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Naïve Bayes classifier assisted automated detection of cerebral microbleeds in susceptibility-weighted imaging brain images

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Biochemistry and Cell Biology 2023年第6期101卷 562-573页

作者： Ateeq, Tayyab Faheem, Zaid Bin Ghoneimy, Mohamed Ali, Jehad Li, Yang Baz, Abdullah Department of Computer Engineering The University of Lahore Lahore 54000 Pakistan Department of Computer Science & IT The Islamia University of Bahawalpur Punjab Bahawalpur 63100 Pakistan Faculty of Computer Science Modern Science & Arts (MSA) University Giza Egypt Department of AI Convergence Network Ajou University Suwon South Korea College of Mechanical and Electrical Engineering Shihezi University Shihezi China Department of Computer Engineering College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia

Cerebral microbleeds (CMBs) in the brain are the essential indicators of critical brain disorders such as dementia and ischemic stroke. Generally, CMBs are detected manually by experts, which is an exhaustive task with limited productivity. Since CMBs have complex morphological nature, manual detection is prone to errors. This paper presents a machine learning-based automated CMB detection technique in the brain susceptibility-weighted imaging (SWI) scans based on statistical feature extraction and classification. The proposed method consists of three steps: (1) removal of the skull and extraction of the brain;(2) thresholding for the extraction of initial candidates;and (3) extracting features and applying classification models such as random forest and naïve Bayes classifiers for the detection of true positive CMBs. The proposed technique is validated on a dataset consisting of 20 subjects. The dataset is divided into training data that consist of 14 subjects with 104 microbleeds and testing data that consist of 6 subjects with 63 microbleeds. We were able to achieve 85.7% sensitivity using the random forest classifier with 4.2 false positives per CMB, and the naïve Bayes classifier achieved 90.5% sensitivity with 5.5 false positives per CMB. The proposed technique outperformed many state-of-the-art methods proposed in previous studies. © 2023 The Author(s).

关键词： brain bleeds cerebral microbleeds hemosiderin deposits naïve Bayes classifier random forest classifier

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A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1476-1489页

作者： Shoufei Han Kun Zhu MengChu Zhou Xiaojing Liu Haoyue Liu Yusuf Al-Turki Abdullah Abusorrah College of Computer Science and Technology Nanjing University of Aeronautics and Astronauticsand also with the Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing 211106China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand also with the K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions *** this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision *** first extend a latest single-objective fireworks algorithm to handle *** we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special ***,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization *** results show that the proposed algorithm is superior to compared algorithms in solving ***,its runtime is less than its peers'.

关键词： Adaptive strategy fireworks algorithm multimodal multiobjective optimization problems(MMOP)

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Reassessing Glove Embeddings in Deep Learning: A Comparative Study with Classical ML Approaches 15

Reassessing Glove Embeddings in Deep Learning: A Comparative...

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15th International Conference on Emerging Ubiquitous systems and Pervasive Networks / 14th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Labd, Zakia Bahassine, Said Housni, Khalid Aadi, Fatima Zahrae Aithamou Laboratory of research in informatics LatRI Department of Computer Science Faculty of Sciences Ibn Tofail University Kenitra Morocco Laboratory of Artificial Intelligence and Complex Systems Engineering Department of Computer Science National Higher School of Arts and Crafts Hassan II University Casablanca Morocco

The importance of text classification algorithms has increased due to the growing availability of large-scale data. This has led to a greater demand for efficient classification techniques and encoding algorithms. Word embedding techniques, like Glove, have shown significant success in encoding semantic relationships between words. This research paper aims to reassess the effectiveness of Glove embeddings coupled with deep learning algorithms. The impact of Glove embedding on two widely used deep learning models: Recurrent Neural Networks (RNN) and Recurrent Convolutional Neural Networks (RCNN) is analyzed. The results highlight the impact of Glove embeddings on deep learning models, showcasing significant performance enhancements in some cases while having minimal effects in others. By examining the impact of Glove embeddings on traditional ML algorithms in a previous study, valuable context for understanding the performance differences between the two approaches is obtained. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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A Coupled Graph Theoric and Deep Learning Approaches for Nonrigid Image Registration 1

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： El Bahi, Omaima Qaraai, Youssef El Allaoui, Ahmad Mathematical and Computer Modelling of Systems Team Applied Mathematics Computing and Systems Laboratory Faculty of Sciences and Techniques Errachidia Moulay Ismail University of Meknes Meknes Morocco Decisional Computing and Systems Modelling Team Engineering Sciences and Techniques Laboratory Faculty of Sciences and Techniques Errachidia Moulay Ismail University of Meknes Meknes Morocco

ISBN: (数字)9783031262548

ISBN: (纸本)9783031262531

Image registration is one of the essential elements in computer vision and several practical domains. Its objective is to recover a spatial transformation that aligns images. This is frequently formulated as an optimization problem based on an objective function that measures the quality of transformation with respect to the picture data and some prior information. This article revisits the concept of graph cuts as an effective optimization technique for image registration. We present a combination of graph cuts with superpixels segmentation via convolutional neural network (CNN), which produces a meaningful graph representation that can overcame the high computation cost. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image registration

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Femoropopliteal Artery Stent Detection in Fluoroscopy Using Otsu's Preprocessing and CNN Segmentation 4

Femoropopliteal Artery Stent Detection in Fluoroscopy Using ...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Damaševičius, Robertas Taniar, David Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania Monash University Faculty of Information Technolog MelbourneVIC3800 Australia

ISBN: (纸本)9798350387490

computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme. © 2024 IEEE.

