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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kaushik, Rohit Sachdeva, Rachit Vashisht, Chirag Choudhary, Krishan Kant University of Illinois Data Analytics Program Cs and Mathematics Department Springfield United States Dr. Akhilesh das Gupta Institute of Technology and Management Electronics and Communication Engineering Department New Delhi India Dr. Akhilesh das Gupta Institute of Technology and Management Information Technology and Engineering Department New Delhi India

ISBN: (纸本)9798350335095

The essential ideas behind haptic technology, including haptic interface and haptic rendering methods, as well as its application to surgical simulation, medical education, and other diverse topics, are presented in this paper. In empirical psychology and physiology, this refers to the sense of contact. The process of analyzing and developing capabilities in response to user interactions with virtual entities is elaborated as haptic rendering. Haptic may indicate haptic transmission, haptic perception, and haptic technology. Humans and other animals can respond to one another by using their feelings through haptic technology. Haptic perception is the process of recognizing objects through *** implies tactile response, which means it delivers feedback in the state of sensation as we may sense the material properties and motions of virtual objects devised by a computer. The use of haptic technology in the medical profession has proven extremely beneficial, and it has a wide range of applications in many different industries. Providing effective exchange via virtual reality helps to ease the surgeon's obligations. Amplify a person's experience in the virtual environment. The haptics ability ranges from computer graphics to psychophysics, cognitive science, and neurosciences. © 2023 IEEE.

关键词： Virtual reality

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data-driven Energy Consumption Modelling for Electric Micromobility using an Open dataset

arXiv

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arXiv 2024年

作者： Ding, Yue Yan, Sen Shah, Maqsood Hussain Fang, Hongyuan Li, Ji Liu, Mingming Dublin City University Ireland SFI Insight Centre for Data Analytics School of Electronic Engineering Dublin City University Dublin Ireland Department of Mechanical Engineering University of Birmingham United Kingdom

The escalating challenges of traffic congestion and environmental degradation underscore the critical importance of embracing E-Mobility solutions in urban spaces. In particular, micro E-Mobility tools such as E-scooters and E-bikes, play a pivotal role in this transition, offering sustainable alternatives for urban commuters. However, the energy consumption patterns for these tools are a critical aspect that impacts their effectiveness in real-world scenarios and is essential for trip planning and boosting user confidence in using these. To this effect, recent studies have utilised physical models customised for specific mobility tools and conditions, but these models struggle with generalization and effectiveness in real-world scenarios due to a notable absence of open datasets for thorough model evaluation and verification. To fill this gap, our work presents an open dataset, collected in Dublin, Ireland, specifically designed for energy modelling research related to E-Scooters and E-Bikes. Furthermore, we provide a comprehensive analysis of energy consumption modelling based on the dataset using a set of representative machine learning algorithms and compare their performance against the contemporary mathematical models as a baseline. Our results demonstrate a notable advantage for datadriven models in comparison to the corresponding mathematical models for estimating energy consumption. Specifically, datadriven models outperform physical models in accuracy by up to 83.83% for E-Bikes and 82.16% for E-Scooters based on an in-depth analysis of the dataset under certain assumptions. © 2024, CC BY.

关键词： Traffic congestion

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A Multi-Objective Evolutionary Machine Learning Method for the Yield Strength Prediction of Strip Steel in Continuous Annealing

A Multi-Objective Evolutionary Machine Learning Method for t...

