Egyptian Informatics Journal最新文献

{"title":"Quantum computing in addressing greenhouse gas emissions: A systematic literature review","authors":"Wahyu Hidayat ,&nbsp;Kridanto Surendro","doi":"10.1016/j.eij.2025.100622","DOIUrl":"10.1016/j.eij.2025.100622","url":null,"abstract":"<div><div>The greenhouse gas (GHG) emissions issue that is directly related to the 13th Sustainable Development Goals; Climate Action has gained attention on a global scale, prompting the utilization of all available technological advancements, including quantum computing. This systematic literature review, employing Kitchenham’s method, explores the realm of quantum computing and its application to the pressing issue of GHG emissions. Through a meticulous analysis of scholarly articles, we identify key trends, influential authors, core sources, and relevant affiliations within this research domain. Notably, our findings underscore a robust connection between quantum computing studies and the fields of machine learning and optimization, where various optimization tasks attempt to minimize GHG emissions, predominantly in the Energy and Logistics problem domain using Quantum-inspired Evolutionary Algorithm, Quantum-inspired Swarm Optimization, or Quantum Annealing. An insightful map reveals the emergence of diverse quantum computing implementations for varied tasks, across various domains, providing nuanced perspectives and identifying potential research directions, particularly in optimization and prediction tasks. This study offers a foundational understanding of trends, challenges, and opportunities associated with quantum computing implementation in addressing GHG emissions, contributing to the ongoing establishment of sustainable technology.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100622"},"PeriodicalIF":5.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving english vocabulary learning with a hybrid deep learning model optimized by enhanced search algorithm","authors":"Fang Zheng","doi":"10.1016/j.eij.2025.100619","DOIUrl":"10.1016/j.eij.2025.100619","url":null,"abstract":"<div><div>In this study, we propose a novel deep-learning architecture that is designed to facilitate vocabulary acquisition for second-language learners of English. A hybridized model combining a tuned LSTM and CaffeNet with the EHGS algorithm. The EHGS was selected from the other algorithms including Manta Ray Foraging Optimization (MRFO), Equilibrium Optimizer (EO), Marine Predators Algorithm (MPA), Runge Kutta Optimizer (RUN), and White Shark Optimizer (WSO) since it is the most balanced algorithm out of all of them in terms of exploration vs. exploitation. From a methodological perspective, we adopt a hybrid CNN-based structural approach to enhance the learning of features and the effective processing of temporal information. It uses Oxford English Corpus and WordNet datasets for pre-training to make sure it is robust and effective. The specified model also outperformed very few with comparative evaluations using metrics of accuracy, F1-score, precision, and mean squared error (MSE). Our model showed an accuracy of 0.92 and an F1-score of 0.91 which far surpassed traditional Gaussian and LSTM methods (accuracy of 0.85 and F1-score 0.84). These findings make clear more advanced NLP techniques that can be applied for the development of intelligent education technology that can help non-native English speakers learn new vocabulary at an unprecedented rate. The better results provided by the proposed model mainly reveal its applicability in novel learning environments and offer students personalized, adapted, and immersive learning experiences.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100619"},"PeriodicalIF":5.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovation of teaching mechanism of music course integrating artificial intelligence technology: ITMMCAI-MCA-ACNN approach","authors":"Xuejing Han","doi":"10.1016/j.eij.2024.100608","DOIUrl":"10.1016/j.eij.2024.100608","url":null,"abstract":"<div><div>The major objective is to teach that makes use of interactive, intelligent technologies, as well as customized utilizing examples to examine concepts in theory and the development of practical skills<strong>.</strong> The manuscript introduced a music teaching system called Attention-based Convolutional Neural Network (ITMMCAI-MCA-ACNN). The system uses data from the Musdb18 dataset and a pre-processing step is performed to remove noise and imperfect records using the Horizontal Gradient Filter. Subsequently, the pre-processed data is passed through a source separationusing Attention-based convolutional neural network (ACNN)optimized withMusical chairs optimization approach to isolate different audio components like drums, bass, vocals, and other sounds, from a mixed audio signal for effective music teaching. The proposed ITMMCAI-MCA-ACNN is implemented in MATLAB, using the Musdb18 dataset for evaluation examination. The proposed method’s efficacy is measured using several success indicators, including precision, accuracy, specificity, error rate, sensitivity, and F1-score. The effectiveness of the suggested ITMMCAI-MCA-ACNN technique works 75.89 %, 61.11 %, and86%high accuracy, and90%, 73 %, and 70 % high precision compared with existing methods such as ITMMCAI-AIT, ITMMCAI-AIT-WN, and ITMMCAI-MDCT respectively.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100608"},"PeriodicalIF":5.