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13th international conference on Society and Information Technologies, ICSIT 2022

作者： Ahmed, Shaftab Ilyas, Mohammad Raja, M. Yasin Akhtar Software Engineering Department Bahria University Islamabad Pakistan College of Engineering and Computer Science Florida Atlantic University Boca RatonFL United States Center for Optoelectronics & Optical Communications University of North Carolina Charlotte NC United States

ISBN: (纸本)9781950492633

Internet of Things (IoT) has assumed great importance in technical and social domains due to desire of smart living and intelligent solutions for industrial operations, home automation and healthcare. The telecommunication networks provide all-time internet connectivity for the devices in physical systems and hand- held devices. The developments have made it easy to remain engaged on all time, anywhere basis, while users interact with one or more applications. Many smart devices may interact in the background resulting in event-driven intelligent activities raising alerts or recording status summary under a policy. The smart solutions are being shaped for the industry, transport, eHealthcare, eEducation and other daily life activities. IoT activities are autonomous and support dynamic Machine-to-Machine (M2M) communication. The challenges of hetrogeneity, dynamic variation in signal quality and large volume of data are being addressed through number of techniques. In this paper, we discuss IoT based smart system technologies, security, vulnerabilities and role of intelligent solutions using Machine Learning (ML) and Artificial Intelligence (AI). A crucial factor hindering the ongoing efforts for widespread IoT-adoption, is security. We propose the requirement of Standard Security Framework (SSF) for platform independent and interoperable hardware/software modules in distributed networks domains. Trends to use Fog- and Edge-computing along with cloud applications has been also reviewed in the context to security and efficiency. © 2022 by the international Institute of Informatics and Systemics. All rights reserved.

关键词： Internet of things

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A deep learning-based approach for hybrid nonlinear systems dynamics approximation 17

A deep learning-based approach for hybrid nonlinear systems ...

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17th international conference on Emerging Technologies (ICET)

作者： Bastos, Vasco Palma, Luis Cardoso, Alberto Gil, Paulo NOVA Sch Sci & Technol Dept Elect & Comp Engn Caparica Portugal NOVA Sch Sci & Technol Dept Elect & Comp Engn CTS UNINOVA Caparica Portugal Univ Coimbra Fac Sci & Techonol Dept Informat Engn CISUC Coimbra Portugal Univ Coimbra CISUC Coimbra Portugal

ISBN: (纸本)9781665459921

An increasingly important class of nonlinear systems includes the nonaffine hybrid systems, in particular those in which the underlying dynamics explicitly depends on a switching signal. When the inherent complexity is treatable and the phenomena governing the system dynamics are known an implicit model can be derived to describe its behaviour over time. When these assumptions are not met the system dynamics can still be approximated by regression-based techniques, provided datasets comprising input/output signals collected from the system are available. One approach relies on intelligent computing-based frameworks, in which artificial neural networks stand out as a class of universal approximation models. This paper, proposes a new approach for capturing nonlinear hybrid system dynamics based on 1D spatio-temporal convolutional neural networks, in which the inputs are represented by regressors and structural configuration parameters. The proposed deep neural network architecture is compared against a shallow multilayer layer perceptron framework, in which each structural configuration is independently approximated. Experimental results point out to the superiority of the 1D spatio-temporal convolutional network.

关键词： Nonlinear systems hybrid systems data driven modelling deep learning spatio-temporal convolutional neural networks

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A Review of Recent advances in Classification and Prediction of Brain Tumor using Deep Learning 3

A Review of Recent Advances in Classification and Prediction...

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3rd IEEE international conference on computing, Communication, and intelligent systems, ICCCIS 2022

作者： Reddy, D.L. Kavya Soumya, D.R. Nagar, Abhayendra Rajpoot, Abha Kiran Sharda University School of Engineering and Technology Greater Noida India

ISBN: (纸本)9781665462006

The latest developments in deep learning to identify brain tumors are reviewed in this article. The aim of this bibliographic review is to provide a thorough understanding of the most current breakthroughs to the researchers that decide to work with deep learning and artificial neural networks for cancer diagnostics. Brain tumors are categorized into two terms malignant (cancerous) and benign (non-cancerous), just like other tumors based on the critical stage. While benign tumors expand slower and rarely spread, malignant tumors grow considerably quicker than benign tumors and frequently do so into the surrounding brain tissue. Only malignant tumors are regarded as cancerous, however benign brain tumors can still pose a threat because of how their growth may affect nearby brain tissue. It may cause death if not treated in the initial phases. Although there have been many substantial efforts and promising results in this field, precise segmentation and classification remain difficult tasks. Because of the differences in tumor location, shape, and size, detecting brain tumors is extremely difficult. The use of AI in medical research has improved the precision and accuracy of brain disease prediction and detection. In this article, we've analyzed methods such as CNN, FCN, U-Net, and others, based on the metrics of accuracy, sensitivity, specificity, precision, and F1-score, which aid in assessing the effectiveness of the algorithm used. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Neural representations of cryo-EM maps and a graph-based interpretation

