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Article Citation - WoS: 11Citation - Scopus: 13Challenges in Agile Software Maintenance for Local and Global Development: an Empirical Assessment(Mdpi, 2023) Almashhadani, Mohammed; Mishra, Alok; Yazici, Ali; Younas, MuhammadAgile methods have gained wide popularity recently due to their characteristics in software development. Despite the success of agile methods in the software maintenance process, several challenges have been reported. In this study, we investigate the challenges that measure the impact of agile methods in software maintenance in terms of quality factors. A survey was conducted to collect data from agile practitioners to establish their opinions about existing challenges. As a result of the statistical analysis of the data from the survey, it has been observed that there are moderately effective challenges in manageability, scalability, communication, collaboration, and transparency. Further research is required to validate software maintenance challenges in agile methods.Article Citation - WoS: 16Citation - Scopus: 20Forecasting Air Quality in Tripoli: an Evaluation of Deep Learning Models for Hourly Pm2.5 Surface Mass Concentrations(Mdpi, 2023) Esager, Marwa Winis Misbah; Unlu, Kamil DemirberkIn this article, we aimed to study the forecasting of hourly PM2.5 surface mass concentrations in the city of Tripoli, Libya. We employed three state-of-the-art deep learning models, namely long short-term memory, gated recurrent unit, and convolutional neural networks, to forecast PM2.5 levels using univariate time series methodology. Our results revealed that the convolutional neural networks model performed the best, with a coefficient of variation of 99% and a mean absolute percentage error of 0.04. These findings provide valuable insights into the use of deep learning models for forecasting PM2.5 and can inform decision-making regarding air quality management in the city of Tripoli.Article Citation - WoS: 9Citation - Scopus: 12On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection(Mdpi, 2022) Mohamed, Ismail; Dalveren, Yaser; Catak, Ferhat Ozgur; Kara, AliIn the development of radiofrequency fingerprinting (RFF), one of the major challenges is to extract subtle and robust features from transmitted signals of wireless devices to be used in accurate identification of possible threats to the wireless network. To overcome this challenge, the use of the transient region of the transmitted signals could be one of the best options. For an efficient transient-based RFF, it is also necessary to accurately and precisely estimate the transient region of the signal. Here, the most important difficulty can be attributed to the detection of the transient starting point. Thus, several methods have been developed to detect transient start in the literature. Among them, the energy criterion method based on the instantaneous amplitude characteristics (EC-a) was shown to be superior in a recent study. The study reported the performance of the EC- a method for a set of Wi-Fi signals captured from a particular Wi-Fi device brand. However, since the transient pattern varies according to the type of wireless device, the device diversity needs to be increased to achieve more reliable results. Therefore, this study is aimed at assessing the efficiency of the EC-a method across a large set ofWi-Fi signals captured from variousWi-Fi devices for the first time. To this end, Wi-Fi signals are first captured from smartphones of five brands, for a wide range of signalto-noise ratio (SNR) values defined as low (3 to 5 dB), medium (5 to 15 dB), and high (15 to 30 dB). Then, the performance of the EC-a method and well-known methods was comparatively assessed, and the efficiency of the EC-a method was verified in terms of detection accuracy.Article Citation - WoS: 20Citation - Scopus: 27Career in Cloud Computing: Exploratory Analysis of In-Demand Competency Areas and Skill Sets(Mdpi, 2022) Ozyurt, Ozcan; Gurcan, Fatih; Dalveren, Gonca Gokce Menekse; Derawi, MohammadThis study aims to investigate up-to-date career opportunities and in-demand competence areas and skill sets for cloud computing (CC), which plays a crucial role in the rapidly developing teleworking environments with the COVID-19 pandemic. In this paper, we conducted a semantic content analysis on 10,161 CC job postings using semi-automated text-mining and probabilistic topic-modeling procedures to discover the competency areas and skill sets as semantic topics. Our findings revealed 22 competency areas and 46 skills, which reflect the interdisciplinary background of CC jobs. The top five competency areas for CC were identified as "Engineering", "Development", "Security", "Architecture", and "Management". Besides, the top three skills emerged as "Communication Skills", "DevOps Tools", and "Software Development". Considering the findings, a competency-skill map was created that illustrates the correlations between CC competency areas and their related skills. Although there are many studies on CC, the competency areas and skill sets required to deal with cloud computing have not yet been empirically studied. Our findings can contribute to CC candidates and professionals, IT organizations, and academic institutions in understanding, evaluating, and developing the competencies and skills needed in the CC industry.Article Citation - WoS: 2Strategic Alignment of Management Information System Functions for Manufacturing and Service Industries With an F-Mcgdm Model(Mdpi, 2022) Bac, UgurConsidering constantly increasing global competition in the market and developing technologies, information systems (ISs) have become an important component of the business world and a vital component of intelligent systems. An IS provides support for planning, controlling, analyzing activities, and support in decisions by managing data throughout the organization to assist executives in their decisions. The main function of an IS is to collect data spread between various parts of the organization and business partners and to process these collected data to form reliable information, which is required for decision making. Another critical function of an IS is to transfer the necessary information to the point-of-need in a timely manner. ISs assist in the conversion of data and information into meaningful outcomes. An IS is a combination of software, data storage hardware, related infrastructure, and people in the organization that use the system. Many business organizations rely on management information systems (MISs), and they conduct their