Muath AlShaikh^1,*, Ahmed Manea Alkhalifah², Sultan Alamri³

Computer Systems Science and Engineering, Vol.49, pp. 353-376, 2025, DOI:10.32604/csse.2025.062433 - 21 March 2025

Abstract Data security is crucial for improving the confidentiality, integrity, and authenticity of the image content. Maintaining these security factors poses significant challenges, particularly in healthcare, business, and social media sectors, where information security and personal privacy are paramount. The cryptography concept introduces a solution to these challenges. This paper proposes an innovative hybrid image encryption algorithm capable of encrypting several types of images. The technique merges the Tiny Encryption Algorithm (TEA) and Rivest-Shamir-Adleman (RSA) algorithms called (TEA-RSA). The performance of this algorithm is promising in terms of cost and complexity, an encryption time which is… More >

Ravi Veerabhadrappa^*, Poornima Athikatte Sampigerayappa

Computer Systems Science and Engineering, Vol.49, pp. 333-351, 2025, DOI:10.32604/csse.2025.061285 - 19 March 2025

Abstract In recent years, the analysis of encrypted network traffic has gained momentum due to the widespread use of Transport Layer Security and Quick UDP Internet Connections protocols, which complicate and prolong the analysis process. Classification models face challenges in understanding and classifying unknown traffic because of issues related to interpret ability and the representation of traffic data. To tackle these complexities, multi-modal representation learning can be employed to extract meaningful features and represent them in a lower-dimensional latent space. Recently, auto-encoder-based multi-modal representation techniques have shown superior performance in representing network traffic. By combining the… More >

Hamayun Khan^1,*, Muhammad Atif Imtiaz², Hira Siddique³, Muhammad Tausif Afzal Rana⁴, Arshad Ali⁵, Muhammad Zeeshan Baig⁶, Saif ur Rehman⁷, Yazed Alsaawy⁵

Computer Systems Science and Engineering, Vol.49, pp. 317-331, 2025, DOI:10.32604/csse.2025.061118 - 19 March 2025

Abstract The migration of tasks aided by machine learning (ML) predictions IN (DPM) is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor. In this paper, we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling (EA-EDF). ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system. The proposed system model allocates processors… More >

Jiacheng Luo, Jiahao Wen, Jian Yang^*

Computer Systems Science and Engineering, Vol.49, pp. 301-316, 2025, DOI:10.32604/csse.2025.060757 - 27 February 2025

Abstract Mitigating tag collisions is paramount for enhancing throughput in Radio Frequency Identification (RFID) systems. However, traditional algorithms encounter challenges like slot wastage and inefficient frame length adjustments. To tackle these challenges, the Slot Prediction Q (SPQ) algorithm was introduced, integrating the Vogt-II prediction algorithm and slot grouping to improve the initial Q value by predicting the first frame. This method quickly estimates the number of tags based on slot utilization, accelerating Q value adjustments when slot utilization is low. Furthermore, a Markov decision chain is used to optimize the relationship between the number of slot groupings (x) More >

Xinxin Liu, Bin Ye^*

Computer Systems Science and Engineering, Vol.49, pp. 287-300, 2025, DOI:10.32604/csse.2025.061655 - 20 February 2025

Abstract In the broader field of mechanical technology, and particularly in the context of self-driving vehicles, cameras and Light Detection and Ranging (LiDAR) sensors provide complementary modalities that hold significant potential for sensor fusion. However, directly merging multi-sensor data through point projection often results in information loss due to quantization, and managing the differing data formats from multiple sensors remains a persistent challenge. To address these issues, we propose a new fusion method that leverages continuous convolution, point-pooling, and a learned Multilayer Perceptron (MLP) to achieve superior detection performance. Our approach integrates the segmentation mask with… More >

Ayman Noor^*

Computer Systems Science and Engineering, Vol.49, pp. 259-286, 2025, DOI:10.32604/csse.2025.060387 - 21 February 2025

