scholarly journals Weather Prediction using Advanced Machine Learning Techniques

2021 ◽  
Vol 2089 (1) ◽  
pp. 012059
Author(s):  
G. Hemalatha ◽  
K. Srinivasa Rao ◽  
D. Arun Kumar
Keyword(s):  
Linear Relationship ◽  
Weather Prediction ◽  
Weather Condition ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Weather Data ◽  
Learning Techniques ◽  
Proposed Model ◽  
Non Linear ◽  
The Relationship

Abstract Prediction of weather condition is important to take efficient decisions. In general, the relationship between the input weather parameters and the output weather condition is non linear and predicting the weather conditions in non linear relationship posses challenging task. The traditional methods of weather prediction sometimes deviate in predicting the weather conditions due to non linear relationship between the input features and output condition. Motivated with this factor, we propose a neural networks based model for weather prediction. The superiority of the proposed model is tested with the weather data collected from Indian metrological Department (IMD). The performance of model is tested with various metrics..

A Heuristic Approach to Crime Prediction based on Generalization of Crime Categories.

2020 ◽  
Vol 14 ◽  
Author(s):  
Gaurav Hajela ◽  
Meenu Chawla ◽  
Akhtar Rasool
Keyword(s):  
Geographical Location ◽  
Weather Conditions ◽  
Heuristic Approach ◽  
Machine Learning Techniques ◽  
Crime Prediction ◽  
The Past ◽  
Learning Techniques ◽  
Proposed Model ◽  
Day Of The Week ◽  
Prediction Approach

Background: With crime rates increasing all over the world, many methods for crime prediction based on data mining has been proposed in the past. Crime prediction finds application in areas like predictive policing, Hotspot evaluation and geographic profiling. It has been observed in the past that crime is closely related to geographical location, time, weather conditions and day of the week. Objective: Thus to tackle the crime events a proactive plocing approach can be developed using crime prediction. The main objective of this manuscript is to provide a heuristic approach to crime prediction. Method: In this work, a crime prediction approach is proposed which utilizes crime history dataset which contains multiple categories of crime. And a heuristic approach based on generalization of crime categories is proposed. A spatiotemporal crime prediction technique based on machine learning techniques is proposed. State of the art classification approaches along with ensemble learning approach is used for prediction. Results: Performance of the proposed model is compared when it used state of art classification techniques without heuristic approach and with heuristic approach and it is found that model with heuristics achieves a better accuracy. Conclusion: Crime events dataset can be utilized to predict future crime events in an area because crime shows geographical pattern. These spatial patterns might vary with the category of crime and it is challenging to deal with lots of crime categories. Thus, a generalization based approach can be a vital asset in crime prediction.

Delay Prediction of Aircrafts Based on Health Monitoring Data

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Flight Delay ◽  
Delay Prediction ◽  
The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

scholarly journals It’s the Weather: Quantifying the Impact of Weather on Retail Sales

2021 ◽  
Author(s):  
Natalie Rose ◽  
Les Dolega
Keyword(s):  
Weather Condition ◽  
Weather Conditions ◽  
Influential Factor ◽  
Retail Sector ◽  
Retail Sales ◽  
Weather Impact ◽  
High Street ◽  
Sector Decision ◽  
The Impact ◽  
The Relationship

AbstractThe weather is considered as an influential factor on consumer purchasing behaviours and plays a significant role in many aspects of retail sector decision making. As a result, better understanding of the magnitude and nature of the influence of variable UK weather conditions can be beneficial to many retailers and other stakeholders. This study addresses the dearth of research in this area by quantifying the relationship between different weather conditions and trading outcomes. By employing comprehensive daily sales data for a major high street retailer with over 2000 stores across England and adopting a random forest methodology, the study quantifies the influence of various weather conditions on daily retail sales. Results indicate that weather impact is greatest in the summer and spring months and that wind is consistently found to be the most influential weather condition. The top five most weather-dependent categories cover a range of different product types, with health foods emerging as the most susceptible to the weather. Also, sales from out-of-town stores show a far more complex relationship with the weather than those from traditional high street stores with the regions London and the South East experiencing the greatest levels of influence. Various implications of these findings for retail stakeholders are discussed and the scope for further research outlined.

