scholarly journals Epileptic Seizures Detection Using DCT-II and KNN Classifier in Long-Term EEG Signals

2020 ◽  
pp. 2687-2694
Author(s):  
Mahmood A. Jumaah ◽  
Ammar Ibrahim Shihab ◽  
Akeel Abdulkareem Farhan
Keyword(s):  
Euclidean Distance ◽  
False Positive Rate ◽  
Age Groups ◽  
Epileptic Seizures ◽  
Detection Algorithm ◽  
Discrete Cosine Transformation ◽  
Maximum Variance ◽  
Knn Classifier ◽  
Positive Rate

     Epilepsy is one of the most common diseases of the nervous system around the world, affecting all age groups and causing seizures leading to loss of control for a period of time. This study presents a seizure detection algorithm that uses Discrete Cosine Transformation (DCT) type II to transform the signal into frequency-domain and extracts energy features from 16 sub-bands. Also, an automatic channel selection method is proposed to select the best subset among 23 channels based on the maximum variance. Data are segmented into frames of  one Second length without overlapping between successive frames. K-Nearest Neighbour (KNN) model is used to detect those frames either to ictal (seizure) or interictal (non-seizure) based on Euclidean distance. The experimental results are tested on 21 patients included in the CHB-MIT dataset. The average F1-score was found to be 93.12, whereas the False-Positive Rate (FPR) average was determined to be 0.07.

scholarly journals ScoreNet: A neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs

2020 ◽  
Author(s):  
Poomipat Boonyakitanont ◽  
Apiwat Lek-uthai ◽  
Jitkomut Songsiri
Keyword(s):  
Neural Network ◽  
Epileptic Seizure ◽  
False Positive Rate ◽  
Imbalanced Data ◽  
Detection Algorithm ◽  
Processing Technique ◽  
Post Processing ◽  
Scalp Eeg ◽  
Positive Rate ◽  
Eeg Recordings

AbstractThis article aims to design an automatic detection algorithm of epileptic seizure onsets and offsets in scalp EEGs. A proposed scheme consists of two sequential steps: the detection of seizure episodes, and the determination of seizure onsets and offsets in long EEG recordings. We introduce a neural network-based model called ScoreNet as a post-processing technique to determine the seizure onsets and offsets in EEGs. A cost function called a log-dice loss that has an analogous meaning to F1 is proposed to handle an imbalanced data problem. In combination with several classifiers including random forest, CNN, and logistic regression, the ScoreNet is then verified on the CHB-MIT Scalp EEG database. As a result, in seizure detection, the ScoreNet can significantly improve F1 to 70.15% and can considerably reduce false positive rate per hour to 0.05 on average. In addition, we propose detection delay metric, an effective latency index as a summation of the exponential of delays, that includes undetected events into account. The index can provide a better insight into onset and offset detection than conventional time-based metrics.

scholarly journals Malicious Domain Names Detection Algorithm Based on N-Gram

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hong Zhao ◽  
Zhaobin Chang ◽  
Guangbin Bao ◽  
Xiangyan Zeng
Keyword(s):  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Detection Algorithm ◽  
Internet Security ◽  
Domain Name ◽  
Domain Names ◽  
Malicious Attack ◽  
Positive Rate ◽  
N Gram

Malicious domain name attacks have become a serious issue for Internet security. In this study, a malicious domain names detection algorithm based on N-Gram is proposed. The top 100,000 domain names in Alexa 2013 are used in the N-Gram method. Each domain name excluding the top-level domain is segmented into substrings according to its domain level with the lengths of 3, 4, 5, 6, and 7. The substring set of the 100,000 domain names is established, and the weight value of a substring is calculated according to its occurrence number in the substring set. To detect a malicious attack, the domain name is also segmented by the N-Gram method and its reputation value is calculated based on the weight values of its substrings. Finally, the judgment of whether the domain name is malicious is made by thresholding. In the experiments on Alexa 2017 and Malware domain list, the proposed detection algorithm yielded an accuracy rate of 94.04%, a false negative rate of 7.42%, and a false positive rate of 6.14%. The time complexity is lower than other popular malicious domain names detection algorithms.

