Python Language Research Articles

Artificial Intelligence (Neural Network) in the Diagnosis of Benign Skin Tumors in Pediatric Patients

Relevance: ten years ago, artificial intelligence (AI), particularly neural networks (NN), as a diagnostic option in practice seemed a distant prospect. Today, the use of AI is becoming an increasingly popular and daily improving approach in all aspects of clinical and fundamental medicine. Purpose: design and learning of a NN to recognize four types of benign melanocytic skin tumors, integration into a mobile app to apply in practice. Material and methods: сlinical and dermatoscopic analysis of skin tumors was carried out in 600 children. In 65 cases the tumors were removed. Histological types were dermal nevus – 43% (n=28), complex nevus - 33.8% (n=22), pyogenic granuloma - 10.8% (n=7), Spitz-nevus - 6.2% (n=4), blue nevus - 3.1% (n=2), melanoma - 3.1% (n=2). Seven patients with pyogenic granulomas and two patients with melanoma were excluded. The test set included 56 dermatoscopic images. Due to the small number of images augmentation was performed. The database has been increased from 600 images to 1800. NN is written in the machine language Python. The machine learning framework was TensorFlow 2.0. The network architecture is based on the pre-trained model “EfficientNet B7”. This model uses the “supervised learning” paradigm. Each element of the sample had a class affiliation. Results: an accuracy of 83% was achieved after a period of learning on the test set. Mathematical metrics calculated in the Scikit-learn library. Sensitivity was 100% (blue nevus), 73% (complex nevus), 93% (dermal nevus), 75% (Spitz-nevus), and specificity were 98%; 94%; 82%; 98%, respectively. AI was integrated into the mobile app “KIDS NEVI”. Conclusion: AI as an auxiliary method for the skin tumors diagnosis in children and adolescents has demonstrated high potential and great opportunities. Dermatoscopic analysis of a skin tumor and a mobile app are able to provide “double control”, quick and correct clinical diagnosis and determination treatment tactics.

Automating Government Report Generation: A Generative AI Approach for Efficient Data Extraction, Analysis, and Visualization

This application paper introduces a transformative solution to address the labour-intensive manual report generation, data searching & report revision process in government entities. Traditional methods of data extraction, analysis, and graph creation for annual reports are not only time-consuming but also prone to human errors. To mitigate these challenges, we propose an innovative system leveraging Generative Artificial Intelligence (GenAI), with a specific focus on large language models (LLMs). Our solution incorporates automated data extraction from diverse sources designated as internal knowledge base, text analysis, summarization using advanced language models, and the generation as well as revisions of informative graphs. Different LLMs like Google's Gemini Pro and OpenAI's GPT 4.0 has been used to read different data visualisation graphs and fetching the information from internal knowledge base, respectively, to update the graphs in automated manner. Solution implementation using Python language on the World Economic Situation and Prospects report by Department of Economic & Social Affairs, United Nation shows that the early result produces almost 0.87% to 17.46% average error rates in the task of factual data visualisation graph. Key benefit of this approach include improved time efficiency, consistency in report format, enhanced insights, and a user-friendly interface. A review and approval workflow facilitate user feedback, contributing to continuous model performance improvement.

PAINe: An Artificial Intelligence–based Virtual Assistant to Aid in the Differentiation of Pain of Odontogenic versus Temporomandibular Origin

IntroductionPain associated with temporomandibular dysfunction (TMD) is often confused with odontogenic pain, which is a challenge in endodontic diagnosis. Validated screening questionnaires can aid in the identification and differentiation of the source of pain. Therefore, this study aimed to develop a virtual assistant based on artificial intelligence using natural language processing techniques to automate the initial screening of patients with tooth pain. MethodsThe PAINe chatbot was developed in Python language using the PyCharm environment and the openai library to integrate the ChatGPT 4 API and the Streamlit library for interface construction. The validated TMD Pain Screener questionnaire and 1 question regarding the current pain intensity were integrated into the chatbot to perform the differential diagnosis of TMD in patients with tooth pain. The accuracy of the responses was evaluated in 50 random scenarios to compare the chatbot with the validated questionnaire. The kappa coefficient was calculated to assess the agreement level between the chatbot responses and the validated questionnaire. ResultsThe chatbot achieved an accuracy rate of 86% and a substantial level of agreement (κ = 0.70). Most responses were clear and provided adequate information about the diagnosis. ConclusionsThe implementation of a virtual assistant using natural language processing based on large language models for initial differential diagnosis screening of patients with tooth pain demonstrated substantial agreement between validated questionnaires and the chatbot. This approach emerges as a practical and efficient option for screening these patients.

