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Ground-penetrating radar (GPR) is one of the most popular and successful sensing modalities that have been investigated for landmine and subsurface threat detection. Many of the detection algorithms applied to this task are supervised and therefore require labeled examples of threat and nonthreat data for training. Training data most often consist of 2-D images (or patches) of GPR data, from which...
By analyzing the disadvantages of the traditional KNN using lazy learning that directly classify the data based on the K neighboring classes using the majority voting method, a new Sigmoid weighted classification algorithm WKS (Weighted KNN Based On Sigmoid) was proposed. WKS provides a new method for learning and training, since each training data di ∊ D contributes to the correct classification...
We show that information extracted from crowd-based testing can enhance automated mobile testing. We introduce Polariz, which generates replicable test scripts from crowd-based testing, extracting cross-app ‘motif’ events: automatically-inferred reusable higher-level event sequences composed of lower-level observed event actions. Our empirical study used 434 crowd workers from Mechanical Turk to perform...
Automatically generating unit tests is a powerful approach to exercise complex software. Unfortunately, current techniques often fail to provide relevant input values, such as strings that bypass domain-specific sanity checks. As a result, state-of-the-art techniques are effective for generic classes, such as collections, but less successful for domain-specific software. This paper presents TestMiner,...
Safety-critical systems in domains such as aviation, railway, and automotive are often subject to a formal process of safety certification. The goal of this process is to ensure that these systems will operate safely without posing risks to the user, the public, or the environment [1]. It is typically expensive and time consuming for companies to certify their software. Therefore, any attempt to automate...
Continuous Integration (CI) and Continuous Delivery (CD) are widespread in both industrial and open-source software (OSS) projects. Recent research characterized build failures in CI and identified factors potentially correlated to them. However, most observations and findings of previous work are exclusively based on OSS projects or data from a single industrial organization. This paper provides...
Recently, multi-label classification has gained prime importance among the classification problems. The applications of classification problems has increased so rapidly that the need for efficient and accurate classifiers has become a vital requirement in the area of data mining. Multi-label classification problem is distinguished from the single label classification because of the capability to handle...
Extreme Learning Machine (ELM) is a neural network architecture with Single Layer Feed-forward Neural Network (SLFN). For meaningful results, the structure of ELM has to be optimized through the inclusion of regularization and the ℓ2 — norm based regularization is mostly used. ℓ2-norm based regularization achieves better performance than the traditional ELM. The estimate of the regularization parameter...
Accurate forecasting of solar time series is challenging due to irregularities and uncertainties of such datasets. This paper develops an advanced hybrid forecasting method for solar radiation. The proposed framework combines a novel data mining technique for clustering the time-series data with an innovative cluster selection method and a multilayer recurrent neural network (RNN) to enhance the forecast...
Graduate employability is an increasingly major concern for academic institutions and assessing student employability provides a way of linking student skills and employer business requirements. Enhancing student assessment methods for employability can improve their understanding about companies in order to get suitable company for them. So, enhanced employability prediction of student can help them...
Refactoring is widely used technique to enhance overall quality of an existing software system by changing its internal structure without modifying its external behavior. Although, it is difficult to implement the refactoring manually, it helps to reduce the defects in the existing software. Three main types of design defects are investigated in the current study namely blob, Spaghetti Code (SC) and...
Intrauterine devices (IUDs) are highly-effective contraceptive methods for preventing unintended pregnancy and related adverse outcomes. Clinical Decision Support (CDS) systems could aid care providers in identifying patients at risk for pregnancy due to lack of contraceptive use. However, research suggests that this information is not reliably documented in structured data fields for query, but rather...
This paper proposes efficient and powerful deep networks for action prediction from partially observed videos containing temporally incomplete action executions. Different from after-the-fact action recognition, action prediction task requires action labels to be predicted from these partially observed videos. Our approach exploits abundant sequential context information to enrich the feature representations...
A method for classifying objects into categories and indexing is proposed to implement object recognition. The relational measurements such as the distance between two points, color comparison is encoded by the attributed relational graph (ARG) representation to provide one-to-one correspondence between models and object features. If the contour is traversed counterclockwise, a sequence can be formed...
Cohesion of a software module broadly refers to the relatedness of the elements of the module. A highly cohesive module has elements that all contribute to a single common purpose. Such modules are believed to be more understandable and maintainable. Most existing object-oriented class cohesion metrics measure the cohesion of a class based on internal connections between the methods of the class where...
Association rule mining is one of the popular topics in data mining. It can be applied with various types of applications. In these days, an organization applies multiple software applications to manage its jobs. These applications are also based on several types of platforms. Hence, an interoperability software development using web service becomes one of popular topics in nowadays. In this paper,...
Credit scoring is an important process in every financial institution and bank. Its high accuracy in classifying customers helps decrease the credit risk and increase reliability and profit. In this paper, we propose a binary classification approach that can classify customers who apply for loans. A statistical technique called Stepwise Regression (SR) is used as a pre-process to select important...
Machine learning and data mining techniques have been widely used in order to improve network intrusion detection in recent years. These techniques make it possible to automate anomaly detection in network traffics. One of the major problems that researchers are facing is the lack of published data available for research purposes. The KDD'99 dataset was used by researchers for over a decade even though...
More and more on-line experiments have been done in E-Commerce in order to understand the behavior of users or customers and then apply the data analysis technique to provide business guidance. One of the techniques is A/B testing. However, there is not clear guidance on the sample size in order for us to have valuable, trustable discovery. The purpose of this work is to find out a way to group customers...
We investigate a variant of the problem of automatic keyphrase extraction from scientific documents, which we define as Scientific Domain Knowledge Entity (SDKE) extraction. Keyphrases are noun phrases important to the documents themselves. In contrast, an SDKE is text that refers to a concept and can be classified as a process, material, task, dataset etc. A SDKE represents domain knowledge, but...
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