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Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

Computers, Materials & Continua 2024年第1期78卷 1443-1460页

作者： Sitara Afzal Imran Ullah Khan irfan mehmood Jong Weon Lee Mixed Reality and Interaction Lab Department of SoftwareSejong UniversitySeoul05006Korea Faculty of Engineering and Digital Technologies School of Computer ScienceAI and ElectronicsUniversity of BradfordBradfordUK

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ***,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and *** this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image *** article explores the potential of seeing-through vehicles as a solution to enhance overtaking ***-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of *** address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both *** server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front *** see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other *** network was trained and tested on the Cityscape dataset using semantic *** transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has *** our findings,we have achieved 97.1% *** article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

关键词： Overtaking safety augmented reality VANET V2V deep learning

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An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection

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Computers, Materials & Continua 2021年第3期66卷 2301-2315页

作者： Sitara Afzal Muazzam Maqsood irfan mehmood Muhammad Tabish Niaz Sanghyun Seo Department of Computer Science COMSATS University IslamabadAttock CampusAttockPakistan Department of Media Design and Technology Faculty of Engineering&InformaticsUniversity of BradfordBradfordBD71AZUK Department of Smart Device Engineering School of Intelligent Mechatronics EngineeringSejong UniversitySeoulSouth Korea School of Computer Art College of Art&TechnologyChung-Ang UniversityAnseong17546South Korea

Cerebral Microbleeds(CMBs)are microhemorrhages caused by certain abnormalities of brain *** can be found in people with Traumatic Brain Injury(TBI),Alzheimer’s disease,and in old individuals having a brain *** research reveals that CMBs can be highly dangerous for individuals having dementia and *** CMBs seriously impact individuals’life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal *** existing work report good results but often ignores false-positive’s perspective for this research *** this paper,an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images(SWI).The proposed framework consists of four main phases(i)making clusters of brain Magnetic Resonance Imaging(MRI)using k-mean classifier(ii)reduce false positives for better classification results(iii)discriminative feature extraction specific to CMBs(iv)classification using a five layers convolutional neural network(CNN).The proposed method is evaluated on a public dataset available for 20 *** proposed system shows an accuracy of 98.9%and a 1.1%false-positive rate *** results show the superiority of the proposed work as compared to existing states of the art methods.

关键词： Microbleeds detection false-positive deep learning CNN

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Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

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Computers, Materials & Continua 2021年第9期68卷 4087-4108页

作者： Sehar Shahzad Farooq Mustansar Fiaz irfan mehmood Ali Kashif Bashir Raheel Nawaz KyungJoong Kim Soon Ki Jung School of Computer Science and Engineering Kyungpook National UniversityDaeguKorea Faculty of Engineering&Informatics School of MediaDesign and TechnologyUniversity of BradfordBradfordBD71DPUK Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Department of Operations TechnologyEvents and Hospitality ManagementManchester Metropolitan UniversityUK Institute of Integrated Technology Gwangju Institute of Science and TechnologyKorea

Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player *** modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the *** behavior focuses on dynamic and static information gathered at the time of *** experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s *** this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral *** Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer *** learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related *** homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’*** not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.

关键词： Game player modeling experience modeling player analytics deep learning LSTM game play data Candy Crush Saga

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An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

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Computers, Materials & Continua 2021年第4期67卷 1191-1206页

作者： Ayesha Jabeen Sitara Afzal Muazzam Maqsood irfan mehmood Sadaf Yasmin Muhammad Tabish Niaz Yunyoung Nam Department of Computer Science COMSATS University IslamabadAttock CampusPakistan Department of Media Design and Technology Faculty of Engineering&InformaticsUniversity of BradfordBradfordBD71AZUK Department of Smart Device Engineering School of Intelligent Mechatronics EngineeringSejong UniversitySeoulKorea Department of Computer Science&Engineering Soonchunhyang UniversityAsan36538Korea

Stock market forecasting is an important research area,especially for better business decision *** stock predictions continue to be significant for business *** short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily ***,major events’news also contains significant information regarding market *** effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock *** research proposes an efficient model for stock market *** current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and *** use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock *** LSTM has the advantage of analyzing relationship between time-series data through memory *** performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.

