scholarly journals Multi-Frequency Radar Micro-Doppler Based Classification of Micro-Drone Payload Weight

2021 ◽  
Vol 1 ◽  
Author(s):  
Dilan Dhulashia ◽  
Nial Peters ◽  
Colin Horne ◽  
Piers Beasley ◽  
Matthew Ritchie
Keyword(s):  
Classification Performance ◽  
Potential Threat ◽  
Detection Systems ◽  
Radar Systems ◽  
W Band ◽  
Knn Classifier ◽  
Threat Level ◽  
Situational Information ◽  
The Impact

The use of drones for recreational, commercial and military purposes has seen a rapid increase in recent years. The ability of counter-drone detection systems to sense whether a drone is carrying a payload is of strategic importance as this can help determine the potential threat level posed by a detected drone. This paper presents the use of micro-Doppler signatures collected using radar systems operating at three different frequency bands for the classification of carried payload of two different micro-drones performing two different motions. Use of a KNN classifier with six features extracted from micro-Doppler signatures enabled mean payload classification accuracies of 80.95, 72.50 and 86.05%, for data collected at S-band, C-band and W-band, respectively, when the drone type and motion type are unknown. The impact on classification performance of different amounts of situational information is also evaluated in this paper.

scholarly journals Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Kun Zeng ◽  
Yibin Xu ◽  
Ge Lin ◽  
Likeng Liang ◽  
Tianyong Hao
Keyword(s):  
Clinical Trial ◽  
Ensemble Learning ◽  
Metric Learning ◽  
Classification Performance ◽  
Ensemble Model ◽  
Automated Classification ◽  
Eligibility Criteria ◽  
Data Imbalance ◽  
The Impact

Abstract Background Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. Methods An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. Results Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. Conclusions A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

scholarly journals Document representations for classification of short web-page descriptions

2008 ◽  
Vol 18 (1) ◽  
pp. 123-138 ◽  
Author(s):  
Milos Radovanovic ◽  
Mirjana Ivanovic
Keyword(s):  
Text Categorization ◽  
Web Search ◽  
Classification Performance ◽  
Document Representation ◽  
Web Page ◽  
Ve Bayes ◽  
Large Hierarchy ◽  
The Impact ◽  
Document Representations

Motivated by applying Text Categorization to classification of Web search results, this paper describes an extensive experimental study of the impact of bag-of- words document representations on the performance of five major classifiers - Na?ve Bayes, SVM, Voted Perceptron, kNN and C4.5. The texts, representing short Web-page descriptions sorted into a large hierarchy of topics, are taken from the dmoz Open Directory Web-page ontology, and classifiers are trained to automatically determine the topics which may be relevant to a previously unseen Web-page. Different transformations of input data: stemming, normalization, logtf and idf, together with dimensionality reduction, are found to have a statistically significant improving or degrading effect on classification performance measured by classical metrics - accuracy, precision, recall, F1 and F2. The emphasis of the study is not on determining the best document representation which corresponds to each classifier, but rather on describing the effects of every individual transformation on classification, together with their mutual relationships. .

scholarly journals Classification of Drones Using Edge-Enhanced Micro-Doppler Image Based on CNN

2021 ◽  
Vol 38 (4) ◽  
pp. 1033-1039
Author(s):  
Ashish Kumar Singh ◽  
Yong-Hoon Kim
Keyword(s):  
Intelligent System ◽  
Relevant Information ◽  
Classification Performance ◽  
Radar System ◽  
Radar Signal ◽  
Essential Requirement ◽  
W Band ◽  
Doppler Image ◽  
Phase Stretch Transform

The development of advanced radar system for detection and classification of UAVs is an essential requirement for today’s societal security. Such intelligent system could able to analyze the received radar signal and extract relevant information by utilizing sophisticated algorithm. In this letter, the utilization of micro-Doppler signature (MDS) for classification of drones, using convolutional neural network (CNN) model has been presented. We have generated images of micro-Doppler signatures using W-band radar system and used it for classification purpose. In this work, phase stretch transform (PST) has been utilized for edge detection and enhancement of the micro-Doppler images, to generate the edge-enhanced micro-Doppler image (EMDI). The comparison based on classification performance of CNN with different input datasets shows that the EMDI based CNN model outperformed the micro-Doppler image (MDI) based model.

