Authors - Aditya Bhabal, Aditi Bharimalla, Shruti Balankhe, Vaibhav Chavan, Vaibhav Narawade Abstract - The overnight growth of OFD service businesses, due to technological advancement and a change in consumer behavior, has made reviews furnished by customers imperative for improvement in service quality, demand forecasting, and customer satisfaction. The vast amount of unstructured data makes the conventional method too ineffective. The following review thus provides valuable insights from diverse studies that are being done to apply deep learning, reinforcement learning, and ensemble learning in analyzing customer reviews of food delivery platforms. It goes on to provide ways through sentiment analysis, demand forecasting, dynamic recommendations of orders, and personalized marketing that these studies have proven how machine learning can make a difference in operatively effectively producing efficiency. It also provides an overview of the challenges in terms of data imbalance, scalability, and sustainability concerns, thus showing perspectives for further research in developing OFD platforms' capabilities for optimized and personalized services that take into account environmental and social impacts.
Authors - Manav Bagthaliya, Madhav Desai, Priyanka Patel Abstract - This paper introduces a black and white image colorization model based on deep learning techniques and OpenCV by combining available open-source toolkits. The model uses the Lab color space, in which a single grayscale image (the luminance or L channel) that is processed by a convolutional neural network (CNN) predicts chromatic values (a and b channels). The colorized version of the image is, therefore, generated by merging it with the original L channel. This approach makes use of deep learning in order to enhance the quality and performance of the reconstructed images. Experimental results verify that the proposed method is both valid and flexible and, hence, can vividly restore color from monochrome photographs in such domains as historical photo restoration and artistic creation.
Authors - Kavisruthi K, Anet Reji, Adhrushta V, Sangeetha Gunasekar Abstract - The aim of the study is to identify and analyze the factors that contribute to positive emotions expressed by visitors about the Taj Mahal based on user-generated content, a top destination for tourists in India. The reviews were scrapped from Trip Advisor.com, from which 77 themes have been identified using BERTopic modeling, reflecting both positive and negative aspects of the visitor experience. These 77 themes were grouped into 9 broader themes like value for money, history, time of visit, architecture, location, facility, tours, memories, and crowd which influence the positive and negative emotions of the visitors to heritage sites. Logistic regression is used to study the impact of the variables on customer satisfaction. Except for the variables location and memories, all the other variables significantly affect whether visitors give a star rating or not. These insights are valuable to tourist destination managers in helping them better strategies for enhancing tourist satisfaction.
Authors - Pushpavati V. Kanaje, Pratik Raj Dahal, Shivam Anant Ghorpade, Aditya Jaikumar Sharma, Atharva Dinesh Phatak, Parth Ravindra Sawant Abstract - In this paper, we strive to explore the various technologies that can be implemented to streamline the process of getting medication, and to make this a patient-centric experience. Exploring all the various solutions and methods that we can implement. Furthermore, these advancements will be beneficial as it will strive to remove the discrepancies that persist in the conventional methods. The advent of all the new technologies open up numerous doors for the medical sector to improve substantially and to make advances beyond what the existing models can do. Incorporation of various techniques like telemedicine, digital health records, and mobile health apps have all been put into test in the past few years. But, with the surge of new methods and techniques these existing techniques can further be enhanced and be made to work a lot more efficiently both figuratively and literally.
Authors - Anil R. Surve, Vijay R. Ghorpade, Ganesh S. Sagade Abstract - Service-Oriented Architecture (SOA) consists of autonomous, standardized, and self-describing components known as services that communicate with each other and are provisioned as per the rules decided. This architecture is being widely used worldwide. SOA is verified for dynamic, automatic, and self-configuring distributed systems such as in automation systems. This paper explores SOA paradigm for active spaces in an IoT environment with devices realized by device profile for web service (DPWS) wherein the context information is acquired, processed, and submitted to a composition engine so as to provision relevant services which suit to the context. Identification of profiled users in the context is achieved using RFID tags accordingly composition plans are created for every user. A six-phased composition process is employed to complete this task. DPWSim simulator is deployed for illustration and testing the system. WS4D explorer is used to scan the available devices in the network. IoT platform is employed for providing context information notifications comprising of various sensors and IoT environments.
Authors - Nilay Vaidya, Kamini Solanki, Krishna Kant, Jay Panchal Abstract - With the seamless integration of state-of-the-art technology and customization of learning experiences, Education 5.0 signifies a transformative shift in the educational landscape. This paradigm leverages big data analytics, virtual reality, and artificial intelligence (AI) to personalize learning paths and facilitate the development of critical 21st-century skills. Unlike outdated one-size-fits-all methods, Education 5.0 emphasizes personalized learning, enhancing student engagement and productivity. This transformation calls for collaborative efforts between educators, students, and industry stakeholders, ensuring education is relevant and future-ready. This chapter highlights the potential of Education 5.0 to foster flexible, inclusive, and progressive learning environments. Blended learning models, which combine AI technology with traditional teaching methods, demonstrate how tailored learning paths can improve outcomes. AI-based solutions offer engaging learning experiences through gamification, virtual reality, and simulations while streamlining administrative tasks like attendance and grading. By providing data-driven insights, AI helps teachers identify growth areas and adjust strategies in real-time to improve student success. This approach enhances accessibility, offering diverse learning methods, and ensuring tools are available for students with impairments. Scalable and adaptable, blended learning encourages lifelong learning, collaborative skills development, and peer interaction through AI powered platforms.
