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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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Lightweight Spiking Neural Network Based Detector for Interweave cognitive Radios

Lightweight Spiking Neural Network Based Detector for Interw...

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ieee international conference on Communications (ICC)

作者： Md Mehedi Hassan Galib Mohamed Younis Sultan Ahmed Dept. of Computer Science and Electrical and Engineering University of Maryland Baltimore County Baltimore Maryland Information Systems Department University of Maryland Baltimore County Baltimore Maryland

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The major advances in wireless communication technology have led to increased adoption across almost all application domains. However, the massive growth has caused spectrum scarcity despite the fact that many of the frequency bands are not fully-utilized. cognitive radios have emerged as a viable means to support dynamic spectrum access. Particularly, supporting opportunistic access through passive spectrum monitoring is of great interest. Existing techniques for detecting white space either require modification to commodity radio transceivers, or involve computationally complex models that do not suit resource-constrained devices. This paper opts to fill the technical gap by proposing a novel lightweight white space detector that employs spiking neural networks (SNN). SNN is a bio-inspired technique for creating data-driven models. The proposed design relies on the sensed energy in the medium to determine whether a primary user is active. The validation results using live LTE data demonstrate the effectiveness of our novel detector. Suitability for edge devices is confirmed through implementation on a Raspberry-PI platform.

关键词： Accuracy Biological system modeling Image edge detection Computational modeling Dynamic spectrum access Detectors Spiking neural networks

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Machine Learning for Early Dementia Detection Integrating Clinical data and MRI Imaging

Machine Learning for Early Dementia Detection Integrating Cl...

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ieee Punecon

作者： Vaidehi Sarag Chhaya Gosavi Jahnavi Patil Department of Computer Engineering Cummins College of Engineering for Women Pune India

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

Dementia, particularly Alzheimer's disease, is a leading cause of cognitive decline, affecting millions globally. Early detection is vital for effective management, yet it remains challenging due to the complex nature of the disease. This paper explores the application of machine learning techniques to predict dementia, focusing on integrating clinical data and MRI images. We propose a hybrid model that combines Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) to enhance prediction accuracy. The study addresses challenges such as data heterogeneity and model interpretability, highlighting the potential of multimodal approaches in dementia research. The results demonstrate the effectiveness of combining clinical and imaging data, offering insights into the future of personalized treatment for dementia.

关键词： Support vector machines Accuracy Magnetic resonance imaging Machine learning Transforms Predictive models data models Vectors Convolutional neural networks Dementia

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A cognitive and Machine Learning-Based Software Development Paradigm Supported by Context 43

A Cognitive and Machine Learning-Based Software Development ...

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43rd ieee/ACM international conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP) / 43rd ACM/ieee international conference on Software Engineering - New Ideas and Emerging Results (ICSE-NIER)

作者： Melo, Glaucia Alencar, Paulo Cowan, Donald Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON Canada

ISBN: (纸本)9780738133249

Advances in the use of cognitive and machine learning (ML) enabled systems fuel the quest for novel approaches and tools to support software developers in executing their tasks. First, as software development is a complex and dynamic activity, these tasks are highly dependent on the characteristics of the software project and its context, and developers need comprehensive support in terms of information and guidance based on the task context. Second, there is a lack of methods based on conversational-guided agents that consider cognitive aspects such as paying attention and remembering. Third, there is also a lack of techniques that make use of historical implicit or tacit data to infer new knowledge about the project tasks such as related tasks, task experts, relevant information needed for task completion and warnings, and navigation aspects of the process such as what tasks to perform next and optimal task sequencing. Based on these challenges, this paper introduces a novel paradigm for human-machine software support based on context, cognitive assistance, and machine learning, and briefly describes ongoing research activities to realize this paradigm. The research takes advantage of the synergy among emergent methods provided in context-aware software processes, cognitive computing such as chatbots, and machine learning such as recommendation systems. These novel paradigms have the potential to transform the way software development currently occurs by allowing developers to receive valuable information and guidance in real-time while they are participating in projects.

关键词： software engineering context software process cognitive assistance machine learning recommendation

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DynaCEP a framework for Dynamic Event Streaming in IoT

DynaCEP a framework for Dynamic Event Streaming in IoT

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ieee/ACM international Symposium on Big data computing (BDC)

作者： Vincenza Carchiolo Michele Malgeri Giulio Samperi Salvatore Sorbello Dip. Ingegneria Elettrica Elettronica Informatica Università di Catania Via Santa Sofia 64 Catania Italy Bax Energy Italia s.r.l Via Sclafani Italy

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

This paper presents a system designed to handle real-time streaming and the prioritization of events in dynamic, high-volume data environments. The system efficiently processes incoming events and organizes them according to customizable prioritization rules, ensuring that high-priority events are addressed promptly. A standout feature of the system is its ability to support multiple prioritization rules simultaneously, offering the flexibility to modify or create new rules on the fly without disrupting the event processing pipeline. This adaptability makes the system particularly well-suited for real-time, data-driven environments such as IoT ecosystems, smart cities, industrial processes, and renewable energy systems, where the ability to prioritize and view events through different lenses is essential for operational efficiency and responsiveness. By dynamically adjusting to new rules and outputting rule-specific timelines, the system offers a powerful solution for managing and analyzing event streams in complex, rapidly evolving contexts.

