A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%–83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.     

A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays

Solar energy is a widely used type of renewable energy. Photovoltaic arrays are used to harvest solar energy. The major goal, in harvesting the maximum possible power, is to operate the system at its maximum power point (MPP). If the irradiation conditions are uniform, the P-V curve of the PV array has only one peak that is called its MPP. But when the irradiation conditions are non-uniform, the P-V curve has multiple peaks. Each peak represents an MPP for a specific irradiation condition. The highest of all the peaks is called Global Maximum Power Point (GMPP). Under uniform irradiation conditions, there is zero or no partial shading. But the changing irradiance causes a shading effect which is called Partial Shading. Many conventional and soft computing techniques have been in use to harvest solar energy. These techniques perform well under uniform and weak shading conditions but fail when shading conditions are strong. In this paper, a new method is proposed which uses Machine Learning based algorithm called Opposition-Based-Learning (OBL) to deal with partial shading conditions. Simulation studies on different cases of partial shading have proven this technique effective in attaining MPP.     

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-the-art approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.     

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R²) during the testing phase (R² = 0.95), whereas the other comparable models generated relatively lower values of R² ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

     

An analysis of characteristic discharges for the Adaim River (Iraq) in multiyear period

A major River Tigris tributary in Iraq, the Adaim River, has a Mediterranean river flow regime with a total basin area of 12,482 km². The river catchment responds almost immediately to rainfall with apparently minimum storage (i.e. flashy stream). The river daily hydrograph showed a daily peak flow of 1,476 m³/s with substantial seasonal and random variability; the flow duration curve followed the two‐parameter lognormal probability distribution. Gamma and the two‐parameter Weibull probability distributions fitted the monthly mean river discharge for the period 1937–2012 well. Normal and gamma probability distributions were found to appropriately describe the distribution of the annual mean river discharge for the same period. Gumbel extreme value, Log Pearson type III, and the two‐parameter lognormal distributions gave a reasonable fit to the annual maximum discharge record for the river. A regression formula was used to fit the annual minimum discharge record, which has a significant number of zero values. There was a positive and significant correlation (r = 0.77) between the annual mean discharge at the measuring site and seasonal rainfall measured at Karkuk meteorological station located in the north central part of the basin. The rainfall record at Karkuk showed a significant decline in seasonal rainfall after 1993.     

An efficient data hiding method based on adaptive directional pixel value differencing (ADPVD)

Steganography is an important secret information communication technology in which one may send messages without others having knowledge of their existence. This paper proposes a new adaptive steganography method for color images using adaptive directional pixel-value differencing (ADPVD). The proposed method increases the capacity of the hidden secret data and improves the security of the stego-color image as well. The hiding capacity of the original PVD method is investigated by considering three directional edges: horizontal, vertical and diagonal directions using color cover image. The cover image is partitioned into 2-pixel blocks in a non-overlapping fashion and scanned in raster-scan order in all three directions. The proposed method adaptively selects the appropriate embedding directions for each color channel according to the largest embedding capacity. The security is improved since different pixel directions are employed adaptively to embed different number of message bits in each color channel. The experimental results show that the proposed method provides both larger embedding capacity and better visual quality of the stego color image compared with other PVD-based algorithms.     

Comparison of the translucency of two multishaded composite resins polymerized with direct or indirect method

The aim of this study was to measure the translucency of one indirect and direct resin composite after polymerization and to examine the effect of color parameters on this property. Tescera and Filtek Ultimate, each containing three shade groups (Enamel, Dentin and Body for Filtek Ultimate; and Incisal, Opaceous Dentin and Body for Tescera), were investigated. The translucency parameter (TP) was measured as the color difference between a specimen over a white and black background. Significant differences were observed in TP values between shade groups. Tukey’s multiple comparison test revealed TP values among the groups of Filtek Ultimate-Enamel > Tescera-Incisal > Filtek Ultimate-Body > Tescera-Body = Filtek Ultimate-Dentin > Tescera-Opaceous Dentin. In conclusion, it is concluded that the translucency of resin composite was affected by polymerization and color parameters.     

All-digital gated ring oscillator $$\Delta \Sigma$$ modulators

This paper presents all-digital time-mode \(\Delta \Sigma\) modulators. The proposed modulators consist of a voltage-to-time integrator, a seven-stage gated ring oscillator functioning as a 3-bit quantizer, and seven digital differentiators. A detailed analysis of the nonlinear characteristics of the modulators is provided. Designed in IBM 130 nm 1.2 V CMOS technology with a 100 mV 100 kHz sinusoidal input and a 4.4 MHz frequency clock, the first-order modulator provides 47 dB SNR over 0–150 KHz bandwidth while consuming 1.1 mW while the second-order modulator provides 55 dB SNR over the same bandwidth while consuming consumes 1.45 mW.     

Monitoring of Overhead Transmission Lines: A Review from the Perspective of Contactless Technologies

This paper describes a comprehensive review of non-contact technologies for overhead power transmission lines. Due to ever increasing emphasis on reducing accidents and speeding up diagnosis for automatically controlled grids, real time remote sensing and actuation is the new horizon for smart grid implementation. The technology overview with emphasis on the practical implementation of advanced non-contact technologies is discussed in this paper while considering optimization of the high voltage transmission lines parameters. In case of fault, the voltage and the current exceed limits of operation and hence real time reporting for control and diagnosis is a critical requirement. This paper aims to form a strong foundation for control and diagnosis of future power distribution systems so that a practitioner or researcher can make choices for a workable solution in smart grid implementation based on non-contact sensing.