Authors - Sakshi Limkar, Chhandavi Gowardhan, Devyani Dahake, Sneha Naik, Arti Vasant Bang Abstract - Chatbots powered by artificial intelligence (AI) are becoming more and more inventive tools in the field of mental health treatment. They provide scalable, affordable, and easily accessible support for people struggling with stress, anxiety, depression, and other mental health conditions. These conversational bots provide real-time therapeutic interventions, such as promoting emotional well-being by mimicking human interaction through Natural Language Processing (NLP) and Machine Learning (ML) techniques. We have therefore developed a chatbot for mental health assistance named CalmConnect. It is designed to assist users in identifying and addressing mental health concerns.
Authors - Akshar Thakor, Tanya Khunteta, Kaushal Shah, Hargeet Kaur Abstract - It is essential to control access to data. Since the beginning of digital communication, scholars have been at work figuring out ways to prevent eavesdropping on data. They have been successful in doing so through cryptography techniques such as RSA, and AES. Modern Quantum computing is shown to be able to break them. Thus, research and development in this field have been rapid in the last decade. It is better to take precautions in its infancy and develop a future-proof cryptography technique. The following article describes the issues with contemporary ciphers and how they are vulnerable against quantum computers, then goes on to suggest lattice-based cryptography as a strong contender for the solution to this vulnerability by providing its benefits and properties synergizing with certain domains in digital technology. It explains why it is a strong contender by providing examples of its performance in IoT devices that are prevalent today and are only going to increase as this era progresses. By providing a comprehensive overview of developments in this realm, it presents to, new researchers in this field, the importance of Lattice-Based Cryptography, by suggesting why Lattice-Based Cryptography should be the focus of the field of cryptography in the future.
Authors - Nitin Pandit, Sandeep Chaware, Adit Bagati, Yashraj Shegokar, Omkar Jadhav, Om Nikam Abstract - DOS attacks or denial of service have become common among hackers who use them as a way to gain reputation and respect in the cyber underground. A denial-of-service attack essentially means denying legitimate and user network services to a target network or server. Its main purpose is to attack so that legitimate users are temporarily unable to use the services on the network. In other words, we can define a DOS attack as an attack that clogs the target’s memory, making legitimate users unable to help. Or, you send packets that the target cannot process, causing the target to fail, reboot, or deny service to legitimate users. We develop an online DOS protection software that can protect web servers.
Authors - Vedant Patil, Bhargavi Bhende, Omkar Jadhav, Gitanjali Shinde, Kavita Moholkar Abstract - The increasing realism of AI-generated faces, driven by advancements in Generative Adversarial Networks (GANs) like StyleGAN and ProGAN, poses significant challenges in security, identity verification, and digital forensics. Current detection methods, primarily relying on Convolutional Neural Networks (CNNs), struggle to identify subtle artifacts in high-quality synthetic imagery. This paper proposes a hybrid model combining Vision Transformers (ViT) and XceptionNet in a soft-voting ensemble framework. ViT captures global spatial patterns, while XceptionNet excels in detecting localized texture inconsistencies. The ensemble achieves 92.3% accuracy, 92.5% precision, and an F1-score of 0.922 on a dataset of 188,800 real and AI-generated faces. Extensive experiments demonstrate the model’s robustness against diverse deepfake architectures, including those with minimal artifacts. This approach offers a state-of-the-art solution for differentiating real and AI-generated faces, with significant implications for fraud prevention, content moderation, and digital forensics.
Authors - Rupali Ramdas Shevale, Monika Sharad Deshmukh Abstract - For efficient real-time decision-making in a variety of domains, including cybersecurity, finance, and the Internet of Things, accurate and trustworthy event categorization is crucial. By maximizing feature integration, this study explores how incorporating Redpanda, a real-time data streaming platform, into predictive algorithms might improve event categorization. Continuous, high-throughput data processing is made possible by Redpanda's low-latency, fault-tolerant architecture, which enables the real-time extraction of a variety of accurate attributes. Predictive models may use Redpanda's capability to access current, augmented feature sets, which will greatly increase classification accuracy and dependability. The integration process is thoroughly examined in the research, along with its effects on feature variety, model accuracy, and system robustness. The benefits of real-time data streaming in predictive analytics are demonstrated by empirical results, which indicate a significant boost in event categorization performance. By improving feature extraction and enhancing the dependability of predictive systems in dynamic contexts, the results establish Redpanda as a scalable and robust solution.
