Search

Diabetes is a chronic disease that is usually caused when the pancreas fails to produce sufficient insulin or when the body is unable to effectively utilize the insulin produced by the pancreas. Early detection of diabetes enables the implementation of a suitable treatment method, which can lead to a healthy lifestyle. A necessity arises for an automated system capable of diagnosing diabetes using clinical and physical data in cases when the conventional approach to detecting diabetes proves to be arduous. In this paper, a new diabetes classification model based on optimized long short-term memory (LSTM) is presented and evaluated on the Pima Indians Diabetes Database (PIDD). To improve the LSTM model, a novel hybrid waterwheel plant and stochastic fractal search (WWPASFS) is proposed for optimizing its parameters. To confirm the performance superiority of the proposed WWPASFS + LSTM model, it is compared to various machine learning models and metaheuristic optimization methods. In addition, the binary WWPASFS is proposed to extract the relevant features in the PIDD dataset, with the aim of improving the accurate classification of diabetes patients. The WWPASFS + LSTM model attained the highest accuracy of 98.2% in classifying diabetes patients on the dataset in hand. The WWPASFS + LSTM model exhibited superior performance compared to the other five models, namely decision tree, K-nearest neighbors, neural networks, random forest, and support vector machines. On the other hand, the statistical analysis of the proposed approach is studied and the results prove its difference and significance. [ABSTRACT FROM AUTHOR]

The cultivation of potatoes is one of the most important parts of the world’s agricultural system, so forecasting methods that can precisely predict the direction of production are needed. We focus on the area of optimization techniques herein in this study and develop a particular use of metaheuristic algorithms applied to improve predictive models. Among such algorithms, the Waterwheel Plant Algorithm (WWPA) is notable for its efficiency in enhancing the autoregressive integrated moving average (ARIMA) model. Feature selection, an essential preprocessing step in machine learning, is of the highest significance in our approach. We apply the bWWPA method to select the most central features from the dataset, which, in turn, improves the whole predictive model’s performance. Through the identification of the main patterns and links in the data, feature selection allows for the model to focus on the most influential factors, giving way to more precise predictions. The WWPA-ARIMA model obtained by our method captures the essential features after optimization and thus involves a very low root mean square error (RMSE) of 0.0001. Such a high level of precision emphasizes the efficiency of our optimization procedure in adjusting the ARIMA model parameters carefully to reveal the hard-to-catch patterns in potato production data. To evaluate the robustness of our method, we employ strong statistical analyses, such as ANOVA and the Wilcoxon signed-rank test. This test also gives additional evidence that our optimization method works better than alternative approaches. [ABSTRACT FROM AUTHOR]

Potato is a food crop at a global scale, bearing a hefty importance for the food security and nutrition of millions of people worldwide. Nonetheless, some obstacles have to be overcome in the cultivation of potatoes, such as susceptibility to a number of diseases that affect quality and yield. Thus, sound disease management approaches are critical to protect potato crops and support maximum production. In this perspective, optimization techniques are vital in improving disease classification accuracy, thus helping in early detection and timely intervention. In this research, we suggest the hybridization of the Greylag Goose Optimizer (GGO) with the Grey Wolf Optimizer (GWO), which is called GGGWO, for the optimization of convolutional neural network (CNN) models for potato disease classification. Through our approach, we are seeking to enhance precision and timeliness in the diagnosis of diseases that will eventually lead to the development of appropriate crop management practices and sustainable agriculture. The performance of the GGGWO-CNN model is assessed in terms of accuracy and is compared to other optimization algorithms using statistical testing methods like ANOVA and Wilcoxon signed rank tests. The results exhibit the excellent performance of the GGGWO-CNN model with an accuracy of 0.9904 and a sensitivity of 0.9421 in identifying potato diseases accurately, highlighting its potential to aid farmers and general agriculture practitioners. Utilizing optimization techniques and CNN models, our research helps in the development of precision agriculture as well as the improvement of resilient potato cropping systems. The proposed method’s approach provides an exciting way of dealing with the problem of potato diseases. It provides an excellent platform for carrying out further studies on improving agricultural decision-making processes aimed at better crop health and productivity. [ABSTRACT FROM AUTHOR]

In the literature, it is well known that there is a bidirectional causality between economic growth and energy consumption. This is why it is crucial to forecast energy consumption. In this study, four deep learning models, i.e., Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), were used to forecast energy consumption in Brazil, Canada, and France. After a training test period, the performance evaluation criterion, i.e., R2, mean square error, root mean square error, mean absolute error, and mean absolute percentage error, was performed for the performance measure. It showed that GRU is the best model for Canada and France, while LSTM is the best model for Brazil. Therefore, the energy consumption prediction was made for the 12 months of the year 2017 using LSTM for Brazil and GRU for Canada and France. Based on the selected model, it was projected that the energy consumption in Brazil was 38 597.14–38 092.88, 63 900–4 800 000 GWh in Canada, and 50 999.72–32 747.01 GWh in France in 2017. The projected consumption in Canada was very high due to the country's higher industrialization. The results obtained in this study confirmed that the nature of energy production will impact the complexity of the deep learning model. [ABSTRACT FROM AUTHOR]

