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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Juan Antonio Vazquez Trejo Adrien Guenard Manuel Adam-Medina Laurent Ciarletta Jean-Christophe Ponsart Didier Theilliol CRAN UMR 7039 CNRS University of Lorraine Campus Sciences BP Nancy France Lorraine Research Laboratory in Computer Science and Its Applications CNRS University of Lorraine Nancy France Electronic Engineering Department TECNM/Cenidet Interior Internado Palmira S/N Palmira Cuernavaca Morelos Mexico

Cooperative systems as fleets of unmanned aerial vehicles often use the exchange of information through communication networks to reach an agreement or complete desired tasks. These networks are mainly vulnerable to cyber-attacks which use them to spread malfunctions to all systems in the network. The main contribution of this paper is the design of a resilient strategy such that the UAVs can follow the trajectories of a UAV leader in a consensus despite a type of cyber-attacks. Linear matrix inequalities (LMIs)-based conditions are obtained to guarantee the stability of the consensus against cyber-attacks. The proposed strategy is evaluated through simulations of a fleet of UAV under false-injection data in the inputs as cyber-attacks. A comparison between the proposed resilient strategy and the classical formation control is provided.

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HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

arXiv

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arXiv 2023年

作者： Jones, Derek Allen, Jonathan E. Zhang, Xiaohua Khaleghi, Behnam Kang, Jaeyoung Xu, Weihong Moshiri, Niema Rosing, Tajana S. Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Global Security Computing Applications Division Lawrence Livermore National Laboratory LivermoreCA United States Biosciences and Biotechnology Division Lawrence Livermore National Laboratory LivermoreCA United States

Purpose: Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis (i.e. combinatorial chemistry). Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug to its protein target. A major drawback of the approaches are that they require exceptional computing capabilities to consider for even relatively small collections of molecules. Methods: Hyperdimensional Computing (HDC) is a recently proposed learning paradigm that is able to leverage low-precision binary vector arithmetic to build effecient representations of the data that can be obtained without the need for gradient-based optimization approaches that are required in many conventional machine learning and deep learning approaches. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated for a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encoding algorithms that leverage the extended connectivity fingerprint (ECFP) algorithm. Results: We show that HDC-based inference methods are as much as 90 × more efficient than more complex representative machine learning methods, and achieve an acceleration of nearly 9 orders of magnitude as compared to inference with molecular docking. We also show that HDC-methods implemented in a variety of tools can match competitive performance in terms of training with respect to simple baseline ML models while retaining competitive predictive performance on a number of tasks ranging from well-studied benchmarks such as MoleculeNet molecular property prediction tasks as well as the DUD-E and LIT-PCBA bind/no-bind activity classification datasets. Conclusions: We demonstrate mu

关键词： Molecules

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An interpretable decision-support model for breast cancer diagnosis using histopathology images

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Journal of Pathology Informatics 2023年 14卷 100319-100319页

作者： Krishna, Sruthi Suganthi, S.S. Bhavsar, Arnav Yesodharan, Jyotsna Krishnamoorthy, Shivsubramani Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India Valeo Navalur Chennai India School of Computing and Electrical Engineering IIT Mandi Himachal Pradesh India Department of Pathology Amrita Institute of Medical Science Kochi India Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

Microscopic examination of biopsy tissue slides is perceived as the gold-standard methodology for the confirmation of presence of cancer cells. Manual analysis of an overwhelming inflow of tissue slides is highly susceptible to misreading of tissue slides by pathologists. A computerized framework for histopathology image analysis is conceived as a diagnostic tool that greatly benefits pathologists, augmenting definitive diagnosis of cancer. Convolutional Neural Network (CNN) turned out to be the most adaptable and effective technique in the detection of abnormal pathologic histology. Despite their high sensitivity and predictive power, clinical translation is constrained by a lack of intelligible insights into the prediction. A computer-aided system that can offer a definitive diagnosis and interpretability is therefore highly desirable. Conventional visual explanatory techniques, Class Activation Mapping (CAM), combined with CNN models offers interpretable decision making. The major challenge in CAM is, it cannot be optimized to create the best visualization map. CAM also decreases the performance of the CNN models. To address this challenge, we introduce a novel interpretable decision-support model using CNN with a trainable attention mechanism using response-based feed-forward visual explanation. We introduce a variant of DarkNet19 CNN model for the classification of histopathology images. In order to achieve visual interpretation as well as boost the performance of the DarkNet19 model, an attention branch is integrated with DarkNet19 network forming Attention Branch Network (ABN). The attention branch uses a convolution layer of DarkNet19 and Global Average Pooling (GAP) to model the context of the visual features and generate a heatmap to identify the region of interest. Finally, the perception branch is constituted using a fully connected layer to classify images. We trained and validated our model using more than 7000 breast cancer biopsy slide images from an

