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International Journal of Advances in Applied Sciences

IJAAS
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Published by Institute of Advanced Engineering and Science

ISSN : 22528814 EISSN : 27222594 DOI : http://doi.org/10.11591/ijaas

Core Subject : Science, Education, Social,

Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

International Journal of Advances in Applied Sciences (IJAAS) is a peer-reviewed and open access journal dedicated to publish significant research findings in the field of applied and theoretical sciences. The journal is designed to serve researchers, developers, professionals, graduate students and others interested in state-of-the art research activities in applied science areas, which cover topics including: chemistry, physics, materials, nanoscience and nanotechnology, mathematics, statistics, geology and earth sciences.

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Articles 680 Documents

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Deep learning model for glioma, meningioma, and pituitary classification Toqa A. Sadoon; Mohammed H. Ali
International Journal of Advances in Applied Sciences Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

One of the common causes of death is a brain tumor. Because of the above mentioned, early detection of a brain tumor is critical for faster treatment, and therefore there are many techniques used to visualize a brain tumor. One of these techniques is magnetic resonance imaging (MRI). On the other hand, machine learning, deep learning, and convolutional neural network (CNN) are the state of art technologies in recent years used in solving many medical image-related problems such as classification. In this research, three types of brain tumors were classified using magnetic resonance imaging namely glioma, meningioma, and pituitary gland on the based of CNN. The dataset used in this work includes 233 patients for a total of 3,064 contrast-enhanced T1 images. In this paper, a comparison is presented between the presented model and other models to demonstrate the superiority of our model over the others. Moreover, the difference in outcome between pre- and post-data pre-processing and augmentation was discussed. The highest accuracy metrics extracted from confusion matrices are a precision of 99.1% for the pituitary, a sensitivity of 98.7% for glioma, a specificity of 99.1%, and an accuracy of 99.1% for the pituitary. The overall accuracy obtained is 96.1%.

Factual power loss reduction by dynamic membrane evolutionary algorithm Lenin Kanagabasai
International Journal of Advances in Applied Sciences Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

This paper presents a dynamic membrane evolutionary algorithm (DMEA) that has been applied to solve optimal reactive power problems. The proposed methodology merges the fusion and division rules of P systems with active membranes and with adaptive differential evolution (ADE), particle swarm optimization (PSO) exploration stratagem. All elementary membranes are amalgamated into one membrane in the computing procedure. Furthermore, the integrated membrane is alienated into the elementary membranes 1, 2, _, m. In particle swarm optimization (PSO) C1 and C2 (acceleration constants) are vital parameters to augment the exploration ability of PSO in the period of the optimization procedure. In this work, Gaussian probability distribution is initiated to engender the accelerating coefficients of PSO. The proposed DMEA has been tested in standard IEEE 14, 30, 57, 118, and 300 bus test systems and simulation results show the projected algorithm reduced the real power loss comprehensively.

Enhanced model based algorithm to reinforce PV system with dynamic MPPT capability Md. Ehtesham
International Journal of Advances in Applied Sciences Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Photovoltaic (PV) power has emerged as the most attractive resource in form of a clean and green energy. However, one major challenge associated with PV interfacing is its intermittent output characteristic which varies dramatically with the operating conditions. Thus designing an effective maximum power point tracking (MPPT) algorithm is a key aspect for optimizing PV system performance. Numerous MPPT algorithms have been proposed earlier having their own specific advantages. However, these are found to have two major limitations which have to be essentially addressed. Firstly, they become ineffective in the dynamic conditions where there is rapid change in environmental parameters like insolation and temperature. Secondly, they fail to discriminate between global and local peaks under partial shading conditions. Therefore, to achieve a reliable and efficient system operation, this paper presents an enhanced model-based (MB) algorithm that overcomes both these deficiencies. Based on new governing equations and precised estimation technique, it predetermines the MPP analytically. First simulated results are obtained where it is tested for dynamic variations of all the three parameters. Then the experimental validation is carried out on a 2 KW installed panel where real time data is recorded through CR1000 data logger and environmental parameters are sensed with elements like pyranometer and humidity sensor. A large number of experimental results are obtained for tracked MPP in the dynamic conditions, which are then summarized in tabular forms. These are finally plotted and compared with simulated results to illustrate the effectiveness of proposed MB algorithm.

