Learning Python Research Articles

Markers of early post-stroke cognitive impairment

Aim. To identify significant indicators of cognitive dysfunction based on discriminant analysis and to assess the influence of the course, nature and localization of ischemic stroke on the cognitive status of the patient.Materials and methods. We examined 290 patients diagnosed with ischemic stroke in the carotid artery area. Depending on presence of cognitive dysfunction according to the Montreal Cognitive Assessment Scale (MoSA) patients were divided into 2 groups: 240 patients with cognitive decline (≤25 point by MoCA) and 50 patients without it. In order to verify the markers, anamnestic characteristics were assessed, cognitive-functional indicators (according to the scales of the National Institutes of Health, MoCA, Bartel, Rankin, IQCODE questionnaire, additional scales to assess praxis, semantic aphasia, perception and executive function), data of neuroimaging studies. For statistical analysis machine learning algorithms and Python with its libraries (Pandas and SciPy) were implied.Results. The main neuropsychological indicators for patients with early post-stroke cognitive impairment were decline in the areas of perception, executive function, memory and semantic information processing, affective disturbances and physical fatigue. Relevant indicators identified during estimation of the instrumental and clinical examination results were severity of IS, left frontal and right parietal localisations of ischemia focus, presence of cortical atrophy and leukoaraiosis.Conclusion. Based on multi-factor analysis of clinical and paraclinical parameters using machine learning algorithms, the main markers of cognitive decline of early post-stroke impairments were identified. This will allow us to optimise the choice of neurocognitive rehabilitation strategies and to personalise the approach in the further management of the stroke patient.

Transforming Wikipedia text into audio through machine learning

Purpose The purpose of this study is to navigate the process of transforming Wikipedia articles into audio files for library readers. The system provides a feasible manner of listening to Wikipedia content, accommodating diverse learning preferences and enlarging knowledge in education society. Design/methodology/approach This framework has been constructed using the Python programming languages in the Linux operating platform. Application programming interface and Google text-to-speech (TTS) are required as additional software packages to design this prototype system. Transform any Wikipedia pages into audio files through Wikitrola for libraries and information centers. Wikipedia articles are directly transformed into audio, as these integrate the content seamlessly for the user experience. The whole system has been designed and configured on the basis of machine learning to provide dynamic services among the readers. Findings The viewer could use the machine learning system to turn Wikipedia articles into audio files, allowing them to listen to Wikipedia content in audio format. This would make information more accessible and adaptable to diverse learning modes, allowing written content to be engaged in novel and visionary ways. Originality/value The insightful observation in connection with the paper is that it shows how to convert text-based material into audio through the Google TTS and machine learning Python programming and finally incorporate them in Wikipedia articles. A harmonious system of information dissemination and technical education is established. This approach shows the effectiveness of imagination and the use of programming tools to enhance learning and knowledge-seeking processes.

An explainable artificial intelligence model for predictive maintenance and spare parts optimization

Maintenance strategies are vital for industrial and manufacturing systems. This study considers a proactive maintenance strategy and emphasizes using analytics and data science. We propose an Explainable Artificial Intelligence (XAI) methodology for predictive maintenance. The proposed method utilizes a machine learning project cycle and Python libraries to interpret the results using the Local Interpretable Model-agnostic Explanations (LIME) method. We also introduce an early concept of spare parts management, presenting insights from predictive maintenance outcomes and providing explanations for decision-makers to enhance their understanding of the influential factors behind predictions. This study demonstrates that utilizing machine learning models in predictive maintenance is highly beneficial; however, the binary outcomes of these models can be misunderstood by decision-makers. Detailed explanations provided to decision-makers will directly impact maintenance decisions and improve spare part management.

