Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

Download Full-text

OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽

10.3390/s21144805 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4805

Author(s):

Saad Abbasi ◽

Mahmoud Famouri ◽

Mohammad Javad Shafiee ◽

Alexander Wong

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Detection Methods ◽

Detection Accuracy ◽

Network Architectures ◽

Design Exploration ◽

Convolutional Autoencoder ◽

Acoustic Anomaly ◽

Human Operators ◽

Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

Download Full-text

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

Applied Sciences ◽

10.3390/app11052177 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2177

Author(s):

Zuo Xiang ◽

Patrick Seeling ◽

Frank H. P. Fitzek

Keyword(s):

Machine Learning ◽

Object Detection ◽

Low Latency ◽

Connected Vehicles ◽

Network Nodes ◽

Machine Learning Model ◽

Fold Reduction ◽

Broad Variety ◽

Computational Resources ◽

Service Latency

With increasing numbers of computer vision and object detection application scenarios, those requiring ultra-low service latency times have become increasingly prominent; e.g., those for autonomous and connected vehicles or smart city applications. The incorporation of machine learning through the applications of trained models in these scenarios can pose a computational challenge. The softwarization of networks provides opportunities to incorporate computing into the network, increasing flexibility by distributing workloads through offloading from client and edge nodes over in-network nodes to servers. In this article, we present an example for splitting the inference component of the YOLOv2 trained machine learning model between client, network, and service side processing to reduce the overall service latency. Assuming a client has 20% of the server computational resources, we observe a more than 12-fold reduction of service latency when incorporating our service split compared to on-client processing and and an increase in speed of more than 25% compared to performing everything on the server. Our approach is not only applicable to object detection, but can also be applied in a broad variety of machine learning-based applications and services.

Download Full-text

An Optimized Stacking Ensemble Model for Phishing Websites Detection

Electronics ◽

10.3390/electronics10111285 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1285

Author(s):

Mohammed Al-Sarem ◽

Faisal Saeed ◽

Zeyad Ghaleb Al-Mekhlafi ◽

Badiea Abdulkarem Mohammed ◽

Tawfik Al-Hadhrami ◽

...

Keyword(s):

Machine Learning ◽

Random Forests ◽

Ensemble Method ◽

Detection Methods ◽

Detection Accuracy ◽

Ensemble Model ◽

Security Attacks ◽

Data Set ◽

Machine Learning Methods ◽

Ensemble Machine Learning

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

Download Full-text

A Research Paper on Traffic Sign Recognition with Machine Learning and IOT

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35309 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 1774-1778

Author(s):

Mahesh Singh

Keyword(s):

Machine Learning ◽

Object Detection ◽

Real World ◽

Research Paper ◽

Raspberry Pi ◽

Detection Accuracy ◽

Great Success ◽

The Real ◽

Road Traffic Safety ◽

Execution Speed

This paper will help to bring out some amazing findings about autonomous prediction and performing action by establishing a connection between the real world with machine learning and Internet Of thing. The purpose of this research paper is to perform our machine to analyze different signs in the real world and act accordingly. We have explored and found detection of several features in our model which helped us to establish a better interaction of our model with the surroundings. Our algorithms give very optimized predictions performing the right action .Nowadays, autonomous vehicles are a great area of research where we can make it more optimized and more multi - performing .This paper contributes to a huge survey of varied object detection and feature extraction techniques. At the moment, there are loads of object classification and recognition techniques and algorithms found and developed around the world. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in object detection tasks. It shows better accuracy or faster execution speed than traditional methods. However, the execution speed and the detection accuracy of the existing CNN methods cannot be obtained at the same time. What's more, the hardware requirements are also higher than before, resulting in a larger detection cost. In order to solve these problems, this paper proposes an improved algorithm based on convolutional model A classic robot which uses this algorithm which is installed through raspberry pi and performs dedicated action.

