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ieee symposium on Computational Intelligence and Games, CIG

作者： Florian Rupp Kai Eckert Department of Computer Science University of Applied Sciences Mannheim Mannheim Germany

ISBN: (数字)9798350350678

ISBN: (纸本)9798350350685

Graph data structures offer a versatile and powerful means to model relationships and interconnections in various domains, promising substantial advantages in data representation, analysis, and visualization. In games, graph-based data structures are omnipresent and represent, for example, game economies, skill trees or complex, branching quest lines. With this paper, we propose G-PCGRL, a novel and controllable method for the procedural generation of graph data using reinforcement learning. Therefore, we frame this problem as manipulating a graph’s adjacency matrix to fulfill a given set of constraints. Our method adapts and extends the Procedural Content Generation via Reinforcement Learning (PCGRL) framework and introduces new representations to frame the problem of graph data generation as a Markov decision process. We compare the performance of our method with the original PCGRL, the run time with a random search and evolutionary algorithm, and evaluate GPCGRL on two graph data domains in games: game economies and skill trees. The results show that our method is capable of generating graph-based content quickly and reliably to support and inspire designers in the game creation process. In addition, trained models are controllable in terms of the type and number of nodes to be generated.

关键词： Analytical models Procedural generation Markov decision processes data visualization Reinforcement learning Evolutionary computation data structures

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Novel Framework for Sentiment analysis of Movie Reviews based on large Language Models and Deep Learning Methods

Novel Framework for Sentiment Analysis of Movie Reviews base...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), ieee International Conference on

作者： Suhag Pandya

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Background: Sentiment analysis has become a crucial research area, enabling businesses to gauge public opinion on products or movies without manually reviewing content. Methods: Using IMDb data that was heavily preprocessed to improve model training, this research investigates sentiment analysis of movie reviews using the ELECTRA model. A multimodal approach incorporating textual, audio, and visual data was explored to improve emotional context understanding. Results: ELECTRA outperformed other models, including BERT, SVM, and CNN, achieving an impressive 95.6% accuracy based on F1score, precision, recall, and accuracy metrics. While CNN also performed well with 90% accuracy, BERT and SVM achieved 84.01% and 86.18%, respectively, demonstrating comparatively lower effectiveness. Conclusion: The findings highlight that deep learning models, particularly ELECTRA, excel in extracting subjective sentiments from movie reviews. As large language models continue to evolve, sentiment analysis methodologies will likely advance further, offering even greater accuracy and contextual understanding in the future.

关键词： Support vector machines Deep learning Sentiment analysis Analytical models visualization Accuracy Reviews large language models Motion pictures data models

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Event-based Bipartite Synchronization of Nonlinear Dynamical Networks With Sampled data

Event-based Bipartite Synchronization of Nonlinear Dynamical...

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2022 ieee International symposium on Product Compliance Engineering - Asia, ISPCE-ASIA 2022

作者： Ayepah, Kwaku Sun, Mei Jia, Qiang School of Mathematical Sciences Jiangsu University Zhenjiang21213 China Institute of Applied System Analysis Jiangsu University Zhenjiang212013 China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

This research examines the bipartite synchronization of dynamical network with switching signed topologies, where the nodes are governed by certain nonlinear dynamics, and an event-triggered control strategy is applied by under periodic sampling communications. It is shown that when the topologies switch within a finite set of signed graph, all nodes are guaranteed to achieve bipartite synchronization if the time average of the algebraic connectivity of the corresponding unsigned graph over certain length of time is large enough, and the main theorem details the impacts of nodal dynamics, network structure on synchronization, and gives a criterion for selecting the involved control parameters. Finally, some numerical simulations are presented to show the validity of our theoretical results and the efficiency of the proposed controller. © 2022 ieee.

关键词： Synchronization

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Deep Learning based Brain Tumor Classification for MR Images using ResNet50 12

Deep Learning based Brain Tumor Classification for MR Images...

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12th International symposium on Digital Forensics and Security, ISDFS 2024

作者： Kokcam, Omer Mirac Boyaci, Aytug Colak, Muhammed Emre Firat University Faculty of Technology Department of Software Engineering Elaziǧ Turkey National Defence University Department of Computer Engineering Air Force Academy İstanbul Turkey Firat University Faculty of Engineering Department of Artificial Intelligence and Data Science Engineering Elaziǧ Turkey

ISBN: (纸本)9798350330366

Brain tumors are abnormal cell growths that occur in various parts of the brain, and the accurate classification of these tumors plays a critical role in determining treatment methods. Classification and diagnosis of brain tumors based on artificial intelligence and deep learning models have been made possible due to advances in medical image processing technologies. For instance, such technologies facilitate quick detection of tumors by health experts thereby improving early diagnosis rate and hence enhancing treatment outcomes. Importantly, however, one considers improvement or breakthroughs in artificial intelligence-based classification systems as a crucial step forward towards brain tumor diagnosis as well as its treatment strategy. In this study we use Google Research's ImageNet-21k pre-trained ResNet50 model for classifying brain tumor cases. This model is a deep learning-based image classification tool and is capable of learning from large data sets. The model is specifically trained for high-resolution image recognition and is designed to achieve high accuracy rates. The results showed a classification accuracy of 99.9 percent, which is an exceptional accuracy rate for this model. This accuracy rate indicates that the model is highly effective in detecting and classifying brain tumors. High accuracy rates allow for early diagnosis of patients' diseases and, as it is often said in medicine, 'Early Diagnosis Saves Lives'. This result is one of the many proofs of how effective artificial intelligence and deep learning are in medical image analysis. © 2024 ieee.

