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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Amardeep Singh Monika Singh Computer Science & Engg. Dept. SVIET Banur India Computer Science & Engg. Apex Institute of Technology Mohali India

An efficient technique for preserving user privacy of users while publishing data is anonymization. Banks, Social Network service providers and hospitals are examples of data owners/stakeholders who anonymize their customers' data before releasing it in order to protect users' privacy. However, trustworthy information consumers still value anonymous data. Numerous anonymization models and techniques have been developed for releasing data while preserving user privacy. These models/algorithms de-identify user data, which is typically presented as graphs. Giving clear perspectives on the entire information privacy field, including recent anonymization research, tabular and SN data, is crucial. In this article, we proposed an approach for anonymization to address privacy issues in social networks specifically addressing semantic similarity attacks. Far as we are aware, our proposed technique is capable of resolving the issues of semantic similarity between sensitive attributes efficiently. The results of the proposed approach have been evaluated using the dataset of around 6000 users of a real-time social network viz. Twitter. Evaluation metrics of APL (Average Path Length) show that our technique can protect privacy and also maintain the utility of data while publishing it for public use.

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Harnessing ECU Data for Real-Time Fuel Consumption Forecasting

Harnessing ECU Data for Real-Time Fuel Consumption Forecasti...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： P. Kranthi Kumari V. Sravani Kumari M. Varshini B. Hari Krishna B. Prem Kumar V. Srinivas Department of Computer Science and Engineering Godavari Global University Rajamahendravaram Andhra Pradesh India Dept. of Computer Science and Engineering Godavari Institute of Engineering & Technology (Autonomous) Rajamahendravaram Andhra Pradesh India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

The Electronic Control Unit (ECU) is an integral part of a vehicle from which we can extract data which makes perfect sense regarding multiple aspects in the automotive sector. This is critical information for analysing driving behaviour. These capabilities are put together to work in the project, utilizing machine learning models to classify drivers based on their fuel efficiency and predict fuel consumption. The feature selection process enables to pinpoint the most noteworthy factors and utilize them to construct accurate fuel usage regression models. The proposed system implements algorithms like Random Forest Regression, AdaBoost Regression for prediction and K-Means Clustering for classification. With an in-dept. review of driving profiles such as Sporty, Eco, Calm, Normal and Aggressive this project enables a broader comprehension of your complete drives and furthermore enabling a smarter fuel-efficient drive, which is noteworthy way for better vehicle execution and lower natural impact.

关键词： Reviews Biological system modeling Predictive models Feature extraction Real-time systems Fuels Forecasting Driver behavior Random forests Vehicles

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How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance

arXiv

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arXiv 2024年

作者： Li, Hongkang Zhang, Shuai Zhang, Yihua Wang, Meng Liu, Sijia Chen, Pin-Yu The Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States New Jersey Institute of Technology United States The Dept. of Computer Science and Engineering Michigan State University United States IBM Research United States

Group imbalance has been a known problem in empirical risk minimization (ERM), where the achieved high average accuracy is accompanied by low accuracy in a minority group. Despite algorithmic efforts to improve the minority group accuracy, a theoretical generalization analysis of ERM on individual groups remains elusive. By formulating the group imbalance problem with the Gaussian Mixture Model, this paper quantifies the impact of individual groups on the sample complexity, the convergence rate, and the average and group-level testing performance. Although our theoretical framework is centered on binary classification using a one-hidden-layer neural network, to the best of our knowledge, we provide the first theoretical analysis of the group-level generalization of ERM in addition to the commonly studied average generalization performance. Sample insights of our theoretical results include that when all group-level co-variance is in the medium regime and all mean are close to zero, the learning performance is most desirable in the sense of a small sample complexity, a fast training rate, and a high average and group-level testing accuracy. Moreover, we show that increasing the fraction of the minority group in the training data does not necessarily improve the generalization performance of the minority group. Our theoretical results are validated on both synthetic and empirical datasets, such as CelebA and CIFAR-10 in image classification. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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SroX: Accelerating Hidden Weight Creation in Neural Networks via Spectral Relaxation and X-Iteration

SroX: Accelerating Hidden Weight Creation in Neural Networks...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Harshit Agarwal Parth Middha Ayush Thakur Sofia Singh Shipra Saraswat Neha Bhatia Dept. of Computer Science & Engineering Amity University Noida India Amity Institute of Information Technology Amity university Noida India Dept. of Information Technology Panipat Institute of Engineering & Technology Panipat India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This paper proposes SroX, a novel approach for accelerating Hidden Weight creation in neural networks, which is a critical component of neural network training that can significantly impact the overall efficiency of the training process. SroX leverages spectral relaxation and X-Iteration to reduce the computational time required for Hidden Weight creation, enabling faster training times without sacrificing accuracy. Our approach addresses the long-standing challenge of efficient Hidden Weight creation, which has been a major bottleneck in neural network training. We demonstrate the efficacy of SroX through extensive experiments on benchmark datasets, including MNIST, CIFAR-10, and ImageNet, where SroX outperforms existing methods, including Random Projection and Gradient-Based Optimization, in terms of training time and accuracy. The proposed approach provides a promising solution for large-scale neural network applications, where efficient training is crucial, and has the potential to revolutionize the field of neural network research and applications, driving innovation and breakthroughs in areas such as computer vision, natural language processing, and beyond. By providing a faster and more efficient way to create Hidden Weights, SroX can enable researchers and practitioners to tackle complex problems with unprecedented speed and efficiency, leading to significant advancements in the field of artificial intelligence.

