Argumentative Insights from an Opinion Classification Task on a French Corpus

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.

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Increasing Depth of Field of Tilted Diffractive Lens in Image Classification Task

2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) ◽

10.1109/fareastcon50210.2020.9271110 ◽

2020 ◽

Author(s):

A.P. Dzyuba ◽

S.N. Khonina ◽

A.V. Nikonorov ◽

S.B. Popov ◽

P.G. Serafimovich

Keyword(s):

Image Classification ◽

Classification Task ◽

Depth Of Field ◽

Diffractive Lens

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Combining texture analysis and deep learning in renal tumour classification task

2020 IEEE 21st International Conference on Computational Problems of Electrical Engineering (CPEE) ◽

10.1109/cpee50798.2020.9238757 ◽

2020 ◽

Author(s):

Aleksandra Maria Osowska-Kurczab ◽

Tomasz Markiewicz ◽

Miroslaw Dziekiewicz ◽

Malgorzata Lorent

Keyword(s):

Deep Learning ◽

Texture Analysis ◽

Renal Tumour ◽

Classification Task ◽

Tumour Classification

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Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01588-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kinshuk Sengupta ◽

Praveen Ranjan Srivastava

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Image Classification ◽

Machine Learning Algorithms ◽

Classification Task ◽

Clinical Image ◽

Prototype Model ◽

Learning Models ◽

Accuracy Measure ◽

Quantum Machine Learning

Abstract Background In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. Methods This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. Results The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. Conclusion The results suggest that quantum neural networks outperform in COVID-19 traits’ classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.

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Student Performance Prediction with Optimum Multilabel Ensemble Model

Journal of Intelligent Systems ◽

10.1515/jisys-2021-0016 ◽

2021 ◽

Vol 30 (1) ◽

pp. 511-523

Author(s):

Ephrem Admasu Yekun ◽

Abrahaley Teklay Haile

Keyword(s):

High School Students ◽

Prediction Model ◽

Student Performance ◽

Performance Prediction ◽

Transformation Method ◽

Classification Task ◽

Support Vector ◽

School Students ◽

K Nearest Neighbors ◽

Classifier Chains

Abstract One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.

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