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Correlation Between Temperature and COVID-19 (Suspected, Confirmed and Death) Cases based on Machine Learning Analysis سلطان احمد علي شير

Currently, the whole world is struggling with the biggest health problem COVID-19 name coined by the World Health Organization (WHO). This was raised from China in December 2019. This pandemic is going to change the world. Due to its communicable nature, it is contagious to both medically and economically. Though different contributing factors are not known yet. Herein, an effort has been made to find the correlation between temperature and different cases situation (suspected, confirmed, and death cases). For a said purpose, k-means clustering-based machine learning method has been employed on the data set from different regions of China, which has been obtained from the WHO. The novelty of this work is that we have included the temperature field in the original WHO data set and further explore the trends. The trends show the effect of temperature on each region in three different perspectives of COVID-19 – suspected, confirmed and death.

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Listed in CV احمد بن عمر عسيري

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Recruitment algorithms for vehicular sensor networks Usman Tariq
Campioni, Fabio, Salimur Choudhury, Usman Tariq, and Ali Kashif Bashir. "Recruitment algorithms for vehicular sensor networks." Computer Communications (2020). Vehicular crowdsensing allows the rapid, predictable movement of vehicles, as well as their wide variety of sensors, to gather sensing data in crowdsensing applications. Recruitment algorithms are used to select a subset of participants in an area that will provide the most complete coverage. In this paper, we explore two variations of the vehicular recruitment problem. In the first problem, which we refer to as the priority based vehicle recruitment problem, we consider coverage areas in which subsets must be covered. In the multisensor variation, we consider coverage areas which require different types of sensors, in which participating vehicles have one or more sensor types onboard. For each, we implement a mixed integer programming model which returns optimal solutions, as well as a heuristic for obtaining approximate solutions. In the unbudgeted priority vehicular recruitment performance evaluation, our heuristic on average obtains only 0.05% lower utility at 1.78% higher recruitment cost. In the budgeted runs, our heuristic obtains on average only 0.02% lower utility at 0.59% higher recruitment costs. In the unbudgeted multisensor vehicular recruitment performance evaluation, our heuristic obtains only 0.04% lower utility at 1.10% higher recruitment cost, and in the budgeted runs we obtain 11.33% lower utility at 0.27% higher recruitment cost.

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A Review on Edge to Cloud: paradigm shift from large data centers to small centers of data everywhere سلطان احمد علي شير

In present, Information Communication Technologies is shifting from large data centers to small and micro data centers, bringing data analytics and storage near to the devices at the edge of networks. This helps to deal with latency and data ransition overhead that helps to make real-time operations more efficient. Advent of IoT and smart mobile technologies, new devices are joining networks and generating enormous data. Instead of cloud servers performing data analysis and storage, edge devices and edge servers are sharing the load of cloud servers which will enable hyper-efficient and smart ecosystem for enterprises. This research work addresses the key benefits of edge-to-cloud computing, its challenges and limitations. It also explored the edge architecture and application which are applicable in the current scenario and future possibilities of edge to cloud computing.

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Real-Time Methodology for Improving Cyber Security in Internet of Things Using Edge Computing During Attack Threats Usman Tariq

T. A. Ahanger, U. Tariq and M. Nusir, "Real-Time Methodology for Improving Cyber Security in Internet of Things Using Edge Computing During Attack Threats," 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2019, pp. 293-297.
doi: 10.1109/ICSSIT46314.2019.8987779
 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8987779&isnumber=8987579

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Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection Usman Tariq
https://doi.org/10.1002/jemt.23447 Keywords: CNN features; database preparation; features selection; gastric infections; handcrafted features. Abstract: Automated detection and classification of gastric infections (i.e., ulcer, polyp, esophagitis, and bleeding) through wireless capsule endoscopy (WCE) is still a key challenge. Doctors can identify these endoscopic diseases by using the computer‐aided diagnostic (CAD) systems. In this article, a new fully automated system is proposed for the recognition of gastric infections through multi‐type features extraction, fusion, and robust features selection. Five key steps are performed—database creation, handcrafted and convolutional neural network (CNN) deep features extraction, a fusion of extracted features, selection of best features using a genetic algorithm (GA), and recognition. In the features extraction step, discrete cosine transform, discrete wavelet transform strong color feature, and VGG16‐based CNN features are extracted. Later, these features are fused by simple array concatenation and GA is performed through which best features are selected based on K‐Nearest Neighbor fitness function. In the last, best selected features are provided to Ensemble classifier for recognition of gastric diseases. A database is prepared using four datasets—Kvasir, CVC‐ClinicDB, Private, and ETIS‐LaribPolypDB with four types of gastric infections such as ulcer, polyp, esophagitis, and bleeding. Using this database, proposed technique performs better as compared to existing methods and achieves an accuracy of 96.5%.

Extrected Data Feature Set (sample): Download

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A Novel PCA-Firefly based XGBoost classification model for Intrusion Detection in Networks using GPU Usman Tariq

Journal: Electronics 

Special Issue: Machine Learning Techniques for Intelligent Intrusion Detection Systems

https://doi.org/10.3390/electronics9020219

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

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A Review Study on the Critical Success Factors of Agile Software Development عبدالله محمد الدهمش

24th European Conference on Systems, Software and Services Process Improvement , EuroSPI 2017, Ostrava, Czech Republic, Sep 6, 2017.

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Evaluating phase level for critical success factors of BPM-system implementation: a case study in a Saudi government organization عبدالله محمد الدهمش

publication descriptionInternational Journal of Modern Engineering Research, Oct 2013.

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publication descriptionInternational Journal of Modern Engineering Research

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Using Factor Analysis to Study the Critical Success Factors of Agile Software Development عبدالله محمد الدهمش

Oct 25, 2017  publication description10th International Conference on Computer Science and Information Technology (ICCSIT) 2017, Florence, Italy.

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