Egyptian Informatics Journal最新文献

英文中文

Multistep prediction for egg prices: An efficient sequence-to-sequence network

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

Egyptian Informatics Journal

Pub Date : 2025-02-19 DOI: 10.1016/j.eij.2025.100628

Minlan Jiang , Liyun Mo , Lingguo Zeng , Azhi Zhang , Youhai Du , Yizhi Huo , Xiaowei Shi , Mohammed A.A. Al-qaness

Egg price has the characteristics of non-stationary, non-linear, and high volatility, which is more difficult to predict accurately. In this paper, we comprehensively consider the multiple factors affecting egg prices and construct a sequence-to-sequence (Seq2seq) model to study the multi-step prediction method of egg prices. Seasonal-trend Decomposition Procedure Based on Loess (STL) is first used to decompose the historical egg price series into trend, seasonal, and residual terms to reduce the interference of sample noise on forecasting performance. Then, Principal Component Analysis (PCA) is used to analyze and downscale the multidimensional factors affecting egg prices, such as feed price, laying hen seedling price, culled chicken price, duck egg price, and consumer index, to eliminate the redundant information in the data. Finally, the above-processed data were introduced into the Seq2seq network for training to establish a multi-step prediction model for egg prices. The experimental results show that the STL-PCA-Seq2seq model proposed in this paper can broadly capture the long-term dependence information of the input series and model the complex nonlinear relationships among the multidimensional factors affecting egg prices with the lowest prediction errors compared to the Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), the Informer model, the Seq2seq model, and the STL-Seq2seq model. The method proposed in this paper can reach R² of 0.9867, 0.9569, and 0.9106 at prediction steps 6, 12, and 18. With a prediction step size of 6, the RMSE is 0.131, MAE is 0.086, and MAPE is 0.813, respectively, which realizes the accurate prediction of egg price at any number of steps, and the results of the study provide a reference for the multi-step prediction of egg prices.

{"title":"Multistep prediction for egg prices: An efficient sequence-to-sequence network","authors":"Minlan Jiang , Liyun Mo , Lingguo Zeng , Azhi Zhang , Youhai Du , Yizhi Huo , Xiaowei Shi , Mohammed A.A. Al-qaness","doi":"10.1016/j.eij.2025.100628","DOIUrl":"10.1016/j.eij.2025.100628","url":null,"abstract":"<div><div>Egg price has the characteristics of non-stationary, non-linear, and high volatility, which is more difficult to predict accurately. In this paper, we comprehensively consider the multiple factors affecting egg prices and construct a sequence-to-sequence (Seq2seq) model to study the multi-step prediction method of egg prices. Seasonal-trend Decomposition Procedure Based on Loess (STL) is first used to decompose the historical egg price series into trend, seasonal, and residual terms to reduce the interference of sample noise on forecasting performance. Then, Principal Component Analysis (PCA) is used to analyze and downscale the multidimensional factors affecting egg prices, such as feed price, laying hen seedling price, culled chicken price, duck egg price, and consumer index, to eliminate the redundant information in the data. Finally, the above-processed data were introduced into the Seq2seq network for training to establish a multi-step prediction model for egg prices. The experimental results show that the STL-PCA-Seq2seq model proposed in this paper can broadly capture the long-term dependence information of the input series and model the complex nonlinear relationships among the multidimensional factors affecting egg prices with the lowest prediction errors compared to the Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), the Informer model, the Seq2seq model, and the STL-Seq2seq model. The method proposed in this paper can reach R<sup>2</sup> of 0.9867, 0.9569, and 0.9106 at prediction steps 6, 12, and 18. With a prediction step size of 6, the RMSE is 0.131, MAE is 0.086, and MAPE is 0.813, respectively, which realizes the accurate prediction of egg price at any number of steps, and the results of the study provide a reference for the multi-step prediction of egg prices.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100628"},"PeriodicalIF":5.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437077","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}

