Problem-solving Algorithm Research Articles

Design of a sweeping robot based on fuzzy QFD and ARIZ algorithms

The design of a multifunctional intelligent robotic vacuum cleaner is essential for meeting user needs and replacing human cleaning tasks, thereby improving the effectiveness and convenience of human-robot interaction. Existing robotic vacuum cleaners can address the issues of small dust and hair accumulation; however, a comprehensive and integrated solution for sweeping, mopping, sanitizing, and general waste cleaning remains necessary. This paper employs the Fuzzy Quality Function Deployment (FQFD) tool and the Algorithm for Inventive Problem Solving (ARIZ) algorithm to address these challenges. The research framework integrates FQFD with the Fuzzy Analytic Hierarchy Process (FAHP) methodology to evaluate the weights of user requirements for a robotic vacuum cleaner. Subsequently, these requirements were translated into technical specifications, thus establishing a link between user needs and technical requirements. The ARIZ algorithm is subsequently employed to resolve conflicts and inconsistencies within the FQFD model. Finally, an innovative design for a robotic vacuum cleaner was proposed by utilizing the ARIZ algorithm's problem-solving principles and qualitative and quantitative insights derived from the FQFD. The design is evaluated using the Fuzzy Order Preference Technique (FTOPSIS) method, which resembles the ideal solution. Furthermore, the operational dynamics of the critical components of the design solution that navigate obstacles are analyzed to ensure their effectiveness. The final product can effectively replace humans in complex cleaning tasks while navigating obstacles during sweeping, mopping, and sanitizing for comprehensive space cleaning. The results of this study provide valuable insights for designers to enhance user experience, improve customer emotional satisfaction, and guide future product designs in related product development initiatives.

МАТЕМАТИКАНЫ ЖАНА АЗЫРКЫ ЗАМАНДЫ ӨНҮКТҮРҮҮ БОЮНЧА КЫРГЫЗ ЫКМАСЫ

The educational process consists of two main types of activities: the teaching activity of the teacher in educating students in knowledge, skills, and life experience, and the learning activity of the student in mastering the content taught by the teacher. Defining the learning process, teaching guides and determines learning. Therefore, the learning process is often explained as the process of educating the student. As a result, the learning process is perceived as a mechanism for the "transmission" of teaching content from teacher to student, with the teacher directly shaping the student. It formalizes the algorithm for solving problems − the problem-solving algorithm using the method of Diophantus al-Khwarizmi. This article presented a solution to text problems in algebra for grade 7 using the Kyrgyz method and a system of linear equations. It should be noted that this type of thinking was successfully used by many ancient Egyptian, Babylonian, Chinese and Indian mathematicians. The generalization of the described method can be successfully used to solve various problems.

Analysis of Ability to Understand Mathematical Concepts Class Students XI in Geometric Transformation Materials

The low ability of students to understand mathematical concepts is proven by the majority of students not being able to reformulate the solution to the problem given. This research aims to describe students' ability to understand mathematical concepts for each indicator so that it is known which indicators students experience problems with. The method used in this research is descriptive qualitative. The instruments in this research are test and interview instruments. The research subjects were 26 students of class XI Science 1 SMAN 5 Sukabumi City. Data were analyzed using data reduction, data presentation, and drawing conclusions. In data reduction, researchers summarize the data and classify the data. Based on the results of data analysis, it was concluded that students with high and medium abilities had excellent concept understanding abilities in restating a concept, classifying objects according to the concept, and presenting concepts in various forms of mathematical representation. while students with low abilities are in the good category. Highly capable students are able to restate the concepts they have learned and apply concepts or problem solving algorithms very well. Meanwhile, students with medium and low ability are in the sufficient category. In linking various concepts, high, medium and low ability students have quite good ability to understand mathematical concepts.