End-to-End Signal Classification in Signed Cumulative Distribution Transform Space.

This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT). We adopt a transport generative model to define the classification problem. We then make use of mathematical properties of the SCDT to render the problem easier in transform domain, and solve for the class of an unknown sample using a nearest local subspace (NLS) search algorithm in SCDT domain. Experiments show that the proposed method provides high accuracy classification results while being computationally cheap, data efficient, and robust to out-of-distribution samples with respect to the existing end-to-end classification methods. The implementation of the proposed method in Python language is integrated as a part of the software package PyTransKit [1].

Food Recommendation System for a Healthy Liver Using Machine Learning

Nowadays, the work routine, the problems we face, make us not pay proper attention to healthy eating. As a result, people get sick. We have created through mathematical knowledge (statistics, probability, linear algebra, geometry), combining the concepts of Machine Learning (Content Based Filtering, TD IDF Vectorizer Algorithm, Conditional Independence, Count Vectorization) with Python language, a Recommendation System for all people suffering from liver. Liver is one of the most important organs of our body, because it makes 500 essential functions in the organism. But we have to be aware about the types of food that we use. Also, we must be very careful with our lifestyle and diet on foods. The aim of this article is to recommend the most appropriate foods for a healthy liver. This article will help the p

Design and implementation of a driving safety assistant system based on driver behavior

<p>These days, road accidents are one of Morocco's biggest problems. Fatigue, drowsiness, and driver behavior are among the primary causes.This research aims to develop an embedded system by image processing and computer vision to ensure driving safety by monitoring driver behavior and assist drivers to awaken from micro-sleep or fatigue due to long driving hours and various other reasons. Indeed, the driver inattention, drowsiness or driver fatigue can be detected. The suggested method is designed to support drivers if needed, based on the vehicle velocity. Once the driver crosses a certain speed limit, the program starts face detection and analyzing this data to determine whether the driver is tired, sleepy, or inattentive. This activates different alarm depending on the criticality level. It can sound a voice alert to help him wake up and drive more cautiously. The system is based on AI algorithms in image processing based on OpenCV libraries and the Python language to capture the movements of the driver's eyes and head when starting the automobile. Every algorithm is run on a Raspberry-Pi 4 card, and numerous experimentation series have demonstrated overall credible performance with success accuracy of over 93% in EAR and MAR calculations.</p>

Модель расчёта момента силы трения при вращательном движении тела

The article discusses one of the types of mechanical movement – rotational movement. To automate the calculation of the frictional moment, an algorithm was proposed and a program was written in Python. The algorithm consists of a block for inputting initial data, blocks for calculating intermediate values and the desired value, and outputting the calculation results. Keywords: FRICTION FORCE, MECHANICAL MOTION, BODY, PYTHON LANGUAGE, ROTATION AXIS, ALGORITHM, LOAD

Foresight study using scenarios and the Delphi method in the leather agroindustrial chain to 2035 - Alignment of results with open innovation

ObjectiveThe objective of this article is to prioritize technologies, innovations, and new businesses related to the leather agroindustrial chain in the department of Quindío (Colombia) for the year 2035. MethodThe prospective scenario and Delphi methods were used to prioritize key variables, technologies, actors, innovations, objectives, and scenarios in the leather agroindustry. As a previous phase, the Scopus database was used with the critical factors of leather, tanning, tanneries, tannery, leather transformation, cowhide, and suede, and the results were used as input for structured, anonymous and reiterative consultations with national and international experts on topics related to the chain and with a time horizon of 2035. The questionnaire was posed by groupings, including product and by-product innovation, sustainability, technology, and genetics, and, for the first round of the Delphi method, was sent to more than 70 national and industry experts/stakeholders, representatives of universities, the public and private sectors, and organizations related to the chain. FindingsAfter conducting a bibliometric analysis, key terms related to the leather sector were visualized and a validation was made for MICMAC methods, with 44 variables found; with topics ranging from raw materials to current regulations. For the MACTOR method, future objectives were determined for clean technologies, the creation of new product development, and an R&D&I Center for productive transformation. Meanwhile, for the Delphi method, 44 technologies and innovations were prioritized through rounds with complementary analysis using Python and natural language processing. ConclusionsTopics such as vegan leather made, from mushrooms; bio-leather or leather made, from mango peel; nanofibers, nanocomposites, biogas production, nanofiltration, 4D Printing, Artificial Intelligence to improve leather quality; augmented reality to improve customer experience; genetic improvement of animals to improve leather quality, among others, in which innovation challenges and open innovation strategies can be generated, as well as in R&D&I projects, were identified.

