American Journal of AI Cyber Computing Management
• Editor- In-Chief: Dr. Mainso Jubarg • Title proper: American Journal of AI Cyber Computing Management (AJACCM) • Subject: AI ML, Data Science, Management, Cyber Security, Computer Science, IOT, Engineering All, Science and Technology • Publisher Name: AJACCM Publications • ISSN: 3069-0102 • Frequency: Quarterly • Language: English • Country: USA • Medium: Online • Indexed by: CROSSREF, MDPI Scilit, Google Scholar Ext • Title DOI of the Journal: http://doi.org/10.64751/issn.3069-0102
Hybrid Cognitive Framework for Fraud Detection using NLP and Transaction Behavior Analytics
Unified Payments Interface (UPI) presents an intelligent framework for detecting fraudulent activity in high-volume transactions by integrating behavioral analytics with machine learning and Natural Language Processing (NLP) on interaction data. India processes over 12 billion UPI transactions per month with year-on-year growth exceeding 40%, while digital payment fraud results in losses of several thousand crores annually, highlighting the urgency for real-time protection mechanisms. Such systems are essential in application scenarios including peer-to-peer transfers, bill payments, and other instant transactions where approvals occur within seconds and delays can lead to irreversible losses. Traditional manual and rule-based fraud detection approaches rely on static thresholds, delayed human intervention, and limited behavioral understanding, making them unsuitable for dynamic fraud patterns and largescale transaction volumes. The proposed UPI- Shield Fraud Detection (UPI-SFD) system utilizes a structured UPI dataset enriched with text-based behavioral information, which undergoes NLP preprocessing, exploratory data analysis, and numerical feature extraction using Statement Bidirectional Encoder Representations from Transformers (SBERT) to capture semantic intent. Synthetic Minority Over-sampling Technique (SMOTE) is applied to balance class distributions, after which existing classifiers such as Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes Classifiers are evaluated as baselines, while the Histogram Gradient Boosting classifier is introduced as the proposed model for improved non-linear learning and real-time performance. The system generates two outputs: UPI Shield classes1 identifying transactions as Normal or Fraud, and UPI Shield classes2 classifying transaction types P2P, Bill Payment, or Other, thereby enabling accurate fraud detection and comprehensive behavioral analysis within real-time payment systems.
UPI Fraud Detection, Real-Time Payment Systems, Natural Language Processing, Histogram Gradient Boosting, Transaction Classification, Digital Payment Security.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
A Dynamic Hybrid Stacking Approach for Real-Time Threat Detection in High-Mobility Vehicular Networks
Vehicular Ad Hoc Networks (VANETs) have become a fundamental part of modern intelligent transportation systems, enabling vehicles to communicate for improved safety and traffic management. Historically, early network security mechanisms in vehicular systems relied on manual monitoring and rule-based approaches to identify malicious activities. However, with the rapid expansion of connected vehicles, these traditional manual systems are no longer sufficient to handle complex and large-scale network data. The major problem lies in accurately detecting malicious nodes involved in attacks such as Denial of Service (DoS), Sybil, and Blackhole, which can severely disrupt communication and compromise system reliability. Manual detection methods are time-consuming, error-prone, and lack scalability, making them ineffective in dynamic and high-mobility VANET environments. This creates a strong need for automated, intelligent, and adaptive detection systems. To address this challenge, the proposed system introduces a Flask-based malicious node detection framework that leverages machine learning models including AdaBoost (AB), Logit Boosting (LB), Gradient Boosting (GB), and Natural Gradient Boosting (NGB). The system incorporates automated preprocessing, real-time exploratory data analysis, and multi-target classification to improve detection accuracy. Among these, NGB demonstrates superior performance, achieving accuracies of 97.61% for DoS attacks, 97.96% for Sybil attacks, and 96.70% for Blackhole attacks due to its probabilistic learning capability and ability to handle uncertainty. The proposed system significantly enhances detection efficiency and accuracy, providing a scalable and user-friendly solution that strengthens network security and ensures reliable communication in intelligent vehicular environments.
Network security, Malicious node detection, VANETs, Machine learning, Natural gradient boosting classification
SmartWSN-IDS: A Hybrid Deep Reservoir and Optimized Tree Model for Routing Attack Detection
Wireless Sensor Networks (WSNs) comprise a large number of low-power sensor nodes deployed in applications such as environmental monitoring, healthcare, military surveillance, and smart cities. Recent studies indicate that over 60% of WSN deployments operate in unattended and hostile environments, making them highly susceptible to security threats. This vulnerability has increased the impact of routing attacks, including Time Division Multiple Access (TDMA) attacks, black-hole attacks, flooding attacks, and gray-hole attacks. These attacks significantly degrade network performance by causing packet loss, excessive energy consumption, and delays in communication. Conventional intrusion detection systems (IDS) for WSNs, which rely on manual rules and predefined thresholds, are inadequate for dynamic and large-scale environments. Such approaches depend heavily on expert knowledge and static monitoring mechanisms, resulting in high false alarm rates, poor scalability, delayed detection, and limited adaptability to emerging or hybrid attacks. To address these limitations, this study proposes a Dual Tree Optimizer-based WSN Routing Attack Detection (DTOWSN-RAD) framework. The framework begins with the utilization of a WSN routing attack dataset, followed by comprehensive data preprocessing steps including normalization, noise removal, and feature selection to improve data quality. For performance benchmarking, existing classification models such as Decision Tree Classifier (DTC), Ridge Classifier (RC), and Linear Discriminant Analysis (LDA) are implemented. The proposed approach integrates Echo State Network (ESN)-based feature extraction with Decision Tree Cost Complexity Pruning (DTCCP) to effectively capture both temporal and structural routing behaviors. This hybrid model enables accurate detection and classification of TDMA, black-hole, flooding, and gray-hole attacks. Experimental results demonstrate that the DTOWSN-RAD framework achieves higher detection accuracy, improved robustness, and better adaptability compared to conventional methods.
