Egyptian Informatics Journal最新文献

{"title":"An innovative approach to advanced voice classification of sacred Quranic recitations through multimodal fusion","authors":"Esraa Hassan ,&nbsp;Abeer Saber ,&nbsp;Omar Alqahtani ,&nbsp;Nora El-Rashidy ,&nbsp;Samar Elbedwehy","doi":"10.1016/j.eij.2025.100640","DOIUrl":"10.1016/j.eij.2025.100640","url":null,"abstract":"<div><div>The Quran is the most important book we have ever read or recited. Perfecting recitation of the Holy Quran is challenging. In this paper, we integrate the use of multimodal fusion to result in advanced voice classification of sacred Quranic recitations. The proposed work called Voice Shortcut Connection Fusion (VSCF) architecture also targets restrictions corresponding to the dataset size and reciters’ variations into which Residual Neural Network (ResNet50) with the Fusion Layer incorporated in voice classification is integrated. The VSCF architecture is designed in a highly complex manner and is designed to be highly sophisticated about the extent to which it can approximate high-level features as well as higher-level features within a wide range of acoustic signals. The Fusion Layer proves to be an important layer that combines the ResNet50 model’s final layer with the Global Average Pooling of the raw MFCC features of the audios. This synergistic fusion enhances the ability of the model by a vast extent to identify the underlying stylistic features inherent in each reciter’s performance. The dataset consists of a Quranic Recitation Dataset having 7144 WAV format audio files from 12 Quran reciters. Compared with the traditional voice classification strategies, VSCF aims at solving issues regarding limitations of the adopted datasets and variations among different reciters. The results from our experiment showcase the effectiveness of the VSCF architecture, achieving an accuracy of 0.97683%. Further metrics include sensitivity at 0.9752, specificity at 0.9785, precision at 0.9875, and an F1 score of 0.9813.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100640"},"PeriodicalIF":5.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641991","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":"PRFE-driven gene selection with multi-classifier ensemble for cancer classification","authors":"Smitirekha Behuria ,&nbsp;Sujata Swain ,&nbsp;Anjan Bandyopadhyay ,&nbsp;Mohammad Khalid Al-Sadoon ,&nbsp;Saurav Mallik","doi":"10.1016/j.eij.2025.100637","DOIUrl":"10.1016/j.eij.2025.100637","url":null,"abstract":"<div><div>In this era, cancer remains a paramount concern due to its pervasive impact on individuals and societies, persistent challenges in treatment and prevention, and the ongoing need for global collaboration and innovation to improve outcomes and reduce its burden. Cancer marked by uncontrolled cell growth is a leading global cause of mortality, necessitating advanced methods for accurate diagnosis. This study introduces an innovative unsupervised feature selection technique Principal Recursive Feature Eliminator (PRFE) for selection of genes and cancer classification. Subsequently, seven different classifiers—Support Vector Machine, Random Forest, CatBoost, Light Gradient Boosting Method, Artificial Neural Network, Convolutional Neural Network, Long Short-Term Memory are used to increase the model’s robustness. The proposed approach is evaluated on nine benchmark gene expression datasets with a combination of two different algorithms. A series of experiments are conducted to assess the proposed method, focusing on the selected features and identifying optimal classifiers. We have calculated F1-Score, accuracy, recall, and precision. The suggested strategy performs better than expected, as the results highlight its potential to improve cancer classification techniques with an accuracy of 99.98%. We conclude from this analysis that, across many datasets, the CatBoost and CNN model outperforms the other models. This research contributes to the ongoing efforts to improve diagnostic precision and treatment outcomes in cancer research.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100637"},"PeriodicalIF":5.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631904","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":"An IoT-based architecture for human resource management in construction sites using software defined networking (SDN) and game theory","authors":"Xuejie Niu","doi":"10.1016/j.eij.2025.100629","DOIUrl":"10.1016/j.eij.2025.100629","url":null,"abstract":"<div><div>Construction sites are notoriously hazardous. This paper explores how the Internet of Things (IoT) can improve worker safety and resource management in these complex environments. Traditional monitoring systems struggle with managing diverse sensor data and ensuring scalability on large construction projects. This research proposes a novel architecture that leverages Software-Defined Networking (SDN) to manage an IoT network of environmental, biometric, and surveillance sensors. This centralized control enables efficient topology control and improved network performance. Furthermore, a game-theoretic approach is employed to optimize resource allocation and routing decisions, minimizing interference and maximizing network throughput. The proposed approach achieves significant enhancements in key network metrics: delay is reduced by 59.49%, energy consumption drops by 18.31%, and packet delivery rate increases by 7.70%. This research demonstrates the potential of SDN-based IoT architectures to revolutionize safety and resource management in construction, paving the way for future advancements in on-site worker well-being and project efficiency.