Predicting Gross Domestic Product (GDP) using a PC-LSTM-RNN model in urban profiling areas

Tarek Z., Elshewey A.M., Shohieb S.M., Elhady A.M., El-Attar N.E., Elseuofi S., Shams M.Y.

Soil erosion, the degradation of the earth’s surface through the removal of soil particles, occurs in three phases: dislocation, transport, and deposition. Factors such as soil type, assembly, infiltration, and land cover influence the velocity of soil erosion. Soil erosion can result in soil loss in some areas and soil deposition in others. In this paper, we proposed the Random Search-Random Forest (RS-RF) model, which combines random search optimization with the Random Forest algorithm, for soil erosion prediction. This model helps to better understand and predict soil erosion dynamics, supporting informed decisions for soil conservation and land management practices. This study utilized a dataset comprising 236 instances with 11 features. The target feature’s class label indicates erosion (1) or non-erosion (−1). To assess the effectiveness of the classification techniques employed, six evaluation metrics, including accuracy, Matthews Correlation Coefficient (MCC), F1-score, precision, recall, and Area Under the Receiver Operating Characteristic Curve (AUC), were computed. The experimental findings illustrated that the RS-RF model achieved the best outcomes when compared with other machine learning techniques and previous studies using the same dataset with an accuracy rate of 97.4%.

M. Elshewey A., Y. Shams M., Tarek Z., Megahed M., M. El-kenawy E., A. El-dosuky M.

Food choice motives (i.e., mood, health, natural content, convenience, sensory appeal, price, familiarities, ethical concerns, and weight control) have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the world. Researchers from several domains have presented several models addressing issues influencing food choice over the years. However, a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making procedure. In this paper, four Deep Learning (DL) models and one Machine Learning (ML) model are utilized to predict the weight in pounds based on food choices. The Long Short-Term Memory (LSTM) model, stacked-LSTM model, Conventional Neural Network (CNN) model, and CNN-LSTM model are the used deep learning models. While the applied ML model is the K-Nearest Neighbor (KNN) regressor. The efficiency of the proposed model was determined based on the error rate obtained from the experimental results. The findings indicated that Mean Absolute Error (MAE) is 0.0087, the Mean Square Error (MSE) is 0.00011, the Median Absolute Error (MedAE) is 0.006, the Root Mean Square Error (RMSE) is 0.011, and the Mean Absolute Percentage Error (MAPE) is 21. Therefore, the results demonstrated that the stacked LSTM achieved improved results compared with the LSTM, CNN, CNN-LSTM, and KNN regressor.

Tarek Z., Y. Shams M., M. Elshewey A., M. El-kenawy E., Ibrahim A., A. Abdelhamid A., A. El-dosuky M.

Wind power is one of the sustainable ways to generate renewable energy. In recent years, some countries have set renewables to meet future energy needs, with the primary goal of reducing emissions and promoting sustainable growth, primarily the use of wind and solar power. To achieve the prediction of wind power generation, several deep and machine learning models are constructed in this article as base models. These regression models are Deep neural network (DNN), k-nearest neighbor (KNN) regressor, long short-term memory (LSTM), averaging model, random forest (RF) regressor, bagging regressor, and gradient boosting (GB) regressor. In addition, data cleaning and data preprocessing were performed to the data. The dataset used in this study includes 4 features and 50530 instances. To accurately predict the wind power values, we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization (SFS-PSO) to optimize the parameters of LSTM network. Five evaluation criteria were utilized to estimate the efficiency of the regression models, namely, mean absolute error (MAE), Nash Sutcliffe Efficiency (NSE), mean square error (MSE), coefficient of determination (R2), root mean squared error (RMSE). The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99% in predicting the wind power values.

Wang L., Liu H., Pan Z., Fan D., Zhou C., Wang Z.

Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain satisfactory accuracy. To overcome this drawback, this paper presents a long short-term memory (LSTM) neural network with transfer learning and ensemble learning and combines it with an unsupervised health indicator (HI) construction method for remaining-useful-life prediction. This study consists of the following parts: (1) utilizing the characteristics of deep belief networks and self-organizing map networks to translate raw sensor data to a synthetic HI that can effectively reflect system health; and (2) introducing transfer learning and ensemble learning to provide the required degradation mechanism for the RUL prediction model based on LSTM to improve the performance of the model. The performance of the proposed method is verified by two bearing datasets collected from experimental data, and the results show that the proposed method obtains better performance than comparable methods.

