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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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  • Frequency of Publication: Biannual (2 Issues Per Year)
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Current Issue

Year: 2026   Volume No: 13, January, Year: 2026 (Special Issue)

Paper Title LOW COST SMALL SIZE PATCH ANTENNA FOR WEARABLE APPLICATIONS
Author Name Sushil Kakkar & Shweta Rani
Country India
DOI https://doi.org/10.5281/zenodo.18933614
Page No. 1-6

Abstract View PDF Download Certificate
LOW COST SMALL SIZE PATCH ANTENNA FOR WEARABLE APPLICATIONS
Author: Sushil Kakkar & Shweta Rani

ABSTRACT
Present day wearable technology possesses a significant contribution in health monitoring systems. A small size cost effective patch antenna for wearable applications has been elaborated in this paper. The presented antenna is square in shape and designed with FR4 substrate. The dimensions of the antenna have been optimized using numerous simulations. In view to obtain the effect of slot on the performance of antenna, a rigorous analysis has also been performed.

Keywords: Antenna, micorstrip, wearable, radiation pattern.

Paper Title AN EXTENSIVE ANALYSIS OF GREEN COMPUTING: BENEFITS, CHALLENGES AND ROLE
Author Name Navneet Kaur Sandhu & Mohammad Wasiq
Country India
DOI https://doi.org/10.5281/zenodo.18933740
Page No. 7-10

Abstract View PDF Download Certificate
AN EXTENSIVE ANALYSIS OF GREEN COMPUTING: BENEFITS, CHALLENGES AND ROLE
Author: Navneet Kaur Sandhu & Mohammad Wasiq

ABSTRACT
The phrase "green computing" refers to the methods employed by the sector to reduce the amount of hazardous elements released into the environment as a result of the use of ICT resources. About 2% of carbon emissions come from this use, which is equivalent to aircraft. This information inspired the idea of green computing, or environmentally friendly computing. Numerous gadgets, mechanisms, and software have been created as a result of advancements in modern technology, and numerous studies have been carried out to maximize and expand the green computing capabilities of these technologies. Therefore, to determine the current developments, difficulties, and prospects for further research, a review and summary of studies based on green computing are necessary. Through an exploration of the twelve areas of green computing, this study reviewed and summarized green computing in each area study. Following a comprehensive comparison and analysis, this study offers answers to the suggested cutting-edge research questions. Additionally, this study outlines the present difficulties and prospects for further research in each field of green computing. This study will offer insights and ideas to institutions, researchers, and organizations involved in green computing research. Additionally, environmental groups, businesses, and government organizations working to lower energy use and carbon emissions will also gain from this review study.

Keywords: Green Computing, ICT, Carbon, Energy, Environment.

Paper Title ENHANCING REAL-TIME MONITORING: THE ROLE OF WIRELESS SENSOR NETWORKS IN MODERN APPLICATIONS WITH VMIMO
Author Name Mandeep Kaur Sekhon & Jagdeep Kaur
Country India
DOI https://doi.org/10.5281/zenodo.18933895
Page No. 11-15

Abstract View PDF Download Certificate
ENHANCING REAL-TIME MONITORING: THE ROLE OF WIRELESS SENSOR NETWORKS IN MODERN APPLICATIONS WITH VMIMO
Author: Mandeep Kaur Sekhon & Jagdeep Kaur

ABSTRACT
This literature review examines the fundamental concepts, applications, and advancements in Wireless Sensor Networks (WSNs), focusing on energy-efficient communication techniques using Single-Input Single-Output (SISO), Single-Input Multiple-Output (SIMO), Multiple-Input Single-Output (MISO), and Multiple-Input Multiple-Output (MIMO) systems. It highlights improvements in MIMO technology, explores energy models with MIMO, and evaluates performance metrics. The need for Virtual MIMO (vMIMO) is discussed, alongside strategies to make it energy efficient. A detailed comparison of vMIMO and traditional MIMO in terms of energy efficiency and an analysis of the challenges in implementing vMIMO in WSNs are provided. Suitable images and diagrams illustrate key concepts. The evolution of wireless communication technology has led to the development of Multiple Input Multiple Output (MIMO) systems, which utilize multiple antennas at both the transmitter and receiver ends to improve communication performance. In recent years, Virtual MIMO (vMIMO) has emerged as a promising alternative, particularly in Wireless Sensor Networks (WSNs), where energy efficiency is paramount due to the limited battery life of sensor nodes. This paper provides a detailed comparison of virtual MIMO and traditional MIMO in terms of energy efficiency, along with the main challenges associated with implementing virtual MIMO in WSNs.

