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2022 2nd international conference on Computer Science, Engineering and Applications, ICCSEA 2022

2022 2nd International Conference on Computer Science, Engin...

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2nd international conference on Computer Science, Engineering and Applications, ICCSEA 2022

ISBN: (纸本)9781665458344

The proceedings contain 124 papers. The topics discussed include: a machine leaning based Parkinson’s detection using acoustic features;performance analysis of various image steganography techniques;development of application software for generating music composition inspired by nature using deep learning;validation of an information diffusion based health social media on yoga and ayurveda;DEVINE: distributed energy-related virtual network embedding approach utilizing multiple objective optimization;masked or unmasked face detection from online video using learning aided pattern recognition method;an experimental study on hyper-parameter optimization for breast cancer risk prediction using IBM snap machine learning techniques;categorizing data for sentimental analysis by auto-focusing mechanism using natural language processing;single camera based real time framework for automated fall detection;secret key generation using genetic algorithm for the hybrid blowfish encryption and substitution ciphers;classification and detection of Covid-19 using deep transfer learning techniques;encrypted cloud storage approach for a multicloud environment using fog computing;and programmable stickers to monitor perishable goods using blockchain.

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Integrate-and-Fire Neurons for Low-Powered pattern recognition 20th

Integrate-and-Fire Neurons for Low-Powered Pattern Recogniti...

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20th international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Bacho, Florian Chu, Dominique Univ Kent Sch Comp CEMS Canterbury CT2 7NF Kent England

ISBN: (纸本)9783030879860;9783030879853

Embedded systems acquire information about the real world from sensors and process it to make decisions and/or for transmission. In some situations, the relationship between the data and the decision is complex and/or the amount of data to transmit is large (e.g. in biologgers). Artificial Neural Networks (ANNs) can efficiently detect patterns in the input data which makes them suitable for decision making or compression of information for data transmission. However, ANNs require a substantial amount of energy which reduces the lifetime of battery-powered devices. Therefore, the use of Spiking Neural Networks can improve such systems by providing a way to efficiently process sensory data without being too energy-consuming. In this work, we introduce a low-powered neuron model called Integrate-and-Fire which exploits the charge and discharge properties of the capacitor. Using parallel and series RC circuits, we developed a trainable neuron model that can be expressed in a recurrent form. Finally, we trained its simulation with an artificially generated dataset of dog postures and implemented it as hardware that showed promising energetic properties.

关键词： Remote system Spiking Neural Networks Integrate-and-fire Neuromorphic hardware

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Online Signature Verification Based on Signer Dependent Sampling Frequency and Dynamic Time Warping 7

Online Signature Verification Based on Signer Dependent Samp...

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7th international conference on soft computing and Machine Intelligence (ISCMI)

作者： Saleem, Mohammad Kovari, Bence Budapest Univ Technol & Econ Dept Automat & Appl Informat Budapest Hungary

ISBN: (纸本)9781728175591

Previous studies have shown that choosing a different sample rate or signature point count provides better accuracy in online signature verification systems. However, the sampling rate that minimizes the error may vary on the database and signer levels. In this work, we studied the effect of choosing individual sampling frequencies for each signer and proposed a signature verification system based on signer-dependent sampling frequency. The system was tested on five different databases, using several features and preprocessing methods. Results showed accuracy improvement in 70% of the overall 500 tests and 92% in the chosen system where z-normalization and six samples used in the preprocessing phase.

关键词： sampling frequency online signature verification dynamic time warping pattern recognition

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Assessing the Impact of Dynamic Backdoor Attacks on Federated Learning Models

Assessing the Impact of Dynamic Backdoor Attacks on Federate...

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Computational Intelligence and Network Systems (CINS), international conference on

作者： Pravija Raj P V Ashish Gupta Department of Computer Science BITS Pilani Dubai Campus Dubai UAE

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

With rapid advances in data-driven techniques, Federated Learning (FL) has evolved as an attractive approach for collaborative model training without compromising the privacy of individual data sources. However, this decentralized structure poses substantial security concerns, especially in the form of backdoor attacks. Backdoor attacks, a subtype of data poisoning attacks, take advantage of the confined control of the server, allowing adversarial participants to inject trigger patterns into local models, compromising the integrity and performance of the global model. A stronger version, called a dynamic backdoor attack, presents an intensive threat, where the adversary uses dynamic trigger patterns across the data inputs, making recognition and prevention even more challenging. In this work, the sensitivity of FL models to such dynamic backdoor attacks and their impact on global model performance is evaluated. Using the two benchmark datasets, MNIST and CIFAR-10, the state-of-the-art methods are tested against distributed backdoor attackers under Independent and Identically Distributed (IID) and non-IID data scenarios to demonstrate the stealth and strength of dynamic triggers, constituting a substantial threat to current FL models.