scholarly journals DAMPAK LETUSAN GUNUNG SINDORO TERHADAP KELESTARIAN SITUS KLASIK DI LERENG TIMUR LAUT GUNUNG SINDORO [THE IMPACT OF MOUNT SINDORO ERUPTION FOR PRESERVATION OF CLASSIC SITES ON THE NORTHEAST ITS SLOPE]

2020 ◽  
Vol 6 (1) ◽  
pp. 57-72
Author(s):  
Reidika Haris Banu Niksa
Keyword(s):  
Risk Index ◽  
Active Volcano ◽  
Potential Threat ◽  
Threat Level ◽  
The Impact

Gunung Sindoro merupakan gunung api aktif yang menyimpan potensi ancaman bagi tinggalan arkeologi yang berada di lerengnya. Berdasarkan fakta tersebut analisis terhadap indeks risiko bencana letusan Gunung Sindoro terhadap keberadaan situs klasik, khususnya di lereng Timur Laut Gunung Sindoro perlu dilakukan. Penelitian ini ditujukan untuk mengetahui tingkat keterancaman situs klasik di lereng timur laut Gunung Sindoro, sehingga kesadaran stakeholder mengenai ancaman kerusakan situs dapat terbangun. Metode yang digunakan adalah deskriptif-eksplanatif, yaitu metode yang dapat memberikan gambaran dan informasi dari suatu gejala tertentu berdasarkan hasil analisis. Selain menganalisis tingkat keterancaman, hasil penelitian ini juga mengusulkan rekomendasi mitigasi terhadap situs klasik tersebut. Mount Sindoro, which is an active volcano, holds a potential threat to archeological remains on its slopes. Based on these facts, analysis of the risk index of Mount Sindoro eruption on the existence of classic sites, especially on the Northeast slope is required. The research aims to discover the threat level of a classic site on the northeast slope of Mount Sindoro so that stakeholder awareness about the threat of site damage can be built. The method used in this paper is descriptive-explanatory that can provide an overview and information of certain indications based on the analysis results. In addition to analyzing the level of threat, the result also proposes recommendations for mitigating the classic site.

scholarly journals Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks

2020 ◽  
Vol 6 (4) ◽  
pp. 24
Author(s):  
Michalis Giannopoulos ◽  
Anastasia Aidini ◽  
Anastasia Pentari ◽  
Konstantina Fotiadou ◽  
Panagiotis Tsakalides
Keyword(s):  
Remote Sensing ◽  
Classification Performance ◽  
Low Rank ◽  
Multispectral Images ◽  
Negative Effects ◽  
Compression Scheme ◽  
Sensing Platforms ◽  
The Impact ◽  
And Storage

Multispectral sensors constitute a core Earth observation image technology generating massive high-dimensional observations. To address the communication and storage constraints of remote sensing platforms, lossy data compression becomes necessary, but it unavoidably introduces unwanted artifacts. In this work, we consider the encoding of multispectral observations into high-order tensor structures which can naturally capture multi-dimensional dependencies and correlations, and we propose a resource-efficient compression scheme based on quantized low-rank tensor completion. The proposed method is also applicable to the case of missing observations due to environmental conditions, such as cloud cover. To quantify the performance of compression, we consider both typical image quality metrics as well as the impact on state-of-the-art deep learning-based land-cover classification schemes. Experimental analysis on observations from the ESA Sentinel-2 satellite reveals that even minimal compression can have negative effects on classification performance which can be efficiently addressed by our proposed recovery scheme.

CLASSIFICATION OF UNSEGMENTED HEART SOUND RECORDING USING KNN CLASSIFIER

2019 ◽  
Vol 19 (04) ◽  
pp. 1950025 ◽  
Author(s):  
SINAM AJITKUMAR SINGH ◽  
SWANIRBHAR MAJUMDER
Keyword(s):  
Heart Sound ◽  
Classification Performance ◽  
Calorie Intake ◽  
Automated Classification ◽  
Power Spectral ◽  
Knn Classifier ◽  
Detailed Classification ◽  
Short Time ◽  
High Calorie