引用次数: 0

A road lane detection approach based on reformer model

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

Egyptian Informatics Journal

Pub Date : 2025-02-17 DOI: 10.1016/j.eij.2025.100625

Dan Li , Zan Yang , Wei Nai , Yidan Xing , Ziyu Chen

Intelligent driving has now become the hot research topics in the field of intelligent transportation system (ITS), and its maturity has a significant impact on road traffic safety in information environment. As one of the key technologies of intelligent driving, lane detection is an important prerequisite for identifying driving environment and driving scenarios, and providing auxiliary decision-making for driving. At present, road lane detection methods based on Transformer model are currently considered to be most effective and accurate; however, Transformer model-based road lane detection methods still have their own drawbacks, like high computational complexity of attention mechanisms and defects in activation function and loss functions. Thus, in this paper, a road lane detection method based on Reformer model, which is in essence an improved version of Transformer model has been proposed. By utilizing local sensitive hashing (LSH) attention mechanism, reversible Transformer structure and partitioning mechanism introduced in Reformer model, the high complexity of Transformer model can be overcome; and by configuring the Mish activation function and Huber loss function, the difficulties in network training and parameter optimization in Transformer model can also be solved. Via numerical analysis and real vehicle scenario experiment on Shanghai-Jiaxing expressway in China, the effectiveness of the proposed Reformer model and its superiority over Transformer models has been demonstrated.

{"title":"A road lane detection approach based on reformer model","authors":"Dan Li , Zan Yang , Wei Nai , Yidan Xing , Ziyu Chen","doi":"10.1016/j.eij.2025.100625","DOIUrl":"10.1016/j.eij.2025.100625","url":null,"abstract":"<div><div>Intelligent driving has now become the hot research topics in the field of intelligent transportation system (ITS), and its maturity has a significant impact on road traffic safety in information environment. As one of the key technologies of intelligent driving, lane detection is an important prerequisite for identifying driving environment and driving scenarios, and providing auxiliary decision-making for driving. At present, road lane detection methods based on Transformer model are currently considered to be most effective and accurate; however, Transformer model-based road lane detection methods still have their own drawbacks, like high computational complexity of attention mechanisms and defects in activation function and loss functions. Thus, in this paper, a road lane detection method based on Reformer model, which is in essence an improved version of Transformer model has been proposed. By utilizing local sensitive hashing (LSH) attention mechanism, reversible Transformer structure and partitioning mechanism introduced in Reformer model, the high complexity of Transformer model can be overcome; and by configuring the Mish activation function and Huber loss function, the difficulties in network training and parameter optimization in Transformer model can also be solved. Via numerical analysis and real vehicle scenario experiment on Shanghai-Jiaxing expressway in China, the effectiveness of the proposed Reformer model and its superiority over Transformer models has been demonstrated.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100625"},"PeriodicalIF":5.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429697","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}

引用次数: 0

A multi-objective fuzzy model based on enhanced artificial fish Swarm for multiple RNA sequences alignment

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

Egyptian Informatics Journal

Pub Date : 2025-02-17 DOI: 10.1016/j.eij.2025.100627

Medhat A. Tawfeek , Ibrahim Alrashdi , Madallah Alruwaili , Gamal Farouk Elhady

Ribonucleic Acid (RNA) sequence alignment is a fundamental operation in bioinformatics, essential for analyzing the physicochemical and functional characteristics of RNA molecules. Traditional cross-alignment methods have significant challenges, particularly in optimizing multiple objectives during RNA sequencing. One of the biggest challenges is working to balance speed and accuracy. Fast methods are accompanied by low accuracy, unlike accurate methods which take a long computational time. Consequently, the alignment task becomes increasingly difficult as the number of RNA sequences grows, requiring tools that adequately handle these conflicting targets. To address these challenges, this study proposes an Enhanced Artificial Fish Swarm Algorithm (EAFSA) integrated with a fuzzy multi-objective model specifically designed for multiple RNA sequence alignment. The proposed EAFSA approach offers various advantages including significantly increased alignment accuracy, preservation of sequence integrity and the ability to search for similar fragments efficiently and quickly while reducing computational costs. Experimental comparisons of the proposed EAFSA with other relevant state-of-the-art alignment tools on benchmark RNA datasets demonstrate the efficiency of the proposed method. The efficiency is also proved by various metrics such as alignment score analysis, time complexity, and accuracy. This work demonstrates the potential of the proposed EAFSA to enhance RNA sequence alignment methods, facilitating additional biological interpretations through sequence alignment applications in genomics.

