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international Symposium on Advanced Computing and Communication (ISACC)

作者： Parijat Saurabh Malik Bader Alazzam Nagarajan S D. Harikishan Reddy Sakar Fatah Sulaiman B Kiran Bala Department of Economics Mahatma Gandhi Kashi Vidyapith Varanasi India Faculty of Information Technology Jadara University Irbid Jordan School of Management Studies Bannari Amman Institute of Technology Erode Tamilnadu India Department of Management Studies Sri Venkateswara College of Engineering (Autonomous) Andhra Pradesh India Department of Business Administration College of Administrative and Financial Sciences Knowledge University Erbil Iraq Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Accurately predicting foreign exchange volatility is crucial for financial institutions, traders, and policymakers. This makes it an extremely complex and dynamic market, hence failing traditional methods of prediction for capturing intricate patterns present in time-series data. Advanced machine learning models, have shown great potential in addressing this challenge. We propose an Autoencoder-Lstm model that leverages the strengths of both autoencoder for feature extraction and LSTM networks for time-series forecasting. These networks have gated mechanisms controlling the flow of information, particularly suitable for financial forecasting in their ability to extract long-term dependencies and trends which improve the forecast's accuracy as far as selective memory in prediction of volatility goes. The autoencoder simplifies input data by extracting important features and dropping out unnecessary noise. After extracting features from the dataset the network processes them efficiently while recognizing past time series connections. The research is implemented using Python software, for model development. The framework is evaluated using real-world foreign exchange data, incorporating both raw price features and derived features such as returns and volatility measures. The model is trained and tested on a dataset of daily forex rates, achieving an impressive prediction accuracy of 99.2%. Performance metrics, including RMSE (0.012), MAE (0.008), and MSE (0.00014), underscore the model's ability to minimize prediction errors, significantly outperforming traditional models. The results demonstrate the Autoencoder-Lstm’s effectiveness in predicting exchange rate volatility, making it a valuable tool for financial forecasting. This model's high accuracy and robustness further establish its potential for broader applications in other time-series prediction tasks.

关键词： Accuracy Autoencoders Time series analysis machine learning Predictive models Feature extraction Software Robustness data mining Forecasting

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Visual recognition and Recommendation System for Cultural Tourism Attractions Based on Deep learning 1st

Visual Recognition and Recommendation System for Cultural To...

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

作者： Li, Si Yang, Xiaoqi Guangzhou College of Commerce Guangzhou China

ISBN: (纸本)9789819633487

This article designs and constructs a deep prediction model that combines deep neural networks and Transformer encoders. Build a prediction model using deep neural networks and Transformer encoders. Due to the powerful feature extraction ability of deep neural networks and their ability to fuse heterogeneous data from multiple sources, they are used to process basic information about users and scenic spots. The Transformer encoder is used to process user comment data, modeling long-distance dependencies between words in sentences through position encoding and self attention at the encoding end, thereby fully mining the comprehensive features of comment data and obtaining more potential user interest features. The features of the last three networks are input into the factorization machine to further learn their potential interaction relationships, thereby improving the prediction accuracy of the model and alleviating the problem of data sparsity. Finally, the effectiveness and accuracy of this recommendation model were verified through experimental comparison with traditional recommendation models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Novel Hybrid Feature Selection Method and Globalization Technique for Text Classification 4th

Novel Hybrid Feature Selection Method and Globalization Tec...

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

作者： Mekhzoumi, Dalila Boulkaboul, Sahar Amroun, Kamal CERIST Research Center Algiers Ben Aknoun Algeria Abderrahmane Mira University Bejaia Algeria

ISBN: (纸本)9783031821523

Feature selection in text classification refers to the critical process of identifying and selecting the most relevant and informative features such as words, phrases, or other linguistic elements from a text dataset. This process, which has been a research topic for decades and finds applications across various fields such as bioinformatics, image recognition, image retrieval, text mining, etc., is essential for optimizing classification accuracy and efficiency. Addressing the challenge of high dimensionality in text data, stemming from the abundance of features like words or n-grams, is crucial to mitigate computational inefficiency and overfitting. Furthermore, the presence of irrelevant or redundant features in text datasets poses another significant challenge, as these features can introduce noise or irrelevant information, thereby undermining the performance of classifier. In this paper, we proposed a new approach GL-SMCHI using improved features selection method to reduce the CHI value of high-frequency words, and globalization technique to incorporate both feature and class information when evaluating the importance of each feature. We compared the results obtained from our proposal against those of existing robust alternatives, The simulation results show that the proposed method outperforms the standard CHI squared, the improved feature selection methods, and the globalization method in term of F-Score and accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Financial data-Driven Classification of Manufacturing Companies: A Case Study of the Malaysian Industry 3rd

Financial Data-Driven Classification of Manufacturing Compan...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Kannan, Rathimala Ramakrishnan, Kannan PSG Institute of Management PSG College of Technology Tamil Nadu Coimbatore641004 India Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya63100 Malaysia

ISBN: (纸本)9783031863011

Manufacturing sector companies are typically categorized based on size or product type to aid in policy formulation and long-term planning. The existing classification systems, however, overlook economic performance, which makes it difficult to plan policies precisely and allocate resources. This study aims to close this gap by identifying economic factors that set manufacturing companies apart using data mining and machine learning. The main objective is to uncover similarity patterns among manufacturing companies and to describe each segment’s characteristics addressed in this study by utilizing historical data from the Manufacturing Economic Census Survey. The Cross-Industry Standard Process for data mining (CRISP-DM) framework guides the extraction of meaningful patterns from the economic census survey data. The study attempts to efficiently group manufacturing organizations according to their economic performance by utilizing unsupervised learning algorithms such as fuzzy C-means clustering and K-means clustering. The findings deviate from the existing categorizing method and yield six groups that are defined by financial metrics such as net book value, staff qualities, and spending distribution. Each segment is described in detail, including key characteristics such as business structure, primary activities, workforce qualifications, and financial metrics. These insights are instrumental in making informed decisions regarding future policy changes and financial aid initiatives, ultimately fostering a more resilient and competitive manufacturing sector. This research underscores the significance of data-driven approaches in elucidating industry dynamics and guiding strategic interventions to propel economic growth and enhance the well-being of Malaysians. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Competition

