We introduce a smart multi-objective optimization framework towards the performance enhancement of the 4G LTE network using an integrated Artificial Intelligence (AI) and Whale Optimization Algorithm (WOA) setup. This approach aims to integrate tested regression-based KPI prediction along with decision-variable-based meta-heuristic optimization in a reproducible experimental environment within MATLAB R2023b. A weighted multi-objective formulation for optimizing the performance of 4 key indicators, i.e., throughput, latency, packet loss ratio, and energy efficiency, is jointly executed. By comparison against existing baseline LTE configuration, we observe a 25.3% throughput improvement, 28.9% reduction in latency, 38.7% reduction in packet loss ratio, and 23.4% improvement in energy efficiency. We validate robustness with weight sensitivity analysis (±10% and ±20%), population scalability testing (N = 20, 30, 50), decay-strategy comparison (linear vs. exponential), and Pareto-front approximation (2,960 non-dominated solutions from 3,000 samples). The above results corroborate that, after approximately 70 iterations, convergence is stable and performance is consistent over the span of 30 independent runs. Thus, the proposed AI–WOA construction offers an organized and replicable process to fine-tune LTE performance optimization, a basis for downstream implementations towards a more distributed and next-generation wireless environment.

Cloud computing has swiftly become a prominent trend in the technology industry, owing to its various benefits such as scalability, high availability, and cost-effectiveness. Consequently, an increasing number of organizations are shifting their operations to cloud-based environments. It is anticipated that cloud technologies will soon form a foundational component of nearly every enterprise. This transformation has significantly influenced sectors like Software as a Service (SaaS), where traditional Database Management Systems (DBMSs) have transitioned into Cloud-based DBMSs (CDBMSs). Alongside this evolution, there has been a movement from conventional single-tenant database structures to multi-tenant architectures, which allow for more efficient utilization of infrastructure and resources. However, despite these benefits, many organizations are still hesitant to adopt multi-tenant database systems due to persistent concerns over data security. The practice of storing data from multiple tenants on the same server—or even within shared tables—introduces considerable risks related to unauthorized access. In response to these concerns, the current research centers on database security in cloud settings, with a specific focus on the unique challenges associated with multi-tenant systems.

The ship's event-triggered tracking algorithm, which combines game theory and remote sensing. The technique addresses issues with conventional continuous and discrete control techniques, which consume excessive amounts of energy and prematurely wear out actuators due to frequent updates. In order to locate ships, this paper first employs the YOLOv8 deep neural network and radar-based remote sensing. After determining the distances and connections between ships, the target trajectory is altered using a game-theoretic framework. In order to reduce needless computations and communications, the controller only activates when significant events take place. This is not the same as conventional time-triggered techniques. Good results were obtained from tests on two benchmark datasets: the Synthetic Aperture Radar Ship Dataset (SAR-Ship-Dataset) and the SAR Ship Detection Dataset (SSDD). On SSDD, the proposed algorithm achieved 92% accuracy and 93% sensitivity, while on SAR-Ship-Dataset, it achieved 93% accuracy and 91% sensitivity. These results lay the foundation for the development of more efficient marine tracking systems by demonstrating that event-triggered tracking can maintain high tracking accuracy while using less energy.

The Internet of Things (IoT) has risen rapidly and created a need for computing architectures with the ability to aid real-time applications with the least latency. Conventional cloud-only setups often experience high latency due to physical distance and network congestion, thus, unsuitable for time-sensitive applications. Four of the architectural models currently proposed in the literature are assessed in the present paper: Cloud-only, Edge-only, Fog Multi-layer, and Smart Hybrid; each of them is evaluated in a large-scale simulation aimed at simulating a smart city environment with 1,000 various IoT devices, such as sensors, cameras, and wearables generating dynamic traffic. The outcomes indicate that Edge-only architecture recorded the least mean latency of 0.2417 seconds compared to Smart Hybrid with 0.3156 seconds and Fog Multi-layer with 0.3874 seconds mean latencies whereas the Cloud-only model presented the highest latency with 0.5231 seconds. Given that there existed significant differences between the architectures (p < 0.0001) based on the results of the statistical analysis carried using the one-way ANOVA. Such results explain the promise of adaptive and context-sensitive hybrid systems and allow reducing latency in any IoT deployment in the future and designing an IoT-oriented system that supports latency-sensitive applications in smart cities in a more scalable manner.

