Journal of Science Engineering Technology and Management Science

Continuous observation of patient health conditions is essential for early detection of medical emergencies and timely intervention. However, conventional monitoring systems often require frequent hospital visits or rely heavily on internet-based infrastructures, which may not always be available in remote or resource-limited environments. To address these challenges, this paper presents a Real-Time Patient Health Monitoring System using an ESP32 microcontroller integrated with GSM-based notification technology. The proposed system continuously measures vital physiological parameters including heart rate, blood oxygen saturation (SpO₂), and body temperature, while also incorporating a fall detection mechanism to identify accidental movements or patient collapse. The collected data from biomedical sensors is processed in real time using the ESP32 controller. The monitored parameters are displayed on a 16×2 LCD screen, allowing patients and caregivers to easily observe health conditions locally. To enhance patient safety, the system includes a threshold-based alert mechanism. When any monitored parameter exceeds predefined safe limits or when a fall event is detected, an audible buzzer alert is triggered locally. Simultaneously, the integrated GSM communication module automatically sends SMS notifications containing patient status information to caregivers, medical staff, or emergency contacts. Unlike many IoT-based monitoring systems that depend on continuous internet connectivity, the proposed solution operates using cellular communication, ensuring reliable alert delivery even in remote areas. The system is particularly beneficial for elderly individuals, patients with chronic illnesses, and home healthcare monitoring environments. Experimental evaluation demonstrates that the system provides stable real-time monitoring, rapid alert response, and reliable emergency notification, making it a cost-effective and practical solution for modern remote healthcare applications.

Keywords :

Author : R.Chinna Rao1, T Rakesh2

Title : Configurable FIFO Buffer ASIC Flow with Gate-Level Simulation & Assertions

Volume/Issue : 2024;01(01)

Page No : 209-216

Behavioural pattern recognition in enterprise software platforms represents a critical capability for product analytics, user experience optimisation, fraud detection, and customer health monitoring. This paper presents a hybrid deep learning architecture that combines Bidirectional Encoder Representations from Transformers (BERT) for contextual feature extraction, Convolutional Neural Networks (CNN) for local pattern detection, and Bidirectional Gated Recurrent Units (BiGRU) for temporal sequence modelling. The proposed architecture addresses the fundamental challenge that enterprise user behaviour is simultaneously contextual, locally patterned, and temporally dependent. We evaluate the hybrid model on three enterprise use cases: user churn prediction, feature adoption forecasting, and anomalous behaviour detection. Experimental results on production datasets from a SaaS platform serving over 200 enterprise clients demonstrate that the hybrid architecture achieves a 7.3% improvement in F1-score over the best single-architecture baseline and a 4.1% improvement over existing ensemble methods. We also present a production deployment framework addressing latency constraints, model serving infrastructure, and continuous learning pipelines for enterprise-grade behavioural analytics.

Keywords : deep learning, BERT, CNN, BiGRU, behavioural analytics, enterprise SaaS, churn prediction, anomaly detection, hybrid architecture, user behaviour modelling

Author : Hitesh Acharya

Title : Applying Hybrid Deep Learning to Behavioural Pattern Recognition in Enterprise Platforms

Volume/Issue : 2024;01(01)

Page No : 217-223

OpenShift has become one of the enterprise container platforms since it has developed Kubernetes to support security, automation, and lifecycle management. The paper is a detailed introduction to OpenShift architecture and its application in the management of enterprise container platforms. It analyses the heart of OpenShift architecture, such as the control plane, worker nodes and their role in enabling scalable, resilient and production-ready containerized environments, along with networking and storage, as well as security functions. The paper also covers the various deployment models such as public, private, hybrid and community cloud, and examines how OpenShift can be used to sustain various requirements of an enterprise within each model. The most important parts of enterprise container platform management, including orchestration, monitoring, observability, application lifecycle management, and automation, are discussed to present efficiency and reliability in operations. Lastly, the paper discusses the enterprise adoption situations and applications, highlighting the role of OpenShift in contemporary application development, microservice scalability, cost-effectiveness, and system reliability in cloud-native and hybrid cloud environments.

Keywords : OpenShift, Kubernetes, Containerization, Enterprise Container Platform, Cloud Deployment Models, Monitoring and Observability

Author : Dharmendra Ahuj

Title : An Overview of OpenShift Architecture and Enterprise Container Platform Management

Volume/Issue : 2024;01(01)

Page No : 224-232

India is in web of social issues beyond its control. The main social issues facing India today include poverty, pollution, illiteracy, corruption, gender inequality, and violence against women. The caste system creates social divisions that can lead to discrimination and inequality, affecting people’s opportunities and rights. Urbanization leads to overcrowding, slums, and inadequate infrastructure, which can worsen living conditions for many. Poverty often prevents children from attending school, leading to high dropout rates and low literacy levels. The government has launched initiatives like ‘Beti Bachao, Beti Padhao’ to promote education and safety for girls and women. Access to healthcare is a major issue, with many people lacking basic services, especially in rural areas. Pollution contributes to serious health problems, including respiratory issues and other diseases, affecting many lives. Social movements raise awareness and advocate for rights, helping to bring about reforms and policy changes. The present paper focuses on the prominent social issues in India and appraises the current trends by virtual analysis.

Keywords : Social Issues, Current Status, Trends, Analysis and Impact on Society

Author : Dr.B.Mukunda Naidu

Title : CURRENT TRENDS AND ANALYSIS OF SOCIAL ISSUES IN INDIA

Volume/Issue : 2024;01(01)

Page No : 233-241

Artificial Intelligence (AI) has become a game-changer in modern business management by intensifying business decision-making by having a smarter way of analyzing data, making predictions, and recommending things automatically. combination of machine learning, big data analytics, natural language processing, and intelligent automation to facilitate the process of making accurate, timely, and strategic decisions by managers. The blistering development of digital transformation and dynamic business landscapes has caused organizations to have greater demands to implement superior technologies that can provide the processing large amounts of structured and unstructured information. In this paper, the author will discuss where AI-driven decision support systems come in the contemporary management of businesses and assess their role in making organizations more efficient, strategic, in managing risks, managing customer relations, and working more productively. The research design is descriptive and analytical and is carried out using secondary data gathered via the journals, books, reports and industry publications. The review shows that is advantageous in enhancing forecast accuracy, minimizing operational risks, maximizing resource allocation, and making data-driven strategic decisions. Nonetheless, there are issues concerning data privacy, ethical considerations, implementation cost, and complexity that impede adoption. The paper concludes that AI-based decision support systems represent the new necessity of contemporary organizations that need to achieve long-term, sustainable growth, innovation.

Keywords : Artificial Intelligence, Decision Support Systems, Business Management, Machine Learning, Predictive Analytics, Data-Driven Decision Making

Author : Dr Prasanna G Deshmukh

Title : “Artificial Intelligence–Driven Decision Support Systems in Contemporary Business Management”

Volume/Issue : 2024;01(01)

Page No : 242-250