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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.

Paper Title CYBER-RESILIENT NETWORK ARCHITECTURE FOR SMART GRID
Author Name Azhar Ashraf, Shishir Singh, Devesh Kumar Upadhyay, Shruti Sharma, & Nimmanagoti Anil
Country India
DOI https://doi.org/10.5281/zenodo.18975445
Page No. 160-167

Abstract View PDF Download Certificate
CYBER-RESILIENT NETWORK ARCHITECTURE FOR SMART GRID
Author: Azhar Ashraf, Shishir Singh, Devesh Kumar Upadhyay, Shruti Sharma, & Nimmanagoti Anil

ABSTRACT—
For smart grids dealing with changing cyber threats, guaranteeing a cyber resilient network is important. This project uses artificial intelligence, specifically Convolutional Neural Networks (CNN), to improve grid security by detecting and mitigating phishing, malware, and DDoS threats. The system correctly detects phishing 33.1% of the time, malware 31.4% of the time, and DDoS attacks 32.4% of the time, with low false positive rates of 0.7% for phishing, 1.0% for malware, and 1.4% for DDoS. To precisely spot all unusual login behaviours, the system thoroughly integrates real-time threat intelligence, in-depth behavioural analysis, and immediate proactive alerts, ultimately achieving a 90% accuracy rate. User feedback confirms its effectiveness and usability through a satisfaction rate of 92% and an adoption rate of 78%. The system secures all communications through encryption, two-factor authentication, and identity verification, fully complying with GDPR and NIST SP 800-63B. This AI framework both strengthens how secure the grid is and keeps operations strong by always
adjusting to new dangers.

Keywords: cybersecurity automation in power systems, secure grid communication, AI-powered security, smart grid security compliance, and machine learning for cybersecurity; Adaptive security architecture; proactive cyber defence; intrusion detection and prevention; threat intelligence in energy networks; and cyber- resilient smart grids.

Paper Title PRIVACY-PRESERVING THREAT SHARING ACROSS ORGANIZATIONS
Author Name Rahul Bhardwaj, Pooja, Ritik Raushan, Akanksha Jain & Azhar Ashraf Gadoo
Country India
DOI https://doi.org/10.5281/zenodo.18975549
Page No. 168-176

Abstract View PDF Download Certificate
PRIVACY-PRESERVING THREAT SHARING ACROSS ORGANIZATIONS
Author: Rahul Bhardwaj, Pooja, Ritik Raushan, Akanksha Jain & Azhar Ashraf Gadoo

ABSTRACT –
Cybersecurity threats have become quite sophisticated, and organizations depend on real-time threat intelligence sharing to protect themselves against the rise of attacks. However, privacy, data confidentiality and competitive risks often restrict their collaboration to the data exchange only. In this paper one, we present a privacy-preserving threat-sharing framework
allowing parties to exchange sensitive threat intelligence information while preventing sensitive internal information leak. only get access to the raw data, while aggregated threat data can be shared with lead analytics. We explore its applicability in threat-sharing within varying contexts, showing organizations manners of leveraging aggregated intelligence without
revealing avenues for proprietary or sensitive data compromise. We show that the use of privacy-preserving mechanisms can greatly facilitate cross-organizational collaboration for cybersecurity and can be compliant with regulatory and legal obligations. This paper provides insights into the broader discussion of secure cyber defence strategies, stressing the role of privacy in promoting collaboration against cyber threats.

Keywords: Cybersecurity, Threat Intelligence Sharing, Privacy-Preserving Framework, Cryptographic Techniques, Differential Privacy, Secure Multi-Party Computation (MPC), Cross- Organizational Collaboration.