Efficient Half Face Detection System Based on Linear Binary Pattern

2020 ◽  
Vol 17 (5) ◽  
pp. 2342-2348
Author(s):  
Ashutosh Upadhyay ◽  
S. Vijayalakshmi
Keyword(s):  
Face Detection ◽  
Detection System ◽  
False Positive Rate ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Data Set ◽  
Computation Cost ◽  
Face Features ◽  
Positive Rate ◽  
The Face

In the field of computer vision, face detection algorithms achieved accuracy to a great extent, but for the real time applications it remains a challenge to maintain the balance between the accuracy and efficiency i.e., to gain accuracy computational cost also increases to deal with the large data sets. This paper, propose half face detection algorithm to address the efficiency of the face detection algorithm. The full face detection algorithm consider complete face data set for training which incur more computation cost. To reduce the computation cost, proposed model captures the features of the half of the face by assuming that the human face is symmetric about the vertical axis passing through the nose and train the system using reduced half face features. The proposed algorithm extracts Linear Binary Pattern (LBP) features and train model using adaboost classifier. Algorithm performance is presented in terms of the accuracy i.e., True Positive Rate (TPR), False Positive Rate (FTR) and face recognition time complexity.

scholarly journals Detection method based on improved faster R-CNN for pin defect in transmission lines

2021 ◽  
Vol 300 ◽  
pp. 01011
Author(s):  
Jun Wu ◽  
Sheng Cheng ◽  
Shangzhi Pan ◽  
Wei Xin ◽  
Liangjun Bai ◽  
...  
Keyword(s):  
Transmission Line ◽  
Defect Detection ◽  
Transmission Lines ◽  
False Positive Rate ◽  
Detection Algorithm ◽  
Detection Model ◽  
Positive Rate ◽  
Aerial Vehicle ◽  
The Stability ◽  
Small Targets

Defects such as insulator, pins, and counterweight in highvoltage transmission lines affect the stability of the power system. The small targets such as pins in the unmanned aerial vehicle (UAV) inspection images of transmission lines occupy a small proportion in the images and the characteristic representations are poor which results a low defect detection rate and a high false positive rate. This paper proposed a transmission line pin defect detection algorithm based on improved Faster R-CNN. First, the pre-training weights with higher matching degree are obtained based on transfer learning. And it is applied to construct defect detection model. Then, the regional proposal network is used to extract features in the model. The results of defect detection are obtained by regression calculation and classification of regional characteristics. The experimental results show that the accuracy of the pin defect detection of the transmission line reaches 81.25%

scholarly journals Adaptive and Lightweight Abnormal Node Detection via Biological Immune Game in Mobile Multimedia Networks

Algorithms ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 368
Author(s):  
Yajing Zhang ◽  
Kai Wang ◽  
Jinghui Zhang
Keyword(s):  
False Positive Rate ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Multimedia Networks ◽  
Mobile Multimedia ◽  
High Detection Rate ◽  
Multimedia Networking ◽  
High Detection ◽  
Positive Rate ◽  
Abnormal Node

Considering the contradiction between limited node resources and high detection costs in mobile multimedia networks, an adaptive and lightweight abnormal node detection algorithm based on artificial immunity and game theory is proposed in order to balance the trade-off between network security and detection overhead. The algorithm can adapt to the highly dynamic mobile multimedia networking environment with a large number of heterogeneous nodes and multi-source big data. Specifically, the heterogeneous problem of nodes is solved based on the non-specificity of an immune algorithm. A niche strategy is used to identify dangerous areas, and antibody division generates an antibody library that can be updated online, so as to realize the dynamic detection of the abnormal behavior of nodes. Moreover, the priority of node recovery for abnormal nodes is decided through a game between nodes without causing excessive resource consumption for security detection. The results of comparative experiments show that the proposed algorithm has a relatively high detection rate and a low false-positive rate, can effectively reduce consumption time, and has good level of adaptability under the condition of dynamic nodes.

scholarly journals Optimized transit detection algorithm to search for periodic transits of small planets

2019 ◽  
Vol 623 ◽  
pp. A39 ◽  
Author(s):  
Michael Hippke ◽  
René Heller
Keyword(s):  
Time Series ◽  
Light Curve ◽  
Signal Detection ◽  
Least Squares ◽  
Detection Efficiency ◽  
False Positive Rate ◽  
Detection Algorithm ◽  
Laptop Computer ◽  
Positive Rate ◽  
Long Time

