3,211 research outputs found
Improvement of the demand forecasting methods for vehicle parts at an international automotive company.
This study aims to improve the forecasting accuracy for the monthly material flows of an area forwarding based inbound logistics network for an international automotive company. Due to human errors, short-term changes in material requirements or data bases desynchronization the Material Requirement Planning (MRP) cannot be directly derived from the Master Production Schedule (MPS). Therefore, the inbound logistics flows are forecast. The current research extends the forecasting methodsÂż scope already applied by the company namely, NaĂŻve, ARIMA, Neural Networks, Exponential Smoothing and Ensemble Forecast (an average of the first four methods) by allowing the implementation of three new algorithms: The Prophet Algorithm, the Vector Autoregressive (Multivariate Time Series) and Automated Simple Moving Average, and two new data cleaning methods: Automated Outlier Detection and Linear Interpolation. All the methods are structured in a software using the programming language R. The results show that as of April 2018, 80.1% of all material flows have a Mean Absolute Percentage Error (MAPE) of less than or equal to 20%, in comparison with the 58.6% of all material flows which had the same behavior in the original software in February 2018. Furthermore, the three new algorithms represent now 29% of all forecasts. All the analysis realized in this research were made with actual data from the company, and the upgraded software was approved by the logistics analysts to make all future material flow forecasts.PregradoINGENIERO(A) EN INDUSTRIA
Customer Churn Detection and Marketing Retention Strategies in the Online Food Delivery Business
The purpose of this thesis is to analyze the behavior of customers within the
Online Food Delivery industry, through which it is proposed to develop a prediction
model that allows detecting, based on valuable active customers, those who will leave
the services of Alpha Corporation in the near future.
Firstly, valuable customers are defined as those consumers who have made at
least 8 orders in the last 12 months. In this way, considering the historical behavior of
said users, as well as applying Feature Engineering techniques, a first approach is
proposed based on the implementation of a Random Forest algorithm and, later, a
boosting algorithm: XGBoost.
Once the performance of each of the models developed is analyzed, and potential
churners are identified, different marketing suggestions are proposed in order to retain
said customers. Retention strategies will be based on how Alpha Corporation works, as
well as on the output of the predictive model. Other development alternatives will also
be discussed: a clustering model based on potential churners or an unstructured data
model to analyze the emotions of those users according to the NPS surveys. The aim of
these proposals is to complement the prediction to design more specific retention
marketing strategies
Fuzzy determination of informative frequency band for bearing fault detection
Detecting early faults in rolling element bearings is a crucial measure for the health maintenance of rotating machinery. As faulty features of bearings are usually demodulated into a high-frequency band, determining the informative frequency band (IFB) from the vibratory signal is a challenging task for weak fault detection. Existing approaches for IFB determination often divide the frequency spectrum of the signal into even partitions, one of which is regarded as the IFB by an individual selector. This work proposes a fuzzy technique to select the IFB with improvements in two aspects. On the one hand, an IFB-specific fuzzy clustering method is developed to segment the frequency spectrum into meaningful sub-bands. Considering the shortcomings of the individual selectors, on the other hand, three commonly-used selectors are combined using a fuzzy comprehensive evaluation method to guide the clustering. Among all the meaningful sub-bands, the one with the minimum comprehensive cost is determined as the IFB. The bearing faults, if any, can be detected from the demodulated envelope spectrum of the IFB. The proposed fuzzy technique was evaluated using both simulated and experimental data, and then compared with the state-of-the-art peer method. The results indicate that the proposed fuzzy technique is capable of generating a better IFB, and is suitable for detecting bearing faults
A Novel Machine learning Algorithms used to Detect Credit Card Fraud Transactions
During the Covid-19 pandemic, the world was under lockdown, and everyone was inside their home. There are so many restrictions for going out, so many companies introduced online shopping, and this online shopping helped more people; the e-commerce platform also increased their revenue; at the same time, online fraud has also risen worldwide. Everyone adopted online shopping during the pandemic. In 2019 India's 2019 credit/debit card fraud rate was 365, according to the National Crime Record Bureau. The developed countries are the highest rate of credit card fraud in 2020 compared to India; for that reason, we have to implement mechanisms that can detect credit theft. The machine learning algorithm with the R program will play an essential role in credit card fraud detection. The following machine learning algorithm will have used for credit card fraud, Random Forest, Logistic regression, Decision trees, and Gradient Boosting Classifiers. The European bank dataset used in our research and the dataset size is 284808. Here we used two classes, the first one is called the positive class (fraud transactions), and the second one is the negative class (genuine transactions). The final result will show us the outperforms of our proposed system
Anomaly detection of android malware using One-Class K-Nearest Neighbours (OC-KNN)
The advent of the Android Operating System has recorded a remarkable ground-breaking opportunities in the Technological world. However, this great breakthrough also has a very dark side – an uncontrollable rapid continuous releases of malware in the wild, targeted at the platform and all its information and human assets. The misuse-based approaches adopted by many detection systems do no longer have the rigidity and the tenacity to accommodate the rapid successive releases of malware that come in great volume in order to keep up with active defenses against unknown and novel attacks. Systems that are capable of offering anomaly protection are thus in dire need. This study developed a normality model that is based on One-Class K-Nearest Neighbour (OC-kNN) Machine Learning approach for anomaly detection of Android Malware. The OC-kNN was trained, using WEKA 3.8.2 Machine Learning Suite, through a semi-supervise procedure that contained mostly benign and a very few outliers Android application samples. The OC-kNN had 88.57% true performance accuracy for normal instances while 71.9% was recorded as true performance accuracy for outliers (unknown) instances. The false alarm rates for both normal and outlier’s instances were recorded as 28.1% and 11.5%. The study concluded that a One-Class Classification model is an effective approach to be used for the detection of unknown Android malware.