Multi-Objective Plum Tree Algorithm and Machine Learning for Heating and Cooling Load Prediction

The prediction of heating and cooling loads using machine learning algorithms has been considered frequently in the research literature. However, many of the studies considered the default values of the hyperparameters. This manuscript addresses both the selection of the best regressor and the tuning of the hyperparameter values using a novel nature-inspired algorithm, namely, the Multi-Objective Plum Tree Algorithm. The two objectives that were optimized were the averages of the heating and cooling predictions. The three algorithms that were compared were the Extra Trees Regressor, the Gradient Boosting Regressor, and the Random Forest Regressor of the sklearn machine learning Python library. We considered five hyperparameters which were configurable for each of the three regressors. The solutions were ranked using the MOORA method. The Multi-Objective Plum Tree Algorithm returned a root mean square error value for heating equal to 0.035719 and a root mean square error for cooling equal to 0.076197. The results are comparable to the ones obtained using standard multi-objective algorithms such as the Multi-Objective Grey Wolf Optimizer, Multi-Objective Particle Swarm Optimization, and NSGA-II. The results are also performant concerning the previous studies, which considered the same experimental dataset.

Learning Python Programming in Middle School - an Innovative path to Digital Literacy

In today's world, digital literacy plays a key role in preparing young people for future challenges. Learning to program, especially in Python, is becoming an increasingly important part of high school education. This article draws attention to innovative methods and approaches to implementing a Python programming curriculum in secondary schools, considering them as an effective path to developing digital literacy in students. The article discusses the benefits of learning Python, its role in developing programming and logical thinking skills, and offers recommendations for using innovative teaching methods such as online courses, project work, and competitions to achieve digital literacy goals in high school.

ASSESSMENT OF THE EFFICIENCY OF THE SUCCESS PREDICTION MODEL USING MACHINE LEARNING METHODS

The work evaluated the created model for predicting the success of users of the Moodle educational platform in order to determine the effectiveness and expediency of its use. The determination of the efficiency and quality of the model was performed according to the following indicators: sensitivity, specificity and balanced accuracy. Also, a ROC curve was constructed to reflect the classifier's ability to correctly recognize positive classes and reject negative classes when the threshold value changes, and the AUC (Area Under Curve) was determined. Methods of assessing success risks and requirements for creating models based on machine learning methods are analyzed. On the basis of user data from the database of the Moodle educational platform, indicators affecting the success of students were formed. A logistic regression model was built for predicting success, which was then tested in practice. The created model makes it possible to predict the success of students with an accuracy of 84%. The overall efficiency of the model is 89%. It was established that the Scikit-learn library provides an opportunity to create an effective model for solving classification problems in machine learning. The use of a logistic regression model to classify the success of users of the Moodle platform will allow creating a model that allows you to predict the success of students based on the collected data. Using machine learning methods and Python libraries, the quality and efficiency of the model was determined. A solution to the problem of predicting students' success is proposed by creating a model based on machine learning methods and the Moodle platform. A Python program was developed to analyze the data of users of the Moodle platform. The presented information shows that choosing the Scikit-Learn library will allow creating an effective model for processing data and predicting results. The use of the created model for forecasting will allow to quickly analyze the success of users and form, if necessary, appropriate ratings. The forecast obtained with the help of the model will be useful both for teachers and educational institutions. Because it will allow planning changes in educational programs and materials, as well as the educational process as a whole.

Geochemistry π: Automated Machine Learning Python Framework for Tabular Data

AbstractAlthough machine learning (ML) has brought new insights into geochemistry research, its implementation is laborious and time‐consuming. Here, we announce Geochemistry π, an open‐source automated ML Python framework. Geochemists only need to provide tabulated data and select the desired options to clean data and run ML algorithms. The process operates in a question‐and‐answer format, and thus does not require that users have coding experience. After either automatic or manual parameter tuning, the automated Python framework provides users with performance and prediction results for the trained ML model. Based on the scikit‐learn library, Geochemistry π has established a customized automated process for implementing classification, regression, dimensionality reduction, and clustering algorithms. The Python framework enables extensibility and portability by constructing a hierarchical pipeline architecture that separates data transmission from the algorithm application. The AutoML module is constructed using the Cost‐Frugal Optimization and Blended Search Strategy hyperparameter search methods from the A Fast and Lightweight AutoML Library, and the model parameter optimization process is accelerated by the Ray distributed computing framework. The MLflow library is integrated into ML lifecycle management, which allows users to compare multiple trained models at different scales and manage the data and diagrams generated. In addition, the front‐end and back‐end frameworks are separated to build the web portal, which demonstrates the ML model and data science workflow through a user‐friendly web interface. In summary, Geochemistry π provides a Python framework for users and developers to accelerate their data mining efficiency with both online and offline operation options.

