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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdu Saif Nor Shahida Mohd Shah Aiman Alnoamani Ali Ameen Edward Curry Vailet Hikmat Faraj Al Khattat Saeed Hamood Alsamhi Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Computer Science Lincoln University College Kuala Lumpur Malaysia Insight Center for Data Analytics University of Galway Galway Ireland Faculty of Engineering Universiti Putra Wireless and Photonics Networks Research Center Malaysia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Due to severe climatic circumstances exacerbated by climate change, deploying Beyond Fifth Generation (B5G) networks is critical. Unmanned Aerial Vehicles (UAVs) have become indispensable for B5G connectivity in inclement weather conditions such as snow, fog, and rain. This paper investigates energy-efficient B5G connectivity and climate-resilient UAVs. We evaluate the performance of UAV coverage and energy efficiency at different elevation angles under various weather conditions, including snow, fog, and rain. Additionally, we discuss the challenges environments and propose solutions to improve climate-resilient and energy-efficient B5G communication. Emphasizing the adverse effects of climate change on communication networks, The paper’s findings highlight the significant impact of weather conditions on UAV coverage, B5G communication networks, and energy efficiency. This research paves the way for a more resilient and sustainable future.

关键词： Climate change Rain Snow Autonomous aerial vehicles Throughput Energy efficiency Loss measurement

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Enabling Intelligent Immersive Learning using Deep Learning-based Learner Confidence Estimation

Enabling Intelligent Immersive Learning using Deep Learning-...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Mohammadreza Akbari Lor Shu-Ching Chen Mei-Ling Shyu Yudong Tao Shahin Vassigh School of Science and Engineering University of Missouri-Kansas City Kansas City MO USA Data Science and Analytics Innovation Center (dSAIC) University of Missouri-Kansas City Kansas City MO USA Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA College of Communication Architecture and the Arts Florida International University Miami FL USA

ISBN: (数字)9798350351187

ISBN: (纸本)9798350351194

In today’s world, augmented reality and virtual reality (AR/VR) technologies have become more accessible to the public than ever. This brings the possibility of immersive learning to the forefront of education for future generations. However, there is still much to discover and improve in using these technologies to analyze and understand learning. This paper explores the utilization of data captured through AR/VR headsets during an immersive training program for industrial robotics. This includes data on time spent, eye gaze, and hand movement during a range of activities to track a learner’s understanding of the content and intelligently estimate learner confidence within these environments using deep learning. Leveraging a dataset that comprises responses and confidence levels from 10 individuals across 35 questions, we aim to improve the uses and applicability of confidence estimation. We explore the possibility of training a model using learners’ data to dynamically fine-tune lessons and activities for each individual, thereby improving performance. We demonstrate that a pre-trained compact LSTM classification model can be fine-tuned with relatively small data, for enhanced performance on an individual basis for better personalized learning.

关键词： Training Adaptation models Solid modeling Tracking Service robots Estimation data models Rough surfaces Long short term memory Immersive learning

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A New Approach for Calculation of Quasi-Normal Modes and Topological Charges of Regular Black Holes

arXiv

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arXiv 2024年

作者： Zhang, M.Y. Hosseinifar, F. Chen, H. Sathiyaraj, T. Hassanabadi, H. College of Computer and Information Engineering Guizhou University of Commerce Guiyang550014 China Department of Physics Faculty of Science University of Hradec Králové Rokitanského 62 Hradec Králové500 03 Czech Republic School of Physics and Electronic Science Zunyi Normal University Zunyi563006 China Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia

This study examines the properties of a special regular black hole. This analysis investigates the Hawking temperature, remnant radius and mass, as well as the effect of parameter ξ on thermodynamic quantities like entropy, heat capacity, and free energy. The emission rate, evaporation process, quasi-normal modes by calculating Rosen-Morse potential, and topological behavior of the black hole are also explored. © 2024, CC0.

关键词： Black holes

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Revolutionizing Industry5.0: Harnessing Advanced Technologies for Human-Robot Collaboration in Post-Pandamic Societal Resilience

SSRN

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SSRN 2024年

作者： Alsamhi, Saeed Hamood Hawbani, Ammar Sahal, Radhya Srivastava, Sumit Kumar, Santosh Nashwan, Farhan Zhao, Liang Al-Qaness, Mohammed A.A. Guizani, Mohsen Curry, Edward Insight Centre for Data Analytics University of Galaway Ireland Faculty of Engineering IBB University Ibb70270 Yemen School of Computer Science Shenyang Aerospace University Shenyang110136 China Hudida University Hudida Ibb Yemen Department of Electronics and Communication Engineering FET MJP Rohilkhand University Uttar Pradesh Bareilly243006 India Department of CSE IIIT Naya Raipur Chhattisgarh India College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Abu Dhabi United Arab Emirates Insight Centre for Data Analytics University of Galway Ireland

