scholarly journals FruitDet: Attentive Feature Aggregation for Real-Time Fruit Detection in Orchards

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2440
Author(s):  
Faris A. Kateb ◽  
Muhammad Mostafa Monowar ◽  
Md. Abdul Hamid ◽  
Abu Quwsar Ohi ◽  
Muhammad Firoz Mridha
Keyword(s):  
Computer Vision ◽  
Learning Strategies ◽  
Precision Agriculture ◽  
Detection System ◽  
Detection Methods ◽  
High Complexity ◽  
Detection Mechanism ◽  
Detection Model ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

Computer vision is currently experiencing success in various domains due to the harnessing of deep learning strategies. In the case of precision agriculture, computer vision is being investigated for detecting fruits from orchards. However, such strategies limit too-high complexity computation that is impossible to embed in an automated device. Nevertheless, most investigation of fruit detection is limited to a single fruit, resulting in the necessity of a one-to-many object detection system. This paper introduces a generic detection mechanism named FruitDet, designed to be prominent for detecting fruits. The FruitDet architecture is designed on the YOLO pipeline and achieves better performance in detecting fruits than any other detection model. The backbone of the detection model is implemented using DenseNet architecture. Further, the FruitDet is packed with newer concepts: attentive pooling, bottleneck spatial pyramid pooling, and blackout mechanism. The detection mechanism is benchmarked using five datasets, which combines a total of eight different fruit classes. The FruitDet architecture acquires better performance than any other recognized detection methods in fruit detection.

Real-Time Intrusion Detection Based on System Call and ARTIS Model

2014 ◽  
Vol 926-930 ◽  
pp. 3157-3160
Author(s):  
Zhan Huang ◽  
Yu Ying Jiang ◽  
Lu Bin Li
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Methods ◽  
System Call ◽  
Detection Model ◽  
Fixed Length ◽  
Computer Intrusion Detection ◽  
Abnormal Behaviors

The main purpose of a computer intrusion detection system is to accurately distinguish between self and non-self. A novel intrusion detection model based on ARTIS model is proposed by introducing the Red Flower and Green Leaf concepts, and by coordinated use of RF variable length and GL fixed length detectors. Intrusion detection methods are optimized to ensure the quick detection of abnormal behaviors making the model more suitable for real-time intrusion detection and more accurately to distinguish between self-and non-self.

scholarly journals A policy conflict detection mechanism for multi-controller software-defined networks

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771984471 ◽  
Author(s):  
You Lu ◽  
Qiming Fu ◽  
Xuefeng Xi ◽  
Zhenping Chen ◽  
Encen Zou ◽  
...  
Keyword(s):  
Network Architecture ◽  
Detection Algorithm ◽  
Conflict Detection ◽  
Detection Methods ◽  
Software Defined Network ◽  
Software Defined Networks ◽  
Network Environment ◽  
Detection Mechanism ◽  
Policy Conflict ◽  
Detection Model

As the network environment expands and becomes more complex, the deficiencies of decision-making capabilities in the single-controller software-defined network architecture are increasingly exposed. Currently, software-defined networks have gradually adopted a multi-controller-based architecture. However, in this architecture, multiple controllers may cause conflicts in the flow policies, which may cause failures such as security and route conflicts. Most of the existing detection methods are only aimed at specific types of conflicts. Aiming at the above insufficiency, this article proposes a policy conflict detection mechanism for multi-controller software-defined network. First, it quantifies and classifies the software-defined policy conflict itself to provide the basis for detection mechanism; then, it proposes a conflict detection model and its deployment scheme for multi-controller software-defined networks; finally, based on the software-defined flow policy’s structure, a multi-branch tree-based policy conflict detection algorithm is proposed to accurately detect the universal types of conflicts. The experimental results under the campus network environment prove that our method can effectively detect the conflict of flow policies existing in the multi-controller software-defined network and has advantages over the existing methods in the integrity, accuracy, and efficiency of the detection.

scholarly journals Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 210 ◽  
Author(s):  
Zhuofan Yan ◽  
Jinsong Chong ◽  
Yawei Zhao ◽  
Kai Sun ◽  
Yuhang Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Detection Method ◽  
Synthetic Aperture ◽  
Detection Methods ◽  
Sar Images ◽  
Detection Thresholds ◽  
Generalization Ability ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

Oceanic phenomena detection in synthetic aperture radar (SAR) images is important in the fields of fishery, military, and oceanography. The traditional detection methods of oceanic phenomena in SAR images are based on handcrafted features and detection thresholds, which have a problem of poor generalization ability. Methods based on deep learning have good generalization ability. However, most of the deep learning methods currently applied to oceanic phenomena detection only detect one type of phenomenon. To satisfy the requirements of efficient and accurate detection of multiple information of multiple oceanic phenomena in massive SAR images, this paper proposes an oceanic phenomena detection method in SAR images based on convolutional neural network (CNN). The method first uses ResNet-50 to extract multilevel features. Second, it uses the atrous spatial pyramid pooling (ASPP) module to extract multiscale features. Finally, it fuses multilevel features and multiscale features to detect oceanic phenomena. The SAR images acquired from the Sentinel-1 satellite are used to establish a sample dataset of oceanic phenomena. The method proposed can achieve 91% accuracy on the dataset.

