Recently, artificial intelligence technology is taking roots in various sectors as a core technology that will lead the knowledge and information society. Interest in Artificial Intelligence education is growing as it is time to prepare for Artificial Intelligence education in addition to existing information literacy education. However, students who are not computer majors tend to perceive Artificial Intelligence technology as a difficult field, which is why a more efficient Artificial Intelligence education is needed. Therefore, this study reviewed educational programming languages and Artificial Intelligence education methods and proposed an Artificial Intelligence education program that offers Artificial Intelligence learning based on the supervised learning theory of machine learning. As for learning suitability test of the program developed in this study, the program was found to be valid as a result of expert validity analysis and it produced positive results when applied to learners. Based on these findings, the program is expected to be the basis of research on Artificial Intelligence education implementable for non-computer majors who are beginning to learn Artificial Intelligence.
The efficient message routing is highly challenging in terms of low power and lossy networks (loT) for transmission of data with overhead and delay. The protocols used for routing need to be designed such that they should be working efficiently. Efficiency in calculated in terms of energy and delivery of packets. RPL protocol is also designed with the aim of making these two parameters efficient. Even then it contains drawbacks. Trickle algorithm is designed with a goal to reduce the drawbacks in RPL. Trickle algorithm is used in RPL protocols for creation of routes between nodes in the network with different intervals. Unfortunately, there exists some more downsides for the trickle algorithm, which made design of several algorithms inorder to analyse different drawbacks. In this paper, on analysing different types of trickle algorithms and locating the drawback in every algorithm, a novel algorithm is designed which helps in reduction of the drawbacks that are found. The description of this algorithm along with the simulation results done using Cooja 3.0 simulator is also discussed in this paper. The Simulation of the algorithm that is newly designed is done by assuming a network with different count of nodes and comparing the results with the previously introduced Trickle algorithms.
Big data defines the state where the size, speed and kind of data go beyond a memory or executing capabilities for precise and timely decision-making. Big data analytics is integrated with ML and statistical methods for processing big data and recognizes the important data. At present times, the generation of online product reviews has exponentially increased at each and every second. These applications have resulted in developing the volumes of data which can be used for prediction and classification for decision making process. Compared with other models, various techniques are applied in solving the big data problem, feature selection (FS) is known to be an efficient method. FS operations could be exploring with the application of a subset of features which is related to the topic of précised definition of the existing datasets. Deplorably, search using this type of sub-sets results in the problems of combinatorial as well as maximum time consuming. The meta-heuristic approaches are typically employed to facilitate the choice of features. This paper presents an optimal extreme learning machine (ELM) based binary particle swarm optimization to precede the FS process. The proposed method develops a Fitness Function (FF) by applying ELM. And the best solution of the FF has been explored under the application of BPSO technique. For instance, the dataset of product review which are derived from Amazon including synthetic data, which is comprised with total of 235,000 positive and 147,000 negative review records is used. The experimental result implied that the ELM-BPSO technique is comparably best
In this paper, it is described a new design of a digital Lock-In amplifier applied to 4-Kelvin- probe electrodes for the measurement of complex electrical variables. The proposed design is based on the operation of a Phase Sensitive Detection (PSD) circuit and on signal acquisition by the Nyquist principle. The hardware basically consists of a programmable embedded system and an analog interfacing circuit. The microcontroller within the circuit was programmed using standard C language for portability and performs the acquisition of the resulting signal along with mathematical operations. Experimental tests on the prototype have shown that it performs as theoretically predicted.
We propose a new generated family of distributions that is known as the generalized alpha-power transformation-G (GAPT-G) family of distributions. The new class of family can be more flexible since the density shapes are left skewed, symmetrical and reversed-J. Some special models derived and discussed. Several of its important properties are derived. The maximum likelihood equations are derived for GAPT-G family parameters. The importance and flexibility of the derived models is assessed using one real dataset.