Transanal Endorectal Pull-Through for Hirschsprung's Disease: Complications and Lessons from Our Practice and the Literature.

Hirschsprung's disease (HD) is a congenital disorder characterised by the absence of ganglion cells in the distal bowel, resulting in functional obstruction. The transanal endorectal pull-through (TEPT) procedure, a minimally invasive approach, aims to treat HD by removing the aganglionic segment. This study assessed the feasibility, safety, and efficacy of single-stage TEPT in paediatric patients, focusing on postoperative complications, bowel function, and quality of life. A retrospective cohort study was conducted on 150 children who underwent single-stage TEPT from January 2005 to December 2023 at the Medical University of Gdansk. Data were collected from medical records, including demographics, preoperative assessments, surgical details, postoperative management, and follow-up outcomes. Statistical analyses were performed using Microsoft Excel 365 and the programming language Python 3.12. The mean age at surgery was 13 months, with a male-to-female ratio of 2.75:1. The mean operative time was 129 min, and the mean hospital stay was seven days. Postoperative complications included anastomotic leak (4%), wound infections (15%), and enterocolitis (26%). Redo surgeries were required in 18% of cases due to persistent constipation and obstructive symptoms. This article includes a comprehensive review of the literature. TEPT demonstrates a favourable safety profile and efficacy in treating HD, though significant concerns include complications such as enterocolitis and the need for additional surgeries. Surgical expertise and thorough preoperative and postoperative management are crucial to optimising patient outcomes.

Educational data mining in a university using LMS Moodle

Educational data mining is a technology for processing big data collected through educational portals and used for assessing the quality of educational content and predicting educational outcomes. Various data analysis methods are applied in the implementation of this technology, including anomaly detection, association rule mining, classification, cluster analysis, regression analysis, and factor analysis. Each of these methods uses special tools, such as spreadsheets, high­level programming languages (Python or R), low­code platforms (Loginom, Orange), or specialized analytics modules integrated into educational portals. The article reveals the features of the “Analytics” module embedded in LMS Moodle for tracking and optimizing the educational process. The authors provide recommendations for configuring and using this module in the educational process. The module “Analytics” allows to create forecasts of the success rate of mastering a separate training course without programming skills , does not require data preprocessing for analysis, and allows to quickly take measures to improve the educational process based on the results of the forecast. However, the module has a number of disadvantages (limited attributes and forecasting models, high requirements for computing power to run model training and build forecasts), so often administrators of educational portals disable this module. In this regard, an experiment was conducted to train a classification model for predicting students’ examination scores based on students’ activities on the educational portal of Nosov Magnitogorsk State Technical University. In the course of the experiment, data on students’ activity on the university’s educational portal for one academic year were taken, and machine learning models were built to predict examination scores for an individual student and subject. The results of the experiment showed F1 74 % in predicting the score that the students would receive on the intermediate assessment. This confirms the effectiveness of using educational data mining based on user data from educational platforms.

MoTion: A new declarative object matching approach in Pharo

Pattern matching is an expressive way of matching data and extracting pieces of information from it. The recent inclusion of pattern matching in the Java and Python languages highlights that such a facility is more and more adopted by developers for everyday development. Other main stream programming languages also offer pattern matching capabilities as part of the language (Rust, Scala, Haskell, and OCaml), with different degrees of expressivity in what can be matched. In the meantime, in graphs, pattern matching takes a slightly different turn; it enhances the expressivity of the patterns that can be defined. Smalltalk currently offers little pattern matching capability to find specific objects inside a large graph of objects using a declarative pattern. In Pharo, the closest library to classical pattern matching that exists is the RBParseTreeSearcher, which allows to express specialized patterns over a Pharo Abstract Syntax Tree to find some inner node. The question arises of what features a flexible pattern matching language should have. In this paper, we review the features found in different existing pattern matching languages, both in General Purpose Languages (like Java) and in declarative graph pattern matching languages. We then describe MoTion, a new pattern matching engine for Pharo smalltalk, combining all these features. We discuss some aspects of MoTion’s implementation and illustrate its use with real case examples.

