Concise Rules Research Articles

Inheriting the glory of PāṆinian grammar into computer science

the integration of Pāṇinian grammar principles into computer science represents a significant advancement in computational linguistics and natural language processing. Pāṇini, an ancient Indian grammarian, formulated an elaborate system of grammar known as Aṣṭādhyāyī around the 4th century BCE, which has been revered for its systematic and concise rules governing Sanskrit language structure. In recent years, there has been a growing interest in adapting Pāṇinian principles to enhance computational frameworks for understanding human languages. This paper explores the theoretical foundations of Pāṇinian grammar and its applicability in the context of modern computational linguistics. The integration of Paninian grammar into computer science enhances the accuracy and efficiency of natural language processing system and contributes to preservation and dissemination of ancient linguistic knowledge. Through this the researchers aim to bridge gap between traditional linguistic scholarship and modern computational methodologies. By embracing the systematic and rigorous framework laid out by Pāṇini millennia ago, researchers are poised to achieve new breakthroughs in understanding and processing human language through innovative computational techniques. The journey from ancient Sanskrit grammar to innovative computer science exemplifies a harmonious blend of tradition and innovation in the quest for linguistic knowledge and technological advancement. Keywords - Pāṇini, Aṣṭādhyāyī, Computational linguistics, Machine translation, semantic analysis, syntactic rules.

Bats integrate multiple echolocation and flight tactics to track prey

The ability of "target tracking," such as keeping a target object in sight, is crucial for various activities. However, most sensing systems experience a certain degree of delay due to information processing, which challenges accurate target tracking. The long history of studies on animal behavior has revealed several tactics for it, although a systematic understanding of how individual tactics are combined into a strategy has not been reached. This study demonstrates a multifaceted tracking strategy in animals, which mitigates the adverse delay effects with small implementation costs. Using an active-sensing bat to measure their sensing state while chasing natural prey, we found that bats use a tracking strategy by combining multiple echolocation and flight tactics. The three echolocation tactics, namely the predictive control of sensing direction accompanied by adjusting the sensing rate and angular range, produce a direct compensation effect. Simultaneously, the flight tactic, the counter maneuver, assists echolocation by stabilizing the target direction. Our simulation results demonstrate that these combined tactics improve tracking accuracy over a wide range of delay constraints. In addition, a concise rule based on the angular velocity between bats and targets explains how bats control these tactics, suggesting that bats successfully reduce the burden of multitasking management. Our findings reveal the sophisticated strategy in animals' tracking systems and provide insights into understanding and developing efficiently integrated strategies in target tracking across various disciplines.

ON THE ISSUE OF THE GOALS AND LEGAL PRINCIPLES OF PROVIDING SOCIAL ASSISTANCE IN THE RUSSIAN FEDERATION

The relevance of the research topic lies in the fact that social assistance is an important component of guarantees of the social security of citizens’ social rights. The article reveals the main task of the state social policy, which is to achieve the well-being of an individual and society as a whole through fair and equal access for all citizens to state social assistance. The article is devoted to the analysis of the goals of providing social assistance to persons in need, enshrined in the Federal Law of 17.07.1999 N 178-FZ "On State Social Assistance". The purpose of the study is to analyze and clarify the objectives set out in the Law on Social Assistance, to define the legal principles of state social assistance and to disclose their content. Materials and methods. The methodological basis of the research is predetermined by the set goal and objectives, as well as by the idea of the existence of universal, general scientific, private scientific and special legal research methods. The paper presents an analysis of legislation, practical experience of its use and scientific doctrine on the goals and principles of social protection and social assistance. The article reveals the concept of state social assistance; the opinions of scientists on the main goals of social protection are given. To conduct a full-fledged analysis, the subject composition of recipients of state social assistance is considered. Such subjects include low-income families, low-income citizens living alone and other categories of citizens who, for reasons beyond their control, have an average per capita income below the subsistence minimum. The result of the study. The authors propose their own formulations of the goals of social protection, assess the implementation of the goals in practice provided for in the legislation, identify the causes of emerging problems with the provision of social assistance, among which the lack of clear and concise rules for the provision of social assistance stands out. These will contribute to the development of legislation. Based on the analysis of legislation and law enforcement practice, their own principles have been put forward. These include targeting, accessibility, humanity, voluntariness, targeted use of budget funds, clarity, and equality. The article reveals the content of each of these principles. The practical and theoretical significance of the study is manifested in the fact that the improvement of legislation on social assistance should take place taking into account the goals and principles discussed in the article, which should be reflected in regulatory legal acts. It will make it possible to create a more effective system of social support for citizens and will contribute to improving the quality of life of the population. Conclusions. It is concluded that it is necessary to clarify the objectives specified in the Law, as well as to supplement it with an article containing the legal principles on which the legislation on social assistance will be based.

