Publications by authors named "K Cengiz"
The Internet of Vehicles (IoV) is a specialized iteration of the Internet of Things (IoT) tailored to facilitate communication and connectivity among vehicles and their environment. It harnesses the power of advanced technologies such as cloud computing, wireless communication, and data analytics to seamlessly exchange real-time data among vehicles, road-side infrastructure, traffic management systems, and other entities. The primary objectives of this real-time data exchange include enhancing road safety, reducing traffic congestion, boosting traffic flow efficiency, and enriching the driving experience.
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- Glaucoma is a progressive eye disease that can lead to permanent blindness, and the paper discusses a new automated diagnosis model that helps doctors quickly classify images as either showing glaucoma or being healthy.
- The model uses an innovative learning technique called fast discrete curvelet transform with wrapping (FDCT-WRP) to extract features from images, combined with principal component and linear discriminant analyses to streamline the data.
- The proposed classification algorithm, which merges modified pelican optimization with extreme learning machine (MOD-POA+ELM), demonstrated high accuracy rates of 93.25% and 96.75% on two standard datasets, while also employing various Explainable AI methods to ensure transparency in the diagnosis.
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- Stroke is a leading cause of illness and death globally, and this study investigates how a year-long pharmaceutical care program impacts stroke patients.
- Conducted as a randomized controlled trial in Türkiye, patients received either enhanced pharmaceutical care (which included medication support) or standard treatment, with outcomes measured after 12 months.
- Results showed significant improvements in medication adherence (86.5% vs 47.1%), quality of life scores (184.9 vs 166.0), and lower stroke recurrence rates (2.2% vs 10.6%) in the care program group compared to usual care.
Atypical neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) can alter the cortex morphology at different levels: (i) a low-order level where cortical regions are examined individually, (ii) a high-order level where the relationship between two cortical regions is considered, and (iii) a multi-view high-order level where the relationship between regions is examined across multiple brain views. In this study, we propose to use the emerging multi-view cortical morphological network (CMN), which is derived from T1-w magnetic resonance imaging (MRI), to profile autistic and typical brains and pursue new ways of fingerprinting 'cortical morphology' at the intersection of 'network neuroscience'. Each CMN view models the pairwise morphological dissimilarity at the connection level using a specific cortical attribute (e.
View Article and Find Full Text PDFThis article presents an evaluation of BukaGini, a stability-aware Gini index feature selection algorithm designed to enhance model performance in machine learning applications. Specifically, the study focuses on assessing BukaGini's effectiveness within the domain of intrusion detection systems (IDS). Recognizing the need for improved feature interaction analysis methodologies in IDS, this research aims to investigate the performance of BukaGini in this context.
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