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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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Algorithm Visualizer for Deaf and Hard of Hearing Students: Enhancing Education through Visual Learning

Algorithm Visualizer for Deaf and Hard of Hearing Students: ...

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computing, Power and Communication Technologies (IC2PCT), ieee international conference on

作者： Shreya Dutta Prakhar Srivastava P. Kanmani Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India

This paper presents an innovative approach to algorithm visualization in the domain of education for individuals with hearing and speech impairments. Recognizing the limited accessibility to algorithm visualization tools for the deaf and dumb community, our project aims to bridge this gap by developing an inclusive and interactive algorithm visualizer. Current educational resources lack practical tools to facilitate the understanding of complex algorithms for this specific user group. The significance of our work lies in its potential to empower deaf and dumb learners with valuable educational resources, enhancing their comprehension of algorithms and computer science concepts. Our work leverages *** and *** tech stacks to create a user-friendly and accessible web application. By utilizing these cutting-edge technologies, we ensure a dynamic, interactive, and responsive user interface tailored to the specific needs of our target audience. Our project stands out by using interactive animations and sidenotes to engage and educate our end users and the target group, setting it apart from existing solutions. This paper portrays the positive feedback received and the tangible enhancements in algorithm comprehension, serving as compelling evidence of the effectiveness of our approach, and affirming its success. In a nutshell, our work contributes to a more equitable educational environment and offers a promising course of action for further research and development in this field.

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cognitive Cardiovascular Disease Prediction Using ECG-Crown Auto Encoder Neural Network for ECG Time Series Signals

Cognitive Cardiovascular Disease Prediction Using ECG-Crown ...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Ameet Shah Dhanpratap Singh Department of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

cognitive computing research in cardiovascular disease prediction is needed in the post-COVID-19 era. Recently, post-vaccination anomalies have been declared at certain levels throughout the world, and in rare cases, the vaccine causes Thrombosis Thrombocytopenia Syndrome, which causes blood clot formation. The core threat is that if such clots travel to heart arteries, there is a possibility of sudden cardiac arrest, cardiac arrhythmia diseases, and other heart diseases. The heart’s function can identify such threats, and the primary tool for cardiac function tests is an electrocardiogram (ECG). So, analyzing the heart rate and heartbeat status more accurately is necessary. The manual analysis for cardiac arrhythmia using ECG requires manual QRS-complex identification, which may mislead the precise disease prediction. Hence, the proposed research focuses on fast decision by enhancing the cognitive computing accuracy of ECG signals through the proposed ECG-Crown autoencoder with an LSTM sequential model. The ECG-Crown auto-encoder neural network gives fast classification results as the downsampling is executed concurrently, making the arrhythmia prediction possible even for a single heartbeat by ECG waveform R-R peak analysis. Hence, the proposed model performance is very impressive compared to existing techniques. The proposed system is tested with ECG time series signal data, and the results are very promising, with 99.7% accuracy. The model loss recorded minimum, which lowers the classification error and subsequently helps to predict a cardiovascular disease very accurately.

关键词： Accuracy Heart beat Arrhythmia Computational modeling Neural networks Time series analysis Manuals Electrocardiography Vaccines cognitive systems

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Design of a Sharing System based on Privacy-Preserving Personal data

Design of a Sharing System based on Privacy-Preserving Perso...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing (SNPD)

作者： Jianxiang Cao Xing Song school of computer and cyber sciences Communication University of China Beijing China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

In recent years, machine learning has been widely used to process large-scale data and train complex models. However, there are certain security risks in the construction process of machine learning models. data trainers may infringe on the privacy of data owners and use personal data to train machine learning models without authorization. To truly prevent unauthorized training of personal data, this paper proposes a privacy-preserving personal data sharing system, called PPDS. And this paper adopts the combination of CP-ABE with outsourced decryption and secure multi-party computation to ensure the secure authorization and training of personal privacy data. In PPDS, data users can only obtain the trained models instead of the authorized data, effectively preventing the privacy leakage of personal data. Moreover, the experimental results show that PPDS is a safe and effective scheme to achieve personal data sharing.

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Systematic cognitive computing Framework Application Using Medical Information Processing

Systematic Cognitive Computing Framework Application Using M...

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ieee UP Section conference on Electrical Computer and Electronics (UPCON)

作者： R. Kishore Kanna A. Ambikapathy Mazin Riyadh AL-Hameed Ippa Sumalatha Navdeep Singh Department of Biomedical Engineering Jeruslaem College of Engineering Chennai India Department of of Electrical and Electronics Engineering Galgotias College of Engineering and Technology India Collage of Engineering National University of Science and Technology Dhi Qar Iraq Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara Punjab India

In recent years, the convergence of cognitive computing and natural language processing (NLP) has emerged as a critical field of study, promising significant breakthroughs in medical imaging. This research digs into the integration of cognitive computing approaches with NLP to increase the interpretation and comprehension of complicated medical narratives. We offer a unique framework that harnesses the cognitive capacities of computers to process, analyze, and interpret huge quantities of unstructured medical material. Our technique combines deep learning architectures and semantic analysis to extract therapeutically important information from radiology reports, patient histories, and other textual data sources. Preliminary findings suggest a considerable increase in the accuracy and efficiency of medical picture annotations, leading to more accurate diagnostic insights. Furthermore, the system exhibits an adeptness in understanding sophisticated medical jargons, acronyms, and context-dependent interpretations. This discovery not only emphasizes the promise of cognitive computing in changing medical imaging but also establishes a precedent for its use in other sectors needing complex language interpretation.

