Search

هدف البحث التعرف الى اسباب سلوك التنمر لدى طلاب الصف الاول المتوسط من وجهة نظر المدرسين والمدرسات واساليب تعديله، واستعمل الباحثان المنهج الوصفي واختيار عينة عشوائية من المدرسين والمدرسات في متوسطة أرض الرافدين ومتوسطة الرحمن للبنين وكان عددهم (46) مدرساً ومدرسة بواقع (32) مدرساً و(14) مدرسة، واعتمد الباحثان الاستبانة أداة للتعرف الى اسباب سلوك التنمر واساليب تعديله، واشارت نتائج البحث الى تنوع اسباب التنمر : ذاتية واسرية ومرتبطة بالبيئة المدرسية ومرتبطة بالبيئة المجتمعية المحيطة ومرتبطة بالإعلام والثورة التقنية ، اذ تضمنت الاستبانة (45) سبباً من أسباب سلوك التنمر المدرسي ، واشتملت استبانة اساليب تعديل سلوكيات التنمر المدرسي على (32) اسلوباً مرتبة تنازليا بحسب وسطها المرجح ووزنها المئوي، وفي ضوء نتائج البحث تبلورت بعض الاستنتاجات والتوصيات والمقترحات

The Internet of Things forensics is a specialised field within digital forensics that focuses on the identification of security incidents, as well as the collection and analysis of evidence with the aim of preventing future attacks on IoT networks. IoT forensics differs from other digital forensic fields due to the unique characteristics of IoT devices, such as limited processing power and connectivity. Although numerous studies are available on IoT forensics, the field is rapidly evolving, and comprehensive surveys are needed to keep up with new developments, emerging threats, and evolving best practices. In this respect, this paper aims to review the state of the art in IoT forensics and discuss the challenges in current investigation techniques. A qualitative analysis of related reviews in the field of IoT forensics has been conducted, identifying key issues and assessing primary obstacles. Despite the variety of topics and approaches, common issues emerge. The majority of these issues are related to the collection and pre-processing of evidence because of the counter-analysis techniques and challenges associated with gathering data from devices and the cloud. Our analysis extends beyond technological problems; it further identifies the procedural problems with preparedness, reporting, and presentation as well as ethical issues. In particular, it provides insights into emerging threats and challenges in IoT forensics, increases awareness and understanding of the importance of IoT forensics in preventing cybercrimes, and ensures the security and privacy of IoT devices and networks. Our findings make a substantial contribution to the field of IoT forensics, as they not only involve a critical analysis of the challenges presented in existing works but also identify numerous problems. These insights will greatly assist researchers in identifying appropriate directions for their future research.

The sneaker industry is continuing to expand at a fast rate and will be worth over USD 120 billion in the next few years. This is, in part due to social media and online retailers building hype around releases of limited-edition sneakers, which are usually collaborations between well-known global icons and footwear companies. These limited-edition sneakers are typically released in low quantities using an online raffle system, meaning only a few people can get their hands on them. As expected, this causes their value to skyrocket and has created an extremely lucrative resale market for sneakers. This has given rise to numerous counterfeit sneakers flooding the resale market, resulting in online platforms having to hand-verify a sneaker's authenticity, which is an important but time-consuming procedure that slows the selling and buying process. To speed up the authentication process, Support Vector Machines and a convolutional neural network were used to classify images of fake and real sneakers and then their accuracies were compared to see which performed better. The results showed that the CNNs performed much better at this task than the SVMs with some accuracies over 95%. Therefore, a CNN is well equipped to be a sneaker authenticator and will be of great benefit to the reselling industry.

Internet scams are fraudulent attempts aim to lure computer users to reveal their credentials or redirect their connections to spoofed webpages rather than the actual ones. Users' confidential information, such as usernames, passwords, and financial account numbers, is the main target of these fraudulent attempts. Internet scammers often use phishing attacks, which have no boundaries, since they could exceed hijacking conventional cyber ecosystems to hack intelligent systems, which emerged recently for the use within smart cities. This paper therefore develops a real-time framework inspired by the honeybee defense mechanism in nature for filtering phishing website attacks in smart cities. In particular, the proposed framework filters phishing websites through three main phases of investigation: PhishTank-Match (PM), Undesirable-Absent (UA), and Desirable-Present (DP) investigation phases. The PM phase is used at first in order to check whether the requested URL is listed in the blacklist of the PhishTank database. On the other hand, the UA phase is used for investigation and checking for the absence of undesirable symbols in uniform resource locators (URLs) of the requested website. Finally, the DP phase is used as another level of investigation in order to check for the presence of the requested URL in the desirable whitelist. The obtained results show that the proposed framework is deployable and capable of filtering various types of phishing website by maintaining a low rate of false alarms.

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people's lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems' effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.

The massive growth of mobile users will spread to significant numbers of small cells for the Fifth Generation (5G) mobile network, which will overlap the fourth generation (4G) network. A tremendous increase in handover (HO) scenarios and HO rates will occur. Ensuring stable and reliable connection through the mobility of user equipment (UE) will become a major problem in future mobile networks. This problem will be magnified with the use of suboptimal handover control parameter (HCP) settings, which can be configured manually or automatically. Therefore, the aim of this study is to investigate the impact of different HCP settings on the performance of 5G network. Several system scenarios are proposed and investigated based on different HCP settings and mobile speed scenarios. The different mobile speeds are expected to demonstrate the influence of many proposed system scenarios on 5G network execution. We conducted simulations utilizing MATLAB software and its related tools. Evaluation comparisons were performed in terms of handover probability (HOP), ping-pong handover probability (PPHP) and outage probability (OP). The 5G network framework has been employed to evaluate the proposed system scenarios used. The simulation results reveal that there is a trade-off in the results obtained from various systems. The use of lower HCP settings provides noticeable enhancements compared to higher HCP settings in terms of OP. Simultaneously, the use of lower HCP settings provides noticeable drawbacks compared to higher HCP settings in terms of high PPHP for all scenarios of mobile speed. The simulation results show that medium HCP settings may be the acceptable solution if one of these systems is applied. This study emphasises the application of automatic self-optimisation (ASO) functions as the best solution that considers user experience.

A real-time roundabout detection and navigation system for smart vehicles and cities using laser simulator-fuzzy logic algorithms and sensor fusion in a road environment is presented in this paper. A wheeled mobile robot (WMR) is supposed to navigate autonomously on the road in real-time and reach a predefined goal while discovering and detecting the road roundabout. A complete modeling and path planning of the road's roundabout intersection was derived to enable the WMR to navigate autonomously in indoor and outdoor terrains. A new algorithm, called Laser Simulator, has been introduced to detect various entities in a road roundabout setting, which is later integrated with fuzzy logic algorithm for making the right decision about the existence of the roundabout. The sensor fusion process involving the use of a Wi-Fi camera, laser range finder, and odometry was implemented to generate the robot's path planning and localization within the road environment. The local maps were built using the extracted data from the camera and laser range finder to estimate the road parameters such as road width, side curbs, and roundabout center, all in two-dimensional space. The path generation algorithm was fully derived within the local maps and tested with a WMR platform in real-time.