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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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Multivariate Conway-Maxwell-Poisson distribution: Sarmanov method and doubly-intractable Bayesian inference

arXiv

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arXiv 2021年

作者： Piancastelli, Luiza S.C. Friel, Nial Barreto-Souza, Wagner Ombao, Hernando School of Mathematics and Statistics University College Dublin Dublin Ireland Insight Centre for Data Analytics Ireland Statistics Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

In this paper, a multivariate count distribution with Conway-Maxwell (COM)-Poisson marginals is proposed. To do this, we develop a modification of the Sarmanov method for constructing multivariate distributions. Our multivariate COM-Poisson (MultCOMP) model has desirable features such as (i) it admits a flexible covariance matrix allowing for both negative and positive non-diagonal entries;(ii) it overcomes the limitation of the existing bivariate COM-Poisson distributions in the literature that do not have COM-Poisson marginals;(iii) it allows for the analysis of multivariate counts and is not just limited to bivariate counts. Inferential challenges are presented by the likelihood specification as it depends on a number of intractable normalizing constants involving the model parameters. These obstacles motivate us to propose a Bayesian inferential approach where the resulting doubly-intractable posterior is dealt with via the exchange algorithm and the Grouped Independence Metropolis-Hastings algorithm. Numerical experiments based on simulations are presented to illustrate the proposed Bayesian approach. We analyze the potential of the MultCOMP model through a real data application on the numbers of goals scored by the home and away teams in the Premier League from 2018 to 2021. Here, our interest is to assess the effect of a lack of crowds during the COVID-19 pandemic on the well-known home team advantage. A MultCOMP model fit shows that there is evidence of a decreased number of goals scored by the home team, not accompanied by a reduced score from the opponent. Hence, our analysis suggests a smaller home team advantage in the absence of crowds, which agrees with the opinion of several football experts. Copyright © 2021, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Dual template using P123-gelatin for synthesized large mesoporous silica for enhanced adsorption of dyes

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South African Journal of Chemical Engineering 2023年 43卷 312-326页

作者： Safitri, Widiya Nur Habiddin, Habiddin Ulfa, Maria Trisunaryanti, Wega Bahruji, Hasliza Holilah, Holilah Rohmah, Alya Awinatul Sholeha, Novia Amalia Jalil, Aishah Abdul Santoso, Eko Prasetyoko, Didik Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember East Java Keputih Sukolilo Surabaya 60111 Indonesia Chemistry Education Study Program Faculty of Teacher Training and Education Universitas Sebelas Maret Jl. Ir. Sutami 36A Surakarta 57126 Indonesia Department of Chemistry Faculty of Science Universitas Gadjah Mada Sekip Utara Sleman Indonesia Centre for Advanced Material and Energy Sciences Universiti Brunei Darussalam Bandar Seri Begawan BE 1410 Brunei Darussalam Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong 16911 Indonesia Department of Food Science and Technology Faculty of Agriculture Halu Oleo University Indonesia College of Vocational Studies Bogor Agricultural University (IPB University) Jalan Kumbang No. 14 Bogor 16151 Indonesia Department of Chemical Engineering Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia 81310 UTM Johor Skudai Johor Bahru Malaysia

Mesoporous Silica with a large mesopore channel was synthesized using the sol-gel method for dye adsorption. A dual template consisting of P123 and gelatin at different ratios was employed in the sol-gel synthesis to enhance surface area and mesopore diameter. Gelatin is a green pore directing agent that strengthens P123 thermal stability during the calcination of silicate gel. Optimization of P123:gelatin ratio to 1:0.02 increased the surface area of mesoporous silica to 561.9 m²/g and expanded the pore diameter to 10 nm. The large pore diameter and surface area enhanced methylene blue adsorption capacity to 363.63 mg/g. The adsorption of methylene blue on mesoporous silica follows the pseudo second-order kinetic and the Langmuir model. Regeneration studies using thermal and chemical treatments exhibited the potential of mesoporous silica as an adsorbent for multiple adsorption-desorption of dyes. © 2022 The Author(s)

关键词： Dual template Dye Gelatin Mesoporous silica Pluronic P123

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Fabrication a sustainable adsorbent nanocellulose-mesoporous hectorite bead for methylene blue adsorption

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Case Studies in Chemical and Environmental Engineering 2024年 10卷

