Systems based on fog computing produce massive amounts of data; accordingly, an increasing number of fog computing apps and services are emerging. In addition, machine learning (ML), which is an essential area, has gained considerable progress in various research domains, including robotics, neuromorphic computing, computer graphics, natural language processing (NLP), decision-making, and speech recognition. Several researches have been proposed that study how to employ ML to settle fog computing problems. In recent years, an increasing trend has been observed in adopting ML to enhance fog computing applications and provide fog services, like efficient resource management, security, mitigating latency and energy consumption, and traffic modeling. Based on our understanding and knowledge, there is no study has yet investigated the role of ML in the fog computing paradigm. Accordingly, the current research shed light on presenting an overview of the ML functions in fog computing area. The ML application for fog computing become strong end-user and high layers services to gain profound analytics and more smart responses for needed tasks. We present a comprehensive review to underline the latest improvements in ML techniques that are associated with three aspects of fog computing: management of resource, accuracy, and security. The role of ML in edge computing is also highlighted. Moreover, other perspectives related to the ML domain, such as types of application support, technique, and dataset are provided. Lastly, research challenges and open issues are discussed.INDEX TERMS Fog computing, machine learning, Internet of Things (IoT), applications.

Wireless Sensor Networks (WSNs) for healthcare have emerged in the recent years. Wireless technology has been developed and used widely for different medical fields. This technology provides healthcare services for patients, especially who suffer from chronic diseases. Services such as catering continuous medical monitoring and get rid of disturbance caused by the sensor of instruments. Sensors are connected to a patient by wires and become bed-bound that less from the mobility of the patient. In this paper, proposed a real-time heart pulse monitoring system via conducted an electronic circuit architecture to measure Heart Pulse (HP) for patients and display heart pulse measuring via smartphone and computer over the network in real-time settings. In HP measuring application standpoint, using sensor technology to observe heart pulse by bringing the fingerprint to the sensor via used Arduino microcontroller with Ethernet shield to connect heart pulse circuit to the internet and send results to the web server and receive it anywhere. The proposed system provided the usability by the user (user-friendly) not only by the specialist. Also, it offered speed andresults accuracy, the highest availability with the user on an ongoing basis, and few cost.

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

At present, Web applications have been used for most of our life activities increasingly, and they affected by Structured Query Language Injection Attacks (SQLIAs). This attack is a method that attackers employ to impose the database in most of the web applications, by manipulate SQL queries, which sent to the Relational Database Management System (RDBMS). Hence, change the behavior of the applications. In This paper, developing Web Application SQLI Protector (WASP) tool in real-time web application to detect SQL injection attacks in stored procedures. Then, evaluated and analyze the developed tool respect to efficiency and effectiveness in practices. The propose technique uses realtime based on positive tainting, accurate and efficiency taint propagation, and syntax aware evaluation of the query strings at the application level to detect illegal queries before they reach at the database by using Microsoft ASP.NET. The developed tool effective due to it capable of detect and stop all SQLI attacks in real-time environment and did not generate any false negative, a few-false positive values in the results and impose minimal deploy requirements.

<p class="JESTECStyleBodyTextIndentComplex10ptFirstline"><span>The tremendous recent involvement of technology in our life generates a lot of advantages and disadvantages. Nevertheless, and to profoundly augment its positive influence, at the expense of the negatives, most technology be deployed to serve humanity and society. Researchers attractive in developing robust and cooperative robotics that capable of solving difficult tasks without any human control. Metal detector robot is one of the robotics principles due to its effectiveness as compared with manually operated and very slow traditional methods. In this article, three main points that are concentrated 1) Design a robot which is vehicle-mounted sensors that capable of carries the sensors of the metal and obstacle; 2) Control and management system wirelessly by a computer-based to command the robot functions by several sets of user’s rules and manage the robot instructions; and 3) Conduct an integrated system that achieving navigated data via metal detector based on online structured query language database registry. Also, discussed a comparison of the previous detector systems and highlights on several merits. The proposed system capable of fully control the robot also, set the robot operator permissions and rules, stored and archived the navigated results and printed reports and stored in an independent database.</span></p>

Power consumption is considered one of the most significant challenges in the wireless network sensors (WSNs). In this paper, an investigation of the power consumption is done by making a comparison between static and dynamic WSNs. We have compared the results of the static network with the results of the dynamic network. Static and dynamic wireless Sensor networks have the same architecture (Homogenous) and proposed protocol. Depending on the suggested protocol, the simulation results show that the energy consumption in the static wireless sensor network was less than the dynamic wireless sensor network. However, moving the sensors in the dynamic WSN present real improvement in delivering packets to the base station. In the proposed routing protocol, transmitting data process is done in a hierarchal way. Cheap sensors are introduced and deploy them intensively to improve the QoS in the network. The final results and the conclusion are reported.

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