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Information Systems (IS) play an important role in business organizations. Implementation of these systems is a difficult task which if not implemented correctly may use up many resources of these organizations. This paper is trying to propose a conceptual framework for the successful implementation of information systems (IS) projects in Yemeni private sector business organizations. The research started with identifying the Critical Success Factors (CSFs) for IS Projects based on a survey of previous works. Then, the proposed conceptual framework was developed, which can help Yemeni private sector business organizations to achieve a successful implementation of IS projects.

Information System (IS), Information System Projects, Critical Success Factors (CSFs). Yemeni Private Sector Business Organizations

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Mokhtar Mohammed Ghilan, "A Conceptual Framework for Successful Implementation of IS Projects in the Yemeni Private Sector Business Organizations," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.57-60, 2020.

COVID-19 outbreak has put the whole world in an unusual situation bringing life around the world to a frightening halt and claiming thousands of lives. To quickly trace the disease for breaking chain governments increased the no. of testings, by increasing the no. of tests there is also a raise in no. of cases but there were only limited facilities. Today Machine Learning plays a crucial role in various sectors like Business, Industries and Health care System and so on for predicting their economy. In the Healthcare system Machine Learning is used to predict the probability of occurring diseases. In this paper by Machine Learning approach we render to Predict whether a patient is corona positive or negative based on the results of laboratory tests collected from clinical dataset SARS-Cov-2 among suspected cases and we also predict whether a patient needs to be admitted into a general ward or a semi-intensive unit or an intensive care unit based on his symptoms by using some Machine Learning models like Principal Component Analysis, and visualize the data by Exploratory Data Analysis(EDA).

PCA (Principal Component Analysis) ,EDA (Exploratory Data Analysis)

[1]. Predicting COVID Cases by Eliminating TPS, FNS, TNS, FPS through Machine Learning Approach by M. Vennela IJCSE vol8 issued on 10 oct 2020
[2]. Saroj S. Date “Forecasting novel Covid-19 confirmed cases in India using Machine Learning Methods” –IJCSE, vol.8, issued on 6,June 2020.
[3]. Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart HealthCare Saad Awadh Alanazi M. M.Kamruzzaman,MadallahAlruwaili,2NasserAlshammari,1Salman Ali Alqahtani,3 and Ali Karime4:Article in hindawi.
[4]. Sanjib Halder “A Mathematical Model to Forecast & Compare Covid-19 Outbreak in Male & Female using Polynomial Regression Analysis”-IJCSE ,vol.8,issued on 5,May 2020.
[5]. Jay Furst-“False-negative COVID-19 test results may lead to a false sense of security”. Source: mayo clinic Steven Woloshin, M.D., Neeraj Patel, B.A., and Aaron.
[6]. John Wiley& sons “Machine Learning: Hands-on for Developers and Technical Professionals”

G. Dinesh Chandra, G. Lavanya Devi, P.R.S. Naidu, "Analysing Covid-19 Cases by Eliminating False Negatives, False Positives by Visual Exploratory Data Analysis Approach," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.61-66, 2020.

In the last decade, biometric techniques for person identification have gained more popularity. Different researchers have proposed various algorithms for feature extraction and classification. Deep learning is a new universally adopted method for classification and regression purpose. Compared to machine learning it is easiest way to train a deep neural network as it does not require feature extraction step. Only the requirement is it needs a voluminous database. In this paper we propose a “AlexNet” a pre-trained network for gender prediction from human iris. We implemented AlexNet for transfer learning based on feature extraction method. In this iris features are extracted using different intermediate layers. These features are further classified using multi-class SVM classifier. We achieved promising results for the proposed method.

Deep neural networks, Deep learning, AlexNet, Iris biometrics, Transfer learning

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[10] J. Tapia, & C. Aravena, “Gender Classification from NIR Iris Images using Deep Learning”, In: B. Bhanu, A. Kumar (Eds) Deep Learning for Biometrics. Advances in Computer Vision and Pattern Recognition. Springer, Cham, 219-239, 2017.
[11] M. Singh, & S. Nagpal, “Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder”, In the proceedings of the IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, pp.666-673, 2017.
[12] J. E. Tapia & C.A. Perez, “Gender Classification from NIR Images by using Quadrature Encoding Filters of the Most Relevant Features”, IEEE Access Open Access Journal, 7, 29114-29127, 2019.
[13] NEM Khalifa, MHN Taha, AE Hassanien, HNET Mohamed, “Deep Iris: Deep Learning for Gender Classification through Iris Patterns”, Acta Informatica Medica: AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku Bih, Vol.27, Issue.2, pp.96-102, 2019.
[14] A. Kuehlkamp & K. Bowyer, “Predicting Gender from Iris Texture May Be Harder Than It Seems”, In the proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, pp. 904- 912, 2019.