{"title":"A multi-objective fuzzy model based on enhanced artificial fish Swarm for multiple RNA sequences alignment","authors":"Medhat A. Tawfeek , Ibrahim Alrashdi , Madallah Alruwaili , Gamal Farouk Elhady","doi":"10.1016/j.eij.2025.100627","DOIUrl":"10.1016/j.eij.2025.100627","url":null,"abstract":"<div><div>Ribonucleic Acid (RNA) sequence alignment is a fundamental operation in bioinformatics, essential for analyzing the physicochemical and functional characteristics of RNA molecules. Traditional cross-alignment methods have significant challenges, particularly in optimizing multiple objectives during RNA sequencing. One of the biggest challenges is working to balance speed and accuracy. Fast methods are accompanied by low accuracy, unlike accurate methods which take a long computational time. Consequently, the alignment task becomes increasingly difficult as the number of RNA sequences grows, requiring tools that adequately handle these conflicting targets. To address these challenges, this study proposes an Enhanced Artificial Fish Swarm Algorithm (EAFSA) integrated with a fuzzy multi-objective model specifically designed for multiple RNA sequence alignment. The proposed EAFSA approach offers various advantages including significantly increased alignment accuracy, preservation of sequence integrity and the ability to search for similar fragments efficiently and quickly while reducing computational costs. Experimental comparisons of the proposed EAFSA with other relevant state-of-the-art alignment tools on benchmark RNA datasets demonstrate the efficiency of the proposed method. The efficiency is also proved by various metrics such as alignment score analysis, time complexity, and accuracy. This work demonstrates the potential of the proposed EAFSA to enhance RNA sequence alignment methods, facilitating additional biological interpretations through sequence alignment applications in genomics.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100627"},"PeriodicalIF":5.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429696","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}

引用次数: 0

Advanced segmentation method for integrating multi-omics data for early cancer detection

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

Egyptian Informatics Journal

Pub Date : 2025-02-15 DOI: 10.1016/j.eij.2025.100624

S.K.B. Sangeetha , Sandeep Kumar Mathivanan , Azath M , Ravinder Beniwal , Naim Ahmad , Wade Ghribi , Saurav Mallik

The global burden of cancer underscores the critical need for early diagnosis. Traditional diagnostic methods, relying on single biomarkers or imaging, often lack comprehensive predictive accuracy. Existing systems often focus on one or two types of omics data, such as genome or transcriptome, but do not comprehensively integrate multiple omics layers (genomic, transcriptomic, proteomic, and epigenomic). This limitation restricts the ability to capture the full biological complexity and heterogeneity of cancer, which can be critical for accurate prediction and understanding of disease mechanisms. We propose an advanced cancer prediction method called Integrated Multi-Omics Segmentation (IMOS), which enhances the processing of multi-omics data by integrating genomic, transcriptomic, proteomic, and epigenomic information. IMOS segments data into biologically meaningful regions, facilitating more precise analysis. IMOS achieves outstanding performance with an average precision of 92 %, sensitivity of 88 %, and specificity of 94 %, outperforming traditional methods by 15 % in precision, 10 % in sensitivity, and 8 % in specificity. Validation using the Genomic Data Commons (GDC) dataset, encompassing diverse cancer types, demonstrated IMOS’s robustness with accuracy of 91 %, sensitivity of 87 %, and specificity of 93 %. The system also excels in clustering evaluation, with a silhouette score ranging from 0.55 to 0.62 and the lowest Davies-Bouldin index achieved with three clusters.

