The novelty of the proposed research lies in its focus on developing and refining MCDM methods to address the complex and multifaceted nature of sustainability challenges, and evaluating the effectiveness and practicality of these methods in real-world decision-making contexts. While previous research has explored the application of MCDM methods to sustainability decision-making, this study aims to advance the field by addressing the following novel aspects: Comprehensive evaluation of MCDM methods: The study aims to comprehensively evaluate the effectiveness and practicality of various MCDM methods in addressing sustainability challenges, including climate change, resource management, and social justice. This evaluation will consider the strengths and limitations of each method, and identify opportunities for improvement. Incorporation of stakeholder values: The study will incorporate stakeholder values into the decision-making process, ensuring that the resulting decisions reflect the diverse perspectives and priorities of all stakeholders. This approach differs from traditional decision-making methods, which often prioritize the perspectives of a select few. Real-world decision-making contexts: The study will evaluate the effectiveness and practicality of MCDM methods in real-world decision-making contexts, providing insights into the challenges and opportunities associated with the implementation of these methods. This will help decision-makers to better understand how to apply MCDM methods to real-world sustainability challenges. The proposed research offers a novel and comprehensive approach to addressing sustainability challenges through the development and application of MCDM methods. By evaluating the effectiveness and practicality of these methods in real-world decision-making contexts, this study aims to provide decision-makers with a more comprehensive and informed approach to sustainability decision-making that reflects the diverse perspectives and priorities of all stakeholders. Future research in the area of MCDM methods to address sustainability challenges could focus on several areas, including: Cross-disciplinary collaborations: Given the complex and multifaceted nature of sustainability challenges, there is a need for cross-disciplinary collaborations between decision-makers, scientists, and stakeholders to develop and implement effective sustainability strategies. Future research could explore how MCDM methods can facilitate these collaborations and promote interdisciplinary dialogue and knowledge sharing. Evaluation of long-term sustainability outcomes: While MCDM methods can help decision-makers to evaluate alternatives based on multiple criteria and stakeholder values, it may be challenging to evaluate the long-term sustainability outcomes of these decisions. Future research could explore how MCDM methods can be used to evaluate the long-term sustainability outcomes of decisions, and how the effectiveness of these methods c
{"title":"MCDM methods to address sustainability challenges, such as climate change, resource management, and social justice","authors":"F. Riandari, Marc Z. Albert, Stanley S. Rogoff","doi":"10.35335/idea.v1i1.4","DOIUrl":"https://doi.org/10.35335/idea.v1i1.4","url":null,"abstract":"The novelty of the proposed research lies in its focus on developing and refining MCDM methods to address the complex and multifaceted nature of sustainability challenges, and evaluating the effectiveness and practicality of these methods in real-world decision-making contexts. While previous research has explored the application of MCDM methods to sustainability decision-making, this study aims to advance the field by addressing the following novel aspects: Comprehensive evaluation of MCDM methods: The study aims to comprehensively evaluate the effectiveness and practicality of various MCDM methods in addressing sustainability challenges, including climate change, resource management, and social justice. This evaluation will consider the strengths and limitations of each method, and identify opportunities for improvement. Incorporation of stakeholder values: The study will incorporate stakeholder values into the decision-making process, ensuring that the resulting decisions reflect the diverse perspectives and priorities of all stakeholders. This approach differs from traditional decision-making methods, which often prioritize the perspectives of a select few. Real-world decision-making contexts: The study will evaluate the effectiveness and practicality of MCDM methods in real-world decision-making contexts, providing insights into the challenges and opportunities associated with the implementation of these methods. This will help decision-makers to better understand how to apply MCDM methods to real-world sustainability challenges. The proposed research offers a novel and comprehensive approach to addressing sustainability challenges through the development and application of MCDM methods. By evaluating the effectiveness and practicality of these methods in real-world decision-making contexts, this study aims to provide decision-makers with a more comprehensive and informed approach to sustainability decision-making that reflects the diverse perspectives