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22nd IEEE Student Conference on Research and Development, SCOReD 2024

作者： Sabri, Nurbaity Sahiq, Aqilah Nadiah Md Hamzah, Hazwa Hanim Mohamed Sain, Hartayuni Mangshor, Nur Nabilah Abu Shari, Anis Amilah Universiti Teknologi MARA Cawangan Melaka College of Computing Informatics and Mathematics Kampus Jasin Melaka Malaysia Universiti Teknologi MARA Cawangan Melaka Faculty of Business and Management Kampus Bandaraya Melaka Malaysia Tadulako University Statistics Study Program Mathematics and Natural Science Faculty Palu94118 Indonesia

ISBN: (纸本)9798331510077

The Middle 40 Per Cent (M40) demographic comprises a substantial proportion of Malaysia's consumer base, comprising the median 40% of income earners. This group is a big part of the country's customer base. The rising cost of living, flat wage growth, and rising family debt make this group especially vulnerable to financial instability. Given these economic pressures, predicting the possibility of bankruptcy in this population is becoming increasingly crucial to allow for early intervention and preventive actions. Support Vector Machines (SVM) are recognized for their efficacy in classification tasks, particularly with small or imbalanced datasets. SVM prediction will be utilized using 14694 samples of the Debt Management program (DMP) dataset from 2016 to 2020. Twenty-three (23) features will be implemented on the dataset and Synthetic Minority Oversampling Technique (SMOTE) to improve the imbalanced datasets. The experiment demonstrates that the SVM classifier obtained 91.1% accuracy when implementing PUK kernels. It suggests that SVM is a plausible alternative model for predicting personal bankruptcy in the M40 group. To anticipate personal bankruptcy for M40, additional prediction models will be evaluated in the future. © 2024 IEEE.

关键词： Prediction models

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Rapid Monitoring for Flood Inundation Detection-Based Sentinel-1 SAR Image Data and Exposure Assessment in Urban Building Area: Makassar City, Indonesia 3rd

Rapid Monitoring for Flood Inundation Detection-Based Sentin...

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3rd International Conference on Radioscience, Equatorial Atmospheric science and Environment, INCREASE 2023

作者： Sekarjati, Dhanu Hanif, Muhammad Komala, Siffa Anastasya Giofandi, Eggy Arya Putri, Febita Safera Bunga, Brigitta Audrynne Rombe Putklang, Wasana Wilis, Ratna Geoinformatics Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Geophysics and Meteorology Faculty of Mathematics and Natural Science IPB University Bogor Indonesia Graduate Program of Regional Planning Science Faculty of Agriculture IPB University Bogor Indonesia Department of Geography Faculty of Social Science Universitas Negeri Padang Padang Indonesia

ISBN: (纸本)9789819707393

Real-time monitoring remains a challenge in providing accurate information, especially in the case of urban flood inundation disasters. The accuracy of data for disaster risk reduction has a positive impact on reducing the loss of livelihood or the regional economy. Understanding flood levels and their impact on urban areas can be crucial information for flood mitigation and effective urban planning. The research aims to conduct a rapid monitoring investigation for flood inundation detection in Makassar City, Indonesia, using Sentinel-1 SAR image data and assess the exposure of urban building areas to flooding. By identifying flood inundation areas using backscatter values, it was determined that Tamalanrea District exhibits a high density and is prone to flooding as a result of urban expansion. Additionally, Biringkanaya District, which is adjacent to Tamalanrea, was also impacted by the flood. The most severely affected area was found to be Tamalanrea District, with an inundation area covering 73,114 m2. It also recorded the highest extent of built-up area affected by flooding, reaching 29,475 m2. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Floods

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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Computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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From Obstacle Problems to Neural Insights: Feed Forward Neural Network Modeling of Ice Thickness

arXiv

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arXiv 2024年

作者： Chawla, Kapil Holmes, William Temam, Roger Department of Mathematics The Institute for Scientific Computing and Applied Mathematics Indiana University IN47405 United States Department of Mathematics and Cognitive Science Program Indiana University IN47405 United States

In this study, we integrate the established obstacle problem formulation from ice sheet modeling [12, 20] with cutting-edge deep learning methodologies to enhance ice thickness predictions, specifically targeting the Greenland ice sheet. By harmonizing the mathematical structure with an energy minimization framework tailored for neural network approximations, our method’s efficacy is confirmed through both 1D and 2D numerical simulations. Utilizing the NSIDC-0092 dataset for Greenland [1] and incorporating bedrock topography for model pre-training, we register notable advances in prediction accuracy. Our research underscores the potent combination of traditional mathematical models and advanced computational techniques in delivering precise ice thickness estimations. © 2024, CC BY.