关键词： Business intelligence decision making stock prediction long short-term memory COVID-19 event sentiment

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An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

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Computers, Materials & Continua 2022年第5期71卷 4151-4166页

作者： Maha Farouk S.Sabir irfan mehmood Wafaa Adnan Alsaggaf Enas Fawai Khairullah Samar Alhuraiji Ahmed S.Alghamdi Ahmed A.Abd El-Latif Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversitySaudi Arabia Centre for Visual Computing Faculty of Engineering and InformaticsUniversity of BradfordBradfordU.K Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahP.O.Box 23713Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia University32511Egypt

Today,due to the pandemic of COVID-19 the entire world is facing a serious health *** to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of *** governmental bodies of different countries imposed that wearing a face mask is compulsory in public ***,it is very difficult to manually monitor people in overcrowded *** research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of *** paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or *** proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked ***,this work applies to real-time and can be implemented in any public service area.

关键词： COIVD-19 deep learning faster-RCNN object detection transfer learning face mask

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ASLP-DL—A Novel Approach Employing Lightweight Deep Learning Framework for Optimizing Accident Severity Level Prediction

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Computers, Materials & Continua 2024年第2期78卷 2535-2555页

作者： Saba Awan Zahid mehmood Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan

Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident *** learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and *** prior success,there is a need for an efficient system recognizing ASL in diverse road *** address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient *** framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved *** and factor contribution analyses identify influential *** on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB *** proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.

关键词： Injury severity prediction deep learning feature

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Quintessence anisotropic stellar models in quadratic and Born-Infeld modified teleparallel Rastall gravity

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Chinese Physics B 2024年第3期33卷 179-189页

作者： Allah Ditta 夏铁成 irfan Mahmood Asif Mahmood Department of Mathematics Shanghai University and Newtouch Center for Mathematics of Shanghai UniversityShanghai 200444China Centre for High Energy Physics University of the PunjabLahorePakistan College of Engineering Chemical Engineering DepartmentKing Saud University RiyadhSaudi Arabia

This study aims to discuss anisotropic solutions that are spherically symmetric in the quintessence field,which describe compact stellar objects in the modified Rastall teleparallel theory of *** achieve this goal,the Krori and Barua arrangement for spherically symmetric components of the line element is *** explore the field equations by selecting appropriate off-diagonal tetrad ***-Infeld function of torsion f(T)=β√λT+1-1 and power law form h(T)=δTn are *** Born-Infeld gravity was the first modified teleparallel gravity to discuss *** use the linear equation of state pr=ξρto separate the quintessence *** obtaining the field equations,we investigate different physical parameters that demonstrate the stability and physical acceptability of the stellar *** use observational data,such as the mass and radius of the compact star candidates PSRJ 1416-2230,Cen X-3,&4U 1820-30,to ensure the physical plausibility of our findings.

关键词： anisotropic spheres quintessence field modified Rastall teleparallel gravity equation of state(EoS) f(T)gravity

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Green hydrogen energy production: current status and potential

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CLEAN ENERGY 2024年第2期8卷 1-7页

作者： Maka, Ali O. M. mehmood, Mubbashar Libyan Ctr Res & Dev Saharian Communities Murzuq Libya Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Scotland

The technique of producing hydrogen by utilizing green and renewable energy sources is called green hydrogen production. Therefore, by implementing this technique, hydrogen will become a sustainable and clean energy source by lowering greenhouse gas emissions and reducing our reliance on fossil fuels. The key benefit of producing green hydrogen by utilizing green energy is that no harmful pollutants or greenhouse gases are directly released throughout the process. Hence, to guarantee all of the environmental advantages, it is crucial to consider the entire hydrogen supply chain, involving storage, transportation and end users. Hydrogen is a promising clean energy source and targets plan pathways towards decarbonization and net-zero emissions by 2050. This paper has highlighted the techniques for generating green hydrogen that are needed for a clean environment and sustainable energy solutions. Moreover, it summarizes an overview, outlook and energy transient of green hydrogen production. Consequently, its perspective provides new insights and research directions in order to accelerate the development and identify the potential of green hydrogen production. Hydrogen is a promising clean energy source and a pathway towards decarbonization and net-zero emissions by 2050. This article provides perspective on techniques for generating green hydrogen that are needed for a clean environment and sustainable energy solutions. Graphical Abstract