General Terms
This paper explores the role of Wireless Sensor Networks (WSNs) in enhancing real-time monitoring through energyefficient communication techniques, including MIMO and vMIMO. It focuses on the advancements in MIMO technology, need for vMIMO and its benefits and main challenges of implementing vMIMO in WSNs are analyzed. A comparison between traditional MIMO and vMIMO is provided, highlighting their architectural and operational differences.

Keywords: WSN, Traditional MIMO, vMIMO, MIMO vs vMIMO.

Paper Title EXPLORING MACHINE LEARNING TECHNIQUES FOR THE DETECTION OF DDOS ATTACKS: A COMPREHENSIVE REVIEW
Author Name Rajni, Daljit Kaur, Inderdeep Kaur, Parminder Kaur & Harmandar Kaur
Country India
DOI https://doi.org/10.5281/zenodo.18933964
Page No. 16-27

Abstract View PDF Download Certificate
EXPLORING MACHINE LEARNING TECHNIQUES FOR THE DETECTION OF DDOS ATTACKS: A COMPREHENSIVE REVIEW
Author: Rajni, Daljit Kaur, Inderdeep Kaur, Parminder Kaur & Harmandar Kaur

ABSTRACT
As DDoS attacks get increasingly sophisticated, traditional detection approaches fail to keep up with the changing threat landscape. Machine learning provides powerful capabilities for detecting and mitigating assaults in real time. This review paper investigates various machine learning algorithms used to detect DDoS attacks, categorizing them as supervised,
unsupervised, and deep learning approaches. Supervised learning algorithms, such as Support Vector Machines (SVM) and Decision Trees, have been widely utilized to categorize attack patterns, although unsupervised learning techniques, such as clustering, provide advantages in detecting novel assaults without the need for labeled data. Deep learning models, notably Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have shown exceptional performance in large-scale, dynamic assault scenarios. This review also examines the role of datasets, named KDDCup99 and CICIDS, which are used to train these models, and their success is evaluated using important performance indicators such as
accuracy, precision, and recall. This study examines recent breakthroughs, datasets, and performance indicators in order to guide future research and improve the resilience of cybersecurity defenses against DDoS attacks.

Paper Title A COMPREHENSIVE STUDY ON TRANSFORMER DESIGN USING NUMERICAL TECHNIQUES
Author Name Sarpreet Kaur
Country India
DOI https://doi.org/10.5281/zenodo.18934030
Page No. 28-35

Abstract View PDF Download Certificate
A COMPREHENSIVE STUDY ON TRANSFORMER DESIGN USING NUMERICAL TECHNIQUES
Author: Sarpreet Kaur

ABSTRACT
The aim of this study was to review the application of finite element techniques for solving complex transformer structures using modern software. The Finite Element Method (FEM), developed over the past 70 years to address intricate problems in civil and aeronautical engineering, has since found valuable applications in electrical engineering for solving complex
design challenges. This paper explores the use of FEM in transformer design, highlighting its effectiveness as a numerical tool for simulating structural components, optimizing materials, enhancing reliability, performing failure analysis, taking corrective actions, and verifying new designs under various loading conditions. The study concludes that FEM is a highly efficient approach for transformer design and analysis.

Keywords- Numerical Techniques, Finite Element Method, Transformer Design.

