Occupational Stress and Mental Health: A Longitudinal Study in High-Stress Professions

This long-term study looks at the complicated link between job stress and mental health in people who have high-stress jobs. The study takes a broad method to figure out how movement changes over time because it knows that work demands have a big effect on people's health and happiness. By carefully choosing high-stress fields like law enforcement, emergency services, and healthcare, the study aims to find the link between low-stress factors in these settings and long-lasting effects on mental health. Get both numeric and personal information This method not only finds similar sources of stress, like problems at work, disagreements with others, and difficult emotions like sadness, but it also looks at how people deal with these problems. The data should help us understand how complicatedly work-related stress and mental health are connected, and they might also shed light on possible ways to avoid stress and help people who are experiencing it. The talk will look at what works in high-stress jobs and make suggestions for changes to the workplace and programs to help with mental health. Even though the study has some flaws, it hopes to serve as a starting point for more research that aims to create healthier workplaces in high-stress fields

Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf

In the whole agriculture plays a very important in country’s economic condition specially in Indian agriculture has a crucial role for raising the Indian economic structure and its level. India’s frequent changing climatic situation, various bacterial disease is much normal that drastically decreases the productivity of crop productivity. Most of the researcher is moving towards into this topic to find the early detection technique to identify the disease in small green leaves plants. A single, micro bacterial infectious disease can destroy all the agricultural small green leaves plants get damaged overnight and hence must be prevented and cured as earliest as possible so that agriculture production. In this research work, we had tried to developed a green small green leaves plants bacterial disease early detection system based on the deep learning network system which will detect the disease at very earlier state of symptoms observed. Deep learning technique is has various algorithms to detect the earliest stage of any of the procedural processing of any bacterial infections or disease. This paper consists of investigations and analysis of latest deep learning techniques. Initially we will explore the deep learning architecture, its various source of data and different types of image processing method that can be used for processing the images captured of leaf for data processing. Different DL architectures with various data visualization’s tools has recently developed to determine symptoms and classifications of different type of plant-based disease. We had observed some issue that was un identified in previous research work during our literature survey and their technique to resolve that issue in order to handle the functional auto-detection system for identifying the certain plant disease in the field where massive growth of green small green leaves plants production is mostly done. Recently various enhancement has been done in techniques in CNN (convolution neural network) that generates much accurate images classification of any object. Our research work is based on deep learning network that will observe and identifies the symptoms generated in leaflet of plant and identifies the type of bacterial infection in progress in that with the help of plant classification stated in the plant dataset. Our research work represents the implementation DCGAN and Hybrid Net Model using Deep learning algorithm for early-stage identification of green plant leaves disease in various environmental condition. Our result obtained shows that it has DCGAN accuracy 96.90% when compared withHybrid Net model disease detection methodologies