ノマン ムハマド

<jats:p>Citrus is manually counted to estimate the yield. By using some innovative agricultural techniques yield and production can be increased. Numerous agricultural innovations have been introduced in recent years. Higher agricultural production, prediction, and reliable crop status information are more important than ever due to the expected growth of the human population. Agriculture has always been the foundation of human society. Current study was aimed to develop a reliable and meaningful information-gathering agricultural field based on image processing during 2020. Citrus yield can be increased in the initial stages by counting it with RGB and HSV-based images taken from an Android phone from various angles using machine learning techniques. Fertilizers such as potash, phosphorus, and nitrogen can then be utilized to boost yield. According to the findings, farmers can control and monitor citrus health production more efficiently and effectively by integrating machine learning with agriculture. The citrus calculation using the given technique compared with manually counted citrus, having difference of up to 5 to 10 citruses for a single plant per plot in a field. The proposed method produced excellent results under varying lighting conditions, leaf occlusion, and fruit overlap on photos taken at various distances from the orange trees.</jats:p>

<jats:p>INTRODUCTION: According to the WHO (World Health Organization), nearly 0.8 million people commit suicide each year, with more than 20 suicide attempts for every self-immolation. Suicidal behaviors have a profound effect on communities, societies, families, friends, and colleagues. After the recognition of public health as a priority by the WHO, various studies are being conducted to prevent it.OBJECTIVES: The investigation's goals were to improve understanding of suicide by identifying socioeconomic indicators correlated with rising suicide rates among divergent legions globally and to develop a prediction model for those who are at a higher risk of suicide by using different predictors of suicide such as tension, depression, anxiety, and so on.METHODS: We used a variety of data mining techniques to create a prediction model for suicide, including Logistic Regression, Multilayer Perceptron, Polynomial/Gaussian/Sigmoid SVM, Decision Tree, and K-Nearest Neighbors. For identifying socioeconomic suicide indicators, we used various descriptive and exploratory analysis techniques such as mean, regression, and correlation.RESULTS: Classification through the Gaussian Kernel - SVM has been shown to have the best results relative to others. Results also stated that many countries saw a decrease in suicide rates between 2006 and 2015, compared to 1996 to 2005. The highest concentrations have been reported in Europe, while the lower has been observed in South America.CONCLUSION: Things are improving, at least according to the statistics. The performance of Gaussian Kernel-SVM has been demonstrated to be superior to the other algorithms for suicide prediction. Data on suicide and suicide attempts are imprecise and difficult to gather. Suicide and suicide attempt monitoring, and surveillance must be improved for suicide prevention initiatives to be effective.</jats:p>