scholarly journals Research on Household Waste Detection System Based on Deep Learning

2021 ◽  
Vol 267 ◽  
pp. 01034
Author(s):  
Li Mingyang ◽  
Li Chengrong
Keyword(s):  
Deep Learning ◽  
Target Tracking ◽  
Urban Environment ◽  
Detection System ◽  
Household Waste ◽  
Data Set ◽  
Style Transfer ◽  
Mechanism Model ◽  
Sorting Tasks ◽  
Day By Day

Household waste is threatening the urban environment increasingly day by day for people’s material needs increasing with the acceleration of urbanization. In this paper, a new waste sorting model is proposed to solve the problems of waste sorting. The style transfer was used to increase the data set to make some objects be sorted well. Then the rotational attention mechanism model was used to increase the accuracy of waste sorting of the blocked objects. The representation vector extraction module in the target tracking algorithm Deep Sort was replaced with Siamese network to make the network more lightweight. As a result, this paper effectively solves the current waste sorting tasks.

Semi supervised inspection algorithm of automatic packaging curve based on deep learning

2021 ◽  
pp. 1-10
Author(s):  
Yong He
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Optimization Algorithm ◽  
Posterior Probability ◽  
Detection Method ◽  
Detection System ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Data Set ◽  
Packaging Process

The current automatic packaging process is complex, requires high professional knowledge, poor universality, and difficult to apply in multi-objective and complex background. In view of this problem, automatic packaging optimization algorithm has been widely paid attention to. However, the traditional automatic packaging detection accuracy is low, the practicability is poor. Therefore, a semi-supervised detection method of automatic packaging curve based on deep learning and semi-supervised learning is proposed. Deep learning is used to extract features and posterior probability to classify unlabeled data. KDD CUP99 data set was used to verify the accuracy of the algorithm. Experimental results show that this method can effectively improve the performance of automatic packaging curve semi-supervised detection system.

scholarly journals DEEP LEARNING: NEW APPROACH FOR DETECTING SCHOLAR EXAMS FRAUD

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 103-107
Author(s):  
S. El Kohli ◽  
Y. Jannaj ◽  
M. Maanan ◽  
H. Rhinane
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Experimental Studies ◽  
Validation Data ◽  
Data Set ◽  
New Approach ◽  
Detection Systems ◽  
Machine Learning Applications ◽  
Worldwide Phenomenon ◽  
3D Cnn

Abstract. Cheating in exams is a worldwide phenomenon that hinders efforts to assess the skills and growth of students. With scientific and technological progress, it has become possible to develop detection systems in particular a system to monitor the movements and gestures of the candidates during the exam. Individually or collectively. Deep learning (DL) concepts are widely used to investigate image processing and machine learning applications. Our system is based on the advances in artificial intelligence, particularly 3D Convolutional Neural Network (3D CNN), object detector methods, OpenCV and especially Google Tensor Flow, to provides a real-time optimized Computer Vision. The proposal approach, we provide a detection system able to predict fraud during exams. Using the 3D CNN to generate a model from 7,638 selected images and objects detector to identify prohibited things. These experimental studies provide a detection performance with 95% accuracy of correlation between the training and validation data set.

scholarly journals Robust Self-Adaptation Fall-Detection System Based on Camera Height

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3768 ◽  
Author(s):  
Kong ◽  
Chen ◽  
Wang ◽  
Chen ◽  
Meng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Fall Detection ◽  
Daily Activities ◽  
Detection Methods ◽  
Depth Camera ◽  
Detection Accuracy ◽  
Data Set ◽  
Human Segmentation ◽  
Self Adaptation

