Search Results for internet-of-things-iot-
Abstract
Rapid evolution of the Internet of Things (IoT) has transformed the urban infrastructure because it is possible to interconnect systems that can improve transportation, healthcare, security, energy management, and public services. Implementation of IoT network in Iraqi cities faces numerous constraints including lack of infrastructural development, network saturation, unstable connections, poor cybersecurity and lack of resource management. These factors largely affect dependability and functionality of smart city applications. This paper examined how Artificial Intelligence (AI) methods applied to improve IoT network performance in Iraqi cities through effective data routing, predictive maintenance, anomaly detection, bandwidth optimization, and intelligent resource management. Machine learning, Deep Learning and reinforcement learning methods were commonly used methods to address the conditions of dynamic networks and maximize operational efficiency. The study aims at the application of AI techniques to strengthen current IoT systems to enhance scalability, minimize latency, enhance security, and energy efficiency. The research, based on analytical modeling and comparative performance analysis, shows that AI-based IoT systems significantly improve technological resilience of cities and assist Iraq in the development of smart cities. The results offer viable advice to policy makers, network engineers and urban planners who wish to develop modern cities in Iraq using intelligent technology.
Abstract
This study investigates the impact of Internet of Things (IoT) technologies on enhancing academic interaction between students and faculty members in higher education institutions, with an in-depth comparative analysis of the Iraqi context in relation to the Arab and global contexts. The study adopts a descriptive-analytical approach based on a literature review and comparative analysis, supported by statistical charts and a field survey. The findings revealed that IoT-enabled smart classrooms improved interaction indicators by between 10% and 96%. The global market reached USD 14.24 billion in 2024 and is projected to reach USD 48.26 billion by 2033, representing a compound annual growth rate of 15.3%. In contrast, the adoption rate of IoT in Iraqi public universities does not exceed 18%, compared with 68% globally, revealing a structural gap that requires urgent strategic intervention.
Abstract
This research aims to measure the impact of using Internet of Things (IoT) and machine learning techniques on achieving the characteristics of accounting information and the extent to which this is reflected in the conceptual statement/8.This is based on the ability of artificial intelligence techniques to solve business problems and overcome obstacles facing companies by using highly advanced devices and applications, as it is a system capable on simulating human intelligence. According to the importance and problem of the research, and for the purpose of testing its hypotheses, a questionnaire was designed using Google Forms and distributed electronically to the research sample members, who were selected using a deliberate (intentional) sample consisting of employees from the departments of (financial affairs, auditing and internal control). (50) responses were obtained. Using the Statistical Package for the Social Sciences (SPSS), the results of the questionnaire were analyzed. According to the applied frame wall of the research, a set of conclusions were reached, the most important of which was that artificial intelligence techniques achieve the qualitative characteristics of accounting information, as stated in Conceptual statement/8. The researchers presented a set of The most prominent recommendations include the adoption by the Accounting Department of the Basrah Municipality Directorate of artificial intelligence techniques when performing various accounting tasks.
Abstract
Smart universities constitute a complex environment in which thousands of Internet of Things (IoT) devices are interconnected, rendering them an increasingly attractive target for sophisticated cyberattacks. This study aims to design an integrated framework combining Artificial Intelligence (AI) and IoT technologies for the early detection of cyberattacks within smart university environments, employing machine learning and deep learning algorithms — namely Random Forest and a hybrid CNN-LSTM model — to classify network traffic and detect anomalous patterns in real time. The study adopted a descriptive-analytical approach supported by a field questionnaire administered to a sample of 150 participants from IT and network security personnel across universities. Results revealed excellent questionnaire reliability (Cronbach's Alpha = 0.927) and a strong positive correlation (r = 0.674) between cybersecurity awareness and AI detection effectiveness, alongside the superiority of the proposed model over traditional systems by an accuracy margin of approximately 25 percentage points (96.3% vs. 71.2%). The study recommends adopting a five-layer architectural framework integrating IoT devices, data processing, AI inference, attack detection, and real-time response layers as a viable model for deployment in Arab smart universities.