Wireless Sensor Networks, Routing Attacks Detection, Echo State Network, Intrusion Detection System, Decision Tree Cost Complexity Pruning.
Transformer Embeddings with Attention-Gated Structural Fusion for HDL Vulnerability Localization
Hardware Description Language (HDL) vulnerabilities present a critical security risk in modern integrated circuit design, often leading to hardware Trojans or side-channel leakage. Traditional vulnerability detection methods, such as static analysis and manual code review, frequently struggle with the complex, non-linear dependencies inherent in structural hardware logic. This research proposes an advanced automated framework for HDL Vulnerability Localization utilizing Transformer Embeddings integrated with Attention-Gated Structural Fusion. The methodology leverages the allMiniLM-L6-v2 sentence-transformer model to map HDL code segments into a high-dimensional semantic vector space, capturing intricate functional relationships that standard tokenization misses. To evaluate the efficacy of the proposed approach, a comparative analysis was conducted against several baseline machine learning architectures, including Decision Tree Classifiers (DTC), Nearest Centroid (NC), and Bernoulli Restricted Boltzmann Machines (RBM). Experimental results demonstrate that the baseline models achieved competitive performance, with the NC and DTC models yielding accuracies of 89.50% and 88.75%, respectively. However, the Proposed Ridge Classifier configuration significantly outperformed all baselines, achieving a perfect 1.0000 score across Accuracy, Precision, Recall, and F1-Score. These results suggest that the combination of Transformer-based semantic extraction and linear regularization effectively separates vulnerable code patterns from secure implementations. This study concludes that embedding-based structural fusion provides a robust, scalable solution for enhancing the security of the hardware supply chain by precisely localizing vulnerabilities within complex HDL datasets.
HDL Security, Transformer Embeddings, Vulnerability Localization, MiniLM, Ridge Classifier, Hardware Security
TWIN TRAP: A LIGHTWEIGHT SECURITY MECHANISM FOR DETECTING ROGUE ACCESS POINTS IN PUBLIC WI-FI NETWORKS
Public Wi-Fi networks are widely used in places such as cafés, airports, universities, and shopping malls due to their convenience. However, these networks also introduce serious security risks, particularly Evil Twin attacks, where attackers create fake access points that mimic legitimate networks. Since these malicious networks use the same SSID as genuine ones, users may unknowingly connect to them, allowing attackers to intercept data, monitor activities, and steal sensitive information such as login credentials. The TwinTrap project demonstrates how Evil Twin attacks can be created and focuses on detecting such threats using a lightweight security mechanism. In this project, a rogue access point is configured to replicate a legitimate network, showing how easily users can be misled. The system scans nearby networks and analyzes parameters such as SSID, BSSID, channel, and signal strength to identify duplicate network names with different identities. When such networks are detected, the system alerts users about potential rogue access points. Additionally, the project emphasizes preventive measures such as verifying network details before connecting, disabling auto-connect features, and using secure communication methods. The system is implemented in a controlled cybersecurity environment to ensure safe and ethical testing. This project helps users and learners understand wireless security threats and highlights the importance of detection systems like TwinTrap in protecting users in public Wi-Fi environments
1Manaswini , 2 V. Mythri, 3 G. Jyoshna, 4 Yaseen
Analysis of Brute Force Attack Techniques Using User-Pattern-Based Password Generation And Implementation of Defensive Counter Measures
In the modern digital world, password-based authentication systems are widely used to protect user accounts, confidential information, and online services. However, weak and predictable passwords created by users often make these systems vulnerable to cyber-attacks. One of the most common methods used by attackers to gain unauthorized access to systems is the brute force attack technique. Brute force attacks involve systematically trying different password combinations until the correct password is discovered. With the advancement of computing power and automated attack tools, brute force attacks have become faster and more effective, posing a serious threat to the security of digital systems. The project titled “Analysis of Brute Force Attack Techniques Using User-Pattern-Based Password Generation and Implementation of Defensive Countermeasures” focuses on studying how brute force attacks exploit predictable password patterns generated by users and how these attacks can be prevented through effective security mechanisms. Many users tend to create passwords based on personal information, simple words, numeric sequences, or common patterns that are easy to remember. While such passwords provide convenience to users, they significantly reduce the overall security of authentication systems. The main objective of this project is to analyze the relationship between user password patterns and the effectiveness of brute force attacks. The system examines common password structures and identifies patterns that can be easily guessed by attackers. By understanding these patterns, the project highlights the vulnerabilities present in traditional password based authentication systems.
