Image watermarking is a robust solution for solving key issues like copyright protection and proof of ownership of digital data. Existing schemes of image watermarking mostly used grayscale or binary images as embedded watermarks, while only a few watermarking schemes are developed for color images. In this paper, we propose a novel robust semi-blind image watermarking scheme based on finite ridgelet transform (FRT), discrete wavelet transform (DWT), singular value decomposition (SVD), particle swarm optimization (PSO), and Arnold transform to protect copyright and verify the authenticity of color images. Firstly, the color image is converted from RGB to YCbCr color space, and the luminance component (Y) is taken into account to insert the watermark data. In this study, the principal component (PC) of the watermark image is directly inserted into the corresponding singular value of the cover image by the scaling factor to avoid the false positive problem (FPP). To further improve security, Arnold transform is applied to process the Y channel of the watermark image before inserting it in the cover image. Besides, PSO optimizes the embedding factor matrices. The qualitative evaluation factors like peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are used to assess the visual quality, while normalized correlation coefficient (NCC) is used to assess the resemblance between the watermarked and the restored watermarked images. The performance of the proposed scheme is evaluated using geometric, non-geometric, and combinational attacks, and its comparison is performed with different image watermarking schemes to prove its robustness.

In order to lower the dependence on textual annotations for image searches, the content based image retrieval (CBIR) has become a popular topic in computer vision. A wide range of CBIR applications consider classification techniques, such as artificial neural networks (ANN), support vector machines (SVM), etc. to understand the query image content to retrieve relevant output. However, in multi-class search environments, the retrieval results are far from optimal due to overlapping semantics amongst subjects of various classes. The classification through multiple classifiers generate better results, but as the number of negative examples increases due to highly correlated semantic classes, classification bias occurs towards the negative class, hence, the combination of the classifiers become even more unstable particularly in one-against-all classification scenarios. In order to resolve this issue, a genetic algorithm (GA) based classifier comity learning (GCCL) method is presented in this paper to generate stable classifiers by combining ANN with SVMs through asymmetric and symmetric bagging. The proposed approach resolves the classification disagreement amongst different classifiers and also resolves the class imbalance problem in CBIR. Once the stable classifiers are generated, the query image is presented to the trained model to understand the underlying semantic content of the query image for association with the precise semantic class. Afterwards, the feature similarity is computed within the obtained class to generate the semantic response of the system. The experiments reveal that the proposed method outperforms various state-of-the-art methods and significantly improves the image retrieval performance.

The most unexpected and fatal kind of cancer, melanoma, has been on the rise in its spread to different parts of the body. Early detection can significantly reduce its fatality rate. The primary stages of melanoma for its identification by the unaided eye are a difficult task that demands extensive training and understanding. Due to a lack of qualified dermatologists, a computerized and automated method is required to correctly detect melanoma. This study achieved this feat through a proposed system that can effectively detect and classify melanoma as benign or malignant. The process begins with image template matching by using normalized cross-correlation technique induction to mark the infected area of skin lesion as the region of interest (ROI) from ISIC datasets 2017, 2019, and 2020 dermoscopic images. Our novel model dynamically calculated number of clusters is assigned to the k -means clustering algorithm, and ROI is extracted. Histogram equalization is applied to the output image for contrast enhancement. Hu Moment method is implemented for shape classification and part recognition from the segmented image. GLCM-based Haralick feature extractor is used in the proposed system to extract textural features generating the feature vector from the segmented skin lesion. It leads the classifier to identify skin lesions as cancerous or non-cancerous. Random Forest, Decision Tree, Support Vector Machine (SVM), Gaussian Naïve Bayes, Logistic Regression, and K-Nearest Neighbor (KNN) Classifiers are used for classification. The KNN, SVM and Random Forest was successful to attain the highest accuracy of 99.29%, 99.38% and 99.46% on the given dermoscopic images datasets ISIC-2019, ISIC-2020 and ISIC-2017 respectively. The proposed method, normalized cross correlation-based k-means clustering model, was found to be more robust and accurate than existing methods and incorporates much more feature information from the images.

Topic modeling is an effective text mining and information retrieval approach to organizing knowledge with various contents under a specific topic. Text documents in form of news articles are increasing very fast on the web. Analysis of these documents is very important in the fields of text mining and information retrieval. Meaningful information extraction from these documents is a challenging task. One approach for discovering the theme from text documents is topic modeling but this approach still needs a new perspective to improve its performance. In topic modeling, documents have topics and topics are the collection of words. In this paper, we propose a new k-means topic modeling (KTM) approach by using the k-means clustering algorithm. KTM discovers better semantic topics from a collection of documents. Experiments on two real-world Reuters 21578 and BBC News datasets show that KTM performance is better than state-of-the-art topic models like LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). The KTM is also applicable for classification and clustering tasks in text mining and achieves higher performance with a comparison of its competitors LDA and LSA.

Alopecia Areata is an autoimmune disorder where the body’s immune system attacks normal cells instead of intruders, leading to hair loss. If not detected early, it can progress to complete scalp baldness (Alopecia Totalis) or total body hair loss (Alopecia Universalis). Therefore, early detection of Alopecia Areata is crucial. Computer vision and deep learning techniques have been used for the last few years in the field of dermatology to detect different relevant diseases. We proposed a robust feature fusion technique, named AlopeciaDet for the timely detection of Alopecia Areata using camera images instead of dermoscopic images that require specialized equipment. AlopeciaDet combines Corner Rhombus Shape HOG (CRSHOG) features with those extracted from the ResNet-50 pre-trained model to detect Alopecia Areata with high accuracy by using the Dermenet dataset. The geometric properties of rhombus shapes make them useful for recognizing patterns in an image. HOG captures local object appearance and shape by computing the distribution of intensity gradients in localized portions of the image. We combined these characteristics into CRSHOG. Alopecia Areata, on the other hand, is characterized by distinctive patterns and shapes of hair loss, with the most common feature being round or oval-shaped patches. These patches can vary in size and usually have well-defined, sharp edges. Consequently, our proposed CRSHOG significantly improves the extraction of local information from images of affected areas. It achieves this by integrating sign and magnitude data, thereby enhancing discrimination capabilities for texture classification tasks. Finally, the magnitudes and directions of these pixel values are calculated. We achieved an accuracy of 99.45% with an error rate of 0.55% using Artificial Neural Network. These results surpass the accuracy of current state-of-the-art techniques in this field.