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Article Citation - WoS: 21Citation - Scopus: 35Deep Learning-Based Computer-Aided Diagnosis (cad): Applications for Medical Image Datasets(Mdpi, 2022) Kadhim, Yezi Ali; Khan, Muhammad Umer; Mishra, AlokComputer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.Review Citation - WoS: 73Citation - Scopus: 118Hybrid Blockchain Platforms for the Internet of Things (iot): a Systematic Literature Review(Mdpi, 2022) Alkhateeb, Ahmed; Catal, Cagatay; Kar, Gorkem; Mishra, AlokIn recent years, research into blockchain technology and the Internet of Things (IoT) has grown rapidly due to an increase in media coverage. Many different blockchain applications and platforms have been developed for different purposes, such as food safety monitoring, cryptocurrency exchange, and secure medical data sharing. However, blockchain platforms cannot store all the generated data. Therefore, they are supported with data warehouses, which in turn is called a hybrid blockchain platform. While several systems have been developed based on this idea, a current state-of-the-art systematic overview on the use of hybrid blockchain platforms is lacking. Therefore, a systematic literature review (SLR) study has been carried out by us to investigate the motivations for adopting them, the domains at which they were used, the adopted technologies that made this integration effective, and, finally, the challenges and possible solutions. This study shows that security, transparency, and efficiency are the top three motivations for adopting these platforms. The energy, agriculture, health, construction, manufacturing, and supply chain domains are the top domains. The most adopted technologies are cloud computing, fog computing, telecommunications, and edge computing. While there are several benefits of using hybrid blockchains, there are also several challenges reported in this study.Article Citation - Scopus: 2The Effects of Paddy Cultivation and Microbiota Members on Arsenic Accumulation in Rice Grain(Mdpi, 2023) Ersoy Omeroglu, Esra; Bayer, Asli; Sudagidan, Mert; Ozalp, Veli Cengiz; Yasa, IhsanAccess to safe food is one of the most important issues. In this context, rice plays a prominent role. Because high levels of arsenic in rice grain are a potential concern for human health, in this study, we determined the amounts of arsenic in water and soil used in the rice development stage, changes in the arsC and mcrA genes using qRT-PCR, and the abundance and diversity (with metabarcoding) of the dominant microbiota. When the rice grain and husk samples were evaluated in terms of arsenic accumulation, the highest values (1.62 ppm) were obtained from areas where groundwater was used as irrigation water, whereas the lowest values (0.21 ppm) occurred in samples from the stream. It was observed that the abundance of the Comamonadaceae family and Limnohabitans genus members was at the highest level in groundwater during grain formation. As rice development progressed, arsenic accumulated in the roots, shoots, and rice grain. Although the highest arsC values were reached in the field where groundwater was used, methane production increased in areas where surface water sources were used. In order to provide arsenic-free rice consumption, the preferred soil, water source, microbiota members, rice type, and anthropogenic inputs for use on agricultural land should be evaluated rigorously.Article Citation - WoS: 2Citation - Scopus: 2Paper-Based Aptasensor Assay for Detection of Food Adulterant Sildenafil(Mdpi, 2024) Kavruk, Murat; Ozalp, Veli CengizSildenafil is used to treat erectile dysfunction and pulmonary arterial hypertension but is often illicitly added to energy drinks and chocolates. This study introduces a lateral flow strip test using aptamers specific to sildenafil for detecting its illegal presence in food. The process involved using graphene oxide SELEX to identify high-affinity aptamers, which were then converted into molecular gate structures on mesoporous silica nanoparticles, creating a unique signaling system. This system was integrated into lateral flow chromatography strips and tested on buffers and chocolate samples containing sildenafil. The method simplifies the lateral flow assay (LFA) for small molecules and provides a tool for signal amplification. The detection limit for these strips was found to be 68.2 nM (31.8 mu g/kg) in spiked food samples.Article Citation - WoS: 10Citation - Scopus: 11Toughening Mechanism Analysis of Recycled Rubber-Based Composites Reinforced With Glass Bubbles, Glass Fibers and Alumina Fibers(Mdpi, 2021) Kabakci, Gamze Cakir; Aslan, Ozgur; Bayraktar, EminRecycling of materials attracts considerable attention around the world due to environmental and economic concerns. Recycled rubber is one of the most commonly used recyclable materials in a number of industries, including automotive and aeronautic because of their low weight and cost efficiency. In this research, devulcanized