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Author: Amit Sharma

India’s multilingual print ecosystem—encompassing education, governance, and media—relies heavily on accurate and culturally sensitive typesetting in over 20 major scripts. However, the structural complexity of Indic scripts such as Devanagari, Tamil, Bengali, and Urdu poses significant challenges in digital rendering, ligature formation, font compatibility, and layout automation. This paper investigates the evolution of typesetting technologies from metal casting and phototypesetting to Unicode-based digital publishing and highlights the persistent limitations in font engineering, rendering engines, and prepress workflows. Case studies from NCERT, regional boards, and vernacular newspapers illustrate how technical fragmentation and inadequate standardization hinder print accessibility and aesthetic integrity. The paper also reviews government-backed standardization efforts (TDIL, Bhashini), community-driven font development (e.g., Google Noto, Lohit), and advances in OpenType and OCR technologies. Finally, it recommends script-specific layout engines, policy-level standardization, and professional training initiatives as strategic pathways for building a robust, scalable, and inclusive multilingual print infrastructure in India.
Keywords: Multilingual typesetting, Indic scripts, Devanagari, Unicode rendering, OpenType features, Indian typography, vernacular printing, font standardization, digital publishing, ligature handling, government publishing, regional language fonts, desktop publishing, script complexity, print accessibility

Author: Kanwaldip Kaur

Green computing (or green IT) refers to the design, use, and disposal of computers and IT infrastructure in a way that reduces environmental impact. This includes energy-efficient hardware, sustainable software, virtualization, cloud computing, and responsible e-waste management.

Author: Gurpreet Singh Sohal

ABSTRACT
Modern libraries are being redefined more and more as locations to have unlimited access to
knowledge from a variety of sources and in a variety of formats. By offering literature that
can be accessed virtually and helping librarians navigate and analyze vast amounts of data
using a range of digital resources, they are expanding their services beyond the physical
boundaries of a building. Libraries are evolving into community centers where people can
participate in lifelong learning. Here, the concept of a smart library evolved, where resources
can be accessed easily and with less staff intervention. Smart Library provides state of the art
automated infrastructure and sophisticated technologies like automatic gates, lighting, selfservice kiosks, mobile library services on the smart phone or handheld devices, offer limitless
digital collection via the Internet, provides e-learning facilities & learning spaces; and uses
the social media platforms to share information about its collection and service. So the Smart
libraries will revolutionize library operations and services. In this paper, we will discuss
various concepts, needs, components and services provided by Smart Libraries.
Keywords: Smart Library, Technology, services, infrastructure, mobile library services

Author: Sahil Luthra

ABSTRACT:
Cloud computing has emerged as a revolutionary paradigm, offering scalable, flexible, and
cost-effective IT services to organizations. Despite its numerous benefits, including reduced
costs and increased efficiency, the adoption of cloud computing is hindered by security
concerns. The outsourcing of sensitive data to third-party providers raises significant risks,
including data breaches and unauthorized access. This paper presents a comprehensive
analysis of the security challenges and issues associated with cloud computing, highlighting
the need for organizations to be vigilant in understanding the risks and benefits of this
technology. By examining the security concerns and potential solutions, this study aims to
provide insights into the secure adoption of cloud computing.
Keywords: Cloud computing; Security in cloud; Security Threats.

Author: Ratul Borah

1. INTRODUCTION:
North eastern region of India comprises the Seven Sisters States of Arunachal Pradesh,
Assam, Monipur, Meghalaya, Mizoram, Nagaland and Tripura. It covers 7.76% of the
country‟s total geographical area of 32.87,240 sq. km. The North East India lies between
latitudes 22 and 29.5 and longitudes 89.70‟ and 97.30‟E. According to 2001 census, the total
population in entire North-East India is 3, 85 core out of which the scheduled tribe population
is 1.06 core. In words, the scheduled tribes constituted 27.42% of the total population N.E.R.
There are many scheduled tribes, scheduled caste, OBC, MOBC, general etc. in the state of
Assam. Assam alone account for 3% of the total scheduled tribe population of the North-East.
According to the Scheduled Caste and Scheduled Tribes others (Amendment) Act, 2002 there
are, 25 scheduled tribes in Assam.
Keywords: Personality, Mishing, Adolescent, Assam, NER , Psychological

Author: Gayathri L , Muralidhara B. L

ABSTRACT:
Classifying Alzheimer’s Disease (AD) using MRI scans is essential for timely detection and
effective treatment planning. This research aims to enhance AD diagnosis by employing
machine learning (ML) models, feature selection methods, and texture-based image analysis.
The study compares the effectiveness of various feature selection strategies and Principal
Component Analysis (PCA) combined with multiple ML algorithms to determine the most
suitable approach for differentiating between Cognitive Normal (CN), Mild Cognitive
Impairment (MCI), and AD cases.
The preprocessing workflow includes N4 bias correction, skull stripping, and linear coregistration, followed by the extraction of texture features that capture statistical
characteristics of image patterns. Different ML classifiers are then trained and tested on these
features to evaluate their ability to accurately categorize patients. Performance is measured
using multiple evaluation metrics to assess the discriminative power of the models across AD
stages.
The findings highlight that combining ML techniques with feature selection and texture
analysis provides a robust framework for early AD detection and personalized treatment
strategies, offering meaningful implications for clinical use.
Keywords— Alzheimer's disease, feature selection, machine learning, texture analysis.

Author: Maya A.K.R, Muralidhara B. L

ABSTRACT
Word embeddings are the basis of machine learning and deep learning models used in NLP
(Natural Language Processing), advancing the methods by which machines interpret and
handle textual data. High-quality embeddings can substantially boost performance in
downstream NLP tasks by better capturing linguistic nuances, including question-answering,
text summarization, text classification, and information retrieval. The present study offers an
in-depth examination of the development of word embeddings in NLP, comparing the
effectiveness of earlier methods to recent developments on both extractive and abstractive
summarization tasks. We explore the radical shift from classical static embeddings like
Word2Vec and GloVe to the dynamic, context-aware representations introduced by
transformer-based models like BERT, T5, and GPT. Additionally, we assess how these
embeddings are integrated with self-attention mechanisms, sequence-to-sequence (Seq2Seq)
architectures, and encoder-decoder models to generate summaries. The study evaluates the
models across standard benchmarks, measuring metrics like ROUGE, BLEU, and model
interpretability. Our analysis reveals a 20% ROUGE improvement with transformer-based
models over static ones on CNN/Daily Mail. Thus, we aim to provide valuable insights into
various word embeddings in text summarization that will be useful for training a new
embedding or using a pre-trained embedding for the NLP task.
Keywords: Contextualized Embeddings, Natural Language Processing, Summarization,
Transformers, Word Embeddings.

