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soft computing for Security Applications (ICSCSA), international conference on

作者： R.RoselinKiruba J. Jude Moses Anto Devakanth L.Sharmila Vasumathy M M Misba C.Saranya Jothi Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Department of Computer Applications Madanapalle Institute of Technology and Science Andra Pradesh India Department of Information Technology Kingston Engineering College Vellore

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Steganography is the method that convert data into bits to make it suitable for hiding. While embedding data the factors such as stego image quality and distortion which cannot be observed by the human eyes. the aim of this study is to increase the stego image with good quality. Initially, the image pixels are separated into different bit planes then it is compared with the secret data bit planes using Fruitfly Optimization Improved Hybridized Algorithm Pixel Similarity (FOIS-PS). For that similar pixel Fitness Function (FF) is applied based on the three important criteria such as edge, entropy and correlation. Finally, the data is embedded in the LSB bits based on their Embedding Capacity (EC). Using this method, the size of the input is reduced. then, the data is embedded based on difference of pixels within each group of similar pixels. Evaluation metrics shows the quality of the stego image is boosted and also reduces the processing time.

关键词： Measurement Image quality Steganography Image coding Image edge detection Entropy Encryption Security Optimization Standards

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Critical Disaster Prediction using Machine Learning Algorithms

Critical Disaster Prediction using Machine Learning Algorith...

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soft computing for Security Applications (ICSCSA), international conference on

作者： E. Nirmala M. Suresh M. Maragatharajan Riya Dadlani School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh India School of Computing Sciences and Artificial Intelligence VIT Bhopal University Kothrikalan Madhya Pradesh India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Prediction of earthquake is a challenging factor in the early warning system. there are a number of geographical locations that cause earthquake very often due to climatic changes. Earthquake is one among the natural disaster that root cause for substantial damage and risk to human life. this article discusses in detail about the earthquake prediction scheme constructed through machine language with automated algorithmic implementation over regression, random forest and SVM model. this accuracy prediction with comparative analysis determines the suitable model for prediction accuracy of earthquake. the objective of this research study predicts the accuracy based on magnitude, magnitude and location or region, number of region and depth of earthquake parameters from the Kaggle dataset of 18029 values. Hence, prediction with three different model generates the possible accurate prediction of 99.9% to predict the magnitude and probability of earthquake occurring in a precise area (California) from the significant data of that county using numerous Machine Learning (ML) models.

关键词： Support vector machines Analytical models Accuracy Disasters Computational modeling Earthquakes Predictive models Prediction algorithms data models Random forests

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PointAF: A Novel Semantic Segmentation Network for Point Cloud 1

引用

Proceedings of the 4th international conference on Neural computing for Advanced Applications, NCAA 2023

作者： Chen, Tianze Wang, Xuhong Li, Dongsheng Liu, Jiepeng Wu, Zhou College of Automation Chongqing University Chongqing China The Hong Kong University of Science and Technology Hong Kong School of Civil Engineering Chongqing University Chongqing China

ISBN: (数字)9789819958443

ISBN: (纸本)9789819958436

Point cloud semantic segmentation is a crucial problem in computer vision, which aims to assign semantic labels to each point in a point cloud. However, the sparsity and irregularity of point cloud data pose significant challenges to achieving accurate segmentation. Unlike traditional image classification tasks, each point in a point cloud not only has location information but also other feature information that must be considered. To address this issue, this paper proposes a novel point cloud semantic segmentation algorithm called PointAF. the proposed method utilizes soft projection operation during downsampling to better combine neighborhood information, an attention mechanism to achieve an adaptive offset effect, and residual connections to solve the problem of gradient disappearance. Experimental results show that the proposed method achieves great performance, with an mIoU of 70.6% and OA of 90.2% on the S3SDIS dataset, as well as mIoU of 69.2% and mACC of 70.1% on the ScanNetV2 dataset. the proposed method demonstrates great potential in point cloud semantic segmentation and may have practical applications in areas such as autonomous driving, robot navigation, and augmented reality. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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Application of Deep Learning for Wildfire Risk Management: Preliminary Results 4th

Application of Deep Learning for Wildfire Risk Management:...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： De Rango, Alessio D’Ambrosio, Donato Mendicino, Giuseppe Department of Environmental Engineering University of Calabria Ponte Pietro Bucci Rende87036 Italy Department of Mathematics and Computer Science University of Calabria Ponte Pietro Bucci Rende87036 Italy

