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检索条件"机构=Computer Science Engineering with Specialization in Artificial Intelligence & Machine Learning"

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Factorised Neural Relational Inference for Particle Decay Reconstruction 5

Factorised Neural Relational Inference for Particle Decay Re...

引用

5th International Seminar on artificial intelligence, Networking and Information Technology, AINIT 2024

作者： Lu, Junjian Liu, Siwei Liang, Shangsong School of Computer Science and Engineering Sun Yat-sen University China Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning United Arab Emirates

ISBN: (纸本)9798350385557

Investigating the interactions among particles in high-energy physics is essential for various tasks, such as reconstructing particle decays. The neural relational inference encoder model, capable of capturing inter-entity dependencies and system dynamics, has been recently introduced for this purpose. However, it faces limitations in accurately capturing diverse relations between complex particles. To address this, we propose the factorised neural relational inference (fNRI) encoder model, which factorises the inferred interaction graph into a multigraph, with each layer encoding a distinct relation. Additionally, we present a simplified version of our model that offers improved efficiency without compromising performance. Moreover, we explore three different inference strategies to leverage the learned relation representations effectively. We demonstrate on a simulated particle physics decay dataset that fNRI improves the reconstruction of the decay tree structure. © 2024 IEEE.

关键词： Graph neural networks

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AI-Driven Diagnostics: Comparative Analysis of machine learning and Deep learning Approaches in Multiple Sclerosis Diagnostics 11

AI-Driven Diagnostics: Comparative Analysis of Machine Learn...

引用

11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Anitha, L. Narmadha, D. Naveen Sundar, G. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350379136

Multiple Sclerosis (MS) is a chronic autoimmune disorder that targets the central nervous system, resulting in considerable physical and cognitive disabilities. Timely and precise diagnosis is essential for the effective management and treatment of Multiple Sclerosis (MS). Predicting MS involves using various types of data to assess the risk and diagnose the disease. The data commonly used are clinical, imaging, speech and gene data. This work presents a review of machine learning and deep learning methods, encompassing a broad spectrum of algorithms such as Support Vector machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNNs), and artificial Neural Networks (ANNs). Furthermore, the survey gives a comparative analysis of the dataset used, algorithms employed and the performance metrics. The survey also compares the implemented results with DL using the gene data. It also identifies current challenges in MS prediction, such as heterogeneity of MS, integration of multimodal data, validation and clinical implementation. Our goal is to offer a valuable resource for researchers and practitioners. © 2024 IEEE.

关键词： Convolutional neural networks

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Develop a Smart Android Application to Provide Laundry Service 15

Develop a Smart Android Application to Provide Laundry Servi...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jeyapradesh, Samuel Jebaseeli, T. Jemima Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798331300579

In the era of the Internet and smart applications, companies and institutions providing various services face significant challenges. The digital landscape, characterized by limitless connectivity and accessibility, poses a new paradigm where services transcend traditional boundaries of time and space. To remain at the forefront in their respective domains, businesses must embrace the electronic realm. Notably, web programming languages like PHP and ASP, alongside mobile application development languages such as Android have emerged as pivotal tools in this technological evolution. This design focuses on the development of an Android application integrated with a web control panel tailored for the service sector, with a specific emphasis on public laundry facilities. The study encompasses the system's architecture technological frameworks employed, and subsequently delves into the target audience, market requirements, and additional services to be offered. Throughout this exploration, the design aims to harness the potential inherent in these programming languages, drawing parallels to human language for enhanced user engagement and accessibility. © Grenze Scientific Society, 2024.

关键词： Laundries

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Unveiling Consumer Sentiments: Leveraging the VADER Algorithm for Product Analysis in the Digital Marketplace 4

Unveiling Consumer Sentiments: Leveraging the VADER Algorith...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kumar Sudabathula, Vijay Sai Rama Swamy, G. Bharath Reddy, S. Gagan Deep Arora Vadher, Harshali Hemant Akhil, Eruva Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad India Malineni Lakshmaiah Womens Engineering College Department of Computer Science & Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350364729

Customer reviews, feedbacks and comments are crucial in digital marketing, shaping product perception and quality. This paper highlights the VADER algorithm's role in product analysis, using sentiment analysis, natural language processing (NLP), keywords, and design patterns to analyze customer reviews. By converting qualitative data into actionable metrics, organizations can measure satisfaction, identify pain points, and recognize trends. The study shows that using cloud computing and big data for product analysis enables rapid capture of extensive feedback, facilitating swift, informed decisions. Results indicate the algorithm's effectiveness in providing actionable insights, supporting innovation, enhancing reputation, and driving growth. Conclusively, this research underscores the significant impact of advanced analytics on understanding consumer sentiments and improving overall customer satisfaction, thereby driving business success. © 2024 IEEE.

