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Cost Forecasting in Organizations
Get PDFIn this paper, the economic relationship between oil prices and operating expenses in the E&P sector is examined, along with the implications for capital budgeting and decision-making. We provide empirical evidence that the price and operating costs are positively correlated, and that failing to take this relationship into account has serious repercussions for the valuation of investment projects when using either the conventional Net Present Value (NPV) methodology or the Real Option approach. In the conventional NPV method, projects that are undervalued as a result of ignoring a price-cost correlation tend to be overvalued in Real Options Analysis, and vice versa. Purpose: The objective of the study was to demonstrate the influence of the relationship between oil prices and operating expenses on the assessment of oil prospects, and to highlight the potential consequences of neglecting or underestimating this correlation. Relevance: It presents the importance of ignoring or overlooking the relationship between the price of oil and operational expenditures, which might lead to the wrong decision when investing in a certain project. Key methodological aspects: This paper examines the impact when the pricecost correlation is ignored in the final investment and decision-making process. To investigate this, used the widespread traditional NPV approach and the Real Options Valuation approach. Data was taken from the local Operating company’s one of future projects. Summary of Key Findings: This paper shows that neglecting price-cost correlation has substantial effects, independent of the valuation methodology used (conventional NPV and/or real options approach). Ignoring the price-cost link leads to an overestimation of risk in NPV assessment, which leads to an underestimating of the investment project's value. When genuine option analysis is used, disregarding crosscorrelation results in more volatility and, as a result, exaggerated project values. iv Key conclusion: The final investigation shows that during the decision-making process, it’s important to use various instruments and not to ignore some economic processes that might impact the project's expected outcomes
Жерге жеке меншік құқығын табыстау процесін цифрландыру кезіндегі тəуекелдер
Get PDF«Цифрлық Қазақстан» бағдарламасы республика экономикасын дамытуға және алдыңғы қатарлы геоақпараттық технологияларды пайдалана отырып, барлық қызметтерді цифрлық форматқа көшіруге және статистикалық деректермен жұмыс істеудің жаңа моделін құруға, біртұтас ақпараттық кеңістікті, сондай-ақ деректердің жаңа сапасын қамтамасыз етуге бағытталған. Цифрландыруға көшу барысында бұл бағыттағы шетелдік тәжірибелер диссертациялық жұмысымда зерделенді. Цифрлы платформа статистикалық ақпаратты жинауға, сақтауға, өңдеуге және тарату процестерін үздіксіз автоматтандыруды қамтамасыз ететін ұлттық деректерді басқару жүйесінің элементтерінің біріде бірегейі болары сөзсіз. Платформаны құру есептілікті азайтуға көмектеседі. Сонымен қатар, бұл осындай ақпаратты тарату көлемін арттыруға, пайдаланушылардың осы деректерді алу қызметтерінің қолжетімділігі мен ыңғайлылығына ықпал етеді. Жер ресурстарын басқарудың тиімділігін, ашықтығын және есептілігін арттыратын бірнеше пайдалы және ауқымды процестерді дамытуға әкеледі. Бұл үдерістер азаматтардың мүмкіндіктерін кеңейтіп, экономикалық өсуді жеңілдетіп, жерді басқаруды цифрландыруды тұрақты дамудың маңызды құралына айналдырады
ҚАЗАҚ ТІЛІНДЕГІ МӘТІНДІ ТҮЗЕТУ
Get PDFБұл жұмыс қазақша мәтіндегі орфографиялық қателерді түзетеді. Нақтырақ айтатын болсақ, мәтін енгізілген кезде түрлі қателер кетуі мүмкін. Жұмыстың мақсаты – сол қателерді дұрыстау жолдарын қарастырып , мүмкін болатын нұсқаларды ұсыну
Automating banking sector monitoring procedures for exceptional situations
Get PDFThe need to detect anomalous events and react to them immediately in real time is becoming increasingly important in the banking sector. The main objective of this thesis is to propose the development of a real time alert notification system that uses outlier detection algorithms to discover unexpected trends in the key performance indicators of a financial industry. In order to enable real-time monitoring of data streams and notify users of anomalous occurrences as they happen, the system will take advantage of the capabilities of cloud computing and big data technologies. The proposed system will be evaluated against traditional outlier identification techniques. The efficacy of the outlier detection algorithms for the banking dataset is assessed using precision, recall, and Fl score measurements. The approaches of sending alerts are evaluated, with the strengths and weaknesses of each method taken into account. This thorough evaluation approach aims to emphasise the advantages and disadvantages of the suggested system as well as identify potential areas for improvement. The suggested system will allow users to take proactive action to lessen the consequences of abnormal events, reduce the risk of costly downtime and other adverse effects
ГЕОМЕТРИЯЛЫҚ ЕСЕПТЕРДІ ШЕШУДІҢ ОҢТАЙЛЫ ТӘСІЛДЕРІН ІЗДЕУ
Get PDFАңдатпа. Қазіргі уақытта біз қоршаған ортада болып жатқан көптеген жаңа құбылыстарды байқаймыз. Бұл құбылыстар кез-келген адамнан жаңа каснепердін бплуын талап етеді. Ең алдымен қоғамға стандарттан тыс ойлайтын. 3 ойы негізінде шешім шығаратын және жаңа бастамаларды ұсынатын тұлғалар қажет. Осындай қасиеттерді математика сабағында немесе сабақтан тыс уақытта есептерді шығару арқылы қалыптастыруға болады. Геометрия оқулығында геометриялық есептерді шешу көбінесе стандартты алгоритмге негізделеді. Бұл алгоритм оқушылар үшін күрделі болуы мүмкін. Сондықтан геометриялық есептерді шешудің басқа әр түрлі әдіс-тәсілдерін іздеп оңтайлы әдісті табу керек. Бұл мақалада геометриялық бір есепті шешудің бірнеше әдістерін көрсетіп оңтайлы әдісті табамыз. Геометриялық есепті әр түрлі әдіспен шешу оқушылардың математикалық білімін тереңдетеді және шығармашылық қабілетін дамытады