and priorities of all stakeholders. Future research in the area of MCDM methods to address sustainability challenges could focus on several areas, including: Cross-disciplinary collaborations: Given the complex and multifaceted nature of sustainability challenges, there is a need for cross-disciplinary collaborations between decision-makers, scientists, and stakeholders to develop and implement effective sustainability strategies. Future research could explore how MCDM methods can facilitate these collaborations and promote interdisciplinary dialogue and knowledge sharing. Evaluation of long-term sustainability outcomes: While MCDM methods can help decision-makers to evaluate alternatives based on multiple criteria and stakeholder values, it may be challenging to evaluate the long-term sustainability outcomes of these decisions. Future research could explore how MCDM methods can be used to evaluate the long-term sustainability outcomes of decisions, and how the effectiveness of these methods c","PeriodicalId":344431,"journal":{"name":"Idea: Future Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129310833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The novelty of this research lies in the integration of machine learning and artificial intelligence techniques into stochastic DEA models. While traditional DEA models have been widely used to measure the efficiency of decision-making units, they may not be able to capture complex and nonlinear relationships between inputs and outputs. By integrating advanced machine learning and AI techniques, this research aims to improve the accuracy, stability, and interpretability of stochastic DEA models, providing decision-makers with more reliable and actionable insights. Moreover, this research explores several novel approaches, including the integration of deep learning techniques, ensemble learning, dynamic stochastic DEA models, and explainable AI, to improve the performance of stochastic DEA models. These approaches have the potential to enhance the accuracy of efficiency scores, increase the stability of the model, provide more actionable insights, and improve the model's interpretability. By integrating these approaches into stochastic DEA models, this research aims to provide a comprehensive and effective solution to the problem of measuring the efficiency of decision-making units. This approach has not been explored extensively in the literature, and thus represents a novel and innovative approach to addressing this important research problem.
{"title":"Data driven approach for stochastic DEA in machine learning and artificial intelligence to improve the accuracy, stability, and interpretability of the model","authors":"Hengki Tamando Sihotang, Zhimin Huang, Aisyah Alesha","doi":"10.35335/idea.v1i1.1","DOIUrl":"https://doi.org/10.35335/idea.v1i1.1","url":null,"abstract":"The novelty of this research lies in the integration of machine learning and artificial intelligence techniques into stochastic DEA models. While traditional DEA models have been widely used to measure the efficiency of decision-making units, they may not be able to capture complex and nonlinear relationships between inputs and outputs. By integrating advanced machine learning and AI techniques, this research aims to improve the accuracy, stability, and interpretability of stochastic DEA models, providing decision-makers with more reliable and actionable insights. Moreover, this research explores several novel approaches, including the integration of deep learning techniques, ensemble learning, dynamic stochastic DEA models, and explainable AI, to improve the performance of stochastic DEA models. These approaches have the potential to enhance the accuracy of efficiency scores, increase the stability of the model, provide more actionable insights, and improve the model's interpretability. By integrating these approaches into stochastic DEA models, this research aims to provide a comprehensive and effective solution to the problem of measuring the efficiency of decision-making units. This approach has not been explored extensively in the literature, and thus represents a novel and innovative approach to addressing this important research problem.","PeriodicalId":344431,"journal":{"name":"Idea: Future Research","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115242772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tambun Sihotang, D. Landgrebe, Firta Sari Panjaitan
The proposed research on developing expert systems for accurately predicting and recommending solutions for various agricultural scenarios is novel in several ways: Integration of Multiple Technologies: The research involves the integration of multiple technologies such as knowledge representation, artificial intelligence and machine learning algorithms, data integration and analysis techniques, and evaluation and validation techniques to develop a comprehensive and effective expert system for agriculture. Interdisciplinary Approach: The research is an interdisciplinary approach that brings together experts from various fields such as computer science, agriculture, and data science to develop an expert system that takes into account the needs of farmers and agriculture professionals. Use