9 research outputs found
Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy
A meta-analysis of machine learning classification tools using rs-fmri data for autism spectrum disorder diagnosis
The Autism Spectrum Disorder (ASD) is a complex and heterogeneous neurodevelopmental condition characterized by cognitive, behavioral, and social dysfunction. Much
effort is being made to identify brain imaging biomarkers and develop tools that could
facilitate its diagnosis - currently based on behavioral criteria through a lengthy and timeconsuming process. In particular, the use of Machine Learning (ML) classifiers based on
resting-state functional Magnetic Resonance Imaging (rs-fMRI) data is promising, but
there is an ongoing need for further research on their accuracy. Therefore, we conducted a
systematic review and meta-analysis to summarize and aggregate the available evidence
in the literature so far. The systematic search resulted in the selection of 93 articles, whose
data were extracted and analyzed through the systematic review. A bivariate randomeffects meta-analytic model was implemented to investigate the sensitivity and specificity
across the 55 studies (132 independent samples) that offered sufficient information for
a quantitative analysis. Our results indicated overall summary sensitivity and specificity
estimates of 73.8% (95% CI: 71.8-75.8%) and 74.8% (95% CI: 72.3-77.1%), respectively,
and Support Vector Machine (SVM) stood out as the most used classifier, presenting
summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except in the subgroup analysis for Artificial Neural Network (ANN) classifiers.
The use of other brain imaging or phenotypic data to complement rs-fMRI information
seem to be promising, achieving specially higher sensitivities (p = 0.002) when compared
to rs-fMRI data alone (84.7% - 95% CI: 78.5-89.4% - versus 72.8% - 95% CI: 70.6-74.8%).
Lower values of sensitivity/specificity were found when the number of Regions of Interest
(ROIs) increased. We also highlight the performance of the approaches using the Automated Anatomical Labelling atlas with 116 ROIs (AAL116). Regarding the features used
to train the classifiers, we found better results using the Pearson Correlation (PC) Fishertransformed or other features in comparison to the use of the PC without modifications.
Finally, our analysis showed AUC values between acceptable and excellent, but given the
many limitations indicated in our study, further well-designed studies are warranted to
extend the potential use of those classification algorithms to clinical settings.Agência 1O Transtorno do Espectro Autista (TEA) é uma condição complexa e heterogênea que
afeta o desenvolvimento cerebral e é caracterizada por disfunções cognitivas, comportamentais e sociais. Muito esforço vem sendo feito para identificar biomarcadores baseados
em imagens cerebrais e desenvolver ferramentas que poderiam facilitar o diagnóstico do
TEA - atualmente baseado em critérios comportamentais, através de um processo longo
e demorado. Em particular, o uso de algoritmos de Aprendizado de Máquina para classificação de dados de Imagens de Ressonância Magnética funcional em estado de repouso
(rs-fMRI) é promissor, mas há uma necessidade contínua de pesquisas adicionais a respeito
da precisão desses classificadores. Assim, este trabalho realiza uma revisão sistemática e
meta-análise de modo a resumir e agregar as evidências disponíveis na literatura da área
até o momento. A busca sistemática por artigos resultou na seleção de 93 deles, que
tiveram seus dados extraídos e analisados através da revisão sistemática. Um modelo
meta-analítico bivariado de efeitos aleatórios foi implementado para investigar a sensibilidade e especificidade dos 55 estudos (132 amostras independentes) que ofereceram
informação suficiente para serem utilizados na análise quantitativa. Os resultados obtidos
indicaram estimativas gerais de sensibilidade e especificidade de 73.8% (95% IC: 71.8-
75.8%) e 74.8% (95% IC: 72.3-77.1%), respectivamente, e os classificadores baseados em