Paper Title A COMPARATIVE SURVEY OF RAO OPTIMIZATION ALGORITHMS: MULTI-OBJECTIVE APPLICATIONS AND HYBRID TECHNIQUES IN ENGINEERING DESIGN
Author Name Shubhangi Jagdish Kamble
Country India
DOI https://doi.org/10.5281/zenodo.18934242
Page No. 36-44

Abstract View PDF Download Certificate
A COMPARATIVE SURVEY OF RAO OPTIMIZATION ALGORITHMS: MULTI-OBJECTIVE APPLICATIONS AND HYBRID TECHNIQUES IN ENGINEERING DESIGN
Author: Shubhangi Jagdish Kamble

ABSTRACT
This paper presents a comprehensive survey of the Rao optimization algorithm focusing on its applications in the omnidirectional domain, including robotics, image processing, machine learning, and renewable energy systems. Rao’s adaptability and robustness make it an effective tool for solving complex, high-dimensional, nonlinear, and dynamic optimization problems. A key contribution is the exploration of hybrid Rao algorithms, such as Rao-Particle Swarm Optimization (PSO), Rao-Differential Evolution (DE), and Rao-Genetic Algorithms (GA) to address challenges like slow convergence in high-dimensional spaces. The paper highlights Rao's potential in real-time applications, such as autonomous robot path planning and machine learning hyper-parameter tuning. Additionally, it examines Rao’s role in multi-objective optimization, a crucial aspect of engineering design and system optimization. The study underscores Rao's strengths in handling dynamic optimization tasks, balancing exploration and exploitation, and improving convergence speed through hybrid approaches. A comparative analysis with other meta-heuristic algorithms like GA, PSO, and DE shows Rao’s superior global search capability and computational efficiency. The results demonstrate Rao’s versatility and potential for solving real-world optimization problems, especially in high-dimensional, dynamic environments. This survey provides valuable insights for researchers and practitioners aiming to use Rao optimization for complex, real-time, and
multi-objective tasks in various domains.

Keywords— Rao optimization, renewable energy systems, hybrid algorithms, multi-objective optimization, robotics, image processing, machine learning

Paper Title DEEP LEARNING FOR REAL-TIME ROUTE OPTIMIZATION IN TOURISM APPLICATIONS
Author Name Disha Sharma, Usman Ali, Aman Kumar Aditya, Gurleen Kaur & Astha Rathore
Country India
DOI https://doi.org/10.5281/zenodo.18934961
Page No. 45-51

Abstract View PDF Download Certificate
DEEP LEARNING FOR REAL-TIME ROUTE OPTIMIZATION IN TOURISM APPLICATIONS
Author: Disha Sharma, Usman Ali, Aman Kumar Aditya, Gurleen Kaur & Astha Rathore

ABSTRACT
Weak environmental factors, such as weather conditions, shifting user preferences, along with road traffic control impact travel efficiency in tourism. Therefore, real-time route optimization models are needed for ensuring smooth and efficient travel for the user. The paper explore how deep learning methods can boost route optimization in tourism systems. The application uses real-time position system and traffic report and weather forecast data to adjust travel routes which delivers customized and optimized routes. Travelers obtain adaptable route recommendations from the system after it factors in their preferences and past travel data and external boundary restrictions to enhance their whole travel experience. If we compare the traditional models over the models used in this paper that is Deep learning-models then we could clearly see a better flexibility and higher accuracy as well as increased computational efficiency. The integration of deep learning
technology improves real-time decision processes in tourism-based navigation systems which leads to time reduction and increases user satisfaction levels.

General Terms
Deep Learning, Route Optimization, Tourism Navigation, Real-time Systems, Traffic Management, Weather Forecasting, User Preferences, Computational Efficiency

Keywords: Deep learning, route optimization, tourism, real time navigation, traffic prediction, personalized travel

Paper Title AI DRIVEN FRAUD DETECTION-TRANSFORMING DIGITAL SECURITY IN AN EVOLVING LANDSCAPE
Author Name Himanshi, Shivansh Mishra, Parichay Sharma & Aditya Raj
Country India
DOI https://doi.org/10.5281/zenodo.18950567
Page No. 52-59

Abstract View PDF Download Certificate
AI DRIVEN FRAUD DETECTION-TRANSFORMING DIGITAL SECURITY IN AN EVOLVING LANDSCAPE
Author: Himanshi, Shivansh Mishra, Parichay Sharma & Aditya Raj

