This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software development in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data.

https://link.springer.com/content/pdf/10.1007%2Fs10462-018-09679-z.pdf

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Mobile Edge Computing (MEC) is considered an essential future service for the implementation of 5G networks and the Internet of Things, as it is the best method of delivering computation and communication resources to mobile devices. It is based on the connection of the users to servers located on the edge of the network, which is especially relevant for real-time applications that demand minimal latency. In order to guarantee a resource-efficient MEC (which, for example, could mean improved Quality of Service for users or lower costs for service providers), it is important to consider certain aspects of the service model, such as where to offload the tasks generated by the devices, how many resources to allocate to each user (specially in the wired or wireless device-server communication) and how to handle inter-server communication. However, in the MEC scenarios with many and varied users, servers and applications, these problems are characterized by parameters with exceedingly high levels of dimensionality, resulting in too much data to be processed and complicating the task of finding efficient configurations. This will be particularly troublesome when 5G networks and Internet of Things roll out, with their massive amounts of devices. To address this concern, the best solution is to utilize Machine Learning (ML) algorithms, which enable the computer to draw conclusions and make predictions based on existing data without human supervision, leading to quick near-optimal solutions even in problems with high dimensionality. Indeed, in scenarios with too much data and too many parameters, ML algorithms are often the only feasible alternative. In this paper, a comprehensive survey on the use of ML in MEC systems is provided, offering an insight into the current progress of this research area. Furthermore, helpful guidance is supplied by pointing out which MEC challenges can be solved by ML solutions, what are the current trending algorithms in frontier ML research and how they could be used in MEC. These pieces of information should prove fundamental in encouraging future research that combines ML and MEC.

Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective

Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.

https://www.mdpi.com/1424-8220/20/10/2809/pdf

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods.

Real-world networks feature weights of interactions, where link weights often represent some physical attributes. In many situations, to recover the missing data or predict the network evolution, we need to predict link weights in a network. In this paper, we first proposed a series of new centrality indices for links in line graph. Then, utilizing these line graph indices, as well as a number of original graph indices, we designed three supervised learning methods to realize link weight prediction both in the networks of single layer and multiple layers, which perform much better than several recently proposed baseline methods. We found that the resource allocation index (RA) plays a more important role in the weight prediction than other topological properties, and the line graph indices are at least as important as the original graph indices in link weight prediction. In particular, the success application of our methods on Yelp layered network suggests that we can indeed predict the offline co-foraging behaviors of users just based on their online social interactions, which may open a new direction for link weight prediction algorithms, and meanwhile provide insights to design better restaurant recommendation systems.

Link Weight Prediction Using Supervised Learning Methods and Its Application to Yelp Layered Network

Support Vector Machines (SVM) is one of machine learning methods that can be used to perform classification task. Many researchers using SVM library to accelerate their research development. Using such a library will save their time and avoid to write codes from scratch. LibSVM is one of SVM library that has been widely used by researchers to solve their problems. The library also integrated to WEKA, one of popular Data Mining tools. This article contain results of our work related to complexity analysis of Support Vector Machines. Our work has focus on SVM algorithm and its implementation in LibSVM. We also using two popular programming languages i.e C++ and Java with three different dataset to test our analysis and experiment. The results of our research has proved that the complexity of SVM (LibSVM) is O(n3) and the time complexity shown that C++ faster than Java, both in training and testing, beside that the data growth will be affect and increase the time of computation.

/pdf/time-complexity-analysis-of-support-vector-machines-svm-in-2fa1y1zq9g.pdf

Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM

Research on Question Answering System (QAS) has been done mainly in English. Unfortunately, for Indonesian, it is still rarely explored whereas Indonesian is the official language used more than 250 million people. Research in the area of Indonesian Question Answering System (IQAS) began in 2005s, and since then only few number of IQAS have been developed. One of the important issues in IQAS is Answer Validation (AV), which is a system that can determine the correctness of QAS. To identify the future scope of research in this area, the need of comprehensive survey on IQAS and AV arises naturally. The goals of
this survey are to find the cutting-edge method used in AV and to prove that AV has not been implemented on IQAS. Based on the results, we suggest new opportunities and research challenges for IQAS community.

/pdf/survey-on-answer-validation-for-indonesian-question-gl33cid18f.pdf

Survey on Answer Validation for Indonesian Question Answering System (IQAS)

Recognition Textual Entailment (RTE) try to solve variability problem that commonly encountered in natural language-based systems. The basic idea is to detect whether the meaning of a text can be inferred by another text. The need dataset in language other than English is necessary to accelerate research development in RTE. We created RTE dataset for Indonesian by retrieval text from Web and generate text-hypothesis pairs as many as possible. The subset technique is used to decide whether Text (T) entails Hypothesis (H). The initial data used 400 question-answer pairs obtained 1,577 entailment pairs, where 481 entailment pairs obtained from the accuracy above 50%.

INARTE: An Indonesian Dataset for Recognition Textual Entailment

Shoot em' up game is the sub-genre of action game. Action game is attractive because the game play usually use the interesting user interface and easily affect human emotion. With the aim to eliminate all the enemy, this kind of game will be boredom the player if the enemy behavior are monotones. This game needs a controller to add dynamic system into the enemy such as the artificial intelligence. Therefore, this paper proposes Fuzzy Takagi Sugeno method that will take several input and give the response as the output. So, the game will manipulate the enemy behavior that make the game more challenging and interesting to be played.

Fuzzy Logic Implementation on Enemy Speed Control to Raise Player Engagement

Research in the field of Open Domain Question Answering System (OD-QAS) generally involves external knowledge which are dynamic and require high-level representation. Strong external knowledge is one of the key success of QAS. Therefore, intensive research is needed in this area. Web is one of the big source of information that can be used as external knowledge by QAS. However, the main problem is the Web contains a lot of unstructured data. Hence, a model is needed to extract information from the web. The model developed in this research based on pipeline architecture and consists three main processes: pre-processing, information extraction processing, and text processing. The input model is factoid questions, and the output are snippets or set of sentences that contains target answers. There are three search engines assist to finding relevant information from the Web, i.e, Yahoo!, Bing, and Ask. The result of average precision and deviation value for the each search engines are slightly different. The highest total number of snippets (true positive) generated by Yahoo! is 65 snippets, while the best average precision obtained by Bing is 25.33%.

Abdiansah Abdiansah

Papers

Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM

Survey on Answer Validation for Indonesian Question Answering System (IQAS)

INARTE: An Indonesian Dataset for Recognition Textual Entailment

Fuzzy Logic Implementation on Enemy Speed Control to Raise Player Engagement

Information Extraction from Web as Knowledge Resources for Indonesian Question Answering System