Hassan E., Y. Shams M., A. Hikal N., Elmougy S.

Infectious diseases are an imminent danger that faces human beings around the world. Malaria is considered a highly contagious disease. The diagnosis of various diseases, including malaria, was performed manually, but it required a lot of time and had some human errors. Therefore, there is a need to investigate an efficient and fast automatic diagnosis system. Deploying deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image analysis. This study proposed a Convolutional Neural Network (CNN) model to detect malaria automatically. A Malaria Convolutional Neural Network (MCNN) model is proposed in this work to classify the infected cases. MCNN focuses on detecting infected cells, which aids in the computation of parasitemia, or infection measures. The proposed model achieved 0.9929, 0.9848, 0.9859, 0.9924, 0.0152, 0.0141, 0.0071, 0.9890, 0.9894, and 0.9780 in terms of specificity, sensitivity, precision, accuracy, F1-score, and Matthews Correlation Coefficient, respectively. A comparison was carried out between the proposed model and some recent works in the literature. This comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics.

Li Q., Yan G., Yu C.

Gross domestic product (GDP) is an important index reflecting the economic development of a region. Accurate GDP prediction of developing regions can provide technical support for sustainable urban development and economic policy formulation. In this paper, a novel multi-factor three-step feature selection and deep learning framework are proposed for regional GDP prediction. The core modeling process is mainly composed of the following three steps: In Step I, the feature crossing algorithm is used to deeply excavate hidden feature information of original datasets and fully extract key information. In Step II, BorutaRF and Q-learning algorithms analyze the deep correlation between extracted features and targets from two different perspectives and determine the features with the highest quality. In Step III, selected features are used as the input of TCN (Temporal convolutional network) to build a GDP prediction model and obtain final prediction results. Based on the experimental analysis of three datasets, the following conclusions can be drawn: (1) The proposed three-stage feature selection method effectively improves the prediction accuracy of TCN by more than 10%. (2) The proposed GDP prediction framework proposed in the paper has achieved better forecasting performance than 14 benchmark models. In addition, the MAPE values of the models are lower than 5% in all cases.

Lai H.

Abstract Gross domestic product (GDP) can well reflect the development of the economy, and predicting GDP can help better grasp the future economic trends. In this article, three different neural network models, the genetic algorithm – back-propagation neural network model, the particle swarm optimization (PSO) – Elman neural network (Elman NN) model, and the bat algorithm – long short-term memory model, were analyzed based on neural networks. The GDP data of Sichuan province from 1992 to 2020 were collected to compare the performance of the three models in predicting GDP. It was found that the mean absolute percentage error values of the three models were 0.0578, 0.0236, and 0.0654, respectively; the root-mean-square error values were 0.0287, 0.0166, and 0.0465, respectively; and the PSO-Elman NN model had the best performance in GDP prediction. The experimental results demonstrate that neural networks were reliable in predicting GDP and can be used for further applications in practice.

Khan S.

Data visualization is graph representation of data. It produces interactive graphs that explain the relationships among the data to viewers of the graph. The aim of data visualization is to communicate data value clearly and effectively through graphs [1]. Here we take the advantage of data visualization to explore the countries dataset to provide a holistic and interpretive view about the world. In addition to examine some hypotheses about gross domestic product (GDP) and Literacy and more of the countries effects on different factors showing on the dataset such as the literacy and the migration.

Assaad R.H., Fayek S.

Abstract There has been a renewed interest in accurately forecasting the price of crude oil and its fluctuations. That said, this paper aims to study whether the price of crude oil in the United States (US) could be predicted using the stock prices of the top information technology companies. To this end, time-series data was collected and pre-processed as needed, and three architectures of computational neural networks were tested: deep neural networks, long-short term memory (LSTM) neural networks, and a combination of convolutional and LSTM neural networks. The findings suggest that LSTM networks are the best architectures to predict the crude oil price. The outcomes of this paper could potentially help in making the oil price prediction mechanism a more tractable task and in assisting decision-makers to improve macroeconomic policies, generate enhanced macroeconomic projections, and better assess macroeconomic risks.

Laygo-Matsumoto S., Samonte M.J.

Maccarrone G., Morelli G., Spadaccini S.

