Anomaly Detection by Utilizing Hybrid Model for Predictive Maintenance in Minnesota PV Solar Plant

The fundamental aim of this research is to develop a face detection system for a quadcopter in order to follow the face object. This research has two main stages, namely the face detection stage and the position control system. The face detection algorithm used in this research is the haar cascade method which is run using the python and OpenCV programming languages. The algorithm worked well, getting around 16fps on a low spec computer without a GPU unit. The results of the face detection algorithm are proven to be able to recognize faces from the camera installed on the DJI Tello mini drone. The mini drone was chosen because it is small and light, so it is harmless, and testing can be carried out indoors. Besides, the DJI Tello can be programmed easily using the python programming language. The drone’s position is then compared with the set point in the middle of the image to obtain errors so that control signals can be calculated for up/down, forward/backward, and right/left movements. From the testing results, the response speed that occurs in the right/left and up/down movements is less than 2 seconds, while for the forward/backward movement, it is less than 3 seconds.

The social media time line, especially Twitter, is still interesting to follow. Various tweets delivered by the public are very informative and varied. This information should be able to be used further by utilizing the topic of conversation trends at one time. In this paper, the authors cluster the tweet data with the TF-IDF algorithm and the K-Mean method using the python programming language. The results of the tweet data clustering show predictions or possible topics of conversation that are being widely discussed by netizens. Finally, the data can be used to make decisions that utilize community sentiment towards an event through social media like Twitter.

Abstract. This article explores the possibilities and advantages of using the Python programming language together with Arduino robotics kits in the process of training future computer science teachers in pedagogical educational institutions. Considerable emphasis is placed on analyzing the principles of operation of the simplest programs and devices that can be used to visualize the possibilities of using the Arduino platform and Python libraries to create robots that can be implemented in the educational process and everyday life. The authors have developed the program code and presented a working model of a robot assembled and programmed on the basis of Arduino components to measure humidity in computer laboratories and create automatic plant irrigation systems to maintain proper conditions in classrooms. The article presents an example of integrating the learning of the Python programming language with the use of Arduino robotics kits. This method is aimed at improving the quality of training of future computer science teachers, expanding their methodological arsenal and ensuring the ability to teach students using innovative means. The results of the study and the developed teaching and learning materials will help to interest students in STEM education and prepare a new generation of computer science teachers for the challenges of the modern technological world, expand their methodological arsenal and develop the ability to use integrated technical, engineering and mathematical solutions to solve theoretical and practical problems. In addition, this study can serve as a guide to popularize the use of Arduino and Python in educational institutions, and encourage future computer science teachers to implement STEM and hands-on teaching methods, which will contribute to better student learning and improve the quality of professional education in the field of information technology in general. In general, the study opens up a wide range of opportunities for using modern digital technologies in professional education and stimulates interest in STEM education among computer science teachers and students, thereby contributing to the development of a new generation of engineers and technology leaders. Further research could be aimed at developing specific training courses and methods for integrating the Python programming language and Arduino robotics kits into STEM education. It is important to create interdisciplinary STEM courses with the participation of teachers of computer science, physics, mathematics, vocational education in the IT industry, etc.

This paper examines the performance of different machine and deep learning algorithms in classifying colon histological images using different feature extraction methods. The relationship between the feature extraction methods and the selected machine learning methods to improve the classification accuracy is analyzed. Widely used methods like local binary patterns, histograms of oriented gradients, Gabor filter and Dobeshi wavelets are investigated for feature extraction from colon histological images. The features extracted by histogram of oriented gradients and Gabor filter methods were used as a single joint feature vector. And popular machine learning methods such as Support vector machine, Decision trees, Random forest, k-nearest neighbors and Naive Bayesian method were used to classify the selected images. The paper also investigates ensemble methods using gradient bousting and voting classifier as examples. The authors also focus on the study of convolutional neural networks as they are one of the main deep learning methods at the moment. The classification methods selected for analysis are compared in terms of classification accuracy and time taken for training and recognition. All pre-defined and adjustable parameters of both feature extraction methods and classification methods were personally selected by the authors as a result of experimental studies, which were conducted using a software tool created in the Python programming language on a set of LC25000 histological images. The software created is easily customizable and can be used in the future to investigate classification methods on other datasets.

