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Öğe A Novel CAD Framework with Visual and Textual Interpretability: Multimodal Insights for Predicting Respiratory Diseases(Institute of Electrical and Electronics Engineers Inc., 2024) Mukhlis, Raza; Saleem, Saied; Kwon, Hyunwook; Hussain, Jamil; Aydin, Ahmet Arif; Al-Antari, Mugahed A.Generating textual interpretability using recent advancements in large language models (LLMs) is crucial for enhancing the efficiency of comprehensive computer-aided diagnosis (CAD) systems. This improves transparency between medical staff, intelligent CAD systems, and end-users by creating a trustworthy and effective intermediate medical diagnosis environment. In this paper, an innovative explainable throughout CAD system is introduced, designed to predict diseases from Chest X-rays (CXR) in a comprehensive scenario. The primary goal is to undertake multiple tasks that reduce the burden on medical staff and enrich CAD outcomes, including classification, visual explanations (heatmaps), and textual report generation. The proposed CAD system is developed through eight key steps: Data Collection and Annotation, Data Preparation, Text Vectorizations (Indexing), Visual Encoder, RAG-Fusion, Structural Prompt, XAI LLmTextual Reasoning (LLM Model), and Final Output (LLM textual report, image classification, and heatmap localization). The AI-based CAD system is trained and evaluated using the public benchmark MIMIC-CXR dataset with 14 different classes. The classification performance achieved an overall accuracy of 70 %, precision of 70 %, and F1-score of 0.60 %, while for text report generation, the system obtained an average BERTScore precision of 0.83, RougeL 0.16, and a Meteor score of 0.28. These promising results suggest the potential for further improvement of the CAD system and its applicability to real-world medical tasks. © 2024 IEEE.Öğe An in-depth Examination of Logical Data Models Utilized in Data Storage Systems to Facilitate Data Modeling(Gazi Univ, 2025) Aydin, Ahmet ArifData is a crucial asset in the current era of big data. Organizations collect, store, and analyze data at different scales, velocities, types, and structures to aid their decision-making. Database management systems (DBMS) also play a key role in properly storing large amounts of data. Understanding data models and selecting the appropriate database are essential for achieving scalable storage and efficient query performance. The motivation and main purposes behind this work are to present important characteristics of prominent logical models of data storage systems in one place in order to accomplish the following goals: First, providing a detailed guide on logical data models of DBMS, starting from legacy ones to modern contemporary systems, all in one place; secondly, presenting a consolidated and comparative overview of the characteristics of logical data models for researchers, database designers, and developers of data-intensive systems to guide them in selecting the appropriate data storage system for data modeling tasks; and lastly, presenting an overview of popular data storage systems and their data models to illustrate current trends in DBMS.Öğe CAP-based Examination of Popular NoSQL Database Technologies in Streaming Data Processing(Ieee, 2019) Doguc, Tugba Beril; Aydin, Ahmet ArifNowadays, when the Internet, software and hardware technologies develop rapidly, we are in a digital era where a wide range of resources generate data in different formats and 7/24. It is essential for organizations to quickly process data in near-real time and store it in a scalable form, especially without loss of data. In addition, the importance of streaming data processing due to the desire to get information quickly instantly increases. In this study, the popular NoSQL databases, which are prominent in streaming data processing, have been examined based on CAP's theorem. This work will help software developers in the selection and use of NoSQL databases for streaming data processing.Öğe A Comparative Perspective on Technologies of Big Data Value Chain(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Aydin, Ahmet ArifData is one of the most valuable assets in the digital era because it may conceal hidden valuable insights. Diverse organizations in diverse domains overcome the challenges of the big data value chain by employing a wide range of technologies to meet their needs and achieve a variety of goals to support their decision-making. Due to the significance of data-oriented technologies, this paper presents a model of the big data value chain based on technologies used in the acquisition, storage, and analysis of data. The following are the paper's contributions: First, a model of the big data value chain is developed to illustrate a comprehensive representation of the big data value chain that depicts the relationships between the characteristics of big data and the technologies associated with each category. Second, in contrast to previous research, this paper presents an overview of technologies for each category of the big data value chain. The third contribution of this paper is to assist researchers and developers of data-intensive systems in selecting the appropriate technology for their specific application development use cases by providing examples of applications and use cases from prominent papers in a variety of fields and by describing the capabilities and stages of the technologies being presented so that the right technology is used at the right time in the big data collection, processing, storage, and analytics tasks.