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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Bomba, Andrii Ostapchuk, Oksana Vovk, Roman Kopnicky, Miroslav National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Software Engineering Ivano-Frankivsk Ukraine Catholic University in Ružomberok Department of Catholic Theology Spišská Kapitula Slovakia

Based on the synthesis of complex analysis methods, perturbation theory, and characteristics, a new approach has been developed for accounting for osmosis and temperature in predicting the migration processes of radionuclides under non-isothermal conditions in quasi-ideal porous media (in curved regions) bounded by streamlines and equipotential lines. The solution to the corresponding degenerate problem was obtained based on the developed method of nonlinear inversion of boundary value problem solutions into quasi-conformal mappings. An algorithm for calculating a uniformly dynamic grid, streamlines, and speed of ideal filtration field was constructed. With the developed method of characteristics, formulas for the approximate solution of diffusion-convection mass transfer problems in perturbed osmosis and heat filtration fields were obtained. © 2023 IEEE.

关键词： Porous materials

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Intelligent Blocking and Prevention of SARS-CoV-2 Based on Evolutionary Reinforcement Learning

Intelligent Blocking and Prevention of SARS-CoV-2 Based on E...

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Congress on Evolutionary Computation

作者： Chengwei He Huaping Hong Lijia Ma Qingling Zhu Yuan Bai College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In recent years, the global repercussions of SARS-CoV-2 and its variants have posed significant challenges to various areas, including the economic order, transportation, healthcare, and education, and the mitigation and prevention of SARS-CoV-2 and other infectious diseases have received much attention. This paper tries to develop a rational and effective intervention strategy in response to the dynamic evolution of the SARS-CoV-2 epidemic. To address this issue, we first use the SEIR model to analyze the population distribution under the SARS-CoV-2 epidemic, and then integrate a deep Q-neural network (DQN) to reflect an economic quantification model contextualized within the SARS-CoV-2 scenario. Next, we propose an evolutionary deep reinforcement learning to generate an optimal and adaptive intervention strategy, by optimizing the weight sequence of the DQN. The experimental results show the effectiveness of the proposed method in maximizing economic benefits, while ensuring the uninterrupted functionality of medical institutions. Importantly, the learned intervention strategy exhibits the applicability to other infectious diseases.

关键词： Economics Epidemics Infectious diseases Prevention and mitigation Heuristic algorithms Sociology Loss measurement

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Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization

arXiv

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arXiv 2023年

作者： Zhu, Yingying Yang, Hongji Lu, Yuxin Huang, Qiang College of Computer Science and Software Engineering Shenzhen University Nanhai Ave 3688 Guangdong Shenzhen518060 China

In this work, we aim at an important but less explored problem of a simple yet effective backbone specific for cross-view geo-localization task. Existing methods for cross-view geo-localization tasks are frequently characterized by 1) complicated methodologies, 2) GPU-consuming computations, and 3) a stringent assumption that aerial and ground images are centrally or orientation aligned. To address the above three challenges for cross-view image matching, we propose a new backbone network, named Simple Attention-based Image Geo-localization network (SAIG). The proposed SAIG effectively represents long-range interactions among patches as well as cross-view correspondence with multi-head self-attention layers. The"narrow-deep" architecture of our SAIG improves the feature richness without degradation in performance, while its shallow and effective convolutional stem preserves the locality, eliminating the loss of patchify boundary information. Our SAIG achieves state-of-the-art results on cross-view geo-localization, while being far simpler than previous works. Furthermore, with only 15.9% of the model parameters and half of the output dimension compared to the state-of-the-art, the SAIG adapts well across multiple cross-view datasets without employing any well-designed feature aggregation modules or feature alignment algorithms. In addition, our SAIG attains competitive scores on image retrieval benchmarks, further demonstrating its generalizability. As a backbone network, our SAIG is both easy to follow and computationally lightweight, which is meaningful in practical scenario. Moreover, we propose a simple Spatial-Mixed feature aggregation moDule (SMD) that can mix and project spatial information into a low-dimensional space to generate feature descriptors. In particular, SMD inherits the property of not being constrained by the strict assumption of model and further improves performance in cross-view tasks. The code is available at https://***/yanghongji200

关键词： Image matching

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Fuzzy based Fall Risk Prediction in Older Adults