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BMC BIOINFORMATICS 2022年第SUPPL 3期23卷 1-20页

作者： Ranno, Nathan Si, Dong Univ Washington Dept Comp & Software Syst Bothell WA 98011 USA

Background advances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Producing a protein structure from the discrete voxel grid data of cryo-EM maps involves interpolation into the continuous spatial domain. We present a novel data format called the neural cryo-EM map, which is formed from a set of neural networks that accurately parameterize cryo-EM maps and provide native, spatially continuous data for density and gradient. As a case study of this data format, we create graph-based interpretations of high resolution experimental cryo-EM maps. Results Normalized cryo-EM map values interpolated using the non-linear neural cryo-EM format are more accurate, consistently scoring less than 0.01 mean absolute error, than a conventional tri-linear interpolation, which scores up to 0.12 mean absolute error. Our graph-based interpretations of 115 experimental cryo-EM maps from 1.15 to 4.0 angstrom resolution provide high coverage of the underlying amino acid residue locations, while accuracy of nodes is correlated with resolution. The nodes of graphs created from atomic resolution maps (higher than 1.6 angstrom) provide greater than 99% residue coverage as well as 85% full atomic coverage with a mean of 0.19 angstrom root mean squared deviation. Other graphs have a mean 84% residue coverage with less specificity of the nodes due to experimental noise and differences of density context at lower resolutions. Conclusions The fully continuous and differentiable nature of the neural cryo-EM map enables the adaptation of the voxel data to alternative data formats, such as a graph that characterizes the atomic locations of the underlying protein or macromolecular structure. Graphs created from atomic resolution maps are superior in finding atom locations and may serve as input to predictive residue classification

关键词： Cryo-EM Neural representation SIREN Interpolation Geometric learning Experimental data

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Cloud computing, Machine Learning, and Secure data Sharing enabled through Blockchain 5

Cloud Computing, Machine Learning, and Secure Data Sharing e...

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5th international conference on Contemporary computing and Informatics, IC3I 2022

作者： Singh, Rajesh Chandra, Akkaraju Sailesh Bbhagat, Babeetta Panduro-Ramirez, Jeidy Gaikwad, Anil Pandurang Pant, Bhasker Uttaranchal Institute of Technology Uttaranchal University Uttarakhand India University Doctoral Scholar School of Business and Management Karnataka Bangalore India Chhatrapati Shivaji Maharaj University Department of Computer Science & Information Technology Navi Mumbai India Universidad Tecnológica Del Perú Department of Business Peru Savitribai Phule Pune University JSPM'S JSCOE-MCA Department Maharashtra Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

Blockchain technologies are sweeping the globe. Cloud computing & secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection & processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms & cloud computing, ML on-device information learning is now a reality. IoT gadgets may outsource training duties to cloud computing services to enable AI at the network's perimeter. Furthermore, these dispersed edges intelligence architectures bring additional issues, also including consumer confidentiality & information safety. Blockchain has been proposed as a viable alternative to these issues. Blockchain, as a dispersed intelligent database, has evolved as a revolutionary innovation for the future phase of multiple industries' uses due to its decentralized, accessible, & safe structure. This system also includes trustworthy automatic scripting running & unchangeable information recordings. As quantum technologies have proven more viable in the latest days, blockchain has faced prospective challenges from quantum computations. In this paper, we summarize the existing material in the study fields of blockchain-based cloud computing, machine learning, and secure data sharing, as well as a basic orientation to post-quantum blockchain to offer a summary of the existing state-of-the-art in these cutting-edge innovations. © 2022 IEEE.

关键词： Blockchain

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international conference on Automation, computing and Renewable systems, ICACRS 2022 - Proceedings

International Conference on Automation, Computing and Renewa...

