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Machine Learning of Medical Applications Involving Complicated Proteins and Genetic Measurements

Computational Intelligence and Neuroscience 2021年 2021卷 1094054页

作者： Bader Alazzam, Malik Mansour, Hoda Hammam, Mohamed M. Alsheikh, Said Bakir, Ali Alghamdi, Saeed Alghamdi, Ahmed S. Faculty of Computer Science and Informatics Amman Arab University Amman Jordan College of Business Administration University of Business and Technology Jeddah Saudi Arabia Theodor Bilharz Research Institute TBRI Giza Egypt University of Business and Technology Jeddah Saudi Arabia Taibah University Taibah Saudi Arabia Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia

Motivations. Breast cancer is the second greatest cause of cancer mortality among women, according to the World Health Organization (WHO), and one of the most frequent illnesses among all women today. The influence is not confined to industrialized nations but also includes emerging countries since the authors believe that increased urbanization and adoption of Western lifestyles will lead to a rise in illness prevalence. Problem Statement. The breast cancer has become one of the deadliest diseases that women are presently facing. However, the causes of this disease are numerous and cannot be properly established. However, there is a huge difficulty in not accurately recognizing breast cancer in its early stages or prolonging the detection process. Methodology. In this research, machine learning is a field of artificial intelligence that employs a variety of probabilistic, optimization, and statistical approaches to enable computers to learn from past data and find and recognize patterns from large or complicated groups. The advantage is particularly well suited to medical applications, particularly those involving complicated proteins and genetic measurements. Result and Implications. However, when using the PCA method to reduce the features, the detection accuracy dropped to 89.9%. IG-ANFIS gave us detection accuracy (98.24%) by reducing the number of variables using the "information gain"method. While the ANFIS algorithm had a detection accuracy of 59.9% without utilizing features, J48, which is one of the decision tree approaches, had a detection accuracy of 92.86% without using features extraction methods. When applying PCA techniques to minimize features, the detection accuracy was lowered to the same way (91.1%) as the Naive Bayes detection algorithm (96.4%). © 2021 Malik Bader Alazzam et al.

关键词： Diseases

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A first principle study of adsorption and dissociation of H2 molecule on MoS2 and Ni-promoted MoS2 surfaces

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AIP Conference Proceedings 2023年第1期2748卷

作者： Wahyu Aji Eko Prabowo Subagjo Subagjo Nugraha Nugraha T. Sutojo Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono Informatics Engineering Department Faculty of Computer Science Dian Nuswantoro University Semarang 50131 Indonesia Catalysis and Reaction Engineering Faculty of Industrial Technology Bandung Institute of Technology Bandung 40132 Indonesia Engineering Physics Department Faculty of Industrial Technology Bandung Institute of Technology Bandung 40132 Indonesia Research Center for Nanoscience and Nanotechnology Bandung Institute of Technology Bandung 40132 Indonesia

Molybdenum disulfide (MoS2) and nickel promoted MoS2 are increasingly used as catalyst in hydrotreating process, which involve adsorption, dissociation and formation of molecular hydrogen. The aims of this study are to investigate the adsorption, dissociation, and formation of H2 molecular over MoS2 and Ni promoted MoS2 (NiMoS) surface by using first-principles density functional theory (DFT). By utilizing climbing image nudged elastic band (CI-NEB) method, the activation energies of H2 dissociation and H2 formation are calculated. The most stable adsorption site is found in MoS2 surface. From the evaluation of H–H formation energy barrier on catalyst surface, NiMoS shows better reactivity than MoS2.

