Egyptian Informatics Journal最新文献

{"title":"Spectral analysis of Cupric oxide (CuO) and Graphene Oxide (GO) via machine learning techniques","authors":"Zeeshan Saleem Mufti , Kashaf Mahboob , Muhammad Nauman Aslam , Sadaf Hussain , Abdoalrahman S.A. Omer , Tanweer Sohail , Sagheer Abbas , Ilyas Khan , Muhammad Adnan Khan","doi":"10.1016/j.eij.2025.100632","DOIUrl":"10.1016/j.eij.2025.100632","url":null,"abstract":"<div><div>Chemical graph theory has recently gained much attraction among researchers due to its extensive use in mathematical chemistry. In this research article, We have studied the spectral properties such as eigenvalues, energy and Estrada index of some chemical structures such as Cupric oxide (<span><math><mrow><mi>C</mi><mi>u</mi><mi>O</mi></mrow></math></span>) and Graphene Oxide (GO). We have computed the energy <span><math><mrow><mi>E</mi><mrow><mo>(</mo><mi>G</mi><mo>)</mo></mrow><mo>=</mo><msubsup><mrow><mo>∑</mo></mrow><mrow><mi>i</mi><mo>=</mo><mn>0</mn></mrow><mrow><mi>n</mi></mrow></msubsup><mrow><mo>|</mo><msub><mrow><mi>λ</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>|</mo></mrow></mrow></math></span> and the other invariant Estrada index <span><math><mrow><mi>E</mi><mi>E</mi><mrow><mo>(</mo><mi>G</mi><mo>)</mo></mrow><mo>=</mo><msubsup><mrow><mo>∑</mo></mrow><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mrow><mi>n</mi></mrow></msubsup><msup><mrow><mi>e</mi></mrow><mrow><msub><mrow><mi>λ</mi></mrow><mrow><mi>i</mi></mrow></msub></mrow></msup></mrow></math></span> of the above mentioned graph structures and obtain the polynomial regression analysis using machine learning techniques. This approach permitted us to predict the spectral values more precisely and analyze the difference between the actual and predicted values. The actual values of energy and Estrada index is represented by <span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>a</mi><mi>v</mi></mrow></msub></math></span> and <span><math><mrow><mi>E</mi><msub><mrow><mi>E</mi></mrow><mrow><mi>a</mi><mi>v</mi></mrow></msub></mrow></math></span> while the predicted values of energy and Estrada index is represented by <span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>p</mi><mi>v</mi></mrow></msub></math></span> and <span><math><mrow><mi>E</mi><msub><mrow><mi>E</mi></mrow><mrow><mi>p</mi><mi>v</mi></mrow></msub></mrow></math></span>, where <span><math><mrow><mi>a</mi><mi>v</mi></mrow></math></span> represents ”actual value” and <span><math><mrow><mi>p</mi><mi>v</mi></mrow></math></span> represents ”predicted value”. We first use traditional method based on softwares and get the actual values (<span><math><mrow><mi>a</mi><mi>v</mi></mrow></math></span>) (see section 2). Then we perform machine learning techniques to generate a best fit model and get the predicted values (<span><math><mrow><mi>p</mi><mi>v</mi></mrow></math></span>) of the energies and Estrada index of Cupric oxide <span><math><mrow><mi>C</mi><mi>u</mi><mi>O</mi></mrow></math></span> and Graphene Oxide <span><math><mrow><mi>G</mi><mi>O</mi></mrow></math></span> by using the best fit second order polynomial for Energy and Estrada Index of <span><math><mrow><mi>C</mi><mi>u</mi><mi>O</mi></mrow></math></span> is obtained as <span><math><mrow><mi>E</mi><mrow><mo>(</mo><mi>CuO</mi><mo>)</mo></mrow><mo>=</mo><mo>−</mo><mn>0</mn><mo>.</mo><mn>007</mn><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>+</mo><mn>5<","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100632"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609489","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":"Multi-strategy fusion binary SHO guided by Pearson correlation coefficient for feature selection with cancer gene expression data","authors":"Yu-Cai Wang,&nbsp;Hao-Ming Song,&nbsp;Jie-Sheng Wang,&nbsp;Xin-Ru Ma,&nbsp;Yu-Wei Song,&nbsp;Yu-Liang Qi","doi":"10.1016/j.eij.2025.100639","DOIUrl":"10.1016/j.eij.2025.100639","url":null,"abstract":"<div><div>Cancer gene expression data is extensively utilized to address the challenges of cancer subtype diagnosis. However, this data is often characterized by high-dimensional, multi-text and multi-classification, which requires an effective feature selection (FS) method. A multi-strategy fusion binary sea-horse optimizer guided by Pearson correlation coefficient was proposed for FS with cancer gene expression data. For the multi-strategy fusion, the rest strategy is introduced in the sea-horse motor behavior stage. Subsequently, a search strategy based on symbiotic organisms of sea horses is designed for the predation stage. Finally, the elementary function dynamic weight strategy is proposed. Multi-strategy fusion enables the sea-horse optimizer (SHO) to perform dynamic exploitation and exploration in the early stage of iteration, expand the search scope initially, and narrow the search scope in the middle and