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IEEE International Symposium on Multimedia

作者： Furkan Kaynar Adrian Michl Eckehard Steinbach Information and Technology Department of Computer Engineering Chair of Media Technology Technical University of Munich School of Computation Munich Institute of Robotics and Machine Intelligence (MIRMI) Munich Germany

ISBN: (纸本)9781665471732

Interactive image segmentation separates the object of interest in the scene from the background, using the help of human annotations. Click-based interactive segmentation methods typically receive positive and negative clicks from the user indicating the object of interest and the background. These clicks are encoded into click-maps to be processed further by a neural network. Although the depth information is known to improve the image segmentation accuracy, the previous work encodes only the locations of the clicks into the click-maps. We propose two novel click-map generation methods that modify the conventional click-maps using relative depth information. This depth information is estimated from the monocular RGB image. After retraining the baseline interactive segmentation method with our novel click-maps, the segmentation accuracy improved without requiring any additional input or increasing the network size. Experimental evaluations showed that our method yields a better mean segmentation accuracy on the Berkeley and DAVIS datasets than the baseline using conventional click-maps, and a comparable performance on the GrabCut dataset.

关键词： Training Image segmentation Image coding Annotations Neural networks Complexity theory

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TANF–Trustworthy Adaptive Neural Framework for Reliable and Scalable 6G Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Awan, Kamran Ahmad Din, Ikram Ud Almogren, Ahmad Adnan, Muhammad Altameem, Ayman Syed, Ikram Ahmad, Shabir University of Haripur Department of Information Technology Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Chair of Cyber Security Department of Computer Science Riyadh11633 Saudi Arabia Higher Colleges of technology Al Ain United Arab Emirates Hankuk University of Foreign Studies Department of Information and Communication Engineering Yongin17035 Korea Republic of Center of Artificial Intelligence for Medical Instruments United States

The increasing complexity of 6G-IoT networks presents challenges in ensuring real-time trust assessment, computational efficiency, and security against adversarial threats. Existing frameworks struggle to dynamically adapt to evolving threats and high-volume data streams, leading to compromised decision reliability. This study proposes TANF (Trustworthy Adaptive Neural Framework), an advanced deep learning-driven trust evaluation system incorporating hierarchical processing, multi-domain trust layers, and Holo-Recursive Memory (HRM) for adaptive optimization. TANF prioritizes high-trust data streams using sensory stream balancing, dynamically allocates resources through task-specific synergy layers, and enhances memory recall by integrating past, present, and predictive state representations. The simulation, conducted in Edge-IIoTset, IoT-23 and CICIDS2017, evaluated trust assessment, computational latency, scalability, and adversarial detection. TANF achieves a precision of 92. 8%, a latency reduction of 34. 5% and a adversarial detection rate of 95. 6%, outperforming ERAI, ROBUST-6G, and IMCS. © 2014 IEEE.

关键词： Data streams

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Self-Supervised Object Recognition Based on Repeated Re-Capturing of Dynamic Indoor Environments

Self-Supervised Object Recognition Based on Repeated Re-Capt...

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IEEE International Symposium on Multimedia

作者： Martin Piccolrovazzi Michael G. Adam Sebastian Eger Marsil Zakour Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Technical University of Munich School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Munich Germany

ISBN: (纸本)9781665471732

Capturing a digital replica of an environment using hand held devices or mobile mapping systems has become increasingly easy in recent years. However, leveraging large amounts of data for various semantic applications is usually impaired by a costly data annotation process. In this paper, we investigate the topic of object recognition in indoor environments without supervision. We approach the problem from a remapping perspective, where we capture RGB images from the same environment at different times and use the naturally occurring changes to identify single objects. In the first step, we create pairs of images from different recordings and generate object candidates using optical flow and an off-the-shelf region proposal algorithm. Then, we use a self-supervised representation learning framework and cluster the extracted objects. We evaluate the performance of several existing clustering methods in an over-clustering setting, since the number of object classes is unknown in an unsupervised setup. Our experimental validation on a real-world dataset shows that the proposed system can successfully recognize objects and pre-annotate a dataset by exploiting a recapturing process.