关键词： Renewable energy sources Smart cities Pipelines Ecosystems Big data Real-time systems Streams Lenses

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A Multi-Class Classification Framework with SMOTE Based data Augmentation Technique for Alzheimer's Disease Progression

A Multi-Class Classification Framework with SMOTE Based Data...

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Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), international conference on

作者： Kaivaram Sivarama Krishna Bommala Deepak Kumar Morumpalli Deekshith Reddy CH Saketh Gayatri Ramasamy Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The detection of Alzheimer's disease is a critical task in medical diagnostics due to its rapid progression and profound impact on cognitive function. Deep learning (DL) offers unprecedented capabilities in analyzing medical imaging data, particularly in neuroimaging like MRI scans. However, class imbalances within the dataset persist, making it difficult to discern intricate patterns indicative of Alzheimer's pathology. This project aims to overcome this challenge by integrating advanced DL techniques with data augmentation methodologies, specifically SMOTE. This strategic integration aims to overcome the limitations of class imbalances within the initial MRI dataset, enhancing the precision and reliability of the classification model. This interdisciplinary exploration aims to redefine Alzheimer's detection capabilities, enhancing diagnoses and offering the potential for broader applications in understanding and managing complex neurodegenerative disorders.

关键词： Deep learning Computational modeling Magnetic resonance imaging Computer architecture data augmentation Resonance Computational efficiency

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A framework for hyperparameter optimization of genomic cloud pipelines: improving the performance of GATK VETS variant filtration

A framework for hyperparameter optimization of genomic cloud...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Wanying Dou Samuel K. Lee Kiran V. Garimella Northwestern University Feinberg School of Medicine Chicago IL USA Broad Institute of MIT and Harvard Data Sciences Platform Cambridge MA USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Running complex machine-learning pipelines in the cloud is standard practice in genomics. However, optimizing these pipelines with respect to their hyperparameters can be challenging, which may limit their effectiveness. To address this gap, we developed an Optuna-based hyperparameter optimization framework and integrated it within the Terra cloud computing platform. To demonstrate our framework, we optimized the Genome Analysis Toolkit Variant Extract-Train-Score (GATK VETS) pipeline for genomic variant filtration. We evaluated the performance of our framework using both human and malaria genomic callsets, demonstrating improved accuracy in detecting sequencing artifacts. Our work underscores the importance of a flexible framework for tuning genomic cloud pipelines and highlights its potential for broader applicability in other genomic machine-learning analyses.

关键词： Cloud computing Sequential analysis Accuracy Filtration Malaria Pipelines Genomics Hyperparameter optimization Bioinformatics Tuning

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Predicting Alzheimer’s Disease and Mild cognitive Impairment with Off-line and On-line House Drawing Tests

Predicting Alzheimer’s Disease and Mild Cognitive Impairmen...

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ieee international conference on e-Science and Grid computing

作者： Nina Hosseini-Kivanani Elena Salobrar-García Lorena Elvira-Hurtado Mario Salas Christoph Schommer Luis A. Leiva University of Luxembourg Luxembourg Ramon Castroviejo Institute of Ophthalmologic Research Universidad Complutense of Madrid Spain Memory Unit Geriatrics Service Hospital Clínico San Carlos Madrid Spain

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

There is growing interest in developing reliable, non-invasive, and cost-effective methods for early diagnosis of neurodegenerative diseases such as Mild cognitive Impairment (MCI) and Alzheimer’s Disease (AD). In this regard, handwriting-based tasks have shown potential in differentiating MCI and AD patients from healthy controls (HCs). However, previous work has reported mixed results when using different symbols and data representations. We address this research gap by developing computational models (convolutional and recurrent neural networks) to differentiate MCI and AD from HCs with off-line (scanned images) and on-line (discrete time series) house drawings. Notably, we observed that augmenting on-line data and then converting it to off-line format, a method we refer to as "OnOff-line", yielded the best performance results in binary classification tasks. These findings highlight the effectiveness of on-line representations in capturing handwriting dynamics more accurately. Ultimately, our work opens new avenues for future research to enhance automated diagnostic of MCI and AD from handwriting analysis.

