Potential of Artificial Intelligence in Boosting Employee Retention in the Human Resource Industry

Artificial intelligence (AI) has the potential to transform the human resource (HR) industry by automating routine tasks, improving decision-making, and enhancing employee engagement and retention. In this paper, we explore the use of machine learning and deep learning techniques to boost employee retention in the HR industry. We review the current state of the art in AI for HR, including the use of predictive analytics, natural language processing, and chatbots for talent management and employee development. We also discuss the challenges and ethical considerations of using AI in HR, including issues of bias and the need for transparent and explainable algorithms. Finally, we present case studies of successful AI-powered HR initiatives that have demonstrated improvements in employee retention and engagement. Our findings suggest that AI has the potential to significantly enhance employee retention in the HR industry, but its implementation requires careful planning and consideration of potential risks and ethical issues

The Intersection of Technology and Public Health: Opportunities and Challenges

The intersection of technology and public health stands as a critical nexus, offering unprecedented opportunities as well as serious challenges. Recent technological advances bring new solutions that can transform healthcare delivery, disease prevention and healthcare. From telemedicine and telehealth services to wearable devices and artificial intelligence, these devices promise sophistication, affordability and efficiency in healthcareSignificant opportunities are emerging in data-driven decision making. Integrating big data analytics enables real-time monitoring of public health data, facilitates rapid response to emerging threats, and optimizes resource allocation Digital healthcare systems enable individual delivery they prioritize their well-being, encouraging a paradigm shift toward preventive health care. In particular, telehealth services bridge geographic gaps, providing remote consultations that extend health care to underserved populations.But the promising land is not without its challenges. Privacy concerns are more pronounced, as the collection and use of personal health information raises ethical dilemmas. Harnessing the power of data and striking a balance between protecting individual privacy is paramount to successfully integrating technology into public health. Additionally, the digital divide presents tangible risks, and disparities in access to technology can exacerbate existing health disparities. The convergence of technology and public health represents a positive path to transformative change. While the opportunities are great, navigating through the challenges requires critical thinking and collaboration

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