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Reducing noise using neighbourhood pixel analysis and interpretable custom kernel in CNN model for CP handwritten digit recognition

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International Journal of Information technology (Singapore) 2024年 1-11页

作者： Muthureka, K. Srinivasulu Reddy, U. Janet, B. Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli India Information Processing Lab Department of Computer Applications National Institute of Technology Tiruchirappalli India

Individuals with Cerebral Palsy (CP) are impacted lifetime barriers in their everyday activities, especially in writing phrase, which results from innate neural motor in co-ordination. Numerous studies have focused on recognizing normal people’s handwritten digits using benchmark datasets. However, there has been limited exploration into recognizing handwritten digits by individuals with CP. The handwriting of CP individuals is significantly more difficult to recognize than that of normal people because the CP digits are inaccurate, uneven, and complex to recognize. In this study, the authors improve the digit recognition accuracy of existing Convolutional Neural Network (CNN) models on collected CP handwritten digit’s dataset using two notable methods, first one is to reduce the noise using neighbourhood pixel analysis through Fast Non-Local Means (FNLM) algorithm and second one is to extract the feature using customized kernel technique. The experimental results show that the accuracy of existing state of art models are improved by 3% to 7.14% with the integration of aforementioned techniques. Moreover, a custom CNN model hybridised with the Prewit, Laplacian, Sobel filters, and custom kernel has following CP digit recognition accuracy values of 97.96%, 98.08%, 99.17%, and 99.60% respectively. © Bharati Vidyapeeth's institute of Computer applications and Management 2024.

关键词： Cerebral Palsy CNN Customized kernel initialization Edge detection Handwritten digit recognition

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Support System for Designing an Operation Plan of Extra Trains on High-Speed Railways

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Japanese Railway Engineering 2023年第1期63卷 1-3页

作者： Okuda, Daiki Matsumoto, Ryosuke Fukasawa, Noriko Data Analytics Laboratory Information and Communication Technology Division Railway Technical Research Institute Japan

We developed a method that can estimate passenger demand for each day in units of 30 minutes, and also developed a system that can propose extra trains that should be operated based on the method. The purpose of these developments is to support designing of a reasonable operation plans of extra trains on high-speed railways. The validity of the system was confirmed through accuracy verification using actual transportation data on one of the high-speed railway lines in Japan. © 2023 Japan Railway Engineers' Association. All rights reserved.

关键词： Railroad transportation

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LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning 50th

LightDiC: A Simple yet Effective Approach for Large-scale Di...

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50th International Conference on Very Large data Bases, VLDB 2024

作者： Li, Xunkai Liao, Meihao Wu, Zhengyu Su, Daohan Zhang, Wentao Li, Rong-Hua Wang, Guoren Beijing Institute of Technology China Peking University National Engineering Laboratory for Big Data Analytics and Applications China

Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and deployment. Compared with undirected graphs, directed graphs (digraphs) fit the demand for modeling more complex topological systems by capturing more intricate relationships between nodes. While some directed GNNs have been introduced, their inspiration mainly comes from deep learning architectures, which lead to redundant complexity and computation, making them inapplicable to large-scale databases. To address these issues, we propose LightDiC, a scalable variant of the digraph convolution based on the magnetic Laplacian. Since topology-related computations are conducted solely during offline pre-processing, LightDiC achieves exceptional scalability, enabling downstream predictions to be trained separately without incurring recursive computational costs. Theoretical analysis shows that LightDiC achieves message passing based on the complex field, which corresponds to the proximal gradient descent process of the Dirichlet energy optimization function from the perspective of digraph signal denoising, ensuring its expressiveness. Experimental results demonstrate that LightDiC performs comparably well or even outperforms other SOTA methods in various downstream tasks, with fewer learnable parameters and higher efficiency. © 2024, VLDB Endowment. All rights reserved.

关键词： Directed graphs

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Rethinking Node-wise Propagation for Large-scale Graph Learning 24

Rethinking Node-wise Propagation for Large-scale Graph Learn...

