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Airline Delay Prediction: A Comparative Study using Machine Learning and Deep Learning Models

Airline Delay Prediction: A Comparative Study using Machine ...

引用

Advances in Technology and computing (ICATC), International Conference on

作者： G.W.I. Dilshani Muditha Tissera Department of Software Engineering Faculty of Computing and Technology University of Kelaniya Sri Lanka

ISBN: (数字)9798331518219

ISBN: (纸本)9798331518226

The goal of this research is to use past flight data to create predictive models that will forecast airline delays, thereby alleviating operational inefficiencies and displeasure among passengers in the aviation industry. A large dataset containing parameters such as flight timings, travel time, transit airport and so on was employed. Preprocessing involved feature engineering and dealing with missing values. Different machine learning methods were tested with hyperparameter tuning done to improve their performance. These included Random Forest, Logistic Regression, Gradient Boosted Trees, Support Vector Machines, and Multilayer Perceptron (MLP). Among these classifiers, it was observed that the MLP model had a prediction accuracy of 98.83%. The importance of hyperparameter tuning for better model performance has been highlighted by the study. Airlines can now make good predictions of when delays are likely to happen, enabling them to counter inefficiencies, hence improving scheduling as well as operational effectiveness.

关键词： Support vector machines Logistic regression Accuracy Atmospheric modeling Predictive models Data models Delays Random forests Tuning Airline industry

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Tidal-Tree-Mem: Toward Read-Intensive Key-Value Stores With Tidal Structure Based on LSM-Tree

Tidal-Tree-Mem: Toward Read-Intensive Key-Value Stores With ...

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作者： Ma, Chenlin Yang, Hao Wu, Shangyu Wang, Yi Mao, Rui Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China City University of Hong Kong Department of Computer Science Hong Kong Shenzhen University College of Computer Science and Software Engineering The Shenzhen Institute of Computing Sciences Shenzhen518060 China

The log-structured merge-tree (LSM-tree)-based key-value store has been widely adopted by many large-scale data storage applications for its excellent write performance. However, such write performance gains mainly come from scarifying read performance due to the leveled and log-structured intrinsic characteristics of the LSM-tree. Therefore, the critical challenge of the existing LSM-tree is how to improve the read efficiency by reducing read amplification. This article for the first time proposes Tidal-tree-Mem, a novel data structure where data flow inside the LSM-tree-like Tidal waves. First, a floating strategy is proposed to allow frequently accessed files at the bottom of the LSM-tree to move to higher positions, reducing read amplification. Second, a stretching strategy is proposed to vary the shape of the LSM-tree to adapt to workloads with different characteristics. To evaluate the performance of Tidal-tree-Mem, we conduct a series of experiments using standard benchmarks from YCSB. The experimental results show that Tidal-tree-Mem can effectively reduce read amplification and the overall latency by over 71.94% and 47.34%, respectively, compared with representative schemes. © 1982-2012 IEEE.

关键词： Benchmarking

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Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Deep Learning-Based E-Learning Solution for Identifying and ...

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2023 International Research Conference on Smart computing and Systems engineering, SCSE 2023

作者： Arunoda, D.P.H. Walpola, S.R. Piumira, S.M.I. Athukorala, A.D.M.P. Thilakarathna, Thusithanjana Chandrasiri, Sanjeevi Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341454

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students' interactions (attention, emotions) with the system to identify students' ability to use the learning tool, identifying gaps in students' knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students' knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy. © 2023 IEEE.

关键词： Students

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Schrödinger Mechanisms: Optimal Differential Privacy Mechanisms for Small Sensitivity

Schrödinger Mechanisms: Optimal Differential Privacy Mechan...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Alghamdi, Wael Asoodeh, Shahab Calmon, Flavio P. Felipe Gomez, Juan Kosut, Oliver Sankar, Lalitha Harvard University School of Engineering and Applied Sciences United States McMaster University Department of Computing and Software Canada Arizona State University School of Electrical Computer and Energy Engineering United States

ISBN: (纸本)9781665475549

We consider the problem of designing optimal differential privacy mechanisms with a favorable privacy-utility tradeoff in the limit of a large number n of compositions (i.e., sequential queries). Here, utility is measured by the average distance between the mechanism's input and output, evaluated by a cost function c. We show that if n is sufficiently large and the sensitivities of all queries are small, then the optimal additive noise mechanism has probability density function fully characterized by the ground-state eigenfunction of the Schrödinger operator with potential c. This leads to a family of optimal mechanisms, dubbed the Schrödinger mechanisms, depending on the choice of the cost function. Instantiating this result, we demonstrate that for c(x) = x2 the Gaussian mechanism is optimal, and for c(x) = |x|, the optimal mechanism is obtained by the Airy function, thereby leading to the Airy mechanism. © 2023 IEEE.

关键词： Cost functions

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Metaverse to Enhance Experimental Learning in Higher Education

Metaverse to Enhance Experimental Learning in Higher Educati...

