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

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

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

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

Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training

As deepfake technology becomes increasingly sophisticated, the proliferation of manipulated images presents a significant threat to online integrity, requiring advanced detection and mitigation strategies. Addressing this critical challenge, our study introduces a pioneering approach that integrates Explainable AI (XAI) with Adversarial Robustness Training (ART) to enhance the detection and removal of deepfake content. The proposed methodology, termed XAI-ART, begins with the creation of a diverse dataset that includes both authentic and manipulated images, followed by comprehensive preprocessing and augmentation. We then employ Adversarial Robustness Training to fortify the deep learning model against adversarial manipulations. By incorporating Explainable AI techniques, our approach not only improves detection accuracy but also provides transparency in model decision-making, offering clear insights into how deepfake content is identified. Our experimental results underscore the effectiveness of XAI-ART, with the model achieving an impressive accuracy of 97.5% in distinguishing between genuine and manipulated images. The recall rate of 96.8% indicates that our model effectively captures the majority of deepfake instances, while the F1-Score of 97.5% demonstrates a well-balanced performance in precision and recall. Importantly, the model maintains high robustness against adversarial attacks, with a minimal accuracy reduction to 96.7% under perturbations

Tumor Categorization Model (TCM) Using Soft Computing Techniques for Providing Efficient Medical Support in Brain Tumor Treatments

Brain cancer identification and segmentation is a prolonged and difficult task in Medical Image Processing, which is most significant for providing appropriate treatment and increase patient’s life span. With the advancements available in medical fields, soft computing techniques are incorporated to accurate detection and classification of brain tumors. Besides brain cancer detection, it is vital to categorize tumor stage based on their features. For that concern, this paper develops a Tumor Categorization Model (TCM) that includes image processing and soft computing techniques. Here, pre-processing is carried out using modified Gabor filter and segmentation process is performed with OTSU thresholding. Following segmentation, region growing is processed based on the pixel intensities of input MRI brain images. Further, Discrete Wavelet Transform is enforced for extorting image features as well as gray-level co-occurence matrix features are also derived for appropriate classifications. Finally, the input MRI images are classified using Boosting Support Vector Machine (BSVM) with the benchmark dataset called DICOM and BraTS dataset. The experimental results demonstrate accurate brain tumor detection and categorization by the efficient incorporation of image processing and soft computing methodologies, provides efficient clinical support in providing treatments.</jats:p