Neural Network Model For Detection Research Articles

Analysis of neural network detectors for network attacks

While network attacks play a critical role in many advanced persistent threat (APT) campaigns, an arms race exists between the network defenders and the adversary: to make APT campaigns stealthy, the adversary is strongly motivated to evade the detection system. However, new studies have shown that neural network is likely a game-changer in the arms race: neural network could be applied to achieve accurate, signature-free, and low-false-alarm-rate detection. In this work, we investigate whether the adversary could fight back during the next phase of the arms race. In particular, noticing that none of the existing adversarial example generation methods could generate malicious packets (and sessions) that can simultaneously compromise the target machine and evade the neural network detection model, we propose a novel attack method to achieve this goal. We have designed and implemented the new attack. We have also used Address Resolution Protocol (ARP) Poisoning and Domain Name System (DNS) Cache Poisoning as the case study to demonstrate the effectiveness of the proposed attack.

A Dynamic Detection Method for Railway Slope Falling Rocks Based on the Gaussian Mixture Model Segmentation Algorithm

Rockfall intrusion detection is crucial for the safety management of railway operations, and video detection methods help reduce deployment costs and improve detection efficiency. Mainstream neural network-based video detection methods have rapidly evolved in recent years but struggle to adapt to complex scenarios such as existing railway slope constructions due to weak generalization ability, low accuracy, and limited information acquisition. Therefore, this paper introduces a dynamic neural network detection model and establishes a dataset for rockfall intrusions in existing railway slope scenarios. The model initially relies on the YOLOv5 neural network, adopting an activation function suitable for the target scenario and addressing overfitting to achieve precise target recognition. Based on the neural network, the model dynamically detects rolling rockfalls by integrating a background subtraction algorithm based on the Gaussian Mixture Model and captures target dimensions using monocular vision technology, thus broadening the dimensions of detection information. Trials conducted on a railway in Shandong, China, demonstrate that the model accurately identifies moving rockfalls along the railway slopes and acquires the dimensions of moving rockfalls, successfully filtering out low-risk targets in the scene.

Design of an efficient neural network model for detection and classification of phase loss faults for three-phase induction motor

AbstractIn industrial applications, three-phase induction motors (IMs) are widely used, and they are subjected to many types of faults. One such fault is the loss of a motor phase caused by a blown fuse, a broken wire, mechanical damage, etc. When this fault occurs during motor operation, it continues to rotate but experiences rapid heating, which can ultimately lead to motor failure. Therefore, various protection devices are available to protect the motor against this fault, but most traditional protection devices do not offer a comprehensive classification of such a fault. So, in this paper, an efficient neural network model is presented for detecting and classifying 12 types of phase loss faults for a three-phase induction motor (IM) based on factors such as the unhealthy phase, fault location, and motor action modes (standstill, transient, and steady-state modes). Thus, the main goal of this work is to determine the motor mode during the fault, the defective phase, and its location to help the maintenance team repair the fault quickly. The system is simulated and tested using the “MATLAB/Simulink” software, employing a feed-forward neural network. The simulation results demonstrate that the proposed network achieves correct detection and classification of phase loss faults within a short time frame from the occurrence of the fault. Therefore, the proposed network model proves to be a simple and reliable solution for integration into the protection system of a three-phase IM, enabling the detection and classification of various phase loss faults.

Performance evaluation of convolution neural network models for detection of abnormal and ventricular ectopic beat cardiac episodes

Performance evaluation of convolution neural network models for detection of abnormal and ventricular ectopic beat cardiac episodes

ARIA: Augmented Reality and Artificial Intelligence enabled mobile application for Yield and grade prediction of tomato crops

Agriculture is the most crucial sector of the Indian economy. Lately, there has been a surge in the usage of technologies like deep learning and computer vision to make the process of agriculture modern and consequently, lessening the mistakes related to conventional processes. This work delivers a user-friendly, accessible, and novel approach for the detection, counting, and grading of tomatoes found on a farm. An Augmented Reality (AR) based mobile application is developed to obtain the images efficiently from a tomato farm in the pre-harvest stage subjected to open situations. The proposed approach uses Faster RCNN, a convolutional neural network model for detection of tomatoes from the input image on a large scale. The proposed model is trained and tested using 2083 images. The results are then analyzed for the overall performance of detection, segmentation, and classification of tomatoes. The results have examined the efficiency of the proposed mobile application and demonstrate the robustness it exhibits for the detection, grading and yield prediction of Tomatoes.

