Published: Jan 1, 2018
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DOI: 10.4018/IJKDB.20180101.pre
Volume 8
Brojo Kishore Mishra, V. Santhi, Sanjaya Kumar Panda, Ashish Khanna, Vishal Gour, Vishal Jain
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Mishra, Brojo Kishore, et al. "Special Issue on Advances in Machine Learning and Data Mining." IJKDB vol.8, no.1 2018: pp.5-6. http://doi.org/10.4018/IJKDB.20180101.pre
APA
Mishra, B. K., Santhi, V., Panda, S. K., Khanna, A., Gour, V., & Jain, V. (2018). Special Issue on Advances in Machine Learning and Data Mining. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 5-6. http://doi.org/10.4018/IJKDB.20180101.pre
Chicago
Mishra, Brojo Kishore, et al. "Special Issue on Advances in Machine Learning and Data Mining," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.1: 5-6. http://doi.org/10.4018/IJKDB.20180101.pre
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Published: Jan 1, 2018
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DOI: 10.4018/IJKDB.2018010101
Volume 8
Saeed Rouhani, Maryam MirSharif
In this article, the authors proposed the method of medical diagnosis in gestational diabetes mellitus (GDM) in the initial stages of pregnancy to facilitate diagnoses and prevent the affection....
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In this article, the authors proposed the method of medical diagnosis in gestational diabetes mellitus (GDM) in the initial stages of pregnancy to facilitate diagnoses and prevent the affection. Nowadays, in industrial modern world with changing lifestyle alimental manner the incidence of complex disease has been increasingly grown. GDM is a chronic disease and one of the major health problems that is often diagnosed in middle or late period of pregnancy, when it is too late for prediction. If it is not treated, it will make serious complications and various side effects for mother and child. This article is designed for answering to the question of: “What is the best approach in timely and accurate prediction of GDM?” Thus, the artificial neural network and decision tree are proposed to reduce the amount of error and the level of accuracy in anticipating and improving the precision of prediction. The results illustrate that intelligent diagnosis systems can improve the quality of healthcare, timely prediction, prevention, and knowledge discovery in bioinformatics.
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Rouhani, Saeed, and Maryam MirSharif. "Data Mining Approach for the Early Risk Assessment of Gestational Diabetes Mellitus." IJKDB vol.8, no.1 2018: pp.1-11. http://doi.org/10.4018/IJKDB.2018010101
APA
Rouhani, S. & Maryam MirSharif. (2018). Data Mining Approach for the Early Risk Assessment of Gestational Diabetes Mellitus. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 1-11. http://doi.org/10.4018/IJKDB.2018010101
Chicago
Rouhani, Saeed, and Maryam MirSharif. "Data Mining Approach for the Early Risk Assessment of Gestational Diabetes Mellitus," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.1: 1-11. http://doi.org/10.4018/IJKDB.2018010101
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Published: Jan 1, 2018
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DOI: 10.4018/IJKDB.2018010102
Volume 8
Gauri Jain, Manisha Sharma, Basant Agarwal
This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging...
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This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.
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Jain, Gauri, et al. "Spam Detection on Social Media Using Semantic Convolutional Neural Network." IJKDB vol.8, no.1 2018: pp.12-26. http://doi.org/10.4018/IJKDB.2018010102
APA
Jain, G., Sharma, M., & Agarwal, B. (2018). Spam Detection on Social Media Using Semantic Convolutional Neural Network. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 12-26. http://doi.org/10.4018/IJKDB.2018010102
Chicago
Jain, Gauri, Manisha Sharma, and Basant Agarwal. "Spam Detection on Social Media Using Semantic Convolutional Neural Network," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.1: 12-26. http://doi.org/10.4018/IJKDB.2018010102
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Published: Jan 1, 2018
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DOI: 10.4018/IJKDB.2018010103
Volume 8
Amit Kumar, Bikash Kanti Sarkar
This article describes how for the last few decades, data mining research has had significant progress in a wide spectrum of applications. Research in prediction of multi-domain data sets is a...
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This article describes how for the last few decades, data mining research has had significant progress in a wide spectrum of applications. Research in prediction of multi-domain data sets is a challenging task due to the imbalanced, voluminous, conflicting, and complex nature of data sets. A learning algorithm is the most important technique for solving these problems. The learning algorithms are widely used for classification purposes. But choosing the learners that perform best for data sets of particular domains is a challenging task in data mining. This article provides a comparative performance assessment of various state-of-the-art learning algorithms over multi-domain data sets to search the effective classifier(s) for a particular domain, e.g., artificial, natural, semi-natural, etc. In the present article, a total of 14 real world data sets are selected from University of California, Irvine (UCI) machine learning repository for conducting experiments using three competent individual learners and their hybrid combinations.
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Kumar, Amit, and Bikash Kanti Sarkar. "Performance Assessment of Learning Algorithms on Multi-Domain Data Sets." IJKDB vol.8, no.1 2018: pp.27-41. http://doi.org/10.4018/IJKDB.2018010103
APA
Kumar, A. & Sarkar, B. K. (2018). Performance Assessment of Learning Algorithms on Multi-Domain Data Sets. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 27-41. http://doi.org/10.4018/IJKDB.2018010103
Chicago
Kumar, Amit, and Bikash Kanti Sarkar. "Performance Assessment of Learning Algorithms on Multi-Domain Data Sets," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.1: 27-41. http://doi.org/10.4018/IJKDB.2018010103
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Published: Jan 1, 2018
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DOI: 10.4018/IJKDB.2018010104
Volume 8
Deepali Virmani, Nikita Jain, Ketan Parikh, Shefali Upadhyaya, Abhishek Srivastav
This article describes how data is relevant and if it can be organized, linked with other data and grouped into a cluster. Clustering is the process of organizing a given set of objects into a set...
