Enhancing the Usability, Visibility, and Responsiveness of an Airline Reservation System: A User-Centered Design Approach

This paper presents the idea, design, and prototype of a flight search and airline booking system based on the perspective of user-centered design. The system is first sketched roughly on paper in the form of a sketched plan and implemented through the proper system by connecting with the rapid API to develop a responsive web application. Booking travel tickets is a hassle and quite stressful because there is a chance that the webpages take time, and several decisions to make, hard to choose a discounted or less expensive flight, and the user will have to put in a lot of effort with many browser tabs may leave open. If a user is looking for the lowest travel options within a range of dates, they need to search a lot of websites looking for better options. As UX designers, it is our responsibility to do some user research and identify the problem areas, then we will recommend some design options based on the research findings. After that, we will create a wireframe and prototype before jumping into web design by collecting all the requirements and analyzing the problems. We will be focusing on UI controls such as location picker, date picker, color contrast, accessibility, and so on. In this paper, we present the design and development of a user-centered flight search and booking system for the airline industry. Our goal is to create a system that would meet the needs and preferences of a diverse set of users. This paper will summarize the design, development, and implementation of an airline reservation system. We have used bubble.io to design the overall system and MYSQL as the database management system for this webpage. Our objective is to upgrade the current website by improving the usability, visibility, and responsiveness of the functions that the user will experience while buying a flight ticket. We have generated and managed the design documentation and a perfect user-based online flight booking system

Business During COVID: An IOT Based Automated Sand Truck Management Solution

As a result of the development in computing technologies have begun to believe the human expectations on these needs in the different sort of components. The eSand Transport System with IOT (eSTSI) is a sand transport system designed to provide secure and accurate data such as gross weight with the sand and the truck, viewing the details of the owner when the RFID card is detected, sending alerts through the mobile application from the Firebase by interconnecting with IOT device, viewing the schedule of the selected truck with the data and destination, and displaying the location once the truck is passed the checkpoints. The main functionalities of eSTSI are to identify the truck with the correct information via the RFID card that retrieves the data who has enrolled with the app and stores the data in the firebase. The expected services are aimed to provide by this system

Deep Learning Model Regression Based Object Detection for Adaptive Driving Beam Headlights

As the world move toward automated driving (AD) continues, the future of adaptive driving beam headlights (ADB), is quickly coming into focus. Engineers, developers and designers are researching hard to identify the most effective combination of components to meet driver requitements for safety and visibility. ADB is a technology used in automotive headlight systems that automatically adjusts the beam pattern of the headlights to provide the best visibility for the driver while also reducing glare for oncoming drivers. The system uses cameras, sensors, and algorithms to detect the presence of other vehicles on the road and adjust the headlight beams accordingly. This allows the driver to have the highest level of visibility while minimizing the risk of dazzling other drivers. ADB is available on many vehicles including Europe, Asia & Middle East. Adaptive capabilities help reveal critical objects such as lane markings, pedestrians, and oncoming cars while avoiding using full high beams that might temporarily blind an oncoming vehicle driver. However, designing and developing a solution for the real road conditions is time-intensive, expensive, and complex. Hence, there is a requirement for adaptive driving beam headlights to detect the oncoming vehicles to reduce the glare for oncoming vehicle drivers. The detection solution needs to be fast, accurate and easy to integrate with automotive vehicular system. This paper reviews various detection techniques that can be used in implementing adaptive headlamps and application of the Machine Learning technique to predict fast and accurate object detection

Time Series Analysis for Tractor Sales using SARIMAX and Deep Learning Models

Time series forecasting is known for playing vital role in many industries to make important decisions and strategies. This study concentrates on providing accurate insights that can help manufactures and stakeholders of agriculture machinery industry on future sales of tractors by applying both traditional and deep learning models like SARIMAX which is extension of SARIMA and deep learning models. Research starts by observing history data which include years of tractor sales then preprocess the data to find its quality and stationarity further applying SARIMAX model to find trends and seasons and cycles in the data and this model is evaluated by famous metrics like Root Mean Squared Error (RMSE).Deep learning models like Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, CNN LSTM Encoder Decoder, Convolutional Neural networks (CNN). They can help in enhancing the forecasting accuracy by handling all the non-linear relationships and their dependencies in the timeseries and this study will provide comparative analysis of deep learning models and SARIMAX model. Where SARIMAX outperformed the deep learning models with RMSE score 0.01 and provide forecast of next year’s tractor sales using SARIMAX model from the study and use q-q plot, residual plots and ACF and PACF graphs to make sure forecast was done accurately

Machine learning-based predictive models for cardiovascular risk assessment in data analysis, model development, and clinical implications

Cardiovascular diseases (CVDs) remain a leading global cause of morbidity and mortality. Timely identification of individuals at risk is paramount for effective interventions and prevention. This study endeavors to develop machine learning approaches for predicting the initial cardiovascular risk level analyzing the dataset encompassing patient demographics, medical history, lifestyle factors, and clinical indicators. Patient characteristics, including age, gender, diabetes or hypertension presence, smoking status, and physical activity level, along with medical indicators such as blood pressure, cholesterol, and glucose levels, are considered. Diverse machine learning algorithms—logistic regression, decision tree classifier, random forests, linear SVC, naive bayes, and neural network—are employed to train and optimize predictive models. Evaluation metrics (accuracy, precision, recall, F1 score, and AUC-ROC) assess model performance. Accurate risk prediction models hold significance in aiding healthcare decisions, optimizing resource allocation, and enhancing patient outcomes. Identifying high-risk individuals early enables preventive strategies and personalized interventions, reducing the CVD burden. Study objectives encompass dataset preprocessing, exploratory analysis, feature selection and engineering, model training and optimization, and performance evaluation. Findings contribute to cardiovascular risk prediction, presenting a robust model for accurate risk assessment and improved patient outcomes