A neural network model for detection and classification of lumbar spinal stenosis on MRI

ObjectivesTo develop a three-stage convolutional neural network (CNN) approach to segment anatomical structures, classify the presence of lumbar spinal stenosis (LSS) for all 3 stenosis types: central, lateral recess and foraminal and assess its severity on spine MRI and to demonstrate its efficacy as an accurate and consistent diagnostic tool.MethodsThe three-stage model was trained on 1635 annotated lumbar spine MRI studies consisting of T2-weighted sagittal and axial planes at each vertebral level. Accuracy of the model was evaluated on an external validation set of 150 MRI studies graded on a scale of absent, mild, moderate or severe by a panel of 7 radiologists. The reference standard for all types was determined by majority voting and in case of disagreement, adjudicated by an external radiologist. The radiologists’ diagnoses were then compared to the diagnoses of the model.ResultsThe model showed comparable performance to the radiologist average both in terms of the determination of presence/absence of LSS as well as severity classification, for all 3 stenosis types. In the case of central canal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.971, 0.864, 0.963) for binary (presence/absence) classification compared to the radiologist average of (0.786, 0.899, 0.842). For lateral recess stenosis, the sensitivity, specificity and AUROC of the CNN were (0.853, 0.787, 0.907) compared to the radiologist average of (0.713, 0.898, 805). For foraminal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.942, 0.844, 0.950) compared to the radiologist average of (0.879, 0.877, 0.878). Multi-class severity classifications showed similarly comparable statistics.ConclusionsThe CNN showed comparable performance to radiologist subspecialists for the detection and classification of LSS. The integration of neural network models in the detection of LSS could bring higher accuracy, efficiency, consistency, and post-hoc interpretability in diagnostic practices.

A study of ensemble feature selection and adversarial training for malicious user detection

The continuously booming of information technology has shed light on developing a variety of communication networks, multimedia, social networks and Internet of Things applications. However, users inevitably suffer from the intrusion of malicious users. Some studies focus on static characteristics of malicious users, which is easy to be bypassed by camouflaged malicious users. In this paper, we present a malicious user detection method based on ensemble feature selection and adversarial training. Firstly, the feature selection alleviates the dimension disaster problem and achieves more accurate classification performance. Secondly, we embed features into the multidimensional space and aggregate it into a feature map to encode the explicit content preference and implicit interaction preference. Thirdly, we use an effective ensemble learning which could avoid over-fitting and has good noise resistance. Finally, we propose a datadriven neural network detection model with the regularization technique adversarial training to deeply analyze the characteristics. It simplifies the parameters, obtaining more robust interaction features and pattern features. We demonstrate the effectiveness of our approach with numerical simulation results for malicious user detection, where the robustness issues are notable concerns.

Fighting against COVID‐19: Innovations and applications

Fighting against <scp>COVID</scp>‐19: Innovations and applications

A Convolutional Neural Network Model for Detection of COVID -19 Disease and Pneumonia

Introduction: COVID -19 has had a devastating impact on public health around the world. Since early diagnosis and timely treatment have an impact on reducing mortality due to infection with COVID -19 and existing diagnostic methods such as RT -PCR test are prone to error, the alternative solution is to use artificial intelligence and image processing techniques. The overall goal is to introduce an intelligent model based on deep learning and convolutional neural network to identify cases of COVID -19 and pneumonia for the purpose of subsequent treatment measures with the help of lung medical images. Method: The proposed model includes two datasets of radiography and CT -scan. These dataset s are pre - processed and the data enhancement process is applied to the images. In the next step, three architectures EfficientNetB4, InceptionV3 , and InceptionResNetV2 are used using transfer learning method. Results: The best result obtained for CT -scan images belongs to the InceptionResNetV2 architecture with an accuracy of 99.366% and for radiology images related to the InceptionV3 architecture with an accuracy of 96.943%. In addition, the results indicate that CT -scan images have more features than radiographic images, and disease diagnosis is performed more accurately on this type of data. Conclusion: The proposed model based on a convolutional neural network has higher accuracy than other similar models. Also, this method by generating instant results can help in the initial evaluation of patients in medical centers, especially during the peak of epidemics, when medical centers face various challenges , such as lacking specialists and medical staffs .