Paper Title SENTIMENT ANALYSIS IN SOCIAL MEDIA: TECHNIQUESAND APPLICATIONS
Author Name Pragya Rajput, Aditya Jain, Lovish Gupta & Abhishek Thakur
Country India
DOI https://doi.org/10.5281/zenodo.18975651
Page No. 177-184

Abstract View PDF Download Certificate
SENTIMENT ANALYSIS IN SOCIAL MEDIA: TECHNIQUESAND APPLICATIONS
Author: Pragya Rajput, Aditya Jain, Lovish Gupta & Abhishek Thakur

ABSTRACT—
Sentiment analysis is one of the important research fields in natural language processing, which is currently gaininga lot of significance due to the increasing growth of social media. This paper provides an overall review of the techniques concerning sentiment analysis, strictly developed for social media contexts. We will discuss a wide variety of methodologies, from classical statistical methods to state-of-the-art machine learning and deep learning methods. It discusses preprocessing methods, feature extraction strategies, and model evaluation metrics by outlining implications for effective sentiment detection. Further, it outlines some of the key applications of sentiment analysison social media platforms, such as brand management, public opinion monitoring, and crisis management. Current challenges such as dealing with noisy data, handling ambiguity in sentiment, and ensuring model generalization across diverse social media platforms are identified. Finally, we discuss some emerging trendsin sentiment analysis research and future directions, among them the integration of multimodal
data and the application of transfer learning. This review intends to provide a holistic viewof sentiment analysis techniques and their practical applications, thus offering insights into important areas for both researchers and practitioners in the field.

Index Terms—Sentiment Analysis, Social Media, Machine Learning, Natural Language Processing, Feature Extraction

Paper Title AI BASED AGRICULTURE SOLUTIONS FOR FARMERS
Author Name Milind Mishra
Country India
DOI https://doi.org/10.5281/zenodo.18975747
Page No. 185-191

Abstract View PDF Download Certificate
AI BASED AGRICULTURE SOLUTIONS FOR FARMERS
Author: Milind Mishra

ABSTRACT:
Agriculture is the spine of numerous economies; however, ranchers regularly confront challenges such as eccentric climate, soil debasement, bug invasions, and wasteful asset utilization. This research about presents an AI-based Android application outlined to enable agriculturists with real-time, data-driven experiences to improve efficiency and maintainability. The proposed framework leverages machine learning and computer vision to give edit illness discovery, climate determining, soil wellbeing investigation, and abdicate expectation. Furthermore, the app coordinating IoT-based
keen cultivating and chatbot back for moment master direction. By utilizing profound learning calculations and lackey symbolism, the framework guarantees exactness farming, decreasing asset wastage whereas maximizing surrender. The consider investigates the effect of AI-driven decision-making in farming, illustrating how the proposed arrangement can
revolutionize conventional cultivating hones and bridge the advanced partition for country agriculturist.

Keywords: AI in Agriculture, Smart Farming, Crop Disease Detection, Precision Agriculture, Machine Learning, Android App.

Paper Title BLOCKCHAIN-BASED DIGITAL ELECTIONS: ENHANCING TRANSPARENCY AND SECURITY
Author Name Bhavi, Azhar Ashraf Gadoo, Anshul Sharma & Prince Gupta
Country India
DOI https://doi.org/10.5281/zenodo.18975887
Page No. 192-199

Abstract View PDF Download Certificate
BLOCKCHAIN-BASED DIGITAL ELECTIONS: ENHANCING TRANSPARENCY AND SECURITY
Author: Bhavi, Azhar Ashraf Gadoo, Anshul Sharma & Prince Gupta

ABSTRACT
While digitalization has continued to alter electoral processes, the integrity, transparency, and security of elections are increasingly complicated. This paper explores ways through which blockchain addresses key challenges in digital elections, such as voter fraud, data manipulation, and lack of transparency, by availing its decentralized nature and cryptographic security
features to increase the trust in digital voting systems. While analyzing theoretical models and case studies, the research shows that blockchain has the potential to fashion an electoral system that will be tamper-proof and transparent, with a guarantee of accuracy and integrity in election results. Implementation of blockchain for digital elections is not all roses, though.
For this technology to realize full potential, issues regarding scalability, privacy concerns, and regulatory compliance need to be addressed. This paper critically analyses these practical chal- lenges and discusses strategies to overcome them. The research, therefore, intends to show how blockchain can change the face of the electoral process and is supposed to provide a roadmap for integrating blockchain technology into digital elections in order for modern democracies to come closer to an increasingly secure, transparent, and reliable voting system.

