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Predicting the Number of Fatalities in an Air Crash 8

Predicting the Number of Fatalities in an Air Crash

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Prajwala, T.R. Manoj, Nandana Kumar, Preethika Ajay Harshita, R. Dept. of Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9798350334012

Air travel is one of the safest forms of travel today. The mortality rate in air travel is much lesser than that in automobile accidents. Nevertheless, aviation accidents have always been a huge cause of concern for people. Crashes could occur due to multiple factors like injury severity, weather condition, amateur built, make, etc. Air crashes could result in fatalities or serious injuries. The outcome of this research is to predict the number of fatalities when factors like the ones mentioned above have been provided. Air travel has a wealth of historical data available, and by analyzing patterns in flight accidents, there is potential to enhance the safety of air travel further, when compared to other modes of transport. This is essential as this can help the rescue team to respond accordingly. Three models namely, Gradient-boosting-regression, Random-Forest-Regressor, XGBoost have been implemented. After reviewing the results of all three models it has been found that Random-Forest comparatively better results. © 2023 IEEE.

关键词： Correlation methods

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Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using Data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

引用

2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Minu, M.S. Rohit Suryaa, S. Jovan Titus, J. Sharan, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350393378

The growing prominence of wind energy as an eco-friendly power alternative amplifies the need for optimal turbine performance. Central to this is the ability to accurately predict and modulate the wind turbine pitch. Our research focuses on integrating adaptive control strategies with advanced power generation analytics to enhance pitch prediction precision. We have processed and analyzed vast amounts of wind data using state-of-the-art data analytics to discern patterns in wind velocity changes accurately. The continuous stream of real-time data is the backbone of our adaptive controls, ensuring perpetual pitch adjustments that optimize energy capture. In tandem with this, we present a thorough analysis linking momentary wind shifts to power production, ensuring consistent and efficient power output. The culmination of these methodologies offers a breakthrough solution in wind energy optimization. Preliminary results highlight the paramount importance of real-time data analytics in revolutionizing green energy advancements. © 2023 IEEE.

关键词： Wind power

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Image Caption Generation in Kannada using Deep Learning Frameworks 1

Image Caption Generation in Kannada using Deep Learning Fram...

引用

1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Chethas, K. Ankita, V. Apoorva, B.S. Sushma, H. Jayashree, R. Pes University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348057

The official language of the state of Karnataka and one of the most frequently spoken languages in India, Kannada is spoken by more than 57 million people worldwide. In this modern era, one of the most important tasks in the field of Natural Language Processing in the present times is the development of image captions. In order to provide a textual description of an image intended to highlight its key qualities, Image Caption generation has long been a study of significant interest to academics in the fields of Artificial Intelligence and Machine Learning. While a great deal of research is being conducted in English, resources in regional languages like Kannada, are severely lacking. Given that 72% of the Karnataka population are Kannada speakers, Image Caption generation in Kannada is the realization of human-computer interaction for native language speakers, enabling the development of regional language virtual assistants which is a breakthrough in this line of work. Through our research, we present a model that generates semantic, syntactic and structured captions in Kannada. © 2023 IEEE.

关键词： Recurrent neural networks

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Fuzzy Computing Testing and Study Hydroponic Dosing Systems for Rural Indian Subsistence Farmers Using AI and IoT 4

Fuzzy Computing Testing and Study Hydroponic Dosing Systems ...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Pai, H. Aditya Jyothi, N.T. Lal Jeyan, J.V. Muruga Chandrappa Jain, Prithviraj Arvindhan, M. Lips Research and Dlcard India Lips Research Foundation India Dept. of Computer Science and Engineering Bengaluru India Deportment of Computer Science and Engineering India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350387490

