Egyptian Informatics Journal最新文献_第2页

IoT enabled data protection with substitution box for lightweight ciphers

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2025-01-31 DOI: 10.1016/j.eij.2025.100620

K.B. Sarmila , S.V. Manisekaran

Rapid growth in communication and networking demands the protection of highly sensitive data in the system. The cryptographic techniques used in various traditional devices and cloud environments are not applicable to resource-constrained devices like sensors, industrial controllers, and RFID tags. A lightweight cryptographic design is required for securing the data revolving around constrained devices. Symmetric block cipher techniques shaped using substitution-permutation network (SPN) structure use the powerful component, the substitution box, which is the only component that contributes to non-linearity. In this paper, a modified 5-bit Dynamic Airy Chaotic (DAC) substitution box is proposed, which uses tent-logistic mapping for obtaining confusion property. This chaotic behavior is incorporated with an improved and crafted logical function. The substitution box exhibits high dynamic chaotic behavior and maintains the structure, balancing the composition of good security strength and resource utilization. The chaotic behavior and security resistance are evaluated based on the standard parameters. The DAC substitution box demonstrates improved security with 66% less memory footprint on an average gate count compared with standard 4- and 5-bit competitors. The solution was able to obtain equally good resistance against differential attacks and increased resistance against linear attacks with 40% less linear probability value in comparison with its competitors. With the increased bit length of 5, it is observed that DAC exhibits excellent flexibility with traditional block cipher techniques, thus simplifying the use of such a solution as a building block of cryptographic primitives.

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引用次数: 0

End-to-end neural automatic speech recognition system for low resource languages

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2025-01-28 DOI: 10.1016/j.eij.2025.100615

Sami Dhahbi , Nasir Saleem , Sami Bourouis , Mouhebeddine Berrima , Elena Verdú

The rising popularity of end-to-end (E2E) automatic speech recognition (ASR) systems can be attributed to their ability to learn complex speech patterns directly from raw data, eliminating the need for intricate feature extraction pipelines and handcrafted language models. E2E-ASR systems have consistently outperformed traditional ASRs. However, training E2E-ASR systems for low-resource languages remains challenging due to the dependence on data from well-resourced languages. ASR is vital for promoting under-resourced languages, especially in developing human-to-human and human-to-machine communication systems. Using synthetic speech and data augmentation techniques can enhance E2E-ASR performance for low-resource languages, reducing word error rates (WERs) and character error rates (CERs). This study leverages a non-autoregressive neural text-to-speech (TTS) engine to generate high-quality speech, converting a series of phonemes into speech waveforms (mel-spectrograms). An on-the-fly data augmentation method is applied to these mel-spectrograms, treating them as images from which features are extracted to train a convolutional neural network (CNN) and a bidirectional long short-term memory (BLSTM)-based ASR. The E2E architecture of this system achieves optimal WER and CER performance. The proposed deep learning-based E2E-ASR, trained with synthetic speech and data augmentation, shows significant performance improvements, with a 20.75% reduction in WERs and a 10.34% reduction in CERs.

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引用次数: 0

Equalizer Design: HBOA-DE-trained radial basis function neural networks

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2025-01-27 DOI: 10.1016/j.eij.2025.100617

Santosh Kumar Das , Satya Ranjan Pattanaik , Pradyumna Kumar Mohapatra , Saroja Kumar Rout , Abdulaziz S. Almazyad , Muhammed Basheer Jasser , Guojiang Xiong , Ali Wagdy Mohamed