Confirmatory factor analysis: model testing of financial ratios with decision support systems approach T. Husain; Maulana Ardhiansyah; Dedin Fathudin
International Journal of Advances in Applied Sciences Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

The decision support systems approach can be developed into both computer-based and quantitative analysis tools. This research uses a model test with a confirmatory factor analysis (CFA) technique on matrix covariance against structural equation modelling (SEM) methods to measure financial ratios. Decision support system (DSS) analysis uses numerical calculations aided by mathematical models through six phases. The first three phases of a structured approach to building multivariate models and the next three phases, namely estimation, interpretation, and validation, are developing from data input that has been selected using LISREL version 8.72. The financial ratio’s testing model with a CFA approach derived into a mathematical (quantitative) model can explain the complexity of the relationship between the goodness-of-fit models (GOF) with a different approach from prior research. The goodness-of-fit test results in this study produced scores on each of the financial ratio measurement models at an accuracy level of CR of 78.49, TATO of 1.26, DER of 41.41, ROA of minus 0.033, and PBV of 540.92. This means that PBV has the highest standardized loading factors to determine the measurement of financial ratios. The CFA measurement based on SEM can be used to make appropriate decisions and combine a model comparison and redevelopment of the CFA technique and model testing with other software such as SPSS, PLS, AMOS, and others.

An application system for the design of the spindle tool clamping mechanism Parthiban Kannan; Ragul Ramanathan
International Journal of Advances in Applied Sciences Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

The heart of the machining center design is the spindle design, and one of the primary functions in the spindle design is a tool clamping system mechanism. The selection of disc spring stack for a tool clamping mechanism is an iterative process that highly depends on the spindle space availability, drawbar design, tool unclamp stroke length, and standard clamping force requirements. For example, even a design space of 0.1 mm may impact one kN clamping force depending on the disc spring stack design. Hence the design of the tool clamping system for a spindle is a time-intensive process and also needed careful attention. The iterative process of disc spring stack selection may lead to an unoptimized tool clamping system, which may not be the best design. This paper explains a dynamic way to find the best spring stack selection to optimize the spindle tool clamping mechanism based on the computational application.

Trainable generator of educational content Vladimir Rotkin
International Journal of Advances in Applied Sciences Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

As the main problem of the research, the possibility of creating a universal educational platform that combines the possibilities of an online generation of educational content with the interface of the training process itself was considered. The methodology of the educational platform has been developed, in which the mass generation of content is carried out at random, based on simulation models of educational objects. A matrix interface is used, which allows performing custom operations by entering a sequence of typical operators. The system forms a reference base of operators, replenishing it from user solutions, which makes it possible to train and improve the system in order to provide methodological support to student users. An active demo layout of an educational content generator was created and tested, using the example of a specific problem from school mathematics. All methodological options function in the layout. There are three interface options: administrative, training and control. It was concluded that the approach based on the simulation of educational objects makes it possible to create a unified algorithmic platform that combines the functions of content generation with educational training. The system contains a unique option to teach yourself based on its interaction with students.