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1st IEEE international conference on Automation, computing and Renewable systems, ICACRS 2022

ISBN: (纸本)9781665460842

The proceedings contain 251 papers. The topics discussed include: optically controlled microwave sensor for biomedical applications;deep learning network for object detection under the poor lighting condition;a brief analysis on machine learning classifiers for intrusion detection to enhance network security;a brief review on melanoma diagnosis models using machine learning techniques;equilibrium optimizer with deep learning model for autism spectral disorder classification;automated intracranial hemorrhage detection and classification using rider optimization with deep learning model;heart disease prediction and classification using machine learning and transfer learning model;review of machine learning algorithms for autism spectrum disorder prediction;novel approach to non-invasive blood glucose monitoring based on visible laser light;a machine learning based approach for breast cancer prediction;covid-19 infection segmentation using deep learning techniques;retinal fundus image retrieval and classification using optimal deep learning model;breast cancer segmentation by k-means and classification by machine learning;an extensive review of machine learning techniques for EEG signal processing;hybrid machine learning based false data injection attack detection and mitigation model for waste water treatment plant;twitter sentiment analysis with machine learning;hybrid particle swarm optimization with deep learning driven sarcasm detection on social media;design of kernel extreme learning machine based intelligent crop yield prediction model;and reliable densely connected network with machine learning based diabetic retinopathy grading approach.

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A Structural Health Monitoring Technique for the Analysis of Big data of Bridges 4th

A Structural Health Monitoring Technique for the Analysis of...

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4th international conference on advances in Civil and Ecological Engineering Research, ACEER 2022

作者： Silik, Ahmed Hong, Weixing Li, Jiawei Mao, Mingjie Noori, Mohammad Altabey, Wael A. Nanjing Zhixing Information Technology Co. Ltd Jiangsu Nanjing China Jiangsu Advance Transportation Institute Jiangsu Nanjing China Department of Civil Engineering Faculty of Engineering Sciences Nyala University Nyala092 Sudan Department of Mechanical Engineering California Polytechnic State University San Luis ObispoCA93405 United States Department of Mechanical Engineering Faculty of Engineering Alexandria University Alexandria21544 Egypt

ISBN: (纸本)9789811957826

Big data (BD) in structural health monitoring for civil engineers has become possible because of recent advances in sensor networks, computing, information, and data acquisition systems technologies (SHM). The time-frequency analysis-based data-driven method provides good opportunities for implementing a strategy of long-term SHM for the bridge by using measured vibration signals. But there are limitations associated with complex and time-consuming feature extraction and decision-making procedures due to high-dimensional big data. Therefore, this paper proposes an innovative strategy that integrates the empirical mode decomposition (EMD) and Hilbert transform (HT) for informative feature extraction for bridge condition assessment. The efficiency of the suggested method is evaluated using data pertinent to a cable-stayed bridge. The findings demonstrate that the proposed technique is effective and robust in extracting informative features for bridge assessments in cases highly characterized by big data without any concerns regarding the information loss about the structural state. It is concluded that the proposed technique can address the issues of identifying data anomalies that are reliable early warning signs for future bridge failure and produce interpretable signal analyses that can handle BD with numerous periodic components, nonlinear functions, and periodic mode amplitudes as well as the problems of identifying data anomalies that are trustworthy early warning indications for upcoming bridge failure. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Detection of Phishing Website Using intelligent Machine Learning Classifiers 1

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5th international conference on Soft computing and Signal Processing, ICSCSP 2022

作者： Pandey, Mithilesh Kumar Singh, Munindra Kumar Pal, Saurabh Tiwari, B.B. Department of Computer Applications VBS Purvanchal University Uttar Pradesh Jaunpur222001 India Department of Electronics and Communication VBS Purvanchal University Uttar Pradesh Jaunpur222001 India

ISBN: (数字)9789811986697

ISBN: (纸本)9789811986680

As a result of the rapid development of the Internet, customers’ tendency has shifted from standard shopping to e-commerce. Nowadays, the criminal party is no longer bank/shop theft, but trying to find their victims on the Web with the purpose of theft. Attackers use the hidden advances of the Internet to propose new advances, such as phishing, and deceive misfortune by using false language conditions, and accumulate their sensitive data, such as account IDs, customer names, passwords, and so on. Knowing whether a Web site page can be authenticated or phishing is an incredible problem, because its semantic-based attack structure fundamentally exploits the mistakes of the PC client. Despite the detail that product organizations dispatch new enemy of phishing items, which use heuristics, visual, and AI-based methodologies, these items can’t forestall all of the phishing assaults. In this article, an anti-phishing framework uses five different classification techniques (logistic regression, K-nearest neighbor, decision tree classifier, random forest classifier, and support vector machine) to predict phishing sites. In order to investigate the demonstration of the framework, a new dataset was collected, and preliminary results were set in it. According to the test and comparison results of the ranking estimation, the random forest calculation gives the best presentation, and the confirmation accuracy of the phishing URL is 97.10%. For validation of this outcome, confusion matrix, precision matrix, recall matrix, and ROC curve have been drawn. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Websites

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Mining fuzzy sub-prevalent co-location pattern with dominant feature 22

Mining fuzzy sub-prevalent co-location pattern with dominant...

