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 >

Deep learning has substantially enhanced facial emotion recognition, an essential element of human--computer interaction. This study evaluates the performance of multiple architectures, including a custom CNN, VGG-16, ResNet-50, and a hybrid CNN-LSTM framework, across FER2013 and CK+ datasets. Preprocessing steps involved grayscale conversion, image resizing, and pixel normalization. Experimental results show that ResNet-50 achieved the highest accuracy on FER2013 (76.85%), while the hybrid CNN-LSTM model attained superior performance on CK+ (92.30%). Performance metrics such as precision, recall, and F1-score were used for evaluation. Findings highlight the trade-off between computational e... More >

The Autism Spectrum Disorder (ASD) diagnosis and detection in its initial stages is a more complex issue in the face of the wide-ranging, diverse nature and causes. Subsequent literature inclined towards a possible correlation of gut microbiome with ASD, and its disclosure presents a more promising attribute for imminent discovery conduits. The dataset on gut microbiome associated with ASD focuses specifically on the microbial compositions obtained through 16S rRNA sequencing. This study presents a novel method that integrates Artificial Intelligence employing various Machine Learning (ML) robust classifiers such that Support Vector Machines (SVM), Random Forest, k-Nearest Neighbors (KNN), L... More >

In the age of increasing machine-mediated communication, the ability to detect emotional nuances in speech has become a critical competency for intelligent systems. This paper presents a robust Speech Emotion Recognition (SER) framework that integrates a hybrid deep learning architecture with a real-time web-based inference interface. Utilizing the RAVDESS dataset, the proposed pipeline encompasses comprehensive preprocessing, data augmentation techniques, and feature extraction based on Mel-Frequency Cepstral Coefficients (MFCCs), Chroma features, and Mel-spectrograms. A comparative experiment was run against a standard machine learning classifier such as K-Nearest Neighbors (KNN), Support... More >

Air pollution has emerged as a critical challenge, directly affecting human health, urban sustainability, and climate systems. Traditional air-quality index (AQI) prediction models often struggle to provide timely alerts because they are not very sensitive to changes over time and are hard to understand. This paper proposes a Neuro-Inspired Alert System for Air Quality Prediction (NAS--AQP) that incorporates an ensemble learning approach using voting regression to enhance input quality, followed by classification through a Spiking Neural Network (SNN). The system is designed such that it captures the temporal and nonlinear relationships between air pollutants such as Nitrogen Dioxide ($NO_2$... More >