Author: Sarala M, Muralidhara B L

ABSTRACT
Forgery detection in educational credentials is a challenging task. Machine learning(ML)
techniques are highly used in fraud detection and spam detection. Our primary objective is to
detect counterfeit educational credentials using ML algorithms. In this research work, we
conducted an empirical study as follows: (i) extracted the features of those credentials using
the Gray Level Histogram Analysis (GLHA) metrics such as standard deviation, mean,
skewness, kurtosis, and entropy, (ii) Trained the ML models with extracted features using
different classification algorithms including Support Vector Machine(SVM), Logistic
Regression, Decision Tree, Naïve Bayes, Random Forest, and K-Nearest Neighbours(KNN).
(iii) Assessed the effectiveness of classification models using hyper-parameters. Random
Forest got 99.38% accuracy, and outperformed well than other algorithms. SVM, Decision
Tree, Logistic Regression, KNN, and Naïve Bayes got accuracies of 98.75%, 98.13%,
95.00%, 92.50%, and 90.00% respectively.
Keywords: machine learning, GLHA, counterfeit detection, supervised algorithms.

Author: Debjani Chakraborti

ABSTRACT
This study introduces an innovative method that employs evolutionary computation to
address interval-valued fractional bilevel programming (IVFBLP) problems. These involve
decision-making at two hierarchical levels, where some data is expressed as intervals rather
than fixed values, enhancing realism but increasing complexity. The proposed method
integrates mixed 0–1 programming, goal programming, and genetic algorithms to efficiently
address these challenges.
The genetic algorithm mimics natural evolutionary processes by generating multiple solution
candidates and iteratively selecting the best, allowing the model to explore a wide solution
space and converge towards optimal or near-optimal decisions. To ensure solutions closely
meet desired goals while minimizing adverse outcomes, termed regrets, the approach
employs two strategies—minsum and minmax—within a combined success-measuring
function.
The solution process follows two phases: first, it establishes optimal target intervals
representing achievable goal ranges; second, it identifies the best decisions for both leader
(upper) and follower (lower) roles. This structured approach delineates responsibilities
between decision-makers.
Utilizing evolutionary computing enables the model to handle uncertainties, nonlinearities,
and complex structures effectively. The paper demonstrates practical applicability through a
numerical example, showcasing its capability to solve real-world problems involving
interval-based objectives and constraints where decisions occur at multiple hierarchical
levels.
Keywords : Fractional bilevel programming, Goal programming, Evolutionary algorithm,
Interval programming, Interval-valued fractional bilevel programming, Multiobjective
decision-making.

Author: Pankaj Mohindru & Jasmeen Kaur

ABSTRACT
Purpose: This study aims to identify the factors that influence consumer’s decision in Punjab
to purchase in Real Estate Sector.
Theoretical framework: The work is primarily to look at how Real Estate Agents affect the
decisions consumers make about buying Home in Punjab and to analyze the link between
major elements influencing consumers' house-buying behavior. The factors in the research
study were determined within a framework of location, type of building, budget, funding
agency, number of bedrooms, and Vastu.
Design/methodology/approach: To collect the necessary data, respondents were selected
from Punjab by using convenience sampling technique and descriptive research design for the
study. Data was collected through a structured questionnaire. Using the statistical tool, a
sample size of 400 units (age group from 35 years and above, income levels from 20,000/- to
80,000/- , education levels of graduation or higher and employment status etc.) was analysed.
Findings: To conclude the findings of the study, the acquired data were first compiled and
then evaluated using statistical tools. The findings of this paper is to show exactly what a
customer needs to think about when deciding whether or not to buy a home in Real Estate
Sector in Punjab.
Conclusion: The paper is helpful for both buyers and real estate agents. It helps buyers
understand the factors that go into making a purchase decision, and it helps real estate agents
understand what buyers want and how they think.
Keywords: Purchasing Behavior, Convenience Sampling, Real Estate, purchase intention,
investment, and consumer’s buying behaviour.

Author: Kumaraswamy. T

ABSTRACT
The Vijayanagara Empire (1336–1565) stands as one of the most remarkable chapters in
South Indian history, remembered for its political strength, economic prosperity, and cultural
brilliance. Centered in Hampi, the capital city, the empire left behind an extraordinary
architectural and artistic legacy that continues to define Karnataka’s cultural identity.
Monumental structures such as the Virupaksha Temple, Vittala Temple, Hazara Rama
Temple, and the royal complexes exemplify the grandeur of Dravidian architecture and the
empire’s syncretic cultural ethos. Beyond architecture, the Vijayanagara period also nurtured
literature, music, and the Bhakti movement, shaping the spiritual and artistic traditions of the
region.
In contemporary Karnataka, the Vijayanagara legacy has acquired new relevance through
heritage preservation and tourism. Hampi, a UNESCO World Heritage Site, attracts
thousands of visitors annually, contributing to local livelihoods and the state’s economy.
Heritage circuits that connect Hampi with sites such as Badami, Aihole, and Pattadakal
strengthen Karnataka’s global image as a cultural tourism hub. Yet, this transformation is not
without challenges. Conservation issues, infrastructural deficits, and the pressures of mass
tourism threaten the fragile archaeological remains. Political contestations over historical
narratives further complicate heritage management. At the same time, sustainable tourism
practices, digital innovations like virtual tours, and community participation offer promising
avenues for balancing preservation with development. This paper argues that Vijayanagara’s
legacy is not confined to the past but actively shapes Karnataka’s socio-economic and
cultural future. Heritage tourism, if guided by sustainability and inclusivity, can transform
Karnataka’s historical assets into engines of both pride and progress.
Keywords: Vijayanagara Empire, Hampi, Cultural Heritage, Tourism Development,
Karnataka, Sustainable Tourism.

Author: Anuradha Averineni, Boddu Bhagya Sree, Shaik Abubakar Siddiq, Attuluri Sri Mani Teja, Chimakurthy Venkata Subramanya Srikanth

ABSTRACT
Artificial Intelligence (AI) and Data Analytics (DA) are transforming how organizations
design, implement, and monitor strategy. This paper explores their role in enhancing strategic
decision-making, competitive advantage, and organizational agility. A survey of 320
managers from IT, manufacturing, and retail organizations in India was conducted to examine
how AI-driven analytics capability affects strategic planning quality, innovation, and business
performance. Regression and mediation analyses reveal that AI and DA adoption positively
influence strategic decision quality and innovation, which in turn drive organizational
performance. The study suggests that embedding AI and analytics into strategic management
processes is essential for sustaining long-term competitiveness.
Keywords
1. Artificial Intelligence (AI)Data Analytics (DA), Strategic Planning, Quality,
Innovation Capability, Decision-Making, Organizational Performance, Competitive
Advantage

Author: Vandana Kapoor, Suman Kumari

ABSTRACT
The present research work was specifically undertaken to study the social skills among senior
secondary school students with respect to genderand family type. Survey technique under
descriptive method of research was adopted in this present investigation. By incidental
sampling technique, total sample of 881senior secondary school students were selected from
four districts of Himachal Pradesh. For data collection Social Skills Rating Scale (SSRSSVAAKS) developed by Sood, Anand and Kumarwas used. The collected data were analyzed
by employing Analysis of Variance (Two way). The major findings of the study revealed that
male and female senior secondary school students possessed similar level of social skills.The
senior secondary school students belonging to different family type differs significantly from
each other with regard to their social skills.In addition to this, the results of the study also
revealed that gender and family type (in combination with each other) did not influence social
skills among senior secondary school students significantly. The end of the paper discussion
on the results and implications of the findings of the investigation have been discussed in
detail.
Keywords: Social Skills, Gender, Family Type, Senior Secondary School Students

