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

共 1561 条记录，以下是341-350 订阅

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E-Commerce data Anomaly Detection Method Based on Variational Autoencoder 3

E-Commerce Data Anomaly Detection Method Based on Variationa...

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3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Li, Jia Liu, Shaojiang Zou, Jiajun Guangzhou Xinhua University Guangzhou China

ISBN: (纸本)9798331530280

With the rapid development of e-commerce, data security issues on e-commerce platforms have become more and more prominent, and anomalous transactions and fraudulent behaviors have brought great challenges to the stable operation of the platform. Traditional anomaly detection methods often show certain limitations when dealing with complex and largescale data. In order to solve this problem, this paper proposes an e-commerce data anomaly detection method based on variational autoencoder (VAE). VAE is able to effectively extract features and identify anomalous data by learning the potential distribution of the data. In terms of model design, this paper introduces an adaptive threshold adjustment mechanism, combined with a lightweight classification network, which improves the detection accuracy and robustness of different types of anomalies. Experimental results show that this paper's method outperforms traditional methods in several performance indicators, especially in key indicators such as precision rate, recall rate, F1 score and ROC-AUC. The method in this paper not only helps to improve the security of e-commerce platforms, but also provides a reference for anomaly detection in other fields. © 2024 ieee.

关键词： data accuracy

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Hybrid Deep Learning for Dyslexia Identification through Heterogeneous cognitive and Behavioral data Analysis 15

Hybrid Deep Learning for Dyslexia Identification through Het...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dewanjee, Swarup Muntaha, Sidratul East Delta University Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798350370249

Dyslexia is a neurological condition, that involves cognitive challenges in processing written languages, impacting reading, writing and spelling abilities. Given that it affects over 10% of global population, early detection of dyslexia in children is crucial. This research proposes an automated system leveraging multiple modalities of data including Electroencephalography (EEG), Eye Tracking (ET) and Handwriting Text images (HTI) for enhanced generalizability. After rigorous preprocessing of data, clean data were used for feature extraction that led to data splitting. For broader understanding, this study employed both Machine Learning (ML) and Deep Learning (DL) architectures and analyses the outcome. A hybrid CNN-BiLSTM DL model was proposed for early detection of dyslexia as it showcased remarkable performance across all modalities, attaining accuracies of 96.57%, 97.62%, 95.81% on EEG, ET and HTI dataset respectively. The proposed model secured its superiority over traditional ML and DL models after thorough hyper parameter tuning, including assessment of various optimizers. The robustness was validated through cross validation with multiple-folds and inspection of Receiver Operating Characteristic (ROC) curves, Confusion Matrix and Accuracy-Loss curves were accomplished for meticulous evaluation. Furthermore, this research compared the proposed model against various Late fusion techniques and Ensemble Learning techniques, in which the proposed model held its consistent superiority. Consequently, the CNN-BiLSTM hybrid model was proposed as a tool for early detection of dyslexia. © 2024 ieee.

关键词： Dyslexia Detection EEG Eye Tracking Handwriting Image Hybrid Deep Learning Learning Disability Prediction Multi-Modal

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Exploring Factors Influencing PCOS: Analysis using data Visualization Techniques 10

Exploring Factors Influencing PCOS: Analysis using Data Visu...

引用

10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Laya, N. Juliet, Sujitha Anitha, J. Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India

ISBN: (纸本)9798350384369

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder worldwide, presenting challenges such as miscarriage and fertility issues. Utilizing data Visualization (DV) tools like bar graphs, scatter plots, and unique charts, this analysis aids in comprehending PCOS demographics, variations, and correlations, contributing to enhanced understanding of the syndrome. Bubble charts depict connections between age, weight, and PCOS, while a Sankey diagram clarifies dataset interconnections, simplifying PCOS comprehension. Stressing the importance of follicle counts as a diagnostic marker, the analysis highlights a higher count's significance in PCOS diagnosis. It advocates for further research to refine our understanding, facilitating more accurate diagnoses. Given PCOS 's prevalence and associated challenges, understanding it is paramount. The chosen DV tools aim to simplify complex PCOS data for researchers and healthcare professionals. Emphasizing follicle counts' diagnostic potential underscores the importance of accurate and timely diagnoses, prompting further investigation to enhance PCOS comprehension and management. © 2024 ieee.

关键词： data visualization

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Enhanced 2D data Clustering Using FCM and K-Means with Euclidean Distance Comparison 4

Enhanced 2D Data Clustering Using FCM and K-Means with Eucli...

