Volume 14 | Issue 5 |

 Abstract

Accurate menstrual health tracking remains a significant challenge due to widespread dependence on manual logging and calendar-based prediction systems that fail to account for individual physiological variability. Existing applications offer limited real-time awareness and are unable to detect reproductive health abnormalities at an early stage. This paper proposes a Smart AI Menstrual Cycle IoT-Based Monitoring System that integrates wearable sensors,and machine learning algorithms to provide automated, continuous, and personalized menstrual health monitoring. The system captures Basal Body Temperature (BBT) via IoT sensors connected to an ESP32 microcontroller and transmits the data wirelessly to a backend where machine learning models--including Random Forest and LSTM--analyze patterns to predict cycle phases, ovulation windows, and irregularities. Validation against historical menstrual datasets demonstrated a prediction accuracy of 85-95%, significantly outperforming traditional calendar-based systems. The system further provides real-time alerts and personalized health recommendations through a web interface. Future work will explore deep learning integration, telemedicine connectivity, and expanded biosensor arrays for comprehensive reproductive health management.

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 Keywords

Menstrual Cycle Prediction, IoT Health Monitoring, Basal Body Temperature, Machine Learning, ESP32, Wearable Biosensors, Reproductive Health Analytics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In today's highly competitive and technology-driven business environment, organizations are increasingly recognizing employees as their most valuable strategic resource. The rapid adoption of artificial intelligence, digital platforms, automation, and hybrid work models has significantly changed the nature of jobs across industries. As a result, organizations are under constant pressure to continuously upgrade employee skills and capabilities. Training and development programs have therefore emerged as essential tools for improving workforce competence, enhancing productivity, and ensuring long-term organizational sustainability. This research examines the influence of training and development initiatives on employee performance within Indian organizations. The study focuses on how structured learning programs contribute to improving productivity, task efficiency, job satisfaction, quality of work, and employee engagement. A quantitative research design was adopted, and primary data was collected from 180 employees working in information technology, banking, manufacturing, and service industries across major Indian cities including Bengaluru, Mumbai, Pune, Hyderabad, and Delhi NCR. The study reveals a strong and positive relationship between training initiatives and employee performance outcomes. Employees who participated in regular and relevant training programs reported greater confidence, improved communication abilities, enhanced technical expertise, and higher workplace efficiency. The findings also indicate that blended learning approaches, which combine online and face-to-face learning experiences, are more effective than conventional methods alone. Additionally, managerial support, post-training feedback, mentoring, and continuous learning opportunities significantly influence the long-term success of training programs. The research highlights the importance of developing employee-centric training systems that align with organizational goals and future industry requirements. The paper concludes that organizations investing in continuous learning and workforce development are more likely to achieve innovation, employee retention, operational excellence, and competitive advantage in the modern business landscape.

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 Keywords

Training and Development, Employee Performance, Human Resource Management, Organizational Learning, Blended Learning, Employee Productivity, Skill Development, Indian Organizations.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The interaction between dye molecules and their surrounding dielectric environment plays a crucial role in determining their photophysical behavior. Dye molecules do not exist in isolation; instead, they are influenced by the medium around them, which alters their electronic structure and energy levels. This paper examines how dielectric effects and environmental factors influence key photophysical processes such as absorption, fluorescence, and non-radiative decay.

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 Keywords

Dielectrics and Photophysical Properties of Dye Molecules

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 Abstract

The rapid advancement of Artificial Intelligence (AI) is transforming educational systems, workplace practices and communication processes across the world. In the digital era, communication skills in English have become essential competencies for academic achievement, employability, professional interaction and global participation. Simultaneously, the growing demand for lifelong learning has highlighted the importance of skilling, re-skilling and up-skilling to help individuals adapt to rapidly changing technological and socio-economic environments. This paper explores the transformative role of Artificial Intelligence in developing communication skills in English within the framework of lifelong learning. AI-powered technologies such as intelligent tutoring systems, speech recognition software, automated writing assistants, chatbots, language learning applications and learning analytics are reshaping English language learning through personalized and adaptive learning experiences. These tools enable learners to improve listening, speaking, reading5 and writing skills through real-time feedback, interactive communication practice and flexible learning opportunities. This paper further examines how AI-driven platforms support inclusive and accessible education barriers related to geography, time language and socio-economic inequalities. It discusses the role of educational institutions, training organisations and policymakers in integrating AI-based communication learning strategies. Additionally, the study highlights important ethical concerns such as the digital divide, data privacy, technological dependence and algorithmic bias. The study concludes that AI has immense potential to enhance communication skills in English and strengthen lifelong learning opportunities. However, responsible implementation, equitable access and balanced human interaction remain essential for ensuring meaningful and inclusive educational transformation.