1 B. RaviKumar , 2 Bhanu Chander, 3 B.Vamshi, 4 N.Manoj
Location tracking has emerged as a critical component of modern technological systems, playing a vital role in applications such as navigation, logistics, emergency response, smart transportation, mobile services, and location-based decision support systems. With the rapid growth of smartphones, wireless communication, and sensor technologies, accurate and reliable location tracking has become increasingly important. This paper presents a comprehensive assessment of location tracking systems, focusing on widely used techniques such as Global Positioning System (GPS), network-based localization, and smartphone assisted geolocation methods. The objective of this study is to evaluate the effectiveness, accuracy, and practical limitations of these techniques under varying environmental conditions. The assessment considers both indoor and outdoor scenarios, where location tracking performance is influenced by factors such as signal strength, environmental obstacles, network availability, and device capabilities. GPS-based tracking is analyzed for its accuracy and reliability in open outdoor environments, while its limitations in indoor spaces and dense urban regions are highlighted. Network-based positioning techniques, including cellular and Wi-Fi based localization, are evaluated for their ability to provide approximate location information in areas where GPS signals are weak or unavailable. Additionally, the role of smartphone sensors in enhancing location accuracy through assisted tracking mechanisms is discussed. The evaluation is carried out using performance parameters such as location accuracy, response time, reliability, and environmental dependency. Comparative analysis is used to identify the strengths and weaknesses of each tracking approach.
1 Tejaswi , 2 A. Manoj, 3 K. Kishore, 4 P. Vikas
ANALYSIS OF COMMAND-LINE AND GUI-BASED NETWORK SCANNING TECHNIQUES USING NMAP COMPARATIVE AND ZENMAP FOR SECURITY ASSESSMENT
Network security assessment plays a vital role in identifying vulnerabilities, monitoring network activities, and protecting systems from potential cyber threats. In today’s digital environment, organizations rely heavily on network scanning tools to detect security weaknesses, discover active hosts, identify open ports, and analyze running services within a network. This project focuses on a comparative analysis of command-line and graphical user interface (GUI)-based network scanning techniques using Nmap and Zenmap. Nmap (Network Mapper) is a powerful open-source command-line tool widely used for network discovery and security auditing. It provides advanced scanning techniques, flexible configurations, and supports automation, making it highly suitable for experienced users and security professionals. On the other hand, Zenmap is the official graphical user interface for Nmap, designed to simplify network scanning tasks. It offers predefined scanning profiles, interactive visualizations, and an easy-to-use interface, making it more accessible for beginners and users with limited command-line experience. The primary objective of this project is to evaluate and compare the efficiency, usability, and functionality of both tools in performing network security assessments. Various scanning techniques, including host discovery, port scanning, service detection, and operating system identification, are performed using both Nmap and Zenmap on selected network targets. The comparison is based on several parameters such as scan accuracy, execution speed, ease of use, flexibility, and output presentation. The results indicate that Nmap provides greater control, precision, and automation capabilities, making it ideal for advanced users. In contrast, Zenmap offers a more user-friendly and visually intuitive environment, which is beneficial for beginners and educational purposes
1Manaswini,2B.Vineeth kumar,3k.Manoj,4.B.Naveen
IMPLEMENTATION OF DETECTION AND PREVENTION TECHNIQUES
With the rapid growth of internet usage and the widespread adoption of digital devices, cybersecurity threats have become a major concern for both individuals and organizations. One of the emerging and serious privacy threats is unauthorized webcam access, where attackers secretly gain control over a user’s camera without their knowledge. These attacks are commonly carried out using malware, spyware, or Remote Access Trojans (RATs), enabling attackers to capture images, record videos, and monitor users remotely. Such unauthorized access can lead to severe privacy violations, data theft, blackmail, and unauthorized surveillance. This project focuses on the investigation of unauthorized webcam access attacks and the implementation of effective detection and prevention techniques. It analyzes how attackers exploit system vulnerabilities, malicious software, and insecure network connections to gain access to webcams. The study also examines different attack methods and their impact on user privacy and system security. To address these challenges, the proposed system incorporates security monitoring techniques to detect suspicious webcam activities. The system continuously monitors running processes, analyzes network traffic, and identifies unauthorized attempts to access the webcam. Preventive mechanisms such as antivirus protection, firewall monitoring, and strict permission control are implemented to block malicious applications. Additionally, the system generates real-time alerts whenever unusual webcam activity is detected, enabling users to take immediate action.
1 Tejaswi , 2 Tulasi, 3 D. Vidya, 4 A. Vishnu
SECURE IMAGE -BASED DATA HIDING USING STEGANOGRAPHY TECHNIQUES
In the modern digital era, secure communication of sensitive information has become increasingly important due to the rising number of cyber threats. This project, titled “Secure Image-Based Data Hiding Using Steganography Techniques,” focuses on protecting confidential data by concealing it within digital images. Steganography is a technique that hides the existence of secret information, making it less detectable compared to traditional encryption methods.The proposed system utilizes image steganography, specifically the Least Significant Bit (LSB) technique, to embed secret data into a cover image. The confidential message is first converted into binary format and then inserted into the pixel values of the image in a manner that preserves its visual quality. To further enhance security, encryption techniques can be applied to the data before embedding, ensuring that even if the hidden data is extracted, it remains unreadable without the appropriate decryption key. This approach allows secure transmission of data over the internet without raising suspicion. The system is evaluated based on parameters such as data hiding capacity, security level, and image quality after embedding. The results demonstrate that the method effectively maintains image integrity while securely storing hidden information.Overall, this project emphasizes the significance of combining steganography and encryption techniques to ensure data privacy and secure digital communication, making it a valuable solution for protecting sensitive information from unauthorized access.
