Scholarly Publications - Information Systems and Technologies
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Item Open Access Factors affecting architectural decision-making process and challenges in software projects: an industrial survey(John Wiley & Sons Ltd., 2024-06-18) Demir, Merve Ö.; Chouseinoglou, Oumout; Tarhan, Ayca K.Software architecture plays a fundamental role in overcoming the challenges of the development process of large-scale and complex software systems. The software architecture of a system is the result of an extensive process in which several stakeholders negotiate issues and solutions, and as a result of this negotiation, a series of architectural decisions are made. This survey study aims to determine the experiences of the software industry experts with respect to architectural decision-making, the factors that are effective in decision-making, and the technical and social problems they encounter. An online questionnaire-based survey was conducted with 101 practitioners. The responses were analyzed qualitatively and quantitatively. Analysis of responses revealed that the majority of the participants prefer to document some or all of the architectural decisions taken and to store these documents in web-based collaboration software. Decisions are usually made by teams of two or three, and discussion-based approaches (brainstorming and consensus) are adopted. In the software architecture decision-making process, “major business impact” is the most challenging situation. Information sharing and keeping track of decisions and decision rationale are areas in need of improvement as identified by most participants. From the participants' feedback and their answers to open-ended questions, we concluded that the software architecture decision-making process has an important role in the industry. Our key findings are that decisions made in the architectural decision-making process are taken by teams and generally all decisions are documented. In projects where decisions are made by a single person, peer pressure is found to be significantly different from pressure in projects where decisions are made by the group. This is an indication that as the number of people in the decision-making process increases, the disagreements also increase.Item Open Access A comparative and practical approach using quantum machine learning (QML) and support vector classifier (SVC) for Light emitting diodes mediated in vitro micropropagation of black mulberry (Morus nigra L.)(Elsevier BV, 2024-03-28) Aasim, Muhammad; Katırcı, Ramazan; Acar, Alpaslan Şevket; Ali, Seyid AmjadIn this study, in vitro regeneration protocol for black mulberry (Morus nigra L.) was optimized using 18 distinct combinations of benzylaminopurine (BAP) with either naphthalene acetic acid (NAA) or Indole butyric acid (IBA). The top two combinations were then utilized to optimize the light intensity given by light-emitting diodes (LEDs). Supplementation of 0.5 mg L-1 BAP and 0.25 mg L-1 IBA with 60 PPFD light intensity yielded a maximum regeneration coefficient (2.53), shoot length (6.01 cm), and number of leaves (10.73). The regenerated plantlets were rooted with IBA under in vitro conditions followed by successful acclimatization of plantlets under greenhouse conditions. The results were further investigated by linking them with an emphasis on improving the Support Vector Classifier (SVC) using quantum computing techniques, and this work embarked on a groundbreaking path to integrate the realms of machine learning (ML) with quantum computing. For this purpose, the traditional Support Vector Classifier (SVC) model was compared with quantum-enhanced algorithms, including SVC with the quantum kernel (SVC Qkernel), SVC with quantum features (SVC Qfeatures), Quantum Support Vector Classifier (QSVC), and the Variational Quantum Circuit (VQC). The quantum-enhanced models showed a range of results, indicating their complex and subtle character, whereas classical SVC performed robustly for multiple metrics. Quantum kernel-based SVC demonstrated an interesting trade-off between recall and precision, indicating its proficiency in processing particular data properties.Item Embargo Zeugodacus fruit flies (Diptera: Tephritidae) host preference analysis by machine learning-based approaches(Elsevier BV, 2024-05-29) Nazir, N.; Fatima, S.; Aasim, M.; Yaqoob, F.; Mahmood, K.; Ali, Seyid Amjad; Awan, S.I.; ul Haq, I.Detecting the host preference of highly polyphagous and economically significant pest species of fruit flies (Diptera; Tephritidae) is important for identifying their species status, their management in orchards and the international trade of fruits and vegetables. In the current study, three fruit fly species Zeugodacus tau, Z. signata, and Z. cucrbitae, (Diptera: Tephritidae) were evaluated for their oviposition preference among three host fruits: pumpkin, cucumber, and bitter gourd. The investigation was conducted under choice