Iraqi Journal for Computers and Informatics
Not a member yet
273 research outputs found
Sort by
Blockchain Metamorphosis: Transforming Traditional Finance through Decentralization and Transparency
Full text linkThis 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
Breast Cancer Detection Using Deep Learning
Full text linkThis research aims to develop an image classification model by integrating long short-term memory (LSTM) with a convolutional neural network (CNN). LSTM, which is a type of neural network, can retain and retrieve long-term dependencies and improves the feature extraction capabilities of CNN when used in a multi-layer setting. The proposed approach outperforms typical CNN classifiers in image classification. The model’s high accuracy is due to the data passing through two stages and multiple layers: first the LSTM layer, followed by the CNN layer for accurate classification. Convolutional and recurrent neural networks are combined in the recommended model, which demonstrates exceptional performance on various classification tasks. The model achieved a training accuracy of 0.9899 and testing accuracy of 0.9463 using real data, which indicates its success and applicability compared with other models
Feature Selection Techniques in Intrusion Detection: A Comprehensive Review
Full text linkThis investigation aims to explore previous research on the implementation of feature selection in intrusion detection. Feature selection has demonstrated its ability to enhance or sustain comparable classification accuracy levels for intrusion detection systems, while simultaneously improving classification efficiency. The evaluation includes an assessment of filter-based, wrapper-based, and hybrid feature selection techniques. Given that Big Data challenges can affect intrusion detection, feature selection’s classification efficiency can aid in lowering computing requirements. Older KDD intrusion detection datasets have received considerable attention in previous feature selection research. Consequently, researchers need more high-quality datasets that are available to the general public
Writer Independent Offline Signature Verification System using Global and Local Geometric Features
Full text linkThe objective of this paper is to propose an offline signature verification system (OSVS) designed for writer-independent applications. The system shall differentiate between original and forged signatures. The system encompasses key stages such as pre-processing, feature extraction, and model training and testing, employing the Support Vector Machine algorithm for classification. One challenge in creating a good OSVS is to find proper signature features to be used in the training/classification phases. In this research, global and local features are utilized. This includes signature area, mean, standard deviation, perimeter, number of connected components, number of vertical and horizontal edges, number of end points, number of branch points, and number of lines. The contributions of this paper are on several aspects of the offline signature verification process. Investigation in this study include data pre-processing techniques (normalization vs. standardization), kernel selection (Poly vs. RBF), dataset distribution for training and testing (80%-20% vs. 5-fold), and variations of the C and gamma parameters (C=1, 10, 100 and gamma=1, 10, 100). Improving the recognition rate involves removing of features with little or bad effect on the recognition rate. An algorithm for model-agnostic feature importance is executed, revealing that the most crucial features in the classification process are mean, standard deviation, perimeter, number of connected components, and number of end points. Signatures are classified as either original or forgery, and the model\u27s performance is assessed on the CEDAR dataset. Experimental results shows a 95.65% accuracy of the proposed system when utilizing standardization with the RBF kernel
Maximum Error Insertion (MEI): A Novel Benchmarking Method for Data Hiding Algorithms
Full text linkData hiding is becoming increasingly important due to the growing threat to the privacy and security of data from intruders and hackers. This situation is accompanied by the advancement in artificial intelligence applications designed to reveal hidden data, making it difficult to choose the most appropriate hiding approach from those presented in literature. The benchmarking method serves as an important roadmap for making decisions. We propose a distinct plain benchmarking method called maximum error insertion (MEI) benchmarking. This approach intends to hide data using maximum error insertion. The MEI refers to the maximum amount of distortion that can be added to host data (such as image or audio) while still ensuring the successful retrieval of hidden data. The maximum error that can be generated by each hiding algorithm is intentionally inserted to the media file, thus giving us maximum error, maximum capacity, and maximum sensitivity to signal processing attacks. Investigation of the two hiding algorithms demonstrates their applicability and precision, and their implementation significantly enhances the reliability of results during the benchmarking stage
Forecasting Energy Consumption in Smart Grids: A Comparative Analysis of Recurrent Neural Networks
