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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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Search Procedures Optimization in Digital Twin data Lakes

Search Procedures Optimization in Digital Twin Data Lakes

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2024 international Russian Smart Industry conference, SmartIndustryCon 2024

作者： Topolsky, Dmitry Beliakov, Alexander Beliakova, Veronica Dep. of Electronic Computing Machines Chelyabinsk Russia

ISBN: (纸本)9798350395044

The development of data Lake technology is observed in many areas of smart manufacturing. data lakes are used to create digital twins in the fuel and energy complex, energy, and metallurgy. The processes of obtaining new materials are no exception in this development direction of science and technology. In the context of the rapid growth of new data and the accumulation of chemical information obtained in the form of digital models of chemical compounds and materials, new information solutions and tools are in great demand, allowing for the search, analysis and visualization of available diverse data. The research is devoted to the development of methods for organizing known information about the structure and properties of compounds and materials, as well as software components for automating and increasing the productivity of search procedures. The issues of organizing the storage and processing of chemical data are considered, ways to improve the performance of search queries are shown, as well as displaying modifications of file structures in a relational database and in a distributed database based on Hadoop tools. File analysis and parsing highlights various properties of crystalline and molecular structures, organizing them into tree structures (JSON format), convenient for indexing and searching information in data Lake of the 'column family' type, which is a sparse matrix of search attributes. The results of accelerating search procedures for some search examples are presented, showing a significant acceleration in obtaining search query results. © 2024 ieee.

关键词： Metadata

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The Impact Of Work Duration On The cognitive Abilities Of Bus Drivers In West Bengal 15

The Impact Of Work Duration On The Cognitive Abilities Of Bu...

引用

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

作者： Mukherjee, Sharbo Sahu, Subhashis Pradhan, Chandan Kumar Brainware University Allied Health Sciences Department West Bengal Barasat Kolkata700125 India University of Kalyani Ergonomics and Occupational Physiology Laboratory Department of Physiology Nodia741235 India Regional Occupational Health Centre Block DP Sector V Salt Lake Kolkata700091 India

ISBN: (纸本)9798350370249

Bus driving job is a part of public service industry. Bus drivers always need to show concern for the passengers in spite of the fact that they must be concentrating on bus driving. In driving performance, the physical and cognitive harmony of the bus driver is a vital factor. Many recent reports related with bus drivers indicate that public transportation employees are at high risk of experiencing traumatic psychological events during the course of their work, with serious threats and assault making up the majority of those reported. This phase of study focuses the changes cognitive ability with duration of working hours. According to year of exposure, 140 subjects were alienated into group 1 and group 2 respectively. Different physical parameter was measured and general questionnaire was administered to know their different responses. Single letter cancellation test, stick drop test were administered to all the subjects for cognitive assessment with different time of the work shift. It was observed from the study that number of incorrect letters was increased with increased duration of working time. As per the reaction time, this was also increased at the end of the shifts and found to be best before the shifts. From the study it can be concluded that their cognitive ability was decreased according to their age. Impairment of motor activity of both groups of drivers was highly related with the time of duration of work. It was also observed in the study that age is another cofactor for the development of that impairment. © 2024 ieee.

关键词： Bus drivers

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Predictive Modeling of Cryptocurrency Prices Using Machine Learning Algorithms 15

Predictive Modeling of Cryptocurrency Prices Using Machine L...

引用

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

作者： Mahmud, Tanjim Akter, Tahmina Chakma, Rishita Barua, Tikle Hossain, Mohammad Shahadat Andersson, Karl Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati4500 Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350370249

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling. ©2024 ieee.

关键词： Adversarial machine learning

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Integrating Big data, ML, and DL for Predictive Analytics in Healthcare: A Comprehensive Analysis and Hybrid Framework Proposal 4

Integrating Big Data, ML, and DL for Predictive Analytics in...