Abstract Precisely forecasting the performance of Deep Learning (DL) models, particularly in critical areas such as Uniform Resource Locator (URL)-based threat detection, aids in improving systems developed for difficult tasks. In cybersecurity, recognizing harmful URLs is vital to lowering risks associated with phishing, malware, and other online-based attacks. Since it directly affects the model’s capacity to differentiate between benign and harmful URLs, finding the optimum mix of hyperparameters in DL models is a significant difficulty. Two commonly used architectures for sequential and spatial data processing, Long Short-Term Memory (LSTM)/Gated Recurrent Unit (GRU) and Convolutional Neural Network… More >

Rana Mohamed^1,2,*, Mostafa Elgendy¹, Mohamed Taha¹

Computer Systems Science and Engineering, Vol.49, pp. 213-238, 2025, DOI:10.32604/csse.2024.059491 - 10 January 2025

Abstract Globally, liver cancer ranks as the sixth most frequent malignancy cancer. The importance of early detection is undeniable, as liver cancer is the fifth most common disease in men and the ninth most common cancer in women. Recent advances in imaging, biomarker discovery, and genetic profiling have greatly enhanced the ability to diagnose liver cancer. Early identification is vital since liver cancer is often asymptomatic, making diagnosis difficult. Imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasonography can be used to identify liver cancer once a sample of liver tissue is… More >

Kwang Chee Seng, Siti Fatimah Abdul Razak^*, Sumendra Yogarayan

Computer Systems Science and Engineering, Vol.49, pp. 239-258, 2025, DOI:10.32604/csse.2024.056582 - 10 January 2025

Abstract Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads. In most scenarios, insufficient and untimely information available to drivers for accessing road conditions and their surrounding environment is the primary factor that causes these incidents. To address these issues, a comprehensive system is required to provide real-time assistance to drivers. Building upon our previous research on a LoRa-based lane change decision-aid system, this study proposes an enhanced Vehicle Overtaking System (VOS). This system utilizes long-range (LoRa) communication for reliable real-time data exchange between vehicles (V2V) and the cloud (V2C). By More >

Tabassum Kanwal¹, Saif Ur Rehman^1,*, Azhar Imran², Haitham A. Mahmoud³

Computer Systems Science and Engineering, Vol.49, pp. 185-212, 2025, DOI:10.32604/csse.2024.056535 - 10 January 2025

Abstract This study presents an energy-efficient Internet of Things (IoT)-based wireless sensor network (WSN) framework for autonomous data validation in remote environmental monitoring. We address two critical challenges in WSNs: ensuring data reliability and optimizing energy consumption. Our novel approach integrates an artificial neural network (ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture. The proposed ANN model is designed to simultaneously detect multiple fault types, including spike faults, stuck-at faults, outliers, and out-of-range faults. We collected sensor data at 5-minute intervals over three months, using temperature and humidity sensors. The ANN was trained on 70%… More >

Ammar Fahem Majeed^1,2,*, Rashidah Arsat¹, Muhammad Ariff Baharudin¹, Nurul Mu’azzah Abdul Latiff¹, Abbas Albaidhani³

Computer Systems Science and Engineering, Vol.49, pp. 159-183, 2025, DOI:10.32604/csse.2024.058741 - 03 January 2025

Abstract Accurate non-line of sight (NLOS) identification technique in ultra-wideband (UWB) location-based services is critical for applications like drone communication and autonomous navigation. However, current methods using binary classification (LOS/NLOS) oversimplify real-world complexities, with limited generalisation and adaptability to varying indoor environments, thereby reducing the accuracy of positioning. This study proposes an extreme gradient boosting (XGBoost) model to identify multi-class NLOS conditions. We optimise the model using grid search and genetic algorithms. Initially, the grid search approach is used to identify the most favourable values for integer hyperparameters. In order to achieve an optimised model configuration,… More >