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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： M.B Marasulov Alka Gangrade Sumanpreet Kaur Aasheesh Shukla Haider Alabdeli B. Lanitha Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Institute of Polymer Chemistry and Physics Tashkent Uzbekistan Western Caspian University Scientific Researcher Baku Azerbaijan Department of Computer Science & Engineering IES College of Technology Bhopal M.P. India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Electronics and Communication Engineering GLA University Mathura India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Artificial Intelligence and Data Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Traffic loads have dramatically grown as a result of the small-cell / Internet of Everything devices' increasing popularity. The present network has undergone a revolution with this expansion, transforming it into 5G wireless technology, which requires ultra-low latency, high data rates, and higher capacity. Maximizing the use of its bands and investigating the spectrum resource are two study topics that are prioritized to satisfy these needs. Nevertheless, obtaining an effective management plan is severely hampered by the spectrum resource's scarcity. The goal of this effort is to provide a thorough assessment of current 5G-enabling technologies and spectrum sharing technologies in relation to 5G development. SS surveys and associated research on SS methods pertinent to 5G networks will are evaluated, and SS techniques are categorized. Surveys and studies are categorized into one of the primary spectrum allocation methodologies based on the network architecture, spectrum access method, and spectrum allocation behavior. Additionally, a thorough analysis of cognitive radio technology in SS in relation to the deployment of 5G is carried out. To ensure a thorough analysis, talks are held about the problems and difficulties with the way SS and CR are currently being implemented, and resources for effective 5G development are offered.

关键词： Surveys Technological innovation 5G mobile communication Interference Telecommunication traffic Switches Ultra reliable low latency communication Telecommunication computing Resource management Cognitive radio

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Boosting Universal Domain Adaptation in Remote Sensing with Dual-Classifiers Consistency Discrimination and Cross-Domain Feature Mixup

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Li, Qingmei Zhang, Yang Zheng, Juepeng Zhang, Yuxiang Huang, Jianxi Fu, Haohuan Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen China Sun Yat-Sen University School of Artificial Intelligence Zhuhai China National Supercomputing Center in Shenzhen Shenzhen China Beijing Institute of Technology School of Information and Electronics Beijing China Southwest Jiaotong University Faculty of Geosciences and Engineering Chengdu China China Agricultural University College of Land Science and Technology Beijing China Ministry of Agriculture and Rural Affairs Key Laboratory of Remote Sensing for Agri-Hazards Beijing China Tsinghua University Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Beijing China

In the field of remote sensing image classification, domain adaptation (DA) methods have been extensively utilized to overcome the challenges posed by data discrepancies between source and target domains that arise from varying imaging conditions, sensor differences, or geographical variations. Stemming from the existence of unseen classes in both the source and target domains, universal DA (UniDA) poses the greatest challenge that demands innovative solutions. Existing UniDA methods often overlook intra-domain variations within the target domain and face difficulties in distinguishing between similar known and unknown classes, which significantly hinder cross-domain transfer. To overcome these challenges, we propose a dual-classifier network tailored for cross-domain classification of remote sensing images, named DCmix. DCmix introduces a dual-classifiers network that utilizes both closed-set and open-set classifiers to improve the accuracy of identifying unknown sample classes. To our knowledge, this is the first attempt to introduce dual classifiers into the UniDA remote sensing image classification task. We further enhance the feature generalization capability of the target domain based on sample neighborhood relations, resulting in a more adaptable and robust feature representation. A cross-domain feature mixup scheme is also designed based on the consistency discrimination of the dual classifiers, achieving smoother decision boundaries and simpler hidden layer representations. Extensive experiments conducted on four hyperspectral image datasets and three RGB datasets prove that the introduced approach attains state-of-the-art performance in remote sensing image classification under the UniDA scenario. © 1980-2012 IEEE.

关键词： Image classification

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Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

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Intelligent Automation & Soft Computing 2024年第3期39卷 417-436页

作者： Muhammad S.Alam Farhan B.Mohamed Ali Selamat Faruk Ahmed AKM B.Hossain Department of Emergent Computing School of ComputingUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Malaysia-Japan International Institute of Technology(MJIIT) Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia Media and Game Innovation Centre of Excellence(MaGICX) Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Engineering Technology University of MemphisMemphisTN38152USA Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Information System and Cyber Security College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance *** scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and *** research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image *** annotated image dataset trains the proposed system and predicts the camera pose in *** system mainly improved the localization performance of indoor cameras by more accurately predicting the camera *** also recognizes the scene changes during the sequence and evaluates the effects of these *** system achieved high accuracy and real-time *** scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact ***,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

关键词： Camera pose estimation indoor camera localization real-time localization scene change detection simultaneous localization and mapping(SLAM)

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Broad learning systems: An overview of recent advances, applications, challenges and future directions

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Neurocomputing 2025年 641卷

作者： Chu, Yonghe Guo, Yanlong Ding, Weiping Cao, Heling Ping, Peng School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Faculty of Data Science City University of Macau 999078 China School of Transportation and Civil Engineering Nantong University Jiangsu Nantong226001 China