1Madhunika,2S.Sumithra,3N.Sahithya,4B.Durga bhavani siri
DETECTION AND PREVENTION OF WEBSITE CREDENTIAL HARVESTING ATTACKS THROUGH SECURITY MONITORING TECHNIQUES
Credential harvesting attacks have emerged as one of the most prevalent and dangerous cyber threats targeting individuals and organizations in the digital era. These attacks involve the unauthorized collection of sensitive user information such as usernames, passwords, and other authentication details through malicious websites, phishing pages, or compromised login forms. Once obtained, attackers exploit these credentials to gain unauthorized access to systems, financial accounts, and confidential data, leading to severe security and privacy breaches. This project focuses on the detection and prevention of website credential harvesting attacks through advanced security monitoring techniques. The proposed system continuously monitors web traffic and analyzes user login activities to identify suspicious patterns and potential phishing attempts. It utilizes a combination of rule-based detection methods, traffic analysis, and log monitoring to detect abnormal behaviors associated with credential theft. Additionally, the system incorporates preventive mechanisms such as domain verification, anomaly detection, real-time alerts, and browser-level security enhancements to block malicious activities before user credentials are compromised. By integrating network monitoring tools and intelligent detection strategies, the system ensures proactive identification and mitigation of threats.
1Ravinder , 2 C. Jitender, 3 V. Bhargav Sharma , 4 N. Harsha Vardhan
In today’s digital era, social media platforms such as Snapchat have become an integral part of daily communication, making them a common target for cyberattacks. One of the most prevalent threats is phishing, where attackers create deceptive login interfaces to steal user credentials. This project focuses on the design and implementation of a Snapchat login phishing simulation to demonstrate how such attacks are carried out and how users can be made aware of them. The simulation replicates a realistic Snapchat login page and captures user input in a controlled and ethical environment for educational purposes. It helps in understanding the techniques used by attackers, including fake URLs, cloned interfaces, and social engineering tactics. Additionally, the project highlights the vulnerabilities in user behavior, such as lack of awareness and inability to identify fraudulent websites. Furthermore, the study emphasizes preventive measures, including user education, secure authentication practices, and detection mechanisms to mitigate phishing risks. By simulating real-world attack scenarios, this project aims to raise cybersecurity awareness and promote safer online practices among users.With the rapid growth of social media platforms like Snapchat, users increasingly rely on these applications for communication and data sharing, making them prime targets for cyber threats. Among these threats, phishing attacks have emerged as one of the most common and dangerous methods used by attackers to steal sensitive information such as login credentials. This project presents the design and implementation of a Snapchat login phishing simulation aimed at understanding and demonstrating how such attacks are executed.
1Manaswini,2Rohan Patil.,3B.SaiKiran,4Shubham Maity
STUDY OF ONION ROUTING , ANONYMITY NETWORKS AND SECURE DARK WEB ACCESS USING THE TOR NETWORK
This project, titled “STUDY OF ONION ROUTING, ANONYMITY NETWORKS AND SECURE DARK WEB ACCESS USING THE TOR NETWORK”, focuses on understanding how anonymity networks protect user privacy and enable secure communication over the internet. With the increasing concerns over data tracking, surveillance, and cyber threats, maintaining user anonymity has become a critical requirement. Traditional internet communication often exposes user identities and IP addresses, making users vulnerable to monitoring and attacks. The study explores the concept of onion routing, a technique that encrypts data in multiple layers and routes it through a series of relays, ensuring that no single node can identify both the sender and the receiver. It further examines the working of the Tor network, which uses this technique to provide anonymous browsing and secure access to online resources. The project also highlights the role of Tor in accessing hidden services, known as .onion websites, within the dark web.Additionally, the project analyzes the advantages, limitations, and security aspects of anonymity networks, emphasizing their importance in safeguarding digital privacy and supporting secure online communication. This project presents a detailed study of onion routing and anonymity networks with a focus on secure dark web access using the Tor network. Onion routing employs layered encryption and multi-hop communication through distributed relays to ensure unlinkability between sender and receiver. The Tor network leverages this mechanism to provide anonymity, traffic obfuscation, and resistance against network surveillance. The study also investigates hidden services (.onion domains), their architecture, and their role in enabling anonymous hosting. Furthermore, the project evaluates performance constraints, potential vulnerabilities, and security considerations of anonymity networks in modern digital environments.
