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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences Jurnal Informatika

Arif Ullah

Riphah International University

Author-ID : 4095486

Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Heart disease classification using various heuristic algorithms Arif Ullah; Shakeel Ahmad Khan; Tanweer Alam; Shkurte Luma-Osmani; Mahanz Sadie
International Journal of Advances in Applied Sciences Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

In the health sector, the computer-aided diagnosis (CAD) system is a rapidly growing technology because medical diagnostic systems make a huge change as compared to the traditional system. Now a day huge availability of medical data and it needs a proper system to extract them into useful knowledge. Heart disease accounts to be the leading cause of death worldwide. Heuristic algorithms have been exposed to be operative in supporting making decisions and classification from the large quantity of data produced by the healthcare sector. Classification is a prevailing heuristic approach which is commonly used for classification purpose some heuristic algorithm predicts accurate result according to the marks whereas some others exhibit limited accuracy. This paper is used to categorize the attendance of heart disease with a compact number of aspects. Original, 13 attributes are involved in classifying heart disease. A reasonable analysis of these techniques was done to conclude how the cooperative techniques can be applied for improving prediction accuracy in heart disease. Four main classifiers used to construct heart disease prediction based on the experimental results demonstrate that support vector machine, artificial bee colony (ABC), bat algorithm, and memory-based learner (MBL) provide efficient results. The accuracy differs between 13 features and 8 features in the training dataset is 1.9% and in the validation, dataset is 0.92% of vector machine which is the most accurate heuristic algorithm.

Cloud computing and 5G challenges and open issues Arif Ullah; Hanane Aznaoui; Canan Batur Şahin; Mahnaz Sadie; Ozlem Batur Dinler; Laassar Imane
International Journal of Advances in Applied Sciences Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

The obtainable fourth-generation technology (4G) networks have been extensively used in the cloud application and are constantly evolving to match the needs of the future cloud applications. The ﬁfth-generation (5G) networks are probable to immense expand today's cloud that can boost communication operations, cloud security, and network challenges and drive the cloud future to the edge and internet of things (IoT) applications. The existing cloud solutions are facing a number of challenges such as large number of connection of nodes, security, and new standards. This paper reviews the current research state-of-the-art of 5G cloud, key-enabling technologies, and current research trends and challenges in 5G along with cloud application.

An evolutionary algorithm for the solution of multi-objective optimization problem Ubaid Ullah; Arif Ullah
International Journal of Advances in Applied Sciences Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Worldwide, COVID-19 widespread has a significant impact on a great number of people. The hospital admittance issue for patients with COVID-19 has been optimized by previous research. Identifying the symptoms that can be used to determine a patient's health status, whether they are dead or alive is a difficult task for medical professionals. To solve this issue, the multi-objective group counseling optimization (MOGCO) algorithm was used to control this problem. First, the zitzler-deb-thiele (ZDT)-2 benchmark function is used to evaluate the MOGCO, multi-objective particle swarm optimization (MOPSO), and non-dominated sorting genetic algorithm (NSGA) II. In comparison to MOPSO and NSGA-II, MOGCO is closest to the Pareto front line according to graphic statistics on different fitness evolution values such as 4000, 6000, 8000, and 10000. As a result, MOGCO is used for COVID-19 data optimization. Moreover, six symptoms (heart rate, oxygen saturation, fever, body pain, flue, and breath) were optimized to see if the COVID-19 patients were still alive. The information was gathered from GitHub. Based on the minimum and maximum values of these symptoms obtained by the suggested method, the optimum study shows that COVID-19 patients can remain alive.

Co-Authors Canan Batur Sahin Canan Batur Şahin Dorsaf Sebai Hanane Aznaoui Hanane Aznaoui Ikram Daanoune Imane Laassar Laassar Imane Mahanz Sadie Mahnaz Sadie Muhammad Azeem khalid Ozlem Batur Dinle Ozlem Batur Dinler Shakeel Ahmad Khan Shkurte Luma-Osmani Tanweer Alam Tanweer Alam Ubaid Ullah

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Journal : International Journal of Advances in Applied Sciences