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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

共 718 条记录，以下是331-340 订阅

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Noncognitive and Affective Attributes of Caregivers Enrolled in Engineering and computing Programs 53

Noncognitive and Affective Attributes of Caregivers Enrolled...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Kim, Sanga Kaczmarczyk, Lisa Rivera, Jessica Gates, Ann Q. Computing Alliance of Hispanic Serving Institutions The University of Texas at El Paso El Paso United States Lisa Kaczmarczyk PhD Consulting Llc San Diego United States The University of Texas at El Paso Office of the Provost El Paso United States

ISBN: (纸本)9798350336429

Historically, student caregivers have been described as parents over the age of 25. However, the role of a caregiver may extend beyond a parental responsibility to include caring for other family members and may include students who are of 'traditional college age.' Failing to acknowledge and provide appropriate support for student caregivers can be particularly problematic for students in engineering and computing. Engineering and computing programs often require extensive work that must be completed outside of class. Meeting these demands is particularly challenging for students with caregiving responsibilities. However, there are significant gaps in our understanding of college experiences and college outcomes of student caregivers enrolled in engineering and computing programs. To understand the experiences of undergraduate student caregivers in engineering and computing fields, this study explored the differences in non-cognitive and affective factors between students who have caregiving responsibilities and those who do not have responsibilities during college. We particularly focused on student caregivers in the College of Engineering at an R1 Hispanic-Serving Institution (HSI). The context of HSIs is important given that these institutions enroll a large number of post-traditional college students, students who have significant life responsibilities that are often at odds with the demands of college. Additionally, this study incorporates a Hispanic-servingness approach, a framework centered on institutions acknowledging the realities and needs of the students they serve. Using data from a single institution, we asked the following research question: To what extent are caregiving responsibilities related to undergraduate engineering and computing students' non-cognitive and affective factors? Our findings indicate that the experiences of students with caregiving responsibilities differ on five non-cognitive and affective factors: open-mindedness, engineering/

关键词： Caregivers Engineering & computing Education Family responsibility Hispanic servingness Non-cognitive & Affective Factors Post-traditional students

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Heart Disease Predictive Analysis Using Machine Learning Approaches 6

Heart Disease Predictive Analysis Using Machine Learning App...

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6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Rana, Anamika Malik, Sushma Chauhan, Madhu Kumar, Prashant Maharaja Surajmal Institute Department of Computer Application New Delhi India Institute of Innovation in Technology & Management Department of Computer Application New Delhi India Jagannath International Management School New Delhi India

ISBN: (纸本)9798350304480

Machine Learning (ML) has found widespread applications in the healthcare sector worldwide, including the diagnosis and treatment of heart diseases, locomotor disorders, and various other medical conditions. ML methods have revolutionized healthcare by enabling the analysis of large and complex medical datasets, leading to valuable insights and predictions that aid healthcare professionals in providing better patient care. ML's ability to analyze vast amounts of healthcare data, uncover patterns, and make predictions has significant potential to improve patient outcomes, optimize medical workflows, and advance medical research. However, it's essential to address privacy and ethical considerations when using ML in healthcare, ensuring the responsible and secure use of sensitive patient information. Supervised Learning methods like SVM, Random Forest, and Logistic Regression are used for the analysis of the dataset downloaded from Kaggle. The various performance parameters such as Precision, F-1 score, Accuracy, and Recall were used to compare the performance of different ML classification techniques. Among the various methods evaluated, the Random Forest classification algorithm was found to outperform the other methods across the fourteen available parameters. © 2023 IEEE.

关键词： Heart Disease Logistic Regression Machine Learning (ML) Random Forest SVM

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IT and I 2023 - Selected Papers of the 10th international Scientific conference "Information Technology and Implementation", Proceedings

IT and I 2023 - Selected Papers of the 10th International Sc...

