Data Reduction Techniques Research Articles

Efficiently approaching vertical federated learning by combining data reduction and conditional computation techniques

In this paper, a framework based on a sparse Mixture of Experts (MoE) architecture is proposed for the federated learning and application of a distributed classification model in domains (like cybersecurity and healthcare) where different parties of the federation store different subsets of features for a number of data instances. The framework is designed to limit the risk of information leakage and computation/communication costs in both model training (through data sampling) and application (leveraging the conditional-computation abilities of sparse MoEs). Experiments on real data have shown the proposed approach to ensure a better balance between efficiency and model accuracy, compared to other VFL-based solutions. Notably, in a real-life cybersecurity case study focused on malware classification (the KronoDroid dataset), the proposed method surpasses competitors even though it utilizes only 50% and 75% of the training set, which is fully utilized by the other approaches in the competition. This method achieves reductions in the rate of false positives by 16.9% and 18.2%, respectively, and also delivers satisfactory results on the other evaluation metrics. These results showcase our framework’s potential to significantly enhance cybersecurity threat detection and prevention in a collaborative yet secure manner.

Penerapan Kuis Wordwall dalam Keaktifan Belajar Peserta Didik Kelas IV C SD Supriyadi Semarang

This research regarding student innovation and activeness in learning can be an important basis for designing effective learning strategies. With the aim of research to find out the results of applying the WordWall quiz in active learning. This type of research uses a qualitative descriptive research method, with a case study research design. Data analysis techniques for data collection, data reduction, data presentation and drawing conclusions. with research subjects totaling 29 students from class IV C SD Supriyadi Semarang. Before implementing WordWall there were still many students who were not enthusiastic about learning. The use of WordWall media in learning has a positive influence on students' activeness in learning. WordWall can help teachers be more creative and make the learning environment interesting and fun because it focuses on students, is technology-based, and makes students feel comfortable and at ease while learning. Apart from that, WordWall media helps learning because it is fun and is useful for increasing student activity.

Application of the polyhedral template matching method for characterization of 2D atomic resolution electron microscopy images

High-throughput image segmentation of atomic resolution electron microscopy data poses an ongoing challenge for materials characterization. In this paper, we investigate the application of the polyhedral template matching (PTM) method, a technique widely employed for visualizing three-dimensional (3D) atomistic simulations, to the analysis of two-dimensional (2D) atomic resolution electron microscopy images. This technique is complementary with other atomic resolution data reduction techniques, such as the centrosymmetry parameter, that use the measured atomic peak positions as the starting input. Furthermore, since the template matching process also gives a measure of the local rotation, the method can be used to segment images based on local orientation. We begin by presenting a 2D implementation of the PTM method, suitable for atomic resolution images. We then demonstrate the technique's application to atomic resolution scanning transmission electron microscopy images from close-packed metals, providing examples of the analysis of twins and other grain boundaries in FCC gold and martensite phases in 304 L austenitic stainless steel. Finally, we discuss factors, such as positional errors in the image peak locations, that can affect the accuracy and sensitivity of the structural determinations.

Improving Credit Card Fraud Detection with Data Reduction Approaches

Detecting fraudulent activities in credit card transactions can be challenging due to issues like high dimensionality and class imbalance that are often present in the datasets. To address these challenges, data reduction techniques such as data sampling and feature selection have become essential. In this study, we compare four approaches for data reduction: using data sampling alone, employing feature selection alone, applying data sampling followed by feature selection, and using feature selection followed by data sampling. Additionally, we include results using all features. We build classification models using five Decision Tree-based classifiers and Logistic Regression, and evaluate their performance using two performance metrics: the Area Under the Receiver Operating Characteristic Curve (AUC) and the Area under the Precision–Recall Curve (AUPRC). In this work, we adopt ensemble supervised feature selection (SFS) techniques and Random Undersampling (RUS) for data reduction. The experimental results demonstrate that all four data reduction techniques have the potential to improve the performance of classifiers. These results are valuable since the classifiers available are dependent upon application domains, computing environments, and licensing agreements. However, these techniques can be applied independently of all these dependencies. We recommend utilizing the ensemble SFS followed by RUS (SFS–RUS) approach as the preferred data reduction method due to its ability to run feature selection and data sampling in parallel. Additionally, we find that XGBoost and CatBoost outperform other classifiers.