关键词： Training Technological innovation computer vision Accuracy Scalability Neural networks Benchmark testing Natural language processing Communications technology Optimization

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DNS Tunnel Problem In Cybersecurity

DNS Tunnel Problem In Cybersecurity

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Güneş Gürsoy Asaf Varol Ahad Nasab Dept. of Computer Engineering College of Eng. and Natural Sciences Maltepe University İstanbul Turkey Dept. of Eng. Management and Technology College of Eng. and Computer Science The Uni. of Tennessee at Chattanooga Chattanooga TN US

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

The Domain Name System (DNS) is the most important building block of the Internet. Websites, file transfer applications, and e-mail services use the DNS service. Therefore, these services can rarely be blocked by firewalls to prevent their access from being affected. Since applications such as firewalls and Intrusion Detection Systems (IDS) do not check the allowed protocols, attackers can open a secret path called DNS Tunnel through the DNS protocol to access sensitive data and cause many attacks. In this study, DNS Tunnel, DNS Tunnel detection methods and detection methods, and DNS attack types are included.

关键词： Deep learning Protocols Machine learning algorithms Firewalls (computing) Tunneling Malware Behavioral sciences

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NestedVGG: A Novel Deep Learning-based architecture for Brain Tumour Diagnosis 3

NestedVGG: A Novel Deep Learning-based Architecture for Brai...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Maurya, Ritesh Thirwani, Parth Gopalakrishnan, T. Dutta, Malay Kishore Panchal, Soumyashree M Amity University Amity Centre for Artificial Intelligence Noida India Amity University Dept. of Artificial Intelligence Noida India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350384598

Brain tumors are sudden outbreak of cells in the brain or on its surface and are categorized into gliomas, meningiomas, and pituitary adenomas. Traditionally, tumors of the brain are categorized using a manual approach that includes visual examination by radiologists on Magnetic Resonance Imaging (MRI). This is arduous and subject to inter-expert variability. In view of the subtle and fine-grained compact variations that exist in MRI images of these three types of brain tumors, this paper proposes an automated brain tumor detection system using deep learning (DL) with MRI scans. This paper presents an automated system for brain tumor detection using DL on MRI scans. The "NestedVGG"model implements transfer learning(TL) with the outer architecture of "Outer VGG"for feature extraction and the inner architecture for fine-grained identification. This "Outer VGG"utilizes a pre-trained VGG16 model to acquire information about features from MRI images. While the inner "Inner VGG"can capture minute variations between tumor types to classify glioma, meningioma, no-tumour, and pituitary cases effectively. Publicly available MRI dataset consists of three classes of tumours were used for testing and this framework has achieves accuracy of 97.71% on the test set, thus outperforming the prior methods in this task. Moreover, it has maintained its high accuracy across various types of tumors, thus making the model flexible. This paper furthers brain tumor diagnosis through an effective architecture for brain tumor identification, which can lead to advanced diagnostic tools and better patient care. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Machine Learning in Agriculture and Crop Management: A Comprehensive Review

Machine Learning in Agriculture and Crop Management: A Compr...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Rachna Rathore Archana Ganesh Said Preeti Jnagra Haritha Murari Saloni Bansal Harshita Singh Dept. of Mathematics Jiwaji University Gwalior India Dept. of Computer Engineering AISSMS’s Institute Of Information Technology Pune India Dept. of Biotechnology Lingayas Vidyapeeth Haryana India Dept. of Software Engineer Spark Infotech Inc Phoenix Arizona USA Dept. of Computer Eng. & Applications GLA University Mathura India Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Machine learning (ML) has transformed agriculture and crop management, revolutionizing modern farming techniques. By leveraging ML, farmers can accurately monitor and analyze agricultural data, gaining valuable insights into crop health, soil conditions, weather patterns, and pest infestations. Through the analysis of satellite imagery, sensor data, and historical records, ML models can predict optimal planting times, forecast yields, and identify potential disease outbreaks, enabling more efficient resource management. These innovations promote precision agriculture, reduce waste, and support sustainability. This paper explores the latest developments in ML applications for agriculture, emphasizing their potential to enhance food security, minimize environmental impact, and boost farm profitability through data-driven decision-making.