A Method of Environmental Sound Classification Based on Residual Networks and Data Augmentation

2021 ◽  
pp. 2150018
Author(s):  
Jinfang Zeng ◽  
Youming Li ◽  
Yu Zhang ◽  
Da Chen
Keyword(s):  
Neural Networks ◽  
Data Augmentation ◽  
Machine Learning Techniques ◽  
Deep Convolutional Neural Networks ◽  
Mel Frequency Cepstral Coefficients ◽  
Environmental Sound ◽  
Sound Classification ◽  
Learning Techniques ◽  
Proposed Model ◽  
Audio Data

Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. To date, a variety of signal processing and machine learning techniques have been applied to ESC task, including matrix factorization, dictionary learning, wavelet filterbanks and deep neural networks. It is observed that features extracted from deeper networks tend to achieve higher performance than those extracted from shallow networks. However, in ESC task, only the deep convolutional neural networks (CNNs) which contain several layers are used and the residual networks are ignored, which lead to degradation in the performance. Meanwhile, a possible explanation for the limited exploration of CNNs and the difficulty to improve on simpler models is the relative scarcity of labeled data for ESC. In this paper, a residual network called EnvResNet for the ESC task is proposed. In addition, we propose to use audio data augmentation to overcome the problem of data scarcity. The experiments will be performed on the ESC-50 database. Combined with data augmentation, the proposed model outperforms baseline implementations relying on mel-frequency cepstral coefficients and achieves results comparable to other state-of-the-art approaches in terms of classification accuracy.

scholarly journals Review of Weather Forecast Services for Ship Routing Purposes

2019 ◽  
Vol 26 (4) ◽  
pp. 80-89
Author(s):  
Marcin Życzkowski ◽  
Joanna Szłapczyńska ◽  
Rafał Szłapczyński
Keyword(s):  
Collision Avoidance ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Weather Data ◽  
Ocean Engineering ◽  
Ship Routing ◽  
Engineering Research ◽  
Fishing Vessels ◽  
Analysis Of Stability

Abstract Weather data is nowadays used in a variety of navigational and ocean engineering research problems: from the obvious ones like voyage planning and routing of sea-going vessels, through the analysis of stability-related phenomena, to detailed modelling of ships’ manoeuvrability for collision avoidance purposes. Apart from that, weather forecasts are essential for passenger cruises and fishing vessels that want to avoid the risk associated with severe hydro-meteorological conditions. Currently, there is a wide array of services that offer weather predictions. These services include the original sources – services that make use of their own infrastructure and research models – as well as those that further postprocess the data obtained from the original sources. The existing services also differ in their update frequency, area coverage, geographical resolution, natural phenomena taken into account and finally – output file formats. In the course of the ROUTING project, primarily addressing ship weather routing accounting for changeable weather conditions, the necessity arose to prepare a report on the state-of-the-art in numerical weather prediction (NWP) modelling. Based on the report, this paper offers a thorough review of the existing weather services and detailed information on how to access the data offered by these services. While this review has been done with transoceanic ship routing in mind, hopefully it will also be useful for a number of other applications, including the already mentioned collision avoidance solutions.

scholarly journals Government Size and Corruption: A Non-linear Analysis in the Case of EMCCA

2021 ◽  
Vol 12 (4) ◽  
pp. 17
Author(s):  
Assoumou Ondo ◽  
Beau Jency Owono Ondo
Keyword(s):  
Linear Relationship ◽  
Central Government ◽  
Central Africa ◽  
Linear Analysis ◽  
Government Size ◽  
Economic Community ◽  
Non Linear ◽  
The Government ◽  
The Impact ◽  
The Relationship

This article analyzes the relationship between Government size and corruption. Unlike the works in the way which suppose a linear relationship between the two variables, we estimate a panel with change of the modes to characterize the impact of the size of the Central Government on corruption, in the countries of the economic community and monetary of Central Africa (EMCCA). The results show that there is a non-linear relationship between these two variables. Indeed, a strong involvement of the Government in economic activity results in a significant increase in corruption when the Government exceeds a size of 13.5508% of the GDP.

scholarly journals An Advance Resource Reservation Approach in a Cloud Database Environment

2017 ◽  
Author(s):  
Vinicius Da S. Segalin ◽  
Carina F. Dorneles ◽  
Mario A. R. Dantas
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Resource Reservation ◽  
Machine Learning Techniques ◽  
Advance Reservation ◽  
Running Time ◽  
Knowing That ◽  
Learning Techniques ◽  
Proposed Model ◽  
Cloud Database

AA well-known challenge with long running time queries in database environments is how much time a query will take to execute. This prediction is relevant for several reasons. For instance, by knowing that a query will take longer to execute than desired, one resource reservation mechanism can be performed, which means reserving more resources in order to execute this query in a shorter time in a future request. In this research work, it is presented a proposal in which the use of an advance reservation mechanism in a cloud database environment, considering machine learning techniques, provides resource recommendation. The proposed model is presented, in addition to some experiments that evaluate benefits and the efficiency of this enhanced proposal.

