A Taxonomy of Academic Abstract Sentence Classification Modelling

Background: Abstract sentence classification modelling has the potential to advance literature discovery capability for the array of academic literature information systems, however, no artefact exists that categorises known models and identifies their key characteristics. Aims: To systematically categorise known abstract sentence classification models and make this knowledge readily available to future researchers and professionals concerned with abstract sentence classification model development and deployment. Method: An information systems taxonomy development methodology was adopted after a literature review to categorise 23 abstract sentence classification models identified from the literature. Corresponding dimensions and characteristics were derived from this process with the resulting taxonomy presented. Results: Abstract sentence classification modelling has evolved significantly with state-of-the-art models now leveraging neural networks to achieve high-performance sentence classification. The resulting taxonomy provides a novel means to observe the development of this research field and enables us to consider how such models can be further improved or deployed in real-world applications

Image Classification Modelling of Beef and Pork Using Convolutional Neural Network

The high price of beef makes some people manipulate sales in markets or other shopping venues, such as mixing beef and pork. The difference between pork and beef is actually from the color and texture of the meat. However, many people do not understand these differences yet. One of the solutions is to create a technology that can recognize and differentiate pork and beef. That is what underlies this research to build a system that can classify the two types of meat. Since traditional machine learning such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) uses manual feature extraction in pattern recognition, we use Convolutional Neural Network (CNN) that can extract the feature automatically through the convolution layer. CNN is one of the deep learning methods and the development of artificial intelligence science that can be applied to classify images. There is no research on using CNN for pork and beef classification. Several regularization techniques, including dropout, L2, and max-norm with several values in them are applied to the model and compared to get the best classification results and can predict new data accurately. The best accuracy of 97.56% and the lowest loss of 0.111 were obtained from the CNN model by applying the dropout technique using p=0.7 supported by hyperparameters such as two convolution layers, 128 neurons in the fully connected layer, ReLU activation function, and two fully connected layers. The results of this study are expected to be the basis for making beef and pork recognition applications

Examining the Trend of Literature on Classification Modelling: A Bibliometric Approach

This paper analyses and reports various types of published works related to classification or discriminant modelling. This paper adopted a bibliometric analysis based on the data obtained from the Scopus online database on 27th July 2019. Based on the ‘keywords’ search results, it yielded 2775 valid documents for further analysis. For data visualisation purposes, we employed VOSviewer. This paper reports the results using standard bibliometric indicators, particularly on the growth rate of publications, research productivity, analysis of the authors and citations. The outcomes revealed that there is an increased growth rate of classification literature over the years since 1968. A total of 2473 (89.12%) documents were from journals (n=1439; 51.86%) and conference proceedings (n=1034; 37.26%) contributed as the top publications in this classification topic. Meanwhile, 2578 (92.9%) documents are multi-authored with an average collaboration index of 3.34 authors per article. However, this classification research field found that the famous numbers of authors’ collaboration in a document are two (with n=758; 27.32%), three (n=752; 27.10%) and four (n=560; 20.18%) respectively. An analysis by country, China with 1146 (41.30%) published documents thus is ranked first in productivity. With respect to the frequency of citations, Bauer and Kohavi (1999)’s article emerged as the most cited article through 1414 total citations with an average of 70.7 citations per year. Overall, the increasing number of works on classification topics indicates a growing awareness of its importance and specific requirements in this research field

Dynamic modelling and optimisation of carbon management strategies in gold processing

