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43rd Conference of the South African Institute of computer Scientists and Information Technologists, SAICSIT 2022

作者： Visser, Ruan Dunaiski, Marcel Department of Computer Science Stellenbosch University Stellenbosch South Africa School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa

Methods such as the h-index and the journal impact factor are commonly used by the scientific community to quantify the quality or impact of research output. These methods rely primarily on citation frequency without taking the context of citations into consideration. Furthermore, these methods weigh each citation equally ignoring valuable citation characteristics, such as citation intent and sentiment. The correct classification of citation intents and sentiments can therefore be used to further improve scientometric impact metrics. In this paper we evaluate BERT for intent and sentiment classification of in-text citations of articles contained in the database of the Association for Computing Machinery (ACM) library. We analyse various BERT models which are fine-tuned with appropriately labelled datasets for citation sentiment classification and citation intent classification. Our results show that BERT can be used effectively to classify in-text citations. We also find that shorter citation context ranges can significantly improve their classification. Lastly, we also evaluate these models with a manually annotated test dataset for sentiment classification and find that BERT-cased and SciBERT-cased perform the best. © 2022, EasyChair. All rights reserved.

关键词： Classification (of information)

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Learning Interpretable Continuous Representation for Alzheimer's Disease Classification

Learning Interpretable Continuous Representation for Alzheim...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhou, Menghui Wang, Mingxia Zhang, Yu Yuan, Zhipeng Lanfranchi, Vitaveska Yang, Po The University of Sheffield Department of Computer Science Sheffield United Kingdom Yunnan University National Pilot School of Software Kunming China

ISBN: (纸本)9798350386226

Alzheimer's disease (AD) is the leading cause of dementia worldwide, characterized by its gradual progression and the subtle variations across disease stages, which pose significant challenges for accurate diagnosis. While deep representation learning algorithms have shown promise in the early detection of AD using MRI data, existing approaches often overlook the meaningful relationships between continuous labels in AD progression, and the learned representations frequently lack interpretability due to the black-box nature of deep learning models. To address these limitations, we propose ICReL (Interpretable Continuous Representation Learning), a novel concept based on coding rate principles that captures continuous representations across AD stages while maintaining a high degree of interpretability. Extensive experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that ICReL not only outperforms multiple baseline methods using 2D slice or 3D MRI in terms of learning continuous representations, but also exhibits enhanced robustness to label corruption and superior predictive performance. This work offers a new, interpretable approach to representation learning for computer-aided diagnosis of neurodegenerative diseases. © 2024 IEEE.

关键词： Adversarial machine learning

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Compact Deep Learning Architecture for Oral Cancer Classification

Compact Deep Learning Architecture for Oral Cancer Classific...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Kalli, Sivanagireddy Kalaimani, G. Sivaprakash, S. Suvathi, P. Kavitha, G. Srinivasan, C. Department of Electronics and Communication Engineering Sridevi Women's Engineering College Telangana Hyderabad India Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India Tamil Nadu Namakkal India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

A compact deep learning model is designed to classify oral cancer based on histopathological images in this work. It has the capacity to learn varying quantities of contextual information by making use of image patches. The proposed model consists of four convolution layers and four sub-sampling layers to extract the features. It uses softmax classification in the fully connected layer for the classification. Because of its straightforward architecture, it is a lightweight model that is suitable for use in settings with limited access to resources and in which it may provide superior results in terms of the amount of space and time required. The performance is evaluated using 1224 histopathological images in two distinct resolutions. The first set has 528 images (100x magnifications) and the second set has 696 images (400 x magnifications). Results prove that the proposed system provides an accuracy of ~98.5% for both sets when data augmentation is employed. © 2024 IEEE.

关键词： Lung cancer

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An In-depth Analysis of Spectroscopic Unmixing for Target Identification in Hyper Spectral Images 5th

An In-depth Analysis of Spectroscopic Unmixing for Target Id...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Yashaswini, K.A. Mishra, Awakash Zaidi, Taskeen Sumit School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

This summary offers an in-intensity evaluation of a system getting-to-know approach, particularly Spectroscopic Unmixing, for target identity in hyperspectral images. This parametric and pixel-smart linear decomposition technique generates spectral functions for each pixel, allowing the identity and extraction of various fabric signatures. The method is applied by fitting the information into a mathematical version, similarly used for unmixing. Different spectral fashions used within the literature have additionally been discussed. The research specializes in implementing the method and its applications for extracting items or substances in complex situations, including city, agricultural, or maritime imaging. Subsequently, the benefits and barriers of Spectroscopic Unmixing have been highlighted. The conclusion provides an insight into the performance of the technique for specific imaging eventualities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pixels

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Brain Tumor Detection and Segmentation Using Deep Learning Models 6

Brain Tumor Detection and Segmentation Using Deep Learning M...