ISBN: (纸本)9783031812439

Due to ongoing climate change and the related increased risk of drought, wildfires in the Mediterranean are expected to become more frequent and intense. this note introduces the use of Deep Learning to produce susceptibility maps for wildfire risk assessment to tackle this scenario. Indeed, thanks to the improved computational capabilities of modern GPUs, recent literature has pointed out that it is possible to train Deep Learning models on big datasets by outperforming standard approaches in several application domains, including the one object of this study. An Artificial Neural Network (ANN) model has been developed and applied to the Calabria region (southern Italy) study area. the network has been trained and validated over ten years of data by considering both meteorological and geomorphological data, and human factors. Preliminary results are promising, evidencing a higher network’s accuracy to the operational state-of-the-art Fire Weather Index (FWI) method, currently applied for forecasting and mitigation purposes in the study area. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Entropy-Based Fuzzy Weighted Logistic Regression for Classifying Imbalanced data 6th

Entropy-Based Fuzzy Weighted Logistic Regression for Classif...

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6th international conference on soft computing in data science (scds)

作者： Harumeka, Ajiwasesa Purnami, Santi Wulan Rahayu, Santi Puteri Inst Teknol Sepuluh Nopember Dept Stat Surabaya Indonesia Cent Bur Stat East Java Surabaya Indonesia

ISBN: (纸本)9789811673344;9789811673337

Logistic regression is a popular classification method that has disadvantages when it is applied to large data. Truncated Regularized Iteratively Reweighted Least Square (TR-IRLS) is a method that overcomes this problem. this method is similar to Support Vector Machine (SVM) because both of them have similar loss functions and parameters that can adjust the bias and variance. Both methods were designed with the assumption of balanced data, so that they are not suitable to be applied on imbalanced data. Both methods were developed to overcome problem on imbalanced data. TR-IRLS was developed into Rare Event Weighted Logistic Regression (RE-WLR) and SVM was developed into Fuzzy Support Vector Machine (FSVM). Both RE-WLR and FSVM use weights based on class differences, so that RE-WLR had better performance than TR-IRLS on imbalanced data whereas FSVM was better than SVM. then, Entropy-based Fuzzy Support Vector Machine (EFSVM) was developed by obtaining weighting values not only based on class differences, but also based on entropy. EFSVM further enhanced minority class interest in imbalanced data than SVM and even FSVM. therefore, Entropy-based Fuzzy Weighted Logistic Regression (EFWLR) is proposed by adopting the success of Entropy-based Fuzzy Membership (EF) as weight on SVM. this study applied EF as weight on Weighted Logistic Regression for binary classification. Experiments on 20 simulation data and 5 benchmark data with various rarity schemes validated that the EFWLR outperformed TR-IRLS and RE-WLR based on AUC. EFWLR had more efficient AUC than RE-WLR on imbalanced data.

关键词： Binary classification Entropy-based Fuzzy Imbalanced data Weighted Logistic Regression

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Enhancing Automated Payments: Impact of Iris Technology's in UPI Transactions

Enhancing Automated Payments: Impact of Iris Technology's in...

引用

soft computing for Security Applications (ICSCSA), international conference on

作者： Bhavna Galhotra Shalini Bhaskar Bajaj Aman Jatain Vivek Jaglan Computer Science Dept Amity University Haryana Jagan Institute of Management Studies Department of Computer Science SOET K.R. Mangalam University Gurgaon ASET Amity University Gwalior MP

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

In the era of cashless transactions, security has become a significant concern for users. this research explores the various models and technologies employed to enhance authentication and payment security in wallets and UPI payments. Biometric authentication, specifically iris recognition, emerges as a promising solution due to its high reliability and wide-ranging applications. the study investigates the working mechanism of iris recognition, comparing captured images with stored data to verify user identity. It highlights the significance of iris authentication in online transactions, presence verification, and other payment-related areas. this study also analyzes the existing models and identifies the most commonly used iris recognition techniques. By understanding the advancements in biometric authentication, this research aims to contribute to the development of more secure and efficient cashless payment systems.

关键词： Protocols Biological system modeling Authentication Information security QR codes Biometric authentication Tokenization Reliability Electronic commerce Iris recognition

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Deep Reinforcement Learning Based Approach for Balanced data Deduplication in Mobile Edge computing

Deep Reinforcement Learning Based Approach for Balanced Data...

