Dealing Crisis Management using AI

Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.

IoT based Smart Irrigation System by Exploiting Distributed Sensorial Network

In this research the Internet of Things (IoT) based smart irrigation system is developed for large scale farming to ensure appropriate water management as well as to minimize unnecessary water utilization. This system can control water wastage for irrigation purpose by using wireless sensor network (WSN) and IoT. Each WSN node contains a unit of combined sensors which has been made by several external sensors such as soil moisture, soil pH, and temperature sensor along with Node MCU for data transmission in the cloud. Other nodes are distributed in the field to collect field data for different positions and this information is sent to the server. Data processing and analysis are performed according to the proposed algorithm. Obtained result as well as weather forecasting report is checked for three days from a developed android app. The accomplished result is sent to the farmers through SMS; depending upon the SMS, farmers take necessary steps for watering or not in the crops field through IoT. Using the particular sensors in this system along with microcontroller board plays an important aspect for bringing automation for a particular model. In this work wireless sensor technology in irrigation purposes can show the direction to the rural farming community to replace some of the traditional techniques.

Using Voice Recognition in E-Learning System to Reduce Educational Inequality During Covid-19

During the COVID -19 pandemic, educational institutions around the world faced problems that have to do with the frustration of students for whom traditional education has been replaced by the online format. Students are experiencing technical difficulties in the digitalization of education. International monitoring of education systems has shown that quite a few countries were ready to move to distance learning, both for technical and economic reasons. The covid pandemic has caused an increase in educational inequality. Elearning systems were expected to reduce inequality in education, but empirical research has shown that learning in this format not only does not reduce, but can increase inequality, increasing the gap in educational outcomes between students with different socioeconomic status. The article describes applications of using voice recognition technology based on artificial intelligence which, by our opinion, may reduce educational inequality during covid-19. We presented a comparative analysis of existing examples artificial intelligence in the educational process. Artificial intelligence uses in specialized software it makes educational process more convenient for both the students and the teachers. There is a description of an application “Academic phrase bank" developed by author. The application consists of two specialising actions for Google assistant. The application allows to increase academic vocabulary, train of creating grammatically correct academic expressions, and memorize templates of academic phrases. In active mode, this application helps to create correct phrases of academic English and improve the abilities of understanding English speech.

Anomaly Detection in Arabic Texts using Ngrams and Self Organizing Maps

Every written text in any language has one author or more authors (authors have their individual sublanguage). An analysis of text if authors are not known could be done using methods of data analysis, data mining, and structural analysis. In this paper, two methods are described for anomaly detections: ngrams method and a system of Self-Organizing Maps working on sequences built from a text. there are analyzed and compared results of usable methods for discrepancies detection based on character n-gram profiles (the set of character n-gram normalized frequencies of a text) for Arabic texts. Arabic texts were analyzed from many statistical characteristics point of view. We applied some heuristics for measurements of text parts dissimilarities. We evaluate some Arabic texts and show its parts they contain discrepancies and they need some following analysis for anomaly detection. The analysis depends on selected parameters prepared in xperiments. The system is trained to input sequences after which it determines text parts with anomalies using a cumulative error and winner analysis in the networks. Both methods have been tested on Arabic texts and they have a perspective contribution to text analysis.

Change Back Feature for Consumer Accounts

The aim of this paper is to propose a new feature called Change Back for consumer accounts, which minimizes the use of cash coins by retail consumer outlets and eliminates the need of carrying the same for banks’ consumer customers. Once implemented in consumer banking applications and point-of-sale devices, the feature will minimize the wastage of coins by shoppers, commission charges incurred by retail vendors on coins, improve money retention by banks in their consumer accounts and minimize of the spread of pandemics. Discussed in detail are how the Cash Back transaction takes place and what needs to be implemented by different parties involved in making it a success. A step-by-step flow of the transaction is pictorially described from a point-of-sale device at a retail cash counter to a bank’s payment processing application.