conditions in the laboratory. Fruit fly species and host fruits were used as input/predictive variables whereas, oviposition preference, number of pupae, weight of pupae, adult emergence, and sex ratio were used as output/response variables to test the host preference through an Artificial Neural Network ANN/machine learning (ML) algorithms. ANN-based on a Multi-Layer Perceptron (MLP) model and decision tree-based Random Forest (RF) models were employed. Results revealed that Z. tau preferred pumpkin > cucumber > bitter gourd in order, Z. cucurbitae preferred bitter gourd > pumpkin > cucumber in order and Z. signata also preferred pumpkin but followed by bitter gourd and cucumber for oviposition. The specific host preferences observed in both Z. tau and Z. signata suggest that they may not be distinct species but rather closely related siblings. These findings highlight host preference as a marker for species delimitation. Moreover, the machine learning (ML) tools, provide better prediction in identifying host preference than statistical methods. These results are discussed in the context of the importance of studying host preferences for fruit flies’ species delimitation, their management, and international trade of fruits and vegetables.Item Open Access Development of a web-based decision support nurse care management system: decision support-n-care(Lippincott Williams & Wilkins, 2024-08-07) Özduyan Kılıç, M.; Korkmaz, F.; Sevgi, Cüneyt; Chouseinoglou, Oumout; Alexander, SusanItem Open Access Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.)(Springer Dordrecht, 2024-03-25) Ali, Seyid Amjad; Aasim, Muhammad; Yildirim, Busra; Say, Ahmet; Aytac, Selim; Nadeem, Muhammad AzharIndustrial hemp (Cannabis sativa L.) is a highly recalcitrant plant under in vitro conditions that can be overcome by employing external stimuli. Hemp seeds were primed with 2.0-3.0% hydrogen peroxide (H2O2) followed by culture under different Light Emitting Diodes (LEDs) sources. Priming seeds with 2.0% yielded relatively high germination rate, growth, and other biochemical and enzymatic activities. The LED lights exerted a variable impact on Cannabis germination and enzymatic activities. Similarly, variable responses were observed for H2O2 x Blue-LEDs combination. The results were also analyzed by multiple regression analysis, followed by an investigation of the impact of both factors by Pareto chart and normal plots. The results were optimized by contour and surface plots for all parameters. Response surface optimizer optimized 2.0% H2O2 x 918 LUX LEDs for maximum scores of all output parameters. The results were predicted by employing Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Moreover, the validity of these models was assessed by using six different performance metrics. MLP performed better than RF and XGBoost models, considering all six-performance metrics. Despite the differences in scores, the performance indicators for all examined models were quite close to each other. It can easily be concluded that all three models are capable of predicting and validating data for cannabis seeds primed with H2O2 and grown under different LED lights.Item Open Access Response surface methodology and artifcial intelligence modeling for in vitro regeneration of Brazilian micro sword (lilaeopsis brasiliensis)(Springer Dordrecht, 2024-04-02) Ali, Seyid Amjad; Aasim, MuhammadIn this study, response surface methodology (RSM) was used to optimize in vitro regeneration of the Brazilian micro sword (Lilaeopsis brasiliensis) aquatic plant, followed by data prediction and validation using machine learning algorithms. The basal salt, sucrose and Benzyaminopurine (BAP) concentrations were derived from Box-Behnken design of RSM. The response surface regression analysis revealed that 1.0 g/L MS + 0.1 mg/L BAP + 25 g/L sucrose was optimized for maximum regeneration (100%), shoot counts (63.2), and fresh weight (1.382 g). The RSM-based predicted scores were fairly similar to the actual scores, which were 100% regeneration, 63.39 shoot counts, and 1.44 g fresh weight. Pareto charts analysis illustrated the significance of MS for regeneration and fresh weight but remained insignificant. Conversely, MS × BAP was found to be the most crucial factor for the shoot counts, with MS coming in second and having a major influence. The analysis of the normal plot ascertained the negative impact of elevated MS concentration on shoot counts and enhanced shoot counts from the combination of MS × BAP. Results were further optimized by constructing contour and surface plots. The response optimizer tool demonstrated that maximum shoot counts of 63.26 and 1.454 g fresh weight can be taken from the combination of 1.0 g/L MS + 0.114 mg/L BAP + 23.94 g/L. Using three distinct performance criterias, the results of machine learning models showed that the multilayer perceptron (MLP) model performed better than the random forest (RF) model. Our findings suggest