关键词： Recurrent neural networks Computational modeling Time series analysis Symbols Medical services Reliability Alzheimer's disease

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DUDPA-TATD: A Lightweight Privacy-Preserving Anomaly Traffic Detection Method for Edge computing Scenarios

DUDPA-TATD: A Lightweight Privacy-Preserving Anomaly Traffic...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Guanghan Li Wenzhong Yang Xiaodan Tian Jiaren Peng College of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In the field of network security, anomaly traffic detection is a key technology for identifying network attacks. It detects abnormal behavior and counteracts it by monitoring changes in traffic patterns. Deep learning-based methods significantly improve detection performance due to their ability to efficiently learn and extract complex feature representations. However, these methods require large amounts of data for training, which exacerbates the risk of privacy breaches and increases training time consumption. This paper proposes an anomaly traffic detection method based on differential privacy and temporal convolutional networks, named DUDPA-TATD. DUDPA-TATD consists of two components: the Dynamic Update Strategy with Differential Privacy Protection Laplace Mechanism (DUDPA) and the Temporal Aggregation Traffic Detection network based on Temporal Convolutional Networks (TATD). DUDPA gradually adjusts the privacy protection strategy according to the inference rules of TATD, while TATD enhances the learning of macro and micro differences in time-series data by aggregating key features, further improving detection performance. We conducted experiments on two public datasets. Compared with baseline methods, extensive experiments on the DAPT2020 and SMAP datasets demonstrate that our DUDPA-TATD not only exhibits superior detection performance but also requires less average training time than most baseline methods, qualifying it as a lightweight detection model.

关键词： Training Measurement Differential privacy Computational modeling Traffic control Feature extraction data models Convolutional neural networks Protection Edge computing

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Research on Natural Language Generation System of BERT Model Computer Big data Technology

Research on Natural Language Generation System of BERT Model...

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ieee international conference on Power, Intelligent computing and Systems (ICPICS)

作者： Jianqin Zhang Nanjing Engineering Branch Jiangsu Union Technical Institute Nanjing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

This paper focuses on the application and research of pre-trained model BERT in natural language generation system to meet the needs of natural language processing tasks in the era of big data. With the emergence of massive data, it is of great significance to use big data technology to understand and generate natural language efficiently. This study aims to build and optimize a natural language generation system based on BERT model, which can fully tap and utilize large-scale unannotated text data and capture complex context dependencies through deep two-way Transformer architecture to achieve accurate and smooth natural language generation. First, this paper discusses the core mechanism of BERT model and its superiority in natural language understanding, and analyzes its ability to effectively learn massive text resources in big data environment. Then, we design a BERT model extension scheme that ADAPTS to big data environment. By combining distributed computing framework and targeted model optimization strategy, the training efficiency and generalization performance of the model can be improved when processing massive data. In the experimental part, we conduct a detailed evaluation of the constructed natural language generation system using large-scale real-world data sets, compare multiple benchmark models, and demonstrate significant effects on specific tasks such as news summary generation and conversation response generation. Experiments show that the natural language generation system based on BERT model effectively utilizes the advantages of big data while maintaining high language quality, and improves the relevance and accuracy of generated content.

关键词： Training Adaptation models Accuracy Computational modeling Natural language generation Distributed databases Big data Transformers data models Optimization

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Design of data-Driven Learning Path Based on Knowledge Graph and Tracing Model

Design of Data-Driven Learning Path Based on Knowledge Graph...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Juli Zhang Ruheng Xia Quan Wang Academy of Advanced Interdisciplinary Research Xidian University Xian China College of Computer Science and Technology Xidian University Xian China

With the explosive growth of online courses, per-sonalized learning path recommendations have gained much attention in E-learning area. Studies aim to provide customized guidance that cater to the needs of each learner. However, generating an appropriate learning path remains a challenge due to the complex cognitive relationships among learning items and learners' different abilities. Existing methods often overlook the impacts of cognitive structure and mastery level on the outcomes of customized instructions, leading to cognitive confusion problem. To address this issue, we propose a model named data-Driven Learning Path based on Knowledge Graph and Tracing Model (LP-KAT), which combines knowledge graph (KG) and knowledge tracing model (KT) to store the relationships among learning items and reflect learners' abilities through the abundant data. The guidance is separated into concept and practice identification; besides, through reinforcement learning, our model can suggest a sequence of practices for different learners. Specifically, we first utilize a knowledge tracing model to track learners' evolving mastery levels at each learning step. Then we design a concept selector that employs knowledge graph navigation algorithm and reinforcement learning to determine which concept to learn next. Finally, we utilize a practice selector to identify practice question based on the relationship between mastery level and difficulty of practice. We evaluate the proposed model against KNN, GRU4Rec, MCS and DQN using a real-world dataset, and the results show an improvement of more than 30 % in the effectiveness of concept mastery level and nearly 20% better than GRU4Rec across various learning path lengths.

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