Python programming language is an increasingly widespread tool used for solving general computational problems in a variety of applications. In 2018, at the Zagreb University of Applied Sciences in Croatia, we introduced laboratory-computer exercises to a physics course in which Python was used to solve problems and to process the results of measurements. Students of Information Technology who attended our course had already completed a course in Programming basics in the previous semester. Our objective was to encourage IT students to apply the programming skills they had acquired to solve problems in other subjects. Before introducing Python, we used to solve problems by explaining the physical framework of the problem, making a sketch, writing down formulae and solving them on the blackboard. With the new approach, we additionally analyze problems using coresponding Python programs which were designed to provide further insights into the problem through graphical representation and additional visualization. This proved to be particularly beneficial during the 2020 lockdown, when we transitioned to online classes. The purpose of this paper is to demonstrate several examples of how we solved problems using the Python programming language and to analyse the reactions of students to this new way of teaching physics.

Background: Frailty syndrome is one of the most important multifactorial medical syndromes, which is characterized by a decrease in functionality of many systems and organs. Material and methods: In order to establish the nutritional determinants that contribute to the onset of frailty syndrome, a study was performed on a group of 50 patients, aged ≥65 years with chronic pathologies and geriatric syndromes. All participants were examined according to clinical features (history, clinical examination), Mini Nutritional Assessment and of the Complex Geriatric Assessment, which included: the data of the frailty tools, age category, Vulnerable Elders Survey, Charlson Comorbidity Index, autonomy – Activity Daily Living, Instrumental Activity Daily Living, Tinetti scale, psychoaffective status – by memory test Mini-Mental State Examination and the Geriatric Scale of Depression in the context of nutrition in the elderly. A clustered analysis (k-means method) of nutritional status showed that the most relevant indicators that separated the clusters were: age category, gender, clinical scale of frailty, comorbidities and polymedication. Results: Frailty through the multidimensional aspects that it meets has an increased prevalence among the elderly with an unfavorable prognosis. Following the proposed study, it was revealed that insufficient nutrition and comorbidities can lead to the weakening of the institutionalized age. The results obtained by evaluating the bio-psycho-social aspects characterize the profile of the institutionalized elderly and can be used as a basis for the development of effective strategies aimed at reducing physical, cognitive and social frailty. Conclusions: The comparative evaluation between both groups of elderly people by gender, showed a normal nutritional status with a higher share in women in the group of 75-84 years, compared to older men, and malnutrition was practically manifested equally in both groups in the study (men/women)

Nowadays machine learning (ML) is used in all sorts of fields like health care, retail, travel, finance, social media, etc. ML system is used to learn from input data to construct a suitable model by continuously estimating, optimizing, and tuning parameters of the model. To attain the stated, Python programming language is one of the most flexible languages, and it does contain special libraries for ML applications, namely SciKit-Learn, TensorFlow, PyTorch, Keras, Theano, etc., which is great for linear algebra and getting to know kernel methods of machine learning. The Python programming language is great to use when working with ML algorithms and has easy syntax relatively. When taking the deep-dive into ML, choosing a framework can be daunting. The most common concern is to understand which of these libraries has the most momentum in ML system modeling and development. The major objective of this paper is to provide extensive knowledge on various Python libraries and different ML algorithms in comparison with meet multiple application requirements. This paper also reviewed various ML algorithms and application domains.KeywordsMachine LearningLibrariesPython

