Bangla handwritten numeral recognition using convolutional neural network

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. Although Bangla is a major language in Indian subcontinent and is the first language of Bangladesh study regarding Bangla handwritten numeral recognition (BHNR) is very few with respect to other major languages such Roman. The existing BHNR methods uses distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. It also automatically provides some degree of translation invariance. In this paper, a CNN based BHNR is investigated. The proposed BHNR-CNN normalizes the written numeral images and then employ CNN to classify individual numerals. It does not employ any feature extraction method like other related works. 17000 hand written numerals with different shapes, sizes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed other prominent exiting methods

Leveraging machine learning to analyze sentiment from COVID-19 tweets: A global perspective

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8 %) F1-score, followed by gradient boost (84.3%), AdaBoost (78.9 %), and XGBoost (83.1 %). Second, it was revealed that during the time of the COVID-19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like

Two-Echelon Vehicle Routing Problems Using Unmanned Autonomous Vehicles

In this thesis, we investigate new multi-echelon vehicle routing problems for logistics operations using unmanned autonomous vehicles. This can provide immediate tangible outcomes, especially in high-demand areas that are otherwise difficult or costly to serve. This type of problem differs from the commonly used multi-echelon supply chain management systems in that here there exist no intermediate facilities that consolidate/separate products for delivery; instead all decisions are made on a per-vehicle basis. We describe here how we can obtain the necessary parameters (data collection) to evaluate the performance of such multi-echelon systems. We also provide three mathematical formulations based on different assumptions and case scenarios. We then study the differences between the three models in practice, as far as routing cost and duration of operations are concerned. We finally show that there are savings to be had by properly employing unmanned vehicles for logistics operations

A statistical analysis of hydrograph data for estimating recharge in the Lower Namoi Valley, N.S.W., Australia

University of Technology, Sydney. Faculty of Science.NO FULL TEXT AVAILABLE. Access is restricted indefinitely. The hardcopy may be available for consultation at the UTS Library.NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- Statistical analyses of borehole hydrographs reveal a new understanding of the spatial distribution of hydrogeological properties throughout the multi-layered alluvial aquifer system of the Lower Namoi Valley, NSW, Australia. Principal component analysis and k-mean clustering divided the Narrabri Formation hydrographs into 6 clusters, and the Gunnedah Formation hydrographs into 7 clusters. The clusters relate to variations in- recharge and extraction throughout the Lower Naomi Valley. A comparison of rainfall residual mass curves with the water level trends displayed in the borehole hydrographs shows that there is a strong correlation between the water level fluctuations and the rainfall history. The analysis also indicates that the irrigation extractions are in balance with recharge near the southern, northern and western boundaries, but in the southeast comer and throughout much of the central region of the Lower Namoi Valley there are numerous areas where extractions exceed recharge. A comparison of the results from the statistical analyses with two previous studies, a MODFLOW model of the aquifers and a chemical investigation of the aquifer chemistry, highlights where there is agreement on recharge and groundwater movement, but it also shows that there are regions where the various approaches provide a different or unique understanding of aquifer processes

Identification, and Heterologous Expression Analysis of Avocado DGAT1 and DGAT2

The neutral lipid triacylglycerol (TAG) is the main storage lipid in plants. When stored in seeds, TAG provides the carbon and energy source during germination. There is significant human nutritional demand for vegetable oil, but its use in production of renewable biomaterials and fuels has intensified the need to increase oil production. In plants, the final and committed step in TAG biosynthesis is catalyzed by diacylglycerol acyltransferases (DGAT) and/or a phospholipid: diacylglycerol acyltransferases (PDAT). Both DGAT and PDAT contribute to seed TAG biosynthesis in an independent or overlapping manner, depending on the species. However, in nonseed tissues such as mesocarp of avocado, the regulation of TAG biosynthesis is not well-studied. Based on the transcriptome data of Persea americana it is hypothesized that both DGAT and PDAT are likely to catalyze the conversion of diacylglycerol to TAG. In this study, putative DGAT1 and DGAT2 were identified and comprehensive in silico analyses were conducted to determine the respective start codons, full-length coding sequences, transmembrane domains, predicted protein structures and phylogenetic relationships with other known DGATs. These data reveal that the putative DGATs of a basal angiosperm species retain features that are conserved not only among angiosperms but also other eukaryotes. For further biochemical characterization, the avocado DGATs were expressed in a TAGdeficient yeast strain and lipotoxicity rescue assays were conducted. The complementation of this yeast strain confirmed enzyme activity and supported the possible role of both avocado DGATs in TAG biosynthesis. Future studies will be focused on determining the substrate specificity of DGAT and its role, relative to PDATs in TAG biosynthesis in avocado mesocarp