Global-scale improvement of the estimation of terrestrial gross primary productivity by integrating optical and microwave remote sensing with meteorological data

Photosynthesis (a key ecological process) is measured based on gross primary productivity (GPP), emphasizing the criticality of accurate GPP estimation to climate change research. The extant remote sensing-based approaches for GPP estimation were typically based on optical remote sensing data, neglecting the potential supplementary information from microwave remote sensing data. Thus, based on the random forest algorithm, we developed a GPP model through the integration of optical and microwave remote sensing with meteorological data (OMM-GPP). The software and tools used for data processing, modeling, and analysis are the standard and third-party libraries based on the Python Language. Our OMM-GPP model was trained and validated using GPP data (referred to as “observed GPP”) retrieved from carbon dioxide fluxes measured at 137 flux towers. The results indicated that GPP estimation by integrating optical and microwave data with meteorological data was more accurate than GPP estimation by integrating single-source remote sensing data (optical or microwave data) with meteorological data across eight vegetation types. The model performed well on daily and monthly scales, with determination coefficients (R2) (root-mean-square errors, RMSE) of 0.85 (1.52 gC m−2 d−1) and 0.83 (1.49 gC m−2 d−1), respectively, which increased (decreased) by 0.17–0.03 (0.58–0.12 gC m−2 d−1) and 0.11–0.02 (0.39–0.10 gC m−2 d−1) compared with the R2 (RMSE) values obtained by integrating single-source remote sensing data with meteorological variables. Further, the OMM-GPP model effectively captured the seasonal variations in daily and monthly GPP across most vegetation types. The eight-day-scale comparison of the model with the VODCA2GPP dataset revealed its enhanced performances, increasing R2 by 0.33 and decreasing RMSE by 0.97 gC m−2 d−1. Overall, the integration of microwave and optical remote sensing data with meteorological data can enhance GPP estimation accuracy, as demonstrated by the established OMM-GPP, across different vegetation types.

PyArc: A python package for computing absorption and radiative coefficients from first principles

Light absorption and radiative recombination are two critical processes in optoelectronic materials that characterize the energy conversion efficiency. The absorption and radiative coefficients are thus key properties for device optimization and design. Here, we develop a python package named pyArc that allows rigorous computation of absorption and radiative coefficients from first principles. By integrating several interpolation strategies to augment k-point sampling in reciprocal space, our code is accurate yet highly efficient. In addition to evaluation of the coefficients, our code is capable of intuitive analysis of carrier distribution, facilitating a deeper understanding of the microscopic mechanisms underlying the radiative coefficients. Utilizing GaAs as a prototypical example, we demonstrate how to employ our package to compute absorption and radiative coefficients and to investigate the key features in the electronic structure that give rise to these coefficients. Program SummaryProgram title: PyArcCPC Library link to program files:https://doi.org/10.17632/5x9g9bvhcv.1Licensing provisions: MIT licenseProgramming language: Python 3Nature of problem: Light absorption and radiative recombination processes in semiconductors critically impact the energy conversion efficiency of optoelectronic devices. Developing a method to calculate coefficients of the two processes based on first-principles theory is essential, which not only can help to obtain the key properties of those semiconductor materials and guide the device design, but also can unveil the underlying microscopic mechanisms.Solution method: PyArc, written in the Python language, implements first-principles methodologies for the computation of absorption and radiative coefficients of semiconductors based on Fermi's golden rule. This package takes the electronic eigenvalues and dipole matrix elements of a material computed from first-principles codes such as VASP as input. Dense k-point sampling for the Brillouin zone is achieved through efficient interpolation schemes implemented in our code to acquire well converged results. The functionality of cross-sectional visualization of carrier distribution in our code provides intuitive insights into the fundamental mechanism beneath the charge-carrier radiative recombination process.