Index Terms—Blockchain Technology, Digital Elections, Elec- toral Integrity, Transparency, Security, Tamper-Proof Voting, Voter Fraud Prevention, Cryptographic Protocols, Decentral- ization, Scalability, Privacy Concerns, Regulatory Compliance, Voting System Transformation

Paper Title REAL-TIME CONNECTIVITY CROSS-PLATFORM ACCESSIBILITY ENHANCED USER EXPERIENCE CENTRALIZED DATA MANAGEMENT
Author Name Bibek Budhathoki, Sumit Arora & Azhar Ashraf
Country India
DOI https://doi.org/10.5281/zenodo.18975944
Page No. 200-207

Abstract View PDF Download Certificate
REAL-TIME CONNECTIVITY CROSS-PLATFORM ACCESSIBILITY ENHANCED USER EXPERIENCE CENTRALIZED DATA MANAGEMENT
Author: Bibek Budhathoki, Sumit Arora & Azhar Ashraf

ABSTRACT
In the age of digital transformation, it has become imperative to provide real-time connectivity and cross-platform access to ensure uninterrupted user interaction across devices. This study delves into novel frameworks and approaches to improve the user experience with a centralized data management system. By combining state-of-the-art cloud computing, edge processing, and AI-optimized optimizations, this research pro- poses a scalable method to provide uninterrupted, synchronized access to data. The envisioned system facilitates smooth data flow, minimizes latency, and supports interoperability, ultimately resulting in an effortless and enriched digital ecosystem.

Index Terms—Real-time connectivity, Cross-platform accessi- bility, Enhanced user experience, Centralized data management, Cloud computing, Edge processing, AI-driven optimization, Data synchronization, Interoperability, Digital transformation.

Paper Title EVOLVEED: AI-DRIVEN PERSONALIZED LEARNING
Author Name Pragya Rajput, Ankit Kumar Singh, Barenya Behera, Prachi Mittal, Radhika & Disha Singh
Country India
DOI https://doi.org/10.5281/zenodo.18976086
Page No. 208-216

Abstract View PDF Download Certificate
EVOLVEED: AI-DRIVEN PERSONALIZED LEARNING
Author: Pragya Rajput, Ankit Kumar Singh, Barenya Behera, Prachi Mittal, Radhika & Disha Singh

ABSTRACT
The swift progressions in artificial intelligence (AI) has been a major reason of the bloom seen by the world of academics facilitating personalized and accommodative learning experiences. The diverse intellectual styles and paces of individuals are generally discarded by conventional learning techniques which can lead to withdrawal and lack of efficient learning journey. The curriculum and the intensity of challenges a student faces are dynamically adjusted based on their progress and learning inclinations by EvolveED, an AI-driven personalized learning platform. An engrossing and effective learning journey is assured by EvolveED for the reason that it capitalizes on adaptive algorithms, real-time feedback mechanisms
and behavioral analysis. The video engagement system formulated on eye-tracking which supervises focus of a student when attending scholastic sessions is one of the most notable aspect of the platform. Any lack of concentration or shifting of gaze from the screen results in a time out making sure the learners are earnestly occupied with the content. EvolveED
not only elevates inclusion but thus also nurtures a more collaborative and learner-oriented environment. The methodologies utilized in the platform, their real-world influence on educational attainment and the overall and
comprehensive significance of AI have been considered in this paper. The challenges of achieving AI-driven in instructive solutions and proposals for future approaches for additional refinement and scaling of the presented approach have also been addressed.

General Terms Personalized learning, Artificial Intelligence, Machine learning, Algorithm Design, Learning Analytics, Educational technology, Behavioral Analysis.

Keywords
AI-driven learning, personalized tuition, adaptive algorithms, real-time feedback, student engagement, eyetracking mechanism.

Paper Title SMART OPTIMIZATION: REVOLUTIONIZING RESEARCH ALGORITHMS FOR SEAMLESS USER EXPERIENCE
Author Name Pragya Rajput, Prikshit Singh, Arnav Kumar & Yoginder Singh
Country India
DOI https://doi.org/10.5281/zenodo.18976198
Page No. 217-225

Abstract View PDF Download Certificate
SMART OPTIMIZATION: REVOLUTIONIZING RESEARCH ALGORITHMS FOR SEAMLESS USER EXPERIENCE
Author: Pragya Rajput, Prikshit Singh, Arnav Kumar & Yoginder Singh

