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Image Registration and Deep NeuroFuzzy Networks for Mitigating Atmospheric Turbulence Effects in Consumer-Based Optical Imaging

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IEEE Transactions on Consumer Electronics 2025年

作者： Khowaja, Sunder Ali Ali, Usman Dev, Kapal Lee, Ik Hyun Dublin City University and ADAPT Centre School of Computing Dublin Ireland Munster Technological University Department of Computer Science Cork Ireland Tech University of Korea Department of Mechatronics Engineering Siheung15073 Korea Republic of IKLAB Inc Seoul08513 Korea Republic of

Consumer-based optical imaging systems are characterized as big data processing systems, which are drastically affected by atmospheric turbulences that add geometric distortions and blur effect to the images when used in outdoor condition. Physics-grounded simulators have been proposed recently to generate synthetic data but the generalization to the real-world turbulent images is not so good. In this paper, we combine the characteristics of image registration, deep neurofuzzy methods, and channel-attention based discriminative learning strategy to propose image registration, neurofuzzy based denoising, and deblurring network (RND2Net). The RND2Net is designed on a principle that it does not require turbulent image pairs (ground truth images) to train the network, which closely resembles the real-world situation used as consumer devices. The registration module focuses on the region-based fusion techniques while the denoising and deblurring module incorporates deep neurofuzzy network along with dense residual blocks and channel attention mechanism to train the network. The RND2Net is also designed to reduce the noise and blur effect from images, while generalizing on the down-stream tasks, such as text recognition. Experimental results show that the RND2Net yields better performance quantitatively as qualitatively on synthetic and real-world datasets in comparison to existing state-of-the-art methods. © 2025 IEEE.

关键词： Image registration

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Optimizing Drug Discovery for Breast Cancer in a Laboratory Environment Using Machine Learning

Optimizing Drug Discovery for Breast Cancer in a Laboratory ...

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International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Gauri Borkhade Jagendra Singh Nitin Arvind Shelke Kamal Upreti Vishakha Kuwar Mohit Tiwar Bharati Vidyapeeth College of Engineering Navi Mumbai India Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Computer Science CHRIST (Deemed to be University) Delhi-NCR Ghaziabad India Dr. D Y Patil Vidyapeeth Centre for online Learning Pune India Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi India

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

Breast cancer therapy can be greatly enhanced by the proposed method that combines experimental and computational techniques. Employing a state-of-the-art in vitro system, we evaluated biopsy tissues at different cancer stages, monitoring them for 48 hours. Later on, our investigation involved the application of machine learning models including naïve Bayes (NB), artificial neural networks (ANN), random forest (RF), and decision trees (DT). Surprisingly, these models reached high test accuracies—ANN 93.2%, NB 90.4%, DT 87.8%, and RF 85.9%. The dataset's impedance dynamics data provide evidence for treatment efficacy. Therapeutic strategies need to be adjusted for particular patients and their stage of cancer since the results underscore the usefulness of personalized breast cancer therapy. This study will significantly contribute to new tailored treatment options available for breast cancer patients.

关键词： Radio frequency Computational modeling Biological system modeling Biopsy Medical treatment Artificial neural networks Breast cancer

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Data Driven Traffic Density Monitoring: A UAV and ResNet50 Deep Learning Framework

Data Driven Traffic Density Monitoring: A UAV and ResNet50 D...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Saddaf Rubab Fida Hussain Dahri Ghulam E Mustafa Abro Nooritawati Md Tahir Muhammad Javed Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates School of Computer Science and Engineering Southeast University Nanjing China Interdisciplinary Research Centre for Aviation and Space Exploration King Fahd University of Petroleum and Minerals (KFUPM) Saudi Arabia School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Cybersecurity International information Technology University Alamty Kazakhstan Department of Computer Science and Technology College of Computer Science Donghua University Shanghai China

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

Urban traffic congestion necessitates innovative monitoring and control solutions. This study leverages UAV footage and deep learning techniques to classify and analyze traffic density levels autonomously. By employing a pre-trained ResN et50 model enhanced with additional layers, the research optimizes traffic density classification using UAV-captured data. A custom dataset of traffic scenarios in Dammam, Saudi Arabia, was developed, incorporating diverse conditions through advanced normalization and data augmentation techniques to improve resilience and robustness. The modified ResN et50 model demonstrated high classification accuracy across varied traffic scenarios, validating its reliability and scalability for real-world applications. Key findings include the superior adaptability of UAV-based monitoring for real-time urban traffic assessment and the enhanced classification precision achieved through model optimization. This research underscores the potential of integrating UAV technology with deep learning to advance urban traffic management and foster more efficient urban planning strategies.