ABSTRACT
New-generation digital security receives a transformation from AI-driven fraud detection because this method achieves higher accuracy and faster efficiency in real-time throughout the fast- evolving cyber environment. Today's fraud detection systems face problems and spots new security threats as they occur which results in monetary damage and reduced public
faith. The research demonstrates how artificial intelligence approaches merge into three classifications to minimize fraudulent detection inaccuracies. The key element of Explainable AI (XAI) ensures transparency through which AI- based decisions become reliably understandable by users. AI obtains immediate processing capability for large data volumes
which allows systems to detect abnormalities to deter cyberattacks during their development phase. Security systems become stronger through Artificial Intelligence because AI protects the digital space from both present and emerging fraud techniques.

General Terms
Pattern recognition, Explainable AI (XAI), Deep learning

Keywords: Artificial Intelligence, Digital Security, Cyber Attacks, Fraudulent Detection, and Digital Space.

Paper Title DETECTION AND IDENTIFICATION OF MEDICINAL PLANT USING AI AND IMAGE PROCESSING
Author Name Mahesh Kini, Rakesh, Sagar M H, Sanjay R & Preethesh Clive D Souza
Country India
DOI https://doi.org/10.5281/zenodo.18950752
Page No. 50-55

Abstract View PDF Download Certificate
DETECTION AND IDENTIFICATION OF MEDICINAL PLANT USING AI AND IMAGE PROCESSING
Author: Mahesh Kini, Rakesh, Sagar M H, Sanjay R & Preethesh Clive D Souza

ABSTRACT:
From ancient times, plants have played a crucial role in Ayurveda as a source of medicine. Accurate recognition of medicinal plants is essential in preparing Ayurvedic formulations, which has traditionally relied on manual expertise. However, due to the increasing demand for large-scale herbal medicine production, automating this process is now necessary. This paper presents a systematic approach for identifying medicinal plants using the Random Forest algorithm, a robust ensemble-based machine learning technique. The method employs a combination of color, texture, and structural characteristics extracted from plant images to classify them effectively. The experimental findings confirm the efficiency of this approach in achieving high classification accuracy, offering a scalable and reliable solution for the herbal medicine industry. By integrating artificial intelligence into this domain, the process not only ensures accuracy but also minimizes
reliance on human expertise, thereby facilitating mass production while maintaining quality and authenticity.

Keywords— Medicinal Plants, Plant Identification, Machine Learning, Image Recognition, Convolutional Neural Networks (CNNs), Support Vector Machines (SVM).

Paper Title BIOMETRIC AUTHENTICATION BEYOND FINGERPRINT SENSORS
Author Name Pragya Rajput, Raghav Somani, Harleet Kaur, Shraddha Sharma, Shruti Pundir & Riya Sharma
Country India
DOI https://doi.org/10.5281/zenodo.18951037
Page No. 56-66

Abstract View PDF Download Certificate
BIOMETRIC AUTHENTICATION BEYOND FINGERPRINT SENSORS
Author: Pragya Rajput, Raghav Somani, Harleet Kaur, Shraddha Sharma, Shruti Pundir & Riya Sharma

ABSTRACT
Modern security systems depend on biometric authentication as their main foundation because it presents better security than conventional authentication methods using passwords and PINs. Fingerprint sensors remain popular. However, their vulnerability to spoofing and sensitivity to environmental conditions necessitate more advanced authentication systems. A study of security/authentication techniques investigates new facial recognition and voice pattern authentication modalities
together with continuous measurement systems and privacy-protecting and AI-related methods. The research introduces transformative frameworks that unite AI with IoT capabilities to handle scalability needs while guaranteeing inclusivity and improving system energy efficiency toward future biometric technology.

Keywords : Biometric authentication, continuous authentication, and adaptive systems, along with artificial intelligence (AI), privacy-preserving techniques, and multimodal biometrics, are increasingly integrated with the Internet of Things (IoT) to enhance security and usability.