Dimeric eremophilane-type sesquiterpenoids from the peeled stems of Syringa pinnatifolia

A continued phytochemical investigation guided by 1H NMR and LC-MS data on the ethanol extract from the peeled stems of Syringa pinnatifolia Hemsl. led to the isolation of 16 undescribed dimeric eremophilane sesquiterpenoids, namely syringenes R–Z (1–9) and A1–G1 (10–16). These structures were elucidated by extensive analysis of spectroscopic data, including HRESIMS, NMR, quantum-mechanics-based computational analysis of NMR chemical shifts, and single-crystal X-ray diffraction analyses, and a concise rule for determination of relative configuration of angular methyl was proposed. The results of the cardioprotective assay demonstrated that 3 exhibits a protective effect against hypoxia-induced injuries in H9c2 cells. This effect was observed at a concentration of 10 μM, with a protective rate of 28.43 ± 11.80%.

Kaidah-Kaidah Kebahasaan dan Aplikasinya dalam Menafsirkan Al-Qur’an

This research aims to find out what rules need to be understood in interpreting the Al-Qur'an. The research method used is literature review. The rules of interpretation are a mufassir's tool in understanding the meaning of the Qur'an. The rules of Tafsir are also closely related to the rules of the Arabic language which are one of the foundations for interpreting the Al-Qur'an. Therefore, mastery of linguistic rules is very necessary in interpreting the Al-Qur'an so that the meaning of the Al-Qur'an verses is not misinterpreted. There are several linguistic rules in interpreting the Qur'an that need to be carefully considered, including: Isim Dhamir, Nakirah and Ma'rifah, repetition of words, questions and answers, number of syartiyah. The linguistic rules in interpreting the Al-Qur'an mentioned above are concise rules that need to be mastered by a mufassir when wishing to interpret the Al-Qur'an.

Concise rule induction algorithm based on one-sided maximum decision tree approach

As the importance of machine learning tools for decision support continues to grow, interpretability has emerged as a key factor. Rule-based classification algorithms, such as decision trees and rule induction, enable high local interpretability by providing transparent reasoning rules in an IF-THEN format. In this context, it is essential to provide concise and clear rules and conditions to achieve high local interpretability. This study proposes a novel Concise Algorithm, designed to effectively remove irrelevant conditions from classification rules. We present a framework incorporating the Concise Algorithm, which employs the One-Sided-Maximum decision tree algorithm for rule generation, followed by the application of the Concise Algorithm to remove irrelevant conditions. This proposed framework produces a rule-based classification model that exhibits an enhanced predictive performance-interpretability trade-off compared to benchmark methods (CART, Ripper, CN2, and modified One-Sided-Maximum), as demonstrated by empirical tests conducted on 19 UCI datasets. A case study focusing on the breast-cancer-wisconsin dataset provides a comprehensive analysis of the rule and condition generation processes.

Feature selection approach for evolving reactive scheduling policies for dynamic job shop scheduling problem using gene expression programming

Dispatching rules are one of the most widely applied methods for solving Dynamic Job Shop Scheduling problems (DJSSP) in real-world manufacturing systems. Hence, the automated design of effective rules has been an important subject in the scheduling literature for the past several years. High computational requirements and difficulty in interpreting generated rules are limitations of literature methods. Also, feature selection approaches in the field of automated design of scheduling policies have been developed for the tree-based GP approach only. Therefore, the aim of this study is to propose a feature selection approach for the Gene Expression Programming (GEP) algorithm to evolve high-quality rules in simple structures with an affordable computational budget. This integration speeds up the search process by restricting the GP search space using the linear representation of the GEP algorithm and creates concise rules with only meaningful features using the feature selection approach. The proposed algorithm is compared with five algorithms and 30 rules from the literature under different processing conditions. Three performance measures are considered including total weighted tardiness, mean tardiness, and mean flow time. The results show that the proposed algorithm can generate smaller rules with high interpretability in a much shorter training time.

