A modified recurrent neural network (MRNN) model for and breast cancer classification system

Breast cancer is most dangerous cancer among women. Image processing techniques are used for Breast cancer detection. A Block-based cross diagonal texture matrix (BCDTM) method is used first to extract Haralick’s features from each mammography ROI. Likewise, wrapper method is utilized to choose the crucial features from the condensed feature vector. There are lot of factors that affects the quality of the images such as salt or pepper noise. As a result, this is less precise and more prone to mistakes because of human. In order to address the problems, input breast image is first pre-processed via median filtering to reduce noise. ROI segmentation is done using weighted K means clustering. Feature extraction, texture and form descriptors based on Centroid Distance Functions (CDF) and BCDTM are used. Kernel Principal Component Analysis (KPCA) is used as dimensionality reduction on the extracted features. Improved Cuckoo Search Optimization (ICSO) is used to compute relevant feature selection. Modified Recurrent Neural Network (MRNN) is utilized to classify breast cancer into benign and malignant. Results show that the suggested model achieved highest accuracy, precision and recall values compared with other state-of-the-art approaches

Tribological Characterisation of Graphene Oxide as Lubricant Additive on Hypereutectic Al-25Si/Steel Tribopair

The performance of a lubricant greatly depends on the additives it involves. However, recently used additives produce severe pollution when they are burned and exhausted. Therefore, it is necessary to develop a new generation of green additives. Graphene oxide (GO) is considered to be environmentally friendly. The scope of this study is to explore the fundamental tribological behavior of graphene, the first existing 2D material, and evaluate its performance as a lubricant additive. The friction and wear behavior of 0.5 wt% concentrations of GO particles in ethanol and SAE20W50 engine oil on a hypereutectic Al-25Si alloy disc against steel ball was studied at 5 N load. GO as an additive reduced the wear coefficient by 60–80% with 30 Hz frequency for 120 m sliding distance. The minimum value of the coefficient of friction (0.057) was found with SAE20W50 + 0.5 wt% GO. A possible explanation for these results is that the graphene layers act as a 2D nanomaterial and form a conformal protective film on the sliding contact interfaces and easily shear off due to weak Van der Waal's forces and drastically reduce the wear. Scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and Raman spectroscopy were used for characterization of GO and wear scars

Removal of salt and pepper noise using adaptive switching modified decision-based unsymmetric trimmed median filter optimized with Hyb-BCO-FBIA

An intriguing area in the IP (image processing) is the recovery of noisy photographs from the noise caused by the salt and pepper. As the mistake rate rises and the image format varies, the issue with the current task does not go away. In this study, Salt and Pepper Denoising, Hybrid Balancing Composite motion Optimization with Adaptive Switching Modified Decision based Unsymmetric Trimmed Median Filter and Forensics-Based Investigation Algorithm is proposed (R-SPN-ASMD-UTMF-Hyb-BCO-FBIA). Initially, the input images are obtained from boat-types-recognition dataset, cat-breeds-dataset, cars-image-dataset, butterfly-images 40-species dataset and birds-200 dataset. The images are pre-processed through an ASMD-UTMF filter. ASMD-UTMF does not expose any adoption of optimization systems to calculate the optimal parameters. Therefore, the proposed Hyb-BCO-FBIA is employed for optimizing the ASMD-UTMF weight parameters. The suggested system is implemented on MATLAB and the assessment metrics as Mean Square Error (MSE), Structural similarity index measurement (SSIM), Peak signal to noise ratio (PSNR), Normalized cross-correlation (NC), Image Enhancement Factor (IEF), Mean Square Error (MSE) are analysed. The proposed method attains higher PSNR, NC related with other SOTA (State-Of-The Art) methods

Detection of glioma on brain MRIs using adaptive segmentation and modified graph neural network based classification

Gliomas constitute the prevalently seen brain tumours in humans. The real-time utilization of Computer Aided Diagnosis system depends on brain Magnetic Resonance Imaging (MRIs) has the ability of helping radiologists and professionals to identify the presence of glioma tumours. It is very difficult to segment brain tumours because of the brain image and it has a complex structure. A fully automated, accurate, segmentation and classification model is developed using a modified Graph Neural Network (MGNN) for brain tumours. Proposed work steps are, image registration, Shift-Invariant Shear let Transform (SIST), adaptive segmentation, feature extraction, and categorization of tumours. At first, image registration and SIST are carried out to improve image quality. Adaptive segmentation is then carried out utilizing Improved Fuzzy C-Means clustering. Next, Grey Level Co-occurrence Matrix, Discrete Wavelet Transform is utilized for the extraction of features in brain MRI data. Finally, MGNN is introduced for the detection of anomalous tumour-infected MR and actual MR brain images. The findings are demonstrated that the proposed model leads in higher accuracy levels for both classification and segmentation

