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International Conference on Information Technology, Computer and Electrical engineering (ICITACEE)

作者： Salamun Rohman Nudin Rahmat Gernowo Maman Somantri Adi Wibowo Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Electrical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

This paper presents a comprehensive review of deep learning methods for the Big Five personality traits prediction using multi-task classification. The purpose of the review is to determine the performance of models developed in previous research to describe research opportunities and challenges in the future. The Big Five model is a framework in psychology that categorizes human personality into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The review begins by analyzing 22 articles from the Science Direct, IEEE Xplore, and Scopus databases by applying screening using the Big Five Personality keywords. Then study the concepts of deep learning for model development such as: input, architecture, method, dataset, and performance to identify the strengths and limitations of each approach. The review results of the highest average accuracy by modality such as unimodal text with essay dataset (80.98 % ), unimodal image with ImageNet dataset (95.36%), unimodal audio with SSPNet dataset (79.48%), bimodal image and audio with Chalearn Video First Impression dataset (91.67 % ), multimodal text, image, and audio with Chalearn Video First Impression dataset (91.88%). The average accuracy of the Big Five Personality traits was highest (95.36 % ) and lowest (59 % ). This highlights the need for standard benchmarks, interpretable models and interdisciplinary collaboration to improve the accuracy, reliability and ethical implications of personality prediction. Future research is expected to pave the way for advancements in understanding human personality through deep learning approaches using other modalities such as EEG, GSR, and PPG.

关键词： Deep learning Accuracy Reviews Psychology Predictive models Brain modeling Multitasking Reliability Information technology Standards

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Tecnological Tests of the Pegamites Residues Extraction at Alto Dois Irmãos/Pb in Borborema Pegmatitic Province/Bpp

SSRN

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SSRN 2023年

作者： Silva, Gerson Ferreira de Brum, Irineu Cordeiro, Vinícius Becheleni, Emily Araújo, Defsson Federal University of Rio Grande do Sul – UFRGS Postgraduate Program in Mining Engineering Metallurgical and of Materials Brazil Federal University of Rio Grande do Sul – UFRGS Department of Metallurgical Engineering Postgraduate Program in Mining Engineering Metallurgical and Materials Brazil Federal University of Campina Grande – UFCG Academic Unity of Mining and Geology Brazil Federal University of Vales do Jequitinhonha e Mucuri – UFVJM Engineering Institute Science and Technology Brazil Federal University of Campina Grande – IFPB Federal Institute of Science Education and Technology of Paraíba Campus Picuí Brazil

The Borborema Pegmatitic Province – BPP situated between Paraiba and Rio Grande do Norte states in Brazilian northeast presents several pegmatites occurrences. In the region of the county of Pedra Lavrada – PB, are observed various mineral occurrences where the pegmatites have been explored with prominence at Alto Dois Irmãos-PB pegmatite, in what quartz, feldspar and mica are extracted. Because an existence of a complex mineralogy of the pegmatites, the understanding about mineral constituent is fundamental to define its industrial employment and also to implement improvements in beneficiation process. The proposal of this research line is the detailed characterization and proof that part of the ore (quartz) considered sterile, presents low poisoning (Fe2O3) and high silica (SiO2) content, to enable its industrial use mainly to nobler purposes. Also will be fundamental to minimize the environmental impact and decrease the grinding energetic consume. Besides that, the obtained results of this research will be used in a future work, as inputs in simulation stage, aiming to optimize the researched beneficiation unity grinding process. To the pegmatite characterization were done density determination tests, mineralogical identification by X-ray diffraction (XRD), chemical analysis by fluorescents X-rays and morphological analysis by scanning electron microscopy (SEM/EDS). Then were done tests to Work Index (WI) calculous. The sterile (quartz extraction) from pegmatite mining in Pedra Lavrada/PB presented medium density of 2,66g/cm3, majority chemical composition of silicon and oxygen and quartz as the mainly mineralogical stage, through X-ray diffractograms and scanning electron microscopy images, what indicates that this material has high purity. The Work Index (WI) was 15,9 kwh/t, showing that this material presents in the pegmatite can be consider resistant to breaking in grinding process. © 2023, The Authors. All rights reserved.

