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Utilizing Smartwatches and Deep Learning Models for Enhanced Avalanche Victim Identification, Localization, and Efficient Recovery Strategies: An In-depth Study 5

Utilizing Smartwatches and Deep Learning Models for Enhanced...

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5th International Conference on information Management and Machine Intelligence, ICIMMI 2023

作者： Jha, Khyati Pasbola, Mansi Rai, Hari Mohan Amanzholova, Saule Department of Computer Science Dronacharya Group of Institutions Greater Noida India Faculty of Computer Technologies and Cyber Security International University of Information Technology Almaty Kazakhstan

ISBN: (纸本)9798400709418

This article explores the transformative impact of modern technology on avalanche response and safety, addressing the longstanding challenges associated with avalanches in mountainous regions. Historically, avalanches have presented significant dangers, compounded by the difficulties of conducting rescue operations in remote and harsh environments. However, recent technological advancements, including smartwatches with SOS features and specialized mobile applications, have ushered in a new era of avalanche safety. Smartwatches have emerged as crucial tools in avalanche safety. Equipped with SOS features and synchronized with mobile apps, these devices have revolutionized the way we approach avalanche emergencies. In this study, we propose a methodology that leverages smartwatch data and deep learning models to detect individuals buried under avalanche debris. This approach aims to significantly improve the chances of successful and timely rescue operations. The proposed methodology involves collecting data from smartwatches, which are equipped with various sensors to monitor location, movement, and environmental conditions. Deep learning models are then deployed to analyze this data, identifying patterns and anomalies associated with avalanche incidents. By harnessing these advanced technologies, our methodology offers the potential for real-time avalanche detection, thereby enhancing overall safety outcomes. One of the key strengths of this methodology is its adaptability to diverse avalanche scenarios. The study envisions a future where technology-driven avalanche safety becomes an integral component of outdoor activities, mitigating risks and enabling more effective response strategies in the face of this natural hazard. © 2023 ACM.

关键词： Deep learning

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A Semi-supervised Protein Complex Identification Algorithm Based on Sparseness Constraint

A Semi-supervised Protein Complex Identification Algorithm B...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

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A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning

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Intelligent Automation & Soft Computing 2023年第8期37卷 2037-2057页

作者： Khalid M.O.Nahar Ammar Almomani Nahlah Shatnawi Mohammad Alauthman Department of Computer Sciences Faculty of Information Technology and Computer SciencesYarmouk University-Irbid21163Jordan School of Computing Skyline University CollegeSharjahP.O.Box 1797United Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken *** proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition *** image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning *** results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of *** offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute *** work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.

关键词： Sign language deep learning transfer learning machine learning automatic translation of sign language natural language processing Arabic sign language

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Enhancing Side-Channel Attacks Prediction using Convolutional Neural Networks 14

Enhancing Side-Channel Attacks Prediction using Convolutiona...

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14th International Conference on Advanced computer information Technologies, ACIT 2024

作者： Alemerien, Khalid Al-Suhemat, Sadeq Alsuhimat, Fadi Altarawneh, Enshirah Tafila Technical University Information Technology Department Tafila Jordan Isra University Faculty of Information Technology Amman Jordan Philadelphia University Cyber Security Department Amman Jordan Hashemite University Computer Engineering Department Zarqa Jordan

ISBN: (纸本)9798350350036

A common type of cyberattack is the side channel attack (SCA), which affects many devices and equipment connected to a network. These attacks have different types, like power attacks such as DPA, electromagnetic attacks, storage attacks, and others. Researchers and information security experts are concerned about SCA targeting devices, as they can lead to the loss and theft of important information. Using deep learning (DL) techniques in SCA analysis can be an efficient tool for detecting the SCA. Many previous works have tried to carry out the SCA in order to mitigate the impact of these attacks, but they encountered difficulties in detecting the SCA, whether in selecting the suitable dataset or applying the most efficient machine learning or deep learning techniques for achieving high performance. Therefore, we developed in this paper a deep learning-based model to detect SCAs using a dataset related to power attacks (the DPAv4 dataset) and the Convolutional Neural Networks (CNN) algorithm to train and test the DPAv4 dataset. The findings of our experiments showed a significant improvement in the performance of DL-based techniques in the detection of SCA. The proposed CNN-based model achieved an accuracy of 0.814 in detecting SCA and a loss rate of 0.581. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

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Intelligent Automation & Soft Computing 2023年第4期36卷 1095-1104页

作者： Liaqat Ali Saif E.A.Alnawayseh Mohammed Salahat Taher M.Ghazal Mohsen A.A.Tomh Beenu Mago College of Engineering and Technology University of Science and Technology of FujairahFujairahUAE Electrical Engineering Department Faculty of EngineeringMutah UniversityJordan School of Information Technology Skyline University CollegeUniversity City Sharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600Malaysia Faculty of Computer Science NCBA&ELahore54660Pakistan School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE

The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent *** an outbreak occurs,each country must have a well-coordinated and preventative plan to address the *** and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily *** intelligent devices and applica-tions have become a vital part of our everyday lives,smart gadgets have also led to several physical and psychological health problems in modern ***,we used an artiﬁcial intelligence AI-based system for disease prediction using an Artiﬁcial Neural Network(ANN).The ANN improved the regularization of the classiﬁcation model,hence increasing its *** unconstrained opti-mization model reduced the classiﬁer’s cost function to obtain the lowest possible *** verify the performance of the intelligent system,we compared the out-comes of the suggested scheme with the results of previously proposed *** proposed intelligent system achieved an accuracy of 0.89,and the miss rate 0.11 was higher than in previously proposed models.