recycled rubber-based composites are designed with glass bubble microsphere, short glass fiber, aluminum chip and fine gamma alumina fiber (gamma-Al2O3) reinforcements. After the determination of the reinforcements with matrix, bending strength and fracture characteristics of the composite are investigated by three-point bending (3PB) tests. Halpin-Tsai homogenization model is adapted to the rubber-based composites to estimate the moduli of the composites. Furthermore, the relevant toughening mechanisms for the most suitable reinforcements are analyzed and stress intensity factor, K-Ic and critical energy release rate, G(Ic) in mode I are determined by 3PB test with single edge notch specimens. In addition, 3PB tests are simulated by finite element analysis and the results are compared with the experimental results. Microstructural and fracture surfaces analysis are carried out by means of scanning electron microscopy (SEM). Mechanical test results show that the reinforcement with glass bubbles, aluminum oxide ceramic fibers and aluminum chips generally increase the fracture toughness of the composites.Article Citation - WoS: 17Citation - Scopus: 24Iot Platform for Seafood Farmers and Consumers(Mdpi, 2020) Jaeger, Bjorn; Mishra, AlokThere has been a strong growth in aquatic products supported by the global seafood industry. Consumers demand information transparency to support informed decisions and to verify nutrition, food safety, and sustainable operations. Supporting these needs rests on the existence of interoperable Internet of Things (IoT) platforms for traceability that goes beyond the minimum "one up, one down" scheme required by regulators. Seafood farmers, being the source of both food and food-information, are critical to achieving the needed transparency. Traditionally, seafood farmers carry the costs of providing information, while downstream actors reap the benefits, causing limited provision of information. Now, global standards for labelling, data from IoT devices, and the reciprocity of utility from collecting data while sharing them represent great potential for farmers to generate value from traceability systems. To enable this, farmers need an IoT platform integrated with other IoT platforms in the value network. This paper presents a case study of an enterprise-level IoT platform for seafood farmers that satisfies consumers' end-to-end traceability needs while extracting data from requests for information from downstream actors.Article Citation - WoS: 3Citation - Scopus: 4Effects of Cerium Oxide on Kidney and Liver Tissue Damage in an Experimental Myocardial Ischemia-Reperfusion Model of Distant Organ Damage(Mdpi, 2024) Gunes, Isin; Dursun, Ali Dogan; Ozdemir, Cagri; Kucuk, Aysegul; Sezen, Saban Cem; Arslan, Mustafa; Ozer, AbdullahBackground and Objectives: Ischemia-reperfusion (I/R) injury is a process in which impaired perfusion is restored by restoring blood flow and tissue recirculation. Nanomedicine uses cutting-edge technologies that emerge from interdisciplinary influences. In the literature, there are very few in vivo and in vitro studies on how cerium oxide (CeO2) affects systemic anti-inflammatory response and inflammation. Therefore, in our study, we aimed to investigate whether CeO2 administration has a protective effect against myocardial I/R injury in the liver and kidneys. Materials and Methods: Twenty-four rats were randomly divided into four groups after obtaining approval from an ethics committee. A control (group C), cerium oxide (group CO), IR (group IR), and Cerium oxide-IR (CO-IR group) groups were formed. Intraperitoneal CeO2 was administered at a dose of 0.5 mg/kg 30 min before left thoracotomy and left main coronary (LAD) ligation, and myocardial muscle ischemia was induced for 30 min. After LAD ligation was removed, reperfusion was performed for 120 min. All rats were euthanized using ketamine, and blood was collected. Liver and kidney tissue samples were evaluated histopathologically. Serum AST (aspartate aminotransferase), ALT (alanine aminotransaminase), GGT (gamma-glutamyl transferase), glucose, TOS (Total Oxidant Status), and TAS (Total Antioxidant Status) levels were also measured. Results: Necrotic cell and mononuclear cell infiltration in the liver parenchyma of rats in the IR group was observed to be significantly increased compared to the other groups. Hepatocyte degeneration was greater in the IR group compared to groups C and CO. Vascular vacuolization and hypertrophy, tubular degeneration, and necrosis were increased in the kidney tissue of the IR group compared to the other groups. Tubular dilatation was significantly higher in the IR group than in the C and CO groups. TOS was significantly higher in all groups than in the IR group (p < 0.0001, p < 0.0001, and p = 0.006, respectively). However, TAS level was lower in the IR group than in the other groups (p = 0.002, p = 0.020, and p = 0.031, respectively). Renal and liver histopathological findings decreased