Identifying spam messages for Kazakh Language using Hybrid Machine Learning Model
Get PDFThe rapid growth of digital communication has led to an increase in unwanted spam messages, which pose a significant challenge for users. While numerous machine learning techniques have been employed to combat spam in widely spoken languages, there is a lack of research on spam detection in less common languages, such as Kazakh. In this study, we propose a hybrid machine learning model for identifying spam messages in the Kazakh language. Our approach combines the strengths of different machine learning techniques to enhance the accuracy and efficiency of spam detection. To evaluate the performance of our hybrid model, we manually collected a dataset of Kazakh language messages, which were labeled as spam or non-spam. The results demonstrate that our hybrid approach surpasses traditional machine learning methods in terms of accuracy, precision, recall, and F1-score. This model not only improves the efficiency of spam filtering but also enhances the user experience for Kazakh-speaking individuals and promotes the development of languagespecific spam detection techniques for other underrepresented languages
Higher education students’ English language level and engagement correlation in programming courses
No full textThis thesis tries to find out if there is a link between how well students know English and how much they participate in the computer class at university. The introduction to the topic gives a general idea of how English is used in Kazakhstan. The literature study talks about things like engagement and how English is used in Kazakhstan. It also defines engagement and talks about tools like Google Forms. As part of the study method, a poll will be given to students and their answers will be analyzed. Also, grades were used in the study, and the link between English level and end course grade was looked at. The method that was used to find this connection is explained. As a result of the work, it was found that how well a student knows English has a big effect on how involved they are in the learning process in a computer class. Given how important the English language is in computing, the results give real suggestions for how to improve the level of education and the efficiency of training
Grobner-Shirshov bases theory for Zinbiel stperalgebras
No full textThis thesis is a collection of 6 chapters .The Grébner-Shirshov basis is an impor- tant mathematical apparatus in algebra and commutative algebra, which is used to study and analyze polynomials and their ideals. The Grdébner-Shirshov basis has a number of important properties that make it a powerful tool for solving various algebraic problems, such as searching for ideals, solving systems of equa- tions and determining the basic invariants of polynomials. In this paper we will construct a Grobner-Shirshov basis for Zinbiel algebras. Algebra with the identity (ab) c = a(bc) + a(cb) is called the Zinbiel algebra. In the process of construct- ing the Grebner-Shirshov basis, two compositions are found and the composition lemma is proved. The method of mathematical induction is used to prove the lemma
Identification of Students at Risk of Not Completing the Course Using Machine Learning
No full textThis dissertation is dedicated to the topic of identifying students at risk of not successfully completing a course at an early stage of their education using machine learning algorithms. In this study, the final exam score, academic performance category, and the risk group of students failing to complete the course are determined for accurate and detailed identification of students at risk. Research shows that machine learning algorithms such as LightGBM Regressor (for the final exam score prediction), Logistic Regression (for identifying two groups of students - those who will complete and those who will not complete the course), and K-Means (for identifying the academic category) can help identify students who need assistance from teachers with high accuracy of prediction. Detecting this group of students at an early stage of their education can enhance students’ motivation for further learning and assist teachers in individually and timely identifying which student requires help
Application for predicting the business orientation based on analysis of user desires
No full textIn today’s competitive business landscape, understanding customer desires and preferences is crucial for the success of any organization. Customer churn is a significant problem for companies and describes moving out of customers to competitors. Predicting such behavior in advance offers companies valuable insights, empowering them to take measures to retain their customer base and potentially d it. One key decision informed by data analysis involves identifying the development. Machine learning algorithms can data and predict business direction. This helps expand the most promising areas for business to be leveraged to analyze customer organizations, make data-driven decisions and tailor their products, services, and marketing strategies to meet customers expectations. The thesis work describes studies using their feedback, and analyzed parte for the company. To solve the problem, hms from different areas were used to how customer preferences and transactions to predict income several stages and machine learning algorithm ect for further use- Neural networks showed the best result in comparison and self business and linear regression to find out a profit. determining the direction. After that, a website and a motion of the company was visualized, and a page page where the saved model was used to predict a new partner and his income