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100629"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563156","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":"IFC: Editorial","authors":"","doi":"10.1016/S1110-8665(25)00045-3","DOIUrl":"10.1016/S1110-8665(25)00045-3","url":null,"abstract":"","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100652"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678989","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":"Mechanical fault diagnosis based on combination of sparsely connected neural networks and a modified version of social network search","authors":"Shang Xu","doi":"10.1016/j.eij.2025.100633","DOIUrl":"10.1016/j.eij.2025.100633","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Swift and precise fault diagnosis is a significant category to guarantee machinery operates reliably and avoid major failures. Conventional methods for monitoring bearing health rely on large datasets of labeled faulty samples, which can be time-consuming and costly to gain. Intelligent fault identification techniques are limited by a lack of defective samples. Although Convolutional Neural Networks (CNNs) are effective tools for diagnosing mechanical faults, they may perform poorly on unseen data due to overfitting when trained with few faulty samples. The small sample problem poses a significant difficulty in mechanical fault diagnosis, as insufficient faulty samples can induce overfitting in models such as CNNs that&lt;!--&gt; &lt;!--&gt;leads to inadequate generalization. For instance, conventional CNNs may extremely adapt to the unique traits of limited training data, whereas SCNNs, characterized by their sparse connectivity, reduce this concern. Additionally, optimizing the hyperparameters of SCNNs can be complex.&lt;!--&gt; &lt;!--&gt;However, the M-SNS algorithm, using&lt;!--&gt; &lt;!--&gt;Lévy flight and self-adjusting population mechanisms&lt;!--&gt; &lt;!--&gt;proficiently solves this issue by enhancing exploitation and exploration. This study suggests a novel approach to solve the small sample problem Sparsely Connected Neural Networks (SCNNs) enhanced by optimizing its hyperparameters based on an improved version of Social Network Search (M-SNS). While standard SNS-based optimizers struggle with local optima, the improved version incorporates Lévy flight to meaningly improve global search performance, guaranteeing better generalization even in small sample scenarios. The proposed SCNN/M-SNS is employed to use a tool for the fault diagnosis. To guarantee the efficiency of the model, its results are applied to a benchmark, called Case Western Reserve University (CWRU) Bearing Dataset which includes a flexible test rig with a 2 hp motor to simulate various load conditions (0, 1, 2, and 3 hp) and controlled fault introduction using Electrical Discharge Machining (EDM) to precisely introduce faults in bearings with different diameters (7, 14, 28, and 40 mils) and at different locations (roller, inner, and outer race faults). Accelerometers are used to collect data at two sampling rates (12 kHz and 48 kHz). The results are compared with some similar state of the art models based on SCNN, including fuzzy neural network model (FNN), Convolution Neural Network (CNN), and LSTM neural network. The results show that the proposed model achieves a mean squared error (MSE) of 0.021 on the test dataset which is higher than the fully connected network with an MSE of 0.035. When compared to other metaheuristic algorithms, the M-SNS-based SCNN model achieves an MSE of 0.021, while Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) achieve MSE values of 0.023 and 0.025, respectively. The proposed model also outperforms other techniques on the CWRU dataset, with a classification ","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100633"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609488","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":"An improved routing technique for energy optimization and delay reduction for Wireless body area networks","authors":"Swati Goel ,&nbsp;Kalpna Guleria ,&nbsp;Surya Narayan Panda ,&nbsp;Fahd S. Alharithi ,&nbsp;Aman Singh ,&nbsp;Aitizaz Ali","doi":"10.1016/j.eij.2025.100630","DOIUrl":"10.1016/j.eij.2025.100630","url":null,"abstract":"<div><div>Wireless Body Area Networks (WBANs) have a lot of potential applications in the medical domain for remote health monitoring. The critical concerns which have a