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arXiv 2024年

作者： Ünsal, Serbulent Özdemir, Sinem Kasap, Bünyamin Kalaycı, M. Erşan Turhan, Kemal Doğan, Tunca Acar, Aybar C. Department of Biostatistics and Medical Informatics Faculty of Medicine Graduate School of Health Sciences Karadeniz Technical University Trabzon Turkey Graduate School of Informatics Middle East Technical University Ankara Turkey Health Sciences University Trabzon Kanuni Training and Research Hospital Medical Microbiology Laboratory Trabzon Turkey Biological Data Science Lab Dept. of Computer Engineering Department of Computer Engineering Hacettepe University Ankara Turkey Dept. of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey

In this study, we propose HOPER (HOlistic ProtEin Representation), a novel multimodal learning framework designed to enhance protein function prediction (PFP) in low-data settings. The challenge of predicting protein functions is compounded by the limited availability of labeled data. Traditional machine learning models already struggle in such cases, and while deep learning models excel with abundant data, they also face difficulties when data is scarce. HOPER addresses this issue by integrating three distinct modalities—protein sequences, biomedical text, and protein-protein interaction (PPI) networks—to create a comprehensive protein representation. The model utilizes autoencoders to generate holistic embeddings, which are then employed for PFP tasks using transfer learning. HOPER outperforms existing methods on a benchmark dataset across all Gene Ontology categories, i.e., molecular function, biological process, and cellular component. Additionally, we demonstrate its practical utility by identifying new immune-escape proteins in lung adenocarcinoma, offering insights into potential therapeutic targets. Our results highlight the effectiveness of multimodal representation learning for overcoming data limitations in biological research, potentially enabling more accurate and scalable protein function prediction. HOPER source code and datasets are available at https://***/kansil/HOPER © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification

arXiv

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arXiv 2025年

作者： Gao, Yibo Zhou, Hangqi Gao, Zheyao Wang, Bomin Gao, Shangqi Wang, Sihan Zhuang, Xiahai School of Data Science Fudan University China Dept. of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Dept. of Oncology University of Cambridge United Kingdom

The pursuit of decision safety in clinical applications highlights the potential of concept-based methods in medical imaging. While these models offer active interpretability, they often suffer from concept leakages, where unintended information within soft concept representations undermines both interpretability and generalizability. Moreover, most concept-based models focus solely on local explanations (instance-level), neglecting the global decision logic (dataset-level). To address these limitations, we propose Concept Rule Learner (CRL), a novel framework to learn Boolean logical rules from binarized visual concepts. CRL employs logical layers to capture concept correlations and extract clinically meaningful rules, thereby providing both local and global interpretability. Experiments on two medical image classification tasks show that CRL achieves competitive performance with existing methods while significantly improving generalizability to out-of-distribution data. The code of our work is available at https://***/obiyoag/crl. © 2025, CC BY.

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Urban Vertical Farming with IoT and Precision Monitoring for a Sustainable Future

Urban Vertical Farming with IoT and Precision Monitoring for...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Unni Krishnn A Amit Dutt Jay Singh Munugapati Bhavana Swathi Baswaraju Baydaa Fathil Mohammed Akila Venkatraman Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh Lovely Professional University Phagwara India Dept. of EEE GL Bajaj Institute of Technology and Management Gr. Noida U.P. India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Computer Science-Data Science New Horizon College of Engineering Bangalore India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Information Technology GRIET Bachupally Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The fast expansion of population worldwide and urbanisation had been estimated to have 80 % of the population in urban areas in the upcoming 50 years leads to an exponential rise in demand of food. These needs provide an urgent call for developing novel approaches to achieve global food demands amid sustainability problems. Urban Vertical Farming (UVF) offers an attractive solution for growing crops in the vertical dimension of structures, the urban skyscrapers, where land use is limited. However, it is fairly limited research which applies the current state of art in cutting edge technologies such as the Internet of Things, to try to automate and monitor farm processing. Thus, in this review paper, gaps in existing research, the potential for IoT enabled vertical farming, as well as challenges such as energy use, economic feasibility and urban policy integration are examined. Vertical farming can be enhanced through resource efficiency, scalability and level of sustainability through the use of IoT systems and AI driven data analytics which will also help promote urban food security. The research gives a summary of the current state, technological capabilities, and potential way forward in the process of successful vertical farming implementation in urban sets.

