The Internet of Things is paving the way for the transition into the fourth industrial revolution with the mad rush of connecting physical devices and systems to the internet. IoT is a promising technology to drive the agricultural industry, which is the backbone for sustainable development especially in developing countries like those in Africa that are experiencing rapid population growth, stressed natural resources, reduced agricultural productivity due to climate change, and massive food wastage. In this paper, we assessed challenges in the adoption of IoT in developing countries in agriculture. We propose a cost effective, energy efficient, secure, reliable and heterogeneous (independent of the IoT protocol) three layer architecture for IoT driven agriculture. The first layer consists of IoT devices and it is made up of IoT driven agriculture systems such as smart poultry, smart irrigation, theft detection, pest detection, crop monitoring, food preservation, and food supply chain systems. The IoT devices are connected to the gateways by low power LoRaWAN network. The gateways and local processing servers co-located with the gateways create the second layer. The cloud layer is the third layer, which exploits the open source FIWARE platform to provide a set of public and free-to-use API specifications that come along with open source reference implementations.

Modern approach to the FOREX currency exchange market requires support from the computer algorithms to manage huge volumes of the transactions and to find opportunities in a vast number of currency pairs traded daily. There are many well known techniques used by market participants on both FOREX and stock-exchange markets (i.e. fundamental and technical analysis) but nowadays AI based techniques seem to play key role in the automated transaction and decision supporting systems. This paper presents the comprehensive analysis over Feed Forward Multilayer Perceptron (ANN) parameters and their impact to accurately forecast FOREX trend of the selected currency pair. The goal of this paper is to provide information on how to construct an ANN with particular respect to its parameters and training method to obtain the best possible forecasting capabilities. The ANN parameters investigated in this paper include: number of hidden layers, number of neurons in hidden layers, use of constant/bias neurons, activation functions, but also reviews the impact of the training methods in the process of the creating reliable and valuable ANN, useful to predict the market trends. The experimental part has been performed on the historical data of the EUR/USD pair.

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Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like average queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging.

The Project is a three devices Microsoft Kinect, Mobile Lego NXT Robot are controlled in a single application whic control the NXT Robot remotely by tracking calibration using voice commands.

Air traffic management systems require constant development. Whenever regular radar devices are out of range, the wide network of the receivers may constitute global air traffic monitoring solutions. Surveillance methods of controlling aircrafts are being improved and the one which stands apart from the others is ADS-B (Automatic Dependent Surveillance Broadcast) introduced in most commercial and private aircrafts, and which was obligatory after the year 2020. Nowadays ADS-B receivers cover about 70% of European and 30% of U.S air traffic. The ADB-S system is based on GPS communication. Aircrafts estimate their position using satellite based navigation systems. Along with plane position, there is a vast number of additional data broadcasted by the ADS-B transponder, including speed, altitude, plane and flight identification data, also emergency codes. The large amount of professional and amateur ADS-B receivers located on most continents, covering significant amount of the airspace, has led to the conclusion that this kind of crowd-processing may establish valuable and reliable source of data using common interface. Currently there is no uniform layer of the ADS-B data presentation and interfaces over the Web. This paper regards standardisation of the data layer using WEB 3.0-Semantic Web principals. It covers acquisition, processing and presentation of the data coming from the ADS-B receiver. A method of unifying data from distributed virtual radar stations has been proposed and is being presented in a way that allows this data to be combined across many sources with existing knowledge. Having ADS-B information integrated and expressed in RDF (Resources Description Format), it would be easy to perform such a query against these data sets, using Protocol and RDF Query Language (SPARQL). Now we stand at the verge of WEB 3.0, where applications vastly utilising Artificial Intelligence, semantic solutions and Natural Language Processing systems are going to become common.

Queues or waiting lines are an integral part of health care facilities such as hospitals, outpatient clinics, medical laboratories, and many other health facilities. Health care management must have waiting lines control strategies for smooth functioning. Due to the lack of proper queuing control and management, patients may become dissatisfied and may leave (renege) the health care facilities without getting service. But, the reneging of patients at two consecutive time marks may be correlated in the sense that if a patient reneges at the current time mark, then there is a probability that a patient may or may not renege at the next time mark. This kind of reneging is referred to as correlated reneging. In this paper, we have introduced the concept of correlated reneging in a finite capacity multi-server queuing model with balking with its application in health care. The steady-state as well as the transient analyses of the model are carried out. We have also derived an expression for the correlation coefficient between the interreneging times and for the rate at which the health facility is losing patients (patient loss probability) due to insufficient capacity, reneging, and balking. We have provided numerical examples in order to demonstrate the effect of balking and correlated reneging on performance measures such as the mean number of patients waiting to be serviced, mean waiting time of patients, and the probability of patient rejection. Further, the effect of the number of servers on performance measures is investigated. Finally, the effect of the correlation coefficient between the inter-reneging times on performance measures is studied. The queuing model discussed in this paper could be useful to the health care firms facing the problem of patient impatience and capacity constraints.