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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Lee, Hyeonsoo Jeong, Won-Ki School of Electrical and Computer Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of College of Informatics Department of Computer Science and Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9783030597092

Segmentation is a fundamental process in microscopic cell image analysis. With the advent of recent advances in deep learning, more accurate and high-throughput cell segmentation has become feasible. However, most existing deep learning-based cell segmentation algorithms require fully annotated ground-truth cell labels, which are time-consuming and labor-intensive to generate. In this paper, we introduce Scribble2Label, a novel weakly-supervised cell segmentation framework that exploits only a handful of scribble annotations without full segmentation labels. The core idea is to combine pseudo-labeling and label filtering to generate reliable labels from weak supervision. For this, we leverage the consistency of predictions by iteratively averaging the predictions to improve pseudo labels. We demonstrate the performance of Scribble2Label by comparing it to several state-of-the-art cell segmentation methods with various cell image modalities, including bright-field, fluorescence, and electron microscopy. We also show that our method performs robustly across different levels of scribble details, which confirms that only a few scribble annotations are required in real-use cases. © Springer Nature Switzerland AG 2020.

关键词： Deep learning

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SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods

SKIN_ML: An Efficient Approach for Skin Cancer Detection Usi...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Mohamed S Farag Ibrahim A Gomaa Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier.

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Pending Interest Table (PIT) Performance Analysis in Named Data Networking on Palapa Ring Topology

Pending Interest Table (PIT) Performance Analysis in Named D...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Adi Sucipto Jupriyadi Syaiful Ahdan Hasan Nur Arifin Eki Ahmad Zaki Hamidi Nana rachmana Syambas School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Department of Electrical Engineering UIN Sunan Gunung Djati Bandung Indonesia

The concept of the internet in the future will prioritize content, by reducing delays in data transmission. Named Data Networking (NDN) is a content-based future internet concept that changes the paradigm of using IP. Inside the NDN router, there are three data structures, namely Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB). Pending Interest Table (PIT) contains a list of unfulfilled interests. This condition occurs when the node has not received a response after the interest forwarding process. Measurable and fast PIT performance is a challenge in Named Data Networks. In this study, we will try to do a simulation to measure and analyze the performance of PIT in NDN in the Palapa Ring topology. The research was conducted using the NDNSim simulator, to see the performance in the PIT. The simulation and analysis of the results show that the granularity of a prefix has an effect on In Satisfied Interest in an NDN network. At the number of interests of 100, the result obtained from the simulation is that there is a decrease in the percentage of interest data served, amounting to more than 20%. At the amount of interest in 1000 about more than 30%. The length of the prefix and the number of interest sent by the consumer affect the performance of the PIT, seen from the number of In Satisfied Interests.

关键词： Analytical models Network topology Simulation Data models Topology Performance analysis Delays

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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Computing (ICOCO), IEEE International Conference on

作者： Mahayaudin M. Mansor Nurain Ibrahim Noor Fadhilah Ahmad radi Nadirah Abdul rahim Syarul Heiry Yahaya Mohd Aftar Abu Bakar Roslinazairimah Zakaria School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA (Penang Campus) Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability.

关键词： Visualization Technological innovation Ethics Emotion recognition Bibliometrics Decision making Focusing Electronic healthcare Object recognition Artificial intelligence

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UAV image acquisition and processing for high-throughput phenotyping in agricultural research and breeding programs

Plant Phenome Journal

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Plant Phenome Journal 2024年第1期7卷

作者： Bongomin, Ocident Lamo, Jimmy Guina, Joshua Mugeziaubwa Okello, Collins Ocen, Gilbert Gilibrays Obura, Morish Alibu, Simon Owino, Cynthia Awuor Akwero, Agnes Ojok, Samson National Crops Resources Research Institute (NaCRRI) Kampala Uganda Department of Manufacturing Industrial and Textile Engineering School of Engineering Moi University Eldoret Kenya Department of Information and Communication Technology National Agricultural Research Organisation (NARO) Secretariat Entebbe Uganda Department of Biosystems Engineering Faculty of Agriculture and Environment Gulu University Gulu Uganda Department of Computer Engineering & Informatics Faculty of Engineering Busitema University Tororo Uganda Department of Electrical and Computer Engineering School of Engineering Makerere University Kampala Uganda School of Agricultural Sciences Makerere University Kampala Uganda

