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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Buenos Aires de Carvalho Denis Mayr Lima Martins Fernando Buarque de Lima Neto Computing Engineering Program University of Pernambuco Recife Brazil Machine Learning and Data Engineering University of Muenster ERCIS Muesnter Germany

ISBN: (纸本)9781728188652

Exploring large datasets in search for valuable insights requires time and sufficient technical knowledge. In order to alleviate this task, we propose and implemented a prototype of a data exploration tool. It is based on Self-Organizing Maps (SOM) and helps non-technical users with limited technical expertise and time. Our proposed approach employs SOM as a clustering mechanism to group and recommend exploratory data views to the user. This recommendation process can also be personalized to meet user’s intention in an interactive manner. Experimental results show that the reported prototype is effective in recommending valuable views, hence, being of aid in data exploration tasks.

关键词： Self-organizing feature maps Training Costs Databases Neurons Prototypes Semiotics

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Self-Organizing Transformations for Automatic Feature Engineering

Self-Organizing Transformations for Automatic Feature Engine...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Ericks da Silva Rodrigues Denis Mayr Lima Martins Fernando Buarque De Lima Neto Computing Engineering Program University of Pernambuco Recife Brazil Machine Learning and Data Engineering University of Muenster ERCIS Muesnter Germany

ISBN: (纸本)9781728190495

Feature Engineering (FE) consists of generating new, better features to improve machine learning models. Very often, FE is performed in a series of trial-and-error steps conducted manually by data scientists. Moreover, FE requires data-specific and domain knowledge, both rarely easy to acquire. To alleviate these problems, we propose the Self-Organizing Automatic Feature Engineering (SOAFE), a novel approach for Automatic Feature Engineering (AFE). Different from the majority of the existing AFEs, SOAFE employs an unsupervised technique (Self-Organizing Maps) to identify patterns in the data, and apply a form of cooperative training, inspired by Generative Adversarial Networks, to improve the feature construction. Our results on several datasets show that SOAFE can improve classification models when compared with existing AFE approaches.

关键词： Self-organizing feature maps Training machine learning Evolutionary computation Generative adversarial networks Iron Optimization

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Identification of potentially relevant metals for the etiology of autism by using a Bayesian multivariate approach for partially censored values

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SCIENTIFIC REPORTS 2023年第1期13卷 1-15页

作者： Wegmann, Bertil Tatemoto, Patricia Miemczyk, Stefan Ludvigsson, Johnny Guerrero-Bosagna, Carlos Linkoping Univ Dept Comp & Informat Sci Div Stat & Machine Learning S-58183 Linkoping Sweden Univ Sao Paulo Ctr Comparat Studies Sustainabil Hlth & Welfare Sch Vet Med & Anim Sci Dept Vet Med & Anim HlthFMVZ BR-13635900 Pirassununga SP Brazil Linkoping Univ Dept Phys Chem & Biol IFM Avian Behav Genom & Physiol Grp S-58183 Linkoping Sweden Linkoping Univ Crown Princess Victor Childrens Hosp Linkoping Sweden Linkoping Univ Dept Biomed & Clin Sci Div Pediat Linkoping Sweden Uppsala Univ Dept Organismal Biol Physiol & Environm Toxicol Program S-75236 Uppsala Sweden

Heavy metals are known to be able to cross the placental and blood brain barriers to affect critical neurodevelopmental processes in the fetus. We measured metal levels (Al, Cd, Hg, Li, Pb and Zn) in the cord blood of newborns and in the serum of the same children at 5 years of age, and compared between individuals with or without (controls) autism spectrum disorder (ASD) diagnosis. The samples were from a biobank associated with the All Babies in Southeast Sweden (ABIS) registry. We proposed a Bayesian multivariate log-normal model for partially censored values to identify potentially relevant metals for the etiology of ASD. Our results in cord blood suggest prenatal Al levels could be indicative of later ASD incidence, which could also be related to an increased possibility of a high, potentially toxic, exposure to Al and Li during pregnancy. In addition, a larger possibility of a high, potentially beneficial, exposure to Zn could occur during pregnancy in controls. Finally, we found decisive evidence for an average increase of Hg in 5-year-old ASD children compared to only weak evidence for controls. This is concordant with previous research showing an impaired ability for eliminating Hg in the ASD group.