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

作者： Kargotra, Poonam Parray, Irfan Ramzan School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331529635

The paper presents a comprehensive in-depth analysis of big data, machine learning (ML), and deep learning (DL) methodologies in predictive healthcare analytics, with a focus on their comparative strengths, research gaps, and applications in real world. The study synthesizes findings from recent research papers related to same objective, highlighting the evolution of data-driven technologies in healthcare sector and the challenges which are associated with data integration, privacy, and real-time processing. The big data analytics provides foundational insights from vast healthcare datasets, while ML algorithms enhance predictive accuracy on structured data, while DL models achieve high precision even in complex tasks, such as medical imaging and real-time IoT embedded systems monitoring. This article introduces a novel hybrid approach that combines the strength of all these approaches, employing common data for heterogenous data integration, federated learning for privacy, and a feedback-driven adaptive system for continuous improvement. Analyzing quantitative comparisons, the effectiveness of each approach is illustrated: DL demonstrate high acuracy, and ML, in turn, flexibility with a moderate rate. Discussing the research gaps of current methodologies, the proposed framework discus the scalability, adaptability, and privacy enhancements of this new conceptual model to create more precise and proactive healthcare analytics. Possible future investigations indicate that it is necessary to enhance these adaptive learning solutions and to expand the arbitrary federative learning approaches to treat different interdisciplinary domains. © 2024 ieee.

关键词： Federated learning

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A Comprehensive Approach to Identify Stroke based on Real-time Medical data 1

A Comprehensive Approach to Identify Stroke based on Real-ti...

引用

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

作者： Maragatharajan, M. Shanmugapriya, S. Sudhan, M.B. Deeptha, R. Paramasivam, Sankar Muthu Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kothrikalan India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu Srivilliputhur India Srm Institute of Science and Technology Department of Computer Science and Engineering RamapuramTamilnadu Chennai India Srm Institute of Science and Technology Department of Information Technology RamapuramTamilnadu Chennai India Palo Alto Networks Inc Distinguished Software Engineer 3000 Tannery Way Santa ClaraCA United States

ISBN: (纸本)9798350372748

Strokes or cardiovascular disorders continue to be a major global health concern. Proactive intervention and timely detection greatly reduce the morbidity and death related to strokes. The goal of this research is to build a prediction model using machine learning techniques to assess a person's risk of stroke based on a variety of sociodemographic and health characteristics. The dataset used in this study consists of a large collection of medical records. It contains important details including age, gender, BMI, smoking habits, heart disease, hypertension, and the type of residential property. Using this information, a Decision Tree Classifier is trained to predict the likelihood of strokes, enabling early detection and customized treatment plans. A Decision Tree Classifier with a predetermined maximum depth is used in the proposed methodology's rigorous data preprocessing, which includes handling missing values, encoding categorical features, and developing models. Both the training and testing datasets are used to evaluate the model's performance using evaluation measures including accuracy, precision, and recall. The model's insights provide important characteristics that influence the prediction of stroke risk, assisting medical professionals in focused risk assessment and preventive intervention. The proposed method has the potential to improve healthcare practices by facilitating prompt interventions and personalized treatment for stroke risk individuals. The proposed model has resulted in 86.78% accuracy for this dataset by using the traditional machine learning algorithms. This work highlights the value of predictive modelling in the healthcare industry by demonstrating how it may improve early detection techniques and lessen the impact of strokes on public health. © 2024 ieee.

关键词： Forecasting

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Neurocognitive Impact of Icon Styles and Semantics on Memory and Recall Speed: An N-Back Study Using EEG 5

Neurocognitive Impact of Icon Styles and Semantics on Memory...