Over the last year, unprecedented Coronavirus Disease 2019 (COVID-19) occurrences have challenged the operation of industrial systems, significantly impacting the society industry. To cope with the COVID-19 challenges, the industry needed to take immediate action to address them for sustainable business. Recently, Industry 5.0 has emerged to focus more on collaborative approaches, including human and robot collaboration. Industry 5.0 combines robotic power with precision and human creativity to work effectively toward a unique solution demanded by the new area. Advanced technologies can transform our world, such as Beyond Fifth Generation (B5G), robotics, Artificial Intelligence (AI), blockchain, Unmanned Aerial Vehicles (UAVs), and Digital Twins (DTs). The bridge between robots and humans leads to an intelligent society that uses advanced technologies in smart healthcare, logistics, and manufacturing. In this paper, we provide a conceptual framework of the applicability of advanced technologies (i.e., AI, DTs, blockchain) to enable human-robot collaboration for healthcare 5.0, such as efficiently controlling the COVID-19 transmission rate. We discuss human-robot collaboration in manufacturing 5.0, healthcare 5.0, and human-UAV for industry 5.0 as application cases. Furthermore, we envision the revolution of Industry 5.0 with advanced technologies to combat the COVID-19-like pandemic impacts. © 2024, The Authors. All rights reserved.

关键词： Blockchain

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Adaptive attention-aware fusion for human-in-the-loop behavioral health detection

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Smart Health 2024年 32卷

作者： Brown, Martin Azmee, Abm Adnan Khan, Md. Abdullah Al Hafiz Thomas, Dominic Pei, Yong Nandan, Monica Department of Data Science and Analytics Kennesaw State University Marietta 30060 GA United States Department of Computer Science Kennesaw State University Marietta 30060 GA United States Department of Information Systems Kennesaw State University Kennesaw 30144 GA United States Department of Social Work and Human Services Kennesaw State University Kennesaw 30144 GA United States

Identifying behavioral health is paramount for law enforcement officers to provide appropriate follow-up community care. In the current practice, law enforcement offices manually identify these behavioral health cases to allow the designation of the relevant follow-up resources. In this work, we develop a tool to automatically detect behavioral health cases from police public narrative reports by identifying behavioral health indicator signals. We propose a novel adaptive attention-aware fusion model for detecting behavioral health signals in sensitive police reports. Our model leverages contextual and semantic information from the reports and relevant behavioral health cues as keywords from a pre-trained attention-weighted keyword-based model. Our model also employs label self-attention mechanisms to correlate label embeddings with the report and keyword representations. Furthermore, we propose a novel clustering-based uncertainty-enabled informative sampling query strategy to integrate humans-in-the-loop in the active learning framework to reduce required annotation from experts. This querying strategy selects the most informative and diverse samples for expert annotation. Our experimental results showed that the proposed model outperforms state-of-the-art classifiers on a dataset of 300 manually annotated ground truth police reports, achieving an accuracy of 87.58% and an F1-score of 85.67%. Applying our querying strategy to our proposed model increased the detection of behavioral health, achieving an accuracy of 92% and an F1-score of 91.1%. Also, our proposed model achieves an accuracy score of 93.75% and an F1-score of 93.61% on unseen samples. Lastly, our proposed model demonstrates its interpretability by extracting the keywords associated with each behavioral health category. © 2024 Elsevier Inc.

关键词： Active learning Adaptive fusion Attention Behavioral health detection Informative clustering Machine learning Mental health NLP Self-attention Sensitive police reports

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Physics-Based Dynamic Models Hybridisation Using Physics-Informed Neural Networks

arXiv

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arXiv 2024年

作者： Lalic, Branislava Cuong, Dinh Viet Petric, Mina Pavlovic, Vladimir Sremac, Ana Firanj Roantree, Mark Faculty of Agriculture University of Novi Sad Novi Sad Serbia School of Computing Dublin City University Dublin 9 Ireland AVIA GIS Zoersel Belgium Department of Computer Science Rutgers University New BrunswickNJ United States Insight Centre for Data Analytics Dublin City University Dublin 9 Ireland

Physics-based dynamic models (PBDMs) are simplified representations of complex dynamical systems. PBDMs take specific processes within a complex system and assign a fragment of variables and an accompanying set of parameters to depict the processes. As this often leads to suboptimal parameterisation of the system, a key challenge requires refining the empirical parameters and variables to reduce uncertainties while maintaining the model’s explainability and enhancing its predictive accuracy. We demonstrate that a hybrid mosquito population dynamics model, which integrates a PBDM with Physics-Informed Neural Networks (PINN), retains the explainability of the PBDM by incorporating the PINN-learned model parameters in place of its empirical counterparts. Specifically, we address the limitations of traditional PBDMs by modelling the parameters of larva and pupa development rates using a PINN that encodes complex, learned interactions of air temperature, precipitation and humidity. Our results demonstrate improved mosquito population simulations including the difficult-to-predict mosquito population peaks. This opens the possibility of hybridisation concept application on other complex systems based on PBDMs such as cancer growth to address the challenges posed by scarce and noisy data, and to numerical weather prediction and climate modelling to overcome the gap between physics-based and data-driven weather prediction models. Copyright © 2024, The Authors. All rights reserved.

