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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111-120 订阅

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Image-Based cognitive States Identification through Deep Learning 2

Image-Based Cognitive States Identification through Deep Lea...

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2nd ieee international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Kundan Rao, S. Ambannavar, Siddarth Sinha, G.R. Bms College of Engineering Dept. of Machine Learning Bangalore560019 India Gfsc University Gujarat Vadodara India

ISBN: (纸本)9798350306415

Yoga and mindfulness practices offer a variety of benefits, promoting both physical and mental well-being. This research intersects with AI, psychology, neuroscience, and more within cognitive science, contributing to the study of the brain, mind, memory, and disorders. The study presents a novel approach utilizing deep learning and image classification techniques to distinguish practitioners from non-practitioners of yoga based on pre-processed data. data pre-processing involves down sampling, filtering, and Independent Component Analysis (ICA) to enhance the extraction of relevant features. The pre-processed data is transformed into images using Gramian Angular Field, facilitating the utilization of Convolutional Neural Network (CNN) models. The proposed methodology leverages custom-built CNN models to classify individuals into their respective groups. The models are trained on the image representations of the pre-processed data, enabling the extraction of intricate patterns and features for accurate classification. © 2024 ieee.

关键词： Deep Learning EEG Gramian Angular Field (GAF) Image Classification Mindfulness

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Learning From The History: Accurately and Efficiently Aggregating Geospatial data under Local Differential Privacy 44

Learning From The History: Accurately and Efficiently Aggreg...

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44th ieee international conference on Distributed computing Systems (ICDCS)

作者： Wang, Mengyuan Jiang, Hongbo Hu, Liang Li, Jie Sun, Peng Li, Jiangchuan Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Cent South Univ Forestry & Technol Sch Comp & Informat Engn Changsha Peoples R China Simon Fraser Univ Sch Comp Sci Burnaby BC Canada

ISBN: (纸本)9798350386066;9798350386059

Aggregating geospatial data plays a crucial role in location-based services. However, collecting such sensitive data raises concerns about location privacy leakage. Local Differential Privacy (LDP), as a de facto privacy paradigm, has been widely employed to ensure individual location privacy. Nonetheless, existing approaches for aggregating geospatial data under LDP either suffer from compromised accuracy or involve complex computations. In this work, we propose a history-aware geospatial data aggregation framework to enhance both accuracy and efficiency while guaranteeing LDP. To this end, we first investigate an efficient aggregation method, namely General Randomized Response (GRR), and find that its variance of aggregation error follows the sum of two zero-mean binomial distributions. This reveals that multiple aggregations can boost the accuracy of GRR. To obtain multiple aggregations without compromising privacy, we adopt a Markov transition model to complement current aggregations from historical ones. However, learning the Markov transition matrix on perturbed data is challenging. Accordingly, we propose a privacy-aware Markov Transition Matrix Estimation (MTME) algorithm. Finally, we introduce a truth discovery-based refinement algorithm to iteratively derive an accurate aggregated result from multiple inaccurate aggregations. We evaluate our proposed method on two real-world trajectory datasets, and thorough experiments demonstrate its superior accuracy and very low time overhead compared to competitors.

关键词： Geospatial data aggregation local differential privacy Markov matrix truth discovery

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Dynamic Offloading of complex Tasks Based on Generative Adversarial Q-Networks in Edge computing 5

Dynamic Offloading of Complex Tasks Based on Generative Adve...

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5th international conference on Big data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Zhang, Lijuan Chen, Zhuo Li, Yan Chongqing University of Technology Chongqing China

ISBN: (纸本)9798331506612

In edge computing environment, dynamic offloading complex tasks is a key issue that affects the overall system performance and user experience. In cloud-edge-end collaborative systems, efficiently offloading tasks with dependencies and data sensitivity, while maximizing resource allocation and quality of service, is a challenging problem for edge service *** address this, we propose an optimization method based on the Generative Adversarial Q Network (GAQN).By incorporating Generative Adversarial Networks (GAN), we enhance Q-value estimation, improving the accuracy and stability of the policy. Specifically, we model complex task offloading in edge computing as a Markov Decision Process (MDP). Then,We design and implement GAQN, combining a generative adversarial network with a dueling deep Q network (Dueling DQN) to optimize task scheduling. Experimental results demonstrate that GAQN exhibits good stability and convergence with proper training, yielding optimal Q values. In terms of service quality, GAQN effectively reduces total system delay and energy consumption. Additionally, GAQN adapts to different network environments and efficiently handles task dependencies and data sensitivity, ensuring data privacy and smooth task execution. © 2024 ieee.

关键词： Generative adversarial networks

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Hybrid CMOS Memristor-Based data Compressor Using Shannon Fano Encoder 1

Hybrid CMOS Memristor-Based Data Compressor Using Shannon Fa...