To improve the prediction accuracy of photovoltaic (PV) power generation, the temperature of PV modules and its prediction is very important. A short-term step-wise temperature prediction model for PV module based on Support Vector Machine ( SVM) is proposed in this paper. Firstly, the primary impact factors of PV module temperature are determined in terms of the PV module physical characteristics and the correlation coefficient between each factor and module temperature. Secondly, two kinds of prediction models, direct and step-wise models, are built to predict module temperature respectively. For direct model, the prediction model is based on SVM using the historical data. For step-wise one, the prediction models of the primary impact factors are built at the first step and then the mapping models between module temperature and the impact factors are established. In the end, the deviations of two different kinds of prediction models are analy%ed and discussed based on actual operation data. The results indicate that the stepwise prediction model has better accuracy than the direct prediction model if other things are equal.

The rapid growth of energy demand has put tremendous pressure on the power system. In order to ease the pressure of the power system and balance supply and demand in the peak periods, we devise an integrated demand response (IDR) program by taking different types of consumers into consideration. Different from most of the existing works that model the responsive demand without classification, we classify consumers into different clusters using the method of k-means. Based on the classification result, the responsive demand models are obtained through historical data. The proposed IDR program is modeled as a nonlinear programming problem. By establishing the Karush-Kuhn-Tucker conditions, the closed-form optimal energy scheduling strategy is given. Moreover, we design an incentive pricing mechanism from which utility company can make optimal decisions. Simulations validate that the proposed IDR can solve the problem of the imbalance between supply and demand in the peak periods and the total costs of utility company can be reduced by implementing different incentive prices for different clusters of consumers.

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2021 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research. Keywords: Research methods in machine learning, machine learning algorithms, machine learning techniques.

The article provides a comparative analysis of web frameworks based on the Python programming language. The right choice of a software development tool will allow developers to speed up the process of designing and software implementation of web resources. With the development of programming languages, solutions have begun to appear that make it easier for programmers to create web applications. In recent years, many tools of this type have been developed, including web frameworks. Some of them are used to develop small web applications, and other frameworks are used to create large-scale systems. The existing web frameworks differ from each other, but choosing them can be a difficult task for a programmer. Therefore, it is necessary to specify several parameters that are common to frameworks, according to which the desired web framework should be selected. The selected criteria make it possible to evaluate the frameworks more qualitatively and more objectively and speed up the process of design and software implementation of web resources. Due to the high demand for web applications, developers need to develop cost-effective, secure and well-coded web applications. Analyzing the Django framework, it can be argued that it is the most popular for web development, written in the Python programming language and using the MVC architecture. It is ideal for application projects with limited time and a small budget. If the application is not very voluminous and only simple URL routing and templates with a simple context are needed, then the Flask framework can be used instead of Django. The framework for building a Flask application in the resulting HTML files contains additional tags and scripts. The mechanism of work of processing forms also works differently. Flask stores more information about form fields on pages. The size of HTML files created using the Flask framework turns out to be significantly larger.

In recent years, different countries have implemented the teaching of computer programming from the first grade in schools, with the aim of incorporating computational thinking as a new way of thinking that is a necessary skill for scientific and technological development, a fundamental axis for development in the 21st century. Recent reviews of the state of the art have shown that this task is only being carried out by countries that have given space to technology and science, incorporating it at the elementary school level. However, developing countries do not yet consider the significance of the issue, so to date they have not taken the necessary steps in this direction. In addition, learning computer programming is fundamental for countries to join technological development, so that they can be creators of technology and not just users of it. The problem is that there is no didactic development for the teaching of programming, nor validated evaluation instruments to quantify the learning of computer programming. The objective of this research is to validate a programming test that evaluates technical skills in the Python programming language. The instrument proposed to be validated is a 90-item test, which after validation by experts was reduced to 70 items, and after the psychometric analysis that considered the calculation of reliability, difficulty and discrimination indexes, resulted in the proposed 45-item instrument, as a standard instrument for the evaluation of learning in the Python programming language. It should be noted that the validation methodology was carried out using the classical theory of psychometric analysis.