Vision-based fall-detection methods have been previously studied but many have limitations in terms of practicality. Due to differences in rooms, users do not set the camera or sensors at the same height. However, few studies have taken this into consideration. Moreover, some fall-detection methods are lacking in terms of practicality because only standing, sitting and falling are taken into account. Hence, this study constructs a data set consisting of various daily activities and fall events and studies the effect of camera/sensor height on fall-detection accuracy. Each activity in the data set is carried out by eight participants in eight directions and taken with the depth camera at five different heights. Many related studies heavily depended on human segmentation by using Kinect SDK but this is not reliable enough. To address this issue, this study proposes Enhanced Tracking and Denoising Alex-Net (ETDA-Net) to improve tracking and denoising performance and classify fall and non-fall events. Experimental results indicate that fall-detection accuracy is affected by camera height, against which ETDA-Net is robust, outperforming traditional deep learning based fall-detection methods.

scholarly journals Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 352
Author(s):  
Anyou Wang ◽  
Rong Hai ◽  
Paul J. Rider ◽  
Qianchuan He
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cancer Screening ◽  
Detection System ◽  
Population Level ◽  
Cancer Type ◽  
Multiple Cancer ◽  
Data Set ◽  
Cancer Types ◽  
Deep Learning Neural Network

Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types. We integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six biomarkers, our approach can easily discriminate any individual cancer type vs. normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can simultaneously multi-classify common cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions.: This detection system provides a promising practical framework for automatic cancer screening at population level. Key points: (1) We developed a practical cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify cancers vs. normal with >96% AUC. (3) In total, 26 individual cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple cancer types simultaneously with >82% accuracy.

scholarly journals Intelligent and Effective Intrusion Detection System using Machine Learning Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 237-240
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Training Time

Intrusion Detection System observes the network traffic and identifies the attack and also inform the admin to corrective action. Powerful Intrusion Detection system is required for detection to various modern attack. There is need of efficient Intrusion Detection system .The focus of IDS research is the application of machine Learning and Deep Learning techniques. Projected work is combination of Deep Learning Technique in which Non Symmetric Deep Auto Encoder and Machine Learning Algorithm, Support Vector Machine Classifier is used to develop the Model. Stack power of the Non symmetric Deep Auto Encoder and Quickness with exactness of the SVM makes the Model very efficient. This Model not only improves the accuracy value but also improve recall and precision. It also cause the reduction of training time .To evaluate the performance of the Model and do the analysis the special Data set which are used are KDD CUP and NSL KDD Dataset.

scholarly journals High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Yaser AbdulAali Jasim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
Plant Disease ◽  
Detection System ◽  
Open Data ◽  
Data Set ◽  
Performance Rate

Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence.  Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers. In this paper, models of the Convolutional neural network were designed to detect (diagnose) plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing (18,000) images of ten different plants, including healthy plants. Several model architectures have been trained to achieve the best performance of (97 percent) when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions.

scholarly journals Network Abnormal Data Detection Based on Deep Learning Model

CONVERTER ◽  
2021 ◽  
pp. 64-73
Author(s):  
Yang Dong
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Detection System ◽  
Feature Learning ◽  
Original Data ◽  
Detection Algorithm ◽  
Training Data ◽  
Data Detection ◽  
Classification Problems ◽  
Data Set

To improve intrusion detection system performance,many algorithms are used to improve the performance of IDS systems, especially deep learning models. This paper presents an algorithm based on the model MLP, the training data set is the KDD99 data set, and the original data of the data set is vectorized by one-hot encoding, and the feature data is processed by Z-Score, and then the feature vector is encoded, and then the multi-layer perception is used The machine network performs feature learning, and finally trains the classifier model for detection. Traditional network anomaly detection algorithm models mainly use manual selection methods, and the accuracy and efficiency of classification problems are not high. This article first proposed the role of multilayer perceptron in Adam optimizer. The test of the KDD99 data set has been completed. The algorithm accuracy rate can reach 99%. For future network abnormal data detection work, an algorithm model that can realize real-time online detection is provided, which will have higher accuracy and better real-time performance.