that the results may be utilized to optimize various input variables using RSM and verified via ML models.Item Embargo Computing artificial neural network and genetic algorithm for the feature optimization of basal salts and cytokinin-auxin for in vitro organogenesis of royal purple (cotinus coggygria scop)(Elsevier BV, 2023-09-01) Aasim, Muhammad; Ayhan, Ayşe; Katırcı, Ramazan; Acar, Alpaslan Şevket; Ali, Seyid AmjadThis study presents the in vitro regneration protocol for Royal purple [(Cotinus coggygria Scop. (syn.: Rhus cotinus L.)] from nodal segment explants followed by optimizing the input variable combinations with the aid of PyTorch ANN and Genetic Algorithm (GA). The Murashige and Skoog (MS) culture medium yielded relatively higher regeneration frequency (91.52 %) and shoot count (1.96) as compared to woody plant medium (WPM), which yielded 84.58 % regeneration and shoot count (1.61) per explant. The supplementation of plant growth regulators (PGRs) + MS medium yielded 80.0–100.0 % shoot regeneration and 1.48–3.25 shoot counts compared to 60.0–100.0 % shoot regeneration and 1.00–2.37 shoots from the combination of PGRs + WPM. In order to predict the shoot count and regeneration with the aid of a mathematical model, the machine learning algorithms of Multilayer Perceptron (MLP), Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Random Forest (RF) models were utilized. The highest R2 values for both output variables were acquired using MLP model in PyTorch platform. The R2 scores for regeneration and shoot counting were recorded as 0.69 and 0.71 respectively. NSGA-II algorithm revealed the 1.25 mg/L BAP (6-Benzylaminopurine), 0.02 mg/L NAA (Naphthalene acetic acid), and 0.03 mg/L IBA (Indole butyric acid) in WPM medium as an optimum combination for 100 % regeneration. On the other hand, the algorithm suggested multiple combination in MS medium for maximum shoot counting.Item Open Access Instructor-Related factors affecting game utilization in software engineering education: a replication study(IGI Global, 2023-08-10) Albayrak, Ö.; Albayrak, DuyguSoftware engineering education is challenging. To cope with various challenges of software engineering education, instructors at universities utilize different ways. One of these ways is to use games in education. In this study, a replication of a previous survey was conducted to check factors that impact on intructors' decision-making on selection of games in undergraduate software engineering education. Out of 287 invitations, a total of 42 valid responses were obtained. Based on the results, the authors observed that “the number of hours per week the instructor plays games,” “the instructor's experience in using games for educational purposes in general,” and “the instructor's experience in designing games for educational purposes” have significant impact on the instructor's decision-making on using games in software engineering education. The authors present the results and limitations of the study as well as plans for future research.Item Open Access Artifcial intelligence–based approaches to evaluate and optimize phytoremediation potential of in vitro regenerated aquatic macrophyte Ceratophyllum demersum L.(2023-01-06) Aasim, M.; Ali, Seyid Amjad; Aydin, S.; Bakhsh, A.; Sogukpinar, C.; Karatas, M.; Khawar, K.M.; Aydin, M.E.Water bodies or aquatic ecosystem are susceptible to heavy metal accumulation and can adversely afect the environment and human health especially in underdeveloped nations. Phytoremediation techniques of water bodies using aquatic plants or macrophytes are well established and are recognized as eco-friendly world over. Phytoremediation of heavy metals and other pollutants in aquatic environments can be achieved by using Ceratophyllum demersum L. — a well-known foating macrophyte. In vitro regenerated plants of C. demersum (7.5 g/L) were exposed to 24, 72, and 120 h to 0, 0.5, 1.0, 2.0, and 4.0 mg/L of cadmium (CdSO4·8H2O) in water. Results revealed signifcantly diferent relationship in terms of Cd in water, Cd uptake by plants, bioconcentration factor (BCF), and Cd removal (%) from water. The study showed that Cd uptake by plants and BCF values increased signifcantly with exposure time. The highest BCF value (3776.50) was recorded for plant samples exposed to 2 mg/L Cd for 72 h. Application of all Cd concentrations and various exposure duration yielded Cd removal (%) between the ranges of 93.8 and 98.7%. These results were predicted through artifcial intelligence–based models, namely, random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). The tested models predicted the results accurately, and the attained results were further validated via three diferent performance metrics. The optimal regression coefcient (R2) for the models was recorded as 0.7970 (Cd water, mg/L), 0.9661 (Cd plants, mg/kg), 0.9797 bioconcentration factor (BCF), and 0.9996 (Cd removal, %), respectively. These achieved results suggest that in