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

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ahead.However,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and fatalities.In 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 segmentation.This article explores the potential of seeing-through vehicles as a solution to enhance overtaking safety.See-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 sight.To 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 cars.The server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front car.Our 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 vehicles.Our network was trained and tested on the Cityscape dataset using semantic segmentation.This transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has obscured.For our findings,we have achieved 97.1% F1-score.The article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

Stock market forecasting is an important research area,especially for better business decision making.Efficient stock predictions continue to be significant for business intelligence.Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily returns.However,major events’news also contains significant information regarding market drivers.An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market.This research proposes an efficient model for stock market prediction.The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and hospitality.We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock prediction.The LSTM has the advantage of analyzing relationship between time-series data through memory functions.The 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.

A Transfer Learning-Based Approach to Detect Cerebral Microbleeds

Cerebral microbleeds are small chronic vascular diseases that occur because of irregularities in the cerebrum vessels.Individuals 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 dementia.In this study,we proposed an efficient approach to automatically identify microbleeds by reducing the false positives in openly available susceptibility-weighted imaging(SWI)data samples.The proposed structure comprises two different pretrained convolutional models with four stages.These 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 classification.The proposed technique was assessed using the SWI dataset available for 20 subjects.For 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 approaches.The results show that our models outperformed the existing approach for the detection of microbleeds.

An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection

Cerebral Microbleeds(CMBs)are microhemorrhages caused by certain abnormalities of brain vessels.CMBs can be found in people with Traumatic Brain Injury(TBI),Alzheimer’s disease,and in old individuals having a brain injury.Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke.The 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 life.The existing work report good results but often ignores false-positive’s perspective for this research area.In 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 subjects.The proposed system shows an accuracy of 98.9%and a 1.1%false-positive rate value.The results show the superiority of the proposed work as compared to existing states of the art methods.

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

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 sector.The 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 quality.Thus,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 AR.In this survey,we provide a detailed overview of the SIQA literature and exclusively focus on objective SIQA methods presented till date.For better understanding,the objective SIQA methods are classified into two classes namely Full-Reference SIQA and No-Reference SIQA approaches.Each class is further categorized into traditional and deep learning-based methods and examined their performance for SIQA task.Further,we shortlist the publicly available benchmark SIQA datasets and evaluation metrices used for quality assessment of panoramic contents.In 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.