Publisher: Al-Kindi Center for Research and Development (Total: 14 journals)   [Sort by number of followers]

Showing 1 - 14 of 14 Journals sorted alphabetically
Intl. J. of Biological, Physical and Chemical Studies     Open Access   (Followers: 2)
Intl. J. of English Language Studies     Open Access   (Followers: 3)
Intl. J. of Law and Politics Studies     Open Access   (Followers: 2)
Intl. J. of Linguistics, Literature and Translation     Open Access   (Followers: 4)
J. of Business and Management Studies     Open Access   (Followers: 4)
J. of Computer Science and Technology Studies     Open Access   (Followers: 2)
J. of Economics, Finance and Accounting Studies     Open Access   (Followers: 3)
J. of English Language Teaching and Applied Linguistics     Open Access   (Followers: 1)
J. of Environmental and Agricultural Studies     Open Access   (Followers: 1)
J. of Humanities and Social Sciences Studies     Open Access   (Followers: 3)
J. of Mathematics and Statistics Studies     Open Access   (Followers: 3)
J. of Mechanical, Civil and Industrial Engineering     Open Access   (Followers: 1)
J. of Medical and Health Studies     Open Access   (Followers: 1)
J. of World Englishes and Education Practices     Open Access  
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Journal of Computer Science and Technology Studies
Number of Followers: 2  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2709-104X
Published by Al-Kindi Center for Research and Development Homepage  [14 journals]
  • Navigation System for Autonomous Vehicle: A Survey

    • Authors: farhat ullah; Muhammad Imad, Muhammad Abul Hassan, Hazrat Junaid, Faiza, Izaz Ahmad
      Abstract: 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.
      PubDate: Sat, 10 Oct 2020 00:00:00 +000
       
  • COVID-19 Classification based on Chest X-Ray Images Using Machine Learning
           Techniques

    • Authors: Muhammad imad; Naveed Khan, Farhat Ullah, Muhammad Abul Hassan, Adnan Hussain, Faiza
      Abstract: 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   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).
      PubDate: Tue, 06 Oct 2020 00:00:00 +000
       
  • Pakistani Currency Recognition to Assist Blind Person Based on
           Convolutional Neural Network

    • Authors: Muhammad Imad; Farhat Ullah , Muhammad Abul Hassan, Naimullah
      Abstract: 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%.
      PubDate: Tue, 06 Oct 2020 00:00:00 +000
       
 
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