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作者： Gaur, Loveleen Bhatia, Ujwal Jhanjhi, N.Z. Muhammad, Ghulam Masud, Mehedi Amity International Business School Amity University Noida India School of Computer Science and Engineering SCE Taylor’s University Subang Jaya Malaysia Research Chair of Pervasive and Mobile Computing King Saud University Riyadh11543 Saudi Arabia Computer Engineering Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Optimization of Multi-Channel Quartz Crystal Microbalance for Gas Sensing Application 8

Optimization of Multi-Channel Quartz Crystal Microbalance fo...

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8th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Zainuddin, Ahmad Anwar Bhattacharjee, Soumik Md Ralib, Aliza Aini Mohd Sukri, Nurain Sufi Sabreena Binti Kamarudin, Saidatul Izyanie Subramaniam, Krishnan Handayani, Dini Reyasudin Basir Khan, M. Muhammad Saifudin, Nik Nor International Islamic University Malaysia Computer Science Kuliyyah of Information Communication Technology Kuala Lumpur Malaysia Universitat Bremen Digital Media Masters Germany International Islamic University Malaysia Department of Electrical and Computer Engineering Kuala Lumpur Malaysia Selangor Shah Alam Malaysia Mila University Department of Mechanical Engineering Nilai Negeri Sembilan Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350315516

Mass detection sensor Quartz Crystal Microbalance (QCM) measures mass sensitivity by piezoelectric, high frequency shaving on nanograms these sensors detect with high precise geometry, low cost and ease of production. Conventional QCM sensors are limited to one electrode structure that minimizes the range of sensing. On a single quartz crystal substrate, three new QCM Multi-Channel sensors are developed and optimized that include 2-channel, 3-channel and 4-channel QCM sensors the COMSOL Multiphysics software is used to design and simulate the multi-channel sensors to enhance the device structure for applications with less interference frequency. From the results, it shows that the least interferences exhibit frequency values of 9.58MHz as both QCMs for the 5mm centre-to-centre (c2c). However, due to small c2c distances that decrease accuracy and operating disadvantages, this design was not selected. For 3 channel sensors, however, three resonant peaks show a similar value of 9.58MHz when c2c is maintained at 7mm. With the highest c2c distance this design shows less interference. Finally, for 4-channel sensors, a distance of 6 mm c2c also has a minimum interference with a 9.58 MHz resonance frequency. This shows that 4 channel sensors with the highest c2c can provide greater operational convenience and less interference at frequencies and thus maximize measurement accuracy. Multi-sensor arrays are advantageous when detecting a large number of analytes at the same time or when only selective coatings (coatings that respond to more than one analyte at the same time) are available these sensor arrays, which use only one sensor and combine it with enhanced signal evaluation methods or prior sample separation to achieve the results of multi-sensor arrays, are becoming increasingly popular. © 2023 IEEE.

关键词： Quartz crystal microbalances

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HCAI4U 2023 - Preface to the First Workshop on User Perspectives in Human-Centred Artificial Intelligence 1

HCAI4U 2023 - Preface to the First Workshop on User Perspect...

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1st Workshop on User Perspectives in Human-Centred Artificial Intelligence, HCAI4U 2023

作者： De Luca, Ernesto W. Purificato, Erasmo Boratto, Ludovico Marrone, Stefano Sansone, Carlo Otto von Guericke University Magdeburg Faculty of Computer Science Magdeburg39106 Germany Leibniz Institute for Educational Media Georg Eckert Institute Brunswick38118 Germany University of Cagliari Department of Mathematics and Computer Science Cagliari09124 Italy University of Naples Federico II Department of Electrical Engineering and Information Technology Naples80125 Italy

The emerging concept of Human-Centred Artificial Intelligence (HCAI) involves the amplification, augmentation, empowerment, and enhancement of individuals. The goal of HCAI is to ensure that AI meets our needs while also operating transparently, delivering fair and equitable outcomes, and respecting privacy, all while preserving human control. This approach involves multiple stakeholders, such as researchers, developers, business leaders, policymakers, and users, who are affected in various ways by the implementation and evaluation of AI systems. The primary focus of the First Workshop on User Perspectives in Human-Centred Artificial Intelligence (HCAI4U) is to examine the potential positive and negative impacts of automated decision-making systems on end-users, as well as how their interaction with AI is influenced by human-centred aspects of reliability, safety, and fairness. The workshop aims to facilitate discussion and exchange of ideas among the community on advances in developing trustworthy, fair, and privacy-preserving systems, as well as user interfaces that are explainable, with a specific focus on the users’ perception in real-world scenarios rather than solely on the algorithmic and model performance. Additionally, HCAI4U aims to foster cross-disciplinary and interdisciplinary discussions between experts from various research fields, such as computer science, psychology, sociology, law, medicine, business, etc., to discuss problems and synergies in this exciting research topic. © 2023 CEUR-WS. All rights reserved.

