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| 1 | Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s显示文摘In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting firearms.which is why an automated weapon detection system is needed.Various automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good results.However,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system.These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos.This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter.The proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm.The proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 score.The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved.Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN.It is promising to be used in the field of security and weapon detection. | Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib | 2022 | Computers, Materials & Continua2022，，2： | 1 |
| 2 | Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance显示文摘:In recent years,video surveillance application played a significant role in our daily lives.Images taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the visibility.The reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery,object detection,target killing,and surveillance.To remove fog and enhance visibility,a number of visibility enhancement algorithms and methods have been proposed in the past.However,these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer vision applications.The existing techniques do not perform well when images contain heavy fog,large white region and strong atmospheric light.This research work proposed a new framework to defog and dehaze the image in order to enhance the visibility of foggy and haze images.The proposed framework is based on a Conditional generative adversarial network(CGAN)with two networks;generator and discriminator,each having distinct properties.The generator network generates fog-free images from foggy images and discriminator network distinguishes between the restored image and the original fog-free image.Experiments are conducted on FRIDA dataset and haze images.To assess the performance of the proposed method on fog dataset,we use PSNR and SSIM,and for Haze dataset use e,r−,andσas performance metrics.Experimental results shows that the proposed method achieved higher values of PSNR and SSIM which is 18.23,0.823 and lower values produced by the compared method which are 13.94,0.791 and so on.Experimental results demonstrated that the proposed framework Has removed fog and enhanced the visibility of foggy and hazy images. | Ghulfam Zahra Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Fayez Eid Alazemi | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 3 | ExpressionHash: Securing Telecare Medical Information Systems Using BioHashing显示文摘The COVID-19 outbreak and its medical distancing phenomenon have effectively turned the global healthcare challenge into an opportunity for Telecare Medical Information Systems.Such systems employ the latest mobile and digital technologies and provide several advantages like minimal physical contact between patient and healthcare provider,easy mobility,easy access,consistent patient engagement,and cost-effectiveness.Any leakage or unauthorized access to users’medical data can have serious consequences for any medical information system.The majority of such systems thus rely on biometrics for authenticated access but biometric systems are also prone to a variety of attacks like spoong,replay,Masquerade,and stealing of stored templates.In this article,we propose a new cancelable biometric approach which has tentatively been named as“Expression Hash”for Telecare Medical Information Systems.The idea is to hash the expression templates with a set of pseudo-random keys which would provide a unique code(expression hash).This code can then be serving as a template for verication.Different expressions would result in different sets of expression hash codes,which could be used in different applications and for different roles of each individual.The templates are stored on the server-side and the processing is also performed on the server-side.The proposed technique is a multi-factor authentication system and provides advantages like enhanced privacy and security without the need for multiple biometric devices.In the case of compromise,the existing code can be revoked and can be directly replaced by a new set of expression hash code.The well-known JAFFE(The Japanese Female Facial Expression)dataset has been for empirical testing and the results advocate for the efcacy of the proposed approach. | Ayesha Riaz Naveed Riaz Awais Mahmood Sajid Ali Khan Imran Mahmood Omar Almutiry Habib Dhahri | 2021 | Computers, Materials & Continua2021，，6： | 0 |
| 4 | Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder显示文摘The exponential increase in new coronavirus disease 2019(COVID-19)cases and deaths has made COVID-19 the leading cause of death in many countries.Thus,in this study,we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images.A stacked denoising convolutional autoencoder(SDCA)model was proposed to classify X-ray images into three classes:normal,pneumonia,and COVID-19.The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images.The proposed model’s architecture mainly composed of eight autoencoders,which were fed to two dense layers and SoftMax classifiers.The proposed model was evaluated with 6356 images from the datasets from different sources.The experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting,respectively.The metrics used for the SDCA model were the classification accuracy,precision,sensitivity,and specificity for both schemes.Our results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%.Therefore,this model can help physicians accelerate COVID-19 diagnosis. | Habib Dhahri Besma Rabhi Slaheddine Chelbi Omar Almutiry Awais Mahmood Adel M.Alimi | 2021 | Computers, Materials & Continua2021，，12： | 0 |
| 5 | A Novel Framework for Multi-Classification of Guava Disease显示文摘Guava is one of the most important fruits in Pakistan,and is gradually boosting the economy of Pakistan.Guava production can be interrupted due to different diseases,such as anthracnose,algal spot,fruit fly,styler end rot and canker.These diseases are usually detected and identified by visual observation,thus automatic detection is required to assist formers.In this research,a new technique was created to detect guava plant diseases using image processing techniques and computer vision.An automated system is developed to support farmers to identify major diseases in guava.We collected healthy and unhealthy images of different guava diseases from the field.Then image labeling was done with the help of an expert to differentiate between healthy and unhealthy fruit.The local binary pattern(LBP)was used for the extraction of features,and principal component analysis(PCA)was used for dimensionality reduction.Disease classification was carried out using multiple classifiers,including cubic support vector machine,Fine K-nearest neighbor(F-KNN),Bagged Tree and RUSBoosted Tree algorithms and achieved 100%accuracy for the diagnosis of fruit flies disease using Bagged Tree.However,the findings indicated that cubic support vector machines(C-SVM)was the best classifier for all guava disease mentioned in the dataset. | Omar Almutiry Muhammad Ayaz Tariq Sadad Ikram Ullah Lali Awais Mahmood Najam Ul Hassan Habib Dhahri | 2021 | Computers, Materials & Continua2021，，11： | 0 |
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