关键词： Intelligent model epidemics artiﬁcial intelligence machine learning techniques

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An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

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computers, Materials & Continua 2023年第5期75卷 2397-2412页

作者： Mohammed Mubarak Mustafa Ahmed Abelmonem Khalifa Korhan Cengiz Nikola Ivkovic Department of Information Technology Sri Krishna College of Engineering&TechnologyCoimbatoreIndia Department of Networking and Security College of Information TechnologyUniversity of FujairahFujairahUAE Switching and Traffic Department National Telecommunication InstituteCairoEgypt Department of Computer Engineering Istinye UniversityZeytinburnuIstanbulTurkey Faculty of Organization and Informatics University of ZagrebVarazdinCroatia

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical *** grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the ***,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions *** this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based *** consecutive segments are *** the covering of adjusted clusters,an ideal number of clusters is determined ***,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor *** heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster ***,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long *** assessment results demonstrate that the IEPA performs better than existing conventions.

关键词： Energy consumption improved energy-proficient algorithm internet of things wireless sensor network

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Realizing the Potential of Metaverse to Revolutionize the Future of Healthcare 2

Realizing the Potential of Metaverse to Revolutionize the Fu...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ishtaiwi, Abdelraouf Hadi, Wael Al-Qerem, Ahmad University Of Petra Department Of Data Science And Artificial Intelligence Amman Jordan University Of Petra Department Of Information Security Amman Jordan Zarqa University Faculty Of Information Technology Computer Science Department Zarqa Jordan

ISBN: (纸本)9798350394962

The emergence of metaverse technology, underpinned by virtual reality, augmented reality, and artificial intelligence, holds profound implications for transforming healthcare. This paper reviews the current applications of metaverse in medical education and training, mental healthcare, remote patient monitoring, surgical procedures, and biomedical research. It analyzes the challenges around adopting this technology, spanning technical limitations, ethical dilemmas, threats to privacy, and regulatory gaps. Finally, it proposes strategies and future directions to harness the metaverse's potential to increase healthcare access, efficiency, personalization and democratization. This encompasses research on innovative use cases, developing ethical governance frameworks, and policy reforms to incentivize equitable metaverse adoption across the healthcare ecosystem. A nuanced, evidence-based approach can enable the metaverse to usher a new paradigm of patient-centric healthcare delivery. © 2024 IEEE.

关键词： Augmented reality

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A Comparative Analysis on Three Duplication Elements in Copy-Move Forgery using PatchMatch-Based Detection Method 8

A Comparative Analysis on Three Duplication Elements in Copy...

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8th IEEE International Conference on Software Engineering and computer Systems, ICSECS 2023

作者： Nor Azaimi, Nur Izzati Abd Warif, Nor Bakiah Ismail, Nor-Syahidatul N Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Centre for Information Security Research Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Department of Computer System and Networking Pekan Pahang Malaysia

ISBN: (纸本)9798350310931

Image forgery is the alteration of a digital image to hide some of the important and useful information. Copy-move forgery (CMF) is one of the most difficult to detect because the copied part of the image has the same characteristics as the original image. Most of the existing datasets only highlight additional attacks in the copied part. Since there are no categories of duplication elements in the datasets, this research analyzed three categories of duplication elements in CMF which are animals, food and non-living things using DEFACTO and CoMo3Dataset. The analysis is performed on PatchMatch-based detection method and the results show that the method able to maintain at least 83% for all duplication elements in both DEFACTO and CoMo3Dataset. Furthermore, the method is able to detect a minimum 92% score for the food category in both datasets. © 2023 IEEE.

关键词： Copy-Move forgery DEFACTO duplication regions PatchMatch

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Classification of Sleep-Related Disorders Using Machine Learning Techniques 25

Classification of Sleep-Related Disorders Using Machine Lear...

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25th International Arab Conference on information technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Al-Safarini, Maram Y. Alamleh, Amneh Zaid, Ali Abu Mansur, Hasan Elrashidi, Ali Alzoubi, Haneen Bani-Bakr, Alaa Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan American University of Madaba Faculty of Business Department of Accounting Madaba Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Private University Faculty of It Applied Science Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Several people suffer from irregular sleep patterns, a problem that negatively affects their daily lives. It presents a multitude of challenges, including daytime distress, disturbances in sleep-wake patterns, feelings of unease, and a variety of other complications. Diabetes, obesity, and psychological disorders are among the diseases that may be responsible for this sleep disorder. Therefore, the primary aim of this research is to employ the robust capabilities of machine learning in forecasting sleep disorders. This research classifies three diseases: diabetes, obesity, and psychological disorders, using thirty-two different classifiers belonging to five learning strategies. The research compares and evaluates their predictive performance on four datasets. The comparative analysis includes four evaluation metrics: Accuracy, Precision, ROC Area, and MCC. The research results indicate that the best classifier is SGD, followed by LMT, while NBM is the weakest classifier. In addition, the research results revealed that the best strategy is Functions, followed by Rules, with Bayes being the least effective strategy. While conducting the experiment and analyzing the data, it is observed that the best feature selection method that yielded the most favorable outcome is the CorrelationAttributeEval. Furthermore, it has been found that the classification algorithm known as SGD achieved the highest Accuracy and Precision in categorizing the data. This indicates that the combination of CorrelationAttributeEval as the feature selection approach and SGD as the classification algorithm proved to be the most successful and efficient strategy for this research © 2024 IEEE.

关键词： Sleep research

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