Paper Title 6G WIRELESS NETWORK: POTENTIAL ARCHITECTURE AND APPLICATIONS
Author Name Rajesh Sachdeva, Vishal Kumar Arora, Ankur Gupta & Shalini Sachdeva
Country India
DOI https://doi.org/10.5281/zenodo.18951252
Page No. 67-74

Abstract View PDF Download Certificate
6G WIRELESS NETWORK: POTENTIAL ARCHITECTURE AND APPLICATIONS
Author: Rajesh Sachdeva, Vishal Kumar Arora, Ankur Gupta & Shalini Sachdeva

ABSTRACT
The standardization activities of the 5G communications are clearly over and deployment has commenced globally. To endure the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the
communication needs after a decade. A new wireless communication system integrated with artificial intelligence and blockchain technology is expected to be launched between 2027 and 2030. Though 5G has not been launched worldwide yet there are some major concerns, that can be addressed. These concerns may include improved QoS, low latency rate and
higher system capacity. This paper presents the architecture and some of the applications of future 6G wireless communication and its network architecture. Many of the emerging technologies such as artificial intelligence, blockchain technology, quantum communications, terahertz communications, three-dimensional networking, big data analytics that
can assist the 6G architecture development in guaranteeing the QoS will be discussed. We present the expected applications with the requirements and the possible technologies for 6G communication. We also outline the possible
applications and research directions to reach this goal.

Keywords 5G, 6G, QoS, Blockchain technology, artificial intelligence, quantum communications

Paper Title ADVANCING BORDER SECURITY AND NATIONAL DEFENSE: THE ROLE OF FACIAL RECOGNITION TECHNOLOGY IN MODERN SURVEILLANCE SYSTEMS
Author Name Aditya Chauhan & Harish Nagar
Country India
DOI https://doi.org/10.5281/zenodo.18951317
Page No. 75-82

Abstract View PDF Download Certificate
ADVANCING BORDER SECURITY AND NATIONAL DEFENSE: THE ROLE OF FACIAL RECOGNITION TECHNOLOGY IN MODERN SURVEILLANCE SYSTEMS
Author: Aditya Chauhan & Harish Nagar

ABSTRACT—
Facial recognition technology is revolutionizing the borders security and national defense scene at an extremely fast pace. This paper explores the role FRT could play in further improving surveillance, identification, and threat prevention mechanisms in critical zones of security. In the current state of practice, we analyze the effectiveness of FRT in identity verification, monitoring, and real- time threat assessment. It is because of its potential, however technology also gives rise to significant issues, including massive concerns, such as privacy issues and ethics, and also the requirement for proper regulatory frameworks. Assessing the security advantages versus the dangers related to privacy might be crucial in knowing how future innovations, especially integration strategies, and policy recommendations may be devised for the use of FRT appropriately and responsibly at national security.

Index Terms—Facial recognition technology, border security, national defense, surveillance, identity verification, privacy,
security policy, threat detection, ethical implications.

Paper Title PAYMENTS AND FACIAL RECOGNITION: THE FUTURE OF CONTACTLESS TRANSACTIONS
Author Name Deepanshi & Harish
Country India
DOI https://doi.org/10.5281/zenodo.18951461
Page No. 83-90

Abstract View PDF Download Certificate
PAYMENTS AND FACIAL RECOGNITION: THE FUTURE OF CONTACTLESS TRANSACTIONS
Author: Deepanshi & Harish

ABSTRACT
Facial recognition technology has emerged as a real game-changing tool in the realm of contactless payments, particularly with increasing claims of security, convenience, and user friendliness. In this paper, the integration of facial recognition in payment solutions is assessed in terms of its impact on transaction speed, fraud prevention, and consumer acceptance. Current advancements, potential security vulnerabilities, and ethical concerns are examined to conduct a deep analysis of how facial recognition can redefine digital transactions. The study also briefly discusses matters of privacy issues and regulation matters with an emphasis on measures that assure user trust and reliability of the system. The result puts forth the promise for the possibility that facial recognition could become one of the very widely accepted, efficient, and safe contactless payment methods very soon.

