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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Shiori Sasaki Ryota Nakamura Prasetyo Wibowo Department of Data Science Musashino University Tokyo Japan Faculty of Data Science Musashino University Tokyo Japan Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

This paper describes a Plastic Waste Hotspot Detection System which has been developed in an international collaborative research project to realize an “Environmental AI-Human Actions integration” with marine plastic waste reduction actions. In this project, various local conditions are obtained to the system to realize an environmentally resilient society. The nature conservation activities such as marine plastic waste collection and reduction actions are implemented based on these conditions. In the process of integration of Environmental AI and Human Actions, the effects of these activities and activists' contribution to environmental improvement and global-scale knowledge are accumulated, shared, analyzed, and visualized through global-scale analysis. This paper describes the Plastic Waste Hotspot Detection system as one of the systems that integrate AI systems and human actions. This paper examines the feasibility and effectiveness of the system through the fieldwork tests in Phuket, Thailand and Surabaya, Indonesia.

关键词： Visualization Collaboration Artificial intelligence Environmental management Plastic waste

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Multi-Criteria Prediction Mechanism for Vehicular Wi-Fi Offloading

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computers, Materials & Continua 2021年第11期69卷 2313-2337页

作者： Mahmoud Alawi Raed Alsaqour Abdi Abdalla Maha Abdelhaq Mueen Uddin Department of Computer Engineering and IT Faculty of EngineeringZanzibar UniversityZanzibarTanzania Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Electronics and Telecommunications Engineering College of ICTUniversity of Dar es SalaamTanzania Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Faculty of Science Universiti Brunei DarussalamNegara Brunei Darussalam

The growing demands of vehicular network applications,which have diverse networking and multimedia capabilities that passengers use while traveling,cause an overload of cellular *** scenario affects the quality of service(QoS)of vehicle and non-vehicle ***,wireless fidelity access points Wi-Fi access point(AP)and fourth generation long-term evolution advanced(4G LTE-A)networks are broadly ***-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A ***,utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult *** condition is due to the short coverage of Wi-Fi APs and weak deployment strategies of *** studies have proposed that offloading mechanisms depend on the historical Wi-Fi connection patterns observed by an interest vehicle in making an offloading ***,depending solely on the historical connection patterns affects the prediction accuracy and offloading ratio of most existing mechanisms even when AP location information is *** present study proposed a multi-criteria wireless availability prediction(MWAP)mechanism,which utilizes historical connection patterns,historical data rate information,and vehicular trajectory computation to predict the next available AP and its expected data capacity in making offloading *** proposed mechanism is decentralized,where each vehicle makes the prediction by *** characteristic helps the vehicle users make a proactive offloading decision that maintains the QoS for different applications.A simulation utilizing MATLAB was conducted to evaluate the performance of the proposed mechanism and benchmark it with related state-of-the-art mechanisms.A comparison was made based on the prediction error and offloading ratio of the proposed mechanism in several *** MWAP mechanism exhibited a lower prediction error(i.e.,below 20%)and higher offloading rat

关键词： Vehicular network markov predictor 4G LTE-A Wi-Fi offloading prediction model heterogeneous network

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Optical flow estimation technique for hazy scenes 2

Optical flow estimation technique for hazy scenes

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Kashif, Rao Rao, Sana Ltd Faculty of Engineering and Computer Science Department of CS/IT National University of Modern Languages Multan Pakistan Xiamen University School of Informatics Xiamen China Fujian Key Laboratory of Sensing and Computing for Smart City Xiamen China

ISBN: (数字)9781665498197

ISBN: (纸本)9781665498197

Hazy scenes limit the visibility and make the optical flow estimation task challenging due to low contrast images. Previous methods are not robust when applied to hazy image sequences because the two assumptions (brightness constancy and smoothness) break down due to the low contrast outdoor effects. Based on the pre-processing work on images, we applied an image dehazing method on image sequences first to handle haze and then estimated optical flow. The proposed method is based on a classical pyramidal technique. Results show that our method performs well on hazy scenes as compared to the other existing method. © 2022 IEEE.

关键词： Optical flows

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Privacy-Preserving Collaborative AI for Distributed Deep Learning with Cross-sectional Data

Privacy-Preserving Collaborative AI for Distributed Deep Lea...

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International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Saeed Iqbal Adnan N. Qureshi Khursheed Aurangzeb Khalid Javeed Department of Computer Science Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah UAE

The article discusses the challenges of using deep learning in healthcare due to the lack of extensive medical datasets and concerns about confidentiality and privacy. The article then focuses on the analysis of skin lesion images, which are complex and difficult to classify. The article presents a unique CNN model (skin-net) using a progressively private federated learning architecture to potentially address these issues. Recent advancements in medical image analysis using convolutional neural networks and federated learning techniques have shown promising results (96.19 accuracy, 96.37 sensitivity and 96.53 specificity) in accurately classifying skin diseases while maintaining data privacy. Using these methods offers a potentially effective way to examine human skin while improving the model’s generalization performance through image augmentation.