Fuzzy based Fall Risk Prediction in Older Adults

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Deepika Mohan Duaa Zuhair Al-Hamid Peter Han Joo Chong Jairo Gutierrez Hui Li Department of Electrical and Electronic Engineering Auckland University of Technology Auckland New Zealand Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand School of Electronic & Computer Engineering Peking University Shenzhen China

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The global elderly population is increasing rapidly, leading to a rise in chronic illnesses and co-existing conditions, which in turn results in higher healthcare expenses. Accidental falls are among the leading causes of injury-related deaths in elderly individuals. This study aims to create a real-time monitoring system using vital signs to foresee a future fall in older adults by identifying abnormalities through continuous monitoring. The proposed fall prediction technique, employing the Fuzzy-based Fall Prediction Algorithm, utilizes Fuzzy rules to learn and execute tasks. The obtained results are then classified according to different levels of predicted risk indicators. The developed model is tested using data from older adults sourced from a public repository and compared with the results of the theoretical evaluation. The simulated outcomes demonstrate that the proposed algorithm achieves 96% accuracy, 93.75% sensitivity, and 100% specificity. Utilizing these advancements in the proposed heterogeneous technology allows for the early prediction of falls in the elderly and can potentially save lives.

关键词： Analytical models Accuracy Sensitivity Predictive models Prediction algorithms Classification algorithms Older adults

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Vision Transformer (ViT)-based Applications in Image Classification

Vision Transformer (ViT)-based Applications in Image Classif...

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： Yingzi Huo Kai Jin Jiahong Cai Huixuan Xiong Jiacheng Pang School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China the College of Computer Science and Electronics Engineering Hunan University Changsha China

In recent years, the ViT model has been widely used in the field of computer vision, especially for image classification tasks. This paper summarizes the application of ViT in image classification tasks, first introduces the image classification imple- mentation process and the basic architecture of the ViT model, then analyzes and summarizes the image classification methods, including traditional image classification methods, CNN-based image classification methods, and ViT-based image classification methods, and provides a comparative analysis of CNN and ViT. Subsequently, this paper outlines the application prospects of ViT in image classification and its future development and also outlines some shortcomings of ViT and its solutions.

关键词： Representation learning Analytical models Visualization Computational modeling Transformers Reservoirs Data models

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Deep Learning-Based Smart Infotainment System for Taxi Vehicles 4

Deep Learning-Based Smart Infotainment System for Taxi Vehic...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Dewalegama, M.P. De Zoysa, A.D.S. Kodikara, L.M Dissanayake, D.M.J.C Kuruppu, Thilmi Anuththara Rupasinghe, Shalini Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Sri Lanka

ISBN: (纸本)9781665468350

Nowadays, people are more intent to use IoT to ease their day-to-day work. As a result of that, the transportation industry is being adopted to more IoT-based approaches rather than traditional methods. When it comes to taxi services, companies need to keep up the competition with their rivals. Along with the high demand, there are also being reporting number of problems around the taxi industry daily. Taking some of the most common problems into consideration, such as 'Smart Infotainment System' will help to resolve most of those problems. Deep learning models such as CNN (Convolutional Neural Network), YOLO and SKLearn used to develop the proposed system. As a result of that, the reputation of the taxi service will be increased, and the taxi service consumers will be able to get a comfortable and safer journey to the end of the day. © 2022 IEEE.

关键词： Convolutional neural networks

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Semantic Rules for Service Discovery in Social Internet of Things 4

Semantic Rules for Service Discovery in Social Internet of T...

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Mohana, S.D. Prakash, S.P. Shiva Krinkin, Kirill Department of IS&E JSS Science and Technology University Karnataka Mysuru India Department of Information Science and Engineering JSS Science and Technology University Karnataka Mysuru India Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University"LETI" Saint Petersburg Russia

ISBN: (纸本)9781665401180

World is moving towards preceding generation Alpha that believes in socializing the objects or devices within the network known as Social Internet of Things(SIoT). In SIoT the services are discovered based on the user preferences by establishing relationship between the objects and user. Thus object intelligent model is necessary to efficiently discover the services based on the context and semantic relationships between the users and objects. SIoT posses certain challenges like service discovery, compositions, etc. To address these challenges, in this work a semantic rules are proposed to discover services and it is evaluated using various machine learning algorithms on SIoT data. Hence, this work focusses on implementing and evaluating the various machine learning algorithms on SIoT device data considering health as an application. The performance is evaluated with dataset ratio of 60:40, 70:30, 80:20 and 90:10. The machine learning classifier techniques like Decision Tree, Naive Bayes, K-nearest neighbour and Artificial Neural Network classifiers gives the average accuracy 100%, 99%, 100%and 100% respectively. The results show that ANN gives best results compared to other algorithms. © 2022 IEEE

关键词： Internet of things

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A Comparative Analysis of Goal-Oriented Requirements engineering and Model-Based Systems engineering Frameworks for Managing Requirements of Systems of Systems

A Comparative Analysis of Goal-Oriented Requirements Enginee...