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cognitive computing with Deep Learning based Cybersecurity Solution for Human Computer Interface Applications

Cognitive Computing with Deep Learning based Cybersecurity S...

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data Science and Network Security (ICDSNS), international conference on

作者： Aviral Srivastava Sarah Stager College of IST Pennsylvania state University

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

In the rapidly evolving domain of cybersecurity, the gap between the need for skilled professionals and the available workforce is widening, with over 35,000 open positions in cyber roles. This challenge is compounded by the intense focus and memory demands of cybersecurity tasks. Our research introduces a novel approach that bridges this gap through a mathematical solution, enhancing the trust and reliability in human-machine collaboration for cybersecurity in Human-Computer Interface (HCI) applications. We present a dynamic arithmetic optimization algorithm with a deep learning-based cybersecurity solution (DAOA-DLCS), which integrates cognitive computing and deep learning (DL) techniques. This innovative approach not only sustains cybersecurity but also advances it by leveraging cognitive systems capable of learning and adapting. The DAOA-DLCS technique fosters active threat recognition, rapid incident response, and dynamic adaptation to new threats, thereby improving user authentication and behavior analysis. This leads to a secure, seamless, and tailored user experience, addressing the constraints of limited human resources in cybersecurity roles. Our solution enhances the trustworthiness and autonomy in human-machine teaming, with the DAOA-DLCS technique employing min-max normalization to scale input data, an LSTM model for precise intrusion detection and classification, and DAOA-based hyperparameter tuning for optimal LSTM model selection. Tested on a benchmark dataset, the DAOA-DLCS technique demonstrates superior performance over existing systems in various measures, marking a significant step towards reliable and autonomous cybersecurity solutions in complex socio-technical systems.

关键词： Deep learning Computational modeling Human-machine systems Collaboration Intrusion detection Reliability Computer security

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Deep Learning Strategies for Multiclass Skin Disease Classification 5th

Deep Learning Strategies for Multiclass Skin Disease Classi...

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5th EAI international conference on cognitive computing and Cyber Physical Systems, IC4S 2024

作者： Venkat, Nakka Lokeswara Satya Tejasri, Masina Naga Vijaya Uppalapati, Padma Jyothi Rao, V. V. R. Maheswara Sripada, V. S. S. Lakshmi Murty, P. Sita Rama Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Andhra Pradesh Bhimavaram534201 India Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534201 India Department of CSE Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram534201 India Andhra Pradesh Bhimavaram534201 India

ISBN: (纸本)9783031770746

Skin diseases represent a significant global health challenge, necessitating accurate and timely diagnosis for effective treatment. In this paper, we delve into the intricate realm of skin disease classification, leveraging advanced deep learning techniques. Skin diseases, ranging from common conditions like eczema and atopic dermatitis to potentially life-threatening melanoma, pose complex diagnostic challenges due to their varied visual characteristics. Accurate classification demands the ability to discern subtle patterns, textures, and features within medical images. Our paper introduces a novel methodology that includes the integration of our proposed model into the existing deep learning frameworks. Unlike conventional approaches involving the replacement of output layers, our model maintains the integrity of pre-trained structures. It achieves this by utilizing a blend of VGG16 and EfficientNet B2, incorporating dense activations for improved regularization. This novel approach seeks to improve the model’s flexibility to various skin disease patterns while maintaining accurate classification without changing the pre-trained models’ fundamental design. The model’s performance is evaluated in-depth using a variety of criteria, including accuracy, precision, recall, and F1 score. The outcomes of this analysis unveil distinct F1 scores for each disease class across different models, thereby illuminating the models’ strengths and limitations in the context of disease classification. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Dermatitis

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Predominant Aspects on Security for Quantum Machine Learning: Literature Review

Predominant Aspects on Security for Quantum Machine Learning...

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Quantum computing and Engineering (QCE), ieee international conference on

作者： Nicola Franco Alona Sakhnenko Leon Stolpmann Daniel Thuerck Fabian Petsch Annika Rüll Jeanette Miriam Lorenz Fraunhofer Institute for Cognitive Systems (IKS) Munich Germany adesso Switzerland Zurich Switzerland Quantagonia Bad Homburg Germany Federal Office for Information Security (BSI) Bonn Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum Machine Learning (QML) has emerged as a promising intersection of quantum computing and classical machine learning, anticipated to drive breakthroughs in computational tasks. This paper discusses the question which security concerns and strengths are connected to QML by means of a systematic literature review. We categorize and review the security of QML models, their vulnerabilities inherent to quantum architectures, and the mitigation strategies proposed. The survey reveals that while QML possesses unique strengths, it also introduces novel attack vectors not seen in classical systems. We point out specific risks, such as cross-talk in superconducting systems and forced repeated shuttle operations in ion-trap systems, which threaten QML's reliability. However, approaches like adversarial training, quantum noise exploitation, and quantum differential privacy have shown potential in enhancing QML robustness. Our review discuss the need for continued and rigorous research to ensure the secure deployment of QML in real-world applications. This work serves as a foundational reference for researchers and practitioners aiming to navigate the security aspects of QML.

