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Controlling the Accuracy and Efficiency of Collision Detection in 2d Games using Hitboxes 1

Controlling the Accuracy and Efficiency of Collision Detecti...

引用

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

作者： Sajo, Paul Gokulapriya, R. Mithun, B.N. University Dept. of Computer Science Bengaluru India

ISBN: (纸本)9798350335774

Collision detection is a process in game development that involves checking if two or more objects have intersected or collided with each other. It is a fundamental aspect of game mechanics that cannot be overlooked. Games invloves assets/sprites, which tend to be drawn digitally with the help of a computer program. This paper discusses controlling and detecting collisions in games that make use of PNG images as game assets. The conventional way to detect collision in a game is to check if the object is within the bounding box of another object or asset. However, such a method lacks realism and doesn't work well with much complex shapes as the game might register a hit when another object collides with the transparent part of the object being checked for collision. In order to overcome these limitations, the proposed algorithm divides the entire image into smaller rectangles and stores its coordinates in an array. The array is then pruned by removing coordinates with no translucent or opaque elements. Collision is detected by checking if any of the points of the collision object is inside the image array. © 2023 IEEE.

关键词： Efficiency

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Neural Machine Translation for Hindi to English Using Sequence-to-Sequence Architecture with Attention Mechanism 1

Neural Machine Translation for Hindi to English Using Sequen...

引用

1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Rathi, Ishan Goel, Paurav Singh, Jaspreet Chandigarh University Dept. of Computer Science & Engg. Mohali India

ISBN: (纸本)9798350392111

In an increasingly linked world, effective cross-linguistic communication has become essential. Machine translation systems play an important role in improving communication among speakers of different languages. This research paper describes a thorough analysis into the creation and testing of a machine translation framework for translating Hindi words into English. The model is trained using a sequence-to-sequence (Seq2Seq) architecture and an attention mechanism on a dataset of concurrent Hindi-English phrase pairings. The training phase, which lasts 50 epochs, produces encouraging results, with the model reaching a training accuracy of 81.68% along with a validation accuracy of 75.71% on a rigorously selected dataset of different English-Hindi phrase pairings. The qualitative examination of the translated result reveals great accuracy and integrity to the original purpose. Comparative comparison using a benchmark translation model confirms the proposed model's ability to capture language intricacies and provide contextually suitable translations. This paper also identifies limits and suggests areas for further research, including the use of advanced approaches like domain adaptation along with transfer learning to improve translation quality. Overall, this study advances machine translation technologies and has implications for increasing cross-lingual communication in many linguistic circumstances. © 2024 IEEE.

关键词： Neural machine translation

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SQL Injection Detection using Machine Learning and Convolutional Neural Networks 5

SQL Injection Detection using Machine Learning and Convoluti...

引用

5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Misquitta, Jason Asha, S. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

SQL Injection Attacks are a concern to databasedriven online applications because they can compromise the security and integrity of information in databases. It gives hackers the potential to read, delete, modify or copy data. Exploiting the flaws in the validation of user inputs provided through login pages is the simplest technique to conduct this attack. Each login page a user accesses can help to reveal more about who they are. Feedbacks sent by the server while running a SQL command can provide details on weaknesses in the database server's validation procedure. This research paper analyses code that focuses on detecting SQL injections. Different supervised Machine Learning algorithms are used to evaluate the model. A CNN model is also created for real-time identification of whether an input is a potential attack or not. © 2023 IEEE.

关键词： Personal computing

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Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches 9

Text-Based Sentimental Analysis to Understand User Experienc...

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Gokulapriya, R. Sambandam, Rakoth Kandan Balamurugan, M. Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350397376

data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general *** primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. © 2023 IEEE.

关键词： Python

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Optimizing Human Activity Recognition for Precision in Yoga Practice Analysis 8

Optimizing Human Activity Recognition for Precision in Yoga ...