Author: Navpreet Kaur, Mamta Sambyal & Krishan Gopal

ABSTRACT
This paper synthesizes secondary literature on artificial intelligence (AI) in education
between 2015 and 2025, drawing from peer-reviewed journals, conference proceedings, and
organizational reports. It explores emerging trends, challenges, and opportunities, with key
themes such as adaptive and personalized learning, intelligent tutoring systems, learning
analytics, ethical considerations, teacher readiness, and equity and inclusion. The review
highlights the substantial potential of AI to improve learning outcomes, engagement, and
efficiency, while exposing critical gaps: insufficient long-term, large-scale empirical
research; uneven global coverage; limited focus on ethics, transparency, and data privacy;
and poor integration of AI concepts into teacher education and policy frameworks. The
abstract concludes with directions for future research to address these deficiencies.
Keywords: Artificial Intelligence, Education, Higher Education, Learning, Teaching,
Assessment, Adaptive Learning

Author: Y. Kanakadurga, Y. Kanakadurga, G. Bhavanarayana, E. Varun Sai, D. Udhaykiran & B. Bhanuprasad

ABSTRACT
Innovation and economic expansion are propelled by startups. However, inadequate financial
management, a lack of strategic direction, and poor decision-making cause many to fail early.
This study presents an AI-powered Decision Support System (AI-DSS) designed to assist
startups in analyzing and producing actionable insights from a variety of business data,
including marketing, operations, and finance. The system predicts financial outcomes, finds
inefficiencies, and makes customized recommendations by combining large language models,
knowledge graphs, and machine learning models. This system can increase financial runway,
enhance decision quality, and reduce early-stage failure rates, as demonstrated by simulated
experiments.
Key words: AI, AI Explainable, AI Decision Support System, Startups, Business
Intelligence, Knowledge Graph, Machine Learning, and Predictive Analytics.

Author: Shruti Handa & Mandeep Kaur

ABSTRACT
With the expansion of urban and economic landscapes, the volume of solid waste generated
globally surges, posing significant environmental and public health challenges. Sustainable
waste segregation is essential for proper disposal, promoting recycling, and reducing landfill
accumulation, thereby supporting ecological balance. Existing studies leverage deep learning
for solid waste classification, but mostly datasets consist of single-object images on plain
backgrounds, which limits real-world applicability. To address this gap, a diverse dataset of
22,000 images spanning 12 waste categories is compiled from multiple public sources. Six
state-of-the-art pre-trained convolutional neural networks—DenseNet201, ResNet101,
EfficientNetB7, ConvNeXtBase, MobileNetV2, and InceptionV3—are fine-tuned using
transfer learning. Among these, ConvNeXtBase achieves the highest individual test accuracy
of 98.13%. To further improve performance, a hybrid model combining DenseNet201 and
ConvNeXtBase is developed using an attention-based fusion mechanism. This model
achieves a test accuracy of 98.45%, outperforming all single models. The results demonstrate
the effectiveness of attention-driven ensemble learning in complex waste classification tasks.
Future research emphasizes real-time deployment, adaptability across diverse waste streams,
and integration with edge devices while promoting sustainable waste management practices.
To further enhance accuracy, the study suggests expanding datasets, optimizing attention
mechanisms, and experimenting with architectures such as Vision Transformers.
Keywords: deep learning, hybrid model, transfer learning, ensemble learning, solid waste,
sustainable waste management

Author: Daisy Wadhwa, Arvind Kumar & Kamal Malik

ABSTRACT
The prevalence of crop diseases presents a major challenge to global food security and
agricultural sustainability, causing significant yield losses and economic damage.
Conventional disease detection methods, which rely on manual inspection, are often
inaccurate, time-consuming, and impractical for large-scale implementation. While deep
learning, especially Convolutional Neural Networks, has shown promise in automating
disease classification, the practical deployment of these models is hindered by high
computational demands. This study proposes an efficient MobileNetV2-based deep learning
model tailored for classifying 36 healthy and unhealthy categories across 16 plant species.
The dataset used in this research combines real-field and lab-controlled images from multiple
public sources, enhancing the model’s generalizability. The model was trained with six
different optimizers, and Nadam was identified as the most effective, yielding 93.51% test
accuracy. To further enhance performance, Optuna-based hyperparameter tuning was
employed. The fine-tuned model attained 98.82% test accuracy, with precision, recall, and
F1-score of 0.9882 and ROC AUC of 0.9999, reflecting a 5.68% improvement over the
baseline model. The findings emphasize the feasibility of deploying a lightweight, highperformance model for real-time crop disease detection. By offering a scalable and
computationally efficient approach, this study advances sustainable agriculture, enabling
timely disease identification and improved crop management.
KEYWORDS:
Deep learning, convolutional neural networks, lightweight model, transfer learning,
sustainable agriculture, plant disease

Author: Y.Kanaka Durga, N.Nichhal, M.Harsha Vardhan Reddy & Ashish Singh

ABSTRACT:
In today’s data-driven business environment, effective decision-making is increasingly reliant
on the integration of Business Intelligence (BI) tools and advanced decision-making
techniques. This paper explores the impact of Business Intelligence on organizational
decision-making processes, focusing on how data analytics, visualization tools, and real-time
reporting systems enhance strategic, tactical, and operational decisions. By examining
various BI frameworks and decision-making models, including data mining, predictive
analytics, and machine learning, this study demonstrates how organizations can achieve
improved accuracy, agility, and competitiveness. Case studies from diverse industries
highlight the tangible benefits of BI adoption, such as enhanced performance metrics,
reduced risks, and more informed business strategies. The findings underscore the critical
role of BI in transforming raw data into actionable insights, ultimately empowering leaders to
make smarter, faster, and more effective decisions in an increasingly complex marketplace
Keywords: Business Intelligence, Decision-Making Techniques, Data Analytics,
Organizational Performance, Strategic Decision Support

Author: Dhanya.S & Resmi R

ABSTRACT:
The link between Quality of Work Life (QWL) and Organizational Citizenship Behavior
(OCB) plays an important role in shaping both employee satisfaction and a company’s
overall performance. QWL reflects how employees perceive their workplace experience —
including their job satisfaction, ability to maintain a healthy work-life balance, access to
career growth opportunities, and the nature of the organization's culture. A strong quality of
work life (QWL) often leads to more meaningful and enjoyable work experiences.
Employees feel more engaged in their roles, supported by leadership, and treated fairly within
the organization. This supportive atmosphere can inspire Organizational Citizenship Behavior
(OCB) — the voluntary actions employees take that aren’t part of their official job duties but
are vital to a healthy and productive workplace. These actions might include helping
teammates, showing initiative, volunteering for extra responsibilities, or contributing to a
positive work culture. This study examines how the quality of work life (QWL) influences
teachers' willingness to engage in Organizational Citizenship Behavior (OCB) at NBAaccredited business schools in Kerala. When educators feel supported, satisfied, and
respected in their professional environment, they’re more inclined to go beyond their basic
responsibilities—offering help to colleagues, showing initiative, and contributing to a
positive workplace culture. These behaviors not only strengthen teamwork and morale but
also enhance overall academic performance and institutional success. When employees
demonstrate positive behaviors, it boosts their own performance as well as that of the
organization, supporting long-term growth and development (Kharisma, R., Siamto, W., &
Astria, K,2022). The paper highlights some key challenges in enhancing Quality of Work
Life (QWL) and encouraging Organizational Citizenship Behavior (OCB), including limited
resources and the difficulty of assessing intangible aspects like job satisfaction. To address
these hurdles, it recommends steps such as investing in career development, implementing
flexible work policies, and fostering supportive leadership. These initiatives can help create a
more positive work atmosphere, leading to better performance, reduced employee turnover,
and a more motivated and engaged academic workforce.
Keywords: Quality of Work Life, Organizational Citizenship Behaviour, Business Schools,
NBA-Accredited