vitro regenerated C. demersum can be efcaciously used for phytoremediation of Cd-contaminated aquatic environments. Likewise, the proposed modeling of phytoremediation studies can further be employed more comprehensively in future studies aimed at data prediction and optimization.Item Open Access Artificial neural network and decision tree–based models for prediction and validation of in vitro organogenesis of two hydrophytes—Hemianthus callitrichoides and Riccia fluitans(Springer, 2023-08-02) Özcan, Esra; Atar, Hasan Hüseyin; Ali, Seyid Amjad; Aasim, MuhammadThe application of plant tissue culture protocols for aquatic plants has been widely adopted in recent years to produce cost-effective plants for aquarium industry. In vitro regeneration protocol for the two different hydrophytes Hemianthus callitrichoides (Cuba) and Riccia fluitans were optimized for appropriate basal medium, sucrose, agar, and plant growth regulator concentration. The MS No:3B and SH + MSVit basal medium yielded a maximum clump diameter of 5.53 cm for H. callitrichoides and 3.65 cm for R. fluitans. The application of 20 g/L sucrose was found appropriate for yielding larger clumps in both species. Solidification of the medium with 1 g/L agar was optimized for inducing larger clumps with rooting for both species. Provision of basal medium with any concentration of 6-benzylaminopurine (BAP) and α-naphthaleneacetic acid (NAA) was found detrimental for inducing larger clumps for both species. The largest clumps of H. callitrichoides (5.51 cm) and R. fluitans (4.59 cm) were obtained on basal medium without any plant growth regulators. The attained data was also predicted and validated by employing multilayer perceptron (MLP), random forest (RF), and extreme gradient boosting (XGBoost) algorithms. The performance of the models was tested with three different performance metrics, namely, coefficient of regression (R2), means square error (MSE), and mean absolute error (MAE). Results revealed that MLP and RF models performed better than the XGBoost model. The protocols developed in this study have shown promising outcomes and the findings can irrefutably assist to produce H. callitrichoides and R. fluitans on a large scale for the local aquarium industry.Item Open Access Factors affecting the adoption of cloud for software development: A case from Turkey(World Scientific Publishing Co. Pte. Ltd., 2023-07-04) Pisirir, E.; Chouseinoglou, Oumout; Sevgi, Cüneyt; Uçar, ErkanCloud-based solutions for software development activities have been emerging in the last decade. This study aims to develop a hybrid technology adoption model for cloud use in software development activities. It is based on Technology Acceptance Model (TAM), Technology–Organization–Environment (TOE) framework, and the proposed extension Personal–Organization–Project (POP) structure. The methodology selected is a questionnaire-based survey and data are collected through personally administered questionnaire sessions with developers and managers, resulting in 268 responses regarding 84 software development projects from 30 organizations in Turkey, selected by considering company and project sizes and geographical proximity to allow face-to-face response collection. Structural Equation Modeling (SEM) is used for statistical evaluation and hypothesis testing. The final model was reached upon modifications and it was found to explain the intention to adopt and use the cloud for software development meaningfully. To the best of our knowledge, this is the first study to identify and understand factors that affect the intention of developing software on the cloud. The developed hybrid model was validated to be used in further technology adoption studies. Upon modifying the conceptual model and discovering new relations, a novel model is proposed to draw the relationships between the identified factors and the actual use, intention to use and perceived suitability. Practical and social implications are drawn from the results to help organizations and individuals make decisions on cloud adoption for software development.Item Open Access Capitalizing the predictive potential of machine learning to detect various fire types using NASA's MODIS satellite data for the mediterranean basin(Association for Computing Machinery, 2024-01-22) Lassem, Nima Kamali; Gaafar, Obai Mohamed Hisham Abdelmohsen ; Ali, Seyid AmjadThis study investigates the realm of machine learning for the classification of different fire types using NASA's FIRMS MODIS satellite data for the Mediterranean basin. Concentrating on the Mediterranean basin and utilizing data spanning from 2019 to 2021 for model training, XGBoost and Random Forest models were subsequently validated for the 2022 data. The findings distinctly illustrate XGBoost's superior predictive precision as compared to Random Forest by showcasing an impressive overall F1 score surpassing 95% and 84% macro F1 score across various fire types. This study emphasizes the prospect of machine learning to improve worldwide wildfire monitoring and response by providing exact, real-time fire type forecasts.Item Open Access Artificial neural network and decision tree facilitated prediction and validation of cytokinin‑auxin induced in vitro organogenesis of sorghum (Sorghum bicolor L.)