关键词： Temperature sensors Food manufacturing Urban areas Urban policy Food security Sustainable development Artificial intelligence Monitoring Water resources Farming

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PREFACE: TECHNICAL COMMISSION II

International Archives of the Photogrammetry, Remote Sensing...

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International Archives of the Photogrammetry, Remote Sensing and Spatial Information sciences - ISPRS Archives 2022年第B2-2022期43卷 7-7页

作者： Yilmaz, A. Wegner, J.D. Qin, R. Remondino, F. Fuse, T. Toschi, I. Dept. of Civil Environmental and Geodetic Engineering The Ohio State University ColumbusOH United States EcoVision Lab Photogrammetry and Remote Sensing Eth Zurich Zurich8093 Switzerland Institute for Computational Science University of Zurich Zurich8057 Switzerland Trento Italy Dept. of Civil Engineering University of Tokyo Hongo 7-3-1 Bunkyo-ku Tokyo 113-8656 Japan

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Enhancing IoT Ecosystems through Effective Master data Management

Enhancing IoT Ecosystems through Effective Master Data Manag...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Ronak Ravjibhai Pansara Balaram Yadav Kasula Piyush Kumar Gupta Tabrej Ahamad Khan Naved Alam Pawan Whig Master Data Specialist Tesla USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of computer science and engineering Jamia hamdard New Delhi India Research Scholar Jamia Millia Islamia New Delhi India Whig Research Scientist Threws VIPS-TC New Delhi India

This study explores the quantitative impact of Master data Management (MDM) implementation on Internet of Things (IoT) ecosystems. Employing a comparative analysis methodology, pre-and post-MDM integration metrics were assessed within the IoT environment. Results showcased a notable enhancement in data accuracy by 20%, attributed to minimized errors and improved consistency following MDM adoption. Operational efficiency exhibited a significant 30% reduction in data integration time across diverse IoT platforms, contributing to streamlined processes. Financially, operational costs decreased by 15%, primarily due to reduced data cleansing efforts post-MDM implementation. Decision-making processes benefited from a 25% faster response time to real-time analytics derived from IoT data insights. Additionally, the IoT ecosystem’s scalability increased by 40%, indicating amplified adaptability to accommodate higher data volumes without compromising performance. These quantitative findings highlight the substantial impact of MDM on fortifying the reliability, efficiency, and scalability of IoT ecosystems, providing empirical evidence for the transformative potential of MDM in optimizing IoT applications.

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MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects

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Computer Methods and Programs in Biomedicine Update 2025年 7卷

作者： Lofù, Domenico Sorino, Paolo Colafiglio, Tommaso Bonfiglio, Caterina Donghia, Rossella Giannelli, Gianluigi Lombardi, Angela Di Noia, Tommaso Di Sciascio, Eugenio Narducci, Fedelucio Dept. of Electrical and Information Engineering (DEI) Politecnico di Bari BA Bari 70125 Italy Dept. of Computer Automatic and Management Engineering (DIAG) Sapienza Università di Roma RM Roma 00185 Italy Unit of Data Science National Institute of Gastroenterology ”Saverio de Bellis” IRCCS Hospital BA Castellana Grotte 70013 Italy Scientific Direction National Institute of Gastroenterology ”Saverio de Bellis” IRCCS Hospital BA Castellana Grotte 70013 Italy

Metabolic dysfunction-associated fatty liver disease (MAFLD) introduces new diagnostic criteria for fatty liver disease that are independent of alcohol consumption and viral hepatitis infection. Therefore, investigating how biochemical and anthropometric factors influence mortality in MAFLD subjects is of significant interest. In this work, we propose MORIX, an Artificial Intelligence-based framework capable of predicting fatal mortality outcomes in subjects with MAFLD. MORIX utilizes data from epidemiological datasets containing carefully selected anthropometric and biochemical information. This selection is achieved through Recursive Feature Elimination (RFE) using a Random Forest (RF) to train Machine Learning (ML) algorithms and provide a mortality risk (Yes/No) output. To provide physicians with a valuable tool, MORIX was trained and tested on a dataset of MAFLD subjects, comparing five different models: Random Forest (RF), eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Light Gradient Boosting Model (LGBM) in a 5-fold cross-validation training strategy. Experimental results identified the RF as the best model, achieving a high accuracy for both mortality risks predicted. Additionally, an eXplainable Artificial Intelligence (XAI) analysis was conducted to clarify the diagnostic logic of the RF model and to assess the impact of each feature to the prediction. Moreover, a web application was developed to predict mortality risk and provide explanations of how the input features influenced the final prediction. In conclusion, the MORIX framework is easy to apply, and the required parameters are readily available in healthcare datasets, making it a practical tool for medical professionals. © 2025 The Authors

关键词： Epidemiology Interpretability Machine learning techniques MAFLD Mortality

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Applications of Transformer Attention Mechanisms in Information Security: Current Trends and Prospects

Applications of Transformer Attention Mechanisms in Informat...