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Studying the wound healing property of biologically and chemically synthesized gold nanoparticles through scratch assay and neural network modeling

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ADVANCED COMPOSITES AND HYBRID MATERIALS 2025年第1期8卷 1-19页

作者： Desai, Anjana S. Ashok, Aparna Wagle, Shivali A. Bhagat, Neeru Ashirova, Zhadyra Abdrassulova, Zhanna T. Abdolla, Nurshat Mukazhanova, Zhazira Ydyrys, Alibek Seilkhan, Ainur Samundra Inst Maritime Studies Dept Appl Sci Pune 410405 Maharashtra India Symbiosis Int Univ Symbiosis Inst Technol Dept Appl Sci Pune 412115 Maharashtra India Symbiosis Int Deemed Univ Symbiosis Inst Technol Dept Artificial Intelligence & Machine Learning Pune 412115 Maharashtra India Al Farabi Kazakh Natl Univ Biomed Res Ctr Al Farabi Ave 71 Alma Ata 050040 Kazakhstan Bolashaq Ctr Int Programs 70 Syganak Str Astana 010000 Kazakhstan MA Aitkhozhins Inst Mol Biol & Biochem Lab Mol Immunol & Immunobiotechnol Alma Ata Kazakhstan Sarsen Amanzholov East Kazakhstan Univ Higher Sch IT & Nat Sci Ust Kamenogorsk 070010 Kazakhstan Abai Kazakh Natl Pedag Univ Educ Program Geog Environm & Serv Sect Alma Ata Kazakhstan St Miras Coll Girls St Miras Coll Pune India

Gold nanoparticles (Au NPs) are commonly used as anti-haemorrhagic and antiseptic agents. Thus, their cytotoxicity should be studied before application. In this study, the Au NPs were synthesized using biological and chemical routes, and the samples were named as AU-BT and AU-C, respectively. The AU-BT were synthesized using three different extracts, i.e., turmeric, aloe-vera, and a mixture of turmeric aloe-vera. The cytotoxicity was studied using scratch assay on HEK-293 cell lines. The number of scratch assays was increased to validate the experimental wound healing results. Finally, a neural networking modeling was performed to predict the results of provided data in terms of the sample toxicity by healing the wound. The experimental results and the neural networking confirmed that the turmeric-derived Au NPs were the best among all the synthesized samples. Moreover, integrating metal NPs in wound healing studies introduces exciting possibilities for enhanced imaging and therapeutic interventions. The combination of advanced image analysis through models like AlexNet and the novel use of nanoparticles opens doors to improved wound care and a deeper understanding of the healing process.

关键词： Gold nanoparticles Scratch assay HEK-293 cell lines Artemisia schrenkiana Neural networking

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Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review

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INFORMATION FUSION 2023年第1期93卷 85-117页

作者： Shoeibi, Afshin Khodatars, Marjane Jafari, Mahboobeh Ghassemi, Navid Moridian, Parisa Alizadehsani, Roohallah Ling, Sai Ho Khosravi, Abbas Alinejad-Rokny, Hamid Lam, H. K. Fuller-Tyszkiewicz, Matthew Acharya, U. Rajendra Anderson, Donovan Zhang, Yudong Gorriz, Juan Manuel Univ Granada Data Sci & Computat Intelligence Inst Granada Spain Internship Biomed Machine Learning Lab Sydney NSW 2052 Australia Islamic Azad Univ Dept Med Engn Mashhad Branch Mashhad Iran Deakin Univ Intelligent Syst Res & Innovat IISRI Geelong Vic 3217 Australia Univ Technol Sydney UTS Fac Engn & IT Ultimo Australia UNSW Sydney Grad Sch Biomed Engn Biomed Machine Learning Lab Sydney NSW 2052 Australia Univ New South Wales UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ Ctr Appl AI Hlth Data Analyt Program Sydney 2109 Australia Kings Coll London Dept Engn Strand London WC2R 2LS England Deakin Univ Sch Psychol Geelong Australia Ngee Ann Polytech Singapore 599489 Singapore Asia Univ Dept Biomed Informat & Med Engn Taichung Taiwan Singapore Univ Social Sci Sch Sci & Technol Dept Biomed Engn Singapore Singapore Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England Univ Cambridge Dept Psychiat Cambridge England

