2023 - Cilt 7 - Sayı 1
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Öğe Creating architectural programming for a student union project(Altınbaş Üniversitesi, 2023) Aydın Aktaş, Büşra Nur; Bozkurt, Afra Nur; Malec, TomaszThe purpose of this study is to establish architectural programming and typology of buildings by analyzing the concrete and abstract values connecting students with the Student Union and the campus community. Understanding the needs of the users and activities is crucial for spatial organizations and architectural programming. This research uses both main and secondary data collection through; literature review, case studies, student survey, and interview methods. The scope of programming is defined to cover student union building design and its programming. Architectural programming is an advanced design tool supporting architectural design. Buildings are often, mostly knowingly, constructed to fulfill particular needs. Architecture and architectural programming describe the starting point and end point of the project, which evolved together, to satisfy the demands and needs of users and clients. In this report, the Student Union project's architectural programming, which serves as a community center in universities, is created. The students' diverse needs are understood, and the technologies and inventions of today are incorporated into the design program in the case of the student union building project. Comprehensive analysis and thorough understanding produce the program. This research plan, as part of the report, is assessed and altered.Öğe XGBoost algorithm for orecasting electricity consumption of Germany(Altınbaş Üniversitesi, 2023) Ibrahim, Abdullahi Abdu; Elzaridi, Khalid Mohammed AbdullahStability requires energy demand prediction. We train and test 24-hour German load forecasting models. ENTSO-E Transparency Platform data covered European energy generation, transmission, and consumption. It uses German load data instead of PJM data for the eastern US, adds holidays and lag features to the XGB model, and benchmarks with a linear model and a random forest. Grid search CV refines the final XGB model. National load forecasting RMSE is 1740MW, which is suitable for the gradient boosting model. H-24 and H-48 lag is the most important for this job. Weekends and holidays help, but less. Regional holidays, average temperatures, and lag characteristics could improve the model (beyond H-48).Öğe Comparison of skyscraper building design : isolated foundations vs. non-isolated foundations(Altınbaş Üniversitesi, 2023) Rahimi, Mahdi; Naimi, SepantaAs the population grows, the need for safe and comfortable places to live and work increases. Tall buildings are becoming a popular option as they enable efficient use of urban space and provide solutions to the challenges of urbanization. However, the safety of these buildings against natural hazards such as earthquakes is crucial. Engineers are putting a lot of effort into the construction and design of tall buildings, some of which have been successfully completed, while others have been halted due to design difficulties. High-rise buildings have challenged engineers' endurance due to their height and number of floors. Efforts have been made to increase the resistance of tall buildings to natural factors and the weight of the building. One of the most important factors is the building's vibration movement, which must be resistant to wind, loads on the building, and earthquakes. One way to increase the strength of the building is to use isolated foundations. This article investigates whether using an isolated foundation has an effect on the strength of tall buildings. Two 51-floor tall buildings were designed, one with an isolated foundation and one without. The differences between the buildings were evaluated in terms of earthquakes, the greatest risk for buildings. Using the ETABS program and following international standards, these two buildings were designed to withstand the forces of wind, earthquakes, and loads on the building. The loads and forces caused by earthquakes, the resistances and possible damages of both types of buildings were also evaluated.Öğe A modified salp swarm optimization algorithm based on the load frequency control of multiple-source power system(Altınbaş Üniversitesi, 2023) Al-Zubaidi, Anas Mahdi; Cansever, GalipThis work proposes a modified Salp Swarm Optimization Algorithm (SSA) for addressing a multi-source power state's Load Frequency Control (LFC). A controller parameter tuning of the SSA method and its application to the LFC of a multi-source power system with several power generating sources. Derive to the controller parameters, a single area telecommunications device that permits two power system with integrated controlles according to each unit is considered first, and the SSA approach is used. The tunned SSA algorithm is used to optimize the integral (I), proportional integral (PI), and proportional integral derivative (PID) parameters. The research is expanded to include a multi-area multi-source power system, as well as an HVDC link is proposed for connectivity of two regions in addition to the current AC point of intersection. This same tunned SSA method is used to improve the parameters of the Integral (I), Proportional Integral (PI), and Proportional - integral - derivative Derivative (PID). Consequently, the suggested system is shown to be resilient and unaffected by changes of the loading situation, system parameters, or SLP size.