作者： Holilah Asranudin El Messaoudi, Noureddine Ulfa, Maria Hamzah, Amir Hamid, Zuratul Ain Abdul Ramadhani, Dini Viandi Suryanegara, Lisman Mahardika, Melbi Melenia, Alvina Tata Pratama, Agus Wedi Prasetyoko, Didik Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Research Collaboration Center for Nanocellulose BRIN-Andalas University Research Collaboration Center for Biomass and Biorefinery Between BRIN and Universitas Padjadjaran Laboratory of Applied Chemistry and Environment Faculty of Sciences Ibn Zohr University Chemistry Education Study Program Faculty of Teacher Training and Education Sebelas Maret University Deputy for Infrastructure Research and Innovation National Research and Innovation Agency of Indonesia (BRIN) School of Materials & Mineral Resources Engineering Universiti Sains Malaysia Department of Biology Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember (ITS) Kampus ITS Sukolilo

In this study, fabrication of mesoporous nanocellulose-hectorite-alginate bead (B-NcH) composites and their application for adsorption of MB from water was carried out. The synthesis of nanocellulose (NC) was conducted from mangosteen waste peel (Garcinia mangostana L.). Garcinia mangostana L. peel had 58.9 ± 0,41 % content of cellulose. NCs was successfully isolated using the acid hydrolysis method. NC from Garcinia mangostana L. peel had a diameter of 24.5 ± 0.2 nm and a length of 381.2 ± 10.4 nm. The superabsorbent was composed of nanocrystalline cellulose, mesoporous hectorite, and alginate. The functional group, phase, and morphology of composite was investigated. The composite with 5 % NC had excellent mechanical strength. The B-NcH5 showed a high adsorption capacity of methylene blue (MB), reaching 98.64 % at an adsorbent dose of 30 mg for 90 min. The presence of hectorite can increase the adsorption ability compared to NCs-alginate. The equilibrium adsorption data followed the Freundlich isotherm;however, the kinetics of adsorption showed the usefulness of adopting the PSO kinetic model. The highest adsorption capacity of B-NcH5 for MB was determined to be 57.59 mg/g. For thermodynamic studies, it was revealed that the adsorption of MB on BNcH-5 is a spontaneous and endothermic process. The removal efficiency was observed in six consecutive cycles, indicating at the favorable reusability of this adsorbent. These results validate the potential use of this B-NcH5 adsorbent for water treatment applications. © 2024

关键词： Adsorption Bead Hectorite Methylene blue Nanocellulose Water treatment

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Comparison of multivariate adaptive Poisson Regression spline and multivariate adaptive generalized Poisson Regression spline

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AIP Conference Proceedings 2023年第1期2536卷

作者： Septia Devi Prihastuti Yasmirullah Bambang Widjanarko Otok Jerry Dwi Trijoyo Purnomo Dedy Dwi Prastyo Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia

The multivariate adaptive regression (MARS) spline is a statistical method introduced by Friedman in 1991. The MARS method has several advantages, especially for data that consider the potential nonlinear pattern. These produce a continuous model on knots where the knot selection process is performed via adaptive procedures, capturing additive effects and interactions between predictor variables, and applied to data modeling with numerical and categorical responses. However, the analyzed data is not only numerical or categorical data, but also count type data. Count type data is often found in the health sector, for example, the number of data on tuberculosis. It invites to modify the MARS model with Poisson regression and generalized Poisson regression, i.e. Multivariate Adaptive Poisson Regression Spline (MAPRS) and Multivariate Adaptive Generalized Poisson Regression Spline (MAGPRS) . In addition, this study aimed to compare MAPRS and MAGPRS when applied to tuberculosis data. By applying MAPRS and MAGPRS to model the number of tuberculosis in Lamongan regency, the MAPRS method outperforms the MAGPRS method. The MAPRS method has a lower RMSE value than the MAGPRS method.

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Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

Embedding Space Augmentation for Weakly Supervised Learning ...

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IEEE International Symposium on Biomedical Imaging

作者： Imaad Zaffar Guillaume Jaume Nasir Rajpoot Faisal Mahmood Department of Computer Science University College London UK Department of Pathology Harvard Medical School Brigham and Women’s Hospital Boston MA USA Department of Pathology Harvard Medical School Massachusetts General Hospital Boston MA USA Cancer Program Broad Institute of Harvard and MIT Cambridge MA USA Data Science Program Dana-Farber Cancer Institute Boston MA USA Department of Computer Science Tissue Image Analytics Centre University of Warwick Coventry UK Department of Pathology University Hospitals Coventry and Warwickshire NHS Trust Coventry UK The Alan Turing Institute London UK

Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL-based analytical pipelines for WSI-level analysis, the WSIs are divided into patches, and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs’ limited RAM. Because of this bottleneck, incorporating patch-level data augmentations during training adds an extra computational burden. To overcome this limitation, we present EmbAugmenter, a data augmentation generative adversarial network (DA-GAN) that can synthesize data augmentations in the embedding space rather than in the pixel space, thereby significantly reducing the computational requirements. Experiments on the SICAPv2 dataset show that our approach outperforms MIL without augmentation and is on par with traditional patch-level augmentation for MIL training while being substantially faster.