Authors - Jay Madane, Aniket Jaiswal, Sejal Balkhande, Kirti Deshpande Abstract - Using machine learning (ML) and artificial intelligence (AI) methods, this article suggests creating a 360-degree feedback software for the Indian government. The system’s objective is to automatically analyze and classify news articles from local media sources according to department and sentiment. Press Information Bureau (PIB) officials will receive real-time notifications concerning unfavorable stories, and stakeholders will be able to efficiently visualize and filter news coverage thanks to an intuitive dashboard. With the use of this program, the Indian government would be able to make more informed decisions by increasing the effectiveness of media monitoring.
Authors - B Shilpa, Shaik Abdul Nabi, G Sudha Reddy, Puranam Revanth Kumar, Thayyaba Khatoon Mohammad Abstract - The Internet of Medical Things (IoMT) has made it possible for digital devices to collect, infer, and disseminate data related to health through the use of cloud computing. Securing data for use in health care has unique challenges. Various studies have been carried out with the aim of securing healthcare data. The best way to protect sensitive data is to encrypt them so that no one can decipher them. Conventional encryption methods are inapplicable to e-health data due to capacity, redundancy, and data size restrictions, particularly when patient data is transmitted across unsecured channels. Due to the inherent dangers of data loss and confidentiality breaches associated with data, patients may no longer be able to fully protect the privacy of their data contents. These security threats have been recognized by researchers, who have then proposed various methods of data encryption to fix the problem. As a result, the area of computer security is deeply concerned with finding solutions to the security and privacy issues associated with IoMT. This research presents an intrusion detection system (IDS) for IoMT that utilizes the machine learning techniques: Decision Tree (DT), Naive bayes (NB), and K-Nearest Neighbor (KNN). Feature scaling using the minimum-maximum (min-max) normalization method was performed on the CIC IoMT 2024 dataset to prevent information leakage into the test data. The effectiveness of the output was then evaluated, ensuring that the scaling process was correctly implemented as the initial step of this work approach. Five types of assaults are identified in this dataset: DDoS, DoS, RECON, MQTT, and Spoofing. Principal Component Analysis (PCA) was used to reduce dimensionality in the subsequent stage. The suggested methods have a high detection rate of accuracy 98.2%, specificity of 97.6%, recall of 98.0%, and f1- score of 97.8%, which offer a viable option for protecting IoMT devices from attacks.
Authors - Vinay Nawandar, Sakshi Rokade, Nitin B. Patil Abstract - This paper reviews recent progress in Smart Surveillance Systems, with an emphasis on their roles in crowd control, crime prevention, and behavior monitoring in educational and workplace environments. The adoption of technologies like Machine Learning and Artificial Intelligence, particularly deep learning models such as YOLO (You Only Look Once), has greatly improved the ability to detect and evaluate events in real time. The paper also explores the drawbacks of traditional surveillance systems, including human error and inefficiencies, and how modern AI-driven solutions are addressing these issues. In addition, it discusses key approaches in face detection, behavior analysis, and anomaly detection, along with a comparative evaluation of various algorithms in intelligent surveillance. It also highlights potential energy-saving strategies and future developments in AI-driven surveillance.
Authors - Pradnya Vishwas Chitrao, Pravin Kumar Bhoyar, Rajiv Divekar Abstract - Background Significant advancements in technology, economy, and society have ushered in the twenty-first century. Advances in IT will significantly expand opportunities in energy, commerce, production, transportation, education, and health. New learning opportunities have multiplied due to advancements in information and communication technology. Numerous educational institutions, including colleges, universities, and organizations, employ the notion of online learning to augment students' knowledge, skills, and capacities in an efficient and participatory way. Through its ability to help students grasp fundamental ideas in topics like physics, arithmetic, and reading, technology can broaden the scope of what they learn. Use of Technology by Businesses to Enhance Education in India Given that both parents work these days, there is a great demand for resources that will meet the needs of the student body as well as the parents. A lot of computer and internet technology has become available in the twenty-first century, and this technology is being employed in the creation of educational resources for students. Zucate is one such business that was started by Dr. Moitreyee Goswami and Ms. Roli Pandey. Objective The company Zucate, which aims to establish “a teacher-learner-parent ecosystem with learner at the center,” is the subject of this paper (https://www.f6s.com/zucate). The aim of this study is to investigate how two women entrepreneurs attempted to help students, particularly school-age children, learn independently beyond school hours by utilizing technology in an economical way. It also aims to study how they survived the pandemic and expanded the enterprise Research Methodology The principal research method employed by the researchers involved conducting in-person interviews with the enterprise's founders. In addition, they made reference to papers from reputed data bases and other secondary sources. Importance of the Research The research is significant because it sheds light on how technical knowledge can be utilized to develop pedagogical and learning materials that help kids and learners understand ideas and learn on their own without having to rely on pricey, time-consuming tuition or even tech-savvy, expensive learning resources provided by traditional businesses. It is also important because it helps us understand how women entrepreneurs strategize and use technology to expand and flourish their enterprises.