Communication systems that rely on wireless technology require signal processing techniques to improve their channel performance. Wireless communications are susceptible to various signal distortions during transmission, including inter-symbol interference, adjacent channel interference, and co-channel interference. As a result, achieving error-free signal transmission in wireless communication is often challenging. To make sure the signal is recovered with a minimum bit error rate, equalizers are needed at the front end of the receiver. As an optimization algorithm, a nature-inspired hybrid algorithm is applied, namely BOA/DE, which is a combination of the Butterfly optimization algorithm (BOA) and differential evolution (DE). Based on a suitable network topology and transfer function, the presented work proposes an algorithm for training radial basis function neural networks (RBFNNs) that is applied to the problem of channel equalization. Both BOA and DE are advantageous in the proposed algorithm, which permits it to produce efficient results by balancing exploration and exploitation. Several methods have also been discussed in the literature that use optimization techniques to deal with the problem of equalization. The same problem is treated in this article as a classification issue. As a further step in the evaluation of the HBOA-DE-based RBFNN equalizer, three non-linear channels and adding different nonlinearities have been simulated. The proposed algorithm is compared with well-known algorithms in terms of Mean Square Error (MSE) and Bit Error Rate (BER). Additionally, the algorithm has been tested against a situation in burst error and evaluated via bit error probability (BEP) to establish its robustness and performance. Results showed that the method performed better in handling burst errors compared to others. It has been shown that the projected method outclasses other methods even in poor signal-to-noise ratio conditions, which is borne out by extensive simulation studies.

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引用次数: 0

A novel feature selection technique: Detection and classification of Android malware

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2025-01-27 DOI: 10.1016/j.eij.2025.100618

Sandeep Sharma , Prachi , Rita Chhikara , Kavita Khanna

Android operating system is not just the most commonly employed mobile operating system, but also the most lucrative target for cybercriminals due to its extensive user base. In light of this, the objective of this research is to uncover a few features that can significantly enhance the detection of Android malware through utilization of feature engineering. This work introduces a novel approach to feature selection that can discover a promising subset of features for effective malware detection. The proposed technique, Multi-Wrapper Hybrid Feature Selection Technique (MWHFST), integrates wrapper-based feature selection techniques to address the limitations of individual wrapper-based feature selection methods. The research employs extensive experiments on the Kronodroid dataset, a comprehensive and large-scale dataset, to gauge how well the proposed technique identifies and classifies malicious Android applications. Experimental results using machine learning algorithms demonstrate that the technique proposed in this research effectively integrates the advantages of individual feature selection techniques and exhibits the potential to identify a brief set of pivotal features for detecting Android malware. The proposed approach successfully identifies and categorizes malicious Android applications, achieving an accuracy of 98.8 % and 88 %, respectively, using only 31 features. This approach surpasses existing methods by delivering comparable performance with a significantly reduced number of features compared to individual approaches.

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引用次数: 0

Fuzzy decision support system for english language teaching with corpus data

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2025-01-26 DOI: 10.1016/j.eij.2025.100612

Meilin Huang

Corpus data from the past and novice observations are useful in improving the adeptness of English language teaching in high schools. Optimization methods support this adeptness through fine-tuning processes acquired from the corpus data. Hence, this article introduces a Progression-focused Teaching System (PTS) optimized by Fuzzy Decision (FD). This system focuses on identifying and providing solutions for lexical placement errors. Focusing on the progression of English teaching with high-quality outputs, lexical arrangement, and error reduction is pursued. The fuzzy decision system identifies a maximum precision output from the possible lexical placement in teaching vocabulary. In this decision process, the teaching efficiency towards the specific output is tuned through personalized training recommendations. The fuzzy output is used to benchmark the maximum precision output for further teaching references. Therefore, a consistent progression in teaching English vocabulary is attained by rectifying the errors in the previous corpus inputs.

