Help Of Human Experts Research Articles

A semi-supervised algorithm for detecting extremism propaganda diffusion on social media

Abstract Extremist online networks reportedly tend to use Twitter and other Social Networking Sites (SNS) in order to issue propaganda and recruitment statements. Traditional machine learning models may encounter problems when used in such a context, due to the peculiarities of microblogging sites and the manner in which these networks interact (both between themselves and with other networks). Moreover, state-of-the-art approaches have focused on non-transparent techniques that cannot be audited; so, despite the fact that they are top performing techniques, it is impossible to check if the models are actually fair. In this paper, we present a semi-supervised methodology that uses our Discriminatory Expressions algorithm for feature selection to detect expressions that are biased towards extremist content (Francisco and Castro 2020). With the help of human experts, the relevant expressions are filtered and used to retrieve further extremist content in order to iteratively provide a set of relevant and accurate expressions. These discriminatory expressions have been proved to produce less complex models that are easier to comprehend, and thus improve model transparency. In the following, we present close to 70 expressions that were discovered by using this method alongside the validation test of the algorithm in several different contexts.

Novel Study of Machine Learning Algorithms with Supervised Learning Approach to Solve Real Time Big Data Applications Using Hadoop and Spark

This review focuses on different machine learning algorithms, descriptions, pros and cons of the algorithms. It also includes task of machine learning and supervised learning process, tools, techniques and programming language to build machine learning model. Machine learning is a building computational art craft, which learns through time from experience. Machine learning is learning from data and it turns input data to information (mapping input value to output value using label data). The objective of machine learning is building the algorithm that can learn from past data without the help of human experts. The learning algorithm contains task T, performance measure P and training experience T. Machine learning algorithm categorized as supervised, unsupervised and reinforcement learning. Supervised machine learning has two tasks, which are classification and regression. Hadoop with Spark and Python programing language most commonly used to build machine learning model. Information gain and Gain ratio, Gini index and Random forest algorithm used to measure feature importance. From this study most of supervised learning algorithms used k-fold cross validation techniques to split the data set in to training set and testing set. Standard value of k will be five or ten but it didn’t fixed, because it depends on the size of data set. The machine learning model performance mainly evaluated using classification accuracy, precision, recall, area under the curve and F-measure (F-score). Generally machine learning algorithms depend on the nature of the data, because the performance of the learning algorithms affected by data set. Therefore it is impossible to say the prediction accuracy of one algorithm is best over others. As each machine learning algorithms have pros and cons, designing of hybrid algorithms might be overcome this problem.

Addressing Noise and Skewness in Interpretable Health-Condition Assessment by Learning Model Confidence

Assessing the health condition has a wide range of applications in healthcare, military, aerospace, and industrial fields. Nevertheless, traditional feature-engineered techniques involve manual feature extraction, which are too cumbersome to adapt to the changes caused by the development of sensor network technology. Recently, deep-learning-based methods have achieved initial success in health-condition assessment research, but insufficient considerations for problems such as class skewness, noisy segments, and result interpretability make it difficult to apply them to real-world applications. In this paper, we propose a K-margin-based Interpretable Learning approach for health-condition assessment. In detail, a skewness-aware RCR-Net model is employed to handle problems of class skewness. Furthermore, we present a diagnosis model based on K-margin to automatically handle noisy segments by naturally exploiting expected consistency among the segments associated with each record. Additionally, a knowledge-directed interpretation method is presented to learn domain knowledge-level features automatically without the help of human experts which can be used as an interpretable decision-making basis. Finally, through experimental validation in the field of both medical and aerospace, the proposed method has a better generality and high efficiency with 0.7974 and 0.8005 scores, which outperform all state-of-the-art deep learning methods for health-condition assessment task by and , respectively.

A Novel Compact Convolutional Neural Network for Real-Time Nondestructive Evaluation of Metallic Surfaces

Due to heightened concerns about quality assurance in the industry, the inspection of high-volume metallic parts has gained significant attention. In this article, we present a compact convolution neural network (CNN)-based optical nondestructive inspection scheme for high-volume metallic components. Our approach enables us to identify surface flaws as small as 2000th of an inch while discriminating them from flaw-like features, such as glue spots, dust, stains, and scratches. First, we capture the images of the metallic parts with a pixel size of $7000\times2500$ and label the images according to flawed and unflawed regions with the help of human experts. Second, we preprocess these images and form more than 120000 tiles of size $224\times 224\times3$ , which, subsequently, are fed into our compact CNN. Finally, we train the neural network by fine-tuning the hyperparameters to reduce overfitting of the data and to obtain maximum accuracy. The quantitative evaluation of this network shows that it can achieve almost 99% precision and recall of over 60 000 test images. The total image acquisition and inference time for one part-under-test is approximately 10 s. As a result, this novel compact CNN for optical inspection of surface flaws can outperform visual/manual inspection of high-volume metallic parts with very high accuracy and within a reasonable evaluation time.

Software Quality Estimation using Machine Learning: Case-based Reasoning Technique

Software quality estimation is one of the most interesting research areas in the domain of software engineering for last few decades. Large numbers of techniques and models have already been worked out in the area of error estimation. The aim of software quality estimation is to identify error prone tasks as the cost can be minimized with advance knowledge about the errors and this early treatment of error will enhance the software quality. In this paper we have explored a set of data in university setting. This paper advocates the use of case-based reasoning (i.e., CBR) to make a software quality estimation system by the help of human experts. CBR relies on historical information from similar past projects, whereby similarities are determined by comparing the projects, and key attributes. We have used different similarity measures to find the best method which increases estimation accuracy & reliability. This paper presents a work in which we have expanded our previous work [24]. The software is a console based application and thus does not use the GUI functions of the Operating System, which makes it very fast in execution. In order to obtain results we have used an indigenous tool for software quality estimation, run in c++ compiler. General Terms Software Engineering: Quality

A survey in the area of machine learning and its application for software quality prediction

This paper explores software quality improvement through early prediction of error patterns. It summarizes a variety of techniques for software quality prediction in the domain of software engineering. The objective of this research is to apply the various machine learning approaches, such as Case-Based Reasoning and Fuzzy logic, to predict software quality. The system predicts the error after accepting the values of certain parameters of the software. This paper advocates the use of case-based reasoning (i.e., CBR) to build a software quality prediction system with the help of human experts. The prediction is based on analogy. We have used different similarity measures to find the best method that increases reliability. This software is compiled using Turbo C++ 3.0 and hence it is very compact and standalone. It can be readily deployed on any configuration without affecting its performance.

Analysis of methods for ischemic stroke region recognition on CT slices

There are developed several computer aided methods for recognition of ischemic stroke region on CT slices at this time. This paper discuss about evaluation of such recognition methods with the help of human experts. Presented evaluation is completed by using an algorithm of fuzzy sets. Performed analysis of 20 methods with modifications showed, that gray level co-occurrence matrix method and hybrid method give highest rates. In addition, the evaluations of the same images by human experts are not uniform. More complex geometrical evaluation methods must be used for archiving of the better accuracy.