Index Terms—Facial Recognition, Contactless Payments, Digital Transactions, Payment Security, Biometric Authentication, Privacy, Consumer Acceptance, Fraud Prevention, User Experience, Regulatory

Paper Title AI-DRIVEN APPROACHES FOR IDENTIFYING GENETIC MUTATIONS
Author Name Aditya, Deepak Yadav & Aashima Narula
Country India
DOI https://doi.org/10.5281/zenodo.18951672
Page No. 91-96

Abstract View PDF Download Certificate
AI-DRIVEN APPROACHES FOR IDENTIFYING GENETIC MUTATIONS
Author: Aditya, Deepak Yadav & Aashima Narula

ABSTRACT
Genetic mutation detection is important for detecting genomic variation to cause disease. Such mutations as single nucleotide changes, insertions, and deletions can be found by a computational approach. This new method correctly identifies genetic variations by analyzing genetic data and comparing it to reference genomes. It thus shows high accuracy results that would allow research in understanding the mechanisms of the disease as well as genetic disorders. This research will help me improve mutation detection techniques, which have applications in the fields of medical
science and genetics.

Keyword: Genetic Mutation Detection, Machine Learning, Random Forest, Mutation Classification, Feature Importance

Paper Title FACIAL AUGMENTATION-DRIVEN ENHANCEMENTS IN DEEPFAKE DETECTION
Author Name Pragya Rajput, Ujjwal Kumar, Parit Rajput, Gautam Das, Raja Siddharth A R & Shubham
Country India
DOI https://doi.org/10.5281/zenodo.18951814
Page No. 97-108

Abstract View PDF Download Certificate
FACIAL AUGMENTATION-DRIVEN ENHANCEMENTS IN DEEPFAKE DETECTION
Author: Pragya Rajput, Ujjwal Kumar, Parit Rajput, Gautam Das, Raja Siddharth A R & Shubham

ABSTRACT
Deep fake technology brings significant concerns regardless of the domain in which it is applied from misinformation to cyber criminals and privacy violation. This new technology is a real danger to several fields as it can disseminate fake news, contribute to the increase of the number of cyberthreats and compromise the protection of personal data. The techniques previously used in detecting deep fake basically do not follow the rather high evolutionary rates of these generation techniques hence yielding a very high level of false positives and false negatives. This work seeks to investigate the viability of FA as an innovative method that strengthens the signal and the spatial resolution of deepfake detection techniques. This research aims to create multiple and complex datasets by combining the changes in facial features
comprising expressions, lighting and occlusion to assist the training of detection models. To assess the proposed approach in depth, the current and one of the most developed machine learning models including CNNs and high-level models are used. Last but not the least, we observed that when the proposed method includes dynamically augmented data, it added
even more value to the detection and reduces error rates substantially; thus it offers more effective ways to counter deep fake threats. These findings outline how knowledge of new strategies to counter the contamination of digital media or the protection against improper use of the deepfake technology is important.

Keywords: Deepfake Detection, Dynamic Face Augmentation, Generative Adversarial Networks (GANs), Machine Learning, Convolutional Neural Networks (CNNs), Data Augmentation, Misinformation, Cybersecurity, Image Analysis, Model Performance.

Paper Title DEVELOPMENT OF AN ONLINE SOCIETY COMPLAINT PORTAL
Author Name Pragya Rajput, Ayush Singh, Ankit kr. Singh, Prashant Chaudhary, Naphees Iqubal & Harsh Vardhan Singh
Country India
DOI https://doi.org/10.5281/zenodo.18952038
Page No. 109-114

Abstract View PDF Download Certificate
DEVELOPMENT OF AN ONLINE SOCIETY COMPLAINT PORTAL
Author: Pragya Rajput, Ayush Singh, Ankit kr. Singh, Prashant Chaudhary, Naphees Iqubal & Harsh Vardhan Singh

ABSTRACT:
The online Society Complaint Portal is designed to offer a simple and accessible platform for citizens to report complaints about societal issues, including infrastructure, public services, and safety concerns. The system features GPS geotagging, user verification, and the ability to handle complaints across multiple departments. Users can submit complaints, monitor their progress, and get timely updates from the appropriate authorities, all while enhancing transparency and efficiency in addressing public issues.

Keywords: Machine Learning, IoT (Internet of Things), Database Management, User Experience (UX), Security Protocols.