(Springer Dordrecht, 2023-04-05) Aasim, M.; Ali, Seyid Amjad; Altaf, M. T.; Ali, A.; Nadeem, M. A.; Baloch, F. S.In this study, in vitro regeneration protocol of sorghum (Sorghum bicolor) was successfully established by using direct organogenesis from a mature zygotic embryo explant. The used basal medium encompassed Murashige and Skoog medium (MS) supplemented with 2–4 mg/L Benzylaminopurine (BAP) alone or with 0.25 mg/L Indole butyric acid (IBA) or Naphthalene acetic acid (NAA). Results demonstrated a significant impact of cytokinin-auxin on shoot count (1.24–3.46) and shoot length (2.80–3.47 cm). Maximum shoot count (3.46) and shoot length (3.97 cm) were achieved on the MS medium enriched with 2 mg/L BAP + 0.25 mg/L NAA and 2.0 mg/L BAP, respectively. To ascertain the impact of BAP alone, BAP + IBA, and BAP + NAA, the data were also analyzed by using a factorial regression model. Pareto chart and normal plots were used to check either the positive or negative impact of input variables on output variables. To further explore the association between BAP + IBA and BAP + NAA on shoot count and shoot length, contour and surface plots were also built. Three different artificial intelligence-based models along with four different performance metrics were utilized to validate the predicted results. Multilayer perceptron (MLP) model performed more efficiently (R2 = 0.799 for shoot count and R2 = 0.831 for shoot length) as compared to the decision tree-based algorithms of random forest (RF) – (R2 = 0.779 for shoot count and R2 = 0.786 for shoot length) and extreme gradient boost (XGBoost) – (R2 = 0.768 for shoot count and R2 = 0.781 for shoot length). As plant tissue culture protocol is a powerful tool for genetic engineering and genome editing of crops, integration of different artificial intelligence-based models can lead to improvement of sorghum with the aid of biotechnological tools.Item Open Access Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L)(Springer (India) Private Ltd., 2023-01-30) Aasim, M.; Akin, F.; Ali, Seyid Amjad; Taskin, M.B.; Colak, M.S.Salt stress is one of the most critical abiotic stresses having significant contribution in global agriculture production. Chickpea is sensitive to salt stress at various growth stages and a better knowledge of salt tolerance in chickpea would enable breeding of salt tolerant varieties. During present investigation, in vitro screening of desi chickpea by continuous exposure of seeds to NaCl-containing medium was performed. NaCl was applied in the MS medium at the rate of 6.25, 12.50, 25, 50, 75, 100, and 125 mM. Different germination indices and growth indices of roots and shoots were recorded. Mean germination (%) of roots and shoots ranged from 52.08 to 100%, and 41.67–100%, respectively. The mean germination time (MGT) of roots and shoots ranged from 2.40 to 4.78 d and 3.23–7.05 d. The coefficient of variation of the germination time (CVt) was recorded as 20.91–53.43% for roots, and 14.53–44.17% for shoots. The mean germination rate (MR) of roots was better than shoots. The uncertainty (U) values were tabulated as 0.43–1.59 (roots) and 0.92–2.33 (shoots). The synchronization index (Z) reflected the negative impact of elevated salinity levels on both root and shoot emergence. Application of NaCl exerted a negative impact on all growth indices compared to control and decreased gradually with elevated NaCl concentration. Results on salt tolerance index (STI) also revealed the reduced STI with elevated NaCl concentration and STI of roots was less than shoot. Elemental analysis revealed more Na and Cl accumulation with respective elevated NaCl concentrations. The In vitro growth parameters and STI values validated and predicted by multilayer perceptron (MLP) model revealed the relatively high R2 values of all growth indices and STI. Findings of this study will be helpful to broaden the understanding about the salinity tolerance level of desi chickpea seeds under in vitro conditions using various germination indices and seedling growth indices.Item Metadata only Software quality and model-based process improvement(CRC Press, 2022-05-30) Bariş, Özkan; Albayrak, Özlem; Demirörs, OnurIn this chapter, we introduced software quality and model based process improvement. Quality is more and more often seen as a critical software attribute and a determinant of business success. The absence of quality in software products and services results in dissatisfied users, financial loss, and may even endanger to our lives. SPI is a process oriented approach to address quality problems. We presented underlying principles by focusing