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IISE Annual Conference and Expo 2024

作者： Liu, Chang Tang, Lixin Zhang, Kainan Guo, Jiajing National Frontiers Science Center for Industrial Intelligence and Systems Optimization North-eastern University Shenyang110819 China Ministry of Education Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China

ISBN: (纸本)9781713877851

In the continuous annealing process, the yield strength is the key property index to evaluate the quality of strip steel and guide the production. It is difficult to perform online quality measurement for numerous products in the steel industry, this brings significant challenges for process monitoring and operation optimization. In this paper, a multiobjective evolutionary machine learning method is proposed for the yield strength prediction of strip steel in continuous annealing. First, the factors affecting the yield strength of strip steel are analyzed and the prediction model based on a hybrid kernel support vector regression (HKSVR) is established. To improve the prediction accuracy, a multi-objective evolutionary optimization algorithm with a knee solution strategy is used to optimize the key parameters of the HKSVR. It is verified by the actual production data that the proposed method has high prediction accuracy and can meet the actual production demand. Moreover, the proposed method can help practitioners to construct quality analytics models for many other similar production lines, providing assurance for quality control and optimization with a good generalization. © IISE Annual Conference and Expo *** rights reserved.

关键词： Prediction models

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Radiomics Features Analysis from Lung Cancer Using CT Images 7

Radiomics Features Analysis from Lung Cancer Using CT Images

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7th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2021

作者： Sherly Angel, S. Nishanimath, Nidhi N Nandish, S. Master of Engineering in Healthcare Data Analytics Manipal School of Information Sciences Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9781665428491

Human cancers display solid phenotypic contrasts that can be visualized non-invasively by clinical imaging. Radiomics alludes to the extensive evaluation of tumour aggregates by applying an enormous number of quantitative image features. A radiomics analysis of features which are extracted from CT data of patients with lung cancer quantifying tumour image intensity, shape, and texture. Many radiomics features have prognostic power. Radiogenomics analysis uncovers that a prognostic radiomics signature, catching intra-tumour heterogeneity. This information recommends that radiomics distinguishes an overall prognostic phenotype existing in lung cancer. This may have a clinical effect as imaging is regularly utilized in clinical work on, giving an exceptional chance to improve in disease treatment easily. In our project we have extracted the radiomics features from the lung cancer dataset of LIDC_IDRI. The images are labelled first based on their malignancy and then over 100 features are extracted from them. data pre-processing and Exploratory data Analysis is performed on the data available to get it ready for the further processes. © 2021 IEEE.

关键词： Medical imaging

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***: a Context-Aware Chatbot for Emotional Detection and Support

***: a Context-Aware Chatbot for Emotional Detection and Sup...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Neave Kallivalappil Kyle D’souza Afif Deshmukh Chinmay Kadam Neha Sharma Computer Engineering Department Fr. Conceicao Rodrigues College of Engineering Mumbai India Data and Analytics Practice Tata Consultancy Services Pune India

The increasing prevalence of mental health disorders highlights the urgent need for innovative technologies that can provide support and improve individuals’ emotional well-being. Intelligent Virtual Friends (IVFs) have emerged as a promising solution to offer personalized and effective therapeutic experiences. This study introduces ***, an emotionally intelligent multimodal chatbot therapist powered by machine learning. *** aims to recognize and respond to users’ emotions, providing a unique and tailored experience by combining cognitive-behavioural therapy techniques with empathetic communication. This integration of facial, text, and voice emotion recognition empowers the chatbot to deliver a holistic and tailored approach to emotional support, ensuring that users receive the assistance they need in a comprehensive and empathetic manner and serves as a valuable resource for those seeking accessible alternatives to traditional therapy methods.

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Unsupervised Anomaly Detection on Node Attributed Networks: A Deep Learning Approach 21

Unsupervised Anomaly Detection on Node Attributed Networks: ...

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4th International Conference on Information Science and Systems, ICISS 2021

作者： Kavehzadeh, Parsa Samadi, Mohammadreza Amir Haeri, Maryam Computer Engineering Department Amirkabir University of Technology Iran Learning Data-Analytics and Technology Department University of Twente Netherlands