Download Full-text

Anomaly based Intrusion Detection by Heuristics to Predict Intrusion Scope of IOT Network Transactions

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.7.10982 ◽

2018 ◽

Vol 7 (2.7) ◽

pp. 797

Author(s):

Ravinder Korani ◽

Dr P. Chandra Sekhar Reddy

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

System Architecture ◽

Data Security ◽

Security And Privacy ◽

Predictive Analysis ◽

Detection Accuracy ◽

Machine Learning Model ◽

Detection Mechanisms ◽

Networking Security

Conventional intrusion detection mechanisms face serious limitations in identifying heterogeneous and distributed type of intrusions over the IoT environment. This is due to inadequate resources and open deployment environment of IoT. Accordingly, ensuring data security and privacy are tough challenges in the practical context. This manuscript discusses various aspects of networking security and related challenges along with the concepts of system architecture. Further, endeavored to define a machine learning model that outlines two heuristics called Intrusion Scope Heuristic ( ), and benign scope heuristic ( ), which further uses in predictive analysis to identify the IOT network transaction is prone to intrusion or benign. The experimental study revealed the significance of the proposal with maximal detection accuracy, and minimal miss rate.

Download Full-text

Object detection and used car price predicting analysis system (ucpas) using machine learning technique

Linguistics and Culture Review ◽

10.21744/lingcure.v5ns2.1660 ◽

2021 ◽

Vol 5 (S2) ◽

Author(s):

Anu Yadav ◽

Ela Kumar ◽

Piyush Kumar Yadav

Keyword(s):

Machine Learning ◽

Object Detection ◽

Research Area ◽

Machine Learning Technique ◽

Car Detection ◽

Machine Learning Model ◽

Implementation Techniques ◽

Learning Technique ◽

Used Car ◽

Analysis System

The highly interesting research area that noticed in the last few years is object detection and find out the prediction based on the features that can be benefited to consumers and the industry. In this paper, we understand the concept of object detection like the car detection, to look into the price of a second-hand car using automatic machine learning methods. We also understand the concept of object detection categories. Nowadays, the most challenging task is to determine what is the listed price of a used car on the market, Possibility of various factors that can drive a used car price. The main objective of this paper is to develop machine learning models which make it possible to accurately predict the price of a second-hand car according to its parameter or characteristics. In this paper, implementation techniques and evaluation methods are used on a Car dataset consisting of the selling prices of various models of car across different cities of India. The outcome of this experiment shows that clustering with linear regression and Random Forest model yield the best accuracy outcome. The machine learning model produces a satisfactory result within a short duration of time compared to the aforementioned self.

Download Full-text

CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression

10.1101/704486 ◽

2019 ◽

Author(s):

Quanhua Mu ◽

Jiguang Wang

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Copy Number ◽

Copy Number Alteration ◽

The Cancer Genome Atlas ◽

Detection Methods ◽

Dna Arrays ◽

Machine Learning Model ◽

Cancer Genome Atlas ◽

Genomic Regions

AbstractCopy number alteration (CNA), the abnormal number of copies of genomic regions, plays a key role in cancer initiation and progression. Current high-throughput CNA detection methods, including DNA arrays and genomic sequencing, are relatively expensive and require DNA samples at a microgram level, which are not achievable in certain occasions such as clinical biopsies or single-cell genomes. Here we proposed an alternative method—CNAPE to computationally infer CNA using gene expression data. A prior knowledge-aided machine learning model was proposed, trained and tested on the transcriptomic profiles with matched CNA data of 9,740 cancers from The Cancer Genome Atlas. Using brain tumors as a proof-of-concept study, CNAPE achieved over 90% accuracy in the prediction of arm-level CNAs. Prediction performance for 12 gene-level CNAs (commonly altered genes in glioma) was also evaluated, and CNAPE achieved reasonable accuracy. CNAPE is developed as an easy-to-use tool at http://wang-lab.ust.hk/software/Software.html.

Download Full-text

Ship Detection Based on YOLOv2 for SAR Imagery

Remote Sensing ◽

10.3390/rs11070786 ◽

2019 ◽

Vol 11 (7) ◽

pp. 786 ◽

Cited By ~ 41

Author(s):