关键词： Quartz

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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Sustainable Emerging Innovations in engineering and Technology (ICSEIET), International Conference on

作者： Maylia Fatmawati Mega Novita Nugroho Dwi Saputro Alok Singh Chauhan Noora Qotrun Nada Amit Sinha Bambang Agus Herlambang Shruti Gupta Department of Informatics Faculty of Engineering and Informatics Universitas PGRI Semarang Semarang Indonesia Postgraduate Program of Science Education Universitas PGRI Semarang Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering ABES Engineering College Ghaziabad Uttar Pradesh India

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks.

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Degradation of the Antibiotic Sulfamethoxazole by Ozonation: A Comprehensive Review

CleanMat

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CleanMat 2024年第1期1卷 16-51页

作者： Jéssica Martini Joshua O. Ighalo Ebuka Chizitere Emenike Jordana Georgin Elvis Carissimi Postgraduate Program in Civil Engineering Federal University of Santa Maria (UFSM) Santa Maria Brazil Department of Chemical Engineering Nnamdi Azikiwe University Awka Nigeria Tim Taylor Department of Chemical Engineering Kansas State University Manhattan Kansas USA Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Nigeria Department of Civil and Environmental Universidad de la Costa CUC Barranquilla Colombia Department of Sanitary and Environmental Engineering Federal University of Santa Maria (UFSM) Santa Maria Brazil

Sulfamethoxazole (SMX) is an antibiotic widely used for the treatment of several diseases, especially respiratory and urinary. Due to its widespread use, its presence in the environment has been on the rise. This study discusses the degradation of the antibiotic SMX through the ozonation process. SMX is preferentially degraded by the direct action of O 3 on the molecule, starting by breaking the double bonds in the aromatic ring. Through ozonation it is possible to obtain complete SMX degradation within 10 min, but the mineralization of the solution is not achieved, reaching total organic carbon removal efficiencies of 5%–60%, depending on the reaction time. Factors such as the presence of organic matter (OM) and the pH of the solution interfere with the degradation of SMX. The OM acts as a radical scavenger, decreasing the efficiency of degradation, while the pH interferes with the decomposition of O 3 into radicals and the dissolution of the oxidant in the medium, in addition to influencing the state of ionization of the SMX making it more or less prone to reactions. In general, most other remediation processes are carried out on a laboratory scale. To increase the level of these processes to a full scale, more pilot-scale studies are needed. In addition, it is necessary to focus on the practice of reusing materials, avoiding the generation of secondary pollution, and increasing the useful life of the material. The review also discusses the reaction mechanisms, intermediate compounds formed, mineralization of the solution, and factors that influence the process, which can help in making decisions for future studies to be carried out in the area. Prospects in the research area revolve around using metal–organic frameworks as molecular imprinting technology to modify heterogeneous catalysts, integration of external energy, bimetallic systems, and evaluation of deactivation mechanisms.

关键词： advanced oxidation processes emerging pollutants ozonation reaction mechanisms sulfamethoxazole water treatment

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Exploring Personal Knowledge Ecologies: Dealing with Digital Platform Asymmetries

Exploring Personal Knowledge Ecologies: Dealing with Digital...

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International Conference on Computer Supported Cooperative Work in Design

作者： Marcos Antonio De Almeida Jano Moreira De Souza António Correia Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The rise of artificial intelligence agents and information technology platforms governed by data-driven algorithms has had profound impacts on the way knowledge work is managed and carried out. Concerning the novel uses of digital work platforms, the arising tensions might suggest that the socio-technical design frameworks adopted by major tech firms are becoming less viable and in some cases ineffective. We propose a conceptual architecture to support a socio-technical framework for exploring Personal Knowledge Ecologies in the context of the digital knowledge economy. This framework aims to increase the degree of individual freedom for the interoperation of these knowledge information infrastructures.