关键词： Deep learning Scalability Heuristic algorithms Urban planning Autonomous aerial vehicles Real-time systems Monitoring Traffic congestion Resilience Residual neural networks

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Data Freshness Optimization Under CAA in the UAV-Aided MECN: A Potential Game Perspective

Data Freshness Optimization Under CAA in the UAV-Aided MECN:...

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作者： Wang, Weizheng Srivastava, Gautam Lin, Jerry Chun-Wei Yang, Yaoqi Alazab, Mamoun Gadekallu, Thippa Reddy City University of Hong Kong Department of Computer Science Hong Kong Brandon University Department of Mathematics and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung406040 Taiwan Western Norway University of Applied Sciences Department of Computer Science Bergen5063 Norway Army Engineering University of Pla Graduate School Nanjing210000 China Melbourne Polytechnic Prahran Campus Department of Information Technology MelbourneVIC3181 Australia Vellore Institute of Technology School of Information Technology Vellore632014 India

As a promising enabler for edge intelligence, Unmanned Aerial Vehicles (UAV) have become more and more important in Mobile Edge computing Networks (MECN), such as communication, computation, collection and control service supply. Although Age of Information (AoI) minimization is indispensable for fresh information collection and computation in the UAV-aided MECN, some attackers can launch attacks to deteriorate the availability of precious channel resources, such as revealed channel access attacks (CAAs). Moreover, recent research has not considered the system's active probability and security issues concurrently, e.g., CAA, in the average AoI minimization process. In this paper, to deal with this problem, we consider an AoI-oriented channel access problem under CAA with a game theory viewpoint. Firstly, to obtain a MECN-based AoI indicator under CAA, the system model with active probability consideration is established. Next, the channel access-based AoI minimization problem is formulated from the viewpoint of the Ordinary Potential Game (OPG). Furthermore, two algorithms called AACSD and DCASD are proposed to determine channel access strategies, by which the Nash Equilibrium (NE) solution of the OPG could be reached. Finally, experiments are conducted under homogeneous and heterogeneous parameter settings, and the simulation results evaluate the correctness and effectiveness of our proposals. © 2000-2011 IEEE.

关键词： Game theory

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Advanced Architectures integrated with Agentic AI for Next-Generation Wireless Networks

arXiv

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arXiv 2025年

作者： Dev, Kapal Khowaja, Sunder Ali Zeydan, Engin Debbah, Merouane Department of Computer Science Munster Technological University Ireland School of Computing Dublin City University Dublin Ireland Centre Tecnològic de Telecomunicacions de Catalunya Barcelona Spain Computer and Information Engineering Khalifa University Abu Dhabi United Arab Emirates

This paper investigates a range of cutting-edge technologies and architectural innovations aimed at simplifying network operations, reducing operational expenditure (OpEx), and enabling the deployment of new service models. The focus is on (i) Proposing novel, more efficient 6G architectures, with both Control and User planes enabling the seamless expansion of services, while addressing long-term 6G network evolution. (ii) Exploring advanced techniques for constrained artificial intelligence (AI) operations, particularly the design of AI agents for real-time learning, optimizing energy consumption, and the allocation of computational resources. (iii) Identifying technologies and architectures that support the orchestration of back-end services using serverless computing models across multiple domains, particularly for vertical industries. (iv) Introducing optically-based, ultra-high-speed, low-latency network architectures, with fast optical switching and real-time control, replacing conventional electronic switching to reduce power consumption by an order of magnitude. © 2025, CC BY-NC-ND.

