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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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ENCL: An Extensive Learning Based Methodology to Predict Liver Disease by Using Enhanced Neural Classification Logic

ENCL: An Extensive Learning Based Methodology to Predict Liv...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ramachandran, R. Bhuvaneswari, J. Prasanth, K. Glory Sheela Shantha Kumari, P.K. Parvathi, A. Lakshmi Manikandan, R. Rajalakshmi Engineering College Department of Biomedical Engineering Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India Department of Mathematics Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India Qis College of Engineering and Technology Department of Cse AndhraPradesh Ongole India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331543617

Effective treatment and handling require precise forecasting and early diagnosis of liver diseases. This paper introduces the Enhanced Neural Classification Logic (ENCL), a novel deep learning framework that is intended to enhance the precision of liver disease predictions. The proposed model is cross-validated with the conventional learning approach, the Random Forest Classifier (RFC), to assess its efficiency. The proposed model ENCL utilizes advanced neural network architecture with optimizations a sequence, to fine-tune classification performance. Our approach includes a multi-layered neural network coupled with improved feature extraction, dynamic attention systems, and refined loss calculus for improving model sensitivity and specificity. We work on a substantially large dataset of the records pertinent to diseases of the liver, which contains both structured clinical data and unstructured imaging data for the assessment of ENCL. Results have indicated that ENCL is significantly better than traditional as well as state-of-the-art approaches regarding predictive reliability as well as classification accuracy. This paper is aimed at describing how the enhanced architecture of proposed Enhanced Neural Classification Logic can avail in order to address such complex issues of prediction within the medical domain towards effective care for the patients. © 2024 ieee.

关键词： Decision trees

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Performance of 16S rRNA Taxonomic Classification Tools in Metagenomic data From a Beetle's Gut Microbiome 6

Performance of 16S rRNA Taxonomic Classification Tools in Me...

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6th ieee international conference on BioInspired Processing, BIP 2024

作者： Ramirez-Mata, Nicole Murillo-Cruz, Catalina Pinto-Tomas, Adrian Calderon-Osorno, Melany Advanced Computing Laboratory San José Costa Rica School of Medicine Universidad de Costa Rica Research Center in Microscopic Structures Biochemistry Department San José Costa Rica San José Costa Rica

ISBN: (纸本)9798350353709

Understanding symbiosis between insects and their gut microbiota is critical for uncovering ecological and evolutionary processes. Taxonomic classification of microbial communities using the 16S rRNA gene is an initial standard approach, yet the accuracy and limitations of bioinformatic tools for this purpose, particularly in non-model organisms like beetles, are not well-studied. This study provides a novel benchmarking of three widely used classification tools-QIIME2, Mothur, and VSEARCH-on beetle gut metagenomic data, revealing new insights into their performance. Our results show that all tools achieved an ASV retrieval accuracy above 99 % with consistent taxonomic assignments. Based on these findings, all three tools are suitable for analyzing 16S data from insect gut samples, with the choice depending on specific research objectives or computational limitations. We emphasize the need for careful selection of reference databases to maximize taxonomic accuracy, contributing valuable guidelines for future studies involving complex microbial communities in underexplored hosts. © 2024 ieee.

关键词： Biotic

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data-Driven with IoT Sensing and Deep Learning Model for Dynamic Skin Cancer Diagnosis 2

Data-Driven with IoT Sensing and Deep Learning Model for Dyn...

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2nd international conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kumar, S. Mohan Thenmozhi, R. Balaji Damodhar, T.S. Epsiba, P. Malathi, N. Meenakshi, B. Cmr University Karnataka Bangalore India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chengalpattu India Madanapalle Institute of Technology & Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

This research introduces an innovative approach to continuously assess the condition of the skin barrier by combining advanced deep learning algorithms with Internet of Things (IoT) sensor technologies. For the system to function, it constantly uses IoT sensors to collect real-time data from various skin metrics, including moisture levels, pH balance, and environmental conditions. A complex analysis, made possible by deep learning, may be built using this vast amount of information. A type of deep learning technique that works well with data, recurrent neural networks (RNNs) are at the heart of this system. RNNs capture subtle changes over time in skin health by capitalizing on the temporal dependsequentialencies in skin data. Training RNNs iteratively on historical data covering a wide range of skin types and conditions allows them to detect patterns indicating healthy and damaged skin barriers. The proposed system has several benefits, such as individualized skincare suggestions, real-time information to guide adaptive skincare regimens, and continuous monitoring to identify skin concerns early. The system can discover complex patterns and correlations in the skin data, which results in better skincare solutions. © 2024 ieee.