Development of a deep convolutional neural network model for detection and delineation of coal mining regions

Development of a deep convolutional neural network model for detection and delineation of coal mining regions

Neural network model for detection of changes in forest environment using multispectral images

The article presents the task of detecting changes in the forest environment using multispectral images for balanced forest management and monitoring of the regional forest ecosystem. The method of semantic segmentation based on assigning each pixel a corresponding class label is used for recognition, object classification on the image, and analysis of multispectral images. By studying the spectral characteristics of pixels, the neural network automatically extracts and memorizes features from the data, using them for classification and change detection. Experimental results based on the developed software prototype confirm the reliability of the theoretical aspects of the model for change detection in the forest environment. Moreover, the neural network model for detecting changes in the forest environment using multispectral images has practical significance and can be applied to solve real tasks of the regional forest ecosystem of the Republic of Buryatia.

Hierarchical neural network detection model based on deep context and attention mechanism

Hierarchical neural network detection model based on deep context and attention mechanism

Hierarchical neural network detection model based on deep context and attention mechanism

Hierarchical neural network detection model based on deep context and attention mechanism

Hybrid deeper neural network model for detection of the Domain Name System over Hypertext markup language protocol traffic flooding attacks

Hybrid deeper neural network model for detection of the Domain Name System over Hypertext markup language protocol traffic flooding attacks

CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays.

Coronavirus (COVID-19) has adversely harmed the healthcare system and economy throughout the world. COVID-19 has similar symptoms as other chest disorders such as lung cancer (LC), pneumothorax, tuberculosis (TB), and pneumonia, which might mislead the clinical professionals in detecting a new variant of flu called coronavirus. This motivates us to design a model to classify multi-chest infections. A chest x-ray is the most ubiquitous disease diagnosis process in medical practice. As a result, chest x-ray examinations are the primary diagnostic tool for all of these chest infections. For the sake of saving human lives, paramedics and researchers are working tirelessly to establish a precise and reliable method for diagnosing the disease COVID-19 at an early stage. However, COVID-19’s medical diagnosis is exceedingly idiosyncratic and varied. A multi-classification method based on the deep learning (DL) model is developed and tested in this work to automatically classify the COVID-19, LC, pneumothorax, TB, and pneumonia from chest x-ray images. COVID-19 and other chest tract disorders are diagnosed using a convolutional neural network (CNN) model called CDC Net that incorporates residual network thoughts and dilated convolution. For this study, we used this model in conjunction with publically available benchmark data to identify these diseases. For the first time, a single deep learning model has been used to diagnose five different chest ailments. In terms of classification accuracy, recall, precision, and f1-score, we compared the proposed model to three CNN-based pre-trained models, such as Vgg-19, ResNet-50, and inception v3. An AUC of 0.9953 was attained by the CDC Net when it came to identifying various chest diseases (with an accuracy of 99.39%, a recall of 98.13%, and a precision of 99.42%). Moreover, CNN-based pre-trained models Vgg-19, ResNet-50, and inception v3 achieved accuracy in classifying multi-chest diseases are 95.61%, 96.15%, and 95.16%, respectively. Using chest x-rays, the proposed model was found to be highly accurate in diagnosing chest diseases. Based on our testing data set, the proposed model shows significant performance as compared to its competitor methods. Statistical analyses of the datasets using McNemar’s, and ANOVA tests also showed the robustness of the proposed model.

A deep learning-based COVID-19 classification from chest X-ray image: case study.

The novel corona virus disease (COVID-19) is a pandemic disease that is currently affecting over 200 countries around the world and more than 6 millions of people died in last 2 years. Early detection of COVID-19 can mitigate and control its spread. Reverse transcription polymerase chain reaction (RT-CPR), Chest X-ray (CXR) scan, and Computerized Tomography (CT) scan are used to identify the COVID-19. Chest X-ray image analysis is relatively time efficient than compared with RT-CPR and CT scan. Its cost-effectiveness make it a good choice for COVID-19 Classification. We propose a deep learning based Convolutional Neural Network model for detection of COVID-19 from CXR. Chest X-ray images are collected from various sources dataset for training with augmentation and evaluating our model, which is widely used for COVID-19 detection and diagnosis. A Deep Convolutional neural network (CNN) based model for analysis of COVID-19 with data augmentation is proposed, which uses the patient’s chest X-ray images for the diagnosis of COVID-19 with an aim to help the physicians to assist the diagnostic process among high workload conditions. The overall accuracy of 93 percent for COVID-19 Classification is achieved by choosing best optimizer.

