In this paper, we introduce an approach via regularization and Homotopy way for resolving the inverse Cauchy problem of the Laplace of system partial differenti

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT), referred to as the Artificial Intelligence of Things (AIoT), has rapidly evolved into a transformative technological paradigm influencing sectors from smart cities to healthcare, agriculture, and beyond. This review explores the convergence of AI and IoT, highlighting the benefits, challenges, and socio-technical implications of their fusion. While AIoT systems promise operational efficiency, sustainability, and enhanced decision-making capabilities, they also pose pressing ethical concerns, especially around data privacy, security, and the societal impact of automation. Through a structured review of thirty recent scholarly contributions, this paper identifies major research themes, gaps in the literature, and opportunities for interdisciplinary innovation. The study emphasizes the need for balanced development—ensuring that technological advancements are accompanied by robust policy frameworks and a nuanced understanding of their social consequences. Ultimately, the paper argues for a more holistic and human-centered approach to AIoT innovation to ensure its responsible and equitable integration into society..

This study presents an AI-driven multi-objective optimization approach using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to enhance hospital operational efficiency in Syria. Using real-world data from the Tishreen University Hospital over a 60-day period, the research addresses three conflicting objectives: minimizing average patient waiting time, reducing daily operational costs, and maximizing the number of patients treated. Six key operational variables were selected to build the optimization model, including bed availability, physician count, and daily admissions. The NSGA-II algorithm successfully generated a set of Pareto-optimal solutions, each reflecting different trade-offs among the objectives. Statistical analysis and visualizations confirmed the complexity and nonlinearity of hospital operations, showing that increases in resources or costs do not always lead to improved outcomes. The results offer decision-makers a range of efficient operational configurations tailored to various institutional priorities. This model provides a valuable decision-support tool, especially in resource-constrained healthcare environments like Syria. Future research will focus on integrating real-time data, expanding operational variables, and validating the model across different institutions to support broader policy implementation and operational standardization.

The prediction of new cases of infection is crucial for authorities to get ready for early handling of the virus spread. Methodology Analysis and forecasting of epidemic patterns in new SARS-CoV-2 positive patients are presented in this research using a hybrid deep learning algorithm. The hybrid deep learning method is employed for improving the parameters of long short-term memory (LSTM). To evaluate the effectiveness of the proposed methodology, a dataset was collected based on the recorded cases in the Russian Federation and Chelyabinsk region between 22 January 2020 and 23 August 2022. In addition, five regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach. The achieved results show that the proposed approach could reduce the mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE), coefficient of determination (R Square), coefficient of correlation (R), and mean bias error (MBE) when compared with the five base models. The achieved results confirm the effectiveness, superiority, and significance of the proposed approach in predicting the infection cases of SARS-CoV-2.

Background: The goal of this study was to forecast pulse production in six countries: Afghanistan, Bangladesh, China, India, Nepal and Pakistan (2020-2027). In this study, time series forecasting was used. Methods: The data series were divided into training set from 1961 to 2015 for model building, testing set from 2016 to 2019 for validation and finally, after selecting the best model, forecast was used from 2020 to 2027, the models were compared. The best-fit model was chosen based on the minimum ME, RMSE, MAE, MPE, MAPE, MASE, ACF1 values on the training data set and the minimum MAPE values on the testing data set. Result: The best fitted model for India was NNAR (1,1). Similar to Afghanistan, the best fit model for forecasting was NNAR (3,2). The best fit model for forecasting in China was ARIMA (0,1,1). The best fit model for forecasting in Nepal was ARIMA (1,1,0). The best fit model for forecasting in Pakistan was ETS (A, N, N) (M, N, N). With a 15.73 per cent growth rate from 2020 to 2027, the best models predict that the production of pulses in (Afghanistan, China, India) will increase until 2027. India will continue to be the largest producer of pulses among the six countries, with production expected to reach 1088.778 thousand tons in 2027. Afghanistan and China have extreme growth rates of 25.19% and 11.95%, respectively, while the rest of the countries have relatively stable production volumes. These results may be crucial for developing an effective agriculture production policy, whether by providing forecasted production values or evaluating such policies.