引用

4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Chilukuri, Rajitha Praveen, Pappula School of Cs&ai Sr University Telangana Warangal506371 India

ISBN: (纸本)9798331529635

In this study, FCM and K-means clustering are in contrast to Fuzzy C Means (FCM), which initializes cluster centrist and calculates membership degrees for each data point, allowing for soft clustering with multiple degrees of membership to multiple clusters. Together, these methods update centroid and membership degrees until convergence. By contrast, K-means employs a hard clustering approach with a random centroid start according to the Euclidean distance;the closest centroids are allocated to each data point. The centroids were then updated interactively by applying an average the data points inside every cluster from start until the focus was reached. The comparative analysis highlights the FCM's flexibility and interoperability for datasets with natural overlap, while K-means is noted for its simple-to-understand implementation and efficiency with distinct clusters. This comparison of algorithms is based on dataset characteristics, with FCM excelling in complex, overlapping scenarios and K-means in clear, well-separated scenarios. © 2024 ieee.

关键词： K-means clustering

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Towards cognitive AI Systems: Workload and Characterization of Neuro-Symbolic AI

Towards Cognitive AI Systems: Workload and Characterization ...

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2024 ieee international Symposium on Performance Analysis of Systems and Software, ISPASS 2024

作者： Wan, Zishen Liu, Che-Kai Yang, Hanchen Raj, Ritik Li, Chaojian You, Haoran Fu, Yonggan Wan, Cheng Samajdar, Ananda Lin, Yingyan Celine Krishna, Tushar Raychowdhury, Arijit Georgia Institute of Technology AtlantaGA United States IBM Research Yorktown HeightsNY United States

ISBN: (纸本)9798350376388

The remarkable advancements in artificial intel-ligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational tra-jectories, limited robustness, and a lack of explainability. To develop next-generation cognitive AI systems, neuro-symbolic AI emerges as a promising paradigm, fusing neural and symbolic approaches to enhance interpretability, robustness, and trustwor-thiness, while facilitating learning from much less data. Recent neuro-symbolic systems have demonstrated great potential in collaborative human-AI scenarios with reasoning and cognitive capabilities. In this paper, we aim to understand the workload characteristics and potential architectures for neuro-symbolic AI. We first systematically categorize neuro-symbolic AI algorithms, and then experimentally evaluate and analyze them in terms of runtime, memory, computational operators, sparsity, and system characteristics on CPUs, GPUs, and edge SoCs. Our studies reveal that neuro-symbolic models suffer from inefficiencies on off-the-shelf hardware, due to the memory-bound nature of vector-symbolic and logical operations, complex flow control, data dependencies, sparsity variations, and limited scalability. Based on profiling insights, we suggest cross-layer optimization solutions to improve the performance, efficiency, and scalability of neuro-symbolic computing. Finally, we discuss the challenges and potential future directions of neuro-symbolic AI from both system and architectural perspectives. © 2024 ieee.

关键词： Program processors

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2024 9th international conference on Image, Vision and computing, ICIVC 2024

2024 9th International Conference on Image, Vision and Compu...

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9th international conference on Image, Vision and computing, ICIVC 2024

ISBN: (纸本)9798350385991

The proceedings contain 78 papers. The topics discussed include: Road Obstacle detection based on 3D information recovery;automated detection of corn leaf blights in natural light images captured by drones using deep learning frameworks;millimeter wave point cloud processing method based on density clustering;generating transferable 3D adversarial point clouds based on multi-scale features;3D reconstruction and path planning of complex parts for laser cladding;implementation of diffusion model in realistic face generation;3D facial model reconstruction and expression generation based on Dirichlet free-form deformations algorithm;occluded image reconstruction under single sample condition using diffusion models aiming facial recognition;and point-cloud data reduction based on projection feature weighting.

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Interactive Visualization of BioMedical data

Interactive Visualization of BioMedical Data

引用

2024 ieee international conference on Big data, Bigdata 2024

作者： Islam, Mohammad Shafiqul Ibn Rafiq, Rahat Tusch, Guenter Grand Valley State University College of Computing MI United States

ISBN: (纸本)9798350362480

Public databases such as the NCBI Gene Expression Omnibus (GEO) house millions of experimental gene expression datasets invaluable for transcriptome meta-analysis, enabling researchers to identify genes, pathways, and biomarkers with enhanced accuracy. However, while these datasets offer great potential, finding relevant biological pathways in disease-related contexts remains challenging. To address this, we introduce a web-based visualization tool that utilizes PubMed abstracts to extract key biomedical entities, such as genes, pathways, diseases, species, and chemicals, and represent their relationships through an interactive graph. By leveraging PubMed's annotated MeSH terms, our tool allows users to explore the literature more intuitively, bypassing traditional dataset search limitations and focusing on published results. The tool transforms complex query outputs into a graph-based network, providing a visual interface for navigating connections between biological entities. This interactive approach helps researchers to analyze relationships within large-scale biomedical data and uncover potential pathways for drug repurposing and disease understanding. We discuss the tool's design using ***, its application in biomedical research, and the challenges faced when visualizing large datasets, offering new insights for improved data interpretation and exploration. © 2024 ieee.

