Search Results for deep-learning
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
The current research presents the idea of using deep learning tools and employing them in financial aspects due to their significant role and ability to explore unobservable aspects in light of financial models governed by a set of restrictions, conditions and linear relationships. On the other hand, the nature of financial data that tends to be non-linear and suffers from the missing of monthly closing prices, which imposes a state of data loss. All of this provides preference for deep learning models, including the neural network tool. The research aims to estimate financial returns in light of the capital asset pricing model CAPM as a financial model and neural networks as a deep learning tool in addition to the mask & padding tool to address the problem of missing data. The knowledge gap was determined by the inability of the capital asset pricing model to explore hidden and invisible aspects and overcome non-linear relationships. The research sample consisted of 42 organizations listed on the Iraq Stock Exchange for the period from 1/1/2021 to 31/12/2024 with 60 observations. The research concluded that the neural network tool is able to overcome the determinants in light of financial models and provide accurate estimates of returns are close to estimates under the capital asset pricing model.
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.