关键词： Technological innovation Systematics Reviews Weather forecasting Machine learning Predictive models Soil Satellite images Sustainable development Crop yield

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Development of a teaching model based on lifelong learning through active methodological strategies 12

Development of a teaching model based on lifelong learning t...

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12th International Conference on Virtual Campus, JICV 2022

作者： Trujillo-Aguilera, F D. Blazquez-Parra, E.B. Almendros, J. Calderon Marti-Campoy, A. Plaza-Garcia, I. University of Malaga Dept. Electronic Technology Malaga Spain University of Malaga Dept. Graphic Engineering Design and Projects Malaga Spain Polytechnic University of Valencia Dept. of Computer and Systems Informatics Valencia Spain University of Zaragoza Dept. of Electronic Engineering and Communications Zaragoza Spain

ISBN: (数字)9781665451062

ISBN: (纸本)9781665451062

Based on one of the fundamental principles of the educational model of the European Higher Education Area concerning the development of the capacity to achieve lifelong learning, this project has been developed in which a set of active teaching activities have been implemented for the acquisition of key competences in the challenge of achieving one of the most important goals that any educational system must provide: lifelong learning, thereby facilitating the incorporation of new university graduates into the world of work. This project has been implemented on the subject of Power Electronics. However, to facilitate the transfer to other subjects, the catalogue of active teaching activities is sufficiently varied to make it easy to extrapolate and use in other subjects, thanks to the fact that teachers have a wide variety of activities to choose from the set. Finally, a survey has been drawn to evaluate this new active methodology's satisfaction, impact and improvement to know the students' perspectives once they have worked with this catalogue of activities. © 2022 IEEE.

关键词： E-learning

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A Machine Learning Approach to Compare Causal Inference Modelling Strategies in the Digital Advertising Industry

A Machine Learning Approach to Compare Causal Inference Mode...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Sanskriti Sanjay Kumar Singh Apoorv Kumar Sinha Trilok Nath Pandey Biswa Mohan Acharya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Computer Application ITER SOA University Bhubaneswar India

Cause and effect relationships are not extensively explored in machine learning but prior to creating a system that understands causal relationships, there needs to be a general ability to grasp and inculcate cause-and-effect statistically. The way of choice for the proposed model is causal inference where the causes are inferred from the data. The study of causality, unlike correlation, falls in the domain of prescriptive analytics, that is, the likelihood of an occurrence given that another occurrence has happened. As a result, the study of causality and predictions based on causal inference are highly important when it comes to the domain of marketing and sales, largely because many practices in the domain of advertising try to fundamentally mimic causal inference such as A/B testing, primarily used in e-commerce and the digital advertising industry. For the purposes of the study, the treatment will be exposure to multiple advertisements and the uplift modelling will be used to direct marketing efforts to the most agreeable customers. Uplift modelling is a causal learning approach that estimates the dataset's individual treatment effect and using the same, calculates the incremental impact of a direct treatment on the individual behavior of the customer, which is why the advertising use-case felt particularly suitable. The usual way causal inference is inculcated in machine learning models is that one attribute is taken as a treatment, that is, the intervention or policy that has been done, while the target label is the desired outcome while accounting for the confounders which are the rest of the attributes. However, for the crux of this study, the proposed model will be taking two treatment attributes so as to see how the causality of two outcomes improves or worsens the impact of a rudimentary machine learning model. At the same time, this comparative study will be in tandem with two other models featuring one causal relationship as well as no causal relations

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Improving Network Efficiency in Clustered Tree Topology through PSO Optimization in IEEE 802.15.4-DSME based IoT Networks

Improving Network Efficiency in Clustered Tree Topology thro...

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Sonali Anand Nikumani Choudhury Tamoghna Ojha Anakhi Hazarika Jay Dave Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Mathematics & Computing Indian School of Mines Indian Institute of Technology Dhanbad India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

The IEEE 802.15.4 standard is designed for low-rate wireless personal area networks (LR-WPANs). Deterministic and Synchronous Multi-channel Extension (DSME) is one of the key Medium Access Control (MAC) modes of the IEEE 802.15.4 standard that was introduced to address the need for higher reliability, lower latency, and better scalability in network communication in Internet of Things (IoT) applications. However, in dynamic network conditions (topology and channel traffic), the network suffers from several limitations, such as low packet delivery ratio, high latency and packet collisions, and finally, higher power consumption. This is because of sub-optimal parameter settings. The standard does not define any mechanism to adapt the parameters based on network conditions. In this paper, we propose a mechanism to adapt/fine-tune the network parameters DSME along with the optimization of QoS using an algorithm based on Particle Swarm Optimization(PSO) for dense and dynamic DSME-based IoT networks. Through simulations, the proposed mechanisms’ performance is analyzed in terms of energy efficiency, transmission overhead, throughput, and latency and is shown to outperform the existing DSME MAC scheme.

关键词： IEEE 802.15 Standard Network topology Heuristic algorithms Wireless personal area networks Throughput Topology Telecommunications Internet of Things Standards Optimization

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