scholarly journals Optimized Scale-Invariant Hog Descriptors for Tobacco Plant Detection

2021 ◽  
Vol 17 ◽  
pp. 787-794
Author(s):  
M. T. Thirthe Gowda ◽  
J. Chandrika
Keyword(s):  
Detection Rate ◽  
Performance Metrics ◽  
Detection System ◽  
Tobacco Plant ◽  
Research Work ◽  
Scale Invariant ◽  
High Detection Rate ◽  
Scale Range ◽  
Computationally Intensive ◽  
The Impact

The histogram of gradient (HOG) descriptor is being employed in this research work to demonstrate the technique of scale variant to identify the plant in surveillance videos. In few scenarios, the discrepancies in the histogram of gradient descriptors along with scale as well as variation in illumination are considered as one of the major hindrances. This research work introduces a unique SIO-HOG descriptor that is approximated to be scale-invariant. With the help of the footage that is captured from the tobacco plant identification process, the system can integrate adoptive bin selections as well as sample resizing. Further, this research work explores the impact of a PCA transform that is based on the process of feature selection on the performance of overall recognition and thereby considering finite scale range, adoptive orientation binning in non-overlapping descriptors, as well as finite scale range are all essential for a high detection rate. The feature vector of HOG over a complete search window is computationally intensive. However, suitable frameworks for classification can be developed by maintaining a precise range of attributes with finite Euclidean distance. Experimental results prove that the proposed approach for detecting tobacco from other weeds has resulted in an improved detection rate. And finally, the robustness of the complete plant detection system was evaluated on a video sequence with different non-linearity's that is quite common in a real-world environment and its performance metrics are evaluated

scholarly journals Network Anomaly Detection System with Optimized DS Evidence Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yuan Liu ◽  
Xiaofeng Wang ◽  
Kaiyu Liu
Keyword(s):  
Anomaly Detection ◽  
Detection Rate ◽  
Computer Network ◽  
Detection System ◽  
Evidence Theory ◽  
Optimization Methods ◽  
High Detection Rate ◽  
Detection Model ◽  
Network Anomaly Detection ◽  
Anomaly Detection System

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor’s regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

scholarly journals Financial Control Systems and Financial Systems Theory: Free Cash Flow and Profitability Nexus: a Comparative Study of Manufacturing Firms in Nigeria and Ghana

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Cash Flow ◽  
Net Present Value ◽  
Performance Metrics ◽  
Positive Impact ◽  
Research Work ◽  
Manufacturing Firms ◽  
Free Cash Flow ◽  
Estimation Model ◽  
Financial Systems ◽  
The Impact

In Financial Systems, the impact of Free Cash Flow (FCF) on the performance of a company has been in the center of academic discourse in recent years. Several studies have tried to ascertain the nature and magnitude of the relationship between free cash flow and firm profitability with conflicting results coming from different scholars. The main objective of this research work was to examine the impact of FCF on the profitability of quoted manufacturing firms in the Nigerian and Ghana stock exchanges. Data were pooled from twenty (20) different companies (ten each from Nigeria and Ghana) for a period of six years (2012 – 2017). A panel data estimation model was used to measure the impact of FCF and other performance metrics on the Return on Assets (ROA), which is our chosen profitability measure. The results show a positive but insignificant relationship between FCF and ROA both for Ghana and Nigerian manufacturing firms. Also, sales growth showed a positive impact on profitability of both countries while leverage negatively impacted on profitability. with Ghana being significant at 5%. The implication of the findings of the study is that it makes no business sense for companies to keep piling up excess funds beyond that which is needed for transactional purposes. The similarity between the results from Ghana and Nigeria in most of the variables shows that the findings of this study can be generalized to other countries. Based on the findings of the study, we recommend that the management of companies should strive to keep only the minimum needed free cash flow while the rest should be invested in other projects with positive net present value

scholarly journals A Comparative Study of Data Mining Algorithms for High Detection Rate in Intrusion Detection System

2018 ◽  
Vol 2 (1) ◽  
pp. 49-57 ◽  
Author(s):  
Nabeela Ashraf ◽  
Waqar Ahmad ◽  
Rehan Ashraf
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Huge Amount ◽  
High Detection Rate ◽  
Data Mining Algorithms ◽  
Secure Networks ◽  
Mining Algorithms ◽  
Speed And Accuracy

Due to the fast growth and tradition of the internet over the last decades, the network security problems are increasing vigorously. Humans can not handle the speed of processes and the huge amount of data required to handle network anomalies. Therefore, it needs substantial automation in both speed and accuracy. Intrusion Detection System is one of the approaches to recognize illegal access and rare attacks to secure networks. In this proposed paper, Naive Bayes, J48 and Random Forest classifiers are compared to compute the detection rate and accuracy of IDS. For experiments, the KDD_NSL dataset is used.

