- Volume 1
- Issue 1
- (5) Articles
Esraa Mohamed
Internet Of Things (IOT) is a network of various devices that are connected over the internet, and they can collect and exchange data with each other. These IOT devices generate a lot of data that needs to be collected and mined for actionable results through use artificial intelligence (AI) to manage huge data flows and storage in the IOT network. In this paper we briefly discussed about what IOT is, what AI is, Algorithm of AI, Challenge AI with IOT, application of artificial intelligence system in the IOT. The self-optimizing network and software defined network are parts of the important parameters in the Artificial Intelligence IoT System. This paper provides a general discussion about importance of the IoT in different applications. The paper covers different applications of IoT and shows the relationship between AI and IoT. The role of the AI in IoT applications is extensively discussed. In the future work, we are planning to work on improving the performance of IoT applications using advanced AI methods and algorithms such as Machine Learning and Deep Learning.
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Doi: https://doi.org/10.54216/JCIM.010101
Vol. 1
Issue. 1
PP. 30-34, (2020)
Dr. Ajay B. Gadicha
,
Dr. Vijay B. Gadicha
In this era of digitization where literally everything is available at the tip of the finger. Huge amount of data used to flow day in day out, where users used to work with various applications like internet websites, cloud applications, various data servers, web servers, etc. This paper provide idea about access control or authentication used to be acting as first line of defense for preserving data secrecy and its integrity, so far it is learned that the usual login password based methods are easy to implement and to use as well but it is also observed that they are more subjected to be get attacked therefore to preserve authentication on the basis of simple alphanumeric passwords is a challenging task now a days. Hence new methods which bring more strength for authentication and access control are so very expected and desirable.
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Doi: https://doi.org/10.54216/JCIM.010102
Vol. 1
Issue. 1
PP. 5-16, (2020)
Hanan Ahmed
Many pests affect plants, and these have a negatively affected agricultural production and cause a lack of quality. These causes an economic loses and high poverty rates. Many types of pests infest trees like insects, viruses, bacteria, and harmful parasitic plants. One of the most dangerous insects that infest trees such as [date, canary, sago, oil, coconut, etc…] is the Red Palm Weevil (RPW). RPW is currently considered as a global pest, killing trees, increases the tree temperature and causes water stress. It lays the eggs inside the trunk of the tree and starts feeding on the tissue of the plant, then begins to move inside the tree and still inside it until the tree dies, then begin move to the neighboring plants. The early detection of this destructive weevil is not easy; because the visible infection symptoms appear only when the infection stage is dangerous. There are many detection methods for discovering the infected trees, a Visual Inspection, Acoustic Detection, Chemical Detection, and Thermal remote sensing. In this research, we will discuss the different methods used for the early detection of this harmful weevil.
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Doi: https://doi.org/10.54216/JCIM.010103
Vol. 1
Issue. 1
PP. 17-20, (2020)
Abdelrahim Koura
,
Hany S. Elnashar
One of the most common health problems that correlated to serious complications is chronic kidney disease. Early detection and treatment can save it from progression. Machine learning is one tool that used historical data to improve future decision about prediction of chronic kidney disease. The aim of this work is to compare the performance of six different models based on accuracy, sensitivity, precision, recall. In this study, the experiments were conducted on 158 records downloaded from UCI repository. Six algorithms ( K-Nearest Neighbor, Naïve Bayes, Support Vector machine, Logistic Regression, Decision Tree, and Random Forest ) were implemented on data after preprocessing stage. Evaluation of models resulted in Naïve Bayes and Random Forest accuracy 100%, Sensitivity 100%, Specificity 100%, precision 100 %, Recall 100% respectively. It is concluded that Naïve Bayes and Random Forest are better than other models.
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Doi: https://doi.org/10.54216/JCIM.010104
Vol. 1
Issue. 1
PP. 21-29, (2020)
Harith Yas
,
Manal M. Nasir
The Internet of Things (IoT) is an ever-expanding network of interconnected devices that enables various applications, such as smart homes, smart cities, and industrial automation. However, with the proliferation of IoT devices, security risks have increased significantly, making it necessary to develop effective intrusion detection systems (IDS) for IoT networks. In this paper, we propose an efficient IDS for complex IoT environments based on convolutional neural networks (CNNs). Our approach uses IoT traffics as input to our CNN architecture to capture representational knowledge required to discriminate different forms of attacks. Our system achieves high accuracy and low false positive rates, even in the presence of complex and dynamic network traffic patterns. We evaluate the performance of our system using public datasets and compare it with other cutting-edge IDS approaches. Our results show that the proposed system outperforms the other approaches in terms of accuracy and false positive rates. The proposed IDS can enhance the security of IoT networks and protect them against various types of cyber-attacks.
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Doi: https://doi.org/10.54216/JCIM.010105
Vol. 1
Issue. 1
PP. 30-37, (2020)