This thesis presents the development and application of a dynamic model of gold adsorption onto activated carbon in gold processing. The primary aim of the model is to investigate different carbon management strategies of the Carbon in Pulp (CIP) process. This model is based on simple film-diffusion mass transfer and the Freundlich isotherm to describe the equilibrium between the gold in solution and gold adsorbed onto carbon. A major limitation in the development of a dynamic model is the availability of accurate plant data that tracks the dynamic behaviour of the plant. This limitation is overcome by using a pilot scale CIP gold processing plant to obtain such data. All operating parameters of this pilot plant can be manipulated and controlled to a greater degree than that of a full scale plant. This enables a greater amount of operating data to be obtained and utilised. Two independent experiments were performed to build the model. A series of equilibrium tests were performed to obtain parameter values for the Freundlich isotherm, and results from an experimental run of the CIP pilot plant were used to obtain other model parameter values. The model was then verified via another independent experiment. The results show that for a given set of operating conditions, the simulated predictions were in good agreement with the CIP pilot plant experimental data. The model was then used to optimise the operations of the pilot plant. The evaluation of the plant optimisation simulations was based on an objective function developed to quantitatively compare different simulated conditions. This objective function was derived from the revenue and costs of the CIP plant. The objective function costings developed for this work were compared with published data and were found to be within the published range. This objective function can be used to evaluate the performance of any CIP plant from a small scale laboratory plant to a full scale gold plant. The model, along with its objective function, was used to investigate different carbon management strategies and to determine the most cost effective approach. A total of 17 different carbon management strategies were investigated. An additional two experimental runs were performed on the CIP pilot plant to verify the simulation model and objective function developed. Finally an application of the simulation model is discussed. The model was used to generate plant data to develop an operational classification model of the CIP process using machine learning algorithms. This application can then be used as part of an online diagnosis tool

Intrusion Detection System using Bayesian Network Modeling

Computer Network Security has become a critical and important issue due to ever increasing cyber-crimes. Cybercrimes are spanning from simple piracy crimes to information theft in international terrorism. Defence security agencies and other militarily related organizations are highly concerned about the confidentiality and access control of the stored data. Therefore, it is really important to investigate on Intrusion Detection System (IDS) to detect and prevent cybercrimes to protect these systems. This research proposes a novel distributed IDS to detect and prevent attacks such as denial service, probes, user to root and remote to user attacks. In this work, we propose an IDS based on Bayesian network classification modelling technique. Bayesian networks are popular for adaptive learning, modelling diversity network traffic data for meaningful classification details. The proposed model has an anomaly based IDS with an adaptive learning process. Therefore, Bayesian networks have been applied to build a robust and accurate IDS. The proposed IDS has been evaluated against the KDD DAPRA dataset which was designed for network IDS evaluation. The research methodology consists of four different Bayesian networks as classification models, where each of these classifier models are interconnected and communicated to predict on incoming network traffic data. Each designed Bayesian network model is capable of detecting a major category of attack such as denial of service (DoS). However, all four Bayesian networks work together to pass the information of the classification model to calibrate the IDS system. The proposed IDS shows the ability of detecting novel attacks by continuing learning with different datasets. The testing dataset constructed by sampling the original KDD dataset to contain balance number of attacks and normal connections. The experiments show that the proposed system is effective in detecting attacks in the test dataset and is highly accurate in detecting all major attacks recorded in DARPA dataset. The proposed IDS consists with a promising approach for anomaly based intrusion detection in distributed systems. Furthermore, the practical implementation of the proposed IDS system can be utilized to train and detect attacks in live network traffi

An improved text classification modelling approach to identify security messages in heterogeneous projects

Security remains under-addressed in many organisations, illustrated by the number of large-scale software security breaches. Preventing breaches can begin during software development if attention is paid to security during the software’s design and implementation. One approach to security assurance during software development is to examine communications between developers as a means of studying the security concerns of the project. Prior research has investigated models for classifying project communication messages (e.g., issues or commits) as security related or not. A known problem is that these models are project-specific, limiting their use by other projects or organisations. We investigate whether we can build a generic classification model that can generalise across projects. We define a set of security keywords by extracting them from relevant security sources, dividing them into four categories: asset, attack/threat, control/mitigation, and implicit. Using different combinations of these categories and including them in the training dataset, we built a classification model and evaluated it on industrial, open-source, and research-based datasets containing over 45 different products. Our model based on harvested security keywords as a feature set shows average recall from 55 to 86%, minimum recall from 43 to 71% and maximum recall from 60 to 100%. An average f-score between 3.4 and 88%, an average g-measure of at least 66% across all the dataset, and an average AUC of ROC from 69 to 89%. In addition, models that use externally sourced features outperformed models that use project-specific features on average by a margin of 26–44% in recall, 22–50% in g-measure, 0.4–28% in f-score, and 15–19% in AUC of ROC. Further, our results outperform a state-of-the-art prediction model for security bug reports in all cases. We find using sound statistical and effect size tests that (1) using harvested security keywords as features to train a text classification model improve classification models and generalise to other projects significantly. (2) Including features in the training dataset before model construction improve classification models significantly. (3) Different security categories represent predictors for different projects. Finally, we introduce new and promising approaches to construct models that can generalise across different independent projects.publishedVersio