SVM (do inglês, Support Vector Machine) se destacaram como os mais utilizados, apresentando estimativas acima de 76%. Estudos que utilizaram amostras maiores tenderam
a obter piores resultados de precisão, com exceção do subgrupo composto por classificadores baseados em Redes Neurais Artificiais. O uso de outros tipos de imagens cerebrais
ou dados fenotípicos para complementar as informações obtidas através da rs-fMRI se
mostrou promissor, alcançando especialmente sensibilidades mais altas ( = 0.002) em
relação aos estudos que utilizaram apenas dados de rs-fMRI (84.7% - 95% IC: 78.5-89.4%
- versus 72.8% - 95% IC: 70.6-74.8%). Valores menores de sensibilidade/especificidade
foram encontrados quando o número de Regiões de Interesse (ROIs, do inglês Regions
of Interest) aumentou. Vale destacar também o desempenho das abordagens utilizando o
atlas AAL (do inglês, Automated Anatomical Labelling) com 116 ROIs. Em relação às
features usadas para treinar os classificadores, foram encontrados melhores resultados nos
estudos que utilizaram a correlação de Pearson em conjunto com a transformação Z de
Fisher ou outras features em comparação ao uso da correlação de Pearson sem modifica-
ções. Finalmente, a análise revelou valores da área sob a curva ROC (do inglês, Receiver
Operating Characteristic) entre aceitável e excelente. Entretanto, considerando as várias
limitações que são indicadas no estudo, mais estudos bem desenhados são necessários para
estender o uso potencial desses algoritmos de classificação a ambientes clínicos
Z-Numbers-Based Approach to Hotel Service Quality Assessment
In this study, we are analyzing the possibility of using Z-numbers for
measuring the service quality and decision-making for quality improvement in the
hotel industry. Techniques used for these purposes are based on consumer evalu-
ations - expectations and perceptions. As a rule, these evaluations are expressed
in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the
respondent opinions based on crisp or fuzzy numbers formalism not in all cases
are relevant. The existing methods do not take into account the degree of con-
fidence of respondents in their assessments. A fuzzy approach better describes
the uncertainties associated with human perceptions and expectations. Linguis-
tic values are more acceptable than crisp numbers. To consider the subjective
natures of both service quality estimates and confidence degree in them, the two-
component Z-numbers Z = (A, B) were used. Z-numbers express more adequately
the opinion of consumers. The proposed and computationally efficient approach
(Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden-
tify the factors that required improvement and the areas for further development.
The suggested method was applied to evaluate the service quality in small and
medium-sized hotels in Turkey and Azerbaijan, illustrated by the example
Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm
Abstract— Online transportation has become a basic
requirement of the general public in support of all activities to go
to work, school or vacation to the sights. Public transportation
services compete to provide the best service so that consumers
feel comfortable using the services offered, so that all activities
are noticed, one of them is the search for the shortest route in
picking the buyer or delivering to the destination. Node
Combination method can minimize memory usage and this
methode is more optimal when compared to A* and Ant Colony
in the shortest route search like Dijkstra algorithm, but can’t
store the history node that has been passed. Therefore, using
node combination algorithm is very good in searching the
shortest distance is not the shortest route. This paper is
structured to modify the node combination algorithm to solve the
problem of finding the shortest route at the dynamic location
obtained from the transport fleet by displaying the nodes that
have the shortest distance and will be implemented in the
geographic information system in the form of map to facilitate
the use of the system.
