Vol. 2 No. 1 (2024)

Open Access

Article

Article ID: 450

Medical image classification using a quantified hazard ratio and a multilayer fuzzy approach

by Kishore Kumar Akula, Monica Akula, Alexander Gegov

Computing and Artificial Intelligence, Vol.2, No.1, pp. 2(1); , 2024; 67 Views, 59 PDF Downloads

We previously developed two AI-based medical automatic image classification tools using a multi-layer fuzzy approach (MFA and MCM) to convert image-based abnormality into a quantity. However, there is currently limited research on using diagnostic image assessment tools to statistically predict the hazard due to the disease. The present study introduces a novel approach that addresses a substantial research gap in the identification of hazard or risk associated with a disease using an automatically quantified image-based abnormality. The method employed to ascertain hazard in an image-based quantified abnormality was the cox proportional hazard (PH) model, a unique tool in medical research for identifying hazard related to covariates. MFA was first used to quantify the abnormality in CT scan images, and hazard plots were utilized to visually represent the hazard risk over time. Hazards corresponding to image-based abnormality were then computed for the variables, ‘gender,’ ‘age,’ and ‘smoking-status’. This integrated framework potentially minimizes false negatives, identifies patients with the highest mortality risk and facilitates timely initiation of treatment. By utilizing pre-existing patient images, this method could reduce the considerable costs associated with public health research and clinical trials. Furthermore, understanding the hazard posed by widespread global diseases like COVID-19 aids medical researchers in prompt decision-making regarding treatment and preventive measures.

Open Access

Article

Article ID: 441

Identifying voices using convolution neural network models AlexNet and ResNet

by Abdulaziz Alhowaish Luluh, Muniasamy Anandhavalli

Computing and Artificial Intelligence, Vol.2, No.1, pp. 2(1); , 2024; 39 Views, 26 PDF Downloads

Deep learning (DL) techniques which implement deep neural networks became popular due to the increase of high-performance computing facilities. DL achieves higher power and flexibility due to its ability to process many features when it deals with unstructured data. DL algorithm passes the data through several layers; each layer is capable of extracting features progressively and passes it to the next layer. Initial layers extract low-level features, and succeeding layers combine features to form a complete representation. This research attempts to utilize DL techniques for identifying sounds. The development in DL models has extensively covered classification and verification of objects through images. However, there have not been any notable findings concerning identification and verification of the voice of an individual from different other individuals using DL techniques. Hence, the proposed research aims to develop DL techniques capable of isolating the voice of an individual from a group of other sounds and classify them based on the use of convolutional neural networks models AlexNet and ResNet, that are used in voice identification. We achieved the classification accuracy of ResNet and AlexNet model for the problem of voice identification is 97.2039 % and 65.95% respectively, in which ResNet model achieves the best result.

Open Access

Article

Article ID: 440

A comparison of cepstral and spectral features using recurrent neural network for spoken language identification

by Irshad Ahmad Thukroo, Rumaan Bashir, Kaiser Javeed Giri

Computing and Artificial Intelligence, Vol.2, No.1, pp. 2(1); , 2024; 55 Views, 107 PDF Downloads

Spoken language identification is the process of confirming labels regarding the language of an audio slice regardless of various features such as length, ambiance, duration, topic or message, age, gender, region, emotions, etc. Language identification systems are of great significance in the domain of natural language processing, more specifically multi-lingual machine translation, language recognition, and automatic routing of voice calls to particular nodes speaking or knowing a particular language. In his paper, we are comparing results based on various cepstral and spectral feature techniques such as Mel-frequency Cepstral Coefficients (MFCC), Relative spectral-perceptual linear prediction coefficients (RASTA-PLP), and spectral features (roll-off, flatness, centroid, bandwidth, and contrast) in the process of spoken language identification using Recurrent Neural Network-Long Short Term Memory (RNN-LSTM) as a procedure of sequence learning. The system or model has been implemented in six different languages, which contain Ladakhi and the five official languages of Jammu and Kashmir (Union Territory). The dataset used in experimentation consists of TV audio recordings for Kashmiri, Urdu, Dogri, and Ladakhi languages. It also consists of standard corpora IIIT-H and VoxForge containing English and Hindi audio data. Pre-processing of the dataset is done by slicing different types of noise with the use of the Spectral Noise Gate (SNG) and then slicing into audio bursts of 5 seconds duration. The performance is evaluated using standard metrics like F1 score, recall, precision, and accuracy. The experimental results showed that using spectral features, MFCC and RASTA-PLP achieved an average accuracy of 76%, 83%, and 78%, respectively. Therefore, MFCC proved to be the most convenient feature to be exploited in language identification using a recurrent neural network long short-term memory classifier.