关键词： Marketplaces

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Accurate Classification of Tessellated Retinal Disease with Deep learning Model 6

Accurate Classification of Tessellated Retinal Disease with ...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Anvesh, Kachi Reshmi, Bharati M Basaveshwar Engineering College Department of Computer Science and Engineeering Bagalkote India Vardhaman College of Engineering Hyderabad India Basaveshwar Engineering College Department of Artificial Intelligence and Machine Learning Bagalkote India

ISBN: (纸本)9798350306613

We provide a novel Tessellated Eye Classifier (TEC) model designed for accurate identification of retinal diseases, with a focus on Tessellation Disease (TSL). Our trials were carried out on the AWS cloud platform, utilizing cutting-edge technology such as optimized RAM and high-performance GPU computers. The TEC model is based on the VGG-16 architecture and has extra Convolutional layers, global average pooling, and a customized output layer designed for the categorization of normal circumstances and retinal disorders, such as TSL. To improve the performance of the model, training and validation were carried out using a variety of fundus images with consistent color channels and resolutions. After undergoing repeated training, the customized TEC model successfully classified retinal disorders with a notable test set accuracy of roughly 79%. This study provides insights into the complexity of the neural network by analyzing parameter distribution in both our proprietary TEC model and the VGG-16 base model in depth. These results highlight the importance of the TEC model for patient care and early diagnosis;more research is being done to optimize the model further to increase its accuracy and therapeutic usefulness. © 2024 IEEE.

关键词： AWS Cloud Platform Deep learning Model Model Optimization Optometry Retinal Disease Classification Tessellated Eye Classifier VGG-16 Architecture

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Smart Security Management using IoT and HC-05 Bluetooth Module 8

Smart Security Management using IoT and HC-05 Bluetooth Modu...

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Sam Michael, S. Ancy Jenifer, J. Benjamin, Ruth Moly Jacob, Cyril Shanthosh, S. Karunya Institute of Technology and Sciences Department of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350386578

Safety and security are essential for every individual worldwide, particularly in the context of home security. Modern advancements in science and technology have introduced various innovative solutions, including smart security locking mechanisms. This research study presents a novel smart lock system that leverages mobile phone applications integrated with built-in fingerprint authentication. Unlike other smart locks that rely on external biometric devices, this system utilizes the smartphone's internal fingerprint sensor, thereby reducing hardware costs and complexity. Additionally, the application supports multiple authentication methods, such as fingerprint and facial recognition, enhancing security and convenience. This solution eliminates the risk of key loss and ensures that only authenticated users can access the home. The lock mechanism operates automatically, allowing the door to open and close without human intervention, providing a seamless and secure user experience. © 2024 IEEE.

关键词： Keys (for locks)

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Understanding Red Wine Quality with Feature Importance in machine learning and Explainable AI 15

Understanding Red Wine Quality with Feature Importance in Ma...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： John, Ayain Damodar, Navya Mohith, N. Vijayan, Bharathy Dayananda Sagar University Dept. Artificial Intelligence and Machine Learning Bangalore India Dayananda Sagar University Dept. Computer Science Engineering Bangalore India

ISBN: (纸本)9798350370249

The research work investigates the factors that influence the quality of red wine and how they contribute to positive outcomes. It uses machine learning techniques like Support Vector machines (SVM) and K-nearest neighbors (KNN) to classify and assess red wine quality data. Traditionally, red wine quality has been evaluated subjectively, which presents challenges. machine learning algorithms offer an objective alternative. This study compares SVM and KNN algorithms in assessing red wine quality and aims to determine the crucial attributes that affect its quality. To evaluate the classification performance of different machine learning algorithms, metrics like specificity, sensitivity, selectivity, and F1 measure are used. Additionally, grid search algorithms are employed for parameter optimization. In the red wine datasets, the Support Vector machine (SVM) model obtains an accuracy rate of 89%, surpassing the K-Nearest Neighbors (KNN) model's accuracy rate of 87%, especially regarding predictive specificity. Furthermore, using grid search algorithms improves the overall performance of the classifiers. This study highlights the effectiveness of machine learning in assessing red wine quality, providing valuable information for producers to optimize wine production processes and ensure high-quality products for consumers. © 2024 IEEE.