关键词： green hydrogen energy hydrogen production energy transition clean energy perspective of green hydrogen TECHNOLOGIES ROADMAP

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A geochemical perspective on the genesis of Cenozoic basic volcanism in northeastern Turkey:an overview of metasomatism and heterogeneity of the sub-continental lithospheric mantle in a post-collisional setting

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Acta Geochimica 2024年第3期43卷 489-506页

作者： Mehmet Arslan irfan Temizel Department of Geological Engineering Karadeniz Technical University61080 TrabzonTurkey

The post-collisional Cenozoic basic volcanic rocks in NE Turkey show temporal variations in whole-rock lithophile element and highly siderophile element(HSE)systematics that are mainly associated with the nature of sub-continental lithospheric mantle(SCLM)sources and parental melt *** far,the traditional whole-rock lithophile geochemical data of these basic volcanic rocks have provided important constraints on the nature of SCLM *** lithophile element and HSE geochemical data of these basic volcanic rocks also reveal the heterogeneity of the SCLM source,which is principally related to variable metasomatism resulting from previous subduction(s)and post-collisional mantle-crust interactions in an extensional *** element geochemical features suggest that the parental magmas have derived from metasomatized spinel-to garnet-bearing SCLM sources for Eocene and Miocene basic volcanic rocks with subduction signatures whereas originated from spinel-to garnet-bearing SCLM sources for Mio-Pliocene and Plio-Quaternary basaltic volcanic rocks without the subduction *** element and HSE geo-chemistry also reveal that Eocene and Miocene basic vol-canic rocks were affected by more pronounced crustal contamination than the basaltic volcanic rocks of Mio-Pliocene and ***,the integrated lithophile element and HSE compositions of these basic volcanic rocks,together with the regional asymmetric lithospheric delamination model,reveal that the compositional variation(especially due to metasomatism)was significant temporally in the heterogeneity of the SCLM sources from which parental magmas formed during the Cenozoic era.

关键词： Basalt Lithospheric mantle Metasomatism Mantle heterogeneity Cenozoic volcanics NE Turkey

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Assessing Multidimensional Vulnerability of Rural Areas to Flooding:An Index‑Based Approach

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International Journal of Disaster Risk Science 2024年第1期15卷 88-106页

作者： Samavia Rasool irfan Ahmad Rana Hassam Bin Waseem Department of Urban and Regional Planning School of Civil and Environmental Engineering(SCEE)National University of Sciences and Technology(NUST)Islamabad 44000Pakistan

Vulnerability assessment is essential for understanding and launching effective flood risk reduction *** study aimed to examine the vulnerability of flood-prone rural communities in southern Punjab,Pakistan to external *** concept of vulnerability encompasses a range of dimensions,including physical,social,institutional,environmental,economic,and *** a composite index method,indices were developed for each dimension and combined to create a multidimensional measure of vulnerability.A sample of 365 communities was selected using the Yamane sampling technique,and data were collected through a questionnaire containing 65 indicators across all *** statistics and ANOVA tests were used to analyze the *** results show that communities near the Chenab River had higher attitudinal and institutional vulnerability compared to other *** attitudinal vulnerabilities were as sociated with poorly perceived flood risks and low preparedness measures,whereas institutional vulnerabilities were driven by conventional flood protection strategies,lack of institutional trust,and lack of flood risk *** research provides insights into the various components of vulnerability in flood-prone rural communities in Pakistan and demonstrates a useful methodology that can be applied to other disasters at different spatial scales.

关键词： Chenab River Flood hazard Indus River Pakistan-Rural areas:Vulnerability to flooding

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