[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

Live migration permits to transfer constantly running Virtual Machine from source host to destination host. This is an unavoidable process in data centre in various scenarios such as load balancing, server maintenance and power management. Now, live migration performance optimization is an active area of research since performance degradation and energy overhead caused by live migration cannot be neglected in modern data centres; particularly if critical business goals and plans are to be satisfied. This work analyses the effect of proper bandwidth allocation during cost-aware live migration. Here, we design a cost function model based on network bandwidth between live migration process and service based on queuing theory. From experimental analysis, we infer that link bandwidth is a critical parameter in determining VM live migration cost.

Cost Evaluation of Virtual Machine Live Migration Through Bandwidth Analysis

The mammogram and MRI used for detection of cancerous cells in breast, requires overlaying the approach to get the best out of two or a combined effort. However, the varying intensity of breast in both modalities make the automatic segmentation of such lesions extremely challenging as both the modalities are different. This paper presents a new approach for automatic registration of mammograms and MR images. The registration of images taken from MRI and mammography is carried out to locate the position of lesion present in breast followed by noise removal, edge enhancement and morphological operations. The chest wall removal is done to extract the breast area, for obtaining the mass area and micro-calcifications to locate its position. The locations of lesions in both images are extracted out using region growing algorithm and thresholding method. Registering the images provide the most accurate location of masses and micro-calcification that is missed in any of the modalities, into single image. The results show that the method gives fast and accurate registration that can be effectively used for detecting masses and micro-calcifications with high level of accuracy. The accuracy obtained in the proposed method is about 87%. Keywords—image pre-processing, chest wall removal, nipple localization, segmentation, registration.

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Positioning Lesion from Breast MRI and Mammogram using Registration Method

kidney stones scientifically known as renal calculus or nephrolith consist of dense crystal masses generally originate in the kidneys and pass through the urinary tract which includes urethra, bladder and ureters. Genetic, dietary and environmental causes can be associated with the occurrence and severity of kidney stones. Imaging studies play a vital part in the treatment of kidney stone patients. CT is a precise diagnostic procedure for gastrointestinal illnesses. In essence, CT sends x-rays in small pieces that are saved on the screen as photographs from the body. The proposed method involves the diagnosis of kidney stones using image processing techniques such as pre-processing,segmentation,feature extraction and classification etc. In the initial stage the salt and pepper noise is removed by using a 3x3 median filter and discrete wavelet transform (DWT).K-Means clustering algorithm is used after segmenting the kidney stones using the watershed segmentation algorithm. The key objective of this study is to extract the features of segmented kidney stones by using the Grey level co-occurrence matrix(GLCM) and classify it using Probabilistic Neural Network (PNN) .The results we got indicate that 194 and 107 as the maximum sensitivity and maximum specificity point which was higher than the conventional renal calculus detection approaches.Also our proposed framework achieves an overall accuracy of 86.8%.

Kidney Stone Detection from CT images using Probabilistic Neural Network(PNN) and Watershed Algorithm

Social networking has become an ubiquitous part of communication in this modern era. Categorization in dynamic social network is very challenging because of the key feature of social networks – continual change. A typical social network grows exponentially over time, as new activities and interactions evolve rapidly. The dynamic social networks are efficient in modeling the behavior of any real life interactions. In this paper, two algorithms are considered, of which one helps to identify the exact community to which a new node belongs. The second algorithm uses a rule based approach to identify the exact community which satisfies a set of constraints. The social network is conceived as a weighted directed graph consisting of a set of nodes, edges , node weights and edge weights. The node weights are computed based on the node rank algorithm and the edge weights based on the people rank algorithm. In the proposed algorithms, the dynamic attributes of the nodes and time-based constraints play a crucial role in the identification of categories in the social network. The main objective of the algorithm is to obtain an optimal community network with high accuracy by using a heuristic approach. This approach helps to categorize a newly added node or entity to one or more communities dynamically.

Elizabeth Sherly

Papers

Cost Evaluation of Virtual Machine Live Migration Through Bandwidth Analysis

Positioning Lesion from Breast MRI and Mammogram using Registration Method

Kidney Stone Detection from CT images using Probabilistic Neural Network(PNN) and Watershed Algorithm

Time Based Constrained Object Identification in a Dynamic Social Network