We present a new method to detect planetary transits from time-series photometry, the transit least squares (TLS) algorithm. TLS searches for transit-like features while taking the stellar limb darkening and planetary ingress and egress into account. We have optimized TLS for both signal detection efficiency (SDE) of small planets and computational speed. TLS analyses the entire, unbinned phase-folded light curve. We compensated for the higher computational load by (i.) using algorithms such as “Mergesort” (for the trial orbital phases) and by (ii.) restricting the trial transit durations to a smaller range that encompasses all known planets, and using stellar density priors where available. A typical K2 light curve, including 80 d of observations at a cadence of 30 min, can be searched with TLS in ∼10 s real time on a standard laptop computer, as fast as the widely used box least squares (BLS) algorithm. We perform a transit injection-retrieval experiment of Earth-sized planets around sun-like stars using synthetic light curves with 110 ppm white noise per 30 min cadence, corresponding to a photometrically quiet KP = 12 star observed with Kepler. We determine the SDE thresholds for both BLS and TLS to reach a false positive rate of 1% to be SDE = 7 in both cases. The resulting true positive (or recovery) rates are ∼93% for TLS and ∼76% for BLS, implying more reliable detections with TLS. We also test TLS with the K2 light curve of the TRAPPIST-1 system and find six of seven Earth-sized planets using an iterative search for increasingly lower signal detection efficiency, the phase-folded transit of the seventh planet being affected by a stellar flare. TLS is more reliable than BLS in finding any kind of transiting planet but it is particularly suited for the detection of small planets in long time series from Kepler, TESS, and PLATO. We make our python implementation of TLS publicly available.

A high-performance brain switch based on code-modulated visual evoked potentials

2022 ◽  
Author(s):  
Li Zheng ◽  
Weihua Pei ◽  
Xiaorong Gao ◽  
Lijian Zhang ◽  
Yijun Wang
Keyword(s):  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Template Matching ◽  
High Performance ◽  
False Positive Rate ◽  
Detection Algorithm ◽  
Fast Reaction ◽  
Idle State ◽  
Positive Rate ◽  
Visual Evoked

Abstract Objective. Asynchronous brain-computer interfaces (BCIs) are more practical and natural compared to synchronous BCIs. A brain switch is a standard asynchronous BCI, which can automatically detect the specified change of the brain and discriminate between the control state and the idle state. The current brain switches still face challenges on relatively long reaction time (RT) and high false positive rate (FPR). Approach. In this paper, an online electroencephalography-based brain switch is designed to realize a fast reaction and keep long idle time (IDLE) without false positives (FPs) using code-modulated visual evoked potentials (c-VEPs). Two stimulation paradigms were designed and compared in the experiments: multi-code concatenate modulation (concatenation mode) and single-code periodic modulation (periodic mode). Using a task-related component analysis-based detection algorithm, EEG data can be decoded into a series of code indices. Brain states can be detected by a template matching approach with a sliding window on the output series. Main results. The online experiments achieved an average RT of 1.49 seconds when the average IDLE for each FP was 68.57 minutes (1.46e-2 FP/min) or an average RT of 1.67 seconds without FPs. Significance. This study provides a practical c-VEP based brain switch system with both fast reaction and low FPR during idle state, which can be used in various BCI applications.

scholarly journals Prevention of Crypto-Ransomware Using a Pre-Encryption Detection Algorithm

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 79 ◽  
Author(s):  
S. Kok ◽  
Azween Abdullah ◽  
NZ Jhanjhi ◽  
Mahadevan Supramaniam
Keyword(s):  
Machine Learning ◽  
Phase I ◽  
Phase Ii ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Application Programming Interface ◽  
Detection Algorithm ◽  
Positive Rate ◽  
New Type ◽  
Two Phases

