Authors - Dhavasironmani R.R, Maria Joel. J, Siddharth S.V, Ajith Sundaram Abstract - Marketing research is essential to get the correct information about the consumers’ needs and their changing preferences. The evaluation of the Consumer Behaviour, attitude, perception and satisfaction level has been the subject of the market research very frequently. Health Drinks indeed are essential for every individual. The quantity of intake may vary according to the age, occupation, income level, size of the family, but everyone accepts that in order to cope up with the energy demands of the day-to-day life, and to defend oneself from the polluted environment, one should definitely consume any health drink supplementary to the food intake. Preferences get converted into a habit which is hard to change. It is evidenced from the study that certain health drinks are being consumed through generations that the customers develop a high degree of brand loyalty towards that brand.
Authors - Bhuvaneswari Perumal, Vaishnavi Moorthy, Gladius Jennifer H Abstract - According to UNICEF-India, 46% of maternal fatalities and 40% of neonatal fatalities transpire during labor or within the initial 24 hours post-delivery. Antepartum care includes routine surveillance, assessment of risks, and appropriate actions to enhance the health of the mother and fetus. Intrapartum care provides for safe labour and delivery with surveillance and appropriate management of complications by skilled personnel.This study aims at identifying the importance of holistic care in these stages and how it helps in preventing complications through the identification of high risk pregnancies which will be useful in avoiding the development of severe problems in future. The study uses analytical tools and Machine Learning models to analyze the health data and risk factors of pregnancy. In existing state of art they have inadequate early risk prediction with poor personalization. So the collected data includes several risk factors identified and classified based on their level of risk. The results of the attempts of applying various machine learning models and EDA methods to define the most important risk factors. This is a very large reduction and in line with the United Nations Sustainable Development Goals for the year 2030.The aim is to reduce maternal and neonatal morbidity and mortality. Lack of adequate management of intrapartum care can lead to postpartum problems to a large extent and thus affect the prenatal and fetal well-being.
Authors - Yash Sharma, Bramhansh Agarwal, Sindhu Chandra Sekharan, C. Kavitha, S. Umamaheswari Abstract - In recent years, interior design has played an increasingly important role in improving the look and usability of residential and commercial spaces. Although professional designers are often employed for this purpose, the process can be time-consuming and costly, with limited flexibility for personalized input. To address these limitations, an AI-assisted solution has been developed. This system employs Conditional Generative Adversarial Networks to analyze photographs of indoor environments alongside text descriptions that reflect user preferences such as desired furniture style, color schemes, and spatial arrangements. After processing the information, the tool provides a range of design suggestions tailored to the user’s specific needs. This method eliminates the need for repeated consultations and allows for rapid generation of unique, realistic interior layouts. The approach supports a more inclusive and affordable design experience, enabling individuals to explore personalized decor ideas efficiently. By merging visual data with linguistic inputs, the system presents a novel pathway for intuitive and responsive interior design support.
Authors - Puja Cholke, Om Yogesh Suhagir, Maroof Mustaq Mohammed Gadiwale, Srushti Pancham Mane, Sanika Suresh Mohite, Shreya Ramesh Phalke Abstract - Snake bites pose a severe public health risk, especially in rural and tropical regions, where delayed treatment often leads to fatalities. Existing systems struggle to classify snakes accurately based on symptoms, causing delays in administering the correct antidote. To address this issue, BiteSage (Snake Bite Antidote Suggester) utilizes data science and machine learning to classify snake bites as venomous or non-venomous based on user-reported symptoms and recommend the appropriate antidote. A chatbot interface assists users in symptom formulation and provides real-time counseling. Additionally, the system offers visualization tools to analyze global trends in snake bites, enhancing awareness and preparedness. The model ensures high precision in bite classification and antidote recommendations, backed by comprehensive data analytics. This research benefits medical professionals in remote areas and educates the public, helping to reduce fatalities and improve emergency response. By integrating AI-driven analysis, real-time assistance, and data visualization, BiteSage enhances medical decision-making and public awareness, ultimately saving lives.
Authors - Kruthiga S, Sindhu Chandra Sekharan, H.Summia Parveen, C. Kavitha, S. Umamaheswari Abstract - The transformative potential of deep learning techniques to revolutionize the landscape of medical image analysis, enabling accurate and efficient multi-disease prediction across a spectrum of critical health conditions. This work provides a solution to the early detection challenge of disease through prediction for Tuberculosis, Pneumonia, Glaucoma, and Brain Tumors using deep learning methods. By leveraging the expressive power of convolutional neural networks and transfer learning strategies, we have developed a robust framework capable of learning intricate patterns and subtle features indicative of diseases such as brain tumor, glaucoma, pneumonia, and tuberculosis. Through meticulous data preprocessing, model selection, and rigorous training and validation procedures, our approach ensures the reliability and generalizability of disease predictions, offering clinicians a powerful tool for early diagnosis and personalized treatment planning. The integration of Python programming language facilitates seamless implementation and deployment of our framework, making it accessible to healthcare practitioners and researchers alike. Overall, our study represents a significant advancement in the field of medical image analysis, with the potential to improve patient outcomes and revolutionize healthcare delivery on a global scale.
