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检索条件"任意字段=International Conference on Information Technology - Coding and Computing"

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New era of gene classification methods using machine learning 4th

New era of gene classification methods using machine learnin...

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4th international conference on Computational Methods in Science and technology, ICCMST 2024

作者： Kaur, Amanpreet Kaur, Harpreet Singh, Amandeep Punjabi University Patiala Department of Information Technology Chandigarh Engg. College-CGC India Punjabi University Patiala India Department of Mechanical Engineering BBSBEC Fatehgarh Sahib India

ISBN: (纸本)9781032911571

New diagnostic technologies like DNA microarray technology have changed illness detection in the fast-changing technological age. This work focuses DNA microarray technology in gene expression research to find and understand genetic anomalies that cause illnesses, including cancer. Gene expression analysis is critical for cancer diagnosis to uncover underlying problems and create novel treatments. The number and size of data make human analysis difficult, requiring automation. Machine learning and statistics are used for data categorization, focusing on cancer prediction. This article discusses various feature selection approaches for large data sets. The related work section discusses KNN-based, MLDA, signal-to-noise ratio, and ensemble algorithms. Sample size, computing costs, and classification accuracy are addressed by each technique. The study emphasizes feature selection and the range of machine learning algorithms. This article proposes a gene classification research, encompassing current gene categorization approaches, feature extraction methods, datasets, result analysis, and conclusion. The article seeks to improve gene expression analysis for illness diagnosis and therapy. © 2025 the Author(s).

关键词： Microarrays

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Protecting Privacy in Federated Time Series Analysis: A Pragmatic technology Review for Application Developers 15

Protecting Privacy in Federated Time Series Analysis: A Prag...

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15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Bachlechner, Daniel Hetfleisch, Ruben Krenn, Stephan Lorünser, Thomas Rader, Michael Fraunhofer Austria Research GmbH Wattens Vienna Austria AIT Austrian Institute of Technology GmbH Vienna Austria Digital Factory Vorarlberg GmbH Dornbirn Austria

ISBN: (纸本)9789897587474

The federated analysis of sensitive time series has huge potential in various domains, such as healthcare or manufacturing. Yet, to fully unlock this potential, requirements imposed by various stakeholders must be fulfilled, regarding, e.g., efficiency or trust assumptions. While many of these requirements can be addressed by deploying advanced secure computation paradigms such as fully homomorphic encryption, certain aspects require an integration with additional privacy-preserving technologies. In this work, we perform a qualitative requirements elicitation based on selected real-world use cases. We match the derived requirements categories against the features and guarantees provided by available technologies. For each technology, we additionally perform a maturity assessment, including the state of standardization and availability on the market. Furthermore, we provide a decision tree supporting application developers in identifying the most promising candidate technologies as a starting point for further investigation. Finally, existing gaps are identified, highlighting research potential to advance the field. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Privacy by design

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Multi-scale Local and Global Context-aware Network for Speech Enhancement in Insect Noise Environments 5

Multi-scale Local and Global Context-aware Network for Speec...

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5th international conference on Advances in Electrical, Electronics and computing technology, EECT 2025

作者： He, Jian Zhang, Chao Li, Guojing School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9798331541545

In the summer and autumn seasons, insect chirping (such as cicadas) emerges as the predominant environmental noise interference in outdoor audio data collection. Although existing speech enhancement models have made progress, they often fail to fully utilize multi-scale information and ignore feature interactions between scales. To address these limitations, this paper introduces a Multi-scale Local and Global Context-aware Network (MLGNet) for solving insect noise interference. MLGNet is based on UNet with Local-Global Scale-Aware (LGSA) module, aiming to learn a wider range of multi-scale contextual features. LGSA consists of three key components. Specifically, Multi-scale Global-based Attention (MGA) module aggregates multi-scale information from different channel levels for global feature modeling and encodes causal position information. The Multi-scale Interactive Convolution (MIC) module achieves cross-scale interaction modeling of local features through dual scale cross learning. Meanwhile, Context-aware Enhancement Fusion (CEF) module adaptivly learns of local and global feature weights and enhances nonlinearity, promoting high-quality dynamic fusion of features. The quantitative evaluation of the Insect Noise dataset and the Voice Bank+DEMAND dataset demonstrate that MLGNet outperformed other models and achieved excellent results in objective evaluation metrics. © 2025 IEEE.

