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The analysis of chromosomes, known as karyotyping, is essential in diagnosing various human genetic disorders and chromosomal aberrations. It can detect a variety of genetic diseases and provide a deeper insight into the human body. However, the process of manual karyotyping is highly time-consuming and requires accomplished professionals with a deep understanding in the field. An automated process is thus highly desirable to assist cytogeneticists and mitigate the cognitive load procured during karyotyping. With that intention, a similarity learning approach is proposed in this paper using ‘Triplet Loss’ for procuring high-dimensional embeddings. The Offline Triplet Loss, Semi-Hard Online mining, and associated hyperparameters are thoroughly tested and explored, and the obtained embeddings are used to classify the images into their respective chromosome classes and Denver groups. A comparative analysis on various embedding-classifying algorithms such as Multi-Layer Perceptron (MLP) and Nearest Neighbours is also demonstrated in this paper, along with experiments on associated distance metrics. The proposed methodologies deliver a superlative performance when compared to a baseline Convolutional Neural Network (CNN), on a publicly available chromosome classification dataset.

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The Coronavirus Pandemic triggered by SARS-CoV-2 has wreaked havoc on the planet and is expanding exponentially. While scanning methods, including CT scans and chest X-rays, are commonly used, artificial intelligence implementations are also deployed for COVID-based pneumonia detection. Due to image biases in X-ray data, bilateral filtration and Histogram Equalization are used followed by lung segmentation by a U-Net, which successfully segmented 83.2\% of the collected dataset. The segmented lungs are fed into a Quadruplet Network with SqueezeNet encoders for increased computational efficiency and high-level embeddings generation. The embeddings are computed using a Multi-Layer Perceptron and visualized by T-SNE (T-Distributed Stochastic Neighbor Embedding) scatterplots. The proposed research results in a 94.6\% classifying accuracy which is 2\% more than the baseline Convolutional Neural Network and a 90.2\% decrease in prediction time.

Copy move forgeries are a type of digital retouching that takes place in the field of image modification. This type of retouching occurs when one element of a picture is replicated over to a different segment of the photograph. This is typically done in an effort to mask or cover up undesirable qualities of the photograph thus copy move forgeries are a form of image modification. When it comes to digital picture modalities, copy move forging is by far the most prevalent form of picture manipulation that may take place. This is mainly observed when certain elements or segments of the recorded file are duplicated in the recorded file. The identification and localization of forgeries is a prominent problem that has piqued the attention of scholars working in the field of digital forensics and continues to do so. The identification of photo forgery has previously been the subject of several methodologies and uncountable publications, all of which were created and published. An abridged description of the present methodology and approaches is provided in this document, along with a survey and condensed summaries of current research and successes, among other things.