关键词： Deep learning

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Fused Fuzzy Deep Learning and Information Steganography for Privacy Preservation in Medical Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Jingxue Zhao, Pengbiao Deng, Erqiang Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Xiong, Hu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China the School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China the KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India the Division of Research and Development Lovely Professional University Phagwara India the University Centre of Research and Development Chandigarh University Mohali India the Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia the Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

—Medical Consumer Electronics (MCE) have greatly facilitated people’s lives, allowing patients to obtain personal medical information including medical images. These medical images obtained from MCE need to be processed using segmentation, diagnosis, recognition to obtain useful information. Deep learning plays an important role in medical image segmentation and recognition. However, other personal information about the patient, such as previous cases, is often required to determine the final diagnosis. However, sending personal information and medical images generated through MCE to the clinic directly exposes patient privacy. Therefore, in this paper, we propose a digital medical image privacy protection framework based on information steganography with fuzzy deep learning. The proposed framework transforms patient privacy information into invisible noise and hides it in the deep covert space of the image. This method plays a crucial role in ensuring patient privacy and data security. Furthermore, this method can ensure a high level of image segmentation accuracy that supports the development of personalized medical services. The experimental results show that the proposed framework can embed patient information in medical images without affecting the segmentation task, and effectively protect privacy. Finally, the proposed framework provides new possibilities and impetus for the popularization of MCE. © 2025 IEEE.

关键词： Medical imaging

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Self-Supervised Superpixel Correspondences For Video Object Segmentation 22

Self-Supervised Superpixel Correspondences For Video Object ...

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Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing

作者： Clement Tan Chai Kiat Yeo Cheston Tan Basura Fernando School of Computer Science and Engineering Nanyang Technological University Singapore and Agency for Science Technology and Research (A*STAR) Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (纸本)9781450397810

Prior self-supervised video object segmentation models directly find pixel correspondences between pairs of frames. Instead, we propose a novel approach of employing superpixel features for learning visual correspondences between frames of a video sequence. With an attention mechanism, we train the model to reconstruct the next frame using superpixel features between adjacent frames of videos trained in a self-supervised manner. As superpixels are able to group pixels based on color and proximity, they reduce repetitive and noisy pixels that may confuse the model during tracking. In addition, the structural information provided by the superpixel features can help improve the segmentation of objects. We benchmark our method (S3CNet) against existing self-supervised pixel correspondence frameworks such as [12, 22, 24] and show that our self-supervised superpixel correspondence method performs better and is a viable alternative for visual tracking on the commonly-used DAVIS 2017 video segmentation benchmark.

关键词： Video Object Segmentation Self-Supervised Learning Superpixels

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Secure and Decentralized Collaboration in Oncology: A Blockchain Approach to Tumor Segmentation

Secure and Decentralized Collaboration in Oncology: A Blockc...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Ramin Ranjbarzadeh Ayse Keles Martin Crane Shokofeh Anari Malika Bendechache School of Computing Faculty of Engineering and Computing Dublin City University Ireland Department of Artificial Intelligence Engineering Faculty of Engineering and Natural Sciences Ankara Medipol University Ankara Turkiye Department of Accounting Economic and Financial Sciences Islamic Azad University South Tehran Branch Tehran Iran ADAPT Research Centre School of Computer Science University of Galway Ireland

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This research presents an innovative framework that uses blockchain technology to improve tumor segmentation in medical imaging. The approach tackles issues related to data security, particularly when dealing with real private dataset, annotation accuracy, and collaboration. With the growing reliance of the medical industry on accurate tumor segmentation from medical images for cancer diagnosis and treatment, current methods are inadequate in maintaining data accuracy and promoting collaboration among experts across different countries. Our suggested approach utilizes blockchain technology to establish a decentralized, secure platform for the collaborative obtaining, annotation, and validation of medical images by data scientists, oncologists, and radiologists. Smart contracts streamline essential procedures such as verification of annotations, consensus among experts, and remuneration of contributors, guaranteeing the dependability and excellence of the data. Furthermore, the unchangeable record of transactions in the blockchain ensures a reliable basis for implementing artificial intelligence and machine learning algorithms. This improves the accuracy of segmenting data and allows for predictive modeling. This strategy not only improves the precision and effectiveness of tumor segmentation but also promotes a worldwide collaborative environment, which has the potential to revolutionize cancer diagnostics and treatment planning. Furthermore, it ensures the privacy and security of patient data.

关键词： Image segmentation Data privacy Accuracy Machine learning algorithms Annotations Collaboration Medical services

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