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2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

作者： Prajapat, Vivek Singh Rathore, Narendra Pal Sethi, Kamal Kumar Rajput, Shiv Shankar Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Machine Learning (ML) has significantly impacted daily life by automating tasks and enhancing decision-making across diverse sectors such as healthcare, finance, and transportation. However, concerns regarding data privacy, particularly in sensitive domains like healthcare and finance, have impeded its widespread adoption. Federated Learning (FL), pioneered by Google in 2016, presents a promising solution by enabling devices to collaborate on model training without sharing raw data. In FL, each device retains its data locally, and only model updates are exchanged with a central server for aggregation. This decentralized approach preserves data privacy while still improving model performance through collaborative learning. FL has garnered interest across industries, with approximately 32% of companies intending to integrate it into their systems soon. Furthermore, investment in FL is projected to rise substantially from 107 million in 2020 to 538 million by 2025, as indicated by forecasts from KPMG. This paper provides an overview of FL, its applications across various sectors, current adoption trends, and future growth prospects, highlighting its significance in addressing data privacy concerns while advancing machine learning capabilities. © 2024 IEEE.

关键词： Federated learning

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Evolutionary Optimization for Biometric Feature Identification and Recognition

Evolutionary Optimization for Biometric Feature Identificati...

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2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

作者： Verma, Shraddha Bhanodia, Praveen Kumar Sethi, Kamal Kumar Rathore, Narendra Pal Singh Rajput, Shiv Shankar Mahajan, Anita Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Performance variability is a major difficulty for multimodal biometric systems, affecting their consistency and dependability in person identification. In order to solve this issue, the use of ensemble learning techniques is suggested in this work. The predictions of several classifiers are combined in ensemble learning to increase system consistency and performance as a whole. The unpredictability inherent in multimodal biometric systems is lessened by ensemble approaches, which integrate many classifiers trained on various subsets of data. By producing identification results that are more reliable and stable under many circumstances, this method improves system reliability. Improved accuracy and consistency can be achieved by integrating complementing data from many biometric modalities using ensemble learning techniques including bagging, boosting, and stacking. In practical applications, the suggested method shows promise in reducing performance variability and improving the dependability of multimodal biometric systems. © 2024 IEEE.

关键词： Contrastive Learning

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Evaluation of Classifiers Based on Neural Network for Handwritten Digit Recognition

Evaluation of Classifiers Based on Neural Network for Handwr...

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2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

作者： Sehta, Neha Saraf, Krupi Vinchi, Aditya Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Handwritten Digit Recognition (HDR) refers to the computer' s ability to identify and categorize hand written digits from various sources into 10 predetermined categories, ranging from 0 to 9. The issue of digit recognition is significant and crucial. Due to variations in size, thickness, location, and orientation of manually written digits, several challenges need to be addressed in order to evaluate the problem of recognizing handwritten digits. The diversity and variety in the composition techniques of different persons also impact the pattern and appearance of the digits. It refers to the approach of recognizing and organizing written numbers. It offers a diverse array of uses, such as automated bank checks, postal addresses, and tax paperwork, among others. The objective of this research is to employ a classification system to accurately identify handwritten numerals. The consequences of the most widely employed deep learning algorithms, such as multilayered perceptron network, in conjunction with convolution neural networks, are being discussed. Comparison of five neural network classifier shows that accuracy up to 99% can be achieved only in few epochs of training with default configuration of hyper-parameters. © 2024 IEEE.

关键词： Deep learning

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Privacy Preserving Techniques in Data science : A Review

Privacy Preserving Techniques in Data Science : A Review

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2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

作者： Panchal, Yuvraj Sheikh, Rashid Sethi, Kamal Kumar Bhanodia, Praveen Kumar Rajput, Shiv Shankar Rathore, Narendra Pal Singh Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

In the field of data science, privacy-preserving methods are essential to guaranteeing the moral and secure use of data. This extensive overview study offers an in-depth examination of privacy-preserving approaches, including federated learning, homomorphic encryption, secure multi-party computation (SMPC), anonymization strategies, and differential privacy. This paper explains the theoretical underpinnings, real-world applications, difficulties, and future possibilities of privacy-preserving strategies in data science. The study explores anonymization strategies such generalization, suppression, k-anonymity, l-diversity, and t-closeness after providing an outline of privacy risks in data science. The concepts and uses of federated learning, homomorphic encryption, SMPC, and differential privacy are then examined, emphasizing how these techniques protect sensitive data and promote teamwork in analysis. Examples from the fields of healthcare, banking, telecommunications, social media, and government are shown to show how useful privacy-preserving techniques are in the real world. We hope to give scholars, practitioners, and policymakers a thorough knowledge of privacy-preserving strategies and their implications for ethical and long-term data-driven decision-making through this extensive review. © 2024 IEEE.