Paper Title POST QUANTUM CRYPTOGRAPHY: PREPARING FOR THE FUTURE
Author Name Vanshika Dhingra, Pragya Rajput & Annanya Nayar
Country India
DOI https://doi.org/10.5281/zenodo.18952416
Page No. 115-124

Abstract View PDF Download Certificate
POST QUANTUM CRYPTOGRAPHY: PREPARING FOR THE FUTURE
Author: Vanshika Dhingra, Pragya Rajput & Annanya Nayar

ABSTRACT
The novel threat posed by quantum computation is undermining classical public-key cryptographic systems that depend on RSA and ECC. To mitigate these difficulties, Block suggests An Adaptive Cryptographic Model to Future Quantum Networks’ which proposes a new model that includes Post quantum cryptography (PQC), Quantum Key Distribution (QKD), and AI based security tools. The exploratory case study approach is used which is composed of multiple components including the literature review, the design of the cryptographic agility framework, the experimental implementation, the conduct of security test, and also the compliance assessment. The block was implemented in simulated environments, monitoring quantum network’s ability to withstand quantum attack, efficiency, as well as the network’s
ability to manage encryption keys in real-time. The research was conducted in accordance to NIST PQC standards that merged with global regulatory frameworks in order to ensure that the provided results are adaptable and compliant across different regions. Cryptographic models which approached the adapted form did appear to meet the adequate level of security and increase the scalability and the agility of the cryptography within the quantum network. The prospective work contains fully homomorphic encryption (FHE) set, quantum identity management with a post-quantum blockchain security paradigm. This work assists tangible endeavours toward the development of quantum-secured next-generation communication system where data will be preserved for long periods of time, while remaining compliant with the regulations and protected from unauthorized access.

Keywords— Post-Quantum Cryptography, Quantum Key Distribution, AI Security, Cryptographic Agility, Quantum Networks.

Paper Title REAL-TIME STRESS DETECTION USING CNN IN DEEP LEARNING
Author Name Tandra Debarati Shome & Laxmi Maurya
Country India
DOI https://doi.org/10.5281/zenodo.18952636
Page No. 125-131

Abstract View PDF Download Certificate
REAL-TIME STRESS DETECTION USING CNN IN DEEP LEARNING
Author: Tandra Debarati Shome & Laxmi Maurya

ABSTRACT
Stress has become a part of everyday life, affecting people of all ages. It creates significant challenges for well-being and productivity. Despite advancements in physiological techniques for stress detection, there are still hurdles in making these solutions real-time, affordable, and accessible to everyone. Psychological stress is closely tied to emotions, and understanding this connection plays a key role in analyzing human behavior, particularly in computational psychology. While deep learning techniques, like Convolutional Neural Networks (CNNs), have shown great promise in detecting facial emotions from images, their potential for identifying mental stress remains underexplored. The system provides a holistic approach to understand and evaluate stress through images and video processing.

Keywords- Stress detection, CNN model, Emotions classes, image processing.

Paper Title SECUREAUTHENTICATION SYSTEM USING BIOMETRIC
Author Name Azhar, Joti Sharma, Himani, Shiv Sharan Dixit, Shubham Kumar & Arpit Negi
Country India
DOI https://doi.org/10.5281/zenodo.18952761
Page No. 132-138

Abstract View PDF Download Certificate
SECUREAUTHENTICATION SYSTEM USING BIOMETRIC
Author: Azhar, Joti Sharma, Himani, Shiv Sharan Dixit, Shubham Kumar & Arpit Negi

ABSTRACT
In today's world of digital transformation, a secure and reliable authentication system is necessary to prevent unauthorized access and breaches in both actual and virtual data. Traditional authentication mechanisms like password-based and PINbased systems are vulnerable to various forms of security threats such as phishing, credential leaks, or brute-force attacks.
Biometric authentication for a good alternative authenticated verification mode is found to either contain unique physiological or behavioral distinct user characteristics fingerprints, face or iris recognition, or biometrics. This research undertakes an investigation into the effectiveness, security, and challenges of biometric authentication. It studies the space mapping of integration multimodal biometrics, encryption, and machine learning algorithms to enhance security and minimize spoof identity risks. The study also addresses the advantage-disadvantage argument of security versus privacy versus user transparency and considers all the topics of concern related to data storage, biometric spoofing, and ethics in
these terms.