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1st international conference on cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Panda, Saswat Dash, Chandra Sekhar Electronics and Communication Engineering Centurion University of Technology and Management Odisha India

ISBN: (纸本)9798350361780

Recently, memristors have been widely employed for building logic circuits as they can be easily integrated with the existing CMOS technology, paving the way for Hybrid Memristor CMOS logic circuits, which mainly decreases the power consumption, die area requirements and further delay associated with the logic circuits. In this work, a Shannon-Fano encoder is designed using Hybrid Memristor CMOS logic, which is widely employed for data compression applications. The key blocks viz. Source Encoder, Decoder, and Priority encoder are built and simulated using Knowm memristor by invoking its SPICE model and TSMC 180nm model files into LT SPICE. Further, the proposed design is found to consume low die area as fewer numbers of transistors were used. © 2024 ieee.

关键词： Memristors

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data Valuation and Pricing in Internet of Things: Survey and Vision 8

Data Valuation and Pricing in Internet of Things: Survey and...

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8th international conference on Smart Internet of Things

作者： Shi, Xinyi Duan, Haihan Shenzhen MSU BIT Univ Artificial Intelligence Res Inst Shenzhen Guangdong Peoples R China Shenzhen MSU BIT Univ Guangdong Hong Kong Macao Joint Lab Emot Intellig Shenzhen Guangdong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350366457;9798350366440

In the digital age, data has become an invaluable asset for decision-making across various industries. Accurate data valuation is essential for businesses to effectively leverage their data assets, optimize strategies, and enhance operational efficiencies. This paper discusses the complex challenges inherent in data valuation methods, focusing on issues of data provenance and the lack of standardized valuation metrics. Moreover, the Internet of Things (IoT) further complicates this landscape by generating vast volumes of real-time data, which requires robust evaluation frameworks. Blockchain technology, with its decentralized and tamper-resistant characteristics, offers promising solutions by ensuring data integrity and traceability. Additionally, smart contracts enable automated and reliable execution of data transactions, reinforcing trust in the data exchange process. Technically, the integration of wireless sensing technology and edge computing facilitates real-time data collection and processing, improving the accuracy and timeliness of data valuation, and machine learning (ML) techniques further enhance these efforts by uncovering patterns and relationships within large datasets. This study explores how these advanced technologies can address the limitations of existing data valuation methods, paving the way for a more transparent, secure, and efficient data marketplace.

关键词： data Valuation Pricing Internet of Things Blockchain

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ENACT - A Framework for Adaptive Scheduling and Deployments of data Intensive Workloads on Energy Efficient Edge to Cloud Continuum 30

ENACT - A Framework for Adaptive Scheduling and Deployments ...

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30th ieee international conference on Engineering, Technology, and Innovation

作者： Nizamis, Alexandros Neises, Juergen Ospina, Diego Wajid, Usman Lopez, Clara Isabel Valero Trakadas, Panagiotis Votis, Konstantinos Lazaro, Oscar Nechifor, Septimiu Palau, Carlos Ctr Res & Technol Hellas CERTH Informat Technol Inst Thessaloniki Greece Fujitsu Technol Solut Warsaw Poland Innovalia Assoc Bilbao Spain Informat Catalyst Valencia Spain Univ Politecn Valencia Valencia Spain Four Dot Infinit Athens Greece Siemens SRL Bucharest Romania

ISBN: (纸本)9798350362442;9798350362435

In the last couple of decades, enterprises have relished and leveraged the capacity, performance, scalability, and quality of cloud computing services. However, a few years ago, the edge computing concept enabled data processing and application execution near compute and data resources, aiming to reduce latency and promote higher security and sovereignty regarding data transfers. The simultaneous use of these two service models by enterprises has lately resulted in the concept of edge-to-cloud continuum, which combines edge and cloud technologies and promotes standards and algorithms for resource orchestration, data management, and the deployment of solutions across the connected edge and cloud resources. Taking a step further in this direction, this paper introduces a framework to realise the cognitive computing Continuum (CCC). The framework leverages AI techniques to address the needs for optimal (edge and cloud) resource management and dynamic scaling, elasticity, and portability of hyper-distributed data-intensive applications. The proposed ENACT framework enables the automated management of distributed (edge and cloud) resources and the development of hyper-distributed applications that can take advantage of distributed deployment and execution opportunities to optimize their behaviors in terms of execution time, resource utilisation and energy efficiency.

关键词： compute continuum edge cloud orchestration

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Activity Recognition of Delivery Personnel by Integrating LSTM and CNN on Wearable Sensory data 17

Activity Recognition of Delivery Personnel by Integrating LS...

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17th ieee international conference on Social computing and Networking, SocialCom 2024

作者： Li, Apei Cheng, Linhui Zhang, Xiao Wang, Yuchen South-Central Minzu University College of Computer Science Wuhan China Wuhan Britain-China High School Wuhan China

ISBN: (纸本)9798331521097

Activity recognition for delivery personnel is growing in significance, as it empowers the quantification and evaluation of their performance by learning their operational behavior related to energy expenditure. This paper presents a deep learning model integrating LSTM and CNN, automating feature extraction and refining activity classification. LSTM captures temporal dependencies, while CNN handles spatial sensitivity, enabling the model to identify complex patterns. The model has demonstrated impressive accuracy, reinforcing its position as a tool for enhancing efficiency and well-being in logistics distribution. © 2024 ieee.