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

scholarly journals A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
R. Sekhar ◽  
K. Sasirekha ◽  
P. S. Raja ◽  
K. Thangavel
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Radial Basis Function Network ◽  
Detection Methods ◽  
Support Vector ◽  
Parameter Method ◽  
List Type ◽  
Fuzzy C Means

Abstract Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

scholarly journals Multiview deep learning based on tensor decomposition and its application in fault detection of overhead contact systems

2021 ◽  
Author(s):  
Xuewu Zhang ◽  
Yansheng Gong ◽  
Chen Qiao ◽  
Wenfeng Jing
Keyword(s):  
High Speed ◽  
Tensor Decomposition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Training Time ◽  
Detection Model ◽  
Railway Line ◽  
Result Show ◽  
Deep Layers

AbstractThis article mainly focuses on the most common types of high-speed railways malfunctions in overhead contact systems, namely, unstressed droppers, foreign-body invasions, and pole number-plate malfunctions, to establish a deep-network detection model. By fusing the feature maps of the shallow and deep layers in the pretraining network, global and local features of the malfunction area are combined to enhance the network's ability of identifying small objects. Further, in order to share the fully connected layers of the pretraining network and reduce the complexity of the model, Tucker tensor decomposition is used to extract features from the fused-feature map. The operation greatly reduces training time. Through the detection of images collected on the Lanxin railway line, experiments result show that the proposed multiview Faster R-CNN based on tensor decomposition had lower miss probability and higher detection accuracy for the three types faults. Compared with object-detection methods YOLOv3, SSD, and the original Faster R-CNN, the average miss probability of the improved Faster R-CNN model in this paper is decreased by 37.83%, 51.27%, and 43.79%, respectively, and average detection accuracy is increased by 3.6%, 9.75%, and 5.9%, respectively.

scholarly journals A Synthetic Biology Approach Using Engineered Bacteria to Detect Perfluoroalkyl Substance (PFAS) Contamination in Water

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 801-807
Author(s):  
Nathaniel A Young ◽  
Ryan L Lambert ◽  
Angela M Buch ◽  
Christen L Dahl ◽  
Jackson D Harris ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Dna Sequences ◽  
Detection System ◽  
The United States ◽  
Detection Methods ◽  
Health Concern ◽  
Contaminated Water ◽  
Engineered Bacteria ◽  
Synthetic Biology Approach ◽  
Rhodococcus Jostii

ABSTRACT Introduction Per- and polyfluoroalkyl substances (PFAS) are a class of synthetic compounds used industrially for a wide variety of applications. These PFAS compounds are very stable and persist in the environment. The PFAS contamination is a growing health issue as these compounds have been reported to impact human health and have been detected in both domestic and global water sources. Contaminated water found on military bases poses a potentially serious health concern for active duty military, their families, and the surrounding communities. Previous detection methods for PFAS in contaminated water samples require expensive and time-consuming testing protocols that limit the ability to detect this important global pollutant. The main objective of this work was to develop a novel detection system that utilizes a biological reporter and engineered bacteria as a way to rapidly and efficiently detect PFAS contamination. Materials and Methods The United States Air Force Academy International Genetically Engineered Machine team is genetically engineering Rhodococcus jostii strain RHA1 to contain novel DNA sequences composed of a propane 2-monooxygenase alpha (prmA) promoter and monomeric red fluorescent protein (mRFP). The prmA promoter is activated in the presence of PFAS and transcribes the mRFP reporter. Results The recombinant R. jostii containing the prmA promoter and mRFP reporter respond to exposure of PFAS by activating gene expression of the mRFP. At 100 µM of perfluorooctanoic acid, the mRFP expression was increased 3-fold (qRT-PCR). Rhodococcus jostii without exposure to PFAS compounds had no mRFP expression. Conclusions This novel detection system represents a synthetic biology approach to more efficiently detect PFAS in contaminated samples. With further refinement and modifications, a similar system could be readily deployed in the field around the world to detect this critical pollutant.

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

Sign in / Sign up

Export Citation Format

Share Document