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All Journal Jurnal Informatika Scientific Journal of Informatics JOIN (Jurnal Online Informatika) International Journal of Applied Sciences and Smart Technologies APTIKOM Journal on Computer Science and Information Technologies (CSIT) Computer Science and Information Technologies IAIC Transactions on Sustainable Digital Innovation (ITSDI)
Alam, Tanweer
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology

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Journal : Jurnal Informatika

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5G-Enabled Tactile Internet for smart cities: vision, recent developments, and challenges Alam, Tanweer
Jurnal Informatika Vol. 13 No. 2 (2019): July 2019
Publisher : Universitas Ahmad Dahlan

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Abstract

The Tactile Internet (TI) is an emerging technology next to the Internet of Things (IoT). It is a revolution to develop smart cities, communities, and cultures in the future. This technology will allow the real-time interaction between humans and machines as well as machine-to-machine with the 1ms challenge to achieve in round trip latency. The term TI is defined by the International Telecommunication Union (ITU) in August 2014. The TI provides a fast, reliable, secure and available internet network that is the requirements of the smart cities in 5G. Tactile internet can develop the part of the world where the machines are strong, and humans are weak. It increases the power of machines so that the value of human power will increase automatically. In this framework, we have presented the idea of tactile internet for the next generation of smart cities. This research will provide a high-performance reliable framework for the internet of smart devices to communicate with each other in a real-time (1ms round trip) using IEEE 1918.1 standard. The objective of this research is expected to bring a new dimension in the research of smart cities.
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

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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.
Co-Authors Abusallama, Firas Adel Ahamed, N. Nasurudeen Alharbi, Yazeed Mohammed Alhejaili, Abdulaziz M. Aljohani, Mohammed Alsharif, Ahmad O. Arif Ullah, Arif Benaida, Mohamed Erqsous, Moath Gupta, Ruchi Hakeem, Ahmad Osama khalid, Muhammad Azeem Salem, Abdulrahman A. Sebai, Dorsaf