关键词： Recurrent neural networks

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SECURING complex IOT PLATFORMS WITH TOKEN BASED ACCESS CONTROL AND AUTHENTICATED KEY ESTABLISHMENT

SECURING COMPLEX IOT PLATFORMS WITH TOKEN BASED ACCESS CONTR...

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2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： William, P. Kotiyal, Arnav Al-Jawahry, Hassan M. Shrivastava, Anurag Yadav, Kanchan Aswini, Kilaru Department of Information Technology Sanjivani College of Engineering SPPU Kopargaon Pune India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Department of Electrical Engineering GLA University Mathura India Institute of Aeronautical Engineering Dundigal Hyderabad India

ISBN: (纸本)9798350355338

The functionality of a security architecture for advanced Internet of Things (IoT) devices is discussed in this article. Validated key configuration and access control that is based on tokens are absolutely necessary in order to guarantee the safety of this idea. In order to keep up with the rapid proliferation of the IoT ecosystem, it is imperative that the availability, integrity, and security of data be maintained at all times. Token-based access control is an opportunity to provide a workable solution to one of the most common concerns raised about the safety of the internet of things (IoT). This article's objective is to improve the safety of the Internet of Things (IoT) by analyzing the guiding principles, implementation tactics, and benefits offered by a variety of security measures that may be included into complex IoT systems. In this article, we explain how token-based access control and verified key generation may be merged into a single, all-encompassing method to strengthen the safety of platforms used for the internet of things (IoT). Token-based access control provides flexibility and adaptability when it comes to regulating rights in dynamic settings that are linked to the Internet of Things. In order to shield oneself from potential dangers such as man-in-the-middle attacks, one must take the necessary precautions, such as generating authorized keys to keep the privacy of one's communication routes secret. This research uses performance measurements, comparisons of code sizes, and statistical analysis to evaluate and examine the efficacy and efficiency of a variety of different security methods. The findings demonstrate how readily the recommended methodology may be adapted to meet the requirements of a wide variety of Internet of Things (IoT) systems. This paper is a helpful resource for enterprises and developers who work in dynamic situations and are interested in strengthening the security of Internet of Things deployments. © 2024 ieee.

关键词： Access control

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Penta-Encoder with Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation 17

Penta-Encoder with Medical Transformer for Incomplete Multim...

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17th ieee international conference on Signal Processing, ICSP 2024

作者： Yu, Guohui Wang, Lihui Chen, Qijian Wang, Li Tang, Kun Cheng, Xinyu Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France

ISBN: (纸本)9798350387384

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. The MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. The MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. The convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets. © 2024 ieee.

关键词： Semantic Segmentation

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Enhancing CS Education with LAs Using AI-Empowered AIELA Program 54

Enhancing CS Education with LAs Using AI-Empowered AIELA Pro...

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54th ieee Frontiers in Education conference, FIE 2024

作者： Ramasamy, Vijayalakshmi Kulpinski, Eli Beaupre, Thomas Antreassian, Aaron Jeong, Yunhwan Clarke, Peter J. Aiello, Anthony Ray, Charles College of Engineering and Computing Georgia Southern University Statesboro United States University of Wisconsin-Parkside Kenosha United States School of Computing and Information Sciences Florida International University FL United States

ISBN: (纸本)9798350351507

This innovative practice full paper delves into the transformative role of Learning Assistants (LAs) in Computer Science education, focusing on enhancing student engagement and improving learning outcomes. The LA model, which aligns with Vygotsky's Social Constructivist Learning Theory, fosters an environment of student-centered learning and social interaction. In a pilot study conducted in Spring 2024 at a public university, the LA program is implemented in two computer science courses. A quasi-experimental design has been used to evaluate the impact of LA-facilitated team activities on student learning outcomes. The study compares a control group receiving traditional instruction with an experimental group participating in LA-facilitated team activities. The experimental group engaged in weekly team-based activities, guided by LAs and faculty, to reinforce class concepts and promote collaboration among team members. Student engagement and learning have been evaluated using feedback from students and LAs collected through Discussion Boards (DBs). Preliminary findings suggest that LA-facilitated in-class activities promote active learning and enhance problem-solving skills. LAs provide valuable support and guidance to students, particularly those struggling to understand complex concepts. The study tested a working model of AIELA, an innovative AI-powered chatbot that assists human LAs in supporting students through knowledge-reinforcing questions and multimodal data analysis, powered by OpenAI API's gpt-4-turbo model. This research is a step towards embracing the challenges of modern CS education, inspiring further innovation in this critical field. The findings will benefit educators seeking innovative strategies to enrich student engagement and learning in engineering and computing disciplines. © 2024 ieee.

