International Journal of Engineering Technologies and Management Research

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

2026-04-14T05:50:45+00:00

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.

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

2026-04-08T11:23:25+00:00

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

2026-04-08T11:23:24+00:00

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

2026-04-10T05:29:40+00:00

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

2026-04-13T12:46:33+00:00

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

2026-04-14T05:50:52+00:00

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.