关键词： Representation learning Clustering methods Semantics Clustering algorithms Self-supervised learning Indoor environment Recording

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Robust Depth Estimation in Foggy Environments Combining RGB Images and mmWave Radar

Robust Depth Estimation in Foggy Environments Combining RGB ...

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IEEE International Symposium on Multimedia

作者： Mengchen Xiong Xiao Xu Dong Yang Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) School of Computation Information and Technology Technical University of Munich Munich Germany

ISBN: (纸本)9781665471732

In this paper, we propose a robust depth estimation strategy that uses RGB images and mmWave radar data to deal with limited visibility in foggy environments. While the state-of-the-art RGB or LiDAR-based depth estimation works well in scenarios with good visibility, their performance dramatically degrades in the presence of fog. In contrast, mmWave radar sensors are not affected by fog and hence are a promising complement. To leverage this property of mmWave radar, we combine RGB image-based depth estimation with radar information. The proposed combination is an extension of the Sparse-to-Dense (S2D) model. Moreover, a weight-based sensor fusion strategy is presented to improve system performance. Our experiments show that a fog density of meteorological optical range (MOR) less than 50m leads to strongly degraded performance for RGB image-based and LiDAR-based depth estimation. For a MOR of 30m in our dataset, the experiments show an improvement of 26% in mean square error for our proposed approach compared to the combination of RGB images and LiDAR data.

关键词： Meteorological radar Laser radar System performance Estimation Radar imaging Sensor fusion Optical imaging

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Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques

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SN computer Science 2021年第3期2卷 177页

作者： Akter, Laboni Ferdib-Al-Islam Islam, Md. Milon Al-Rakhami, Mabrook S. Haque, Md. Rezwanul Department of Biomedical Engineering Khulna University of Engineering & Technology Khulna 9203 Bangladesh Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna 9203 Bangladesh Research Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Cervical cancer growth is the fourth maximum of regular diseases in females. It is one of the sicknesses which is compromising ladies' wellbeing everywhere in the world and it is difficult to notice any sign in the beginning phase. But the screening process of cervical cancer sometimes is being hampered due to some social-behavioral factors. There is still a limited number of researches directed in cervical cancer identification dependent on the behavior and machine learning in the area of gynecology and computer science. In this research, we have proposed three machine learning models such as Decision Tree, Random Forest, and XGBoost to predict cervical cancer from behavior and its variables and we got significantly improved outcomes than the current methods with 93.33% accuracy. Moreover, we have shown the top features from the dataset according to the feature important scores to know their impacts on the development of the classification model. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Behavior risk Cervical cancer Correlation Feature importance Machine learning

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Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

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computers, Materials & Continua 2022年第11期73卷 4123-4138页

作者： Areej A.Malibari Marwa Obayya Mohamed K.Nour Amal S.Mehanna Manar Ahmed Hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease *** cancer is the most widely affected cancer worldwide,with an increased death rate *** to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast *** detection of breast cancer will reduce the death rate *** early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive *** paper proposed an efficient and optimized deep learning-based feature selection approach with this *** model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm *** learning is used in the extraction of features *** ext,a convolution neural network,is used to extract the *** two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant *** optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant *** feature extraction and classification process used two datasets,breast,and *** to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the MIAS dataset.

关键词： Breast cancer detection computer-aided diagnosis(CAD) deep learning CNN entropy butterfly optimization

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The past, present and future of learning analytics

Proceedings of the Annual Hawaii International Conference on...

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Proceedings of the Annual Hawaii International Conference on System Sciences 2021年 2020-January卷 1507-1508页

作者： Islind, Anna Sigridur Óskarsdóttir, María Willermark, Sara Deeva, Galina Reykjavik University Department of Computer Science School of Technology Iceland University West School of Business Economics and IT Department of Media and Design Sweden KU Leuven Research Centre for Information Systems Engineering Belgium

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Energy management system based on wireless sensor networks and power line communications

Energy management system based on wireless sensor networks a...

