Gestational Diabetes Mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. Fifty percent of GDM patients develop type 2 Diabetes in next twenty years and as well as the newborn can also be affected by diabetes in their lifetime. So the long term complications for both the mother and the child cannot be ignored. In view of maternal morbidity and mortality as well as fetal complications, early diagnosis is an utmost necessity in the present scenario. In developing country like Bangladesh, early detection and prevention is not cost effective and usually troublesome. So, there is an urgent need for a well-designed method for the detection of gestational diabetes mellitus. The purpose of this study is to predict the GDM in the first trimester. This research presents and compares some Artificial Neural Network (ANN) models on the early detection of Gestational diabetes mellitus and chooses the best neural network model among them to detect GDM early.

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In all respects of the last five decades, integrated circuit technology has advanced at exponential rates in both productivity and performance. Giga-Scale Integration (GSI) System-On-A-Chip (SoC) designs have become one of the main drivers of the integrated circuit technology in recent years. The objective of this work is to understand the challenges of Giga-scale SoC integration in nanometer technologies, and identify promising conveniences for innovation. Physical designs are crucial for SoC integration and in our work we identify them with details. In future the couplings and interactions among system components will increase as we put more of the system on a silicon die. Therefore the system designers will face challenges in several areas and we describe these future challenges briefly. Developing a design driver for GSI SoC design is important. With the help of this design driver we provide the design methodology, which ensures the high performance of the design. We present two noteworthy solutions which overcome the challenges of GSI SoC design. One is reuse and integration and another is efficient bus architecture. We also provide the challenges for verification of GSI SoC and methods to overcome these challenges.

The effortless expansion of Internet access has eventually transformed the dissemination behavior towards E-Mode. Thus the usage of online or, more specifically, ‘Digital’ texts has expanded abruptly. ‘Bangla’, the seventh most spoken language globally, has no different nature. Communication in the Bangla language has also been exposed on the Internet, which describes the feelings of individuals in any specific context. These enormously generated data from diverse sources have drawn the interest of the researchers working in the Natural Language Processing domain. Despite its relatively complicated structure, a lesser amount of annotated data, as well as a limited number of frameworks and approaches, exist. This lacking of resources has kept several stones unturned in this diverse, emotion-rich and widely spoken language. To bridge the lacking and absence of resources, this article aims to provide a generalized deduced working procedure in this domain. To do so, the existing research work in the domain of sentiment analysis using Bangla text has been collected, evaluated and summarized. Also, in this article, the techniques used in pre-processing, feature extraction, and eventually used algorithms have been identified and discussed. Considering these facts, this research work sketches a tentative blueprint of sentiment analysis using Bangla text. Additionally, this article discusses existing regional language corpora such as Tamil, Urdu, and Hindi, as well as English and methodologies used to extract emotional essence from Bangla language comparing other languages. That will assist in determining the probable chosen path of exploring Bangla in a more deeper aspect. Moreover, this work has deduced and presented a generalized framework that will direct aspiring researchers to decide the pathway of choosing data vis-à-vis methodologies based on their interests.