关键词： Ice

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Ontology-Driven IoT System for Monitoring Hypertension 25

Ontology-Driven IoT System for Monitoring Hypertension

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25th International Conference on Enterprise Information Systems, ICEIS 2023

作者： de Souza, Pedro Lopes de Souza, Wanderley Lopes Pires, Luís Ferreira Moreira, João Luiz Rebelo da Silva Rodrigues, Ronitti Juner Ciferri, Ricardo Rodrigues Semantics Cybersecurity & Services Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede Netherlands Ubiquitous Computing Group Graduate Program in Computer Science Computing Department Federal University of São Carlos P.O. Box 676 São Carlos Brazil

ISBN: (纸本)9789897586484

Hypertension is a noncommunicable disease (NCD) that causes global concern, high costs and a high number of deaths. Internet of Things, Ubiquitous computing, and Cloud computing enable the development of systems for remote and real-time monitoring of patients affected with NCDs like hypertension. This paper reports on a system for monitoring hypertension patients that was built by employing these techniques. This system allows the vital signs of a patient (blood pressure, heart rate, body temperature) to be captured via sensors built in a wearable device similar to a wristwatch. These signals are transmitted to the patient's mobile device for processing, and the generated clinical data are sent to the cloud to be properly presented and analysed by the health professionals responsible for the patient. To deal with semantic interoperability issues that arise when multiple different devices and system components must interoperate, a semantic model was conceived for this system in terms of ontologies for diseases and devices. This paper also presents the semantic module that we developed and implemented in the cloud to perform reasoning based on this model, demonstrating the potential benefits of incorporating semantic technologies in our system. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Ontology

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Sequences analysis and phylogenetic of leukemia DNA 2

Sequences analysis and phylogenetic of leukemia DNA

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2nd International Seminar on science and Technology 2020, ISST-2 2020

作者： Lusiyanti, D. Nacong, N. Irawan, M.I. Mathematics Study Program Science Faculty Tadulako University Indonesia Dapertment of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya Indonesia

DNA is one part of a biological sequence other than RNA and protein. DNA is a nucleic acid that stores all the unique biological information of every living thing and several viruses. Someone who has an illness can be seen in the structure of the DNA which can then be compared with other DNA structures. This process is part of sequence analysis which is the core of bioinformatics. This study aims to analyze the results of sequences alignment of cancer. The sequence was obtained from DNA leukemia taken from GenBank (***). In the sequence analysis, the most basic thing is the sequence alignment. Alignment results can provide information about the level of similarity in the structure of the two sequences. This study will apply the Super Pairwise Alignment method that is implemented in JAVA-based programming languages. Alignment results are carried out by giving different values of gap penalty and gap extend. The results of the study obtained different alignment values, this proves that there is a significant effect of the selection of core parameters used in alignment. © Published under licence by IOP Publishing Ltd.

关键词： DNA

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InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data

arXiv

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arXiv 2025年

作者： Johnson, David R. Krishnaswamy, Smita Perlmutter, Michael Program in Computing Boise State University BoiseID United States Department of Computer Science Department of Genetics Yale University New HavenCT United States Department of Mathematics Program in Computing Boise State University BoiseID United States

Diffusion wavelets extract information from graph signals at different scales of resolution by utilizing graph diffusion operators raised to various powers, known as diffusion scales. Traditionally, the diffusion scales are chosen to be dyadic integers, 2j. Here, we propose a novel, unsupervised method for selecting the diffusion scales based on ideas from information theory. We then show that our method can be incorporated into wavelet-based GNNs via graph classification experiments. Copyright © 2025, The Authors. All rights reserved.