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ResNet-50-NTS digital painting image style classification based on Three-Branch convolutional attention","authors":"Xiaohong Wang,&nbsp;Qian Ye,&nbsp;Lei Liu,&nbsp;Haitao Niu,&nbsp;Bangbang Du","doi":"10.1016/j.eij.2025.100614","DOIUrl":"10.1016/j.eij.2025.100614","url":null,"abstract":"<div><div>Addressing the difficulties and challenges faced by current traditional digital painting image style classification methods, the study enhances the residual neural network model by incorporating a three-branch convolutional attention mechanism. Furthermore, it integrates the improved residual neural network model with a fine-grained image classification model, ultimately presenting a novel approach for digital painting image style classification. The experimental results show that the final model can reach 100%, 98.61%, and 99.31% for the image classification precision, recall, and F1 value of ancient Greek pottery style, respectively. The improved residual neural network model proposed in this study has significant advantages in the task of digital painting image style classification, and can provide an efficient and reliable solution for classifying and recognizing digital painting image styles.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100614"},"PeriodicalIF":5.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Entropy-extreme concept of data gaps filling in a small-sized collection","authors":"Viacheslav Kovtun ,&nbsp;Krzysztof Grochla ,&nbsp;Mohammed Al-Maitah ,&nbsp;Saad Aldosary ,&nbsp;Oleksii Kozachko","doi":"10.1016/j.eij.2025.100621","DOIUrl":"10.1016/j.eij.2025.100621","url":null,"abstract":"<div><div>The article investigates the process of filling data gaps in a small-sized collection, which generalizes information about periodic measurement of input and output parameters of a target object. To fill the data gaps, a concept is proposed based on generating a committee of entropy-optimal trajectories through sampling probability density functions of parameters from a stochastic parameterized model trained on relevant data. The concept is generalized to cases of filling gaps in output data, input data, and both those data spaces. Filling gaps in output data is implemented using entropy-extreme estimation of probability density functions for parameters of the model and errors of measurement. In the case of addressing missing values in input data, these are interpreted as results of transforming a sequence of independent stochastic vectors introduced into a model structurally identical to that formalized for filling gaps in output data. Thus, the proposed concept inherits the benefits of both parametric estimation and using a trained model of the target process and non-parametric estimation of undefined characteristics that distort data. The proposed concept was tested on the task of filling gaps in a collection consisting of 35 tuples with measurement results of three attributes. It was considered that the imperfection of the measurement procedure caused variability in the obtained data at the level of 15% of their absolute value. Less than 20% of the data from the collection was used to train the corresponding entropy-extreme model. The relative error of the filled missing data was 0.21.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100621"},"PeriodicalIF":5.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overseas short video recommendations: A multimodal graph convolutional network approach incorporating cultural preferences","authors":"Xishi Liu ,&nbsp;Haolin Wang ,&nbsp;Dan Li","doi":"10.1016/j.eij.2025.100616","DOIUrl":"10.1016/j.eij.2025.100616","url":null,"abstract":"<div><div>In an age of cultural globalization, short video platforms are springing up around the globe, making it challenging to cater to a diverse mix of users with varied preferences and cultural backgrounds. In our research, we propose a novel suggestion model of short video material for international video apps through user preference modelling via hybrid multi-modal GCN (graph convolutional network). Unlike traditional methods that rely on the overall metadata of the short movies only, our approach jointly considers visual, linguistic and audio features of short movies, as well as user interactions, to propose personalized recommendations. Due to the effectiveness of the proposed method on TikTok and MovieLens dataset with a recall of 0.590 and video label classification accuracy more than 94.9%, The approach demonstrates effective use of resources with a maximum CPU utilization of only 44% whilst maintaining high user satisfaction across different age groups. Overall, the results have an implication that the proposed approach can lead to better user interaction and satisfaction in a culturally diverse environment.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100616"},"PeriodicalIF":5.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143271625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT enabled data protection with substitution box for lightweight ciphers","authors":"K.B. Sarmila ,&nbsp;S.V. Manisekaran","doi":"10.1016/j.eij.2025.100620","DOIUrl":"10.1016/j.eij.2025.100620","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100620"},"PeriodicalIF":5.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"End-to-end neural automatic speech recognition system for low resource languages","authors":"Sami Dhahbi ,&nbsp;Nasir Saleem ,&nbsp;Sami Bourouis ,&nbsp;Mouhebeddine Berrima ,&nbsp;Elena Verdú","doi":"10.1016/j.eij.2025.100615","DOIUrl":"10.1016/j.eij.2025.100615","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100615"},"PeriodicalIF":5.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Equalizer Design: HBOA-DE-trained radial basis function neural networks","authors":"Santosh Kumar Das ,&nbsp;Satya Ranjan Pattanaik ,&nbsp;Pradyumna Kumar Mohapatra ,&nbsp;Saroja Kumar Rout ,&nbsp;Abdulaziz S. Almazyad ,&nbsp;Muhammed Basheer Jasser ,&nbsp;Guojiang Xiong ,&nbsp;Ali Wagdy Mohamed","doi":"10.1016/j.eij.2025.100617","DOIUrl":"10.1016/j.eij.2025.100617","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100617"},"PeriodicalIF":5.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel feature selection technique: Detection and classification of Android malware","authors":"Sandeep Sharma ,&nbsp;Prachi ,&nbsp;Rita Chhikara ,&nbsp;Kavita Khanna","doi":"10.1016/j.eij.2025.100618","DOIUrl":"10.1016/j.eij.2025.100618","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100618"},"PeriodicalIF":5.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}