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Information Technology (ACIT)

作者： Suhair AlHajHassan Samer Murrar Ahmad Samhan Mohammed Odeh Ali Elrashidi Software Engineering Department Applied Science Private University Amman Jordan Computer Science Department Applied Science Private University Amman Jordan Software Engineering Department Zarqa University Zarqa Jordan Global Academy for Digital Health Bristol United Kingdom Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Goal-oriented requirements engineering (GORE) for Systems of Systems (SoS) includes combining individual operational systems local goals to achieve higher-level goals. GORE offers a structured approach to managing complex requirements, ensuring that strategic goals are translated into operational tasks. This paper provides a review of GORE frameworks, including those incorporating Model-Based Systems engineering (MBSE), that could be used in the management of complex Systems of Systems. The paper analyzes recent GORE frameworks such as CGS4Adaptation which combines Goals and SysML for managing adaptive Socio-Cyber-Physical Systems (SCPSs); GORE-based approach to Energy Management Systems (EnMS); Model-Based and Goal-Oriented Approach for the conceptual design of smart grid services; GORE and reference architecture approach for microgrid systems; and Adaptation-Oriented Requirement Modeling approach (ADORE). A comparative analysis is conducted to assess the extent of effectiveness these frameworks provide for improving SoS traceability, adaptability, and system design integrity. The paper concludes with key findings on the strengths and limitations of the considered frameworks, on the basis of which, major conclusions on how GORE combined with MBSE can be used for managing SoS requirements in Smart Grid (SG) and socio-cyber-physical systems could be drawn. This review paper also contributes to the requirements engineering domain by outlining effective strategies for designing and managing complex, adaptive Systems of Systems.

关键词： Adaptation models Analytical models Adaptive systems Reviews Microgrids Smart grids Requirements engineering Information technology System analysis and design System of systems

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Detection and Forecasting of Parkinson Disease Progression from Speech Signal Features Using Multi-Layer Perceptron and LSTM

arXiv

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arXiv 2024年

作者： Ali, Majid Shakir, Hina Samreen, Asia Ahmed, Sohaib Department of Electrical Engineering Bahria University Karachi Pakistan Department of Software Engineering Bahria University Karachi Pakistan Department of Computer Science Bahria University Karachi Pakistan

Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The application of machine learning techniques helps improve the diagnostic accuracy of Parkinson’s disease detection but only few studies have presented work towards the prediction of disease progression. In this research work, Long Short-Term Memory(LSTM) was trained using the diagnostic features on Parkinson patients speech signals, to predict the disease progression while a Multilayer Perceptron(MLP) was trained on the same diagnostic features to detect the disease. Diagnostic features selected using two well-known feature selection methods named Relief-F and Sequential Forward Selection and applied on LSTM and MLP have shown to accurately predict the disease progression as stage 2 and 3 and its existence respectively. © 2024, CC BY.

关键词： Neurodegenerative diseases

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Sinhala Sign Language Learning System for Hearing Impaired Community

Sinhala Sign Language Learning System for Hearing Impaired C...

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2023 4th International Informatics and software engineering Conference (IISEC)

作者： Idushan K. A. S Dilshan P. G. T Jayasundera P. S Madhushan K. L. B Jenny Krishara Dinuka Wijendra Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

This research aims to develop a comprehensive system for Sinhala Sign Language (SSL) that includes a learning system, dynamic sign detection, audio/video to sign conversion, and vocal training. SSL plays a crucial role in facilitating communication for individuals who are deaf or hard of hearing in Sri Lanka. The learning system provides a platform for learning SSL and includes a text-to-sign language interpreter. The dynamic sign detection system uses computer vision techniques to identify and interpret dynamic signs accurately. The audio/video to sign conversion system bridges the gap between spoken language and SSL by converting auditory information into visual representations. The vocal training system focuses on enhancing the vocal skills of cochlear implanted children. This research contributes to the development of effective communication and language skills for SSL users.

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