In this study, we look at how well smart hydroponics systems supported by the Internet of Things (IoT) work for small-scale farmers in India's rural areas. The framework undergoes training using Fuzzy Logic artificial intelligence. A fuzzy logic model was created as part of the research carried out in this study. The monitor is connected to the Internet of Things (IoT) collects the data., this framework accurately finds and modifies the output. These instruments are useful in observing the environment variables of the hydroponic setup as well as keeping the fertilizer solution in check. An initial test carried out shows that the real time data generated and collected for testing made up the model evaluation. These are the phases that are undertaken to ensure that the accurate determination of the values are noted for the purpose of changing the environment variables of the hydroponic setup. The study findings describe how the system successfully determines the correct values that are required for the microcontroller., thus confirming that efficacy of Fuzzy Computing and AI used in the hydroponics setup. Here., the emphasis is given to provide accuracy towards the Fuzzy Computing and AI for rural subsistence farmers., especially in the lower to middle income nations. The research also mentions about importance of integrating solar power-enabled system with nearby available Wi-Fi networks., in contrast with the cloud-based IoT platforms so as to optimize the use of Fuzzy Computing - AI in the hydroponics system within the particular environments. © 2024 IEEE.

关键词： Hydroponics

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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Clustering Data Log CITA Blockchain Performance Monitoring with Mean Shift Method

Clustering Data Log CITA Blockchain Performance Monitoring w...

引用

2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Ubaya, Huda Stiawan, Deris Suprapto, Bhakti Yudho Perdana, Muhammad Iqbal Universitas Sriwijaya Faculty of Computer Science Dept. of Computer Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Engineering Dept. of Electrical Engineering Palembang Indonesia

ISBN: (纸本)9798350307306

CITA is a high-performance enterprise blockchain system with six microservices (RPC, Auth, Consensus, Chain, Executor, and Network) that exchange information through Message Bus. There is a dataset in the form of performance logs from CITA in the format of CPU, Memory, Network, PReads, PWrite, and VMem taken in real-time using the Performance Monitoring Framework for Blockchain. This framework has the advantage of lower overhead, more details, and better scalability than other performance monitoring approaches, but there is no clustering feature in the monitoring results. Thus, it is necessary to include a clustering feature so that users know the performance directly without the need to analyze it more deeply. This research uses the Mean Shift algorithm, which does not require the number of clusters for the initial process but only determines the maximum distance of centroid displacement through calculation. The number of clusters estimated is 2 clusters, where CITA produces 7 cluster samples with cluster evaluation results with the highest average silhouette score from CITA, which is 0.555 in the Network-Received Network-Sent feature pair. So, it is known that the Mean Shift clustering algorithm produces goodness of fit results from clustering on the CITA Blockchain performance log, which means that the clusters are well separated and can be clearly distinguished. © 2023 IEEE.

关键词： Blockchain

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Shanketik: Revolutionizing Communication for the Deaf and Mute - A Reinforcement Learning-Enhanced Bangla AAC Application 26

Shanketik: Revolutionizing Communication for the Deaf and Mu...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Monalisa, Natasha Tanzila Himi, Shinthi Tasnim Parvin, Kohinur Uddin, Mohammad Shorif Jahangirnagar University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

This research addresses the communication challenges faced by the deaf and mute community, leveraging advanced technology to enhance augmentative and alternative communication (AAC). In light of the limitations of existing methods, we propose a novel approach, 'Shanketik', that integrates Reinforcement Learning (RL) into an AAC mobile application in Bangla language. This system bridges the gap in accessible communication tools, particularly in languages like Bangla, where options are limited. The 'Shanketik' system functions as an agent, interacting with users through symbols in AAC. It incorporates a dynamic feedback loop, learning from positive and negative user responses. The system's core architecture involves the innovative use of the Deep Q-Network (DQN) algorithm, a fusion of Q-learning and deep neural networks. The 'Shanketik' application not only refines text and speech generation for accuracy and contextual relevance but also offers a user-friendly interface, ensuring navigation flexibility and enhanced efficiency in usability. The research presents a significant advancement in AAC technology, marking a transformative step toward inclusive communication for the deaf and mute community. © 2023 IEEE.