Integrating Remote Sensing Methods for Monitoring Lake Water Quality: A Comprehensive Review

Remote sensing methods have the potential to improve lake water quality monitoring and decision-making in water management. This review discusses the use of remote sensing methods for monitoring and assessing water quality in lakes. It explains the principles of remote sensing and the different methods used for retrieving water quality parameters in complex waterbodies. The review highlights the importance of considering the variability of optically active parameters and the need for comprehensive studies that encompass different seasons and time frames. The paper addresses the specific physical and biological parameters that can be effectively estimated using remote sensing, such as chlorophyll-α, turbidity, water transparency (Secchi disk depth), electrical conductivity, surface salinity, and water temperature. It further provides a comprehensive summary of the bands, band combinations, and band equations commonly used for remote sensing of these parameters per satellite sensor. It also discusses the limitations of remote sensing methods and the challenges associated with satellite systems. The review recommends integrating remote sensing methods using in situ measurements and computer modelling to improve the understanding of water quality. It suggests future research directions, including the importance of optimizing grid selection and time frame for in situ measurements by combining hydrodynamic models with remote sensing retrieval methods, considering variability in water quality parameters when analysing satellite imagery, the development of advanced technologies, and the integration of machine learning algorithms for effective water quality problem-solving. The review concludes with a proposed workflow for monitoring and assessing water quality parameters in lakes using remote sensing methods.

Философия изобретения Ж. Симондона и Г.С. Альтшуллера на примере задачи о ледоколе

The article aims to study the possibilities of combining the theories of inventive creativity of the French philosopher J. Simondon and the Soviet engineer G.S. Altshuller. As an ex­ample, we take The Icebreaker Problem, which has often been offered to students of in­troductory courses of G.S. Altshuller’s theory of inventive problem solving (TRIZ). The presuppositions for the ideas about the technical evolution of both authors are con­sidered. The main provisions of the theory of technical evolution are demonstrated on the basis of the evolution of icebreakers. Technological inventions are considered from Simondon’s point of view. Then, we consider the principles of Altshuller’s algorithm for inventive problem solving (ARIZ) to solve The Icebreaker Problem. Although both authors describe this process from different perspectives, the general theory of the in­vention allows us to combine them both, justifying the discrepancies. TRIZ naturally complements Simondon’s inventive creativity theory as a method. Simondon’s theory, in its turn, explains the origins of this method and other unusual characteristics of TRIZ (such as the “splitting” of the subject within the framework of the idea of directed evolu­tion that organizes TRIZ).

A density clustering-based differential evolution algorithm for solving nonlinear equation systems

Solving nonlinear equation systems (NESs) is one of the most important tasks in numerical computing. The NESs usually have multiple roots, and quickly locating their roots in a single algorithm run with a limited number of iterations has always been the algorithm improvement direction. In order to further enhance the efficiency of the existing methods, a density clustering-based differential evolution algorithm (DCDE) for the NESs problem solving is proposed in this paper. Firstly, density-based spatial clustering of applications with noise (DBSCAN) is used to divide the population into clusters and noises, which provides a better direction for population evolution. Secondly, a cluster evaluation factor is proposed, which not only divides the clusters into excellent clusters and non-excellent clusters, but also prevents the migration of excellent clusters and maintain the diversity of populations. Then, a migration strategy and individual generation mechanism are proposed to guide non-excellent clusters to migrate to promising regions. Finally, combined with an archive technique, the individuals which satisfy the solution requirements are archived and randomly initialized to further improve population diversity. To verify the effectiveness of the proposed algorithm, comparative experimental results of the propsoed DCDE with other state-of-the-art algorithms for thirty NESs problems solving show that the proposed DCDE is the most effective algorithm to locate more roots in a single run.

Nature-inspired optimization algorithms based feature selection: Application in credit scoring

Nature-inspired algorithms as problem-solving methodologies are extremely effective in discovery of optimized solutions in multi-dimensional and multi-modal problems. Because of qualities like “self-optimization”, “flexibility” and etc., nature-inspired algorithms for problem solving are effectively optimal. Feature selection is an approach to find approximate optimal subset of the features which are more relevant towards the particular outcome. In this study, we focused on how feature selection may improve the credit scoring model’s performance for prediction. Nature-inspired algorithms are applied for feature selection to improve the predictive performance of the credit scoring model. Additionally, four benchmark credit scoring datasets collected from the UCI repository are used to test feature selection by several Nature-inspired algorithms aggregated with “Random Forest (RF)”, “Logistic Regression (LR),” and “Multi-layer Perceptron (MLP)” for classification and results are compared in terms of classification accuracy and G-measures.