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Exploring Advanced Ensemble learning Strategies in machine learning and data mining for Predictive Modeling of Marathon Running Time 3rd

Exploring Advanced Ensemble Learning Strategies in Machine L...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Panwala, Brijal Buch, Sanjay College of Computer Applications SDJ International College Vesu Surat India College of Computer Applications Bhagwan Mahavir University Surat India

ISBN: (纸本)9783031862922

This study explores state-of-the-art advanced ensemble learning methodologies for predictive modeling in marathon running times. The research emphases on enhancing the precision and reliability of marathon time predictions by utilizing a varied array of machine learning methods, given the multifaceted nature of influencing factors, including historical performance data. The process initiates with the deployment of individual machine learning models, followed by the application of normalization techniques to standardize the dataset, ensuring uniform feature scaling. To further refine model performance, ensemble methods such as bagging, boosting, and stacking are employed, leveraging the collective advantages of multiple models to generate more robust predictions. Comprehensive experimental analysis and comparisons reveal that ensemble learning significantly improves predictive accuracy and uncovers the intricate relationships between variables influencing marathon outcomes. This research contributes meaningfully to the evolving domain of sports analytics, providing valuable insights and tools for athletes, coaches, and sports scientists aiming to optimize training regimens and race strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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On the size of a classification tree

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3rd international conference on machine learning and data mining in pattern recognition

作者： Scaringella, A Univ Roma La Sapienza Fac Sociol I-00198 Rome Italy

We discuss some estimates for the misclassification rate of a classification tree in terms of the size of the learning set, following some ideas introduced in [3]. We develop some mathematical ideas of [3], extending ... 详细信息

ISBN: (纸本)3540405046

关键词： learning systems

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Fusion of learning automata theory and granular inference systems: ANLAGIS. Applications to pattern recognition and machine learning

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NEUROCOMPUTING 2011年第8期74卷 1237-1242页

作者： Maravall, Dario de Lope, Javier Univ Politecn Madrid Ctr Automat & Robot CSIC Dept Appl Intelligent Syst Madrid Spain Univ Politecn Madrid Dept Artificial Intelligence Fac Comp Sci Sch Telecommun Engn Madrid Spain

In this paper the fusion of artificial neural networks, granular computing and learning automata theory is proposed and we present as a final result ANLAGIS, an adaptive neuron-like network based on learning automata and granular inference systems. ANLAGIS can be applied to both pattern recognition and learning control problems. Another interesting contribution of this paper is the distinction between presynaptic and post-synaptic learning in artificial neural networks. Tc illustrate the capabilities of ANLAGIS some experiments on knowledge discovery in data mining and machine learning are presented. The main, novel contribution of ANLAGIS is the incorporation of learning Automata Theory within its structure;the paper includes also a novel learning scheme for stochastic learning automata. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks Granular computing learning automata theory pattern recognition machine learning

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Detecting the boundary curve of planar random point set

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3rd international conference on machine learning and data mining in pattern recognition

作者： Imiya, A Tatara, K Ootani, H Hlavác, V Chiba Univ Inst Media & Informat Technol Chiba Japan Chiba Univ Sch Sci & Technol Chiba Japan Czech Tech Univ Ctr Machine Percept CR-16635 Prague Czech Republic

In this paper, we develop an algorithm for the learning of the boundary and the medial axis of random point sets, employing the principal curve analysis. The principal curve analysis is a generalization of principal a... 详细信息

ISBN: (纸本)3540405046

关键词： pattern recognition

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Graph-based tools for data mining and machine learning

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3rd international conference on machine learning and data mining in pattern recognition

作者： Bunke, H Univ Bern Inst Informat & Angew Math CH-3012 Bern Switzerland

ISBN: (纸本)3540405046

Many powerful methods for intelligent data analysis have become available in the fields of machine learning and data mining. However, almost all of these methods are based on the assumption that the objects under consideration are represented in terms of feature vectors, or collections of attribute values. In the present paper we argue that symbolic representations, such as strings, trees or graphs, have a representational power that is significantly higher than the representational power of feature vectors. On the other hand, operations on these data structure that are typically needed in data mining and machine learning are more involved than their counterparts on feature vectors. However, recent progress in graph matching and related areas has led to many new practical methods that seem to be very promising for a wide range of applications.

关键词： graph matching graph edit distance graph clustering unique node labels edit cost learning

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Coevolutionary feature learning for object recognition

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3rd international conference on machine learning and data mining in pattern recognition

作者： Krawiec, K Bhanu, B Univ Calif Riverside Ctr Res Intelligent Syst Riverside CA 92521 USA

ISBN: (纸本)3540405046

In this paper, we consider the task of automatic synthesis/leaming of pattern recognition systems. In particular, a method is proposed that, given exclusively training raster images, synthesizes complete feature-based recognition system. The proposed approach is general and does not require any assumptions concerning training data and application domain. Its novelty consists in procedural representation of features for recognition and utilization of coevolutionary computation for their synthesis. The paper describes the synthesis algorithm, outlines the architecture of the synthesized system, provides firm rationale for its design, and evaluates it experimentally on the real-world task of target recognition in synthetic aperture radar (SAR) imagery.

关键词： Object recognition

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