later stages of the algorithm, so as to avoid the algorithm falling into the local optimal and increase the possibility of the algorithm jumping out of the local optimal, and avoid the blind search caused by elite influence. In the FS part, Pearson correlation coefficient guided strategy is proposed firstly to add or delete features. Then eight binary algorithms are derived from S-type and V-type transfer functions. The simulation experiment was divided into four parts. Firstly, the CEC-2022 test functions were used to test the performance of the multi-strategy fusion SHO, from which the best variant TanASSHO was selected, and then compared with other nine swarm intelligent optimization algorithms. Performance tests of various algorithm variants on 18 UCI datasets show that V1PTASSHO is the most effective binary version. Finally, V1PTASSHO was compared with other nine swarm intelligent optimization algorithms on 18 cancer gene expression datasets. The results demonstrate that V1PTASSHO effectively reduces feature subsets, improve classification accuracy and obtain lower fitness value. Friedman test and Wilcoxon rank sum test were used for statistical analysis to verify the effectiveness of the proposed algorithm.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100639"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576894","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":"EFFSIP: Efficient forest fire system using IoT and parallel computing","authors":"Khalid Mohammad Jaber ,&nbsp;Ahmad A.A. Alkhatib","doi":"10.1016/j.eij.2025.100631","DOIUrl":"10.1016/j.eij.2025.100631","url":null,"abstract":"<div><div>The escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the paper introduces the Efficient Forest Fire System using an innovative Internet of Things (IoT) and Parallel computing (EFFSIP) solution. The EFFSIP system leverages a wireless sensor network to efficiently detect and analyze fire behavior, providing real-time data on fire spread, speed, and direction. The system processes environmental parameters such as temperature (T), relative humidity (RH), and the Chandler Burning Index (CBI) against set thresholds to enable early fire detection.</div><div>Designed for the challenging forest environment, the EFFSIP system prioritizes minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures that the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. The system’s efficiency is enhanced through the use of Pthreads for parallel processing, allowing multiple tasks such as data collection, processing, and fire checking to be handled concurrently. This approach significantly reduces response time by processing sensor data in parallel, ensuring rapid detection and accurate prediction of fire behavior.</div><div>Field tests of the EFFSIP system in various Jordanian forest locations, including Burgish-Ajloun, demonstrated its effectiveness. The system detected a fire at 10:42 am with an initial CBI value of 32.5, which increased sharply to 97.92 as the fire progressed. Additionally, the system recorded a decrease in humidity from 53% to 22% and an increase in temperature from 28 °C to 48.6 °C. For example, the system predicted a fire at node 12 would occur in 0.477 min, allowing preemptive actions to be taken before the fire started. The system’s ability to provide real-time alerts and detailed analysis of fire spread, speed, and direction makes it a valuable tool for forest fire management. The strategic placement of sensor nodes and the use of durable components reduce the risk of system damage due to environmental extremities. The EFFSIP system offers a significant contribution to fighting forest fires, and future enhancements may include leveraging GPGPU (General-Purpose computing on Graphics Processing Units) technology to further increase computational power and system efficiency.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100631"},"PeriodicalIF":5.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479482","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 real-time framework for opinion spam detection in Arabic social networks","authors":"Cherry A. Ezzat ,&nbsp;Abdullah M. Alkadri ,&nbsp;Abeer Elkorany","doi":"10.1016/j.eij.2025.100626","DOIUrl":"10.1016/j.eij.2025.100626","url":null,"abstract":"<div><div>In today’s interconnected digital landscape, social media platforms serve as the primary avenue for global conversations, encompassing various topics and opinions. Opinion spam entails spreading misleading content masked as authentic opinions. The propagation of opinion spam poses a significant challenge, undermining the authenticity and trustworthiness of online interactions and disturbing the unrestricted exchange of ideas. One of the main challenges in spam detection is the rapid flow of spam content, which necessitates real-time detection mechanisms. Additionally, another