A Travel Salesman Problem Solving Algorithm Based on Feature Enhanced Attention Model

<p>In order to address the challenges of low accuracy and weak generalization capabilities in solving the traveling salesman problem (TSP) using end-to-end deep reinforcement learning (DRL) algorithms, this paper introduced a novel solution. This solution consisted of a feature enhanced attention model (FEAM) and a rotation expanded inference method. The FEAM combined a feature filtering layer, a graph embedding layer, and Transformer architecture in the encoder to better capture the complex relationships between cities, obtain richer and more accurate node representations, and thereby improved the model’s solving accuracy and generalization ability. The rotation expanded inference method generated new problem instances through coordinate rotation, enabling the model to consider path planning strategies from multiple perspectives, thus further enhancing solution accuracy. Numerous experiments on randomly generated benchmark datasets and public benchmark datasets show that the proposed end-to-end DRL algorithm performed better than other DRL algorithms in terms of solution quality and generalization ability.</p> <p> </p>

Physics on autopilot: exploring the use of an AI assistant for independent problem-solving practice

This study investigates the efficacy of large language model (LLM)-powered chatbots in guiding physics problem-solving, examining whether they can effectively supplement teacher-led learning. A customised chatbot was developed leveraging ChatGPT to provide step-by-step assistance through a structured problem-solving algorithm. Its impact was evaluated via an experimental study with 12th-grade physics students (N = 24) randomly assigned to a teacher-guided or chatbot-guided group for problem-solving practice. A Mann-Whitney U test revealed no significant differences in problem-solving competency between conditions. Qualitative analysis of conversational logs indicates the chatbot successfully emulated key teacher scaffolding behaviours. Our findings suggest AI tutors can deliver personalised, interactive support akin to human teachers, offering viable supplements to augment physics learning. Further research should explore optimising LLM training, human-chatbot balances, and impacts across diverse educational settings.

Modified firefly algorithm and different approaches for sentiment analysis

The firefly algorithm is the tool of swarm intelligence in almost all areas of optimization and engineering practice. Firefly algorithm and its variants have been used to solve diverse problems of real-world and motivate new researchers and algorithm developers to use this simple algorithm for problem-solving. The population of the initial solution, firefly light intensity rank selection and position update are important parameters of the firefly algorithm. The optimization result will vary in the variation of type of function selection by the researchers and developers. Therefore, here an effort has been made to propose a hybrid firefly algorithm where a population of initial solution generation done by the differential evolution algorithm (DEA) and selection of light intensity and change in position of firefly position will be done by position updating techniques of PSO. The hybrid firefly algorithm has been tested on five benchmark data set with nine benchmark algorithms and experiment result showed that firefly outperformed the other used algorithm. The aim of this study is also to motivate researchers to apply this algorithm for sentiment analysis also.

Nonlinear Tolerancing: Variation Simulation and Assembly Analysis with Regard to Contact Interaction of Parts

The variation analysis is a key tool for ensuring the high quality assembly in the process of developing the technology for manufacturing of aircraft parts. One of the main factors in variations is the deviations in the positioning procedure. This paper is devoted to the development of an approach that allows taking into account the variations during positioning and merging it with the special algorithm of contact problem solving. The impact of varied boundary conditions is incorporated into an additional vector of forces that can be interpreted as reactions to the shift of supports. The obtained results are illustrated with a case of wing-to-fuselage assembly.

Research on the “Trinity” Teaching Mode of Management Operations Research

Operations research has been widely applied in various fields such as production planning, marketing, and engineering technology, provided the scientific basis for management decision-making, and promoted modernization of management. Operations research, as a discipline based on data decision optimization, has made significant development and progress. However, the teaching mode of operations research has not undergone reform and innovation with the arrival of the big data era. In the traditional teaching process of operations research, teachers often overemphasize mathematical theoretical derivation and problem-solving algorithms, while neglecting students’ ability to solve practical problems. To further improve the teaching quality of operations research and the training quality of management professionals, this paper analyzes the main problems existing in the teaching of management operations research courses and proposes a ‘Trinity’ teaching mode of management operations research. Based on the above, establish an integrated teaching method that integrates mathematical modeling ideas, computer experiments, and subject competition training. Through the “Trinity” teaching mode, we can deepen the understanding and application of theoretical knowledge, establish students’ modeling thinking ability, and cultivate students’ ability to use relevant software and solve practical problems. Through the study of operations research, students can use the knowledge and methods of operations research to analyze, model, and solve some typical problems in economics and management with the help of computers.