significant impact on WBAN’s performance are energy efficiency and end-to-end latency. This paper proposes an energy-efficient routing technique for WBAN that decreases network delay, lowers the bit error rate, and increases the network’s total lifespan. To improve the performance of WBAN the proposed Energy-Efficient Adaptive Routing Technique (EEART) performs clustering, scheduling, and optimisation namely, Glowworm Swarm Optimisation (GSO). The network is initially split into clusters, and each cluster is assigned a Cluster Head (CH). During scheduling, the load is distributed among the cluster nodes based on time, priorities, and energy of nodes for effective operation of the network. The GSO adjusts the configuration dynamically in accordance with the conditions of the network for finding an optimal path. The simulation results demonstrate that the energy and latency of the WBAN network improves considerably with an increased efficiency of 30–35%. The proposed technique performs better in terms of energy consumption, throughput, Packet Delivery Ratio (PDR), and end-to-end delay in comparison to other existing routing protocols.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100630"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508594","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 machine learning neural network architecture for the accelerating universe based modified gravity","authors":"Zulqurnain Sabir ,&nbsp;Basma Souayeh ,&nbsp;Zahraa Zaiour ,&nbsp;Alyn Nazal ,&nbsp;Mir Waqas Alam ,&nbsp;Huda Alfannakh","doi":"10.1016/j.eij.2025.100635","DOIUrl":"10.1016/j.eij.2025.100635","url":null,"abstract":"<div><div>The current investigations present the numerical outputs of the mathematical accelerating universe based modified gravity model (MAUMGM) by designing a computational stochastic structure using the Bayesian regularization neural network. The classification of the mathematical MAUMGM is presented into five different nonlinear classes. A dataset is designed using the explicit Runge-Kutta scheme, which is divided into training as 82% and 9%, 9% for testing and verification. The designed stochastic process for solving the MAUMGM contains log-sigmoid activation function, thirty neurons in the hidden layer, dataset based explicit Runge-Kutta, and Bayesian regularization for the optimization. The correctness of the stochastic solver is perceived by comparing the outcomes along with absolute error 10^-06 to 10^-09. The best training values are reported around 10^-13 to 10^-14, which also signify the solver’s perfection. To authenticate the accuracy, and competence of the solver, some tests have been taken using the parameters of regression, state transition, and error histogram.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100635"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535112","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 non-invasive computer-aided personalized diagnosis system for Osteopenia and Osteoporosis","authors":"Hadeel Osama El-Sisi,&nbsp;Fatma El-Zahraa Ahmed El-Gamal,&nbsp;Noha Ahmed Hikal","doi":"10.1016/j.eij.2025.100634","DOIUrl":"10.1016/j.eij.2025.100634","url":null,"abstract":"<div><h3>Background:</h3><div>Osteoporosis is a common bone related disease that is characterized by a severe decrease in bone mineral density and an elevated risk of fracture. To achieve an effective disease management and fractures avoidance, the detection of the disease at its early stage, the Osteopenia stage, is extremely beneficial.</div></div><div><h3>Methods:</h3><div>For this purpose, this paper presents a non-invasive computer aided diagnosis system for disease’s screening using knee X-ray scans in its two basic stages (i.e., Osteopenia and Osteoporosis). Furthermore, a probabilistic diagnosis is produced for each scan, offering a personalized diagnosis that in turn indicates the severity of the disease, if exist, for each individual independently. Accordingly, the proposed methodology consists of three main steps: (1) the X-ray scans of three groups (i.e., normal, Osteopenia, and Osteoporosis) are pre-processed to improve the scans’ quality, and to serve the feature extraction and the construction of the model; (2) the pre-trained VGG16 model is used to identify the descriminative characteristics of each studied group that are then; at stage (3) fed to the SVM classifier to accomplish the diagnosis task, including the severity grading task.</div></div><div><h3>Results:</h3><div>Evaluating the proposed framework showed promising results with an average overall accuracy of 94.85%. In the groups base, the results were around 93.75%, 96.77%, and 100% for the normal group’s recall, F1-score, and precision, respectively. For the Osteopenia group, the results were around 93.95%, 100%, and 88.60% for F1-score, recall, and precision, respectively. Finally, the Osteoporosis group’s results achieved an average of 93.91%, 90%, and 98% for F1-score, recall, and precision, respectively.