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6th International Conference on Image Information Processing, ICIIP 2021

作者： Kumar, Raj Singh, Anupam Datta, Goutam Kumar, Ashwani Garg, Harshit KIET Group of Institutions Delhi-NCR Department of Computer Science Ghaziabad India UPES Informatics SoCS Dehradun India Sreyas Institute of Engineering and Technology Hyderabad India ABES IT Department of Computer Science Ghaziabad India

ISBN: (纸本)9781665433617

Brain tumors are taken into consideration to be an extreme form of ailment with inside the medical field. Brain Tumors are a purpose for the peculiar and out of control division and growth of cells with inside the brain region itself. If this out-of-control increase will become greater than 60% then the affected person is not able to recover. Human inspection is the usual approach for detecting any contamination in MR brain images. This technique is impractical for a big quantity of data. Therefore, computerized tumor detection strategies are advanced as they might keep radiologists time. The step for tumor detection begins off evolved with the acquisition of an MRI test photo of the tumor. MRI images have grey and white matter and the vicinity affected by the tumor is of excessive intensity. The proposed work is split into four parts as preprocessing, feature extraction, augmentation after which classification has finished the usage of a machine learning algorithm. © 2021 IEEE.

关键词： Magnetic resonance imaging

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An Hybrid Modelling for Optimal Utilisation on Cloud Resources using Machine Learning Algorithm

An Hybrid Modelling for Optimal Utilisation on Cloud Resourc...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Atyam, Nageswara Rao Babu P., Ramesh Nayak, Nihar Ranjan Saikia, Utpal Ananda Kumar, K.S. Aravindharamanan, S. Cmr Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India Wollega University Department of Computer Science College of Engineering Technology Nekemte Ethiopia Department of Mca Andhra Pradesh Chittoor India Silapathar College Department of Mathematics Silapathar Assam Dhemaji India School of Computing and Informatics College of Engineering and Technology Dilla University Snnpr Dilla Ethiopia Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9781665484251

The great majority of data centres are now underutilised, which is a concern. It is estimated that they are only used at 15% of their maximum capacity the great majority of the time. As a result of significant increases in server demand, additional space in these data centres has been added. This study presents the development of a hybrid paradigm for the efficient utilisation of cloud resources. Cloud resource provisioning is made more efficient through the use of machine learning and particle swarm optimization techniques. In order to determine if the model performs as intended, CloudSim is used to simulate the model's performance, and the results are compared against a range of performance measurements, such as the make span, delay, and throughput. © 2022 IEEE.

关键词： Machine learning

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Investigation on data adaptation techniques for neural named entity recognition 59

Investigation on data adaptation techniques for neural named...

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2021 Student Research Workshop, SRW 2021 at the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Tokarchuk, Evgeniia Thulke, David Wang, Weiyue Dugast, Christian Ney, Hermann Informatics Institute University of Amsterdam Netherlands Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Germany

ISBN: (纸本)9781954085558

Data processing is an important step in various natural language processing tasks. As the commonly used datasets in named entity recognition contain only a limited number of samples, it is important to obtain additional labeled data in an efficient and reliable manner. A common practice is to utilize large monolingual unlabeled corpora. Another popular technique is to create synthetic data from the original labeled data (data augmentation). In this work, we investigate the impact of these two methods on the performance of three different named entity recognition tasks. © 2021 Association for Computational Linguistics.

关键词： Data handling

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Word-Based Turkish Lip Reading System with Two Stream 3dcnn and Bilstm

SSRN

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SSRN 2023年

作者： Erbey, Ali Barışçı, Necaattin Department of Computer Programming Distance Education Vocational School Usak University Usak64200 Turkey Department of Information Systems Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara06500 Turkey

Lip reading is a technique that aims to understand spoken words by analyzing people's lip movements. Deep learning algorithms are used as a powerful tool for detecting and recognizing lip movements more accurately. In this study, we present a lip-reading architecture for the Turkish language. The architecture is trained to recognize Turkish words using 3DCNN (3D Convolutional Neural Network) architecture and two-stream deep learning methods. The model was trained on video data representing lip movements and learned to classify words accurately. The Turkish lip reading system has been trained and tested on a large Turkish lip reading dataset. In the experiments, an accuracy of 87.12% was achieved with the proposed model, indicating successful results in the Turkish language. The proposed model demonstrates high accuracy rates in recognizing Turkish words, showcasing its effectiveness. This paper introduces a novel architecture, a lip-reading system that employs deep learning techniques, providing a robust tool for successful Turkish lipreading applications. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Enhancing Martian Mineral Identification Using an Artificial Neural Network With Extracted Spectral Features In CRISM MTRDR Data