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Abiodun, Moses Kazeem Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Aworinde, Halleluyah Tinuoye, George Imoize, Agbotiname Lucky Li, Chun-Ta Lee, Cheng-Chi Landmark University Department of Computer Science Kwara State Omu-Aran Nigeria Bowen University Department of Computer Science Osun State Iwo Nigeria University of Ilorin Department of Computer Science Kwara State Ilorin Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Nigeria Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City Taiwan Fu Jen Catholic University Department of Library and Information Science New Taipei City Taiwan

ISBN: (纸本)9798350374162

This study focuses on brain tumor detection and segmentation using Convolutional Neural Networks (CNN) with architectures of Fully Convolutional Net-work (FCN) and VGG16. The dataset imported for this study consists of brain MRI images, which were preprocessed to improve the overall image resolution. data augmentation techniques were applied to heighten the variability and volume of the training data. For feature extraction, CNN models were utilized, specifically the VGG16 architectures. This model was trained on a preprocessed dataset to learn the important features associated with the presence of brain tumor. Image segmentation was performed to precisely outline and identify the tumor region within the brain images. FCN architecture was employed for this task, which allowed pixel-level classification of tumor regions. Finally, image classification was carried out to group brain images based on the absence or presence of a tumor. The trained models were evaluated using accuracy, precision, recall and F1score as the performance metrics. The results of the study demonstrated promising outcomes, achieving an accuracy score of 93% and a precision, recall and f1score ranging from 75% to 100% based on the specific type of tumor enlisted. This indicates that the developed CNN models, with the FCN and VGG16 architectures, effectively detect and segment brain tumors from MRI images. The findings of this study contribute to the field of medical image analysis and gives a foundation for further research and development in brain tumor diagnosis and treatment. The study recommends the use of more deep learning algorithms for the process of detecting brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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MRI-Based Biomarker in the Diagnosis of Alzheimer’s Disease Using Attention-UNet

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SN computer science 2025年第3期6卷 1-15页

作者： Arumugam, J. Prasanna Venkatesan, V. Beigh, Tabiya Department of Computer Science School of Engineering and Technology Pondicherry University Kalapet Puducherry Puducherry 605014 India Department of Banking Technology School of Management Pondicherry University Kalapet Puducherry Puducherry 605014 India

Dementia can occur in various forms;some types of dementia are curable and some are not. Among the non-curable forms, Alzheimer’s disease (AD) is the prominent one. There is no effective treatment to cure it but it may reduce its impact if diagnosed early. Accurate classification of AD is necessary for diagnosing and giving the right effective treatment to patients. Specific regions in the brain serve as hotspots for AD, potential imaging biomarkers that contribute effectively thereby improving classification accuracy. We introduce a new Attention-based ’U’ shaped Convolution Neural Network in this study to identify imaging biomarkers of AD using 3D T1-weighted MRI data, which excels at distinguishing between gray matter, white matter, and cerebrospinal fluid (CSF). Our model is improved UNet with an enhanced convolution block attention module named EnCBAMUNet. This makes it ideal for brain imaging, where the differentiation between these tissues is crucial for identifying pathology, such as in neurodegenerative diseases. Our method is tested on both ADNI and OASIS datasets. Classification accuracy of 99.8% for Healthy as control normal(CN) vs Alzheimer’s disease(AD) is obtained in binary classification and 95.5% for multi-classifier Alzheimer’s Disease, Mild Cognitive Impairment(MCI) is a transient state from healthy to disease state and Control Normal as Healthy(AD vs MCI vs CN) in multiclass classification. We visualize regions (hippocampus, ventricles, and some parts of cortex) of the brain responsible for AD using the Three Dimensional Gradient-weighted Class Activation Mapping (3D-Grad-CAM) method of our model due to the deep learning model(DL) that is black-box in nature. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alzhimer disease Classification Deep learning MRI imaging biomarker Visualization

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Towards Robust Multimodal Sentiment Analysis with Incomplete data

arXiv

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arXiv 2024年

作者： Zhang, Haoyu Wang, Wenbin Yu, Tianshu School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Computer Science University College London United Kingdom School of Computer Science Wuhan University China