Multi-classification neural network model for detection of abnormal heartbeat audio signals

Nowadays, heart disease is the leading cause of death. The high mortality rate and escalating occurrence of heart diseases worldwide warrant the requirement for a fast and efficient diagnosis of such ailments. The purpose is to design an automated system for the classification of abnormal heartbeat audio signals to assist cardiologists. To the best of our knowledge, this is the first study that uses a single neural network model for the classification of eight different types of heartbeat audio signals. The proposed recurrent neural network (RNN) model using Long short-term memory (LSTM) is developed on two publically available databases such as the PASCAL challenge and the 2017 PhysioNet challenge. Mel frequency cepstrum coefficient (MFCC) is applied to extract the dominant features, and a bandpass filter is used to remove the noise from both of the datasets. Afterward, the downsampling technique is used to fix the size of the sampling rate of each sound signal to 20KHz and 300 Hz for the Pascal and PhysioNet database, respectively. The proposed model is compared with multi-layer perceptron (MLP) in terms of different performance evaluation matrices. Furthermore, the outcomes of five machine learning (ML) models are also analyzed. The proposed model has achieved the highest classification accuracy of 0.9971 on the Pascal database, and 0.9870 accuracy on the PhysioNet challenge dataset, which is consistently superior to its competitor approaches. The proposed model provides significant assistance to the cardiac consultant in detecting heart valve diseases.

LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images.

The syndrome called COVID‐19 which was firstly spread in Wuhan, China has already been declared a globally “Pandemic.” To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence‐based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network‐based “LiteCovidNet” model is proposed that detects COVID‐19 cases as the binary class (COVID‐19, Normal) and the multi‐class (COVID‐19, Normal, Pneumonia) bifurcated based on chest X‐ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi‐class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID‐19 infected patients at an early stage with convenient cost and better accuracy.

Integrating Neural Network for Pest Detection in Controlled Environment Vertical Farm

Background: An integrated system for creating and maintaining controlled environment ideal for vertical farming prototype is demonstrated. The requirement of optimal artificial light for different growth stages of tomato and chilli plants is studied in detail and CNN model-based method for detection and classification of Leaf disease is also developed. Methods: The artificial environment ensuring adequate artificial lighting, moisture, and minerals was create by implanting various sensors and actuators to the plant beds and connected in network through a cloud based remote server. A CMOS image sensor module was used to monitor the various stages of plant growth. Findings: The duration and intensity requirement for germination, vegetation and flowering of both tomato and chilli plants are relatively lesser with artificial light condition than with sunlight. At the end of fifth epoch the developed convolution neural network model for detection and classification of leaf disease produced training and validation accuracies of 84.8% and 67.2%, respectively. Novelty: For different growth stages of tomato and chilli plants in north eastern India, the requirement of optimal artificial light is studied by exposing them to different light intensities. The study was conducted during summer (May-June) when the average sun exposure in eastern India was ~130- 190 hours. The captured images and data generated were used to monitor the status of the crops and identifying diseases with the application of Deep Learning models. Convolutional Neural Network (CNN) model-based method for detection and classification of leaf disease is presented. Keywords: Convolution Neural Network; Vertical Farming; Artificial Light; Pest Detection; Light Emitting Diode

Comparative analysis on deep neural network models for detection of cyberbullying on Social Media

Social media usage has been increased and it consists of both positive and negative effects. By considering the misusage of social media platforms by various cyberbullying methods like stalking, harassment there should be preventive methods to control these and to avoid mental stress. These extra words will expand the size of the vocabulary and influence the performance of the algorithm. Therefore, we come up with variant deep learning models like LSTM, BI-LSTM, RNN, BI-RNN, GRU, BI-GRU to detect cyberbullying in social media. These models are applied on Twitter, public comments data and performance were observed for these models and obtained improved accuracy of 90.4%.