关键词： Attention branch network Breast cancer computer-aided diagnosis Convolutional neural networks Histopathology images

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Utilizing Deep Reinforcement Learning for Advanced Pattern Extraction in Big Data Analytics and Ontology Systems

Utilizing Deep Reinforcement Learning for Advanced Pattern E...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： A Karthik Asha V Garima Bhardwaj Ginni Nijhawan Irfan Khan Nabaa M. Bader Institute of Aeronautical Engineering Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Amity Business School Amity University Greater Noida Uttar Pradesh India Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The development of big data has provided unparalleled prospects for uncovering novel patterns and insights in several domains. However, the complex structure and volume of data need the use of advanced methods to successfully extract significant information. This research presents a new framework that utilizes Deep Reinforcement Learning (DRL) to improve pattern extraction in big data analytics and ontology systems. DRL, which combines deep learning with reinforcement learning, excels at dealing with data spaces that have a large number of dimensions. This makes it a good option for effectively exploring and analyzing complex datasets. The suggested framework effectively utilizes sophisticated ontological structures to integrate DRL, enabling it to accurately identify and extract complex patterns that may be overlooked by standard approaches. The study showcases the effectiveness of the framework by conducting extensive tests on diverse big datasets, demonstrating that the DRL-enhanced system surpasses previous methods in terms of both accuracy and speed. The study also addresses architectural design, the choice of reinforcement learning methods, and the found implementation issues. Moreover, it delves into the consequences of these discoveries for subsequent investigations and real-world implementations, emphasizing the capacity of DRL to transform the identification of patterns in large-scale data settings. This work enhances the area of big data analytics by offering a strong technique for extracting complex patterns, which in turn enables better informed decision-making and discovery in scientific and commercial sectors.

关键词： Data analysis Computational modeling Decision making Transforms Big Data Ontologies Deep reinforcement learning

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ON DISCRETE AND SEMIMETRIC FIXED POINT THEOREMS FOR NONEXPANSIVE MAPPINGS

arXiv

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arXiv 2022年

作者： Adraoui, A. El Kabil, M. Kamous, A. Lazaiz, Samih Laboratory of Analysis Modeling and Simulation Department of Mathematics and Computer Sciences Faculty of Sciences Ben M’sick University Hassan II Casablanca Morocco Laboratory of Mathematics Computer Science and Applications Faculty of Sciences and Technologies Mohammedia Hassan II University of Casablanca Morocco ENSAM Casablanca Hassan II University Casablanca Morocco

This work is a comparative study between the existence of fixed point for homomorphisms in a class of binary relationnal systems and the existence of fixed point for nonexpansive mappings in semimetric spaces. © ... 详细信息

关键词： Fixed point arithmetic

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Reliable Densely Connected Network with Machine Learning based Diabetic Retinopathy Grading Approach

Reliable Densely Connected Network with Machine Learning bas...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Srinivasulu Sirisala Nihar Ranjan Behera D. Amuthaguka Sachin Vasant Chaudhari Jhakeshwar Prasad Swetha Reddy A. Department of Computer Science and Engineering CVR College of Engineering Hyderabad Telangana India Swiss School of Business and Management Geneva Petit-Lancy Switzerland Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics & Computer Engineering Sanjivani College of Engineering Kopargaon Department of Pharmacology Shri Shankaracharya College of Pharmaceutical Sciences Bhilai Chhattisgarh India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Puduvoyal Thiruvallur District Tamilnadu India

ISBN: (纸本)9781665460859

Lately, diabetic retinopathy (DR) is deliberated to be the major cause of blindness in today's world. There are various reasons for loss of eyesight but DR has greatly contributed to blindness. The main challenge of DR is that it is incurable at an early stage, therefore basic analysis is of great significance. Consequently, it is mentioned as a classic difficulty in medical schemes due to minimum number of medicinal experts and facilities and massive patients. Accordingly, it resulted in deploying automated diagnosing methodology that is very beneficial for the analysis of DR. With this aspect, this article develops a reliable Densely Connected Network with Machine Learning based Diabetic Retinopathy Grading Approach, called RDCN-MLDR. The presented RDCN-MLDR model follows two major stages namely feature extraction and DR classification. Initially, the DenseNet model processes the input fundus images. In addition, squirrel search algorithm (SSA) is applied for the hyperparameter tuning of the DenseNet model. As soon as the features are created, they are fed to the LR module for proper identification of DR. The performance assessment of the RDCN-MLDR model is tested using open access dataset from Kaggle dataset and it produces the 97.8% accuracy. The attained outcomes highlighted that the RDCN-MLDR model can accomplish enhanced DR classification performance.

