Abstract-Liver diseases like cirrhosis and hepatitis are major causes of global morbidity and mortality, highlighting the need for early detection. Traditional diagnostic methods often identify liver damage at later stages, limiting preventive interventions. This study develops a machine learning model to predict liver damage earlier using clinical features and lab results. By analyzing a data-set with patient demographics and biochemical markers, we apply machine learning algorithms, including Random Forest, Decision Tree, and Logistic Regression, and evaluate their performance using metrics like accuracy, precision, recall, F1 score, and ROC-AUC. The Random Forest model outperformed others, showing high accuracy and robustness. Feature importance analysis revealed critical clinical factors, such as serum bilirubin and liver enzymes, in predicting liver damage. These results suggest that machine learning, especially Random Forest, could aid in the early detection of liver disease, improving patient outcomes. Future work will focus on using larger, more diverse data-sets and advanced models to improve predictive accuracy.

Abstract-This system is designed to solve a common problem: running out of gas without knowing when it’s about to happen. The system keeps track of how much gas is left in the container by continuously checking its weight. If the gas is running low, it can automatically place a new gas order using the Internet of Things (IoT) technology. A device called a load cell is used to measure the weight of the gas container, and this data is sent to an Arduino Uno (a small computer) to compare with a standard weight. If the gas is low, the system sends a message to the user via SMS, using a GSM modem. For safety, the system also has sensors to detect gas leaks (MQ-2 sensor) and monitor the surrounding temperature (LM35 sensor). If any unusual changes are detected by these sensors, such as a gas leak or a sudden change in temperature, a siren will sound to alert the user.

Abstract-In this research paper we have proposed a device that helps visually impaired people recognise currency denomination in order to detect the denomination of Indian currency. The members of this community have challenges particular to them when it comes to dealing with money, and as such there is an ever-growing need for quick and accurate identification tools appropriate for their scenario. We describe the process we have followed to develop the device, offering a blend of image processing and machine learning to allow currency identification in real time. Surveys of potential users revealed important preferences and needs for accessibility and ease of use, guiding the design of a new device system. According to test results, the device achieves high accuracy in denominations recognition and effective user satisfaction, demonstrating a potential device providing financially independent life for visually impaired users. These findings underscore the value of blending cutting-edge technology with user-centered design to create impactful solutions for underserved communities. The paper hence concludes with recommendations for the further enhancements and future research to expand the device’s features and accessibility.

Abstract-The field of healthcare is turning more towards tools to improve access, to services and make the experience better for patients and providers alike. A specific example is “Appointify,” a web platform for booking doctor appointments that was created using the MERN technology stack— MongoDB, Express.js, React and Node.js—with a goal of simplifying the appointment process and connecting patients, with healthcare professionals seamlessly. This document provides an outline of “Appointify ” a system created to tackle the issues encountered in appointment handling like extended waiting periods and disorganized scheduling well as the absence of efficient communication, between patients and healthcare providers.”Appointify” allows patients to search for doctors based on their expertise area request appointments access their history and update their profiles. It also equips doctors with functions to control their availability, schedule appointments. Engage with patients effectively. The platform includes functions such, as role based access control for security measures and encryption to safeguard data privacy It also features responsive design for user friendly interaction, on various devices

Abstract-In light of the increasing gun violence incidents worldwide, there is a pressing need for automated visual surveillance systems capable of detecting handguns. This paper presents a method for real-time handgun detection in video streams using the YOLO algorithm, comparing its performance in terms of false positives and false negatives against the Faster CNN algorithm. To enhance detection accuracy, we compiled a custom dataset featuring handguns from various angles and merged it with the Roboflow dataset. The YOLO model was trained on this combined dataset and validated using four different videos. The results indicate that YOLO effectively detects handguns across diverse scenes, demonstrating superior speed and comparable accuracy to Faster CNN, making it suitable for real-time applications.

Abstract-This study introduces the Car Surveillance System (Driver Negligence and Dissuader System), integrating advanced lane detection, drowsiness detection, pedestrian detection, and object detection technologies to boost road safety. Much like the luggage storage website, it presents a user-friendly interface and real-time alerts to avert accidents. Intelligent functionalities ensure efficacy and security, simplifying driving experiences and encouraging hassle-free travel. Tailored settings and transparent pricing cater to individual driver requirements, tackling prevalent challenges and nurturing safer roads for all users.

Abstract-Due to the large amount of information generated every day, automatic writing is an important part of knowledge management. The discipline has made great progress, especially with the emergence of abstraction, abstraction and hybrid content models. In the extraction method, the main idea is preserved by selecting the main sentence or phrase from the text, while in the abstraction method, all the information is repeated to create new sentences. As the name suggests, hybrid models include the features of both extraction and abstraction systems to get the best of both approaches. However, issues remain, particularly in how to address the authenticity, coherence, and length of the text. This article examines the current state of writing concepts and topics in practice and future research.

Abstract-The enhancement in web technologies has contributed to the evolution of web applications that are very dynamic and engaging. This research work focuses on the creation of an online image editing application that is based on cloud infrastructure and modern web layouts/development tools such as Next.js, TailwindCSS, and Cloudinary’s APIs, among other resources, to deliver advanced image editing features. The application incorporates Clerk to allow users to create login accounts and easily register, while data is managed using MongoDB to facilitate the security of users and edited pictures across several devices. Necessary and basic features such as object removal, editing backgrounds, recoloring pictures, restoring, and changing the size of images are handled within the cloud and therefore benefit the functionality of the application and users as well. In addition, a contact form utilizing EmailJS has been integrated to enable communication with users. This research work highlights the legitimacy of cloud-based solutions as well as their expanded geographic reach in catering to an advanced user experience within image editing applications, thus supporting the growth of cloud computing and web technology.

Abstract-Diabetes is one of the most frequent diseases worldwide where yet no remedy is discovered for it. Every year a great deal of money has to be spent for caring for patients with diabetes. Therefore, it is crucial that prediction should be very accurate and a very dependable method must be adopted for doing so. One of these methods is the use of artificial intelligence systems, and in particular, the use of Artificial Neural Networks, or ANN. So, in this paper, we used artificial neural networks in order to predict whether or not a person has diabetes. The criterion was to minimize the error function in neural network training with the help of a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 70%

Abstract-This paper examines the pivotal role of mechanical engineering in advancing transportation through innovations like electric vehicles, lightweight materials, and dual-fuel systems. It highlights their impact on sustainability, efficiency, and safety while addressing challenges such as costs, regulations, and public acceptance. Emerging technologies like Hyperloop and hydrogen propulsion are also explored, emphasizing their potential to redefine global mobility.