scholarly journals Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10132
Author(s):  
Robert Szczepanek
Keyword(s):  
Machine Learning ◽  
Public Space ◽  
Learning Algorithm ◽  
Pedestrian Detection ◽  
Computation Time ◽  
Movement Control ◽  
Time Lapse ◽  
Efficient Detection ◽  
Detection And Counting ◽  
Standard Application

At the turn of February and March 2020, COVID-19 pandemic reached Europe. Many countries, including Poland imposed lockdown as a method of securing social distance between potentially infected. Stay-at-home orders and movement control within public space not only affected the touristm industry, but also the everyday life of the inhabitants. The hourly time-lapse from four HD webcams in Cracow (Poland) are used in this study to estimate how pedestrian activity changed during COVID-19 lockdown. The collected data covers the period from 9 June 2016 to 19 April 2020 and comes from various urban zones. One zone is tourist, one is residential and two are mixed. In the first stage of the analysis, a state-of-the-art machine learning algorithm (YOLOv3) is used to detect people. Additionally, a non-standard application of the YOLO method is proposed, oriented to the images from HD webcams. This approach (YOLOtiled) is less prone to pedestrian detection errors with the only drawback being the longer computation time. Splitting the HD image into smaller tiles increases the number of detected pedestrians by over 50%. In the second stage, the analysis of pedestrian activity before and during the COVID-19 lockdown is conducted for hourly, daily and weekly averages. Depending on the type of urban zone, the number of pedestrians decreased from 33% in residential zones to 85% in tourist zones located in the Old Town. The presented method allows for more efficient detection and counting of pedestrians from HD time-lapse webcam images compared to SSD, YOLOv3 and Faster R-CNN. The result of the research is a published database with the detected number of pedestrians from the four-year observation period for four locations in Cracow.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

scholarly journals Sampling Algorithms Combination with Machine Learning for Efficient Safe Trajectory Planning

2021 ◽  
Vol 11 (1) ◽  
pp. 1-11
Author(s):  
Amit Chaulwar ◽  
Keyword(s):  
Machine Learning ◽  
Trajectory Planning ◽  
Learning Algorithm ◽  
Computation Time ◽  
Deep Learning Algorithm ◽  
Sampling Algorithms ◽  
Computationally Intensive ◽  
Critical Traffic ◽  
Computational Resources ◽  
Hardware Platforms

The planning of safe trajectories in critical traffic scenarios using model-based algorithms is a very computationally intensive task. Recently proposed algorithms, namely Hybrid Augmented CL-RRT, Hybrid Augmented CL-RRT+ and GATE-ARRT+, reduce the computation time for safe trajectory planning drastically using a combination of a deep learning algorithm 3D-ConvNet with a vehicle dynamic model. An efficient embedded implementation of these algorithms is required as the vehicle on-board micro-controller resources are limited. This work proposes methodologies for replacing the computationally intensive modules of these trajectory planning algorithms using different efficient machine learning and analytical methods. The required computational resources are measured by downloading and running the algorithms on various hardware platforms. The results show significant reduction in computational resources and the potential of proposed algorithms to run in real time. Also, alternative architectures for 3D-ConvNet are presented for further reduction of required computational resources.

Support Optimization for Piping System With Machine Learning

2017 ◽  
Author(s):  
Jongho Ham ◽  
Jungeun An ◽  
Bongjae Kim ◽  
Jaewoong Choi ◽  
Booki Kim
Keyword(s):  
Machine Learning ◽  
Stress Analysis ◽  
Hybrid Method ◽  
Learning Algorithm ◽  
Computation Time ◽  
Pattern Search ◽  
Design Criteria ◽  
Piping System ◽  
Search Optimization ◽  
Design Variables

Piping stress analysis is performed by the manipulations of support type, location and pipe arrangement based on many specific design criteria. A classical way to find good engineering solutions satisfying design criteria among lots of combinations is obviously time-consuming work. In field practice, it also highly depends on engineer’s experiences and abilities. This paper proposes a hybrid method by combining several global search optimization algorithms and predication model generation in order to automatically control the combinations of support types as the engineering solutions. Here, we use some efficient and popular algorithms such as genetic algorithm, swarm intelligence and Gaussian pattern search to develop initial design of experiments. From the set of the initials, we build and update a prediction model by applying machine learning algorithm such as artificial neural network. As a result of using the hybrid method, the engineering solution is sufficiently optimized for the classical solution. Design variables for this problem are the types of restraints (or the pipe support type). The nonlinearity conditions such as gaps and frictions are also treated as key design variables. Each restraint is initially identified as a binary set of design variables, and transformed to integer numbers to run on the n-dimensional design space. The number of dimension corresponds to the number of pipe supports. Currently, pipe stress analysis problems are divided into a certain size that is enough to run on one computer for project management purpose. If we have bigger system with more design variables to consider, the hybrid machine learning method plays a key role in saving computation time with the help of additional parallel computation technique.

scholarly journals Detection of Crop Hail Damage with a Machine Learning Algorithm Using Time Series of Remote Sensing Data

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2078
Author(s):  
Leandro Sosa ◽  
Ana Justel ◽  
Íñigo Molina
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Learning Algorithm ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Sensing Data ◽  
Hail Damage ◽  
Efficient Detection

Hailstorms usually result in total crop loss. After a hailstorm, the affected field is inspected by an insurance claims adjuster to assess yield loss. Assessment accuracy depends largely on in situ detection of homogeneous damage sectors within the field, using visual techniques. This paper presents an algorithm for the automatic detection of homogeneous hail damage through the application of unsupervised machine learning techniques to vegetation indices calculated from remote sensing data. Five microwave and five spectral indices were evaluated before and after a hailstorm in zones with different degrees of damage. Dual Polarization SAR Vegetation Index and Normalized Pigment Chlorophyll Ratio Index were the most sensitive to hail-induced changes. The time series and rates of change of these indices were used as input variables in the K-means method for clustering pixels into homogeneous damage zones. Validation of the algorithm with data from 91 soybean, wheat, and corn plots showed that in 87.01% of cases there was significant evidence of differences in average damage between zones determined by the algorithm within the plot. Thus, the algorithm presented in this paper allowed efficient detection of homogeneous hail damage zones, which is expected to improve accuracy and transparency in the characterization of hailstorm events.

Efficient detection and prediction of flood severity using machine learning algorithm

2021 ◽  
Author(s):  
R. Gowthamani ◽  
K. Sasi Kala Rani ◽  
S.R. Abishek ◽  
G. Akash ◽  
A. Kavin Kumar
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Efficient Detection

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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