We are in a race against time to combat climate change and increase food production by 70% to feed the ever-growing world population, which is expected to double by 2050. Agricultural research plays a vital role in improving crops and livestock through breeding programs and good agricultural practices, enabling sustainable agriculture and food systems. While advanced molecular breeding technologies have been widely adopted, phenotyping as an essential aspect of agricultural research and breeding programs has seen little development in most African institutions and remains a traditional method. However, the concept of high-throughput phenotyping (HTP) has been gaining momentum, particularly in the context of unmanned aerial vehicle (UAV)-based phenotyping. Although research into UAV-based phenotyping is still limited, this paper aimed to provide a comprehensive overview and understanding of the use of UAV platforms and image analytics for HTP in agricultural research and to identify the key challenges and opportunities in this area. The paper discusses field phenotyping concepts, UAV classification and specifications, use cases of UAV-based phenotyping, UAV imaging systems for phenotyping, and image processing and analytics methods. However, more research is required to optimize UAVs’ performance for image data acquisition, as limited studies have focused on the effect of UAVs’ operational parameters on data acquisition. © 2024 The Authors. The Plant Phenome Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of America.

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A New Multimodal Medical Image Fusion based on Laplacian Autoencoder with Channel Attention

arXiv

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arXiv 2023年

作者： Wankhede, Payal Das, Manisha Gupta, Deep radeva, Petia Bakde, Ashwini M. The Department of Electronics and Comm. Engineering Visvesvaraya National Institute of Technology Nagpur40010 India The Department de Mathematics and Informatics Universitat de Barcelona Barcelona08007 Spain The Computer Vision Center Cerdanyola08193 Spain The Department of Radio-Diagnosis All India Institute of Medical Sciences Nagpur441108 India

Medical image fusion combines the complementary information of multimodal medical images to assist medical professionals in the clinical diagnosis of patients’ disorders and provide guidance during preoperative and intra-operative procedures. Deep learning (DL) models have proved to achieve end-to-end image fusion with high robust and accurate fusion performance. However, most of the DL-based fusion models perform down-sampling on the input images to minimize the number of learnable parameters and computations. During this process, salient features of the source images become irretrievable leading to the loss of crucial diagnostic edge details and contrast of various brain tissues. In this paper, we propose a new multimodal medical image fusion model is proposed that is based on integrated Laplacian-Gaussian concatenation with attention pooling (LGCA). We prove that our model preserves effectively complementary information and important tissue structures. Extensive experimental results demonstrate a notable improvement in the fusion performance of the proposed method compared to the recently developed state-of-the-art fusion approaches on 4 different image modalities using 6 different statistics metrics. © 2023, CC BY.

关键词： Image fusion

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Riemannian manifold-based geometric clustering of continuous glucose monitoring to improve personalized diabetes management

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computers in Biology and Medicine 2024年 183卷 109255-109255页

作者： Song, Jiafeng McNeany, Jocelyn Wang, Yifei Daley, Tanicia Stecenko, Arlene Kamaleswaran, Rishikesan Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Biomedical Informatics Emory University AtlantaGA30322 United States Department of Biomedical Engineering Duke University DurhamNC27708 United States Department of Pediatrics Emory University AtlantaGA30322 United States Department of Biological Sciences Georgia Institute of Technology AtlantaGA30322 United States Department of Surgery Duke University School of Medicine DurhamNC27708 United States Department of Anesthesiology Duke University DurhamNC27708 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States