ABSTRACT
This research addresses the limitations of traditional research algorithms, such as data scarcity, cold-start Problems, scalability challenges, and inefficient ranking. The proposed optimization framework integrates hybrid recommender systems, deep learning-based ranking methods, and a cluster-based algorithm selection mechanism to enhance user experience and retrieval efficiency. It employs collaborative filtering, content-based filtering, and hybrid approaches while leveraging ranking metrics like Rank-Biased Precision (RBP) and Normalized Discounted Cumulative Gain (NDCG) to improve content relevance. Additionally, a network-friendly optimization model enhances computational efficiency without compromising search quality. Experimental evaluation on real-world datasets, including financial product recommendations and digital libraries, demonstrates significant improvements in precision, recall, and user satisfaction. The research introduces a personalized, adaptive approach that optimizes recommendation algorithms, bridging computational efficiency with enhanced user engagement for next-generation intelligent systems.

Keywords—Research Algorithm Optimization, Machine Learning, Recommender Systems, User Experience Enhancement, Ranking-based Optimization, Deep Learning, Adaptive Research Models

Paper Title INVESTIGATING THE IMPACT OF CLIMATE CHANGE ON AGRICULTURE: ANALYZING CROP YIELD VARIABILITY AND ADAPTIVE STRATEGIES
Author Name Pragya Rajput, Harshmeet Singh, Komaldeep Singh & Noorpreet Singh Saini
Country India
DOI https://doi.org/10.5281/zenodo.18976339
Page No. 226-233

Abstract View PDF Download Certificate
INVESTIGATING THE IMPACT OF CLIMATE CHANGE ON AGRICULTURE: ANALYZING CROP YIELD VARIABILITY AND ADAPTIVE STRATEGIES
Author: Pragya Rajput, Harshmeet Singh, Komaldeep Singh & Noorpreet Singh Saini

ABSTRACT—
Climate change is one of the great challenges facing agriculture in the world because it changes weather patterns, affects the health of the soil, and lowers crop production. This study explores the complex relationship between climate change and agriculture with an emphasis on how variability in crop yields arises due to a change in climatic variables, including temperature fluctuations, changes in precipitation, and extreme weather events. The paper integrates short- and long-term impacts of climate change on various crops in different regions with special attention to the vulnerable farming
community. Adaptive strategies, including climate-resilient crops, improved irrigation techniques, and technological innovations, are further considered as potential sources of mitigation against the adverse effects of climate change on agricultural productivity. Results suggest that sustainable agricultural practices are supported by a confluence of multiple factors over research and findings from science, policy intervention, and technological innovation in this changing climate.

Index Terms—Climate change, agriculture, crop yield variabil- ity, adaptive strategies, extreme weather events, soil health, irriga- tion techniques, climate-resilient crops, agricultural productivity.

Paper Title EMERGENCY SUPPORT SYSTEM: AN INTEGRATED ANDROID AND WEB-BASED PORTAL FOR LIFELINE SERVICES
Author Name Harsh Gaur, Bhavya Kapoor & Azhar Ashraf
Country India
DOI https://doi.org/10.5281/zenodo.18976540
Page No. 234-239

Abstract View PDF Download Certificate
EMERGENCY SUPPORT SYSTEM: AN INTEGRATED ANDROID AND WEB-BASED PORTAL FOR LIFELINE SERVICES
Author: Harsh Gaur, Bhavya Kapoor & Azhar Ashraf

ABSTRACT—
During emergencies, prompt access to essential ser- vices is vital to reducing loss of lives and risks. This study pro- poses an integrated emergency support system that uses Android apps and web portals to offer convenient access to lifeline services like healthcare, police response, firefighting, and disaster relief. The system provides real-time communication, tracking, and automated alerting to facilitate efficiency in response. Through the use of AI-powered analytics and cloud infrastructure, the envisioned platform will make emergency response processes efficient, eliminate delays, and enhance access. The performance of the system is analyzed in case studies and user responses to prove its feasibility for deployment in smart cities and rural towns at scale.

Index Terms—Emergency support system, Android applica- tion, Web portal, Lifeline services, Real-time communication, AI-driven analytics, Cloud-based infrastructure, Disaster man- agement, Location tracking, Smart city deployment.