关键词： Speech enhancement

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Research on microservice gateway technology based on data processing characteristics of Service-Oriented Architecture design models 2

Research on microservice gateway technology based on data pr...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： He, Hongjie Hu, Yashan Wang, Fei Fan, Zikai Dai, Jiao Li, Wenpu Wang, Xingtao Xue, Meng State Grid Jiangsu Electric Power Co. Ltd. Economic and Technical Research Institute Nanjing210000 China State Grid Information and Communication Industry Group Co. Ltd. Beijing100052 China

ISBN: (纸本)9781510689053

With the rapid development of cloud computing, big data and IoT (Internet of Things) technologies, microservice architecture has gradually become the mainstream design model for enterprise IT (information technology) systems. Based on the SOA (Service-Oriented Architecture) design model, this study systematically analyzes the data processing capabilities of microservice gateways and proposes a comprehensive set of technical frameworks. The research centers around the key functions of microservice gateways, including message format conversion, data validation and filtering, as well as service routing and load balancing, to explore and innovate the technology. By implementing a unified message format standard and JSON Schema validation, this research ensures data accuracy for cross-service communication. In addition, by integrating JSON (JavaScript Object Notation) Schema and regular expressions, efficient data validation and security filtering are realized. For service management, this study employs Consul and Netflix Ribbon techniques to achieve dynamic discovery of services and load balancing. The experimental evaluation reveals the potential of the proposed technologies in enhancing data processing efficiency and system stability, providing professional guidance and practical basis for further research and application development of microservice gateways. © 2025 SPIE.

关键词： Service oriented architecture (SOA)

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Blockchain-Enabled Supply Chain Management with Role-Based Access Control and AES Encryption

Blockchain-Enabled Supply Chain Management with Role-Based A...

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2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Dharshan, J.Y. Amarnath, Arjun R. Reddy, Pothamsetti Sai Srujan Rajagopal, Shinu M. Prashanth, Mrudula Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9798331538538

Blockchain-Enabled Supply Chain Management with Role-Based Access Control and AES Encryption will use blockchain technology in order to enable safe, transparent, and decentralized supply chain management. Using smart contracts deployed on Polygon Amoy Testnet, the system ensures data integrity, confidentiality, and traceability while implementing role- based access control (RBAC) in order not to give unauthorized access to data. AES encryption is set forth for secretive treatment of information, thus affording a double-layered security measure for protection against on-chain as well as off-chain transactions. The solution is intended for deployment and testing in both localhost environments for rapid prototyping and the Polygon Amoy Testnet for scalable and distributed implementation. The Polygon Amoy Testnet provides an inexpensive development environment: developers can acquire test tokens (0.807 POL) via the Polygon Faucet for complete testing purposes. Deployment and interactions of smart contracts can be checked via the Polygon Testnet Explorer. The project combines blockchain's immutability with cryptographic security to address traditional supply chain inefficiencies, enhancing trust, scalability, and data accuracy. © 2025 IEEE.

关键词： Authentication

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Cryptography Innovations for Securing Data in the Quantum computing Era: Integrating Machine Learning for Enhanced Security

Cryptography Innovations for Securing Data in the Quantum Co...