Predictive self-organizing neural networks for in-home detection of Mild Cognitive Impairment

In-home sensing of daily living patterns from older adults coupled with machine learning is a promising approach to detect Mild Cognitive Impairment (MCI), a potentially reversible condition with early detection and appropriate intervention. However, the number of subjects involved in such real-world studies is typically limited, posing the so-called small data problem to most predictive models which rely on a sizable number of labeled data. In this work, a predictive self-organizing neural network known as fuzzy Adaptive Resonance Associate Map (fuzzy ARAM) is proposed to detect MCI using in-home sensor data collected from a unique Singapore cross-sectional study. Specifically, mean and standard deviation of nine in-home behavioral attributes of 49 subjects over two months were derived for each subject from the raw sensor data. We first applied fuzzy ARAM to the 49-subject data set with missing data, and achieved a F1-score of 58.3% to detect MCI from cognitive healthy. To eliminate the effect of missing data, we next conducted our study using an even smaller 25-subject data set with no missing values, of which fuzzy ARAM achieved a F1-score of 63.6%. To derive concise rules for prediction and interpretation, antecedent pruning was subsequently employed. For the 25-subject data set, the F1-score improved to 76.2%, while the symbolic IF–THEN rules revealed that behavior metrics such as variation of forgetfulness and sleep contained notable predictive utility. Compared with Support Vector Machines (SVM), Decision Tree, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), our benchmark experiments show that fuzzy ARAM provided the highest predictive performance and yielded unique rules for MCI detection. These results demonstrate the potential of fuzzy ARAM to detect MCI using in-home monitoring sensor data.

DOES EXISTING ISLAMIC SOCIAL REPORTING INDEX BETTERTO ARTICULATE SHARIA VALUE?

Purpose: This study aims to investigate the Islamic Social Reporting index whether it reflects sharia value implementation within companies listed in Jakarta Islamic Index compare to Sustainability Reporting Award winner. Design/methodology/approach: Data was taken from companies listed in Jakarta Islamic Index and those whom awarded as first and second place in Sustainability Reporting Award. This research uses quantitative approach to test the consistency of Sharia index implemented in JII companies compare to Sustainable Reporting Awardee companies. Using content analysis, the hypothesis is test uses one sample t-test. Findings: This study shows that there is ISR disclosure index difference between JII and SRA awardee companies. The SRA awardee outperforms JII companies, which is claimed as sharia holding value in running the business. This paper contributes to stakeholder literature by reshape disclosure index based on the shariah value in relation to social responsibility practices. The findings are noteworthy to the investor that concern with sharia value implementation through concise and consistent sharia rules. This study provides insights to how Islamic organization manage sharia implementation by redesign its indexing using more advanced methodology as well as adapted instead of adopted other guidelines without sufficient methods. There are disclosure items that the company naturally does not want to disclose. So, it cannot be detected through the content of information disclosed by the company. As a result, measurements may become biased. Originality: This paper give additional insight that even sharia based assessment need to reconstruct its sharia methodology to provide fruitful information for investor. We recommend considering assessment method to reflect sharia value.

Concise Rule Induction Algorithm Based on One-Sided Maximum Decision Tree Approach

Concise Rule Induction Algorithm Based on One-Sided Maximum Decision Tree Approach

One-Dimensional Convolutional Neural Networks with Feature Selection for Highly Concise Rule Extraction from Credit Scoring Datasets with Heterogeneous Attributes

Convolution neural networks (CNNs) have proven effectiveness, but they are not applicable to all datasets, such as those with heterogeneous attributes, which are often used in the finance and banking industries. Such datasets are difficult to classify, and to date, existing high-accuracy classifiers and rule-extraction methods have not been able to achieve sufficiently high classification accuracies or concise classification rules. This study aims to provide a new approach for achieving transparency and conciseness in credit scoring datasets with heterogeneous attributes by using a one-dimensional (1D) fully-connected layer first CNN combined with the Recursive-Rule Extraction (Re-RX) algorithm with a J48graft decision tree (hereafter 1D FCLF-CNN). Based on a comparison between the proposed 1D FCLF-CNN and existing rule extraction methods, our architecture enabled the extraction of the most concise rules (6.2) and achieved the best accuracy (73.10%), i.e., the highest interpretability–priority rule extraction. These results suggest that the 1D FCLF-CNN with Re-RX with J48graft is very effective for extracting highly concise rules for heterogeneous credit scoring datasets. Although it does not completely overcome the accuracy–interpretability dilemma for deep learning, it does appear to resolve this issue for credit scoring datasets with heterogeneous attributes, and thus, could lead to a new era in the financial industry.