Author: Irrinki Mohan Krishna, Ch. Nissy Vennela, G N Harshita, Lavanya Sindhu & Sahasra

ABSTRACT
This research explores how investors behave during economic booms and busts, focusing on
psychological biases that influence decision-making.1 By analyzing historical financial data
from the 2008 Global
Financial Crisis and the 2020 COVID-19 crash, the study examines the differences between
retail and
institutional investors.1 Key behavioral patterns such as overconfidence, herd mentality, and
loss aversion are highlighted.1 Case studies provide real-world context, and findings offer
insights into strategies that can mitigate irrational investment behavior during market
turmoil.1 The study aims to provide a comprehensive understanding of investor psychology,
helping investors, policymakers, and financial advisors make informed decisions during
volatile periods.1
Key Words: A Behavioral Study of Investor, Financial Crisis, Key Behavioral patterns such
as overconfidence, herd mentality and loss aversion are highlighted.

Author:

Author: Raja Pavan Kumar Malladi, Muthyreddy Venkata Akhilesh, Kanneganti Antony Suhith, Shaik Mohammad Sohail & Maadu Mohan Dharma Teja

ABSTRACT
The purpose of this study is to examine the level of financial literacy among Indian youth and
to identify the factors that influence their knowledge, attitudes, and behaviours toward
financial decision-making. With increasing access to digital financial products, credit
facilities, and investment opportunities, understanding youth financial literacy is essential for
building long-term financial security and economic stability. The study employed a
quantitative survey-based approach. A structured questionnaire was distributed to 500 youth
respondents aged between 18 and 30 years, selected through stratified random sampling
across urban and semi-urban regions of India. The instrument covered three domains of
financial literacy as per the OECD framework Financial Knowledge (basic concepts of
saving, inflation, interest rates, and risk diversification), Financial Attitude (long-term
planning, responsible credit use, and attitude towards savings), Financial Behaviour
(budgeting, investment practices, debt management, and use of digital financial services)
Data were analysed using descriptive statistics, independent t-tests, and regression models to
identify demographic and behavioural predictors of financial literacy. The findings of the
study are Overall financial literacy levels among youth were moderate, with knowledge
scores averaging 56%, attitude scores 63%, and behaviour scores 48%, Male respondents and
those from commerce/business education backgrounds demonstrated higher knowledge levels
compared to female and non-commerce students. It was also found that Urban youth showed
stronger financial behaviour (use of banking apps, digital wallets, investment awareness)
compared to semi-urban respondents, A significant gap was observed between financial
knowledge and behaviour—youth often knew financial principles but failed to apply them
effectively, Regression analysis indicated that education level, monthly family income, and
access to financial education programs were significant predictors of higher financial literacy.
Key words: Financial literacy, Financial Education, Financial Planning

Author: Raja Pavan Kumar Malladi, Velidhi Rohith, Abhinav Shanmukha Srinivasa Ramisetti, Kattiboiena Meghana & Kesavarapu Tejaswini

ABSTRACT
The rise of digital marketing has significantly transformed how businesses in the automotive
service sector engage with customers. Among the various digital platforms, Instagram and
Facebook have emerged as dominant social media channels influencing consumer behavior
and brand engagement. This study aims to evaluate the effectiveness of Instagram and
Facebook promotions in enhancing customer awareness, engagement, and service purchase
intentions in the automotive service industry. The research draws upon digital marketing and
consumer engagement theories to examine factors such as content quality, advertisement
frequency, influencer marketing, and customer trust. A mixed-method approach combining
surveys and secondary data was used to analyze the impact of social media promotions on
brand visibility and service inquiries. The findings indicate that visual storytelling on
Instagram drives stronger emotional engagement, while Facebook promotions are more
effective in generating leads and conversions. The study concludes by offering managerial
implications for optimizing social media strategies to improve customer retention and brand
loyalty within the automotive service sector.
Keywords: Social Media Marketing; Instagram Promotions; Facebook Advertising;
Automotive Services; Consumer Engagement; Brand Awareness; Digital Marketing;
Influencer Strategy.

Author: Puneet Kumar, Arvind Kumar & Kamal Malik

ABSTRACT:
Sentiment analysis of movie reviews employs sophisticated natural language processing and
machine learning algorithms to analyze audience sentiment patterns systematically. This
facilitates predictive analytics for box office performance and enables data-driven decisionmaking in content strategy and production investments. In this Research Paper, a hybrid
model is constructed which includes the ensembling of K-Nearest Neighbor (KNN), KMeans clustering, Decision Tree, Random Forest, and Artificial Neural Network (ANN).
Experimental results demonstrate that ANN significantly outperformed other models,
achieving an accuracy of 88.04% and the highest F1 score of 88.30%. All the performance
metrics for various models have been calculated individually and compared. The comparative
analysis reveals a clear advantage of neural network architectures in capturing complex
semantic patterns within movie reviews. These findings contribute to the growing body of
research in sentiment analysis by providing empirical evidence for the efficiency of different
AI-based approaches, especially highlighting the robustness of neural networks in processing
and classifying textual sentiment data.
Keywords:
Artificial Intelligence; Sentiment Analysis; Supervised Learning; Unsupervised Learning

Author: Y. Kanaka Durga & A.Ajay Kumar Redddy

ABSTRACT
The COVID-19 pandemic catalyzed a global shift to remote work, transforming how
organizations manage performance, collaboration, and culture. This study investigates the
dual impact of remote work on team productivity and organizational culture through an
integrated framework combining the Job Demands–Resources (JD-R) model and Social
Exchange Theory (SET). A sequential mixed-methods design is proposed: qualitative
interviews to explore employee experiences and leadership practices, followed by a
quantitative survey across sectors. Key constructs include communication effectiveness,
autonomy, work-life balance, technology support, trust, engagement, and cultural cohesion.
We hypothesize that remote work enhances productivity when autonomy and digital
infrastructure are high but can erode culture when social connectedness and trust are weak.
The study contributes evidence-based insights for leaders seeking to sustain both
performance and cohesion in hybrid or remote-first organizations.
Keywords: Remote Work, Job Demands–Resources, etc.