1Madhunika,2A.Sagargoud,3A.Praveen kumar,4K.Ajay
DESIGN AND IMPLEMENTATION OF A VULNERABLE WEB APPLICATION FOR SQL INJECTION ATTACK DEMONSTRATION AND PREVENTION
Web applications play a vital role in modern digital environments by providing users with access to various online services such as e commerce, banking, soc ial networking, and information management systems. These applications interact with databases to store and retrieve important data, including user credentials and personal information. However, if web applications are developed without proper security mea sures, they become vulnerable to cyber attacks. One of the most common and critical vulnerabilities affecting web applications is SQL Injection. SQL Injection is a type of cyber attack in which malicious SQL commands are inserted into i nput fields such as login forms or search boxes. When an application fails to properly validate or sanitize user inputs, the attacker’s input becomes part of the SQL query executed by the database. This allows attackers to manipulate database queries to by pass authentication mechanisms, retrieve sensitive information, modify records, or even delete entire databases. Because of its severe impact on application security, SQL Injection is considered one of the most dangerous web vulnerabilities and is included in the OWASP Top 10 web application security risks. The main objective of this project is to design and implement a vulnerable web application that demonstrates how SQL Injection attacks occur and how they can be prevented using secure coding techniques. The application includes a login interface where users enter their username and password to access the system. In the initial implementation, the application is intentionally designed with insecure SQL queries that directly concatenate user inp ut with SQL statements. This allows attackers to manipulate the query logic and bypass authentication using SQL Injection techniques.After demonstrating the vulnerability, the project implements a secure solution to prevent SQL Injection attacks.The preven tion mechanism uses parameterized queries through Prepared Statement, which separates SQL commands from user input. By treating user input as data rather than executable code, this method effectively prevents SQL Injection attacks and enhances the security of the application. The system is developed using HTML and CSS for the frontend user interface, Spring Boot for backend application logic, and MySQL as the database management system. The project provides a practical understanding of SQL Injection vulnera bilities and highlights the importance of secure coding practices in webapplication development. Through this implementation, developers and students can better understand how web application vulnerabilities arise and how thy can be mitigated using proper security techniques
1 Ravinder, 2 G.Neha, 3 A.Sri vidhya , 4 Chandrakanth
Analysis of Microphone Exploitation Attacks and Development of Security Mitigation Strategies
With the rapid growth of smartphones, laptops, and Internet of Things (IoT) devices, microphones have become essential components for communication and voice-based services. However, these devices are increasingly vulnerable to microphone exploitation attacks, where malicious applications or attackers gain unauthorized access to record conversations and collect sensitive information without the user's knowledge. Such attacks pose serious threats to user privacy and data security. This project focuses on the analysis of microphone exploitation attacks and the development of effective security mitigation strategies to protect users from unauthorized audio surveillance. The study examines various attack techniques, including malware-based microphone access and unauthorized background recording in modern devices. A monitoring framework is proposed to track microphone usage, detect abnormal activity, and alert users when suspicious access is identified. The system architecture and methodology are designed to implement security mechanisms such as permission monitoring, activity logging, and anomaly detection. The system is developed and tested in a controlled environment using appropriate tools and technologies. The results demonstrate that the proposed approach can effectively identify suspicious microphone activity and enhance device security. Although certain limitations exist, such as the possibility of false alerts and additional system resource usage, the proposed solution significantly improves user privacy protection. Overall, this project contributes to increasing cybersecurity awareness and provides a practical approach to preventing microphone exploitation attacks in modern digital devices
1B. RaviKumar , 2 B. Dharani, 3 D. Kalyani, 4 B. Sanjeev
Design and Analysis of a Cam Shaft Using FEA
The camshaft is a critical engine component that controls valve timing by converting crankshaft rotary motion into the reciprocating motion needed to operate intake and exhaust valves. This paper presents the design and analysis of a camshaft for a multi-cylinder engine using finite element methods. The 3D model is created in Pro/Engineer and analyzed in ANSYS Workbench using three materials: Structural Steel, Cast Iron, and Aluminium Alloy. Static analysis determines von Mises stress distribution and deformation under operational torsional and bending loads, while modal analysis identifies natural frequencies to assess vibration susceptibility. Results demonstrate that Structural Steel provides the best performance with maximum von Mises stress of 98.7 MPa (safety factor 2.53), minimum deformation of 0.012 mm, and adequate separation from resonant frequencies, making it the recommended material for high-performance camshaft applications.
G. Guna Sekhar, D. Rohith, M. Ajay, N. Rajesh, Mr. G. Hareesh
Planning, Analysis and Design of Shopping Mall Using STAAD.Pro
This paper presents the complete planning, structural analysis, design, and estimation of a G+3 Shopping Mall building located in Etcherla. The building layout is planned on a 50m × 42m plot with architectural drafting completed in AutoCAD. The structural analysis and design of the entire reinforced concrete frame (beams, columns, slabs, and footings) is performed using STAAD.Pro software with M30 grade concrete and Fe500 reinforcement steel. The analysis determines forces acting on structural members under various load combinations including dead load, live load, and seismic load as per IS codes. STAAD.Pro generates member schedules, concrete take-off quantities, and reinforcement bar weights. The foundation is designed as isolated footings using STAAD Foundation with medium soil bearing capacity. Results include optimized member sizes, reinforcement details, and a comprehensive cost estimate of ₹4.85 crores for the complete structure, demonstrating the effectiveness of integrated software-based structural design for commercial building projects.
K. Venkata Bhargava Raju, S. Harika, K. Gowthami, K. Surya Vardhan, V. Hemakanth,Dr. G.V. Usha Rani
Modelling and Layout of Building by Using Revit
This paper presents the comprehensive modelling and layout design of a G+1 residential building using Autodesk Revit, a Building Information Modeling (BIM) software. The project develops a detailed 3D architectural model incorporating walls, doors, windows, floors, roofs, columns, beams, and staircases with proper dimensions, alignment, and structural considerations. Revit's parametric modeling capabilities enable systematic design with accurate representations of the structure for design, construction, and documentation purposes. The model provides both 2D plan views and 3D perspective visualizations, allowing stakeholders to understand the building layout before construction. The project demonstrates that BIM-based design reduces design errors by approximately 40% compared to traditional 2D CAD drafting, improves coordination between architectural and structural elements, and generates automated quantity take-offs for cost estimation. The resulting model serves as a comprehensive digital prototype facilitating informed decision-making throughout the building lifecycle.
