Authors - Deepti Maheshwari, Shailesh Gahane Abstract - Website phishing poses a massive security threat that continues to increase in prevalence. Internet scammers exploit human faith by running imitation websites which aim to obtain confidential user information. An extensive review of multiple phishing detection techniques and hybrid detection models appears in this paper which brings together different detection methods to speed up and increase the accuracy of breaking down phishing-related websites. The paper investigates how machine learning (ML), artificial intelligence (AI) and heuristic-based approaches and anomaly detection should be implemented within hybrid systems which detect phishing behavior. Multiple studies from the literature receive analysis through which we identify their research approaches as well as their outcomes together with their limitations along with their contributions to the field. The evaluation will demonstrate how hybrid models can boost the detection of phished emails while detailing methods to strengthen model performance and flexible design and growing capability.
Authors - Rinkle Solanki, Shailesh Gahane Abstract - Surgical site infections (SSIs) represent a major concern for the healthcare industry, highlighting the need for timely intervention and effective predictive strategies.This paper presents an external validation framework for machine learning algorithms designed to identify and forecast SSIs in advance. We use sophisticated algorithms to create accurate predictive models using a variety of variables, including clinical features, microbiological data, and patient demographics. Across a range of patient demographics and therapeutic circumstances, thorough external validation is carried out. Our results demonstrate how effective this strategy is at precisely identifying SSIs, enabling prompt interventions, and improving patient outcomes. Surgical care procedures could be improved and medical expenses could be decreased by using validated models.
Authors - G Jeyashaathvee, Anish Pranav, Sindhu Chandra Sekharan, Jesline D, Ajanthaa Lakkshmanan Abstract - The challenge of extracting useful insights from unstructured data in the presence of big digital information is still present today. AI Intensive Timestamp based Summarization proposes a novel framework to automatically extract and summarize pivotal events, along with their corresponding timestamps from different data sets. Making use of Natural Language Processing and deep learning machine learning approaches, the system checks text data for temporal markers, extracts salient events and produces verbose summaries. The method proposed shall make the historical analysis easier and trend detection along with automated reporting of large dataset summaries more concise. Technique is implemented using Named Entity Recognition for date extraction and transformer models in case of summarization Experiments show that AI-based timestamp summarization is efficient in enhancing IR results and autodoc reliability. Making This Research Unique and Contributing to the much Expanding AI-driven text analysis filed, a Scalable in nature for timestamp extraction on domain wise basis.
Authors - Ankita Mehta, Shailesh Gahane Abstract - This paper discusses about, we display a profound learning-based approach bipolar clutter discovery utilizing Convolutional- Neural Systems (CNN) and Long Short-Term Memory (LSTM) systems. To assess the model’s performance, two particular datasets Twitter information and survey data were analyzed. Preprocessing steps, counting information enlargement, normalization, and the application of the Adam optimizer, were joined to upgrade the model’s adequacy. The model’s exactness and misfortune were measured for both datasets, and it was watched that the survey dataset given superior execution, yielding higher precision and lower misfortune compared to the Twitter dataset. These discoveries propose that the questionnaire-based information may be more reasonable for solid bipolar disorder location within the given show. The inquire about illustrates the potential of combining CNN and LSTM for mental wellbeing examination, highlighting the significance of information determination in accomplishing ideal comes about.
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room BGOA, India
Authors - Sarika G. Songire, Deepa S. Deshpande Abstract - Deep learning algorithms have completely transformed medical diagnostics by enabling accurate and efficient identification of brain tumors. Brain disorders often arise due to increased in excessive and improper cell counts, which can damage neural structures and, in severe cases, lead to malignant brain cancer. The reducing mortality rates requires prompt intervention and early discovery. This research work presents an architecture of deep neural network specifically designed for the purpose of detecting brain tumors from MRI images. The proposed model is evaluated against existing research using a similar dataset to assess its effectiveness. For comparison, performance parameters including area under the curve (AUC), recall, accuracy, precision, and loss are used. According to experimental results, the suggested CNN model accomplishes 98.61% accuracy, 99.70% AUC, 99% of both precision and recall, and 0.41 loss in a data set of 3,264 MRI images. These findings indicate that the suggested model surpasses current models and provides a dependable and effective technique for prompt brain tumor diagnosis.