引用

8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Salian, Supriya Preethi Salian, K. Rodrigues, Maria Viola Ritesh Muhammed Elham, P.N. Mannan, Mohammed Abdul St. Joseph Engineering College Vamanjoor Dept. of Computer Science and Engineering Mangaluru India Dept. of Information Science and Engineering Udupi India

ISBN: (纸本)9798350350593

The proposed system on enhancing human activity recognition in yoga presents an innovative solution using advanced technologies to accurately and quickly analyze data of yoga poses. Through meticulous data collection and preprocessing, including noise removal, normalization, and handling missing data, the system ensures a clean and standardized dataset. Pose estimation algorithms contribute to accurately mapping the body's posture, extracting essential features like joint angles and body orientation. The features form the basis for a powerful machine learning model, trained on a labelled dataset and employing neural networks, enabling the system to recognize yoga poses with remarkable precision. The study embraces an iterative enhancement process, continuously refining the model based on feedback and performance metrics, ensuring ongoing improvements in accuracy. Ultimately, the proposed system represents a cutting-edge solution for advancing human activity recognition in yoga, providing practitioners with real- time feedback and guidance, fostering improved techniques, and enhancing the overall yoga experience. © 2024 IEEE.

关键词： Mapping

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Comprehensive Methodologies for Breast Cancer Classification: Leveraging XAI LIME, SHAP Bagging Boosting, and Diverse Single Classifiers 4

Comprehensive Methodologies for Breast Cancer Classification...

引用

4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Avlani, Dhruvi T. Abhijna, M.B. Sai Disha, G.S. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Information Science and Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Artificial Intelligence and & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350354218

Breast cancer is one of the most common causes for deaths owed to cancer. It is very crucial and necessary to detect cancer at early phases. There are various Machine Learning techniques and computational techniques available for the aim of diagnosis of breast cancer data. This paper offers a Machine Learning model to perform automated diagnosis for the classification of breast cancer. This method employs single classifier model and bagging boosting for feature selection. Also, four algorithms SVM, Decision Tree, KNN and Logistic Regression which gives the highest accuracy of 88%. The system was trailed on BCGENES dataset. The performance of the system is measured on the basis of accuracy and precision. The incorporation of XAI methods, such as SHAP values and LIME, provides a nuanced understanding of feature importance and individual predictions, elevating model interpretability. The amalgamation of machine learning classifiers, hyperparameter tuning, and XAI techniques contributes to advanced screening tools, fostering trust among clinicians and researchers. © 2024 IEEE.

关键词： Logistic regression

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Prompt-Based Domain Incremental Learning with Modular Classification Layer 27

Prompt-Based Domain Incremental Learning with Modular Classi...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Wang, Boyu Ma, Yue Qiu, Qinru Dept. of Electrical Engineering and Computer Science Syracuse University United States

ISBN: (纸本)9781643685489

Continual learning is referred to as machine learning model's ability to learn a sequence of tasks or data over time without forgetting previously learned knowledge. In particular, we focus on domain incremental learning where the model trained on one domain, e.g., real-life photos, has to be augmented to accommodate data from new domains, e.g., cartoon images. Typical incremental learning relies on rehearsal-based methods, which store trained samples in a buffer and replay them during training alongside new data. This results in significant memory overhead and raise concerns about data privacy. Recently, prompt-based methods address these challenges and outperform them by utilizing the pre-trained Vision Transformer (ViT) and replacing the replay buffer with a prompt pool. However, existing prompt-based models fail to capture domain-specific knowledge and perform poorly in domain incremental learning. In this paper, we propose "domain prompt incremental learning via dynamic neural network", which combines the advantages of architecture-based and prompt-based methods. Specifically, our framework maintains two types of prompt: a instance-level prompt that improves the model's generalization ability is shared across all input samples;and a domain prompt that encodes domain-specific knowledge is assigned for each task. Furthermore, a separated classification head is trained for each domain so that the model has a pre-trained ViT and an ensemble of classification layers, one for each domain. The experimental results shows that our approach outperforms state-of-the-art methods by 2.3% in average on two domain incremental learning (DIL) benchmarks. © 2024 The Authors.

