International Journal of Engineering Technologies and Management Research

ASSESSMENT OF THE INFLUENCE OF CLASSROOM LIGHTING AND ACOUSTIC CONDITIONS ON LEARNING OUTCOMES: A CASE OF A SCHOOL IN BENGALURU, INDIA

Aditi Nayak — 2026-04-08

Aim: Classroom lighting and acoustics strongly affect comfort, attention, and communication. This study aims to evaluate how the classroom lighting and acoustic conditions influence learning outcomes by assessing environmental parameters and collecting perceptual feedback from students and teachers in a selected school in South Bengaluru.

Methodology: A mixed-methods research methodology was used, which combined environmental measurements and user perception surveys. A BEETECH B-105 light meter and a BEETECH B-401 sound level meter were used to obtain objective data. Subjective data was collected using structured Likert-scale questionnaires provided to 106 students and 14 teachers at a private school in Bengaluru. These data were analyzed and presented.

Findings: Results showed that 53% of students experienced visual discomfort, primarily due to glare and uneven brightness. In high-illumination classrooms (above 340.5 lux), 50% reported visual fatigue, aligning with findings on over-illumination. Some students (29%) mentioned strong echo effects, whereas just under a quarter said they understood teachers well, matching earlier findings about unclear speech. In noisy spaces above 75-80 dB, teachers described effortful speaking, which aligns with prior observations on classroom sound issues.

Implications: The research highlights including both sound and light elements in school building guidelines. While supporting low-cost renovation methods, it also fosters better classroom conditions that benefit students’ performance, along with staff health.

ASSESSING THE IMPACT OF PRADHAN MANTRI JAN DHAN YOJANA (PMJDY) ON RURAL DIGITAL ADOPTION: A SECONDARY DATA ANALYSIS

Mallikarjun K. Chougala — 2026-04-08

Pradhan Mantri Jan Dhan Yojana (PMJDY) is the flagship financial inclusion programme of the Government of India and the foundation of the Jan Dhan–Aadhaar–Mobile (JAM) trinity. Over the last decade, PMJDY has expanded basic savings bank accounts to more than 55 crore beneficiaries, with around two-thirds of accounts located in rural and semi-urban areas. This paper assesses whether the rapid scaling-up of PMJDY has been associated with deeper digital adoption in rural India. Using exclusively secondary data from official sources such as the Ministry of Finance, Reserve Bank of India, Parliament documents, and the National Payments Corporation of India, supplemented by recent survey evidence and academic literature, the study constructs a consolidated data set for the period 2015–2025. Descriptive statistics and trend analysis are used to track the evolution of PMJDY accounts, deposits, RuPay card issuance and rural account shares alongside digital payment indicators such as the RBI Digital Payments Index and aggregate digital transaction volume. A simple correlation analysis for 2020–2025 indicates a very strong positive association between the growth in PMJDY accounts and the RBI-DPI, suggesting that expansion of basic accounts has moved broadly in tandem with the deepening of digital payments infrastructure and usage. However, evidence from rural UPI and AePS usage and from recent survey-based studies shows that gaps in digital literacy, connectivity and trust still constrain active digital use, especially among older and less educated rural account holders. The paper concludes that PMJDY has been a necessary but not sufficient condition for rural digital adoption; complementary investments in digital and financial literacy, cybersecurity safeguards and last-mile infrastructure remain critical for converting access into sustained usage.

TOWARDS INDIA 2050: INTEGRATING SMART TECHNOLOGIES AND HUMAN CAPITAL FOR SUSTAINABLE AND INCLUSIVE GROWTH

Anushka Mishra — 2026-04-10

This study examines India’s pathway toward becoming a globally competitive economy by 2050 through the strategic integration of smart technologies and human capital. It investigates how the synergy between “Smart Machines” (advanced technologies) and “Smart Minds” (a skilled workforce) can drive sustainable and inclusive economic growth.
The study employs a conceptual and analytical research design grounded in secondary data, including government policy documents, reports from international organizations (UNDP, WEF, FAO, ADB), and peer-reviewed academic literature. A thematic analysis framework is applied to identify convergent patterns across three focal sectors: Micro, Small, and Medium Enterprises (MSMEs), smart cities, and technology-driven agriculture.
The study finds that Artificial Intelligence (AI), blockchain, and cybersecurity substantively enhance productivity, transparency, and financial inclusion. MSMEs, when digitalized, emerge as pivotal engines of inclusive economic growth. Circular economy models and precision agriculture significantly bolster environmental resilience. Critically, technological gains remain constrained without commensurate investment in human capital, revealing a technology–skills interdependency at the core of India’s development challenge.
Unlike prior studies that examine technology or human capital in isolation, this research proposes an integrated conceptual framework that links emerging technologies, workforce capabilities, and key sectoral actors within a unified long-term development vision for India. The paper bridges a critical gap in the literature by providing a holistic perspective on India 2050.
Policymakers should prioritize the co-development of digital infrastructure and skill ecosystems. MSME digitalization, smart agricultural extension, and urban innovation corridors are identified as high-leverage intervention points for inclusive growth.