关键词： Medical imaging

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Automated Image Captioning Using Sparrow Search Algorithm With Improved Deep Learning Model

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ieee ACCESS 2023年 11卷 104633-104642页

作者： Arasi, Munya A. Alshahrani, Haya Mesfer Alruwais, Nuha Motwakel, Abdelwahed Ahmed, Noura Abdelaziz Mohamed, Abdullah King Khalid Univ Coll Sci & Arts Rijal Almaa Dept Comp Sci Abha 62529 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn POB 22459 Riyadh 11495 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm Hawtat Bani Tamim Dept Management Informat Syst Al Kharj 11942 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 11942 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11845 Egypt

Image captioning is a deep learning technique that intends to create and generate textual descriptions or captions for images. It integrates computer vision and natural language processing (NLP) to comprehend the visual content of an image and generate human-like descriptions. Deep learning (DL) based image captioning models can be trained on large-scale datasets, allowing them to generalize various types of images and generate captions that apply to a wide range of visual scenarios. By combining computer vision and natural language processing, DL-enabled image captioning models can understand both visual and textual information, which enables them to generate captions that not only describe the visual content but also incorporate contextual and semantic information. This study develops an Automated Image Captioning using Sparrow Search Algorithm with Improved Deep Learning (AIC-SSAIDL) technique. The major intention of the AIC-SSAIDL technique lies in the automated generation of textual captions for the input images. To accomplish this, the AIC-SSAIDL technique utilizes the MobileNetv2 model to generate feature descriptors of the input images and its hyperparameter tuning process takes place using SSA. For the image captioning process, the AIC-SSAIDL technique utilizes an attention mechanism with long short-term memory (AM-LSTM) network. Finally, the hyperparameter selection of the AM-LSTM model is performed by the fruit fly optimization (FFO) algorithm. A wide range of experiments has been conducted on benchmark data to depict the better performance of the AIC-SSAIDL method. The comprehensive result analysis highlighted the enhanced captioning results of the AIC-SSAIDL method with maximum CIDEr of 46.12, 61.89, and 137.45 on Flickr8k, Flickr30k, and MSCOCO datasets, respectively.

关键词： Convolutional neural networks visualization Feature extraction Convolution Deep learning Natural language processing Computational modeling Image capture Search methods Image captioning deep learning natural language processing sparrow search algorithm computer vision

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Automated Optimization of Electrode Placement in Transcutaneous Electrical Nerve Stimulation Using Numerical Simulation

Automated Optimization of Electrode Placement in Transcutane...

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ieee International symposium on Biomedical Imaging

作者： Takashi Ota Hideaki Kuzuoka Tomohiro Amemiya Kazuma Aoyama The University of Tokyo Japan Gunma University Japan

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Electrode placement is a crucial parameter in transcutaneous electrical nerve stimulation. However, it is challenging to identify the optimal electrode placement that effectively stimulates a specific tissue because of the complex structure of the human body, which comprises multiple tissues. In this study, we developed a system that automatically identifies the optimal electrode placement from a large number of potential electrode positions. The system uses the finite element method to calculate the current density distribution in the target tissue for all conditions and outputs the optimal placement that stimulates the target tissue most efficiently. To evaluate the effectiveness of this system, we conducted experiments to optimize the electrode placement for electrical stimulation of the lower limbs and head. We confirm that the optimal elec-trode placement determined by the proposed system is reasonable since it was similar to that in a previous study. Fur-thermore, the automation enables verification of several conditions that would be impractical to verify using conventional methods.

关键词： Electrodes Wrist visualization Limbs Force Electrical stimulation Numerical simulation Finite element analysis Optimization Tendons

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A Review on visualization of Educational data in Online Learning 19th

A Review on Visualization of Educational Data in Online Lear...