When religion meets mathematics: From mathematical anxiety to mathematical well-being for minority group student

The mission of positive psychology is to mitigate negative emotions while bolstering positive ones, thereby influencing student engagement in classroom learning activities, including mathematics. This study elucidates the Realistic Mathematics Education (RME) approach within the framework of Islamic teachings—specifically, inheritance law and charity—in the context of fractions and number operations. Its objective is to alleviate mathematics anxiety, a prevalent negative emotion, and enhance well-being during mathematical learning. Drawing upon flow theory from positive psychology, the research endeavors to optimize mathematical well-being by crafting appropriately challenging questions. Employing a descriptive qualitative methodology, the study focuses on an Indonesian mother and her ten-year-old son, residing in Uithoorn, Amsterdam, Netherlands, belonging to minority groups in ethnicity and identity, with religion being a significant aspect of their lives. Data collection involves observation, interviews, tests, and documentation, with analysis employing data reduction, presentation, triangulation, and verification techniques. The study utilizes socio-religious problems as a context, one of the characteristics of RME, such as inheritance division, to design mathematical challenges. The findings demonstrate a marked transition from math anxiety to math well-being, manifested through heightened enthusiasm and cheerfulness in learning mathematics, particularly by the mother. Noteworthy transformations include increased engagement, generous gestures towards the teacher, and frequent smiling, contrasting with previous instances of math anxiety during learning sessions. Furthermore, the results indicate that RME facilitates comprehension of mathematical concepts and fosters understanding of the intersection between mathematics and Islamic life among minority groups, as detailed in this article.

Penggunaan E-Commerce Di Kalangan Para Pedagang Di Pasar Baru Permai (Sudimampir) Banjarmasin

Based on DataIndonesia.id (28/02/2023) in 2022 in Indonesia there will be more and more online business actors. This research aims to (1) Find out the motives of traders at Baru Permai Market, Banjarmasin who use e-commerce. (2) Knowing the motives of traders at Baru Permai Market, Banjarmasin who do not use e-commerce. This research uses qualitative research methods. The data sources selected used purposive sampling and the informants in this study were 6 people. Data collection was carried out using observation, interviews and documentation techniques. The data analysis technique in this research uses data reduction techniques, data presentation, and drawing conclusions. The results of the research show that (1) The motives of traders who use e-commerce are because of cheaper prices, to fulfill stock of goods, and a more diverse choice of goods. The objective motive is for promotional activities, and is more flexible. (2) The motives of traders who do not use e-commerce are because they don't know how, it's difficult to access, and it's difficult. The goal motive is not having to buy a quota, and not creating content first. Based on the research results, it is recommended that future researchers use different theories and methods to those used by researchers, taking a different market from Pasar Baru Permai because Sudimampir Market itself is a combination of several markets and sells different goods.

Arti Petuah Matiti Suara dalam Upacara Ngusaba Kedasa di Pura Ulun Danu Batur, Kintamani, Bangli

Matiti Suara is one of the traditions to complete Yadnya carried out in Ulun Danu Batur Temple. Matiti Suara Derived from two words, namely Titi which means Jalan Utawi Bridge, Suara means Sound and gets the prefix -ma, so Matiti Suara is a sound or sound that becomes a road or bridge so that you do good in life. Ulun Danu Batur. The formulations of the research problem are: (1) What is the Order of Matiti Suara at Ngusaba Kedasa at Ulun Danu Batur Temple, (2) What are the benefits of Matiti Suara for the people in Batur Village? (3) What is the Meaning of Matiti Suara that is carried out at Ulun Danu Batur Temple. The theory used to research this research is Structural Functional Theory and Value Theory. The data sources obtained are based on observation methods, interviews, literature and documentation. After the data is obtained, then the data is analyzed or processed based on data reduction techniques, data display, and data verification. The data obtained based on this research is the meaning of the first Matiti Suara's Piteket, namely Baas Barak Baas Putih, Ané Daak Dados Sugih, the second, namely Mula Kliki, Mula Biu, Mula Kidik Mupu Liu, the third, namely Sampunang Nganggé Kriya Usaha Dhana Prestasiné, and the third four, namely giving or giving Uang Képeng.

