Bag-Based Feature-Class Correlation Analysis for Multi-Instance Learning Application

Multi-instance Learning (MIL) is widely applied in image classification. In MIL, an image is presented as a bag. A bag consists of multi-instance which is known as patches. Irrelevant features of the image presented to the classifier affects the classification performance. Feature selection is one of the essential phases to select relevant. However, limited studies discuss the feature selection phase in MIL. Correlation between feature-class (FC) relationship is one important criterion to analyse features’ relevance. However, it cannot be performed directly in MIL. To address this gap, this study proposed the MultiBag-FCCorr feature selection technique. It consists of three steps: transformation, evaluation and fusion. The bags of feature information are acquired from summarization from different statistical central tendency measures of trimmed mean, mode and median. In feature evaluation step, extended point biserial correlation has been used to measure FC correlation and then the FC score has been analysed. The selected features are validated via two prominent classifiers (Support Vector Machine (SVM) and K-Nearest Neighbour (KNN)) on benchmark MI image datasets: UCSB Breast Cancer, Tiger, Elephant and Fox datasets. The selected features of UCSB Breast Cancer dataset were reduced to 92% number of features from the proposed technique giving the best result of average accuracy with 86.8.% using SVM and 84.5% using KNN. The average accuracy improved 6.3% using SVM and 16.4% using KNN compared without implementing the proposed feature selection. The results proved that the selected feature set improved the performance of MI image classification

Synthesis and characterisation of hydroxyapatite from Fringescale sardinella for biomedical applications

Hydroxyapatite (HAp) from fish by-product exhibits good biocompatibility and bioactivity on implants. The aim of the study is to investigate the elemental composition, crystalline phases, and functional groups of HAp synthesised from fringescale sardinella fish bones by heat decomposition method at temperature of 600, 900, and 1200 °C. The synthesised powders were characterized using fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and energy dispersive spectroscopy (EDS). After calcination of the raw fish bone to 600, 900, and 1200 °C, the FTIR data showed the existence of phosphate and hydroxyl peaks in the calcined fish bones. At 900 and 1200°C, the XRD data observed shows well-defined peaks of HAp pattern. The elemental composition evaluated by EDS provides information on the calcium to phosphate formation into apatite with a Ca/P ratio of 2.80, 0.98, 1.64 and 1.79 atomic % for raw fish bones and calcined samples, respectively. It can be concluded that the fringescale sardinella fish bones show promising findings particularly on the synthesisation of HAp for biomedical application

Daylight-adaptive lighting control techniques: a comparative analysis of particle swarm optimization and firefly algorithm

Lighting in commercial buildings consumes a substantial amount of energy. Therefore, this paper developed particle swarm optimization (PSO) and firefly algorithm (FA) as control techniques for lighting systems to improve energy efficiency and satisfy occupantsꞌ visual comfort in an indoor environment. An office room was considered to test the performance of the PSO and FA techniques. The proposed methods showed superior performance in minimizing the cost of energy consumption by more than 60% while satisfying illuminance-based metrics mentioned by the European Standard EN 12464-1. Based on the comparative result, PSO outperformed FA by 3% in energy savings. Due to its performance, the proposed PSO method can be utilized for other types of building

Characterization and microstructure analysis of sodium alginate incorporate with iron (III) oxide for biomedical application

This research focuses on the characterisation of sodium alginate incorporated with Iron (III) oxide for biomedical applications. First, biofilm and bead samples with and without 0.1 g, 0.2 g, and 0.3 g iron (III)oxide particles are prepared by manual syringe technique and solution casting. Next, sodium alginate biofilms and beads incorporated with and without Iron (III) particles were analysed by microstructure using Scanning Electron Microscope (SEM). Energy–dispersive X-Ray Spectroscopy (EDS) analysis was applied as well to reveal the chemical elements present in the sample. The samples were characterised using X-Ray Diffraction (XRD) and Atomic Force Microscopy (AFM) analysis. Microstructure analysis results revealed that the microstructure of 0.1g, 0.2g, and 0.3g beads varied due to the amount of iron (III) oxide particles. Meanwhile, EDS detected that the chemical elemental present were mainl

The influenced of steel fiber in hot mix asphalt mixture to enhance the tensile strength

Deformation of the road was the main factor of the repeated loaded at high temperature to the asphalt pavements and one of the main distress mechanisms. Asphalt mixture incorporated with steel fiber to enhance the tensile strength would have investigated by used grade 60/70 of asphalt binder and 5% of optimum asphalt binder. The percentages of steel fiber that were used for the studied was 0, 1.2, 1.5, 2.1, 2.4% by total weight of aggregate. Purpose of this studied is to investigate the effect of steel fiber toward the asphalt mixture on improved the asphalt pavement. To decrease the road deformation and to compute the pavement cracked at incorporating steel fiber using Marshall Stability Test. The result from this study has shown that the mixture incorporating steel fibers for stability is decrease between control sample with 2.7% steel fiber which is 21.357 kN and 13.193 kN. The performance test due to stiffness analysis passed the requirement JKR, which is more than 2.6 kN/mm, even if the value of the steel fiber modified sample is lower than the control sample and it still relevance to implemen