Risk Factors Discovery for Cancer Survivability Analysis Using Graph-Rule Mining

Mining and understanding patients’ disease-development pattern is a major healthcare need. A huge number of research studies have focused on medical resource allocation, survivability prediction, risk management of diagnosis, etc. In this article, we are specifically interested in discovering risk factors for patients with high probability of developing cancers. We propose a systematic and data-driven algorithm and build around the idea of association rule mining. More precisely, the rule-mining method is firstly applied on the target dataset to unpack the underlying relationship of cancer-risk factors, via generating a set of candidate rules. Later, this set is represented as a rule graph, where informative rules are identified and selected with the aim of enhancing the result interpretability. Compared to hundreds of rules generated from the standard rule-mining approach, the proposed algorithm benefits from a concise rule subset, without losing the information from the original rule set. The proposed algorithm is then evaluated using one of the largest cancer data resources. We found that our method outperforms existing approaches in terms of identifying informative rules and requires affordable computational time. Additionally, relevant information from the selected rules can also be used to inform health providers and authorities for cancer-risk management.

Extracting boolean and probabilistic rules from trained neural networks

This paper presents two approaches to extracting rules from a trained neural network consisting of linear threshold functions. The first one leads to an algorithm that extracts rules in the form of Boolean functions. Compared with an existing one, this algorithm outputs much more concise rules if the threshold functions correspond to 1-decision lists, majority functions, or certain combinations of these. The second one extracts probabilistic rules representing relations between some of the input variables and the output using a dynamic programming algorithm. The algorithm runs in pseudo-polynomial time if each hidden layer has a constant number of neurons. We demonstrate the effectiveness of these two approaches by computational experiments.

Rule Extraction Model Based on Decision Dependency Degree

Rule extraction is the core in rough set. Two procedures are contained in rule extraction: one is attribute reduction and another is attribute value reduction. It was proved through computational complexity perspective that obtaining all the reduction, minimum attribute reduction, and minimum attribute value reduction is an NP problem. So, generally, a heuristic reduction method is used to solve attribute reduction and attribute value reduction. However, for most heuristic methods, it is hard to put into practice and has high cost on computational complexity. Moreover, part of the methods extracted redundant rules. To approach a quick and effective model for rule extraction in decision systems, against the concept of distinguishable relation, relevant concepts and basic theorems of rule extraction are proposed. In order to get concise and accurate rules quickly, algorithms for finding conflict object set, finding duplicate object set, and finding redundant rules are given. After that, using decision dependency degree as attribute importance to determine the importance of each attribute in rule object, a new rule extraction model based on decision dependency degree is proposed in this paper. Compared with the previous models, this model does not generate matrix; instead, it finds conflict object set and duplicate object set by equivalence class, and consequently, improves the time performance tomaxOCU,OC2U/C, andOREDU/C/RED2. The theoretical analysis and experimental research show that the new model more accurately and effectively reduces the redundant data and extracts more concise decision rules from dataset.

Towards explicit representation of an artificial neural network model: Comparison of two artificial neural network rule extraction approaches

In the quest for interpretable models, two versions of a neural network rule extraction algorithm were proposed and compared. The two algorithms are called the Piece-Wise Linear Artificial Neural Network (PWL-ANN) and enhanced Piece-Wise Linear Artificial Neural Network (enhanced PWL-ANN) algorithms. The PWL-ANN algorithm is a decomposition artificial neural network (ANN) rule extraction algorithm, and the enhanced PWL-ANN algorithm improves upon the PWL-ANN algorithm and extracts multiple linear regression equations from a trained ANN model by approximating the hidden sigmoid activation functions using N-piece linear equations. In doing so, the algorithm provides interpretable models from the originally trained opaque ANN models. A detailed application case study illustrates how the generated enhanced-PWL-ANN models can provide understandable IF-THEN rules about a problem domain. Comparison of the results generated by the two versions of the PWL-ANN algorithm showed that in comparison to the PWL-ANN models, the enhanced-PWL-ANN models support improved fidelities to the originally trained ANN models. The results also showed that more concise rule sets could be generated using the enhanced-PWL-ANN algorithm. If a more simplified set of rules is desired, the enhanced-PWL-ANN algorithm can be combined with the decision tree approach. Potential application of the algorithms to domains related to petroleum engineering can help enhance understanding of the problems.

Extracting Production Rules for Cerebrovascular Examination Dataset through Mining of Non-Anomalous Association Rules

Today, patients generate a massive amount of health records through electronic health records (EHRs). Extracting usable knowledge of patients’ pathological conditions or diagnoses is essential for the reasoning process in rule-based systems to support the process of clinical decision making. Association rule mining is capable of discovering hidden interesting knowledge and relations among attributes in datasets, including medical datasets, yet is more likely to produce many anomalous rules (i.e., subsumption and circular redundancy) depends on the predefined threshold, which lead to logical errors and affects the reasoning process of rule-based systems. Therefore, the challenge is to develop a method to extract concise rule bases and improve the coverage of non-anomalous rule bases, i.e., one that not only reduces anomalous rules but also finds the most comprehensive rules from the dataset. In this study, we generated non-anomalous association rules (NAARs) from a cerebrovascular examination dataset through several steps: obtaining a frequent closed itemset, generating association rule bases, subsumption checking, and circularity checking, to fit production rules (PRs) in rule-based systems. Toward the end, the rule inferencing part was performed by PROLOG to obtain possible conclusions toward a specific query given by a user. The experiment shows that compared with the traditional method, the proposed method eliminated a significant number of anomalous rules while improving computational time.