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Application of Graph-Curvature Features in Auxiliary Diagnosis for Histopathological Image Identification of Gastric Cancer

SSRN

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SSRN 2023年

作者： He, Ruilin Li, Chen Yang, Xinyi Yang, Jinzhu Jiang, Tao Grzegorzek, Marcin Sun, Hongzan Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology Chengdu China Institute for Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering

Background: Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors, but it has some drawbacks. This paper explores a computer-aided diagnostic method that can identify benign and malignant gastric cancer with histopathology images. Method: This article obtains the most suitable process through multiple experiments, compared multiple methods and features for classification. Firstly, the U-net is applied to segment the image. Next, the nucleus is extracted from the segmented image and the Minimum Spanning Tree (MST) diagram structure is drawn. The third step is to extract the graph-curvature features of histopathology image according to the MST image. Finally, by inputting graph-curvature features into the classifier, the recognition results for benign or malignant can be obtained. Result: During the experiment, we use various methods for comparison. In the image segmentation stage, U-net, watershed algorithm and Otsu threshold segmentation methods are used respectively. Combined with multiple indicators, we find that the U-net method is the most suitable for segmentation of histopathology images. In the feature extraction stage, in addition to extracting graph-edge feature and graph-curvature feature, several basic image features are also extracted, including Red, Green, Blue feature, Gray-Level Co-occurrence Matrix feature, Histogram of Oriented Gradient feature, and Local Binary Pattern feature. In the classifier design stage, we experimented with various methods, such as Support vector machine (SVM), Random forest, Artificial Neural Network, K Nearest Neighbors, VGG-16 and Inception-V3. Through the comparison and analysis, the classification results with an accuracy of 98.57% can be obtained by inputting the graph-curvature feature into SVM classifier. Conclusion: This paper has created a unique feature, graph-curvature feature based on MST to represent and analyze histopathology images. This graph-based feature can be used

关键词： Local binary pattern

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Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss

arXiv

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arXiv 2023年

作者： Zhu, Jiarui Chen, Weixing Sun, Hongfei Zhi, Shaohua Zhao, Mayang Lap, Lam Yu Zhou, Zihan Peng, Tao Cai, Jing Ren, Ge Department of Health Technology and Informatics The Hong Kong Polytechnic University 999077 Hong Kong School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Department of Radiation Oncology Xijing Hospital Fourth Military Medical University Xian710032 China Department of Clinical Oncology Queen Mary Hospital 999077 Hong Kong Department of Radiation Oncology Jinshazhou Hospital Guangzhou University of Chinese Medicine Guangzhou510168 China School of Future Science and Engineering Soochow University Suzhou215299 China Research Institute for Intelligent Wearable Systems The Hong Kong Polytechnic University 999077 Hong Kong

Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy (IGRT) to provide updated patient anatomy information for cancer treatments. However, CBCT images often suffer from streaking artifacts and noise caused by under-rate sampling projections and low-dose exposure, resulting in low clarity and information loss. While recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts, they have limited performance on preserving anatomical details since conventional pixel-to-pixel loss functions are incapable of describing detailed anatomy. To address this issue, we propose a novel feature-oriented deep learning framework that translates low-quality CBCT images into high-quality CT-like imaging via a multi-task customized feature-to-feature perceptual loss function. The framework comprises two main components: a multi-task learning feature-selection network(MTFS-Net) for customizing the perceptual loss function;and a CBCT-to-CT translation network guided by feature-to-feature perceptual loss, which uses advanced generative models such as U-Net, GAN and CycleGAN. Our experiments showed that the proposed framework can generate synthesized CT (sCT) images for the lung that achieved a high similarity to CT images, with an average SSIM index of 0.9869 and an average PSNR index of 39.9621. The sCT images also achieved visually pleasing performance with effective artifacts suppression, noise reduction, and distinctive anatomical details preservation. Functional imaging tests further demonstrated the pulmonary texture correction performance of the sCT images. Comparison experiments with pixel-to-pixel loss also showed that the proposed perceptual loss significantly enhances the performance of involved generative models. Our experiment results indicate that the proposed framework outperforms the state-of-the-art models for pulmonary CBCT enhancement. This framework holds great promise for generat

