Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training

Sirtuin 1 inhibition: a promising avenue to suppress cancer progression through small inhibitors design

SIRT1 is a protein associated with vital cell functions such as gene regulation, metabolism, ageing, and cellular energy restoration. Its association with the tumor suppressor protein p53 is essential for controlling the growth of cells, apoptosis, and response to DNA damage. By raising p53 acetylation, encouraging apoptosis, and reducing cell proliferation, inhibiting SIRT1's catalytic domain, which interacts with p53, shows potential as a cancer treatment. The aim of the study is to find compounds that could inhibit SIRT1 and thus lower the proliferation of cancer cells. Employing molecular docking techniques, a virtual screening of ∼900 compounds (isolated from medicinal plants and derivatives) gave us 13 active compounds with good binding affinity. Additional evaluation of pharmacokinetic and pharmacodynamic properties led to the selection of eight compounds with desirable properties. Docking analysis confirmed stable interactions between the final eight compounds (C1-C8) and the SIRT1 catalytic domain. Molecular dynamics simulations show overall stability and moderate changes in protein structure upon compound binding. The compactness of the protein indicated the protein's tight packing upon the inhibitors binding. Binding free energy calculations revealed that compounds C2 (-49.96 ± 0.073 kcal/mol and C1 (-44.79 ± 0.077 kcal/mol) exhibited the highest energy, indicating strong binding affinity to the SIRT1 catalytic domain. These compounds, along with C8, C5, C6, C3, C4 and C7, showed promising potential as SIRT1 inhibitors. Based on their ability to reduce SIRT1 activity and increase apoptosis, the eight chemicals discovered in this work may be useful in treating cancer.Communicated by Ramaswamy H. Sarma.

Sensitivity Analysis of Parameters Affecting Wetland Water Levels: A Study of Flood Detention Basin, Colombo, Sri Lanka

Wetlands play a vital role in ecosystems. They help in flood accumulation, water purification, groundwater recharge, shoreline stabilization, provision of habitats for flora and fauna, and facilitation of recreation activities. Although wetlands are hot spots of biodiversity, they are one of the most endangered ecosystems on the Earth. This is not only due to anthropogenic activities but also due to changing climate. Many studies can be found in the literature to understand the water levels of wetlands with respect to the climate; however, there is a lack of identification of the major meteorological parameters affecting the water levels, which are much localized. Therefore, this study, for the first time in Sri Lanka, was carried out to understand the most important parameters affecting the water depth of the Colombo flood detention basin. The temporal behavior of water level fluctuations was tested among various combinations of hydro-meteorological parameters with the help of Artificial Neural Networks (ANN). As expected, rainfall was found to be the most impacting parameter; however, apart from that, some interesting combinations of meteorological parameters were found as the second layer of impacting parameters. The rainfall-nighttime relative humidity, rainfall-evaporation, daytime relative humidity-evaporation, and rainfall-nighttime relative humidity-evaporation combinations were highly impactful toward the water level fluctuations. The findings of this study help to sustainably manage the available wetlands in Colombo, Sri Lanka. In addition, the study emphasizes the importance of high-resolution on-site data availability for higher prediction accuracy.

ARTIFICIAL NEURAL NETWORK MODELS FOR ESTIMATION OF ELECTRIC FIELD INTENSITY AND MAGNETIC FLUX DENSITY IN THE PROXIMITY OF OVERHEAD TRANSMISSION LINE

This paper considers the application of artificial neural network (ANN) models for electric field intensity and magnetic flux density estimation in the proximity of overhead transmission lines. Specifically, two distinct ANN models are used to facilitate independent estimation of electric field intensity and magnetic flux density in the proximity of overhead transmission lines. The considered ANN approach is systematically evaluated under different scenarios. An example of an overhead transmission line with horizontal phase conductor configuration is used to enable a direct comparison of the electric field intensity and magnetic flux density estimates generated by the two ANN models to measurement results obtained over the lateral profile. Further investigation of ANN models involves an extensive study whereby 13 different overhead transmission lines of horizontal configurations are used as the basis for comparing measurement results to estimates provided by the ANN models. In this study, the performance analysis of the ANN models was evaluated using coefficient of determination and root mean square error. The obtained results demonstrate that the considered ANN approach can be used to estimate the electric field intensity and magnetic flux density in the proximity of overhead transmission lines.

Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg-Marquardt (LM), Conjugate Gradient with Fletcher-Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children's hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the nonlinear system based on neural network and time series analysis is proposed to deal with the one-month forecast of the produced power from photovoltaic modules (PVM). The PVM is a monocrystalline cell with a rated production of 175 watts that is placed at Heliopolis University, Bilbéis city, Egypt. The NARX model is considered powerful enough to emulate the nonlinear dynamic state-space model. It is extensively performed to resolve a variety of problems and is mainly important in complex process control. Moreover, the NARX method is selected because of its quick learning and completion times, as well as high appropriateness, and is distinguished by advantageous dynamics and interference resistance. The neural network (NN) is trained and optimized with three algorithms, the Levenberg–Marquardt Algorithm (NARX-LMA), the Bayesian Regularization Algorithm (NARX-BRA) and the Scaled Conjugate Gradient Algorithm (NARX-SCGA), to attain the best performance. The forecasted results using the NARX method based on the three algorithms are compared with experimentally measured data. The NARX-LMA, NARX-BRA and NARX-SCGA models are validated using statistical criteria. In general, weather conditions have a significant impact on the execution and quality of the results.

Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE < 0.22, and BIAS < 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.

A comparison of machine learning classifiers for smartphone-based gait analysis

This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected by using a smartphone. The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs. Graphical Abstract. This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected and processed by using a smartphone(see figure). The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs.

Fuzzy Classification Methods Based Diagnosis of Parkinson's disease from Speech Test Cases

BACKGROUND

Together with the Alzheimer's disease, Parkinson's disease is considered as one of the two serious known neurodegenerative diseases. Physicians find it hard to predict whether a given patient has already developed or is expected to develop the Parkinson's disease in the future. To overcome this difficulty, it is possible to develop a computing model, which analyzes the data related to a given patient and predicts with acceptable accuracy when he/she is anticipated to develop the Parkinson's disease.

OBJECTIVES

This paper contributes an attractive prediction framework based on some machine learning approaches for distinguishing people with Parkinsonism from healthy individuals.

METHODS

Several fuzzy classifiers such as Inductive Fuzzy Classifier, Fuzzy Rough Classifier and two types of neuro-fuzzy classifiers have been employed.

RESULTS

The fuzzy classifiers utilized in this study have been tested using the "Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set" of 40 subjects available on the UCI repository.

CONCLUSION

The results achieved show that FURIA, MLP- Bagging - SGD, genfis2 and scg1 performed the best among the fuzzy rough, WEKA, adaptive neuro-fuzzy and neuro-fuzzy classifiers, respectively. The worst performance belongs to nearest neighborhood, IBK, genfis3 and scg3 among the formerly mentioned classifiers. The results reported in this paper are better in comparison to the results reported in Sakar et al., where the same dataset was used, with utilization of different classifiers. This demonstrates the applicability and effectiveness of the fuzzy classifiers used in this study as compared to the non-fuzzy classifiers used by Sakar et al.

Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, machine learning and neuro-fuzzy classifiers

To design machine learning classifiers to facilitate the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the differential diagnosis of patients with asthma and restrictive respiratory diseases. FOT and spirometric exams were performed in 97 individuals, including controls (n = 20), asthmatic patients (n = 38), and restrictive (n = 39) patients. The first experiment of this study showed that the best FOT parameter was the resonance frequency, providing moderate accuracy (AUC = 0.87). In the second experiment, a neuro-fuzzy classifier and different supervised machine learning techniques were investigated, including k-nearest neighbors, random forests, AdaBoost with decision trees, and support vector machines with a radial basis kernel. All classifiers achieved high accuracy (AUC ≥ 0.9) in the differentiation between patient groups. In the third and fourth experiments, the use of different feature selection techniques allowed us to achieve high accuracy with only three FOT parameters. In addition, the neuro-fuzzy classifier also provided rules to explain the classification. Neuro-fuzzy and machine learning classifiers can aid in the differential diagnosis of patients with asthma and restrictive respiratory diseases. They can assist clinicians as a support system providing accurate diagnostic options.

A novel trust model for fog computing using fuzzy neural networks and weighted weakest link

Purpose

Trust is one of the main pillars of many communication and interaction domains. Computing is no exception. Fog computing (FC) has emerged as mitigation of several cloud computing limitations. However, selecting a trustworthy node from the fog network still presents serious challenges. This paper aims to propose an algorithm intended to mitigate the trust and the security issues related to selecting a node of a fog network.

Design/methodology/approach

The proposed model/algorithm is based on two main concepts, namely, machine learning using fuzzy neural networks (FNNs) and the weighted weakest link (WWL) algorithm. The crux of the proposed model is to be trained, validated and used to classify the fog nodes according to their trust scores. A total of 2,482 certified computing products, in addition to a set of nodes composed of multiple items, are used to train, validate and test the proposed model. A scenario including nodes composed of multiple computing items is designed for applying and evaluating the performance of the proposed model/algorithm.

