Alkadhim Journal for Computer Science

This study presents an enhancement to the Direct Sequence Spread Spectrum (DSSS) algorithm aimed at transmitting high data rate signals over low-frequency pass-bands. By utilizing a random number to generate a new encryption key, the complexity of both encryption and decryption processes is significantly increased. The original signal undergoes multiplication with a spreading code, resulting in a random matrix that obscures the original data and enhances security. The research employs Binary Phase Shift Keying (BPSK) modulation to effectively transmit the spread signal, optimizing bandwidth utilization and mitigating channel impairments. Simulation results conducted using MATLAB demonstrate that the proposed DSSS method achieves low Bit Error Rates (BER) under various conditions, confirming its robustness against interference. Furthermore, the findings indicate that the combination of high data rate transmission and low-frequency pass-bands using DSSS has promising applications in diverse fields such as industrial automation, remote sensing, and military communications. Overall, this research contributes to advancing secure and efficient communication systems by leveraging DSSS techniques.

Diabetes is one of the most prevalent diseases in the modern era, leading to a significant number of deaths annually, as reported by the World Health Organization. Early prediction of diabetes can substantially improve patient outcomes and save lives. This study introduces a new model for predicting diabetes using the Random Forest algorithm, known for its powerful ability to split data until reaching an optimal state. Two datasets are utilized: the Multiclass Diabetes dataset and the PIMA Indian Diabetes dataset. The data are preprocessed by removing outliers, handling missing values, and balancing the classes. These preprocessed data are then classified using the Random Forest algorithm through continuous splitting until the stopping criteria are met, aiming to predict diabetic individuals. The proposed model demonstrated superior performance with the Multiclass Diabetes dataset, it achieves a validation accuracy of 100%, a precision of 98.20%, and recall and F1 scores of 98.11% and 98.12%, respectively. With the PIMA dataset, the proposed model achieves a validation accuracy of 85.30%, with precision, recall, and F1 scores of 88.07%, 87.50%, and 87.50%, respectively. In addition to our proposed model, we built many machine learning models with the first dataset such as SVM, logistic regression, logistic regression with L1/ L2 regularization, K-NN, and naïve bayes. Our results indicate that the Random Forest algorithm significantly outperforms other machine learning techniques in predicting diabetes, offering a highly accurate and reliable tool for early diagnosis. This research underscores the potential of ensemble learning in healthcare, particularly in managing chronic diseases like diabetes.

Short Message Service (SMS) spam remains a significant threat to users and businesses, with spammers constantly adopting more sophisticated techniques. This paper comprehensively surveys SMS spam detection methods, categorizing existing approaches into five primary groups: rule-based methods, traditional machine learning techniques, deep learning models, hybrid models, and ensemble methods. Each category is examined in detail, highlighting its strengths, limitations, and evolution. Rule-based methods, though historically significant, are limited by their inability to handle new or evolving spam tactics. Traditional machine learning techniques, such as Naive Bayes and support vector machines (SVM), offer improved accuracy but depend on handcrafted features. In contrast, deep learning models, including recurrent neural networks (RNN) and convolutional neural networks (CNN), excel in feature extraction and adaptability yet face challenges with model complexity and the need for large labeled datasets. Hybrid and ensemble methods combine the benefits of various models to improve performance, reduce bias, and enhance robustness. This review aims to provide a structured overview of the state of SMS spam detection, identify emerging trends, and suggest future research directions, including improving generalization, reducing data dependency, and exploring the integration of contextual information. The findings underscore the need for continued innovation to address the evolving landscape of SMS spam.

Low-light image enhancement (LLIE) is an important area of research as many applications such as photography, video surveillance, and security are confronted with image degradation in low-light environments. This paper presents an intelligent agent-based method for LLIE using the Asynchronous Advantage Actor-Critic (A3C) framework. The enhancement task is effectively cast in this work into the framework of a Markov Decision Process. This method enables an agent to learn a policy that successively improves image quality. In the agent, features are extracted by a Fully Convolutional Network (FCN), a policy network for choosing an action, and a value network for estimating the reward. In training, non-reference loss functions are also used to measure image quality without the availability of the reference image or ground truth images. Such functions include spatial consistency loss, exposure control loss, and illumination smoothness loss, and the approach achieves end-to-end enhancement without reference image. The experimental results on LOL and MIT-Adobe dataset also show that the proposed technique enhances image brightness, Contrast, and structure much better as compared to other state-of-the-art methods. Especially, the methodology proposed scored 25.93 PSNR, 0.932 SSIM, and 0.053 LPIPS on the LOL dataset, achieving better results than related strategies. The designed agent-based approach works under a wide range of low-light situations. This approach allows obtaining enhancement results that will be satisfactory in terms of the users’ preferences and needs of the specific applications. The findings highlight the method's robustness and flexibility, making it suitable for various practical applications. This work demonstrates that reinforcement learning agents have promising applications in improved image processing capabilities, and establishes a new record for low-light image improvement