Background: Continuous Glucose Monitoring (CGM) provides a detailed representation of glucose fluctuations in individuals, offering a rich dataset for understanding glycemic control in diabetes management. This study explores the potential of Riemannian manifold-based geometric clustering to analyze and interpret CGM data for individuals with Type 1 Diabetes (T1D) and healthy controls (HC), aiming to enhance diabetes management and treatment personalization. Methods: We utilized CGM data from publicly accessible datasets, covering both T1D individuals on insulin and HC. Data were segmented into daily intervals, from which 27 distinct glycemic features were extracted. Uniform Manifold Approximation and Projection (UMAP) was then applied to reduce dimensionality and visualize the data, with model performance validated through correlation analysis between Silhouette Score (SS) against HC cluster and HbA1c levels. Results: UMAP effectively distinguished between T1D on daily insulin and HC groups, with data points clustering according to glycemic profiles. Moderate inverse correlations were observed between SS against HC cluster and HbA1c levels, supporting the clinical relevance of the UMAP-derived metric. Conclusions: This study demonstrates the utility of UMAP in enhancing the analysis of CGM data for diabetes management. We revealed distinct clustering of glycemic profiles between healthy individuals and diabetics on daily insulin indicating that in most instances insulin does not restore a normal glycemic phenotype. In addition, the SS quantifies day by day the degree of this continued dysglycemia and therefore potentially offers a novel approach for personalized diabetes care. © 2024 Elsevier Ltd

关键词： Continuous glucose monitoring (CGM) Type 1 diabetes (T1D) Uniform manifold approximation and projection (UMAP) Glycemic control Personalized diabetes management

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Ibrahim Abd Elatif Gomaa Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%.

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Anticipating for Smart Healthcare System by Deep Ensemble Model Using Multi-Layer Perceptron in Early Human Facial Emotion Recognition

SSRN

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SSRN 2024年

作者： Riaz, Waqar Gong, Tao Ji, Jiancheng Zaman, Khalid Ullah, Inam Alwabli, Abdullah Almutairi, Sulaiman Sulmi Institute of Intelligent Manufacturing Shenzhen Polytechnic University 4089 Shahe West Road Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Computer Engineering Gachon University Seongnam13120 Korea Republic of Department of Electrical Engineering College of Engineering and Computing in Al-Qunfudhah Umm al-Qura University Mecca Saudi Arabia Department of Health Informatics College of Public Health and Health Informatics Qassim University Qassim51452 Saudi Arabia

In the advancement of Artificial Intelligence, this study anticipates towards smart healthcare systems by pioneering anticipation in humans (being a patient) emotions classification. Followed by the investigation delves into the challenge of accurately classifying human emotions, a critical factor in understanding patients' recent mood and situation (if critical). Integrating Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Multi-Layer Perceptron (MLP) for comprehensive facial emotion analysis. Initiated with Faster Region-Based Convolutional Neural Network (R-CNN) for face detection, the methodology extract features from three CNN models and employ advanced feature fusion techniques to combine them. These amalgamated features are then used to train an ensemble classification model. The process commences with the deployment of a Faster Region-Based Convolutional Neural Network (Faster R-CNN) for the swift and accurate identification of human’s face in real-time and recorded video feeds, including advanced feature extraction across three CNN models and innovative fusion techniques by strengthened the InceptionV3 for superior accuracy, replacing improved Faster R-CNN feature learning module. This replacement aims to enhance the accuracy and efficiency of face detection in our proposed framework. Validation on EMOTIC, CK+, FER-2013, and AffectNet confirms notable accuracy results of 98.01%, 99.53%, 99.27% and 96.81%, respectively with accuracy rates highlighting its potential to revolutionize medical environment and measures in smart healthcare systems. It is important to note that these datasets were carefully acquired in a simulated environment. © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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Electrical and computer engineering Researches (ICECER), International Conference on

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Munir Ahmad Tahir Abbas Karamath Ateeq College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul Republic of Korea Department of Computer Science TIMES Institute Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah UAE

ISBN: (数字)9798331539733

ISBN: (纸本)9798331539740

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems.

关键词： Renewable energy sources Accuracy Load forecasting Machine learning Predictive models Power system stability Data models Forecasting Sustainable development Load modeling

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