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Specific chemical bioexergy and microwave-assisted torrefaction optimization via statistical and artificial intelligence approaches

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Fuel 2023年 333卷

作者： Chen, Wei-Hsin Aniza, Ria Department of Aeronautics and Astronautics National Cheng Kung University Tainan701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung411 Taiwan International Doctoral Degree Program on Energy Engineering National Cheng Kung University Tainan701 Taiwan

The artificial neural network (ANN) in artificial intelligence (AI) is a computational model that portrays how nerve cells (neurons) work in the human brain. Meanwhile, specific chemical bioexergy (SCB) is a vital indicator to provide essential information on how high the actual energy may be contained within biomass fuel (biofuel). In this study, the Taguchi method, analysis of variance (ANOVA), and artificial neural network (AI-ANN) are utilized to predict the SCB of biofuels from the spent mushroom substrate (SMS) torrefaction via microwave-assisted heating (MAH). In Taguchi's orthogonal array, washing, catalyst, and power operating parameters are considered. Acid and water washings lower the SMS's ash content by 58.45 % and 36.29 %, respectively. The optimum conditions for combining with acid washing, a catalyst with higher Fe2O3 (35 %), and microwave power 540 W render the highest total SCB in biofuels (biochar + bio-oil) of 47.90 MJ·kg−1, which is close to the SCB of crude oil derivatives (41–49 MJ·kg−1). The enhancement of biomass's SCB value from optimal torrefaction approximately 3 folds (256.30 %) from 12.84 (raw) to 47.90 MJ·kg−1. The ANN model with an architecture of 1 hidden layer (sigmoid activation function) with 3 neurons and the output layer (piecewise linear activation function) with a quick propagation algorithm for the training process of all layers poses excellent prediction with high accuracy R2 = 1. This result demonstrates that ANN with the designed scheme is suitable for predicting the SCB of SMS-derived biofuels. © 2022 Elsevier Ltd

关键词： Taguchi methods

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A Study on Medicinal Plant Leaf Recognition Using Artificial Intelligence 3

A Study on Medicinal Plant Leaf Recognition Using Artificial...

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3rd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Ayumi, Vina Ermatita, Ermatita Abdiansah, Abdiansah Noprisson, Handrie Purba, Mariana Utami, Marissa Universitas Sriwijaya Doctoral Program in Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Universitas Mercu Buana Faculty of Computer Science Jakarta Indonesia Program of Informatics Management Politeknik Anika Palembang Indonesia Muhammadiyah University of Bengkulu Faculty of Engineering Bengkulu Indonesia

ISBN: (纸本)9781665427333

Medicinal plant recognition manually takes a lot of time and money. Moreover, to reduce these resources, some researchers propose to implement artificial intelligence technology. This paper aims are to conduct a systematic literature review of medicinal plant leaf recognition published in the last two years (2019-2020) from IEEE, Springer and Science Direct. We obtained 15 studies in the field of medicinal plant leaf recognition using artificial intelligence. The dataset used for medicinal plant leaf recognition is mostly used private dataset, however, there are public dataset named Leaf, Flavia, Swedish dataset. We also found robust method that can be used for medicinal plant leaf recognition is Multichannel Modified Local Gradient Pattern (MCMLGP) and Gray Level Co-Occurrence Matrix (GLCM) as feature extraction;and Convolutional Neural Network (CNN), Multi-Layer Perceptron trained with Backpropagation algorithm (MLP-BP), Support Vector Machine (SVM), and Transfer Learning (VGG19) as classifier. © 2021 IEEE.

关键词： Image processing

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids

Integrating Noise Classification and Speech Enhancement Mode...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Shin-Chi Lai Wen-Ho Juang Ming-Hwa Sheu Jeng-Dao Lee Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm.

关键词： Accuracy Noise Noise reduction Speech recognition Speech enhancement Hearing aids Classification algorithms Convolutional neural networks Human voice Older adults

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports Science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport Science recognizes and prevents injuries. Sensors and statistics formalize Sports Science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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Analysis of the motivations for ISO 9001:2015 adoption in the Brazilian business context

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Quality Management Journal 2021年第2期28卷 76-85页

作者： Siltori, Patricia F. S. Rampasso, Izabela Simon Martins, Vitor W. B. Anholon, Rosley Silva, Dirceu Souza Pinto, Jefferson Leal Filho, Walter School of Mechanical Engineering University of Campinas Campinas Brazil PNPD/CAPES Program Doctoral Program in Sustainable Management Systems Federal Fluminense University Niterói Brazil Department of Production Engineering State University of Pará Belém Brazil Faculty of Education University of Campinas Campinas Brazil Federal Institute of São Paulo Bragança Paulista Brazil Faculty of Life Sciences Hamburg University of Applied Sciences Hamburg Germany

This study aims to analyze the reasons that motivate Brazilian companies to implement Quality Management Systems, according to ISO 9001:2015 requirements. Based on information collected in the literature, a questionnaire was developed and answered by professionals that work with the mentioned standard in their companies. An Exploratory Factor Analysis was conducted with the collected data to group the validated variables into two constructs that present the most part of information variance. These constructs were entitled “internal improvements related motives” and “market related motives”. The findings of this research confirm and complement previous studies performed in Brazil and in other countries. © 2021 American Society for Quality.