关键词： Active learning

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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

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5th international conference on Circuits, Control, Communication and computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 ieee.

关键词： Convolutional neural networks

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Assessing GPT's Legal Knowledge in Japanese Real Estate Transactions Exam

Assessing GPT's Legal Knowledge in Japanese Real Estate Tran...

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2024 international conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Inoshita, Keito Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9798331533137

In recent years, large language models (LLMs) like GPT-3.5 and GPT-4, developed by OpenAI, have demonstrated impressive capabilities in tasks such as natural language understanding, code generation, and even passing professional exams. Trained on vast datasets, these models have shown high accuracy across various fields. One challenging area for LLMs is professional certification exams, which require specialized knowledge and understanding of complex regulations. The Real Estate Transaction Specialist Examination (RETSE) in Japan is a key certification for professionals managing real estate transactions. Passing the RETSE qualifies individuals to handle legal responsibilities like ensuring the legality of property deals. The exam covers civil law, tax law, and real estate regulations, making it a comprehensive test of legal knowledge. With a pass rate of around 15%, it is considered one of Japan’s more difficult professional exams, making it a strong benchmark for evaluating LLM performance. This study evaluates the ability of GPT-3.5 and GPT-4 to answer RETSE questions, comparing their performance to official passing criteria and analyzing the effect of prompt engineering. While GPT-4 outperformed GPT-3.5, neither model met the passing standard. Prompt strategies such as "Consider customary laws" improved response accuracy, especially for complex legal questions. These findings suggest that while GPT models cannot fully replace human expertise in legal exams like RETSE, they offer potential as supplementary learning tools for verifying basic knowledge. To mitigate the risk of job displacement by AI, it is crucial for professionals to enhance their expertise in areas where LLMs struggle, such as recent legal changes and ethical judgment. This study highlights both the limitations and potential of GPT in legal contexts, providing insights into how AI can support learners and practitioners in real estate law. ©2024 ieee.

关键词： Geological surveys

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Research on the Applicability of Convolutional Neural Network Models in the Integration of Industry and Education in Vocational Colleges

Research on the Applicability of Convolutional Neural Networ...

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cognitive computing and complex data (ICCD), international conference on the

作者： Ruishuang Qi Lili Dong Tiantian Yang School of Economics and Management Beijing Polytechnic Beijing China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

At present, the integration effect of industry and education in vocational schools is not satisfactory, and there are still a series of problems in the integration process, such as inconsistency, lack of cooperation, lack of close coordination, and insufficient cooperation, which cause the integration of industry and education to become superficial. This study focuses on constructing and validating an evaluation index system for the integration of industry and education in vocational colleges. It also proposes an evaluation algorithm based on convolutional neural networks. The algorithm uses convolutional neural networks to determine the fusion evaluation score and obtain the corresponding evaluation level. The effectiveness of the proposed evaluation algorithm was demonstrated through instance testing. The evaluation index system developed in this research is rational and provides significant guidance for improving the integration of industry and education in vocational schools.

关键词： Industries Education Production Indexes Convolutional neural networks Stakeholders cognitive systems Investment Testing

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Access Control Attribute Mining Model Based on ALBERT-PFN

Access Control Attribute Mining Model Based on ALBERT-PFN

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cognitive computing and complex data (ICCD), international conference on the

作者： Zhaoxiong Bu Li Ma Jihui Li Linjiu Guo Yong Zhong Zhongrong Zeng Foshan University Foshan China Guangdong Strong Metal Technology Co. Ltd Foshan China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

In the evolving field of cybersecurity, efficiently managing Attribute-Based Access Control (ABAC) remains crucial due to complex attribute relationships. This paper introduces the ALBERT-PFN model, a novel access control attribute mining approach that employs deep learning to transform ABAC challenges into joint entity and relationship extraction tasks. The model leverages ALBERT for contextual word embeddings and a Positional Feature Network (PFN) that dynamically interprets relationships between entities, significantly enhancing both the accuracy and automation of attribute mining. Experimental results show that ALBERT-PFN achieves a precision of 88.13%, a recall of 91.80%, and an F1 score of 89.93% in entity recognition, outperforming existing methods. In relationship extraction, it further excels with a precision of 81.55%, a recall of 72.94%, and an F1 score of 77.01%, underscoring its effectiveness in developing dynamic and precise ABAC policies.

关键词： Access control Deep learning Computational modeling Transforms Feature extraction data models data mining Computer security cognitive systems Context modeling

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