scholarly journals DeLTA: GPU Performance Model for Deep Learning Applications with In-Depth Memory System Traffic Analysis

2019 ◽  
Author(s):  
Sangkug Lym ◽  
Donghyuk Lee ◽  
Mike O'Connor ◽  
Niladrish Chatterjee ◽  
Mattan Erez
Keyword(s):  
Deep Learning ◽  
Traffic Analysis ◽  
Memory System ◽  
Performance Model

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

scholarly journals Machine and Deep Learning Applied to Predict Metabolic Syndrome Without a Blood Screening

2021 ◽  
Vol 11 (10) ◽  
pp. 4334
Author(s):  
Guadalupe O. Gutiérrez-Esparza ◽  
Tania A. Ramírez-delReal ◽  
Mireya Martínez-García ◽  
Oscar Infante Infante Vázquez ◽  
Maite Vallejo ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Deep Learning ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Dietary Habits ◽  
Smoking Habit ◽  
Performance Model ◽  
Whole Milk ◽  
Corn Tortilla ◽  
Blood Screening

The exponential increase of metabolic syndrome and its association with the risk impact of morbidity and mortality has propitiated the development of tools to diagnose this syndrome early. This work presents a model that is based on prognostic variables to classify Mexicans with metabolic syndrome without blood screening applying machine and deep learning. The data that were used in this study contain health parameters related to anthropometric measurements, dietary information, smoking habit, alcohol consumption, quality of sleep, and physical activity from 2289 participants of the Mexico City Tlalpan 2020 cohort. We use accuracy, balanced accuracy, positive predictive value, and negative predictive value criteria to evaluate the performance and validate different models. The models were separated by gender due to the shared features and different habits. Finally, the highest performance model in women found that the most relevant features were: waist circumference, age, body mass index, waist to height ratio, height, sleepy manner that is associated with snoring, dietary habits related with coffee, cola soda, whole milk, and Oaxaca cheese and diastolic and systolic blood pressure. Men’s features were similar to women’s; the variations were in dietary habits, especially in relation to coffee, cola soda, flavored sweetened water, and corn tortilla consumption. The positive predictive value obtained was 84.7% for women and 92.29% for men. With these models, we offer a tool that supports Mexicans to prevent metabolic syndrome by gender; it also lays the foundation for monitoring the patient and recommending change habits.

scholarly journals To Identify and Recognize the Object for Traffic Analysis System using Deep Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 1621-1624
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Traffic Analysis ◽  
High Accuracy ◽  
Object Identification ◽  
Learning Systems ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Analysis System

The object identification has been most essential field in development of machine vision which should be more efficient and accurate. Machine Learning & Artificial Intelligence, both are on their peak in today’s technology world. Playing with these can leads towards development. The field has actually replaced human efforts. With the approach of profound learning systems (i.e. deep learning techniques), the precision for object identification has expanded radically. This project aims to implement Object Identification for Traffic Analysis System in real time using Deep Learning Algorithms with high accuracy. The differentiation among objects such as humans, Traffic signs, etc. are identified. The dataset is so designed with specific objects which will be recognized by the camera and result will be shown within seconds. The project purely based on deep learning approaches which also includes YOLO object detection & Covolutionary Neural Network (CNN). The resulting system is fast and accurate, therefore can be implemented for smart automation across global stage

An Efficient Approach for Detecting and Classifying Moving Vehicles in a Video Based Monitoring System

2020 ◽  
Vol 38 (6A) ◽  
pp. 832-845
Author(s):  
Sajidah S. Mahmood ◽  
Laith J. Saud
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Road Traffic ◽  
Computation Time ◽  
Traffic Analysis ◽  
Traffic Monitoring ◽  
Traffic Load ◽  
Surveillance Systems ◽  
Modern Approach ◽  
Moving Vehicles

Moving objects detection, type recognition, and traffic analysis in video-based surveillance systems is an active area of research which has many applications in road traffic monitoring. This paper is on using classical approaches of image processing to develop an efficient algorithm for computer vision based on traffic surveillance system that can detect and classify moving vehicles, besides serving some other traffic analysis issues like finding vehicles speed and heading, tracking specified vehicles, and finding traffic load. The algorithm is designed to be flexible for modification to fulfill the changes in design objectives, having limited computation time, giving good accuracy, and serves inexpensive implementation.  A 92% of success is achieved for the considered test, with the missed cases being abnormal that are not defined to the algorithm. The computation time, with a platform (hardware and software) dependent, the algorithm took to produce results was parts of milliseconds. A CNN based deep learning classifier was built and evaluated to judge the feasibility of involving a modern approach in the design for the targeted aims in this work. The modern NN based deep learning approach is very powerful and represents the choice for many very sophisticated applications, but when the purpose is restricted to limited requirements, as it is believed the case is here, the reason will be to use the classical image processing procedures.  In making choice, it is important to consider, among many things, accuracy, computation time, and simplicity of design, development, and implementation.  

A Disaggregated Memory System for Deep Learning

IEEE Micro ◽  
2019 ◽  
Vol 39 (5) ◽  
pp. 82-90 ◽  
Author(s):  
Youngeun Kwon ◽  
Minsoo Rhu
Keyword(s):  
Deep Learning ◽  
Memory System

scholarly journals Construction of reliable image captioning system for web camera based traffic analysis on road transport application

2021 ◽  
Vol 3 (2) ◽  
pp. 114-130
Author(s):  
R Dhaya
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Visual Information ◽  
Short Term Memory ◽  
Road Transport ◽  
Traffic Analysis ◽  
Natural Images ◽  
Image Captioning ◽  
Web Camera

The automated captioning of natural images with appropriate descriptions is an intriguing and complicated task in the field of image processing. On the other hand, Deep learning, which combines computer vision with natural language, has emerged in recent years. Image emphasization is a record file representation that allows a computer to understand the visual information of an image in one or more words. When it comes to connecting high-quality images, the expressive process not only requires the credentials of the primary item and scene but also the ability to analyse the status, physical characteristics, and connections. Many traditional algorithms substitute the image to the front image. The image characteristics are dynamic depending on the ambient condition of natural photographs. Image processing techniques fail to extract several characteristics from the specified image. Nonetheless, four properties from the images are accurately described by using our proposed technique. Based on the various filtering layers in the convolutional neural network (CNN), it is an advantage to extract different characteristics. The caption for the image is based on long short term memory (LSTM), which comes under recurrent neural network. In addition, the precise subtitling is compared to current conventional techniques of image processing and different deep learning models. The proposed method is performing well in natural images and web camera based images for traffic analysis. Besides, the proposed algorithm leverages good accuracy and reliable image captioning.

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