引用次数: 0

Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2025-01-17 DOI: 10.1016/j.eij.2025.100613

Ramiro Saltos , Ignacio Carvajal , Fernando Crespo , Richard Weber

Dynamic clustering algorithms play a crucial role in numerous real-world applications by continuously adapting to evolving data patterns and identifying changes within the underlying cluster structure. However, unlike static clustering, where a plethora of validation indices exist to assess the solution’s quality, evaluating the effectiveness of dynamic clustering algorithms remains a challenge. This paper addresses this gap by proposing a novel set of six validation indices specifically designed for dynamic clustering. These indices assess the quality of solutions generated at three distinct granularities: individual clusters, individual observation periods, and the entire observation horizon. Our focus centers on cluster creation and elimination, recognized as the most critical structural changes within the dynamic clustering literature. To illustrate the application of these novel indices, we introduce an improved version of the dynamic fuzzy c-means algorithm (I-DFCM) which offers enhanced computational stability for handling dynamic data. We demonstrate the effectiveness of both the I-DFCM algorithm and the new validation indices through computational experiments using both synthetic and real-world datasets. The experiments showcase how these indices can effectively validate dynamic clustering solutions and guide parameter tuning for optimal performance, and support practical applications such as dynamic community detection in social networks and informed decision-making in dynamic environments. The results highlight the significant potential of these new validation indices and the I-DFCM algorithm in advancing the field of dynamic clustering.

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引用次数: 0

Benchmark Arabic news posts and analyzes Arabic sentiment through RMuBERT and SSL with AMCFFL technique

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2025-01-15 DOI: 10.1016/j.eij.2024.100601

Mustafa Mhamed , Richard Sutcliffe , Jun Feng

Sentiment analysis aims to extract emotions from textual data; sentiment analysis and text recognition are two of the most common tasks associated with natural language processing. Emergent technologies have been developed and employed in various fields, including marketing, health care, and policy making. However, with the growth of social media platforms and the flow of data, especially in the Arabic language, substantial difficulties have emerged that call for the creation of new frameworks to address problems, such as the lack of datasets related to news platforms, the complicated formation of the Arabic language, and complications with classifying, and system challenges, whether in machine learning, deep learning, or online analysis tools. This paper provides a new framework that helps address ASA challenges and work on various tasks based on the state-of-the-art ASA. First, it presents a new collection named (ANP5) from Arabic news posts from several Arabic platforms, then uses SSL with AMCFFL technique to analyze the Arabic sentiment and generate a second dataset (ANPS2). Next, applied ML classifiers, RF and SVM, do the best among the other classifiers, with an accuracy of 82.00%; however, the measurement distributions for each class are different (Experiment 1). Following that, DL models, BIGRU, CNN-LSTM, LSTM, and CNN, had accuracies of 88.10%, 89.30%, 89.85%, and 90.10% (Experiment 2). Experiments 1 and 2 represent the initial benchmark classification as the first baseline. Afterward, a new RMuBERT Model was developed and compared with four transformers on the two datasets: ANPS2 accuracy (90.87%) and ANP5 (90.33%). RMuBERT performed better than the baselines (Experiment 3). Further testing of RMuBERT on various Arabic corpora with different classes, lengths, and sizes: ArSarcasm (3C), STD (2C), AJGT (2C), and AAQ (2C), revealed accuracies of 77.76%, 91.79%, 94.07%, and 93.48%, respectively. Still, RMuBERT performed better than the baselines (Experiment 4). Finally, on the largest Arabic sentiment corpora with six million Arabic tweets, the performance is up to (91.12%); RMuBERT works efficiently with less training time (Experiment 5).

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引用次数: 0

Next-Generation energy Management: Particle Density algorithm for residential microgrid optimization

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2025-01-11 DOI: 10.1016/j.eij.2025.100611

