Proposed study on evaluating and forecasting the resident property value based on specific determinants by case base reasoning and artificial neural network approach

Diwan, Sinan Adnan

doi:10.11591/ijeecs.v17.i3.pp1467-1473

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…According to historical datasets in the legal context, judicial decisions' prediction is standard and widely practised in the worldwide legal system. Machine learning is a budding scientific algorithms study, and statistical models are artificial intelligence's parts that enable systems to automatically learn and improvise experience from the test data [23]- [30].…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review of machine learning methods in predicting court decisions

Rosili

Hassan

Zakaria

et al. 2021

IJ-AI

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<span>Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.</span>

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Section: Introductionmentioning

confidence: 99%

A systematic literature review of machine learning methods in predicting court decisions

Rosili

Hassan

Zakaria

et al. 2021

IJ-AI

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“…Artificial neural networks (ANNs) have been widely explored for real estate price prediction due to their ability to model complex nonlinear relationships [5]. The study in [6] combined case-based reasoning (CBR) and ANNs, achieving high accuracy while acknowledging challenges in data availability and model refinement. These studies highlight the potential of ANNs, aligning with the objective of leveraging advanced ML techniques for predictive modeling.…”

Section: Neural Network Approachmentioning

confidence: 99%

Advanced Machine Learning Techniques for Predictive Modeling of Property Prices

Mathotaarachchi,

Hasan,

Mahmood

2024

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Real estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing literature lacks a cohesive synthesis of methodologies, findings, and research gaps in ML-based real estate price prediction. This study addresses this gap through a comprehensive literature review, examining various ML approaches, including neural networks, ensemble methods, and advanced regression techniques. We identify key research gaps, such as the limited exploration of hybrid ML-econometric models and the interpretability of ML predictions. To validate the robustness of regression models, we conduct generalization testing on an independent dataset. Results demonstrate the applicability of regression models in predicting real estate prices across diverse markets. Our findings underscore the importance of addressing research gaps to advance the field and enhance the practical applicability of ML techniques in real estate price prediction. This study contributes to a deeper understanding of ML’s role in real estate forecasting and provides insights for future research and practical implementation in the real estate industry.

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“…Prior work on FIS has been conducted. The inference system is no longer get the linguistic information from an expert only but also adapting fuzzy system using numerical data (input/output pairs) to get better performance, in this case, the accuracy of the data analysis results [2]. The development of fuzzy logic shows that fuzzy logic can model various systems, map an input into an output without losing sight of the factors, be very flexible, and have a tolerance to the existing data [3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house

Kushadiani

Buono²,

Nugroho

2022

Comput. Sci. Inf. Technol.

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As a scientific publishing house, Indonesian Institute of Sciences (LIPI) Press' encountered some problems in publication planning, mainly predicting the optimum production of publications. This study aimed to enhance a fuzzy inference system (FIS) parameters using the genetic algorithm (GA). The enhancements led to optimally predict the number of LIPI Press publications for the following year. The predictors used were the number of work units, the number of workers, and the publishing process duration. The dataset covered a five years range of total production of LIPI Press. Firstly, an expert set up the parameters of the fuzzy inference system denoted as a FIS expert. Next, we performed a FIS GA by applying the genetic algorithm and K-fold validation in splitting the training data and testing data. The FIS GA revealed optimum prediction with parameters that were composed of both population size (30), the probability of crossover (0.75), the probability of mutation (0.01), and the number of generations (150). The experiment results show that our enhanced FIS GA outperformed FIS expert approach.

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Proposed study on evaluating and forecasting the resident property value based on specific determinants by case base reasoning and artificial neural network approach

Cited by 4 publications

References 9 publications

A systematic literature review of machine learning methods in predicting court decisions

A systematic literature review of machine learning methods in predicting court decisions

Advanced Machine Learning Techniques for Predictive Modeling of Property Prices

Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house

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