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Information Technology,Big data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Yitong Sun Yun Wu School of Information Science and Technology Donghua University Shanghai China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Mobile edge computing (MEC) migrates computing, storage, and network resources to the network edge, providing users with low-latency data access. As a crucial part of MEC, edge storage systems (ESS) offer storage capabilities near data sources or users. However, the limited server storage resources present challenges for ESS in optimizing storage utilization efficiency. this paper investigates a balanced data deduplication problem in ESS involving multiple collaborative servers and different tasks while comprehensively considering deduplication rate, storage balance, and data storage efficiency. To maximize overall system performance and overcome the limitations of traditional methods that rely on static rules, we formulate the problem as a Markov decision process (MDP) and propose a deep deterministic policy gradient (DDPG)-based approach to find the optimal deduplication solution. We employ a dual-network structure combined with a learning rate scheduler and implement prioritized experience replay to enhance training stability. Simulation results demonstrate that our approach outperforms the baselines across various parameter settings.

关键词： Training Multi-access edge computing Simulation System performance soft sensors Markov decision processes Memory Stability analysis Servers Low latency communication

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A Study of Optimizations in Rainfall Prediction using Machine Learning Models

A Study of Optimizations in Rainfall Prediction using Machin...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Chaitanya Kumar Sinha Udit Sharma M. Maragatharajan J. Manikandan School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Rainfall prediction is essential for many industries, such as agriculture, water resource management, and disaster relief. Recently, there has been a lot of interest in machine learning techniques to increase the accuracy of rainfall forecasts. To improve rainfall prediction models' accuracy, we provide a hybrid strategy in this study that combines conventional algorithms with cutting-edge optimization techniques. We utilize the XG Boost, Random Forest, and Linear Regression algorithms as baseline models. these are then combined with the Crow Search Algorithm (CSA) optimization technique. Crow collective behavior inspired the nature-inspired metaheuristic algorithm, CSA, which has demonstrated promising results in solving complicated problems. this study intends to improve the baseline models' performance by dynamically adjusting their parameters through the use of CSA. the proposed approach incorporates CSA with the chosen machine learning algorithms, which adaptively modify parameters to increase prediction accuracy. We validate the suggested approach with historical rainfall data and conduct extensive experimentation to show its efficacy.

关键词： Adaptation models Rain Accuracy Machine learning algorithms Heuristic algorithms Humidity Predictive models Prediction algorithms data models Forecasting

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An Object Detection Algorithm with Multi-scale Context Information Based on YOLOv4 4

An Object Detection Algorithm with Multi-scale Context Infor...

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4th international conference on Natural Language Processing, ICNLP 2022

作者： Ma, Sugang An, Wen Yang, Xiaobao Hou, Zhiqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Xi'an University of Posts and Telecommunications Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

Most current object detection algorithms have the issue of missing objects due to occlusion. As the great difference of scale between occlusion objects and their integrity is affected, how to reduce the missing rate of occlusion objects has become a challenging problem in object detection algorithms. Aiming at the problem, this paper proposes an object detection algorithm based on multi-scale context information on the basis of the YOLOv4 algorithm framework. Firstly, the object features are extracted through the CSPDarkNet53 feature extraction network. then, the proposed attention-guided multi-scale context information module (AMCM) enhances the ability to perceive object context information and endows features with different influence factors through attention weighting to improve feature discrimination. the experimental results show that the detection accuracy and recall of the proposed algorithm reach 83.2% and 96.4% respectively on PASCAL VOC dataset, and 83.2% and 91% on KITTI dataset, which improves the detection performance of YOLOv4 algorithm. © 2022 IEEE.

关键词： Object detection

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Security and Privacy-Preserving Techniques of Federated Learning in Edge computing: A Comparative Study

Security and Privacy-Preserving Techniques of Federated Lear...

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Innovative Research in Applied science, Engineering and Technology (IRASET),conference on

作者： Nouhaila Aasoum Ismail Jellouli Souad Amjad Yahya M.A. Mohammed Department of Emerging Computer Technologies Faculty of Science Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

Edge computing (EC) is an innovative architecture that aims to provide cloud computing services near data sources. In conventional deep learning systems that use edge computing, data producers frequently need to transmit and exchange data with other entities, such as edge or cloud servers, to train their models. Federated Learning (FL) has recently emerged as a potential remedy for the problems of undesired bandwidth use, data confidentiality, and legal compliance. To enhance the status of research in this field and effectively implement the federated learning strategy, it is essential to identify and analyze its security and privacy issues first. Federated learning (FL) is the optimal method for addressing security and privacy concerns in edge computing. A profound grasp of risk factors allows FL implementers to create a secure environment while researchers benefit from a clear understanding of prospective research areas. the primary objective of this study is to evaluate previous contemporary studies in relevant disciplines to understand the issues and subjects covered in recent surveys. Furthermore, FL research is categorized into architectures, applications, and issues. Moreover, we analyze substantial security and privacy risks and propose new mitigation strategies.

关键词： Surveys Privacy data privacy Cloud computing Federated learning soft sensors Computer architecture

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