Minakshi R.Rajput, Ganesh S. Sable, "Alexnet Based Transfer Learning Approach for Extracting Soft Attributes From Iris Biometrics," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.67-71, 2020.

Diabetes is one of the disease increases in humanity across the world-wide. Data mining is a process of extracting the information from a large dataset and transforms it into understandable structure. Medical data mining has been a great capability for finding hidden patterns from the large data sets of the medical dataset. The Data mining techniques used for the prediction of diseases like heart diseases, cancer, kidney stones, EEG etc. Prediction of Diabetes is an emerging and fastest growing technology in the medical analysis data. This research paper concentrates on the clustering method for grouping diabetic data based on cluster head attributes. In this paper the popular clustering algorithms K-Means, Fuzzy C-Means (FCM) and Gaussian Kernel based Fuzzy C-Means (GKFCM) clustering algorithms are selected and analyzed based on their fundamentals by using diabetes dataset. The algorithms performance is tested based on its various analyses. The results are compared with three algorithm algorithms. Finally we obtained that the GKFCM has best than K-means and FCM algorithm.

K-means, Fuzzy C-Means (FCM), Gaussian Kernel based Fuzzy C-Means (GKFCM), Clustering, and Diabetes dataset.

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K. Gandhimathi, N. Umadevi, "Prediction of Type 2 Diabetics besed on Clustering Algorithm," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.72-78, 2020.

Most of the Organizations and governments consider security as an undeniable prerequisite have in view of the extending movement of attacks which is hostile both security and assurance. In this paper, we present an investigation of IDPS which drove us to play out a gathering of systems depending upon the strategies used in interferences acknowledgment and neutralization structures. We talk about the focal points and disadvantages of these strategies. A brief timeframe later, we analyze the various issues tangling the most ideal value and efficiency of the current IDPS and moreover research its troubles in appropriated registering, PDAs and sharp metropolitan networks.

Networking, Security challenges, Cloud computing, intrusion prevention, Intrusion detection,ids, ips, idps

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R. Hamsaveni, Vasumathy M., "A Taxonomy and Survey of Security challenges in intrusion detection and prevention for Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.79-84, 2020.

The successful software project estimation is critically dependent upon measurement of productivity. The overall goal of productivity metric is to improve the ability and performance of information system evaluation as well as maintenance, moreover determine the performance of the information technology (IT) performance in business applications. The activities corresponding to productivity metrics vary at distinct management levels. The activities used within project management leads to dynamic framework that could be opted in changing or dynamic environments. At the project management level, productivities activities concentrates on effectiveness. For instance, during estimation of effort required to reproduce a system, assumption must be manufacture regarding the ability that could lead to successful development of software project and this is fetched from past projects. This paper focuses on identifying the productivity metrics that are essential and appropriate for project management process.

Software productivity, ability, effectiveness, efficiency, cost, Information technology

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Monika, Deepak, "Productivity Metric Estimation: Comprehensive Examine of Efficiency, Effectiveness and Value Based Metrics," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.85-92, 2020.

The prevalence of devices for programming quality examination has expanded throughout the years, with uncommon regard for instruments that ascertain Technical debt dependent on infringement of a lot of rules. SonarQube is one of the most utilized devices and gives an estimation of the time expected to remediate technical debt. Notwithstanding, experts are as yet suspicious about the precision of their remediation time estimation. In this paper, we investigate the exactness of programming on a set open-source Java ventures. The outcomes call attention to that Technical debt remediation time, contrasted with the genuine time for paying off technical debt, is for the most part overestimated, and that the most precise estimation identifies with code smells, while the least exact concerns bugs.

SonarQube, Technical debt, Security, Code Quality, Open Source Software

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Monika, Deepak, "Technical Debt Prediction: Using Predictive Model," International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.93-99, 2020.