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

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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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Covid-19 agent-based model: An epidemiological simulator applied in vaccination scenarios for quezon city, philippines

Covid-19 agent-based model: An epidemiological simulator app...

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2021 Philippine Geomatics Symposium 2021

作者： Bongolan, V.P. Ang, K.K. Celeste, J.J. Minoza, J.M. Caoili, S. Rivera, R.L. De Castro, R. Department of Computer Science College of Engineering University of the Philippines Diliman Quezon City Philippines College of Medicine University of the Philippines Manila Manila Philippines College of Social Work and Development University of the Philippines Diliman Quezon City Philippines Center for Informatics University of San Agustin Iloilo City Philippines

COVID-19 vaccines are rolling out in the Philippines but the supply remains limited;there is a need to optimize the distribution. In this study, we developed a COVID-19 agent-based model for Quezon City, a COVID-19 hotspot in the country. This model, in conjunction with a multi-objective linear programming model for equitable vaccine distribution, was then used to simulate four vaccination scenarios. Experiments were conducted with the front-line workers always added to the groups: mobile workers, elderly and low-income. Main results are: prioritizing the mobile workers minimizes infections the most (by 4.34%), while prioritizing the low-income groups minimizes deaths the most (by 1.93%). These results demonstrate that protecting the population with the most interactions (mobile workers) effectively reduces future infections. On the other hand, protecting the most vulnerable population (low income and elderly) decreases the likelihood of death. These results may guide the policy-makers in Quezon City. © International Archives of the Photogrammetry, Remote Sensing and Spatial Information sciences - ISPRS Archives

关键词： COVID-19

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Detection and Classification of COVID-19 Using Machine Learning

Research Square

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Research Square 2021年

作者： Shahin, Osama R. Alshammari, Hamoud H. Taloba, Ahmed I. Abd El-Aziz, Rasha M. Department of Computer Science College of Science and Arts in Gurayat Jouf University Saudi Arabia Information Systems Department College of Computer and information sciences Jouf University Sakaka Saudi Arabia

As people all over the world are vulnerable to be affected by the COVID-19 virus, the automatic detection of such a virus is an important concern. The paper aims to detect and classify coronavirus using machine learning. To spot and identify coronavirus in CT-Lung screening and computer-Aided diagnosis (CAD) system is projected to distinguish and classifies the COVID-19. By utilizing the clinical specimens obtained from the corona-infected patients with the help of some machine learning techniques like Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function. While some specialists believe that the RT-PCR test is the best option for diagnosing Covid-19 patients, others believe that CT scans of the lungs can be more accurate in diagnosing coronavirus infection, as well as being less expensive than the PCR test. The clinical specimens include serum specimens, respiratory secretions, and whole blood specimens. Overall, 15 factors are measured from these specimens as the result of the previous clinical examinations. The proposed CAD system consists of four phases starting with the CT lungs screening collection, followed by a pre-processing stage to enhance the appearance of the ground glass opacities (GGOs) nodules as they originally lock hazy with fainting contrast. A modified K-means algorithm will be used to detect and segment these regions. Finally, we will use the detected, infected areas that obtained in the detection phase with a scale of 50x50 and we will crop the solid false positives that seem to be GGOs as inputs and targets for the machine learning classifiers, here we will use a support vector machine (SVM) and Radial basis function (RBF). Moreover, a GUI application is developed which avoids the confusion of the doctors for getting the exact results by giving the 15 input factors obtained from the clinical specimens. © 2021, CC BY.

关键词： Coronavirus

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Comprehensive Assessment of OCR Tools for Gene Name Recognition in Biological Pathway Figures

Comprehensive Assessment of OCR Tools for Gene Name Recognit...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Stuart Aldrich Micheal Olaolu Arowolo Fei He Mihail Popescu Dong Xu Department of Electrical Engineering and Computer Science University of Missouri Columbia Missouri USA College of Information Science and Technology Northeast Normal University Changchun China Department of Health Management and Informatics University of Missouri Columbia Missouri USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Optical Character Recognition (OCR) is becoming more and more effective in text detection in images. However, OCR’s performance in special applications may vary. In particular, OCR in visual representations of complex processes known as pathway figures in the biomedical literature is challenging. The information depicted in a pathway graphic usually represents the article’s most important conclusions. Still, the huge number of pathway figures cannot be automatically processed for large-scale search, data mining, and downstream analysis. Assisted by recent developments in OCR, we have developed a method to extract gene names from pathway images. For usage in the method, we thoroughly evaluated and compared major available OCR tools using 563 genes from 45 pathway images, 1000 images of alphanumeric characters and gene names from HUGO, and KEGG data with 20 random pathway genes curated routes. Our study showed that Google Cloud Vision and MMOCR are best suitable for gene name recognition in pathway figures.

