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Leveraging Neural Networks to Enhance Large-Scale data processing Performance

Leveraging Neural Networks to Enhance Large-Scale Data Proce...

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Smart Generation computing, Communication and Networking (SMART GENCON), international conference on

作者： Megha Pandey Vinay Kumar S B Jatin Khurana B. Kalpana Kishor R. Pathak Mukesh Kumar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India −1 Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Mechanical Engineering Vivekananda Global University Jaipur India

Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This has enabled neural networks to be applied to large-scale data processing problems, where they have been shown to perform very well. However, due to the large amount of data involved in such tasks, it is essential that the processing performance is also improved. This paper presents several methods to leverage available resources such as distributed computing and improved algorithmic techniques to improve the performance of large-scale data processing tasks using neural networks. Examples of such techniques are given for both supervised and unsupervised learning tasks, which can significantly enhance the performance of large-scale data processing applications. The results of several experiments are presented, demonstrating the effectiveness of the proposed techniques. The authors also provide insights for future work that could further enhance the capabilities of neural networks when applied to large-scale data processing applications.

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A Principled Approach to Model Validation in Domain Generalization

A Principled Approach to Model Validation in Domain Generali...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Boyang Lyu Thuan Nguyen Matthias Scheutz Prakash Ishwar Shuchin Aeron Department of Electrical and Computer Engineering Tufts University Medford MA Department of Computer Science Tufts University Medford MA Department of Electrical and Computer Engineering Boston University Boston MA

Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen domains with different data distributions. State-of-the-art domain generalization methods typically train a representation function followed by a classifier jointly to minimize both the classification risk and the domain discrepancy. However, when it comes to model selection, most of these methods rely on traditional validation routines that select models solely based on the lowest classification risk on the validation set. In this paper, we theoretically demonstrate a trade-off between minimizing classification risk and mitigating domain discrepancy, i.e., it is impossible to achieve the minimum of these two objectives simultaneously. Motivated by this theoretical result, we propose a novel model selection method suggesting that the validation process should account for both the classification risk and the domain discrepancy. We validate the effectiveness of the proposed method by numerical results on several domain generalization datasets.

关键词： Signal processing algorithms Signal processing data models Acoustics Numerical models Classification algorithms Speech processing

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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy

Application Research of Vertical Federated Learning Technolo...

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IEEE international conference on Big data and Cloud computing (BdCloud)

作者： Yong Luo Zhi Lu Xiaofei Yin Songfeng Lu Yiting Weng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound.

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Improved Fruit Detection by Image processing using Deep Learning

Improved Fruit Detection by Image Processing using Deep Lear...

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Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： Pillagolla Jayakrishna Mulaga Naveen R M Balajee Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Greenfields Vaddeswaram Guntur(Dist) AndhraPradesh India

In the food market today, where everyone is concerned about their health, the capability of a deep learning architecture to recognize fruits based on their quality is crucial. Fruits come in a variety of varieties. However, in order to specifically identify the fruits with the highest quality, the most effective classifier for fruit classification is found with the use of dimensional analysis during the pre-processing of images in terms of time complexity. Finally, this study discusses about how the proposed deep learning model may be applied and how it might be expanded to suit new demands. The proposed CNN-based model was loaded into the special program to test its performance on 200 different apples, and its efficiency is achieved as the measure of accuracy with 92% and with a processing time of less than 72 MS for six photos of an apple fruit. The overall results showed that there was an increasing possibility for implementing the proposed CNN-based classification model in commercial packaging lines. The proposed system starts the process by clicking the picture of the fruit. The characteristics of the fruit samples, such as their size, shape, and colour are predicted with 92.7% accuracy.

关键词： Deep learning Training Analytical models Computational modeling Predictive models data models Time measurement

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A data-Driven Framework for Identifying Misuse of Multidrug-Resistant Organisms

A Data-Driven Framework for Identifying Misuse of Multidrug-...

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Technology, Engineering, and computing Applications (ICTECA), international conference on

作者： Putri Bernawati Teddy Mantoro Dwi Retnowati Postgraduate Program of Medical Intelligence National Intelligence College Bogor Indonesia Department of Computer Science Sampoerna University Jakarta Indonesia

Multidrug-resistant organisms (MDROs) pose a significant threat to public health due to their ability to cause various diseases. The emergence and evolution of superbugs have been accelerated by existing drug conservation policies, hindering the development of novel antibiotics. As a result, researchers are focusing on understanding disease causation pathways and developing advanced classes of antibiotics. Despite challenges related to transparency, regulations, ethics, and technical limitations, technology such as MDRO Dual Use Quickscan shows promise in combating MDRO-based threats. This work presents a web-based tools design that incorporates a data-driven framework and utilizes artificial intelligence (AI) to detect potential cases of dual-use research involving MDROs. A comprehensive literature review identified 15 questions relevant for evaluating MDRO dual-use research using the Quickscan tool. The Quickscan can be developed as a valuable tool for assessing the potential dual-use characteristics of MDRO research. It is recommended that expert opinions guide risk management in biologics and address potential perilous scenarios arising from dual-use activities. Artificial intelligence models, natural language processing techniques, and binary classification algorithms are suggested as viable options for detecting and assessing dual-use traits associated with MDROs. However, human supervision and assessment remain critical for interpreting outcomes and making informed decisions.