关键词： Training Sensitivity Federated learning soft sensors Prevention and mitigation Data models pattern recognition Servers Security Network systems

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Fusion-Based Automated Domain Ontology Construction Method

Fusion-Based Automated Domain Ontology Construction Method

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international conference on Computer and Communications (ICCC)

作者： Dongsheng Yang Chang Liu Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China Chinese Academy of Sciences Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Ontology is a crucial component of the Semantic Web, enabling knowledge sharing and reuse across data sources by organizing information into a knowledge network. Currently, domain ontologies are primarily constructed manually by domain experts. However, due to the typically unstructured or semi-structured nature of data sources, combined with large data volumes and the difficulty of content evaluation, the construction process is inefficient. Furthermore, relying on a single data source often fails to ensure the completeness of an ontology. To address these challenges, this paper proposes a fusion-based method for automated domain ontology construction. This approach integrates two types of data sources: relational databases and domain-specific texts (such as research papers and books). The method involves converting relational databases into domain ontology using the RDB2RDF standard, training a domain concept extraction model to identify concepts from texts, and employing pattern matching to extract relationships between concepts for ontology construction. Finally, ontology merging techniques are used to combine data from both sources, generating the final ontology. Using the field of electronic components as a case study, the proposed method was evaluated by domain experts, who confirmed the ontology's comprehensiveness and validated the approach's effectiveness.

关键词： Training Knowledge engineering Semantic Web soft sensors Computational modeling Electronic components Relational databases Ontologies Standards pattern matching

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Evaluating Communication pattern Representations in Execution Trace Gantt Charts

Evaluating Communication Pattern Representations in Executio...

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IEEE international Workshop on Visualizing software for Understanding and Analysis (VISsoft)

作者： Connor Scully-Allison Katherine E. Isaacs Scientific Computing and Imaging Institute (SCI) University of Utah Salt Lake City USA

ISBN: (数字)9798331528485

ISBN: (纸本)9798331528492

Gantt charts are frequently used to explore execution traces of large-scale parallel programs. In these visualizations, each parallel processor is assigned a row showing the computation state of a processor at a particular time. Lines are drawn between rows to show communication between these processors. When drawn to align equivalent calls across rows, visual patterns can emerge reflecting communication behavior of the executing code. However, though these patterns have the same definition at any scale, they can be obscured by the density of rendered lines when displaying more than a few hundred processors. We seek to understand the effectiveness of various strategies for recognizing these patterns in Gantt charts. Specifically, we conduct a pre-registered user study comparing recognition of patterns when viewing all processors, a subset of processors, or a set of abstracted glyphs overlaid on the chart. We find that all strategies have limitations when scaling, motivating further designs. Our results further indicate that for simple patterns, the glyphs are more effective in general pattern recognition while the zoomed subsets provide nuance to specific characteristics, such as offsets, in patterns. These results suggest the development of a combined approach may be appropriate to enable pattern comprehension in large-scale Gantt charts.

关键词： Context Visualization Codes Navigation software pattern recognition Lenses

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Machine Learning: An Analytical Approach for pattern Detection in Diabetes 1

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8th international conference on soft computing: Theories and Applications, SoCTA 2023

作者： Chauhan, Ritu Goel, Anika Kaur, Harleen Alankar, Bhavya Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Jamia Hamdard Delhi India

ISBN: (数字)9789811998584

ISBN: (纸本)9789811998577

In current times, the amount of data is increasing exponentially but generated data is not transformed into knowledge data which eventually serves no purpose. This issue can be resolved by applying machine learning approaches which prove to play a promising role in the field of prediction. Similarly, in healthcare industries, there is a supply of large amount of data for which it is necessary to extract information for prognosis, diagnosis, and medication development. In this paper, we have focused on diagnosis of diabetes which is a chronic disease with fastest global growth rate. The current study is carried out in two phases where in first phase data is pre-processed to remove the unwanted and missing values and in second phase the data is analyzed by model building using machine learning algorithm. Additionally, we have attempted machine learning techniques in our study such as decision tree classifier and logistic regression which can be used for diagnosis of diabetes, and we attain the accuracy of 75.9% for decision tree classifier and 74.6% for logistic regression. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Low-light image enhancement model based on self-calibration illumination and color adjustment 2

Low-light image enhancement model based on self-calibration ...