VitaFALL: Advanced multi-threshold based reliable fall detection system

2020 ◽  
Vol 13 ◽  
Author(s):  
Warish D. Patel ◽  
Chirag Patel ◽  
Monal Patel
Keyword(s):  
Detection System ◽  
Wearable Sensors ◽  
Research Work ◽  
Text Message ◽  
Vital Signs ◽  
Early Death ◽  
Fall Detection ◽  
High Detection Rate ◽  
Aged People ◽  
Fall Prediction

Background: The biggest challenge in our technologically advanced society is the healthy being of aging individuals and differently-abled people in our society. The leading cause for significant injuries and early death in senior citizens and differently-abled people is due to falling off. The possibility to automatically detect falls has increased demand for such devices, and the high detection rate is achieved using the wearable sensors, this technology has a quite social and monetary impact on society. So even for the daily activity in the life of aged people, an automatically fall detecting system and vital signs examining system become a necessity. Objectives: This research work aims at helping aged people and every other necessary human by monitoring their vital signs and fall prediction. A fall detection VitaFALL (Vital Signs and Fall Monitoring) device, could analyze the measurement in all three orthogonal directions using a triple-axis accelerometer, and Vital Signs Parameters (Heartrate, Heartbeat, and Temperature monitoring) for the aged and differently-abled people. Methods: Comparison with Present Algorithms, there are various benefits regarding privacy, success rate, and design of devices upgraded using an implemented algorithm over the ubiquitous algorithm. Results: As concluded from the experimental outcomes, the accuracy achieved is up to 94%, ADXL335 is a 3-Axial Accelerometer Module that collects the accelerations of aged people from a VitaFALL device. A guardian can be notified by sending a text message via GSM and GPRS module so that aged can be helped. Conclusion: However, a delay in the time can be noticed while comparing the gradient and minimum value to predetermine the state of the older person. The experiment results show the adequacy of the proposed approach.

scholarly journals Detection of Attacks in Pervasive Computing Using Gated Recurrent Unit Based on Bidirectional Weighted Feature Averaging

2021 ◽  
Author(s):  
P. Rajasekaran ◽  
V Magudeeswaran
Keyword(s):  
Pervasive Computing ◽  
Detection Rate ◽  
Performance Metrics ◽  
Personal Data ◽  
Cyber Attacks ◽  
High Detection Rate ◽  
The Social ◽  
High Detection ◽  
Different Types ◽  
Gated Recurrent Unit

Abstract In the era of information technology, the new types of cyber-attacks affect the performance of the network, which is very risky and cannot be restored quickly. In pervasive computing, there are more chances for such types of attacks since the personal data of the user is closely connected to the social environment. The research is performed using SNMP-MIB dataset, and feature selection are made by using the Enhanced Salp Swarm Optimization to select the optimal features to identify the attacks by using wrapper techniques. Then, various types of attacks are appropriately distinguished with proposed classifier Gated Recurrent Unit Neural Network based on Bidirectional Weighted Feature Averaging for high detection rate and accuracy. The value of performance metrics obtained from the proposed method outperforms the existing methods in terms of 99.9% accuracy, 99.8% in precision and detection rate is 99% in classifying different types of attacks.

A robust method to authenticate car license plates using segmentation and ROI based approach

2019 ◽  
Vol 9 (4) ◽  
pp. 737-747 ◽  
Author(s):  
Akarsh Aggarwal ◽  
Anuj Rani ◽  
Manoj Kumar
Keyword(s):  
False Positive ◽  
Detection Rate ◽  
Performance Metrics ◽  
Detection System ◽  
False Positive Rate ◽  
Input Image ◽  
License Plate ◽  
Phase Detection ◽  
Content Type ◽  
Positive Rate

Purpose The purpose of this paper is to explore the challenges faced by the automatic recognition systems over the conventional systems by implementing a novel approach for detecting and recognizing the vehicle license plates in order to increase the security of the vehicles. This will also increase the societal discipline among vehicle users. Design/methodology/approach From a methodological point of view, the proposed system works in three phases which includes the pre-processing of the input image from the database, applying segmentation to the processed image, and finally extracting and recognizing the image of the license plate. Findings The proposed paper provides an analysis that demonstrates the correctness of the algorithm to correctly capture the license plate using performance metrics such as detection rate and false positive rate. The obtained results demonstrate that the proposed algorithm detects vehicle license plates and provides detection rate of 93.34 percent with false positive rate of 6.65 percent. Research limitations/implications The proposed license plate detection system eliminates the need of manually used systems for managing the traffic by installing the toll-booths on freeways and bridges. The design implemented in this paper attempts to capture the license plate by using three phase detection process that helps to increase the level of security and contribute in making a sustainable city. Originality/value This paper presents a distinctive approach to detect the license plate of the vehicles using the various image processing techniques such as dilation, grey-scale conversion, edge processing, etc. and finding the region of interest of the segmented image to capture the license plate of the vehicles.

