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An Impact of Poisoning Attacks on machine learning Algorithms

An Impact of Poisoning Attacks on Machine Learning Algorithm...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Lourdu Mahimai Doss P M Gunasekaran Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

machine learning models, ubiquitous in domains like natural language processing and image recognition, are vulnerable to adversarial poisoning attacks, where malicious actors manipulate training data to induce erroneous predictions. This study delves into the susceptibility of Support Vector machine (SVM) models to such attacks, employing the Gradient Ascent Poisoning (GAP) approach. The findings reveal a substantial impact of GAP on SVM performance, leading to a notable decline in accuracy from 81% to 73%. Beyond presenting empirical evidence, the paper highlights the broader challenges of detecting and mitigating poisoning attacks, underscoring the need for robust security measures in machine learning systems. By illuminating the effectiveness of adversarial poisoning techniques and their ramifications, this research contributes to the ongoing endeavor to enhance the resilience of machine learning models in practical applications.

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recognition of Active Deception Jamming for LFM Radar Based on Hyperdimensional Computing 3

Recognition of Active Deception Jamming for LFM Radar Based ...

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2022 3rd international conference on Electrical, Electronic Information and Communication Engineering, EEICE 2022

作者： Dong, Hao Wang, Yan Hu, Wen Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time-frequency diagrams as the input, which time-frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Hamming distance

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Integrating Bi-LSTM and Extended Geological Constraints Method for Automatic Logging Curve Stratification

Integrating Bi-LSTM and Extended Geological Constraints Meth...

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Cloud Computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Xiang Li XiaLin Zhang ZhangLin Li ZhengPing Weng School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Logging curve stratification plays a crucial role in the interpretation of geological data. As the volume of logging data increases, logging curve stratification becomes increasingly challenging and time-consuming. Current automated log stratification methods primarily rely on pattern recognition techniques, which are limited to processing single curves and overlook the complex nonlinear relationships between different logging curves, resulting in suboptimal performance in thin layer stratification. In this study, we propose a deep learning-based approach that simultaneously handles multiple logging curves while incorporating geological constraints to enhance the impact of spatial features on logging curve stratification. Furthermore, we optimize the loss function and introduce a new evaluation metric to better assess the model's performance. Experimental results demonstrate that our proposed method performs better in terms of stratification error and thin layer detection.

关键词： Measurement Deep learning Cloud computing Accuracy Geology Computational modeling Big data applications data models pattern recognition Software engineering

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A Review of Healthcare Recommendation Systems Using Several Categories of Filtering and machine learning-Based Methods 3

A Review of Healthcare Recommendation Systems Using Several ...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Kumar, Pardeep Kumar, Ankit Baba Mastnath University Asthal Bohar Rohtak India

ISBN: (纸本)9781665462006

Health-care Recommendation Systems (RSs) is a famous application of AI (artificial intelligence) that involves the investigators worldwide. Several ML methods are utilized to develop health-care RSs. Selecting the best ML method to give customers a service/product is an interesting task in health-care RSs. Currently, it is observed in the buying pattern of individuals from in-shop to online the outcome in accessibility of OI (online information) which is exponentially improved day by day. In shopping scenarios, these RSs must be capable of advising reliable solutions to the customers. The medical health-care RSs have to manage a large quantity of data by cleaning the reliable data depending on the data analysis, generated on the user behaviour made by the customers during their online terms. RSs can suggest suitable items to customers based on their interests and existing favourites, leading to improved sales. This paper described the overview of health-care RS, phases of RSs such as IC (information collection), learning, and recommender or predictor. This paper presents an overview of three main methods used to construct health-care RSs that are content-based, collaborative filtering (CF)-based and hybrid (HB)-based filtering methods and discuss several ML-based methods used in health-care RSs with their performance comparison. The ML-based methods include;MFM (matrix factorization model), CNN, MLP, SVD, etc. © 2022 IEEE.

关键词： Health care

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Harmonic Representation for CNN-LSTM Automatic Chord recognition 3

Harmonic Representation for CNN-LSTM Automatic Chord Recogni...