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30th ACM SIGSPATIAL international conference on advances in Geographic Information systems (ACM SIGSPATIAL GIS)

作者： Xiong, Kaifang Chen, Hongmei Wang, Lizhen Xiao, Qing Yunnan Univ Sch Informat Sci & Engn Kunming Peoples R China Yunnan Univ Yunnan Key Lab Intelligent Syst & Comp Kunming Peoples R China

ISBN: (纸本)9781450395298

Prevalent co-location pattern mining aims to find a subset of spatial features whose instances are frequently located together in geo-space. Sub-prevalent co-location pattern mining attains co-location patterns with richer spatial relationships based on star instance model instead of clique instance model. Further, discovering dominant features in a sub-prevalent co-location pattern is important to reveal the interaction relationship between spatial features and improve the application of the pattern. However, the methods for mining sub-prevalent co-location pattern with dominant feature use a distance threshold to determine the binary neighbor relationship between spatial instances, and ignore the fuzziness of neighbor relationship which can give a rise to the fuzziness of dominance relationship between spatial features. Thus, this paper presents mining fuzzy subprevalent co-location pattern with dominant feature by exploring spatial instance distribution based on the fuzzy theory. Specifically, a novel pattern, fuzzy sub-prevalent co-location pattern with dominant feature, is proposed by defining the fuzzy neighbor relationship between spatial instances and the fuzzy dominant relationship between spatial features. Then, an efficient algorithm to mine the proposed patterns is designed by utilizing the anti-monotonicity of fuzzy star participation index and fuzzy star dominance index to prune unpromising patterns. The experimental results show that the proposed patterns are practical and the mining algorithm is efficient.

关键词： spatial data mining sub-prevalent co-location pattern fuzzy neighbor relationship fuzzy dominant relationship

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ILQR-VAE: CONTROL-BASED LEARNING OF INPUT-driven DYNAMICS WITH APPLICATIONS TO NEURAL data 10

ILQR-VAE: CONTROL-BASED LEARNING OF INPUT-DRIVEN DYNAMICS WI...

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10th international conference on Learning Representations, ICLR 2022

作者： Schimel, Marine Kao, Ta-Chu Jensen, Kristopher T. Hennequin, Guillaume Department of Engineering University of Cambridge Cambridge United Kingdom Gatsby Computational Neuroscience Unit University College London London United Kingdom

Understanding how neural dynamics give rise to behaviour is one of the most fundamental questions in systems neuroscience. To achieve this, a common approach is to record neural populations in behaving animals, and model these data as emanating from a latent dynamical system whose state trajectories can then be related back to behavioural observations via some form of decoding. As recordings are typically performed in localized circuits that form only a part of the wider implicated network, it is important to simultaneously learn the local dynamics and infer any unobserved external input that might drive them. Here, we introduce iLQR-VAE, a control-based approach to variational inference in nonlinear dynamical systems, capable of learning both latent dynamics, initial conditions, and ongoing external inputs. As in recent deep learning approaches, our method is based on an input-driven sequential variational autoencoder (VAE). The main novelty lies in the use of the powerful iterative linear quadratic regulator algorithm (iLQR) in the recognition model. Optimization of the standard evidence lower-bound requires differentiating through iLQR solutions, which is made possible by recent advances in differentiable control. Importantly, the recognition model is naturally tied to the generative model, greatly reducing the number of free parameters and ensuring high-quality inference throughout the course of learning. Moreover, iLQR can be used to perform inference flexibly on heterogeneous trials of varying lengths. This allows for instance to evaluate the model on a single long trial after training on smaller chunks. We demonstrate the effectiveness of iLQR-VAE on a range of synthetic systems, with autonomous as well as input-driven dynamics. We further apply it to neural and behavioural recordings in non-human primates performing two different reaching tasks, and show that iLQR-VAE yields high-quality kinematic reconstructions from the neural data. © 2022 ICLR 2022 - 10th

关键词： Dynamics

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