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Predicting Synthetic Lethality in Human Cancers via Knowledge Graph Summarization 23

Predicting Synthetic Lethality in Human Cancers via Knowledg...

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3rd international conference on Bioinformatics and Intelligent Computing, BIC 2023

作者： Wu, Yunfei School of Artificial Intelligence and Computer Jiangnan University Wuxi China

ISBN: (纸本)9798400700200

Synthetic lethality (SL) is of great significance for the development of anticancer drugs. Thanks to the increasing richness of biomedical knowledge graph (KG), machine learning algorithm combined with knowledge graph has been used for SL prediction and achieved good performance. However, how to effectively utilize biomedical KG to learn stronger gene feature representation is an open problem. Therefore, a self-attention based graph summarization scheme is introduced to delete unimportant nodes and edges in the graph. Firstly, the knowledge graph is used to construct the subgraph of the target gene node, and then the self-attention mechanism is used to calculate the edge signal intensities score in the subgraph, so as to remove the entities that are not important to the target gene in the subgraph. Finally, latent features of gene node are learned by graph neural network for SL prediction. The experimental findings demonstrate that our method performs better than the best baseline method, demonstrating the viability of our method. © 2023 ACM.

关键词： Knowledge graph

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Towards Exploring Adversarial Learning for Anomaly Detection in Complex Driving Scenes 1

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4th international conference on Deep Learning Theory and Applications (DeLTA)

作者： Habib, Nour Cho, Yunsu Buragohain, Abhishek Rausch, Andreas Tech Univ Clausthal Inst Software & Syst Engn Arnold Sommerfeld Str 1 D-38678 Clausthal Zellerfeld Germany

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

One of the many Autonomous Systems (ASs), such as autonomous driving cars, performs various safety-critical functions. Many of these autonomous systems take advantage of Artificial intelligence (AI) techniques to perceive their environment. But these perceiving components could not be formally verified, since, the accuracy of such AI-based components has a high dependency on the quality of training data. So machine learning (ML) based anomaly detection, a technique to identify data that does not belong to the training data could be used as a safety measuring indicator during the development and operational time of such AI-based components. Adversarial learning, a sub-field of machine learning has proven its ability to detect anomalies in images and videos with impressive results on simple data sets. Therefore, in this work, we investigate and provide insight into the performance of such techniques on a highly complex driving scenes dataset called Berkeley DeepDrive.

关键词： Adversarial learning Artificial intelligence Anomaly detection Berkeley DeepDrive (BDD)

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Anonymizing Dysarthric Speech: Investigating the Effects of Voice Conversion on Pathological Information Preservation 27th

Anonymizing Dysarthric Speech: Investigating the Effects of ...

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27th international conference on Text, Speech, and Dialogue (TSD)

作者： Hernandez, Abner Perez-Toro, Paula Andrea Arias-Vergara, Tomas Vasquez-Correa, Juan Camilo Yang, Seung Hee Orozco-Arroyave, Juan Rafael Maier, Andreas Friedrich Alexander Univ Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany Univ Antioquia UdeA Fac Engn GITA Lab Medellin Colombia Basque Res & Technol Alliance BRTA Fdn Vicomtech Donostia San Sebastian Spain Friedrich Alexander Univ Erlangen Nurnberg Speech & Language Proc Lab Erlangen Germany

ISBN: (纸本)9783031705656;9783031705663

Acquiring speech data is a crucial step in the development of speech recognition systems and related speech-based machine learning models. However, protecting privacy is an increasing concern that must be addressed. This study investigates voice conversion (VC) as a strategy for anonymizing the speech of individuals with dysarthria. We specifically focus on training a variety of VC models using self-supervised speech representations, such as Wav2Vec and its multi-lingual variant, Wav2Vec2.0 (XLSR). The converted voices maintain a word error rate that is within 1% with respect to the original recordings. The Equal Error Rate (EER) showed a significant increase, from 1.52% to 41.18% on the LibriSpeech test set, and from 3.75% to 42.19% on speakers from the VCTK corpus, indicating a substantial decrease in speaker verification performance. A similar trend is observed with dysarthric speech, where the EER varied from 16.45% to 43.46%. Additionally, our study includes classification experiments on dysarthric vs. healthy speech data to demonstrate that anonymized voices can still yield speech features essential for distinguishing between healthy and pathological speech. The impact of voice conversion is investigated by covering aspects such as articulation, prosody, phonation, and phonology.