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10th international Scientific conference "Information Technology and Implementation", IT and I 2023

The proceedings contain 42 papers. The topics discussed include: towards software development for maintainability evaluation of business process models using fuzzy logic;on Newman’s lemma and non-termination;complex system for radio location field quality assessment;data processing of digital productions under conditions of uncertainty using system information models;information technology development for social services consumers' choice;enhancing foreign language learning in Ukraine: immersive technologies as catalysts for cognitive interest and achievement;a methodical approach to designing secure adaptive embedded systems implemented via reconfiguring their structures;assessing the readiness of UAS operators based on the simulator training results;methods for automatically determining the level of disease damage to plant leaves from their raster image;and application of machine learning techniques for forecasting electricity generation and consumption in Ukraine.

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CMG: Class and mask guided network for hippocampus and its subregions segmentation 4

CMG: Class and mask guided network for hippocampus and its s...

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4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

作者： Wang, Zimu Zuo, Zhentao Sun, Dengdi Zhou, Tiangang AHU-IAI Ai Joint Laboratory Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China State Key Laboratory of Brain and Cognitive Science Institute of Biophysics Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computing School of Artificial Intelligence Anhui University Hefei230601 China

ISBN: (纸本)9798400708831

The hippocampus is involved in many recognition functions. Accurate segmentation of the hippocampus and its subregions is crucial for exploring the cognitive functions of hippocampus and early diagnosis of related diseases. However, this task faces challenges such as small target volume and blurred boundaries between its subregions. We introduce the Class and Mask Guided Network for precise hippocampal subregion segmentation in MRI images. Our network is based on the U-Net architecture and incorporates Class Guided Blocks and Mask Guided Blocks in the decoder section to address the challenges faced in hippocampus segmentation. Among them, the Mask Guide Block allows each subregion to be individually feature extracted, with channel attention competing within subregions, mitigating the issue of texture blurriness, and the Class Guide Block assigned a class token to each subregion and interacts with the image to ensure that the model does not overlook small subregions. Furthermore, we obtained and processed data from public datasets to create a dataset for fine segmentation of hippocampal subregions. We conducted comparative evaluations of our model against other state-of-the-art medical segmentation models using these datasets, and our model exhibited superior performance in terms of Dice and HD_95 metrics (in both the healthy and diseased population datasets, the average dice coefficient increased by 1.1%, 1.2%, and the average HD_95 decreased by 0.04, 0.35). © 2023 ACM.

关键词： Semantic Segmentation

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Factor Space: cognitive Computation and Systems for Generalized Genes 1st

Factor Space: Cognitive Computation and Systems for Generali...

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1st international conference on cognitive Computation and Systems, ICCCS 2022

作者： Wang, Peizhuang Zou, Xiaohui Zeng, Fanhui Guo, Sizong Shi, Yong He, Jing Institute of Intelligent Engineering and Math Liaoning Technology University Fuxin China Searle Research Center No. 100 Renshan Road Guangdong Hengqin519000 China Chinese Academy of Sciences University Beijing China Oxford University Oxford United Kingdom

ISBN: (纸本)9789819927883

The purpose is to generalize and expand the vector space to factor analysis, and then simplify the knowledge graph to the factor graph, focusing on the formal expression of concept, which is essentially an attempt to construct the mathematical foundation of the three categories of information, intelligence and data science & technology. The method is: first, it is clear that the vector space can only be used for mathematical analysis of quantitative real variables, and it should be generalized and extended to the qualitative causal analysis with the factor space, and that could be regarded as a special factor space;then, it is pointed out that the advantages and disadvantages of the knowledge graph, which should be simplified to the factor graph;finally, the formal expression of the factor genealogy focusing on concepts, it is essentially an attempt to construct the mathematical basis of the three major categories of science and technology of information, intelligence and data that that is different from material science and technology. The result is the discovery of the mathematics of factor as a meta-word, namely factor space. Its significance lies in: its mission is to provide the smart mathematical tools as cognitive computing system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vector spaces

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NCDFSA: Neural cognitive Diagnostic Focusing on Students' Attention to Knowledge Concepts

NCDFSA: Neural Cognitive Diagnostic Focusing on Students' At...