关键词： Visualization Image recognition Sensitivity Limiting Text recognition Biological system modeling Optical character recognition

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Development of a Calibration-Free Brain-computer Interface Utilizing Common Spatial Patterns and Artificial Neural Networks for EEG Signal Analysis

Development of a Calibration-Free Brain-Computer Interface U...

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

作者： Korhan Cengiz Shreyas J Udayaprasad P K Gururaj H L Dilip Kumar S M College of Information Technology University of Fujairah UAE Faculty of Organization and Informatics University of Zagreb Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology.

关键词： Training Machine learning algorithms Artificial neural networks Electroencephalography Brain-computer interfaces Calibration Reliability

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Performance assessment and multiobjective optimization of a biomass waste-fired gasification combined cycle for emission reduction

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Chemosphere 2023年第1期334卷 138980页

作者： Hai, Tao Alshahri, Abdullah H. Mohammed, Amin Salih Sharma, Aman Almujibah, Hamad R. Mohammed Metwally, Ahmed Sayed Ullah, Mirzat School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Electronics and Information Engineering Ankang University Ankang China Universiti Teknologi MARA Shah Alam40450 Malaysia Department of Civil Engineering College of Engineering Taif University P.O. Box 11099 Taif City21974 Saudi Arabia Department of Software and Informatics Engineering College of Engineering Salahaddin University-Erbil Kurdistan Region Iraq Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Institute of Engineering and Technology GLA University UP Mathura281406 India Department of Mathematics College of Science King Saud University Riyadh11451 Saudi Arabia Graduate School of Economics and Management Ural Federal University Yekaterinburg620002 Russia

The use of renewable fuels leads to reduction in the use of fossil fuels and environmental pollutants. In this study, the design and analysis of a CCPP based on the use of syngas produced from biomass is discussed. The studied system includes a gasifier system to produce syngas, an external combustion gas turbine and a steam cycle to recover waste heat from combustion gases. Design variables include syngas temperature, syngas moisture content, CPR, TIT, HRSG operating pressure, and PPTD. The effect of design variables on performance components such as power generation, exergy efficiency and total cost rate of the system is investigated. Also, through multi-objective optimization, the optimal design of the system is done. Finally, it is observed that at the final decisioned optimal point, the produced power is 13.4 MW, the exergy efficiency is 17.2%, and the TCR is 118.8 $/h. © 2023

关键词： Gasification

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Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

arXiv

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arXiv 2023年

作者： Banupriya, V. Anusuya, S. Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Tamilnadu Karur639113 India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Tamil Nadu Chennai602117 India

In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy (DR), which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy (DR) can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying the lesions are high. Similarly, training for Convolutional Neural Networks (CNN) is more time-consuming. This proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model (EDLM) for timely DR identification that is potentially more accurate than existing CNN-based models. The proposed model will detect various lesions from retinal images in the early stages. First, characteristics are retrieved from the retinal fundus picture and put into the EDLM for classification. For dimensionality reduction, EDLM is used. Additionally, the classification and feature extraction processes are optimized using the stochastic gradient descent (SGD) optimizer. The EDLM’s effectiveness is assessed on the KAGGLE dataset with 3459 retinal images, and results are compared over VGG16, VGG19, RESNET18, RESNET34, and RESNET50. Experimental results show that the EDLM achieves higher average sensitivity by 8.28% for VGG16, by 7.03% for VGG19, by 5.58% for ResNet18, by 4.26% for ResNet 34, and by 2.04% for ResNet 50, respectively. © 2023, CC BY.

关键词： Feature extraction

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MADP-IIME: malware attack detection protocol in IoT-enabled industrial multimedia environment using machine learning approach

MADP-IIME: malware attack detection protocol in IoT-enabled ...