关键词： Knowledge engineering Sociotechnical systems Computer architecture Ecology Software Heterogeneous networks Complexity theory

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Classification of Paddy Leaf Disease Using MobileNet Model

Classification of Paddy Leaf Disease Using MobileNet Model

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International Conference on Computing, engineering, and Design (ICCED)

作者： Fauzan Masykur Kusworo Adi Oky Dwi Nurhayati Doctoral Program Of Information System School Of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Informatics Engineering Muhammadiyah University of Ponorogo Ponorogo Indonesia

ISBN: (纸本)9781665453905

Paddy is a rice-producing plant as a staple food raw material for most of the population in Indonesia. The rice planting process cannot be separated from pests and diseases that can cause crop failure and impact on rice production stocks. Currently, the process of monitoring the development of rice plants is done conventionally by direct observation by farmers. Meanwhile, in this paper, a classification model and determination of object detection in rice plants infected with pests and diseases is proposed based on leaf color. Leaf classification applies convolution conditional network algorithm with mobilenet as the architecture. The leaf image dataset was taken from public data from *** by involving 4 classes, namely healthy leaf images, brownspot, hispa and leafblast images. In each class the leaf images are grouped into 2 groups, namely 70% training and 30% validation. On the mobilenet backbone, it produces 97% accuracy and 22% loss with the time required for 195s 19s/step in 1 epoch round.

关键词： Training Image color analysis Sociology Computer architecture Production Object detection Raw materials

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Electrochemical Biosensor Utilizing Alkaloids and Tannins from Areca Nuts for Environmental Quality Application

Electrochemical Biosensor Utilizing Alkaloids and Tannins fr...

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Agrosystem engineering, Technology & Applications (AGRETA), IEEE International Conference on

作者： Rudi Salam Suprihatin Nastiti Siswi Indrasti Khaswar Syamsu Wahid Sidik Sarifuddin Muhammad Gazali Rachman Adriyansyah Postgraduate Program in Agro-industrial Engineering IPB University Bogor Indonesia Department of Agro-industrial Engineering IPB University Bogor Indonesia Centre for Advanced Material and Energy Sciences (CAMES) Universiti Brunei Darussalam Brunei Faculty of Geosciences Universiti Brunei Darussalam Brunei PT. Freeport Indonesia Timika Indonesia

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

The increasing need for sustainable and efficient methods to monitor environmental quality has led to the exploration of natural compounds in biosensing. This paper presents the design and evaluation of an electrochemical biosensor based on alkaloids and tannins for environmental quality applications. The primary objective is to develop a highly sensitive and selective biosensor for detecting pollutants in water, air, and soil. The methodology involves the use of SACMES software to simulate and analyze the performance of the biosensor, specifically focusing on relative sensitivity and confinement loss within the frequency range of 1.0 to 2.0 THz. Results demonstrate that the proposed biosensor achieves sensitivities over 96% and confinement losses below 10 -13 dB/m. These findings indicate the potential of alkaloid and tannin-based biosensors as effective tools for environmental monitoring, offering high precision and environmental sustainability.

关键词： Electric potential Sensitivity Green products Focusing Soil Water pollution Software Biosensors Environmental monitoring Compounds

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Mechanistic insights into CO2 photoreduction to formic acid using ZnO catalysts

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Chemical engineering Research and Design 2025年 218卷 664-673页

作者： Astuti, Andi Rina Ayu Ariono, Danu Wenten, I. Gede Sasongko, Dwiwahju Saputera, Wibawa Hendra Kurnia, Kiki Adi Department of Chemical Engineering Faculty of Industrial Technology Universitas Muslim Indonesia Jl. Urip Sumoharjo Km. 05 Makassar90231 Indonesia Department of Chemical Engineering Faculty of Industrial Technology Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Research Center for Nanosciences and Nanotechnology Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Research Group on Energy and Chemical Engineering Processing System Department of Chemical Engineering Faculty of Industrial Technology Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Center for Catalysis and Reaction Engineering Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Research Center for New and Renewable Energy Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Center of CO2 Utilization and Flare Gas Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Postgraduate program of Science in Sustainability Faculty of Infrastructure Planning Universitas PERTAMINA Jl. Teuku Nyak Arif Jakarta12220 Indonesia