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3rd international conference on Cybernetics and Intelligent System (ICORIS)

作者： Ito, Tsuyoshi Arai, Shuichi Tokyo City Univ Grad Sch Engn Setagaya Ku 1-28-1 Tamazutsumi Tokyo 1588557 Japan

ISBN: (纸本)9781665425803

Paper Since Chord progression is the element that determines the harmony of a piece of music, Automatic Chord recognition (ACR) from audio signals is a crucial task in the field of Music Information Retrieval(MIR). Recently, various models using deep learning have been proposed, but there are few studies on their input features. Notes parts of the chord are the fundamental note, and its overtone ringed simultaneously. In order to model these audio signals efficiently, feature transforms such as "Constat-Q-Transform(CQT)" is used. However, due to the super-position of fundamental notes and overtones of various instruments in polyphonic music, it is considered difficult to model chords even by deep learning. Therefore, we focused on the structure, including fundamental notes are on the logarithm and its overtones are on the linear. In this paper, we propose a feature representation that can represent overtone structure for each fundamental note. Based on these feature representations, data-driven approach to learn the chord by CNN-LSTM model. We evaluated performance using 383 songs with publicly available annotations, and achieved the same performance with approximately one-tenth of the number of parameters than the existing methods.

关键词： Deep learning Signal Processing pattern recognition Music Information Retrieval Automatic Chord recognition

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Online Multi-label Streaming Feature Selection Based on Neighborhood Approximation Error Rate and Label Correlation 11th

Online Multi-label Streaming Feature Selection Based on Nei...

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11th international Joint conference on Rough Sets, IJCRS 2025

作者： Pan, Siping Zhong, Juda Mao, Yu Lin, Yaojin School of Computer Science Minnan Normal University Fujian Zhangzhou 363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Fujian Zhangzhou 363000 China

ISBN: (纸本)9783031927430

Multi-label learning has garnered significant attention and application in the domains of machine learning, data mining, and pattern recognition. However, existing multi-label learning algorithms often fall short in adequately capturing the complex dynamic correlations among labels. To address this limitation, this paper proposes an online multi-label stream feature selection algorithm based on neigh-borhood approximation error rate and label correlation. Initially, the similarity between labels is computed, and correlated labels are partitioned into multiple subsets. The strongly correlated label sets thus obtained serve as the label space for the subsequent algorithm. Subsequently, instance boundaries are introduced to granulate all instances. Within the multi-label neighborhood approximation system, neighborhoods are determined based on the average intervals of samples, and a method for calculating feature importance is provided, offering a quantitative basis for feature selection. Finally, a dynamic stream feature selection framework is designed to iteratively select a subset of features that minimize the approximation error, thereby enhancing the efficiency and accuracy of feature selection. Experimental results demonstrate that the proposed algorithm consistently outperforms five other multi-label feature selection algorithms across all evaluation metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dynamic environments Multi-label learning Neighborhood approximation error rate

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Bug recognition using Hybrid Fuzzy Logic algorithm and Support Vector machine classification 4

Bug Recognition using Hybrid Fuzzy Logic algorithm and Suppo...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Ananthi, K. Balakrishnan, M. Pandi, M. Senthil Madasamy, N. Dr.Mahalingam College of Engineering and Technology Artificial Intelligence &data Science Tamil Nadu Coimbatore642003 India Dr.Mahalingam College of Engineering and Technology Computer Science and Engineering Tamil Nadu Coimbatore642003 India

ISBN: (纸本)9781665474368

In data mining and pattern recognition, the Support Vector machine is an extensively used classification technique. The tuning weight methods and the distance of the metrics used in a Support Vector based classification system are important. The problem of determining the best feature weight values and measuring the distance between neighbors, which affects the Support vector machine's classification accuracy, is a key obstacle. For parameter optimization, a hybrid approach is applied in this paper. To quantify the equalities between thetraining data set and the test data set are observed and the similarity measuring approach known as the fuzzy distance metric is used. Additional work, such as creating a unique fuzzy membership function and membership degrees for each dataset, adds time to the design process for these hybrid systems. To improve the classification model's precision, this automated approach will heuristically explore the optimal bounds of fuzzy membership functions for each dataset. Additionally, looking into the optimal boundaries may decrease error rates and improve accuracy in the proposed system. The experimental results will be implemented into a real-world classification challenge sourced from the UCI machine-learning benchmark library. © 2022 IEEE.