ISBN: (纸本)9781450389136

Anomaly detection has been one of the important issues in social network analysis in recent years due to the crucial role it plays in different applications such as fraud and spammer detection. Using both graph and node characteristics leads to more accurate results in detecting anomalous nodes of node attributed networks. Most of the research works in this field are concentrated on supervised methods for anomaly detection. However, in real-world problems, there is not enough labeled data to use supervised methods for anomaly detection. This paper proposes an unsupervised method for detecting anomalous nodes in node attributed networks. The methods used a two-step deep learning approach. In the first step, structural features of the network are extracted using node2vec;in the next step, Variational AutoEncoder (VAE) is used to detect the anomalies considering both structural and node attributes. The anomalous nodes are recognized by their higher reconstruction loss. Our experimental results on two datasets, BlogCatalog and Flickr, show that the suggested method can compete with the state-of-the-art approaches of anomaly detection in attributed networks. Our method (Deep2NAD) outperforms the state-of-the-art result on the Flickr dataset based on AUC. Moreover, it receives an acceptable AUC over the BlogCatalog dataset in comparison to the state-of-the-art methods. © 2021 ACM.

关键词： Anomaly detection

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AI-based Catalytic Performance Prediction for CO2 Electrochemical Reduction using Ionic Liquids

AI-based Catalytic Performance Prediction for CO2 Electroche...

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Annual Conference of Industrial Electronics Society

作者： Rabeea Jaffari Sulafa Abdalmageed Saadaldeen Mohammed Lakshitha Gunasekara Software Engineering Department Mehran University of Engineering and Technology Jamshoro Pakistan Department of Energy Technology Aalborg University Aalborg Denmark Centre for Data Analytics and Cognition La Trobe University Australia

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Recently, ionic liquids (ILs) have garnered remarkable attention as electrolytes for CO 2 electrochemical reduction (CO 2 ER) due to their unique properties viz. thermal and chemical stability, good CO 2 solubility, and their ability to reduce the reaction overpotential. The catalytic performance of ILs in CO 2 ER has been explored via experimental methods which have limitations, given the unclear understanding of the complex reaction mechanisms. Recently, Artificial Intelligence (AI) methods have gained increased attention across diverse applications including chemical engineering. These methods play a pivotal role in extracting insights, understanding patterns, and mitigating uncertainty within datasets. Hence, in this study, we investigate two categories of AI models (ML and DL) for predicting the CO 2 ER catalytic performance using ILs via Gibbs free energy and capacity. For this task, we formulate a novel dataset (CO 2 ERIL) from 90 ILs in two formats using: (1) 30 electronic and geometric IL properties, (2) IL chemical structures in SMILES data format. The dataset versions are formulated using the Conductor-like Screening Model for Realistic Solvents (COSMO-RS) and TURBOMOLE software. The prediction results of the AI models on the two dataset versions depict a similar trend in the predicted target variables signifying that the dataset format does not really affect the model performance. Moreover, the best ML model outperforms the DL model for one target variable, Free energy predictions, but lags in the other target variable, capacity predictions. Our study is novel in terms of AI methodology as well as the CO 2 ER dataset. Our research outcomes will contribute to the more effective selection of ILs for CO 2 ER catalysis.

关键词： Solvents Liquids Uncertainty Predictive models Market research Stability analysis Software Artificial intelligence Thermal stability Chemicals

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Adaptive and non-adaptive fusion algorithms analysis for digital surface model generated using census and convolutional neural networks 24

Adaptive and non-adaptive fusion algorithms analysis for dig...

引用

2021 24th ISPRS Congress Commission II: Imaging Today, Foreseeing Tomorrow

作者： Albanwan, Hessah Qin, Rongjun Geospatial Data Analytics Laboratory Department of Civil Environmental and Geodetic Engineering The Ohio State University ColumbusOH United States Department of Electrical and Computer Engineering The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States

