24 research outputs found
Elephant Search with Deep Learning for Microarray Data Analysis
Even though there is a plethora of research in Microarray gene expression
data analysis, still, it poses challenges for researchers to effectively and
efficiently analyze the large yet complex expression of genes. The feature
(gene) selection method is of paramount importance for understanding the
differences in biological and non-biological variation between samples. In
order to address this problem, a novel elephant search (ES) based optimization
is proposed to select best gene expressions from the large volume of microarray
data. Further, a promising machine learning method is envisioned to leverage
such high dimensional and complex microarray dataset for extracting hidden
patterns inside to make a meaningful prediction and most accurate
classification. In particular, stochastic gradient descent based Deep learning
(DL) with softmax activation function is then used on the reduced features
(genes) for better classification of different samples according to their gene
expression levels. The experiments are carried out on nine most popular Cancer
microarray gene selection datasets, obtained from UCI machine learning
repository. The empirical results obtained by the proposed elephant search
based deep learning (ESDL) approach are compared with most recent published
article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl
DBBRBF- Convalesce optimization for software defect prediction problem using hybrid distribution base balance instance selection and radial basis Function classifier
Software is becoming an indigenous part of human life with the rapid
development of software engineering, demands the software to be most reliable.
The reliability check can be done by efficient software testing methods using
historical software prediction data for development of a quality software
system. Machine Learning plays a vital role in optimizing the prediction of
defect-prone modules in real life software for its effectiveness. The software
defect prediction data has class imbalance problem with a low ratio of
defective class to non-defective class, urges an efficient machine learning
classification technique which otherwise degrades the performance of the
classification. To alleviate this problem, this paper introduces a novel hybrid
instance-based classification by combining distribution base balance based
instance selection and radial basis function neural network classifier model
(DBBRBF) to obtain the best prediction in comparison to the existing research.
Class imbalanced data sets of NASA, Promise and Softlab were used for the
experimental analysis. The experimental results in terms of Accuracy,
F-measure, AUC, Recall, Precision, and Balance show the effectiveness of the
proposed approach. Finally, Statistical significance tests are carried out to
understand the suitability of the proposed model.Comment: 32 pages, 24 Tables, 8 Figures
DSTATCOM deploying CGBP based icosϕ neural network technique for power conditioning
AbstractPresent investigation focuses design & simulation study of a three phase three wire DSTATCOM deploying a conjugate gradient back propagation (CGBP) based icosϕ neural network technique. It is used for various tasks such as source current harmonic reduction, load balancing and power factor correction under various loading which further reduces the DC link voltage of the inverter. The proposed technique is implemented by mathematical analysis with suitable learning rate and updating weight using MATLAB/Simulink. It predicts the computation of fundamental weighting factor of active and reactive component of the load current for the generation of reference source current smoothly. It’s design capability is reflected under to prove the effectiveness of the DSTATCOM. The simulation waveforms are presented and verified using both MATLAB & real-time digital simulator (RTDS). It shows the better performance and maintains the power quality norm as per IEEE-519 by keeping THD of source current well below 5%
Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP
With the advances in computationally efficient artificial Intelligence (AI)
techniques and their numerous applications in our everyday life, there is a
pressing need to understand the computational details hidden in black box AI
techniques such as most popular machine learning and deep learning techniques;
through more detailed explanations. The origin of explainable AI (xAI) is
coined from these challenges and recently gained more attention by the
researchers by adding explainability comprehensively in traditional AI systems.
This leads to develop an appropriate framework for successful applications of
xAI in real life scenarios with respect to innovations, risk mitigation,
ethical issues and logical values to the users. In this book chapter, an
in-depth analysis of several xAI frameworks and methods including LIME (Local
Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive
exPlanations) are provided. Random Forest Classifier as black box AI is used on
a publicly available Diabetes symptoms dataset with LIME and SHAP for better
interpretations. The results obtained are interesting in terms of transparency,
valid and trustworthiness in diabetes disease prediction.Comment: Chapter-6: Accepted Book Chapter in: Transparent, Interpretable and
Explainable AI Systems, BK Tripathy & Hari Seetha (Editors), CRC Press, May
202
Application of Swarm Intelligence Based Routingprotocols for Wireless Adhoc Sensor Network
The enormous growth of wireless sensor network (WSN) research has opined challenges about their ease in implementation and performance evaluation. Efficient swarm intelligence based routing protocols that can be used to obtain the application specific service guarantee are the key design issues in designing a WSN model. In this paper, an experimental testbed is designed with 100 sensor nodes deployed in a dense environment to address the scalability and performance issues of WSN. In this paper, we use Flooded Piggyback (FP) and SC-MCBR ant colony based routing along with AODV and MCBR Tree in order to design an efficient WSN model. Finally, simulation results are presented with various performance measures to understand the efficacy of the proposed WSN design
An Education Process Mining Framework: Unveiling Meaningful Information for Understanding Students’ Learning Behavior and Improving Teaching Quality
This paper focuses on the study of automated process discovery using the Inductive visual Miner (IvM) and Directly Follows visual Miner (DFvM) algorithms to produce a valid process model for educational process mining in order to understand and predict the learning behavior of students. These models were evaluated on the publicly available xAPI (Experience API or Experience Application Programming Interface) dataset, which is an education dataset intended for tracking students’ classroom activities, participation in online communities, and performance. Experimental results with several performance measures show the effectiveness of the developed process models in helping experts to better understand students’ learning behavioral patterns
Performance Measures of Ultra-Wideband Communication System
Ultra-Wideband (UWB) has the potential to become a viable and competitive wireless technology for short-range high rate Wireless personal area networks (WPANs) as well as lower-rate and low power causing low cost devices and networks. This paper discusses properties of Gaussian pulses and derivatives thereof used for UWB signalling. The power spectral density(PSD) of signals sent into channel appear as discrete components resembling spikes with amplitudes that could adversely interfere with established communication systems. In this, mathematical model for the analysis of PSD in the channel is introduced. PSD for Time Hopping UWB (TH-UWB) signals using PPM (Pulse Position Modulation) technique is derived and simulated in the MATLAB 7.0 environment. Results show that TH-UWB PPM is a potential candidate for the fourth generation broadband wireless networks. Finally, it is established that the introduction of randomness into transmitted signal has the effect of decreasing the amount of interference power
DSTATCOM deploying CGBP based icosϕ neural network technique for power conditioning
Present investigation focuses design & simulation study of a three phase three wire DSTATCOM deploying a conjugate gradient back propagation (CGBP) based icosϕ neural network technique. It is used for various tasks such as source current harmonic reduction, load balancing and power factor correction under various loading which further reduces the DC link voltage of the inverter. The proposed technique is implemented by mathematical analysis with suitable learning rate and updating weight using MATLAB/Simulink. It predicts the computation of fundamental weighting factor of active and reactive component of the load current for the generation of reference source current smoothly. It’s design capability is reflected under to prove the effectiveness of the DSTATCOM. The simulation waveforms are presented and verified using both MATLAB & real-time digital simulator (RTDS). It shows the better performance and maintains the power quality norm as per IEEE-519 by keeping THD of source current well below 5%. Keywords: CGBP based icosϕ neural network, DSTATCOM, MATLAB, RTD