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International Conference on Information Technology Research (ICITR)

作者： M.H.M. Wickramasinghe S. R. Liyanage Software Engineering Department Faculty of Computing and Technology University of Keniaya Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

The rapid growth of technology has resulted in innovative methods to improve education, and traditional learning has evolved. The purpose of this study is to explore how the education sector can utilise the ecosystem of the metaverse to improve 21st-century skills. It emphasises critical thinking, problem-solving, and collaborative information exchange in immersive and interactive learning spaces. The concept of the Metaverse was first introduced in American writer Neal Stephenson's science fiction novel Snow Crash in 1992. In Snow Crash, the characters build into symbols and interact in the Metaverse, a three-dimensional (3D) virtual world. [1] The metaverse refers to a virtual reality existing beyond reality. It is a compound word of “meta,” meaning transcendence and virtuality, and “universe,” meaning world and universe. The term “digitized earth” refers to a new world created by digital media like smartphones and the internet. The metaverse is a collection of developing virtual and expanded reality technologies that will provide a more vivid experience than the current internet. [2] The study aims to discover the possibility of using the metaverse to achieve experiential learning in education, to facilitate learning by fostering learners' cognitive, behavioural, and sociocultural engagement with the subject matter in the metaverse, and to explore the readiness of learners, teachers, and stakeholders to integrate this innovative system into the higher education. The literature review forms the basis for developing the methodology's conceptual framework and the hypotheses. This systematic investigation incorporates a mixed research approach, including quantitative and qualitative approaches to develop a model for education ecosystems to create a simulated education platform for a complex subject. A list of questions was given to selected students to find out whether students find out where metaverse has enhanced students' experimental learning and its effect on th

关键词： Training Electronic learning Three-dimensional displays Metaverse Snow Education Ecosystems Collaboration Games Computer crashes

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A Short Review of Truck and Drone Collaborative Delivery Problem 10

A Short Review of Truck and Drone Collaborative Delivery Pro...

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhu, Yena Liu, Min Jin, Dongyao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

ISBN: (纸本)9798350312461

With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. The advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. Therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented. © 2023 IEEE.

关键词： Reinforcement learning

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Optimal Multidimensional Differentially Private Mechanisms in the Large-Composition Regime

Optimal Multidimensional Differentially Private Mechanisms i...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

ISBN: (纸本)9781665475549

We construct vector differentially-private (DP) mechanisms that are asymptotically optimal in the limit of the number of compositions growing without bound. First, we derive via the central limit theorem a reduction from DP to a KL-divergence minimization problem. Second, we formulate the general theory of spherically-symmetric DP mechanisms in the large-composition regime. Specifically, we show that additive, continuous, spherically-symmetric DP mechanisms are optimal if one considers a spherically-symmetric cost (e.g., bounded noise variance) and an l2 sensitivity metric. We then formulate a finite-dimensional problem that produces noise distributions that can get arbitrarily close to optimal among monotone mechanisms. Finally, we demonstrate numerically that our proposed mechanism achieves better DP parameters than the vector Gaussian mechanism for the same variance constraint. © 2023 IEEE.

关键词： Information theory

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Enhancing Early Childhood Learning using Image Processing for Sinhala Medium Education 5

Enhancing Early Childhood Learning using Image Processing fo...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Saranga, R.G.V. Jayasinghe, A.H.N. Divyantha, U.A.S. De Silva, S.W.L.P. Swarnakantha, N.H.P. Ravi Supunya Kumari, P. K. Suriya Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children. © 2023 IEEE.

关键词： Image enhancement

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Analysis of Sentiments on Russia-Ukraine Conflict using Machine Learning Techniques 1

Analysis of Sentiments on Russia-Ukraine Conflict using Mach...

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1st IEEE International Conference on Contemporary computing and Communications, InC4 2023

作者： Negi, Sakshi Dawar, Ishaan Prakash, Rishabh Tiwari, Prashant Bhushan, Megha Kumar, Ajay DIT University School of Computing Uttarakhand Dehradun India DTU Department of Software Engineering Delhi India KIET Group of Institutions CSIT Department Gaziabad India

ISBN: (纸本)9798350335774

Nowadays, the internet has become an essential part of the current scenario. Due to the rapid expansion of the social network industry, people are using its platforms to share their opinions on current events. It is important to collect and evaluate consumer feedback on products, services, and other topics. It is of utmost importance to determine if an endorsement or criticism of war operations leads to the universal message as the crisis between Russia and Ukraine has worsened and more foreign powers are involved. The abundant writings on the critical connection between public opinion to international relations support the above-mentioned objective. This study focuses mainly on Twitter text data extracted by Twitter API during the Ukraine-Russia war timeline and is available on Kaggle. Sentimental analysis using various supervised machine learning techniques, including Random Forest, Xtreme Gradient Boosting, Support Vector Machine, and Logistic Regression. Also, the comparison of their performance based on various metrics, including the accuracy and precision of the data is presented. As a result, it investigates the opinions and reactions of the global world to Russia's invasion of Ukraine. © 2023 IEEE.

关键词： Sentiment analysis

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Network Coverage Improvement during Natural Disaster using Self-Organizing Maps

Network Coverage Improvement during Natural Disaster using S...

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2023 International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Hoang, Nam Devabhakthini, Prathyusha Shukla, Raj Mani Bhunia, Suman Miami University Department of Computer Science and Software Engineering OxfordOH United States Anglia Ruskin University Computing and Information Science Cambridge United Kingdom

ISBN: (纸本)9798350322972

This paper presents an emergency response management system to tackle the problem of the absence of network connectivity during the time of a natural disaster. Network connectivity is often enabled by the base stations on the ground. However, during the time of the disaster, the connectivity is disrupted due to the base station being damaged. During such scenarios, the Unmanned Aerial Vehicles (UAV) based stations could help in partially providing the network connectivity and help in the rescue operations. But, the UAVs need to be quickly deployed and placed at a suitable location based on the population coverage and base stations being impacted due to the disruptions. In this paper, we propose the Self Organizing Map (SOM) based optimal UAV deployment to enhance the network coverage, and increase the percentage of people having network access. In contrast to other Artificial Intelligence-based approaches, like Deep Neural Networks, our method does not require to be heavily trained using train and the test dataset. © 2023 IEEE.

关键词： Self organizing maps

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