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

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

引用

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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Research on attention-based multiscale information fusion with the real-time instance segmentation method 2

Research on attention-based multiscale information fusion wi...

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

作者： He, Yifan Huang, Hua Li, Qishen Zhang, Guiwen School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Instance segmentation is a comprehensive computer vision task that involves a wide range of other tasks. Recently, the study of real-time instance segmentation methods has received more attention for the development of autonomous driving. Although existing real-time instance segmentation methods are fast, their accuracy does not meet practical needs. Most methods go for segmentation based on object detection, and their effectiveness is overly dependent on the effectiveness of detection. This paper proposes a new attention-based multiscale information fusion method based on Cheng, T. et al. [1]. Firstly, the PPM module of the baseline network is replaced with the module Multiscale Context Attention (MSCA) designed in this paper based on the baseline network, which uses atrous convolution with different ratios to obtain information of four scales, and then uses non-local attention to enhance the information of features. It can effectively suppress the interference of redundant information on the instance segmentation results. Secondly, a new feature fusion approach is designed, which no longer uses bilinear interpolation, but sub-pixel up sampling combined with attention. We did experiments related to this module on the coco dataset and demonstrated its effectiveness, with a 0.5% improvement over the baseline network. © 2022 IEEE.

关键词： Computer vision

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AITA: Accurate Network Topology recognition via Active Interception and Topology Analysis

AITA: Accurate Network Topology Recognition via Active Inter...

引用

Electronic Technology, Communication and Information (ICETCI), IEEE international conference on

作者： Rui Sun Department of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

Recent advances in wireless communication technologies have ushered in a significant opportunity to revolutionize the new network communication and combat strategies. The high efficiency of modern communication war requires that the key nodes and topology of wireless communication organization can be easily mastered with low computing cost. In complex and diverse communication situations, accurate identification of network topology becomes a key problem to improve communication efficiency and performance optimization. Traditional topology identification algorithms often rely on a single technology or data source, there are various communication environments and dynamic topology, so a more comprehensive and accurate method is needed. In this paper, we proposed a novel method called AITA for accurate topology identification of complex communication networks by a structured fusion of active listening and topology analysis. Specially, it involves sending out the interfering crafted packets to devices within the network and observing their responses to infer the topology. The topology analysis strategies, including graph theory and structured modeling, are automatically triggered to enhance the precision of topology identification in the presence of a significant error in the estimated location of active interception. Experimental results show that the proposed method performs well in accuracy, power consumption and recognition performance, and is superior to the traditional single detection and recognition methods. It provides a feasible and efficient solution to the topology identification problem in modern communication warfare, which lays the foundation for the future intelligent and adaptive communication system.

关键词： Wireless communication Analytical models Accuracy Adaptive systems Network topology Heuristic algorithms soft sensors

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Lunar Ejecta pattern Detection and Analysis Using Faster R-CNN and GAN Augmentation

Lunar Ejecta Pattern Detection and Analysis Using Faster R-C...

引用

international conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Premanand Ghadekar Chanchal Rathad Sourav Jangral Rushikesh Unde Adwait Mali Aryan Karande Dept. Of Information Technology Vishwakarma Institute of Technology Pune India

Gaining understanding of the Moon's geological and impact history requires a thorough analysis and inspection of its surface. Understanding the history of impact events, planetary development, crater formation mechanisms, geological composition, lunar regolith, planetary defense strategies, space weathering effects, and the broader field of comparative planetary science depend greatly on the analysis of ejecta patterns. This study examines the automated recognition and interpretation of complex lunar patterns from high-resolution lunar pictures using region-based convolutional neural networks. The model is customized and fine-tuned to identify a wide range of craters with prominent ejecta patterns. An essential component for enhancing the model's effectiveness and reliability is the integration of Generative Adversarial Networks. GAN s, a class of generative models, are employed to generate additional lunar images that closely resemble real lunar surface features. This augmentation strategy serves multiple technical purposes, such as improving resistance to noise and artifacts, expanding the training dataset, and enhancing overall robustness. The initial step encompasses essential tasks such as orthorectification and radiometric calibration, aimed at optimizing image quality and ensuring accurate geometric and radiometric characteristics. Subsequently, this study trained the model for feature extraction and region proposal generation. This is followed by region classification and bounding box regression to accurately pinpoint and categorize lunar crater patterns. The analysis encompasses characterizing ejecta patterns based on their size, shape, distribution, and their relationship with other lunar surface features. Proposed model has achieved an outstanding accuracy rate of 85.35% in the automatic recognition and interpretation of intricate lunar patterns.

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3rd international Symposium on Automation, Mechanical and Design Engineering, SAMDE 2022

3rd International Symposium on Automation, Mechanical and De...