关键词： Large scale systems

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A Swarm Artificial Intelligence Approach for Effective Treatment of Chronic Conditions

A Swarm Artificial Intelligence Approach for Effective Treat...

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International Workshop on Design of Reliable Communication Networks (DRCN)

作者： Kitty Kioskli Spyridon Papastergiou School of Computer Science and Electronic Engineering University of EssexInstitute of Analytics and Data Science (IADS) Essex United Kingdom Gruppo Maggioli Research and Development Lab Athens Greece trustilio B.V. Amsterdam Netherlands Department of Informatics Piraeus University of Piraeus Greece Focal Point Brussels Belgium

Long-term conditions or chronic diseases are multifaceted and challenging. Current treatment options, for patients with long-term conditions, are mainly pharmacological, causing numerous adverse drug events and pressing for alternative management strategies such as personalized interventions. Areas of machine learning, such as deep learning, would enable researchers to develop predictive modelling algorithms, using continuous monitoring and allowing assessing the medical risk for long-term conditions and their related complications. In this paper, we claim that harmonization of data, novel machine learning algorithms, swarm-based technologies, and the involvement of the entire healthcare community will lead to acceptable and effective personalized healthcare. Our proposed approach aims to amplify the intelligence of the healthcare community. Based upon the patients’ characteristics empowers better decisions, personalised medical risk prediction and recommendations of acceptable and effective interventions. Our future work includes the validation of the SwarmAI framework by actively engaging relevant stakeholders.

关键词： Drugs Machine learning algorithms Medical services Pressing Predictive models Reliability engineering Prediction algorithms

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A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition

A Hybrid Neural Network with Smart Skip Connections for High...

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IEEE International Conference on Multi Topic

作者： Hafsa Wazir Jawad Ahmad Muazzam A. Khan Sana Ullah Jan Fadia Ali Khan Muhammad Shahbaz Khan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Computer Science HITEC University Taxila Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. EMG signals measure the electrical activity of muscles during different motions. EMG signals play a key role in gesture recognition studies, such as hand gesture recognition. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DB1 dataset. The proposed model boasts an accuracy of 99.7% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. The fast inference time and lightweight architecture of the proposed model makes it suitable for resource constrained IoT devices. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen’s kappa coefficient, Matthew’s correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data.

关键词： Hands Performance evaluation Correlation coefficient Accuracy Neural networks Gesture recognition Electromyography data models Internet of Things Reliability

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The Role of Machine Learning Algorithms in Developing Android App and to do Malware Detection

The Role of Machine Learning Algorithms in Developing Androi...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Gadug Sudhamsu Rahul Bhatt Geethamanikanta Jakka Muhsin J. Jweeg Badria Sulaiman Alfurhood Vikas Tripathi Department of Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore India Department of CSE Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Data Analytics Information Technology University of the Cumberlands USA College of Technical Engineering Al-Farahidi University Baghdad Iraq Department of Computer Sciences College of Computer and Information Sciences Princess Νourah Hint Ahdulrahman University Saudi Arabia Department CSE Graphic Era Deemed To Be University

Because there are so many issues that have had suitable solutions, cybersecurity is now the subject that worries computer scientists and system administrators the most. The rapid development of information technology and its inherent integration into every aspect of our lives have made it possible to analyze and identify a broad variety of harmful software packages and the targets of these programmes. Malware ***, Android malware has gotten some notice. It seems to attract a lot of attention on the internet. Android is one of the most well-liked operating systems that dominates the operating system market. In 2020, 85% of smartphones will run on the Android operating system, according to statistics from the system. Mobile virus attacks have increased in frequency over time. Known to infect a phone system with applications that seem harmless but really contain malware is one of the most popular approaches. Malware on cellphones has already been detected using machine learning. Deep learning and supervised learning techniques have been used to achieve this. Drawing on consumer rights, this study evaluates the effectiveness of multiple categorization approaches to find virus in Android apps.

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Impact of energy efficiency on the morphology and behaviour of evolved robots

arXiv

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arXiv 2021年

作者： Rebolledo, Margarita Zeeuwe, Daan Bartz-Beielstein, Thomas Eiben, A.E. Institute for Data Science Engineering and Analytics TH Köln Gummersbach Germany Department of Computer Science Vrije Universiteit Amsterdam Netherlands

Most evolutionary robotics studies focus on evolving some targeted behavior without taking the energy usage into account. This limits the practical value of such systems because energy efficiency is an important property for real-world autonomous robots. In this paper, we mitigate this problem by extending our simulator with a battery model and taking energy consumption into account during fitness evaluations. Using this system we investigate how energy awareness affects the evolution of robots. Since our system is to evolve morphologies as well as controllers, the main research question is twofold: (i) what is the impact on the morphologies of the evolved robots, and (ii) what is the impact on the behavior of the evolved robots if energy consumption is included in the fitness evaluation? The results show that including the energy consumption in the fitness in a multi-objective fashion (by NSGA-II) reduces the average size of robot bodies while at the same time reducing their speed. However, robots generated without size reduction can achieve speeds comparable to robots from the baseline set. © 2021, CC BY-NC-SA.

关键词： Health

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