Liang Wang , Nan Sun

This study presents an innovative approach to optimize energy management in residential microgrids, in light of the rising demand for energy and mounting environmental concerns. The research underscores the vital role of efficient energy management and responsive load control to improve energy efficiency and reduce consumer costs. To this end, a framework is proposed in which a power aggregator operates within a microgrid to manage residential electricity consumption. The primary goal of this framework is to minimize energy costs while considering subscriber preferences and the capacity limitations of the distribution network. The improved particle swarm optimization (IPSO) algorithm is employed to optimize energy management, resolve convergence challenges, and ensure user requirements are effectively prioritized. Integrating emergency, economic, and planned strategies provides cost savings, ensures grid stability, and enhances user satisfaction. The incorporation of Internet of Things (IoT) technology enables seamless communication, precise device control, and data-driven decision-making, empowering households to manage their energy loads more effectively and contribute to grid efficiency. Through scenario analysis, this research demonstrates the IPSO algorithm’s potential for significant cost reductions and improved grid stability. In Scenario 1, focused exclusively on affordability, numerical analyses present the total cost of electricity under different load conditions over three months. Scenario 2, also prioritizing affordability, highlights the impact of economic considerations on electricity expenses. Furthermore, Scenario 3 (80 % emergency + 20 % affordable) and Scenario 4 (50 % emergency + 20 % affordable + 30 % planned) showcase the potential for cost reduction through various priority combinations. These insights reflect the effectiveness of load management strategies facilitated by IoT technology. This comprehensive energy management approach lays a strong foundation for a resilient and adaptable energy infrastructure that meets society’s evolving demands.

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引用次数: 0

The financial impact of human resources configuration: A quantitative analysis based on modified single candidate optimizer

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2025-01-11 DOI: 10.1016/j.eij.2024.100584

Zhuozhuo Zhang , Jun Lu , Qi Wang

Recently, by complicated and fast changing business environments, the effective allocation of Human Resources (HR) is considered as an important task to achieve success within organizations. However, the optimal allocation of HR is considered as a complicated challenge due to the uncertainties that are inherent in the process. Traditional approaches often rely on manual decision-making, which can result in less effective allocations and reduced productivity. With the rise of big data and advanced analytics, there is an increasing demand for data-driven methodologies to enhance HR allocation. This paper presents an innovative HR optimization framework that uses a modified metaheuristic model, called the Modified Single Candidate Optimizer (MSCO) algorithm to resolve this task. The framework integrates big data analytics and system analysis to establish a quantitative management strategy for optimizing HR configurations. By using the advantages of the proposed MSCO, the framework can effectively address the HR allocation problems to provide an optimal solution. The results indicate that the proposed framework significantly improves HR utilization rates, labor productivity.

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引用次数: 0

The role of hybrid models in financial decision-making: Forecasting stock prices with advanced algorithms

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal

Pub Date : 2025-01-11 DOI: 10.1016/j.eij.2025.100610

Xiaoyi Zhu

Stock price volatility is influenced by many factors, which are significant obstacles to achieving accurate stock price forecasting in the financial market. This study introduces a novel hybrid model to tackle the abovementioned issues by integrating various algorithms, including bidirectional long short-term memory and random forest. Additionally, it incorporates ensemble empirical mode decomposition, sample entropy clustering, and sea-horse optimizer as part of its methodology. Exponential moving average 30, relative strength index 14, simple moving average 30, moving average convergence divergence, on-balance-volume, and daily open price, high price, low price, close price, and trading volume of the S&P 500 index between 01/04/2013 and 12/29/2022 were utilized as the dataset. To reduce the complexity of the time series decomposition and clustering methods were employed. Then, the high sequences underwent processing using the optimized random forest algorithm, and the remaining sequences were subjected to processing utilizing optimized bidirectional long short-term memory. This approach allowed the model to generalize effectively across a variety of global indices, as demonstrated by its high prediction accuracy (coefficient of determination (

R^{2}

) values exceeding 0.98 for the Dow Jones, CSI, Nikkei, and DAX indices). Additionally, robustness testing was conducted by introducing incremental noise levels to simulate real-market conditions, which demonstrated that the model remains highly accurate even at the highest noise level. In comparison to other methods, the proposed model demonstrated superior performance on the S&P 500, with an R² of 0.99 and low error metrics. This model’s adaptability and reliability in diverse and volatile market conditions are emphasized by this robust framework, which renders it a potent financial forecasting tool.

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引用次数: 0