Author: Pulin Bharali, Upala Baruah & Bijoy Das

ABSTRACT
In the 21st century, social scientists have become increasingly concerned with economics and
material wealth rather than with society itself, emphasizing the maximization of income over
the expansion of opportunities for people. Although the obsession with materialism may be
relatively recent, economists, educationists, social philosophers, social scientists,
geographers, and policymakers have long been preoccupied with augmenting the “national
treasure” through surplus trade balances, often prioritizing material success over the
development of human lives and values within society. As Segall (1984) defines, “Culture is
nothing but a bunch of independent variables, which might include basic institutions,
subsistence patterns, social organizations, languages, and social rules governing interpersonal
relations.” In this context, the present study seeks to examine the influence of the sociocultural environment of the Deori community in Assam, and to explore the socioeconomic background of the Deori people. The concepts of culture and society are complex
and constantly evolving. While society represents a web of social relationships that
continually change, culture embodies accumulated knowledge and practices transmitted from
generation to generation, adapting over time to shape human behavior and social life.
Key words: Socio-cultural, environment, community.

Author: Eda Harsha Vardhan, P.Mouli & G.Chaiteesh Reddy

ABSTRACT:
Employee engagement and motivation are critical drivers of organizational success,
influencing productivity, job satisfaction, and retention rates. This research explores the
factors that contribute to employee engagement and the different motivational theories that
explain employee behavior in the workplace. The study examines the role of intrinsic and
extrinsic motivation, highlighting the importance of aligning organizational goals with
individual aspirations. It also investigates the impact of leadership styles, organizational
culture, and work environment on engagement levels, particularly in the context of hybrid
and remote work settings. By analyzing both qualitative and quantitative data from employee
surveys and interviews, the research identifies key drivers of engagement, such as
recognition, opportunities for growth, and work-life balance. Additionally, it explores the
connection between employee motivation and organizational performance, presenting
strategies for enhancing engagement through personalized motivation techniques. The
findings suggest that while financial incentives are important, non-monetary factors—such as
autonomy, meaningful work, and a supportive organizational culture—are essential to
fostering sustained employee engagement. This paper provides practical recommendations
for HR professionals and leaders seeking to develop a motivated, engaged, and high performing workforce in the evolving work landscape.
Keywords: Employee Engagement, Motivation, Organizational Performance, Job
Satisfaction, HRM

Author: Gurpreet Kaur, Arvind Kumar & Kamal Malik

ABSTRACT
Due to rapid advancements in network technologies, the digital landscape has become quite
complex. Accurate prediction of future traffic patterns have become crucial and critical for
optimal resource allocation and effective network management. So, in response to the challenge,
this study introduces a comprehensive deep learning framework that is designed to enhance the
accuracy of network traffic classification. Through the integration of CNN-based spatial feature
extraction and LSTM-driven temporal modeling, the hybrid design enhances classification
accuracy, compared to the conventional methods. The proposed model is applied to WSN-DS
dataset that achieved an impressive classification accuracy of 98%, verified using standard
performance evaluation metrics. A comprehensive comparative analysis against state-of-the-art
techniques further demonstrated its enhanced effectiveness across multiple performance
indicators. These outcomes highlight the framework‟s robustness, scalability, and practical
applicability, establishing it as a promising solution for real-time network monitoring and
intelligent traffic analysis in increasingly complex network environments.
KEYWORDS:
Deep learning, network traffic prediction, convolutional neural networks, long-short term memory,
wsn-ds dataset

Author: Gurleen Kaur, Mandeep Kaur & Punam Rattan

ABSTRACT
High-velocity transactions are made possible by mobile and portable wallets, but they also
increase the attack surface for low-signal, real-time fraud—often under stringent latency and
computation limitations. We present a low-compute, security-conscious hybrid learner for
wallet fraud detection that uses soft voting over a compact, domain-specific seven-feature
signature extracted from PaySim (step, amount, type, oldbalanceOrg, newbalanceOrig,
oldbalanceDest, and newbalanceDest) to couple logistic regression with a shallow decision
tree. In order to reduce false alarms and inference costs, the pipeline uses a rule-based
prefilter to exclude zero-information/system-generated records and explicitly handles extreme
class imbalance using transaction-type-aware SMOTE restricted to TRANSFER and
CASH_OUT. The trained artifact is serialized and sealed with SHA-256 to facilitate integrity
verification and governance checks, hence hardening deployment. With training and
inference performed on a low-resource laptop, the method achieves ROC-AUC 0.9917, F1
0.96 (precision 0.95, recall 0.98), and 0.96 accuracy on Pay Sim, indicating edge practicality.
While SHAP/LIME studies offer clear global and local explanations appropriate for
operations and compliance, benchmarks against LightGBM, Cat Boost, Random Forest, and a
CNN-LSTM demonstrate competitive or superior recall and ROC-AUC at significantly
reduced complexity. The contribution is a detection stack that is deployable, interpretable,
and governance-aligned while providing cutting-edge accuracy without the need for complex
models. We go over the drawbacks of evaluating synthetic data and provide strategies for
drift monitoring, live-stream validation, and privacy-preserving updates. These findings show
that on devices with limited resources, correctly designed, security-conscious hybrids may
offer dependable, real-time wallet fraud screening.
KEYWORDS:
High-velocity transactions, Logistic regression,Smote,SHA-256 , Cat Boost, Random Forest,
and CNN-LSTM

Author: Golak Kumar Mandal & Manish Kumar

ABSTRACT
Hexagonal yttrium manganite (YMnO₃) is a prototypical multiferroic material exhibiting a
unique coexistence of ferroelectric and antiferromagnetic ordering at relatively low
temperatures. This paper presents a detailed structural, dielectric, and magnetic
characterization of sol-gel synthesized YMnO₃ for potential use in multifunctional sensor
devices. X-ray diffraction (XRD) and Rietveld refinement confirm the hexagonal P6₃cm
crystal structure with high phase purity. Dielectric measurements exhibit strong frequency
dispersion, indicating space-charge and interfacial polarization effects. Magnetic
measurements reveal weak antiferromagnetism with signatures of spin canting. The
correlation between microstructure, dielectric behavior, and magnetism is explored to assess
the applicability of YMnO₃ in capacitive, magnetic field, and temperature sensor devices. The
intrinsic stability, high resistivity, and eco-friendly nature of YMnO₃ make it a promising
candidate for multifunctional sensor technologies.
Keywords: YMnO₃, multiferroics, dielectric relaxation, antiferromagnetism, multifunctional
sensors, sol-gel synthesis, magneto-dielectric effect, hexagonal perovskite

Author: Pooja Kumari

ABSTRACT
Van der Waals (vdW) solids, comprising layered two-dimensional (2D) materials such as
graphene, hexagonal boron nitride (h-BN), transition-metal dichalcogenides (TMDs), and
artificially stacked heterostructures, have emerged as a transformative class of quantum
materials where electronic dispersion can be tuned through stacking, twist angles, external
fields, and strain. The ability to modulate band curvature, Fermi velocity, effective mass, and
bandgap via interlayer coupling has opened promising avenues in nanoelectronics,
valleytronics, and optoelectronics. This paper presents an analytical study of electronic
dispersion engineering in vdW solids through continuum band modelling, tight-binding
approximations, and perturbative analysis of interlayer interactions. The study identifies how
weak vdW interlayer coupling preserves individual layer symmetry while enabling tunable
hybridisation near high-symmetry points such as K, K′, M, and Γ. Analytical derivations
highlight how interlayer distance and twist angle modify the overlap integral and hopping
parameters, leading to reconstructed mini-bands, flatband formation, and moiré-induced
dispersion renormalisation. Graphene bilayers illustrate how small twist angles (<2°) reduce
Fermi velocity and yield flatbands associated with strong correlation, while similar
engineering in MoS₂, WS₂, and MoSe₂ demonstrates tunable direct–indirect band transitions
under vertical fields and interlayer shear. The study further examines dispersion anisotropy in
black phosphorus, where effective mass variation along armchair and zigzag directions can
be analytically captured via k·p theory. In all these systems, the analytical models align with
experimentally observed electro-absorption spectra, ARPES measurements, and magnetotransport signatures.
Keywords: Van der Waals solids; electronic dispersion engineering; moiré superlattices;
twisted bilayer graphene; transition-metal dichalcogenides (TMDs); black phosphorus; tightbinding model; k·p perturbation theory.