on quality, process and quality, and the Co Q. We explained quality using different defining approaches, such as transcendental, product, user, manufacturing, and value based approaches. We then defined process and qualitystartingwiththeconceptofprocessaswidelyappreciatedastheproper ground for improving product quality and productivity. We highlighted the importance of SPC, plan do check act, and TQM. We also explained Co Q. Co Q analysis and technique shave been in use for more than 50 years and there are multiple models for Co Q. These models are the effective tools in feasibility analysis of SPI programs and the measurement and evaluation of the program performance. Both theory and experience advise investing on prevention and appraisal costs to get the highest returns from the decreased costs of appraisal and failure. In terms of best practices, we focused on software process maturity, models for SPI, and results from implementations. The use of maturity models has been popularized in software engineering through the SEI software CMM, which was published in 1991. In 1993,inEurope, ISO started the SPICE initiative. Both these models define capability levels for software processes and corresponding key process areas. Not every organization that has attempted model based process improvement has succeeded. A group of problems were observed to be general and related to the management of change and to underestimated costs and timeframes. Survey results also included evidence that SPI efforts were overcome by crisis due political struggles within the organizations. Software processes are characterized by a vast number off actors, that is, business goals, organizational culture, accumulated knowledge and experience, company size, the market, domain and environmental and regulatory constraints, etc. SPI is thus challenged by this process diversity, and there is no generic reference model that suits all software development projects and organizations. Furthermore, our analysis showed that the main are as of future research should focus on SPI for small organizations and agile development, measurement, and using SPC and automation/tools.Item Open Access Innovation in the breeding of common bean through a combined approach of in vitro regeneration and machine learning algorithms(Frontiers Media S.A., 2022-08-24) Aasim, Muhammad; Katirci, Ramazan; Baloch, Faheem Shehzad; Mustafa, Zemran; Bakhsh, Allahv; Nadeem, Muhammad Azhar; Ali, Seyid Amjad; Hatipoğlu, Rüştü; Çiftçi, Vahdettin; Habyarimana, Ephrem; Karaköy, Tolga; Chung, Yong SukCommon bean is considered a recalcitrant crop for in vitro regeneration and needs a repeatable and efficient in vitro regeneration protocol for its improvement through biotechnological approaches. In this study, the establishment of efficient and reproducible in vitro regeneration followed by predicting and optimizing through machine learning (ML) models, such as artificial neural network algorithms, was performed. Mature embryos of common bean were pretreated with 5, 10, and 20 mg/L benzylaminopurine (BAP) for 20 days followed by isolation of plumular apice for in vitro regeneration and cultured on a post-treatment medium containing 0.25, 0.50, 1.0, and 1.50 mg/L BAP for 8 weeks. Plumular apice explants pretreated with 20 mg/L BAP exerted a negative impact and resulted in minimum shoot regeneration frequency and shoot count, but produced longer shoots. All output variables (shoot regeneration frequency, shoot counts, and shoot length) increased significantly with the enhancement of BAP concentration in the post-treatment medium. Interaction of the pretreatment × post-treatment medium revealed the need for a specific combination for inducing a high shoot regeneration frequency. Higher shoot count and shoot length were achieved from the interaction of 5 mg/L BAP × 1.00 mg/L BAP followed by 10 mg/L BAP × 1.50 mg/L BAP and 20 mg/L BAP × 1.50 mg/L BAP. The evaluation of data through ML models revealed that R2 values ranged from 0.32 to 0.58 (regeneration), 0.01 to 0.22 (shoot counts), and 0.18 to 0.48 (shoot length). On the other hand, the mean squared error values ranged from 0.0596 to 0.0965 for shoot regeneration, 0.0327 to 0.0412 for shoot count, and 0.0258 to 0.0404 for shoot length from all ML models. Among the utilized models, the multilayer perceptron model provided a better prediction and optimization for all output variables, compared to other models. The achieved results can be employed for the prediction and optimization of plant tissue culture protocols used for biotechnological approaches in a breeding program of common beans. Copyright © 2022 Aasim, Katirci, Baloch, Mustafa, Bakhsh, Nadeem, Ali, Hatipoğlu, Çiftçi, Habyarimana, Karaköy and Chung.Item Open Access Classroom management in higher education: a systematic literature review(Routledge, 2022-02-17) Ateşkan, Armağan; Albayrak, DuyguThis paper presents the findings of a systematic literature review (performed