A novel intuitionistic fuzzy rough instance selection and attribute reduction with kernelized intuitionistic fuzzy C-means clustering to handle imbalanced datasets

Due to advancement of internet and lab based technologies, large volume of high dimensional data are generated every day. These data usually consisted of several issues such as class imbalance, noise, later uncertainty, irrelevant and/or redundant features, and redundancy in size. These issues degrade the overall performance measures of the various machine learning algorithms. An efficient method to cope with such issues for a large sized datasets is to apply efficient data reduction techniques. In the recent years, numerous data reduction techniques have been presented based on fuzzy rough set theory to tackle these obstacles. However, building such a method that can handle all the above mentioned issues simultaneously is still a challenging task. In this study, we handle all these obstacles simultaneously by introducing a novel approach to obtain the reduced dataset by combining kernelized intuitionistic fuzzy C-means with intuitionistic fuzzy rough set model. Intuitionistic fuzzy rough set handles the vagueness and uncertainty in a better way than fuzzy set aided models as it takes use of membership, non-membership as well as hesitancy to capture the uncertainty of real-valued datasets. To generate the membership and non-membership grades, kernelized intuitionistic fuzzy C- means based notion is introduced. Further, an intuitionistic fuzzy rough set model is established by addressing lower and upper approximations based on a novel similarity relation. Moreover, all the necessary conditions are justified with the relevant mathematical theorems. Next, this model is employed for dimensionality reduction based on the concept of discernibility matrix by using the idea of different classes’ ratios to avoid the noise. Thereafter, positive region is defined with the help of lower approximation. The positive region information is applied to tackle problematic instances available in both minority and majority class for the imbalanced dataset after generation of artificial samples by synthetic minority oversampling technique (SMOTE). A comprehensive experimental study is added to show the effectiveness of the proposed technique. Finally, a framework is established to improve the prediction of animal toxin peptides.

Scaling and merging macromolecular diffuse scattering with mdx2.

Diffuse scattering is a promising method to gain additional insight into protein dynamics from macromolecular crystallography experiments. Bragg intensities yield the average electron density, while the diffuse scattering can be processed to obtain a three-dimensional reciprocal-space map that is further analyzed todetermine correlated motion. To make diffuse scattering techniques more accessible, software for data processing called mdx2 has been created that is both convenient to use and simple to extend and modify. mdx2 is written in Python, and it interfaces with DIALS to implement self-contained data-reduction workflows. Data are stored in NeXus format for software interchange and convenient visualization. mdx2 can be run on the command line or imported as a package, for instance to encapsulate a complete workflow in a Jupyter notebook for reproducible computing and education. Here, mdx2 version 1.0 is described, a new release incorporating state-of-the-art techniques for data reduction. The implementation of a complete multi-crystal scaling and merging workflow is described, and the methods are tested using a high-redundancy data set from cubic insulin. It is shown that redundancy can be leveraged during scaling to correct systematic errors and obtain accurate and reproducible measurements of weak diffuse signals.

Educational Values in the Mepopoidulu Tradition in the Bada Tribe of Poso Regency, Indonesia

This study discusses the Mepopoidulu tradition implemented by the Bada tribe, Poso Regency, in Indonesia. The aim of this study is to reveal the cultural values contained in the Meppoidulu tradition in the Bada tribe. This study used a qualitative method with an ethnography approach. The data was gathered through field observation, in-depth interviews with local communities, and written document analysis. The data, then, was analyzed using data reduction techniques, data presentation, and data verification, and ended with checking validity and drawing conclusions. The results of this study show that the implementation of the Mepopoidulu tradition consists of five main elements: Pousuana Katuwon'na Tauna, and the womb salvation ceremony during a mother's first pregnancy. Second, Pompetimbali Poadakana Tauna. Namely, manners are polite habits agreed upon in the social environment between people when they are adults in the stages leading to marriage. Third, poparewa, which implies ownership, means that humans will always live and strive and will own and have property rights. The fourth is Maroro'u, which means the implementation of expressions of gratitude to the almighty creator God for the agricultural harvests of the Bada community. The fifth is Mpegiwuw, which sanctions or punishes those who violate the traditional rules/customs of the Bada tribe. The educational values contained in the mepopoidulu tradition include the Value of religious education, an awareness deeply manifested in the depths of the human heart as human nature. Then, the Value of social education is a collection of attitudes and feelings manifested through behavior that influences the behavior of a person with values. Furthermore, the Value of moral education is a way of life that differentiates humans from other creatures. Cultural education is a value that has been embedded and agreed upon by the Bada tribe in the form of habits, behavior, and responses to a situation after or before it occurs. Finally, the Value of character education, which is an effort designed and implemented systematically to help understand human behavior, including the Value of caring, the ability to be sensitive to the needs of other people, and the ability to feel the feelings of different people and put oneself in other people's situations (empathy).

