Authors - Kaushal Kotkar, Samiran Deore, Riddhi Tak, Tejal Deshmukh, Rupali Vairagade, Nilakshi Jain Abstract - Traditional CAPTCHAs often hinder users more than they stop bots. This project proposes a passive, user-friendly alternative that monitors behavior—like mouse movement, typing speed, and clicks—to distinguish humans from bots. Built with Python, FastAPI, MongoDB, and XGBoost, the system defends against threats like DoS/DDoS attacks while remaining seamless. It adapts over time through model updates and achieved 95.3% accuracy with minimal false positives. With response times under a second, it outperforms conventional CAPTCHAs in both speed and usability.
Authors - Samrudhi Pustole, Hemal Rajput, Linisha Thakor, Dhanashri Gawai, R. B. Murumkar Abstract - Fraud detection is a critical challenge in financial transactions, requiring advanced machine learning models to distinguish between genuine and fraudulent activities. This project focuses on LSTMbased fraud detection, leveraging historical transaction data to identify suspicious patterns. The model processes multiple attributes, including transaction amount, category, user job type, geolocation, and time-based parameters, to assess fraud risk. In addition to the LSTM model, we conducted single-attribute fraud analysis using various models, evaluating their individual impact on fraud detection. This helped determine the most influential features in predicting fraudulent transactions. The system is integrated into a web-based application built with React and Flask, allowing users to input transaction details and receive a fraud score in real-time. The backend ensures efficient data preprocessing using feature scaling and categorical encoding, aligning new transactions with the trained model’s feature space. Through extensive testing with high-risk and low-risk transaction scenarios, the system demonstrates its ability to detect fraudulent transactions with high accuracy, making it a valuable tool for financial security. . . .
Authors - Shirina Samreen, K. Shailaja Abstract - The goal of this research is to accurately classify Android applications as either malware or legitimate software using machine learning techniques. This accuracy is attained through a well-organized approach for dimensionality reduction utilizing an Autoencoder to transform a high-dimensional feature space to a compact representative feature space. This approach is crucial for reducing dimensionality, especially since the novel NATICUSdroid dataset used for Android malware classification contains a large number of features, including both native and custom permissions. The primary contribution of the research is the design of a Machine Learning Pipeline that prioritizes the most relevant features, ensuring high accuracy with a minimal set of features. Classification is performed using various ensemble classifiers. Predictive ability of the proposed MLP is assessed through various evaluation metrics using a confusion matrix.
Authors - Akshat Sharma, Alka Chaudhary Abstract - This study aims to develop an Automated Incident Response System (AIRS) for real-time detection and mitigation of cybersecurity threats. The system employs Random Forest for anomaly detection using live network traffic data. SMOTE is applied to address class imbalance, and Optuna optimizes model parameters for enhanced accuracy. A web-based dashboard provides real-time visualization of security incidents. Performance evaluation on the UNSW-NB15 dataset demonstrates high accuracy (94.7%) and reduced false positives, ensuring robust cyber defense.
Tuesday August 25, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
Authors - Swati Suman, Sumit Ray, Ajay Kumar Prusty, Umesha C, Girish Prasad Rath, Sabyasachi Patnaik, Ankita Priyadarshini, Swagat Shubhadarshi, Pavan Kumar Pandey, Lalithamma M Abstract - Climate change has a significant influence on agriculture, affecting developing nations' food security and financial condition. Thus, the use of Digital Twins, Internet of Things (IoT) devices, and Artificial Intelligence (AI) may play an important role in transforming agriculture that is data-enabled in real time for crop development, high productivity, or climate mitigation. These technologies would aid in predicting drought start periods, optimizing irrigation scheduling to react to any specific climatic shift, and driving crop rotations in a given area. To power climate-resilient farming development, AI and IoT must be combined, resulting in DTs. This technology incorporates agricultural offices, animal monitoring, crop harvests, crop protection, and a DT for predictive maintenance purposes. AI is transforming agriculture by analyzing large volumes of data to forecast climate change consequences. Precision agriculture, a key AI tool, uses micro-localized applications based on syntactic sensory data, drones, and satellite data. Smart agriculture uses IoT, AI, Big Data analytics, and DTs to gather, integrate, and analyze data from various sources. AI-powered models can forecast future weather patterns, insect infestations, and disease outbreaks, enabling earlier intervention and higher output. These insights enable improved resource allocation, agricultural practice optimization, and enhanced farm output in the face of climate change and hence making the DT the possible game changer in the field of agriculture while keeping sustainability as one of its important cornerstones.
