Training For Developing Digital Presences With Zoho Form For Office Automation And Office Governance

Teachers in facing the challenges of rapid technological advances are expected to always adapt to the times, which are required to always innovate and be creative to be able to create quality graduates. So that students have good provisions in facing the challenges ahead. Teachers who teach at Vocational High Schools majoring in office automation and governance include Office Technology subjects. The Office Technology subject contains practical material regarding the use of applications for office or administrative activities, which as the name suggests is currently the office administration department changed to the office automation and governance department. However, the applications used for learning related to the use of applications and the application of digital offices, especially the creation of digital presences to switch to paperless, are still very minimal or even non-existent. Learning office technology, which has been difficult for teachers to provide and provide skills to students related to applications that support the digitization of offices through office technology subjects. In the era of computerization, it affects administrative management in an organization, as well as a student majoring in office automation and governance must have competence in mastering digitalization and office automation skills.

Convolutional neural network-based ensemble methods to recognize Bangla handwritten character

In this era of advancements in deep learning, an autonomous system that recognizes handwritten characters and texts can be eventually integrated with the software to provide better user experience. Like other languages, Bangla handwritten text extraction also has various applications such as post-office automation, signboard recognition, and many more. A large-scale and efficient isolated Bangla handwritten character classifier can be the first building block to create such a system. This study aims to classify the handwritten Bangla characters. The proposed methods of this study are divided into three phases. In the first phase, seven convolutional neural networks i.e., CNN-based architectures are created. After that, the best performing CNN model is identified, and it is used as a feature extractor. Classifiers are then obtained by using shallow machine learning algorithms. In the last phase, five ensemble methods have been used to achieve better performance in the classification task. To systematically assess the outcomes of this study, a comparative analysis of the performances has also been carried out. Among all the methods, the stacked generalization ensemble method has achieved better performance than the other implemented methods. It has obtained accuracy, precision, and recall of 98.68%, 98.69%, and 98.68%, respectively on the Ekush dataset. Moreover, the use of CNN architectures and ensemble methods in large-scale Bangla handwritten character recognition has also been justified by obtaining consistent results on the BanglaLekha-Isolated dataset. Such efficient systems can move the handwritten recognition to the next level so that the handwriting can easily be automated.

Design, development and performance evaluation of a smart office automation system

This research work aimed at the design, development and performance of Office Automation System, using the Internet of Things (IoT) based and Voice Recognition Command. The data is stored in personal computer (PC) or android smartphone. Arduino IDE was the framework used in the development of the system, while C++ was the programming language used for programming the system. Similarly, a voice control was built using the This Then That (IFTTT) platform. The system developed was capable of enabling users to control office appliances like printer, air conditioner, light, fan, etc. with voice command using the Google home mini device. Results obtained from the laboratory test proved that the system had a very high (about 98%) performance rating. This system had shown that office automation system can use voice command to preform its task