关键词： Adoption motives ISO 9001 motivation quality management

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Alternative determination of customer classification of wastewater management services

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AIP Conference Proceedings 2024年第1期3110卷

作者： Ida Ayu Rai Widhiawati Ngakan Made Anom Wiryasa Dewa Ketut Sudarsana Kadek Diana Harmayani Doctor of Engineering Study Program Udayana University Denpasar Indonesia Department of Doctoral Program Faculty of Engineering Udayana University Denpasar Indonesia

To fulfill the operating and maintenance costs of waste water management in Bali, Bali Provincial Regulation No. 2 of 2011 concerning Public Service Retribution, where the manager began to collect wastewater levies starting from September 2011. However, from 2011 to 2021 there was still no change in rates. In real conditions, it was found that household customers and the lodging sector did not match the registered classification. The purpose of this research is to design a new customer classification and levy rates that can meet the needs for the operation and maintenance of centralized wastewater management. The results of this study found that 26.67% of households and 82.35% of non-star hotels were not by their classification. Two household alternatives and one alternative for the lodging sector were designed to increase equitable income for the community. The alternative category of household classification assessment uses an assessment category based on the type of house (design I) and income (design II). In the classification of the lodging sector, star hotels are divided into 4 & 5-star hotels, and 1 – 3-star hotels (design III). The results of the income for the first-third design increased by 33.68%; 25.25%; and 25.39%.

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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Expert System Research for Prostate Cancer : A Literature Review

Expert System Research for Prostate Cancer : A Literature Re...

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Networking, Electrical engineering, Computer Science, and Technology (IConNECT), International Conference on

作者： Rusliyawati Rusliyawati Sutyarso Sutyarso Agus Wantoro Doctoral Program of Mathematics and Natural Science Faculty University of Lampung Bandar Lampung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandar Lampung Indonesia Faculty of Mathematics and Natural Sciences University of Lampung Bandar Lampung Indonesia

In the field of computer science, expert systems have attracted many computer scientists. In recent years, expert systems have been widely used in the medical field, particularly for diagnosing and treating various diseases, especially prostate cancer. Reality has shown that expert systems hold great promise for accurate diagnosis and treatment of prostate cancer. This research summarizes several studies related to the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores potential future research. Articles and reviews on the application of expert systems in prostate cancer from 2015 to 2022 were selected from the PubMed, Embase, and Web of Science databases. Microsoft Excel 2019 and GraphPad Prism 8 were used for target variable analysis. A total of 37 articles were selected for this research. Research on artificial intelligence in prostate cancer has grown exponentially in recent years, particularly in Indonesia. Image data usage is more predominant (62%) than numerical data types in prostate cancer research. Artificial intelligence research in prostate cancer, especially in the field of treatment cases, and the use of Deep Neural Network (DNN), Human Neural Network (HNN), Fully Convolutional Network (FCN), QT Environment for C++, Artificial Neural Network (ANN) algorithms, are highly feasible for further research.

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Cigarette butt filter as membrane material with tannic acid and FeCl₃ additives for improve antifouling properties

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental engineering 2025年 11卷

作者： Luthfiana, Anisa Mulyati, Sri Arahman, Nasrul Bilad, Muhammad Roil Aulia, Muhammad Prayogie Doctoral Program School of Engineering Science Universitas Syiah Kuala Banda Aceh Indonesia Department of Chemical Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Chemical and Process Engineering Faculty of Integrated Technologies Universiti Brunei Darussalam Bandar Seri Begawan Brunei Darussalam Department of Chemical Science and Engineering Kobe University Kobe Japan

Membrane fouling remains a critical challenge in wastewater treatment, particularly in applications involving oil-water emulsions. This study addresses this issue by fabricating antifouling membranes from repurposed cigarette filter waste, modified with tannic acid and FeCl₃ (ferric chloride) coatings. These modifications enhance membrane hydrophilicity, antifouling properties, flux recovery, and selectivity through an environmentally friendly approach. The membranes were prepared using the non-solvent induced phase separation (NIPS) method and subsequently coated through vacuum filtration. Key performance metrics included pure water flux, oil emulsion selectivity, and antifouling properties. The antifouling mechanism was attributed to the hydrophilic and protective layers formed by the tannic acid and FeCl₃ modifications, which reduced fouling and improved flux recovery. Characterization revealed that the tannic acid and FeCl₃ modifications created a hydrophilic layer with uniform pore distribution, leading to an oil rejection rate of up to 97 % and an increased flux recovery ratio of 85 %, compared to 65 % in unmodified membranes. The results highlight the potential of waste-derived membranes as a sustainable alternative for industrial wastewater treatment, aligning with the principles of circular economy and green chemistry. Future work should explore long-term stability, surface charge effects, and optimization of additive concentrations to enhance performance and antifouling efficiency further. © 2025 The Authors

关键词： Antifouling membrane Cigarette filter waste FeCl3 Oil-water emulsion tannic acid

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Exploring Regression and Classification in Big Data with Deep Learning

Exploring Regression and Classification in Big Data with Dee...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Ravindra Pandey Aruna Sri Rongali S. Gopinath Nilesh Anute Forda Nama Sachin Pund Department - Management Hult International Business School Doctoral Information Technology/Information Technology Emphasis/Information Technology University of The Cumberlands US Department of Electronics And Communication Engineering Karpagam Institute of Technology Coimbatore Management ASM's Institute of Business Management and Research Pune Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India

Deep gaining knowledge is a synthetic intelligence approach used to recognize complex facts. It uses many facts to recognize the complicated relationships between various factors. With deep learning, models can be built which might be capable of understanding the complicated patterns within big datasets. In this paper, we can explore using deep mastering in regression and type tasks on large datasets. In particular, we can use deep learning strategies to build a regression model that predicts a target value primarily based on the functions in a massive dataset and a classification version that can classify an object into one of the many classes of an extensive dataset. We can then discuss the overall performance of the fashions in comparison to standard models and talk about capability areas for development. Moreover, we will analyze the scalability of deep mastering strategies used on large datasets and discuss exceptional practices for building deep mastering fashions on massive datasets. Finally, we will summarize our findings and offer our conclusions approximately the use of deep studying in regression and type obligations on large datasets.

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