critical operations based on these systems. The existence of an efficient MIS is a requirement for the sustainability of any business. However, MIS's efficiency depends on the business's requirements and nature. The compatibility of MIS with business in the company is vital for the successful implementation of these systems. The current study analyzes differences in expectations of manufacturing and service industries from MISs. For this aim, a fuzzy multi-criteria group decision-making (F-MCGDM) model is proposed to determine the differentiating success factors of MIS in both manufacturing and service industries. Findings indicate that there are considerable differences in the needs of both industries from MIS.Article Citation - WoS: 8Citation - Scopus: 7Nodular Cast Iron Ggg40, 60, 70 Mechanical Characterization From Bars and Blocks Obtained From Brazilian Foundry(Mdpi, 2022) Fernandes, Daniel de Oliveira; Mota Anflor, Carla Tatiana; Vaz Goulart, Jhon Nero; Baranoglu, BesimNodular cast iron has been commonly applied in industry and many engineering applications due to its low production cost and the similarity of its mechanical properties to carbon steel. The mechanical properties of nodular cast iron are very dependent on its microstructure and also on the characteristics of the graphite nodules. In this sense, the main objective of this paper was to evaluate and characterize the nodular cast iron grades GGG40, GGG60 and GGG70 in the absence of heat treatment. In addition, specimens were obtained from casted bars and blocks without the Y-block casting process. The microstructure was analyzed by optical microscopy with the support of computational image analysis for determination of the attributes of the graphite nodules and the quantification of each phase present in the microstructure of the nodular cast iron. The results showed that the microstructure has a strong effect on the material's strength, especially the density of graphite nodules in the material. This difference reinforces the idea that cast iron can undergo mechanical changes due to changes in the casting process, confirming the importance of checking the characteristics of the cast batch before engineering applications of the material.Article Citation - WoS: 6Citation - Scopus: 6Optimized Porous Carbon Particles From Sucrose and Their Polyethyleneimine Modifications for Enhanced Co2 Capture(Mdpi, 2024) Ari, Betul; Inger, Erk; Sunol, Aydin K.; Sahiner, NurettinCarbon dioxide (CO2), one of the primary greenhouse gases, plays a key role in global warming and is one of the culprits in the climate change crisis. Therefore, the use of appropriate CO2 capture and storage technologies is of significant importance for the future of planet Earth due to atmospheric, climate, and environmental concerns. A cleaner and more sustainable approach to CO2 capture and storage using porous materials, membranes, and amine-based sorbents could offer excellent possibilities. Here, sucrose-derived porous carbon particles (PCPs) were synthesized as adsorbents for CO2 capture. Next, these PCPs were modified with branched- and linear-polyethyleneimine (B-PEI and L-PEI) as B-PEI-PCP and L-PEI-PCP, respectively. These PCPs and their PEI-modified forms were then used to prepare metal nanoparticles such as Co, Cu, and Ni in situ as M@PCP and M@L/B-PEI-PCP (M: Ni, Co, and Cu). The presence of PEI on the PCP surface enables new amine functional groups, known for high CO2 capture ability. The presence of metal nanoparticles in the structure may be used as a catalyst to convert the captured CO2 into useful products, e.g., fuels or other chemical compounds, at high temperatures. It was found that B-PEI-PCP has a larger surface area and higher CO2 capture capacity with a surface area of 32.84 m(2)/g and a CO2 capture capacity of 1.05 mmol CO2/g adsorbent compared to L-PEI-PCP. Amongst metal-nanoparticle-embedded PEI-PCPs (M@PEI-PCPs, M: Ni, Co, Cu), Ni@L-PEI-PCP was found to have higher CO2 capture capacity, 0.81 mmol CO2/g adsorbent, and a surface area of 225 m(2)/g. These data are significant as they will steer future studies for the conversion of captured CO2 into useful fuels/chemicals.Article Citation - WoS: 18Citation - Scopus: 23A Novel Hybrid Machine Learning Based System To Classify Shoulder Implant Manufacturers(Mdpi, 2022) Sivari, Esra; Guzel, Mehmet Serdar; Bostanci, Erkan; Mishra, AlokIt is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient's previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of the proposed hybrid machine learning models (p < 0.05). The proposed hybrid machine learning algorithms achieve the goal of low cost and high performance compared to other studies in the literature. The results lead the authors to believe that the proposed system could be used in hospitals as an automatic and objective system for assisting orthopedists in the rapid and effective determination of shoulder implant types before performing revision surgery.Article Citation - WoS: 5Citation - Scopus: 8A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification(Mdpi, 2024) Kadhim, Yezi Ali; Guzel, Mehmet Serdar; Mishra, AlokMedicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.Article Citation - WoS: 4Citation - Scopus: 4Further Development of Polyepichlorohydrin Based Anion Exchange Membranes for Reverse Electrodialysis by Tuning Cast Solution Properties(Mdpi, 2022) Eti, Mine; Cihanoglu, Aydin; Guler, Enver; Gomez-Coma, Lucia; Altiok, Esra; Arda, Muserref; Kabay, NalanRecently, there have been several studies done regarding anion exchange membranes (AEMs) based on polyepichlorohydrin (PECH), an attractive polymer enabling safe membrane fabrication due to its inherent chloromethyl groups. However, there are still undiscovered properties of these membranes emerging from different compositions of cast solutions. Thus, it is vital to explore new membrane properties for sustainable energy generation by reverse electrodialysis (RED). In this study, the cast solution composition was easily tuned by varying the ratio of active polymer (i.e., blend ratio) and quaternary agent (i.e., excess diamine ratio) in the range of 1.07-2.00, and 1.00-4.00, respectively. The membrane synthesized with excess diamine ratio of 4.00 and blend ratio of 1.07 provided the best results in terms of ion exchange capacity, 3.47 mmol/g, with satisfactory conductive properties (area resistance: 2.4 omega center dot cm(2), electrical conductivity: 6.44 mS/cm) and high hydrophilicity. RED tests were performed by AEMs coupled with the commercially available Neosepta CMX cation exchange membrane (CEMs).