This paper compares the predictive power of different models to forecast the real U.S. GDP. Using quarterly data from 1976 to 2020, we find that the machine learning K-Nearest Neighbour (KNN) model captures the self-predictive ability of the U.S. GDP and performs better than traditional time series analysis. We explore the inclusion of predictors such as the yield curve, its latent factors, and a set of macroeconomic variables in order to increase the level of forecasting accuracy. The predictions result to be improved only when considering long forecast horizons. The use of machine learning algorithm provides additional guidance for data-driven decision making.

Nosair A.M., Shams M.Y., AbouElmagd L.M., Hassanein A.E., Fryar A.E., Abu Salem H.S.

To monitor groundwater salinization due to seawater intrusion (SWI) in the aquifer of the eastern Nile Delta, Egypt, we developed a predictive regression model based on an innovative approach using SWI indicators and artificial intelligence (AI) methodologies. Hydrogeological and hydrogeochemical data of the groundwater wells in three periods (1996, 2007, and 2018) were used as input data for the AI methods. All the studied indicators were enrolled in feature extraction process where the most significant inputs were determined, including the studied year, the distance from the shoreline, the aquifer type, and the hydraulic head. These inputs were used to build four basic AI models to get the optimal prediction results of the used indicators (the base exchange index (BEX), the groundwater quality index for seawater intrusion (GQISWI), and water quality). The machine learning models utilized in this study are logistic regression, Gaussian process regression, feedforward backpropagation neural networks (FFBPN), and deep learning-based long-short-term memory. The FFBPN model achieved higher evaluation results than other models in terms of root mean square error (RMSE) and R2 values in the testing phase, with R2 values of 0.9667, 0.9316, and 0.9259 for BEX, GQISWI, and water quality, respectively. Accordingly, the FFBPN was used to build a predictive model for electrical conductivity for the years 2020 and 2030. Reasonable results were attained despite the imbalanced nature of the dataset for different times and sample sizes. The results show that the 1000 μS/cm boundary is expected to move inland ~9.5 km (eastern part) to ~10 km (western part) to ~12.4 km (central part) between 2018 and 2030. This encroachment would be hazardous to water resources and agriculture unless action plans are taken.

Muchisha N.D., Tamara N., Andriansyah A., Soleh A.M.

GDP is very important to be monitored in real time because of its usefulness for policy making. We built and compared the ML models to forecast real-time Indonesia's GDP growth. We used 18 variables that consist a number of quarterly macroeconomic and financial market statistics. We have evaluated the performance of six popular ML algorithms, such as Random Forest, LASSO, Ridge, Elastic Net, Neural Networks, and Support Vector Machines, in doing real-time forecast on GDP growth from 2013:Q3 to 2019:Q4 period. We used the RMSE, MAD, and Pearson correlation coefficient as measurements of forecast accuracy. The results showed that the performance of all these models outperformed AR (1) benchmark. The individual model that showed the best performance is random forest. To gain more accurate forecast result, we run forecast combination using equal weighting and lasso regression. The best model was obtained from forecast combination using lasso regression with selected ML models, which are Random Forest, Ridge, Support Vector Machine, and Neural Network.

Fan C., Chen M., Wang X., Wang J., Huang B.

The rapid development in data science and the increasing availability of building operational data have provided great opportunities for developing data-driven solutions for intelligent building energy management. Data preprocessing serves as the foundation for valid data analyses. It is an indispensable step in building operational data analysis considering the intrinsic complexity of building operations and deficiencies in data quality. Data preprocessing refers to a set of techniques for enhancing the quality of the raw data, such as outlier removal and missing value imputation. This article serves as a comprehensive review of data preprocessing techniques for analysing massive building operational data. A wide variety of data preprocessing techniques are summarised in terms of their applications in missing value imputation, outlier detection, data reduction, data scaling, data transformation, and data partitioning. In addition, three state-of-the-art data science techniques are proposed to tackle practical data challenges in the building field, i.e., data augmentation, transfer learning, and semi-supervised learning. In-depth discussions have been presented to describe the pros and cons of existing preprocessing methods, possible directions for future research and potential applications in smart building energy management. The research outcomes are helpful for the development of data-driven research in the building field.

Wu X., Zhang Z., Chang H., Huang Q.

Gross domestic product (GDP) is a general reference to comprehensive measure the level of a country or region’s economic development and diagnoses the health of economy. Traditional economic census-based methods for GDP forecasting are often expensive and resource-consuming, more importantly, economic census results lag significantly. This paper presents a data-driven GDP forecasting model that integrates multidimensional data from the aspects of electricity consumption, climate and human activities. Specifically, the model is built up based on the long-short-term-memory neural network with particle swarm optimization algorithm. The input multidimensional data are analyzed by correlation-based feature selection, and then filtered to five influencing factors. The experimental results show that these influencing factors are obviously related to economic development, at the same time, GDP can be well predicted based on the proposed model in a timely and relatively accurate manner.

