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2022 International Conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2022

作者： Ma, Wei Qi, Yongjun Tang, Hailin Guangzhou Institute of Science and Technology School of Computer Science & Engineering Guangzhou510540 China Guangdong Baiyun University Faculty of Megadate and Computing Guangzhou510450 China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

Wind power generation is based on the use of natural wind energy, driven by a lower wind speed, and converting the kinetic energy passed by the wind wheel into mechanical power to drive the generator to rotate. Therefore, it occupies a very important position in the power system. This paper intends to study the automatic detection technology of wind turbine blade cracks from the multi-scale feature fusion of intelligent technology, with the purpose of real-time monitoring of wind turbine failures and prevention of the stop of wind power generation. In this paper, experimental methods and case analysis methods are mainly used to study the automatic detection technology of fan blade cracks and multi-scale feature fusion. The experimental results show that the accuracy of the multi-feature extraction method in the transverse cracks of the fan blade reaches 95%, which basically meets the requirements. © 2022 IEEE.

关键词： Crack detection

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Federated Ensemble for Lung Pneumonia Detection

Federated Ensemble for Lung Pneumonia Detection

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International Conference on computing and Networking Technology (ICCNT)

作者： Vishwash Sharma VenkataHemant Kumar Reddy Challa Pasupuleti Pranavi Tripty Singh Rekha R Nair Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Enginnering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the realm of medical imaging, the detection of lung pneumonia through automated methods has garnered significant attention due to its potential to augment diagnostic processes. This study proposes a novel approach, termed Federated CNN Ensemble, aiming to enhance the accuracy and robustness of pneumonia detection from chest X -ray images through federated learning and ensemble techniques. The proposed method leverages convolutional neural networks trained locally at distributed medical institutions, thereby preserving data privacy and security while harnessing collectiveintelligence. By aggregating the learned models through an ensemble strategy, our approach achieves superior performance in pneumonia detection compared to standalone CNN models. This research contributes to the advancement of automated medical diagnosis systems, offering a promising avenue for scalable and collaborative disease detection in healthcare settings.

关键词： Pneumonia Accuracy Federated learning Computational modeling Lung Robustness Convolutional neural networks Security Medical diagnosis Medical diagnostic imaging

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Recursive Descent Parser for Abstract Syntax Tree Visualization of Mathematical Expressions

Recursive Descent Parser for Abstract Syntax Tree Visualizat...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Shiva Teja Pecheti Basavadeepthi H M Nithin Kodurupaka Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

This study presents a novel method for finding and evaluating mathematical expressions using Abstract Syntax Trees (AST). The approach is particularly adaptable for both academic and professional applications since it accurately parses complex equations and accommodates a large number of mathematical operations. The AST model ensures a hierarchical expression structure, enabling accurate and efficient evaluation. Parsed expressions are improved by incorporating interactive graph vi-sualisation, which simplifies mathematical computations and engineering tasks for understanding and application. The project's complete approach to detecting mathematical expressions stands out since it takes into account a wide range of functions and variables and ultimately enhances the system's accuracy and adaptability. The visual representation of AST also helps with debugging and checking the accuracy of parsed expressions, which adds to its significance in the field of mathematical expression recognition.

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Automation of Short Answer Grading Techniques: Comparative Study using Deep Learning Techniques

Automation of Short Answer Grading Techniques: Comparative S...

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International Conference on Electronics, Communication and computing Technologies (ICECCT)

作者： Arunima Divya Vivek Haridas Jayasree Narayanan Deportment of Computer Science and Engineering Amrita School of Computing Amritapuri India

Automatic short answer grading (ASAG) techniques have been shown to cut down on the time and work needed to grade exams, and it is a method that is becoming more and more common, especially with the rise in popularity of online courses. This study compares the results of 7 pre-trained embedding models using just one feature to automatically grade brief responses: the similarity between the model answer's and the student answer's embeddings. Regression models are developed and evaluated to predict a short answer's score based on the similarities between all pairs of answers in the Mohler dataset. The predictions are evaluated by comparing the Root Mean Squared Error (RMSE) and Pearson correlation scores of each model.