关键词： Human computer interaction

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A transformer generative adversarial network for multi-track music generation

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CAAI Transactions on Intelligence technology 2022年第3期7卷 369-380页

作者： Cong Jin Tao Wang Xiaobing Li Chu Jie Jiessie Tie Yun Tie Shan Liu Ming Yan Yongzhi Li Junxian Wang Shenze Huang School of Information and Communication Engineering Communication University of ChinaBeijingChina School of Information and Engineering Zhengzhou UniversityZhengzhouChina Central Conservatory of Music BeijingChina Department of computer Science University of TorontoTorontoCanada School of Data Science and Media Intelligence Communication University of ChinaBeijingChina School of Light Industry and Engineering South China University of TechnologyGuangzhouChina School of Animation and Digital Arts Communication University of ChinaBeijingChina

This study proposes a new generation network based on transformers and guided by the music theory to produce high-quality music *** this study,the decoding block of the transformer is used to learn the internal information of single-track music,and cross-track transformers are used to learn the information amongst the tracks of different musical instruments.A reward network based on the music theory is proposed,which optimizes the global and local loss objective functions while training and discriminating the network so that the reward network can provide a reliable adjustment method for the generation of the *** method of combining the reward network and cross entropy loss is used to guide the training of the generator and produce high-quality music *** with other multi-track music generation models,the experimental results verify the validity of the model.

关键词： artificial intelligence deep learning multimedia

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Implementing Deep Learning Techniques into Healthcare System Improvement 2

Implementing Deep Learning Techniques into Healthcare System...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Kochhar, Harmeet Kaur Kilaru, Mrudula Aljohani, Nasser Goyal, Himanshu Rai Singh, Devendra Saini, DilipKumar Jang Bahadur Chandigarh University University Institute of Media Studies India Velagapudi Ramakrishna Siddhartha Engineering College India Islamic University of Madinah Information Systems Department Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun India Sawmi Rama Himalayan University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350346640

Artificial intelligence (AI) has the potential to transform clinical decision-making and assistance. In any case, the utilization of current AI methods remains only as a supportive solution to make clinical decisions. The existing methodologies fail to perform in-depth analysis to deliver accurate predictions. This study reviews the integration of advanced technologies AutoML in healthcare sector by considering the data privacy, heterogeneity and interpretability. This review will be a useful resource for medical practitioners interested in applying information science methods to their field of study. © 2023 IEEE.

关键词： Data privacy

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Sensor-Based Emotion Tracking System for computer Games 3

Sensor-Based Emotion Tracking System for Computer Games

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Wickramasinghe, W.R.M.G.K. Devduni, R.M.B. Dasanayaka, D.T.C.B. Mohomed, M.N.N. Kumari, Suriyaa Dassanayake, Thamali Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Interactive Media Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

The game development industry is among the leading industries globally, and in 2020, gaming emerged as a popular entertainment activity upon the COVID-19 outbreak. Thus, competition among gaming companies is high. Hence, they try to adopt new technologies often. Gaming brings multiple feelings for the gamer. At times, the conditions may get even worse from the game's end where the gamer may end up venting out his rage and annoyance. Hence, there is a massive possibility for the gamer to switch to another game which may result in the company to lose its customers. In that scenario, this system can monitor the emotional states of the gamer while playing and manipulate the gaming environment, sound environment, enemy behavior, and gamer mechanism according to the emotional state of the gamer. The sensor-based emotion tracking system identifies the gamer's emotional state using facial emotions, detected through a webcam and heart rate, detected through sensors. The development was carried out through the machine learning models, open cv, Arduino techniques, and reactive programming. The emotional state and facial emotions that will be tracked will count to an accuracy of above 95%. Through that, the target will be to make the gamer satisfied by building appreciation for the services given and by improving the gamer's gaming experience and retain the gamer with the game provider. © 2021 IEEE.

关键词： computer games

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S2CMAF: Multi-Method Assessment Fusion for Scan-to-CAD Methods

S2CMAF: Multi-Method Assessment Fusion for Scan-to-CAD Metho...

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IEEE International Symposium on Multimedia

作者： Driton Salihu Adam Misik Markus Hofbauer 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) Siemens Technology Munich Germany

ISBN: (纸本)9781665471732

Scan-to-CAD-based 3D reconstruction of indoor environments has become increasingly more popular in recent years. The inherent structure of Scan-to-CAD consists of object detection, model retrieval, and alignment. Therefore, a variety of metrics are required to assess these three aspects. This can lead to ambiguous evaluation results and incorrect quality assumptions. To impede the problem of incorrect evaluation, we introduce S2CMAF, a multi-method assessment fusion approach for Scan-to-CAD pipelines. S2CMAF merges several metrics used in evaluating these pipelines into one unique quality score. We show that S2CMAF significantly improves the correlation between Scan-to-CAD results and the ground truth, compared to the conventionally used Scan2CAD benchmark. Additionally, we train S2CMAF using different optimization techniques and demonstrate the advantages of our approach on real-world data.

关键词： Measurement Point cloud compression Correlation Three-dimensional displays Pipelines Object detection Benchmark testing

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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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Interactive RGB Image Segmentation via Depth-modified Click Encoding and Estimated Depth

Interactive RGB Image Segmentation via Depth-modified Click ...

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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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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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