Building Customized Chatbots for Document Summarization and Question Answering using Large Language Models using a Framework with OpenAI, Lang chain, and Streamlit

This research presents a comprehensive framework for building customized chatbots empowered by large language models (LLMs) to summarize documents and answer user questions. Leveraging technologies such as OpenAI, LangChain, and Streamlit, the framework enables users to combat information overload by efficiently extracting insights from lengthy documents. This study discussed the framework's architecture, implementation, and practical applications, emphasizing its role in enhancing productivity and facilitating information retrieval. Through a step-by-step guide, this research has demonstrated how developers can utilize the framework to create end-to-end document summarization and question-answering application

Enhanced Early Detection of Thyroid Abnormalities using a Hybrid Deep Learning Model: A Sequential CNN and K-Means Clustering Approach

The thyroid gland, often referred to as the butterfly gland due to its shape, is located in the neck and plays a crucial role in regulating metabolism. It is susceptible to various health conditions, including hypothyroidism, hyperthyroidism, thyroid cancer, and thyroid nodules. Early detection of these conditions is essential for accurate diagnosis and effective treatment. Detecting thyroid nodules using machine learning and deep learning techniques presents a challenging yet promising research avenue. The choice of model depends on the characteristics of the patient's thyroid data, the dataset size, and the available computational resources. Hybrid models can be employed to handle complex data more effectively. In this study, a sequential Convolutional Neural Network (CNN) model was developed due to its capability to automate feature extraction and focus on Regions-of-Interest (ROIs) for detecting thyroid abnormalities. The proposed model achieved an accuracy of 81.5%, with a precision of 97.4% and a sensitivity of 83.1%, indicating its robustness in classifying images as benign or malignant. The confusion matrix provided further performance insights. Data segmentation was enhanced using K-means clustering for its scalability and efficiency in processing large medical image datasets. Compared to traditional models, the proposed hybrid approach demonstrated a significant improvement in diagnostic accuracy and precision, achieving performance gains of approximately 15-20% over baseline methods. These advancements underscore the potential of integrating machine learning and deep learning in medical diagnostics, paving the way for more reliable and efficient diagnostic tools for healthcare professionals

Enhancing the Usability, Visibility, and Responsiveness of an Airline Reservation System: A User-Centered Design Approach

This paper presents the idea, design, and prototype of a flight search and airline booking system based on the perspective of user-centered design. The system is first sketched roughly on paper in the form of a sketched plan and implemented through the proper system by connecting with the rapid API to develop a responsive web application. Booking travel tickets is a hassle and quite stressful because there is a chance that the webpages take time, and several decisions to make, hard to choose a discounted or less expensive flight, and the user will have to put in a lot of effort with many browser tabs may leave open. If a user is looking for the lowest travel options within a range of dates, they need to search a lot of websites looking for better options. As UX designers, it is our responsibility to do some user research and identify the problem areas, then we will recommend some design options based on the research findings. After that, we will create a wireframe and prototype before jumping into web design by collecting all the requirements and analyzing the problems. We will be focusing on UI controls such as location picker, date picker, color contrast, accessibility, and so on. In this paper, we present the design and development of a user-centered flight search and booking system for the airline industry. Our goal is to create a system that would meet the needs and preferences of a diverse set of users. This paper will summarize the design, development, and implementation of an airline reservation system. We have used bubble.io to design the overall system and MYSQL as the database management system for this webpage. Our objective is to upgrade the current website by improving the usability, visibility, and responsiveness of the functions that the user will experience while buying a flight ticket. We have generated and managed the design documentation and a perfect user-based online flight booking system

Unleashing the Power of AI in Healthcare: Transforming the Future of Medicine

The healthcare sector is undergoing a revolutionary transformation, and at the heart of this evolution lies Artificial Intelligence (AI). This groundbreaking technology is poised to revolutionize the way we approach healthcare, from diagnosis and treatment to drug discovery and patient care. In our upcoming blog, we will explore the incredible potential of AI in healthcare- Revolutionizing Heart Disease Prediction and Prevention with AI. We'll delve into how AI is enhancing medical diagnostics, enabling personalized treatment plans, and even contributing to drug development. We'll also shed light on the challenges and ethical considerations that come hand in hand with this transformative technology. Join us as we take a journey through the remarkable world of AI in healthcare and discover how it is already making a profound impact on the lives of patients and the future of medicine itself. Stay tuned for insights into AI-powered healthcare solutions that are set to shape a healthier, more connected, and more compassionate future for us all

Deep Learning Approaches for Accurate Sentiment Analysis of Online Consumer Feedback

Over the earlier time, a category of machine learning, called deep learning, has attained significant achievements in several computer vision tasks such as image classification, object detection, semantic segmentation, pattern recognition and image classification generation. Deep learning objectives at finding various levels of dispersed representations, which have been proven to be discriminatively effective in many tasks. Distributed statement depicts similar information highlights across different adaptable and reliant layers. Each layer characterizes the data with a similar degree of exactness, however adapted to the degree of scale. The implementation of deep learning techniques depends greatly on the variety of data interpretation (or features) on which they are used. Artificial intelligence plans to understand interpretations of information regularly by changing over it or isolating components as of it, which creates it simpler to play out an undertaking like order or extrapolation