An efficient breast cancer classification model using bilateral filtering and fuzzy convolutional neural network

Abstract BC (Breast cancer) is the second most common reason for women to die from cancer. Recent workintroduced a model for BC classifications where input breast images were pre-processed using median filters for reducing noises. Weighed KMC (K-Means clustering) is used to segment the ROI (Region of Interest) after the input image has been cleaned of noise. Block-based CDF (Centre Distance Function) and CDTM (Diagonal Texture Matrix)-based texture and shape descriptors are utilized for feature extraction. The collected features are reduced in counts using KPCA (Kernel Principal Component Analysis). The appropriate feature selection is computed using ICSO (Improved Cuckoo Search Optimization). The MRNN ((Modified Recurrent Neural Network)) values are then improved through optimization before being utilized to divide British Columbia into benign and malignant types. However, ICSO has many disadvantages, such as slow search speed and low convergence accuracy and training an MRNN is a completely tough task. To avoid those problems in this work preprocessing is done by bilateral filtering to remove the noise from the input image. Bilateral filter using linear Gaussian for smoothing. Contrast stretching is applied to improve the image quality. ROI segmentation is calculated based on MFCM (modified fuzzy C means) clustering. CDTM-based, CDF-based color histogram and shape description methods are applied for feature extraction. It summarizes two important pieces of information about an object such as the colors present in the image, and the relative proportion of each color in the given image. After the features are extracted, KPCA is used to reduce the size. Feature selection was performed using MCSO (Mutational Chicken Flock Optimization). Finally, BC detection and classification were performed using FCNN (Fuzzy Convolutional Neural Network) and its parameters were optimized using MCSO. The proposed model is evaluated for accuracy, recall, f-measure and accuracy. This work’s experimental results achieve high values of accuracy when compared to other existing models

A modified recurrent neural network (MRNN) model for and breast cancer classification system

Breast cancer is most dangerous cancer among women. Image processing techniques are used for Breast cancer detection. A Block-based cross diagonal texture matrix (BCDTM) method is used first to extract Haralick’s features from each mammography ROI. Likewise, wrapper method is utilized to choose the crucial features from the condensed feature vector. There are lot of factors that affects the quality of the images such as salt or pepper noise. As a result, this is less precise and more prone to mistakes because of human. In order to address the problems, input breast image is first pre-processed via median filtering to reduce noise. ROI segmentation is done using weighted K means clustering. Feature extraction, texture and form descriptors based on Centroid Distance Functions (CDF) and BCDTM are used. Kernel Principal Component Analysis (KPCA) is used as dimensionality reduction on the extracted features. Improved Cuckoo Search Optimization (ICSO) is used to compute relevant feature selection. Modified Recurrent Neural Network (MRNN) is utilized to classify breast cancer into benign and malignant. Results show that the suggested model achieved highest accuracy, precision and recall values compared with other state-of-the-art approaches

Evaluation of the biomethanation potential of enriched methanogenic cultures on gelatin

Abstract Purpose Biomethane is an environment-friendly, economic, and alternative energy resource for a clean and green future. In the present study, we have evaluated the biomethanation potential of acetate-utilizing methanogenic culture (AUMC) and gelatin-enriched mixed culture (GEMC) with Clostridium acetobutylicum NCIM 2841 (GEMC-CA.) on gelatin as a sole carbon and nitrogen source. Methods We conducted experiments for examining the specific-methanogenic activity of these cultures in the metabolic assay media containing 1% gelatin. The produced methane and consumed gelatin were quantified by standard experimental methods. Exchange metabolites produced by these cultures were qualitatively analyzed by mass spectrometry. Results Results of our study show that the growth-associated amino acid catabolism partially or completely supported the methanogenesis of these defined cultures. AUMC and GEMC found to be suitable for enhanced methanogenic activity on gelatin but a rapid degradation of amino acid was attributed by GEMC-CA. The ammonia released from these cultures was directly proportional to gelatin degradation. Mass spectral data analysis identified some key exchange metabolites from acidogenic culture and methanogenic culture for confirming the growth-associated methanogenesis. Conclusion The biomethanation potential of these cultures on gelatin is coupled with the Stickland reactions-directed methanogenesis in a syntrophic manner. The present study provides the importance for the development of a starter culture for the biomethanation of protein-based industrial wastes in effective ways