Elshewey A.M., Youssef R.Y., El-Bakry H.M., Osman A.M.

Jallow H., Mwangi R.W., Gibba A., Imboga H.

Insights into the magnitude and performance of an economy are crucial, with the growth rate of real GDP frequently used as a key indicator of economic health, highlighting the importance of the Gross Domestic Product (GDP). Additionally, remittances have drawn considerable global interest in recent years, particularly in The Gambia. This study introduces an innovative model, a hybrid of recurrent neural network and long-short-term memory (RNN-LSTM), to predict GDP growth based on remittance inflows in The Gambia. The model integrates data sourced both from the World Bank Development Indicators and the Central Bank of The Gambia (1966–2022). Pearson’s correlation was applied to detect and choose the variables that exhibit the strongest relationship with GDP and remittances. Furthermore, a parameter transfer learning technique was employed to enhance the model’s predictive accuracy. The hyperparameters of the model were fine-tuned through a random search process, and its effectiveness was assessed using RMSE, MAE, MAPE, and R2 metrics. The research results show, first, that it has good generalization capacity, with stable applicability in predicting GDP growth based on remittance inflows. Second, as compared to standalone models the suggested model surpassed in term of predicting accuracy attained the highest R2 score of 91.285%. Third, the predicted outcomes further demonstrated a strong and positive relationship between remittances and short-term economic growth. This paper addresses a critical research gap by employing artificial intelligence (AI) techniques to forecast GDP based on remittance inflows.

Kaddes M., Ayid Y.M., Elshewey A.M., Fouad Y.

Breast cancer (BC) is a global problem, largely due to a shortage of knowledge and early detection. The speed-up process of detection and classification is crucial for effective cancer treatment. Medical image analysis methods and computer-aided diagnosis can enhance this process, providing training and assistance to less experienced clinicians. Deep Learning (DL) models play a great role in accurately detecting and classifying cancer in the huge dataset, especially when dealing with large medical images. This paper presents a novel hybrid model of DL models combined a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for binary breast cancer classification on two datasets available at the Kaggle repository. CNNs extract mammographic features, including spatial hierarchies and malignancy patterns, whereas LSTM networks characterize sequential dependencies and temporal interactions. Our method combines these structures to improve classification accuracy and resilience. We compared the proposed model with other DL models, such as CNN, LSTM, Gated Recurrent Units (GRUs), VGG-16, and RESNET-50. The CNN-LSTM model achieved superior performance with accuracies of 99.17% and 99.90% on the respective datasets. This paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that our model CNN-LSTM can enhance the performance of breast cancer classifiers compared with others with 99.90% accuracy on the second dataset.

Chen X., Kim M.G., Lin C., Na H.J.

In the 21st century, the increasing complexity and uncertainty of the global economy have heightened the need for accurate economic forecasting. Per capita GDP, a critical indicator of living standards, economic growth, and productivity, plays a key role in government policy-making, corporate strategy, and investor decisions. However, predicting per capita GDP poses significant challenges due to its sensitivity to various economic and social factors. Traditional methods such as statistical analysis, regression, and time-series models have shown limitations in capturing nonlinear interactions and volatility of economic data. To address these limitations, this study develops a per capita GDP forecasting model based on deep learning, incorporating key macroeconomic variables—the Consumer Price Index (CPI) and unemployment rate (UR)—to enhance predictive accuracy. This study employs five deep-learning regression models (RNN, LSTM, GRU, TCN, and Transformer) applied to real and placebo datasets, each incorporating combinations of CPI and UR. The results demonstrate that deep learning models can effectively capture complex, nonlinear relationships in economic data, significantly improving predictive accuracy compared to traditional models. Among the models, the Transformer consistently achieves the highest R-squared and lowest error values across various metrics (MSE, RMSE, and MSLE), indicating its superior ability to model intricate economic patterns. In addition, including CPI and UR as additional predictors enhances model robustness, with the TCN and Transformer models showing particularly strong performance in capturing short-term economic fluctuations. The findings suggest that the deep learning models, especially the Transformer, offer valuable tools for policymakers and business leaders, providing reliable GDP forecasts that support economic decision-making, resource allocation, and strategic planning. Academically, this study advances the understanding of deep learning applications in economic forecasting, particularly in integrating significant macroeconomic variables for enhanced predictive performance. The developed model is a foundation for informed economic policy and strategic decisions, offering a robust and actionable framework for managing economic uncertainties. This research contributes to theoretical and applied economics, providing insights that bridge academic innovation with practical utility in economic forecasting.