S. Harika, T. Induvadana, P. Praneetha, B. Anil Kumar, S. Manohar,V.K. Chakravarthi
Design and Analysis of a Spur Gear: Contact Stress Variation with Eccentricity
Contact stress analysis of gear drives is critical for predicting gear life and preventing surface failure. This paper investigates the variation of contact stresses in spur gear pairs due to eccentricity of the gear central hole, a manufacturing defect from poor workmanship. The analysis is conducted using ANSYS FEA software with eccentricity varied from 0.0 mm to 0.5 mm in 0.1 mm increments. Results show that von Mises stresses and von Mises strain increase with increasing eccentricity, while maximum deformation decreases. At 0.5 mm eccentricity, von Mises stress increases by 23.4% compared to the concentric case (0.0 mm). The FEM results at zero eccentricity are validated against Hertz analytical theory and AGMA standards, showing agreement within 4.2%, confirming the accuracy of the finite element model and demonstrating the significant impact of manufacturing eccentricity on gear contact stress distribution.
G. Mahesh, M. Appana, P. Vamsi, V. Mohan Rao, A. Surendra, Mr. K. Prema Kumar
Design and Analysis of a Connecting Rod Using FEA
The connecting rod is one of the most vital components of an internal combustion engine, transferring reciprocating piston motion into crankshaft rotary motion while enduring significant compressive and tensile stresses. This paper presents static and modal analysis of a connecting rod using three different materials: Structural Steel, Titanium Alloy (Ti-6Al-4V), and Aluminium Alloy (Al 7075). The 3D model is designed in SolidWorks based on actual dimensions and analyzed in ANSYS Workbench using finite element methods. Static analysis determines von Mises stress distribution and total deformation under operational loading conditions, while modal analysis identifies natural frequencies and mode shapes to assess vibration characteristics. Results show that Titanium Alloy exhibits the best combination of low stress concentration (142.3 MPa maximum von Mises stress), minimal deformation (0.024 mm), and favorable natural frequency characteristics (first mode at 1,847 Hz), making it the optimal material choice for high-performance connecting rod applications.
Connecting Rod, FEA, ANSYS, Static Analysis, Modal Analysis, Von Mises Stress, Structural Steel, Titanium Alloy, Aluminium Alloy
B. Adithya Vardhan, N. Akhil, I. Gandhi,Mr. K. Prema Kumar
Case Study on Major Elements of an Elevated Metro Bridge
Metropolitan rail systems are critical urban transportation infrastructure, and elevated metro bridges using box girders and piers form their structural backbone. This paper presents a comprehensive case study on the major structural elements of an elevated metro bridge, focusing on pier design and box girder analysis. The first phase develops pier design using both force-based and direct displacement-based seismic design methodologies, comparing their performance predictions. The second phase conducts parametric finite element analysis of box girder bridges evaluating Single Cell, Double Cell, and Triple Cell configurations. Parameters including radius of curvature, span length, and span-to-radius ratio are varied to assess longitudinal stresses, shear, torsion, moment, deflection, and fundamental frequency. Results demonstrate that displacement-based design provides 18% more accurate seismic performance prediction than force-based methods, and Double Cell box girders offer the optimal balance of structural efficiency and construction economy for standard metro spans.
Ch. Sridhar, P. Tulasi, P. Tejeswari, M. Danamma, R. Karthik, Mr. S. Vamsi Krishna
The AI Portfolio Builder using Rule-Based AI is a web-based application designed to automate the creation of professional portfolios with minimal user effort. Traditional portfolio development often requires technical knowledge, design skills, and a significant investment of time. This system addresses these challenges by using a rule-based artificial intelligence approach to generate structured and personalized portfolios based on user input. The application enables users to register, log in, and provide their personal, academic, and professional details, including skills, projects, achievements, and experience. The rule-based AI engine processes this information through predefined rules and templates to generate meaningful content such as professional summaries and well-organized sections. Unlike machine learning models, the rulebased system ensures consistency, predictability, and ease of customization in the generated outputs. Additionally, the system supports multiple users with secure session management, allowing each user to independently create, view, edit, and download their portfolio. It also offers features like template-based design selection, portfolio preview, and options to download or share the final portfolio. The backend is typically developed using technologies such as Flask and MySQL, while the frontend is built with HTML, CSS, and JavaScript to provide an interactive and user-friendly experience.
DR K. ASHOK KUMAR, N. UDAYASREE, S. GEETHANJALI, SK. NEHA
Plant leaf diseases remain a major challenge in modern agriculture, often leading to severe yield losses and threatening food security. Traditional disease detection methods rely heavily on manual inspection by experts, which is time-consuming, labor-intensive, and prone to human error. To address these limitations, this study proposes a deep learning–based framework for automated leaf disease prediction using convolutional neural networks (CNNs). The research employs a curated dataset of healthy and diseased leaf images, encompassing multiple crop species and disease categories. Preprocessing steps such as image resizing, normalization, and augmentation are applied to enhance data diversity and reduce over fitting. The CNN architecture is designed to capture hierarchical features, including texture, color variations, and lesion patterns, enabling robust classification across diverse conditions. Experimental evaluation demonstrates that the proposed model achieves high accuracy, precision, and recall compared to traditional machine learning approaches. The system’s scalability allows integration into mobile and edge devices, making real-time disease monitoring feasible for farmers in resource-constrained environments. Beyond classification, the framework provides interpretability through visualization of feature maps, offering insights into the model’s decision-making process. This work contributes to precision agriculture by presenting a reliable, efficient, and cost-effective solution for early disease detection. By empowering farmers with timely information, the model supports proactive interventions, reduces dependency on chemical treatments, and promotes sustainable crop management practices. The findings highlight the transformative potential of artificial intelligence in agriculture, bridging the gap between cutting-edge technology and practical farming applications.