The digital surface models (DSM) fusion algorithms are one of the ongoing challenging problems to enhance the quality of 3D models, especially for complex regions with variable radiometric and geometric distortions like satellite datasets. DSM generation using Multiview stereo analysis (MVS) is the most common cost-efficient approach to recover elevations. Algorithms like Census-semi global matching (SGM) and Convolutional Neural Networks (MC-CNN) have been successfully implemented to generate the disparity and recover DSMs;however, their performances are limited when matching stereo pair images with ill-posed regions, low texture, dense texture, occluded, or noisy, which can yield missing or incorrect elevation values, in additions to fuzzy boundaries. DSM fusion algorithms have proven to tackle such problems, but their performance may vary based on the quality of the input and the type of fusion which can be classified into adaptive and non-adaptive. In this paper, we evaluate the performance of the adaptive and nonadaptive fusion methods using median filter, adaptive median filter, K-median clustering fusion, weighted average fusion, and adaptive spatiotemporal fusion for DSM generated using Census and MC-CNN. We perform our evaluation on 9 testing regions using stereo pair images from Worldview-3 satellite to generate DSMs using Census and MC-CNN. Our results show that adaptive fusion algorithms are more accurate than non-adaptive algorithms in predicting elevations due to their ability to learn from temporal and contextual information. Our results also show that MC-CNN produces better fusion results with a lower overall average RMSE than Census. © 2021 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. All rights reserved.

关键词： Textures

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Finger Vein Authentication Based on Wavelet Scattering Networks

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Computers, Materials & Continua 2022年第8期72卷 3369-3383页

作者： Amjad Rehman Majid Harouni Maedeh Omidiravesh Suliman Mohamed Fati Saeed Ali Bahaj Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering Dolatabad BranchIslamic Azad UniversityIsfahanIran Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityIsfahanIran MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two *** biometrics such as fingerprint,palm,iris,palm vein and finger vein and other biometrics have been *** of the challenges in biometrics is physical *** of finger vein is of the biometrics least exposed to physical *** methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification *** paper presents a novel method of scattering wavelet-based identity *** wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural *** distinguishes this algorithm from other popular feature extraction methods such as deep learning methods,filter-based methods,statistical methods,etc.,is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes,even when the image is subject to serious damage such as noise,angle changes or pixel location,this descriptor still generates feature vectors in away thatminimizes classifier *** improves classification and *** proposed method has been evaluated using two databases Finger Vein USM(FV-USM)and Homologous Multimodal biometrics Traits(SDUMLA-HMT).In addition to having reasonable computational complexity,it has recorded excellent identification rates in noise,rotation,and transmission *** best,it has a 98.2%identification rate for the SDUMLA-HMT database and a 96.1%identification rate for the FV-USM database.

关键词： Biometrics finger veins wavelet scattering vein authentication disaster risk reduction

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BREAKING THE BARRIER: SELECTIVE UNCERTAINTY-BASED ACTIVE LEARNING FOR MEDICAL IMAGE SEGMENTATION

arXiv

引用

arXiv 2024年

作者： Ma, Siteng Wu, Haochang Lawlor, Aonghus Dong, Ruihai The Insight Centre for Data Analytics School of Computer Science University College Dublin Dublin Ireland School of Electrical and Electronic Engineering University College Dublin Dublin Ireland

Active learning (AL) has found wide applications in medical image segmentation, aiming to alleviate the annotation workload and enhance performance. Conventional uncertainty-based AL methods, such as entropy and Bayesian, often rely on an aggregate of all pixel-level metrics. However, in imbalanced settings, these methods tend to neglect the significance of target regions, eg., lesions, and tumors. Moreover, uncertainty-based selection introduces redundancy. These factors lead to unsatisfactory performance, and in many cases, even underperform random sampling. To solve this problem, we introduce a novel approach called the Selective Uncertainty-based AL, avoiding the conventional practice of summing up the metrics of all pixels. Through a filtering process, our strategy prioritizes pixels within target areas and those near decision boundaries. This resolves the aforementioned disregard for target areas and redundancy. Our method showed substantial improvements across five different uncertainty-based methods and two distinct datasets, utilizing fewer labeled data to reach the supervised baseline and consistently achieving the highest overall performance. Our code is available at https://***/HelenMa9998/Selective Uncertainty AL. Copyright © 2024, The Authors. All rights reserved.

关键词： Redundancy

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