of Real-World Data: The research uses real-world data to test and validate the performance of the expert system, which increases its applicability and effectiveness in practical agricultural scenarios. Customization and Personalization: The proposed expert system can be customized and personalized based on the unique needs of individual farmers and agriculture professionals, which will make it more useful and user-friendly. Potential to Enhance Agriculture Productivity and Sustainability: The development of an effective expert system for agriculture has the potential to enhance productivity and sustainability in agriculture, which will benefit not only the farmers and agriculture professionals but also the wider society by improving food security and reducing environmental impact. In summary, the proposed research on developing expert systems for accurately predicting and recommending solutions for various agricultural scenarios is a novel and interdisciplinary approach that has the potential to transform agriculture by improving productivity, sustainability, and profitability. Future research in the development of expert systems for agriculture can build on the proposed research in several ways: Integration of Internet of Things (IoT) Technology: The integration of IoT technology can provide real-time data on various parameters such as soil moisture, temperature, and humidity, which can be used to improve the accuracy of the expert system predictions and recommendations. Integration of Remote Sensing Technology: The integration of remote sensing technology such as satellite imagery can provide a broader view of agricultural landscapes and enable the expert system to predict and recommend solutions for large-scale agricultural scenarios. Development of User-Friendly Interfaces: Future research can focus on developing user-friendly interfaces that enable easy access and understanding of expert system predictions and recommendations by farmers and agriculture professionals. Use of Explainable AI Techniques: Future research can explore the use of explainable AI techniques that enable the expert system to provide explanations for its predictions and recommendations,
{"title":"Expert system approach to improve the accuracy of prediction and solution of various agricultural scenarios","authors":"Tambun Sihotang, D. Landgrebe, Firta Sari Panjaitan","doi":"10.35335/idea.v1i1.5","DOIUrl":"https://doi.org/10.35335/idea.v1i1.5","url":null,"abstract":"The proposed research on developing expert systems for accurately predicting and recommending solutions for various agricultural scenarios is novel in several ways: Integration of Multiple Technologies: The research involves the integration of multiple technologies such as knowledge representation, artificial intelligence and machine learning algorithms, data integration and analysis techniques, and evaluation and validation techniques to develop a comprehensive and effective expert system for agriculture. Interdisciplinary Approach: The research is an interdisciplinary approach that brings together experts from various fields such as computer science, agriculture, and data science to develop an expert system that takes into account the needs of farmers and agriculture professionals. Use of Real-World Data: The research uses real-world data to test and validate the performance of the expert system, which increases its applicability and effectiveness in practical agricultural scenarios. Customization and Personalization: The proposed expert system can be customized and personalized based on the unique needs of individual farmers and agriculture professionals, which will make it more useful and user-friendly. Potential to Enhance Agriculture Productivity and Sustainability: The development of an effective expert system for agriculture has the potential to enhance productivity and sustainability in agriculture, which will benefit not only the farmers and agriculture professionals but also the wider society by improving food security and reducing environmental impact. In summary, the proposed research on developing expert systems for accurately predicting and recommending solutions for various agricultural scenarios is a novel and interdisciplinary approach that has the potential to transform agriculture by improving productivity, sustainability, and profitability. Future research in the development of expert systems for agriculture can build on the proposed research in several ways: Integration of Internet of Things (IoT) Technology: The integration of IoT technology can provide real-time data on various parameters such as soil moisture, temperature, and humidity, which can be used to improve the accuracy of the expert system predictions and recommendations. Integration of Remote Sensing Technology: The integration of remote sensing technology such as satellite imagery can provide a broader view of agricultural landscapes and enable the expert system to predict and recommend solutions for large-scale agricultural scenarios. Development of User-Friendly Interfaces: Future research can focus on developing user-friendly interfaces that enable easy access and understanding of expert system predictions and recommendations by farmers and agriculture professionals. Use of Explainable AI Techniques: Future research can explore the use of explainable AI techniques that enable the expert system to provide explanations for its predictions and recommendations,","PeriodicalId":344431,"journal":{"name":"Idea: Future Research","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131603534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hengki Tamando Sihotang, J. Lavemaau, F. Riandari, Firta Sari Panjaitan, Sonya Enjelina Gorat, Juliana Batubara
