Computer Technology and Information Systems

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  • ItemEmbargo
    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 Amjad
    This 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.
  • ItemOpen Access
    Instructor-Related factors affecting game utilization in software engineering education: a replication study
    (IGI Global, 2023-08-10) Albayrak, Ö.; Albayrak, Duygu
    Software 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.
  • ItemOpen 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.
  • ItemEmbargo
    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, 0202-08-02) Özcan, Esra; Atar, Hasan Hüseyin; Ali, Seyid Amjad; Aasim, Muhammad
    The 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.
  • ItemOpen 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, Erkan
    Cloud-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.
  • ItemOpen 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 Amjad
    This 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.
  • ItemOpen 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.
  • ItemOpen 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.
  • ItemMetadata only
    Software quality and model-based process improvement
    (CRC Press, 2022-05-30) Bariş, Özkan; Albayrak, Özlem; Demirörs, Onur
    In 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.
  • ItemOpen 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 Suk
    Common 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.
  • ItemOpen Access
    Classroom management in higher education: a systematic literature review
    (Routledge, 2022-02-17) Ateşkan, Armağan; Albayrak, Duygu
    This 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.
  • ItemOpen 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, Duygu
    The 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.
  • ItemOpen 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 Amjad
    Optimization 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.
  • ItemOpen Access
    Divide-and-conquer: A systematic approach for subcontractor selection in defense industry projects
    (International Journal of Industrial Engineering, 2022) Şehitoğlu, Anıl; Chouseinoglou, Oumout
    Defense 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.
  • ItemOpen Access
    Machine learning and artificial neural networks-based approach to model and optimize ethyl methanesulfonate and sodium azide induced in vitro regeneration and morphogenic traits of water hyssops (Bacopa monnieri L.)
    (2022-09-10) Mirza, K.; Aasim, M.; Katırcı, R.; Karataş, M.; Ali, Seyid Amjad
    Application of chemical mutagens is used for artificially induced in vitro mutation to develop new cultivars with elite characteristics. However, the optimization of selecting proper mutagen, its concentration, and exposure time is of utmost importance, especially for plants containing noteworthy secondary metabolites. In this study, the effect of sodium azide (NaN3) and ethyl methanesulfonate (EMS) in different concentrations (0.025, 0.05, 0.1, and 0.2 mg l−1), and treatment time (30, 60, and 120 min) was investigated on Bacopa monnieri; an important medicinal plant. The maximum shoot counts (57.0) were achieved from the combination of 0.10 mg l−1 EMS × 60 min. Whereas, maximum shoot length (4.07 cm), node numbers (4.97) and leaf numbers (12,23) were achieved from the combination of 0.20 mg l−1 EMS × 120 min, respectively. Combination of 0.025 mg l−1 NaN3 × 120 mg/l yielded maximum shoot counts (52.30), shoot length (3.23 cm), node numbers (6.07) and leaf numbers (12.13). The trained model to predict the outputs were designed and calibrated with machine learning (ML) algorithms. Support Vector Classifier (SVC), Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), Random Forest (RF) models, and Multilayer Perceptron (MLP) neural network algorithms were used to discover the best models and their hyperparameters. The RF model gave exceptional results in the prediction of the outputs. F1 scores of the RF were acquired in the range of 0.98–1.00 for different outputs. The other models’ F1 scores varied in the range of 0.65 and 0.85. The present work opens the new era of applying ML and artificial neural network (ANN) models in plant tissue culture with the possibility of application for other economic crops.
  • ItemOpen Access
    Achieving aging well through senior entrepreneurship: a three-country empirical study
    (Springer New York LLC, 2021-11-12) Zhu, Y.; Collins, Ayşe; Sardana, D.; Çavuşgil, S. T.
    Seniors strive to achieve aging well by engaging in entrepreneurial activities subsequent to ceasing their organizational employment. While this is a common practice in many societies, scant research exists on what motivates seniors to engage in entrepreneurial activities once they end their formal employment. We adopt the self-determination theory (SDT) to investigate the effects of goal contents and motives on the well-being among seniors who launch their entrepreneurship journeys. Based on in-depth interviews with senior entrepreneurs in China, India, and Turkey, we contribute to extant knowledge by linking separate paradigms. These are as follows: goal contents and intrinsic motivation-driven entrepreneurship, management of inner and outer challenges, and achievement of the eventual outcome of aging well. We also investigate the culture-specific drivers of senior entrepreneurship in a comparative framework.