{"title":"Advanced segmentation method for integrating multi-omics data for early cancer detection","authors":"S.K.B. Sangeetha , Sandeep Kumar Mathivanan , Azath M , Ravinder Beniwal , Naim Ahmad , Wade Ghribi , Saurav Mallik","doi":"10.1016/j.eij.2025.100624","DOIUrl":"10.1016/j.eij.2025.100624","url":null,"abstract":"<div><div>The global burden of cancer underscores the critical need for early diagnosis. Traditional diagnostic methods, relying on single biomarkers or imaging, often lack comprehensive predictive accuracy. Existing systems often focus on one or two types of omics data, such as genome or transcriptome, but do not comprehensively integrate multiple omics layers (genomic, transcriptomic, proteomic, and epigenomic). This limitation restricts the ability to capture the full biological complexity and heterogeneity of cancer, which can be critical for accurate prediction and understanding of disease mechanisms. We propose an advanced cancer prediction method called Integrated Multi-Omics Segmentation (IMOS), which enhances the processing of multi-omics data by integrating genomic, transcriptomic, proteomic, and epigenomic information. IMOS segments data into biologically meaningful regions, facilitating more precise analysis. IMOS achieves outstanding performance with an average precision of 92 %, sensitivity of 88 %, and specificity of 94 %, outperforming traditional methods by 15 % in precision, 10 % in sensitivity, and 8 % in specificity. Validation using the Genomic Data Commons (GDC) dataset, encompassing diverse cancer types, demonstrated IMOS’s robustness with accuracy of 91 %, sensitivity of 87 %, and specificity of 93 %. The system also excels in clustering evaluation, with a silhouette score ranging from 0.55 to 0.62 and the lowest Davies-Bouldin index achieved with three clusters.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100624"},"PeriodicalIF":5.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421393","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}

引用次数: 0

Quantum computing in addressing greenhouse gas emissions: A systematic literature review

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

Egyptian Informatics Journal

Pub Date : 2025-02-14 DOI: 10.1016/j.eij.2025.100622

Wahyu Hidayat , Kridanto Surendro

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.

{"title":"Quantum computing in addressing greenhouse gas emissions: A systematic literature review","authors":"Wahyu Hidayat , 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}

引用次数: 0

Improving english vocabulary learning with a hybrid deep learning model optimized by enhanced search algorithm

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

Egyptian Informatics Journal

Pub Date : 2025-02-14 DOI: 10.1016/j.eij.2025.100619

Fang Zheng

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.

{"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}

引用次数: 0

Innovation of teaching mechanism of music course integrating artificial intelligence technology: ITMMCAI-MCA-ACNN approach

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

Egyptian Informatics Journal

Pub Date : 2025-02-14 DOI: 10.1016/j.eij.2024.100608

Xuejing Han

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. 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.

{"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}

引用次数: 0

ResNet-50-NTS digital painting image style classification based on Three-Branch convolutional attention

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

Egyptian Informatics Journal

Pub Date : 2025-02-10 DOI: 10.1016/j.eij.2025.100614

Xiaohong Wang, Qian Ye, Lei Liu, Haitao Niu, Bangbang Du

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.

引用次数: 0

Entropy-extreme concept of data gaps filling in a small-sized collection

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

Egyptian Informatics Journal

Pub Date : 2025-02-10 DOI: 10.1016/j.eij.2025.100621

Viacheslav Kovtun , Krzysztof Grochla , Mohammed Al-Maitah , Saad Aldosary , Oleksii Kozachko

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.

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引用次数: 0

Overseas short video recommendations: A multimodal graph convolutional network approach incorporating cultural preferences

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

Egyptian Informatics Journal

Pub Date : 2025-02-06 DOI: 10.1016/j.eij.2025.100616

Xishi Liu , Haolin Wang , Dan Li

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.

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