Full text linkIn the present era of smart grids, accurate prediction of energy uses is becoming increasingly essential to guarantee optimal energy efficiency. This study contributes to the field by utilizing advanced machine learning techniques to perform predictions of energy consumption using the data from Internet of Things (IoT) devices. Specifically, the approach utilizes regression neural network (RNN) structures, such as long short-term memory (LSTM) and gated recurrent units (GRUs). The data from IoT sensors are more extensive and detailed than those of conventional smart meters, allowing for the development of more complex models of energy use patterns. This study utilizes Adam-optimized LSTM, RNN, and GRU models, along with stochastic gradient descent, to evaluate their performance in addressing the complexity of time-series data in energy forecasting on different network configurations. Result of the analysis indicates that LSTM models, which are run with the Adam optimizer, are more accurate in terms of predictions compared with the other models. This conclusion is supported by the test results of these models that are within the lowest root mean square error and mean absolute error scores. All the models under the analysis exhibit signs of overfitting based on the performance indicators for the training and the testing data. This notion implies that regularization should be utilized to ensure the improved generalizability of the models. These findings show that deep learning can have a lasting influence in improving energy consumption management systems to meet sustainability and energy efficiency requirements. These observations are beneficial for the gradual improvements of smart grids
Machine Learning and Computer Vision Techniques in Self-driving Cars
Full text linkThis study explores the remarkable advancements in self-driving vehicles achieved through the application of computer vision and machine learning techniques. We examine various algorithms designed for critical functions, such as object detection, image segmentation, behavior prediction, and adaptive learning, which are all integral components of autonomous driving systems. Our research highlights key performance metrics, emphasizing accuracy, efficiency, and safety. Simulated environments and real-world testing are essential for validating the effectiveness of these methodologies.Our findings underscore the transformative potential of self-driving technology in enhancing transportation safety and its far-reaching effect on numerous industries. Notably, self-driving cars demonstrate the ability to reduce traffic accidents and improve traffic flow, which can lead to substantial economic and social benefits. Moreover, we discuss future research avenues, including the enhancement of system robustness and safety measures, the improvement of human–AI interaction, and the utilization of edge computing and edge AI. We also address the ethical and regulatory challenges associated with the widespread adoption of autonomous vehicles.Our comprehensive analysis indicates that self-driving technology is poised to revolutionize the transportation sector, offering safer, more efficient, and more accessible mobility solutions. As technology continues to evolve, ongoing research and development will be crucial in overcoming current limitations and realizing the full potential of autonomous driving systems
Enhanced Hybrid Algorithm for E-AbdulRazzaq and Fast Online Hybrid Matching Algorithms for Exact String Matching
Full text linkAlgorithms for string matching are considered one of the most extensively researched topics in the field of computer science due to their substantial role in various applications, such as information retrieval, editing, security, firewalls, and biological applications. String matching involves examining the optimal alignment by comparing the characters in the pattern and the text. Over the past two decades, it has gained considerable attention due to technological advancements. The need to address string-matching problems has also emerged because of its wide-ranging applications. This study presents the E-ARFO hybrid string-matching algorithm, which combines the best features of two original algorithms, namely, E-AbdulRazzaq and fast online hybrid matching. Compared with other algorithms, the proposed method demonstrates outstanding performance in terms of the number of attempts and character comparisons conducted across multiple databases, including DNA and protein sequences. Results indicate that irrespective of the number of attempts or character comparisons made, E-ARFO consistently ranks first for short and lengthy patterns in most databases. Results also reveal reduced runtimes and competitive character comparisons. Moreover, results underscore the potential effect of E_ARFO on computational biology, offering a new paradigm for precision and efficiency in string matching
Smart Farming Platform Using IoT and UAVs
Full text linkWith the advancement of communication technology, many innovative applications have developed in agriculture as a result of the integration of the Internet of Things (IoT) with unmanned aerial vehicles (UAVs), leading to the modernization of agriculture. This study seeks to propose an effective and low-cost platform for comprehensive monitoring of environmental parameters using IoT and drones.The preparation of this paper was based on a platform that was tested in a realistic environment on a farm near the city of Al-Median in Tunisia, where the platform was built to suit the realistic environment of a farm in Baghdad, through the use of sensors above and below the ground, which meets the experimental work and standards for automated and real-time monitoring. For environmental standards, the unified theory of acceptance and use of technology model was used, which is a model based on four basic elements: 1) expected performance, 2) expected effort, 3) social impact, and 4) facilitating conditions for obtaining results. The unique integration of IoT sensors with drones has shown impressive experimental results, indicating the possibility of performing both automatic and manual actions by humans. These smart moves contribute significantly to promoting precision agriculture, leading to a significant increase in agricultural production and conservation of natural resources
Using Artificial Intelligence Algorithms to Study the Relative Importance of Macroeconomic Variables on Foreign Trade in Iraq
Full text linkInternational 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