  • ItemOpen Access
    Exploiting linearity of modular multiplication
    (Springer, 2020) Yıldırım, Hamdi Murat
    The XOR Open image in new window and the addition ⊞⊞ operations have been widely used as building blocks for many cryptographic primitives. These operations and the multiplication ⊙⊙ operation are successively used in the design of IDEA and the MESH block ciphers. This work presents several interesting algebraic properties of the multiplication operation. By fixing one operand, we obtain vector valued function ggZggZ on Zn2Z2n, associated with ⊙⊙. In this paper we show that the nonlinearity of ggZggZ remains the same under some transformations of Z and moreover we give an upper bound for the nonlinearity of ggZggZ when Z is a power of 2. Under weak-key assumptions, we furthermore present a list of new linear relations for 1-round IDEA cipher, some of directly derived and others algorithmically generated using these relations and known ones. We extend the largest linear weak key class for IDEA cipher with size 223223 to derive such a class with sizes 224224. Under the independent key subblocks (subkeys) and weak-key assumptions we derive many linear relations for IDEA cipher using linear relations for 1-round IDEA cipher.
  • ItemOpen Access
    Preservice teachers' Facebook usage and their perspectives about Facebook as a professional development tool
    (Ahi Evran Üniversitesi Kırşehir Eğitim Fakültesi, 2011) Köşkeroğlu-Büyükimdat, M.; Albayrak, Duygu; Uğur-Erdoğmuş, F.; Yıldırım, S.; Eryol, G.; Ataman, Y. E.
    In this study, an explanatory mixed method design was employed to evaluate preservice information technology teachers’ Facebook usage and their perspectives about Facebook as a professional development tool. In quantitative phase, data were collected through a Facebook usage survey on a total number of 338 undergraduate Computer Education and Instructional Technology (CEIT) students from three representative state universities in Turkey. Survey results indicated that preservice teachers favored and utilized Facebook. To find out more in-depth data, the results of quantitative data analysis were used to develop a structured interview to be done with four preservice teachers who were selected purposefully. The data collected from the interviews were subjected to content analysis where coding was conducted to create meaningful organization of the data. According the results of this explanatory study especially communication, sharing and socializing capacity of Facebook were found important in terms of using Facebook as a professional development tool.
  • ItemOpen Access
    3. yılında Türkiye'nin internetle savaşı: Donkişot, Devekuşu,Harakiri
    (Türk Kütüphaneciler Derneği, 2010) Akgül, Mustafa
    Türkiye'de İnternet yasaklarını düzenleyen 5651 numaralı kanun çıkalı 3 yılı aştı. Bu kanunun çıkış sürecinde ve bugüne kadar uygulamasında, ülkemiz bir yandan dünyaya önderlik etmeye çalıştı, bir yandan Youtube/ Google gibi devlere meydan okudu; onlara büyük vergi cezaları kesti. Ülkemiz, kendi internet algılaması ve değerlendirmesini dünyaya empoze etmeye çalışıyor. Böylece, bu konuda, uluslararası hukuku tesis etmeye çalışıyor. Bunu, uluslararası forumlarda, Birleşmiş Milletlerde önererek, savunarak, müzakere ederek yapmıyor. İnterneti, basın gibi algılayarak, basına uygulanan yasaklama alışkanlıkları ile yasaklıyor. Başbakan dahil internet yasaklarını önemli bir çoğunluk deliyor. Bu yasaklara Cumhurbaşkanı, Avrupa Birliği’nden sorumlu Devlet Bakanı, hatta Ulaştırma Bakanı ve BTK (Bilgi Teknolojileri ve İletişim Kurumu) Başkanı da karşı beyanlar veriyor. Bu arada Hukuk'un temel ilkeleri, kuvvetler ayrılığı, adil yargılama, özgürlüklerin özüne dokunulmaz ilkesi gözardı ediliyor. Bir başka deyişle, ülkenin hukukçuları ve düşünen insanların gözü önünde bir Hukuk Faciası yaşanıyor. Ve ülkemiz, matbaada olduğu gibi, interneti anlamayarak, harakiri yapıyor.Bu yazıda, Türkiye'nin internetle savaşının 3 yıllık macerasının boyutları değerlendirecektir.
  • ItemOpen Access
    Average distance estimation in randomly deployed wireless sensor networks (WSNs): an analytical study
    (Inderscience Enterprises, 2019) Sevgi, Cüneyt
    A wireless sensor network (WSN) is an energy-scarce network in which the energy is primarily dissipated by the nodes during data transmission to the base station (BS). The location of the BS dramatically affects the energy dissipation, the throughput, and the lifetime. While in certain studies the optimal positioning of a BS is considered, the system parameters are optimized when the BS location is known in advance in many others. Herein, we provide a general-purpose mathematical framework to find the expected distance value between every point within any n-sided simple polygon shaped sensing field and an arbitrarily located BS. Knowing this value is imperative particularly in random deployment as it is used for energy-efficient clustering. Although similar derivations appear in the related literature, to the best of our knowledge, this study departs from them, since our derivations do not depend on the shape of the field and the orientation of BS relative to it.