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NRJITIS - National Research Journal of Information Technology & Information Science


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The National Research Journal of Information Technology and Information Science (NRJITIS) (ISSN: 2350-1278)  is a peer reviewed Journal academic publication dedicated to advancing research and knowledge in the fields of Information Technology (IT) and Information Science. The journal serves as a platform for scholars, practitioners, and industry professionals to share innovative research findings, emerging technologies, and practical applications. It covers a broad range of topics including data science, cybersecurity, artificial intelligence, software engineering, information systems, digital transformation, and human-computer interaction & Library Sciences and other multidisciplinary related topics. The journal aims to foster interdisciplinary collaboration and contribute to the evolving landscape of IT and information science through high-quality, original research.

The Journal is Published By "National Press Associates"

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  • Journal Name: National Research Journal of Information Technology and Information Science
  • ISSN: 2350-1278
  • Impact Factor: 7.9
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Current Issue

Year: 2025   Volume-12, Issue-2 (July-December)

Paper Title CHALLENGES IN MULTILINGUAL TYPESETTING AND TYPOGRAPHY FOR INDIAN VERNACULAR PRINTING
Author Name Amit Sharma
Country India
DOI https://doi.org/10.5281/zenodo.16014579
Page No. 1-11

Abstract View PDF Download Certificate
CHALLENGES IN MULTILINGUAL TYPESETTING AND TYPOGRAPHY FOR INDIAN VERNACULAR PRINTING
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

Paper Title OPTIMIZING AWARENESS IN GREEN COMPUTING INITIATIVES
Author Name Kanwaldip Kaur
Country India
DOI https://doi.org/10.5281/zenodo.16521563
Page No. 12-16

Abstract View PDF Download Certificate
OPTIMIZING AWARENESS IN GREEN COMPUTING INITIATIVES
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.

Paper Title SMART LIBRARIES: ENHANCING THE LIBRARY EXPERIENCE
Author Name Gurpreet Singh Sohal
Country India
DOI https://doi.org/10.5281/zenodo.16634127
Page No. 17-23

Abstract View PDF Download Certificate
SMART LIBRARIES: ENHANCING THE LIBRARY EXPERIENCE
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

Paper Title CYBER SECURITY THREATS IN CLOUD COMPUTING
Author Name Sahil Luthra
Country India
DOI https://doi.org/10.5281/zenodo.16678363
Page No. 24-35

Abstract View PDF Download Certificate
CYBER SECURITY THREATS IN CLOUD COMPUTING
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.

Paper Title INFLUENCE OF CULTURAL AND SOCIO-ECONOMIC BACKGROUND ON PERSONALITY PATTERN OF MISHING ADOLESCENTS IN LAKHIMPUR DISTRICTS OF ASSAM
Author Name Ratul Borah
Country India
DOI https://doi.org/10.5281/zenodo.17043561
Page No. 36-47

Abstract View PDF Download Certificate
INFLUENCE OF CULTURAL AND SOCIO-ECONOMIC BACKGROUND ON PERSONALITY PATTERN OF MISHING ADOLESCENTS IN LAKHIMPUR DISTRICTS OF ASSAM
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

Paper Title EVALUATING MRI PLANE AND MACHINE LEARNING ALGORITHM PERFORMANCE IN ALZHEIMER'S DISEASE CLASSIFICATION USING HARALICK TEXTURE FEATURES
Author Name Gayathri L , Muralidhara B. L
Country India
DOI https://doi.org/10.5281/zenodo.17097235
Page No. 48-57

Abstract View PDF Download Certificate
EVALUATING MRI PLANE AND MACHINE LEARNING ALGORITHM PERFORMANCE IN ALZHEIMER'S DISEASE CLASSIFICATION USING HARALICK TEXTURE FEATURES
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.

Paper Title BEYOND WORDS: A COMPARATIVE STUDY OF SUMMARIZATION MODELS AND EMBEDDING TECHNIQUES
Author Name Maya A.K.R, Muralidhara B. L
Country India
DOI https://doi.org/10.5281/zenodo.17097274
Page No. 58-66

Abstract View PDF Download Certificate
BEYOND WORDS: A COMPARATIVE STUDY OF SUMMARIZATION MODELS AND EMBEDDING TECHNIQUES
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.

Paper Title COUNTERFEIT DETECTION IN EDUCATIONAL CREDENTIALS USING MACHINE LEARNING TECHNIQUES
Author Name Sarala M, Muralidhara B L
Country India
DOI https://doi.org/10.5281/zenodo.17097310
Page No. 67-74

Abstract View PDF Download Certificate
COUNTERFEIT DETECTION IN EDUCATIONAL CREDENTIALS USING MACHINE LEARNING TECHNIQUES
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.

