Nahier Aldhafferi

Investigating the effect of correlation based feature selection on breast cancer diagnosis using artificial neural network and support vector machines

The breast cancer is one of the most popular cause of death among women. It is also one of the diseases that can be cured and has high healing chances when it is detected in the early stages [1]. Detecting the cancer and differentiating between the diagnosis that affirm whether a patient has breast cancer or not has been considered as a big challenge. In order to have an accurate diagnosis, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been selected in many research papers to solve this problem with high classification accuracy. In this paper the breast cancer diagnosis is addressed using SVM and ANN combined with feature selection. The feature selection is based on the correlation coefficient of each feature against the target class where different feature subsets are used. The model is tested on the popular Wisconsin Diagnosis Breast Cancer (WDBC) dataset to conduct the experiments. 10- Fold Cross validation has been used for data partitioning while developing the model and the outcome indicates better classification accuracy. As for comparison between SVM and ANN, empirical studies outcome indicated that SVM outperformed ANN with classification accuracy of 97.14 and 96.71 respectively.

Estimation of Curie temperature of manganite-based materials for magnetic refrigeration application using hybrid gravitational based support vector regression

Magnetic refrigeration (MR) technology stands a good chance of replacing the conventional gas compression system (CGCS) of refrigeration due to its unique features such as high efficiency, low cost as well as being environmental friendly. Its operation involves the use of magnetocaloric effect (MCE) of a magnetic material caused by application of magnetic field. Manganite-based material demonstrates maximum MCE at its magnetic ordering temperature known as Curie temperature (TC). Consequently, manganite-based material with TC around room temperature is essentially desired for effective utilization of this technology. The TC of manganite-based materials can be adequately altered to a desired value through doping with appropriate foreign materials. In order to determine a manganite with TC around room temperature and to circumvent experimental challenges therein, this work proposes a model that can effectively estimates the TC of manganite-based material doped with different materials with the aid of suppor...

Robust and Fragile Medical Image Watermarking: A Joint Venture of Coding and Chaos Theories

A secure spatial domain, hybrid watermarking technique for obtaining watermark (authentication information) robustness and fragility of the host medical image (content integrity) using product codes, chaos theory, and residue number system (RNS) is proposed. The proposed scheme is highly fragile and unrecoverable in terms of the host image, but it is significantly robust and recoverable in terms of the watermark. Altering the medical image may result in misdiagnosis, hence the watermark that may contain patient information and organization logo must be protected against certain attacks. The host medical image is separated into two parts, namely, the region of interest (ROI) and region of noninterest (RONI) using a rectangular region. The RONI part is used to embed the watermark information. Moreover, two watermarks are used: one to achieve authenticity of image and the other to achieve the robustness against both incidental and malicious attacks. Effectiveness in terms of security, robustness, and fragility of the proposed scheme is demonstrated by the simulations and comparison with the other state-of-the-art techniques.

Reversible and Fragile Watermarking for Medical Images

A novel reversible digital watermarking technique for medical images to achieve high level of secrecy, tamper detection, and blind recovery of the original image is proposed. The technique selects some of the pixels from the host image using chaotic key for embedding a chaotically generated watermark. The rest of the pixels are converted to residues by using the Residue Number System (RNS). The chaotically selected pixels are represented by the polynomial. A primitive polynomial of degree four is chosen that divides the message polynomial and consequently the remainder is obtained. The obtained remainder is XORed with the watermark and appended along with the message. The decoder receives the appended message and divides it by the same primitive polynomial and calculates the remainder. The authenticity of watermark is done based on the remainder that is valid, if it is zero and invalid otherwise. On the other hand, residue is divided with a primitive polynomial of degree 3 and the obtained remainder is appended with residue. The secrecy of proposed system is considerably high. It will be almost impossible for the intruder to find out which pixels are watermarked and which are just residue. Moreover, the proposed system also ensures high security due to four keys used in chaotic map. Effectiveness of the scheme is validated through MATLAB simulations and comparison with a similar technique.

Differential Evolution Assisted MUD for MC-CDMA Systems Using Non-orthogonal Spreading Codes

In this paper, receiver optimization techniques are being investigated into a Differential Evolution (DE) assisted Multiuser Detection scheme for a synchronous, MC-CDMA system. In multiuser detection, the induced multiple access interference (MAI) makes the detection very inefficient and critical. However, the proposed system is less vulnerable to this issue in MC-CDMA communication. In this proposed scheme, for sake of attaining frequency diversity gain, Orthogonal Frequency Division Multiplexing (OFDM) has been used. That is, same signal is transmitted over different sub-carrier frequencies and these sub-carrier frequencies being adequately separated in frequency domain, do not interfere with each other and hence end of the day capacity is added up. Moreover, the role of Walsh (orthogonal but less practical) and Gold spreading sequences (non-orthogonal) which are more practical in nature, is also investigated and the results are demonstrated for different number of users communicating at the same time. The proposed scheme can perform sufficiently well with very low computational complexity compared to the optimum maximum likelihood (ML) detection scheme with increasing users.

Modeling energy band gap of doped TiO2 semiconductor using homogeneously hybridized support vector regression with gravitational search algorithm hyper-parameter optimization

Titanium dioxide (TiO2) semiconductor is characterized with a wide band gap and attracts a significant attention for several applications that include solar cell carrier transportation and photo-catalysis. The tunable band gap of this semiconductor coupled with low cost, chemical stability and non-toxicity make it indispensable for these applications. Structural distortion always accompany TiO2 band gap tuning through doping and this present work utilizes the resulting structural lattice distortion to estimate band gap of doped TiO2 using support vector regression (SVR) coupled with novel gravitational search algorithm (GSA) for hyper-parameters optimization. In order to fully capture the non-linear relationship between lattice distortion and band gap, two SVR models were homogeneously hybridized and were subsequently optimized using GSA. GSA-HSVR (hybridized SVR) performs better than GSA-SVR model with performance improvement of 57.2% on the basis of root means square error reduction of the testing dataset. Effect of Co doping and Nitrogen-Iodine co-doping on band gap of TiO2 semiconductor was modeled and simulated. The obtained band gap estimates show excellent agreement with the values reported from the experiment. By implementing the models, band gap of doped TiO2 can be estimated with high level of precision and absorption ability of the semiconductor can be extended to visible region of the spectrum for improved properties and efficiency.Titanium dioxide (TiO2) semiconductor is characterized with a wide band gap and attracts a significant attention for several applications that include solar cell carrier transportation and photo-catalysis. The tunable band gap of this semiconductor coupled with low cost, chemical stability and non-toxicity make it indispensable for these applications. Structural distortion always accompany TiO2 band gap tuning through doping and this present work utilizes the resulting structural lattice distortion to estimate band gap of doped TiO2 using support vector regression (SVR) coupled with novel gravitational search algorithm (GSA) for hyper-parameters optimization. In order to fully capture the non-linear relationship between lattice distortion and band gap, two SVR models were homogeneously hybridized and were subsequently optimized using GSA. GSA-HSVR (hybridized SVR) performs better than GSA-SVR model with performance improvement of 57.2% on the basis of root means square error reduction of the testing datas...