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33rd ACM Web Conference, WWW 2024

作者： Li, Xunkai Ma, Jingyuan Wu, Zhengyu Su, Daohan Zhang, Wentao Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University National Engineering Laboratory for Big Data Analytics and Applications Beijing China

ISBN: (纸本)9798400701719

Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale graph-based web applications. However, (i) Most scalable GNNs tend to treat all nodes with the same propagation rules, neglecting their topological uniqueness;(ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes' local properties is required. Intuitively, different nodes in web-scale graphs possess distinct topological roles, and therefore propagating them indiscriminately or neglecting local contexts may compromise the quality of node representations. To address the above issues, we propose Adaptive Topology-aware Propagation (ATP), which reduces potential high-bias propagation and extracts structural patterns of each node in a scalable manner to improve running efficiency and predictive performance. Remarkably, ATP is crafted to be a plug-and-play node-wise propagation optimization strategy, allowing for offline execution independent of the graph learning process in a new perspective. Therefore, this approach can be seamlessly integrated into most scalable GNNs while remaining orthogonal to existing node-wise propagation optimization strategies. Extensive experiments on 12 datasets have demonstrated the effectiveness of ATP. © 2024 ACM.

关键词： Graph neural networks

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity 40

AdaFGL: A New Paradigm for Federated Node Classification wit...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Li, Xunkai Wu, Zhengyu Zhang, Wentao Sun, Henan Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University China National Engineering Labratory for Big Data Analytics and Applications Beijing China

ISBN: (纸本)9798350317152

Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break data silos. Existing FGL studies employ community split on the homophilous global graph by default to simulate federated semisupervised node classification settings. Such a strategy assumes the consistency of topology between the multi-client subgraphs and the global graph, where connected nodes are highly likely to possess similar feature distributions and the same label. However, in real-world implementations, the varying perspectives of local data engineering result in various subgraph topologies, posing unique heterogeneity challenges in FGL. Unlike the well-known label Non-independent identical distribution (Non-iid) problems in federated learning, FGL heterogeneity essentially reveals the topological divergence among multiple clients, namely homophily or heterophily. To simulate and handle this unique challenge, we introduce the concept of structure Non-iid split and then present a new paradigm called Adaptive Federated Graph Learning (AdaFGL), a decoupled two-step personalized approach. To begin with, AdaFGL employs standard multi-client federated collaborative training to acquire the federated knowledge extractor by aggregating uploaded models in the final round at the server. Then, each client conducts personalized training based on the local subgraph and the federated knowledge extractor. Extensive experiments on the 12 graph benchmark datasets validate the superior performance of AdaFGL over state-of-the-art baselines. Specifically, in terms of test accuracy, our proposed AdaFGL outperforms baselines by significant margins of 3.24 % and 5.57 % on community split and structure Non-iid split, respectively. © 2024 IEEE.

关键词： Graph neural networks

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Fabrication and fracture behaviors of the continuous brittle fiber reinforced tungsten composites fabricated via field-assisted sintering technology

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Tungsten 2025年第1期7卷 172-182页

作者： Rui Shu Yi-Ran Mao Jan W. Coenen Alexis Terra Stephan Sch?nen Johann Riesch Christian Linsmeier Christoph Broeckmann Juelich Research Center Institute of Energy and Climate Research Plasma Physics (IEK-4) Partner in the Trilateral Euregio Cluster Institute of Materials Applications in Mechanical Engineering (IWM) RWTH Aachen University Department of Nuclear Engineering & Engineering Physics University of Wisconsin Madison Central Institute of Engineering Electronics and Analytics Engineering and Technology (ZEA-1) Juelich Research Center Max Planck Institute for Plasma Physics Institute of Materials Applications in Mechanical Engineering IWM RWTH Aachen University