Implementation of Road Infrastructure Development Program in East Lombok Regency (Study at the East Lombok Regency Pupr Service)

This research is entitled Implementation of the Road Infrastructure Development Program in East Lombok Regency. The aim of this research is to find out the extent of the implementation of the Public Works and Spatial Planning Service program in Road Infrastructure Development in East Lombok Regency. The research method used is descriptive qualitative with data collection techniques through observation, interviews, documentation and triangulation. Informants in this research included various related parties, ranging from officials from the Public Works and Spatial Planning Department to the general public. The data obtained was analyzed through data reduction techniques, data presentation, and drawing conclusions. The research results show that the implementation of the road infrastructure development program in East Lombok Regency by the Public Works and Spatial Planning Service has not gone well. Communication is still lacking, as can be seen from the lack of response to community proposals, resources, especially in terms of budget and human resources, are still an obstacle, even though the disposition of the Department is considered good. The bureaucratic structure is also considered not optimal because there are still damaged and potholed roads.

ANALISIS PENYEBAB ANAK PUTUS SEKOLAH (STUDI DI DESA LANTAGI KECAMATAN KULISUSU KABUPATEN BUTON UTARA)

This research aims to analyze the causes of children dropping out of school in Lantagi Village, Kulisusu District, North Buton Regency. The phenomenon of children dropping out of school is a common phenomenon that often occurs in both urban and rural communities. This research uses symbolic interaction theory from Hebert Blumer to examine more deeply the phenomenon of children dropping out of school. This research uses descriptive qualitative research methods. Data collection techniques in this research used observation, interview and documentation techniques. The research location is located in Lantagi Village, Kulisusu District, North Buton Regency. The data collection technique in this research used a purposive sampling technique with 12 informants. The data analysis technique in this research uses data reduction techniques, data presentation and drawing conclusions. The data validation technique in this research uses the Member Check technique, which is a data collection technique by re-interviewing informants so that it is said to be valid. The results of the research show that the cause of children dropping out of school in Lantagi Village, Kulisusu District, North Buton Regency is caused by several factors, namely economic factors, family factors, internal desire factors and children's mindset about school.

MENINGKATKAN KEMAMPUAN MEMBACA MELALUI IMPLEMENTASI PROGRAM LITERASI DI MIN 1 GRESIK

This research was carried out because based on the results of observations, the researcher found that the reading ability of students in the MIN 1 Gresik educational institution was known to still not meet the indicators of students' reading ability in general, this case was highlighted a lot from the lower class. Researchers feel interested in this case and want to examine in depth the case that occurred at MIN 1 Gresik. Researchers want to provide an in-depth understanding of this research through a narrative description of the research results using data collection techniques through interviews, observation and documentation, later the data will be analyzed through data reduction techniques, data presentation and conclusion drawing based on the theory of miles and huberman with the subject of students who take part in the literacy program at MIN 1 Gresik. Based on the results of the study, researchers found that: 1) The implementation of the literacy program at MIN 1 Gresik 2) students after being given treatment in the form of implementing a literacy program have improved in reading skills, while this program has a fairly good strategy implemented by the madrasah, especially the Head of Madrasah, Class Teachers and Literacy Teachers, collaboration between parties and also communication with student guardians is a supporting basis in improving students' reading skills.