Keywords— Shortest Path, Algorithm Dijkstra, Node
Combination, Dynamic Location (key words
Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids
This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader
New Fundamental Technologies in Data Mining
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining
Safety and Reliability - Safe Societies in a Changing World
The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management
- mathematical methods in reliability and safety
- risk assessment
- risk management
- system reliability
- uncertainty analysis
- digitalization and big data
- prognostics and system health management
- occupational safety
- accident and incident modeling
- maintenance modeling and applications
- simulation for safety and reliability analysis
- dynamic risk and barrier management
- organizational factors and safety culture
- human factors and human reliability
- resilience engineering
- structural reliability
- natural hazards
- security
- economic analysis in risk managemen
Collected Papers (on Neutrosophic Theory and Applications), Volume VII
This seventh volume of Collected Papers includes 70 papers comprising 974 pages on (theoretic and applied) neutrosophics, written between 2013-2021 by the author alone or in collaboration with the following 122 co-authors from 22 countries: Mohamed Abdel-Basset, Abdel-Nasser Hussian, C. Alexander, Mumtaz Ali, Yaman Akbulut, Amir Abdullah, Amira S. Ashour, Assia Bakali, Kousik Bhattacharya, Kainat Bibi, R. N. Boyd, Ümit Budak, Lulu Cai, Cenap Özel, Chang Su Kim, Victor Christianto, Chunlai Du, Chunxin Bo, Rituparna Chutia, Cu Nguyen Giap, Dao The Son, Vinayak Devvrat, Arindam Dey, Partha Pratim Dey, Fahad Alsharari, Feng Yongfei, S. Ganesan, Shivam Ghildiyal, Bibhas C. Giri, Masooma Raza Hashmi, Ahmed Refaat Hawas, Hoang Viet Long, Le Hoang Son, Hongbo Wang, Hongnian Yu, Mihaiela Iliescu, Saeid Jafari, Temitope Gbolahan Jaiyeola, Naeem Jan, R. Jeevitha, Jun Ye, Anup Khan, Madad Khan, Salma Khan, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Darjan Karabašević, Kifayat Ullah, Kishore Kumar P.K., Sujit Kumar De, Prasun Kumar Nayak, Malayalan Lathamaheswari, Luong Thi Hong Lan, Anam Luqman, Luu Quoc Dat, Tahir Mahmood, Hafsa M. Malik, Nivetha Martin, Mai Mohamed, Parimala Mani, Mingcong Deng, Mohammed A. Al Shumrani, Mohammad Hamidi, Mohamed Talea, Kalyan Mondal, Muhammad Akram, Muhammad Gulistan, Farshid Mofidnakhaei, Muhammad Shoaib, Muhammad Riaz, Karthika Muthusamy, Nabeela Ishfaq, Deivanayagampillai Nagarajan, Sumera Naz, Nguyen Dinh Hoa, Nguyen Tho Thong, Nguyen Xuan Thao, Noor ul Amin, Dragan Pamučar, Gabrijela Popović, S. Krishna Prabha, Surapati Pramanik, Priya R, Qiaoyan Li, Yaser Saber, Said Broumi, Saima Anis, Saleem Abdullah, Ganeshsree Selvachandran, Abdulkadir Sengür, Seyed Ahmad Edalatpanah, Shahbaz Ali, Shahzaib Ashraf, Shouzhen Zeng, Shio Gai Quek, Shuangwu Zhu, Shumaiza, Sidra Sayed, Sohail Iqbal, Songtao Shao, Sundas Shahzadi, Dragiša Stanujkić, Željko Stević, Udhayakumar Ramalingam, Zunaira Rashid, Hossein Rashmanlou, Rajkumar Verma, Luige Vlădăreanu, Victor Vlădăreanu, Desmond Jun Yi Tey, Selçuk Topal, Naveed Yaqoob, Yanhui Guo, Yee Fei Gan, Yingcang Ma, Young Bae Jun, Yuping Lai, Hafiz Abdul Wahab, Wei Yang, Xiaohong Zhang, Edmundas Kazimieras Zavadskas, Lemnaouar Zedam
Factors Influencing Customer Satisfaction towards E-shopping in Malaysia
Online shopping or e-shopping has changed the world of business and quite a few people have
decided to work with these features. What their primary concerns precisely and the responses from
the globalisation are the competency of incorporation while doing their businesses. E-shopping has
also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce
industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction
while operating in the e-retailing environment. It is very important that customers are satisfied with
the website, or else, they would not return. Therefore, a crucial fact to look into is that companies
must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s
point of view. With is in mind, this study aimed at investigating customer satisfaction
towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students
randomly selected from various public and private universities located within Klang valley area.
Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for
further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer
satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust,
design of the website, online security and e-service quality. Finally, recommendations and future
study direction is provided.
Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia