Authors - Karthika M, Raghu Nandan KS, Salanke Anni Rao, Ramkumar S Abstract - Fire detection and prevention are essential to preventing fire spread and substantial loss or damage, especially in remote regions like lakes where traditional approaches are almost useless. This review covers fire alarm system advances, focusing on machine learning (ML) to improve detection. The paper also examines how these innovations improve remote fire alarm systems functioning and how they integrate with emerging IoT protocols, sensor networks, and radio technologies like LoRaWAN. It focuses on ML models like CNNs and deep learning to analyse sensor data and detect fires accurately and quickly. This paper discusses fire detection innovation and how ML can improve future systems' coverage and accuracy.
Authors - Vishal Karpe, Meetu Kandpal Abstract - In order to better understand how Amazon Web Services (AWS) charges for its services and how users can efficiently manage expenses, this research examines the price choices offered by AWS. It illustrates how various pricing strategies such as pay- as-you-go, volume discounts, and cost savings affect users' AWS spending by explaining them and providing examples of how they work. To emphasize the advantages and disadvantages of each, the research also contrasts the costs of Microsoft Azure and Amazon Web Services. In order to track and optimize user spending, it also comes with features like AWS Budgets, AWS Pricing Calculator, and AWS Trusted Advisor. The goal is to provide clients with an extensive manual on how to maximize their financial and material investments when utilizing cloud services.
Authors - Ritveek Rana, Manisha Manoj, Anitha Dhanasekaran Abstract - The escalating threat of climate change has made it imperative to understand and mitigate the environmental impact of human activities, particularly by reducing carbon footprints. This research ventures on predicting carbon emissions for India using autoregressive integrated moving average (ARIMA) models. The findings may signal appreciable implications for decisions in governmental policies and energy sector. This study highlights a potential situation for India in the coming years due to increased expenditure of carbon-based fuel sources to meet the need for increased manufacturing and demand. The ARIMA models developed in this research can serve as a valuable tool for forecasting carbon emissions and guiding future energy policies.
Authors - Amoggha C H, Padmapriya R, Adithya Narayana Holla, Manoj C Aradhya Abstract - This paper presents the development of a deep learning model for recognizing handwritten Kannada characters. Kannada character recognition presents unique challenges due to the complexity of the script and the variety of symbols. To address these, we utilize a hybrid model combining ResNet50 and VGG16 architectures. ResNet50 is leveraged for its ability to train deep networks on complex patterns, while VGG16 excels in capturing detailed feature representations. The model is trained on carefully pre-processed datasets, optimized through iterative parameter tuning to ensure high accuracy and robustness. The backend infrastructure uses Flask and TensorFlow, with the frontend built using java script, HTML, and CSS. The system features a sketchpad where users can draw Kannada characters, which are then processed by the deep learning model for recognition. An interactive tool further supports language learning. Through extensive testing, the system has proven to be reliable and effective. This project represents a significant advancement in automated Kannada language processing, offering a powerful tool for character recognition. By enabling accurate, efficient recognition, it contributes to promoting linguistic diversity and inclusivity, making it an invaluable resource for Kannada language processing applications.
Authors - Edidiong Akpabio, Sudhir Agarmore, Akshay Kumar Abstract - As digital technologies become increasingly ingrained in critical energy infrastructure, a looming threat is cyberattacked as the sector has absorbed all the data acquisition and supervisory control systems, smart grids, and industrial control systems, with associated operational efficiencies, but at the cost of an expanded attack surface in terms of cyber threats. This paper aims at identifying the unique cybersecurity issues in CEI that pose threat scenarios that include, for instance, their vulnerability to legacy system vulnerabilities, insider threats, and more complex attack vectors such as advanced persistent threats and ransomware. Finally, it points out the need for proactive risk assessment, network segmentation, advanced defence mechanisms such as intrusion prevention and detection systems, and zero trust architectures. Newer technologies like machine learning, blockchain, artificial intelligence, and quantum cryptography offer new opportunities for better cybersecurity. It can foresee the occurrence of a particular attack through AI-based threat detection systems. Blockchain provides security in energy transactions while making unbreakable encryption of critical communications. This paper insists on better, much more comprehensive disaster recovery and incident response plans to minimize the impacts caused by cyberattacks and it concludes by advocating a multi-layered cybersecurity strategy with the intent of integrating advanced detection systems and risk management practices into a solid collaboration between the government and private sectors aimed at enhancing the stability of global energy supplies.
Authors - Archana L. Rane, Sanskruti R. Talele, Rashika A. Ghavate, AditiS.Khairnar, Harisha A. Chothani Abstract - Nowadays, the world is increasingly focused on health care, with hair care emerging as a key aspect of personal well-being. Many people face confusion when selecting the best shampoo based on their scalp and hair health. The purpose of this study is to provide a natural alternative to conventional shampoos by incorporating eggshell powder, a readily available, eco-friendly resource, into future hair care formulations. A comprehensive study was conducted to evaluate various shampoos currently available and to identify the benefits of eggshell powder. This study highlights the potential of eggshell powder in enhancing shampoo production. Machine learning algorithms such as Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest were employed to analyze manufacturing parameters and optimize the absorption of eggshell powder. The results of the analysis revealed varying accuracies for each model: Naive Bayes (52%), KNN (71%), SVM (72%), and Random Forest (82%). These techniques allowed for precise adjustments to ingredient concentrations and interactions, improving the overall efficacy of the shampoo. The results demonstrate that shampoos formulated with eggshell powder offer several advantages, including stronger hair, better moisture retention, and enhanced scalp health. Additionally, eggshell powder proved to be a sustainable material, aligning with growing consumer demand for environmentally friendly products. This study highlights the potential of using natural resources and machine learning to drive data-driven improvements in hair care formulations, offering a promising alternative to conventional products while meeting the increasing preference for sustainability.