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Skin Cancer Diagnosis Using High-Performance Deep Learning Architectures 3rd

Skin Cancer Diagnosis Using High-Performance Deep Learning A...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Bindhu, A. Ahilan, A. Vallisree, S. Maria Jesi, P. Muthu Kumar, B. Marriwala, Nikhil Kumar Sabr, Aznul Qalid Md Department of Computer Science Infant Jesus College of Arts and Science for Women Tamil Nadu Kanyakumari District Mulagumoodu India Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Electronics Engineering MIT Campus Anna University Tamil Nadu Chennai India Department of Computer Science and Engineering Loyola Institute of Technology and Science Loyola Nagar KK District Thovalai India School of Computing and Information Technology REVA University Karnataka Kattigenahalli560064 India Department of Electronics and Communication Engineering University Institute of Engineering and Technology Kurukshetra University Kurukshetra Thanesar India Department of Artificial Intelligence FCSIT University in Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9789819986453

Skin cancer is one of the dangerous diseases which causes human death. It is originated by the abnormal growth of melanocytic cells. The malignant Skin cancer is also referred by another name as melanoma. The key source for the computer aided diagnosis is Dermoscopy image which acquires the skin cancer. Fortunately, a high percentage of skin cancers can be cured if they are found early. Automatic early detection approaches are being used as quick and practical skin cancer screening due to the expense and morbidity of this biopsy procedure. The existing methods of melanoma detection meet-up the demerits such as less accuracy, false segmentation and more time consumption. To overcome these issues, the proposed High performance Deep Learning Architecture method for classifying the skin cancer. It involves three types of techniques are used for comparison study are MobileNet, ResNeXt101, and Multi-Model Ensemble. The performance metrics used for the comparison are accuracy, precision, and recall. The comparison of the accuracy value for MobileNet, ResNeXt101 and Multi-model ensemble techniques are 97.2%, 84.2% and 86% respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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Collaborative Intelligent Resource Trading for RAN Slicing: A Federated Policy Distillation Approach

Collaborative Intelligent Resource Trading for RAN Slicing: ...

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2023 International Conference on Networks, Communications and Intelligent computing, NCIC 2023

作者： Ayepah-Mensah, Daniel Sun, Guolin School of Computer Science and Engineering UESTC Chengdu China National Key Laboratory for Intelligent Collaborative Computing Chengdu China Intelligent Terminal Key Laboratory of Sichuan Province Yibin China

ISBN: (纸本)9798350371949

In Radio Access Network (RAN), the sharing of resources can be modeled as a trading process in which multiple Mobile Virtual Network Operators (MVNOs) buy and sell resources according to their needs. This process can be competitive, with each MVNO strategically pricing and managing resources to maximize utility. Despite the dynamic nature of RAN slicing, deep-reinforcement learning (DRL) solutions often perform best but can be impractical due to their centralized nature and the need for full cooperation. These methods may face inconsistency due to MVNO heterogeneity, leading to imbalanced data distribution and potential selfishness, complicating optimal solution achievement. This paper proposes a collaborative intelligent framework for resource trading based on the Federated Deep Reinforcement Learning framework with Mutual Policy Distillation (FDRL-MPD), which enables MVNOs to collaborate and learn personalized trading models. Furthermore, we proposed a reward-shaping mechanism based on the proxy policy optimization (PPO) algorithm for local resource trading. Simulations performed with several MVNOs confirm the effectiveness of the proposed framework, especially concerning the algorithm's robustness to not independent and identically distributed data. © 2023 IEEE.

关键词： Reinforcement learning

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Extraction of Water and Riverine Sand using Deep Learning on Multispectral Remote Sensing Images 7

Extraction of Water and Riverine Sand using Deep Learning on...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Sharma, Megha Supriya, M. Kumar, Anil Dhyani, Kartik Chaturvedi, Parth Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Indian Institute of Remote Sensing Dehradun India Cambridge Institute of Technology Bengaluru India

ISBN: (纸本)9798350340600

The study of land-use-land cover (LULC) has become a necessity with the advancement of the urbanization process. Increased erosion of soil, increased silting, and sedimentation of the rivers are key effects that require study and analysis. Deep learning has a significant impact on classification tasks, particularly in the field of remote sensing image analysis. The proposed framework classifies LULC classes by employing the characteristics of deep learning. In this work, we compared the proposed method with the traditional machine learning methods in extracting water and riverine sand from multispectral remote sensing images. Further, we analyse the impact of Stochastic Gradient Descent (SGD) and Adam optimizers. The Adam optimizer implemented in this work gives higher accuracy than other combinations. © 2023 IEEE.

