This study focuses on developing a robust flight delay prediction model by analyzing historical flight data, incorporating factors such as departure and arrival times, weather conditions, aircraft type, and airline details. Machine learning approaches, specifically the Artificial Neural Network (ANN) Algorithm and Auto Regressive Integrated Moving Average (ARIMA), were employed to predict estimated flight delays. Two groups were sampled during the pretest power analysis with an 80% confidence level. The ANN algorithm showed an accuracy level of 86.90%, surpassing the ARIMA algorithm with an accuracy level of 85.94%. By conducting independent sample T-tests with a significance level of 0.05, the ANN algorithm was proven to be superior, with a statistically significant difference in accuracy. These results underscore the potential of the ANN model in enhancing the precision of flight delay predictions, contributing to the optimization of air travel planning and operations, and ultimately improving the overall efficiency of the aviation industry.

Flight Delay Prediction for Air Travel Management Using Artificial Neural Network Compared with Auto Regressive Integrated Moving Average Algorithm

This research aims to evaluate the performance of these two approaches in a curated dataset that reflects the challenges of detecting and classifying maritime debris. By comparing SVMs and ResNets, this work seeks to provide insights into the strengths and limitations of each method, offering guidance for researchers and practitioners working towards more efficient and accurate maritime surveillance systems. Materials and Methods: In this work, we applied both the SVM algorithm and the ResNet algorithm, each involving 10 iterations (N=10). These two algorithms were assessed across two distinct groups, and a total of 100 samples were considered in the analysis. The Gpower statistical test was employed, with a power setting of 85% (g power parameters configured with $\boldsymbol{\alpha}=\mathbf{0.05}$ and power=0.85). This choice of power setting was made to ensure that the study had a robust ability to detect statistically significant differences or effects, in line with established statistical standards. Results: The research outcomes reveal a notable accuracy of 93.21% for Support Vector Machines (SVMs) and 91.73% for Residual Networks (ResNets) in maritime object detection and classification. This underscores the effectiveness of SVMs, suggesting their practical applicability in real-world scenarios for tasks like environmental conservation and sustainable marine practices. Conclusion: In conclusion, our research lays the foundation for advancing maritime surveillance with SVMs and ResNets, acknowledging current limitations and emphasizing the need for ongoing improvements to foster sustainable marine practices and environmental conservation.

A Novel Approach on Automatic Maritime Object Detection and Classification Using Support Vector Machine in Comparison with Residual Networks

Today, Branch Predictors have become a sophisticated component in all modern processors. While contemporary predictors have good accuracy, the cost of a misprediction is quite high. Hence, active research is being conducted towards improving the performance of branch predictors, while reducing the resources consumed. Modern computer architectures increasingly rely on speculation to boost instruction-level parallelism (ILP). As the pipelines deepen and instructions issued per cycle increase, the accurate branch prediction mechanisms become more and more important in increasing the processor performance. This study explores some branch predictors and compares them on the basis of their performance on certain benchmarks applications.

Exploration of Performance of Dynamic Branch Predictors used in Mitigating Cost of Branching

In the contemporary-day global, the IoT is at its peak. The global is turning into smarter, domestic automation is emerging. Smart Door manage gadget is the state-of-the-art era in domestic automation. The foremost motive of clever Door era is to offer entire safety to the door, ease and luxury for users. The goal of this paper is to extend the door automation approach the use of Embedded and android phone. The PC shooting an photograph of someone as quickly because the bell is being pressed and sends the nevertheless photograph to the android tool via telegram app and signals the proprietor approximately the character status on the door. The whole structures programming has been hooked up in python and the Embedded C for a Controller.

Intelligent Door Assist system using Chatbot and Facial Recognition

Educational institutes across India have closed due to COVID-19 pandemic which has jeopardized academic schedules. To maintain their academic activities, several Indian educational institutes have shifted to online learning platforms. However, there are still questions about the effectiveness, design, and readiness of e-learning. In light of this fact, E-learning still tends to be controversial. As a result, it is inevitable to design an application with greater usability. In this paper, a novel application tool is developed and the design is proposed to enhance student knowledge and facilitate their study process, so they can study in comfort at home. The proposed system allows students to receive personalized educational assistance and also allows the students to get instant responses to all their questions throughout the day via a voice-enabled chatbot. It facilitates the connection between students and tutors, as well as the awarding of mind coins and badges based on how well they do, motivating them to learn more. Data analytics is incorporated and usability is measured. The result shows that the proposed system has greater usability resulting in a progressive improvement in the student’s performance.

AI Enabled Educational Bot to Improve Learning Outcomes using Bag of Words Algorithm

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Exploration of Performance of Dynamic Branch Predictors used in Mitigating Cost of Branching

Intelligent Door Assist system using Chatbot and Facial Recognition

AI Enabled Educational Bot to Improve Learning Outcomes using Bag of Words Algorithm