EXAMINING THE ROLE OF FINANCIAL LITERACY AND PERCEIVED BEHAVIORAL CONTROL IN INVESTMENT DECISION-MAKING: EVIDENCE FROM GEN Z AND MILLENNIALS

Surbhi — 2026-04-13

Investment decision-making has become increasingly important in contemporary financial environments, particularly among younger and middle-aged individuals who are exposed to expanding investment opportunities, digital financial platforms, and information-rich market environments. In this context, financial literacy and perceived behavioral control have emerged as two important determinants of how individuals evaluate, plan, and execute investment decisions. The present study examined the role of financial literacy and perceived behavioral control in investment decision-making among Gen Z and Millennial respondents. The study also considered the influence of social factors and subjective norms in order to provide a broader behavioral explanation of investment behaviour. Primary data were collected from 480 respondents through a structured questionnaire, and the relationships among the constructs were assessed through structural equation modeling. The findings indicate that financial literacy significantly improves investment decision-making both directly and indirectly through perceived behavioral control. The results further show that perceived behavioral control acts as an important explanatory mechanism, suggesting that financial knowledge alone is not sufficient unless individuals also feel confident in their capacity to make sound investment choices. The generation-wise analysis reveals that these relationships remain meaningful for both Gen Z and Millennials, although the relative influence of confidence and social inputs may vary across age groups. The study contributes to the growing literature on financial behaviour by highlighting that rational investment participation is shaped not only by knowledge but also by perceived capability and behavioural readiness. The findings offer useful implications for policymakers, educators, and financial service providers seeking to improve financial decision-making among emerging and active investor groups.

PREDETERMINED TIME SYSTEMS APPLIED IN SEWING PROCESSES: PROPOSAL FOR ADAPTING MOST SYSTEM TO AUTOMOTIVE SEWING

Genovevo Gonzalez de la Rosa — 2026-04-14

This article presents a literature review for determining standard times in automotive sewing operations by adapting the Maynard Operation Sequence Technique (MOST). The most commonly used methods in sewing processes are MTM and GSD, which are widely disseminated, but have limitations due to their complexity. The MOST system has been successfully applied to improve productivity in various processes, but rarely in sewing. Given this research need, it is proposed to develop a sequence of sub-operations that combines MOST’s movement categories with technical sewing parameters (revolutions per minute, stitch length, stitches per inch). This approach represents methodological advancement that can be replicated in other industrial garment manufacturing processes and contributes to the development of hybrid work measurement models.

ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION OF MENTAL HEALTH DISORDERS USING SOCIAL MEDIA DATA

Sukhpreet Kaur — 2026-04-14

Mental health conditions can be considered one of the most serious social disasters of the twenty-first century. “World Health Organization” (WHO) states that a global population of over one billion is living with some mental health problem, that over half a billion suffer depression and other disorders of anxiety and that each year, suicide kills about 727,000 with more than 580 million people affected. Timely intervention and early detection is desperately wanting especially in low and middle-income countries where more than three quarters of victims go untreated. The growth of social networks such as Twitter/X, Reddit, and Facebook produces large amounts of user-generated data that can record current emotional states, behavioural tendencies, and linguistic indicators and can serve as an unprecedented source of non-invasive data to monitor mental health. The paper is a systematic review of the use of artificial intelligence (AI)-based tools in the early prediction of mental health issues, such as depression, anxiety, bipolar disorder, and suicidal thoughts, with the help of social media data. The review summarizes more recent “natural language processing” (NLP), deep learning systems, including BERT, RoBERTa, and Bidirectional LSTM networks, multimodal fusion models, and “Explainable AI” (XAI) models related to improving clinical interpretability. Empirical results suggest state of the art transformer designs can do so with a depression detection accuracy of over 91, a suicidal ideation detection rate of up to 94.29 and the AI systems are able to detect other crisis telltales on average 7.2 days before professional clinicians. Data privacy, cross-cultural generalizability, and the Ethical aspects of autonomous mental health screening are highlighted as key issues of autonomous systems in healthcare. This review offers a guide on how AI-driven social media analytics can be responsibly integrated into the proactive mental health care systems.

STRUCTURE, SEGMENT AND GRID RELIABILITIES: A PROPOSAL

Sriram Kalaga — 2026-04-15

The paper proposes the outline of a method for estimating electric utility grid structural reliability as a function of reliabilities of individual poles and segments. Structural pole reliability is defined in terms of selected weather and load cases. Probability distributions for ice and wind loads, along with their associated statistical parameters, are discussed. It is suggested that separating the effects of ice and wind may help to simplify the computations. An example segment is analyzed to illustrate one of the concepts described in the paper. Suggestions for further studies and extensions are offered.