Öğe Data modelling for large-scale social media analytics: design challenges and lessons learned(Inderscience Enterprises Ltd, 2020) Aydin, Ahmet Arif; Anderson, Kenneth M.We live in a world of big data; organisations collect, store, and analyse large volumes of data for various purposes. The five V's of big data introduce new challenges for developers to handle when performing data processing and analysis. Indeed, data modelling is one of the most challenging and critical aspects of big data because it determines how data will be structured and stored; these decisions then impact how that data can be processed and analysed. In this paper, we report on designing a data model for storing and analysing Twitter data in support of crisis informatics. In this work, we leverage the data model provided by columnar NoSQL data stores to design column families that can efficiently index, sort, store and analyse large Twitter datasets. In particular, our column families are designed to achieve efficient batch data processing. We evaluate these claims and discuss our future work.Öğe Effect of outside temperature on energy consumption of electric vehicles: Real-time big data and artificial intelligence-aided seahorse optimization approach(Gazi Univ, Fac Engineering Architecture, 2025) Ekici, Yunus Emre; Karadag, Teoman; Akdag, Ozan; Aydin, Ahmet ArifIn calculating the energy consumption of electric vehicles (EVs); it is very important to optimize the consumption efficiency and driving range by considering the outdoor temperature. Studies have shown that very low and very high temperatures reduce engine efficiency and significantly increase energy consumption, while affecting regenerative energy recovery. Therefore, in the presented study, the effects of outdoor temperature on range and energy consumption were investigated using real-time big data obtained from Electric Buses (EO). The field application of the study was carried out with 22 24.7-meter EOs. The EO route was divided into 4 different regions and the energy consumption for each region and the analysis of the outdoor temperature corresponding to this consumption were obtained using regression techniques. First, the energy consumption model was created and the driving cycle was calculated for each region. Then, the driving cycle for the entire route was created and the energy consumption on the route was expressed as a mathematical model. Trilayered Neural Network (TNN) gave the best result in the calculations of the entire route. Finally, the mathematical model obtained as a result of TNN was reconsidered using the SeaHorse optimization method. Considering the analysis for the entire route (R), it was calculated that the most efficient consumption is 3.02 kWh/km and this consumption value can be achieved with a temperature of 21.5oC. This study has become a reference study for other electric vehicle manufacturers in determining the range of their vehicles in different climate conditions.Öğe Energy consumption model with real-time data for driving range extension of electric buses(Elsevier, 2025) Ekici, Yunus Emre; Aydin, Ahmet Arif; Karadag, Teoman; Akdag, Ozan; Ates, AbdullahPreventing range anxiety in electric vehicles (EVs) requires efficient energy use and an accurate estimation of the battery capacity needed for the desired range. A longer range leads to reduced consumption and extends operational activities. Thus, extended driving range can be achieved, promoting a more environmentally sustainable transportation model. This contributes significantly to reducing greenhouse gas emissions and mitigating the environmental impact of transportation. In this study, 250,000 rows of real-world data were collected from electric Trolleybus vehicles for a realistic energy consumption estimation of EVs. First, a mathematical model was obtained from these data using Gaussian Process Regression (GPR) method. To reduce the error rate of this model and increase the accuracy of consumption estimation, it was necessary to re-analyze it with an optimization technique. The accuracy of the consumption prediction model is extremely important for increasing the range of EVs and enabling uninterrupted travels. To solve range anxiety problem, the mathematical model obtained by GPR method is re-optimized by SeaHorse optimization and a new energy consumption prediction