The conversion of CO2 into formic acid (HCOOH) is vital due to rising atmospheric CO2 levels and the high market value of HCOOH among CO2 utilization products. A photocatalytic technology offers a promising method for this transformation using sustainable energy. However, the lack of the exact mechanism of CO2 reduction into HCOOH using heterogeneous catalyst and light energy remains unclear. Therefore, this study explores the reaction pathways validated with experimental data and density functional theory (DFT) calculation. Based on the experiment, ZnO semiconductor catalysts achieved the highest formic acid production, with a concentration of 345.44 µmol/L·gcat after 4 hours of operation at a gas flow rate of 800 mL/min. Among the four proposed mechanisms, CO₂ photoreduction to HCOOH follows the Eley-Rideal adsorption mechanism, where only reactant of 2H⁺ undergoes chemisorption, while CO₂ remains physisorbed. Additionally, HCOOH exhibits strong adsorption on ZnO surface, as confirmed by experimental data and adsorption energy calculations. DFT calculations using CRYSTAL14 and the B3LYP method support this finding. The weak physisorption energy (less than 0.5 eV) allows CO2 to undergo direct reactions on the catalyst surface. This study elucidates the role of each component in enhancing CO2 photoreduction to HCOOH and provides a foundation for further research on CO2 utilization. © 2025

关键词： Local density approximation

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Graphene's photonic and optoelectronic properties——A review

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Chinese Physics B 2020年第3期29卷 13-35页

作者： A J Wirth-Lima P P Alves-Sousa W Bezerra-Fraga Department of Physics Federal Institute of EducationScience and Technology of Ceara Campus SobralCearaBrazil Postgraduate Program in Electrical Engineering and Computing Federal University of Ceara(U.F.C.)Sobral CearaBrazil

Due to its remarkable electrical and optical properties,graphene continues to receive more and more attention from researchers around the *** excellent advantage of graphene is the possibility of controlling its charge density,and consequently,the management of its conductivity and dielectric constant,among other *** is noteworthy that the control of these properties enables the obtaining of new optical/electronic devices,which would not exist based on conventional ***,to work in this area of science,it is necessary to have a thorough knowledge regarding the electrical/optical properties of *** this review paper,we show these graphene properties very well detailed.

关键词： absorbance conductivity dielecric constant dispersion relation

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Exploring the Potential of Artificial Intelligence in Special Education: A Bibliometric Analysis to Support Educators and Students with Autism Spectrum Disorder (ASD)

Exploring the Potential of Artificial Intelligence in Specia...

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Frontiers in Education (FIE) Conference

作者： Lisiane Corrêa Gomes Silveira Patrícia Warnke de Alvarenga Luis Otoni Meireles Ribeiro Eder Mateus Gonçalves Rosa Vicari Regina Barwaldt Postgraduate Program in Education Federal Institute Sul-rio-grandense Pelotas Brazil Postgraduate Program in Science Education Federal University of Rio Grande Rio Grande Brazil Computer Science Center of FURG Federal University of Rio Grande Rio Grande Brazil Computer Engineering Federal University of Rio Grande Rio Grande Brazil Computer Science Center Federal University of Rio Grande do Sul Porto Alegre Brazil Computer Science Center Federal University of Rio Grande Rio Grande Brazil

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This full paper describes a complete article in the research category. This work presents a Bibliometric Review that aims to characterize the current scenario of academic research in Artificial Intelligence in education to support educators in the teaching and learning processes in Special Education in different contexts, becoming a tool with potential use in cases of students with Autism Spectrum Disorder (ASD). In this sense, this work aims to contribute to a better understanding of research associated with the area of Artificial Intelligence aimed at educational processes involving students with autism. To do this, we used the EndNote reference manager, an online tool designed to support researchers in conducting Literature Reviews, following the bibliometric protocol proposed by Guedes and Borschiver (2005). From the definition of search strings, (“artificial intelligence”) AND (“autism” OR “ASD” OR “autism spectrum disorder”) AND (“education” OR “teaching”), scientific databases were explored like Web of Science (WoS), Scopus, ERIC, Emerald, Scielo, Portal CAPES, and IEEE, to locate existing studies on the topic, between 2019 and 2024. As a result, 298 articles were mapped and 20 scientific works were selected that address the aforementioned specific theme, written by 89 authors belonging to 50 institutions in 19 countries on four continents, enabling the creation of a significant theoretical basis. By analyzing the information contained in scientific publications in this specific field, it was possible to infer that research is divided into distinct areas of concentration: 1) the exploration of algorithms and data analysis based on Artificial Intelligence for analytical-predictive issues in the field of special education; 2) the use of robots in the teaching and learning processes of students with autism; 3) the development of personalized educational intervention models for learning pedagogical, social and communication skills. The data show that research on

关键词： Autism Protocols Databases Education Bibliometrics Learning (artificial intelligence) Data models Robots Portals Systematic literature review

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