APPLICATION OF DEMONSTRATION METHOD IN LEARNING MAHARAH KALAM IN THE DARUSSALAM SCHOOL LAWANG MALANG

This thesis discusses the Application of Demonstration Methods in Learning Maharah Kalam in the Darussalam School Lawang Malang. In learning Maharah kalam rarely used by the teacher when learning then learning is given classic and does not vary, many students do not know the benefits of maharah kalam and are not confident in speaking Arabic. With this the researchers implemented a new program, The Demonstration Method.This research methodology uses a qualitative approach. The research data collection was obtained by participatory observation techniques, interviews, and document studies. Analysis of the data of this study was obtained using data reduction techniques, data presentation, and drawing conclusions.The results of the study revealed the findings that Application of Demonstration Method in Learning Maharah Kalam in the Darussalam School Lawang Malang was as follows: 1) Learning the Arabic Club Program was carried out through 3 learning activities, namely: Arabic dialogue, Arabic games, and Arabic

SHOULD I STAY OR SHOULD I GO-A rapid qualitative study of principal deteriorating factors experienced by nurses relating to their intentions to leave their current position in a medical hospital department.

High nurse turnover in hospitals is a worldwide problem with dire consequences for patient care such as increased mortality and a decrease in patient safety. A specific effort to retain nurses is urgently needed due to the many vacant nursing positions, especially across the Medical hospital departments. To identify the principal deteriorating factors experienced by nurses relating to their intentions to leave the medical department at a university hospital. A rapid qualitative research methodology was used. Participants comprised 22 registered nurses employed in five university hospital medical department units. The Rigorous and Accelerated Data Reduction (RADaR) technique was therefore used for data collection through qualitative semi-structured interviews and analysis. COREQ was used for reporting the study. The RADaR analysis detected a downward spiral of five principal deteriorating factors influencing nurses' intentions to leave their position in the medical department. The factors were resignations from several nursing colleagues combined with too few and inexperienced nurses present during shifts, additional tasks assigned, a management refraining from improving the problematic issues, leading to decisive consequences for patient care and a declining feeling of professional care. The lack of nurses in the department caused missed nursing care, which affected the nurses' job satisfaction and intentions to leave their positions. Future research initiatives must focus on evaluating successful interventions to maintain the nurses in the positions. Further knowledge, is also needed, to investigate how we can change the downward spiral to a story of retention success.

A novel approach to model cumulative stress: Area under the s-factor curve

ObjectiveUsing a large longitudinal sample of adults from the Midlife in the United States (MIDUS) study, the present study extended a recently developed hierarchical model to determine how best to model the accumulation of stressors, and to determine whether the rate of change in stressors or traditional composite scores of stressors are stronger predictors of health outcomes. MethodWe used factor analysis to estimate a stress-factor score and then, to operationalize the accumulation of stressors we examined five approaches to aggregating information about repeated exposures to multiple stressors. The predictive validity of these approaches was then assessed in relation to different health outcomes. ResultsThe prediction of chronic conditions, body mass index, difficulty with activities of daily living, executive function, and episodic memory later in life was strongest when the accumulation of stressors was modeled using total area under the curve (AUC) of estimated factor scores, compared to composite scores that have traditionally been used in studies of cumulative stress, as well as linear rates of change. ConclusionsLike endogenous, biological markers of stress reactivity, AUC for individual trajectories of self-reported stressors shows promise as a data reduction technique to model the accumulation of stressors in longitudinal studies. Overall, our results indicate that considering different quantitative models is critical to understanding the sequelae and predictive power of psychosocial stressors from midlife to late adulthood.

Motion acquisition of gait characteristics one week after total hip arthroplasty: a factor analysis

IntroductionClinical gait analysis can be used to evaluate the recovery process of patients undergoing total hip arthroplasty (THA). The postoperative walking patterns of these patients can be significantly influenced by the choice of surgical approach, as each procedure alters distinct anatomical structures. The aim of this study is twofold. The first objective is to develop a gait model to describe the change in ambulation one week after THA. The secondary goal is to describe the differences associated with the surgical approach.Materials and methodsThirty-six patients undergoing THA with lateral (n = 9), anterior (n = 15), and posterior (n = 12) approaches were included in the study. Walking before and 7 days after surgery was recorded using a markerless motion capture system. Exploratory Factor Analysis (EFA), a data reduction technique, condensed 21 spatiotemporal gait parameters to a smaller set of dominant variables. The EFA-derived gait domains were utilized to study post-surgical gait variations and to compare the post-surgical gait among the three groups.ResultsFour distinct gait domains were identified. The most pronounced variation one week after surgery is in the Rhythm (gait cycle time: +32.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+32.9\\mathrm{\\%}$$\\end{document}), followed by Postural control (step width: +27.0%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+27.0\\mathrm{\\%}$$\\end{document}), Phases (stance time: +11.0%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+11.0\\%$$\\end{document}), and Pace (stride length: − 9.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$9.3\\%$$\\end{document}). In postsurgical walking, Phases is statistically significantly different in patients operated with the posterior approach compared to lateral (p-value = 0.017) and anterior (p-value = 0.002) approaches. Furthermore, stance time in the posterior approach group is significantly lower than in healthy individuals (p-value < 0.001).ConclusionsThis study identified a four-component gait model specific to THA patients. The results showed that patients after THA have longer stride time but shorter stride length, wider base of support, and longer stance time, although the posterior group had a statistically significant shorter stance time than the others. The findings of this research have the potential to simplify the reporting of gait outcomes, reduce redundancy, and inform targeted interventions in regards to specific gait domains.