Exact solutions of some OR-library test instances for the p-next center problem

OR-Library is a platform that provides standardized examples for testing problem-solving algorithms in many fields of the operational research and combinatorial optimization. One of the problems emerged in the previous decade is the p-next center problem. The solution to the p-next center problem implies locating p centers in order to minimize the maximum user distance to the closest center plus the distance between that center and the center closest to it. There are several heuristic algorithms for solving this NP-hard problem that give optimal or near-optimal solutions. We propose an algorithm for solving the p-next center problem based on the variable neighborhood search method, capable of recognizing whether some of the found solutions from the OR-Library test set are exact. As a result of the algorithm execution, more than 50% of the solutions are identified as globally optimal. The paper presents a table with the found exact solution values for the p-next center problem from the OR-Library test set.

Analysis of Students' Mathematical Understanding in Gradient Material Linear Function Graphics Class IX UPTD SMP Negeri 3 Pematang Siantar

This research aims to find out how to analyze students' mathematical understanding of gradient materials for linear function graphs for Class IX 7 UPTD SMP Negeri 3 Pematang Siantar. The research method used in this research is a qualitative method with descriptive data analysis. The subjects in this research are all students in class IX 7 of SMP Negeri 3 Pematang Siantar in the 2023/2024 academic year. The number of subjects in this research was 30 students. The instrument used in this research is a mathematical understanding test in the form of a description using 3 indicators, namely restating concepts that have been studied, presenting concepts in various forms of mathematical representation, applying concepts or problem-solving algorithms. These 3 indicators are used in 6 questions. Based on the data analysis carried out, the overall mathematical understanding ability of students is at a medium level, namely with a percentage of 47.8%. The indicators used are 1) restating mathematical concepts, 2) presenting mathematical representations, 3) applying concepts or problem-solving algorithms. Based on the previous explanation, it can be concluded that the mathematical understanding of students in class IX 7 UPTD SMP Negeri 3 Pematang Siantar as a whole is at a medium level.

A method for recovering linear block codes over an arbitrary finite field from sets of distorted code words

The article is devoted to one of the practically important problems of information security and cryptanalysis, which consists in recovering an unknown linear block code over an arbitrary field from a set of distorted code words. This is a hard computational problem, and the known problem-solving methods are proposed only for codes over the field of two elements and are based on the algorithms for searching words of small weight in (undistorted) linear block codes.
 The main result of the article is a method for solving the problem posed, which differs in essence from the known ones and consists in recovering the desired code by solving the LPN (Learning Parity with Noise) problem, namely, recovering the solutions of systems of linear equations with distorted right-hand sides and a random equally probable matrix of coefficients over specified field. The LPN problem is well known from the Theory of Computational Algorithms and Cryptanalysis. It is equivalent to the problem of random linear block code decoding, and the security of many modern post-quantum cryptosystems are based on its hardness.
 The proposed method provides an opportunity to apply a wider class of algorithms for recovering linear block codes in comparison with the previously known methods, in particular, algorithms like BKW and also the low weight words search algorithms in co-sets of linear block codes. Moreover, in contrast to previously known ones, the complexity of the proposed method depends linearly on the length of the required code (and increases with increasing of its dimension according to which algorithm for the LPN problem-solving is applied). Thus, the basic parameter determined the complexity of recovering a linear block code is its dimension (not its length), which, in principle, makes it possible to speed up known algorithms for recovering linear block codes from a set of corrupted code words.

