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Real-time Privacy Preserving Framework for Covid-19 Contact Tracing

computers, Materials & Continua 2022年第1期70卷 1017-1032页

作者： Akashdeep Bhardwaj Ahmed A.Mohamed Manoj Kumar Mohammed Alshehri Ahed Abugabah School of Computer Science University of Petroleum&Energy Studies(UPES)Dehradun248007India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Information Technology Faculty of Computer and InformationAssiut UniversityAssiut71515Egypt Department of Information Technology College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Information System College of Technological InnovationZayed UniversityAbu DhabiUnited Arab Emirates

The recent unprecedented threat from COVID-19 and past epidemics,such as SARS,AIDS,and Ebola,has affected millions of people in multiple *** have shut their borders,and their nationals have been advised to *** variety of responses to the pandemic has given rise to data privacy *** prevention and control strategies as well as disease control measures,especially real-time contact tracing for COVID-19,require the identification of people exposed to *** tracing frameworks use mobile apps and geolocations to trace ***,while the motive may be well intended,the limitations and security issues associated with using such a technology are a serious cause of *** are growing concerns regarding the privacy of an individual’s location and personal identifiable information(PII)being shared with governments and/or health *** study presents a real-time,trust-based contact-tracing framework that operateswithout the use of an individual’sPII,location sensing,or gathering GPS *** focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the *** research validates the trust-based framework using Bluetooth as practical and *** our proposed methodology,personal information,health logs,and location data will be secure and not *** research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users.

关键词： Privacy contact tracing mobile apps Bluetooth Covid epidemic

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Optimizing Monkeypox Lesions Detection with a Lightweight Hybrid Model

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IEEE Access 2025年 13卷 103709-103732页

作者： Al-Gaashani, Mehdhar S. A. M. Alkanhel, Reem Hassan, Dina S. M. Ba Mahel, Abduljabbar S. Aziz, Ahmed Khayyat, Mashael M. Muthanna, Ammar University of Electronic Science and Technology of China School of Resources and Environment 1st Ring Rd East 2 Section Sichuan Chengdu610056 China Princess Nourah bint Abdulrahman University Department of Information Technology College of Computer and Information Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia University of Electronic Science and Technology of China School of Life Science and Technology Chengdu610054 China Benha university Department of computer science Faculty of computer and Artificial intelligence Egypt Central Asian University Engineering school Tashkent Uzbekistan University of Jeddah Department of Information Systems and Technology Collage of Computer Science and Engineering Jeddah Saudi Arabia Peoples'Friendship University of Russia RUDN University Department of Applied Probability and Informatics 6 Miklukho-Maklaya St Moscow117198 Russia

The increasing global prevalence of monkeypox (mpox) has necessitated the development of accurate, efficient, and interpretable diagnostic models for timely disease identification. Although deep learning has advanced medical image analysis, diagnosing diseases with complex lesions, such as monkeypox, remains challenging in resource-limited settings. Existing methods often rely on large architectures that demand high computational resources and lack interpretability. To address these issues, we propose a lightweight, interpretable architecture that truncates MobileNetV2 for efficient feature extraction, incorporates Ghost Modules to reduce redundancy, and integrates a SA-MobileViT block to capture both local and global dependencies in lesion images. Grad-CAM visualizations enhance interpretability, and Optuna is used for hyperparameter optimization. Our model achieved an accuracy of 98.72%, precision of 98.76%, recall of 98.72%, and F1-score of 98.72%, outperforming existing approaches, especially in handling irregular lesion patterns. The model's design is ideal for deployment in resource-constrained settings, offering a robust solution for real-world healthcare applications. Grad-CAM, saliency maps, and other visualization techniques help improve clinical trust by highlighting relevant regions in input images, emphasizing key features of the lesions. The proposed model is lightweight with only 630,204 parameters, making it 92.3× smaller than ResNet152V2 (58,152,004 parameters) and 28.7× smaller than DenseNet201 (18,100,612 parameters). It requires 321.61M FLOPs, 35.8× fewer than ResNet152V2 (11.51G FLOPs) and 13.4× fewer than DenseNet201 (4.29G FLOPs). With a size of just 72.92 MB, it is 8.14× smaller than ResNet152V2 (594.08 MB) and 4.57× smaller than DenseNet201 (333.47 MB), making it an efficient solution for mobile and edge deployment in resource-constrained environments. © 2013 IEEE.