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This article describes how data is relevant and if it can be organized, linked with other data and grouped into a cluster. Clustering is the process of organizing a given set of objects into a set of disjoint groups called clusters. There are a number of clustering algorithms like k-means, k-medoids, normalized k-means, etc. So, the focus remains on efficiency and accuracy of algorithms. The focus is also on the time it takes for clustering and reducing overlapping between clusters. K-means is one of the simplest unsupervised learning algorithms that solves the well-known clustering problem. The k-means algorithm partitions data into K clusters and the centroids are randomly chosen resulting numeric values prohibits it from being used to cluster real world data containing categorical values. Poor selection of initial centroids can result in poor clustering. This article deals with a proposed algorithm which is a variant of k-means with some modifications resulting in better clustering, reduced overlapping and lesser time required for clustering by selecting initial centres in k-means and normalizing the data.
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Virmani, Deepali, et al. "Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology." IJKDB vol.8, no.1 2018: pp.42-59. http://doi.org/10.4018/IJKDB.2018010104
APA
Virmani, D., Jain, N., Parikh, K., Upadhyaya, S., & Srivastav, A. (2018). Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 42-59. http://doi.org/10.4018/IJKDB.2018010104
Chicago
Virmani, Deepali, et al. "Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.1: 42-59. http://doi.org/10.4018/IJKDB.2018010104
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Published: Jan 1, 2018
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DOI: 10.4018/IJKDB.2018010105
Volume 8
Mohammad Ahsan, Madhu Kumari, Tajinder Singh, Triveni Lal Pal
This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some...
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This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e., number of followers, followees, tweets generated by the user so far, account age, tweet age. In order to explore the relationship between sentiment content and information diffusion, the authors first checked the features' significance as an indicator of diffusibility by using random forests. Finally, support vectors and k-Neighbors regression models are used to capture the complete dynamics of information diffusion. Experiments and results clearly reveal that sentiment prominently helps in making a better prediction of information diffusion.
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Ahsan, Mohammad, et al. "Sentiment Based Information Diffusion in Online Social Networks." IJKDB vol.8, no.1 2018: pp.60-74. http://doi.org/10.4018/IJKDB.2018010105
APA
Ahsan, M., Kumari, M., Singh, T., & Pal, T. L. (2018). Sentiment Based Information Diffusion in Online Social Networks. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 60-74. http://doi.org/10.4018/IJKDB.2018010105
Chicago
Ahsan, Mohammad, et al. "Sentiment Based Information Diffusion in Online Social Networks," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.1: 60-74. http://doi.org/10.4018/IJKDB.2018010105
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Published: Jan 1, 2018
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DOI: 10.4018/IJKDB.2018010106
Volume 8
Sumitra Kisan, Sarojananda Mishra, Ajay Chawda, Sanjay Nayak
This article describes how the term fractal dimension (FD) plays a vital role in fractal geometry. It is a degree that distinguishes the complexity and the irregularity of fractals, denoting the...
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This article describes how the term fractal dimension (FD) plays a vital role in fractal geometry. It is a degree that distinguishes the complexity and the irregularity of fractals, denoting the amount of space filled up. There are many procedures to evaluate the dimension for fractal surfaces, like box count, differential box count, and the improved differential box count method. These methods are basically used for grey scale images. The authors' objective in this article is to estimate the fractal dimension of color images using different color models. The authors have proposed a novel method for the estimation in CMY and HSV color spaces. In order to achieve the result, they performed test operation by taking number of color images in RGB color space. The authors have presented their experimental results and discussed the issues that characterize the approach. At the end, the authors have concluded the article with the analysis of calculated FDs for images with different color space.
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Kisan, Sumitra, et al. "Estimation of Fractal Dimension in Different Color Model." IJKDB vol.8, no.1 2018: pp.75-93. http://doi.org/10.4018/IJKDB.2018010106
APA
Kisan, S., Mishra, S., Chawda, A., & Nayak, S. (2018). Estimation of Fractal Dimension in Different Color Model. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 75-93. http://doi.org/10.4018/IJKDB.2018010106
Chicago
Kisan, Sumitra, et al. "Estimation of Fractal Dimension in Different Color Model," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.1: 75-93. http://doi.org/10.4018/IJKDB.2018010106
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Published: Jan 1, 2018
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DOI: 10.4018/IJKDB.2018010107
Volume 8
Libi Hertzberg, Assif Yitzhaky, Metsada Pasmanik-Chor
This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been...
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This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.
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Hertzberg, Libi, et al. "GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists." IJKDB vol.8, no.1 2018: pp.94-105. http://doi.org/10.4018/IJKDB.2018010107
APA
Hertzberg, L., Yitzhaky, A., & Pasmanik-Chor, M. (2018). GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 94-105. http://doi.org/10.4018/IJKDB.2018010107
Chicago
Hertzberg, Libi, Assif Yitzhaky, and Metsada Pasmanik-Chor. "GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.1: 94-105. http://doi.org/10.4018/IJKDB.2018010107
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