Öğe A missing data imputation method based on grey wolf algorithm for diabetes disease(Altınbaş Üniversitesi, 2023) Ahmed, Anas; İnan, TimurThe bulk of medical databases contain coverage gaps due in large part to the expensive expense of some tests or human error in documenting these tests. Due to the absence of values for some features, the performance of the machine learning models is significantly impacted. Consequently, a specific category of techniques is necessary for the aim of imputing missing data. In this study, the Grey Wolf Algorithm (GWA) is used to generate and impute the missing values in the Pima Indian Diabetes Disease (PIDD) dataset. The proposed method is known as the Pima Indian Diabetes Disease (PIDD) Algorithm (IGW). The obtained results demonstrated that the classification performance of three distinct classifiers, namely the Support Vector Machine (SVM), the K-Nearest Neighbor (KNN), and the Naive Bayesian Classifier (NBC), was enhanced in comparison to the dataset prior to the application of the proposed method. In addition, the results indicated that IGW performed better than statistical imputation procedures such as removing samples with missing values, replacing them with zeros, mean, or random values.Öğe An integrated method for refocusing of moving targets in spotlight SAR(Altınbaş Üniversitesi, 2023) Papila, Ibrahim; Paker, Selçuk; Kartal, MesutA new target-refocusing technique based-on re-centering phase computation of previously recorded moving target raw data is implemented to the Spotlight SAR data in order to obtain refocused moving targets. The technique is tested on the integrated simulated data; background real spotlight SAR Raw data with the synthetically generated data domes of civilian moving targets. First Polar format Algorithm is applied to detect and estimate the speed of ground-moving targets on the integrated raw data. At the next step, re-organize the integrated raw data by selecting and arranging target focusing center with a new technique based on re-centering phase computation to each moving target speed. At the third step re-organize the raw data by re-centering the phase computation to each moving target location. Finally, Polar Format Algorithm is applied to each reorganized raw data to obtain highly focused moving targets individually.Öğe A platform proposal for the evaluation of construction and demolition wastes within the concept of zero waste in İstanbul(Altınbaş Üniversitesi, 2023) Sawaby, Amany Lofty A. S.This research investigates the economic benefits of zero-waste strategies in Istanbul's construction and demolition (C&D) waste sector. Current waste management practices, challenges, and stakeholder perspectives are analyzed to identify improvement opportunities. A case study demonstrates the potential of zero-waste principles in the C&D sector, emphasizing financial savings. This research introduces the Zero Waste Construction Material Exchange (ZWCME) platform, a digital solution connecting waste generators with potential users and recyclers, promoting reuse, recycling, and upcycling of C&D waste materials. The research explores industry implications, identifies enablers and barriers to zero-waste adoption, and examines the policy and regulatory framework needed to support the ZWCME platform. Results highlight significant potential for economic benefits through zero-waste strategies in Istanbul's C&D waste sector, emphasizing stakeholder collaboration, supportive policies, and innovative solutions like the ZWCME platform.Öğe Malware detection using deep learning algorithms(Altınbaş Üniversitesi, 2023) Altaiy, Mohammed; Yıldız, İncilay; Uçan, BahadırBackground/aim: The aim of this study is to benefit from deep learning algorithms in the classification of malware. It is to determine the most effective classification algorithm by comparing the performances of deep learning algorithms. Materials and methods: In this study, three deep learning methods, namely Long-Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Multitasking Deep Neural Network (DNN) were used. Results: According to the findings obtained in malware detection, the best results were obtained from LTSM, CNN and DNN methods, respectively. With the three deep learning algorithms, the average Accuracy was 96%, the Precision average was 97%, and the Recall average was 97%. Conclusion: According to the most effective results obtained from this study, Accuracy 0.982, Precision 0.988 and Recall 0.990.Öğe Using feature selection and ACO algorithm for optimizing smart classroom(Altınbaş Üniversitesi, 2023) Abd Ali, Dhuha Abdulameer Abd Ali Abd Ali; Balık, Hasan HüseyinThe smart education had a huge impact on learning and teaching, so it must be effective and highly efficient. An efficient smart campus or smart classroom will make the learning more and more easily, the students could learn and give the best activities. In addition, the teachers will be able to make right decisions. To achieve this goal, the smart classroom's conditions must be ideal. Since ACO (ant colony optimization algorithm) is a meta heuristic algorithm, in this paper, it is found that ACO, in conjunction with a machine learning classifier, was an effective method used in feature selection for selecting best features from an intelligent campus data set to create an environment that is conducive to academic success and student learning, such as (humidity and temperature), lighting and sound pressure levels, wind direction, and raw rainfall amounts (among other variables). In this contribution to get the most accurate results, the ACO algorithm was combined with a logistic regression classifier that was used to select the best features. The accuracy of the proposed model was 0.927438624 and 0.898268071 for two sets of data back to the School of Design and Environment 4, building located at the National University of Singapore.
