scholarly journals FastText-Based Intent Detection for Inflected Languages

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 161 ◽  
Author(s):  
Kaspars Balodis ◽  
Daiga Deksne
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Detection System ◽  
Detection Accuracy ◽  
Word Embeddings ◽  
Neural Network Classifier ◽  
Dialogue System ◽  
Baltic Countries

Intent detection is one of the main tasks of a dialogue system. In this paper, we present our intent detection system that is based on fastText word embeddings and a neural network classifier. We find an improvement in fastText sentence vectorization, which, in some cases, shows a significant increase in intent detection accuracy. We evaluate the system on languages commonly spoken in Baltic countries—Estonian, Latvian, Lithuanian, English, and Russian. The results show that our intent detection system provides state-of-the-art results on three previously published datasets, outperforming many popular services. In addition to this, for Latvian, we explore how the accuracy of intent detection is affected if we normalize the text in advance.

scholarly journals Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Positive Impact ◽  
Detection System ◽  
Training Data ◽  
Detection Accuracy ◽  
Current State ◽  
Alignment Task ◽  
Scanned Image ◽  
Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

scholarly journals Anomaly Detection Using Deep Neural Network for IoT Architecture

2021 ◽  
Vol 11 (15) ◽  
pp. 7050
Author(s):  
Zeeshan Ahmad ◽  
Adnan Shahid Khan ◽  
Kashif Nisar ◽  
Iram Haider ◽  
Rosilah Hassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Detection System ◽  
Traffic Monitoring ◽  
Detection Accuracy ◽  
Learning Models ◽  
Prime Concern ◽  
Entry Points

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

scholarly journals Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 458
Author(s):  
Zakaria El Mrabet ◽  
Niroop Sugunaraj ◽  
Prakash Ranganathan ◽  
Shrirang Abhyankar
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Power Systems ◽  
Real Time ◽  
Fault Location ◽  
State Of The Art ◽  
Economic Consequences ◽  
Support Vector ◽  
Detection Accuracy ◽  
Data Driven Approach

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

scholarly journals Hybrid-Based Analysis Impact on Ransomware Detection for Android Systems

2021 ◽  
Vol 11 (22) ◽  
pp. 10976
Author(s):  
Rana Almohaini ◽  
Iman Almomani ◽  
Aala AlKhayer
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Hybrid System ◽  
State Of The Art ◽  
Detection System ◽  
Detection Accuracy ◽  
Accuracy Rate ◽  
Hybrid Detection ◽  
Dynamic Analyses ◽  
Data Files

Android ransomware is one of the most threatening attacks that is increasing at an alarming rate. Ransomware attacks usually target Android users by either locking their devices or encrypting their data files and then requesting them to pay money to unlock the devices or recover the files back. Existing solutions for detecting ransomware mainly use static analysis. However, limited approaches apply dynamic analysis specifically for ransomware detection. Furthermore, the performance of these approaches is either poor or often fails in the presence of code obfuscation techniques or benign applications that use cryptography methods for their APIs usage. Additionally, most of them are unable to detect ransomware attacks at early stages. Therefore, this paper proposes a hybrid detection system that effectively utilizes both static and dynamic analyses to detect ransomware with high accuracy. For the static analysis, the proposed hybrid system considered more than 70 state-of-the-art antivirus engines. For the dynamic analysis, this research explored the existing dynamic tools and conducted an in-depth comparative study to find the proper tool to integrate it in detecting ransomware whenever needed. To evaluate the performance of the proposed hybrid system, we analyzed statically and dynamically over one hundred ransomware samples. These samples originated from 10 different ransomware families. The experiments’ results revealed that static analysis achieved almost half of the detection accuracy—ranging around 40–55%, compared to the dynamic analysis, which reached a 100% accuracy rate. Moreover, this research reports some of the high API classes, methods, and permissions used in these ransomware apps. Finally, some case studies are highlighted, including failed running apps and crypto-ransomware patterns.

scholarly journals A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

2020 ◽  
Vol 9 (4) ◽  
pp. 609
Author(s):  
Abdulrahman Jassam Mohammed ◽  
Muhanad Hameed Arif ◽  
Ali Adil Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Multilayer Perceptron ◽  
Detection System ◽  
Denial Of Service ◽  
Detection Accuracy ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.</p>

scholarly journals IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 286 ◽  
Author(s):  
Jiangqiao Yan ◽  
Hongqi Wang ◽  
Menglong Yan ◽  
Wenhui Diao ◽  
Xian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Ground Truth ◽  
Detection Performance ◽  
Candidate Region ◽  
Detection Accuracy ◽  
Remote Sensing Images

Recently, methods based on Faster region-based convolutional neural network (R-CNN)have been popular in multi-class object detection in remote sensing images due to their outstandingdetection performance. The methods generally propose candidate region of interests (ROIs) througha region propose network (RPN), and the regions with high enough intersection-over-union (IoU)values against ground truth are treated as positive samples for training. In this paper, we find thatthe detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially,detection performance of small objects is poor when choosing a normal higher threshold, while alower threshold will result in poor location accuracy caused by a large quantity of false positives.To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework formulti-class object detection. Specially, by analyzing the different roles that IoU can play in differentparts of the network, we propose an IoU-guided detection framework to reduce the loss of small objectinformation during training. Besides, the IoU-based weighted loss is designed, which can learn theIoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspectratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves theprecision of the results. Extensive experiments validate the effectiveness of our approach and weachieve state-of-the-art detection performance on the DOTA dataset.

Detection of Lung Nodules in Thoracic MDCT Images Based on Temporal Changes from Previous and Current Images

2011 ◽  
Vol 15 (6) ◽  
pp. 707-713 ◽  
Author(s):  
Shinya Maeda ◽  
◽  
Yasuyuki Tomiyama ◽  
Hyoungseop Kim ◽  
Noriaki Miyake ◽  
...  
Keyword(s):  
Neural Network ◽  
Temporal Change ◽  
Lung Nodules ◽  
Detection Accuracy ◽  
Multidetector Row Computed Tomography ◽  
Neural Network Classifier ◽  
Shape Features ◽  
Temporal Subtraction ◽  
Nodule Detection ◽  
Positive Results

Temporal subtraction enhances temporal change by subtracting images captured at different times. Medical images captured currently (current images) and in previous examination (previous images) are subtracted to enhance new lesions and temporal change in existing lesion shadows. Temporal subtraction using chest MultiDetector-Row Computed Tomography (MDCT) images and currently being developed is to be applied to nodule detection in pulmonary regions. Nodule detection using conventional temporal subtraction, however, yields many false-positive results for those 20 mm or less in diameter, requiring improvement. We discuss improvements in nodule detection accuracy using temporal subtraction, first extracting rough nodules from temporal subtraction images as candidate shadows. Features are then acquired from current, previous, and temporal subtraction images. We use intensity features in previous images and shape features in the current images and in features used in conventional methods. Using acquired features, we build a neural network classifier, then extract final pulmonary candidates in unknown shadows.

Research on High-Density Network Intrusion Features Detection Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 1054-1057
Author(s):  
Tai Fu Lv
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Systems ◽  
Feature Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
High Density ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
The Neural Network

Research on high-density network intrusion features problems, which improves the detection accuracy. For high-density network, an intrusion feature detection system based on intelligent expert systems and neural networks in is proposed. First, use expert systems for known high-density network intrusion detection. Use the neural network expert system to detect those which cannot find the unknown high-density network intrusion. Finally test results using neural network expert system rule library to be updated. Experimental results show that this method can effectively detect high-density network intrusion features, which ensures the security of the network and achieves satisfactory results.

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