Paper Title MALWARE TRAFFIC ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING: A COMPARATIVE STUDY WITH LSTM, XGBOOST, AND RANDOM FOREST
Author Name Vaibhav Bajaj, Taniya Mukhija, Azhar Asroof & Harshita Dhingra
Country India
DOI https://doi.org/10.5281/zenodo.18976678
Page No. 240-247

Abstract View PDF Download Certificate
MALWARE TRAFFIC ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING: A COMPARATIVE STUDY WITH LSTM, XGBOOST, AND RANDOM FOREST
Author: Vaibhav Bajaj, Taniya Mukhija, Azhar Asroof & Harshita Dhingra

ABSTRACT—
With cyber threats evolving constantly, we needed something beyond traditional rule-based detection, which struggles against polymorphic attacks. We tested machine learning models, starting with Random Forest (RF) and XGBoost on the CICIDS2017 dataset, and while they gave a solid baseline, they missed sequential attack patterns. That’s when we moved to Deep Learning (DL), specifically Long Short-Term Memory (LSTM) networks, which handle time-series network traffic way better. But then we ran into another issue—class imbalance, where rare attacks were barely represented. So, we used GANs and SMOTE to fix that, generating synthetic attack traffic to train the model better. We evaluated everything with accuracy, precision, recall, F1-score, and AUC-ROC, and the pattern was clear—LSTM outperformed RF and XGBoost, improving malware detection by capturing sequential dependencies in network traffic. Our results highlight the tradeoff between accuracy and computational cost, showing that while LSTM is powerful, hybrid approaches may work even better in balancing detection efficiency and real-time processing.

Keywords— Malware Traffic Analysis, Machine Learning, Deep Learning, LSTM, XGBoost, Random Forest, GANs, SMOTE, Network Security

Paper Title DEEPLEARNING FOR AGE AND GENDER ESTIMATION
Author Name Bharath Yalagi & Sangeetha J
Country India
DOI https://doi.org/10.5281/zenodo.18978344
Page No. 248-259

Abstract View PDF Download Certificate
DEEPLEARNING FOR AGE AND GENDER ESTIMATION
Author: Bharath Yalagi & Sangeetha J

ABSTRACT:
Automatic age and gender prediction via facial recognition is an important feature in modern applications, such as personalized marketing, surveillance, and security systems. This work focuses on applying CNNs in order to learn
and extract automatically features from face images, doing away with manually engineered features. The system predicts age within certain ranges and accurately classifies gender, even under tough environments characterized by variable illumination, facial expressions, and occlusions. Using a diverse and labelled dataset, the model generalizes to unseen data while dealing with real-world complexities. Used Techniques like transfer learning and data augmentation enhance the robustness and accuracy of system. The suggested approach not only enhances the reliability of prediction but also shows significant promise for deployment in areas such as forensic investigations, demographic studies, and human-computer interaction. This novel approach is expected to provide a scalable and efficient framework for automated demographic analysis.

Keywords: Convolutional Neural Networks, ResNet, VGGNet, deep learning, data augmentation

Paper Title SECURE COMMUNICATION IN CRITICALWIRELESS INFRASTRUCTURE NETWORK
Author Name Subhajit Paul, Azhar Ashraf, Abhay Tiwari, Abhijeet Kumar, Shubham Kumar Jha & Shreyansh Shrey
Country India
DOI https://doi.org/10.5281/zenodo.18977770
Page No. 260-266

Abstract View PDF Download Certificate
SECURE COMMUNICATION IN CRITICALWIRELESS INFRASTRUCTURE NETWORK
Author: Subhajit Paul, Azhar Ashraf, Abhay Tiwari, Abhijeet Kumar, Shubham Kumar Jha & Shreyansh Shrey

ABSTRACT
Critical infrastructure networks (CINs), such as power grids, transportation systems, and water supply networks, provide the foundation of modern society. As we continue scaling these networks, the required protection to secure communication within these networks against cyber threats, unauthorized access, and potential system failures becomes critical. This
article describes the major risks and vulnerabilities in CIN communication, covering threats like Man-in-the-Middle attacks, Denial-of-Service (DoS) attacks, and Advanced Persistent Threats (APTs). We examine current security frameworks, encryption methodologies, and authentication strategies, underlining how cryptographic protocols, blockchain, and AI-based anomaly detection can be pivotal in strengthening resilience. In addition, we introduce a multitiered security architecture and describe how incorporating real-time monitoring mechanisms, secure network
communication protocols, and a zero-trust network design paradigm can secure data transmission between elements of CINs. Future research directions: What are we going to do?

