Journal of Intelligent Systems and Internet of Things

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2690-6791ISSN (Online) 2769-786XISSN (Print)

Hybrid Machine Learning Model for Rainfall Forecasting

Hatem Abdul-Kader , Mustafa.Abd-El salam , Mona Mohamed

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.  

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Vol. 1 Issue. 1 PP. 5-12, (2020)

A comparative study on Internet of Things (IoT): Frameworks, Tools, Applications and Future directions

Mona Mohamed

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.    

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Vol. 1 Issue. 1 PP. 13-39, (2020)

A survey on gel images analysis software tools

Mahmoud H.Alnamoly , Ahmed M. Alzohairy , Ibrahim M. El-Henawy

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.

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Vol. 1 Issue. 1 PP. 40-47, (2021)

A Survey on Meta-heuristic Algorithms for Global Optimization Problems

Abdel Nasser H. Zaied, Mahmoud Ismail and Salwa El- Sayed*

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.

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Vol. 1 Issue. 1 PP. 48-60, (2020)

A Machine Learning Approach for Energy-Efficient IoT Systems

Mahmoud M. Ismail

  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. 

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Vol. 1 Issue. 1 PP. 61-69, (2020)