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2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wei, Guoxiong He, Zhenyu Guan, Quanlong Fang, Liangda Luo, Weiqi Chen, Guanliang College of Information Science and Technology Jinan University Guangdong Guangzhou510632 China Guangdong Institution of Smart Education Jinan University Guangdong Guangzhou510632 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Monash University Faculty of Information Technology Australia

ISBN: (纸本)9798350337020

The primary aim of cognitive diagnosis is to predict students' performance and knowledge structures by analyzing their learning behavior and answering results, thereby enabling educators can provide personalized instruction. Scholars have proposed many cognitive diagnostic models. However, most of the models do not fully extract and utilize the relevant data and parameters of cognitive diagnosis. Moreover, some models only rely on artificially designed simple functions to analyze the cognitive process of students, which cannot fully capture the complex relationship between students and the exercises. To address these limitations, this paper proposes a neural cognitive diagnostic model named NCDFSA, which focuses on students' attention to knowledge concepts. The model utilizes neural networks to diagnose students' knowledge and considers students' implicit relationships with knowledge concepts. We introduce the concept of attention matrix(AM) and define the importance of knowledge concepts by the frequency of usage of knowledge concepts to improve the prediction effect. This paper compares NCDFSA with existing classical models on four real datasets and finds that the model has higher accuracy and rationality in predicting student performance. © 2023 IEEE.

关键词： Attention Matrix cognitive Diagnosis Knowledge Concepts Neural Network

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2023 4th international conference on Computer Engineering and Application, ICCEA 2023

2023 4th International Conference on Computer Engineering an...

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4th international conference on Computer Engineering and Application, ICCEA 2023

ISBN: (纸本)9798350347548

The proceedings contain 175 papers. The topics discussed include: view self-attention network for 3d object recognition;vulnerability assessment for directed weighted complex network by quantifying importance of community nodes;network node importance evaluation based on directed overlapping box coverage algorithm;incremental learning for multi-view 3d shape recognition based on knowledge distillation;multi-armed bandit with time-variant budgets;research on gas outburst prediction model based on improved beluga whale optimization algorithm and temporal convolutional network;two-way prototypical network based on word embedding mixup for few-shot event detection;safety assessment of human with metal implants exposed to magnetic field based on numerical method;topological robustness of supply chain networks under disruptions;research on evaluation strategy of heterogeneous computing chip based on improved common origin grey clustering;joint task offloading, resource allocation and data caching in MEC-assisted vehicular network;dynamic detection method for android terminal malware based on native layer;and search recommendation model based on attention compressed interaction network.

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Deep Fuzzy cognitive Map methodology for Non-Small Cell Lung Cancer diagnosis based on Positron Emission Tomography imaging 14

Deep Fuzzy Cognitive Map methodology for Non-Small Cell Lung...

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14th international conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Feleki, Anna Apostolopoulos, Ioannis D. Papageorgiou, Elpiniki Moustakidis, Serafeim Papathanasiou, Nikolaos D. Apostolopoulos, Dimitris J. Kokkinos, Konstantinos Papandrianos, Nikolaos University of Thessaly Department of Energy Systems Larissa41500 Greece Aideas Oü Tallinn Estonia School of Medicine University General Hospital of Patras University of Patras Department of Nuclear Medicine Patras Greece

