Stress among employees is a crucial element that has a big impact on output and general well-being at work. Maintaining a productive and healthy workforce requires early intervention in the form of stress level identification and management. This study offers a machine learning method that uses emotional intelligence algorithms to identify employees’ stress levels and determine which ones would benefit from therapy for stress management. Through the integration of sophisticated machine learning algorithms with emotional intelligence measurements, the suggested model provides a reliable framework for evaluating employee stress in real-time. A large dataset is used to assess the model’s efficacy, demonstrating how well it predicts stress-related problems and suggests relevant counseling actions. The results show how this strategy has the ability to promote employee well-being and, as a result, enhance organizational performance through proactive.

Diabetes is a severe disease within which the deceased cannot appropriately manage the amount of sugar in the blood since it does not have adequate insulin, it is also a situation within which the extent of blood glucose stage is elevated than regular. In the area of medicine to discover patient's data as well as to attain a predictive model or a set of rules, Classification techniques have been persistently used. The key purpose of this is to explore and facilitate a better diagnosis of diabetes by predicting the blood glucose level in advance that is before 2 hours. Presently there are quite a lot of other methodologies do endure on classification for the diabetes disease (DD). The planned methodology that has been adopted for the classification and prediction, on the selected feature is J48 Decision Tree algorithm. Primal Diagnosis of DD provides a way with less cost which is always preferable. J48 algorithm is noted for its accuracy.

The field of image processing is distinguished by the variety of functions it offers and the wide range of applications it has in biomedical imaging. It becomes a difficult and time-consuming process for radiologists to do the manual identification and categorization of the tumour. It is a complex and time-consuming procedure conducted by radiologists or clinical professionals to remove the contaminated tumour region from magnetic resonance (MR) pictures. It is the goal of this study to improve the performance and reduce the complexity of the image segmentation process by investigating FCM predicted image segmentation procedures in order to reduce the intricacy of the process. Furthermore, relevant characteristics are collected from each segmented tissue and aligned as input to the classifiers for autonomous identification and relegation of encephalon cancers in order to increase the accuracy and quality rate of the neural network classifier. An evaluation, validation, and presentation of the experimental performance of the suggested approach have been completed. A unique APSO (accelerated particle swarm optimization) based artificial neural network model (ANNM) for the relegation of benign and malignant tumours is presented in this study effort, which allows for the automated identification and categorization of brain tumours. Using APSO training to improve the suggested ANNM model parameters would give a unique method to alleviate the stressful work of radiologists performing manual identification of encephalon cancers from MR images. The use of an APSO-based ANNM (artificial neural network model) model for automated brain tumour classification has been presented in order to demonstrate the resilience of the classification model. It has been suggested to utilise the improved enhanced fuzzy c means (IEnFCM) method for image segmentation, while the GLCM (gray level co-occurrence matrix) feature extraction approach has been employed for feature extraction from magnetic resonance imaging (MR pictures).

In the current educational landscape, the integration of Artificial Intelligence is transforming the way students learn and teachers instruct. The “Personalized Adaptive Learning Platform Empowered by Artificial Intelligence” is an innovative project aimed at revolutionizing the educational landscape by leveraging artificial intelligence (AI) to provide a personalized and interactive learning experience. In this paper we present an educational technology platform which harnesses the capabilities of artificial intelligence (AI) to emulate the role of a human teacher in delivering lectures, offering personalized explanations through visual animations, and closely monitoring student engagement via webcam-based interactions. The purpose of this endeavor is multifaceted. Firstly, it aims to narrow the divide between conventional teaching methods and the changing requirements of modern learners by introducing an AI-driven, personalized approach. Secondly, the integration of webcam monitoring ensures active student participation and attentiveness during lectures.

Breast cancer is a leading cause of mortality worldwide, emphasizing the need for accurate and timely prediction of risk factors. This study proposes a novel approach using deep learning algorithms to predict breast cancer. The self-adaptive sea lion optimization-based recurrent neural network (SA-SLnO-RNN) is developed to select optimal features that enhance prediction accuracy. Evaluated on the breast cancer Wisconsin diagnostic (BCWD) dataset, the SA-SLnO-RNN achieves superior performance compared to existing methods, with accuracy, precision, recall, specificity, and F1-score values of 98.82%, 98.78%, 98.71%, 99.06%, and 98.75%, respectively. The proposed model’s self-adaptive nature prevents overfitting, while the RNN with optimization algorithm enables quick convergence and captures complex temporal dependencies, ensuring early and accurate breast cancer prediction.