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作者： Pundir, Sumit Obaidat, Mohammad S. Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Rodrigues, Joel J. P. C. Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun 248 002 India College of Computing and Informatics University of Sharjah UAE with King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan University of Science and Technology Beijing Beijing 100083 China Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Federal University of Piauí (UFPI) PI Teresina 64049-550 Brazil Instituto de Telecomunicações Covilhã 6201-001 Portugal

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such

关键词： Internet of things

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AiGet: Transforming Everyday Moments into Hidden Knowledge Discovery with AI Assistance on Smart Glasses 25

AiGet: Transforming Everyday Moments into Hidden Knowledge D...

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2025 CHI Conference on Human Factors in Computing systems, CHI 2025

作者： Cai, Runze Janaka, Nuwan Kim, Hyeongcheol Chen, Yang Zhao, Shengdong Huang, Yun Hsu, David Synteraction Lab School of Computing National University of Singapore Singapore Singapore Smart Systems Institute Synteraction Lab National University of Singapore Singapore Singapore College of Design and Engineering National University of Singapore Singapore Singapore School of Creative Media Department of Computer Science City University of Hong Kong Hong Kong School of Information Sciences University of Illinois at Urbana-Champaign Champaign IL United States School of Computing Smart Systems Institute National University of Singapore Singapore Singapore

ISBN: (纸本)9798400713941

Unlike the free exploration of childhood, the demands of daily life reduce our motivation to explore our surroundings, leading to missed opportunities for informal learning. Traditional tools for knowledge acquisition are reactive, relying on user initiative and limiting their ability to uncover hidden interests. Through formative studies, we introduce AiGet, a proactive AI assistant integrated with AR smart glasses, designed to seamlessly embed informal learning into low-demand daily activities (e.g., casual walking and shopping). AiGet analyzes real-time user gaze patterns, environmental context, and user profiles, leveraging large language models to deliver personalized, context-aware knowledge with low disruption to primary tasks. In-lab evaluations and real-world testing, including continued use over multiple days, demonstrate AiGet's effectiveness in uncovering overlooked yet surprising interests, enhancing primary task enjoyment, reviving curiosity, and deepening connections with the environment. We further propose design guidelines for AI-assisted informal learning, focused on transforming everyday moments into enriching learning experiences. © 2025 Copyright held by the owner/author(s).

关键词： AI HMD human-ai interaction incidental learning informal learning knowledge discovery large language model multimodal information smart glasses wearable-AI assistance

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An investigation of cyber attack impact on consumers’ intention to purchase online

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Decision Analytics Journal 2023年 8卷

作者： Wahab, Fazal Khan, Imran Kamontip Hussain, Tariq Amir, Abbas College of Computer Science and Technology Northeastern University Shenyang 110169 China Department of Informatics University of Sussex United Kingdom School of Computer Science and Information Engineering Zhejiang Gongshang University Hangzhou China School of Statistics and Mathematics Zhejiang Gongshang University Hangzhou China Shaheed zulfiqar ali Bhutto University Islamabad Pakistan

E-commerce technologies are increasingly adopted and used because they give companies a competitive edge and boost their profitability through access to international markets. Even though global e-commerce has grown over the past few years, many consumers lack confidence in e-commerce purchases. Several factors, including safety, trust, and use of technology, influence the users’ intent to purchase. This study uses the Theory of Planned Behavior (TPB), trust in e-commerce sellers, trust in the internet medium, and cyber-fraud perception to determine what factors influence customers’ intention to buy online. We surveyed 258 Thai customers of e-commerce websites via Google Forms. Cronbach's alpha and correlation are used to verify the analysis's reliability and validity analysis. Data analysis included simple linear and multiple linear regression. We show that attitude toward behaviour and perceived behavioural controls are ideal predictors of customers’ e-commerce purchasing intentions. Customers’ behaviour also depends on their trust in the internet medium and the vendor. We further demonstrate subjective norms, online vendor trust, cybercrime perception, and Internet trust do not affect users’ intent to purchase, and cybercrime does not change behaviour either. It also does not moderate the association between trust in e-commerce companies and clients’ disposition toward buying online. © 2023 The Author(s)

关键词： Customers’ behaviour Cyber attacks E-commerce Linear regression Online shopping Theory of planned behaviour

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