Brain diseases, including tumors and mental and neurological disorders, seriously threaten the health and well-being of millions of people worldwide. Structural and functional neuroimaging modalities are commonly used by physicians to aid the diagnosis of brain diseases. In clinical settings, specialist doctors typically fuse the magnetic resonance imaging (MRI) data with other neuroimaging modalities for brain disease detection. As these two approaches offer comple-mentary information, fusing these neuroimaging modalities helps physicians accurately diagnose brain diseases. Typically, fusion is performed between a functional and a structural neuroimaging modality. Because the functional modality can complement the structural modality information, thus improving the performance for the diagnosis of brain diseases by specialists. However, analyzing the fusion of neuroimaging modalities is difficult for specialist doctors. Deep learning (DL) is a branch of artificial intelligence that has shown superior performances compared to more conventional methods in tasks such as brain disease detection from neuroimaging modalities. This work presents a comprehensive review paper in the field of brain disease detection from the fusion of neuroimaging modalities using DL models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), pretrained, generative adver-sarial networks (GANs), and Autoencoders (AEs). First, neuroimaging modalities and the need for fusion are discussed. Then, review papers published in the field of neuroimaging multimodalities using AI techniques are explored. More-over, fusion levels based on DL methods, including input, layer, and decision, with related studies conducted on diagnosing brain diseases, are discussed. Other sections present the most important challenges for diagnosing brain diseases from the fusion of neuroimaging modalities. In the discussion section, the details of previous research on the fusion of neuroimaging moda

关键词： Brain diseases MRI Neuroimaging Fusion Multimodality Deep learning

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Bridging the Gap: Towards an Expanded Toolkit for AI-Driven Decision-Making in the Public Sector

arXiv

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arXiv 2023年

作者： Fischer-Abaigar, Unai Kern, Christoph Barda, Noam Kreuter, Frauke Dept. of Statistics LMU Munich Germany Munich Center for Machine Learning Germany Joint Program in Survey Methodology University of Maryland United States Dept. of Software and Information Systems Engineering Dept. of Epidemiology Biostatistics and Community Health Sciences Ben-Gurion University of the Negev Israel

AI-driven decision-making systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. While these systems offer great potential benefits to institutional decision-making processes, such as improved efficiency and reliability, these systems face the challenge of aligning machine learning (ML) models with the complex realities of public sector decision-making. In this paper, we examine five key challenges where misalignment can occur, including distribution shifts, label bias, the influence of past decision-making on the data side, as well as competing objectives and human-in-the-loop on the model output side. Our findings suggest that standard ML methods often rely on assumptions that do not fully account for these complexities, potentially leading to unreliable and harmful predictions. To address this, we propose a shift in modeling efforts from focusing solely on predictive accuracy to improving decision-making outcomes. We offer guidance for selecting appropriate modeling frameworks, including counterfactual prediction and policy learning, by considering how the model estimand connects to the decision-maker’s utility. Additionally, we outline technical methods that address specific challenges within each modeling approach. Finally, we argue for the importance of external input from domain experts and stakeholders to ensure that model assumptions and design choices align with real-world policy objectives, taking a step towards harmonizing AI and public sector objectives. © 2023, CC BY-NC-ND.