Authors - Vidhi Aakash Pandya, Meetu Joshi Abstract - The present research looks at how artificial intelligence (AI) is affecting various interpersonal sectors and offers opportunities, problems, and potential solutions. It investigates how artificial intelligence (AI) has developed into a crucial instrument for tackling social problems and providing answers in a variety of fields, including healthcare, education, the environment, and agriculture. The history of AI's development from historical turning points to modern deep learning applications opens the study. It then dives into a thorough review of the literature, highlighting important research and the condition of AI application in many industries at the moment. The study examines AI's potential applications in healthcare, with a focus on tailored treatment methods, diagnostics, and disease prediction. It also addresses ethical issues. AI is being used in education to investigate how diversity and specific instruction might be achieved using voice assistants and virtual mentors, among other technologies.
Authors - Yash Dargude, Jui Ambekar, Yash Gadakh, S.T Gandhe Abstract - Mental health disorders, such as depression, anxiety, and stress, are global challenges that significantly affect individuals’ well-being and productivity. Early detection and diagnosis are crucial for effective intervention, yet traditional methods often rely on subjective assessments, leading to potential delays. Electroencephalography (EEG) has emerged as a promising non-invasive tool for objectively monitoring brain activity, offering valuable insights into mental health conditions. This survey paper explores the current state-of-the-art in mental health detection using EEG signals. We provide an overview of EEG-based systems, highlighting key signal processing techniques such as filtering, artifact removal, and noise reduction. Feature extraction methods, including time-domain, frequency-domain, and time-frequency domain techniques, are reviewed to emphasize how patterns in brainwave activity correlate with mental health states. Additionally, we examine various machine learning and deep learning algorithms, such as Support Vector Machines (SVM), Random Forest, and Convolutional Neural Networks (CNNs), which have been applied to classify mental health conditions based on EEG data. The paper also presents a comprehensive analysis of the effectiveness of these models in detecting specific mental health conditions like depression, anxiety, and stress. We discuss the challenges faced in using EEG for mental health detection, such as signal variability and the need for large datasets, and propose future directions for enhancing the accuracy and generalizability of these models. This survey aims to contribute to the development of more reliable, EEG-based diagnostic tools for mental health assessment.
Authors - Krishan Pal Singh, Emmanuel S. Pilli, Vijay laxmi Abstract - Tor network provides anonymity and privacy to online users. Hence, analyzing Tor traffic to identify applications and services, especially when encrypted tunnels and pluggable transports are used, remains a significant challenge. This paper presents a novel framework for identifying obfuscation techniques by analyzing their unique traffic characteristics, such as packet sizes, inter-arrival times, byte sizes, and byte frequencies. A custom-built network traffic collection environment is established to evaluate the proposed framework. A large Tor traffic dataset is created that contains Obfs4 and Snowflake Plugin traffic, ensuring realistic user behavior simulation utilizing modified Tor browser configurations. The framework leverages a combination of statistical analysis of encrypted payloads, examines timing sequences during authentication, and packet length filtering. The Traffic data is evaluated on diverse deep learning models, such as Neural Networks, Adaboost, and XGBoost, achieving high accuracy rates (95% to 98%) across different Tor plugins. The proposed framework demonstrates robustness with low false positive rates. It is also adaptable to new Tor obfuscation techniques such as Obfs4 and Snowflake. The research findings highlight the importance of using up-to-date and diverse datasets to train effective Tor plugin identification models, with potential applications for improving Tor network security.
Authors - Vinayak Suresh Bhajantri, Aishwarya B Kalatippi, Rahul B Sajjan, Babusingh Ramsingh Rajput, Kiran M R, Suneeta Budhihal Abstract - In recent years, natural disasters like earthquakes, tsunamis, floods, and storms have happened frequently, causing severe damage. These disasters have shown how crucial it is to have reliable communication for rescue operations. Often, disasters damage communication network. The heavy demand for data transfer on the Internet is pushing its infrastructure to the limit, making it difficult to respond quickly to emergencies and disasters. To solve this problem, Internet networks need to prioritize certain types of data traffic: Security, Health, and Emergency (SHE) data traffic. These specialized networks work in private domains to support specific tasks for particular groups of users. We proposed network flow priority management system based on Software-Defined Networking (SDN) to give SHE data traffic the highest priority. Using the Mininet simulator, we tested our system extensively. The results show significant improvements in handling SHE data traffic, ensuring that during network congestion, SHE data is transmitted quickly, improving the effectiveness of emergency response efforts.