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 Keywords

Keywords: Artificial Intelligence, Communication Skills in English, Lifelong Learning, Skilling, Re-skilling, Up-skilling, Inclusive Education

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Business decision-making in Micro, Small, and Medium Enterprises (MSMEs) is increasingly challenged by dynamic market conditions, uncertain customer demand, resource constraints, and operational complexity. Traditional decision support systems primarily depend on static historical analysis and fail to provide adaptive, predictive, and intelligent recommendations in real-time environments. To address these limitations, this research proposes a Cognitive Digital Twin (CDT)-based intelligent business decision framework integrating machine learning, reinforcement learning, knowledge graphs, and simulation analytics for predictive and adaptive business optimization. The proposed system develops a virtual cognitive replica of business operations capable of continuously monitoring, learning, simulating, and optimizing strategic decisions. The framework consists of five major layers: data acquisition layer, digital twin modeling layer, cognitive intelligence layer, simulation and optimization engine, and decision visualization interface. Real-time and historical business datasets including sales records, inventory data, workforce allocation, customer demand patterns, and pricing information are integrated into the system for intelligent analysis. A machine learning-based prediction module utilizing Random Forest and Long Short-Term Memory (LSTM) models is implemented to forecast sales trends and operational performance. Furthermore, reinforcement learning-based optimization techniques are employed to identify optimal pricing strategies, inventory management policies, and workforce allocation decisions under varying business scenarios. A knowledge graph model is incorporated to capture semantic relationships among business entities, enabling intelligent reasoning and dependency analysis. The proposed CDT framework is implemented using Python, TensorFlow, Neo4j, Flask, and Power BI, and evaluated using simulated MSME business datasets. Experimental results demonstrate that the proposed system improves decision prediction accuracy by 23%, reduces operational cost by 17%, and decreases strategic decision-making time by 41% compared to traditional analytics approaches. Simulation-based "what-if" analysis further enables proactive risk assessment and adaptive business planning. The major contribution of this work lies in extending Cognitive Digital Twin technology beyond industrial automation into intelligent business decision intelligence systems for MSMEs. The proposed framework provides a scalable, cost-effective, and AI-driven solution for next-generation business optimization and establishes a foundation for future research in cognitive simulation systems and intelligent enterprise analytics.

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 Keywords

Cognitive Digital Twin, MSME, Reinforcement Learning, Business Intelligence, Machine Learning, Knowledge Graph, Simulation Analytics, Decision Optimization.

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 Abstract

On 1 July 2024, the Bharatiya Nagarik Suraksha Sanhita, 2023 (BNSS) came into force, replacing the Code of Criminal Procedure, 1973 in its entirety. The most consequential innovation for the investigative process is contained in Section 176(3): for every offence punishable with imprisonment of seven years or more, the officer in charge of a police station is now under a statutory duty to cause a forensic expert to visit the crime scene, collect forensic evidence, and videograph the process using a mobile phone or other electronic device. This provision represents a fundamental doctrinal shift from the regime of the old Section 157 CrPC, under which no such obligation existed and scientific evidence collection remained wholly discretionary. This article maps the architecture of that shift--its legislative genealogy, its precise statutory scope, and its intersection with the digital evidence provisions of the Bharatiya Sakshya Adhiniyam, 2023. It then undertakes a critical audit of the implementation landscape, examining the gap between the mandate's ambitions and the realities of India's forensic science infrastructure: acute vacancies in Forensic Science Laboratories, pendency-driven backlogs, inadequate digital forensic capacity, and uneven state-level readiness. Drawing on comparative experience from the United Kingdom, the United States, and Australia, the article evaluates the judicial consequences of non-compliance with the BNSS forensic mandate--particularly in light of the Kerala High Court's landmark direction in Suresh v. State of Kerala (2025)--and examines whether the absence of an explicit evidentiary sanction for non-compliance creates a lacuna that courts must fill. The article concludes by proposing a calibrated reform pathway combining legislative clarification, executive investment through the National Forensic Infrastructure Enhancement Scheme, and judicial enforcement mechanisms to ensure that Section 176(3) evolves from a paper promise into an operative standard of scientific policing.