</div></div><div><h3>Conclusion:</h3><div>These results reflect the powerful ability of the proposed work especially that it could outperform the related efforts. Accordingly, these results encourage further analysis to extract more related medical insights for consequent assistance in the relevant healthcare diagnosis and treatment plans.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100634"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593154","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 hybrid GRASP and VND heuristic for vehicle routing problem with dynamic requests","authors":"Shifeng Chen ,&nbsp;Yanlan Yin ,&nbsp;Haitao Sang ,&nbsp;Wu Deng","doi":"10.1016/j.eij.2025.100638","DOIUrl":"10.1016/j.eij.2025.100638","url":null,"abstract":"<div><div>This paper describes a hybrid heuristic that integrates the Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Descent (VND) to address the Vehicle Routing Problem with Dynamic Requests (VRPDR). The VRPDR, a dynamic offshoot of the classical Vehicle Routing Problem (VRP), features customer requests emerging over time, with the objective of minimizing the total travel distance by devising a set of routes to serve all customers. The proposed method initially employs GRASP to construct an initial solution, followed by VND for exploration and refinement. The hybrid approach aims to utilize the strengths of both algorithms. Through testing on two sets of benchmark instances, namely dynamic pickup instances and dynamic delivery instances, 15 new optimal solutions are identified for the former and 11 for the latter. These results clearly demonstrate that the proposed algorithm competes favorably with the algorithms documented in the literature.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100638"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549452","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":"Spectral analysis of Cupric oxide (CuO) and Graphene Oxide (GO) via machine learning techniques","authors":"Zeeshan Saleem Mufti ,&nbsp;Kashaf Mahboob ,&nbsp;Muhammad Nauman Aslam ,&nbsp;Sadaf Hussain ,&nbsp;Abdoalrahman S.A. Omer ,&nbsp;Tanweer Sohail ,&nbsp;Sagheer Abbas ,&nbsp;Ilyas Khan ,&nbsp;Muhammad Adnan Khan","doi":"10.1016/j.eij.2025.100632","DOIUrl":"10.1016/j.eij.2025.100632","url":null,"abstract":"&lt;div&gt;&lt;div&gt;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 (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;mi&gt;u&lt;/mi&gt;&lt;mi&gt;O&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) and Graphene Oxide (GO). We have computed the energy &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;G&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msubsup&gt;&lt;mrow&gt;&lt;mo&gt;∑&lt;/mo&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;mrow&gt;&lt;mo&gt;|&lt;/mo&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;|&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and the other invariant Estrada index &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;G&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msubsup&gt;&lt;mrow&gt;&lt;mo&gt;∑&lt;/mo&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; 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 &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;v&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;v&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; while the predicted values of energy and Estrada index is represented by &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;v&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;v&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, where &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;v&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; represents ”actual value” and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;v&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; represents ”predicted value”. We first use traditional method based on softwares and get the actual values (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;v&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) (see section 2). Then we perform machine learning techniques to generate a best fit model and get the predicted values (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;v&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) of the energies and Estrada index of Cupric oxide &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;mi&gt;u&lt;/mi&gt;&lt;mi&gt;O&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and Graphene Oxide &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;G&lt;/mi&gt;&lt;mi&gt;O&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; by using the best fit second order polynomial for Energy and Estrada Index of &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;mi&gt;u&lt;/mi&gt;&lt;mi&gt;O&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; is obtained as &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;CuO&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;007&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;+&lt;/mo&gt;&lt;mn&gt;5&lt;","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}