The novelty of integrating neural networks (NNs) into the stochastic DEA model lies in the ability to address some of the limitations of traditional DEA models and improve the accuracy and efficiency of efficiency measurement and prediction. By integrating NNs, the stochastic DEA model can capture the complex and non-linear relationships between the input and output variables of the decision-making units (DMUs) and handle uncertainty in the input and output data. This is achieved by using the NN to estimate the distribution of the input and output data and then using the stochastic DEA model to calculate the efficiency scores based on these estimated distributions. Furthermore, the integration of NNs into the stochastic DEA model allows for the development of hybrid models that combine the strengths of both techniques. For example, some researchers have proposed using genetic algorithms or other optimization techniques to optimize the input and output weights of the stochastic DEA model, which are then used to calculate the efficiency scores based on the estimated distributions from the NN. This results in a more accurate and efficient efficiency measurement and prediction model. Another novelty of integrating NNs into the stochastic DEA model is the potential for enhancing the interpretability of the model. While NNs are often considered as black-box models, several methods have been proposed to enhance the interpretability of NN-based stochastic DEA models. These methods include using feature importance analysis or visualization techniques to identify the most important input and output variables that contribute to the efficiency scores. Overall, the integration of NNs into the stochastic DEA model represents a novel approach to addressing the limitations of traditional DEA models and improving the accuracy and efficiency of efficiency measurement and prediction under uncertainty. The development of hybrid models and methods to enhance interpretability further add to the novelty and potential impact of this research.
{"title":"Integrating the neural network into the stochastic DEA model","authors":"Hengki Tamando Sihotang, J. Lavemaau, F. Riandari, Firta Sari Panjaitan, Sonya Enjelina Gorat, Juliana Batubara","doi":"10.35335/idea.v1i1.2","DOIUrl":"https://doi.org/10.35335/idea.v1i1.2","url":null,"abstract":"The novelty of integrating neural networks (NNs) into the stochastic DEA model lies in the ability to address some of the limitations of traditional DEA models and improve the accuracy and efficiency of efficiency measurement and prediction. By integrating NNs, the stochastic DEA model can capture the complex and non-linear relationships between the input and output variables of the decision-making units (DMUs) and handle uncertainty in the input and output data. This is achieved by using the NN to estimate the distribution of the input and output data and then using the stochastic DEA model to calculate the efficiency scores based on these estimated distributions. Furthermore, the integration of NNs into the stochastic DEA model allows for the development of hybrid models that combine the strengths of both techniques. For example, some researchers have proposed using genetic algorithms or other optimization techniques to optimize the input and output weights of the stochastic DEA model, which are then used to calculate the efficiency scores based on the estimated distributions from the NN. This results in a more accurate and efficient efficiency measurement and prediction model. Another novelty of integrating NNs into the stochastic DEA model is the potential for enhancing the interpretability of the model. While NNs are often considered as black-box models, several methods have been proposed to enhance the interpretability of NN-based stochastic DEA models. These methods include using feature importance analysis or visualization techniques to identify the most important input and output variables that contribute to the efficiency scores. Overall, the integration of NNs into the stochastic DEA model represents a novel approach to addressing the limitations of traditional DEA models and improving the accuracy and efficiency of efficiency measurement and prediction under uncertainty. The development of hybrid models and methods to enhance interpretability further add to the novelty and potential impact of this research.","PeriodicalId":344431,"journal":{"name":"Idea: Future Research","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122070152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hengki Tamando Sihotang, Marc Z. Albert, F. Riandari, L. Rendell