This study aims to explore the application of artificial intelligence algorithms in the development of a simple 2D game called Cuypy using the Python programming language. Cuypy is designed to provide an interactive, educational, and challenging gameplay experience. Artificial intelligence is implemented to enhance enemy intelligence, game adaptability to player behavior, and overall system responsiveness. The development focuses on the integration of pathfinding algorithms and machine learning techniques to improve enemy strategy, enabling non-player characters to learn from player actions and adjust their behavior dynamically to increase the level of challenge over time. Additionally, this study emphasizes the use of reinforcement learning mechanisms that allow enemies to predict player movement patterns and automatically adapt their strategies. Pygame is utilized as the graphical library to build the user interface and animations, while Python serves as the main programming language supporting efficient AI implementation. The novelty of this research lies in the real-time behavioral adaptation of game enemies based on player interactions, a feature that is rarely explored in simple Python-based games. This study also highlights the integration of pathfinding algorithms to ensure more realistic, adaptive, and engaging enemy movements. Overall, the research contributes to the development of interactive Python-based games and provides insights into applying AI to simple educational games with automatically adjustable difficulty levels

Compiler design plays an important role in ensuring that the translation of the programs written in high-level language into executable code is correct. However, in todays’ safety-critical environments, security gaps, visible and hidden defects in compiler models are liability factors that needed to be addressed in the process of examining that a system meets specifications and requirements of its intended objectives. Nevertheless, defects in this model are errors in coding which bring about incorrect or unexpected results from a compiler design that does not meet the actual requirements. Hence, this paper developed a novel approach by integrating the computational power of Communicating Stream X-Machine (CSXM) to address the problem associated with compiler correctness. In addition, this paper outlines the development of a novel compiler design called X-compiler, emphasizing its important role in software development. CSXM technique was implemented in Visual Studio (2022) to develop the X-compiler. The X-compiler utilize state-of-the-art techniques to translate program source code written in high-level programming language which is the blend of C# and Python programming language into executable code. The development process includes the lexical analysis, syntax parsing, semantic analysis, and code generation ensuring the correctness of the X-compiler. The results of the compiled code were run to produce output in command Windows environment for user interactions. Also, highlights the potential for enhanced language interoperability and the development of efficient compiler that leverage the strengths of multiple programming paradigms. Overall, the study on CSXM contributed to a deeper understanding of concurrent systems, while the design and implementation of the compiler showcased the feasibility of creating a synergistic blend of C# and Python programming languages.

Relevance. Artificial intelligence tools are playing an increasingly important role in food technology and biotechnology, significantly accelerating and improving various processes. Operational control of colorimetric parameters of cherry extracts using computer vision allows for a quick assessment of the content of bioactive substances without the cost of experimental studies. Methods. The objects of the study are samples of cherry extracts. Dilutions from 2 to 0.25% were prepared to obtain images of the test samples. The color characteristics of the extracts were determined using an NR60CP colorimeter. Quantitative determination of the total content of polyphenols in solutions of extracts of different concentrations was carried out using the Folin — Ciocalteu method. RGB images from a digital camera (50 MP) were obtained as input data for classifying objects and compiling a database. The Python programming language, OpenCV library and TensorFlow were used to develop image processing software. TensorFlow extracts features from photographs and adds them to the database. Results. The research results showed that with an increase in the content of bioactive substances — polyphenols — the color of the extracts changes and becomes darker, which is confirmed by the results of the evaluation of color characteristics with a colorimeter: with an increase in concentration, the redness increases and the lightness of the samples decreases. A database was created by extracting features using the TensorFlow library from ready-made sample images. A program for assessing the content of polyphenols in extracts was developed in the Python programming language. The described model of the system for monitoring the quality of extracts using computer vision can facilitate the determination of the content of bioactive substances on an industrial scale, which requires significant time and resources.