Research on Pedestrian Attitude Detection Algorithm from the Perspective of Machine Learning

It is the capability of humans and as well as vehicles to automatically detect object level motion that results into collision less navigation and also provides sense of situation. This paper presents a technique for secure object level motion detection which yields more accurate results. To achieve this, python code has been used along with various machine learning libraries. The detection algorithm uses the advantage of background subtraction and fed in data to detect even the slightest movement this system makes use of a webcam to scan a premise and detect movement of any sort; on the recognition of any activity it immediately sends an alert message to the owner of the system via mail. Any person requiring a surveillance system can use it.

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IMPROVED TOW-END DETECTION FOR FIBER PLACEMENT INSPECTION USING MACHINE LEARNING

10.12783/asc36/35826 ◽

2021 ◽

Author(s):

ADRIANA W. (AGNES) BLOM-SCHIEBER ◽

WEI GUO ◽

EKTA SAMANI ◽

ASHIS BANERJEE

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Image Data ◽

Detection Algorithm ◽

Automated Inspection ◽

Machine Learning Algorithm ◽

Fiber Placement ◽

Inspection Systems ◽

Automated Fiber Placement ◽

The University

A machine learning approach to improve the detection of tow ends for automated inspection of fiber-placed composites is presented. Automated inspection systems for automated fiber placement processes have been introduced to reduce the time it takes to inspect plies after they are laid down. The existing system uses image data from ply boundaries and a contrast-based algorithm to locate the tow ends in these images. This system fails to recognize approximately 10% of the tow ends, which are then presented to the operator for manual review, taking up precious time in the production process. An improved tow end detection algorithm based on machine learning is developed through a research project with the Boeing Advanced Research Center (BARC) at the University of Washington. This presentation shows the preprocessing, neural network and post‐processing steps implemented in the algorithm, and the results achieved with the machine learning algorithm. The machine learning algorithm resulted in a 90% reduction in the number of undetected tows compared to the existing system.

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Detection of duplicate and non-face images in the eRecruitment applications using machine learning techniques

IAES International Journal of Robotics and Automation (IJRA) ◽

10.11591/ijra.v10i2.pp114-122 ◽

2021 ◽

Vol 10 (2) ◽

pp. 114

Author(s):

Manjunath K. E. ◽

Yogeen S. Honnavar ◽

Rakesh Pritmani ◽

Sethuraman K.

Keyword(s):

Machine Learning ◽

Detection Algorithm ◽

Similarity Score ◽

Machine Learning Techniques ◽

Human Face ◽

Face Images ◽

Research Organisation ◽

Learning Techniques ◽

Satellite Centre ◽

Recruitment Activities

The objective of this work is to develop methodologies to detect, and report the noncompliant images with respect to indian space research organisation (ISRO) recruitment requirements. The recruitment software hosted at U. R. rao satellite centre (URSC) is responsible for handling recruitment activities of ISRO. Large number of online applications are received for each post advertised. In many cases, it is observed that the candidates are uploading either wrong or non-compliant images of the required documents. By non-compliant images, we mean images which do not have faces or there is not enough clarity in the faces present in the images uploaded. In this work, we attempt to address two specific problems namely: 1) To recognise image uploaded to recruitment portal contains a human face or not. This is addressed using a face detection algorithm. 2) To check whether images uploaded by two or more applications are same or not. This is achieved by using machine learning (ML) algorithms to generate similarity score between two images, and then identify the duplicate images. Screening of valid applications becomes very challenging as the verification of such images using a manual process is very time consuming and requires large human efforts. Hence, we propose novel ML techniques to determine duplicate and non-face images in the applications received by the recruitment portal.

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Algorithm for detecting attacks on Web applications based on machine learning methods and attributes queries

Journal of Science and Technology on Information security ◽

10.54654/isj.v2i14.118 ◽

2022 ◽

Vol 2 (14) ◽

pp. 26-34

Author(s):

Nguyen Manh Thang ◽

Tran Thi Luong

Keyword(s):