关键词： Pixels

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Enhancing COVID-19 vaccination and medication distribution routing strategies in rural regions of Morocco: A comparative metaheuristics analysis

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informatics in Medicine Unlocked 2024年 46卷

作者： Mzili, Toufik Mzili, Ilyass Riffi, Mohammed Essaid Kurdi, Mohamed Ali, Ali Hasan Pamucar, Dragan Abualigah, Laith Department of Computer Science Faculty of Science Chouaib Doukkali University EI Jadida Morocco Department of Business Management Al-imam University College Balad Iraq Department of Computer Science Faculty of Economics and Management Hasan 1st University Settat Morocco Faculty of Informatics Engineering Idlib University Idlib Syrian Arab Republic Institute of Mathematics University of Debrecen Pf. 400 Debrecen H-4002 Hungary College of Engineering Technology National University of Science and Technology Dhi Qar 64001 Iraq Technical Engineering College Al-Ayen University Dhi Qar 64001 Iraq Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia College of Engineering Yuan Ze University Taiwan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan

The optimization of the vaccination campaign and medication distribution in rural regions of Morocco conducted by the Ministry of Health can be significantly improved by employing metaheuristic algorithms in conjunction with a tour planning system. This research proposes the utilization of six metaheuristic algorithms: genetic algorithm, rat swarm optimization, whale optimization, spotted hyena optimizer, penguins search optimization, and particle swarm optimization, to determine the most efficient routes for equipped trucks carrying vaccines and medications. These algorithms consider critical field constraints, such as operating hours of vaccination centers, vaccine availability, and distances between centers while minimizing the overall journey duration. In addition, a comprehensive tour planning system is integrated into the optimization framework accounting for transportation costs such as fuel expenses and truck maintenance costs. By incorporating these factors, the Ministry of Health aims to achieve the maximum efficiency while minimizing the financial burden associated with the vaccination campaign in rural areas. The integration of metaheuristics and the tour planning system presents a robust and data-driven solution for the Ministry of Health to enhance the effectiveness of their vaccination and medication distribution campaigns in rural regions of Morocco. This approach not only minimizes costs but also improves overall efficiency by ensuring timely access to vaccines and medications for the rural population. The findings of this research contribute to the growing body of knowledge in the field of healthcare logistics optimization and provide valuable insights for policymakers and practitioners involved in similar campaigns worldwide. © 2024 The Authors

关键词： Healthcare logistics Medication distribution Metaheuristics Route optimization Tour planning system Transportation Vaccination campaign

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Cyber security after COVID 19: A review

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AIP Conference Proceedings 2023年第1期2839卷

作者： Muthana Alisawi Layth Hammood Alaan Ghazi Sirwan Saber Abdullah Aras Al-Dawoodi Abbas Hussein Ali Ashraf Nabeel Almallah Nidhal Mohsin Hazzaa Yousif Mohammed Wahab Asmaa Yaseen Nawaf Department of Computer science college of computer science and information technology University of Kirkuk. Kirkuk Iraq Computer science informatics institute Gazi university Çankaya Ankara 06690 Turkey Institute of science faculty of technology computer engineering Gazi university Çankaya Ankara 06690 Turkey Department of Computer Technical Engineering Al-Qalam university College Kirkuk Iraq Faculty of Applied and Human Sciences University Malaysia Perlis Perlis Malaysia Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia Information Technology and Communication Department Rafidain Bank Baghdad Iraq Department of Quality Assurance and Academic Performance University of Kirkuk Kirkuk Iraq Department of computer science college of computer and mathematics University of Mosul Mosul Iraq Gazi University - Turkey Çankaya Ankara 06690 Turkey