The research on efficient optimization algorithms for machine learning is novel because it addresses several gaps in previous research and proposes new solutions to improve the efficiency and accuracy of machine learning models. Firstly, the proposed research focuses on developing more efficient algorithms for large-scale deep learning. While there have been many optimization algorithms proposed for deep learning, the proposed research aims to develop new algorithms that can handle the complexity and scale of these models and improve their efficiency. Secondly, the proposed research aims to explore the effectiveness of optimization algorithms for different types of machine learning tasks. While many studies have focused on deep learning, the proposed research aims to evaluate the effectiveness of optimization algorithms for other types of machine learning tasks, such as reinforcement learning, unsupervised learning, and semi-supervised learning. Thirdly, the proposed research aims to develop optimization algorithms that can handle noisy and incomplete data, which is a significant challenge for machine learning models. The proposed research aims to develop algorithms that can handle noisy and incomplete data and improve the accuracy of machine learning models. Fourthly, the proposed research aims to develop optimization algorithms that can handle non-convex objective functions. While some optimization techniques have been proposed for non-convex optimization, the proposed research aims to develop new algorithms that can handle these functions and improve the accuracy of machine learning models. The proposed research aims to investigate the trade-off between optimization efficiency and model performance. While previous research has explored this trade-off to some extent, the proposed research aims to develop algorithms that can balance these factors and optimize both efficiency and performance. The proposed research is novel because it addresses several gaps in previous research and proposes new solutions to improve the efficiency and accuracy of machine learning models for various tasks, including classification, regression, and clustering. By developing new algorithms and evaluating their effectiveness for different types of machine learning tasks, the proposed research can advance the field of machine learning and improve the accuracy and efficiency of machine learning models.
{"title":"Efficient optimization algorithms for various machine learning tasks, including classification, regression, and clustering","authors":"Hengki Tamando Sihotang, Marc Z. Albert, F. Riandari, L. Rendell","doi":"10.35335/idea.v1i1.3","DOIUrl":"https://doi.org/10.35335/idea.v1i1.3","url":null,"abstract":"The research on efficient optimization algorithms for machine learning is novel because it addresses several gaps in previous research and proposes new solutions to improve the efficiency and accuracy of machine learning models. Firstly, the proposed research focuses on developing more efficient algorithms for large-scale deep learning. While there have been many optimization algorithms proposed for deep learning, the proposed research aims to develop new algorithms that can handle the complexity and scale of these models and improve their efficiency. Secondly, the proposed research aims to explore the effectiveness of optimization algorithms for different types of machine learning tasks. While many studies have focused on deep learning, the proposed research aims to evaluate the effectiveness of optimization algorithms for other types of machine learning tasks, such as reinforcement learning, unsupervised learning, and semi-supervised learning. Thirdly, the proposed research aims to develop optimization algorithms that can handle noisy and incomplete data, which is a significant challenge for machine learning models. The proposed research aims to develop algorithms that can handle noisy and incomplete data and improve the accuracy of machine learning models. Fourthly, the proposed research aims to develop optimization algorithms that can handle non-convex objective functions. While some optimization techniques have been proposed for non-convex optimization, the proposed research aims to develop new algorithms that can handle these functions and improve the accuracy of machine learning models. The proposed research aims to investigate the trade-off between optimization efficiency and model performance. While previous research has explored this trade-off to some extent, the proposed research aims to develop algorithms that can balance these factors and optimize both efficiency and performance. The proposed research is novel because it addresses several gaps in previous research and proposes new solutions to improve the efficiency and accuracy of machine learning models for various tasks, including classification, regression, and clustering. By developing new algorithms and evaluating their effectiveness for different types of machine learning tasks, the proposed research can advance the field of machine learning and improve the accuracy and efficiency of machine learning models.","PeriodicalId":344431,"journal":{"name":"Idea: Future Research","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121101374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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