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2nd international conference on Algorithms, High Performance computing and Artificial Intelligence, AHPCAI 2022

作者： Zhang, Guiwen Li, Qishen Li, Qiufeng Wu, Taida School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Jiangxi Nanchang China

ISBN: (纸本)9798350334784

to improve the clarity of objects in a dark-light environment, and to facilitate the identification and detection of targets behind. People perceive the color and brightness of a point not only depending on the pixel value of the point but also the absolute light entering the human eye is related to the color and brightness around the point. The self-calibration model we used considers the surrounding information, which avoids the information pollution, such as large regions of dark background information. In this model, we introduce a heterogeneous convolution filter, which makes reasonable use of different parts of the filter. Through this operation, information from multiple different scale spaces can be fused, and the field of view when applying the convolution layer is greatly increased without increasing the hyper-parameters, thus producing a more distinctive feature representation. Then the self-calibration model is combined with the backbone reinforcement network, which can not only retain the information of the original scale space but also efficiently collect the latent space information to guide the feature transformation in the original space. After the different channels of the image are processed separately, the dependency between the channels can be established by using the heterogeneous convolution filter. Finally, the test on the ExDark data set proves that our dark target enhancement effect has been significantly improved. © 2022 IEEE.

关键词： Convolution

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Real-Time Medical Electronic Data Mining Based on Shapelet pattern recognition

Real-Time Medical Electronic Data Mining Based on Shapelet P...

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2022 international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2022

作者： Mao, Yi Li, Yun Han, Jingyu Chen, Yixin Nanjing China Washington University in St.Louis Department of Computer Science and Engineering St.Louis United States

ISBN: (数字)9781665498586

ISBN: (纸本)9781665498586

Data mining of clinical data has big feasibility for refining the quality of hospital remedy and increasing survival probability of patients. Data-driven prediction techniques rely heavily on data gathering and analysis of patient vital signs. Time series shapelets are lately discovered short discriminant subseries that are not only accurate, but also explainable for the classification of time series problems. Similarity between shapelets and the whole series are employed in shapelet-based classification as a discriminatory feature, which are more comprehensible in the problem domain. This paper aims to exploit features in a distance-based classifier by replacing the distance between wireless sensor networks medical electronic data by the distance between shapelet transformed features. By using the cross relationship of different vital signs, we got the shapelets regions. Also, we introduced Multi-metric Large Margin Nearest Neighbor Classification(LMNN) to study Mahalanobis distance matrixes for the purpose of reducing the dimension of the transformed feature while keep the non-Target patients' features are far away from the target patients'. Experiments show that the combination of shapelets and Multi-metric LMNN can significantly improve the classification precision and also gives better explanation to doctors in units. © 2022 IEEE.

关键词： Time series

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Big Data Analytics for pattern recognition and Technological Decision-Making

Big Data Analytics for Pattern Recognition and Technological...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), international conference on

作者： Vidhya. N C. Meenakshi Department of Computer Applications Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

The purpose of this study's analysis of big data is to clarify the following ideas and classifications regarding big data, sorts of big data, what they are, and what characteristics they have as determined by the literature review. The discussion continues through the effective result it might produce thereby promising a bright future. The terminology and concepts of big data intellectuals and the information technology industry that pay closer attention to big data are to be discussed. Data is generated and retrieved at a rate that quickly exceeds the threshold range in the digital and computing world. The amount of data and pace of market expansion both rise when information is shared and transferred at the speed of light through wireless and broadband networks. When compared to conventional database systems, high-performance analytics of big data is the effective framework for the analysis and pattern development of processed data.

关键词： Wireless communication Industries Terminology Big Data Prediction algorithms software Sustainable development Information technology Optimization Systematic literature review

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