Machine Learning ◽

Web Application ◽

Cloud Storage ◽

Web Applications ◽

Detection Algorithm ◽

Learning Methods ◽

Google Docs ◽

Effective Work ◽

Web Resources ◽

Machine Learning Methods

Abstract—Almost developed applications tend to become as accessible as possible to the user on the Internet. Different applications often store their data in cyberspace for more effective work and entertainment, such as Google Docs, emails, cloud storage, maps, weather, news,... Attacks on Web resources most often occur at the application level, in the form of HTTP/HTTPS-requests to the site, where traditional firewalls have limited capabilities for analysis and detection attacks. To protect Web resources from attacks at the application level, there are special tools - Web Application Firewall (WAF). This article presents an anomaly detection algorithm, and how it works in the open-source web application firewall ModSecurity, which uses machine learning methods with 8 suggested features to detect attacks on web applications. Tóm tắt—Hầu hết các ứng dụng được phát triển có xu hướng trở nên dễ tiếp cận nhất có thể đối với người dùng qua Internet. Các ứng dụng khác nhau thường lưu trữ dữ liệu trên không gian mạng để làm việc và giải trí hiệu quả hơn, chẳng hạn như Google Docs, email, lưu trữ đám mây, bản đồ, thời tiết, tin tức,... Các cuộc tấn công vào tài nguyên Web thường xảy ra nhất ở tầng ứng dụng, dưới dạng các yêu cầu HTTP/HTTPS đến trang web, nơi tường lửa truyền thống có khả năng hạn chế trong việc phân tích và phát hiện các cuộc tấn công. Để bảo vệ tài nguyên Web khỏi các cuộc tấn công ở tầng ứng dụng, xuất hiện các công cụ đặc biệt - Tường lửa Ứng dụng Web (WAF). Bài viết này trình bày thuật toán phát hiện bất thường và cách thức hoạt động của tường lửa ứng dụng web mã nguồn mở ModSecurity khi sử dụng phương pháp học máy với 8 đặc trưng được đề xuất để phát hiện các cuộc tấn công vào các ứng dụng web.

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A building block attitude detection algorithm based on robot self-error-correction

10.1117/12.2623052 ◽

2021 ◽

Author(s):

ZeYuan Cai ◽

ZhiQuan Feng ◽

LiRan Zhou ◽

XiaoHui Yang

Keyword(s):

Error Correction ◽

Building Block ◽

Detection Algorithm ◽

Attitude Detection

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Improved Antibiotic Detection in Raw Milk Using Machine Learning Tools over the Absorption Spectra of a Problem-Specific Nanobiosensor

Sensors ◽

10.3390/s20164552 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4552

Author(s):

Pablo Gutiérrez ◽

Sebastián E. Godoy ◽

Sergio Torres ◽

Patricio Oyarzún ◽

Ignacio Sanhueza ◽

...

Keyword(s):

Machine Learning ◽

Milk Sample ◽

Learning Algorithm ◽

State Of The Art ◽

Raw Milk ◽

Detection Algorithm ◽

Learning Tools ◽

Antibiotic Detection ◽

Biosensor System ◽

Spectral Detection

In this article we present the development of a biosensor system that integrates nanotechnology, optomechanics and a spectral detection algorithm for sensitive quantification of antibiotic residues in raw milk of cow. Firstly, nanobiosensors were designed and synthesized by chemically bonding gold nanoparticles (AuNPs) with aptamer bioreceptors highly selective for four widely used antibiotics in the field of veterinary medicine, namely, Kanamycin, Ampicillin, Oxytetracycline and Sulfadimethoxine. When molecules of the antibiotics are present in the milk sample, the interaction with the aptamers induces random AuNP aggregation. This phenomenon modifies the initial absorption spectrum of the milk sample without antibiotics, producing spectral features that indicate both the presence of antibiotics and, to some extent, its concentration. Secondly, we designed and constructed an electro-opto-mechanic device that performs automatic high-resolution spectral data acquisition in a wavelength range of 400 to 800 nm. Thirdly, the acquired spectra were processed by a machine-learning algorithm that is embedded into the acquisition hardware to determine the presence and concentration ranges of the antibiotics. Our approach outperformed state-of-the-art standardized techniques (based on the 520/620 nm ratio) for antibiotic detection, both in speed and in sensitivity.

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Machine learning-based fracture-hit detection algorithm using LFDAS signal

The Leading Edge ◽

10.1190/tle38070520.1 ◽

2019 ◽

Vol 38 (7) ◽

pp. 520-524 ◽

Cited By ~ 5

Author(s):

Ge Jin ◽

Kevin Mendoza ◽

Baishali Roy ◽

Darryl G. Buswell

Keyword(s):

Machine Learning ◽

Low Frequency ◽

Detection Algorithm ◽

Data Sets ◽

Fracture Zones ◽

Learning Techniques ◽

Distributed Acoustic Sensing ◽

Probability Of Fracture ◽

Simple Neural Network

Low-frequency distributed acoustic sensing (LFDAS) signal has been used to detect fracture hits at offset monitor wells during hydraulic fracturing operations. Typically, fracture hits are manually identified, which can be subjective and inefficient. We implemented machine learning-based models using supervised learning techniques in order to identify fracture zones, which demonstrate a high probability of fracture hits automatically. Several features are designed and calculated from LFDAS data to highlight fracture-hit characterizations. A simple neural network model is trained to fit the manually picked fracture hits. The fracture-hit probability, as predicted by the model, agrees well with the manual picks in training, validation, and test data sets. The algorithm was used in a case study of an unconventional reservoir. The results indicate that smaller cluster spacing design creates denser fractures.

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