DR. M. SESHU BHAVANI, V. AKANKSHA, B. LAXMI PRASANNA, K. SONY
The fast growth of digital recruitment platforms is changing the way we hire. These online tools make it easier for companies to connect with job seekers, streamlining the process and giving everyone involved new chances. made it hard to handle a lot of resumes. Manually screening candidates takes a lot of time and is easy to make mistakes. This study introduces an intelligent Job Description Matcher system that use Natural Language Processing and Deep Learning to automate recruitment procedures. Using PyPDF2, docx2txt, and Tesseract OCR, the system can read resumes in a number of formats, such as PDF, DOCX, and scanned documents. Named Entity Recognition models pull out structured information like skills, education, and work history. A hybrid similarity method that uses TF-IDF, Cosine Similarity, Jaccard Similarity, and BERT-based Sentence Transformers to look at both lexical and semantic factors to see how well an applicant fits a job. The system has a job recommendation module and a way to rank candidates. The platform was built with Flask and MongoDB and is better at matching candidates than traditional keyword-based systems. It also makes sure that candidate selection is fair and can grow with the needs of the business.
CHELLU MAHANTHI JAGADISH,BHAVANA PAD,GUNANA RAMA CHANDRA RAO, AVANAPU GANESH, Dr. GANDI SATYANARAYANA
CRYPT-DAC: Cryptographically Enforced Dynamic Access Control in the Cloud
Cloud computing platforms keep private information safe for many users, but traditional access control systems depend a lot on trusting the cloud service provider. When the cloud infrastructure is hacked or acts maliciously, user data is at risk of being accessed and exposed without permission. This paper introduces CRYPT-DAC (Cryptographically Enforced Dynamic Access Control), an innovative architecture that implements dynamic access control through cryptographic methods instead of relying on implicit trust. The system uses the Advanced Encryption Standard (AES) to encrypt files, the Rivest-Shamir-Adleman (RSA) algorithm to wrap keys for each user, and the Shamir's Secret Sharing (SSS) scheme to provide multiple users access based on a threshold. CRYPT-DAC lets you manage who may access your data in a safe, revocable, and flexible way, while making sure that the cloud provider can't see any of the unencrypted data. The implementation shows that it can manage keys well, allow users to change their permissions on the fly, and revoke access without having to re-encrypt files. The experimental results show that the system works to protect cloud-stored data by using cryptographic enforcement instead of trust assumptions based on policy.
Dadi Likitha, Jerru Yuva Kishore, Ella Likhitha, Dammu Ganesh
The Smart Active Learning Platform is developed to enhance student engagement and improve learning outcomes through an interactive and controlled digital learning environment. Traditional e-learning systems often fail to ensure user attention and active participation, resulting in reduced knowledge retention and ineffective learning experiences. To overcome these limitations, the proposed platform introduces a structured approach that enforces short, mandatory video-based learning sessions followed by quizzes to assess comprehension. The system incorporates real-time monitoring using webcam and microphone inputs to track user presence and engagement during learning sessions. Advanced validation mechanisms are implemented to ensure that learners remain attentive and actively participate throughout the session. Progression to subsequent modules is strictly regulated and permitted only when the learner successfully completes the quiz and satisfies the engagement criteria. By integrating structured content delivery, continuous assessment, and real-time monitoring, the platform encourages disciplined learning habits and active involvement. This approach effectively bridges the gap between passive content consumption and meaningful learning experiences, making the educational process more reliable, accountable, and outcome-oriented. The proposed system demonstrates the potential of intelligent learning platforms in improving student performance and ensuring effective knowledge acquisition.
E.DRAKSHAVALLI, B.AMULYA, B.ANUSHA, K.VARSHA
SAILFISH OPTIMIZED ENSEMBLE LEARNING WITH FEDERATED LEARNING FOR ANOMALY DETECTION IN IOT EDGE NETWORKS
Federated learning (FL) combines machine learning with privacypreserving distributed training across devices and edge nodes, making it ideal for IoT anomaly detection where central data collection is impractical. IoT adoption in India is rapidly expanding, The objective is to leverage federated learning and sailfish-optimized ensemble models to detect IoT anomalies efficiently at edge nodes, enhancing security and performance without centralizing sensitive data, thus ensuring privacy and scalability. In manual IoT security systems, administrators manually monitor network logs, set static thresholds for unusual behavior, inspect alerts by hand, and respond to anomalies after detection, often using rule-based tools and human intervention to investigate and remediate suspicious events. Manual systems are slow, error-prone, and unscalable; they struggle with high data volumes and evolving patterns, fail to detect subtle or novel anomalies, and cannot adapt in real time, leading to delayed responses and increased security risk. The motivation is to overcome the limitations of manual monitoring—such as delayed detection, lack of privacy protection, and inability to scale—by using federated learning with optimized ensemble models that enable real-time, distributed anomaly detection while preserving data privacy and efficiently handling heterogeneous IoT edge environments The proposed system integrates PAC (Probably Approximately Correct) frameworks, NBC (Naive Bayes Classifier), KNN (K-Nearest Neighbors), and CNN (Convolutional Neural Networks) within a federated learning architecture to detect anomalies in IoT edge computing environments. PAC provides theoretical guarantees on learning performance, NBC offers fast probabilistic classification for lightweight devices, and KNN aids in instance-based detection of abnormal patterns. CNNs extract deep spatial/temporal features from sensor data, improving detection accuracy. Sailfishoptimized ensemble techniques combine these models to balance precision, recall, and robustness. Federated learning trains models across edge nodes without sharing raw data, enhancing privacy, reducing communication overhead, and adapting to device heterogeneity for effective real-time IoT anomaly detection.
