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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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Visual Cryptography and Blockchain for Protecting Against Phishing Attacks on Electronic Voting Systems 12

Visual Cryptography and Blockchain for Protecting Against Ph...

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12th International Conference and Exposition on Electrical and Power engineering, EPE 2022

作者： rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Malik, Shilpa Neagu, Bogdan Constantin raboaca, Maria Simona Verma, Chaman Sandip University School of Computer Science and Engineering Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida201302 India Central Institute of Educational Technology Delhi India 'Gheorghe Asachi' Technical University of Iasi Department of Power Engineering Iasi700050 Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

A recent poll suggests that a lot of people don't vote because they feel like they already have too much to do. This is because of many things, like the fact that everyone has to go to a polling place, wait in a long line, and be mentally and physically tired before they can vote. We have pushed for people to be able to vote online, but there are a growing number of problems and risks that could come with this. When websites are attacked by things like phishing, it might be hard to log in. With the help of Visual Cryptography (VC), We proposed Visual Cryptography and Blockchain for Protecting Against Phishing Attacks on Electronic Voting Systems we made a voting system that lets people discuss and decide on important business ideas without giving away any information. © 2022 IEEE.

关键词： Cryptography

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Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

arXiv

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arXiv 2023年

作者： Zhang, Xuan Wang, Limei Helwig, Jacob Luo, Youzhi Fu, Cong Xie, Yaochen Liu, Meng Lin, Yuchao Xu, Zhao Yan, Keqiang Adams, Keir Weiler, Maurice Li, Xiner Fu, Tianfan Wang, Yucheng Strasser, Alex Yu, Haiyang Xie, YuQing Fu, Xiang Xu, Shenglong Liu, Yi Du, Yuanqi Saxton, Alexandra Ling, Hongyi Lawrence, Hannah Stärk, Hannes Gui, Shurui Edwards, Carl Gao, Nicholas Ladera, Adriana Wu, Tailin Hofgard, Elyssa F. Tehrani, Aria Mansouri Wang, Rui Daigavane, Ameya Bohde, Montgomery Kurtin, Jerry Huang, Qian Phung, Tuong Xu, Minkai Joshi, Chaitanya K. Mathis, Simon V. Azizzadenesheli, Kamyar Fang, Ada Aspuru-Guzik, Alán Bekkers, Erik Bronstein, Michael Zitnik, Marinka Anandkumar, Anima Ermon, Stefano Liò, Pietro Yu, Rose Günnemann, Stephan Leskovec, Jure Ji, Heng Sun, Jimeng Barzilay, Regina Jaakkola, Tommi Coley, Connor W. Qian, Xiaoning Qian, Xiaofeng Smidt, Tess Ji, Shuiwang Department of Computer Science & Engineering Texas A&M University College StationTX United States Department of Chemical Engineering Massachusetts Institute of Technology Cambridge United Kingdom AMLab University of Amsterdam Amsterdam Netherlands Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical & Computer Engineering Texas A&M University College StationTX United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United Kingdom Department of Materials Science & Engineering Texas A&M University College StationTX United States Department of Physics & Astronomy Texas A&M University College StationTX United States Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY United States Department of Computer Science Stony Brook University Stony BrookNY United States Department of Computer Science Cornell University IthacaNY United States Department of Computer Science Technical University of Munich München Germany Department of Computer Science Stanford University StanfordCA United States Department of Computer Science & Engineering University of California San Diego La Jolla CA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Nvidia Santa ClaraCA United States Department of Chemistry and Chemical Biology Harvard University Cambridge United Kingdom Department of Chemistry University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Department of Computer Science University of Oxford Oxford United Kingdom Department of Biomedical Informatics Harvard University BostonMA United States Department of Computing & Mathematical Sciences California Institute of Technology PasadenaCA United States Computational Science Initiative Brookhaven National Laboratory UptonNY United States

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science;namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Pilot Study on Using Hybrid–Cascade Filtering on Brain Signals for the Control Purposes