model, SHO-EBECM (Seahorse Optimized-Electric Bus Energy Consumption Model), is obtained. The trained SHO-EBECM was applied to 20 real routes of public transportation with internal combustion engine buses in a metropolitan city and the RMSE (Root Mean Square Error) value has been calculated to be between 0.1470 and 0.2920. Based on the achieved error rate, it can be inferred that SHO-EBECM offers a solution with a reduced error rate in comparison to four other optimization techniques. Furthermore, considering global warming, carbon emissions and ecological balance, it is concluded that approximately 12,060 tons/year of CO2, 372.75 tons/ year of NO and NO2 gases can be prevented from being emitted to nature by converting internal combustion engine buses on 20 different routes to electric buses (E-Bus) with the help of SHO-EBECM.Öğe Enhancing electric vehicle range through real-time failure prediction and optimization: Introduction to DHBA-FPM model with an artificial intelligence approach(Elsevier, 2025) Ekici, Yunus Emre; Karadag, Teoman; Akdag, Ozan; Aydin, Ahmet Arif; Tekin, Hueseyin OzanElectrical and mechanical failures in electric vehicles (EVs) during passenger operation cause significant operational losses and elevated energy consumption, amplifying range anxiety. To address this issue, we utilized 250,000 rows of real-time data from electric trolleybuses operating in T & uuml;rkiye to develop a robust artificial intelligence (AI)-based optimization model for failure mitigation. Initially, Tri layered Neural Network (TNN) was employed to create a predictive function for electrical and mechanical failures, followed by comparative analyses across six optimization algorithms widely adopted in failure prediction studies. Among these, the Developed Honey Badger Algorithm with AI Approach (DHBA) emerged as the most effective, achieving a predictive accuracy improvement of 15 % over the standard Honey Badger Algorithm (HBA). The DHBA incorporates a Dynamic Fitness-Distance Balance (DFDB) mechanism and a novel spiral motion feature to enhance search precision, leading to the DHBA-FPM (Developed-Honey Badger Algorithm - Failure Prediction Model). The final DHBA-FPM model was applied to the 10 highest-density bus routes in T & uuml;rkiye to predict and optimize failures. Results indicate that applying the DHBA-FPM model across these routes yielded a 3.96 % average range increase in EVs, extending the total range by approximately 79,200 km annually. It can be concluded that the model could prevent the release of 238.7 tons/year of CO2, NO, and NO2 emissions through its potential to improve both the operational efficiency and sustainability of EVs in public transit networks.Öğe Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review(Springer, 2025) Al-Antari, Mugahed A.; Salem, Saied; Raza, Mukhlis; Elbadawy, Ahmed S.; Butun, Ertan; Aydin, Ahmet Arif; Aydogan, MuratLumbar spinal stenosis (LSS) involves the narrowing of the spinal canal, leading to compression of the spinal cord and nerves in the lower back. Common causes include injuries, degenerative age-related changes, congenital conditions, and tumors, all of which contribute to back pain. Early diagnosis is critical for symptom management, preventing progression, and preserving quality of life. This study systematically reviews AI-based approaches for predicting LSS using MRI axial and sagittal imaging. The review focuses on various AI tasks: detection, segmentation, classification, hybrid approaches, spinal index measurements (SIM), and explainable AI frameworks. The aim is to highlight current knowledge, identify limitations in existing models, and propose future research directions. Following PRISMA guidelines and the PICO method (Population, Intervention, Comparison, Outcome), the review collects data from databases like PubMed, Web of Science, ScienceDirect, and IEEE Xplore (2005-2024). The Rayyan AI tool is used for duplicate removal and screening. The screening process includes an initial review of titles and abstracts, followed by full-text appraisal. The Meta Quality Appraisal Tool (MetaQAT) assesses the quality of selected articles. Of 1323 records, 97 duplicates were removed. After screening, 895 records were excluded, leaving 331 for full-text review. Among these, 184 articles were excluded for lacking AI relevance. Ultimately, 95 key articles (91 technical papers and 4 reviews) were identified for their contributions to AI-based LSS prediction. This review provides a comprehensive analysis of AI techniques in LSS prediction, guiding future research and advancing understanding in areas like explainable AI and large language models (LLMs).