Zhu J., Dai W., Wu J., Zhang X., Chen H.

The accuracy of economic forecasting directly influences the formulation of economic policies and profoundly impacts the stable operation of the economy. As a pivotal indicator of economic activity, predicting the producer price index (PPI) is crucial. Although most existing research is focused on the overall PPI, economic and financial institutions are increasingly interested in its partially disaggregated components. Therefore, this paper proposes a hybrid hierarchical deep network prediction framework called Attention-HRNN-GRU (AHG) with parameter adaptive transfer, which integrates algorithms such as the attention mechanism, the Hierarchical Recurrent Neural Network (HRNN) and the Gated Recurrent Unit (GRU). First, an independent GRU network is designed and trained separately for each PPI level to perform preliminary predictions. The internal parameters of each level’s network are preserved to facilitate interlevel information transfer, forming an initial HRNN framework. An attention mechanism is then introduced to adaptively adjust the parameters of the upper-level prediction model that are used as the prediction parameters for the lower-level model. This process enables effective information transfer across multiple levels, producing high-accuracy prediction outcomes. This method effectively addresses a common issue in traditional hierarchical data prediction, where the direct application of upper-level parameters to lower-level data often overlooks variations between sequences. Experimental results show that the proposed AHG model markedly reduces prediction errors compared with those of the current advanced HRNN model. For example, the root mean square error (RMSE) of the producing materials index improved by 13.11% over that of the advanced model.

Elshewey A.M., Abed A.H., Khafaga D.S., Alhussan A.A., Eid M.M., El-kenawy E.M.

Heart disease is a category of various conditions that affect the heart, which includes multiple diseases that influence its structure and operation. Such conditions may consist of coronary artery disease, which is characterized by the narrowing or clotting of the arteries that supply blood to the heart muscle, with the resulting threat of heart attacks. Heart rhythm disorders (arrhythmias), heart valve problems, congenital heart defects present at birth, and heart muscle disorders (cardiomyopathies) are other types of heart disease. The objective of this work is to introduce the Greylag Goose Optimization (GGO) algorithm, which seeks to improve the accuracy of heart disease classification. GGO algorithm’s binary format is specifically intended to choose the most effective set of features that can improve classification accuracy when compared to six other binary optimization algorithms. The bGGO algorithm is the most effective optimization algorithm for selecting the optimal features to enhance classification accuracy. The classification phase utilizes many classifiers, the findings indicated that the Long Short-Term Memory (LSTM) emerged as the most effective classifier, achieving an accuracy rate of 91.79%. The hyperparameter of the LSTM model is tuned using GGO, and the outcome is compared to six alternative optimizers. The GGO with LSTM model obtained the highest performance, with an accuracy rate of 99.58%. The statistical analysis employed the Wilcoxon signed-rank test and ANOVA to assess the feature selection and classification outcomes. Furthermore, a set of visual representations of the results was provided to confirm the robustness and effectiveness of the proposed hybrid approach (GGO + LSTM).

Shen Y., Yu C., Li J., Wang S., Wei S., Ye D.

This article explored the application and effectiveness of the LSTM (Long Short-Term Memory) model algorithm in predicting and analyzing urbanization economic development. In recent years, accurately predicting the trend of urban economic development has become very important. Traditional economic forecasting methods suffer from limited data processing capabilities and insufficient model flexibility. For this purpose, this article designed an LSTM model algorithm to improve the accuracy and efficiency of urban economic development prediction. In the experimental stage, four experiments were designed to evaluate the predictive performance of urbanization economic development based on the LSTM model. In the benchmark model performance evaluation experiment, the AUC (Area Under the Curve) value of the LSTM model reached 0.92. In the time span prediction ability experiment, the mean square error of the LSTM model in each period ranged between 0.02 and 0.04. In the predictive evaluation experiment of data volume, when the data volume increased from 1000 to 10000, the accuracy of the LSTM model increased from 65% to 90%. In the final model parameter tuning experiment, by adjusting the LSTM model parameters, the accuracy of the model reached the highest value of 92%. From the data conclusion, it can be seen that the LSTM model is suitable for predicting urbanization and economic development tasks due to its excellent performance, and can provide strong data support for urban planning and economic policy formulation.