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5th international conference on data Intelligence and cognitive Informatics, ICDICI 2024

作者： Thomas-Hercules, Daniel Palaniappan, Ramaswamy University of Kent Ai and Data Analytics Research Group School of Computing Chatham United Kingdom

ISBN: (纸本)9798350389609

This research investigates the effectiveness of icons in memory-related tasks, focusing on two icon styles (Solid and Outline) and two semantic classifications (Resemblance and Abstract). Through the N-Back task and electroencephalogram (EEG) measurements, the study aims to provide quantitative insights into memory load and recall speed. Twelve University of Kent students participated in an experiment involving 1-Back and 2-Back tests, using four icon combinations: Solid Resemblance, Solid Abstract, Outline Resemblance, and Outline Abstract. Each participant completed eight tests, during which N-Back reaction times, accuracy, and EEG data were recorded using a 32-channel wireless brain-computer interface (BCI). Statistical analysis was performed using the Wilcoxon signed rank test to evaluate reaction times and event-related potentials (ERPs). The results revealed no significant differences in recall speed between the Solid and Outline icon styles. However, contrary to previous research favoring Resemblance icons, Abstract icons were recalled significantly faster, particularly in the Outline style. However, Outline Abstract icons exhibited a significantly smaller P300 amplitude compared to Outline Resemblance icons, indicating a higher memory load. These findings suggest that Outline Resemblance icons may offer better usability due to their lower memory load but Outline Abstract icons if recall speed is the key. The study also employed N400 analysis to further explore the semantic processing of the icon combinations. These findings underscore the need for further research into the various dimensions of icons to better understand their impact on memory performance. © 2024 ieee.

关键词： Brain computer interface

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Optimization of data Structures and Trade-Offs with Concurrency Control in Multithread Software Structures Using Artificial Intelligence 5

Optimization of Data Structures and Trade-Offs with Concurre...

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5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Cherukuri, Bangar Raju Arulkumar, V. Germantown MD United States School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

ISBN: (纸本)9798350383522

The optimization of data structures and trade-offs is a complex problem. This is simplified and provides solutions through the aid of artificial intelligence. There are various synchronization problems occur due to the concurrent access to the data structures. This affects the overall performance of the system. To overcome various challenges, deep learning with optimization algorithms is implemented. This helps to achieve the potential areas for optimization in multithread software by evaluating the historical patterns and performance information. Trade-offs are defined as the choices that are initiated in the optimization process for data structures in concurrency. Deep learning helps to analyze the impacts of these trade-offs by developing training models on the historical data. A large amount of data is retrieved through the deep learning techniques. This includes extracting image data for CNN and time series data for RNN and LSTM. The reduction of using data compression techniques helps in obtaining optimal performance parameters. The various forms of threats are neglected through data loading and preprocessing stages. The concurrency is essential due to the usage of multiple threats working on the neural networks. This is further accessed through synchronizing access to shared data structures. They must be properly done because excess synchronization leads to the origin of bottlenecks. A balance between data structure optimization and concurrency control must be maintained properly. Excessive concurrency control may reduce the performance. The execution speed of the CNN is enhanced through the aid of hardware accelerators. Thus the fine-tuning of trade-offs and creation of software are done through training and testing to obtain increased use of multi-core processors. © 2024 ieee.

关键词： Artificial intelligence Concurrency data structures Deep learning Multi-core processors Multithread software Trade-offs

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Resilience of Aviation Cyber-Physical Systems to Malicious Attacks

Resilience of Aviation Cyber-Physical Systems to Malicious A...

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2024 international conference on Computer and Applications, ICCA 2024

作者： Abozaid, Fatemah Baydoun, Ali Oryx Universal College in Partnership with Liverpool John Moores University Department of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350367560

The inter-connectivity of critical aviation operations are the prime targets for Advance Persistent Threats (APTs) and others malicious actors due to increasingly relying on the Cyber-Physical Systems (CPS). This paper examines the current cyber resilience of aviation CPS to such cyber threats, emerging trends, and proposing new and effective resilience strategies. Using a multi-pronged approach, data were collected through survey from the aviation professionals in the state of Qatar. Quantitative analysis was conducted on survey data to assess trends in threat perception and resilience measures, while qualitative analysis of interviews provided in-depth insights into current vulnerabilities and effective mitigation strategies show that outdated software, insufficient access controls, and complex supply chains increase the risk to advanced persistent threats (APTs) and other sophisticated attacks. Our findings can assist the industry leaders, policymakers, and cybersecurity professionals to get valuable insights to safeguard the aviation ecosystem against the growing cyber threats. © 2024 ieee.

