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6th IFIP TC 12 International Conference on Intelligence science, ICIS 2024

作者： Wang, You Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang State Key Laboratory of Novel Software Technology National Insititute of Health-care Data Science Nanjing University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Data and Computer Science Shandong Woman’s University Jinan China

ISBN: (纸本)9783031712524

In this paper, we introduce an innovative approach to weakly supervised medical image segmentation with box annotations. Different from the previous methods which simply utilize a single conventional network with the same augmentation techniques widely used in supervised segmentation, we aim to introduce diverse augmentations and heterogenous networks to leverage the box annotations for promising generalization ability. Specifically, to amplify the diversity between the contents within the box and its surroundings, we propose the interior and exterior box augmentation (IEBA) technique, in which distinct augmentation techniques are employed for regions inside and outside the bounding boxes. Also, for the purpose of selecting pseudo-labels of superior quality, we propose the pseudo-label filter module (PLFM) to eliminate unreliable pseudo-labels. Besides, as CNN demonstrates superior capabilities in acquiring local information, and ViT specializes in capturing global context, we facilitate a bidirectional learning process between CNN and ViT through quadruple cross consistency losses (QCCL). In inference, we only employ the superior model from the validation set to obtain parameter efficiency. Our approach is evaluated across four tasks on two public datasets, utilizing the 3D dice similarity coefficient as the evaluation metric. The experimental results show that the proposed method outperforms the state-of-the-art comparison methods. © IFIP International Federation for Information Processing 2025.

关键词： Image segmentation

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Accurate Anomaly Detection in Medical Images using Transfer Learning and Data Optimization: MRI and CT as Case Studies 5

Accurate Anomaly Detection in Medical Images using Transfer ...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Nizamli, Yasser Filatov, Anton Yu. Fadel, Weaam Shichkina, Yulia A. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Computer Science Saint Petersburg Russia

ISBN: (纸本)9798350363739

Today, medical imaging techniques are widely used to detect a variety of human conditions and diseases. To speed up the diagnostic process, systems are often automated using deep learning methods, which have been proven to yield outstanding results. However, these models may be limited to a specific domain, prone to overfitting, and difficult to update. In this paper, we present a transfer learning approach that achieves high accuracy for detecting anomalies in both MRI and CT images. Our system utilizes the ConvNeXt network, inspired by the Swin Transformer architecture, as a high-level feature extractor from processed and augmented medical images. The resulting data is then optimized using the Edited Nearest Neighbors algorithm, which selects the most effective observations and discards the rest. Finally, the data is passed to the K-Nearest Neighbors classifier to detect anomalies in numerically represented images. Our model achieves high performance for two different types of medical images and can easily be updated without the need for retraining. © 2024 IEEE.

关键词： Magnetic resonance imaging

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A Pre-selected Local Weighted Method for Improving the Performance of LMKNN Classifier 6

A Pre-selected Local Weighted Method for Improving the Perfo...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Zhang, Yuzhi Guan, Ziyan Xiong, Chen School of Aeronautic Science and Engineering Beihang University Beijing China School of Big Data & Software Engineering Chongqing University Chongqing China School of Computer Science Xi'an Jiaotong University City College Xi'an China

ISBN: (纸本)9798350374315

The K-nearest neighbor (KNN) algorithm and its variant Local Mean K-nearest neighbor (LMKNN) have been widely used in the field of data mining due to their simplicity and intuition. However, these methods have limitations when dealing with unbalanced datasets and edge samples, such as sensitivity to the choice of nearest neighbor size and degradation of classification performance for outlier points. To address these issues, this paper proposes a pre-selected locally weighted mean K-nearest neighbor (PLWMKNN) algorithm, which aims to improve the performance of LMKNN through an advanced pre-selection mechanism and a weighted average method. PLWMKNN firstly pre-selects favorable classes as the nearest neighbors based on the distance weights, and then computes the weighted center vectors of each class to classify them by the minimum distance principle. Through experimental validation on four real datasets, PLWMKNN shows significant performance improvement over traditional KNN and LMKNN for unbalanced datasets. Our results demonstrate the effectiveness of the PLWMKNN algorithm in improving classification accuracy and robustness, especially when dealing with extremely unbalanced datasets. © 2024 IEEE.

关键词： Nearest neighbor search

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Double Encryption Technique for Sharing and Storing the Images in the Cloud Environment 4

Double Encryption Technique for Sharing and Storing the Imag...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Anusha, P. Maruthi, R. Kumar, D. Maghesh Begum, H. Parveen Periyar Maniammai Institute of Science and Technology Department of Software Engineering Tamil Nadu Thanjavur India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350343670

Cryptography is used by all organizations to protect the data files and ensures confidentiality mainly at the time of sharing and storing in the cloud data storage. The cloud service providers use a wide range of tools and techniques for encryption and storage technologies. The intruders are finding number of ways to break or attack the cryptographic systems, so that new ways are constantly being explored to safeguard our files from the attackers This paper explores double encryption cryptographic technique based on Elliptic curve cryptography (ECC) and Advanced Encryption Standard (AES) to perform end-to end encryption for sharing and storing files in the cloud environment. © 2024 IEEE.

关键词： Advanced Encryption Standard Cloud Cryptography Elliptic Curve Cryptography File Sharing

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Graph-Based Feature Learning for Cross-Project software Defect Prediction

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computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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A Data Management Model for Malaria Control: A Case of Rwanda 16

A Data Management Model for Malaria Control: A Case of Rwand...