关键词： Deep learning

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Environment-Aware 3D Mobility Simulation for the 5G and 6G Wireless Networks 1

Environment-Aware 3D Mobility Simulation for the 5G and 6G W...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Raddwan, Basheer Ameen Al-Baltah, Ibrahim Ahmed Ghaleb, Mukhtar Department of Information Technology Faculty of Computer Science and IT Sana’a University Sana’a Yemen College of Computing and Information Technology University of Bisha Bisha Saudi Arabia

ISBN: (纸本)9798331533557

This paper addresses a significant challenge for future wireless networks, particularly those assisted by unmanned aerial vehicles (UAVs) and multi-access edge computing (MEC), which is the impact of 3D mobility in urban environments. Existing mobility models cannot facilitate 3D urban movement owing to control, collision avoidance, and awareness of the three-dimensional urban environment. Panda5gSim, is an open-source simulation framework, models 3D-controlled mobility in synthetic urban settings using a gaming engine. We presented a proof-of-concept, environment-dependent SINR (Signal-to-Interference plus noise), throughput, spectrum efficiency, and handover rate model for omnidirectional and 3rd Generation Partnership Project (3GPP) directional antennas. These results serve as benchmarks for this methodology. Furthermore, Panda5gSim can collect realistic transformations of objects and ray-tracing line-of-sight, rendering;it is precise and economic which make it a good alternative to the tracing-based mobility method, particularly at millimeter-wave and terahertz frequencies. © 2024 IEEE.

关键词： 5G mobile communication systems

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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Experimental Validation of RGB Dataset Analysis for Distribution system Operator Infrastructure Inspections 11

Experimental Validation of RGB Dataset Analysis for Distribu...

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11th IEEE Workshop on Advances in information, Electronic and Electrical Engineering, AIEEE 2024

作者： Gauce, Diana Litvinenko, Anna Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia

ISBN: (纸本)9798331527761

This article focuses on experimentally validating techniques for analyzing red-green-blue (RGB) datasets in infrastructure inspections carried out by Distribution system Operators (DSOs). Given the increasing demand for efficient and precise inspection methods in the energy sector, leveraging RGB datasets presents a promising opportunity. The research investigates the effectiveness of RGB dataset analysis in identifying and evaluating critical infrastructure assets essential for DSO operations. The experimental validation underscores the potential of RGB data analysis as a valuable tool for improving infrastructure inspection procedures, streamlining maintenance schedules, and ensuring dependable energy distribution networks. © 2024 IEEE.

关键词： Decision making

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Clustering Mechanism in Particle Swarm Optimization Algorithm for Data Aggregation 13

Clustering Mechanism in Particle Swarm Optimization Algorith...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Sharmin, Sharmin Ahmedy, Ismail Noor, Rafidah Md Ismail, Habibah Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Univesrsiti Teknologi MARA Computer Science Unit Centre of Foundation Studies Selangor Dengkil43800 Malaysia

ISBN: (纸本)9798350347319

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes with distinct resources working together to achieve a common objective efficiently. A critical issue is extending the network lifetime of WSNs, especially for data transfer, as sensor nodes depend on their internal batteries to carry out essential functions and communicate with one another. Efficient energy use per sensor is essential for ensuring the overall energy efficiency of the network. Clustering has been the most energy-efficient approach but lacking a method for selecting the most effective cluster head results in decreased data aggregation efficiency, leading to the increased power consumption of sensor nodes. The project aims to enhance energy efficiency and extend network service life by using Particle Swarm optimization (PSO) for clustering in WSNs. PSO is used to select the cluster head based on member node distance from the base station and remaining energy. The proposed PSO technique was tested against Low Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Zonal Stable Election Protocol Z-SEP, and Extended Stable Election Protocol (EZ-SEP) and found to be more effective in reducing energy usage and prolonging the network's lifespan. The recommended technique can extend the service life of WSNs and reduce energy consumption, ultimately leading to more cost-effective and eco-friendly solutions for various applications such as environmental monitoring, surveillance, and healthcare. © 2023 IEEE.

关键词： Energy utilization

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A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning

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Neural Computing and Applications 2024年第33期36卷 20723-20750页

作者： Abdel-salam, Mahmoud Kumar, Neeraj Mahajan, Shubham Faculty of Computer and Information Science Mansoura University Mansoura Egypt Department of CSE Thapar Institute of Engineering and Technology Deemed to Be University Patiala147004 India Delhi-NCR India

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. © The Author(s) 2024.

