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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

引用

engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, ScienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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Text-based automatic personality prediction: A bibliographic review

arXiv

引用

arXiv 2021年

作者： Feizi-Derakhshi, Ali-Reza Feizi-Derakhshi, Mohammad-Reza Ramezani, Majid Nikzad-Khasmakhi, Narjes Asgari-Chenaghlu, Meysam Akan, Taymaz Ranjbar-Khadivi, Mehrdad Zafarni-Moattar, Elnaz Jahanbakhsh-Naghadeh, Zoleikha Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department of Software Engineering Ayvansaray University Istanbul Turkey Clinical Informatics Louisiana State University Health Sciences Center Shreveport Shreveport United States Department of Computer Engineering Shabestar Branch Islamic Azad University Shabestar Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Naghadeh Branch Islamic Azad University Naghadeh Iran

Personality detection is an old topic in psychology and Automatic Personality Prediction (or Perception) (APP) is the automated (computationally) forecasting of the personality on different types of human generated/exchanged contents (such as text, speech, image, video). The principal objective of this study is to offer a shallow (overall) review of natural language processing approaches on APP since 2010. With the advent of deep learning and following it transfer-learning and pre-trained model in NLP, APP research area has been a hot topic, so in this review, methods are categorized into three;pre-trained independent, pre-trained model based, multimodal approaches. Also, to achieve a comprehensive comparison, reported results are informed by datasets. © 2021, CC BY-NC-SA.

关键词： Forecasting

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A Consensus Privacy Metrics Framework for Synthetic Data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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Detection of adversarial supports in few-shot classifiers using self-similarity and filtering

arXiv

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arXiv 2020年

作者： Tan, Yi Xiang Marcus Chong, Penny Sun, Jiamei Cheung, Ngai-Man Elovici, Yuval Binder, Alexander ST Engineering-SUTD Cyber Security Laboratory ISTD pillar Singapore University of Technology and Design Singapore Department of Software and Information Systems Engineering Ben-Gurion University Israel Department of Informatics University of Oslo Norway

Few-shot classifiers excel under limited training samples, making them useful in applications with sparsely user-provided labels. Their unique relative prediction setup offers opportunities for novel attacks, such as targeting support sets required to categorise unseen test samples, which are not available in other machine learning setups. In this work, we propose a detection strategy to identify adversarial support sets, aimed at destroying the understanding of a few-shot classifier for a certain class. We achieve this by introducing the concept of self-similarity of a support set and by employing filtering of supports. Our method is attack-agnostic, and we are the first to explore adversarial detection for support sets of few-shot classifiers to the best of our knowledge. Our evaluation of the miniImagenet (MI) and CUB datasets exhibits good attack detection performance despite conceptual simplicity, showing high AUROC scores. We show that self-similarity and filtering for adversarial detection can be paired with other filtering functions, constituting a generalisable concept. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Study of Block Chain and its Impact on Internet of Health Things (IoHT):Challenges and Opportunities

Study of Block Chain and its Impact on Internet of Health Th...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muhammad Raza Naqvi Muhammad Aslam Muhammad Waseem Iqbal Syed Khuram Shahzad Marium Malik Muhammad Usman Tahir Department of Computer Science The Superior College Lahore Pakistan Department of Computer Science University of Engineering & Technology Lahore Pakistan Department of Software Engineering The Superior College Lahore Pakistan Department of Informatics & Systems University of Management and Technology Lahore Pakistan

ISBN: (数字)9781728193526

ISBN: (纸本)9781728193533

Blockchain has been making some vital progress in these days because of its secure and amiable structure. It is a distributed and decentralized database that deals with transactions. The blockchain is also emerging in the healthcare sector rapidly. Healthcare has now become a prestigious part of our life as it is making life-changing innovations day by day whether it is about artificial intelligence influence in the diagnostic system, expert system, patient's data storage, retrieval, security, and any other healthcare smart services. Smart health data is the most important ingredient to help us in solving our healthcare needs. Many researchers are now emerging blockchain in smart health because of its secure, flexible, and reliable architecture. In this paper, the major progress made in implementing the blockchain and its vulnerabilities are discussed in the area of smart health. The major focus is on the issues of patient's electronic health record, retrieval, security, and interoperability in Internet of Health Things (IoHT).

关键词： Interoperability Contracts Computer science

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Importance of Big Data in Precision and Personalized Medicine

Importance of Big Data in Precision and Personalized Medicin...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Muhammad Raza Naqvi Muhammad Arfan Jaffar Muhammad Aslam Syed Khuram Shahzad Muhammad Waseem Iqbal Amjad Farooq Department of Computer Science The Superior College Lahore Pakistan Department of Software Engineering The Superior College Lahore Pakistan Department of Computer Science University of Engineering & Technology Lahore Pakistan Department of Informatics & Systems University of Management and Technology Lahore Pakistan

ISBN: (数字)9781728193526

ISBN: (纸本)9781728193533

The rapidly increasing adaptation of Big data technologies in biomedicine, has introduced a revolution in and medical research practice. Trending high-throughput data analysis techniques, have converted the appearance of the biological system to acquire idolization methods for complicated diseases. Majority of the acquired Big-data models govern the materialization of illustrating medicine. This transformation aims at quantification of the period of P4 medicine that will then progressively be more predictive, personalized, pre-emptive, and participatory. It layouts a track to modernize antiseptic methods for the patient's concern center. P4 medicine besides being a scientific face of systems medicine has two highlighted purposes first of which is to evaluate wellness, while the other is, to identify and expose disease. Patients are major operators in the cognizance of P4 medicine as they directly get engaged with a medically familiar network that helps them boost their health. This article will discuss the maturity in big data planning and correlated challenges in biomedicine.