关键词： Adversarial machine learning

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CampusSight—College Management Automation Using Full Stack and RPA 12th

CampusSight—College Management Automation Using Full Stack ...

引用

12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Manohar, Eashaan Duraimurugan, N. Vijay, K. Narayana, K.E. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Information Technology Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The college management diaspora encapsulates various stakeholders such as students, faculty members, administrative personnel, and other staff. Unfortunately, most of the management workflows are undertaken manually, leading to silos, long paper work and admin overhauls. The pivotal aim of this research project is to digitalize the college management by bringing the entire setup into an intelligent application, leveraging key technologies like Automation, data analytics, full stack development. This application ultimately streamlines cumbersome tasks and enhance working across the college environment, promoting transparency and facilitating seamless interfaces for the entire user base. Swapping the monotonous manual workings with state-of-art digital platform will elevate the overall student experience by bringing down the high latency issue with the manual undertakings. Prime features of the application features management in terms of attendance, absences, on-duty requests, result disbursement, course registration, and facilitation of placement processes. Also, the system will promote enhanced organizing of events related to campus, industrial visits, increasing the net student engagement within the college and opening new opportunities. The system prioritizes core functionalities such as maintaining clean design and nimble experience, aligning with the increasing trend of digital transformation, granting greater college management autonomy. The application not only brings down the complexities prevailing in the existing college administration but also helps the higher education and placement processes alike, by leveraging key concepts like Data Analysis, machine learning and artificial intelligence from the data and insights stored, thereby adopting agile college management practices and emphasizing rapid digital growth, contributing to the entire education community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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End-to-End Hyperspectral Image Classification Using Hybrid GCNN with SWIN Transformer 1

End-to-End Hyperspectral Image Classification Using Hybrid G...

引用

1st IEEE International Conference on Ambient intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Ghotekar, Rahul Shaw, Kailash Rout, Minakhi Kiit University Department of Computer Science and Engineering Odisha India Department of Artificial Intelligence and Machine Learning Maharashtra Pune Lavale India

ISBN: (纸本)9798350316469

Hyperspectral image (HSI) classification holds immense significance in remote sensing applications like land cover mapping and environmental monitoring. This study introduces Graph Convolutional Neural Networks (GCNN) with the SWIN Transformer model (G_SwTr) to enhance HSI classification accuracy. GCNNs are utilized to incorporate spatial information through graph representations, capturing spectral-spatial dependencies. Graph convolutions, effectively harnesses spectral and spatial attributes, further the integration of the SWIN Transformer, recognized for its proficiency in capturing long-range dependencies via shifted windows and hierarchical processing, elevates feature extraction and contextual understanding. The Proposed G_SwTr model is evaluated across three HSI datasets (Pavia-University, Indian-Pines, and Salinas) and is compared against traditional machine learning and Deep learning methods. The results achieved outperform state-of-art methods, with an outstanding Overall Accuracy (O.A) of 99.7, 99.9, and 99.5, an average accuracy (A.A) of 99.5, 99.6, and 98.2, and a kappa coefficient (K) of 99.5, 99.6, and 98.3 for all three datasets. © 2023 IEEE.

关键词： Deep learning Graph Convolution Neural Network Hybrid Model Hyperspectral Image Classification SWIN Transformer

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Review on Event Extraction for BioNLP with a Survey 2

Review on Event Extraction for BioNLP with a Survey

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Pattankar, Veena V Priyanga, P. Computer Science and Engineering Global Academy of Technology Bangalore India Artificial Intelligence and Machine Learning Global Academy of Technology Bangalore India

ISBN: (纸本)9781665475174

The scientific literature contains essential information connected to proteins, drugs, and symptoms. Researchers are extracting organized, brief, and understandable information from unstructured texts to keep up with the growing number of publications. Within the context of the biomedical area, this study investigates event extraction and natural language comprehension. We will build a flexible description of an event by first outlining several terminological methods. Second, we demonstrate event extraction, its challenges, and annotated corpora. Third, we investigate representative methodologies and provide an up-to-date analysis with performance discussion. We provide a taxonomy to assist academics in navigating the deluge of event extraction works. Fourth, we compare biomedical solutions to those in other domains to uncover fresh research opportunities and approaches. Finally, we discuss potential applications, explainability, and knowledge injection. © 2023 IEEE.

关键词： Semantics

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