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19th International Conference on Web-Based Learning / 5th International symposium on Emerging Technologies for Education ICWL/SETE)

作者： Dewan, M. Ali Akber Pachon, Walter Moreno Lin, Fuhua Athabasca Univ Sch Comp & Informat Syst Edmonton AB Canada

ISBN: (纸本)9783030669058;9783030669065

Higher educational institutions capture huge amounts of educational data, especially in online learning. data mining techniques have shown promises to interpret these data using different patterns. However, understanding the mining patterns and extracting meaningful information from the data require reasonable skills and knowledge for the users. Information visualization, due to its potential to display large amount of data, may fill this gap. In this paper, we present a short review of such visualization systems that focus on extracting meaningful information from the educational data. visualizations have been used in different applications dealing with educational data, especially for monitoring student performance, understanding learning style, analyzing course and program status, and dropout prediction. In this paper, we reviewed the existing visualization systems, their design considerations, and their strengths and weaknesses to analyze educational data in the context of online learning. Research findings indicate that although some progress has been achieved in educational data mining and visualizations, designing and developing effective and easy to understand visualizations and having the functionalities of interactivity and time-series analysis are still challenging. This review provides insight into how to build a learner and instructor focused effective visualization system for an online learning environment.

关键词： Information visualization Educational data mining Pattern recognition Learning management system Visual analytics

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Learning From large-Scale Noisy Web data With Ubiquitous Reweighting for Image Classification

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ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2021年第5期43卷 1808-1814页

作者： Li, Jia Song, Yafei Zhu, Jianfeng Cheng, Lele Su, Ying Ye, Lin Yuan, Pengcheng Han, Shumin Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Natl Engn Lab Video Technol Beijing 100871 Peoples R China Comp Vis Technol Dept Baidu Beijing 100871 Peoples R China

Many important advances of deep learning techniques have originated from the efforts of addressing the image classification task on large-scale datasets. However, the construction of clean datasets is costly and time-consuming since the Internet is overwhelmed by noisy images with inadequate and inaccurate tags. In this paper, we propose a Ubiquitous Reweighting Network (URNet) that can learn an image classification model from noisy web data. By observing the web data, we find that there are five key challenges, i.e., imbalanced class sizes, high intra-classes diversity and inter-class similarity, imprecise instances, insufficient representative instances, and ambiguous class labels. With these challenges in mind, we assume every training instance has the potential to contribute positively by alleviating the data bias and noise via reweighting the influence of each instance according to different class sizes, large instance clusters, its confidence, small instance bags, and the labels. In this manner, the influence of bias and noise in the data can be gradually alleviated, leading to the steadily improving performance of URNet. Experimental results in the WebVision 2018 challenge with 16 million noisy training images from 5000 classes show that our approach outperforms state-of-the-art models and ranks first place in the image classification task.

关键词： Noise measurement Deep learning Task analysis Training Annotations Solid modeling visualization Image classification noisy web data CNNs ubiquitous reweighting deep learning

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5th ieee International Conference on Computer and Communication Engineering Technology, CCET 2022

5th IEEE International Conference on Computer and Communicat...

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5th ieee International Conference on Computer and Communication Engineering Technology, CCET 2022

ISBN: (纸本)9781665467353

The proceedings contain 58 papers. The topics discussed include: an improved lion swarm optimization algorithm based on tent-map and differential evolution;a self-attentive interest retrieval recommender;marine predators algorithm based on trigonometric function;research on hybrid intelligence wargame method;automatic current transformer verification technology for high-speed railway power based on edge-cloud collaborative computing;grasshopper optimization algorithm based on adaptive curve and reverse learning;research on a user context model based on data mining;large-scale distance matrix calculation method based on contraction hierarchies;epidemic data analysis based on data visualization technology;improving aerospace big data infrastructure and applications with distributed file system and massive parallel calculation;arbitrary-shaped text detection with gaussian probability distance distribution;similarity computation of heterogeneous ontology based on graph attention network;and a small target detection method based on feature enhancement and positioning optimization.

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Principal Component analysis (PCA) of Surface Contamination by TOF-SIMS

Principal Component Analysis (PCA) of Surface Contamination ...

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ieee International symposium on the Physical and Failure analysis of Integrated Circuits (IPFA)

作者： Sek, Kei Lin Lee, Pei Lin Pang, Khin Yin Hua, Younan Zhu, Lei Li, Xiaomin WinTech Nanotechnol Serv Pte Ltd Surface & Chem Anal Div 10 Sci Pk Rd 03-26Sci Pk 2 Singapore 117684 Singapore

ISBN: (纸本)9781665439886

Static time of flight secondary ions mass spectrometry (TOF-SIMS) is a qualitative analysis technique with a high detection limit capable of achieving parts per million (ppm) levels [1] and sub monolayer surface detection. Due to its high sensitivity, the data obtained may contains more than hundreds of peaks including information about sample composition, chemical compound, fragments ions, sample purity, and etc. [2]. Therefore, TOF-SIMS data interpretation is complicated and time consuming especially for unknown contamination. These challenges could be overcome by Principal Component analysis (PCA). PCA is one of a multivariate method used to identify mass peak spectral in various principal component loadings when processing large data sets such as TOF-SIMS data. This paper presents a study of surface contamination analysis using image based PCA to find out the parent ion of the contaminant.

关键词： TOF-SIMS Imaging PCA contamination

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