关键词： Crime

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Cosmology with Galaxy Photometry Alone

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ASTROPHYSICAL JOURNAL LETTERS 2024年第1期966卷 L18-L18页

作者： Hahn, ChangHoon Villaescusa-Navarro, Francisco Melchior, Peter Teyssier, Romain Princeton Univ Dept Astrophys Sci Peyton Hall Princeton NJ 08544 USA Flatiron Inst Ctr Computat Astrophys 162 5th Ave New York NY 10010 USA Princeton Univ Ctr Stat & Machine Learning Princeton NJ 08544 USA Princeton Univ Program Appl & Computat Math Fine Hall Washington Rd Princeton NJ 08544 USA

We present the first cosmological constraints using only the observed photometry of galaxies. Villaescusa-Navarro et al. recently demonstrated that the internal physical properties of a single simulated galaxy contain a significant amount of cosmological information. These physical properties, however, cannot be directly measured from observations. In this work, we present how we can go beyond theoretical demonstrations to infer cosmological constraints from actual galaxy observables (e.g., optical photometry) using neural density estimation and the CAMELS suite of hydrodynamical simulations. We find that the cosmological information in the photometry of a single galaxy is limited. However, we combine the constraining power of photometry from many galaxies using hierarchical population inference and place significant cosmological constraints. With the observed photometry of similar to 20,000 NASA-Sloan Atlas galaxies, we constrain Omega m = 0.323 - 0.095 + 0.075 and sigma 8 = 0.799 - 0.085 + 0.088 .

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3D Non-Rigid Alignment of Low-Dose Scans Allows to Correct for Saturation in Lower Extremity Cone-Beam CT

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IEEE ACCESS 2021年 9卷 71821-71831页

作者： Maier, Jennifer Maier, Andreas Eskofier, Bjoern Fahrig, Rebecca Choi, Jang-Hwan Friedrich Alexander Univ Erlangen Nurnberg Dept Comp Sci Pattern Recognit Lab D-91058 Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Dept Comp Sci Machine Learning & Data Analyt Lab D-91052 Erlangen Germany Siemens Healthcare GmbH D-91301 Forchheim Germany Ewha Womans Univ Grad Program Syst Hlth Sci & Engn Div Mech & Biomed Engn Seoul 03760 South Korea

Detector saturation in cone-beam computed tomography occurs when an object of highly varying shape and material composition is imaged using an automatic exposure control (AEC) system. When imaging a subject's knees, high beam energy ensures the visibility of internal structures but leads to overexposure in less dense border regions. In this work, we propose to use an additional low-dose scan to correct the saturation artifacts of AEC scans. Overexposed pixels are identified in the projection images of the AEC scan using histogram-based thresholding. The saturation-free pixels from the AEC scan are combined with the skin border pixels of the low-dose scan prior to volumetric reconstruction. To compensate for patient motion between the two scans, a 3D non-rigid alignment of the projection images in a backward-forward-projection process based on fiducial marker positions is proposed. On numerical simulations, the projection combination improved the structural similarity index measure from 0.883 to 0.999. Further evaluations were performed on two in vivo subject knee acquisitions, one without and one with motion between the AEC and low-dose scans. Saturation-free reference images were acquired using a beam attenuator. The proposed method could qualitatively restore the information of peripheral tissue structures. Applying the 3D non-rigid alignment made it possible to use the projection images with inter-scan subject motion for projection image combination. The increase in radiation exposure due to the additional low-dose scan was found to be negligibly low. The presented methods allow simple but effective correction of saturation artifacts.

关键词： Histograms Knee Image reconstruction Detectors Computed tomography X-ray imaging Legged locomotion Automatic exposure control C-arm cone-beam CT detector saturation lower extremity overexposure artifacts non-rigid reconstruction X-ray imaging and computed tomography

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Detection of Leukemia from Histopathological Image using Deep learning Techniques

Detection of Leukemia from Histopathological Image using Dee...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： V Sunil Kumar Renukadevi S Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Deepak Gupta Ashish Khanna Department Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Department of Artificial intelligence and Machine Learning Dayananda Sagar college of engineering Bengaluru Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceará Brazil CSE dept Maharaja Agrasen Institute of Technology GGSIPU CSE Dept Maharaja Agrasen Institute of Technology GGSIPU