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Extrapolation and option-implied kurtosis in volatility forecasting

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Pacific Basin Finance Journal 2024年 84卷

作者： Pan, Ging-Ginq Shiu, Yung-Ming Wu, Tu-Cheng International Bachelor Degree Program in Finance National Pingtung University Science and Technology 1 Shuefu Road Pingtung Neipu Taiwan Department of Risk Management and Insurance Risk and Insurance Research Center College of Commerce National Chengchi University 64 Sec. 2 Zhi-Nan Road Wen-Shan District Taipei Taiwan Department of Data Science and Analytics I-Shou University 1 Sec. 1 Syuecheng Road Dashu District Kaohsiung Taiwan

Prior studies have employed extrapolation to reduce truncation errors when computing risk-neutral moments. However, extrapolation may have a disadvantage in that it obscures the predictive power of risk-neutral skewness and kurtosis. Our out-of-sample results show that extrapolation does not enhance the predictive power of the volatility forecasting models when risk-neutral volatility, skewness, and kurtosis are included. Under this model specification, extrapolation generates less accurate forecasts and obscures the performance of risk-neutral kurtosis in volatility forecasting. © 2024 Elsevier B.V.

关键词： Extrapolation Risk-neutral kurtosis Risk-neutral skewness Volatility forecasting

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Eye tracking data analytics in virtual reality training: Application in Deepwater Horizon oil drilling operation 64th

Eye tracking data analytics in virtual reality training: App...

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64th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2020

作者： Kang, Ziho Jeon, Jiwon Salehi, Saeed Industrial and Systems Engineering University of Oklahoma NormanOK United States Data Science and Analytics Program University of Oklahoma NormanOK United States Petroleum and Geological Engineering University of Oklahoma NormanOK United States

Virtual reality (VR) enable us to train in a safe environment using computer-generated simulations. One such simulated environment is the Deepwater Horizon operation, and VR enables us to evaluate trainees’ and operators’ situation awareness (SA) in a non-hazardous environment. One unobtrusive and viable SA evaluation method might be the use of eye movements, specifically the time-ordered visual scan paths. In this research, we investigated how SA can be associated with visual scan paths in an anomaly detection task within the oil drilling rig virtual reality simulator. The results show that the trainees having lower SA tended to create random visual scan paths whereas the trainees having higher SA tended to create concentrated and refined visual scan paths. The results show promise in developing timely intervention methods through analyzing the visual scan path characteristics of the trainees. © 2020 by Human Factors and Ergonomics Society. All rights reserved.

关键词： Eye tracking

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Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport

Deep reinforcement learning of event-triggered communication...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kazuki Shibata Tomohiko Jimbo Takamitsu Matsubara Autonomous Distributed Cooperative Control Program Data Analytics Research-Domain Toyota Central R&D Labs. Inc. Japan Graduate School of Science and Technology Division of Information Science Nara Institute of Science and Technology Nara Japan

In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network policies may be insufficient for covering communication and control; these methods cannot decide the timing of communication and can only work with fixed-rate communications. Therefore, our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again. Such event-triggered control policies are efficiently optimized using a multi-agent deep deterministic policy gradient. We confirmed that our approach could balance the transport performance and communication savings through numerical simulations.

关键词： Deep learning Automation Conferences Reinforcement learning Computer architecture Numerical simulation Timing

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Random Forest vs. SVM vs. KNN in classifying Smartphone and Smartwatch sensor data using CRISP-DM

Random Forest vs. SVM vs. KNN in classifying Smartphone and ...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Sadiq Jaffer Saleh Sayed Qasim Ali Ahmed M. Zeki Big Data Science & Analytics Master Program University of Bahrain College of Information Technology University of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Nowadays IoT sensors become one of the major sources of data, starting from home automation going to daily devices. Focusing on handsets that have the most significant contribution factor on data blooming and booming. The problem at hand is data mining methods comparison, for selecting the best method that serves sensor data. Methodology used was CRISP-DM. This paper found Random Forest data mining method to work best under the described circumstances.

关键词： Support vector machines Legged locomotion data mining Business Activity recognition Accelerometers Bibliographies

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