from 2010 to 2020) about classroom management (CM) in higher education. The purpose of this article is to present the state of CM in higher education. Search terms identified 129 papers, from which 42 relevant articles met the inclusion criteria of the current review. Data extraction was initially conducted based on title, keywords, and abstract; it continued with a full-text analysis for the final set of 42 included studies. Based on the reviewed articles factors affecting CM are classified according to students, instructors, and the system. The results show that novice instructors need training about CM and instructors should integrate active learning strategies for better CM. The results also point to a need for researches in online CM. Finally, the findings provide suggestions for future research on CM in higher education.Item Open Access A phenomenological analysis of primary school teachers’ lived distance education experience during the COVID-19 pandemic in Turkey(Routledge, 2022-09-19) Ugur-Erdogmus, F.; Albayrak, DuyguThe purpose of this phenomenological study was to investigate lived distance education (DE) experiences of primary school teachers and their perceptions about DE during the COVID-19 pandemic in Turkey. Twenty primary school teachers who actively taught online participated in online interviews. Phenomenological analysis of the interviews sought to reveal (1) the primary school teachers’ lived DE experience, and (2) their perceptions about DE during the pandemic. The current status of DE, effects of DE, and teachers’ perceptions of DE were the themes revealed. Results showed that teaching practice, interactivity, difficulties, needs, and inequality were the main issues revealed from the primary school teachers’ lived experience. The results also identified the perceived effects of DE on both teachers and students. According to their online experiences, the teachers’ perceptions about DE and their future plans with respect to online teaching were reported.Item Open Access Light-emitting diodes induced in vitro regeneration of Alternanthera reineckii mini and validation via machine learning algorithms(Springer, 2022-10-22) Aasim, M.; Ali, Seyid AmjadOptimization of in vitro regeneration protocol using multiple input variables is highly significant, and can be achieved by validating the data using machine learning algorithms. Shoot tip and nodal segment explants of Alternanthera reineckii mini were inoculated on Murashige and Skoog (MS) medium enriched with different concentrations of benzylaminopurine (BAP), and cultured under five different monochromic light-emitting diodes (LEDs). The attained results were validated through the application of four different supervised machine learning models (RF, XGBoost, KNN, and GP). The prediction of the data were validated by using regression coefficient (R2), mean squared error (MSE), and mean absolute percentage error (MAPE) performance metrics. Results revealed R2 values of 0.61 and 0.59 for shoot counts and shoot length, respectively. The results of MSE were registered between 3.48–5.42 for shoot count and 0.40–0.74 for shoot length, whereas, 28.9–35.1% and 13.2–18.4% MAPE values were recorded for both shoot count and shoot length. Among the utilized models, the RF model validated and predicted the results more accurately, followed by the XGBoost model for both output variables. The results confirm that ML models can be used for data validation, and opens a new era of employing ML modeling in plant tissue culture of other economically important plants. Graphical abstract: Schematic structure presenting input features and outputs together with ML models, used validation and performance metrics [Figure not available: see fulltext.]. © 2022, The Society for In Vitro Biology.Item Open Access Divide-and-conquer: A systematic approach for subcontractor selection in defense industry projects(International Journal of Industrial Engineering, 2022) Şehitoğlu, Anıl; Chouseinoglou, OumoutDefense industry projects generally are of large size and may be broken down into subparts of different granularity levels, where each subpart may be assigned to a different subcontractor. On the other hand, the problem of subcontractor selection to each subpart is a complex decision-making problem that requires evaluating a number of criteria and the characteristics of each subpart. This study aims to model the problem of subcontractor selection in a defense industry project decomposed to multiple subprojects by combining the Analytic Hierarchy Process (AHP) and Integer Linear Programming (ILP). A project carried out at a defense industry company in Turkey has been used as a case study. An extensive set of criteria specific to the defense industry have been identified, and AHP has been applied to the relevant criteria and alternative subcontractors for each subpart. Finally, ILP has been used to include a set of constraints regarding the project specifications.