The Effect of Teaching Physical Programming on Computational Thinking Skills and Self-Efficacy Perceptions Towards Computational Thinking

The study examined the effect of teaching text-based programming with a physical programming tool on secondary school students’ computational thinking skills and self-efficacy perceptions. The study was conducted according to a sequential explanatory design as a mixed method research. The study group consisted of 85 secondary school students. Within the scope of the study, a physical programming tool called Micro:bit was used to teach Python programming for a period of 6 weeks. Data were collected using the Self-Efficacy Perception Scale for Computational Thinking Skill, Bebras: International Challenge on Informatics and Computational Thinking Tasks, tests focused on programming tool, concepts, and processes, and through semi-structured interview questioning. According to the findings obtained from pretests and posttests, a significant and positive difference was found in the students’ computational thinking skills and self-efficacy perceptions towards computational thinking skill. As a result of having received instruction in programming, the students satisfactorily learnt the required programming concepts and processes. Through learning Python programming with a physical programming tool, the students not only gained the skills required to write appropriate syntax, and to test and debug code, but they also learnt programming concepts such as variables, conditional expressions, loops, and functions.

Data-Driven Circular Economy of Biowaste to Bioenergy with Conventional Prediction Modelling and Machine Learning

Rapid population growth has not only increased energy demand, but waste generation that has increased and introduced emerging pollutants into waste streams, posing sanitary and environmental risks. The purpose of this research was to investigate waste to energy (anaerobic digestion-bioenergy process) on integrated waste management and digitalization of biomethane production as a data-driven circular economy model across the value chain of the carbon cycle (transition from take-make-dispose to use-make-return) through waste quantification, characterization, biomethane potential test (BMP) to prediction of the biomethane production. Anaerobic digestion experiment was conducted at a laboratory scale to analyze biomethane production from diverse substrates such as food waste, cow manure, sewage sludge, and chicken manure, with an average pH of 7.58. The experimental results obtained were then modelled and simulated with Modified Gompertz, Logistic, and Richards models and compared to machine learning simulation using Python with Gompertz, Logistics, and Richards models. Modified Logistic model was shown to be the best-fit curve, with a coefficient of determination (R2) > 0.9 validating the conventional mathematical modeling and simulation performance. During simulation with machine learning (Python), the experimental results obtained from the cow manure substrate provided the best fitting curve to the training curve compared to other substrates with the highest average of R2 as 1.0 for training, validation, and test data. Cow manure had the best validation performance at MSE (mean squared error) of 25.36 at epoch 1.Graphical

Brain Tumor Detection and Classification Using VGG16 Deep Learning Algorithm and Python Imaging Library

Early diagnosis and treatment of brain cancer depend on the detection and categorization of brain tumors. Deep learning algorithms have produced amazing results in medical imaging applications including tumor identification. Most of this field's research has concentrated on applying CNN algorithms like VGG16, DNN, and ANN to this problem. This work describes the identification and classification of brain tumors using the Python Imaging Library (PIL) and the VGG16 deep learning algorithm. A dataset of 7000 MRI pictures categorized by tumor type served as the foundation for the research. The main objective of this study was to develop a high-efficiency, high-accuracy model. We suggested utilizing the VGG16 architecture and preprocessing images with PIL to ensure consistent images for training on a sizable dataset of brain magnetic resonance imaging (MRI) images. A novel technique we have used in our work is one that can analyze a single image and predict the presence of a tumor from the results. The research's methods produced robust tumor detection across the dataset with 96, 9% accuracy, indicating the value of the method in helping medical professionals make informed decisions when diagnosing the presence of tumors.