Nowadays, when talking about cybersecurity, the talk is associated with the most important event in the world during the year 2020, which is COVID-19. Although the digital world exists parallel to the real world, humanity did not have to rely on the Internet to accomplish its tasks. However, the situation changed after the Corona pandemic, as the world found itself in front of one option, which is to work remotely to overcome the impact of the pandemic. Therefore, with this massive and sudden digital transformation on the global level, cyber criminals have many opportunities through cyber vulnerabilities that appear due to the rapid shift to remote work. In this study, we presented an accurate review of the effects that accompanied the occurrence of the Coronavirus pandemic on cybersecurity. Moreover, the world has seen a growing number of cyber-attacks due to these effects, which led to attract the attention to the importance of cyber security to ensure full benefit from digital services without exposure to damage. Also, we indicated in this paper a number of recommendations that contribute to mitigating the damages of cyber-attacks or preventing their occurrence. Furthermore, Cyber security should be a complete approach and not limited to the technical side, where the awareness of the community about cyber risks should contribute to preventing cyber criminals from achieving their goals.

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Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics

arXiv

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arXiv 2023年

作者： Rosas, Fernando E. Candia-Rivera, Diego Luppi, Andrea I. Guo, Yike Mediano, Pedro A.M. School of Engineering and Informatics University of Sussex United Kingdom Centre for Psychedelic Research Department of Brain Science Imperial College London United Kingdom Centre for Complexity Science Imperial College London London United Kingdom Centre for Eudaimonia and Human Flourishing University of Oxford United Kingdom Paris Brain Institute ICM Paris75013 France University Division of Anaesthesia University of Cambridge Cambridge United Kingdom Montreal Neurological Institute McGill University Montreal Canada Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Computing Imperial College London South Kensington London United Kingdom Department of Psychology University of Cambridge Cambridge United Kingdom

Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and parasympathetic outflow from the autonomic nervous system, which is known to play a key role in modulating attention, memory, decision-making, and emotional processing. However, extracting useful information from heartbeats about the autonomic outflow is still challenging due to the noisy estimates that result from standard signal-processing methods. To advance this state of affairs, we propose a paradigm shift in how we conceptualise and model heart rate: instead of being a mere summary of the observed inter-beat intervals, we introduce a modelling framework that views heart rate as a hidden stochastic process that drives the observed heartbeats. Moreover, by leveraging the rich literature of state-space modelling and Bayesian inference, our proposed framework delivers a description of heart rate dynamics that is not a point estimate but a posterior distribution of a generative model. We illustrate the capabilities of our method by showing that it recapitulates linear properties of conventional heart rate estimators, while exhibiting a better discriminative power for metrics of dynamical complexity compared across different physiological states. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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GA-lapagos, an open-source c framework including a python-based system for data analysis 20

GA-lapagos, an open-source c framework including a python-ba...

引用

2020 Genetic and Evolutionary Computation Conference, GECCO 2020

作者： Jamieson, Peter Ferreira, Ricardo Nacif, José Augusto M. Department of Electrical and Computer Engineering Miami University OxfordOH United States Departament of Informatics Universidade Federal de Viçosa Viçosa Minas Gerais Brazil Science and Technology Institute Universidade Federal de Viçosa Florestal Minas Gerais Brazil

ISBN: (纸本)9781450371278

In this work, we introduce, GA-lapagos, an open-source genetic algorithm framework written in 'C' for 3 exemplar optimization problems, and an accompanying Python-based data analysis scripts to extract and produce results. We created this system to help researchers implement a fast GA solving system for their problems allowing them to implement and leverage many ideas implemented in the research literature. Additionally, we provide a number of compilation paths to parallel computational systems such as multi-core, GPUs, and FPGAs. By building an executable framework and outputting results in comma separated values (CSV) format, we create a set of Python scripts to read the data and create graphs. © 2020 ACM.