Keywords—Biometrics, Authentication, security, Encryption, Credentials, machine learning, PINs.

Paper Title HARNESSING MACHINE LEARNINGTECHNIQUES TO DIAGNOSE TOMATO PLANT DISEASES
Author Name S. Aruna, R. Abinaya, A. Vanithasr & A. Vasanthakumar
Country India
DOI https://doi.org/10.5281/zenodo.18953033
Page No. 139-149

Abstract View PDF Download Certificate
HARNESSING MACHINE LEARNINGTECHNIQUES TO DIAGNOSE TOMATO PLANT DISEASES
Author: S. Aruna, R. Abinaya, A. Vanithasr & A. Vasanthakumar

ABSTRACT—
Agriculture is basic in the development of any nation and also contributes to economic stability. Tomato production constitutes a significant aspect of agriculture in Tamil Nadu and India. It is facing yield and quality issues. However, disease in crops lowers the health of tomato leaves and the
productivity of a tomato plant. This study has focused on an approach to develop a Convolutional Neural Network (CNN) model improved with data augmentation for detecting diseases in tomato leaves. It identifies and classifies multiple diseases on tomato leaves accurately at 89% with over 35
epochs during training. All validation metrics support the strength and effectiveness of the model: AUC score, precision, and recall. The software solution also serves both detection and practical application by suggesting appropriate chemicals for recognized diseases such as early blight, septoria
leaf spot, and powdery mildew. This function makes it easier to manage the disease as a whole and a loss on crops is not so severe. It was trained on images of tomato leaves, some of which were obtained from the Plant Village repository in order to have variation in the datasets to make them practical for use in the real world. The research underlines the possibility of technology integration in agricultural practices and proposes an effective method of focusing on the prevention of disease outbreak and its link to appropriate management activities. This adds crop productivity but also promotes sustainable agricultural practices which enhances economic and environmental stability.

Keywords— Convolutional Neural Network, Data Augmentation, Tomato Leaf Disease Detection.

Paper Title DYNAMIC MULTI-SUBSCRIPTION AZURE RESOURCE AUTOMATION USING TERRAFORM
Author Name Kartik Bhardwaj, Alish Pandey, Rhythmpreet Kaur & Ramandeep Singh
Country India
DOI https://doi.org/10.5281/zenodo.18953393
Page No. 150 -154

Abstract View PDF Download Certificate
DYNAMIC MULTI-SUBSCRIPTION AZURE RESOURCE AUTOMATION USING TERRAFORM
Author: Kartik Bhardwaj, Alish Pandey, Rhythmpreet Kaur & Ramandeep Singh

ABSTRACT
Governance and provisioning of Azure resources across various subscriptions remains a challenging task, largely due to the limitations of Terraform’s azurerm provider by design. While Terraform enjoys widespread acclaim as an Infrastructure-as-Code (IaC) capabilities fall short in tool, multi-subscription automation. It describes a novel approach combining python scripting, pipeline automation using YAML in azure devops and Terraform to achieve a scalable, performant and fully automated resource provisioning spanning multiple Azure subscriptions. In particular, Terraform variable files ( .tfvars ) with subscription data imported from a structured CSV file, eliminating the need for any manual configuration. A matrix strategy on a YAML pipeline orchestrates the run of Terraform steps—init, plan and apply— in parallel across multiple workspaces significantly speeding up deployment while reducing overhead. For large-scale, enterprise deployments, this setup is particularly useful as storing Terraform state files in a centralized Azure Storage account enhances both maintainability and conflict prevention. So presenting a systematized automation-based method that minimizes human interaction and streamlines the provisioning process while improving consistency of state is the significant finding of this work, translating into a better cloud automation approach. Well suited for multi-cloud deployments, the approach is both a sound architectural and operational best practice that help alleviate some of the most common scalability and operational challenges that all cloud practitioners experience.

Keywords: Azure, Terraform, Multi-Subscription Automation, YAML Pipelines, Python Scripting, Cloud Automation.

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