关键词： Voice Anonymization Voice Conversion Speech Representation Dysarthria Medical Data

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Joint Unsupervised Deep Temporal Clustering for Modeling Human Behavior in Vestibular Dysfunction: A Study of Navigation pattern 1

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3rd international conference on Digital Technologies and Applications

作者： Annaki, Lhabab Abdelbasset Rahmoune, Mohammed Bourhaleb, Mohammed Rahmoun, Noureddine Zaoui, Mohamed Castilla, Alexander Berthoz, Alain Cohen, Bernard Univ Mohammed Premier Natl Sch Appl Sci Res Lab Appl Sci PB 669 Oujda 60000 Morocco CIRB Coll France 11 Pl Marcelin Berthelot F-75231 Paris France Sorbonne Univ Fac Med Bd Hop F-75013 Paris France

ISBN: (数字)9783031298608

ISBN: (纸本)9783031298592;9783031298608

Human behavior modeling aims to extract and understand patterns of behavior in one's daily life. A seemingly intuitive walk to a destination following a specific path raises questions about the existence of a model that can explain the choices and strategies predetermined by the brain through physical behavior. Tasks such as finding objects, achieving goals, avoiding obstacles, and returning home can be challenging when one lacks navigational abilities. In our previous work. We employed machine learning (ML) approaches to evaluate and classify cognitively challenged individuals to identify crucial markers that could be used to simulate human navigation behavior. However, the challenge lies in the possibility of missing key elements. Our proposed method utilizes deep time series clustering architectures, in which we employ auto-encoders to extract features and then cluster our data using unsupervised machine learning techniques. This approach will provide us with initial insights into human navigation patterns and meaningful clustering. Our developed framework combines unsupervised learning with a dataset obtained from a neuropsychological evaluation in a virtual reality setting called "The VR Magic Carpet", a variant of "the Corsi block-tapping task".

关键词： Artificial intelligence Deep-Learning Deep time-series clustering Virtual Reality Neuropsychological assessments Cognitive Impairments

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Anti-pattern Based IoRT-Aware Business Process Structure Verification Approach 3rd

Anti-pattern Based IoRT-Aware Business Process Structure Ver...

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3rd international conference on Language Processing and Knowledge Management-LPKM

作者： Fattouch, Najla Ben Lahmar, Imen Boukadi, Khouloud Sfax Univ MIRACL Lab FSEG Sfax Sfax Tunisia Sfax Univ ReDCAD Lab ISIM Sfax Sfax Tunisia

ISBN: (纸本)9783031850660;9783031850677

The IoRT-aware Business Process is a new business type. It embedded IoT and robot technologies to automate classic BP. To effectively execute an IoRT-aware BP, it is significant to ensure that is accurately modeled. Referring to the literature, various modeling languages can be used to model an IoRT-aware BP. The Business Process Model and Notation (BPMN) is among the most attractive and descriptive languages. However, the latter lacks a formal aspect, potentially leading to inconsistencies and ambiguities. Therefore, to effectively check an IoRT-aware BP modeled using BPMN, it seems relevant to cope with a consistent formalism (e.g., Petri Net (PN), Discrete Event System Specification (DEVS), etc.), simplifying the process structure verification. From this perspective, we propose an approach that deals with DEVS formalism to check IoRT-aware BP structure (e.g., deadlock, loop, dead task, etc.). To evaluate the proposed approach, we consider a case study of IoRT-aware BP in agriculture by checking its process model structure. The findings show the effectiveness of our approach in capturing structure errors.

关键词： Structure Verification Anti-pattern DEVS IoRT-aware BP

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Enhancing Endometrial Tumor Detection: Early Diagnosis with Advanced Vision Transformer Architecture 1

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3rd international conference on Artificial intelligence and Knowledge Processing (AIKP)

作者： Tejavath, Abhinaya Swarnkar, Bhawna Khare, Nilay Maulana Azad Natl Inst Technol MANIT Ctr Artificial Intelligence Bhopal India Maulana Azad Natl Inst Technol MANIT Dept Comp Sci & Engn Bhopal India

ISBN: (数字)9783031686177

ISBN: (纸本)9783031686160;9783031686177

Introduction: Early detection and treatment are key to improving the prognosis of endometrial cancer. However, conventional machine learning approaches have limited capacity to simulate the complex links between histopathological images and their interpretations, making it challenging to achieve accurate results. A vision transformer-based image classification model has been proposed to assist medical professionals in detecting endometrial cancer and improving patient outcomes. Objective: This study aims to develop and evaluate a vision transformer-based model for accurately detecting histopathology images of endometrium, and compare its performance against existing fine-tuning methods such as MobilenetV2, Xception, and VGG16. Methods: A publicly accessible histopathology imaging dataset of endometrium was used to train and validate the proposed model. The performance of the model was evaluated against state-of-the-art approaches in the field. Results: The validation results showed that the proposed model attained an accuracy of 99.36%, surpassing the performance of existing fine-tuning methods and achieving the state-of-the-art performance in the widely used endometrial cancer benchmark dataset. These findings highlight the potential of vision transformer-based models in accurately detecting histopathology images of endometrium, which could lead to better patient outcomes. Conclusions: The proposed vision transformer-based model provides a highly accurate and efficient approach to detecting endometrial cancer. This study underscores the potential of this model as a valuable tool for medical professionals in the early detection and treatment of endometrial cancer, ultimately improving patient outcomes.

关键词： Endometrial Cancer Hematoxylin and Eosin (H&E) image Vision Transformer Deep Learning Medical Image Classification

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An Empirical Evaluation of machine Learning Algorithms for Groundwater Quality Classification 3

An Empirical Evaluation of Machine Learning Algorithms for G...

