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The Internet of Things (IoT) has the potential to revolutionize the education sector by creating smart learning environments that enable personalized learning experiences, improved teacher-student interactions, and enhanced collaboration. The traditional education system has been based on a one-size-fits-all approach, which may not be the most effective for all students. IoT-based education can provide personalized learning experiences that are tailored to individual students' needs and abilities. IoTenabled devices can collect data on students' learning habits, preferences, and progress, which can be used to create customized learning paths that meet the specific needs of each student. Moreover, IoT in education can enhance collaboration among students and between students and teachers. However, its adoption and integration into the traditional education system pose significant challenges, including privacy concerns, cybersecurity risks, and the need for technical expertise to manage and maintain the IoT-enabled devices. This paper provides a comprehensive review of the literature on IoT in education, with a focus on its potential benefits, challenges, and implications. The paper also discusses the current state of IoT in education, and provides recommendations for future research on IoT in education. Overall, this paper highlights the potential of IoT to transform education, but also emphasizes the need for careful consideration of the challenges and implications associated with its adoption. [ABSTRACT FROM AUTHOR]

With the advent of social networks, the users have obtained a golden opportunity to express their opinions using text and multimedia. However, some users abused these platforms by introducing acts such as Cyber-Bullying and Cyber-Harassment. Despite the various negative health and social effects, the works proposed toward the detection of these acts are still limited, especially in non-English languages. In Arabic, few works studied this phenomenon. These works had limited datasets. As the number of available training datasets are limited, it is still hard to train classifiers to detect these acts. Therefore, clustering has posed as an alternative solution to tackle this difficulty. In this work, we propose the use of clustering to detect Cyber-Bullying and Cyber-Harassment. We adopted various clustering algorithms including K-Means and Expectation Maximization (EM). Moreover, we used various natural language processing (NLP) tools for this objective. The results illustrate that the training time of K-Means is significantly smaller than that of EM in all the conducted experiments. As for the accuracy, the two clustering methods showed different performance based on the variance in the used NLP settings. [ABSTRACT FROM AUTHOR]

In recent years, the use of social media has rapidly increased and developed significant influence on its users. In the study of the behavior, reactions, approval, and interactions of social media users, detecting the polarity (positive, negative, neutral) of news posts is of considerable importance. This proposed research aims to collect data from Arabic social media pages, with the posts comprising the main unit in the dataset, and to build a corpus of manually-processed data for training and testing. Applying Natural Language Processing to the data is crucial for the computer to understand and easily manipulate the data. Therefore, Stop-Word removal, Stemming, and Normalization are applied. Several classifiers, such as Support Vector Machine, Naïve Bayes, K-Nearest Neighbor, Random Frost, and Decision Tree are used to train the dataset, and their accuracy is determined by data testing. These two steps are carried out using the open-source WEKA tool. As a result, each post is categorized into three different classes: positive, negative, and neutral. This research concludes that among the classifiers, SVM reaches the highest level of accuracy with a percentage of 83% for the F1-measure. [ABSTRACT FROM AUTHOR]

Forests are considered an essential part from the nature and the environmental system since it forms the key ecological contributor in the balance of the nature. Unfortunately, this balance is greatly affected by forest fires that regularly happen in all forest all over the world. These fires have a long-term effect on money, environmental and social aspects. There is usually a loss in human lives besides properties' destruction and hence uninterrupted effect on individual or collective level occur. A great deal of attention was recently given for developing the suitable systems that can reactively and proactively act against these fires. In fact, WSNs is considered one of the promising solution to be used in detecting and mentoring the forest fires efficiently. Different schemes for forests fire detection based on WSNs was proposed in the literature. our proposed system has a distinctive benefit since it gathers between detection and fire behavior prediction analysis. The early detection before its too late using novel designed system for sensor coverage and fire detection with a reduced faulty alarm and distinguishing ability between argent alarm and no action required alarms. Our techniques extended the network life time almost 3 times in comparison with other techniques. [ABSTRACT FROM AUTHOR]