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16th IEEE AFRICON, AFRICON 2023

作者： Mary, Happiness Rose Mukamakuza, Carine Pierrette Tuyishimire, Emmanuel College of Engineering Carnegie Mellon University Africa Kigali Rwanda Academy of Computer Science and Software Engineering University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9798350336214

We are in an era when the diagnosis of diseases benefits greatly from digitizing their control. On the other hand, malaria is a long-standing yet one of the deadliest diseases. Its diagnosis processes still use old mechanisms, even when we have embarked on the Fourth Industrial Revolution. It is, therefore, more important than ever to digitize its control to enhance its diagnosis. This paper studies a digital data management model for malaria control in the context of a typical African country, Rwanda. Existing data management models are revisited, exploited, and contextualized to suit the Rwandan scenario, with the aim of their adoption. The proposed model in this study is the first of its kind for the developing world. © 2023 IEEE.

关键词： Information management

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Artificial Intelligence-based Speech Signal for COVID-19 Diagnostics 6

Artificial Intelligence-based Speech Signal for COVID-19 Dia...

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6th International Conference on Future Networks and Distributed Systems, ICFNDS 2022

作者： Alfaidi, Aseel Alshahrani, Abdullah Aljohani, Maha Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Saudi Arabia Department of Software Engineering College of Computer Science and Engineering University of Jeddah Saudi Arabia

ISBN: (纸本)9781450399050

The speech signal has numerous features that represent the characteristics of a specific language and recognize emotions. It also contains information that can be used to identify the mental, psychological, and physical states of the speaker. Recently, the acoustic analysis of speech signals offers a practical, automated, and scalable method for medical diagnosis and monitoring symptoms of many diseases. In this paper, we explore the deep acoustic features from confirmed positive and negative cases of COVID-19 and compare the performance of the acoustic features and COVID-19 symptoms in terms of their ability to diagnose COVID-19. The proposed methodology consists of the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images to extract deep audio features. In addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 from acoustic features compared to COVID-19 symptoms, achieving an accuracy of 97%. The experimental results show that the proposed method remarkably improves the accuracy of COVID-19 detection over the handcrafted features used in previous studies. © 2022 ACM.

关键词： COVID-19

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GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching 38

GoMatching: A Simple Baseline for Video Text Spotting via Lo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： He, Haibin Ye, Maoyuan Zhang, Jing Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science Nanyang Technological University Singapore

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets. © 2024 Neural information processing systems foundation. All rights reserved.

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Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

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computers, Materials & Continua 2022年第11期73卷 2967-2984页

作者： Gulzar Ahmed Tahir Alyas Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science University of South AsiaLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Handwritten character recognition systems are used in every field of life nowadays,including shopping malls,banks,educational institutes,*** is the national language of Pakistan,and it is the fourth spoken language in the ***,it is still challenging to recognize Urdu handwritten characters owing to their cursive *** paper presents a Convolutional Neural Networks(CNN)model to recognize Urdu handwritten alphabet recognition(UHAR)offline and online *** research contributes an Urdu handwritten dataset(aka UHDS)to empower future works in this *** offline systems,optical readers are used for extracting the alphabets,while diagonal-based extraction methods are implemented in online ***,our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text *** this end,we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline ***,we carried out detailed experiments for character recognition,as detailed in the *** proposed CNN model outperformed as compared to previously published approaches.

关键词： Urdu handwritten text recognition handwritten dataset convolutional neural network artificial intelligence machine learning deep learning

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Generating synthetic data on agricultural crops with DCGAN 11

Generating synthetic data on agricultural crops with DCGAN

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11th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2024

作者： Pasaribu, Fieter Brain Dewi, Luh Joni Erawati Aryanto, Kadek Yota Ernanda Seputra, Ketut Agus Varnakovida, Pariwate Kertiasih, Ni Ketut Computer Science Universitas Pendidikan Ganesha Singaraja Indonesia Software Engineering Universitas Pendidikan Ganesha Singaraja Indonesia King Mongkut's University of Technology Faculty of Science Thonburi Thailand

ISBN: (纸本)9798331520311

Convolutional Neural Network (CNN) is one of the deep learning architectures that is very effective for handling images. CNN is able to automatically extract important features from images, making it very suitable for various image processing tasks such as classification, object detection, and segmentation. However, even though CNN has great capabilities, one important thing to note is the amount of data. A considerable amount of data is needed for the CNN model to work optimally and avoid overfitting. To handle this problem, a synthetic data augmentation process is used using the Deep Convolutional Generative Adversarial Network (DCGAN) method. The generator network contained in the DCGAN model has a latent space dimension input whose value can vary. The size of the latent space dimension is very important in enabling data or image reconstruction during the training process. This study tested latent space dimension values on a corn plant dataset totaling 9159. The latent space values used in this experiment were 64, 100, and 128. In addition, this study also tested different batch sizes, namely 64 and 128. The model was evaluated using Fre'chet Inception Distance (FID) and Inception Score (IS). From the evaluation results, the best score on FID was 0.018001 and IS 1.239421. The greater the latent space value, the more realistic and clear the image results will be. Likewise, the smaller the batch size value, the more realistic and clear the image results. © 2024 IEEE.

关键词： Generative adversarial networks

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