Valarmathi V., Ramkumar J.

The increasing frequency and severity of wildfires present critical challenges to ecosystems, human safety, and property and underline the inefficiency of traditional methods of fire detection and management. This chapter will present how integration between DL and IoT could give way to a revolution in fire ecology by providing innovative tools for real-time fire prediction, detection, monitoring, and response. DL, in particular, through Convolutional and Recurrent Neural Networks, looks into terabytes of data ranging from historical fire data to weather patterns and topography to predict and assess wildfire risks. IoT aids this with real-time data that emanates from a network of sensors, drones, and cameras spread across susceptible areas. This synergy therefore offers DL and IoT more accurate, timely, and proactive fire management. Future technologies will focus on 5G, blockchain, and advanced robotics for more resilient fire management strategies.

Elkenawy E.M., Alhussan A.A., Khafaga D.S., Tarek Z., Elshewey A.M.

Lung cancer is an important global health problem, and it is defined by abnormal growth of the cells in the tissues of the lung, mostly leading to significant morbidity and mortality. Its timely identification and correct staging are very important for proper therapy and prognosis. Different computational methods have been used to enhance the precision of lung cancer classification, among which optimization algorithms such as Greylag Goose Optimization (GGO) are employed. These algorithms have the purpose of improving the performance of machine learning models that are presented with a large amount of complex data, selecting the most important features. As per lung cancer classification, data preparation is one of the most important steps, which contains the operations of scaling, normalization, and handling gap factor to ensure reasonable and reliable input data. In this domain, the use of GGO includes refining feature selection, which mainly focuses on enhancing the classification accuracy compared to other binary format optimization algorithms, like bSC, bMVO, bPSO, bWOA, bGWO, and bFOA. The efficiency of the bGGO algorithm in choosing the optimal features for improved classification accuracy is an indicator of the possible application of this method in the field of lung cancer diagnosis. The GGO achieved the highest accuracy with MLP model performance at 98.4%. The feature selection and classification results were assessed using statistical analysis, which utilized the Wilcoxon signed-rank test and ANOVA. The results were also accompanied by a set of graphical illustrations that ensured the adequacy and efficiency of the adopted hybrid method (GGO + MLP).

Jin X., Song A., He M.

Pu Y., Huang Z., Wang J., Zhang Q.

This paper addresses the challenges of automated pricing and replenishment strategies for perishable products with time-varying deterioration rates, aiming to assist wholesalers and retailers in optimizing their production, transportation, and sales processes to meet market demand while minimizing inventory backlog and losses. The study utilizes an improved convolutional neural network–long short-term memory (CNN-LSTM) hybrid model, autoregressive moving average (ARIMA) model, and random forest–grey wolf optimization (RF-GWO) algorithm. Using fresh vegetables as an example, the cost relationship is analyzed through linear regression, sales volume is predicted using the LSTM recurrent neural network, and pricing is forecasted with a time series analysis. The RF-GWO algorithm is then employed to solve the profit maximization problem, identifying the optimal replenishment quantity, type, and most effective pricing strategy, which involves dynamically adjusting prices based on predicted sales and market conditions. The experimental results indicate a 5.4% reduction in inventory losses and a 6.15% increase in sales profits, confirming the model’s effectiveness. The proposed mathematical model offers a novel approach to automated pricing and replenishment in managing perishable goods, providing valuable insights for dynamic inventory control and profit optimization.

Rizk F.H., Mohamed M.E., Sameh B., Zaki A.M., Eid M.M., El-kenawy E.M.

Ahammad M.S., Sinthia S.A., Hossain M., Ahmed M.M., Ikram M.N., Asif N.

Velmurugan B., Dharmalingam S., Binith Muthukrishnan K., Senthilkumar K.R.

India's trajectory toward digital eminence is intricately woven into the fabric of its burgeoning digital library ecosystem. Through the lens of artificial intelligence (AI) insights, this chapter delineates the pivotal role of digital libraries in India's ascent within the global digital milieu. At the heart of this exploration lies the profound impact of digital libraries as reservoirs of knowledge, catalyzing innovation, and fostering inclusive growth. By employing a multifaceted analysis, the authors uncover the transformative potential inherent in these repositories, elucidating their capacity to democratize access to information, propel research and education, and underpin socioeconomic advancement.