关键词： Anonymity

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Automated Subject Allotment to Faculty in an Academic institute Using Maxflow Problem

Automated Subject Allotment to Faculty in an Academic Instit...

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2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

作者： Sehta, Neha Shah, Naman Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Presently, educational organizations are utilizing manual procedures to assign subjects to faculties depending on their own preferences, which gives rise to prejudice and therefore, discontentment. The best approach would be to allocate the most qualified individual to instruct the appropriate course, taking into account their knowledge and experience. Many instructional units rely on manual trial-and-error methods, which are time-consuming, inefficient, and prone to biased judgment. Moreover, the distribution is likely disadvantageous for several academic personnel. This study introduces an automated allocation of subjects to the faculties, taking into account the preferences in an effective way. The subject allocation problem is mapped into a maxflow problem and solved using Edmonds-Karp algorithm. A satisfaction metric can be computed to evaluate the effectiveness of the solution proposed which is average calculated on the distribution of subject as per the choice of faculty. © 2024 IEEE.

关键词： Automation

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Privacy-Preserving Human Activity Recognition System for Assisted Living Environments

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2342-2357页

作者： Jain, Ankit Akerkar, Rajendra Srivastava, Abhishek Indian Institute of Technology Indore Department of Computer Science and Engineering Madhya Pradesh Indore453552 India Western Norway Research Institute Sogndal6856 Norway

Automatic human activity recognition has numerous applications, especially in elderly support and healthcare. Several approaches for human activity recognition (HAR) using a variety of sensors are available in literature. While such frameworks are effective, each has limitations related to privacy, convenience, cost, and performance. In this article, a robust framework for automatic human activity recognition is proposed that uses depth sensors that preserve privacy and are cost-effective. The depth sensors provide two data modalities, namely, depth maps and skeleton sequences, used together for activity recognition. Two novel descriptors, joint position descriptor based on the position of joints;and bone angle descriptor based on bone inclination, are generated from the skeleton sequence data. The descriptors convey both spatial and temporal information and are scale and view-point invariant. Depth video clips are used along with the descriptors to deal with the issue of noisy and missing skeleton sequences. The data modalities and descriptors are fused using a two-level fusion strategy for a multichannel convolutional neural network (CNN) framework. The proposed system is validated and shown to be superior to the existing state of the art through comparisons over four widely used public datasets. A computational complexity analysis of the system confirms its efficacy in real time. A prototypical implementation of the proposed system further validates its practicability. © 2020 IEEE.

关键词： Neural networks

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Rumor detection from online social media through feature selection and machine learning

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Multimedia Tools and Applications 2025年 1-25页

作者： Gurrapu, Neelima Baydeti, Nagaraju Department of Computer Science and Engineering National Institute of Technology Nagaland Nagaland Chumukedima797 103 India

Recently, Rumor Spreading over Online Social Media is found as one of the serious issue, which causes severe damage to society, organization and individuals. To control the rumor spread, rumor detection is found as one of the probable solution and gained huge interest in the research community. However, most of the past methods characterize the rumormonger behavior based on their own profiles and completely neglected the reputation features. A hybrid method for rumor identification by combining the user reputation feature with other features like user features, meta-content based features and linguistic features is proposed. The major novelty lies at the representation of users through their interaction with his/her followers in different communities. The own profile based features can be evaded easily but the reputation of follows cannot be evaded easily. Totally 44 features are used to characterize each tweet and trained through different classifiers. Four classifiers namely K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are used to train the system and the performance is analyzed through two standard datasets namely Zubiaga and Kwon. The obtained results show that the proposed method can recognize rumor with higher F1-score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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research progress and future prospects in the service security of key blast furnace equipment

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International Journal of Minerals,Metallurgy and Materials 2024年第10期31卷 2121-2135页

作者： Yanxiang Liu Kexin Jiao Jianliang Zhang Cui Wang Lei Zhang Xiaoyue Fan School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing 100083China Research Institute of Macro-Safety Science University of Science and Technology BeijingBeijing 100083China

The safety and longevity of key blast furnace(BF)equipment determine the stable and low-carbon production of *** pa-per presents an analysis of the heat transfer characteristics of these components and the uneven distribution of cooling water in parallel pipes based on hydrodynamic principles,discusses the feasible methods for the improvement of BF cooling intensity,and reviews the pre-paration process,performance,and damage characteristics of three key equipment pieces:coolers,tuyeres,and hearth ***-thermoere,to attain better control of these critical components under high-temperature working conditions,we propose the application of optimized technologies,such as BF operation and maintenance technology,self-repair technology,and full-lifecycle management ***,we propose further researches on safety assessments and predictions for key BF equipment under new operating conditions.

关键词： blast furnace equipment service security blast furnace campaign self-repair

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6243-6258页

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2025 IEEE.

关键词： Federated learning

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