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Automatic Difficulty Balance in Two-Player Games with Deep Reinforcement Learning

Automatic Difficulty Balance in Two-Player Games with Deep R...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Simão Reis Rita Novais Luís Paulo Reis Nuno Lau Artificial Intelligence and Computer Science Laboratory (LIACC) Faculty of Engineering University of Porto Porto Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Aveiro Portugal

Regardless of the goal of a game, it should be a pleasant and fun experience for its players. For some games to be enjoyable, the level of difficulty must be carefully calibrated, otherwise, players will feel bored or frustrated. Multiplayer scenarios in particular, where one player’s satisfaction might not translate to the enjoyment of other players and poses extra challenges in balancing the difficulty. The performance of one player is relative to the opponent, versus single-player scenarios where we can fully control the environment. We propose an AI automation framework for difficulty balancing in two-player games, where balancing is seen as a Reinforcement Learning task. A Game Master (GM) agent learns how to use handicap game mechanics, signaled by a reward function that evaluates a weighted combination of aesthetic criteria that encourages dramatization and allows a player in the lead to go back and a player in the rear to catch up, creating the desired rubber banding effect that balances out skill gaps. The quality of the games with the trained GM embedded is examined by measuring the same aesthetic criteria on the resulting games, and by analyzing the resulting changes in the game.

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Structural RC computer aided intelligent analysis and computational performance via experimental investigations

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Structural engineering and Mechanics 2024年第3期90卷 253-261页

作者： Huang, Y.C. Lulios, TuMuli Chang, Chu-Ho Noor, M. Nasir Shao, Jen-Chung Chiu, Chien-Liang Lee, Tsair-Fwu Wang, Renata Information Technology Faculty Duy Tân University 254 Nguyen Van Linh Danang Viet Nam Princess Margaret Hospital Funafuti Island Private Mail Bag Tuvalu College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan College of Maritime National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Medical Physics and Informatics Laboratory of Electronics Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Information and Research Center of China 1/9 Road: 2 Block: D Section: 15 Dhaka1216 Bangladesh

This research explores a new finite element model for the free vibration analysis of bi-directional functionally graded (BDFG) beams. The model is based on an efficient higher-order shear deformation beam theory that incorporates a trigonometric warping function for both transverse shear deformation and stress to guarantee traction-free boundary conditions without the necessity of shear correction factors. The proposed two-node beam element has three degrees of freedom per node, and the inter-element continuity is retained using both C1 and C0 continuities for kinematics variables. In addition, the mechanical properties of the (BDFG) beam vary gradually and smoothly in both the in-plane and out-of-plane beam’s directions according to an exponential power-law distribution. The highly elevated performance of the developed model is shown by comparing it to conceptual frameworks and solution procedures. Detailed numerical investigations are also conducted to examine the impact of boundary conditions, the bi-directional gradient indices, and the slenderness ratio on the free vibration response of BDFG beams. The suggested finite element beam model is an excellent potential tool for the design and the mechanical behavior estimation of BDFG structures. © 2024 Techno-Press, Ltd.

关键词： Boundary conditions

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Investigation of the separation zone for split-domain light field visualization

Investigation of the separation zone for split-domain light ...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Kamran Javidi Maria G. Martini Peter A. Kara Department of Computer Science Wireless Multimedia and Networking Research Group School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Penrhyn Road Campus Kingston upon Thames London UK Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution.

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Methods and tools for automatic or semi-automatic recognition of selected emotions using machine learning algorithms

Methods and tools for automatic or semi-automatic recognitio...

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Progress in Applied Electrical engineering (PAEE)

作者： Piotr Schneider Dariusz Mikołajewski Anna Bryniarska Magdalena Igras-Cybulska Artur Cybulski Walery Marcinowicz Maciej Janiszewski Aleksandra Kawala-Sterniuk Department of Neuroinformatics and Biomedical Engineering Institute of Computer Science Maria Curie-Sklodowska University Lublin Poland Hishoo Sp. z o.o Gdynia Poland Faculty of Computer Science Kazimierz Wielki University Bydgoszcz Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

Automatic or semi-automatic recognition of selected emotions using Machine Learning (ML) algorithms requires practical, fast and accurate implementation of the steps involved in acquiring the actual signals (e.g. speech, facial expression, posture and body movements), recording them in the form of an optimal feature vector/matrix, correctly interpreting the above data and reproducing and generalising this process within the ML system. Depending on the accuracy and speed required and the degree of its ‘invasiveness’ (e.g. by attaching sensors to the user’s body), single or multi-modal interpretations of biometric signals associated with different groups of emotions are possible. This group of studies, despite their relative non-invasiveness, requires the caution prescribed by the Artificial Intelligence (AI) Act and RODO Act and the possibility of being replaced (if necessary) by alternatives. This article aims to analyse the opportunities and risks associated with this group of technologies, with a particular focus on directions for further development.

关键词： Emotion recognition Machine learning algorithms Accuracy Video on demand Speech recognition Machine learning Feature extraction Recording Convolutional neural networks Web sites

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