ISBN: (纸本)9798350318067

Non-Small Cell Lung Cancer (NSCLC) constitutes the major cause of cancer deaths worldwide in both men and women, accounting for approximately 85% of the total lung cancers. Early characterization of Solitary Pulmonary Nodules (SPNs) enables early treatment and can increase the survival rate. The present study focuses on classifying 2D SPN representations from a PET/CT scanner into two possible classes, namely benign and malignant SPN s. In this regard, 2D images were acquired from the scanner, where the SPN is alienated. Also, the SUVmax and SPN diameter data were included. A pre-trained RBG convolutional neural network processed the images to predict the class of each instance. The predictions were handled by a Fuzzy cognitive Map with Particle Swarm Optimization in combination with the SUVmax and SPN diameter to provide a final decision. The proposed model, DeepFCM, has been evaluated with robust metrics like accuracy, loss, sensitivity, specificity, and precision and extracted 94.71±2.99, 0.05, 92.06, 96.98, 91.91, accordingly. DeepFCM provides explainability and transparency by modeling a complex system in concepts and providing the importance of each feature. © 2023 IEEE.

关键词： Fuzzy cognitive Maps

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An AI-Centred Adaptive cognitive Network Traffic Modelling & Measurement Technique for EIS 3

An AI-Centred Adaptive Cognitive Network Traffic Modelling &...

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3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Bharathi, L. Vasoya, Nimish H. Kansal, Lavish Gehlot, Anita Swathi, Bajila Khan, Suyeb Ahmed Ramachandra College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Eluru India Children's University Department of Student Startup Gujarat Gandhinagar382021 India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Hoshiar Nagar Amritsar Baba Kuma Singh Ji Engineering College India

ISBN: (纸本)9798350399264

The IoT is the foundation of the Enterprise Information System (EIS), which collects a lot of business information. For EIS, timely, accurate data analysis transfer are crucial. For net management traffic optimization in EIS, Network infrastructure traffic is crucial. Nevertheless, the needs for fine-grain traffic monitoring are in conflict with the monitoring overheads precision. Long-standing promises of artificial intelligence (AI) include the ability to recognise the physical attributes of network activity take appropriate steps in response. In order to decrease network monitoring overheads increase measurement precision, we present an AI centred between light Adapt Measuring System (ALAMM) for SDN in this study. Initially, we simulate forecast the network congestion information using AI. We suggest an adaptive strategy to choose the rate of traffic sample centred on the outcomes of traffic forecasting. Next, we instruct switching devices to evaluate the coarse-grain traffic of flow pattern networks using the measuring primitives. In order to retrieve the Fine grained (FG) assessment optimise the measured values, the matrices refilling optimizing approach is finally suggested. Simulated outcomes demonstrate that our method can accurately and efficiently determine network activity. © 2023 IEEE.

关键词： Software defined networking

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Prospects and Challenges in COVID-19 Study: A Review Based on Influencing Factors and Analysis Methods 23

Prospects and Challenges in COVID-19 Study: A Review Based o...

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6th international conference on Big data Technologies, ICBDT 2023

作者： Liu, Lisai Zhu, Dashuang Zheng, Tianlong Niu, Yiheng Zhang, Zhao Zhang, Zihan Zhu, Xu Liu, Sheng College of Computer Science and Technology Huaibei Normal University Huaibei235000 China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei235000 China

ISBN: (纸本)9798400707667

The unprecedented Coronavirus Disease 2019 had spread and diffused in many countries and regions worldwide, posing a serious threat to people's health and lives. This review presents a systematic analysis and summary of representative research on the role of AI technology in the prediction and containment of COVID-19 and influencing factors of its transmission. To further explore the influences of multiple factors on the incidence and transmission of COVID-19, we used total cases per million (TCPM) and total deaths per million (TDPM) as dependent variables and influencing factors as explanatory variables to conduct multiple linear regression, and constructed four training models: xgboost model (XGBR), random forest regenerator (RFR), extra trees regenerator (ETR), and gradient boosting regenerator (GBR) to predict the test data. This study highlights the contribution and application prospects of AI related technologies in the study of the spread of COVID-19, which will help deepen the global understanding of the COVID-19 pandemic, and provide a reference for curbing the wanton spread of COVID-19 and other diseases again. © 2023 ACM.

关键词： COVID-19

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