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2022 International Conference Automatics and Informatics, ICAI 2022

作者： Omar, Azhar-Husain Malekian, Reza Bogatinoska, Dijana Capeska University of Pretoria Department of Electrical Electronic and Computer Engineering Pretoria082 South Africa Malmö University Department of Computer Science and Media Technology Malmö205 06 Sweden Mach. Intell. and Robotics University of Information Science and Technology St. Paul the Apostle Faculty of Applied IT Ohrid Macedonia

ISBN: (数字)9781665476256

ISBN: (纸本)9781665476256

In this paper, we developed a power line communication (PLC) system design, power measurement sensor design, light sensor design, temperature sensor design, and the integration of these components into an advanced sensor network to allow for energy metering and environment monitoring. A power measurement sensor was implemented through a current and voltage sensing circuitry was interfaced multi-plug power adapter to allow for non-invasive measurement of power usage of appliances. The sensors produce signals corresponding to the drawn voltage and current, which are then sampled and processed to estimate power usage. The PLC communications operated at an average accuracy of 95%. The power measurement sensor had an accuracy of 92%, which made it appropriate for home user estimations. The light sensor had an accuracy of between 91-97%, which was suitable for home lighting measurement. © 2022 IEEE.

关键词： Wireless sensor networks

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Temporal-guided Mixture-of-Experts for Zero-Shot Video Question Answering

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Qin, Yixin Zhao, Lei Gao, Lianli Zhang, Haonan Zeng, Pengpeng Shen, Heng Tao University of Electronic Science and Technology of China Center for Future Media School of Computer Science and Engineering Chengdu611731 China Sichuan Artificial Intelligence Research Institute Yibin644000 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China Tongji University Department of Information Engineering and Computer Science China

Video Question Answering (VideoQA) is a challenging task in the vision-language field. Due to the time-consuming and labor-intensive labeling process of the question-answer pairs, fully supervised methods are no longer suitable for the current increasing demand for data. This has led to the rise of zero-shot VideoQA, and some works propose to adapt large language models (LLMs) to assist zero-shot learning. Despite recent progress, the inadequacy of LLMs in comprehending temporal information in videos and the neglect of temporal differences, e.g., the different dynamic changes between scenes or objects, remain insufficiently addressed by existing attempts in zero-shot VideoQA. In light of these challenges, a novel Temporal-guided Mixture-of-Experts Network (T-MoENet) for zero-shot video question answering is proposed in this paper. Specifically, we apply a temporal module to imbue language models with the capacity to perceive temporal information. Then a temporal-guided mixture-of-experts module is proposed to further learn the temporal differences presented in different videos. It enables the model to effectively improve the capacity of generalization. Our proposed method achieves state-of-the-art performance on multiple zero-shot VideoQA benchmarks, notably improving accuracy by 5.6% on TGIF-FrameQA and 2.3% on MSRVTT-QA while remaining competitive with other methods in the fully supervised setting. The codes and models developed in this study will be made publicly available at https://***/qyx1121/T-MoENet. © 1991-2012 IEEE.

关键词： Zero-shot learning

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A Comprehensive Survey of Joint Radar-Communication Technologies and their future Applications in Healthcare Sector

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IEEE Access 2024年 1-1页

作者： Al-Salehi, Abdulrahman Sufyan, Mubarak Mohammed Al-Ezzi Malik, Aqdas Naveed Al-Asaly, Mahfoudh Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Department of Networks and cyber security AlJanad University Of Science and Technology Taiz Yemen Department of Electrical Engineering University of Engineering and Technology G.T Road Lahore Pakistan STC’s Artificial Intelligence Chair College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

The integration of joint radar and communication technologies has emerged as a transformative approach with promising applications across various sectors, including healthcare. This paper presents a comprehensive examination of the current state of the art in this field, including design considerations, technical challenges, and key research directions. Furthermore, this paper compares the current review with previous literature reviews in the field and emphasizes its comprehensiveness and indepth examination of the key issues and trends in the field of unified radar and communication systems. The ultimate goal of this paper is to provide researchers and practitioners with a comprehensive and up-to-date understanding of the current state of this field and its future research directions. Authors

关键词： Health care

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