The classification of Capsicum spp. varieties is often hindered by their morphological similarities, making accurate identification a challenging task. To address this issue, this study applies a hybrid computational approach that combines data dimensionality reduction techniques using Principal Component Analysis and Factor Analysis with various supervised Machine Learning algorithms. The dataset, which is unprecedented in the literature and was collected under controlled agricultural conditions, enables a robust evaluation of models including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest, Decision Tree, and Gradient Boosting. Model performance was assessed... More >

The healthcare sector has both opportunities and challenges as a result of the rapid expansion of unstructured clinical text data in electronic health records (EHRs). Physician notes, reports from radiologists, and summaries of discharge are examples of narrative medical documents from which relevant and actionable information can be extracted using clinical text analytics driven by Natural Language Processing (NLP). Named entity recognition, conceptual normalization, relation extraction, and temporal reasoning are just a few of the core methods and approaches in clinical natural language processing that are thoroughly covered in this paper. It covers cutting-edge deep learning models like B... More >

Deep learning is a great success primarily because it encodes large amounts of data and manipulates billions of model parameters. Despite this, it is challenging to deploy these cumbersome deep models on devices with limited resources, such as mobile phones and embedded devices, due to the high computational complexity and the amount of storage required. Various techniques are available to compress and accelerate models for this purpose. Knowledge distillation is a novel technique for model compression and acceleration, which involves learning a small student model from a large teacher model. Then, that student network is fine-tuned on any downstream task to be applicable for resource-constr... More >

The paper carries out a comparative study that is based on the use of credibility theory to examine pentagonal and hexagonal fuzzy membership functions of machine learning systems. These fuzzy memberships can be used to manage the uncertainty and imprecision of a data driven-model which allows better decision-making in the case of vague or incomplete information. The credibility theory is used to determine quantitatively the reliability of the inferences obtained through each function. Both the membership functions are modelled, incorporated in machine learning framework and tested on randomly generated as well as application specific datasets. The results obtained indicate that the performa... More >

This paper investigates the core principles of quantum computation, providing an in-depth understanding of quantum phenomena and illustrating how these principles form the scientific foundation of the field. The pivotal physical concepts, such as properties of subatomic particles, including electrons and photons, as well as their mathematical description through linear algebra are examined. It focuses on the qubit, the quantum analogue of a classical bit, featuring properties like superposition, entanglement, and wave function collapse, which redefine the traditional concept of information processing. The mathematical structures that underlie quantum system modelling—vector spaces, tensor... More >