Authors - Jayashri D.Palkar, Anuradha S. Deshpande Abstract - The crop protection plays vital roles in the food supply and depends on how healthy the crops are, which influences the agricultural production; any adverse condition on crops will be leading to economic loss. Grapes find much use, being important and widely cultivated crops primarily in the Mediterranean regions that control an outgoing market of over 189 billion United States dollars. They are grown for consumption as fresh fruits, as well as in various processed forms such as drinks and sweets. These would be grapes, which, unlike many other plants, thrive and develop despite sickness, thus their control mechanisms must also function well. At the same time, many instances of diagnosis of these infections being wrong can lead to inadequate treatments for the known diseases, inducing even more generalized losses amounting from 5-80% on the crop under inspection. Current computer-based solutions may not be precise enough, leading to high running costs, operational difficulties, and image quality issues due to distortions. The body of literature based on different algorithms for the detection and classification of grape crop diseases remains vast and continues to grow rapidly with the newly emerging algorithms. It presents the overview of different disease-detection algorithms for optimizing grape disease detection, thereby aiding farmers in choosing the appropriate algorithm based on particular diseases and weather condition. This study presents a systematic review of various methods implemented in literature and provides a framework for use of AI-ML for effective detection of disease.
Authors - Shilpa M Katikar, Vikas B Maral, Nagaraju Bogiri, Vilas D Ghonge, Pawan S Malik, Suyash B Karkhele Abstract - Effective forecasting and modeling in food demand supply chains are critical to minimizing waste, reducing costs, and ensuring product availability. This paper explores a comprehensive approach to forecasting food demand by leveraging regression-based models for analysis. We investigate how various machine learning regressors can predict food demand more accurately by examining key supply chain factors such as seasonal trends, price fluctuations, and consumer behavior. The study implements and compares multiple regressors to assess their performance in predicting demand. Metrics Evaluation is done by predicting various models which are Ensemble Learning Models and Neural Network Models to calculate the model’s accuracy. By doing prediction, we identified that Gradient Boosting and XGBoost have overall good accuracy in forecasting and it has provided optimized solutions in the supply food chain. This research mainly focuses on using the best modeling techniques which will help the end users to make proper decisions and bring efficiency in food demand management.
Authors - S. B. Hema Anjali, Manikanta Sai Sumeeth, Sushama Rani Dutta Abstract - This study makes use of a machine learning system that predicts health insurance costs, a relevant issue given the increasing need for such estimates in a post-COVID-19 world. Using the Medical Cost Personal Dataset available at Kaggle offering 1,338 entries, we applied various ensemble models, notably XGBoost, Gradient Boosting Machine (GBM), Random Forest, and Support Vector Machines (SVM). Among our results, XGBoost gives out the best accuracy of the estimates, but the implementation of this technique was expensive. Random Forest was non-intrusive and went on to be of high efficacy. We also discussed how the big data paradigm was implemented using Spark as a means to enhance performance in working on large datasets. As a whole, this work positions XGBoost the ban for the cost of health insurance prediction claiming that there exists scope for improvement by deploying ML methods in decision making in healthcare processes.
Authors - Shrikant Bhopale, Tahseen Mulla, Madhav Salunkhe, Sagarkumar Dange, Sagar Patil, Rohit Raut Abstract - Cardio-Vascular Disease (CVD) continues to be a prominent issue in worldwide health, emphasizing the crucial importance of accurate forecasting and timely prevention. Machine learning (ML) has become a vital tool in the quest to improve CVD diagnosis. The present study aims to conduct a comparative analysis of various machine learning (ML) algorithms in terms of their performance, which includes Naïve Bayes, Logistic Regression, Random Forest, Decision Tree, Artificial Neural Network, Support Vector Machine and XGBoost, in the prediction of CVD. Our results reveal that XGBoost outshines other models, achieving outstanding accuracy, precision, recall, and F-measure. Its exceptional ability to balance precision and recall makes it an excellent choice for the early identification of CVD. This study makes a valuable addition to the expanding field of study on CVD prediction. It underscores the significance of employing advanced ML algorithms, that have the possibility to significantly influence public health outcomes.
Authors - Yathin Reddy Duvuru, Seshank Mahadev, Saranya P Abstract - In this paper, we implement a deep learning model for photovoltaic (PV) power forecasting using Global Horizontal Irradiance (GHI) values which are the major determiner of photovoltaic cell power output. We use a multilayer Long Short-Term Memory (LSTM) model combined with explainable AI (XAI) techniques, aimed at improving the interpretability of predictions across various forecasting horizons. The model utilizes global horizontal irradiance (GHI) data, which undergoes thorough pre-processing, including cleaning and downsampling to ensure data quality and computational efficiency. The LSTM model is designed with multiple layers to capture temporal dependencies and nonlinearities, which are crucial for accurately forecasting PV power under variable environmental conditions. To evaluate model performance, multiple error metrics such as R², MAE, RMSE, and MAPE are utilized. In addition, a benchmark model is built as a reference to compare against the LSTM-based model, providing a baseline for assessing performance improvements. The use of XAI further enables the interpretation of the LSTM model’s predictions, providing an understanding of feature importance and model behavior. We use the SHAP library to perform XAI analysis by calculating Shapley Values. We demonstrate how the SHAP library can be used on 3D LSTM data. Furthermore, the SHAP graphs provide a sense of the importance of each feature’s role in the prediction.