A review on biosynthesis zinc oxide nanoparticles by using leaves extract

A nanoparticle is a branch of nanotechnology that deals with nano-scale materials with very small particle sizes ranging from 1 to 100 nm. Metal oxide nanoparticles are more promising than the many other nanoparticles available because they have unique physical, chemical, and biological properties. Zinc oxide is one of the abundantly produced metal oxides after silicon dioxide and titanium dioxide. However, these methods of production are typically costly, labourintensive, and can harm the environment and living organisms. Therefore, green synthesis (biosynthesis) is a good alternative where plants are used to assist nanoparticles synthesis which has eco- friendly benefits compared to chemical and physical methods. This biosynthesis method uses simple procedures, easily accessible raw materials, and a conducive environment for the synthesis process, where the precursors are safe and reduce the possibilities of harmful by-products being produced. Therefore, this review paper is focused on summaries of the biosynthesis of Zinc oxide nanoparticles from leaf extract such as Mangifera Indica (Mango), Ixora Cocconea (Jungle Geranium), Corymbia Citridora (Lemon-scented Gum) as a new development of green technology beneficial to the environment and to the plant itself. It also describes the progress made in the understanding of the mechanism routes reported in this revie

Zero-inflated regression models for measuring accident

Worldwide, hundreds of thousands of deaths and thousands more are injured every year in traffic accidents around the world. This is owing to an increase in road traffic throughout time, as well as a wide range of traffic compositions. Nowadays, road accidents have become a major concern, and analyzing accidents data has become an important concern for analysts. Therefore, analysis of accidents data requires a lot of attention because accident data is very complex. The road accidents process results in various frequency calculations, for example, deaths and injured number, and/or involved cars in the accidents. However, the probability distribution governing the occurrence of this count may be different. In addition to the problem of excess zeros, lack of data is a common occurrence in results of traffic accidents. Thus, this study discusses the use of the zero-inflated model in analyzing traffic accidents and the variables used by the researchers by reviewing the literature related to the use of zero-inflated models in accident cases. To find a better zero-inflated model that can be used to calculate accident data and to identify the variables that are commonly used to calculate traffic accidents. The result showed that the models that are more widely used by researchers to calculate traffic accidents are commonly known as (ZINB) the zero-inflated negative binomial model and (ZIP) the zero-inflated Poisson model. Both model types have been used since they are approaches for resolving the problem of overdispersio

I4.0 readiness index in electric power distribution in serving modern consumers

Nowadays, the needs of modern human life are inseparable from the use of electrical energy. The development of eco-friendly technology continues to emerge rapidly to help facilitate daily life, such as electric vehicles (EV), electric stoves, and other modern equipment. A variety of modern equipment will have a technical impact on the distribution of electric power. Perusahaan Listrik Negara (PLN) is a state-own company, as one of the electricity service providers, must serve consumers reliably and efficiently. The existence of modern loads on the consumers side must be addressed with modern electricity supply, such as the preparation of smart grid technology, communication technology between EV charging stations, disturbance management, and efficient asset management. All of that is a readiness index for PLN to serve modern consumers. PLN needs to prepare technology and knowledge in the transformation of industry 4.0. This research helps PLN to assess the readiness of industry 4.0, which we call INDIST 4.0 (Power distribution readiness Index I4.0). The results of this study contain 5 Pillars, 15 Fields as a reference. Assessment for all distribution work areas in Indonesia with a value of 1.9 out of 4, meaning PLN is a newcomer and learne

CFD Based on The Visualisation of Aortic Valve Mechanism in Aortic Valve Stenosis for Risk Prediction at The Peak Velocity

Aortic valve disease plays a crucial role in the development of cardiovascular disease (CVD), leading to increased rates of mortality and morbidity. Two diseases, aortic valve regurgitation and aortic valve stenosis are known to occur in the aortic valve. However, aortic valve stenosis is gaining attention due to its severe impact on the patient. The malfunction of the aortic valve might be affected by blood flow, which leads to stenosis. This study aims to investigate the blood flow re-circulation on the aortic valve in different stenotic regions when the blood’s velocity reaches the pick flow of the time in the systole phases. Four different models of aortic valve stenotic are designed using computer-aided design (CAD) software. The computational fluid dynamics (CFD) approach governed by the Navier-Stokes equation is imposed to identify the characteristics of the blood backflow at the left ventricle. Several hemodynamic factors are considered, such as time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI) and relative residence time (RRT). The blood flow characteristic is expected to be chaotic, especially at the highest percentages of aortic valve stenosis, presenting the worst condition to the heart. This finding supports healthcare providers in foreseeing the deterioration of the patient’s condition and opting for aorta valve surgery replacement

Synthesis and characterisation of hydroxyapatite from Fringescale sardinella for biomedical applications

Hydroxyapatite (HAp) from fish by-product exhibits good biocompatibility and bioactivity on implants. The aim of the study is to investigate the elemental composition, crystalline phases, and functional groups of HAp synthesised from fringescale sardinella fish bones by heat decomposition method at temperature of 600, 900, and 1200 °C. The synthesised powders were characterized using fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and energy dispersive spectroscopy (EDS). After calcination of the raw fish bone to 600, 900, and 1200 °C, the FTIR data showed the existence of phosphate and hydroxyl peaks in the calcined fish bones. At 900 and 1200°C, the XRD data observed shows well-defined peaks of HAp pattern. The elemental composition evaluated by EDS provides information on the calcium to phosphate formation into apatite with a Ca/P ratio of 2.80, 0.98, 1.64 and 1.79 atomic % for raw fish bones and calcined samples, respectively. It can be concluded that the fringescale sardinella fish bones show promising findings particularly on the synthesisation of HAp for biomedical application