Keywords— Critical Infrastructure, Secure Communication, Cybersecurity, Cryptography, Zero-Trust Architecture, Anomaly Detection, Blockchain.

Paper Title ADVANCING CYBERSECURITY: A COMPREHENSIVE REVIEW OF FEDERATED LEARNING APPROACHES FOR DISTRIBUTED INTRUSION DETECTION SYSTEMS
Author Name Anupam Sharma, Arbaz Raza, Kunal Chauhan, Simranpreet Kaur, Udit Dagar & Digvijay Singh Shekhawat
Country India
DOI https://doi.org/10.5281/zenodo.18978263
Page No. 267-275

Abstract View PDF Download Certificate
ADVANCING CYBERSECURITY: A COMPREHENSIVE REVIEW OF FEDERATED LEARNING APPROACHES FOR DISTRIBUTED INTRUSION DETECTION SYSTEMS
Author: Anupam Sharma, Arbaz Raza, Kunal Chauhan, Simranpreet Kaur, Udit Dagar & Digvijay Singh Shekhawat

ABSTRACT—
The review paper analyzes cybersecurity through examining how federated learning works with distributed intrusion detection systems for implementation and performance effectiveness. The increasing danger from cyberattacks forces traditional intrusion detection systems to struggle in their ability to respond to present-day cybersecurity threats. Federated learning presents itself as a solution to improve distributed network detection through decentralized machine learning models. The abstract presents a thorough research on federated learning approaches together with their intrusion detection system applications that boost detection precision and operational speed. The paper explores both the advantages and difficulties implied by federated learning systems while discussing security-related issues along with privacy protection needs and network traffic management problems and model distribution mechanisms. The study uses case studies and
experimental results to illustrate the functional advantages that result from federated learning implementation across different network configurations. The paper provides essential research and practical guidance about present-day distributed intrusion detection breakthroughs to scholars and security experts and technical professionals. Based on existing research synthesis and important discoveries we intend to steer upcoming research directions for building improved cybersecurity solutions suited to distributed computing systems.

Keywords— Federated learning, cybersecurity, intrusion detection, distributed networks, machine learning, privacy preservation, model synchronization, communication overhead, network security, adaptive cybersecurity.

Paper Title REAL TIME PHISHING DETECTION USING AI IN CORPORATE NETWORKS
Author Name Azhar, Shanu Kumar, Onkar Nath & Bevan Mehra
Country India
DOI https://doi.org/10.5281/zenodo.18978852
Page No. 276-283

Abstract View PDF Download Certificate
REAL TIME PHISHING DETECTION USING AI IN CORPORATE NETWORKS
Author: Azhar, Shanu Kumar, Onkar Nath & Bevan Mehra

ABSTRACT
The phishing attacks targeting today corporate networks have fully exploited email, messaging, and collaboration platforms. Even traditional security measures like signature-based and rule-based systems have a hard time keeping up with advancing phishing strategies. In this, we delve into a real-time AI driven phishing detection system using machine learning, deep learning, and natural language processing that promises exceptionally high accuracy in detecting and responding to threats. The anomaly detection integration with AI, behavior analysis, and multi-layered automated response
mechanisms help in securing multi-channel corporate communication. The system is capable of real-time phishing detection by performing sophisticated analyses of email metadata, hyperlinks, and message content. Proactive AI-response measures such as content filtering, user alerting, etc. further improve corporate defense. Challenges such as conducted
adversarial AI attacks, detection of false positives, and compliance to data privacy regulations are notable, yet, progress in federated learning and Explainable AI provide answers to the unique problems posed. Ultimately, this research shed light on the powerful potential of AI being able to combat phishing attack in real-time.

Keyword : AI-based Phishing Detection, Real-time Cybersecurity, Machine Learning, Deep Learning, Corporate Networks.

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