Yang-Lang Chang ◽

Amare Anagaw ◽

Lena Chang ◽

Yi Wang ◽

Chih-Yu Hsiao ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Real Time ◽

Experimental Results ◽

Detection Methods ◽

Computational Time ◽

Detection Accuracy ◽

Single Shot ◽

Ship Detection ◽

Sar Imagery

Synthetic aperture radar (SAR) imagery has been used as a promising data source for monitoring maritime activities, and its application for oil and ship detection has been the focus of many previous research studies. Many object detection methods ranging from traditional to deep learning approaches have been proposed. However, majority of them are computationally intensive and have accuracy problems. The huge volume of the remote sensing data also brings a challenge for real time object detection. To mitigate this problem a high performance computing (HPC) method has been proposed to accelerate SAR imagery analysis, utilizing the GPU based computing methods. In this paper, we propose an enhanced GPU based deep learning method to detect ship from the SAR images. The You Only Look Once version 2 (YOLOv2) deep learning framework is proposed to model the architecture and training the model. YOLOv2 is a state-of-the-art real-time object detection system, which outperforms Faster Region-Based Convolutional Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) methods. Additionally, in order to reduce computational time with relatively competitive detection accuracy, we develop a new architecture with less number of layers called YOLOv2-reduced. In the experiment, we use two types of datasets: A SAR ship detection dataset (SSDD) dataset and a Diversified SAR Ship Detection Dataset (DSSDD). These two datasets were used for training and testing purposes. YOLOv2 test results showed an increase in accuracy of ship detection as well as a noticeable reduction in computational time compared to Faster R-CNN. From the experimental results, the proposed YOLOv2 architecture achieves an accuracy of 90.05% and 89.13% on the SSDD and DSSDD datasets respectively. The proposed YOLOv2-reduced architecture has a similarly competent detection performance as YOLOv2, but with less computational time on a NVIDIA TITAN X GPU. The experimental results shows that the deep learning can make a big leap forward in improving the performance of SAR image ship detection.

Download Full-text

Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion

Security and Communication Networks ◽

10.1155/2017/6451260 ◽

2017 ◽

Vol 2017 ◽

pp. 1-14 ◽

Cited By ~ 8

Author(s):

Xin Wang ◽

Dafang Zhang ◽

Xin Su ◽

Wenjia Li

Keyword(s):

Machine Learning ◽

Information Fusion ◽

Malware Detection ◽

Parallel Machine ◽

Detection Methods ◽

Detection Accuracy ◽

Android Malware ◽

Detection Model ◽

Android Apps ◽

Android Malware Detection

In recent years, Android malware has continued to grow at an alarming rate. More recent malicious apps’ employing highly sophisticated detection avoidance techniques makes the traditional machine learning based malware detection methods far less effective. More specifically, they cannot cope with various types of Android malware and have limitation in detection by utilizing a single classification algorithm. To address this limitation, we propose a novel approach in this paper that leverages parallel machine learning and information fusion techniques for better Android malware detection, which is named Mlifdect. To implement this approach, we first extract eight types of features from static analysis on Android apps and build two kinds of feature sets after feature selection. Then, a parallel machine learning detection model is developed for speeding up the process of classification. Finally, we investigate the probability analysis based and Dempster-Shafer theory based information fusion approaches which can effectively obtain the detection results. To validate our method, other state-of-the-art detection works are selected for comparison with real-world Android apps. The experimental results demonstrate that Mlifdect is capable of achieving higher detection accuracy as well as a remarkable run-time efficiency compared to the existing malware detection solutions.

Download Full-text

A Feature Extraction Method for P2P Botnet Detection Using Graphic Symmetry Concept

Symmetry ◽

10.3390/sym11030326 ◽

2019 ◽

Vol 11 (3) ◽

pp. 326 ◽

Cited By ~ 4

Author(s):

Zhixian Yang ◽

Buhong Wang

Keyword(s):

Machine Learning ◽

Information Entropy ◽

Feature Detection ◽

Denial Of Service ◽

Attack Detection ◽

Machine Learning Techniques ◽

Detection Accuracy ◽

Feature Extraction Method ◽

Denial Of Service Attack ◽

Ddos Attack

A DDoS (Distributed Denial of Service) attack makes use of a botnet to launch attacks and cause node congestion of wireless sensor networks, which is a common and serious threat. Due to the various kinds of features required in a Peer-to-Peer (P2P) botnet for DDoS attack detection via current machine learning methods and the failure to effectively detect encrypted botnets, this paper extracts the data packet size and the symmetric intervals in flow according to the concept of graphic symmetry. Combined with flow information entropy and session features, the frequency domain features can be sorted so as to obtain features with better correlations, which solves the problem of multiple types of features required for detection. Information entropy corresponding to the flow size can distinguish an encrypted botnet. This method is implemented through machine learning techniques. Experimental results show that the proposed method can detect the P2P botnet for DDoS attack and the detection accuracy is higher than that of traditional feature detection.

Download Full-text