scholarly journals Convolutional Neural Network based Intelligent Network Intrusion Detection System

2020 ◽  
Vol 6 (9) ◽  
pp. 1-4
Author(s):  
Levina Bisen ◽  
Sumit Sharma
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Research Work ◽  
Visual Fields ◽  
Intelligent Network ◽  
High Detection Rate ◽  
Network Intrusion

Today cyberspace is developing tremendously, and the Intrusion Detection System (IDS) plays a key role in information security. The IDS, which operates at the network and host levels, should be able to identify various malicious attacks. The job of network-based IDSs is to distinguish between normal and malicious traffic data and trigger an alert in the event of an attack. In addition to traditional signature-based and anomaly-based approaches, many researchers have used various deep learning (DL) techniques to detect intruders, as DL models are capable of automatically extracting salient features from the input data packets. The application of the Convolutional Neural Network (CNN), which is often used to solve research problems in the visual and visual fields, is not much explored for IDS. In this research work the proposed model for intrusion detection is based on feature selection and reduction using CNN and classification using random forest. As compared to some existing work the proposed algorithm proves its efficiency in terms of high accuracy and high detection rate.

LSO-AdaBoost Based Face Detection for IP-CAM Video

2013 ◽  
Vol 284-287 ◽  
pp. 3543-3548 ◽  
Author(s):  
Chuang Jan Chang ◽  
Shu Lin Hwang
Keyword(s):  
Video Surveillance ◽  
Face Detection ◽  
Motion Detection ◽  
Detection Rate ◽  
Detection System ◽  
Training Data ◽  
Surveillance Systems ◽  
High Detection Rate ◽  
Data Set ◽  
Adaboost Algorithm

The IP-CAM plays a major role in the context of digital video surveillance systems. The function of face detection can add extra value and can contribute towards an intelligent video surveillance system. The cascaded AdaBoost-based face detection system proposed by Viola can support real-time detection with a high detection rate. The performance of the Alt2 cascade (from OpenCV) in an IP-CAM video is worse than that with regard to static images because the training data set in the Alt2 cannot consider the localized characters in the special IP-CAM video. Therefore, this study presents an enhanced training method using the Adaboost algorithm which is capable of obtaining the localized sampling optimum (LSO) from a local IP-CAM video. In addition, we use an improved motion detection algorithm that cooperates with the former face detector to speed up processing time and achieve a better detection rate on video-rate processing speed. The proposed solution has been developed around the cascaded AdaBoost approach, using the open-CV library, with a LSO from a local IP-CAM video. An efficient motion detection model is adopted for practical applications. The overall system performance using 30% local samples can be improved to a 97.9% detection rate and reduce detection time by 54.5% with regard to the Alt2 cascade.

scholarly journals Enhanced Host-Based Intrusion Detection Using System Call Traces

2019 ◽  
Vol 8 (2) ◽  
pp. 93-109
Author(s):  
Yaqoob S. Ikram Yaqoob S. Ikram
Keyword(s):  
Intrusion Detection ◽  
Detection Rate ◽  
Environmental Changes ◽  
Detection System ◽  
Computational Cost ◽  
System Call ◽  
High Detection Rate ◽  
Learning Time ◽  
System Calls ◽  
Almost All

To detect zero-day attacks in modern systems, several host-based intrusion detection systems are proposed using the newly compiled ADFA-LD dataset. These techniques use the system call traces of the dataset to detect anomalies, but generally they suffer either from high computational cost as in window-based techniques or low detection rate as in frequency-based techniques. To enhance the accuracy and speed, we propose a host-based intrusion detection system based on distinct short sequences extraction from traces of system calls with a novel algorithm to detect anomalies. To the best of our knowledge, the obtained results of the proposed system are superior to all up-to-date published systems in terms of computational cost and learning time. The obtained detection rate is also much higher than almost all compared systems and is very close to the highest result. In particular, the proposed system provides the best combination of high detection rate and very small learning time. The developed prototype achieved 90.48% detection rate, 22.5% false alarm rate, and a learning time of about 30 seconds. This provides high capability to detect zero-day attacks and also makes it flexible to cope with any environmental changes since it can learn quickly and incrementally without the need to rebuild the whole classifier from scratch.

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