Machine learning technology in biodiesel research: A review

Detection of ECG characteristic points using Multiresolution Wavelet Analysis based Selective Coefficient Method

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Analysis And Design Principles Of Mems Devices

In this paper, a novel self-learning gene expression programming (GEP) methodology named SL-GEP is proposed to improve the search accuracy and efficiency of GEP. In contrast to the existing GEP variants, the proposed SL-GEP features a novel chromosome representation in which each chromosome is embedded with subfunctions that can be deployed to construct the final solution. As part of the chromosome, the subfunctions are self-learned or self-evolved by the proposed algorithm during the evolutionary search. By encompassing subfunctions or any partial solution as input arguments of another subfunction, the proposed SL-GEP facilitates the formation of sophisticated, higher-order, and constructive subfunctions that improve the accuracy and efficiency of the search. Further, a novel search mechanism based on differential evolution is proposed for the evolution of chromosomes in the SL-GEP. The proposed SL-GEP is simple, generic and has much fewer control parameters than the traditional GEP variants. The proposed SL-GEP is validated on 15 symbolic regression problems and six even-parity problems. Experimental results show that the proposed SL-GEP offers enhanced performances over several state-of-the-art algorithms in terms of accuracy and search efficiency.

Self-Learning Gene Expression Programming

Identification of persons by way of biometric features is an emerging phenomenon. Over the years, biometric recognition has received much attention due to its need for security. Amongst the many existing biometrics, fingerprints are considered to be one of the most practical ones. Techniques such as watermarking and steganography have been used in attempt to improve security of biometric data. Watermarking is the process of embedding information into a carrier file for the protection of ownership/copyright of music, video or image files, whilst steganography is the art of hiding information. This paper presents an overview of steganography techniques applied in the protection of biometric data in fingerprints. It is novel in that we also discuss the strengths and weaknesses of targeted and blind steganalysis strategies for breaking steganography techniques.

/pdf/an-overview-of-steganography-techniques-applied-to-the-10uj8un6cz.pdf

An overview of steganography techniques applied to the protection of biometric data

Obstructive sleep apnea (OSA) is the most common and severe breathing dysfunction which frequently freezes the breathing for longer than 10s while sleeping. Polysomnography (PSG) is the conventiona...

A novel approach osa detection using single-lead ecg scalogram based on deep neural network

These days, with technological advancement, it is very easy for miscreants to produce illegal multimedia data copies. Various techniques of copyright protection of free data are being developed daily. Digital watermarking is one such technique, where digital embedding of the copyright information/watermark into the data to be protected. The two major ways of doing so are spatial domain and the robust transform domain. In this study, method for watermarking of digital images, with biometric data is presented. The usage of biometric instead of the traditional watermark increases the security of the image data. The biometric used here is iris. After the retinal scan, it is the most unique biometric. In terms of user friendliness in extracting the biometric, it comes after fingerprint and facial scan. The iris biometric template is generated from subject's eye images. The discrete cosine values of templates are extracted through discrete cosine transform and converted to binary code. This binary code is embedded in the singular values of the host image's coefficients generated through wavelet transform. The original image is thus firstly applied with the discrete wavelet transform followed up by the singular value decomposition  of the subband coefficients. The algorithm has been tested with popular attacks for analysis of false recognition and rejection of subjects.

Singular value decomposition and wavelet-based iris biometric watermarking

Due to low physical workout, high-calorie intake, and bad behavioral character, people were affected by cardiological disorders. Every instant, one out of four deaths are due to heart-related ailme...

Classification of unsegmented heart sound recording using knn classifier

Gene Expression Programming

This work deals with the implementation of back propagation based artificial neural network (BPANN) model for prediction of the different diesel engine parameters, i.e., mean effective pressure, mechanical efficiency, fuel consumption, air-fuel ratio, and torque, to overcome the difficulties of practical experimentation and minimization of time and cost in this endeavor. The parameters provided as input to this model were engine speed, load and different biodiesel, and diesel fuel blends. The model has been trained with approximately 85% of the test results, and the rest were kept for prediction. The number of hidden layers and the number of neuron in each layer were varied to achieve the best predicting model. Once the model was ready, it had been used to predict the remaining data where the mean square error was as low as 6.51×10−4 and had a very low total R value. This work hereby shows that the BPANN based model can predict the performance of diesel engine fed with biodiesel and diesel fuel blends.

Swanirbhar Majumder

Papers

A novel approach osa detection using single-lead ecg scalogram based on deep neural network

Singular value decomposition and wavelet-based iris biometric watermarking

Classification of unsegmented heart sound recording using knn classifier

Gene Expression Programming

Back propagation artificial neural network (BPANN) based performance analysis of diesel engine using biodiesel