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2025 international conference on Computer, Electrical and Communication Engineering, ICCECE 2025

作者： Temara, Sheetal Bhagyalakshmi, L. Suman, Sanjay Kumar Shakunthala, M. Chitra, N. Thulasi Golla, Kishor University of the Cumberlands Department of Computer and Information Sciences United States Rajalakshhmi Engineering College Dept. of ECE TN Chennai India St. Martin's Engineering College Dept. of Ece and Dean R&d Telangana Secunderabad India R.M.D. Engineering College Dept. of ECE TN Thiruvallur India Mlr Institute of Technology Dept. of CSE Hyderabad India St. Martin's Engineering College Dept. of CSE Telangana Secunderabad India

ISBN: (纸本)9798331513894

The rise of quantum computing introduces substantial risks to traditional cryptographic protocols, which are vulnerable to quantum decryption methods such as Shor's algorithm. To address these emerging threats, this paper proposes an innovative cryptographic framework that integrates machine learning (ML) techniques to enhance data security in the quantum computing era. Our approach leverages ML-driven anomaly detection, adaptive key management, and predictive analytics to create a flexible and resilient cryptographic defense. The system's anomaly detection module utilizes neural networks to identify potential quantum-based decryption attempts, while reinforcement learning optimizes key generation and distribution in response to detected threats. Experimental results demonstrate that the proposed ML-augmented framework significantly improves anomaly detection accuracy and reduces vulnerability to quantum decryption attempts by dynamically adjusting cryptographic parameters. These findings underscore the potential of machine learning to strengthen cryptographic systems, making them adaptable to the advanced threats posed by quantum computing. © 2025 IEEE.

关键词： Predictive analytics

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Comparative Analysis of Execution Time for Sequential and Data-Decomposed Parallel Program

Comparative Analysis of Execution Time for Sequential and Da...

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international Scientific-Professional conference on information technology (IT)

作者： Radovan Čikić Miloš Milić Siniša Vlajić Faculty of Organizational Sciences University of Belgrade Jove Ilića 154 Belgrade Serbia

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

When complex computing in programming is mandatory, it’s important to choose a proper way to minimize execution time. Sequential and parallel approaches to source code execution each have their own pros and cons, and programmers must make valid decisions based on these factors. In this paper, we will conduct the comparative analysis of the performances of both approaching ways, and consider their application in different business and mathematics areas. Based on the experimental results, it was concluded that as the volume of the data processed by the program increases, parallel execution becomes more favorable, whereas sequential execution is more efficient for smaller data sizes. Additionally, the point at which sequential execution ceases to be more efficient was identified.

关键词： Source coding Programming Mathematics information technology Business

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Research on human pose and action detection algorithm based on YOLOv8 2

Research on human pose and action detection algorithm based ...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Cheng, Weizhong Wang, Xuefei School of Information Engineering Suihua University Heilongjiang Suihua China School of Electrical Engineering Suihua University Heilongjiang Suihua China

ISBN: (纸本)9781510689053

Human pose and action detection technology has significant applications in areas such as smart security, sports monitoring, and human-computer interaction. The development of deep learning has driven the demand for real-time and efficient detection algorithms in this domain. Although existing human action detection algorithms have improved accuracy, their model complexity and computational demands hinder practical use in portable devices and edge computing platforms. To address this, the paper presents a lightweight human pose and action detection algorithm based on the YOLOv8 framework, designed to meet the high-precision, low-latency needs of edge computing and portable devices. In the feature extraction phase, GhostNet's lightweight structure is used to reduce model parameters, while the CBAM attention mechanism, applied in the feature fusion phase, enhances the model's attention to critical features. This approach significantly decreases computational load while maintaining high accuracy. Experimental results indicate that the algorithm achieves a real-time detection rate exceeding 40 frames per second on edge computing platforms, demonstrating high accuracy and robustness across various human action scenarios. This offers strong technical support for human pose detection in smart security and wearable devices. © 2025 SPIE.

