Journal of Cybersecurity and Information Management

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.

  The energy challenge in IoT refers to the significant energy consumption of IoT devices, which can lead to sustainability issues, shorter battery life, and increased operating costs. IoT devices are known for their high energy consumption, and optimizing their energy usage can have a significant impact on sustainability and cost. Machine learning (ML) can learn from data and patterns to predict and control energy consumption in IoT systems, making them more energy efficient. The main contribution of this paper is the establishment of a novel deep learning framework for enhanced predictive modeling of energy consumption in IoT networks to help realize Energy-efficient IoT systems. our framework applies recurrent processing to capture long-term relations in the energy consumption of IoT appliances. Then, the self-attention mechanism is devised to help the model to focus on important predictive features.  Simulation experiments against the competing ML baselines demonstrate the predictive capability of our framework. 

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.

Optimization is a more important field of research. With increasing the complexity of real-world problems, the more efficient and reliable optimization algorithms vital. Traditional methods are unable to solve these problems so, the first choice for solving these problems becomes meta-heuristic algorithms. Meta-heuristic algorithms proved their ability to solve more complex problems and giving more satisfying results. In this paper, we introduce the more popular meta-heuristic algorithms and their applications in addition to providing the more recent references for these algorithms.

The proliferation of the smart and sensing devices in the field of communicating networks support in to develop the so-called Internet of Things (IoT). IoT considers a new paradigm for evolutionary of internet connectivity. IoT refers to connect objects around the real world with the Internet to accomplish the common goals and monitor these objects via wire/wireless communications. It plays a large and important role in human life through its use in many applications of human interest. Through using a variety of enabling wireless technologies as Wireless Sensor Networks (WSN), Radio Frequency Identification (RFID), Near Filed Communication (NFC), and barcode in the applications. These technologies will support IoT to transform the internet into a fully integrated future internet. This paper attempts to provide a comprehensive survey of the available literature related to IoT technologies and its applications in many areas of modern-day living. Identify the trend and directions of future research in IoT applications, depend on a comprehensive literature review and the discussion of the achievements of the researchers.    

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.

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.

One of the most severe sources of information for a molecular biologist is the gel image generated by using gel electrophoresis during the experiment of issr-pcr, sds-pages, and rapd-pcr. DNA and protein gel images are obtained through the gel electrophoresis separations techniques of DNA and protein fragments. The separation of the polymorphic bands is based on the sizes of the negatively charged DNA fragments running from the negative cathode toward the positive anode. Each gel image has some vertical lanes; each lane corresponds to one sample and has several horizontal bands. The resulting images produced by Gel electrophoresis are sometimes difficult to interpret so that it was important to develop software tools to analyze the gel images to help biologists in the process of analyzing gel image as they draw their conclusions according to the results that generated from gel image analyzer software. In this article, we present a survey of some commercial and non-commercial software tools that are used for analyzing gel images. We develop a novel software for processing and analyzing the gel electrophoresis images, computing the molecular weights, saving them as excel sheet, clustering the bands based on their molecular weights using k-means algorithm, Applying band matching using a tolerance value entered by the user, determine the similarities between samples, drawing the corresponding phylogenetic tree, saving a report of the experiment as a pdf, and printing this report. The novel software will provide the biologist with the ability of manual processing, automatic processing, and semi-automatic processing.

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.

The state of the weather became a point of attraction for researchers in recent days. It control  in  many  fields  as  agriculture,  the  country  determines  the  types  of  crops  depend  on  state of the atmosphere. It is therefore important to know the weather in the coming days to take precautions. Forecasting the weather in future especially rainfall won the attention of many researchers, to prevent flooding and other risks arising from rainfall. This Paper presents a vigorous hybrid technique was applied to forecast rainfall by combining Particle Swarm Optimization (PSO) and  Multi-Layer  Perceptron  (MLP)  which  is  popular  kind  used  in  Feed Forward Neural Network (FFNN). The purpose of using PSO with MLP is not just to forecast the rainfall but, to improve the performance of the network;  this  was  proved  by  comparison  with  various  Back  Propagation  (BP)  an algorithm  such  as Levenberg-Marquardt (LM) through results of Root Mean Square Error (RMSE). RMSE for MLP based PSO is 0.14 while RMSE for MLP based LM is 0.18.