关键词： BioInformatics BioMedical data Visualization PubMed

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Analysis and Treatment of Missing data in Happiness Score Using Machine Learning 21

Analysis and Treatment of Missing Data in Happiness Score Us...

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21st ieee international conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Touati, Wassim El Touati, Yamen Hadj-Alouane, Nejib Ben Jemai, Abderrazek Polytechnic School of Tunisia Carthage University SERCOM Laboratory Tunis Tunisia Northern Border University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia ENIT University of Tunis EI Manar OASIS Laboratory Tunisia American University in Dubai Electrical Engineering and Computer Engineering Department Dubai United Arab Emirates

ISBN: (纸本)9798350378078

Machine learning, a vital part of artificial intelli-gence, improves our ability to make predictions from complex data. The success of these predictions relies heavily on the model's fit with its data and the data's consistency. Accurate predictions depend on closely matching the model to the data patterns. However, missing data can seriously disrupt the accuracy of predictions, causing errors. To address this, we recommend adopting a proposed framework that enhances predictions by effectively filling in missing data. Our objective is to refine the accuracy of our predictions, especially in scenarios where the data might be incomplete and features exhibit patterns of dependency. In the context of missing data, our approach concentrates on enhancing regression performance to minimize the effects of missing data as effectively as possible. © 2024 ieee.

关键词： Adversarial machine learning

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Machine Learning-Based Security Mechanism for Detecting Phishing Attacks

Machine Learning-Based Security Mechanism for Detecting Phis...

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2024 ieee international conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Bhatt, Pragya Obaidat, Mohammad S. Dangwal, Gagan Das, Ashok Kumar Wazid, Mohammad Sadoun, Balqies Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248 002 India University of Science and Technology Beijing Beijing100083 China SRM University Kattankulathur603203 India Amity University Noida201301 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India College of Engineering Al-Balqa' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350349832

Phishing attacks pose a serious threat to online users by impersonatina trustworthy entities and stealing sensitive data. This can result in reputational damage, financial loss, ransomware, or additional spyware outbreaks. Phishing scams take into consideration roughly 22% of all data violations, making it one of the most prominent cybercrimes, in accordance with the FBI's 2021 IC3 report. According to phishing email statistics, roughly 1.2% of all emails received are fraudulent, resulting in 3.4 billion phishing emails per day. One out of every 4,200 emails sent is almost certainly a phishing attempt. cognitive methods, such as machine learning (ML) and deep learning (DL), are widely used in cybersecurity to extract information and detect possible intrusions and phishing attempts. This research investigates the procedure of employing machine learning methods for recognizing URLs that are used for phishing attacks. We have used the Pristine and Malicious URLs dataset to train, test, and evaluate the performance of a set of algorithms to develop a robust binary class URL phishing attack detection mechanism. The obtained results show that the proposed mechanism outperforms the other existing competing mechanisms with 99.6% accuracy. © 2024 ieee.

关键词： Phishing

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The Significance of Big data Analytics in the Global Healthcare Market

The Significance of Big Data Analytics in the Global Healthc...

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2024 international conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Manikandan, M. Venkatesh, P. Illakya, T. Krishnamoorthi, M. Senthilnathan, C.R. Maran, K. Sri Sairam Engineering College Department Of Management Studies Chennai India Sri Sairam Institute Of Technology Chennai India Kristu Jayanti College Bengaluru Faculty Of Commerce And Management Bengaluru India

ISBN: (纸本)9798350352689

Large The worldwide healthcare industry cannot function without big data analytics. It completely changes the way healthcare is provided, administered, and studied. Through the use of big data analytics, healthcare firms are able to enhance the entire sector by capitalising on vast amounts of data. Personalised therapy, increased operational efficiency, and decreased costs are all ways in which big data analytics may enhance patient care and outcomes. Healthcare providers can utilise it to make data-driven decisions, identify patterns, and develop targeted remedies for complex problems. Drug development, collaborative research, and the creation of new technologies and therapies are all sped up by big data analytics. New treatment and disease prevention strategies can be developed with the support of healthcare organisations through advanced analytics. To realise its full potential, big data analytics in healthcare must conquer challenges related to data privacy, interoperability, and data governance. Consistent spending on healthcare IT, training for new employees, and adherence to regulations are all necessary for the ethical and efficient use of big data. There is great promise for innovation, better patient outcomes, and better public health as a result of big data analytics in the healthcare industry around the world. As healthcare systems around the world face increasingly complex challenges and possibilities, data-driven approaches will become increasingly important. © 2024 ieee.

关键词： international trade

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