Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to increase the interpretability and explainability of black box Machine Learning (ML) algorithms. LIME typically creates an explanation for a single prediction by any...
We developed an automated system for detecting Fusarium infection in pineapple leaves and fruits using a two-input onvolutional neural network. Implemented on a Raspberry Pi 5 with a high-quality camera, the system analyzes image pairs, fruit, and le...
The Controller Area Network (CAN) facilitates efficient communication among vehicle components. While it ensures fast and reliable data transmission, its lightweight design makes it susceptible to data manipulation in the absence of security layers....
An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on ho...
The rapid advancement of Machine Learning (ML) has significantly transformed polymer science by enabling efficient prediction and design of polymer properties through high-throughput screening. However, current methods still struggle with nonlinear S...
There is a lot of research on the neural models used for short-term load forecasting (STLF), which is crucial for improving the sustainable operation of energy systems with increasing technical, economic, and environmental requirements. Neural networ...
This paper focuses on explaining changes over time in globally sourced annual temporal data with the specific objective of identifying features in black-box models that contribute to these temporal shifts. Leveraging local explanations, a part of exp...
According to recent global public health studies, chronic kidney disease (CKD) is becoming more and more recognized as a serious health risk as many people are suffering from this disease. Machine learning techniques have demonstrated high efficiency...
Accurate classification of defects in the semiconductor manufacturing process is critical for improving yield and ensuring quality. While previous works have mainly focused on improving classification accuracy, we propose a model that can simultaneou...
Diabetes, a chronic condition caused by insufficient insulin production in the pancreas, presents significant health risks. Its increasing global prevalence necessitates the development of accurate and efficient predictive algorithms to support timel...
Background: Diabetes remains a major global health challenge, contributing significantly to premature mortality due to its potential progression to organ failure if not diagnosed early. Traditional diagnostic approaches are subject to human error, hi...
Universities compete for funding, and their positions depend on the results of national assessments and rankings, which are expensive to produce and based on difficult-to-predict expert opinions. Assessment results have a significant impact on a univ...
The escalating global incidence of malware presents critical cybersecurity threats to manufacturing, automation, and industrial process control systems. Given the fast-developing web applications and IoT devices in use by industry operations, securin...
Background: Industry 4.0’s development requires digitalized manufacturing through Predictive Maintenance (PdM) because such practices decrease equipment failures and operational disruptions. However, its effectiveness is hindered by three key c...
An important hurdle in medical diagnostics is the high-quality and interpretable classification of audio signals. In this study, we present an image-based representation of infant crying audio files to predict abnormal infant cries using a vision tra...
Customers heavily depend on reviews for product information. Fake reviews may influence the perception of product quality, making online reviews less effective. ChatGPT’s (GPT-3.5 and GPT-4) ability to generate human-like reviews and responses...
The increasing complexity and importance of medical data in improving patient care, advancing research, and optimizing healthcare systems led to the proposal of this study, which presents a novel methodology by evaluating the sensitivity of artificia...
28 February 2023
In the last few years, many types of research have been conducted on the most harmful pandemic, COVID-19. Machine learning approaches have been applied to investigate chest X-rays of COVID-19 patients in many respects. This study focuses on the deep...
Many methods have been proposed for visualizing and interpreting the results of artificial intelligence (AI) algorithms. AI explainability (XAI) methods vary in mathematical basis, effectiveness, and scope of application. Knowing this, an important q...
Deep learning has obtained remarkable achievements in computer vision, especially image and video processing. However, in synthetic aperture radar (SAR) image recognition, the application of DNNs is usually restricted due to data insufficiency. To au...
COVID-19 is an infectious disease that has greatly affected worldwide healthcare systems, due to the high number of cases and deaths. As COVID-19 patients may develop cardiac comorbidities that can be potentially fatal, electrocardiographic monitorin...
Driver behavior is a critical factor in road safety, contributing to the majority of traffic crashes. The i-DREAMS project introduced the concept of a Safety Tolerance Zone (STZ) to enhance driving safety through real-time and post-trip interventions...
Diabetic retinopathy (DR) remains a major cause of vision loss in patients with diabetes, and earlier recognition of retinal vascular abnormalities may improve risk stratification and clinical follow-up. Optical coherence tomography angiography (OCTA...
Background: First-trimester screening for preterm preeclampsia relies on validated competing-risks models integrating maternal characteristics with biochemical and biophysical markers to generate individualized risk estimates. Large language models (...