Due to low physical workout, high-calorie intake, and bad behavioral character, people were affected by cardiological disorders. Every instant, one out of four deaths are due to heart-related ailments. Hence, the early diagnosis of a heart is essential. Most of the approaches for automated classification of the heart sound need segmentation of Phonocardiograms (PCG) signal. The main aim of this study was to decline the segmentation process and to estimate the utility for accurate and detailed classification of short unsegmented PCG recording. Based on wavelet decomposition, Hilbert transform, homomorphic filtering, and power spectral density (PSD), the features had been obtained using the beginning 5 second PCG recording. The extracted features were classified using nearest neighbors with Euclidean distances for different values of [Formula: see text] by bootstrapping 50% PCG recording for training and 50% for testing over 100 iterations. The overall accuracy of 100%, 85%, 80.95%, 81.4%, and 98.13% had been achieved for five different datasets using KNN classifiers. The classification performance for analyzing the whole datasets is 90% accuracy with 93% sensitivity and 90% specificity. The classification of unsegmented PCG recording based on an efficient feature extraction is necessary. This paper presents a promising classification performance as compared with the state-of-the-art approaches in short time less complexity.

scholarly journals Voxel-Wise Feature Selection Method for CNN Binary Classification of Neuroimaging Data

2021 ◽  
Vol 15 ◽  
Author(s):  
Domenico Messina ◽  
Pasquale Borrelli ◽  
Paolo Russo ◽  
Marco Salvatore ◽  
Marco Aiello
Keyword(s):  
Feature Selection ◽  
Performance Enhancement ◽  
Binary Classification ◽  
Group Analysis ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Imaging Data ◽  
Neuroimaging Data ◽  
The Impact

Voxel-wise group analysis is presented as a novel feature selection (FS) technique for a deep learning (DL) approach to brain imaging data classification. The method, based on a voxel-wise two-sample t-test and denoted as t-masking, is integrated into the learning procedure as a data-driven FS strategy. t-Masking has been introduced in a convolutional neural network (CNN) for the test bench of binary classification of very-mild Alzheimer’s disease vs. normal control, using a structural magnetic resonance imaging dataset of 180 subjects. To better characterize the t-masking impact on CNN classification performance, six different experimental configurations were designed. Moreover, the performances of the presented FS method were compared to those of similar machine learning (ML) models that relied on different FS approaches. Overall, our results show an enhancement of about 6% in performance when t-masking was applied. Moreover, the reported performance enhancement was higher with respect to similar FS-based ML models. In addition, evaluation of the impact of t-masking on various selection rates has been provided, serving as a useful characterization for future insights. The proposed approach is also highly generalizable to other DL architectures, neuroimaging modalities, and brain pathologies.

ON THE CLASSIFICATION OF DIGITAL TECHNOLOGIES IN CIVIL PROCESS

2019 ◽  
pp. 27-35
Author(s):  
Alexandr Neznamov
Keyword(s):  
Information Processing ◽  
Scientific Community ◽  
Digital Technologies ◽  
Significant Feature ◽  
Legal Proceedings ◽  
Civil Process ◽  
Civil Proceedings ◽  
Processing Information ◽  
The Impact

Digital technologies are no longer the future but are the present of civil proceedings. That is why any research in this direction seems to be relevant. At the same time, some of the fundamental problems remain unattended by the scientific community. One of these problems is the problem of classification of digital technologies in civil proceedings. On the basis of instrumental and genetic approaches to the understanding of digital technologies, it is concluded that their most significant feature is the ability to mediate the interaction of participants in legal proceedings with information; their differentiating feature is the function performed by a particular technology in the interaction with information. On this basis, it is proposed to distinguish the following groups of digital technologies in civil proceedings: a) technologies of recording, storing and displaying (reproducing) information, b) technologies of transferring information, c) technologies of processing information. A brief description is given to each of the groups. Presented classification could serve as a basis for a more systematic discussion of the impact of digital technologies on the essence of civil proceedings. Particularly, it is pointed out that issues of recording, storing, reproducing and transferring information are traditionally more «technological» for civil process, while issues of information processing are more conceptual.

Influence of internal and external risks on sustainability of financial system of Аltai krai

2018 ◽  
Vol 35 (4) ◽  
pp. 133-136
Author(s):  
R. N. Ibragimov
Keyword(s):  
Sustainable Development ◽  
Financial Stability ◽  
Management Strategy ◽  
Financial System ◽  
Risk Management Strategy ◽  
The Sustainable Development ◽  
The Stability ◽  
The Impact

The article examines the impact of internal and external risks on the stability of the financial system of the Altai Territory. Classification of internal and external risks of decline, affecting the sustainable development of the financial system, is presented. A risk management strategy is proposed that will allow monitoring of risks, thereby these measures will help reduce the loss of financial stability and ensure the long-term development of the economy of the region.

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