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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]

PURPOSE Oncology education at medical schools is often fragmented, under-represented, and nonstandardized. Medical students lack essential knowledge, skills, and attitudes necessary for them to provide optimal primary care to patients with cancer upon graduation. METHODS In this study, we designed and assessed the impact of a compact oncology teaching conference on medical students. The conference covered topics in cancer biology, public health, diagnosis, patient management, treatment, and communication skills. Medical students completed voluntary presurveys and postsurveys regarding their perceived knowledge, attitudes, and perspectives. The event was promoted by student groups, particularly our ASCO Oncology Student Interest Group, and took place online. RESULTS A total of 228 responses from medical students representing 50 universities were analyzed. We revealed significant baseline confidence and perceived knowledge deficits especially in diagnosis and patient management, and treatment modalities. Our conference positively affected self-assessed knowledge acquisition among students, with the most pronounced differences seen in diagnosis and patient management (2.51 6 1 v 3.87 6 0.81) and treatment modalities (2.54 6 0.96 v 3.79 6 0.88), P < .001. Students believed the program was beneficial, felt more confident in applying their knowledge in the clinic and in delivering bad news, and were more interested in pursuing oncology-related fields, P < .001. CONCLUSION We demonstrated that a compact teaching conference resulted in significant improvements in students' confidence and perceived knowledge about oncology. Our successful teaching model can be adapted and implemented at medical schools globally. Development and evaluation of teaching programs are important to urgently reform undergraduate medical education in oncology. [ABSTRACT FROM AUTHOR]

Since the early days of 20 20, COVID-19 has tragic effects on the lives of human beings all over the world. To combat this disease, it is important to survey the infected patients in an inexpensive and fast way. One of the most common ways of achieving this is by performing radiological testing using chest X-Rays and patient coughing sounds. In this work, we propose a Convolutional Neural Network-based solution which is able to identify the positive COVID-19 patients using chest X-Ray images. Multiple CNN models have been adopted in our work. Each of these models provides a decision whether the patient is affected with COVID-19 or not. Then, a weighted average selection technique is used to provide the final decision. To test the efficiency of our model we have used publicly available chest X-ray images of COVID positive and negative cases. Our approach provided a classification performance of 88.5%. [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]

Due to the increasing use of social media applications, it is essential to ensure that the connection is secure because sometimes it is required to fill secret information such as username and password. Many anonymous messages reach to the computer client, which may contain Trojan, virus (Malware). If the user is not an expert in the field of information security, then the user will open the incoming messages without ensuring its integrity, which allows hackers to enter the devices and planting malicious software in the client devices without their knowledge. In this paper, we will identify the concept of syntactic attack and its types, which leads to Cyber-Bullying attack--and also discussing the effect of penetration of devices on the Jordanian society. As shown in the result, the most common electronic crimes are crimes related to defamation, threats and extortion. The results also show the prevalence of Cyber-crime in densely populated places. Besides, Cyber-crime decreases in the regions where clans and tribes live. At the same time, the most age groups that have been exposed to electronic crimes are the groups 18-29 and 30-44. [ABSTRACT FROM AUTHOR]

Recommender systems are used to suggest items to users based on their interests. They have been used widely in various domains, including online stores, web advertisements, and social networks. As part of their process, recommender systems use a set of similarity measurements that would assist in finding interesting items. Although many similarity measurements have been proposed in the literature, they have not concentrated on actual user interests. This paper proposes a new efficient hybrid similarity measure for recommender systems based on user interests. This similarity measure is a combination of two novel base similarity measurements: the user interest–user interest similarity measure and the user interest–item similarity measure. This hybrid similarity measure improves the existing work in three aspects. First, it improves the current recommender systems by using actual user interests. Second, it provides a comprehensive evaluation of an efficient solution to the cold start problem. Third, this similarity measure works well even when no corated items exist between two users. Our experiments show that our proposed similarity measure is efficient in terms of accuracy, execution time, and applicability. Specifically, our proposed similarity measure achieves a mean absolute error (MAE) as low as 0.42, with 64% applicability and an execution time as low as 0.03 s, whereas the existing similarity measures from the literature achieve an MAE of 0.88 at their best; these results demonstrate the superiority of our proposed similarity measure in terms of accuracy, as well as having a high applicability percentage and a very short execution time. [ABSTRACT FROM AUTHOR]

Medical images have a very significant impact in the diagnosing and treating process of patient ailments and radiology applications. For many reasons, processing medical images can greatly improve the quality of radiologists' job. While 2D models have been in use for medical applications for decades, wide-spread utilization of 3D models appeared only in recent years. The proposed work in this paper aims to segment medical volumes under various conditions and in different axel representations. In this paper, we propose an algorithm for segmenting Medical Volumes based on Multiresolution Analysis. Different 3D volume reconstructed versions have been considered to come up with a robust and accurate segmentation results. The proposed algorithm is validated using real medical and Phantom Data. Processing time, segmentation accuracy of predefined data sets and radiologist's opinions were the key factors for methods validations. [ABSTRACT FROM AUTHOR]

Arabic news articles in electronic collections are difficult to study. Browsing by category is rarely supported. Although helpful machine-learning methods have been applied successfully to similar situations for English news articles, limited research has been completed to yield suitable solutions for Arabic news. In connection with a Qatar National Research Fund ( QNRF)-funded project to build digital library community and infrastructure in Qatar, we developed software for browsing a collection of about 237,000 Arabic news articles, which should be applicable to other Arabic news collections. We designed a simple taxonomy for Arabic news stories that is suitable for the needs of Qatar and other nations, is compatible with the subject codes of the International Press Telecommunications Council, and was enhanced with the aid of a librarian expert as well as five Arabic-speaking volunteers. We developed tailored stemming (i.e., a new Arabic light stemmer called P- Stemmer) and automatic classification methods (the best being binary Support Vector Machines classifiers) to work with the taxonomy. Using evaluation techniques commonly used in the information retrieval community, including 10-fold cross-validation and the Wilcoxon signed-rank test, we showed that our approach to stemming and classification is superior to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]