关键词： Portable equipment

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Advanced Machine Learning Approaches for Liver Disease Prediction: A Focus on Feature Selection and Hybrid Ensemble Model 6

Advanced Machine Learning Approaches for Liver Disease Predi...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Pavani, Pasupuleti Sravanthi, P. Suneetha, Chalasani Azhar, Mohammad Hema supriya Reddy, L. Maddineni, Tejaswi Department of CSE MLR Institute of Technology Hyderabad India Department of CSE CMR College of Engineering and Technology Hyderabad India Department of ECE Vignana Bharathi Institute of Technology Hyderabad India Malla Reddy college of Engineering and Technology Secunderabad India United States

ISBN: (纸本)9798331522667

This paper classifies liver disease using advanced techniques of authentication such as comprehensive datasets upon which the advance machine learning methods are based. In the methodology, careful data preprocessing included data cleaning and treatment of class imbalances through Random Oversampling, outlier detection by the Local Outlier Factor method, and Min-Max Scaling for feature standardization to ensure uniformity in the presence of such features across the overall dataset. However, in the case of LARS feature selection for LASSO-ANOVA Recursive Selection approach, the hybrid combination of LASSO algorithm and Recursive Feature Elimination and ANOVA-a that successfully picked five major features is involved, namely, Direct Bilirubin, Aspartate Aminotransferase, Alkaline Phosphatase, Age, and Albumin. These features enhance quite a margin above the predictability of the model. During the model-building phase, an ensemble fusion strategy was employed using Random Forest, AdaBoost, and CatBoost algorithms that utilize the overall strength of the algorithm. Outstanding performances were delivered with 96.67% accuracy in the test dataset, a weighted F1 score of 90.62%, precision of 90.67%, and a root mean square error of 0.3055. This scientific work brings to the fore the potential application of advanced algorithms of machine learning in the diagnosis of liver disease, providing health professionals with information crucial to enrich their clinical decision-making and the ultimate outcome for patients suffering from these diseases. © 2025 IEEE.

关键词： Random forests

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Optimizing Latency and Communication in Federated Edge computing with LAFEO and Gradient Compression for Real-Time Edge Analytics 6

Optimizing Latency and Communication in Federated Edge Compu...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Namratha, P. Chaganti, Koushik Reddy Elicherla, Sivananda Lahari Reddy Guddati, Surekha Swarna, Akarapu Thiramal Reddy, P. Department of CSE GATES Institute of Technology and Science Anantapur India Dept. of Computer and Information Systems University of the Cumberlands KY United States Dept of CSE Dayananda Sagar University Bangalore India Dept of CSE Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

The increasing need for real-time data analytics in edge computing is furthered by the latency pressures from computational and communication bottlenecks, thus demanding novel solutions. Fundamental approaches to federated learning and distributed machine learning do not accommodate changes in dynamic latency conditions very well, thereby being inefficient in terms of synchronization, communication overhead, and delay in task execution. This paper presents a holistic framework consisting of three innovative approaches to solve these problems: Latency-Aware Federated Edge-Aware Optimization (LAFEO), Latency-Driven Gradient Compression with Adaptive Sparsity (LAGCAS), and Predictive Latency Compensation with Dynamic Replication (PLCDR). The LAFEO framework consists of a double-layer optimization framework, combining latency-weighted scheduling with reinforcement learning to dynamically adapt the synchronization frequencies. This results in 25-30% end-to-end latency reduction with an accuracy deviation that is capped at 2% centralized models. LAGCAS reduces communication overhead by applying adaptive gradient scarification methods, bringing compressions of gradients under real-time accuracy thresholds: it reduces 40% communication volume and accelerates convergence by 20%. PLCDR uses predictive modelling to predict latency bottlenecks and dynamically replicates critical computations across the edge nodes. It hence offers 15-25% decrease in the completion time of tasks and up to 30% enhancement in the resilience of system sets. The existing methods will be addressed with the key limitations due to the introduction of latency-aware mechanisms that adapt to real-time edge conditions along with high accuracy and efficiency. Simulation results have shown marked scalability improvements, latency reductions, and resilience;in its positioning, this work's effort is a transformative advancement in the latency-sensitive edge computing process. © 2025 IEEE.

关键词： Federated learning

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