关键词： Remote sensing

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EvoX: A Distributed GPU-Accelerated Framework for Scalable Evolutionary Computation

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Huang, Beichen Cheng, Ran Li, Zhuozhao Jin, Yaochu Tan, Kay Chen Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Engineering Westlake University Hangzhou China Department of Computing Hong Kong Polytechnic University Hong Kong

Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established itself as a cornerstone of Artificial Intelligence. Recently, with the surge in data-intensive applications and large-scale complex systems, the demand for scalable EC solutions has grown significantly. However, most existing EC infrastructures fall short of catering to the heightened demands of large-scale problem solving. While the advent of some pioneering GPU-accelerated EC libraries is a step forward, they also grapple with some limitations, particularly in terms of flexibility and architectural robustness. In response, we introduce EvoX: a computing framework tailored for automated, distributed, and heterogeneous execution of EC algorithms. At the core of EvoX lies a unique programming model to streamline the development of parallelizable EC algorithms, complemented by a computation model specifically optimized for distributed GPU acceleration. Building upon this foundation, we have crafted an extensive library comprising a wide spectrum of 50+ EC algorithms for both single-and multi-objective optimization. Furthermore, the library offers comprehensive support for a diverse set of benchmark problems, ranging from dozens of numerical test functions to hundreds of reinforcement learning tasks. Through extensive experiments across a range of problem scenarios and hardware configurations, EvoX demonstrates robust system and model performances. EvoX is open-source and accessible at: https://***/EMI-Group/EvoX. IEEE

关键词： Evolutionary algorithms

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LyEmoBERT: Classification of lyrics' emotion and recommendation using a pre-trained model

LyEmoBERT: Classification of lyrics' emotion and recommendat...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Revathy, V.R. Pillai, Anitha S. Daneshfar, Fatemah School of Computing Sciences Hindustan Institute of Technology and Science Chennai India Department of Computer Engineering University of Kurdistan Sanandaj Iran

Music plays a significant role in evoking human emotions. Thanks to the quick proliferation of smartphones and mobile internet, music streaming applications and websites have made the music emotion recognition task even more active and exciting. However, music emotion recognition faces significant challenges too. These include inaccessibility of data, unavailability of large data volume, and the lack of other emotionally relevant features. While emotionally relevant features can be identified by analyzing lyrics and audio signals, the availability of datasets annotated with a lyrical emotion remains a constant challenge. This study uses the Music4All dataset to evaluate the lyrical features relevant for the identification of four important human emotions-happy, angry, relaxed, and sad. This was done with the help of several machine learning algorithms based on a semantic psychometric model. A transfer learning approach was also used to understand the feelings of the lyrics from an in-domain dataset and then predict the emotion of the target dataset. Further, it was observed that the BERT model improves the overall accuracy of the model (92%). A simple lyrics recommender system is also built using the Sentence Transformer model. © 2023 The Authors. Published by Elsevier B.V.

关键词： Music

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Navigating Cinematic Choices: A Comprehensive Examination of Movie Recommendation System 3

Navigating Cinematic Choices: A Comprehensive Examination of...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Jain, Palak Bhatt, Chandradeep Vishvkarma, Ashish Chauhan, Rahul Singh, Teekam Graphic Era Hill University Cse Department Dehradun India School of Computing Uttaranchal University Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350342338

Recommendation plays a vital role in today's competitive world;it helps an individual to discover best essential for what an individual is looking for by filtering personalized information and based on the attributes present in previously liked. Recommendation can be seen in many areas such as recommendation of products on Amazon, recommendation of movies on Netflix, recommendation of songs on you tube music and many others and it also help an individual to take better decision in variety of information provided on internet. Since the technology is getting advanced each day new techniques are being developed to provide user a great experience in this new era. Movie Recommendation helps any individual discover movie according to their own choice and preference and by reducing time consuming process of searching movies according to their interest and then watching them. This article aims to describe the movie recommendation system based on different kinds of recommendation systems and describing the pros and cons of every type available. © 2023 IEEE.

关键词： Hybrid Filtering Memory Based Approach Recommendation System

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