Abstract:With the increased use of the Internet in today\'s digital world, a massive amount of data is generated at an accelerated rate that must be handled. This data must be handled as soon as it arrives because it is continuous, and cannot be kept for a long period of time. Various methods exist for mining data from streams. However, this work concentrates on Very Fast Decision Tree, which is the most often used technique in data flow classification, despite the fact that it wastes a huge amount of energy on trivial calculations. The research presents a proposed mechanism for upgrading the algorithm\'s energy usage and restricts computational resources, without compromising the algorithm\'s efficiency. The mechanism has two stages: the first is to eliminate a set of bad features that increase computational complexity and waste energy, the second is to group the good features into a candidate group that will be used instead of using all of the attributes in the next iteration. Experiments were conducted on real-world benchmark and synthetic datasets to compare the proposed method to state-of-the-art algorithms in previous works. The proposed algorithm works considerably better and faster with less energy while maintaining accuracy
Abstract:Multilevel inverters are very popular because of rise the overall staging of the network with low harmonic distortion, less stresses and give higher staging in industrial and electrical vehicle applications. This paper demonstrates, a single phase multilevel inverter (MLI) consists less count of switches and drive circuits compare to conventional MLI. It has the capability to operate as a Symmetrical 5-Level (5-L) inverter and by adding one bidirectional switch to the above inverter it is able to act a asymmetrical 7-Level (7-L) inverter, under normal running conditions. The control of inverter is implemented by Variable Amplitude Sinusoidal Pulse Width Modulation (VASPWM) Techniques. In this technique a single carrier signal is divided into three equal carriers with different magnitudes with same frequency, then it reduce the volt-sec of output. So that it gives less conduction period and provides less Total Harmonic Distortion (THD) in both the waveforms of the voltage and current compare to other exiting PWM techniques and Conventional Space Vector Pulse Width Modulation (CSVPWM). The ease of conversion capability with less count of switches and operation with less THD shows the converter\'s ability to convert. The converter is modeled in MATLAB/SIMULINK, and the results are discussed.
Abstract:Complex systems composed of interacting elements such as protein-protein interaction networks or social networks are generally modeled by graphs. Link prediction is fundamental to study the evolution of these networks. It aims to predict missed or future relationships based on current connections. Methods based on similarity metrics are very interesting in link prediction where similar elements will be likely connected in the future. In this article, we propose a new similarity metric called CNDPR that is based on the common neighbors, distance and the degree of im-portance of nodes obtained from the PageRank algorithm. After testing our algorithm on a set of ten real-world networks, our proposed measure shows a remarkable performance compared to ex-isting methods currently in use.
Abstract:In mathematics, a semigroup is an algebraic structure consisting of a set together with an associative binary operation. The first to use the term \"semigroup\" in the modern sense was Harold Hilton in his book on finite groups in 1908 [6]. The semigroups have a lot of generalizations. One of them is the neaerness semigroup. Near set theory which is a generalization of rough set theory is based on the determination of universal sets according to the available information of the objects. İnan and Öztürk applied the notion of near sets defined by J. F. Peters to the semigroups [10]. \nIn this paper, our aim is to define ordered semigroups on weak near approximation spaces. Moreover, we explored some properties of these ordered nearness semigroups.
Abstract:A grid-connected system, in particular, relies heavily on the generation of electricity from Renewable Energy Sources. Because of the RES\'s connection to a grid, problems with power quality have arisen. Harmonics, voltage swells, sags and other grid concerns are caused by power quality issues. As solar and wind energy are both free and environmentally beneficial, they are regarded as the finest options for remote (or rural) electricity. The combination of solar power and wind power is a reliable source of energy creating a constant energy flow by avoiding the fluctuations. But this hybrid system gives rise to complications related to power system stability. Most of the industrial loads are controlled by power electronic converters that are sensitive to power system disturbances. Hence the power quality issues diminution is more focused in recent times as it is vital in power supply industry. A number of power semiconductor devices have been developed to overcome the above power quality issues. Distributed Power Flow Controller (DPFC), which is emerged from Unified-Power-Flow- Controller is considered as the best reliable device among the others. To report these expanded issues, the authors recognized an advanced custom power device entitled Distributed Power Flow Controller. The proposed Solar-Wind hybrid energy system is studied initially with Distributed Power Flow Controller. Later the system is examined with Genetic Algorithm based ANFIS Controller for Shunt control of Distributed Power Flow Controller. The results of the investigation demonstrate DPFC with Genetic Algorithm-AdaptiveNeuroFuzzyInferenceSystem has improved achievement in conditions of harmonics reduction and voltage compensation. MATLAB/Simulink has been used to study the anticipated integrated hybrid system under unbalanced voltage situations.