important obstacle in detecting spam on Arabic social networks is the limited availability of labeled data. This paper proposes a framework for Real-Time Arabic Opinion Spam Detection (RTAOSD) that was developed to effectively detect opinion spam within Arabic social networks. This framework integrates advanced machine learning models, sentiment Analysis, and real-time processing techniques to achieve accurate and efficient detection of opinion spam. Furthermore, RTAOSD categorizes the non-spam content according to its relevance to topic of interest in to purify the content appear to social network users. Experimental evaluations conducted on real-world datasets demonstrate the effectiveness of RTAOSD in detecting opinion spam which leads to provide users with filtered content that match with their interest and overcome the problem of information overloading. The proposed framework achieved macro-F1 scores for spam detection ranging from 91% to 99% on different Arabic datasets surpassing previous work. While for topic relevance classification, RTAOSD achieved a macro-F1 of 86% for binary relevance and 78% for categorical relevance outperforming previous approaches used. The outcomes of this research is a real-time Arabic spam detector that accurately detects spam content and classifies non-spam text according to its relevance to topic . In addition to providing a visualization module for analyzing and reporting the characteristics of the filtered text.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100626"},"PeriodicalIF":5.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479481","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":"Optimizing fault prediction in software based on MnasNet/LSTM optimized by an improved lotus flower algorithm","authors":"Wang Long ,&nbsp;Zhao Qixin ,&nbsp;Michail A. Zakharov ,&nbsp;Sangkeum Lee","doi":"10.1016/j.eij.2025.100623","DOIUrl":"10.1016/j.eij.2025.100623","url":null,"abstract":"<div><div>Software quality and reliability are very important problems in the field of software production. Software error and defect detection technology is one of the most important research goals in the field of software system reliability that prevents software failure. Therefore, the performance of the defect prediction model in order to accurately predict defects is important in improving and effectiveness of models. In this paper, an attempt has been made to present a hybrid and efficient classification model based on deep learning and metaheuristic models for predicting defects of software. The basis of the suggested model is utilizing a combination of MnasNet (for extracting the semantics of AST tokens) and LSTM (for keeping the key features). It has been improved with the help of an improved variant of Lotus Flower Algorithm (ILFA) so that appropriate coefficients and acceptable results can be produced with the optimization power of metaheuristic algorithms and the learning power of the network. For evaluating the results of the suggested model, the model is applied to a practical dataset and the results are compared with some different methods. The new combined model worked best for the Xerces project, reaching 93% accuracy, which was much better than other models. It also performed well on different projects, improving accuracy by 3.3% to 7.9% after cleaning the data and fixing the issue of uneven class sizes. The results indicate that the proposed model can achieve the highest values ​​of efficiency.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100623"},"PeriodicalIF":5.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464251","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":"Multistep prediction for egg prices: An efficient sequence-to-sequence network","authors":"Minlan Jiang ,&nbsp;Liyun Mo ,&nbsp;Lingguo Zeng ,&nbsp;Azhi Zhang ,&nbsp;Youhai Du ,&nbsp;Yizhi Huo ,&nbsp;Xiaowei Shi ,&nbsp;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 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.</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}

{"title":"A road lane detection approach based on reformer model","authors":"Dan Li ,&nbsp;Zan Yang ,&nbsp;Wei Nai ,&nbsp;Yidan Xing ,&nbsp;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}

{"title":"A multi-objective fuzzy model based on enhanced artificial fish Swarm for multiple RNA sequences alignment","authors":"Medhat A. Tawfeek ,&nbsp;Ibrahim Alrashdi ,&nbsp;Madallah Alruwaili ,&nbsp;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}

{"title":"Advanced segmentation method for integrating multi-omics data for early cancer detection","authors":"S.K.B. Sangeetha ,&nbsp;Sandeep Kumar Mathivanan ,&nbsp;Azath M ,&nbsp;Ravinder Beniwal ,&nbsp;Naim Ahmad ,&nbsp;Wade Ghribi ,&nbsp;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}

{"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}