ISBN: (纸本)9781665493970

Leukemia is a kind of cancer that is brought on by abnormalities in the body's white blood cells. An inspection of a blood smear that has been collected from the peripheral blood and then seen under a microscope is the method that is considered to be the gold standard for diagnosing leukemia. Techniques such as counting the cells in the blood and doing morphological analysis are two examples of methods that may be used to identify and diagnose leukemia. It is necessary to carefully examine a stained blood smear or an aspiration of bone marrow in order to correctly detect and diagnose leukemia. Both of these procedures involve the removal of bone marrow. This requires prior training and specialized expertise on the subject matter. A tedious and time-consuming operation, the manual evaluation of microscopic pictures of blood samples is one of the steps in the diagnostic procedure. The results are highly dependent on the degree of knowledge and experience that the laboratory worker has. Pathologists are often burdened with an excessive quantity of such data that has to be properly evaluated in order to arrive at a decision. This may be a time-consuming and difficult process. Red blood cells, white blood cells, and blood platelets are the three primary cell types that make up human blood. Platelets are also present in blood. Erythrocyte disease might refer to a collection of diseases that have an effect on hemoglobin. Hemoglobin is a protein found in red blood cells that is responsible for transporting oxygen to cells located throughout the body. This condition is referred to as sickle cell anemia. In sickle cell anemia, the blood contains abnormal hemoglobin molecules known as hemoglobin S. These molecules distort the structure of red blood cells, causing them to take on the form of a crescent or a reaping hook. There is a genetic form of anemia known as sickle cell anemia. This kind of anemia is caused by defective hemoglobin, which causes red blood cells to take on t

关键词： White blood cells Training Thresholding (Imaging) Red blood cells Microscopy Blood platelets Cells (biology)

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Importance of Wearable Health Monitoring Systems Using IoMT;Requirements, Advantages, Disadvantages and Challenges 22

Importance of Wearable Health Monitoring Systems Using IoMT;...

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IEEE Joint 22nd International Symposium on Computational Intelligence and Informatics / 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo)

作者： Khozeimeh, Fahime Roshanzamir, Mohamad Shoeibi, Afshin Darbandy, Mohammad Tayarani Alizadehsani, Roohallah Alinejad-Rokny, Hamid Ahmadian, Davood Khosravi, Abbas Nahavandi, Saeid Deakin Univ IISRI Geelong Vic 3217 Australia Fasa Univ Dept Comp Engn Fasa *** Iran Macquarie Univ AI Enabled Proc AIP Res Ctr Internship Hlth Data Analyt Program Sydney NSW 2109 Australia Islamic Azad Univ Taft Sch Architecture Taft *** Iran UNSW Sydney Grad Sch Biomed Engn BioMed Machine Learning Lab Sydney NSW 2052 Australia Univ Tabriz Fac Math Stat & Comp Sci Tabriz Iran

ISBN: (纸本)9798350398823

Providing better quality of service is one of the most important goals in medical/healthcare systems. The services provided should have features such as low latency, appropriate geographic distribution, and real-time interaction. Internet of Medical Things (IoMT) technology is one of the solutions to achieve high quality of services with these features. The wearable health monitoring systems which have become popular in the last decade are one of the foundations of achievement to these features. In this paper, the importance of wearable devices under IoMT technology is investigated. There are several reasons for using wearable devices under IoMT technology, such as reducing healthcare costs and long waiting times. Therefore, anyone working on wearable devices needs to have a general understanding of how they are designed and function. In fact, the integration of new devices to monitor and alert patients about their physical health is a promising option for improving health care systems around the world. Overcrowding in medical centers will be prevented by upgrading the facilities for the virtual presence of patients. It can also ensure real-time monitoring and reduce operating costs. In addition, it can accelerate treatment of patients and reduce the cost of investment and increase its coverage.

关键词： Internet of Medical Things Wearable Devices Healthcare Systems Internet of Things

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