Enhancing Martian Mineral Identification Using an Artificial...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Priyanka Kumari Sampriti Soor Amba Shetty B. S. Daya Sagar Department of Water Resources and Ocean Engineering NIT Karnataka Surathkal India School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Systems Science and Informatics Unit Indian Statistical Institute Bangalore India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Creating a supervised learning model for mineral identification is challenging due to the lack of ground-truth data. This study utilizes a method from existing literature that generates a training dataset by augmenting available spectra in the MICA spectral library. However, rather than using entire spectra for identification, this study extracts spectral features from each spectrum for model training. It employs the apparent continuum removal method, Segmented Curve Fitting, to identify the most informative or distinguishable parts in the spectral domain. Spectral features are then extracted based on band-centers and band-areas for each selected part. The model is evaluated against a Targeted Reduced Data Record (TRDR) dataset obtained using a hierarchical Bayesian model, demonstrating improved identification performance than the existing supervised models. Finally, using this model, dominant minerals are identified in MTRDR data from the Nilli Fossae region of Mars, and a corresponding mineral map is presented.

关键词： Training Absorption Supervised learning Artificial neural networks Feature extraction Data models Libraries

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Non-Destructive Egg Fertility Detection in Incubation Using SVM Classifier Based on GLCM Parameters

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Procedia computer Science 2022年 207卷 3254-3263页

作者： Shofan Saifullah Rafał Dreżewski Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta Indonesia Institute of Computer Science AGH University of Science and Technology Cracow Poland

The research presented in this paper aims at egg fertility detection during incubation based on the presence of the embryo. To obtain an optimal detection method that does not adversely affect the incubation process, the presented approach uses computer technology to analyze the images obtained from the egg candling process, and to obtain information about the presence of an egg embryo. This study applies the Support Vector Machine (SVM) classifier method using the second-order statistical feature extraction input to detect the chicken egg's fertility. The feature extraction is based on the Gray-Level Co-occurrence Matrix (GLCM) approach with 6 parameters: Energy (En), Contrast (Ct), Entropy (Et), Variance (V), Correlation (Cr), and Homogeneity (H). This study develops a manual detection process that takes a long time and improves the research accuracy of the backpropagation method. The dataset used consisted of 100 images of chicken eggs, including 50 images for each type: fertile and infertile. The preprocessing of the images included cropping, grayscaling, and image enhancement using CLAHE (Contrast Limited Adaptive Histogram Equalization) and HE (Histogram Equalization) methods. Such process can improve the image to get its GLCM parameters. The extracted GLCM parameters are input to the egg fertility detection process performed by the SVM classifier-based method. The presented SVM-GLCM approach was able to detect fertile and infertile eggs with a success rate of 98.20%, which was an improvement over the previous research. This research can be a reference for implementing the detection of chicken egg fertility in the incubation machine to obtain optimal hatching results.

关键词： SVM classifier GLCM image processing egg fertility detection

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Data-driven soft sensing towards quality monitoring of industrial pasteurization processes

Data-driven soft sensing towards quality monitoring of indus...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Gabriel Filios Andreas Kyriakopoulos Stavros Livanios Fotis Manolopoulos Sotiris Nikoletseas Stefanos H. Panagiotou Paul Spirakis Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute and Press “Diophantus” (CTI) Greece Heineken Patras Plant Greece Department of Computer Science University of Liverpool UK

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

In the food and beverage industry many foods, beers and soft drinks usually need to get pasteurized, a process that holds a significant role in the quality and taste of the final product but is difficult to monitor due to the process nature. Soft sensing techniques, also called virtual sensing or surrogate sensing, can be leveraged to monitor the product quality, by using information available from other measurements and process parameters to calculate an estimation of the quantity of interest. In this paper, we develop a soft sensing methodology that is based on machine learning algorithms for continuous, end-to-end estimation of the temperature of products during the pasteurization process, with the vision to serve as an intermediate step towards monitoring live the final quality of the pasteurized products. This work studies a real beer pasteurization process in collaboration with Heineken’s plant in Patras, Greece and the results demonstrate notable performance in temperature prediction accuracy, with average root mean square error (RMSE) of 1.85°C in the test sets. Thus, we claim that it is possible to obtain measurements quite similar to the ones by the respective physical sensors with sufficient accuracy, and our methodology can be considered as a virtual low-cost solution for monitoring product quality in legacy pasteurizer operation.