Broad learning system (BLS) as a novel training framework derived from the random vector functional link neural network (RVFLNN). Unlike RVFLNN, which directly applies raw data to network learning, BLS first transforms input data into feature nodes through feature mapping. These feature nodes are then nonlinearly transformed into enhanced nodes. Both feature nodes and enhanced nodes are concatenated and connected to the output layer, and the corresponding output weights are derived via pseudo-inverse. Due to its shallow architecture and the need to train only the output weights, BLS achieves highly efficient learning ***, when adding new nodes, BLS does not require retraining from scratch;it only adjusts the weights associated with the new nodes. Different from deep learning, BLS aims to expand the broad rather than the depth of the neural network to solve complex problems. It not only overcomes the drawback of the long-training process in deep learning but also enables the rapid incremental construction of network models. Therefore, BLS has received extensive attention from both the academic and industrial communities. In view of this, this paper conducts a systematic review of BLS. First, we outline the research background of the BLS. Then, we elaborate on the relevant concepts and definitions of BLS. Furthermore, recent advances in BLS are introduced. In addition, we present the extensive applications of BLS in various fields. Finally, several possible development directions for BLS are proposed. © 2025 Elsevier B.V.

关键词： Optical character recognition

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Intelligent Hardware Solutions for COVID -19 and Alike Diagnosis - A survey

Intelligent Hardware Solutions for COVID -19 and Alike Diagn...

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Computational intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Manisha Aeri Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Mersin Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Mersin Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Mersin Turkey Computer Science and Engineering Graphic Era Hill University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) Greater Noida India

Globally, 210 nations have been affected by the 2019 Novel Coronavirus (COVID-19), which has been classified as a pandemic. The modern health system, as well as the economic, educational, and social sides of society, have all been severely impacted. While the rate of transmission keeps increasing, several cooperative strategies between stakeholders to create cutting-edge methods of screening and detecting COVID-19 instances among people at a comparable rate have been noticed. Also, the importance of computational models connected to the technologies of the fourth industrial revolution in accomplishing the desired feat has been emphasized. Unfortunately, there is a gap in the precision of COVID-19 case detection, prediction, and contact tracking. For patients with COVID-19 in isolation units, teleultrasound (TUS), particularly with the support of fifth generation (5G) wireless transmission technology, can offer rapid monitoring, quick clinical progress assessment, and assistance with guiding interventional procedures. Also, it helps conserve medical resources like equipment and supplies while lowering the risk of infection among medical personnel. The review of computer models presented in this work can be used to improve the effectiveness of COVID-19 pandemic case detection and prediction. We concentrate on adoptable big data, AI, and nature-inspired computing solutions for the current pandemic. According to the review, models inspired by nature have shown strong performance in feature selection for medical problems. In pandemic-related cases like COVID-19, contact tracing using big data analytics should also be investigated.

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Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

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Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters

arXiv

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arXiv 2022年

作者： Wu, Junde Fang, Huihui Yang, Yehui Liu, Yuanpei Gao, Jing Duan, Lixin Yang, Weihua Xu, Yanwu Baidu Inc. Beijing China Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong Electrical and Computer Engineering Purdue University West LafayetteIN United States School of Computer Science and Technology University of Electronic Science and Technology of China Sichuan Chengdu China Big Data and Artificial Intelligence Institute Shenzhen Eye Hospital Jinan University Guangdong Shenzhen China

In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias. But when data is multiply annotated, standard deep learning models are often not applicable. In this paper, we propose a novel neural network framework, called Multi-Rater Prism (MrPrism) to learn the medical image segmentation from multiple labels. Inspired by the iterative half-quadratic optimization, the proposed MrPrism will combine the multi-rater confidences assignment task and calibrated segmentation task in a recurrent manner. In this recurrent process, MrPrism can learn inter-observer variability taking into account the image semantic properties, and finally converges to a self-calibrated segmentation result reflecting the inter-observer agreement. Specifically, we propose Converging Prism (ConP) and Diverging Prism (DivP) to process the two tasks iteratively. ConP learns calibrated segmentation based on the multi-rater confidence maps estimated by DivP. DivP generates multi-rater confidence maps based on the segmentation masks estimated by ConP. The experimental results show that by recurrently running ConP and DivP, the two tasks can achieve mutual improvement. The final converged segmentation result of MrPrism outperforms state-of-the-art (SOTA) strategies on a wide range of medical image segmentation tasks. © 2022, CC BY.

关键词： Medical imaging

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Facial expression animation through action units transfer in latent space

Facial expression animation through action units transfer in...