关键词： Convolutional neural networks

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Life Cycle Evaluation of Photovoltaic Panels 31

Life Cycle Evaluation of Photovoltaic Panels

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2023 31st National Conference with International Participation, TELECOM 2023

作者： Stanchev, Plamen Vacheva, Gergana Hinov, Nikolay Klimenta, Dardan Technical University of Sofia Faculty of Computer Systems and Technologies Departement of Information Techonology in Industry Sofia Bulgaria Technical University of Sofia Faculty of Electronic Engineering and Technologies Departement of Power Electronics Sofia Bulgaria University of Priština in Kosovska Faculty of Technical Science Chair of Power Engineering Serbia

ISBN: (纸本)9798350303292

In the presented paper a life cycle evaluation of photovoltaic panels was presented. A comparative analysis was made of two types of panels with the same power, provided by one manufacturer. The main advantages and disadvantages of photovoltaic panels are presented. Through this examination, a suitable type for building a photovoltaic plant can be easily and efficiently determined. Estimating the lifetime and efficient operation of the individual modules required for construction could significantly reduce construction and maintenance costs. © 2023 IEEE.

关键词： Energy efficiency

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study 4

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Taniar, David Damaševičius, Robertas Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Monash University Faculty of Information Technology MelbourneVIC3800 Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas44249 Lithuania

ISBN: (纸本)9798350387490

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database. © 2024 IEEE.

关键词： Classification Ensemble of deep-features Healthcare Illness Monkeypox

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Constructing Varied and Attractive Shortlists from Databases: A Group Decision Approach 7th

Constructing Varied and Attractive Shortlists from Databas...

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7th Seminar on Quantitative Methods of Group Decision Making, 2021

作者： Ramsey, David M. Mariański, Aleksander Department of Computer Science and Systems Engineering Faculty of Computer Science and Telecommunications Wroclaw University of Science and Technology Wroclaw Poland Department of Applied Computer Science Faculty of Computer Science and Telecommunications Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783662665961

Thanks to the Internet, basic information can be found about a very large number of offers at very little cost. Many sites give basic information about real estate offers, including price, size, location and number of rooms. Information of this kind is sufficient to assess whether an offer is potentially attractive or not, but does not suffice in making an ultimate decision. The number of real estate offers in a city may be very large. In such a case, it can be beneficial to use an automatic procedure to first eliminate offers that do not satisfy the basic criteria of an individual or family and then construct a shortlist of varied and potentially attractive offers. This article recalls an algorithm that derives such a shortlist for an individual searcher. The novelty of this article lies in adapting this algorithm to scenarios in which a group decision is made. A practical example based on real estate offers in Warsaw is used to illustrate the algorithm. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Software engineering

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Coverless Image Steganography System Based on Maze Game Generation

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Intelligent Automation & Soft Computing 2023年第11期38卷 125-138页

作者： Al Hussien Seddik Saad Mohammed S.Reda GamalM.Behery Ahmed A.El-harby Mohammed Baz Mohamed Abouhawwash Ahmed Ismail Ebada Department of computer science Faculty of ScienceMinia UniversityShalabyMiniaEgypt Department of computer science Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaDamiettaEgypt Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityP.O.Box 11099Taif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA Department of Information Systems Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaDamiettaEgypt

The trend of digital information transformation has become a topic of *** data are threatening;thus,protecting such data from attackers is considered an essential ***,a new methodology for data concealing has been suggested by researchers called coverless *** steganography can be accomplished either by building an image database to match its image subblocks with the secret message to obtain the stego image or by generating an *** paper proposes a coverless image steganography system based on pure image generation using secret message bits with a capacity higher than the other traditional *** system uses the secret message to generate the stego image in the form of one of the Intelligence Quotient(IQ)games,the ***,a full grid is generated with several specific rows and columns determined from the number of bits of the secret ***,these bits are fed to the full grid to form the maze game stego ***,the generated maze game stego image is sent to the *** experimental results,using the Bit Error Rate(BER),were conducted,and confirmed the strength of this system represented by a high capacity,perfect performance,robustness,and stronger hiding system compared with existing coverless steganography systems.

关键词： Coverless data hiding digital image steganography intelligence quotient games maze game

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Brownian-Distribution Algorithm Optimized Efficientnet Features for Normal/Glaucoma Retinal Fundus Image Classification 4

Brownian-Distribution Algorithm Optimized Efficientnet Featu...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

ISBN: (纸本)9798350387490

The eye is a critical sensory organ in human physiology, and any abnormality in the eye will result in vision problems ranging from mild to severe. Image-guided methods are frequently employed to conduct clinical-level eye health assessments, and retinal fundus imaging (RFI) is among the most frequently employed modalities for eye analysis. This research aims to implement a Deep-Learning (DL)-based technique for classifying the Normal/Glaucoma RFI database. This scheme consist following phases, image collection and labelling, feature extraction with EfficientNet, feature optimization using a novel Brownian-Distribution Algorithm (BDA), and binary classification with 3-fold cross validation. Initially, the proposed approach performs the classification with EfficientNet-model features and documents the results. Subsequently, the BDA is employed to optimize the features in order to identify the most significant ones. Serial features-fusion is then implemented to generate a new features vector. The results of this study confirm that this approach is capable of achieving a 100 % accuracy rate when the BDA optimized feature vector is utilized for the classification task. In the future, this application may be utilized to analyze the RFI collected from hospital. © 2024 IEEE.

关键词： Ophthalmology

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