Keywords: Android; Machine Learning, Malware, One-Class Classification, Anomaly Detection, Outlier Detection, Novelty Detection, Concept Learning, k-N
07181 Abstracts Collection -- Parallel Universes and Local Patterns
From 1 May 2007 to 4 May 2007 the Dagstuhl Seminar 07181 ``Parallel
Universes and Local Patterns\u27\u27
was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl. During the seminar, several participants
presented their current research, and ongoing work and open problems
were discussed. Abstracts of the presentations given during the
seminar as well as abstracts of seminar results and ideas are put
together in this paper. The first section describes the seminar
topics and goals in general. Links to extended abstracts or full
papers are provided, if available
A strategy for predicting waste production and planning recycling paths in e-logistics based on improved EMD-LSTM
With the rapid development of e-commerce, express delivery has been chosen and accepted by consumers, and a large number of express packages have resulted in serious waste of resources and environmental pollution. Because of the irregularity of online goods purchases by users in real life, logistics parks are unable to accurately judge the recycling needs of various regions. In order to solve this problem, we propose an improved empirical mode decomposition (IEMD) algorithm combined with a long-short-term memory (LSTM) network to deal with the addresses and categories in logistics data, analyze the distribution of recyclable logistics waste in the logistics park service area and in the express recycling station within the logistics park, judge the value of recyclable logistics waste, optimize the best path for recycling vehicles and improve the success rate of logistics waste recycling. In order to better research and verify the IEMD-LSTM prediction model, we model and simulate the algorithm behavior of the express waste packaging recycling prediction model system, and compare it with other classification methods through specific logistics data experiments. The prediction accuracy, stability and advantages of the four algorithms are analyzed and compared, and the application reliability of the algorithm proposed in this paper to the logistics waste recycling process is verified. The application in the actual express logistics packaging recycling case shows the feasibility and effectiveness of the waste recycling scheme proposed in this paper
HiER 2015. Proceedings des 9. Hildesheimer Evaluierungs- und Retrievalworkshop
Die Digitalisierung formt unsere Informationsumwelten. Disruptive Technologien dringen verstärkt und immer schneller in unseren Alltag ein und verändern unser Informations- und Kommunikationsverhalten. Informationsmärkte wandeln sich. Der 9. Hildesheimer Evaluierungs- und Retrievalworkshop HIER 2015 thematisiert die Gestaltung und Evaluierung von Informationssystemen vor dem Hintergrund der sich beschleunigenden Digitalisierung. Im Fokus stehen die folgenden Themen: Digital Humanities, Internetsuche und Online Marketing, Information Seeking und nutzerzentrierte Entwicklung, E-Learning
Multiple reference consistency check for LAAS: a novel position domain approach
Since the traditional Maximum Likelihood-based range domain multiple reference consistency check (MRCC) has limitations in satisfying the integrity requirement of CAT II/III for civil aviation, a Kalman filter-based position domain method has been developed for fault detection and exclusion in the Local Area Augmentation System MRCC process. The position domain method developed in this paper seeks to address the limitations of range domain-based MRCC by focusing not only on improving the performance of the fault detection but also on the integrity risk requirement for MRCC. In addition, the issue of the stability of the Kalman filter in relation to the position domain approach is considered. GPS range corrections from multiple reference receivers are fused by the adaptive Kalman filter at the master station for detecting and excluding the single reference receiver’ failure. The performance of the developed Kalman filter-based MRCC has been compared with the traditional method using experimental data. The results reveal that the vertical protection level is slightly better in the traditional method compared with the developed Kalman filter-based approach under the fault-free case. However, the availability can be improved to over 97% in the proposed method relative to the traditional method under the single-fault case. Furthermore, the fault-tolerant positioning result with an accuracy improvement of more than 32% can be achieved even if different fault types are considered under the single-fault case. In particular, the algorithm can be a candidate option as an augmentable complement for the traditional MRCC and can be implemented in a master station element of the LAAS integrity monitoring architecture
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