关键词： Training Adaptation models Differential privacy Quantum computing Machine learning Computer architecture Vectors data models Security Resilience

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The Impact of Generative AI on Cloud data Security: A Systematic Study of Opportunities and Challenges

The Impact of Generative AI on Cloud Data Security: A System...

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ieee international conference on Utility and Cloud computing (UCC)

作者： Hardik Ruparel Harshal Daftary Videet Singhai Pramod Kumar School of Computer Science University of California Irvine San Jose CA USA School of Computer Science Stony Brook University New York Seattle WA USA College of Engineering Carnegie Mellon University Seattle WA USA Computer Science and Engineering The Pennsylvania State University San Jose CA USA

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

Since the launch of ChatGPT, Generative AI (GenAI) has transformed virtually every domain, addressing complex problems in areas previously considered insurmountable. At the same time, the adoption of cloud computing for storing confidential data has been rapidly increasing. This swift shift to the cloud has highlighted the critical need for robust data protection measures. In this paper, we merge these two emerging fields to examine how GenAI influences cloud data security. While research on traditional AI-based solutions for cyber threats has been ongoing, the recent surge in GenAI adoption calls for a focused, systematic study to address specific research questions related to GenAI. We formulate 7 research questions and analyze data from 4 major digital repositories (ieee, Springer, ACM, Wiley) to provide an extensive overview of how GenAI contributes to mitigating key cloud security threats such as Malware, Denial-Of-Service (DoS), and Phishing, among others. We also discuss the potential risks posed by GenAI in cloud security and highlight the operational and ethical challenges that impede its widespread adoption. This systematic study aims to provide valuable insights for fellow researchers and IT professionals, guiding them in effectively leveraging GenAI to enhance cloud data security while being mindful of its limitations and challenges.

关键词： Ethics Systematics Generative AI Shape data security Cloud computing security Surge protection Solids Surges Research and development

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A Multi-Modal Bio-Medical data Analysis Framework: A case study for Pandemic Preparedness

A Multi-Modal Bio-Medical Data Analysis Framework: A case st...

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ieee international conference on Utility and Cloud computing (UCC)

作者： Aaisha Makkar Myra Conway Farid Meziane Vinaytosh Mishra Ashutosh Kumar Singh Department of Computing University of Derby UK Department of Bio-Medical Sciences University of Derby UK Gulf Medical University UAE Indian Institute of Information Technology Bhopal India

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

Recently, the world has witnessed the emergence of various infectious diseases that have led to public health emergencies. These outbreaks have highlighted the need for effective pandemic preparedness strategies. One of the key components of such strategies is the efficient handling of large amounts of medical data by healthcare professionals and organizations. The scientific community is actively contributing in the introduction of innovative approaches such as artificial intelligence in medicine and healthcare systems. However, effectively managing such a large volume of data remains a considerable challenge for the World Health Organisation (WHO) and medical practitioners. In this work, the authors present a deep learning and explainable Artificial Intelligence (XAI)-empowered scheme for multi-modal bio-medical data analysis applications. The proposed scheme addresses these challenges using data garbling, class balancing, feature selection, and harnessing the power of these techniques. The proposed framework is evaluated using the case study of COVID-19 based on the NCBI COVID-19 dataset. Pre-processing medical data using the proposed scheme, resulted in a significant understanding of data, demonstrating the effectiveness of the proposed scheme for complex medical data.

关键词： COVID-19 Deep learning Pandemics Infectious diseases Organizations Predictive models Feature extraction Bioinformatics Public healthcare Biomedical imaging

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DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting

DAM: A Universal Dual Attention Mechanism for Multimodal Tim...

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Metaverse computing, Networking and Applications (MetaCom), ieee international conference on

作者： Yihang Fu Mingyu Zhou Luyao Zhang Duke Kunshan University Suzhou China Data Science Research Center and Social Science Division Duke Kunshan University

ISBN: (数字)9798331515997

ISBN: (纸本)9798331516000

In the realm of distributed systems, blockchain technology has spurred the development of cryptocurrencies, merging enhanced security and decentralization with significant investment potential. Despite their promise, existing cryptocurrency trend forecasting often oversimplifies sentiment data, failing to fully capture the complex interplay between financial market dynamics and external sentiment influences. This paper introduces a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach integrates key cryptocurrency metrics with sentiment data from news and social media, analyzed through CryptoBERT, effectively addressing the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models, such as LSTM and Transformer, by up to 20% in prediction accuracy. This advancement not only deepens the understanding of distributed systems but also has practical implications for stakeholders in the cryptocurrency and blockchain sectors.

关键词： Attention mechanisms Dams Social networking (online) Market research Transformers Cryptocurrency Blockchains Stakeholders Security Forecasting

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