Authors - Pranay Meshram, Prakash Prasad Abstract - In the rapidly evolving digital landscape, data security has become paramount, necessitating innovative encryption techniques that balance computational efficiency with robust protection. This research introduces the New Efficient Selective Encryption Algorithm (DSEA), a novel approach to selective text encryption that addresses critical challenges in current cryptographic methods. By leveraging intelligent message analysis and strategic encryption, by providing a robust approach to safeguard valuable information at the same time as minimizing resource usage, DSEA addresses the need for privacy in a progressive manner. The approach utilizes proximity to structural properties of the message, such as the ratio of alphabetic characters, presence of vowels, and semantic connections, to inform the selection of encryption techniques. DSEA thus allows encryption to be applied at a more granular scale, using its identification and prioritization of sensitive text segments, which results in a significantly lower computation overhead compared to traditional techniques for full-document encryption. Experimental results show that DSEA has a better performance comparing with the existing selective encryption schemes, especially in the encryption time percentage, encryption processing time, and encryption proportion.
Authors - Bendre M. R., Vikhe V.P., Vanve G.B. Abstract - Within the carrier and business industries, there would be an ongoing demand for employees who are promoted to higher positions in the service and corporate sectors. The human resource team faces significant pressure to maintain employee commitment and motivation. Incentives such as promotions, bonuses, and wages are applied to motivate employees to feel closer to their work. The employee promotions are primarily deliberate, expressing gratitude for the employee's dedication to enhancing business standards, ensuring team competency, preventing talent from seeking other opportunities, and upholding the excessive degree of overall performance, all through the assessment year, human resources gather a significant quantity of facts on all elements of worker engagement events and activities. The data collected is continuously expanding in terms of employee service, but it is of little value if it does not provide meaningful insights. As a result, machine learning plays a crucial role in human resource analytics by extracting valuable information from collaborative employee data. The issue lies in the conventional approach to promotion, which is both time- and resource-intensive due to the numerous steps required for segregating and promoting employees. This had a significant impact on the smooth transition of employees into their new positions. Because of this reason, it's miles greater sensible if human assets can predict which workers are more legal and appropriate for advancement or upgrade, earnings increase, and so on. This research aims to propose or expect worker promotion. Utilizing machine learning techniques to forecast which employee might be eligible for a promotion, contingent on the data gathered and their previous achievements. To determine the likelihood of advancement probabilities the classification algorithms together with decision trees (DT), logistic regression (LR), random forests (RF), and k-means clustering are considered broadly utilized within the field. The k-nearest neighbors (K- NN), random forest (RF), and decision tree (DT) classifiers are applied to make the expected forecast.
Authors - Nishant Survase, Chitti Saharsh, Sachin Dhadwe, Krishnadeep Thakare, Yash Ishwarkar, Nilesh Pinjarkar Abstract - Railway stations, key transportation nodes, frequently have complicated layouts that disorient travelers, leading to delays. Conventional signage alone is not enough for effective navigation, particularly with increasing urban populations. Augmented Reality (AR) becomes a solution, superimposing virtual, step-by-step directions onto actual views through smartphones or AR glasses. This paper explores AR's capability to improve navigation in stations by combining GPS, GLONASS, and adaptive machine-learning algorithms. Both marker-based and markerless AR approaches, combined with realtime locationing, also offer custom guidance. Analytics of learning further refine user engagement, with increased feedback mechanisms as well as operational effectiveness. As such, AR can efficiently handle congestion, enhance accessibility for the disabled, and optimize passenger flows. Keywords: Augmented Reality (AR), Railway
Authors - Prafulla Bafna, Punam Nikam Abstract - Mental illness can be the reasons of extreme behavioral, emotional, and physical health issues. Majorly there are 4 mental disorders which are based on disposition, uneasiness, identity and insanity. Most of the times symptoms pertaining to these mental diseases are common. But remedies on each mental disorder is different. Due to the commonly existing symptoms of each disease, identifying the exact type of mental disorder is difficult. To smoothen the process of identifying exact mental disorder we use machine learning algorithms. The algorithms are executed on 1020 patient records containing nine parameters which show mental status such as l consciousness level, general behavior, and so on. To predict the exact type of mental clutter/disorder , KNN and SVM are implemented using 80 :20 ratio of training-to-testing data. SVM proved to be more accurate that is low misclassification error and greater recall. The accuracy of prediction is steady for 300 to 1020 records.
Authors - Supriya, Ananya G Bhat, Chandana B A, Niharika P Abstract - This study seeks to enhance the accuracy of credit card fraud detection by utilizing advanced machine learning techniques, with a specific focus on the XG Boost algorithm. Various ML approaches, including Decision Trees, Logistic Regression, Naive Bayes, Random Forest, and XG Boost, are evaluated for their efficiency in detecting fraudulent transactions using patterns derived from historical data. Recent advancements highlight the integration of diverse authentication methods and randomized training datasets to mitigate vulnerabilities in fraud detection systems.
