Jigna Jadav

In today's busy world, stress is common because people must think about many things simultaneously. To effectively deal with the harmful effects of worry on your health, a person needs to notice them as soon as they appear. This study supports recognizing stress as a helpful method. It shows how critical physiological signs are as a reliable way to detect stress, mainly because these signals cannot be changed purposefully. Heart Rate Variability (HRV), a physiological signal, is used in this study to investigate how stress can be detected using the SWELL knowledge work (SWELL-KW) dataset of 25 Subjects. PCA (Principal Component Analysis) and IQR (Interquartile Range) Preprocessing techniques are applied to select 26 features and detect outliers. The proposed model used a long short-term memory (LSTM) model to sort stress levels from biosensors in real-time and gives 98% accuracy. This study goes even further by using explainable artificial intelligence (XAI) models to explain their performance by pointing out the factors the model thought were important when making a decision. The SHAP (SHapley Additive Explanations) model is used to understand results by making them easier to interpret. It also promotes acknowledging stress as a beneficial method for managing mental health, highlighting the significance of early identification and intervention for a proactive and comprehensive approach to mental well-being. The contributions provide significant insights and techniques for resolving stress-related difficulties and developing mental health awareness and resilience.

In today's fast-paced lifestyle, pursuing holistic well-being has increased interest in monitoring and managing stress levels. Heart rate variability (HRV), a non-invasive measure of autonomic nervous system activity, has emerged as a valuable tool for assessing individual responses to stress. This study focuses on utilizing the capabilities of the Apple Watch to collect continuous HRV data in real-world contexts. A diverse dataset from individuals working in software companies was gathered, including HRV recordings during various stress-inducing scenarios. By employing HRV Time Domain, Frequency Domain, and Nonlinear features, the study uses Principal Component Analysis (PCA) to extract relevant features, considering the personalized nature of stress reactions. Addressing variations in stress responses among individuals, the study introduces an innovative approach using Long Short-Term Memory (LSTM) networks. A hybrid model, combining feature selection, dimensionality reduction, and ensemble techniques, is developed to predict stress levels based on individualized HRV patterns. Rigorous training and validation reached to an 88% accuracy rate. These findings demonstrate the effectiveness of the proposed methodology. The LSTM model accurately forecasts stress responses, highlighting the potential of Apple Watch-acquired HRV data for stress assessment. Beyond prediction, the study enhances understanding of the complex interplay between HRV dynamics and unique stress reactions. This novel approach, leveraging Apple Watch features and intelligent computing, offers a personalized method to predict stress levels using K-Means Clustering Algorithm. Through integrating K-means clustering and person-specific HRV analysis, the research endeavours to advance our comprehension of the intricate interplay between physiological responses and stressors. The study offers a novel perspective on stress response variations by delving into the distinct autonomic patterns characterizing each cluster. It sets the stage for developing targeted interventions and personalized stress management strategies.

Face recognition is one of the most relevant applications of image analysis. It’s a true challenge to build an automated system which equals human ability to recognize faces. Although humans are quite good identifying known faces, we are not very skilled when we must deal with a large amount of unknown faces. The computers, with an almost limitless memory and computational Speed, should overcome human’s limitations. Face recognition is one of the most important biometric which seems to be a good compromise between actuality and social reception and balances security and privacy well. The goal of face reorganization is to implement the system for a particular face and distinguish it from a large number of stored faces with some real-time variations as well.

Data mining is use to discover a knowledge and hidden pattern from data [15]. OLAP (Online Analytical Processing) is a tool of data mining and data warehouse that performs different operations on data that is store in multidimensional database but the limitation of OLAP is that it is not capable to explain relationships between data that resides in data cube. So that’s why OLAM is used it is also known as OLAP mining that takes advantages of both OLAP and data mining and gives accurate results so it is consider as a business intelligence (BI).different mining techniques are there that can be apply on OLAP cube in paper we will how to apply association rules mining method on data cube.

Face recognition is one of the most relevant applications of image analysis. It’s a true challenge to build an automated system which equals human ability to recognize faces. Although humans are quite good identifying known faces, we are not very skilled when we must deal with a large amount of unknown faces. The computers, with an almost limitless memory and computational Speed, should overcome human’s limitations. Face recognition is one of the most important biometric which seems to be a good compromise between actuality and social reception and balances security and privacy well. The goal of face reorganization is to implement the system for a particular face and distinguish it from a large number of stored faces with some real-time variations as well.