Öğe Impact of Outside Temperature on Driving Range and Energy Consumption Using Real-Time Big Data for Electric Buses(Institute of Electrical and Electronics Engineers Inc., 2024) Ekici, Yunus Emre; Karadag, Teoman; Aydin, Ahmet Arif; Akdag, OzanCalculating the energy consumption of electric vehicles (EVs) is crucial to optimize efficiency and driving range, taking into account the outdoor temperature. Research shows that low temperatures significantly increase motor and battery energy consumption while inhibiting regenerative energy recovery, with optimum efficiency achieved at around 20-30 degrees Celsius. Furthermore, the use of heating and cooling systems in different seasons also affects the overall efficiency by affecting battery energy consumption. Therefore, outdoor temperature and driving conditions must be taken into account to accurately assess and optimize the energy consumption of EVs. In this study, the effects of outdoor temperature on range and energy consumption are analyzed using real-time big data from Electric Buses (EB). The field application of the study is based on the EB route currently in operation in Malatya. The EB route is divided into 4 different regions and the energy consumption and the corresponding outdoor temperature for each region are analyzed using regression analysis techniques. As a result of the calculations, it was calculated that the most efficient consumption for the entire EB route is 3,02 kWh / km and this consumption value can be achieved with a temperature of 21,5° C. © 2024 IEEE.Öğe A Novel Energy Consumption Prediction Model of Electric Buses Using Real-Time Big Data From Route, Environment, and Vehicle Parameters(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Ekici, Yunus Emre; Akdag, Ozan; Aydin, Ahmet Arif; Karadag, TeomanElectric vehicles (EVs) have positive impacts on reducing oil dependence and exhaust emissions. However, the range problem of EVs is a factor that raises concerns for individual users and bus operators. For this reason, studies on increasing the range of the electric buses in public transportation is extremely important to ensure optimum operation. In this study, a novel energy consumption model, MTECM (Malatya Trolleybus Energy Consumption Model), is developed using the multi-parameter linear regression method. The real-time big data was collected on the field of Trolleybus vehicles, which have been operated for 8 years in Malatya / Turkiye. Firstly, by calculating the correlation of the parameters affecting this model, the parameters that are suitable for the purpose of our study are determined and regression analysis is performed on the original Trolleybus dataset. A total of 75.497.472 data are analyzed for this model. The RMSE (Root Mean Square Error) of MTECM is calculated as 0.29996. The trained model is applied to the 10 busiest routes in Malatya in terms of passenger density. The RMSE value on these routes is calculated between 0.30299 and 0.31421. Based on the results, with lower error rates, the proposed novel model is more efficient than other studies in the literature. In addition, energy consumption can be calculated for any route planned to establish an electric bus operation with MTECM. Therefore, according to the consumption obtained, the correct determination and selection of parameters that significantly affect the investment cost such as route, vehicle length, engine power, and battery capacity can be made.Öğe Optimization of Proportional-Integral-Derivative Parameters for Speed Control of Squirrel-Cage Motors with Seahorse Optimization(Aves, 2024) Ekici, Yunus Emre; Akdag, Ozan; Aydin, Ahmet Arif; Karadag, TeomanThe two different motion behaviors of seahorses in nature served as inspiration for the seahorse optimization (SHO) method, which is a new metaheuristic swarm intelligence-based approach to solving fundamental engineering problems. In this study, the propo rtion al-in tegra l-der ivati ve (PID) parameters for the simplified speed control of the manipulator joint using squirrel-cage induction motors were calculated with the SHO algorithm. As a result of these calculations, Kp, Ki, and Kd values were obtained as 0.0430, 0.00474, and 0.03254, respectively. Then, the time for the squirrel-cage motor to reach 50 rpm (revolutions per minute) and 90 rpm was calculated with the help of SHO. In PID + SHO operation, the squirrel-cage electric motor reached 50 rpm in 3 seconds and 90 rpm in 8 seconds. In this study, in which the SHO optimization method was used, it was calculated that the acceleration of the squirrel-cage motor and reaching the desired value gave 50% better results compared to the particle swarm optimization algorithm.Öğe An Overview of Quality Attributes for Data Intensive Systems in Crisis Informatics(Ieee, 2017) Aydin, Ahmet ArifIn today's digital world, we have been exposed by tremendous amounts of data generated by numerous sources and the developers of data intensive systems are confronted with the challenges of collecting, analyzing and storing large amounts of data. Dealing with those challenges and designing software systems provides demanded set of quality attributes require engaging with sophisticated approaches, developing clever techniques, and carefully making use of cutting edge technologies. In this paper, first, an outline of crisis informatics research and crisis management phases are introduced, next, an overview of quality attributes for data intensive systems is presented, last, a classification of frequently demanded quality attributes for crisis data intensive system is provided by taking into account crisis management phases and the type of data analytics performed.