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3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Domakonda, Vinay Kumar Sasirekha, K. Sangeetha, S. Umasankar, L. Nagendiran, S. Kumar, M Jogendra Vignan's Foundation for Science and Technology Department of Mechanical Engineering Andhra Pradesh India R.M.D Engineering College Department of Computer Science Kavarapettai India Veltech Multitech Department of Information Technology Chennai India R.M.K College of Engineering and Technology Department of Electrical and Electronics Chennai India R.M.K Engineering College Department of Artificial Intelligence Kavaraipettai India KoneruLakshmaiah Education Foundation Department of Computer Science Vaddeswaram India

ISBN: (纸本)9798350336238

Groundwater is an effective monitoring system is essential, one of the most vulnerable resources. The use of spatial data to measure spatial changes in groundwater one of the most key things of soil monitoring. As a result, the most important water constituents based on groundwater characteristics is critical for an effective soil monitoring programmed the development of an efficient reference system that estimates. We evaluated the performance of neural network (NN)-based algorithms and event prediction models (EPM)) to estimate the severity of SS in some Indian regions throughout this study. Using 16 years of We developed a regional and local model remote sensing dataset to estimate the SS of the entire Indian basin and each catchment in the study area. Based on EPM and NN regional models had accuracy and SS of 88%, 96%, 88%, and 87%, The estimation and SS outperformed both the regional and spatial NN by 50-84% and 71-84%, whereas the local model was the empirically derived model, respectively. Consequently, according to the findings, machine learning methods should be used to accurately and continuously monitor groundwater quality parameters. In complex topography of India and other similar land classifications. © 2023 IEEE.

关键词： Landsat

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Explainable AI for Industry 4.0: Semantic Representation of Deep Learning Models 3

Explainable AI for Industry 4.0: Semantic Representation of ...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Terziyan, Vagan Vitko, Oleksandra Univ Jyvaskyla Fac Informat Technol Jyvaskyla 40014 Finland Kharkiv Natl Univ Radio Elect Dept Artificial Intelligence UA-61166 Kharkiv Ukraine

Artificial intelligence is an important asset of Industry 4.0. Current discoveries within machine learning and particularly in deep learning enable qualitative change within the industrial processes, applications, systems and products. However, there is an important challenge related to explainability of (and, therefore, trust to) the decisions made by the deep learning models (aka black-boxes) and their poor capacity for being integrated with each other. Explainable artificial intelligence is needed instead but without loss of effectiveness of the deep learning models. In this paper we present the transformation technique between black-box models and explainable (as well as interoperable) classifiers on the basis of semantic rules via automatic recreation of the training datasets and retraining the decision trees (explainable models) in between. Our transformation technique results to explainable rule-based classifiers with good performance and efficient training process due to embedded incremental ignorance discovery and adversarial samples ("corner cases") generation algorithms. We have also shown the use-case scenario for such explainable and interoperable classifiers, which is collaborative condition monitoring, diagnostics and predictive maintenance of distributed (and isolated) smart industrial assets while preserving data and knowledge privacy of the users. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： Explainable Artificial intelligence Industry 4.0 semantic web deep learning predictive maintenance

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ICBASE 2022 - Proceedings of the 2022 3rd international conference on Big Data and Artificial intelligence and Software Engineering

ICBASE 2022 - Proceedings of the 2022 3rd International Conf...

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3rd international conference on Big Data and Artificial intelligence and Software Engineering, ICBASE 2022

The proceedings contain 39 papers. The topics discussed include: vibration region extraction method of bridge based on ground-based MIMO radar;academic paper knowledge graph, the construction and application;research on test case generation method of airborne software based on NLP;3D dynamic hand gesture recognition with fused RGB and depth images;refined balanced resource allocation based on Kubernetes;visible region enhancement network for occluded pedestrian detection;a hierarchical-based frequent itemset mining method under local differential privacy;similarity-aware attention network for multimodal fake news detection;pedestrian attribute recognition based on multi-scale feature fusion over a larger receptive field and strip pooling;stowaway mining: a selfish mining against strategy;spatiotemporal attention networks for traffic demand prediction;and benefits analysis of Chinese cities on urban networks based on evolutionary games.

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Predicting Student Attention And Engagement Through Eye-Tracking Using machine Learning

Predicting Student Attention And Engagement Through Eye-Trac...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： Kirubakaran R Suganthi N Lokeshwaran V Akilan V E Shahidha S Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India Department of Information Science and Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper presents a comprehensive approach to predicting student engagement and learning behavior by analyzing eye-tracking data combined with behavioral analysis using camera-based recognition algorithms. The proposed system leverages WebGazer for eye-tracking to monitor gaze patterns and uses computer vision algorithms to recognize behaviors and emotional states. Feelings like engagement, frustration, or perplexity are identified using facial expression analysis. The system aims to provide real-time, actionable insights to students and educators by relating to emotional and concentration states by combining different modalities, thereby improving personalized learning strategies and educational outcomes.

关键词： Emotion recognition Computer vision Gaze tracking machine learning Educational technology Prediction algorithms Real-time systems Telecommunication computing pattern recognition Monitoring

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