Second order noise shaping for data-weighted averaging technique to improve sigma-delta DAC performance Ali Kerem Nahar; Ansam Subhi Jaddar; Hussain K. Khleaf; Mohmmed Jawad Mortada Mobarek
International Journal of Advances in Applied Sciences Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

In general, the noise shaping responses, a cyclic second-order response is delivered by the method of data weighted averaging (DWA) in which the output of the digital-to-analog converter (DAC) is restricted to one of two states. DWA works efficiently for rather low levels of quantizing; it begins presenting considerable difficulties when internal levels of quantizing are extended further. Though, each added bit of internal quantizing causes an exponentially increasing in power dissipation, complexity, and size of the DWA logic and the DAC. This gives a controlled second-order response accounting for the mismatch of the elements of DAC. The multi-bit DAC is made up of numerous single-bit DACs having values thereof chosen via a digital encoder. This research presents a discussion of the influence of mismatching between unit elements of the delta-sigma DAC. This results in a constrained second-order response accounting for a mismatch of DAC elements. The results of the simulation showed how the effectiveness of the DWA method in reducing band tones. Furthermore, the DWA method has proved its efficiency in solving the mismatching of DAC unit elements. The noise of the mismatching elements is enhanced by 11 dB at 0.01 with the proposed DWA, thereby enhancing the efficiency of the DAC in comparison to the efficiency of the DAC with no use of the circuit of DWA.

Real power loss diminution by rain drop optimization algorithm Lenin Kanagasabai
International Journal of Advances in Applied Sciences Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

In this work, the rain drop optimization (RDO) algorithm is projected to reduce power loss. Proceedings of rain drop have been imitated to model the RDO algorithm. The natural action of rain drop is flowing downwards from the peak and it may form small streams during the headway from the mountain or hill. As by gravitation principal rain drop flow as a stream as a river from the peak of mountains or hill then it reaches the sea as global optimum. Proposed rain drop optimization (RDO) algorithm evaluated in IEEE 30, bus test system. Power loss reduction, voltage deviation minimization, and voltage stability improvement have been achieved.

Optimal generation capacity mix in microgrid to meet demand Virendra Sharma; Piyush Kumar Choubey; Amit Kumar; Lata Gidwani
International Journal of Advances in Applied Sciences Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

This paper presents an approach for optimal generation capacity mix to fulfill future power demand using a micro-grid model which is operated in both the on-grid and off-grid modes. This is achieved using the solar photovoltaic (PV) system, fuel-cell, and battery energy storage system (BESS) with and without the grid-connected mode. Different control approaches and optimal size of the generators are presented. Proposed micro grid with solar PV system, solid oxide fuel cell (SOFC) and back scattered electron detector (BESD) is tested for different operational scenarios of loads. Comparative index of performance (CIP) is introduced to indicate effectiveness of the micro-grid operations in the off-grid mode. This is based on difference in the total harmonic distortions (THD) in both the on-grid and off-grid modes. This is established that CIP indicates that the micro-grid works efficiently in the both the on-grid and off-grid modes during the simulated events of the switching ON/OFF the loads at different test conditions. The optimal generation mix successfully met the load demand with and without grid having conventional generation.

True power loss reduction by mountain zebra, augmented bat, and improved kidney search algorithms Lenin Kanagasabai
International Journal of Advances in Applied Sciences Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

In this paper optimal reactive power problem is solved by mountain zebra algorithm (MZA), augmented bat algorithm (AB), and improved kidney search (IKS) algorithm. In the proposed algorithm, an intermediate state has been established at first, and then explores the intermediate state in order to obtain the global optima. Iterative local search implemented in this proposed algorithm. This technique enhances the search procedure in rapid mode. Then in this work, IKS algorithm has been proposed for solving optimal reactive power problem. In initial phase, a random population of probable solutions is created and re-absorption, secretion, excretion are imitated in the search process to check various conditions entrenched to the algorithm. The algorithm has been built to advance the search even a potential solution moved to waste (W) and it will be brought back to the filtered blood (FB). Glomerular filtration rate (GFR) test is utilized to verify the fitness of kidneys. Better efficiency of the proposed MZA, AB, and IKS algorithm confirmed by successful evaluation in standard IEEE 14-bus, 118-bus, and 300-bus test systems. The results show that active power loss has been reduced.

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