A parametric modeling method for 2.5D warp-reinforced woven composites considering the extruded distortion of yarns

2.5D warp-reinforced woven composites (2.5DWRWC) are widely used in various industries, but it is currently difficult to model parametrically considering the molding effects due to their complex structure. Accordingly, two special parameters (ψ and φ) are introduced to regulate the degree of yarn extrusion, where ψ is used to characterize the extrusion degree of the binder yarn on the weft yarn in the thickness direction, and φ is used to characterize the twisting degree of the warp yarn. On this basis, a parametric modeling method for 2.5DWRWC is constructed by uniting the essential weaving parameters, and the modeling code is written in TexGen software using Python language. Subsequently, the established model is verified experimentally, and the results show that the average comparison error of the geometric parameters is only 5.07 %, the maximum prediction error of the mechanical properties is only 4.78 %, validating the reasonableness of the proposed modeling method. Finally, the effects of the two special parameters on the mechanical properties are emphasized, and the results show that the mechanical properties in weft direction are enhanced with the increase of ψ, and the mechanical properties in warp direction are weakened with the increase of φ. This work can provide guidance for the molding process and mechanical design of 2.5DWRWC, which has significant theoretical value and engineering significance.

Gamma–lindley regression cure model for corporate credit default prediction

Predicting the firm’s default over time is marked persistently with certain flaws for firms seeking to avoid their exposure to credit risk and losses induced by defaults. From this perspective, a Gamma–lindley cure model, relying on a Gamma–lindley distribution and the survival analysis framework, was introduced to model the default probability and time of corporate credits referring to their inference ability once the firm is likely to default. In addition, it extends the standard mixture cure model allowing more flexibility for modeling lifetime data through relaxing the independence assumption of the probability and the time of default. Furthermore, it handles the missing defaulting labels as a latent variable and identifies three classes of default probability distribution, namely short, medium, and long-term through its potentially accurate classification method. A relevant parameter-learning procedure was described resting on the Maximum Likelihood approach. The results of an empirical analysis on a firms dataset reveal that the advanced Gamma–lindley cure model has a rather effective performance in terms of default time prediction compared to Exponential, Gamma, Weibull, and Cox proportional hazards regression modeling specifications, which yield a meaningful solution for timely credit risk management and cluster firms defaulter in three classes, enabling a deeper analysis of firm’s defaults situation. The numerical experiments were conducted with Python language.

Flood Risk Analysis in Sindh, Pakistan: Predicting the Most Affected Tehsil Using Statistical and Machine Learning Models with Comprehensive Data

Extreme weather events pose significant risks and threats to the Sindh region, shedding light on the potential havoc they can wreak on livestock, human lives, and public infrastructure. Unusual monsoon rains in 2022 transformed the geography of Sindh, leading to devastating floods and significant damage. The current flood management strategies are effective, but there is some gap in early warning systems. Different findings suggest a need for refined approaches to improve predictive accuracy and response efficiency. The primary goal is to assess and improve existing flood response measures, identify opportunities for improvement, and strengthen resilience against flood events. In response to these challenges, this study provides actionable insights for policymakers and emergency planners to refine flood management strategies, ultimately aiming to bolster community resilience and preparedness. This paper presents a novel framework for a hybrid predictive model that combines machine learning and statistical analysis to improve the prediction of flood-induced displacement, providing timely warnings and preparing for future flood events. The selected dataset used for this research is from the 'Sindh Flood Data Analysis and Prediction 2022', which includes both demographic and geographical variables. By integrating machine learning and statistical models, the research evaluates their effectiveness in predicting flood impacts and identifying the most affected Tehsils using Python language and Seaborn library for analysis. The Gradient Boosting Classifier achieved the highest accuracy at 97.67%, followed by Logistic Regression at 95%. In contrast, Decision Trees and Lasso Regression demonstrated lower accuracies of 25% and 55%, respectively.