significantly in the CO-IR group compared to the IR group. A decrease in the TOS level and an increase in the TAS level were found compared to the IR group. The AST, ALT, GGT, and Glucose levels are shown. Conclusions: CeO2 administered before ischemia-reperfusion reduced oxidative stress and ameliorated IR-induced damage in distant organs. We suggest that CeO2 exerts protective effects in the myocardial IR model.Article Citation - WoS: 1Citation - Scopus: 1Recycling Decommissioned Wind Turbine Blades for Post-Disaster Housing Applications(Mdpi, 2025) Turhan, Cihan; Durak, Murat; Saleh, Yousif Abed Saleh; Kalayci, AlperThe growing adoption of wind energy has resulted in an increasing number of decommissioned wind turbine blades, which pose significant disposal challenges due to their size, material composition, and environmental impact. Recycling these blades has thus become essential. To this aim, this study explores the potential of using recycled wind turbine blades in post-disaster housing applications and examines the feasibility of re-purposing these durable composite materials to create robust, cost-effective, and sustainable building solutions for emergency housing. A case study of a post-earthquake relief camp in Hatay, T & uuml;rkiye, affected by the 2023 earthquake, is used for analysis. First, the energy consumption of thirty traditional modular container-based post-disaster housing units is simulated with a dynamic building simulation tool. Then, the study introduces novel wind turbine blade-based housing (WTB-bH) designs developed using the same simulation tool. The energy consumption of these (WTB-bH) units is compared to that of traditional containers. The results indicate that using recycled wind turbine blades for housing not only contributes to waste reduction but also achieves 27.3% energy savings compared to conventional methods. The novelty of this study is in demonstrating the potential of recycled wind turbine blades to offer durable and resilient housing solutions in post-disaster situations and to advocate for integrating this recycling method into disaster recovery frameworks, highlighting its ability to enhance sustainability and resource efficiency in construction. Overall, the output of this study may help to present a compelling case for the innovative reuse of decommissioned wind turbine blades, providing an eco-friendly alternative to traditional waste disposal methods while addressing critical needs in post-disaster scenarios.Article Citation - WoS: 14Citation - Scopus: 26Deep Learning-Based Vehicle Classification for Low Quality Images(Mdpi, 2022) Tas, Sumeyra; Sari, Ozgen; Dalveren, Yaser; Pazar, Senol; Kara, Ali; Derawi, MohammadThis study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effectiveness, the proposed model is tested on a new dataset containing tiny (100 x 100 pixels) and low resolution (96 dpi) vehicle images. The proposed model is then compared with well-known VGG16-based CNN models in terms of accuracy and complexity. Results indicate that although the well-known models provide higher accuracy, the proposed method offers an acceptable accuracy (92.9%) as well as a simple and lightweight solution for vehicle classification in low quality images. Thus, it is believed that this study might provide useful perception and understanding for further research on the use of standard low-cost cameras to enhance the ability of the intelligent systems such as intelligent transportation system applications.Article Citation - WoS: 10Citation - Scopus: 13Stress Detection Using Experience Sampling: a Systematic Mapping Study(Mdpi, 2022) Dogan, Gulin; Akbulut, Fatma Patlar; Catal, Cagatay; Mishra, AlokStress has been designated the "Health Epidemic of the 21st Century" by the World Health Organization and negatively affects the quality of individuals' lives by detracting most body systems. In today's world, different methods are used to track and measure various types of stress. Among these techniques, experience sampling is a unique method for studying everyday stress, which can affect employees' performance and even their health by threatening them emotionally and physically. The main advantage of experience sampling is that evaluating instantaneous experiences causes less memory bias than traditional retroactive measures. Further, it allows the exploration of temporal relationships in subjective experiences. The objective of this paper is to structure, analyze, and characterize the state of the art of available literature in the field of surveillance of work stress via the experience sampling method. We used the formal research methodology of systematic mapping to conduct a breadth-first review. We found 358 papers between 2010 and 2021 that are classified with respect to focus, research type, and contribution type. The resulting research landscape summarizes the opportunities and challenges of utilizing the experience sampling method on stress detection for practitioners and academics.