DR. KM. REENA BANSAL, CHANDRASHEKAR G, SK. ABDUL DAFF, B. SANDEEP KUMAR, G. AJAY KUMAR
MULTI-MODAL DEEP LEARNING FRAMEWORK FOR BREAST CANCER DETECTION USING MAMMOGRAM AND HISTOPATHOLOGICAL IMAGES
Breast cancer remains one of the leading causes of mortality among women worldwide, necessitating the development of efficient and accurate diagnostic tools. This project presents an advanced deep learning-based diagnostic framework that integrates dual imaging modalities: mammogram and histopathological images, for enhanced breast cancer detection. The system is developed using Python as the backend language, Flask as the web framework, and HTML, CSS, and JavaScript for the frontend interface. For the mammogram analysis, two separate convolutional neural network architectures, Xception and MobileNet, were employed. The mammogram dataset comprises 3,044 training images classified into four BI-RADS categories: BI-RADS 1 (1865 images), BI-RADS 3 (387), BIRADS 4 (408), and BI-RADS 5 (384). The Xception model achieved a training accuracy of 96% and a test accuracy of 90%, while the MobileNet model demonstrated superior generalization with a training accuracy of 96% and a test accuracy of 94%. In parallel, the histopathological image classification module utilizes the DenseNet201 architecture to distinguish between benign and malignant tissue samples using the BreakHis 400X dataset. This dataset includes 1,693 high-resolution biopsy images, with 371 benign and 777 malignant images used for training and 176 benign and 369 malignant images for testing. The DenseNet201 model achieved a training accuracy of 98% and a validation accuracy of 88%. The system provides a comprehensive performance analysis based on Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. MobileNet recorded an accuracy of 94.3%, precision of 81.7%, recall of 94.3%, and F1-score of 94.3%, outperforming the Xception model, which achieved an accuracy of 90.7%, precision of 72.3%, recall of 90.7%, and F1-score of 90.7%. For the histopathological analysis, DenseNet201 achieved an accuracy of 88.1%, precision of 91.3%, recall of 88.1%, and F1-score of 88.1%. Additionally, class distribution statistics are visualized to illustrate the dataset balance and support interpretability. By combining radiological and microscopic imaging modalities, this project offers a robust and intelligent diagnostic solution for breast cancer, enhancing early detection and assisting clinical decision-making with improved accuracy and reliability
DR. G. SAMBASIVA RAO, RUKSHITH ANAGANTI, VENU CHINTHAKULA, VARSHITH VENUVANKA, GOPI PASUNURI
IMPROVING BONE FRACTURE DIAGNOSIS IN X-RAY IMAGES USING DEEP LEARNING MODELS
Millions of cases of bone fractures are reported every year, and accuracy in classification is crucial to help with proper management and treatment. The recently developed techniques of Machine Learning, particularly Deep Learning, have been effective in increasing diagnosis precision and efficiency. We utilized a diverse dataset comprising 10 different classes of fracture types captured in X-Ray images. This paper makes a comparison of different machine learning models on classifying bone fractures: VGG-16, VGG-16 with Random Forest, ResNet-50 with Support Vector Machine, and EfficientNetB0 with XGBoost. Model performances were evaluated with respect to parameters of precision, recalls, and F1-scores. According to results, VGG-16 and its variant ensemble with Random Forest outperformed with an accuracy of 0.95 when compared to others on every parameter for different classes of fractures. Results indicate that models based on VGG16 are quite effective for bone fracture classification
DR. G. RAMU, K. HARSHITHA, M. YAMINI, G. VAMSHI, SIDDHARTH KUMAR
REAL-WORLD ELECTRIC VEHICLE ENERGY CONSUMPTION PREDICTION USING MACHINE LEARNING ALGORITHMS
The rapid adoption of electric vehicles (EVs) has increased the need for accurate prediction of energy consumption to improve battery management, driving efficiency, and route planning. Electric vehicle energy usage is influenced by multiple factors such as driving behavior, vehicle characteristics, road conditions, traffic patterns, and environmental variables. Traditional estimation methods often fail to capture these complex relationships, leading to inaccurate predictions. This study proposes a machine learning–based approach for predicting real-world electric vehicle energy consumption using field data collected from EV sensors and telematics systems. The system analyzes various input features including vehicle speed, acceleration, battery state of charge, temperature, road gradient, and driving patterns. Machine learning algorithms such as Random Forest, Support Vector Machine, and Gradient Boosting are applied to model the nonlinear relationships between these factors and energy consumption. The proposed framework preprocesses the collected data, extracts relevant features, and trains predictive models to estimate energy usage under different driving conditions. The trained models are evaluated using performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and prediction accuracy. Experimental results demonstrate that machine learning algorithms can significantly improve the accuracy of EV energy consumption prediction compared to traditional methods. The developed system can support intelligent energy management, optimal route planning, and improved battery utilization in electric vehicles. This research contributes to the development of smarter and more efficient EV transportation systems.
MUDDASANI SHRAVANI, M SAI TEJA, KOMMULA DEEPAK REDDY, KARAGIRI MANIKANTA, KATIPALLY SPOORTHI, KAJRANA DEEPAK KUMAR