Findings

The results show a well-performing trust model with an accuracy of 0.9996. Thus, the end-users of FC services adopting the proposed approach could be more confident when selecting elected fog nodes. The trained, validated and tested model was able to classify the nodes according to their trust level. The proposed model is a novel approach to fog nodes selection in a fog network.

Research limitations/implications

Certainly, all data could be collected, however, some features are very difficult to have their scores. Available techniques such as regression analysis and the use of the experts have their own limitations. Experts might be subjective, even though the author used the fuzzy group decision-making model to mitigate the subjectivity effect. A methodical evaluation by specialized bodies such as the security certification process is paramount to mitigate these issues. The author recommends the repetition of the same study when data form such bodies is available.

Originality/value

The novel combination of FNN and WWL in a trust model mitigates uncertainty, subjectivity and enables the trust classification of complex FC nodes. Furthermore, the combination also allowed the classification of fog nodes composed of diverse computing items, which is not possible without the WWL. The proposed algorithm will provide the required intelligence for end-users (devices) to make sound decisions when requesting fog services.

Detection and classification of bearing faults in industrial geared motors using temporal features and adaptive neuro-fuzzy inference system

This paper concerns the automatic diagnosis of ball bearing defects in industrial geared motor based on statistical indicators and the Adaptive Neuro-Fuzzy Inference System (ANFIS). The approach consists of three essential steps: the first is the extraction of statistical indicators from the root mean square (RMS) of the raw vibration signals measured experimentally for different states of the bearing (healthy and in the presence of defects). The second step consists of the selection of the more relevant indicators, and finally the introduction of these indicators to the ANFIS network in order to classify the various defects in the bearing (inner and outer race faults, and combined fault). A test campaign was conducted on an industrial installation (Wheeled Conveyor) to collect data as the RMS trend of the raw vibrations using adequate instrumentation in order to verify the validity of the method in real test conditions. The obtained results show that the proposed approach can reliably detect and classify various faults at different speeds of rotation of the electric motor. The effectiveness of the proposed method was also approved by using additional test data.

Automated sleep staging of OSAs based on ICA preprocessing and consolidation of temporal correlations

An automated sleep staging based on analyzing long-range time correlations in EEG is proposed. These correlations, indicating time-scale invariant property or self-similarity at different time scales, are known to be salient dynamical characteristics of stage succession for a sleeping brain even when the subject suffers a destructive disorder such as Obstructive Sleep Apnea (OSA). The goal is to extract a set of complementary features from cerebral sources mapped onto the scalp electrodes or from a number of denoised EEG channels. For this purpose, source localization/extraction and noise reduction approaches based on Independent Component Analysis were used prior to correlation analysis. Feature extracted segments were then classified in one of the five classes including WAKE, STAGE1, STAGE2, SWS and REM via an ensemble neuro-fuzzy classifier. Some techniques were employed to improve the classifier's performance including Scaled Conjugate Gradient Method to speed up learning the ANFIS classifiers, a pruning algorithm to eliminate irrelevant fuzzy rules and the 10-fold cross-validation technique to train and test the system more efficiently. The performance of classification for two strategies including (1) feature extraction from effective cerebral sources and (2) feature extraction from selected channels of denoised EEG signals was compared and contrasted in terms of training errors and test accuracies. The first and second strategies achieved 92.23 and 88.74% agreement with human expert respectively which indicates the effectiveness of the staging system based on cerebral sources of activity. Our results further indicate that the misclassification rates were almost below 10%. The proposed automated sleep staging system is reliable due to the fact that it is based on the underlying dynamics of sleep staging which is less likely to be affected by sleep fragmentations occurred repeatedly in OSA.

Early classification of pathological heartbeats on wireless body sensor nodes

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections.

A neuro-fuzzy approach in the classification of students' academic performance

Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions.

TOWARDS A UNIFIED PRINCIPLE FOR REASONING ABOUT HETEROGENEOUS DATA: A FUZZY LOGIC FRAMEWORK

Human knowledge about monitoring process variables is usually incomplete. To deal with this partial knowledge many types of representation other than the quantitative one are used to describe process variables (qualitative, symbolic interval). Thus, the development of automatic reasoning mechanisms about the process is faced with this problem of multiple data representations. In this paper, a unified principle for reasoning about heterogeneous data is introduced. This principle is based on a simultaneous mapping of data from initially heterogeneous spaces into only one homogeneous space based on a relative measure using appropriate characteristic functions. Once the heterogeneous data are represented in a unified space, a single processing for various analysis purposes can be performed using simple reasoning mechanisms. An application of this principle within a fuzzy logic framework is performed here to demonstrate its effectiveness. We show that simple fuzzy reasoning mechanisms can be used to reason in a unified way about heterogeneous data in three well known machine learning problems.