Abstract:Objective: To find out whether mental mindfulness predicts psychological well-being and to see what impact (gender) factors have on that connection.\nMethod: This study looked at the mental mindfulness/psychological well-being link in a sample of 150 university students. A self-administered questionnaire was used in conjunction with a quantitative research method. The study\'s results revealed a favorable connection between several aspects of mental awareness and the overall degree of psychological well-being. \nResults: There was no correlation between the observation dimensions on the mental mindfulness scale and the self-acceptance, life goals, and positive social relationships with others on the psychological well-being scale at a significance level of 0.01, according to the findings. Mental mindfulness aspects may be used to predict university students\' psychological well-being, which has been shown. Mental awareness and its investigated aspects were not found to vary significantly between men and women, according to the results. The research provides new information and knowledge that helps the decision-maker, especially the universities responsible for preparing a generation that enjoys mental health standards and the quality of life in general. It also sheds light on a set of important variables such as mindfulness, psychological well-being, and their impact on the academic work environment.\nConclusion: There are disparities between men and women on the factors (independence - personal maturity - total degree of psychological well-being) in favor of men in addition to emphasizing the distinctions between them (self-acceptance - the purpose of life - social relations - mastery of the environment).
Abstract:Principal component analysis (PCA) has been well known for useful statistical techniques in many disciplines. One of the prevalent uses of PCA is the dimension reduction technique, in which a vast number of variables can be summarized into a few important variables without losing the original information. There are currently few resources for step-by-step instructions for using PCA dimension reduction techniques, particularly in the social sciences field. In this work, the PCA analysis was employed to identify the components used as items measurement in the Open Government Data (OGD) post-adoption study. Four component retention methods were utilized to assess the components: the eigenvalues, proportion of total variance, scree plot test, and the interpretability criterion. The data was garnered from 125 OGD adopters among public agencies. Nine components were extracted to represent 39 items of measurement of OGD post-adoption independent factors. This paper’s contribution is two-fold: (i) it describes the process of developing the questionnaire items to measure OGD post-adoption among public agencies using principal component analysis; and (ii) it demonstrates the use of principal component analysis to extract important factors from a set of correlated variables as a different technique to factor analysis.
Abstract:This paper aims to analyse the performance of machine learning algorithms in detecting skin cancer types. Skin cancer has various types, some of which may lead to death. Therefore, early detection of skin cancer can help reduce the death rate. Our dataset was extracted from the archive of The International Skin Cancer Collaboration of imaging (ISIC). The dataset consists of 2637 benign and malignant images for training and 660 benign and malignant images for testing. The goal of this paper is to identify the algorithm that gives the maximum accuracy in detecting skin cancer types when applied on dataset images. The algorithms applied are Convolution Neural Network (CNN), Support Vector Machine (SVM) and Artificial Neural Network (ANN). The obtained results showed that Convolution Neural Network algorithm provided the highest accuracy (0.90) comparing to the Artificial Neural Network (0.84) and Support Vector Machine (0.83) algorithms.
Abstract:This paper presents results from experiments dealing the ovalization and critical ovalization of 7005-T6 aluminum alloy round-hole tubes (7005-RHTs) with different hole diameters of 2, 4, 6, 8, and 10 mm submitted to cyclic bending. It can be observed that the cross-section of the 7005-RHT continues to ovalize to a critical value before breaking. A larger controlled curvature or hole diameter leads to a greater ovalization. In addition, the relationship between the ovalization at negative extreme curvature and number of cycles can be divided into three stages (stages I, II and III). However, most of the number of cycles are in stages I and II. Finally, the empirical formula introduced by Lee et al. in 2010 was employed and material parameters related to the hole diameter were proposed for simulating the above-mentioned relationships in stages I and II. In addition, an empirical formula was also proposed to simulate the critical ovalization-controlled curvature relationships. It was found that the experimental and simulated data agreed quite well.
Abstract:With the proliferation of machine learning applications in all the surrounding fields and the rapid spreading of the internet of things (IoT), there is a growing need to merge between both of them. For this reason, there had to be an intersection between machine learning (ML) and the tiny edge in the internet of things (IoT) called Tiny machine learning (TinyML). TinyML is a recent technology that can be used to improve edge devices with low power consumption to engage machine learning (ML)and deep learning (DL) as well. This technology can change the way of communication between IoT devices and their data by allowing the device to interact with the data locally without sending it to the cloud. In this paper, we present an overview of the revolution of TinyML and its benefits. As well, we provide holistic coverage of TinyML studies that present the studies based on the DL methodologies, models, metrics and studies concerning the devices design considerations. Moreover, the used datasets in TinyML applications are demonstrated and the different devices are also explained.