关键词： Deep reinforcement learning

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Defending against Co-Resident Attacks in Multi-datacenter Environments 1

Defending against Co-Resident Attacks in Multi-Datacenter En...

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1st international conference on cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Mohapatra, Soumya Snigdha Kumar, Rakesh Ranjan Dehury, Mohan Kumar Bebortta, Sujit Sahoo, Devismita DRIEMS University Department of CSE Cuttack754022 India C. V. Raman Global University Department of CSE Bhubaneswar752045 India Amity Inst. of Information Technology Amity University Jharkhand Ranchi834001 India Ravenshaw University Dept. of Computer Science Cuttack753003 India

ISBN: (纸本)9798350361780

With cloud computing, customers can use a variety of computing resources whenever they need them without having to worry about maintenance. But security is one of the main issues with cloud computing. Making the move to the cloud presents end users with new security problems, especially because of shared resources that are available to other users. This study focuses on the colocation or co-resident attack, a malicious strategy in which attackers use backdoors to steal sensitive data from Virtual Machine (VM)s housed on the same server. The study investigates detection and prevention techniques for co-resident attacks. Our focus is on the Previously Selected Servers First (PSSF) VM allocation policy, which was selected because it can offer increased security while using less energy to address this problem. Later, we extend this PSSF to study its impact on the Multi-data center environment. © 2024 ieee.

关键词： Sensitive data

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Ant Colony Optimization for Network Customization in cognitive Human-Computer Communication 1

Ant Colony Optimization for Network Customization in Cogniti...

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1st ieee international conference on cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Khan, Shamim Ahmad Pande, S.S. Al Ansari, Mohammed Saleh Siddiqui, Asfar H. Abdurasul, Bobonazarov Ponni Valavan, M. Glocal University U.P India IT Pict Pune India University of Bahrain Department of Chemical Engineering Bahrain Yeshwantrao Chavan College of Engineering Department of Science and Humanities Maharashtra Nagpur India Turin Polytechnic University Tashkent Uzbekistan K. Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350372748

In the era of cognitive computing, seamless and efficient human-computer communication is paramount for enhancing user experience and system performance. This paper introduces a novel approach, the Ant Colony Optimized-Long Short-Term Memory (ACO-LSTM) framework, designed for network customization to optimize communication channels in cognitive computing environments. Inspired by the collective intelligence of ant colonies, the ACO-LSTM framework combines the adaptive learning capabilities of LSTM neural networks with the optimization prowess of ant colony algorithms. The ACO-LSTM framework operates by leveraging ant-like agents to explore and exploit the solution space for optimal network configurations. These agents dynamically adapt the communication pathways based on environmental cues and user interactions, ensuring adaptability to changing conditions. The LSTM component of the framework captures temporal dependencies and patterns in the communication data, facilitating intelligent decision-making for network customization. To validate the efficacy of the proposed approach, experiments were conducted in diverse cognitive computing scenarios. The proposed ACO-LSTM model shows better accuracy with 97.9% which is 13.1% higher when compared with other methods. The integration of ant colony optimization and LSTM provides a synergistic solution for optimizing network configurations in cognitive human-computer communication, offering a promising avenue for advancing the field of intelligent systems. © 2024 ieee.

关键词： Human computer interaction

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Early Diagnosis of Schizophrenia in Patients Using Deep Learning Techniques 10

Early Diagnosis of Schizophrenia in Patients Using Deep Lear...

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

作者： Priyanka, G. Nivaashini, M. Pavithra, S. Srimaha, T.S. Kanishgaa, P.R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

While Brain, emotion, and behaviours abnormalities are hallmarks of schizophrenia, a complex and long-lasting mental illness. Its symptoms are divided into three categories: positive, negative, and cognitive. Positive symptoms include delusions, disordered thinking, abnormal gait, and hallucinations. Diminished ability to express emotions, alogia, anhedonia, avolition, and social disengagement are detrimental symptoms. Deficiencies in working memory, problems paying attention, and trouble making decisions are examples of cognitive symptoms. Environmental variables, genetic predisposition, and neurochemical imbalances interact intricately in an etiology of schizophrenia. Typically, antipsychotic drugs, psychotherapy, and social support are used in conjunction for treatment. The management of symptoms and enhancement of the general health of persons diagnosed with schizophrenia need early intervention and customized treatment strategies. The CNN, BiLSTM, GRU, and Transformer deep learning models are implemented in this study, and their respective performances are assessed. The transformer and GRU models outperform the other models when hyperparameter adjustment is carried out to increase the models' efficacy. © 2024 ieee.

关键词： Diseases

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