Advances in Information Technology 

About: Advances in Information Technology is an academic conference. The conference publishes majorly in the area(s): The Internet & Deep learning. Over the lifetime, 705 publications have been published by the conference receiving 2782 citations.

Mining Top-K Periodic-Frequent Pattern from Transactional Databases without Support Threshold

Abstract: Temporal periodicity of patterns can be regarded as an important criterion for measuring the interestingness of frequent patterns in several applications. A frequent pattern can be said periodic-frequent if it appears at a regular interval. In this paper, we introduce the problem of mining the top-k periodic frequent patterns i.e. the periodic patterns with the k highest support. An efficient single-pass algorithm using a best-first search strategy without support threshold, called MTKPP (Mining Top-K Periodic-frequent Patterns), is proposed. Our experiments show that our proposal is efficient.

Impact of Foreign Direct Investment on Economic Growth in India: A Co integration Analysis

Abstract: : The role of FDI in the growth process has been a burning topic of debate in several countries including India. This paper is an attempt to analyze the causal relationship between Foreign Direct Investment (FDI) and economic growth in India and tries to analyze and empirically estimate the effect of FDI on economic growth in India, using the cointegration approach for the period, 1990-91 to2010-11. The empirical analysis on basis of ordinary Least Square Method suggests that there is positive relationship between foreign direct investment(FDI)investment and GDP and vice versa. The unit root test clarified that both economic growth and foreign direct investment were found to be integrated of order one using the Kwiatkowski, Phillips, Schmidt and Shinn (KPSS) test for unit root only. The cointegration test confirmed an existence of long run equilibrium relationship between the two as confirmed by the Johansen cointegration test results. The Granger causality test finally confirmed the presence of uni-directional causality which runs from economic growth to foreign direct investment. The error correction estimates gave evidence that the Error-Correction Term is statistically significant and has a negative sign, which confirms that there isn’t any problem in the long-run equilibrium relation between the independent and dependent variables. For FDI to be a noteworthy provider to economic growth, India would do better by focusing on improving infrastructure, human resources, developing local entrepreneurship, creating a stable macroeconomic framework and conditions favourable for productive investments to augment the process of development.

Modified Contrast Limited Adaptive Histogram Equalization Based on Local Contrast Enhancement for Mammogram Images

Abstract: Optimal Contrast enhancement for detection of masses and micro calcification of mammogram images using Contrast Limited Adaptive Histogram Equalization (CLAHE) based on local contrast modification (LCM) is presented in this paper. The LCM-CLAHE is proposed to highlight the finer hidden details in mammogram images and to adjust the level of contrast enhancement. The proposed method is tested for mammographic images from MIAS database. The performance of the proposed method is obtained using Peak Signal to Noise Ratio (PSNR). The results are compared with other standard enhancement techniques such as Histogram Equalization, Unsharp Masking (USM) and CLAHE. The experimental results of proposed method show that this method provides better contrast enhancement with preserving all the local information of the mammogram images.

Fake News Detection Using Deep Learning Techniques

Abstract: News is crucial part of our life. In day to day life current news are helpful to enhance knowledge what happen around the world. So most of peoples prefer watching news most of the peoples generally prefer reading newspaper early in the morning enjoying with cup of tea. If news is fake that will mislead peoples sometimes fake news utilized to spread rumors about things or it will affect some political leader positions just because of fake news. So it’s crucial to find the fake news. So we proposed system to detect fake news but now a day’s data on web or social media is increasing vastly and it is so hectic to detect news is fake or not by looking all data and it is time consuming so we utilize classification techniques to classify huge data. Here we proposed fake news detection system based on classification such as Logistic regression (LR), Naive bayes (NB), Support vector machine (SVM), Random forest (RF) and deep neural network (DNN). We compare all machine learning techniques for detecting fake news.

Hybrid Classroom: Designing for the New Normal after COVID-19 Pandemic

Abstract: In the end of December 2019, there was a reported of novel coronavirus in Wuhan, China. Later, this virus named COVID-19 became one of a major outbreak of the world. Since the virus can be spread through the air, the social distancing is essential. Many reports indicated that this will make the world change into a new normal. Working or learning from home by using information and communication technologies will become a new standard of the future. This paper proposed the ideas of the King Mongkut's University of Technology Thonburi to design and implement the hybrid classroom for teaching and conducting university's business after the pandemic crisis. The hybrid classroom concept aims to reducing the number of involved people in each activity by offloading some group of people to online from their home. The hybrid room has also been designed to allow online and physical attendees interact during operating sessions. The conclusion of this paper describes the problems and obstacles that arise during the implementation of prototype rooms, as well as the solutions and future direction of this work.