Author: Premakumari L

ABSTRACT
Dr. B.R. Ambedkar, the Principal architect of the Indian Constitution, was a visionary
economist and advocate for agrarian justice. His contributions to rural development, often
overshadowed by his work in social justice and constitutional law, remain relevant in
contemporary India. This paper examines Ambedkar's blueprint for agrarian transformation,
highlighting his advocacy for equitable land distribution, state ownership of agricultural
resources, and cooperative farming to promote economic democracy. Drawing on a
theoretical framework combining Ambedkar's ideas with Rawlsian justice and Amartya Sen's
capability approach, this study investigates the intersection of caste, land, and rural
inequality. The analysis draws on secondary data from government sources, academic
literature, and Ambedkar's original works.
The findings reveal structural disparities, particularly landlessness among Scheduled Castes
and Tribes, and inadequate post-independence policies to achieve Ambedkar's vision. By
comparing his proposals with current agrarian issues, including farmer suicides and
unsustainable farming practices, the paper advocates for rural development policies aligned
with Ambedkar's principles. This study proposes solutions including effective land
redistribution, cooperative farming revitalization, and incorporation of Ambedkar's justice
framework into policy-making and rural education. The paper argues that Ambedkar's
agrarian ideals align with constitutional mandates while presenting a sustainable, inclusive
approach to rural upliftment in 21st-century India.
Keywords: Dr. B. R. Ambedkar, Agrarian Justice, Rural Development, Sustainable Farming,
Land Reforms, Economic Democracy, Cooperative Farming, Social Equity.

Author: Pooja Kumari

ABSTRACT
As MOS transistors approach sub-5-nm channel lengths, traditional drift–diffusion transport
breaks down, and charge carriers increasingly propagate through the channel via quasiballistic and direct source-to-drain tunnelling pathways. Understanding the ballistic–
tunnelling transition thus becomes essential for predicting device behaviour, assessing scaling
limits, and designing next-generation CMOS technologies. This paper presents a
comprehensive theoretical model describing the continuous evolution of electronic transport
from semi-classical ballistic injection to quantum-mechanical tunnelling in nanoscale MOS
transistors. The analysis is based on a hybrid approach integrating Landauer–Büttiker
formalism, non-equilibrium Green’s functions (NEGF), and Wentzel–Kramers–Brillouin
(WKB) tunnelling approximations. These frameworks collectively capture mode-resolved
carrier injection, transmission probability, quantum confinement, and barrier thinning within
aggressively scaled channels. Analytical derivations reveal that ballistic transport dominates
when the channel length is comparable to or smaller than the mean free path (≈5–15 nm
for Si and ≈20–30 nm for III–V materials), whereas tunnelling becomes prominent when
effective barrier height decreases due to short-channel electrostatics, high-k dielectrics, and
subthreshold drain fields. The model identifies a critical “crossover regime”, typically within
nm, where neither conventional drift–diffusion nor pure tunnelling models adequately
describe current flow. Instead, carrier transmission is governed by combined thermionic–
ballistic injection and direct/phonon-assisted tunnelling across a triangular or trapezoidal
barrier. The proposed analytical expressions for transmission coefficient , quantum
capacitance, and injection velocity are benchmarked against NEGF simulation data and
experimentally measured short-channel transfer characteristics. Results show excellent
agreement in predicting off-state leakage, subthreshold swing degradation, and saturation
current roll-off. The model further highlights how gate oxide thickness, material effective
mass, channel orientation, and dielectric engineering influence the ballistic–tunnelling
balance in nanoscale devices.
Keywords: Ballistic transport; quantum tunneling; nanoscale MOS transistors; Landauer–
Büttiker formalism; WKB approximation; non-equilibrium Green’s function (NEGF); shortchannel effects; electrostatic barrier engineering;

Author: Prasanna R & Bhavani S

ABSTRACT:
In the rapidly evolving landscape of education, libraries have undergone significant
transformations to support remote learning. This article explores how libraries are adapting
their services to meet the needs of online education, highlighting their crucial role in providing
access to digital resources, virtual reference services, and user education. The shift from
traditional to digital resources has enabled libraries to expand their offerings, including ebooks, online journals, and academic databases, making high-quality information accessible to
remote learners. Virtual reference services, such as real-time chat and video consultations, have
become essential in maintaining personalized support. Integration with Learning Management
Systems (LMS) has streamlined access to library resources, enhancing the learning experience.
Additionally, libraries are committed to inclusivity and accessibility, ensuring resources are
available in formats that accommodate diverse needs.
Keywords: Remote Learning, Online Education, Library Services, Digital Resources, Virtual
Reference Services, Academic Databases, Learning Management Systems (LMS), User
Education, Digital Divide, Technology Integration, Virtual Reality, Artificial Intelligence,
Digital Literacy, Library Adaptation, Online Tutorials

Author: Isha Malik Arora, Mandeep Kaur & Kamal Malik

ABSTRACT
Hurricanes are highly destructive tropical systems that pose significant threats to human life,
coastal infrastructure, and global economies, making accurate forecasting an essential
component of disaster preparedness. Because traditional numerical weather prediction models
often struggle with the nonlinear dynamics governing hurricane behaviour, artificial
intelligence (AI) has emerged as a promising approach for improving predictive accuracy.
This systematic review examines advancements in AI-based hurricane prediction by
synthesizing research published across major scientific databases, including Web of Science,
Scopus, and IEEE Xplore, following PRISMA guidelines for study identification, screening,
and selection. The review highlights how machine learning and deep learning models—such
as artificial neural networks, support vector machines, random forests, convolutional neural
networks, LSTMs, and hybrid architectures—have been applied to predict hurricane
formation, track, intensity, and rapid intensification patterns. These models leverage satellite
imagery, atmospheric reanalysis data, oceanographic variables, and historical storm records
to uncover complex spatial and temporal relationships that conventional methods may
overlook. The findings indicate that deep learning approaches, particularly CNN–LSTM
hybrids and transformer-based networks, outperform traditional techniques by capturing both
multi-dimensional spatial features and sequential atmospheric dependencies. Despite these
advancements, several challenges persist, including limited labeled hurricane datasets,
inconsistencies across ocean basins, model interpretability issues, climate change-driven
variability, and difficulty integrating AI systems with operational forecasting frameworks.
Overall, the review demonstrates that AI-driven approaches have substantial potential to
enhance early warning systems, improve risk assessment, and support more informed
decision-making by disaster management authorities. Continued progress will depend on
expanding high-quality datasets, developing physically interpretable models, and integrating
hybrid systems that combine data-driven learning with established meteorological principles.
Keywords :-Hurricane Prediction, Artificial Intelligence, Machine Learning, Deep Learning,
Tropical Cyclone Forecasting, Meteorological Modelling