Combining Building Block Process With Computational Thinking Improves Learning Outcomes of Python Programming With Peer Assessment

The capability of computer programming language logic is one of the basics of technical education. How to improve students “interest in program logic design and help overcome students” fears of coding has become vital for educators. Cultivating practical talents with information technology application and basic programming development will become one of the important topics in the department of information related science. The objective of this research is to improve the ability of learning basic programming courses by using Zuvio interactive software. Zuvio employs the mathematical logic of computational thinking to analyze problems and enhance learners’ interest in learning programming skills through a graphical interface tool with building blocks. It uses innovative interactive teaching to use peer and self-assessment to study the content of the course. Zuvio improves the design ability of different groups of class learning Python programming. In line with the innovative teaching policy of the schools and the current stage of the learner’s learning model, learning effectiveness can be achieved. The research results were analyzed by midterm and final experimental group scores, and the progress of the experimental group’s scores was examined through descriptive statistics. The average and standard deviation of the assessment were used to analyze the progress of the experimental group students in the programming course. In the classroom, assessment criteria were set up as the basis for peer assessment scoring. After the midterm and final exams, the teacher assessment and peer assessment scores were analyzed for cognitive differences, and possible learning differences were analyzed. The students’ professional ability was examined to see if it met the professional standards required by the course, and whether innovative teaching methods could improve the learning outcomes of learners with different professional backgrounds in Python programming.

Grading of Pomegranate Fruit using CNN

Farmers require an automated system to grade Pomegranate fruits rather than a manual system to increase productivity and quality of Pomegranate fruits. Manual grading of fruits does not produce adequate results and requires additional time for disease identification and gradation, as well as the expertise of an expert, making it ineffective. The suggested system is an efficient module that identifies various pomegranate fruit disorders and determines the stage of sickness. Effective growth stage monitoring and disease detection are critical for maximising pomegranate fruit yield and quality. This paper describes a method for monitoring the growth stages of pomegranate fruit utilising image processing techniques and disease detection approaches based on machine learning algorithms. The suggested method analyses colour, shape, and texture information taken from photos captured at various phases of development to track the growth stages of pomegranate fruit. The obtained data is then utilised to train machine learning models that appropriately distinguish the growth stages. The models are trained on a dataset of annotated photos containing numerous pomegranate fruit illnesses. Farmers and agricultural specialists can use the developed technology to correctly monitor the growing phases of pomegranate fruit and detect problems. The camera in this project catches various pomegranate fruit stages and classifies pomegranate fruits into two classes: infected and non-infected, using a machine learning algorithm and Python tools. This study employs a CNN, K-mean method, and image processing technique to detect illnesses at various phases of fruit development.

Bio-oil production from ripe pawpaw seeds and its optimal output: Box-Behnken Design and Machine Learning approach

The purpose of this study is to use ripe pawpaw seeds for a rapid Soxhlet extractor process with n-hexane to increase the yield of the bio-oil and to make the bio-oil produced a beneficial addition to the field of bio-oil production parameter optimization and utilization for industrial applications. Thus, the produced bio-oil from kwale ripe pawpaw seeds was modeled and optimized (Ripe Carica papaya seeds) via Soxhlet extractor using Box-Behnken Design from Response Surface Methodology and Machine Learning (Python code) techniques. The result from Box-Behnken Design shows that the highest yield obtained was 23.93 wt.% at 55 g of Sample weight, 50 min of Extraction time, and 250 ml of Solvent volume while the highest value of the bio-oil yield from the Machine Learning approach is 23.97 wt.%, which is closely related to the value (23.93 wt.%) obtained from Box-Behnken Design. The R2 of the model from Box-Behnken Design was 0.9786 while the R2 from Machine Learning was 1.0. Also, the visualization in Machine Learning was more appealing than in Box-Behnken Design. Thus, Machine Learning via python coding was more reliable and effective than Box-Behnken Design in terms of prediction and accuracy of bio-oil production. Both models, however, delivered a reliable reaction under the operational conditions considered. The physicochemical characteristics of the bio-oil from ripe pawpaw seeds also meet the requirements for bio-oil. Thus, this study concluded that green biowaste oil obtained from Kwale ripe pawpaw seeds waste might be used to produce biodiesel as well as to cool spinning equipment parts.