Feature Selection and Rule Extraction Based on Variable Precision Rough Set

Main factors in feature selection is dimensionality in data and decision making on partial information handling as precision classification errors. This study will demonstrate the feature selection using the rough set method, especially in handling missing information along with an ambiguous decision system with a precision variable-based approach to finding significant features and extracting the required rules. The experiment was carried out with 12 Dermatology datasets from the UCI Machine Learning Repository consisting of 11 conditional attributes and a decision variable. The experimental results show the results of the dominant conditional feature selection of 45% of the overall existing conditional features, along with more concise rules based on selected features.

TOWARD A CONVERGENT MODEL OF FLEXIBILITY IN ARCHITECTURE

With recent advances in technology, fundamental changes have occurred in architectural flexibility methods, a great deal of experience has been gained, and many strategies and tactics have been developed in this field. However, a convergence is lacking in these experiences, methods, and tactics, which raises a need for providing a convergent approach in this regard. In this paper, it is going to propose such a model of flexibility. To do this, it is first reviewed the general concepts of flexibility, and provide a short review of its history and its main executional tactics. It is then divided its main tactics into eight main groups of: ‘open plan’, ‘pre-fabricated modules’, ‘similar spaces’, ‘extendable unit᾽, ‘attachment and detachment of adjacent units’, ‘common space between adjacent units’, ‘portable walls’, and ‘retractable furniture in a multifunctional space’. Analyzing these tactics, it is extracted the three fundamental principles of flexibility: ‘soft connection’, ‘diversity and multiplicity of spaces’, and ‘multifunctional space’. Accordingly, this paper proposes a concise rule for flexibility as: ‘soft connections between spaces’ which implies to the three principles if some special attentions are paid. Although the rule seems to be simple, the paper argumentations show that paying attention to what it implies, can produce much more flexible spaces than what is created generally. At the same time, new creative ideas along supported by new emerging technologies can enrich these simple solutions extensively.

Use of a Deep Belief Network for Small High-Level Abstraction Data Sets Using Artificial Intelligence with Rule Extraction.

We describe a simple method to transfer from weights in deep neural networks (NNs) trained by a deep belief network (DBN) to weights in a backpropagation NN (BPNN) in the recursive-rule eXtraction (Re-RX) algorithm with J48graft (Re-RX with J48graft) and propose a new method to extract accurate and interpretable classification rules for rating category data sets. We apply this method to the Wisconsin Breast Cancer Data Set (WBCD), the Mammographic Mass Data Set, and the Dermatology Dataset, which are small, high-abstraction data sets with prior knowledge. After training these three data sets, our proposed rule extraction method was able to extract accurate and concise rules for deep NNs trained by a DBN. These results suggest that our proposed method could help fill the gap between the very high learning capability of DBNs and the very high interpretability of rule extraction algorithms such as Re-RX with J48graft.

Data-Driven Elastic Fuzzy Logic System Modeling: Constructing a Concise System With Human-Like Inference Mechanism

The construction of fuzzy logic systems (FLSs) using data-driven techniques has become the most popular modeling approach. However, this approach still faces critical challenges, including the difficulty in obtaining concise models for high-dimensional data and generating accurate fuzzy rules to simulate human inference mechanism. To tackle these issues, a new FLS modeling framework called data-driven elastic FLS (DD-EFLS) is proposed in this paper. The DD-EFLS has two key characteristics. First, the fuzzy rules in the rule base can use different feature subspaces that are extracted from the original high-dimensional space to yield simple and accurate rules in feature spaces of lower dimensionality. Second, fuzzy inferences from various views are implemented by embedding different rules in the corresponding subspaces to imitate human inference mechanism. Based on the DD-EFLS framework, an elastic Takagi–Sugeno–Kang (TSK) FLS modeling method (ETSK-FLS) is proposed to train the elastic TSK FLS using the concise rules and a more human-like inference mechanism for modeling tasks based on high-dimensional datasets. The characteristics and advantages of the proposed framework and the ETSK-FLS method are validated experimentally using both synthetic and real-world datasets.