Exploring multi-modalities in weather prediction using a univariate graph based on machine learning techniques

2021 ◽  
Author(s):  
Natacha Galmiche ◽  
Nello Blaser ◽  
Morten Brun ◽  
Helwig Hauser ◽  
Thomas Spengler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Probability Distributions ◽  
Weather Prediction ◽  
A Priori ◽  
Clustering Algorithms ◽  
Quantitative Information ◽  
Machine Learning Techniques ◽  
Topological Data Analysis ◽  
Learning Techniques

<p>Probability distributions based on ensemble forecasts are commonly used to assess uncertainty in weather prediction. However, interpreting these distributions is not trivial, especially in the case of multimodality with distinct likely outcomes. The conventional summary employs mean and standard deviation across ensemble members, which works well for unimodal, Gaussian-like distributions. In the case of multimodality this misleads, discarding crucial information. </p><p>We aim at combining previously developed clustering algorithms in machine learning and topological data analysis to extract useful information such as the number of clusters in an ensemble. Given the chaotic behaviour of the atmosphere, machine learning techniques can provide relevant results even if no, or very little, a priori information about the data is available. In addition, topological methods that analyse the shape of the data can make results explainable.</p><p>Given an ensemble of univariate time series, a graph is generated whose edges and vertices represent clusters of members, including additional information for each cluster such as the members belonging to them, their uncertainty, and their relevance according to the graph. In the case of multimodality, this approach provides relevant and quantitative information beyond the commonly used mean and standard deviation approach that helps to further characterise the predictability.</p>

Detection of Botnet Based Attacks on Network

2018 ◽  
pp. 101-116
Author(s):  
Prachi
Keyword(s):  
Large Scale ◽  
Flow Analysis ◽  
Traffic Analysis ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Botnet Detection ◽  
Learning Techniques ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Traffic Flow Analysis

This chapter describes how with Botnets becoming more and more the leading cyber threat on the web nowadays, they also serve as the key platform for carrying out large-scale distributed attacks. Although a substantial amount of research in the fields of botnet detection and analysis, bot-masters inculcate new techniques to make them more sophisticated, destructive and hard to detect with the help of code encryption and obfuscation. This chapter proposes a new model to detect botnet behavior on the basis of traffic analysis and machine learning techniques. Traffic analysis behavior does not depend upon payload analysis so the proposed technique is immune to code encryption and other evasion techniques generally used by bot-masters. This chapter analyzes the benchmark datasets as well as real-time generated traffic to determine the feasibility of botnet detection using traffic flow analysis. Experimental results clearly indicate that a proposed model is able to classify the network traffic as a botnet or as normal traffic with a high accuracy and low false-positive rates.

scholarly journals Flame Temperatures Saturate with Increasing Dead Material in Ulex europaeus, but Flame Duration, Fuel Consumption and Overall Flammability Continue to Increase

Fire ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 6 ◽  
Author(s):  
Jennifer Dent ◽  
Hannah Buckley ◽  
Audrey Lustig ◽  
Timothy Curran
Keyword(s):  
Linear Relationship ◽  
Fire Hazard ◽  
Maximum Temperature ◽  
Ulex Europaeus ◽  
Plant Trait ◽  
Non Linear ◽  
Dead Material ◽  
Components Analysis ◽  
Fuel Accumulation ◽  
The Relationship

A key determinant of wildfire behaviour is the flammability of constituent plants. One plant trait that influences flammability is the retention of dead biomass, as the low moisture content of dead material means less energy is required to achieve combustion. However, the effect of the dead-to-live ratio of fuel on plant flammability has rarely been experimentally quantified. Here we examine the nature of the relationship between dead fuel accumulation and flammability in Ulex europaeus (common gorse). Shoots with varying proportions of dead material were ignited in a purpose-built plant-burner. Three components of flammability were measured: sustainability (flame duration), consumability (proportion burnt biomass) and combustibility (maximum temperature). While flame duration and proportion burnt biomass had a positive linear relationship with the proportion of dead material, the response of maximum temperature was positive but non-linear. All three flammability components were reduced to a single variable using principal components analysis; this had a non-linear relationship with the proportion of dead material. The response of maximum temperature to dead material plateaued at 39%. These findings have implications for the management of habitats invaded by gorse; to mitigate fire hazard associated with gorse, stands should be kept at a relatively young age when dead fuel is less prevalent.

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