[서평]「The Unified Modeling Language User Guide」

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Handbook of Data Mining and Knowledge Discovery

Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.

Corpus creation for sentiment analysis in code-mixed Tamil-English text

Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.

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Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

In this paper, we contribute to a recent and successful modelization of uncertainty, which the practitioner often encounters in the formulation of multicriteria group decision making problems. To be precise, in order to approach the uncertainty issue we introduce a novel type of soft rough covering by means of soft neighborhoods, and then we use it to improve decision making in a multicriteria group environment. Our research method is as follows. Firstly we introduce the soft covering upper and lower approximation operators of soft rough coverings. Then its relationships with well-established types of soft rough coverings are analyzed. Secondly, we define and investigate the measure degree of our novel soft rough covering. With this tool we produce a new class of soft rough sets. Finally, we propose an application of such soft rough covering model to multicriteria group decision making by means of an algorithmic solution. A fully developed example supports the implementability of this decision making method.

A novel type of soft rough covering and its application to multicriteria group decision making

Identifying the wide range of applications, machine learning algorithms proved its ability to learn without being explicitly programmed. Classifying the images through machine learning algorithms is getting wide range of acceptability nowadays. Being a branch of Artificial Intelligence, machine learning implies the study of systems which has the capability to learn from data. Machine learning involves two parts - representation and generalization. Representation implies labeling seen data instances and generalization determines whether the system can perform well on unlabelled data instances. In this article, we focused on the performance of machine learning algorithms [1]. A CBIR (Content Based Image Retrieval) frame work has been developed and obtained a reduced texture feature data set using Caltech101 image database [2]. We highlight the top five algorithms such as Logistic, Bagging, LMT, Multiclass classifier and Attribute selection classifier which can be used for image classification. In introduction, an overview of the selected techniques is presented. We have extracted 2037 feature vectors from Caltech101 image database. These data are used to distinguish the performance of machine learning algorithms. Having checked all machine learning algorithms supported, we identified top five algorithms that have a better performance compared to other machine learning algorithms. The software used for testing is WEKA [3], which is an open source software developed by University of Waikato, New Zealand.

Performance analysis of classification algorithms applied to Caltech101 image database

The Wireless Sensor Network (WSN) is a promising network with wireless infrastructure to provide secure service to the users anywhere and anytime. In this type of wireless infrastructure, reliability is a major issue because it affects the multicasting due to link or node failure, opportunistic packet loss, and lowest degree of latency. These three deficiencies may affect the reliable multicast in WSN. The comprehensive solutions of Hybrid Position-based Adaptive Low Packet Loss Routing (H-PAL-PLR) protocol of WSNs are against opportunistic packet loss. In addition to this, Hybrid Optimized Localization with Long-Lifetime and Low-Latency Data Aggregation Scheduling algorithm (HOL-5-DAS) for low latency reliable multicast routing in wireless transmission. H-PAL-PLR Routing protocol consists of combination of position-based protocols and adaptive low packet loss routing protocol is used to protect the wireless sensor networks from the packet loss. The proposed model of MRM, H-PAL-PLR, and HOL-5-DAS has been proved to be efficient in case of link or node failure, packet loss and latency respectively. Likewise the proposed models show better performance in terms of packet delivery ratio, network life time, throughput, latency ratio and packet loss ratio parameters and those are experimentally proved.

A New Hybrid Algorithm for Tolerating Security Threats in Wireless Sensor Networks

Machine Translation has made significant achievements for the past decades However, in many languages, the complexity with its rich inflection and agglutination poses many challenges, that forced for manual translation to make the corpus available The divergence in lexical, syntactic and semantic in any pair of languages makes machine translation more difficult And many systems still depend on rules heavily, that deteriates system performance In this paper, a study on divergence in Malayalam-Tamil languages is attempted at source language analysis to make translation process easy In Malayalam-Tamil pair, the divergence is more reported in lexical and structural level, that is been resolved by using bilingual dictionary and transfer grammar The accuracy is increased to 65 percentage, which is promising KeywordsTranslational divergence; semantic; syntactic; lexical;

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A Study on Divergence in Malayalam and Tamil Language in Machine Translation Perceptive

Word Sense Disambiguation (WSD) is one of the pressing problems in Natural Language Processing (NLP), which determines the right sense of an ambiguous word in the specific context. WSD is considered as a harder problem as it depends on a set of classes, which vary depending on the context. There have been a number of researches performed in WSD in many languages, from dictionary-based methods to supervised learning methods and unsupervised learning approaches. In this paper, an attempt is made for the disambiguation of Malayalam words, which has a rich set of ambiguous words having different meanings. A semi supervised machine learning techniques combined with statistical approach mainly Maximum Entropy is experimented for Malayalam, which shows promising result for a set of corpus trained.

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Malayalam Word Sense Disambiguation using Maximum Entropy Model

The new education paradigm changes the classrooms to E-Learning platforms. The student engagement monitoring is a key problem in E-Learning. This work put forward an efficient mechanism to measure the visual attention of a learner by analyzing various face attributes and the fatigue level. Eye closure is considered as an indicator of fatigue in humans. The novel architecture employs machine learning algorithms to predict the fatigue through bi-state eye classification. Different experimentation are done using various classifiers and feature extraction techniques. The accuracy of the best model derived reaches up to 93.6% through principal component analysis based feature extraction and random forest classifier. The testing of the model is performed on different datasets and on real-time video. The results indicate the efficacy of the model in analyzing the visual attention score during learning.

Elizabeth Sherly

Papers

Performance analysis of classification algorithms applied to Caltech101 image database

A New Hybrid Algorithm for Tolerating Security Threats in Wireless Sensor Networks

A Study on Divergence in Malayalam and Tamil Language in Machine Translation Perceptive

Malayalam Word Sense Disambiguation using Maximum Entropy Model

Visual Attention Score and Fatigue Level Measure of Students through Eye Analysis–Machine Learning Approach