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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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A Transfer Learning-Based Approach to Detect Cerebral Microbleeds

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Computers, Materials & Continua 2022年第4期71卷 1903-1923页

作者： sitara afzal Imran Ullah Khan Jong Weon Lee Mixed Reality and Interaction Lab Department of SoftwareSejong UniversitySeoul143-747Korea

Cerebral microbleeds are small chronic vascular diseases that occur because of irregularities in the cerebrum *** and elderly people with brain injury and dementia can have small microbleeds in their brains.A recent study has shown that cerebral microbleeds could be remarkably risky in terms of life and can be riskier for patients with *** this study,we proposed an efficient approach to automatically identify microbleeds by reducing the false positives in openly available susceptibility-weighted imaging(SWI)data *** proposed structure comprises two different pretrained convolutional models with four *** stages include(i)skull removal and augmentation,(ii)making clusters of data samples using the k-mean classifier,(iii)reduction of false positives for efficient performance,and(iv)transfer-learning *** proposed technique was assessed using the SWI dataset available for 20 *** our findings,we attained an accuracy of 97.26%with a 1.8%false-positive rate using data augmentation on the AlexNet transfer learning model and a 1.1%false-positive rate with 97.89%accuracy for the ResNet 50 model with data augmentation *** results show that our models outperformed the existing approach for the detection of microbleeds.

关键词： Microbleeds deep convolutional neural network ResNet50 AlexNet computer-vision

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Perceptual quality assessment of panoramic stitched contents for immersive applications:a prospective survey

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Virtual Reality & Intelligent Hardware 2022年第3期4卷 223-246页

作者： Hayat ULLAH sitara afzal Imran Ullah KHAN Department of Computer Science Kansas State UniversityManhattanKS 66506USA Department of Software Sejong UniversitySeoul 143-747Republic of Korea

The recent advancements in the field of Virtual Reality(VR)and Augmented Reality(AR)have a substantial impact on modern day technology by digitizing each and everything related to human life and open the doors to the next generation Software Technology(Soft Tech).VR and AR technology provide astonishing immersive contents with the help of high quality stitched panoramic contents and 360°imagery that widely used in the education,gaming,entertainment,and production *** immersive quality of VR and AR contents are greatly dependent on the perceptual quality of panoramic or 360°images,in fact a minor visual distortion can significantly degrade the overall ***,to ensure the quality of constructed panoramic contents for VR and AR applications,numerous Stitched Image Quality Assessment(SIQA)methods have been proposed to assess the quality of panoramic contents before using in VR and *** this survey,we provide a detailed overview of the SIQA literature and exclusively focus on objective SIQA methods presented till *** better understanding,the objective SIQA methods are classified into two classes namely Full-Reference SIQA and No-Reference SIQA *** class is further categorized into traditional and deep learning-based methods and examined their performance for SIQA ***,we shortlist the publicly available benchmark SIQA datasets and evaluation metrices used for quality assessment of panoramic *** last,we highlight the current challenges in this area based on the existing SIQA methods and suggest future research directions that need to be target for further improvement in SIQA domain.

关键词： Virtual reality Augmented reality Panoramic image Immersive contents Stitched image quality assessment Deep learning Convolutional neural networks

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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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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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