Open Access

Article

Article ID: 416

Revolutionizing Neurosurgery and Neurology: The transformative impact of artificial intelligence in healthcare

by Habib Hamam

Computing and Artificial Intelligence, Vol.2, No.1, pp. 2(1); , 2024; 62 Views, 29 PDF Downloads

The integration of artificial intelligence (AI) has brought about a paradigm shift in the landscape of Neurosurgery and Neurology, revolutionizing various facets of healthcare. This article meticulously explores seven pivotal dimensions where AI has made a substantial impact, reshaping the contours of patient care, diagnostics, and treatment modalities. AI’s exceptional precision in deciphering intricate medical imaging data expedites accurate diagnoses of neurological conditions. Harnessing patient-specific data and genetic information, AI facilitates the formulation of highly personalized treatment plans, promising more efficacious therapeutic interventions. The deployment of AI-powered robotic systems in neurosurgical procedures not only ensures surgical precision but also introduces remote capabilities, mitigating the potential for human error. Machine learning models, a core component of AI, play a crucial role in predicting disease progression, optimizing resource allocation, and elevating the overall quality of patient care. Wearable devices integrated with AI provide continuous monitoring of neurological parameters, empowering early intervention strategies for chronic conditions. AI’s prowess extends to drug discovery by scrutinizing extensive datasets, offering the prospect of groundbreaking therapies for neurological disorders. The realm of patient engagement witnesses a transformative impact through AI-driven chatbots and virtual assistants, fostering increased adherence to treatment plans. Looking ahead, the horizon of AI in Neurosurgery and Neurology holds promises of heightened personalization, augmented decision-making, early intervention, and the emergence of innovative treatment modalities. This narrative is one of optimism and collaboration, depicting a synergistic partnership between AI and healthcare professionals to propel the field forward and significantly enhance the lives of individuals grappling with neurological challenges. This article provides an encompassing view of AI’s transformative influence in Neurosurgery and Neurology, highlighting its potential to redefine the landscape of patient care and outcomes.

Open Access

Article

Article ID: 535

Enhancing user experience in large language models through human-centered design: Integrating theoretical insights with an experimental study to meet diverse software learning needs with a single document knowledge base

by Yuchen Wang, Yin-Shan Lin, Ruixin Huang, Jinyin Wang, Sensen Liu

Computing and Artificial Intelligence, Vol.2, No.1, pp. 2(1), 535; , 2024; 96 Views, 35 PDF Downloads, 0 Supp. file Downloads, 0 Supp. file Downloads

This paper begins with a theoretical exploration of the rise of large language models (LLMs) in Human-Computer Interaction (HCI), their impact on user experience (HX) and related challenges. It then discusses the benefits of Human-Centered Design (HCD) principles and the possibility of their application within LLMs, subsequently deriving six specific HCD guidelines for LLMs. Following this, a preliminary experiment is presented as an example to demonstrate how HCD principles can be employed to enhance user experience within GPT by using a single document input to GPT’s Knowledge base as new knowledge resource to control the interactions between GPT and users, aiming to meet the diverse needs of hypothetical software learners as much as possible. The experimental results demonstrate the effect of different elements’ forms and organizational methods in the document, as well as GPT’s relevant configurations, on the interaction effectiveness between GPT and software learners. A series of trials are conducted to explore better methods to realize text and image displaying, and jump action. Two template documents are compared in the aspects of the performances of the four interaction modes. Through continuous optimization, an improved version of the document was obtained to serve as a template for future use and research.