An automated deep learning pipeline based on advanced optimisations for leveraging spectral classification modelling

Na modelagem de deep learning (DL) para dados espectrais, um grande desafio está relacionado à escolha da arquitetura de rede DL e à seleção dos melhores hiperparmetros. Muitas vezes, pequenas mudanças na arquitetura neural ou seu hiperparômetro podem ter uma influência direta no desempenho do modelo, tornando sua robustez questionável. Para lidar com isso, este estudo apresenta uma modelagem automatizada de aprendizagem profunda baseada em técnicas avançadas de otimização envolvendo hyperband e otimização bayesiana, para encontrar automaticamente a arquitetura neural ideal e seus hiperparmetros para alcançar modelos robustos de DL. A otimização requer uma arquitetura neural base para ser inicializada, no entanto, mais tarde, ajusta automaticamente a arquitetura neural e os hiperparmetros para alcançar o modelo ideal. Além disso, para apoiar a interpretação dos modelos DL, foi implementado um esquema de pesagem de comprimento de onda baseado no mapeamento de ativação de classe ponderada por gradiente (Grad-CAM). O potencial da abordagem foi mostrado em um caso real de classificação da variedade de trigo com dados espectrais quase infravermelhos. O desempenho da classificação foi comparado com o relatado anteriormente no mesmo conjunto de dados com diferentes abordagens DL e quimiométrica. Os resultados mostraram que, com a abordagem proposta, foi alcançada uma precisão de classificação de 94,9%, melhor do que a melhor precisão relatada no mesmo conjunto de dados, ou seja, 93%. Além disso, o melhor desempenho foi obtido com uma arquitetura neural mais simples em comparação com o que foi usado em estudos anteriores. O deep learning automatizado baseado na otimização avançada pode suportar a modelagem DL de dados espectrais.info:eu-repo/semantics/publishedVersio

Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining

The notion of meta-mining has appeared recently and extends the traditional meta-learning in two ways. First it does not learn meta-models that provide support only for the learning algorithm selection task but ones that support the whole data-mining process. In addition it abandons the so called black-box approach to algorithm description followed in meta-learning. Now in addition to the datasets, algorithms also have descriptors, workflows as well. For the latter two these descriptions are semantic, describing properties of the algorithms. With the availability of descriptors both for datasets and data mining workflows the traditional modelling techniques followed in meta-learning, typically based on classification and regression algorithms, are no longer appropriate. Instead we are faced with a problem the nature of which is much more similar to the problems that appear in recommendation systems. The most important meta-mining requirements are that suggestions should use only datasets and workflows descriptors and the cold-start problem, e.g. providing workflow suggestions for new datasets. In this paper we take a different view on the meta-mining modelling problem and treat it as a recommender problem. In order to account for the meta-mining specificities we derive a novel metric-based-learning recommender approach. Our method learns two homogeneous metrics, one in the dataset and one in the workflow space, and a heterogeneous one in the dataset-workflow space. All learned metrics reflect similarities established from the dataset-workflow preference matrix. We demonstrate our method on meta-mining over biological (microarray datasets) problems. The application of our method is not limited to the meta-mining problem, its formulations is general enough so that it can be applied on problems with similar requirements