Öğe Prominent quality attributes of crisis software systems: a literature review(Tubitak Scientific & Technological Research Council Turkey, 2020) Aydin, Ahmet ArifDeveloping software systems to meet user-demanded functionality is critical. Achieving the design goals by providing the needed functionality is a necessary task, and it is about figuring out a proper set of quality attributes and implementing each one by reflecting a complete set of quality attributes. This study presents popular quality attributes of crisis software systems by conducting a literature review. Each crisis software system has been studied by concentrating on crisis management phases where the system is used, design purposes, and the data processing style. The findings of this research shed light on the crisis software development process by presenting a quality attribute-oriented perspective, addressing design challenges, and recommending to developers remedies to handle challenges.Öğe SpineAutoCAD: Multimodal CAD System for Lumbar Spine MRI Analysis and Structured Report Generation(Institute of Electrical and Electronics Engineers Inc., 2025) Salem, Saied; Habib, Afnan; Raza, Mukhlis; Aydin, Ahmet Arif; Gu, Yeong Hyeon; Al-Antari, Mugahed A.Automated analysis of lumbar spine MRI is essential for improving diagnostic consistency and enhancing clinical workflow efficiency in the evaluation of chronic low back pain (CLBP). In this study, we present a computer-aided diagnosis (CAD) framework designed to automate both the analysis of lumbar spine MRI scans and the generation of structured diagnostic reports. The system processes 3D DICOM MRI volumes by extracting mid-sagittal slices for the segmentation of vertebrae and intervertebral discs (IVDs), followed by a 3D cross-projection method to localize the corresponding axial slices. The SegResNet architecture is employed as segmentation model to delineate anatomical structures in both sagittal and axial views. From these segmentations, quantitative measurements of key spinal anatomy are extracted, enabling automated anatomical indices measurements, disorders detection, spinal stenosis severity grading, and evaluation of spinal alignment abnormalities. These assessments serve as the diagnostics information feed into the input prompt to large language model (LLM) for report generation. The proposed system leverages a novel retrievalaugmented generation (RAG) approach that integrates semantic retrieval and knowledge graph-based reasoning to generate detailed, level-specific diagnostic report. The system demonstrates high segmentation performance (Dice: 97.79% sagittal, 93.52% axial) and generates clinically coherent reports using AgenticRAG, achieving a BERT F1-score of 83.58%. These results highlight its effectiveness for accurate, level-specific diagnosis and streamlined clinical reporting. © 2025 IEEE.Öğe Tailoring Energy Efficiency for Urban Electric Buses: The GTECM Model for Enhanced Range and Sustainable Operation Using Real-Time Big Data(Ieee-Inst Electrical Electronics Engineers Inc, 2025) Ekici, Yunus Emre; Karadag, Teoman; Akdag, Ozan; Aydin, Ahmet Arif; Tekin, Huseyn OzanThe increasing depletion of fossil fuels and growing environmental concerns are increasing the need for energy efficient and sustainable solutions, particularly in transport. At this point, especially in public transport, electric vehicles (EVs) offer a promising alternative; however, issues such as range anxiety and energy efficiency require comprehensive solutions. This study introduces the Gauss-based Trolleybus Energy Consumption Model (GTECM) for electric buses, harnessing real-time big data to mitigate range anxiety and enhance energy efficiency. This model employs Gaussian Process Regression to a large-scale dataset including 100,000 entries collected over six months in T & uuml;rkiye. With an overall Root Mean Square Error (RMSE) of 0.013905, GTECM substantially outperforms linear approaches across T & uuml;rkiye's primary routes, exhibiting route-specific RMSE values between 0.28117 and 0.30540. Empirical findings suggest potential energy savings of up to 50%, alongside a 10% extension in driving range, thereby mitigating an estimated 4,220 tons of CO2 and 129.88 tons of NO2 emissions annually. Moreover, the projected amortization period for diesel-to-electric bus conversion stands at 6.83 years, underscoring GTECM's pragmatic utility for sustainable urban transit optimization. The findings of the study can form the basis for future research and guide policy makers and urban planners in the development of more efficient and sustainable transport networks.