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作者： Fan, Yachun Tian, Feng Tan, Xiaohui Cheng, Housen School of Artificial Intelligence Beijing Normal University Beijing China Faculty of Science and Technology Bournemouth University Poole United Kingdom Information Engineering College Capital Normal University Beijing China

Automatic animation synthesis has attracted much attention from the community. As most existing methods take a small number of discrete expressions rather than continuous expressions, their integrity and reality of the facial expressions is often compromised. In addition, the easy manipulation with simple inputs and unsupervised processing, although being important to the automatic facial expression animation applications, is relatively less concerned. To address these issues, we propose an unsupervised continuous automatic facial expression animation approach through action units (AU) transfer in the latent space of generative adversarial networks. The expression descriptor which is depicted with AU vector is transferred into the input image without the need of labeled pairs of images and even without their expressions and further network training. We also propose a new approach to quickly generate input image's latent code and cluster the boundaries of different AU attributes with their latent codes. Two latent code operators, vector addition and continuous interpolation, are leveraged for facial expression animation simulating align with the boundaries in the latent space. Experiments have shown that the proposed approach is effective on facial expression translation and animation synthesis. © 2020 John Wiley & Sons, Ltd.

关键词： Generative adversarial networks

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Hybrid vision transformer and Xception model for reliable CT-based ovarian neoplasms diagnosis

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intelligence-Based Medicine 2025年 11卷 100227-100227页

作者： Alshdaifat, Eman Hussein Gharaibeh, Hasan Sindiani, Amer Mahmoud Madain, Rola Al-Mnayyis, Asma'a Mohammad Abu Mhanna, Hamad Yahia Almahmoud, Rawan Eimad Akhdar, Hanan Fawaz Amin, Mohammad Nasayreh, Ahmad Hamad, Raneem Department of Obstetrics and Gynecology Faculty of Medicine Yarmouk University Irbid 211633 Jordan Artificial Intelligence and Data Innovation Office King Hussein Cancer Center Jordan Amman 11622 Jordan Department of Obstetrics and Gynecology Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Internal Medicine College of Medicine Yarmouk University Irbid 211633 Jordan School of Physics University Sains Malaysia (USM) Penang 11800 Malaysia Physics Department Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 13318 Saudi Arabia Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Faculty of Medicine Yarmouk University-Irbid 21163 Jordan

Ovarian cancer is a major global health concern, characterized by high mortality rates and a lack of accurate diagnostic methods. Rapid and accurate detection of ovarian cancer is essential to improve patient outcomes and formulate appropriate treatment protocols. Medical imaging methods are essential for identifying ovarian cancer;however, achieving accurate diagnosis remains a challenge. This paper presents a robust methodology for ovarian cancer detection, including the identification and classification of benign and malignant tumors, using the Xception_ViT model. This hybrid approach was chosen because it combines the advantages of traditional CNN-based models (such as Xception) with the capabilities of modern Transformers-based models (such as ViT). This combination allows the model to take advantage of Xception, which extracts features from images. The Vision Transformer (ViT) model is then used to identify connections between diverse visual elements, enhancing the model's understanding of complex components. A Multi-Layer Perceptron (MLP) layer is finally integrated with the proposed model for image classification. The effectiveness of the model is evaluated using three computed tomography (CT) image datasets from King Abdullah University Hospital (KAUH) in Jordan. The first dataset consists of the ovarian cancer computed tomography dataset (KAUH-OCCTD), the second is the benign ovarian tumors dataset (KAUH-BOTD), and the third is the malignant ovarian tumors dataset (KAUH-MOTD). The three datasets collected from 500 women are characterized by their diversity in ovarian tumor classification and are the first of their kind collected in Jordan. The proposed model Xception_ViT achieved an accuracy of 98.09 % in identifying ovarian cancer on the KAUH-OCCTD dataset, and an accuracy of 96.05 % and 98.73 % on the KAUH-BOTD and KAUH-MOTD datasets, respectively, in distinguishing between benign and malignant ovarian tumors. The proposed model outperformed the pre-trai

关键词： CT Ovarian cancer Ovarian diagnosis Ovarian neoplasms Transformer Xception

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Renewable energy-powered robot for fully-automated tea leaf harvesting: A solution for labor shortage and environmental sustainability

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AIP Conference Proceedings 2024年第1期3161卷

作者： G. Lavanya K. S. Murthy S. Sivakumar Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India Department of Mechanical Engineering Paavai Engineering College Namakkal India Asia Pacific University of Technology and Innovation School of Technology Wilayah Persekutuan Kuala Lumpur Malaysia

The primary reason for the development of this project is the shortage of labour and increase in wages. Robotizing tea leaf harvesting will not only increase the production rate but also compensate for the labour issues. The main intent of the proposed system is to efficiently use renewable energy and provide an alternate solution to the shortage of labours in harvesting tea leaf. In India, tea harvesting is carried out manually by hand plucking or by machinery plucking. The main limitations of semi-automated tea harvesting machines are the power supply generation for the machine operation is using fuels which increased fuel cost and pollution among the surrounding environments. The project is proposed with the following benefits for society: 1. It increases the tea harvesting by minimal human resources; 2. It reduces the manual operation in the harvesting process; 3. It avoids the carbon emission in tea harvesting machines; 4. It utilizes minimum power and draws renewable energy for charging the batteries; 5. It regains the power by its own motion; 6. It saves operating human health without inducing body pain, asthma and other health issues.

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