Author: Parveen Kumar Goyal & Garima Tyagi

ABSTRACT
This work investigates the urgent need for models that are simultaneously robust and responsible in Data Privacy Threat detection. In this paper a Hybrid Machine Learning Framework is to be used that fuses Convolutional Neural Network for feature learning, also an XGBoost classifier to be implemented which has been carefully optimized using optuna bayesian approach. It resulted in better classification performance with an AUC 0.999 and F1-Score of 0.9886, clearly outperforming unoptimized baselines. Importantly, conceptualization is taken beyond mere performance, such as by introducing a way to measure model interpretability and ethical reasoning. Hear a new comparative study quantifying important operational statistics such as and the Traceability Index to steer deploy resource efficiency, leverage Natural Language Processing to investigate and verify model explanations against the ethical compliance benchmarks. This paper devising a novel metricthat use the feature of textual model outputs, i.e., Inference Privacy Score to measure the privacy leakage risk and guarantees solution being not only high-performing but fully traceable and responsible.

Keywords: Hybrid Machine Learning Framework, Data Privacy Threat Detection, Optuna Bayesian Optimization, XGBoost Classifier, Inference Privacy Score, Ethical Compliance Audit.

Author: Shyam Kumar Saphi

Fifth-generation (5G) mobile systems increasingly exploit millimetre-wave (mm-wave) spectrum, especially in the 24–30 GHz and 37–43 GHz bands, to meet stringent capacity and latency requirements. Accurate electromagnetic propagation modelling at these frequencies is essential for the design of phased-array antennas, beamforming strategies and dense small-cell deployments. This study presents a comprehensive discussion of propagation characteristics at mm-wave frequencies, surveys standardised and measurement-based channel models, and develops a hybrid link-level modelling framework combining 3GPP TR 38.901–style large-scale fading with physics-based atmospheric and rain attenuation and realistic antenna array patterns. Using representative 28 GHz urban microcell (UMi) scenarios, we generate numerical results for path loss, additional attenuation due to oxygen absorption and rain, and the impact of beamforming gain on received signal-to-noise ratio (SNR). Data tables and illustrative plots show how link range and reliability depend on environment (LOS/NLOS), frequency, and antenna configuration. The results highlight that, while severe blockage and high basic path loss are inherent at mm-wave, directional beamforming with large arrays and careful link budgeting can deliver viable coverage for 5G access and backhaul, particularly in line-of-sight (LOS) and short-range non-LOS (NLOS) conditions. The proposed modelling framework is generic and can be used to benchmark antenna designs, evaluate deployment strategies, and extend towards emerging 5G-Advanced and early 6G systems.
Keywords: 5G, millimetre-wave, electromagnetic propagation, 28 GHz, 60 GHz, 3GPP TR 38.901, channel modelling, phased arrays, beamforming, rain attenuation.

Author: Navneet Kaur Bains, Navdeep Kaur

Background: The rapid expansion of digital health platforms has revolutionised healthcare delivery by enhancing access, improving efficiency, and fostering patient engagement. However, variations in patients’ digital competencies, particularly eHealth literacy, continue to influence the extent to which these platforms are adopted and used effectively. Understanding the factors that drive or hinder digital health adoption is essential for achieving inclusive and sustainable digital healthcare.
Aim/Objectives: The study aims to examine patients’ adoption of digital health platforms using the UTAUT2 framework and to analyze the role of eHealth literacy in shaping behavioural intention and actual usage. Specifically, it investigates the influence of UTAUT2 constructs on behavioural intention, the intention–use relationship, and the moderating effect of eHealth literacy.
Methodology: A quantitative, descriptive research design was adopted. Primary data were collected from 400 patients in the Tricity region (Chandigarh, Panchkula, and Mohali) using a structured questionnaire based on validated scales. Data were analysed using descriptive statistics, regression, structural analysis, mediation analysis, and moderation analysis through PROCESS Macro.
Results: The findings reveal that performance expectancy, facilitating conditions, habit, and price value significantly influence behavioural intention, while behavioural intention strongly predicts actual use behaviour. Effort expectancy, social influence, and hedonic motivation show limited direct effects. eHealth literacy significantly moderates the relationship between UTAUT2 constructs and behavioural intention, strengthening adoption outcomes.
Conclusion: The study concludes that digital health adoption is driven not only by technological features but also by patients’ ability to understand and use digital health information. Enhancing eHealth literacy is critical for translating access into effective and sustained digital health usage.
Keywords: Digital health platforms; eHealth literacy; UTAUT2; Behavioural intention; Use behaviour

Author: Munmi Gogoi, Jonison Daulagajau

The purpose of this study is to explore the intricate conflicts that may arise from an identity crisis within a community as it endeavors to integrate into a larger social group. To truly understand one's identity, individuals must embark on a journey of introspection, grappling with profound questions such as "Who am I?" and "Who are we?" This introspective journey underscores the notion that identity is not only a fundamental human desire but also a critical factor in many enduring social conflicts. Historically, Assam has stood as a vibrant crossroads of various ethnicities and cultural groups, often referred to as the gateway to Northeast India. Among the notable communities in Assam are the Adivasis, whose history is deeply intertwined with colonial narratives. These individuals are primarily the descendants of migrant laborers transported by the British colonial administration from what are now the states of Jharkhand, Chhattisgarh, Odisha, Andhra Pradesh, Bihar, and parts of West Bengal. During the 19th and early 20th centuries, as the tea industry rapidly expanded in Assam, the British East India Company sought to fill the labor gap by bringing in these workers, largely because the indigenous Assamese population was often hesitant to engage in plantation work. This study will delve into the identity conflicts faced by the Adivasis as they navigate the complexities of adapting to a dominant culture that overshadows their own. The central focus of the essay will be to conduct a thorough theoretical analysis of the pertinent facts and an extensive review of the existing literature related to this significant issue. By examining these dynamics, the study aims to shed light on how identity struggles manifest in the context of cultural integration and community belonging.
Keywords: Identity, Migration, Conflicts, Assimilation.

Author: Ruchi Kohli, Supriya Duggal

The National Education Policy is a transformative revolution aimed at reshaping India’s education structure and mechanisms to meet the demands of the future. This handwriting explores how the NEP 2020 is a necessity in achieving the dream of “Viksit Bharat2047 ”( Developed India) by aligning its objects with the Sustainable Development Goals ( SDG 2030). We've concentrated on strategies, benefits and challenges in areas like quality education, gender equivalency and sustainable profitable growth and also demonstrate how the NEP 2020 can serve as a catalyst to the nation’s overall development and attainment of the global sustainability.
Keywords – National Education Policy 2020 (NEP 2020), Sustainable Development Goals 2030 (SDG 2030), Education, Viksit Bharat, Skill.

