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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Ambika, R. Lahane, Prashant Kumar, G. Rabbi, Fazle Maarif, Muhammad Anas Qureshi, Mohmad Kashif Bms Institute of Technology and Management Department of Ece India Mit World Peace University Faculty of Engineering India Srm Institute of Science and Technology Department of Management India Australian Computer Society Australia Institut Pesantren Kh. Abdul Chalim Pacet Mojokerto Indonesia Sanskriti University Computer Science & Engineering Professor Cum Data Scientist India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Intelligent control system is a control technique that utilises different computing approaches such as fuzzy logic, Bayesian probability, neural networks and machine learning. Information processing is change of information within a manner detectable through an observer. It describes everything that happens in universe from falling of rocks to printing text files from a digital computer system. This paper aimed to analyse the impact of intelligent control systems (ICS) to assist humans with complex information systems. In order to achieve this aim, the paper has conducted a literature review to understand other authors' perspectives. It has discussed concept of ICS and its role in assisting information systems. Additionally, the paper has analysed information processing and other information about it. In order to collect relevant data on selected research topic, paper has conducted a secondary data collection method. It has selected data from secondary resources and analysed data with help of thematic analysis. This data analysis method helped to understand data patterns on selected research topic. Based on data analysis, results found that key methods in intelligence control are fuzzy control, neural network control, and expert control and these controls offer useful and relevant programming regarding complex information systems. Additionally, intelligent control can adapt, learn and plan under a huge uncertainty as well as cope with a huge amount of information. Finally, based on robust discussion, paper has formulated an appropriate conclusion for this research. © 2022 IEEE.

关键词： Fuzzy control

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Hybrid CNN-RNN Models for Multimodal Analysis of Autism Spectrum Disorder Neuroimaging

Hybrid CNN-RNN Models for Multimodal Analysis of Autism Spec...

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Automation and Computation (AUTOCOM), International Conference on

作者： Rajat Kishor Varshney Alok Katiyar Prashant Johri School of Computing Science & Engineering Research Scholar Galgotias University Greater Noida India School of Computing Science & Engineering Professor Galgotias University Greater Noida India School of Computer Application & Technology Professor Galgotias University Greater Noida India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

ASD is a developmental disorder in the neurological category and has numerous and diverse symptoms. Diagnosis is still problematic because symptoms of parkinsonism are diverse, and the dimensionality of the neuroimaging data is high. This paper presents a combination of CNN and RNN for classifying the ASD by analyzing multimodal neuroimaging data such as fMRI data, MRI data, and DTI data. The CNN component analyzes spatial characteristics of the neuroimaging data, and the RNN learns temporal patterns in fMRI series. Enhancements include a novel preprocessing step called 3D Brain Region Alignment (3D-BRA) as well as the 3D version of Adaptive Temporal, minimizing dimensionality through an innovative feature selection technique employing EN-CC. Predictive analysis with the hybrid model utilizes standard benchmark datasets such as ABIDE I, ABIDE II, HCP, and SFARI and records classification performances over 95 percentage. To enhance the interpretability of the model, the Grad-CAM algorithm is used, which marks specific regions of the brain important in the context of ASD. The proposed framework seems to possess considerable feasibility for enhancing the diagnosis of ASD and improving the understanding of its neural substrates, making the developed method highly advantageous for applications in neuroinformatics and clinical utility.

关键词： Neuroimaging Adaptation models Analytical models Three-dimensional displays Accuracy Functional magnetic resonance imaging Predictive models Brain modeling Feature extraction Data models

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Human Gait Recognition with Tensor Train Subspace Analysis

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SSRN 2024年

作者： Zdunek, Rafal Switonski, Adam Wroclaw University of Science and Technology Faculty of Electronics Janiszewskiego 11/17 Wroclaw50-372 Poland Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Akademicka 16 Gliwice44-100 Poland

The paper proposes a new computational approach to classification of motion capture data, which is based on a low-rank approximation of multivariate time series containing information on angles of skeletal body segments. In the first stage, the continuous wavelet transform is applied to generate a set of 2D representations. Then, the tensor train decomposition model in conjunction with tensor subspace analysis is used for robust feature extraction. Validation experiments are conducted for highly precise motion capture data and a challenging human gait identification problem. A dataset containing recordings of 30 male individuals with 436 gait samples and 72-dimensional pose space is used. Different variants of preprocessing for motion capture data are applied, resulting in various time series lengths and pose parameters taken into consideration. The comparative analysis with other low-rank approximation methods, such as non-negative matrix factorization, higher-order singular value decompositions, and baseline tensor train, as well as the selected state-of-the-art approaches of mocap data classification, is carried out. The proposed approach outperforms all the tested techniques and achieves a high classification rate, that is, exceeding 96%. © 2024, The Authors. All rights reserved.