Empowerment of the Poor in Non-Cash Food Assistance Program in Tanjung Pinang City, Indonesia

The purpose of this study is to determine how the empowerment of the poor in the non-cash food assistance program and the factors that can hinder the empowerment and efforts to overcome the obstacles. The method used in this study is a descriptive method with a qualitative approach. Data collection and information in this study was done using interview techniques, observation, and documentation. The data that has been collected is analyzed using data reduction techniques, data presentation, and decision making/verification. Based on the results of the research that has been done by the researcher, the conclusion obtained is empowerment through the approach of basic needs, namely non-cash food assistance in its implementation successfully meets three aspects in maintaining household strength, namely: a) social aspects, b) political aspects, and c) psychological aspects. With the fulfillment of the indicators of these three aspects, the community, in this case, the family of beneficiaries, can be said to be empowered through the Non-Cash Food Assistance program.

Hybrid whale algorithm with evolutionary strategies and filtering for high-dimensional optimization: Application to microarray cancer data.

The standard whale algorithm is prone to suboptimal results and inefficiencies in high-dimensional search spaces. Therefore, examining the whale optimization algorithm components is critical. The computer-generated initial populations often exhibit an uneven distribution in the solution space, leading to low diversity. We propose a fusion of this algorithm with a discrete recombinant evolutionary strategy to enhance initialization diversity. We conduct simulation experiments and compare the proposed algorithm with the original WOA on thirteen benchmark test functions. Simulation experiments on unimodal or multimodal benchmarks verified the better performance of the proposed RESHWOA, such as accuracy, minimum mean, and low standard deviation rate. Furthermore, we performed two data reduction techniques, Bhattacharya distance and signal-to-noise ratio. Support Vector Machine (SVM) excels in dealing with high-dimensional datasets and numerical features. When users optimize the parameters, they can significantly improve the SVM's performance, even though it already works well with its default settings. We applied RESHWOA and WOA methods on six microarray cancer datasets to optimize the SVM parameters. The exhaustive examination and detailed results demonstrate that the new structure has addressed WOA's main shortcomings. We conclude that the proposed RESHWOA performed significantly better than the WOA.

A neural network approach for population synthesis

This work explores techniques and metrics applied to the process of population synthesis used in activity-based modeling for traffic and transport simulation. The paper presents a novel population synthesis approach based on applying artificial neural networks (ANNs) and evaluates the approach against techniques derived from iterative proportional fitting (IPF), Bayesian networks, and data sampling methods. The documented research also investigates the appropriateness of goodness-of-fit measures and the need to consider similarity measures in assessing technique effectiveness with a focus on measures derived from Jaccard similarity coefficient. We established that IPF techniques should be preferred when datasets with the required composition are available, targeting few output variables and in relatively large zones of 5% region size. However, in smaller zones with sparser datasets, or inadequate dataset composition, the proposed ANN technique and identified sampling method are favorable. The proposed ANN method shows suitability for the population synthesis problem compared with the examined methods, but further work is required to improve model fitting speed, explore mixture models of multiple ANNs, and apply data reduction techniques to reduce the observation–decision space. The research findings also established that comparing scenarios of varying sizes and variable numbers is challenging when employing specific goodness-of-fit measures. Furthermore, the mentioned similarity measures can reveal concerns regarding inconsistent archetypes and low-quality populations that can remain concealed when using error metrics.