Author: Arun Kalia, A J Singh

Vehicle Number Identification is central to in intelligent transportation systems, traffic monitoring, law enforcement, and security applications. Traditional rule-based approaches, which relied on edge detection, color segmentation, and optical character recognition (optical character deciphering), often failed under real-world conditions such as poor lighting, motion blur, and diverse plate formats. In recent developments in intelligent algorithmic models (ML) and advanced neural network techniques (DL) significantly reshaped LPR (License Plate Recognition) by introducing robust feature learning, end-to-end recognition, and real-time detection capabilities. The present study reviews and validates modern ML approaches for license plate recognition, focusing on object detection models such as You Only Look Once framework and Region-based Convolutional Neural Network for plate localization, and sequence learning methods such as Convolutional Neural Networks, Convolutional Recurrent Neural Networks, and context-aware Transformer modeless for character recognition. Furthermore, the contribution of data augmentation and Generative Adversarial Network-based synthetic image generation in enhancing robustness under challenging environments is discussed. Experimental evidence from benchmark datasets, including CCPD, AOLP, and UFPR-ALPR, confirms that ML-based approaches consistently achieve 97–99% accuracy in ideal conditions and maintain 90–95% accuracy in real-world scenarios, far outperforming traditional methods. The findings validate that intelligent algorithmic models significantly enhances both accuracy and reliability in LPR systems. The study concludes that while ML-powered models excel in robustness and real-time processing, future research should address challenges such as adverse weather conditions, multilingual license plates, and privacy concerns in automated vehicle tracking.
Keywords: Vehicle Number Identification (LPR), Automatic Number Plate Recognition (ANPR), Machine Learning, Deep Learning, You Only Look Once frame work, Convolutional Neural Network-optical character deciphering, Smart Traffic Systems.

Author: ਸੁਰਭੀ ਸ਼ਰਮਾ

ਮੀਡੀਆ ਸਮਾਜਿਕ, ਰਾਜਨੀਤਿਕ ਅਤੇ ਸੱਭਿਆਚਾਰਕ ਮੁੱਦਿਆਂ ਨੂੰ ਵਿਅਕਤੀਆਂ ਦੁਆਰਾ ਕਿਵੇਂ ਸਮਝਿਆ ਜਾਂਦਾ ਹੈ, ਇਸ ਨੂੰ ਪ੍ਰਭਾਵਿਤ ਕਰਕੇ ਜਨਤਕ ਰਾਏ ਨੂੰ ਆਕਾਰ ਦੇਣ ਵਿੱਚ ਮਹੱਤਵਪੂਰਨ ਭੂਮਿਕਾ ਨਿਭਾਉਂਦਾ ਹੈ। ਭਾਰਤ ਵਰਗੇ ਵਿਭਿੰਨ ਲੋਕਤੰਤਰੀ ਦੇਸ਼ ਵਿੱਚ, ਮੀਡੀਆ ਜਨਤਾ ਲਈ ਜਾਣਕਾਰੀ ਅਤੇ ਵਿਆਖਿਆ ਦੇ ਇੱਕ ਮਹੱਤਵਪੂਰਨ ਸਰੋਤ ਵਜੋਂ ਕੰਮ ਕਰਦਾ ਹੈ। ਟੈਲੀਵਿਜ਼ਨ ਖ਼ਬਰਾਂ ਅਤੇ ਡਿਜੀਟਲ ਪਲੇਟਫਾਰਮਾਂ ਦੇ ਵਿਸਥਾਰ ਦੇ ਨਾਲ, ਰਾਏ ਨਿਰਮਾਣ 'ਤੇ ਮੀਡੀਆ ਦਾ ਪ੍ਰਭਾਵ ਵਧੇਰੇ ਤੁਰੰਤ ਅਤੇ ਵਿਆਪਕ ਹੋ ਗਿਆ ਹੈ। ਇਹ ਪੇਪਰ ਭਾਰਤੀ ਸੰਦਰਭ ਵਿੱਚ ਜਨਤਕ ਰਾਏ ਨੂੰ ਆਕਾਰ ਦੇਣ ਵਿੱਚ ਮੀਡੀਆ ਦੇ ਵੱਖ-ਵੱਖ ਰੂਪਾਂ ਦੀ ਭੂਮਿਕਾ ਦੀ ਜਾਂਚ ਕਰਦਾ ਹੈ। ਇਹ ਮੀਡੀਆ ਪੱਖਪਾਤ, ਵਪਾਰੀਕਰਨ ਅਤੇ ਗਲਤ ਜਾਣਕਾਰੀ ਦੇ ਫੈਲਾਅ ਵਰਗੀਆਂ ਵੱਡੀਆਂ ਚੁਣੌਤੀਆਂ 'ਤੇ ਵੀ ਚਰਚਾ ਕਰਦਾ ਹੈ। ਅਧਿਐਨ ਸੂਚਿਤ ਜਨਤਕ ਭਾਸ਼ਣ ਅਤੇ ਲੋਕਤੰਤਰੀ ਕਦਰਾਂ-ਕੀਮਤਾਂ ਦਾ ਸਮਰਥਨ ਕਰਨ ਲਈ ਨੈਤਿਕ ਅਤੇ ਜ਼ਿੰਮੇਵਾਰ ਮੀਡੀਆ ਅਭਿਆਸਾਂ ਦੀ ਜ਼ਰੂਰਤ 'ਤੇ ਜ਼ੋਰ ਦਿੰਦਾ ਹੈ।
ਕੀਵਰਡਸ: ਮੀਡੀਆ, ਰਾਇ, ਜਨ ਸੰਚਾਰ, ਲੋਕਫੋਰਟ, ਭਾਰਤ

Author: Ilyas P A, Navdeep Kaur

Artificial Intelligence (AI) has increasingly been adopted in large-scale cultural destinations to improve crowd regulation, visitor safety, and operational performance during peak events. This study investigates how AI-powered crowd management systems contribute to organizational development and performance from the visitors’ perspective, using Katara Cultural Village, Qatar as the case setting. A quantitative research design was employed using a structured questionnaire administered to visitors attending cultural events at Katara. The study assessed key AI-driven strategies including predictive analytics, real-time crowd monitoring, automated crowd control alerts, and intelligent resource allocation. Findings indicate that visitors perceive AI-enabled crowd management as highly effective in reducing congestion, improving movement comfort, strengthening safety assurance, and enhancing service quality. Furthermore, the results suggest that positive visitor perceptions of AI-based crowd management significantly influence evaluations of organizational effectiveness and institutional image. The study highlights that AI technologies function not only as operational tools but also as strategic drivers that shape visitor satisfaction and destination credibility in cultural tourism contexts. Practical implications include the need for policymakers and destination managers to support ethical AI deployment through transparency, privacy safeguards, and public communication to sustain visitor trust. This visitor-centered study contributes to existing literature by linking AI crowd management directly to perceived organizational performance within a real cultural destination environment.
Keywords: Artificial Intelligence; Crowd Management; Visitor Perception; Organizational Performance; Katara Cultural Village