This study goes into the essential challenge of estimating potato output in order to ensure sustainable agricultural practices while also providing vital insights into global market patterns. The potato production data series compares the accuracy of two popular forecasting models, ARIMA (AutoRegressive Integrated Moving Average) and ETS (Error-Trend-Seasonality), in predicting potato production. The study assesses the efficacy of these models with a particular focus on their relevance to the agricultural markets of India, China, and the USA, three major potato-producing countries. This research builds ARIMA and ETS models and thoroughly assesses their forecasting performance using historical production data series from these important nations. The results show that the ETS model, especially when considering the chosen countries, consistently performs better in predicting potato production for the testing data set than the ARIMA model. According to the models, China and India will keep contributing more to the potato market, solidifying their positions as key players. It is anticipated that the US economy will plateau and stabilize. For the anticipated year 2027, the expected potato output for China, India, and the USA is 100,417, 61,882, and 18,229 thousand tonnes, respectively. Nonetheless, the increasing diversity of confidence intervals in extended forecasts illustrates the intricacy of agricultural productivity and the numerous factors that could impact outcomes. We believe that this research significantly advances sustainable farming methods by offering a thorough analysis of worldwide potato production projections. It also improves our comprehension of the dynamics of the potato market, providing insightful information that can guide decision-making at different levels. In the conclusions, we stated that the studies not only have consequences for the potato sector, but they also highlight how crucial it is to use cutting-edge forecasting methods in order to promote sustainable food production and guarantee future food security. [ABSTRACT FROM AUTHOR]

Nanoemulsions are gaining interest in a variety of products as a means of integrating easily degradable bioactive compounds, preserving them from oxidation, and increasing their bioavailability. However, preparing stable emulsion compositions with the desired characteristics is a difficult task. The aim of this study was to encapsulate the Tinospora cordifolia aqueous extract (TCAE) into a water in oil (W/O) nanoemulsion and identify its critical process and formulation variables, like oil (27–29.4 mL), the surfactant concentration (0.6–3 mL), and sonication amplitude (40% to 100%), using response surface methodology (RSM). The responses of this formulation were studied with an analysis of the particle size (PS), free fatty acids (FFAs), and encapsulation efficiency (EE). In between, we have studied a fishbone diagram that was used to measure risk and preliminary research. The optimized condition for the formation of a stable nanoemulsion using quality by design was surfactant (2.43 mL), oil concentration (27.61 mL), and sonication amplitude (88.6%), providing a PS of 171.62 nm, FFA content of 0.86 meq/kg oil and viscosity of 0.597 Pa.s for the blank sample compared to the enriched TCAE nanoemulsion with a PS of 243.60 nm, FFA content of 0.27 meq/kg oil and viscosity of 0.22 Pa.s. The EE increases with increasing concentrations of TCAE, from 56.88% to 85.45%. The RSM response demonstrated that both composition variables had a considerable impact on the properties of the W/O nanoemulsion. Furthermore, after the storage time, the enriched TCAE nanoemulsion showed better stability over the blank nanoemulsion, specially the FFAs, and the blank increased from 0.142 to 1.22 meq/kg oil, while TCAE showed 0.266 to 0.82 meq/kg. [ABSTRACT FROM AUTHOR]

Lead-based deep brain stimulation (DBS) electrodes have been employed to treat Parkinson's disease (PD), but their limitations have led to the development of lead-free piezoelectric nanoparticle-based DBS (LF-PND-DBS). This novel approach utilizes non-invasive biocompatible piezoelectric nanoparticles to generate electrical stimulation, offering a promising alternative to traditional DBS. In this study, an innovative machine learning (ML)-optimized LF-PND-DBS system for diagnosing and evaluating PD is proposed. By leveraging ML algorithms, the optimized design of LF-PND electrodes and stimulation parameters is derived, ensuring precise and personalized treatment delivery. The ML-optimized LF-PND-DBS system was evaluated in a cohort of PD patients, demonstrating an exceptional diagnostic accuracy with a sensitivity of 99.1% and a specificity of 98.2%. It effectively assessed PD severity and response to DBS treatment, providing valuable guidance for treatment monitoring. The findings highlight the immense potential of the ML-optimized LF-PND-DBS system as a transformative tool for PD diagnosis and evaluation. This novel approach has the potential to enhance DBS efficacy, safety, and personalization, paving the way for improved patient outcomes and quality of life. [ABSTRACT FROM AUTHOR]