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Adequate Basis for the data-Driven and Machine-Learning-Based Identification 1

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22nd international conference on Artificial Intelligence and Soft computing, ICAISC 2023

作者： Rojahn, Marcel Ambros, Maximilian Biru, Tibebu Krallmann, Hermann Gronau, Norbert Grum, Marcus University of Potsdam Potsdam Germany Krallmann AG Berlin Germany

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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13th international Symposium on Business Modeling and Software Design, BMSD 2023

13th International Symposium on Business Modeling and Softwa...

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13th international Symposium on Business Modeling and Software Design, BMSD 2023

ISBN: (纸本)9783031367564

The proceedings contain 29 papers. The special focus in this conference is on Business Modeling and Software Design. The topics include: Preface;modeling Change in Business Processes;Development of a Capability Maturity Model for Organization-Wide Green IT Adoption;From Conceptual Specification to OO Specification;Architecting Agility: Unraveling the Impact of AI Capability on Organizational Change and Competitive Advantage;enterprise Architecture Artifacts’ Role in Improved Organizational Performance;A Semiotic Analysis of the Representativeness of BPMN Graphic Elements;A Systematic Approach to Derive User Stories and Gherkin Scenarios from BPMN Models;composing an Initial Domain-Specific Modeling Language Notation by Reusing Icons;validating Trust in Human Decisions to Improve Expert Models Based on Small data Sets;how is Affect Social Justice Tensions: A Case Study of Asylum Management;applying Augmented Reality in Tourism: Analyzing Relevance as It Concerns Consumer Satisfaction and Purchase Intention;realizing Appropriate Process Standardization – Basis for Effective Digital Transformation;comparing Sensor-Based computing and Predictive data Analytics for Usage in Context-Aware Applications;a Model of a Multi-sensor System for Detection and Tracking of Vehicles and Drones;a Web-Based Approach for Traceability in Rule-Based Business Information Systems;a Conceptual Model for the Selection of Methods for Software Engineering Process Improvement;Towards Log-Driven Monitoring of Technical Degradation: An ERP Perspective;A Development Example: From Conceptual Specification to OO Specification;managing database Trigger Design;a View on Vulnerabilities Within IoT Devices in the Smart Home Environment;on Tools for Practical and Effective Security Policy Management and Vulnerability Scanning;linking Computers to the Brain: Overview of Cybersecurity Threats and Possible Solutions;serious Game-Based Haptic Modeling - An Application-Oriented Approach for Sequentially Devel

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data Cataloging based SINDy Approach for Weather Forecasting

Data Cataloging based SINDy Approach for Weather Forecasting

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international conference on Advanced computing, ICAC

作者： X Regis J John Mathuram V Padiyarajan Department of Mechanical Engineering Loyola-ICAM college of Engineering and Technology Chennai India Department of Chemical Engineering Alagappa college of Technology Anna University Chennai India

In the present era, climatic change is a crucial phenomenon that poses a great threat to human and animal life. Accurately simulating and forecasting the temperature of a particular geographical location have been a great conundrum throughout history particularly in tropical regions due to the sophisticated and multi-faceted oceanic transactions. Nonetheless, a smattering number of machine learning techniques such as random forests, deep learning algorithms, neural networks, support vector machines, stacked autoencoders have been exploited to forecast the weather and other volatile field variables with encouraging prospects. These algorithms require dense training data and also consumes large CPU time to perform future weather predictions. This article proposes a data cataloging based Sparse regression of Nonlinear Dynamics (SINDy) method for weather prediction. It differs from the existing prediction techniques by formulating an elevated time series data from the actual measured dataset. By adopting this empirical prediction method, the governing relationship between the input and output data is attained in simple mathematical form rather than complex functions. The MATLAB simulations of temperature and relative humidity confirm the superiority of the proposed approach in aspects of performance criteria (Mean square error).

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The Development of Machine Learning innovation technology for data Mining In Smart Healthcare

The Development of Machine Learning innovation technology fo...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： V. Manikandan Sonal Sharma Department of Computer Science and Engineering Jain (Deemed-to-be University) Bangalore India

The field of Smart Healthcare has undergone a revolution due to the development of machine learning technologies. Artificial intelligence (AI) has a subfield called machine learning that allows computers to learn from data and develop over time without explicit programming. Healthcare workers can now discover trends, diagnose diseases, and make decisions based on more precise and timely data thanks to machine learning’s capacity to comprehend and analyze enormous amounts of data. Finding patterns and links in massive datasets is known as data mining. data mining is used in Smart Healthcare to locate patterns in patient data that can be utilized to comprehend a patient’s health status better, diagnose specific disorders, and forecast the most effective course of therapy. data mining can create novel cures and treatments customized to a person’s genetic profile. data mining in Smart Healthcare is now considerably more effective and efficient, thanks to machine learning. Using complex algorithms, machine learning models can analyze vast amounts of data markedly faster and more correctly than conventional methods. Healthcare experts may not immediately notice hidden patterns and connections in data that machine learning algorithms can find. As a result, machine learning can assist healthcare practitioners in making decisions that are better informed, improving patient outcomes.

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BAV-DSS: Blockchain Assisted Verifiable data Sharing Scheme with Fast Encryption and Outsourced Decryption for IoT 24th

BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme ...

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24th international conference on algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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