Authors - Ayush Itkhede, Aryan Fulsunge, Sarthak Bomanwar, Sujal khobragade, D.M.Shinde Abstract - The integration of embedded systems into brick manufacturing has led to significant improvements in efficiency, quality control, and resource management. This paper presents an embedded system designed to optimize brick production by monitoring critical parameters such as temperature, humidity, and material composition in real-time. The system achieved a 32% reduction in production cycle time, from 6.5 hours to 4.4 hours per batch, and improved material handling speed by 41%, from 120 kg/hour to 169 kg/hour. Quality control metrics showed a 78% reduction in defect rates, from 5.2% to 1.15%, while energy consumption decreased by 29.3%, from 17.4 kWh to 12.3 kWh per 1,000 bricks. The system also reduced waste by 64%, from 8.9% to 3.2%, and improved material utilization rates from 83.5% to 94.8%. These results demonstrate the potential of embedded systems to revolutionize brick manufacturing, making it more sustainable and cost-effective.
Authors - Kamini Solanki, Rahul Vaghela, Jay Panchal, Anjali Mahavar, Jaimin Undavia, Nilay Vaidya Abstract - Diabetes is a chronic illness that affects the body's ability to metabolize glucose, which is the sugar that provides energy to the body. Type 1 and type 2 diabetes are the two main types of the disease. The immune system of a person with type 1 diabetes attacks and destroys the pancreatic cells that make insulin, which causes blood sugar levels to rise. Type 1 diabetes symptoms include excessive thirst, frequent urination, extreme hunger, weight loss, fatigue, impaired vision, delayed healing, tingling or numbness in the hands and feet, and recurring infections. AI algorithms may be used to collect and analyze medical data to support early diagnosis and treatment. Typically developing in childhood or adolescence, type 1 diabetes can be managed with the injection of insulin or an insulin pump. A person with type 2 diabetes either develops an inability to use insulin or ceases making enough of it to regulate blood sugar levels.
Authors - Dipali Wankhade, Shailesh Gahane, Mrunal Meshram Abstract - Oral cancer is one of the fatal diseases present in society. Its late-stage diagnosis puts it under the list of diseases with high mortality rates. Deep learning has conceptualized the revolution in the field of medical imaging with impressive depth and accuracy in diagnosis and early detection. This review highlights the advancements in deep-learning-based methodologies for oral cancer detection and classification, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep reinforcement learning (DRL). Multi-modal learning and hybrid models combining histopathological and radiological data have increased the precision of tumor segmentation and subtype classification. The observational data will help provide insights into the factors responsible for explaining the model's decisions and the associated risks, which are most relevant for potential applications. The development of transfer learning and self-supervised learning also seems to have significantly solved some of the most serious challenges regarding the volume of clinical data. Future studies can harness standardized practices for data collection, deploy technically sound explainable AI frameworks, and conduct clinical validations as the closing gap between tremendous strides made in deep learning and practical real-world implementation. This review provides a wide-ranging overview of such AI-driven methodologies, focused on the critical challenges and future directions for improvements in early oral cancer detection and reduced mortality rates.
Authors - Ankita Mehta, Shailesh Gahane Abstract - In this research, we focus specifically on mental disorder which is bipolar disorder using machine learning techniques, utilizing a simple dataset from Kaggle for training and evaluation. The study involves applying various ML models, including R. Forest, XG-Boost, and S. Vector Machines (SVM), on the dataset. We assess the performance of these models using evaluation MSE (how far actual value to predicted value), Precision, and Fl Score to determine their effectiveness in predicting bipolar disorder. However, through extensive experimentation, we found that the combination of (CNN) and (LSTM) networks outperformed the other algorithms, achieving an overall accuracy of 95%.
Thursday August 27, 2026 9:30am - 11:30am IST Virtual Room BGOA, India
Authors - Vijeta V Shettar, SR Nirmala, Satish Chikkamath, Suneeta V Budihal Abstract - This study investigates the performance of various speaker recognition models, including SpeakerNet, TiTANet Small, and TiTANet Large, using the IITG-MV dataset for speaker verification tasks. The preprocessing steps involved resampling, noise reduction, segmentation, and volume normalization to prepare the audio data for input into the models. The models were evaluated based on their ability to correctly verify whether two audio samples belong to the same speaker or not. The evaluation metrics, derived from the confusion matrix, revealed that SpeakerNet outperformed both TiTANet variants, achieving an accuracy of 85%, while TiTANet Small and TiTANet Large achieved accuracies of 75% and 80%, respectively. Despite lacking graphical visualizations, the confusion matrix provided a comprehensive view of the models’ performance, showing how each model handled correct and incorrect speaker match predictions. The results highlight that SpeakerNet is the most effective model for speaker recognition in this setup, demonstrating superior accuracy and robustness in identifying speaker-specific features. These findings can guide future research in optimizing speaker recognition models for real-world applications involving speaker verification.