关键词： Big Data Genomics Bioinformatics Cancer Medical diagnostic imaging

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Design, monitoring, and testing of microservices systems: The Practitioners’ perspective

arXiv

引用

arXiv 2021年

作者： Waseem, Muhammad Liang, Peng Shahin, Mojtaba Salle, Amleto Di Márquez, Gastón School of Computer Science Wuhan University Wuhan430072 China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Melbourne3800 Australia Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’AquilaI-67100 Italy Department of Electronics and Informatics Federico Santa María Technical University Concepción4030000 Chile

Context: Microservices Architecture (MSA) has received significant attention in the software industry. However, little empirical evidence exists on design, monitoring, and testing of microservices systems. Objective: This research aims to gain a deep understanding of how microservices systems are designed, monitored, and tested in the industry. Method: A mixed-methods study was conducted with 106 survey responses and 6 interviews from microservices practitioners. Results: The main findings are: (1) a combination of domain-driven design and business capability is the most used strategy to decompose an application into microservices, (2) over half of the participants used architecture evaluation and architecture implementation when designing microservices systems, (3) API gateway and Backend for frontend patterns are the most used MSA patterns, (4) resource usage and load balancing as monitoring metrics, log management and exception tracking as monitoring practices are widely used, (5) unit and end-to-end testing are the most used testing strategies, and (6) the complexity of microservices systems poses challenges for their design, monitoring, and testing, for which there are no dedicated solutions. Conclusions: Our findings reveal that more research is needed to (1) deal with microservices complexity at the design level, (2) handle security in microservices systems, and (3) address the monitoring and testing challenges through dedicated solutions. © 2021, CC BY.

关键词： Surveys

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The use of a genetic algorithm in the problem of distribution of information security organizational and financial resources 2

The use of a genetic algorithm in the problem of distributio...

引用

2nd IEEE International Conference on Advanced Trends in Information Theory, ATIT 2020

作者： Lakhno, Valeriy Akhmetov, Bakhytzhan Adilzhanova, Saltanat Blozva, Andrii Svitlana, Rzaieva Dmytro, Rzaiev National University of Life and Environmental Sciences of Ukraine Department of Computer Systems and Networks Kyiv Ukraine Abai Kazakh National Pedagogical University Department of Information Technology Almaty Kazakhstan Al-Farabi Kazakh National University Department of Information Technology Almaty Kazakhstan Kyiv National University of Trade and Economics Department. of Software Engineering and Cybersecurity Kyiv Ukraine Kyiv National Economics University after Vadym Hetman Department Of. Informatics and Systemology Kyiv Ukraine

ISBN: (纸本)9781728197999

A model of the application of the genetic algorithm (GA) for solving the problem of multicriteria optimization in the distribution of resources allocated for the cybersecurity of the protected object is proposed. In the presented model, in contrast to the existing solutions, the Bellman-Zade principle is involved, which makes it possible to optimize the distribution, for example, of the organizational and financial resources of the protected object. It is shown that the use of GAs in the tasks of multicriteria optimization of cybersecurity resources will make it possible, based on multivariate simulation modeling, to select the optimal variant of resource allocation, which is especially important when these resources are limited in volume, and the defense side does not have data on the attackers' resources. © 2020 IEEE.

关键词： Resource allocation

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Optimization of Neural Network Parameters Based on a Genetic Algorithm for Prediction of Time Series

Optimization of Neural Network Parameters Based on a Genetic...

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2020 International Multi-Conference on Industrial engineering and Modern Technologies, FarEastCon 2020

作者： Tormozov, V.S. Zolkin, A.L. Vasilenko, K.A. Pacific National University Senior Lecturer of the Software Engineering for Computers and Computer-based Systems Sub-faculty Tikhookeanskaya St. 136 Khabarovsk680054 Russia Ph.D. in Engineering Science Povolzhskiy State University of Telecommunications and Informatics Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Samara443010 Russia Highest Category Lecturer Service and Design College of the Vladivostok State University of Economics and Service Service and Design College of the Vladivostok State University of Economics and Service Vladivostok690092 Russia

ISBN: (纸本)9781728169514

The article proposes a method that allows to solve the complex combinatorial problem of structural optimization of an artificial neural network with a large dimension of the space of optimization parameters using the stochastic mutation function with a constant coefficient of variation. A study on assessment of applicability of this approach to solve the problem is given. This method is proposed to be used for the forecasting of time series values. © 2020 IEEE.

关键词： Structural optimization

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Toward Scalable and Unified Example-based Explanation and Outlier Detection

arXiv

引用

arXiv 2020年

作者： Chong, Penny Cheung, Ngai-Man Elovici, Yuval Binder, Alexander The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore The Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva84105 Israel The Department of Informatics University of Oslo Oslo0373 Norway

When neural networks are employed for high-stakes decision-making, it is desirable that they provide explanations for their prediction in order for us to understand the features that have contributed to the decision. At the same time, it is important to flag potential outliers for in-depth verification by domain experts. In this work we propose to unify two differing aspects of explainability with outlier detection. We argue for a broader adoption of prototype-based student networks capable of providing an example-based explanation for their prediction and at the same time identify regions of similarity between the predicted sample and the examples. The examples are real prototypical cases sampled from the training set via our novel iterative prototype replacement algorithm. Furthermore, we propose to use the prototype similarity scores for identifying outliers. We compare performances in terms of the classification, explanation quality, and outlier detection of our proposed network with other baselines. We show that our prototype-based networks beyond similarity kernels deliver meaningful explanations and promising outlier detection results without compromising classification accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistics

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