关键词： Cyber attacks

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Efficient ECG Signal Enhancement using Partial Update Augmented complex LMS Algorithm

Efficient ECG Signal Enhancement using Partial Update Augmen...

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2024 international conference on Intelligent computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Gowri, T. Surekha, Sala Rahman, Md Zia Ur GITAM Deemed to Be University ECE Department Andhra Pradesh Visakhapatnam530045 India K L Deemed to Be University Koneru Lakshmaiah Education Foundation Department of IoT Andhra Pradesh Vaddeswaram522302 India K L Deemed to Be University Koneru Lakshmaiah Education Foundation Dept. of ECE Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350369175

When an Electrocardiogram (ECG) is acquired by a sensor device in a clinical setting, it encounters many kinds of artefacts. These artefacts obscure the minute elements of the ECG signal's PQRST complex, which are crucial for clinical monitoring and diagnosis. When the data signal in a practical biotelemetry-based sensing system is too lengthy, standard filters require a lot of iterations to process the samples. In this research, we present effective ECG signal improvement strategies that make use of Augmented complex Least Mean Square Algorithm (ACLMS) to address this issue. By partial update processing of ECG signals, these techniques considerably minimize the computing burden on the filter and extract high-resolution, noise-free signals. In order to minimise the mean square error in comprehensive signal occurrence, the deepest descent technique is solved by the ACLMS algorithm. Compared to the traditional Least Mean Squared (LMS) based Adaptive Noise Canceler (ANC), this is demonstrated to be a steady state unbiased approach with less variance. Finally, we used these ANCs on the original ECG signals that were procured from the MIT-BIH open database, comparing their results to those of conventional LMS-based ANC. The findings indicate that when the signal length is abnormally long, the block implemented algorithms outperform their LMS counterparts with regard to Signal to Noise Ratio (SNR). © 2024 ieee.

关键词： Mean square error

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Real-Time Transaction Classification and Fraud Detection in Banking using AI and Advanced data Processing 4

Real-Time Transaction Classification and Fraud Detection in ...

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

作者： Beeram, Divya Logeshwaran, J. San Jose State University United States Christ University Department of Computer Science Karnataka Bengaluru560029 India

ISBN: (纸本)9798331529635

Banks need real-time transaction classification and fraud detection to secure end-to-end financial transactions. However, the time it currently takes to classify and detect manually is too slow for an industry that can lead to financial loss if done incorrectly or lead customers in some forward-oriented industries. Artificial Intelligence (AI) and Advanced data Processing techniques have sharpened the speed and accuracy of transaction classification and fraud detection in banking systems. By analyzing transaction history, customer behaviour, and the geographical location of a shop overplayed in tens to hundreds of different real-time data sources, an AI algorithm can accurately classify the level of fraud risk on card transactions. With that data in place, problematic patterns suggestive of fraud can be identified immediately. Sophisticated data processing techniques like data mining and machine learning can identify fraudulent patterns hidden in large amounts of actions, and even the most complex fraud cases will be detected. Moreover, these technologies constantly learn, and with new fraud patterns, they adapt on the go, giving banks a 'pre-fraud' approach to detection. Banks can now classify transactions and detect fraud in real-time using AI and Advanced data Processing, significantly reducing financial losses and eliminating the frustration of yet another chargeable transaction. As technology advances, these strategies will continue to strengthen and adapt to fight against new types of fraud, leading transactions towards more safety. © 2024 ieee.

关键词： data handling

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