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3rd international Symposium on Automation, Mechanical and Design Engineering, SAMDE 2022

ISBN: (纸本)9783031400698

The proceedings contain 38 papers. The special focus in this conference is on international Symposium on Automation, Mechanical and Design Engineering. The topics include: UWB Localization Based on Improved SO-Robust KF Fusion Algorithm;class-Imbalanced and Fine-Grained Object Detection in Video Surveillance for Production Safety;NLFA: A Non Local Fusion Alignment Module for Multi-Scale Feature in Object Detection;Image Reconstruction for Rain Removal in Both Wavelet and Spatial Frequency Sub-bands Using W-CycleGANs;Pedestrian Multi-object Tracking Based on ResNeXt and FairMOT;recognition of Leftover Food Based on Deep Learning;Task Offloading for UAV-Assisted of Collaborative Mobile Edge computing Network Based on Deep Reinforcement Learning;Multi-scale Rain Removal Across Multiple Frequencies Sub-bands Using MS-CycleGANs;research on the Optimization of the Assembly Sequence of Continuous Steel Arch Bridges with Multi-span Deck;different Structural Parameters Impacted on the Rough Surfaces Contact Force and Area Between the Base and the Magnetic Film Cantilever;performance Prediction Method of Laser Device Energy Component Based on Neural Network;Preliminary Design of 2D Ultrasonic Vibration Assisted Micro-milling (UVAMM) Using Piezoelectric Stack Actuator;self-Fitting Hearing Aid Algorithm Based on Improved Interactive Genetic Algorithm;bending Process Parameters Optimization of the 316L Tube with Small R/D Ratio: A Physical Prior Knowledge Based Bayesian Optimization Approach;Modified Johnson–Cook Model of AA5005 Alloy for Thermal Tensile Tests;vibration Prediction Method for New Buildings in Existing Metro Operation Area with soft Soil;mechanical Structural Improvement Design and Vibration Test of Coupling Guard for Pump System;effect of Horizontal Plates on the Sliding of Breakwater Caissons.

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Promo-Zen: A Natural Language Processing Based Promotional Text Message Management System

Promo-Zen: A Natural Language Processing Based Promotional T...

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Advancements in computing (ICAC), international conference on

作者： Chamath Udugampola Nethmi Weerasingha Department of Computer and Data Science NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Promotional Short Message Service (SMS) messages, which provide frequent updates about deals and discounts to consumers, are crucial in developing countries like Sri Lanka, as they help alleviate financial pressure during economic crises. However, poor practices by the brands, SMS technology limitations and human memory limitations challenge the ability of consumers to make effective use of this facility. As a solution, this project aimed to develop an Android application to organize and enhance the readability of promotional messages, while also providing mechanisms to prevent users from forgetting promotions. These objectives were achieved by creating a Kotlin app that displays promotional information in a date-sorted weekly dashboard, offers deal-expiration reminders, and optimizes message content for readability. The system utilizes a novel workflow/pipeline combining a Natural Language Processing (NLP) model with Named Entity recognition (NER) capabilities, Regular Expressions and custom logic, to understand the context of and extract the crucial information from the promotional messages. User-acceptance testing carried out demonstrates positive results, reducing traditional access times to promotions of 8–10 seconds to approximately 2 seconds through this application. Additionally, user-testers reported multiple occurrences of the reminders feature ensuring that promotions weren't forgotten before use.

关键词： Named entity recognition Developing countries Message services Feature extraction Logic Data mining pattern matching Testing Context modeling

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Intrusion Detection on Industrial Networks Using Neuromorphic computing

Intrusion Detection on Industrial Networks Using Neuromorphi...

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international conference on Electrical, Control and Instrumentation Engineering (ICECIE)

作者： Ali Mehrabi André Van Schaik International Centre for Neuromorphic Systems Western Sydney University

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

Cyber-attacks on Industrial Control Systems (ICS) present critical risks to operational stability, public safety, and national security. As industrial networks become more integrated and interconnected, their susceptibility to sophisticated cyber threats increases. Traditional security measures often fall short in detecting evolving attack patterns. This paper investigates the application of neuromorphic computing, which emulates the human brain's pattern recognition capabilities, as an innovative approach to intrusion detection in industrial networks. We introduce a hardware-based Spiking Neural Network (SNN) engineered to monitor and analyze communication packets in real time, detecting anomalies indicative of potential intrusions. The neuromorphic approach offers a promising solution to the growing challenge of cyber-attacks in critical infrastructure. The proposed system is implemented on an FPGA, focusing initially on monitoring key packet types such as LLDP, ARP, UDP, TCP, and Profibus RT, with scalability to accommodate additional protocols. The results demonstrate the efficacy of this SNN-based intrusion detection system in providing real-time, efficient, and accurate monitoring of industrial networks.

关键词： Neuromorphic engineering Scalability Intrusion detection Spiking neural networks Real-time systems Stability analysis Public security Security Monitoring Cyberattack

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