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2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs)

作者： M. Vubangsi Sarumi Usman Abidemi Olukayode Akanni Auwalu Saleh Mubarak Fadi Al-Turjman Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Mersin Turkey Department of Computer Science HTTTC Bambili Computational materials Science lab University of Bamenda NWR Cameroon Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin Turkey

In this work, we present a comprehensive survey on applications of the most recent transformer architecture based on attention in information security. Our review reveals three primary areas of application: Intrusion detection, Anomaly Detection and Malware Detection. We have presented an overview of attention-based mechanisms and their application in each cybersecurity use case, and discussed open grounds for future trends in Artificial Intelligence enabled information security.

关键词： Neural networks Intrusion detection Transformers Market research Real-time systems Malware Internet of Things

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Summarization of Investment Reports Using Pre-trained Model

Summarization of Investment Reports Using Pre-trained Model

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Hiroki Sakaji Ryotaro Kobayashi Kiyoshi Izumi Hiroyuki Mitsugi Wataru Kuramoto School of Engineering The University of Tokyo Tokyo Japan Data-driven Science Dept. Frontier Technologies Research Center. Daiwa Institute of Research Ltd. Tokyo Japan Fund Management Division Daiwa Asset Management Co.Ltd. Tokyo Japan

In this paper, we attempt to summarize monthly reports as investment reports. Fund managers have a wide range of tasks, one of which is the preparation of investment reports. In addition to preparing monthly reports on fund management, fund managers prepare management reports that summarize these monthly reports every six months or once a year. The preparation of fund reports is a labor-intensive and time-consuming task. Therefore, in this paper, we tackle investment summarization from monthly reports using transformer-based models. There are two main types of summarization methods: extractive summarization and abstractive summarization, and this study constructs both methods and examines which is more useful in summarizing investment reports.

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Malicious Traffic Classification in WSN using Deep Learning Approaches

Malicious Traffic Classification in WSN using Deep Learning ...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Chhote Lal Prasad Gupta Dinesh Rajassekharan Dilip Kumar Sharma Mohanraj Elangovan Varatharaj Myilsamy Kamal Upreti Dept. of Computer Science and Engineering Bansal Institute of Engineering and Technology Lucknow India Faculty of Computer Science Peninsula College Peninsula Malaysia Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India Department of Artificial Intelligence and Data Science K. S. Rangasamy College of Technology Tiruchengode Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science CHRIST(Deemed to be University) Delhi-NCR Ghaziabad India

Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data.

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The doctor will polygraph you now: ethical concerns with AI for fact-checking patients

arXiv

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arXiv 2024年

作者： Anibal, James T. Gunkel, Jasmine Huth, Hannah Nguyen, Hang Awan, Shaheen Bensoussan, Yael Wood, Bradford J. BethesdaMD United States Computational Health Informatics Lab Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford United Kingdom BethesdaMD United States Ho Chi Minh City Viet Nam Dept. of Communication Sciences & Disorders University of Central Florida United States Morsani College of Medicine University of South Florida Tampa Bay FL United States

Clinical artificial intelligence (AI) methods have been proposed for predicting social behaviors which could be reasonably understood from patient-reported data. This raises ethical concerns about respect, privacy, and patient awareness/control over how their health data is used. Ethical concerns surrounding clinical AI systems for social behavior verification were divided into three main categories: (1) the use of patient data retrospectively without informed consent for the specific task of verification, (2) the potential for inaccuracies or biases within such systems, and (3) the impact on trust in patient-provider relationships with the introduction of automated AI systems for "fact-checking". Additionally, this report showed the simulated misuse of a verification system and identified a potential LLM bias against patient-reported information in favor of multimodal data, published literature, and the outputs of other AI methods (i.e., "AI selftrust"). Finally, recommendations were presented for mitigating the risk that AI verification systems will cause harm to patients or undermine the purpose of the healthcare system. © 2024, CC BY.

关键词： Electronic health record

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