关键词： Time series

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ELSA: Euler-Lagrange Skeletal Animations - Novel and Fast Motion Model Applicable to VR/AR Devices 21st

ELSA: Euler-Lagrange Skeletal Animations - Novel and Fast Mo...

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21st International Conference on Computational science, ICCS 2021

作者： Wereszczyński, Kamil Michalczuk, Agnieszka Foszner, Pawel Golba, Dominik Cogiel, Michal Staniszewski, Michal Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland KP Labs Gliwice Poland

ISBN: (纸本)9783030779764

Euler Lagrange Skeletal Animation (ELSA) is the novel and fast model for skeletal animation, based on the Euler Lagrange equations of motion and configuration and phase space notion. Single joint’s animation is an integral curve in the vector field generated by those PDEs. Considering the point in the phase space belonging to the animation at current time, by adding the vector pinned to this point and multiplied by the elapsed time, one can designate the new point in the phase space. It defines the state, especially the position (or rotation) of the joint after this time elapses. Starting at time 0 and repeating this procedure N times, there is obtained the approximation, and if the N→ ∞ the integral curve itself. Applying above, to all joint in the skeletal model constitutes ELSA. © 2021, Springer Nature Switzerland AG.

关键词： Animation

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First Competition on Presentation Attack Detection on ID Card

First Competition on Presentation Attack Detection on ID Car...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Juan E. Tapia Naser Damer Christoph Busch Juan M. Espin Javier Barrachina Alvaro S. Rocamora Krištof Ocvirk Leon Alessio Borut Batagelj Sushrut Patwardhan Raghavendra Ramachandra Raghavendra Mudgalgundurao Kiran Raja Daniel Schulz Carlos Aravena Hochschule Darmstadt (h_da) da/sec-Biometrics and Internet Security Research Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Facephi Company Spain Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Norwegian University of Science and Technology (NTNU) Gjøvik Norway ID VisionCenter (IDVC) Santiago Chile

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

This paper summarises the Competition on Presentation Attack Detection on ID Cards (PAD-IDCard) held at the 2024 International Joint Conference on Biometrics (IJCB 2024). The competition attracted a total of ten registered teams, both from academia and industry. In the end, the participating teams submitted five valid submissions, with eight models to be evaluated by the organisers. The competition presented an independent assessment of current state-of-the-art algorithms. Today, no independent evaluation on cross-dataset is available; therefore, this work determined the state-of-the-art on ID cards. To reach this goal, a sequestered test set and baseline algorithms were used to evaluate and compare all the proposals. The sequestered test dataset contains ID cards from four different countries. In summary, a team that chose to be "Anonymous" reached the best average ranking results of 74.80%, followed very closely by the "IDVC" team with 77.65%.

关键词： Biometrics Training Metalearning Industries Accuracy Biological system modeling Benchmark testing Proposals Few shot learning Stress

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In pursuit of Japanese-style Research and Education of computer Software: Experiences with Software-Centered Research Projects

In pursuit of Japanese-style Research and Education of Compu...

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International Conference on computer Theory and Applications (ICCTA)

作者： Kazunori Ueda Computer Science and Engineering Waseda University Visiting Professor Egypt-Japan University of Science and Technology

The conception of E-JUST, established with the Japanese partnership, was to provide problem-solving-oriented, lab-based approach in research and education. While this model is common and considered successful in Japanese universities, how to put it into practice in developing full-fledged system software in academia is not *** this talk, I will discuss the role of lab-based (or project-based) approach to the research and education of computer software based on his recent projects on programming language design and *** projects turned out to be extremely long-lived and evolutionary, and contributed to fostering meta-skills as well as domain-specific knowledge and skills. Designing and implementing novel software from scratch as a lab project can be viewed also as an embodiment of “mono-zukuri” (the art of making things), a traditional principle of Japanese manufacturing, applied to the making of complex software.

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Developing smart devices with automated Machine learning Approach: A review

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Materials Today: Proceedings 2022年 51卷 826-831页

作者： Sunil A. Patel Sanjay P. Patel Yagna Bhupendra Kumar Adhyaru Santosh Maheshwari Pankaj Kumar Mukesh Soni Assistant Professor Computer Science and Engineering Department Government Engineering College Patan Gujarat India Assistant Professor Computer Engineering Department Hansaba College of Engineering & Technology Gokul Global University Gujarat India Head Computer Engineering Department Hansaba College of Engineering & Technology Gokul Global University Gujarat India Department of Computer Science & Engineering Noida Institute of Engineering & Technology Greater Noida India Research Scholar Computer Science and Engineering Department University Bhopal 462044 India

In the current era, Artificial Intelligence is playing a major role in smart devices. Apart from incorporating intelligence in the devices, it has extended its range beyond that. Now new AI development tools such as automated machine learning (AutoML) approach are increasing rapidly because of its performance on smart devices. AutoML is used to design a machine learning model with self-adapting capability which can perform the task in less time with advanced skills. This approach does not need any explicit coding and the data science experts but this can build smart sensor codes that will get executed automatically on the microcontrollers embedded within the IoT devices. Due to these capabilities, the developers can build efficient smart devices with real-time responsiveness, security, and data privacy without the help of machine learning experts. In this paper, we will show how AutoML will be used to create smart de-vices. The objective of this paper is to develop the skills that will help to develop smart edge devices which include planning, design-ing, implementation, and the evaluation of the smart IoT applications.