Authors - Sreelasya Changalasetty, Lalitha Saroja Thota, Seshagiri Rao Kandukuri, Suresh Babu Changalasetty, Ahmed Said Badawy, Wade Ghribi, Sajid Ali Khan, Syed Asif Basha, Firdouse Banu Abstract - Child-related criminal offenses are amongst the utmost heinous, targeting the vulnerable youth of the society. These crimes include physical & sexual abuse of children, child labor, child trafficking, cyberbullying etc. The World Health Organization (WHO) estimates that up to 1 billion crimes against children have occurred globally. In India alone, more than 350 such crimes are reported each day. This study focuses on identifying crime hotspots related to children in India using hierarchical clustering, a machine learning technique. The research utilized crime data from the National Crime Records Bureau (NCRB) India for 2016–2020, alongside state-wise child population estimates, to group states according to the gravity of offenses committed against children. The data was pre-processed, normalized, and analyzed using KNIME software, which applied a bottom-up hierarchical clustering approach to create a dendrogram for visualizing crime clusters. The results revealed three category Indian states of crime zones in India: high, medium, and low. Delhi state was identified as the primary hotspot with a very high crime rate, followed by 17 states in the medium-risk category, and 12 states in the low-risk category. These findings underscore the need for targeted interventions and enhanced child protection policies, especially in Delhi. The study illustrates how hierarchical clustering can be effectively applied to criminology for identifying high-risk areas and informing policy decisions. Future research may include the use of localized data and exploration of other clustering algorithms to refine and improve crime analysis and prevention strategies.
Authors - G. B. Sambare, Gauri Pawar, Soham Vhanamane, Tejas Sonar, Omkar Gouroji Abstract - Phishing, a deceptive practice aimed at acquiring sensitive information through fraudulent websites mimicking legitimate ones, remains a significant cybersecurity threat. This paper presents a survey of machine learning (ML) algorithms applied for the detection of phishing URLs. We explore various feature categories derived from URL structure, domain characteristics, and HTML/JavaScript content. In particular, the characteristics of interest involve address-bar-based features (i.e., URL length, redirection patterns, existence of IP addresses), domain-based features (e.g., DNS records, website age, web traffic), and HTML/JavaScript-based features (e.g., iframe redirects, disabling the right click). The accuracy of a variety of classification methods, namely Decision Tree, Random Forest, XGBoost, and Support Vector Machines (SVM), is covered. By our results as well as available literature, we point out the effectiveness of XGBoost, which, in our testing, reached a comparatively high value of 86.8% accuracy and exhibited its prowess as a solid detector of phishing URLs. This paper offers some vision into the benefits and shortcomings different machine learning approaches are for phishing assaults.
Authors - Ajay K.Talele, Gayatri Bhurguda, Siddhant Yenpure, Purva Ratnaparkhi, Jidnya Santosh Jadhav, Rugvedi Nimbhore, Advait Mhalungekar, Tanishka Kalokhe, Riddhi Rathi, Roshan Raut, Pratha Sawant Abstract - In an era where skill development is essential for career growth, learners often struggle to track progress, find curated resources, and stay motivated. SkillTrax is a personalized learning tracker built to address this gap. The platform helps users enter their skills, select proficiency levels, set learning goals, and track their progress with recommended resources and quizzes. It is powered by object-oriented design principles, ensuring modularity and scalability. This paper outlines the motivation, design, and implementation details of SkillTrax, focusing on its core features, backend architecture, and educational impact.
Tuesday August 25, 2026 3:30pm - 5:30pm IST Virtual Room EGOA, India
Authors - Owais Ansari, Hemangini Patel, Tejas Maroo, Morvi Panchal, Nikita Raichada Abstract - In recent years, emotional voice conversion and expressive speech synthesis have gained attention due to their applications in areas such as automated dubbing, human-computer interaction, and assistive technologies. Our research proposes an AI-based dubbing system, SyncVox, which presents a seamless voice dubbing custom pipeline designed to provide seamless voice conversion across languages. It addresses low-resource video dubbing using various advanced technologies like speech recognition, translation, and style transfer. The pipeline combines speaker embeddings with advanced techniques to produce natural-sounding, speaker-alike voice synthesis. By employing multitask learning with Text-To-Speech, the pipeline is capable of capturing rich linguistic information while retaining languages; this allows content creators to dub videos without compromising the original speaker’s intent and naturalness. Early results show that this system effectively synthesizes natural-sounding speech with high emotional fidelity.
Authors - Sunitha Ratnakaram, Venkamaraju Chakravaram, Chakravaram Sri Surya Narayan Raj Abstract - Artificial Intelligence (AI) has significantly reshaped the landscape of Information and Communication Technology (ICT), particularly in the corporate sector. Integrating AI into programming and ICT development has led to automation, enhanced efficiency, and optimized business operations. AI-driven solutions are not only transforming software development but also impacting key business functions such as cybersecurity, finance, marketing, and decision-making processes. AI-powered automation has enabled businesses to achieve unprecedented productivity levels, making operations faster and more accurate while reducing human intervention. Companies now rely on AI for predictive analytics, customer insights, fraud detection, and even real-time strategic decision-making. This research paper explores the transformative role of AI in ICT programming, highlighting its vast impact on corporate efficiency, software development methodologies, cybersecurity frameworks, and business functions such as marketing and finance. It also delves into the ethical considerations of AI implementation, the challenges posed by AI-driven ICT automation, and the potential of AI to reshape the global economy. This study presents various case studies and empirical findings that illustrate how organizations have successfully incorporated AI into their ICT programming strategies to gain competitive advantages. Furthermore, the research investigates future trends, exploring how AI is expected to evolve within the corporate sector and the potential risks it may pose. The researcher used descriptive exploratory research methodology. The findings of this paper contribute to the ongoing discourse on AI's role in shaping the digital landscape, offering insights into both opportunities and concerns surrounding AI adoption in the modern corporate world.