MACHINE LEARNING UNET-BASED SEGMENTATION OF SENTINEL-1A SATELLITE IMAGES

Image segmentation is an area of study which has generated a lot of interest in engineering in the last few years. Segmentation of Sentinel 1A satellite images is an area that is not as common as that of its optical counterparts as a result of the appearance of its images. The focus of this paper is the segmentation of Sentinel-1A Synthetic Aperture Radar (SAR) satellite images using UNet, a Deep learning technique designed specifically for semantic segmentation of images. The model was developed with Python programming language in keras, which is a deep learning Python API running on the Tensorflow framework. The satellite images were acquired from the European Space Agency’s (ESA) Copernicus satellite acquisition hub. The performance evaluation was carried out with Jaccard Index or Intesection over Union (IoU) and Accuracy metrics. The IoU metric measures the number of pixels common between the target and prediction masks divided by the total number of pixels present across both masks while the Accuracy metric reports the percent of pixels in the image which were correctly classified. The results recorded in the evaluation had IoU as 0.42 and an Accuracy of 95%. This show a high performance of the model in segmenting images.

Review of assessment methods for dementia

AbstractBackgroundMore than 50 million people worldwide live with various types of dementia, which incorporates a progressive decline in patients' cognition. Early detection of dementia in older adults is crucial and helps them apply for intervention programs. Numerous assessment methods have been developed to be used in clinical practice and help clinicians accurately detect dementia and diagnose its types. The recent advancement in the artificial intelligence domain has revolutionized the early detection of dementia and allowed clinicians to use AI‐based assessment technologies for detecting dementia at its very mild decline stage. The AI community aims to develop valid, efficient, practical, reliable, and accurate AI‐based assessment technologies to detect dementia; thus, to help the community, this paper reviews clinical assessment methods and recently developed AI‐based assessment technologies, particularly AI‐based language and speech assessment techniques. Moreover, it collects information about available datasets, python libraries for developing AI assessment methodologies to detect dementia.MethodWe have extracted around 300 peer‐reviewed articles from PubMed and MEDLINE related to AI‐based assessment technologies (i.e., focusing on automated language and speech‐based assessment methods) and cognitive, behavioral, neurophysiological, functional assessment methods.ResultBy reviewing more than 50 various clinical and AI assessment methods, we listed the advantages and limitations of clinical and AI assessment methods and available machine learning datasets, python scripts, and libraries to develop AI assessment methods to detect dementia.ConclusionThe paper could provide valuable information for the AI community to have better progress in developing quick, accurate, multicultural, practical assessment technologies with high specificity and sensitivity. The paper can be beneficial for mental health clinicians to learn about known AI‐based assessment methods to detect dementia.

Multiple Disease Prediction Using ML

Accurate and on-time analysis of any health-related drawback is vital for the interference and treatment of the sickness. The normal method of diagnosing might not be sufficient within the case of a significant illness. Developing a medical diagnosing system supported machine learning (ML) algorithms for prediction of any unwellness will facilitate during a lot of correct diagnosis than the standard methodology. We've designed a disease prediction system using ML. Disease Prediction System using Machine Learning could be a system that predicts the sickness supported data or symptoms that he/she enter into the system and gives correct results supported that data. This predictive disease using Machine Learning is completed entirely with the assistance of Learning Machines and Python programing language with its Flask Interface and mistreatment antecedently offered databases with hospitals that use that we'll predict the unwellness.