International trade is considered the central link in the complex system of contemporary international economic relations. It links all countries of the world in a unified economic system whose goal is to address economic problems at the international level through developing productive capacity, expanding employment opportunities, and enhancing the flow of production factors between countries to achieve economic growth. Our study aimed to clarify the considerable impact of some macroeconomic variables (exchange rate, gross domestic product, public spending, foreign direct investment) on foreign trade in Iraq and to determine the degree of relative importance of the macroeconomic variables affecting foreign trade in Iraq. The study followed the descriptive analytical approach by collecting data related to the study for the period from 2003 to 2020 and then analyzing the data obtained through artificial neural networks.

Boosting algorithms are a set of machine learning techniques that are predicated on the notion that a weak learner's acquisition of multiple basic classifiers might yield results that are superior to those of any one simple classifier used alone. A comprehensive evaluation of regularly used boosting techniques against highly investigated diseases is lacking, despite the fact that boosting approaches have been used for disease prediction in many studies. Thus, the purpose of this work is to highlight the main algorithms and strategies in the boosting learning. The results of this work will help academics identify a more appropriate boosting approach to predict disease, as well as better understand current patterns and hotspots in diseases prediction models that use boosting learning. The results showed that adaboost algorithm outperformed other algorithms in terms of accuracy, achieving above 90%. This review also demonstrates how combining two boosting methods can increase the basic classifier's accuracy. By using AdaBoost and LightGBM, the accuracy reached 99.75%. XGBoost and Gradient Boosting techniques were employed more frequently in researches than other boosting algorithms.

Numerous sensor nodes make up a wireless sensor network. Sensor nodes work together to send information about the environment to the base station. Nodes are susceptible to failure during these operations due to power failure, hardware or software failure, etc.. Consequently, delivering dependable packages necessitates achieving two important objectives: energy efficiency and minimized error rate. A novel approach is proposed to distribute the workload among each sensor node to accomplish these objectives. When estimating the scope of communication between nodes, uncertainties are dealt with using the fuzzy logic method. During some weather hazards, the features and cables are particularly challenging to monitor with simple devices in the WAN area. Remote sensing units are used to check and display the feature conditions to resolve this issue. Using machine learning algorithms, the collected data will be analyzed and processed to create a prediction horizon that will be useful for environmental studies and early warning systems. The MatLab2020b simulation program, which has useful tools and learning applications, will be used to carry out this task. The FIS algorithm obtained a cost distance of just 629 m out of the total WSN sensor lengths of 2600 m, which is the best cost among all implemented routing protocol techniques in this study.

This study investigates how decentralization and transparency offered by blockchain technology could revolutionize traditional finance. Even with the rise of well-known cryptocurrencies such as Bitcoin and Ethereum, a general understanding of blockchain’s influence on the financial industry is still lacking. We identified five major application cases—transparent credit scoring, effective consumer identification, expedited insurance settlements, improved cybersecurity, and the emergence of decentralized finance—where blockchain technology is well positioned to tackle persistent issues. We show how blockchain technology may address problems such as opaque credit scoring, poor customer identity, convoluted insurance settlement procedures, and susceptibility to cyberattacks by thoroughly examining various use cases. According to our research, a greater number of traditional financial institutions need to embrace and integrate blockchain innovations into their functions to promote inclusivity, transparency, and decentralization.

This study describes a lightweight deep learning model trained on a self-made image dataset taken inside farms and open areas of the Holy Shrine of Al-Hussainiya in the City of Karbala, Iraq. This dataset includes fire and smoke images taken using a Samsung A52S camera in different weather conditions. The overall goal is to create a fire detection system model that can successfully replace the existing physical sensor-based fire detectors and lessen the issues that come with such fire detectors, including false and delayed triggering. Another goal is to control fires on farms or open areas and prevent crop damage as much as possible. Previous studies were reviewed. Moreover, the architecture of the You Only Look Once version 8 (YOLOv8) model was briefly explained, and the results it achieved were compared with those achieved by previous versions. Then, the proposed system was trained and evaluated with the YOLOv8 large model. Results showed that the proposed system outperformed the rest of the current systems in mAP, which reached 98.5%.

Iraqi Journal for Computers and Informatics is an open access, double-blind peer-reviewed, international academic journal published biannually (since 2017). It is an institutional journal issued by the University of Information Technology and Communications (UoITC) in Baghdad- Iraq. IJCI aims to contribute to the constant scientific research and to provide a sober scientific journal that enriches scholars around the world.