The field of Multimodal Sentiment Analysis (MSA) has recently witnessed an emerging direction seeking to tackle the issue of data incompleteness. Recognizing that the language modality typically contains dense sentiment information, we consider it as the dominant modality and present an innovative Language-dominated Noise-resistant Learning Network (LNLN) to achieve robust MSA. The proposed LNLN features a dominant modality correction (DMC) module and dominant modality based multimodal learning (DMML) module, which enhances the model’s robustness across various noise scenarios by ensuring the quality of dominant modality representations. Aside from the methodical design, we perform comprehensive experiments under random data missing scenarios, utilizing diverse and meaningful settings on several popular datasets (e.g., MOSI, MOSEI, and SIMS), providing additional uniformity, transparency, and fairness compared to existing evaluations in the literature. Empirically, LNLN consistently outperforms existing baselines, demonstrating superior performance across these challenging and extensive evaluation metrics.1 Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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A Decision Tree-Based Approach to Object-Oriented Classification of Hyper Spectral Imagery 5th

A Decision Tree-Based Approach to Object-Oriented Classifica...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Suneetha, K. Sumit Akshatha, Y. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Hyperspectral imagery is a faraway sensing fact that gives one-of-a-kind spectral reflectance measurements across the electromagnetic spectrum. This form of imagery has various uses for monitoring the environment and tracking flora, wooded area cowl, and urban increase. However, successful analysis of hyperspectral imagery requires correct item-orientated classification, which may be challenging and time-consuming to acquire with traditional methods. This paper affords a choice tree-based method to the object-oriented type of hyperspectral imagery. This technique uses statistical class strategies and, blended with professional information, picks out the numerous objects described in the imagery. The proposed approach has been tested on hyperspectral imagery from exceptional environments, and the consequences reveal high accuracy and occasional processing times. This approach can offer a more accurate object-oriented class of hyperspectral imagery than conventional strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hyperspectral imaging

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Internet of Medical Things: A Bibliometric Analysis of Research Publications from 2018–2022 4th

Internet of Medical Things: A Bibliometric Analysis of Resea...

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4th International Conference on Electronics, Biomedical Engineering, and Health Informatics, ICEBEHI 2023

作者： Maulana, Fairuz Iqbal Lestari, Dian Adi, Puput Dani Prasetyo Arifin, Mohammad Nazir Purnomo, Agung Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Medicine Universitas Airlangga Surabaya60132 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9789819714629

The field of the Internet of Medical Things (IoMT) is experiencing significant growth as it involves the integration of medical devices and systems with the internet and various digital technologies. This study provides a bibliometric analysis of scholarly articles pertaining to the Internet of Medical Things (IoMT) during the time frame of 2018 to 2022. The analysis is conducted using data sourced from the Scopus database. A total of 1165 documents were identified and analyzed to gain insights into the current state of research in this field. The analysis includes trends in publication output, top authors and institutions, and key research topics. The results of the analysis indicate a steady increase in publication output on IoMT over the past five years, with the highest number of publications in 2022. The top authors in this field were Mohanty, S.P., Kougianos, E., and Pirbhulal, S., while the top institutions were King Saud University, Chinese Academy of sciences, and University of North Texas. Co-citation analysis revealed that wearable devices, telemedicine, and data analytics were among the key research topics in IoMT. This bibliometric analysis offers useful insights into the present research landscape of the Internet of Medical Things (IoMT) spanning the years 2018 to 2022. The findings can be beneficial for researchers, practitioners, policymakers, and funding agencies in understanding the trends and priorities in this field. The top authors and institutions identified in this analysis can serve as valuable resources for collaboration and staying updated on the latest developments in IoMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Digital devices

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An Ensemble Deep Learning Model for Diabetic Retinopathy Identification 9

An Ensemble Deep Learning Model for Diabetic Retinopathy Ide...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Hussain, Mohammad Manzoor Shanmugam, P. Moorthi, K. Sakthivelu, U. Rajasekar, A. Kumar, R. Naresh B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Rajalakshmi Engineering College Department Computer Science and Engineering Chennai India Sairam Engineering College Department Electronics and Communication Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Tamilnadu Kattankulathur India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350384208

Diabetic Retinopathy (DR) is an eye disease that can potentially cause significant injury in those diagnosed with diabetes. The condition arises due to issues with blood circulation to the retina, the delicate tissue in the posterior part of the eye. DR is recognized as the leading factor contributing to visual impairment among those affected by diabetes. However, timely identification and intervention might mitigate the occurrence of this condition. Conversely, the timely identification of DR poses challenges, while the diagnostic process can be laborious and time-intensive. The automated detection of DR necessitates categorizing colour retinal fundus pictures. This article presents a computer-aided diagnostic strategy that utilizes Deep Learning (DL) algorithms. In order to classify retinal fundus pictures, this study employed a combination of DL models, specifically ResNet50 and Inception V3. The studies are being conducted on the DIARETDB1 dataset. An analysis is conducted to compare the results of the suggested methodology. The comparison study results show that the proposed approach has more significant accuracy levels than the other methods. A high degree of accuracy, that is, 98.37%, has been attained by utilising the proposed techniques. © 2023 IEEE.

关键词： Deep learning

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