The Effect of Automated Error Message Feedback on Undergraduate Physics Students Learning Python: Reducing Anxiety and Building Confidence

STEM fields, such as physics, increasingly rely on complex programs to analyse large datasets, thus teaching students the required programming skills is an important component of all STEM curricula. Since undergraduate students often have no prior coding experience, they are reliant on error messages as the primary diagnostic tool to identify and correct coding mistakes. However, such messages are notoriously cryptic and often undecipherable for novice programmers, presenting a significant learning hurdle that leads to frustration, discouragement, and ultimately a loss of confidence. Addressing this, we developed a tool to enhance error messages for the popular Python language, translating them into plain English to empower students to resolve the underlying error independently. We used a mixed methods approach to study the tool’s effect on first-year physics students undertaking an introductory programming course. We find a broadly similar distribution of the most common error types to previous studies in other contexts. Our results show a statistically significant reduction in negative student emotions, such as frustration and anxiety, with the mean self-reported intensity of these emotions reducing by (73 ± 12)% and (55 ± 18)%, respectively. This led to a corresponding decrease in discouragement and an increase in student confidence. We conclude that enhanced error messages provide an effective way to alleviate negative student emotions and promote confidence. However, further longer-term investigations are necessary to confirm if this translates into improved learning outcomes. To our knowledge, this is the first physics-specific investigation of the effect of Python error message enhancement on student learning.

블록 프로그래밍 언어를 선행조직자로 한 파이썬 교수⋅학습 전략 개발 및 적용

Objectives The Purpose in this study, a teaching⋅learning strategy was developed to lower the cognitive load when novice learners with block programming experience learn a text programming. Methods According to previous studies, Python was selected as a text programming language, and Python teaching⋅learning strategies with block programming language as a preceding organizer were developed and reviewed by experts. In addition, the developed teaching⋅learning strategy was applied to 19 elementary school students (8 experimental groups and 11 control groups) to measure computing thinking in a preliminary inspection and cognitive load in a post inspection. Results The teaching⋅learning strategy was finally completed after receiving a validity review from computer education professors and field experts. As a result of applying teaching⋅learning strategies, it was confirmed that they were homogeneous groups in the preliminary examination, and when teaching⋅learning strategies were applied, the cognitive load was measured lower than that of the control group. Conclusions Through prior research and expert review, teaching⋅learning strategy and learning materials with block programming language as a advanced organizer were developed. When learning Python with the developed strategy and learning materials, it was confirmed that the cognitive load was measured lower than with the direct teaching strategy. This is expected to increase academic achievement by allowing learners to have room for cognitive capacity and promoting learning due to the lower cognitive load when learning Python.

Use of machine learning techniques for effective prediction of heart disease

One of the most important problems now affecting the globe is heart disease. A significant problem in the field of clinical knowledge analysis might be disorder prediction. Many medical conditions can be identified, detected and predicted using machine learning. This study uses machine learning methods and Python programming to study heart disease prediction. Heart disease has become a prevalent and fatal condition in the last few years due to the suppression of fat. Excessive pressure in the human body causes this disease to develop. Using multiple features from the dataset, researchers can predict heart disease. To assess patient performance, a dataset consisting of 12 parameters as well as 70000 unique data values ​​was used. The main goal of this study is to increase the accuracy of heart disease detection by using algorithms where the target output determines whether the subject has heart disease. This study provides the base for future heart disease prediction by using the machine learning method.

Using MOOC to Learn the Python Programming Language

The article carries out a quantitative analysis of online courses on learning the Python programming language as of October 2022. Such online platforms as Codecademy, Alison, FutureLearn, Udemy, Edx were considered. The main requirements for software engineers training, as well as features of online courses on learning programming languages were highlighted. The authors analyzed samples of online courses, which were a) generated with an automatic search by the keyword “Python” and b) found manually by relevant thematic sections. The analysis was carried out separately for each sample according to various criteria: a number of courses, an approximate level of student training, the cost of taking an online course, and a possibility of obtaining a certificate/diploma. The largest number of courses on learning Python is presented on Udemy platform. Alison platform offers the fewest number of courses for learning the Python language. All platforms are aimed at different level of student training (beginner, intermediate, advanced). On Alison and Edx platforms, 100% of the courses are free, while on FutureLearn platform, all courses are payable. After completing online courses, students can receive a certificate (all MOOC) and/or a diploma (Alison, Edx). As Python popularity is growing, further research is planned to analyze online courses which are represented on various MOOC and are related to the use of the Python programming language in different areas.