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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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Heart Failure: Machine Learning Prediction Within a 5-Year Framework

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Konstantina-Helen Tsarapatsani Vasilios D. Tsakanikas Antonis Sakellarios Hans J. Trampisch Efterpi Karapintzou Henrik Rudolf George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greeece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina and Biomedical Research Institute-FORTH Ioannina Greece

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction.

关键词： Accuracy Sensitivity Explainable AI Biological system modeling Peptides Receivers Predictive models Indexes Cardiovascular diseases Random forests

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Enhancing Sentiment Classification in Twitter Data Through Context-Driven Text Processing and Tweet Embeddings

Enhancing Sentiment Classification in Twitter Data Through C...

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IEEE Region 10 Humanitarian technology Conference (R10-HTC)

作者： Vassilis C. Gerogiannis Andreas Kanavos Nikos Antonopoulos Amrita Bhola Biswaranjan Acharya Department of Digital Systems University of Thessaly Larissa Greece Department of Informatics Ionian University Corfu Greece Department of Digital Media and Communication Ionian University Argostoli Greece Indira Gandhi Institute of Technology Sarang Odisha India Department of Computer Engineering- AI & BDA Marwadi University Rajkot India

Sentiment analysis and text classification tasks heav-ily rely on text processing techniques. However, existing approaches often neglect domain-specific factors and rely on generic routines and pre-built dictionaries. In this paper, we investigate the impact of text processing steps on sentiment classification using Twitter data. Our approach introduces skip gram-based word embeddings that effectively capture Twitter-specific fea-tures, such as informal language and emojis. Through rigorous experimentation, we identify the detrimental consequences of conventional text processing steps like stop word removal and simple averaging of term vectors for tweet representation. To optimize sentiment classification, we propose new effective steps, including the inclusion of emoji characters, measuring word importance from embeddings, aggregating term vectors into tweet embeddings, and creating a linearly separable feature space. Our results demonstrate the superiority of context-driven word embeddings in selecting important words for tweet clas-sification, outperforming pre-built word dictionaries. Moreover, the proposed tweet embedding reduces reliance on multiple text processing steps, resulting in more accurate sentiment analysis on Twitter data.

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PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction

PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutra...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhang Wan Zhuoyi Lin Shamima Rashid Shaun Yue-Hao Ng Rui Yin J. Senthilnath Chee-Keong Kwoh School of Computer Science and Engineering Nanyang Technological University Singapore Institute for Infocomm Research Agency for Science Technology and Research (A*STAR) Singapore School of Chemical and Biomolecular Engineering Nanyang Technological University Singapore Department of Health Outcome and Biomedical Informatics University of Florida Gainesville FL USA

Prediction of neutralization antibodies is important for the development of effective vaccines and antibody-based therapeutics. Traditional methods rely on features based on first principles derived from the binding interface. However, they are burdened by arduous data preprocessing from a limited quantity of protein structures. In comparison, deep learning allows automatic substructure characterization and representation without hand-crafted feature engineering. In particular, large language models (LLMs) based method predicts neutralization using Fv sequences of antibody and antigen. Despite LLM’s success, incorporating full-length Fv sequences suffers from: 1) inaccurate sequence-level labels in existing datasets, 2) inefficient modeling due to noisy non-contributing motifs, and 3) ignorance of non-bonded interactions that play a key role in facilitating epitope-paratope pairing. In this paper, we propose a novel approach that incorporates only the paratope and epitope for antibody-antigen neutralization prediction while adopting a novel set modeling that regards the paratope and epitope as bags of residues. Specifically, we hand-crafted a dataset containing neutralizing paratope-epitope pairs where epitopes are potentially generalizable to future unseen variants of SARS-CoV-2. Training on such a dataset enables deep learning models to predict neutralizing antibodies for prospective mutated variants of SARS-CoV-2, meanwhile addressing the problem of inaccurate sequence-level labels. A higher modeling efficiency is also achieved by disregarding non-contributing motifs. Furthermore, we also propose paratope-epitope set interaction (PESI), a set modeling model inspired by first principles that learns intra-inter non-covalent interactions through a global attention mechanism. To validate PESI, we perform a 10-fold cross-validation on our dataset. Experimental results show that PESI achieves a more balanced overall performance and a significant improvement on MCC as c

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The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification

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Healthcare Analytics 2023年 4卷 100247-100247页

作者： T R, Mahesh V, Vinoth Kumar V, Dhilip Kumar Geman, Oana Margala, Martin Guduri, Manisha Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore 562112 India School of Computer Science Engineering & Information Systems (SCORE) Vellore Institute of Technology (VIT) 632014 India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai 600062 India Department of Computers Electronics and Automation Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava 720229 Romania School of Computing and Informatics University of Louisiana at Lafayette United States

Breast cancer is one of the most common causes of death among women, and early diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used machine-learning breast cancer prediction and diagnosis methods. We use synthetic minority over-sampling to handle imbalanced data in the breast cancer diagnosis dataset obtained from the Wisconsin Machine Learning Repository. We use a variety of machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Classification and Regression Tree (CART), Naive Bayes (NB), and well-known ensembles methods like Majority-Voting, eXtreme Gradient Boosting algorithm (XGBoost), and Random Forest (RF) for the breast cancer classification. The findings show that the Majority-Voting ensemble method, built on the top three classifiers (LR, SVM, and CART), outperforms all other individual classifiers and offers the highest accuracy of 99.3%. © 2023 The Author(s)

关键词： Breast cancer Class-balancing Cross-validation Diagnostic analytics Ensemble method Machine learning

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