Pilot Study on Using Hybrid–Cascade Filtering on Brain Sign...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Mariusz Pelc Dariusz Mikołajewski Edward Jacek Gorzelańczyk Anna Wieczorek Henryk racheniuk Adam Sudoł Kayhan Latifzadeh Luis A. Leiva Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland Faculty of Physical Education and Physiotherapy Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Department of Computer Science University of Luxembourg Luxembourg Luxembourg

We present an initial study conducted on fNIRS signals using Hybrid-Cascade filters for the purpose of their quality improvement. Whilst many studies focus on filtering brain signals, so that their frequency domain properties would allow e.g. widely understood diagnostics, here we focus on the study of time-domain signal characteristics, which is relevant for potential control purposes. Taking into account various kinds of artifacts, we propose a novel cascade 1D Kalman filter to handle fNIRS signals.

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Deep Learning and CNN-Based Approach for Efficient Detection of Potato Leaf Diseases

Deep Learning and CNN-Based Approach for Efficient Detection...

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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing technology (AGERS)

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Muhammad Salah-ud-Din Iqbal Waqas Iqbal Munir Ahmad Muhammad Saleem Faculty of Information Science and Technology College of Arts & Science Applied Science University Manama Bahrain & Center for Cyber Security Universiti Kebangsaan Malaysia Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain School of Computer Science Minhaj University Lahore Pakistan Department of forestry and Range Management University of Agriculture Faisalabad Pakistan College of Informatics Korea University Seoul Republic of Korea

ISBN: (数字)9798331543105

ISBN: (纸本)9798331543112

Potato is a maj or food crop worldwide, providing essential nutrients to millions. However, various diseases can severely affect potato crops, leading to reduced yield and quality. Early disease detection is crucial for effective disease management and control. This study presents a deep learning-based approach using CNN to autonomously diagnose potato leaf diseases. The model leverages the Plant Village dataset, which contains images of potato leaves in both healthy and diseased states. The proposed method demonstrates high accuracy in identifying multiple potato leaf diseases, offering promising results for application in precision agriculture. By utilizing CNN s, the model effectively detects and classifies disease symptoms, providing a reliable tool for early intervention in agricultural practices. The approach's potential impact on improving crop yield and minimizing losses due to diseases is highlighted, making it a significant contribution to smart farming techniques. The study underscores the importance of deep learning in modern agricultural practices and its role in enhancing disease management strategies.

关键词： Deep learning Precision agriculture Smart agriculture Plants (biology) Geoscience Nutrients Reliability Remote sensing Diseases Testing

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Evaluation of the Montenegrin Academic Digital Innovation Hub 27

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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27th International Conference on Information technology, IT 2023

作者： Lakovic, Luka Kara, Peter A. Ognjanovic, Ivana Bokor, Laszlo Sendelj, ramo Reich, Christoph Roganovic, Milovan Zoulias, Emmanouil Erakovic, Jevto radusinovic, Tanja rakocevic, Nada Simon, Aniko University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Faculty of Science Engineering and Computing Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro Sigma Technology Budapest1093 Hungary

ISBN: (纸本)9798350397512

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro. © 2023 IEEE.

关键词： Life cycle

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AI in Industry: Real-World Applications and Case Studies

TechRxiv

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TechRxiv 2023年

作者： rahmaniar, Wahyu Maarif, Alfian ul Haq, Qazi Mazhar Iskandar, Muchammad Edo Institute of Innovative Research Tokyo Institute of Technology Kanagawa226-8503 Japan Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55166 Indonesia International Bachelor Program in Informatics and Computer Science Yuan Ze University Taoyuan320315 Taiwan Ezy Industries Jakarta Indonesia

Artificial intelligence (AI) has advanced rapidly and is becoming a cornerstone technology that drives innovation and efficiency in various industries. This paper examines the real-world application of AI in multiple sectors, including healthcare, finance, agriculture, retail, energy, and automotive. Several case studies are described to understand better the practical applications, results, and challenges of implementing AI. While many industries have reaped enormous benefits from AI, inherent challenges include data privacy, the potential for bias, and the continuing demand for skilled labor. This comprehensive review aims to provide AI application insights to professionals and researchers. Thus, as AI grows, there may be challenges and avenues for future research. © 2023, CC BY.

关键词： Agriculture

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