关键词： Wavelet transforms

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OSM: Leveraging model checking for observing dynamic 1 behaviors in aspect-oriented applications

arXiv

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arXiv 2024年

作者： AlSobeh, Anas Mohammad Ramadan Department of Information Systems Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan Program of Information Technology School of Computing College of Engineering Computing Technology and Mathematics Southern Illinois University CarbondaleIL United States

In the intricate domain of software systems verification, dynamically model checking multifaceted system characteristics remains paramount, yet challenging. This research proposes the advanced observe-based statistical model-checking (OSM) framework, devised to craft executable formal models directly from foundational system code. Leveraging model checking predicates, the framework melds seamlessly with aspect-oriented programming paradigms, yielding a potent method for the analytical verification of varied behavioral attributes. Exploiting the transformative capacity of OSM framework, primary system code undergoes a systematic metamorphosis into multifaceted analysis constructs. This not only simplifies the model verification process but also orchestrates feature interactions using an innovative observing join point abstraction mechanism. Within this framework, components encompassing parsing, formal verification, computational analytics, and rigorous validation are intrinsically interwoven. Marrying the principles of model checking with aspect-oriented (AO) modularization, OSM framework stands as a paragon, proficiently scrutinizing and affirming system specifications. This ensures the unyielding performance of electronic health record systems amidst shifting preconditions. OSM framework offers runtime verification of both object-oriented and AO deployments, positioning itself as an indispensable open-source resource, poised to automate the enhancement of system performance and scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Aspect oriented programming

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Investigation of thermal properties of ethylene glycol-based Williamson hybrid-nanofluid over stretchable/shrinking flat plate and their effects on solar panels

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International Journal of Thermofluids 2024年 24卷

作者： Aselebe, L.O. Adeosun, A.T. Kasali, K.B. Yisa, B.M. Salaudeen, K.A. Adesina, R.O. Federal School of Surveying Oyo Nigeria Department of Mathematics Federal College of Education Iwo Nigeria Department of Mathematics and Statistics First Technical University Ibadan Nigeria Department of Mathematics Faculty of Physical Sciences University of Ilorin Ilorin Nigeria Department of Mathematics and Computing Science Education Faculty of Science Education Emmanuel Alayande University of Education Oyo Nigeria Energy System Science and Engineering Program Department of Electronic and Electrical Engineering Obafemi Awolowo University Ile-Ife Nigeria

The global requirement for sustainable energy supply to enhance industrial productivity and reduce production costs has focused researchers’ attention to renewable energy in recent years. Solar energy mitigates the dangers connected with the use of fossil fuels in electricity generation. This work is set to evaluate the heat transmission capacities of Williamson hybrid nanofluid flow across a flat plate with viscous dissipation and heat source. The mathematical model explaining the flow interaction of Williamson hybrid nanofluid, combining viscous dissipation, heat source, and temperature-variation thermal conductivity and viscosity, is created using conservation laws. The specified system of non-linear coupled partial differential equations undergoes non-similarity transformation. The resulting non-dimensional model is solved using the bivariate spectral weighted residual method. The accuracy of the method is proven by comparing obtained results with those in the literature, and a good agreement is observed. Graphs are utilized to explain the thermophysical properties that are being considered. The results show that the fluid temperature rises when there is a source of heating and viscous dissipation. The velocity and the fluid parameter (We) have an inverse connection, whereas the temperature of the fluid has the opposite impact. Moreover, when nanoparticles are present, the thermal boundary layer rises along with the nanoparticles, thickening the velocity boundary layer and decreasing fluid velocity. Findings also show that for the Vd∈[0.1,0.5], the skin drag force and Nusselt number retard by 0.64%,14.06% for the shrinking sheet and 0.21%,6.57% for the stretching sheet respectively. In the same vein, an 100% surge in the porosity parameter escalate the skin friction coefficient by 19.13% and 26.91% and the Nusselt number by 4.92% and 2.06% respectively for both the contracting and elastic sheet. The findings in the research will provide more insight in the desig

关键词： Nusselt number

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Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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