Journal of Big Data Technology and Business Analytics

Machine Learning-Based Traffic Flow Prediction Model

2024-05-21T09:55:26+00:00

This project aims to create a web application using Flask that forecasts traffic flow on roads by utilizing cutting-edge deep learning algorithms. The program uses a Multi-Layer Perceptron (MLP) regression model to estimate real-time traffic volume based on various input data, such as the date, time, temperature, and weather, taking holidays into consideration. The study thoroughly investigates data preparation procedures, including categorical variable encoding, feature extraction, and sorting. Moreover, it entails the MLP regression model's thorough implementation, which includes training, hyperparameter adjustment, and assessment. The careful execution of the traffic prediction model integration into the Flask framework allows for smooth application setup and interaction. This project aims to provide stakeholders and developers with a powerful tool for comprehending and controlling road traffic, with possible uses in urban planning and transportation management.

PG Hostels Advertisement

2024-05-24T11:44:34+00:00

This abstract encapsulates a website's user journey and functionalities dedicated to the hostel and PG accommodation seekers, emphasizing ease of navigation and comprehensive features. Users are welcomed to an intuitive homepage offering many features, including facilities, hostels, and more. Within the hostels section, users can explore detailed information and select accommodations based on their preferences, aided by transparent descriptions of amenities and contact details. The website facilitates seamless communication between users and hostel managers, allowing for the submission of contact details and prompt responses. Users can also access ratings and reviews to inform their decisions while contributing their feedback to enrich the communal knowledge base. The booking process is streamlined, providing users with assurance and confirmation of their reservations, while insights into PG rules and regulations ensure clarity and transparency. The website aims to enhance the accommodation search experience through its user-centric design and comprehensive features, empowering users and fostering trust and reliability in their quest for accommodations.

Nurture Nest- Smart Cradle System

2024-07-05T09:17:15+00:00

In today's technologically grown surroundings, data is becoming digitized. We can bridge the gap between children and working parents. We're working on a strategy to benefit modern parents struggling to find enough time to meet their children's needs. This idea suggests an Internet of Things-based Nurture Nest—Smart Cradle System that includes voice activation, video surveillance, a temperature sensor, and a GSM Module to ensure complete safety. The system's central hub facilitates the seamless functioning of its constituent parts, enabling hands-free communication through voice prompts to set off alerts and requests. Real-time video surveillance allows remote monitoring by recording suspicious activities, and a temperature sensor detects physiological abnormalities that point to distress. When a GSM Module detects an urgent situation, it sets preset algorithms in motion that alert parents to live audio-visual transmission. Users may change security settings and get timely notifications from any location with the help of mobile applications or web interfaces thanks to the IoT design's remote access and administration capabilities. By integrating this cutting-edge technology, the system aims to provide women.

A Comprehensive Review on Digital Twin Applications in Healthcare System

2024-07-09T10:05:33+00:00

This paper provides an in-depth exploration of the utilization of Digital twin technology in the Healthcare sector by synthesizing insights from distinct research works. The primary objectives of this paper are to examine various facets of Digital twin applications in healthcare, including Cloud-based healthcare systems, Precision wellness monitoring, Chronic wound management, Health facility management, and Patient-specific digital twin development. The paper analyzes the methodologies and frameworks employed in the implementation of Digital Twins and, at the same time, identifies the challenges and opportunities allied with integrating Digital Twins into healthcare systems.
Through an analysis of diverse perspectives, we explore the capabilities of digital twin technology, which is revolutionizing healthcare delivery while improving patient outcomes. In addition, the paper discusses the hurdles, such as data security, interoperability, and technical complexity, as well as prospects for future research and implementation strategies. By furnishing valuable insights, this paper aims to guide stakeholders in the healthcare industry in leveraging Digital Twin technology for enhanced patient care and operational efficiency.

Performance Analysis of Cotton Leaf Disease Detection System

2024-07-11T09:47:49+00:00

In India, agriculture is the primary source of farmers' revenue. India's most widely grown and traded crop is cotton. It allows farmers to make good capital and will boost their revenue. Cotton's vulnerability to many diseases is a significant issue. Plant illnesses must be detected as soon as feasible to prevent productivity loss. A method for automatic disease detection will be needed for this. In this study, we suggest an automated process that uses deep learning techniques to identify prevalent illnesses that affect cotton leaves. One of Ethiopia's most significant crops in terms of economic importance is cotton; however, there are numerous limits to its use in some areas. Typically, these are limited to detecting the majority of leaf diseases. In this project, we used fungus databases for training. For prediction, we propose using the modified CNN to classify different types of fungus. The fungus dataset consists of 1951 images divided into four classes.