Kabir, H.M. Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand*** Iran Department of Information Technology Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering Qom University of Technology Qom Iran Department of Electronic Engineering Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia School of Science and Technology Singapore University of Social Sciences Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods (goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods (energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication (M2M), Train-to-Train(T2T) communication network, Peer-to-Peer (P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems;Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process. © 2022, CC BY-SA.

关键词： Reinforcement learning

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Automated Recognition of Structures in Scanning Electron Microscopy Images Using Specialized Algorithms in MATLAB and Python: An Overview

Automated Recognition of Structures in Scanning Electron Mic...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Valentina Marascu Marius Iulian Mihailescu Stefania Loredana Nita Marius Rogobete Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science “Spiru Haret” University Bucharest Romania Low Temperature Plasma Department National Institute for Laser Plasma and Radiation Physics Bucharest Romania Department of Computers and Cybersecurity “Ferdinand I” Military Technical Academy Bucharest Romania Telematics Harman International Bucharest

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

Applied physics serves as the bridge connecting theoretical principles with their practical implementations in everyday situations. informatics plays a crucial role in this subject because of its comprehensive and intricate capacity to simulate and forecast physical phenomena. This research highlights the indispensability of computer-based algorithms for analyzing the Scanning Electron Images of the obtained materials, particularly materials synthesized by laboratory plasma. Herein, the conducted research highlights the advantages of image processing via Matlab and Python algorithms, in order to analyze the synthesized structures. This approach can offer perspectives on physical processes by employing image processing techniques. The approaches and future directions were identified.

关键词： Scanning electron microscopy Software algorithms Tungsten Process control Software Oxidation Plasmas

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Fraud Detection and Prevention for a Secure Financial Future Using Artificial Intelligence

Fraud Detection and Prevention for a Secure Financial Future...

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Emerging Smart Computing and informatics (ESCI), Conference on

作者： Rishi Chaudhry Sandeep Kaur Jagdeep Singla Ruchi Mittal Varun Malik Institute of Management Studies and Research Maharshi Dayanand University Rohtak Haryana India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar Punjab India HPKV Business School Dhauladhar Parisar-II Central University of Himachal Pradesh Dharamshala Distt Kangra HP India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The demand for reliable and intelligent solutions to identify and prevent financial fraud is growing in proportion to the complexity and sophistication of this crime. This article outlines a comprehensive plan to improve financial security via the use of state-of-the-art AI techniques. The suggested architecture consists of three main parts: a K-Means clustering for dataset preparation, an extended deep Q network (EDQN) for analysis and prediction, and a principal component analysis (PCA) for feature selection. An enhanced K-Means clustering method that can manage complex and large-scale financial data is our primary contribution. A key motivation for this strategy is the hope that it would facilitate the detection of legitimate and fraudulent activities by making it simpler to see clear patterns in the data. By streamlining the grouping process and producing more accurate clusters, a more efficient clustering technique improves the overall effectiveness of fraud detection. In the aftermath of getting the dataset ready, PCA feature selection follows. By lowering the dataset's dimensionality without sacrificing any of the valuable information, principal component analysis (PCA) finds and retains the most relevant characteristics. This guarantees that the chosen qualities greatly aid in the identification and prevention of fraudulent transactions, while also improving the computational efficiency of future activities. Lastly, an EDQN is used in a system for classification and prediction. By including characteristics that enable the model to comprehend more complex links in financial data, the EDQN enhances previous Deep Q Networks.

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Pending Interest Table (PIT) Performance Analysis in Named Data Networking on Palapa Ring Topology

Pending Interest Table (PIT) Performance Analysis in Named D...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Adi Sucipto Jupriyadi Syaiful Ahdan Hasan Nur Arifin Eki Ahmad Zaki Hamidi Nana Rachmana Syambas School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Department of Electrical Engineering UIN Sunan Gunung Djati Bandung Indonesia

The concept of the internet in the future will prioritize content, by reducing delays in data transmission. Named Data Networking (NDN) is a content-based future internet concept that changes the paradigm of using IP. Inside the NDN router, there are three data structures, namely Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB). Pending Interest Table (PIT) contains a list of unfulfilled interests. This condition occurs when the node has not received a response after the interest forwarding process. Measurable and fast PIT performance is a challenge in Named Data Networks. In this study, we will try to do a simulation to measure and analyze the performance of PIT in NDN in the Palapa Ring topology. The research was conducted using the NDNSim simulator, to see the performance in the PIT. The simulation and analysis of the results show that the granularity of a prefix has an effect on In Satisfied Interest in an NDN network. At the number of interests of 100, the result obtained from the simulation is that there is a decrease in the percentage of interest data served, amounting to more than 20%. At the amount of interest in 1000 about more than 30%. The length of the prefix and the number of interest sent by the consumer affect the performance of the PIT, seen from the number of In Satisfied Interests.

关键词： Analytical models Network topology Simulation Data models Topology Performance analysis Delays

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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