Paper Title AN EVOLUTIONARY APPROACH TO FRACTIONAL BILEVEL PROGRAMMING
Author Name Debjani Chakraborti
Country India
DOI https://doi.org/10.5281/zenodo.17097510
Page No. 75-87

Abstract View PDF Download Certificate
AN EVOLUTIONARY APPROACH TO FRACTIONAL BILEVEL PROGRAMMING
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.

Paper Title ANALYSIS OF FACTORS INFLUENCING CONSUMER PURCHASE BEHAVIOR IN THE REALTY SECTOR IN PUNJAB
Author Name Pankaj Mohindru & Jasmeen Kaur
Country India
DOI https://doi.org/10.5281/zenodo.17191554
Page No. 88-98

Abstract View PDF Download Certificate
ANALYSIS OF FACTORS INFLUENCING CONSUMER PURCHASE BEHAVIOR IN THE REALTY SECTOR IN PUNJAB
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.

Paper Title THE LEGACY OF VIJAYANAGARA EMPIRE: CULTURAL HERITAGE AND TOURISM DEVELOPMENT IN CONTEMPORARY KARNATAKA
Author Name Kumaraswamy. T
Country India
DOI https://doi.org/10.5281/zenodo.17200990
Page No. 99-104

Abstract View PDF Download Certificate
THE LEGACY OF VIJAYANAGARA EMPIRE: CULTURAL HERITAGE AND TOURISM DEVELOPMENT IN CONTEMPORARY KARNATAKA
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.

Paper Title ARTIFICIAL INTELLIGENCE AND DATA ANALYTICS IN STRATEGIC MANAGEMENT
Author Name Anuradha Averineni, Boddu Bhagya Sree, Shaik Abubakar Siddiq, Attuluri Sri Mani Teja, Chimakurthy Venkata Subramanya Srikanth
Country India
DOI https://doi.org/10.5281/zenodo.17223264
Page No. 105-108

Abstract View PDF Download Certificate
ARTIFICIAL INTELLIGENCE AND DATA ANALYTICS IN STRATEGIC MANAGEMENT
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

Paper Title SOCIAL SKILLS AMONG SENIOR SECONDARY SCHOOL STUDENTS WITH RESPECT TO GENDER AND FAMILY TYPE
Author Name Vandana Kapoor, Suman Kumari
Country India
DOI https://doi.org/10.5281/zenodo.17233637
Page No. 109-115

Abstract View PDF Download Certificate
SOCIAL SKILLS AMONG SENIOR SECONDARY SCHOOL STUDENTS WITH RESPECT TO GENDER AND FAMILY TYPE
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

Paper Title ARTIFICIAL INTELLIGENCE IN EDUCATION: A REVIEW OF EMERGING TRENDS, CHALLENGES, AND OPPORTUNITIES
Author Name Navpreet Kaur, Mamta Sambyal & Krishan Gopal
Country India
DOI https://doi.org/10.5281/zenodo.17284754
Page No. 116-124

Abstract View PDF Download Certificate
ARTIFICIAL INTELLIGENCE IN EDUCATION: A REVIEW OF EMERGING TRENDS, CHALLENGES, AND OPPORTUNITIES
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

Paper Title AI-DRIVEN DECISION SUPPORT SYSTEM FOR GUIDING AND REVIVING FAILING STARTUPS
Author Name Y. Kanakadurga, Y. Kanakadurga, G. Bhavanarayana, E. Varun Sai, D. Udhaykiran & B. Bhanuprasad
Country India
DOI https://doi.org/10.5281/zenodo.17310832
Page No. 25-128

Abstract View PDF Download Certificate
AI-DRIVEN DECISION SUPPORT SYSTEM FOR GUIDING AND REVIVING FAILING STARTUPS
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.

Paper Title AN ATTENTION-BASED HYBRID DEEP LEARNING APPROACH FOR SOLID WASTE CLASSIFICATION
Author Name Shruti Handa & Mandeep Kaur
Country India
DOI https://doi.org/10.5281/zenodo.17338006
Page No. 129-152

Abstract View PDF Download Certificate
AN ATTENTION-BASED HYBRID DEEP LEARNING APPROACH FOR SOLID WASTE CLASSIFICATION
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

Paper Title DEEP LEARNING FOR SUSTAINABLE AGRICULTURE: OPTIMIZED MOBILENETV2 FOR MULTI-CLASS CROP DISEASE IDENTIFICATION
Author Name Daisy Wadhwa, Arvind Kumar & Kamal Malik
Country India
DOI https://doi.org/10.5281/zenodo.17346239
Page No. 153-175

Abstract View PDF Download Certificate
DEEP LEARNING FOR SUSTAINABLE AGRICULTURE: OPTIMIZED MOBILENETV2 FOR MULTI-CLASS CROP DISEASE IDENTIFICATION
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

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