Ransomware is a relatively new type of intrusion attack, and is made with the objective of extorting a ransom from its victim. There are several types of ransomware attacks, but the present paper focuses only upon the crypto-ransomware, because it makes data unrecoverable once the victim’s files have been encrypted. Therefore, in this research, it was proposed that machine learning is used to detect crypto-ransomware before it starts its encryption function, or at the pre-encryption stage. Successful detection at this stage is crucial to enable the attack to be stopped from achieving its objective. Once the victim was aware of the presence of crypto-ransomware, valuable data and files can be backed up to another location, and then an attempt can be made to clean the ransomware with minimum risk. Therefore we proposed a pre-encryption detection algorithm (PEDA) that consisted of two phases. In, PEDA-Phase-I, a Windows application programming interface (API) generated by a suspicious program would be captured and analyzed using the learning algorithm (LA). The LA can determine whether the suspicious program was a crypto-ransomware or not, through API pattern recognition. This approach was used to ensure the most comprehensive detection of both known and unknown crypto-ransomware, but it may have a high false positive rate (FPR). If the prediction was a crypto-ransomware, PEDA would generate a signature of the suspicious program, and store it in the signature repository, which was in Phase-II. In PEDA-Phase-II, the signature repository allows the detection of crypto-ransomware at a much earlier stage, which was at the pre-execution stage through the signature matching method. This method can only detect known crypto-ransomware, and although very rigid, it was accurate and fast. The two phases in PEDA formed two layers of early detection for crypto-ransomware to ensure zero files lost to the user. However in this research, we focused upon Phase-I, which was the LA. Based on our results, the LA had the lowest FPR of 1.56% compared to Naive Bayes (NB), Random Forest (RF), Ensemble (NB and RF) and EldeRan (a machine learning approach to analyze and classify ransomware). Low FPR indicates that LA has a low probability of predicting goodware wrongly.

An Innovative Approach for Detection of Armoured Vehicle in Airborne Thermal Imagery Using Morphological Processing and Texture Feature Extraction

2017 ◽  
Vol 26 (2) ◽  
pp. 359-370
Author(s):  
Asheesh Kumar Gautam ◽  
Lokesh K. Sinha ◽  
Mahendra R. Bhutiyani
Keyword(s):  
Gabor Filter ◽  
False Positive Rate ◽  
Mean Shift ◽  
Texture Feature ◽  
True Positive Rate ◽  
Detection Algorithm ◽  
Thermal Image ◽  
Morphological Processing ◽  
Thermal Imagery ◽  
Positive Rate

AbstractAutomatic detection of a vehicle in an airborne thermal imagery is a challenging research topic in computer vision, especially the detection of military tanks in the field. Various methodologies for detection in forward-looking infrared imagery, which has higher spatial resolution, has been discussed by a number of researchers in literature. The algorithm we developed in the present study detects tanks not only in higher resolution but in lower resolution imagery as well. Detection algorithm is initiated by the segmentation of thermal image using mean shift, which provides possible targets present in the field other than the background. To reduce clutter and uneven illumination in a thermal image, a pre-processing morphological algorithm based on top-hat filtering has been implemented. After convolution of image window with Gabor filter banks, we extracted the energy feature of each image generated after convolution. The energy vector of such a target and the neighbouring background window has been calculated, and the similarity between the target and background using distance-measuring method has been measured. The minimum distance is used as the threshold to decide the target. A comparative study has been carried out between tanks and various targets/objects that appear similar to tanks in a thermal image. This validates our target detection algorithm. The false-positive rate and true-positive rate have been calculated for performance evaluation. Overall, this algorithm shows promising results for tank detection using single-band thermal imagery.

scholarly journals Hue–saturation–intensity and texture feature-based cloud detection algorithm for unmanned aerial vehicle images

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142090353
Author(s):  
Wang Yi ◽  
Zhang Jing ◽  
Gao Shuang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Target Identification ◽  
False Positive Rate ◽  
Texture Feature ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Detection Algorithms ◽  
Saturation Intensity ◽  
Positive Rate ◽  
Aerial Vehicle

There are a large number of cloud-covered areas in most unmanned aerial vehicle images and lead to the loss of information in the image and affect image post procession such as image fusion and target identification. Finding the cloud-occluded area in an image is a key step in image processing. Based on the differences of color and texture characteristics between cloud and ground, a cloud detection algorithm for the unmanned aerial vehicle images is proposed. Simulation results show that the proposed algorithm is better than the classical cloud detection algorithms in accuracy rate, false-positive rate, and kappa coefficient.

Sign in / Sign up

Export Citation Format

Share Document