scholarly journals Pedestrian Re-identity Based on ResNet Lightweight Network

2021 ◽  
Vol 2083 (3) ◽  
pp. 032087
Author(s):  
Xingxing Li ◽  
Chao Duan ◽  
Panpan Yin ◽  
Ningxing Wang
Keyword(s):  
Deep Learning ◽  
Target Tracking ◽  
Loss Function ◽  
Cross Entropy ◽  
Learning Technology ◽  
Data Set ◽  
Tracking Tasks ◽  
Multi Target Tracking ◽  
Identity Based ◽  
And Migration

Abstract With the development of deep learning technology, pedestrian re-identity technology has been widely used in multi-target tracking and cross mirror tracking tasks. In this paper, the classical deep learning ResNet18 network is used for pedestrian re-identity tasks. The advantage of the network is that it can easily realize lightweight deployment. In addition, the labeled smooth cross entropy loss function and migration learning technology are used in the process of training the network, which can realize the accuracy of map 67.8 on the Market1501 data set while lightening the network, and promote the engineering landing of pedestrian re-identity network.

scholarly journals Software Defined Network Enabled Fog-to-Things Hybrid Deep Learning Driven Cyber Threat Detection System

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ihtisham Ullah ◽  
Basit Raza ◽  
Sikandar Ali ◽  
Irshad Ahmed Abbasi ◽  
Samad Baseer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Detection System ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Detection Accuracy ◽  
Software Defined Network ◽  
Centralized Control ◽  
Data Set ◽  
Iot Devices

Software Defined Network (SDN) is a next-generation networking architecture and its power lies in centralized control intelligence. The control plane of SDN can be extended to many underlying networks such as fog to Internet of Things (IoT). The fog-to-IoT is currently a promising architecture to manage a real-time large amount of data. However, most of the fog-to-IoT devices are resource-constrained and devices are widespread that can be potentially targeted with cyber-attacks. The evolving cyber-attacks are still an arresting challenge in the fog-to-IoT environment such as Denial of Service (DoS), Distributed Denial of Service (DDoS), Infiltration, malware, and botnets attacks. They can target varied fog-to-IoT agents and the whole network of organizations. The authors propose a deep learning (DL) driven SDN-enabled architecture for sophisticated cyber-attacks detection in fog-to-IoT environment to identify new attacks targeting IoT devices as well as other threats. The extensive simulations have been carried out using various DL algorithms and current state-of-the-art Coburg Intrusion Detection Data Set (CIDDS-001) flow-based dataset. For better analysis five DL models are compared including constructed hybrid DL models to distinguish the DL model with the best performance. The results show that proposed Long Short-Term Memory (LSTM) hybrid model outperforms other DL models in terms of detection accuracy and response time. To show unbiased results 10-fold cross-validation is performed. The proposed framework is so effective that it can detect several types of cyber-attacks with 99.92% accuracy rate in multiclass classification.

scholarly journals Intrusion detection systems using long short-term memory (LSTM)

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
FatimaEzzahra Laghrissi ◽  
Samira Douzi ◽  
Khadija Douzi ◽  
Badr Hssina
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Short Term Memory ◽  
Detection System ◽  
Principal Component ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Short Term Memory

AbstractAn intrusion detection system (IDS) is a device or software application that monitors a network for malicious activity or policy violations. It scans a network or a system for a harmful activity or security breaching. IDS protects networks (Network-based intrusion detection system NIDS) or hosts (Host-based intrusion detection system HIDS), and work by either looking for signatures of known attacks or deviations from normal activity. Deep learning algorithms proved their effectiveness in intrusion detection compared to other machine learning methods. In this paper, we implemented deep learning solutions for detecting attacks based on Long Short-Term Memory (LSTM). PCA (principal component analysis) and Mutual information (MI) are used as dimensionality reduction and feature selection techniques. Our approach was tested on a benchmark data set, KDD99, and the experimental outcomes show that models based on PCA achieve the best accuracy for training and testing, in both binary and multiclass classification.

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