关键词： Support vector regression

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On The Symbolic 2-plithogenic Fermat's Non-Linear Diophantine Equation

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Neutrosophic Sets and systems 2023年 59卷 13-21页

作者： Alrawashdeh, Heba Al-Basheer, Othman Hajjari, Arwa Faculty of Computer Information Systems The University of Jordan Aqaba Jordan Sudan University of Science and Technology Faculty of Science Khartoum Sudan Cairo University Department of Mathematics Cairo Egypt

This paper is dedicated to find all symbolic 2-plithogenic integer solutions for the symbolic 2-plithogenic Fermat's Diophantine equation Xn + Yn = Zn for n ≥ 3. We prove that it has exactly 27 solutions, and we ... 详细信息

关键词： Fermat's Diophantine equation symbolic 2-plithogenic integer symbolic 2-plithogenic ring

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Addressing the Libyan Arabic Dialect Identification: A Comparative Study of Ensemble Classification Methods 4

Addressing the Libyan Arabic Dialect Identification: A Compa...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Ghumeid, Najah A. Essgaer, Mansour Sebha University Faculty of Sciences Computer Science Department Sebha Libya Sebha University Faculty of Information Technology Information Systems Department Sebha Libya

ISBN: (纸本)9798350372632

Dialect identification presents a challenging task within the realm of Natural Language Processing. The growing utilization of Arabic dialects in written form, particularly on social media platforms, has created new demands for accurate identification of these dialects. In multilingual datasets, distinguishing the Libyan Arabic dialect from neighboring Arabic dialects has emerged as a crucial goal. To address this, we conducted experiments using different lengths of word n-gram features to determine the most suitable combination of word n-grams and term frequency-inverse document frequency. Our aim was to capture contextual information effectively while mitigating linguistic similarities between Arabic dialects of neighboring countries. The QCRI Arabic Dialects Identification dataset was employed, and we evaluated the performance of three ensemble tree-based classification methods: Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. The evaluation, based on the f-measure metric, yielded respective scores of 68%, 37%, and 52% for Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. These results underscore the effectiveness of our approach in identifying the Libyan Arabic dialect and highlight the potential of ensemble tree-based methods in Arabic dialect identification tasks. © 2024 IEEE.

关键词： Computational linguistics

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A Comparative Analysis of Deep Learning Approaches for Fire Hotspots Prediction in Thailand 23

A Comparative Analysis of Deep Learning Approaches for Fire ...

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23rd International Symposium on Communications and information Technologies, ISCIT 2024

作者： Phawandee, Sansern Sangurai, Nuanprang Putpuek, Narongsak Cooharojananone, Nagul Rajabhat Rajanagarindra University Faculty of Science and Technology Department of Information Technology Chachoengsao Thailand Chulalongkorn University Department of Mathematics and Computer Science Facluty of Science Bangkok Thailand

ISBN: (纸本)9798350353952

Northern Thailand faces a recurring ecological threat: forest fires. These fires, prevalent during the dry season, spread rapidly and consume vital forest fuels. Human activities exacerbate the problem, with agricultural burning of forests, grasses, and crop residues contributing to the fire regime. These fires not only alter forest structure, but also produce harmful PM10 and PM2.5 particles that threaten public health. The purpose of this work is to perform a comparative analysis of two deep learning approaches, including single and hybrid, for fire hotspots prediction. The dataset was acquired from NASA's MODIS and VIIRS S-NPP satellite, covering the period from January 2012 to February 2024. The dataset values were pre-processed by coding and normalization. The pairwise correlation coefficient between each feature and the number of hotspots was calculated and then sorted in descending order. Based on these correlation values, a dimensionality reduction step was performed to reduce the number of features from 4 to 2 for each experiment. The prepared dataset was then divided into 80% and 20%. Four deep learning models were trained and three error metrics were used to objectively evaluate the model performance. The investigation showed that the single approach using LSTM algorithm had promising results in identifying fire hotspots in Thailand, especially when using data from the VIIRS S-NPP dataset with two or three attributes: confidence, bright-ti5, and frp or confident and bright-ti5. © 2024 IEEE.

关键词： NASA

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