Tungsten（W） is a promising candidate material for the plasma-facing components in future fusion ***,it has issues regarding the intrinsic brittleness as well as operational *** fiber reinforced tungsten（W_f/W） composites overcome these issues by using extrinsic toughening *** to neutron irradiation and high-temperature recrystallization during fusion operation,the ductility of the fibers may degrade over a long period of ***,it is necessary to investigate the fracture behavior of W_f/W composites with brittle *** the present work,the tungsten fibers were embrittled by ***,the continuous brittle W_f/W composite with yttrium oxide（Y2O3） interface was fabricated by the field-assisted sintering technology *** microstructure of the prepared W_f/W composites was *** fracture behavior and toughening mechanisms were discussed in detail based on the experimental results of 3-point bending tests and the corresponding finite element *** composites show a pseudo-ductile fracture *** are hindered and deflected by the de-bonded fiber-matrix *** extrinsic toughening mechanisms of interface de-bonding,crack bridging,and fiber pull-out are *** indicates the reinforcement concept still works even though all components in the W_f/W are brittle.

关键词： Interface Fracture behavior Numerical simulation W_f/W composites Field-assisted sintering technology(FAST)

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Findings of the WILDRE Shared Task on Code-mixed Less-resourced Sentiment Analysis for Indo-Aryan Languages 7

Findings of the WILDRE Shared Task on Code-mixed Less-resour...

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7th Workshop on Indian Language data Resource and Evaluation, WILDRE 2024

作者： Rani, Priya Negi, Gaurav Jha, Saroj Kumar Suryawanshi, Shardul Ojha, Atul Kr. Buitelaar, Paul McCrae, John P. SFI Centre for Research and Training in AI Data Science Institute University of Galway Ireland Insight SFI Research Centre for Data Analytics Data Science Institute University of Galway Ireland Indian Institute of Technology Patna India

ISBN: (纸本)9782493814371

This paper describes the structure and findings of the WILDRE 2024 shared task on code-mixed less-resourced sentiment analysis for Indo-Aryan Languages. The participants were asked to submit the test data’s final prediction on CodaLab. A total of fourteen teams registered for the shared task. Only five participants submitted the system for evaluation on CodaLab, with only two teams submitting the system description paper. All the submitted systems exceed baseline scores, with the best F1 Scores of 0.97, 0.54, 0.45, 0.60 and 0.49 for Bangla-Hindi-English, Hindi-English, Magahi-Hindi-English, Combined, and Maithili-Hindi-English, respectively. This significant improvement from the baseline score highlights notable progress in the performance of the systems. This underscores the advancement and refinement of methodologies, highlighting the potential for further innovation for code-mixed tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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Telugu Meme dataset and Baseline System for Automatic Identification of Domain, and Troll in Memes 2nd

Telugu Meme Dataset and Baseline System for Automatic Iden...

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2nd International Conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Lohith, N. Adnan Raqeeb, S. Sai Manoj Reddy, Poreddy Sunil Kumar, Chekuri Venkata Anand Kumar, M. Chakravarthi, Bharathi Raja Department of Information Technology National Institute of Technology Karnataka Surathkal India Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Galway Ireland

ISBN: (纸本)9783031584947

Everyone now uses social media to communicate information and ideas, which has become a requirement for everyone. Social media posts can offend people or be helpful or educational for them. Memes are one type of media that is disseminated in this way through direct messages, videos, or photographs. A meme is an image or video that captures the opinions and sentiments of a particular group of people. Memes can be trolling or not, and they include an image and text that express an emotion. Most memes are unique to a particular linguistic group of people. The most frequent cause of this is different languages. Memes must be categorized since studying them can help us understand the interests of each user. As both are necessary for interpretation, the meme cannot be considered acceptable if we only gaze at the text or the image. In this article, we provided a method for categorizing memes in Telugu that considers both the meme’s text and visual features, classifies trolling and non-trolling memes, attempt to determine the emotion the meme conveys and categorizes the meme according to the domain it belongs to. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Social networking (online)

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