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Automated Pharmaceutical Drug and Vitamin Classification Using the EfficientNetB3 Model in Deep Learning

Automated Pharmaceutical Drug and Vitamin Classification Usi...

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International Conference on Information science and Communications Technologies (ICISCT)

作者： Goldy Verma Rahul Chauhan Gotte Ranjith Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand School of Computer Science & Artificial Intelligence SR University Warangal

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

In this research, the fine-tuned EfficientNetB3 model is utilised for detecting the pharmaceutical drugs and vitamins. Emphasizing the need of correct medication identification in healthcare, this paper seeks to improve patient safety and avoid drug distribution mistakes. Collected via the open-source Kaggle platform, ten different products as Alaxon, Bactidol, Bioflu, Biogesic, and others have high- resolution images in the dataset, painstakingly annotated to guarantee correct classification. Refined using batch normalization, dropout, and dense layers, the EfficientNetB3 model attained an overall accuracy of 99%, proving its ability in differentiating between like-looking pharmaceutical goods. Classification reports, confusion matrices, and training- validation loss curves among other detailed analyses show the model's strong performance over all classes. With a consistent method for automated medicine and vitamin identification, the results of this study could have major consequences for pharmaceutical management. Expanding the dataset and improving model generalization will be main priorities of next development.

关键词： Drugs Training Information science Accuracy Image resolution Biological system modeling Medical services Safety Biomedical imaging Context modeling

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Sentiment Analysis on Drug Reviews Using Diverse Classification Techniques 3

Sentiment Analysis on Drug Reviews Using Diverse Classificat...

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3rd International Conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Preethaa, K.R.Sri Mouthami, K. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Research Professor Intelligent Construction Automation Center Kyungpook National University Deagu Korea Republic of

ISBN: (纸本)9781665474979

Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordinary people. Reviews are distributed about the medicines through online feeds, posts and blogs, which are merely helpful in identifying the side effects and their adverse reactions in combination with another drug or standalone adverse reactions. Spelling mistakes, jargon and short names are normalized throughout the medical terminologies using Natural language processing (NLP) and RxNorm from the reviews extracted from these sources. The concept of Bidirectional Encoder Representations from Transformers (BERT) is used to mine the association rules for the drug interactions when used in conjugation with another drug, and the idea of Adaptive Fuzzy logic neural networks is used to validate the results of association rules derived from the BERT. Inconsistencies identified by the regulations generated were rejected efficiently by the Support vector machines (SVM), Radial Basis Function (RBF)and the system shows 60.11 percentage of accuracy with 10 cross-fold validation, whereas the results are equivalent to the accuracy obtained using SVM with RBF kernel. The data set is reduced from its dimension using Linear discriminant analysis (LDA) by applying the mining process and BERT on two drugs, namely 'Azithromycin' in conjugation with Buprenorphine has Type 'A' side effects, and Prozac in conjugation with Metronidazole shows different side effects. Deplin and Zoloft give very positive symptoms and are independent of dosage interactions. © 2022 IEEE.

关键词： Bidirectional Encoder Representations from Transformers (BERT) Linear discriminant analysis (LDA) Radial Basis Function (RBF) Support vector machines (SVM)

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Attention based Abstractive Summarization of Malayalam Document

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Procedia computer science 2021年 189卷 250-257页

作者： Sindhya K Nambiar David Peter S Sumam Mary Idicula Research Scholar Department of Computer Science Cochin University of Science and Technology Professor Department of Computer Science Cochin University of Science and Technology

There are different textual content summarization processes available in natural Language Processing. Amongst them abstractive textual content summarization is one of the challenging problems in natural language processing and that too, with very little research done in regional languages. Unlike other summarization techniques, which reuses the words and phrases from the source text, abstractive text summarization builds a short and concise precis of a huge text document built from the underlying message of the text not necessarily using the same words and phrases from the source. The objective of the proposed work is to create a brief and understandable abstractive summary of a Malayalam document. Malayalam is one of the 22 scheduled languages of India spoken by over 34 million people and is designated as a Classical Language in India. Being a Classical language, Malayalam has a very unique syntactic and semantic rules which makes this work more important. The proposed work attempts to create an attention mechanism to generate the summary of the source document. In this work, the goal was to compare the efficiency of Attention model with sequence to sequence baseline model of Malayalam text and thereby implementing a better abstractive text summarizer for a malayalam document.

关键词： Abstractive Summarization Text Summarization Sequence to sequence Attention model Malayalam Language Deep learning

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