Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Informatika Computer Science and Information Technologies
Arif Ullah, Arif
Unknown Affiliation
Computer Science & IT Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

 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.
Cloud computing needs to explore into sky computing Ullah, Arif; Remmach, Hassnae; Aznaoui, Hanane; Şahin, Canan Batur; Mrhari, Amine
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p294-306

Abstract

This paper evaluates key issues in cloud computing and introduces a novel model, known as sky computing, to address these challenges. Cloud computing, a transformative technology, has played a critical role in reshaping modern operations—especially following the COVID-19 pandemic, when many human activities shifted to technology-driven platforms. It offers multiple service models, including Software as a Service, Hardware as a Service, Desktop as a Service, Backup as a Service, and Network as a Service, each tailored to user requirements. However, the rapid expansion of cloud-based technologies and interconnected systems has intensified infrastructure and scalability challenges. Sky computing, or the “cloud of clouds,” emerges as an advanced layer above traditional cloud models, enabling dynamically provisioned, distributed domains built over multiple serial clouds. Its core capability lies in offering variable computing capacity and storage resources with dynamic, real-time support, providing a robust and unified platform by integrating diverse cloud resources. This paper reviews related technologies, summarizes prior research on sky computing, and discusses its structural design. Furthermore, it examines the limitations of current cloud computing frameworks and highlights how sky computing could overcome these barriers, positioning it as a pivotal architecture for the future of distributed computing.
Co-Authors Alam, Tanweer Aznaoui, Hanane khalid, Muhammad Azeem Mrhari, Amine Remmach, Hassnae Şahin, Canan Batur Sebai, Dorsaf