关键词： Python

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The Localized Subtraction Approach For EEG and MEG Forward Modeling

arXiv

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arXiv 2023年

作者： Höltershinken, Malte B. Lange, Pia Erdbrügger, Tim Buschermöhle, Yvonne Wallois, Fabrice Buyx, Alena Pursiainen, Sampsa Vorwerk, Johannes Engwer, Christian Wolters, Carsten H. Institute for Biomagnetism and Biosignalanalysis University of Münster Münster Germany Institute of Medical Informatics University of Münster Münster Germany Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience University of Münster Münster Germany INSERM U1105 Research Group on Multimodal Analysis of Brain Function Jules Verne University of Picardie Amiens France Pediatric functional exploration of the nervous system department CHU Picardie Amiens France Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Computing Sciences Unit Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Institute of Electrical and Biomedical Engineering Private University for Health Sciences Medical Informatics and Technology Hall in Tyrol Austria Faculty of Mathematics and Computer Science University of Münster Münster Germany

In FEM-based EEG and MEG source analysis, the subtraction approach has been proposed to simulate sensor measurements generated by neural activity. While this approach possesses a rigorous foundation and produces accurate results, its major downside is that it is computationally prohibitively expensive in practical applications. To overcome this, we developed a new approach, called the localized subtraction approach. This approach is designed to preserve the mathematical foundation of the subtraction approach, while also leading to sparse right-hand sides in the FEM formulation, making it efficiently computable. We achieve this by introducing a cut-off into the subtraction, restricting its influence to the immediate neighborhood of the source. In this work, this approach will be presented, analyzed, and compared to other state-of-the-art FEM right-hand side approaches. Furthermore, we discuss how to arrive at an efficient and stable implementation. We perform validation in multi-layer sphere models where analytical solutions exist. There, we demonstrate that the localized subtraction approach is vastly more efficient than the subtraction approach. Moreover, we find that for the EEG forward problem, the localized subtraction approach is less dependent on the global structure of the FEM mesh, while the subtraction approach is sensitive to the mesh structure even at a distance to the source. Additionally, we show the localized subtraction approach to rival, and in many cases even surpass, the other investigated approaches in terms of accuracy. Especially for the MEG forward problem, where Biot-Savart’s law is employed, we show the localized subtraction approach and the subtraction approach to produce highly accurate approximations of the volume currents close to the source. The localized subtraction approach thus reduces the computational cost of the subtraction approach to an extent that makes it usable in practical applications without sacrificing the rigorousness and

关键词： Mesh generation

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Path Priority Routing Protocol for Underwater Wireless Sensor Network

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IEEE Internet of Things Journal 2025年

作者： Ogundile, Olayinka Babalola, Oluwaseyi Agboola, Emmanuel Ogundile, Olabisi Davidson, Innocent Tai Solarin University of Education Department of Industrial Technical Education Nigeria Cape Peninsula University of Technology French-South African Institute of Technology Department of Electrical Electronic and Computer Engineering Bellville South Africa Bournemouth University Department of Computing and Informatics Bournemouth United Kingdom Government Senior College Ketu-Epe Nigeria

Underwater wireless sensor networks (UWSNs) are rapidly expanding, enabling advancements in marine technologies like seabed excavation, seismic monitoring, and environmental monitoring. UWSNs consist of small, battery-powered devices that communicate to forward data to a base station. Due to their seabed location, replacing batteries is challenging, making energy-efficient (E-E) communication crucial to extending the network’s lifespan. Many routing protocols have been developed to address the challenges of underwater communication, but balancing energy consumption while maintaining high-quality service (QoS) remains difficult, especially in large-scale UWSNs. This article introduces a path priority routing protocol (PPRP) for UWSNs, which adapts the mobile selective-path priority table (MSPT) routing protocol from terrestrial wireless sensor networks (WSNs) to underwater conditions. The PPRP prioritises paths to the base station using simple yet effective rules, reducing overall energy consumption while balancing QoS requirements. Comparative analysis with recent routing protocols show that the PPRP is a performance-efficient alternative routing protocol for UWSNs. © 2014 IEEE.

关键词： Sensor nodes

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