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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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Articles

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.

CloudIoT paradigm acceptance for e-learning: analysis and future challenges Arif Ullah; Hanane Aznaoui; Canan Batur Sahin; Ikram Daanoune; Ozlem Batur Dinle
Jurnal Informatika Vol 16, No 3 (2022): September 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i3.a21744

E-learning is the theme interrelated to the virtualized distance learning with the help of electronic communication machines, certainly with the help of Internet. CloudIoT paradigm is the combination of cloud resource and internet of thing which become prevalent now days due to the flexibility and fast access for those reason different countries used CloudIoT paradigm different purposes. E-learning is one of the best examples where virtual environment provides cost-effective alternative to physical labs as well as to run scientific applications. The world order change in education sector due to Covid-19 all activity shift in to e-learning system. In this paper we present the review about CloudIoT paradigm and it usage in e-learning system as well as we extant taxonomy of CloudIoT paradigm for e-leaning purpose. In the related work section we present the existing contribution in the field of e-learning using CloudIoT paradigm are highlighted. We also contemporaneous the most standard framework which carried out for e-leaning using CloudIoT paradigm is discuss. The contribution section of the paper present the issue being faced by in adopting CloudIoT paradigm for e-learning are discussed along with recommendation and future work.

Cloud and internet-of-things secure integration along with security concerns Arif Ullah; Imane Laassar; Canan Batur Şahin; Ozlem Batur Dinle; Hanane Aznaoui
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v12i1.pp62-71

Cloud computing is a new technology which refers to an infrastructure where both software and hardware application are operate for the network with the help of internet. Cloud computing provide these services with the help of rule know as you pay as you go on. Internet of things (IoT) is a new technology which is growing rapidly in the field of telecommunications. The aim of IoT devices is to connect all things around us to the internet and thus provide us with smarter cities, intelligent homes and generally more comfortable lives. The combation of cloud computing and IoT devices make rapid development of both technologies. In this paper, we present information about IoT and cloud computing with a focus on the security issues of both technologies. Concluding we present the contribution of cloud computing to the IoT technology. Thus, it shows how the cloud computing technology improves the function of the IoT. Finally present the security challenges of both technologies IoT and cloud computing.

A Hybrid Approch Tomato Diseases Detection At Early Stage Arif Ullah; Muhammad Azeem khalid; Dorsaf Sebai; Tanweer Alam
Jurnal Informatika Vol 17, No 1 (2023): January 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v17i1.a24759

In traditional farming practice, skilled people are hired to manually examine the land and detect the presence of diseases through visual inspection, but the visual inspection method is ineffective. High accuracy of disease detection is one of the most important factors in crop production and reducing crop losses. Meanwhile, the evolution of deep convolutional neural networks for image classification has rapidly improved the accuracy of object detection, classification and system recognition. Previous tomato detection methods based on faster region convolutional neural network (RCNN) are less efficient in terms of accuracy. Researchers have used many methods to detect tomato leaf diseases, but their accuracy is not optimal. This study presents a Faster RCNN-based deep learning model for the detection of three tomato leaf diseases (late blight, mosaic virus, and leaf septoria). The methodology presented in this paper consists of four main steps. The first step is pre-processing. At the second stage, segmentation was done using fuzzy C Means. In the third step, feature extraction was performed with ResNet 50. In the fourth step, classification was performed with Faster RCNN to detect tomato leaf diseases. Two evaluation parameters precision and accuracy are used to compare the proposed model with other existing approaches. The proposed model has the highest accuracy of 98.6% in detecting tomato leaf diseases. In addition, the work can be extended to train the model for other types of tomato diseases, such as leaf mold, spider mites, as well as to detect diseases of other crops, such as potatoes, peanuts, etc.

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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