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 Keywords

Keywords: Bharatiya Nagarik Suraksha Sanhita 2023; Section 176(3) BNSS; Forensic Investigation Mandate; Bharatiya Sakshya Adhiniyam 2023; Digital Evidence; Forensic Science Laboratories.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Environmental pollution caused by non-degradable plastic waste has created a growing demand for eco-friendly alternatives. This study focuses on the development of a biodegradable bioplastic film using pectin, a natural polysaccharide extracted from plant sources. The film was prepared using the solvent casting method. Its mechanical strength, flexibility, water resistance, and visual appearance were evaluated to determine its suitability for practical use. The results showed that the pectin-based film possesses moderate strength, good transparency, and biodegradability, highlighting its potential as a sustainable material for pharmaceutical and packaging applications.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

bioplastic ,biodegradability, orange peel, film, phytochemicals

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Adulteration of milk has become a major problem for the dairy sector, endangering food safety and public health. To boost volume and profit, hazardous materials like water, detergents, starch, urea, and synthetic chemicals are frequently added. This can result in health problems such food poisoning, kidney damage, and nutritional deficiencies. Although accurate, traditional laboratory testing techniques are costly, time-consuming, and need specialised personnel, making them unfeasible for small-scale dairy businesses and households. The design and development of an affordable, portable, and effective milk adulteration detection system with real-time impurity detection capabilities is presented in this study. To monitor milk quality characteristics, the system combines an Arduino Uno microcontroller with sensors for pH and Total Dissolved Solids (TDS). Whereas the TDS sensor finds dissolved particles, the pH sensor gauges acidity levels. The Arduino program continuously processes sensor inputs and compares them to predetermined threshold levels of pure milk. The device recognises the sample as contaminated and shows the outcome on an LCD screen if deviations are found. Without requiring complicated laboratory processes, the suggested system provides quick detection, dependable performance, and user-friendly operation. It is appropriate for homes, dairy farms, and milk collection facilities because to its small size and low cost. By tracking changes in pH and TDS levels, experimental observations validate its efficacy in identifying adulteration. IoT integration, cloud storage, mobile apps, and sophisticated sensors for identifying certain adulterants are possible future improvements. All things considered, this system promotes better food quality monitoring, safer dairy consumption, and increased public health protection.

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 Keywords

Milk adulteration, Food safety, Food safety, Real-time detection, Embedded Systems,Ardino UNO

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 Abstract

The Advanced Waiter Robot with Smart Ordering System is an innovative automation solution designed to improve efficiency, accuracy, and hygiene in restaurants and hospitality environments. This system integrates robotics, wireless communication, and smart user interfaces to automate food ordering and serving processes. The robot is equipped with sensors such as ultrasonic sensors for obstacle detection, ensuring safe and smooth navigation within the restaurant. Customers can place orders through a digital interface, such as a mobile application or touchscreen system, which directly communicates with the kitchen using wireless technologies like Wi-Fi or Bluetooth. Once the order is prepared, the robot delivers the food to the respective table, reducing human effort and minimizing delays. The system also includes features like LED indicators, buzzer notifications, and predefined path navigation to enhance user interaction and operational efficiency. By reducing dependency on the robot helps minimize human errors, improves service speed, and maintains better hygiene standards.

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 Keywords

Waiter Robot,Smart Ordering System, Automation,Robotics, Internet of Things (IoT),Ultrasonic Sensor,Obstacle Detection,Wireless Communication,Arduino / Microcontroller,Embedded Systems,Food Delivery Robot,Human-Machine Interaction,Smart Restaurant,Artificial Intelligence (basic automation),Navigation System,Digital Ordering,Sensor Technology.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Teachers have always played a crucial role in preparing communities and societies towards exploring new horizons and achieving higher levels of progress and development. They are the prime agents of change. The significance of the emerging role of teachers has never been so critical as at this juncture. Professionalism in teacher education has been a matter of concern and attention in India. Ancient teacher had heightened emotional concern towards their pupils in order to inculcate desired virtues. In contract, the behavior of modern teacher, in general, is not exemplary in facilitating pupil to imbibe any quality that is virtually virtuous. Further, modern teacher in general had little emotional concern towards the pupils. The present day teacher is mostly concerned to transact the content and neglecting entirely the affective aspect of inculcating right attitudes and virtuous behavior. This theoretical paper discusses how values inculcation among students and role of the parents and teachers

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Values inculcation, Teachers, Parents

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Creative Commons Attribution 4.0 and The Open Definition