South Africa faces significant challenges in monitoring air pollution from different provinces due to the sparse nature of the sensor network and heterogeneous pollutant sources. Notably, some provinces continue to record a limited amount of data on...
Ensemble Learning (EL) has been used for almost ten years to classify heart diseases, but it is still difficult to grasp how the “black boxes”, or non-interpretable models, behave inside. Predicting heart disease is crucial to healthcare,...
1 April 2023
The reduction of childhood mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In ligh...
Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis. Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow Universi...
Recently, explainability in machine and deep learning has become an important area in the field of research as well as interest, both due to the increasing use of artificial intelligence (AI) methods and understanding of the decisions made by models....
1 September 2023
Artificial intelligence is gaining interest among clinicians, but its results are difficult to be interpreted, especially when dealing with survival outcomes and censored observations. Explainable machine learning (XAI) has been recently extended to...
Air pollution remains one of the environmental issues affecting some countries, which leads to health issues globally. Though several machine learning and deep learning models are used to analyze air pollutants, model interpretability is a challenge....
Automated detection of defects in welds are inevitable in the assurance of structural integrity, but this faces serious challenges due to the microscopic characteristics of the discontinuities, low visual contrast and infrequent occurrence of defect...
27 February 2023
Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing...
Alzheimer’s disease (AD) remains a critical global health challenge for which early diagnosis is essential for effective intervention. However, AD detection is a challenging and complex task due to the scarcity of labeled clinical data, class i...
Tomato cultivation is a vital agricultural practice worldwide, yet it faces significant challenges due to various diseases that adversely affect crop yield and quality. This paper presents a novel tomato disease detection system within an operational...
Machine learning (ML) has become a cornerstone of critical applications, but its vulnerability to data poisoning attacks threatens system reliability and trustworthiness. Prior studies have begun to investigate the impact of data poisoning and propos...
Skin cancer is one of the most prevalent of all cancers. Because of its being widespread and externally observable, there is a potential that machine learning models integrated into artificial intelligence systems will allow self-screening and automa...
Fossil fuels are crucial to the global energy supply, with pipelines being a vital transportation method. However, these vital assets are highly susceptible to pitting corrosion, an insidious form of degradation that can lead to catastrophic failures...
Credit risk is one of the most important issues in the rapidly growing and developing finance sector. This study utilized a dataset containing real information about the bill payments of individuals who made transactions with a payment institution op...
The increase in malicious cyber activities has generated the need to produce effective tools for the field of digital forensics and incident response. Artificial intelligence (AI) and its fields, specifically machine learning (ML) and deep learning (...
Although machine learning (ML) models are widely used in many fields, their prediction processes are often hard to understand. This lack of transparency makes it harder for people to trust them, especially in high-stakes fields like healthcare and fi...
Deep learning researchers believe that as deep learning models evolve, they can perform well on many tasks. However, the complex parameters of deep learning models make it difficult for users to understand how deep learning models make predictions. I...
The lack of interpretability in AI-based intrusion detection systems poses a critical barrier to their adoption in forensic cybersecurity, which demands high levels of reliability and verifiable evidence. To address this challenge, the integration of...
Interpretability is essential for deploying deep learning (DL) models in electroencephalography (EEG)-based neurotechnology, particularly in brain–computer interfaces and clinical decision-support settings. Existing post hoc explainable artific...
Accurately predicting well productivity is crucial for optimizing gas production and maximizing recovery from tight gas reservoirs. Machine learning (ML) techniques have been applied to build predictive models for the well productivity, but their hig...
Background. Leukemic relapse remains the primary cause of treatment failure and death after allogeneic hematopoietic stem cell transplant. Changes in post-transplant donor chimerism have been identified as a predictor of relapse. A better predictive...
Obesity is ranked as one of the biggest health challenges facing humanity today. Globally, the number of obese people has almost tripled since 1975, and this lifestyle disease currently affects hundreds of millions of adults who suffer from major hea...
Sarcopenia is an age-associated condition characterized by a muscle mass and function decline. This condition poses significant health risks for the elderly. This study developed a machine-learning model to predict sarcopenia using data from 664 part...
Optimization-based design tools for energy systems often require a large set of parameter assumptions, e.g., about technology efficiencies and costs or the temporal availability of variable renewable energies. Understanding the influence of all these...
Hyperkalemia is a major complication of chronic kidney disease (CKD). However, owing to the absence of specific symptoms in its early stages, hyperkalemia frequently remains undiagnosed. This study aimed to develop a machine learning model for predic...
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