The majority of countries rely largely on agriculture for employment. Irrigation accounts for a sizable amount of water use. Crop irrigation is an important step in crop yield prediction. Field harvesting is very reliant on human supervision and experience. It is critical to safeguard the field’s water supply. The shortage of fresh water is a major challenge for the world, and the situation will deteriorate further in the next years. As a result of the aforementioned challenges, smart irrigation and precision farming are the only viable solutions. Only with the emergence of the Internet of Things and machine learning have smart irrigation and precision agriculture become economically viable. Increased efficiency, expense optimization, energy maximization, forecasting, and general public convenience are all benefits of the Internet of Things (IoT). As systems and data processing become more diversified, security issues arise. Security and privacy concerns are impeding the growth of the Internet of Things. This article establishes a framework for detecting and classifying intrusions into IoT networks used in agriculture. Security and privacy are major concerns not only in agriculture-related IoT networks but in all applications of the Internet of Things as well. In this framework, the NSL KDD data set is used as an input data set. In the preprocessing of the NSL-KDD data set, first all symbolic features are converted to numeric features. Feature extraction is performed using principal component analysis. Then, machine learning algorithms such as support vector machine, linear regression, and random forest are used to classify preprocessed data set. Performance comparisons of machine learning algorithms are evaluated on the basis of accuracy, precision, and recall parameters.

Fiscal policy is a crucial government tool for influencing and managing the national economy and creating a strong incentive for low carbon investment. Previous literature has reputable evidence that improving fiscal policy enhances environmental quality. However, the literature fails to classify the exact turning level (threshold point) below/above which the association may be negative or positive. In this regard, this research investigates the nexus between fiscal policy, foreign direct investment, financial development, trade openness, urban population, gross capital formation, labour force, and CO₂ emissions in the era of globalization. The panel data set contained 105 countries over the period from 1990 to 2016. The empirical findings are estimated through linear and nonlinear panel data approaches such as fully modified ordinary least square and panel threshold regression. The subsequent findings are established: first, fiscal policy and globalization significantly increase environmental pollution. Second, the empirical results confirm the existence of the pollution haven hypothesis (PHV). Third, financial development and gross fixed capital formation are also considered some of the most crucial indicators to increase pollution levels. Fourth, trade openness, urban population, and labour force improve environmental quality. Fifth, panel threshold regression discovers that countries maintain a minimum level of fiscal policy at −1.2889. Based on these empirical findings, this study suggests that policymakers and governments of these countries should take steps to restructure their industrial sector and design macroeconomic-level carbon-free policies to support the implementation of low-energy-intensive and lower carbon production technologies.

Due to the growing worries of communities and governments about the depletion of natural resources and environmental contamination, green innovation (GI) is currently receiving much attention on a global scale. This study intends to investigate how Green Innovation Adoption (GIA) influences Sustainable Performance (SNP) in Saudi Arabia’s small- and medium-sized enterprises (SMEs) based on primary research. A conceptual framework model is developed to better comprehend the relationships of Government Support (GS), External Partnership and Cooperation (EPC), Rules and Regulatory Factors (RR), Market and Customer Factors (MC), Organization and Human Factors (OH), Green Innovation Strategy (GIS), and Technology Factors (TF) with GIA. The evaluation of hypotheses is performed using the Partial Least-Squares Structural Equation Modeling (PLS-SEM) method. The study’s findings are obtained using the SPSS 24.0 and AMOS 24.0 software programs. The results of this study reveal that GS, EPC, RR, MC, OH, and TF all have a positive impact on GIA. Furthermore, it has been noted that GIA has a positive impact on the economic, social, and environmental performance of SMEs in the Kingdom of Saudi Arabia. In accordance with the findings, corporate units that use GI would produce more acceptable eco-friendly and long-term performance.

Alzheimer’s disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning. This article presents an Alzheimer’s disease detection framework consisting of image denoising of an MRI input data set using an adaptive mean filter, preprocessing using histogram equalization, and feature extraction by Haar wavelet transform. Classification is performed using LS-SVM-RBF, SVM, KNN, and random forest classifier. An adaptive mean filter removes noise from the existing MRI images. Image quality is enhanced by histogram equalization. Experimental results are compared using parameters such as accuracy, sensitivity, specificity, precision, and recall.