关键词： Contrastive Learning

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ConfigMaster: An Interactive Solution for System Management 8

ConfigMaster: An Interactive Solution for System Management

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ganapathi, Ananya Sivakumar, Parikshith Divya, K.V. Elango, Aditya Gupta, Hardik Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331505462

The 'ConfigMaster: An interactive solution for system management' is a simple utility written using Bash scripting. It is designed for Unix-like systems. The tool lets users easily configure system settings and do administrative tasks without having to remember complex commands. The tool minimizes user's work by automating tasks. The tool can be used even by users with very little technical knowledge, as it has a simple menu-based interface to manage things like network settings, software packages, and wireless connections directly from the terminal. By automating common tasks, the tool makes system management more efficient and accessible. By automating common tasks, the tool makes system management more efficient and accessible. It's very useful to simplify system configuration on Unix-like systems using scripting languages like Bash. 'ConfigMaster' aims to offer a smooth user experience with the incorporation of core management principles from an operating system, such as disk scheduling, process synchronization, and memory management etc. Being flexible itself, this tool will bring further improvements to make effective responses to emerging user needs. The framework being flexible, it can be expanded to add new features to configure under different scenarios. By integrating essentials operating system operations, 'ConfigMaster' enables users to efficiently perform system configurations along with promoting ease of use. © 2024 IEEE.

关键词： Wi-Fi

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Navigating Breakthroughs in Pediatric Brain Stroke Detection: A Comprehensive Survey

Navigating Breakthroughs in Pediatric Brain Stroke Detection...

引用

2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Deepak, N.R. Paital, Santosh Kumar Verma, Saritha Choeyang, Tenzin Singh, Udit Kumar Bhavya Hkbk College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Brain strokes, characterized by sudden interruptions in cerebral blood flow, pose a significant health concern, especially in children, where detection is intrinsically challenging due to limitations in existing methodologies. Recent attempts leveraging Machine Learning with Deep Learning techniques have grappled with accuracy and efficiency shortcomings in addressing this critical issue. In response, our innovative model integrates the Extreme Learning Machine (ELM), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs) algorithms. Rigorously evaluated using a comprehensive dataset, our ELM + CNN + GAN model demonstrates unparalleled accuracy, precision, and recall rates, surpassing benchmarks set by conventional approaches like the Random Forest algorithm and the AlexNet + SVM hybrid model. Notably, our model excels in diagnostic speed, offering a promising avenue for swift and efficient early detection of pediatric brain strokes. The strategic incorporation of ELM, recognized for its efficiency in single layer neural networks, along with the analytical power of CNN for image analysis and GAN for synthetic data generation, establishes a robust and comprehensive model. By outperforming existing methods, our approach signifies a substantial leap forward in addressing this critical health concern, revolutionizing stroke detection with enhanced accuracy and speed. © 2024 IEEE.

关键词： Generative adversarial networks

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Understanding the Evolution of Abusive Behaviour in Online Social Networks 3

Understanding the Evolution of Abusive Behaviour in Online S...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Gashroo, Ovais Bashir Mehrotra, Monica Jamia Millia Islamia Dept. of Computer Science New Delhi India

ISBN: (纸本)9798350381931

This paper presents a comprehensive investigation into the intricate dynamics of abusive behaviour in online social networks, utilizing cutting-edge methodologies at the intersection of computer science, natural language processing (NLP), and social network analysis. Cross-platform and cultural variances are rigorously examined, addressing challenges posed by platform-specific features and linguistic nuances. Innovative approaches including transfer learning, adversarial training, and multimodal analysis showcase the adaptability of models to diverse online environments. The technical dept. of the paper is evident in the exploration of NLP advancements, leveraging state-of-the-art models such as BERT and GPT-3 for a nuanced understanding of abusive language. Machine learning models, including ensemble approaches and deep learning architectures like CNNs and RNNs, are harnessed for precise classification tasks. Social network analysis metrics uncover patterns of influence and community dynamics contributing to abusive behaviour. The research navigates ethical considerations with finesse, addressing issues of privacy, algorithmic fairness, and bias mitigation. The cross-cultural and cross-platform analyses are underpinned by carefully curated datasets that encapsulate the diverse nature of online interactions, ensuring the robustness and generalizability of the proposed methodologies. In conclusion, this paper not only contributes to the evolving landscape of abusive behaviour research but also establishes a benchmark for interdisciplinary studies. The technical acumen demonstrated in the methodologies, coupled with a commitment to ethical practices, positions this research at the forefront of endeavours to comprehend, predict, and mitigate abusive behaviour in the dynamic realm of online social networks. © 2024 IEEE.

关键词： Social networking (online)

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