IMPACT OF STRENGTHENING RELATIONSHIP BETWEEN HR AND EMPLOYEES ON ORGANIZATIONAL PERFORMANCE, A CRITICAL STUDY

Vinod Kumar Mishra — 2026-04-18

This study examines the effect of strengthening relationships between Human Resources (HR) and employees on organizational performance. The study analyzes the impact of HR practices on organizational outcomes, utilizing a sample of 200 respondents from 20 firms, with employee performance serving as a mediating variable. Data were gathered using online questionnaires and processed with SPSS 22.0. The findings demonstrate that HR practices exert a substantial direct beneficial influence on organizational performance (β = 0.4928, p < .001) and an indirect effect via employee performance (indirect effect = 0.1180, 95% CI [0.0052, 0.2293]). Human Resource practices contributed to 60.67% of the variance in employee performance and, when joined with employee performance, elucidated 51.17% of the variance in organizational performance. The study indicates that effective HR policies directly enhance organizational performance and indirectly improve it by elevating employee performance. These findings underscore the necessity of investing in HR-employee interactions and connecting HR strategies with business objectives to attain optimal performance results.

IMPACT OF INFLUENCER MARKETING ON CONSUMER ENGAGEMENT AND BRAND PERCEPTION

Muskan Yadav — 2026-04-20

Influencer marketing in the digital world has turned into a trendy approach that enables businesses to connect with consumers through the voice of reputable figures on platforms such as Instagram, YouTube, and Tik Tok. Conventional advertising was often not relatable, sincere, or trustworthy, which this form of marketing offers. This study aims to explore the impact of influencer marketing on brand perception and consumer engagement. Specifically, it examines the impact of marketing strategies, the type of content, and the reputation of influencers on customer perceptions and brand trust. A primary data gathering method was used in a quantitative study design. One hundred randomly chosen respondents were given a standardized questionnaire. Regression and correlation tests were used in SPSS to evaluate the data, which was gathered via Google Forms, and look at the correlations between the variables. Findings indicate that influencer marketing techniques can account for more than three-quarters of the variance, and they significantly enhance consumer interactions. Furthermore, there is a high correlation between influencer credibility and brand authenticity and trust, and 56.4% of consumers' perceptions are influenced by the sort of material. These results underscore the importance of realistic engaging content when it comes to establishing positive brand relationships. Influencer marketing is one of the most effective tactics to engage with more people and increase brand awareness. By investing in authentic influencer collaborations and numerous content programs, brands are in a more favorable position to foster loyalty and build long-lasting customer relationships.

ML AND RAG-BASED INTELLIGENT SYSTEM FOR YOGA POSE RECOGNITION AND CORRECTIVE GUIDANCE

Harish Barapatre — 2026-04-30

Yoga pose recognition has gained significant importance in digital health and fitness systems, where accurate posture assessment and corrective feedback are critical for safe practice. Traditional computer vision–based approaches rely on pose estimation models but often lack contextual understanding and personalized guidance. To address this limitation, this paper proposes a hybrid framework that integrates Machine Learning (ML)–based pose recognition with Retrieval-Augmented Generation (RAG) for intelligent feedback generation. The system utilizes human pose estimation techniques to extract skeletal keypoints and classify yoga poses using supervised learning models. Subsequently, a RAG module retrieves relevant expert knowledge from a curated yoga knowledge base and generates context-aware corrective suggestions. This dual-layer architecture ensures both high recognition accuracy and meaningful interpretability of results. The proposed approach aims to bridge the gap between static classification systems and interactive AI-driven coaching by enabling real-time feedback and adaptive recommendations. The framework is designed as a conceptual model with potential applicability in mobile health applications, smart fitness systems, and remote yoga training platforms. By combining data-driven learning with knowledge retrieval mechanisms, the system enhances both usability and reliability in real-world scenarios.

CYBER-ATTACK DETECTION SYSTEM FOR CYBER-PHYSICAL SYSTEMS USING MACHINE LEARNING-BASED ANOMALY DETECTION

Harish Barapatre — 2026-04-30

Cyber-Physical Systems (CPS) are increasingly deployed in critical domains such as smart grids, industrial automation, healthcare, and transportation, where the integration of computational and physical components introduces significant security challenges. These systems are highly vulnerable to cyber-attacks such as false data injection, denial-of-service, and stealthy manipulation, which can lead to severe physical and economic consequences. Traditional security mechanisms are insufficient due to the dynamic and heterogeneous nature of CPS environments [1], [2].
This paper proposes a conceptual machine learning-based cyber-attack detection framework tailored for CPS environments. The framework focuses on real-time anomaly detection by analyzing multi-source data streams from sensors, network traffic, and control signals. A hybrid detection approach is designed that combines statistical feature analysis and supervised learning models to identify deviations from normal system behavior. The proposed model introduces a risk scoring mechanism that evaluates system states based on behavioral inconsistencies, enabling early detection of potential threats.
The framework emphasizes scalability, adaptability, and low-latency detection, which are critical for real-time CPS applications. Unlike conventional signature-based systems, the proposed approach is capable of detecting unknown and zero-day attacks. The study provides a structured design and mathematical formulation for anomaly scoring and decision-making processes, making it suitable for further implementation and validation in real-world CPS scenarios [3].