关键词： Augmented and Alternative Communication (AAC) Bangla Language Processing Human-computer Interaction Mobile Application Reinforcement Learning

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Learning to Communicate Using Action Probabilities for Multi-Agent Cooperation

Learning to Communicate Using Action Probabilities for Multi...

引用

2023 IEEE International Conference on Agents, ICA 2023

作者： Bai, Yidong Sugawara, Toshiharu Waseda University Dept. Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350312782

While communication is an essential tool for cooperation in multi-agent systems (MAS), existing approaches generally assume high-dimensional messages genereated by deep networks, which are uninterpretable for humans and require expensive transmission and complex encoding/decoding networks in agents. Uninterpretability may in turn raise reliability and security issues for the systems. To achieve low-dimensional and interpretable communication, we demonstrate that each agent's action probabilities can be used as messages, inspired by the fact that humans often share likely actions during collaboration. Our proposed method, communication based on action probabilities (CAP), is a simple yet effective communication architecture for coordination and collaboration in MAS, which facilitates our understanding of the agents' learned coordinated and cooperative behaviors. Moreover, because messages can be generated by the actor network, which is usually implemented in deep reinforcement learning agents, we can eliminate the requirement of extra message-generator networks. Our experiments show that CAP can achieve comparable performance to those of the state-of-the-art methods, with quicker convergence, simpler network structures and better interpretability. © 2023 IEEE.

关键词： Reinforcement learning

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Hybrid ANN-Based Model for Lung Cancer Disease Prediction 3

Hybrid ANN-Based Model for Lung Cancer Disease Prediction

引用

3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Paul, Banibrata Mir, Tawseef Ahmad Murugan, Stalin Alliance University Dept. of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350317022

Currently, lung cancer ranks as the world's leading cause of cancer-related fatalities in both men and women. The primary cause of lung cancer may be attributed to smoking. Although lung cancer can develop in any part of the lung, 90% to 95% of cases are believed to start in the epithelial cells that line the larger and smaller airways (bronchi and bronchioles). This study's main goal is to identify lung cancer by employing artificial neural networks with scaled conjugate gradient back propagation and k-fold cross validation. We have utilized the Lung Cancer dataset through kaggle. Data from 1000 lung cancer patients, ages 14 to 73, is used to train the network. How many neurons are in the hidden layer affects how accurate the results are. The proposed system uses 11 input attributes to assess the presence and absence of lung cancer during testing, yielding a minimum accuracy percentage of 83.1429% and a maximum accuracy percentage of 100%. © 2023 IEEE.

关键词： Artificial Neural Network Back propagation Lung Cancer Lung cancer dataset Scaled Conjugate Gradient Algorithm

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Bringing computer science Education to Schools in Qatar: Challenges of the implementation 5

Bringing Computer Science Education to Schools in Qatar: Cha...

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5th International Conference on computer and applications, ICCA 2023

作者： Alhazbi, Saleh Qatar University Dept. of Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350303254

Many countries have started national curriculum changes to make computer science (CS) as a compulsory subject for all students in schools. In 2018, a new national computing curriculum has been developed in Qatar for all k-12 grades. Comparing to the old curriculum, the new one focuses on teaching computer science instead of digital literacy. This paper discusses the challenges of implementing this new curriculum from CS teachers' perspectives in Qatar public schools. A survey was conducted of over 392 CS teachers who taught the new curriculum during the academic year 2018/2019. From the analysis of the collected data, it is found that teachers face different challenges including more working loads, lack of support to master the new contents, as well as the need of professional developments to improve their pedagogical skills in order to deliver the new CS concepts effectively. Moreover, the results shows that teachers in preparatory and secondary stages struggle with these challenges more than those in the elementary stage. Although all teachers are computing majors, the majority indicated that programming subjects in all grades are the topics where they need the most support. Accordingly, more support in term of improving teachers' content knowledge, and pedagogical content knowledge is essential in order to achieve the objectives of this reform. © 2023 IEEE.

关键词： Curricula

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