Research on the Prediction of Boston House Price Based on Linear Regression, Random Rorest, Xgboost and SVM Models

Regression problems, which make up a significant portion of machine learning research, include the Boston housing price forecast problem. Regression problems are a subset of artificial intelligence. Regression problem-solving algorithms that are more frequently used four different machine learning models—the linear regression model, the Random Forest model based on an integrated learning technique, the XGBoost model, and the SVM model—are utilized for training and testing in this study. These models were chosen based on the real-world Boston house price prediction problem. From various angles, the effectiveness of the various algorithmic models on the regression problem of house price prediction was evaluated, and it was determined that among the many algorithmic models. For this problem, the best model is XGBoost Regression and the worst model was the SVM model. The most typical benefit of the XGBoost model is that It can capture complex non-linear relationships between variables.The disadvantage of the SVM model is its computational complexity.

Evaluation of Frameworks for MLOps and Microservices

Information Technology involves solutions for many kinds of industries and organizations, offering conditions for solving problems of different types and complexities. Artificial Intelligence, and more specifically applications that considers Machine Learning (ML) and Software Technology are part of these solutions for solving problems, including solutions for solving problems that involve smart cities approach. In order to present frameworks that deal with the operationalization of Machine Learning and Software technology, this article is based on the study and evaluation of frameworks that involve Machine Learning Operations (MLOps) and microservices. Specifically, three frameworks that integrate ML algorithms with microservices are evaluated based on a bibliographical review in scientific journals of relevance to the area. From an exploratory analysis of these frameworks, it was possible to highlight their main objectives, their benefits, and their ability to offer solutions that favor the large-scale use of Machine Learning algorithms in problem solving. The main results are highlighted in the article through a qualitative analysis that considers six evaluation criteria, such as: capacity for sharing resources, scope of use by users, and use in a cloud environment. The results achieved are satisfactory since the work allows, through a qualitative view of the evaluated frameworks, a perspective of how the integration of MLOps and microservices has been carried out, its benefits and possible results achieved through this integration.

Analisis kesulitan belajar siswa pada materi perbandingan kelas VII SMPN 02 cililin

This study looks at how difficult it is for seventh grade students of junior high school in working on comparison problems in terms of gender. This study uses a descriptive method with a qualitative approach used for this study. 10 students consisting of 5 male students and 5 female students from SMPN 02 Cililin were used as research subjects. The instrument used in the study was five items in the form of descriptions. The results of student test scores based on comparison questions are used in collecting research data, with indicators 1) restating a concept 2) applying concepts or algorithms in problem solving, 3) using and utilizing and having certain procedures or operations, based on the conclusions from the research results that is, it was concluded that the ability of class VII male students of SMPN 02 Cililin in working on comparison questions was still weak compared to female students. Lack of enthusiasm in participating in learning, student attitudes in participating in learning because they are used to online school, so students do not really understand the material that has been delivered.

Stochastic scheduling of autonomous mobile robots at hospitals.

This paper studies the scheduling of autonomous mobile robots (AMRs) at hospitals where the stochastic travel times and service times of AMRs are affected by the surrounding environment. The routes of AMRs are planned to minimize the daily cost of the hospital (including the AMR fixed cost, penalty cost of violating the time window, and transportation cost). To efficiently generate high-quality solutions, some properties are identified and incorporated into an improved tabu search (I-TS) algorithm for problem-solving. Experimental evaluations demonstrate that the I-TS algorithm outperforms existing methods by producing high-quality solutions. Based on the characteristics of healthcare requests and the AMR working environment, scheduling AMRs reasonably can effectively provide medical services, improve the utilization of medical resources, and reduce hospital costs.

Enhancing Employability for Autistic Graduates: Using TRIZ Theory to Design Virtual Reality Solutions for Fostering Inclusive Communications in Workplace Environments

The underemployment of post-secondary graduates with autism (AP-PSD) is a critical concern. This issue often stems from difficulties in workplace integration and communication. This research explores the potential of virtual reality (VR) systems to alleviate these challenges by simulating work environments. The methodology, rooted in the TRIZ Theory, a problem-solving algorithm, refines the use of VR systems for enhanced adaptability and efficiency. The primary objectives include enhancing employers' understanding of AP-PSD-related issues and identifying significant workplace challenges faced by AP-PSDs. Through literature reviews, surveys, and focus groups, the study investigates the factors impacting AP-PSDs and identifies key components to develop a more effective VR system to support their workplace integration.