The use of Unmanned Aerial Vehicles (UAVs), colloquially known as drones, has grown rapidly over the past two decades and continues to expand at a rapid pace. This has resulted in the production of many research papers addressing the use of UAVs in a variety of applications, such as forest firefighting. The main purpose of this paper is to provide a comprehensive overview of UAV-based forest-fire-extinguishing activity (FFEA) operations. To achieve this goal, a systematic literature review was conducted to answer a specific set of questions, which were carefully formulated to address the results of research conducted between 2008 and 2021. This study aims to (i) expand our understanding of the development of UAVs and their current contributions to the FFEA; (ii) identify particularly novel or unique applications and characteristics of UAV-based fire-extinguishing systems; (iii) provide guidance for exploring and revising further ideas in this field by identifying under-researched topics and other areas in which more contributions are needed; and (iv) explore the feasibility of using UAV swarms to enable autonomous firefighting in the forest without human intervention. Of the 1353 articles systematically searched across five databases (Google Scholar, ACM Digital Library, Science Direct, Scopus, and IEEE Explore), 51 highly relevant articles were found to meet the inclusion criteria; therefore, they were analyzed and discussed. The results identified several gaps in this field of study among them the complexity of coordination in multi-robotic systems, the lack of evaluation and implementation of fire extinguishing systems, the inability of handling multiple spot fires, and poor management of time and resources. Finally, based on the conducted review, this paper provides significant research directions that require further investigations by researchers in this field including, the deployment of UAV-based Swarm Robotics, further study on the characteristics of the fire extinguishing systems; design more effective area coverage; and the propose of a self-firefighting model that enables individuals to decide on the course of events efficiently and locally for better utilization and management of time and resources.

Wildfires are among the biggest problems faced worldwide. They are increasing in severity and frequency, causing economic losses, human death, and significant environmental damage. Environmental factors, such as wind and large forest areas, contribute to the fire spreading over multiple fire spots, all of which grow continuously, making fire suppression extremely difficult. Therefore, fire spots should be coverage simultaneously to contain the spread and prevent coalescence. Therefore, this study presents a new model based on the principles of nature-inspired metaheuristics that uses Swarm Intelligence (SI) to test the effectiveness of using an autonomous and decentralized behaviour for a swarm of Unmanned Aerial Vehicles (UAVs) or drones to detect all distributed fire spots and extinguishing them cooperatively. To achieve this goal, we used the improved random walk algorithm to explore the distributed fire spots and a self-coordination mechanism based on the stigmergy as an indirect communication between the swarm drones, taking into account the collision avoidance factor, the amount of extinguishing fluid, and the flight range of the drones. Numerical analysis and extensive simulations were performed to investigate the behaviour of the proposed methods and analyze their performance in terms of the area-coverage rate and total energy required by the drone swarm to complete the task. Our quantitative tests show that the improved model has the best coverage (95.3%, 84.3% and 65.8%, respectively) compared to two other methods Levy Flight (LF) algorithm and Particle Swarm Optimization (PSO), which use the same initial parameter values. The simulation results show that the proposed model performs better than its competitors and saves energy, especially in more complicated situations.

Business intelligence is a collection of methodologies, methods, architectures, and technologies that convert raw data into significant and useful information used by organizations to enable more effective strategic, tactical, and operational insights and decision-making. In spite of several studies have examined the critical success factors and development of business intelligence System, but few relevant studies have investigated perceptions of end-users Business Intelligence Systems. Furthermore, none of those studies was performed in a Higher Education Sector in Iraq. Consequently, the study aims to determine the business intelligence system features influencing perceived impact end users’ and of using business intelligence systems in Iraqi educational institutes. A technology acceptance model and technology organization environment framework were syntheses as a basis to develop a research model for business intelligence users' perceived impact and adopt of business intelligence systems named (SMUPI-BIS). Later, an online instrument (questionnaire) was designed to gather data from the business intelligence system users in five Iraqi universities. Twenty-one hypotheses were proposed and later tested. Then, for data analysis, the authors used several methods such as hierarchical regression, one-way ANOVA, descriptive statistics as well as structural equation modeling (SEM). The main outcomes of this study suggest that decision support, information quality, and real-time reporting are the most significant system characteristics influencing end users' perceived impact and their usage of business intelligence systems.

The satisfaction of E-learners has the main effect on the success of the E-learning process and leads to improvements in the E-learning system's quality and several factors affect this satisfaction. Based on the dimensions of e-learning, the main objective of this study was to evaluate the factors that contributed to students' satisfaction with e-learning during pandemic the Covid-19 and to give a thorough understanding and knowledge of different data mining techniques that have been used to predict student performance and development, as well as how these techniques help in the identification of the most relevant student attribute for prediction. Currently, to search for information in large databases, data mining techniques have become very popular and proven itis effective. Because of the performance and effectiveness of data mining techniques, it has been adopted by many areas such as telecommunication, education, sales management, banking, etc. In this paper, data mining algorithms were relied on to build e-learning classification models for a "student performance" data set, the proposed model includes 1000 instances with 35 attributes. Data mining algorithms have been implemented on the student performance data set in E-learning. Among these algorithms are the Decision Tree algorithm, Random Tree algorithm, Naive Bayes algorithm, Random Forest algorithm, REP Tree algorithm, Bagging algorithm and KNN algorithm. After comparing the results and conducting the assessment, the impact of the proposed features in e-learning on the student's performance was clarified. The final result of this study is important for providing greater insight into evaluating student performance in the COVID-19 pandemic and underscores the importance of data mining in education.

Wireless Sensor Nodes are interconnected sensor nodes, that communicate wirelessly to collect surrounding data such as temperature, sound, pressure, motion, or pollutants, and pass the data to the sink where it can be observed are processed. In our sensor nodes is non-rechargeable. Therefore, when a node dies, it causes an interruption in the sensor network as well as creates a huge energy wastage since the problem is faced in real time deployment. A lot of research has been done on WSNs until now. Scientists and researchers have come up with intelligent and efficient routing algorithms to increase the speed and efficiency of the network. One such factor that determines how good a network is, Lifetime. The proposed E-LEACH protocol has better performance in comparison to LEACH and TTDFP in this comparison. The improvement of the efficiency results from choosing the best communication protocol, which decreases packet loss significantly. Therefore, packet loss reduction would eliminate excessive energy-consuming transmissions. The E-LEACH performs about 60% higher than LEACH, while the TTDFP is 20% higher. However, TTDFP effectively transmits its application of a set of rules which take longer to make decisions.

The rapid increase in the growth of text information over the past two decades has led to the need for the use of text classification techniques, particularly in the area of information retrieval, data mining and data management. The precise results and simplicity of the K-Nearest Neighbor Classification Algorithm (K-NN) in knowledge mining is the reason that made it one of the most important classification algorithms used in many tasks such as pattern recognition, regression, and text classification. Through experiments and analysis of the results of the use of the traditional algorithm of the (K-NN), there are some deficiencies in their performance, especially when the data are large such as the algorithm was unable to process big data by rapid extraction with minimal storage space and generate useless samples computation and probability problems. In this paper, we have developed an enhanced algorithm and get the best results and perform better than that in the traditional algorithm. The significant improvement in our model performance is due to the improvement by removing unnecessary computational samples in the traditional algorithm. The performance is further improved by using the lost value computational method to define results as a prelude to avoid wasting time by correcting and filtering noise, examining the database, and eliminating unwanted records. Additionally, the inverse logarithmic function was used to solve the probability problems the algorithm encounters. The experimental results showed the efficiency of the modified algorithm in reducing the sample size and speeding up the search for the required data.