Journal of Computer Science and Technology Studies https://al-kindipublisher.com/index.php/jcsts <p>Founded in 2019, the Journal of Computer Science and Technology Studies (JCSTS) is a double-blind peer-reviewed, open-access journal published by <a href="https://www.al-kindipublishers.com/">Al-Kindi Center for Research and Development</a>. It covers the latest developments in the broad areas of computer science and information technology. The journal offers readers free access to all new research issues relevant to computer science and technology. While the journal strives to maintain high academic standards and an international reputation through the suggestions of the international advisory board, it welcomes original, theoretical and practical submissions from all over the world. The Journal publishes original papers, review papers, case studies, conference reports, academic exchanges, conceptual framework, empirical research, technical notes, and book reviews in the fields of Artificial Intelligence, Bioinformatics, Cluster Computing and Performance, Computer Graphics and Visualization, Computer Network and Internet, Cryptography and Security, Data Warehouse and Applications […] <a href="https://al-kindipublisher.com/index.php/jcsts/Aims-and-Scope">Read More</a></p> en-US editor@jcsts.one (Managing Editor) editor@jcsts.one (Technical Support) Tue, 06 Oct 2020 00:00:00 +0000 OJS 3.2.0.3 http://blogs.law.harvard.edu/tech/rss 60 COVID-19 Classification based on Chest X-Ray Images Using Machine Learning Techniques https://al-kindipublisher.com/index.php/jcsts/article/view/531 <p>The coronavirus (COVID-19) pandemic rapidly spread from the infected person who has a severe health problem around the world. World Health Organization (WHO) has identified the coronavirus as a global pandemic issue. The infected person has a severe respiratory issue that needs to be treated in an intensive health care unit. The detection of COVID-19 using machine learning techniques will help in healthcare system about fast recovery of patients worldwide. One of the crucial steps is to detect these pandemic diseases by predicting whether COVID19 infects the human body or not. The investigation is carried out by analyzing Chest X-ray images to diagnose the patients. In this study, we have presented a method to efficiently classify the&nbsp;&nbsp; COVID-19 infected patients and normally based on chest X-ray radiography using Machine Learning techniques. The proposed system involves pre-processing, feature extraction, and classification. The image is pre-processed to improve the contrast enhancement. The Histogram of Oriented Gradients (HOG) is used to extract the discriminant features. Finally, In the classification step, five different Machine Learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbors, Random Forest, Naïve Bayes algorithm, and Decision Tree) are used to efficiently classify between COVID-19 and normal chest X-ray images. The different metric measures like accuracy, precision, recall, specificity and F1are used to analyze the results. The result evaluation shows that SVM provides the highest accuracy of 96% among the other four classifiers (K-Nearest Neighbors and Random Forest achieved 92% accuracy, 90% accuracy of Naïve Bayes algorithm and 82% accuracy of Decision Tree).</p> Muhammad imad, Naveed Khan, Farhat Ullah, Muhammad Abul Hassan, Adnan Hussain, Faiza Copyright (c) 2020 Journal of Computer Science and Technology Studies https://al-kindipublisher.com/index.php/jcsts/article/view/531 Tue, 06 Oct 2020 00:00:00 +0000 Pakistani Currency Recognition to Assist Blind Person Based on Convolutional Neural Network https://al-kindipublisher.com/index.php/jcsts/article/view/529 <p>A visually impaired person faces many difficulties in their daily life, such as having trouble finding their ways, recognize the person and objects. One of the crucial problems is to recognize the currencies for a blind or visually impaired person. In this research article, we have proposed a system to recognize a Pakistani currency for a blind or visually impaired person based on Convolutional Neural Network (CNN) and Support Vector Machine (SVM). In the proposed system, seven different Pakistani paper currency notes (Rs.10, 20, 50, 100,500, 1000 and 5000) are used for training and testing. Experimental results show that the proposed system can recognize seven notes of Pakistan's Currency (Rs. 10, 20, 50, 100, 500, 1000, 5000) successfully with an accuracy of 96.85%.</p> Muhammad Imad, Farhat Ullah , Muhammad Abul Hassan, Naimullah Copyright (c) 2020 Journal of Computer Science and Technology Studies https://al-kindipublisher.com/index.php/jcsts/article/view/529 Tue, 06 Oct 2020 00:00:00 +0000 Navigation System for Autonomous Vehicle: A Survey https://al-kindipublisher.com/index.php/jcsts/article/view/538 <p>Advanced Driver Assistance Systems (ADAS) apply to various high-tech in-vehicle systems designed to enhance road traffic protection by making drivers become more mindful of the road and its potential hazards, as well as other vehicles around them. The design of traffic sign, traffic light, traffic cone, car, road lane, pedestrian and road blocker detection and Recognition, a significant ADAS subsystem, has been a problem for many years and thus becomes an essential and successful research topic in the field of smart transport systems. This paper present different approaches Devised over the last 3 years for the diverse modalities. We present a survey of each challenge in form of table in terms of “algorithm, parameter, result, advantage, and disadvantage. For each survey, we describe the possible implementations suggested and analyze their underlying assumptions, while impressive advancements were demonstrated at limited scenarios, inspection into the needs of next generation systems reveals significant gaps. We identify these gaps in disadvantage block and suggest research directions that may bridge them. we identify the future solutions proposed and examine their underlying assumptions, although promising development has been shown in restricted contexts, analysis of next-generation applications requirements shows significant gaps. We define certain holes in the block of drawbacks and propose avenues for work that can cross them.</p> farhat ullah, Muhammad Imad, Muhammad Abul Hassan, Hazrat Junaid, Faiza, Izaz Ahmad Copyright (c) 2020 Journal of Computer Science and Technology Studies https://al-kindipublisher.com/index.php/jcsts/article/view/538 Sat, 10 Oct 2020 00:00:00 +0000