关键词： Support vector machines

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SGBCD: A data Discretization Algorithm Based on Granular-ball Computing and Attribute Significance

SGBCD: A Data Discretization Algorithm Based on Granular-bal...

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Electrical Engineering, Big data and Algorithms (EEBDA), IEEE international conference on

作者： Weiqi Chen Chongqing Vocational College of Transportation Chongqing China

As an important method in data preprocessing, discretization can effectively reduce the size of data, generate concise semantic representation, obtain valuable knowledge and information contained in big data, which is of great significance in the fields of data mining and machine learning. Nevertheless, most of the traditional discretization algorithms don't consider the distribution of sample and difficulty in setting parameters during interval partitioning, which leads to a decrease in efficiency and classification accuracy. A data discretization algorithm based on granular-ball computing and attribute significance is proposed in this paper, which is a multi-granularity method. Firstly, by introducing the granular-ball computing, the difficulty of parameter optimization is reduced and the efficiency of the algorithm is improved. At the same time, interval partitioning adaptively fits the original data distribution, reducing the occurrence of uneven interval partitioning and improving classification accuracy. Then, the randomness of attribute selection leads to instability in interval partitioning results. By introducing attribute significance and prioritizing the selection of attributes with high importance, this paper further improves the classification accuracy. Compared to other excellent discretization algorithms in the experiments, the proposed algorithm shows an ideal performance.

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OSOL: Open Linked data Generation Using Semantics Oriented learning Paradigms 15th

OSOL: Open Linked Data Generation Using Semantics Oriented L...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Aravind Krishnan, A. Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031889912

In the era of Web 3.0, there is an imminent need for a strategic framework for open linked data and meta tag generation for web pages as they would be useful in indexing which would serve as a roadmap for retrieval and also several other applications such as text summarization, text mining, etc. This paper proposes an open-linked data generation framework in which the XGBoost classifier classifies the web URL dataset. Category extraction and URL canonicalization are performed which helps in strategic maximal utilization of the term set. Strategic knowledge base repositories like CYC and NELL are used for entity enrichment in the model. The RBFNN classifies the web-based directories extracted from the current structure of the web using a focused crawler. Unlike most traditional frameworks, the OSOL uses Semantics-oriented reasoning, which is achieved through the SimRank measure with an empirically decided threshold. The Eagle optimization algorithm (a metaheuristic, nature-inspired algorithm) generates the optimal solution set. Shannon’s entropy helps in feature selection for classifying the dataset. This framework can achieve an overall mean precision percentage of 95.17%, a mean recall percentage of 96.75%, and an FDR value of 0.05, making it the best-in-class model for linked open data and meta-tag generation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge engineering

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Emotional Analysis of Clothing Product Reviews Based on machine learning 3

Emotional Analysis of Clothing Product Reviews Based on Mach...

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3rd international conference on Applied machine learning, ICAML 2021

作者： Hong, Ying Shao, Xiwen Basic Course Department Beijing Institute of Fashion Technology Beijing China

ISBN: (纸本)9781665421256

Emotional analysis of product reviews is a hot spot in current data mining research. Whether it is in academic or economic fields, text emotional analysis of e-commerce product reviews has great research value. This article used machine learning to conduct sentiment analysis for clothing e-commerce product reviews. Here, we used the data collection program to collect comment data. Then we performed preprocessing, word segmentation and sentiment labeling on the data. By setting different parameters to train the data, we build a sentiment classifier to automatically classify unknown data. We show experimentally that using machine learning methods to perform sentiment classification on product reviews can achieve good results. © 2021 IEEE

关键词： Sentiment analysis

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