Smart Scheduling: A Pilot Project of Workforce Scheduling in Radiation Oncology

Introduction: In the case of the radiation oncology department, the large number of visits faced by patients illuminates the critical need for optimal time management. Focused on three central themes: daily waiting times, diagnosis-to-treatment waiting times, and appropriate staffing for the present workload, the research highlights the impact of inefficient time management on patient satisfaction and overall operational efficiency. The time and energy invested in a schedule are high and frequently many scheduling conflicts occur even after the schedule is made. The ability to schedule different employees in the most optimal manner would increase the productivity of the radiation oncology department. Methods:The scheduling software was constructed using Python language and importations of libraries from the Tkinker software for the Graphical User Interface. The software is a constraint-based algorithm that allocates staff to different sites based on each radiation therapy clinic’s staffing requirements. Results and Discussion: This work developed a basic software that creates a randomized schedule of employees. While this would benefit the team by curating a schedule that has no functional mistakes, the algorithm provides a foundation for the data collection that will facilitate the future incorporation of artificial intelligence (AI). This would allow for deeper learning overtime of the software to develop a schedule that is optimal for the success of the individual and, thus, the entire team. This pilot project aimed to generate interest regarding the introduction of AI to current scheduling software in the context of the radiation oncology department.

Ranking English Apps From the Google Play Store Based on Customer Reviews Analysis

Nowadays, several educational technology and English learning apps on the Google Play Store cater to various learning levels and objectives. However, the parents had difficulty finding an appropriate app for their kids. It is challenging when there is a diversity of English learning apps in the market. Not only product brands but also prices affect the customer‘s choice behavior. A good product will achieve customer satisfaction. And the praise of experienced customers will be a suggestion for potential customers. This paper uses sentiment analysis as a factor of criteria set for evaluating English learning Apps from Vietnamese companies. The Python language was used in all the analysis processes, and the sentiment score of 8,756 reviews for English learning apps was calculated. The criteria to evaluate the App include overall rating, sentiment scores, and number of downloads. The results will be a suggestion for improving the quality of companies‘ products and a recommendation for users when choosing a suitable Edtech product.

Design and implementation of power socket soldering robot arm system

Abstract In order to solve the problem of high cost and low efficiency in manual welding of power socket welding components, a soldering robot arm system was developed. The gripper, 5-DOF manipulator, clamp, support, and control system were designed. The coordinate system of the robot arm was constructed, and the working space of the robot arm was analyzed by using the MATLAB robot toolbox. It was concluded that the x0 coordinate range of the welding working area was [175mm, 215mm], the y0 coordinate range was [55mm, -55mm], and the z0 coordinate was 90mm. At the same time, the motion trajectory of the robot arm was determined. Using Raspberry PI as a controller, the control system is developed by programming Python language. The prototype is made, and a welding test is carried out. The test shows that the system has completed the welding operation, and the welding effect is good.

Evaluating Daily Water Stress Index (DWSI) Using Thermal Imaging of Neem Tree Canopies under Bare Soil and Mulching Conditions

Water stress on crops can severely disrupt crop growth and reduce yields, requiring the accurate and prompt diagnosis of crop water stress, especially in semiarid regions. Infrared thermal imaging cameras are effective tools to monitor the spatial distribution of canopy temperature (Tc), which is the basis of the daily water stress index (DWSI) calculation. This research aimed to evaluate the variability of plant water stress under different soil cover conditions through geostatistical techniques, using detailed thermographic images of Neem canopies in the Brazilian northeastern semiarid region. Two experimental plots were established with Neem cropped under mulch and bare soil conditions. Thermal images of the leaves were taken with a portable thermographic camera and processed using Python language and the OpenCV database. The application of the geostatistical technique enabled stress indicator mapping at the leaf scale, with the spherical and exponential models providing the best fit for both soil cover conditions. The results showed that the highest levels of water stress were observed during the months with the highest air temperatures and no rainfall, especially at the apex of the leaf and close to the central veins, due to a negative water balance. Even under extreme drought conditions, mulching reduced Neem physiological water stress, leading to lower plant water stress, associated with a higher soil moisture content and a negative skewness of temperature distribution. Regarding the mapping of the stress index, the sequential Gaussian simulation method reduced the temperature uncertainty and the variation on the leaf surface. Our findings highlight that mapping the Water Stress Index offers a robust framework to precisely detect stress for agricultural management, as well as soil cover management in semiarid regions. These findings underscore the impact of meteorological and planting conditions on leaf temperature and baseline water stress, which can be valuable for regional water resource managers in diagnosing crop water status more accurately.