关键词： Temperature measurement Temperature sensors Machine learning algorithms Soft sensors Estimation Product design Sensor systems

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WEIGHTED SUM OF SEGMENTED CORRELATION: AN EFFICIENT METHOD FOR SPECTRA MATCHING IN HYPERSPECTRAL IMAGES

arXiv

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arXiv 2024年

作者： Soor, Sampriti Kumari, Priyanka Sagar, B.S. Daya Shetty, Amba School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Water Resources and Ocean Engineering NIT Karnataka Surathkal India Systems Science and Informatics Unit Indian Statistical Institute Bangalore India

Matching a target spectrum with known spectra in a spectral library is a common method for material identification in hyperspectral imaging research. Hyperspectral spectra exhibit precise absorption features across different wavelength segments, and the unique shapes and positions of these absorptions create distinct spectral signatures for each material, aiding in their identification. Therefore, only the specific positions can be considered for material identification. This study introduces the Weighted Sum of Segmented Correlation method, which calculates correlation indices between various segments of a library and a test spectrum, and derives a matching index, favoring positive correlations and penalizing negative correlations using assigned weights. The effectiveness of this approach is evaluated for mineral identification in hyperspectral images from both Earth and Martian surfaces. © 2024, CC BY-NC-ND.

关键词： Hyperspectral imaging

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SONICSIM: A CUSTOMIZABLE SIMULATION PLATFORM FOR SPEECH PROCESSING IN MOVING SOUND SOURCE SCENARIOS

arXiv

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arXiv 2024年

作者： Li, Kai Sang, Wendi Zeng, Chang Yang, Runxuan Chen, Guo Hu, Xiaolin Department of Computer Science and Technology Institute for AI BNRist Tsinghua University Beijing100084 China IDG/McGovern Institute for Brain Research Tsinghua University Beijing100084 China National Institute of Informatics Japan Beijing100010 China

Systematic evaluation of speech separation and enhancement models under moving sound source conditions requires extensive and diverse data. However, real-world datasets often lack sufficient data for training and evaluation, and synthetic datasets, while larger, lack acoustic realism. Consequently, neither effectively meets practical needs. To address this issue, we introduce SonicSim, a synthetic toolkit based on the embodied AI simulation platform Habitat-sim, designed to generate highly customizable data for moving sound sources. SonicSim supports multi-level adjustments—including scene-level, microphone-level, and source-level—enabling the creation of more diverse synthetic data. Leveraging SonicSim, we constructed a benchmark dataset called SonicSet, utilizing LibriSpeech, Freesound Dataset 50k (FSD50K), Free Music Archive (FMA), and 90 scenes from Matterport3D to evaluate speech separation and enhancement models. Additionally, to investigate the differences between synthetic and real-world data, we selected 5 hours of raw, non-reverberant data from the SonicSet validation set and recorded a real-world speech separation dataset, providing a reference for comparing SonicSet with other synthetic datasets. For speech enhancement, we utilized the real-world dataset RealMAN to validate the acoustic gap between SonicSet and existing synthetic datasets. The results indicate that models trained on SonicSet generalize better to real-world scenarios compared to other synthetic datasets. Code is publicly available at https://***/SonicSim/. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech enhancement

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Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model

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computers, Materials & Continua 2021年第8期68卷 2413-2429页

作者： Tapan Kumar Das Pradeep Kumar Roy Mohy Uddin Kathiravan Srinivasan Chuan-Yu Chang Shabbir Syed-Abdul School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering Indian Institute of Information TechnologySurat395007India Research Quality Management Section King Abdullah International Medical Research CenterKing Saud bin Abdulaziz University for Health SciencesMinistry of National Guard-Health AffairsRiyadh11426Kingdom of Saudi Arabia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Graduate Institute of Biomedical Informatics Taipei Medical UniversityTaipeiTaiwan

Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s ***,the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory,clinical mapping,and delusion *** this proposed study,a deep learning based framework that employs deep convolution neural network(Deep-CNN),by utilizing both clinical presentations and conventional magnetic resonance imaging(MRI)investigations,for diagnosing tumors is *** research aims to develop a model that can be used for abnormality detection over MRI data quite efficiently with high *** research is based on deep learning and Deep-CNN was deployed to examine the MR brain image for tracing the *** system runs on Tensor flow and uses a feature extraction module in DeepCNN to elicit the factors of that part of the image from where underlying issues are identified and subsequently succeeded in prediction of the disease in the MR *** results of this study showed that our model did not have any adverse effect on classification,achieved higher accuracy than the peers in recent years,and attained good detection outcomes including case of *** the future work,further improvement can be made by designing models that can drastically reduce the parameter space without affecting classification accuracy.

关键词： Deep learning convolutional neural network brain tumor magnetic resonance imaging

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