关键词： Data analysis Retinopathy Open Access Machine learning Blindness Feature extraction Diabetes

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Determination of Brain Image Segmentation Method for Medical Disease Diagnosis by using Techniques in Soft Computing 6

Determination of Brain Image Segmentation Method for Medical...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Paikrao, Prashant Laxmanrao Sivaramakrishnan, A. Thimmiaraja, J. Pattanaik, Balachandra Ghosh, Sudipta Das, Saswata Government College of Engineering E & Tc Engineering Department Maharashtra Amravati India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Computer Science and Applications Andhra Pradesh Guntur India Kalasalingam Academy of Research and Academy Krishnankoil School of Computing Tamil Nadu Srivilliputhur India Wollega University College of Engineering and Technology Department of Ece Nekemte Ethiopia Calcutta Institute of Engineering and Management Ashok Nagar Ece Department West Bengal Kolkata India Cognizant Technology Solutions India Pvt. Ltd. West Bengal Kolkata India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

In this paper, the Cuckoo Search Optimization (CSO) method has been used to provide a context-sensitive multi-level thresholding for optimal segmentation of images. In order to determine the most appropriate thresholds for colour images with uncertainty and dense features, CSO segmentation has been used. Furthermore, due to the complexity and multidimensionality of colour images, obtaining the best thresholding values consumes more time and is also computationally intensive. The proposed method is more advantageous than other existing models due to its accuracy in estimating thresholds. When determining the energy curve of an image using a histogram-based approach, the information available in nearby pixels has been ignored. © 2022 IEEE.

关键词： Image segmentation

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Machine learning advances aiding recognition and classification of Indian monuments and landmarks

arXiv

引用

arXiv 2021年

作者： Paul, Aditya Jyoti Nethaji, Niketha Ghose, Smaranjit Pal, Shivam Aggarwal, Kanishka Purkayastha, Arnab Dutta Cognitive Applications Research Lab India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India

— Tourism in India plays a quintessential role in the country's economy with an estimated 9.2% GDP share for the year 2018. With a yearly growth rate of 6.2%, the industry holds a huge potential for being the primary driver of the economy as observed in the nations of the Middle East like the United Arab Emirates. The historical and cultural diversity exhibited throughout the geography of the nation is a unique spectacle for people around the world and therefore serves to attract tourists in tens of millions in number every year. Traditionally, tour guides or academic professionals who study these heritage monuments were responsible for providing information to the visitors regarding their architectural and historical significance. However, unfortunately this system has several caveats when considered on a large scale such as unavailability of sufficient trained people, lack of accurate information, failure to convey the richness of details in an attractive format etc. Recently, machine learning approaches revolving around the usage of monument pictures have been shown to be useful for rudimentary analysis of heritage sights. This paper serves as a survey of the research endeavors undertaken in this direction which would eventually provide insights for building an automated decision system that could be utilized to make the experience of tourism in India more modernized for visitors. Copyright © 2021, The Authors. All rights reserved.

关键词： computer vision

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Checking for non-Euclidean latent geometry of biological networks

Checking for non-Euclidean latent geometry of biological net...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Paola Lecca Angela Re Faculty of Computer Science Free University of Bozen-Bolzano Bolzano Italy National Group for Mathematical Analysis Probability and their Applications Francesco Severi’ National Institute of High Mathematics Rome Italy Department of Applied Science and Technology Politecnico di Torino Turin Italy

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

We present non-iterative methods for graph embedding in flat and curved manifold and acriterion to assess the quality of the embedding for the purpose of determining what is the optimal latent geometry of a graph. To date, graph embedding procedures have been used in data dimension reduction problems for the purpose of simpler and more efficient data analysis, but little has been done to use them as a tool for the estimation of the latent geometry. The latent geometry of a graph is important since it explains graph organizational structure and dynamics. Advancing the state of the art in this direction is of particular importance for the study of biological networks, since these networks exhibit a high degree of organizational complexity that is difficult to identify with standard analyses of node centrality measures.

关键词： Geometry Visualization Codes Regulators Estimation Regulation Topology

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