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

ISBN: (纸本)9783031862922

The proceedings contain 28 papers. The special focus in this conference is on Advancements in Smart Computing and Information Security. The topics include: Prediction of Anti-Cholinesterase Inhibitor for Alzheimer Disease Using machine learning Techniques;scrutinize Search Engine Optimization Strategies with Artificial Intelligence to Rank a Website;a Study on machine learning Algorithms: Forecasting Crop Prices;improving Student Stress Analysis: Novel Methods for Collecting, Preprocessing, and Fusing Features;extraction of Normal and Abnormal Region in Colposcopy Image to Support Cervical Cancer Clinical Decision;optimizing Pricing Strategies: A Comprehensive Framework Using Bayesian Inference and Game Theory;hybrid Ensemble Gradient Boosting Algorithm to Predict Diabetes Health Care Analytics;a Review on machine learning Algorithms for Real-Time Traffic Management;hyperparameter-Tuned Intention mining for Mental Health Diagnosis Using Logistic Regression;machine learning for Accessible and Precise Assessment in Smart Monitoring Systems;an Efficient Model for Academic Performance Prediction of the University Students;comprehensive Comparative Study on data mining and machine learning Approaches for Fraud Detection in Financial Services;prediction of Stock Price and Detection of Stock Market Trends Using Adaptive learning Techniques;asthma Prediction Using Fuzzification and machine learning Based Ensemble Classifier;detection and Analysis of Features of Optic Nerve Head Using Retinal Fundus Images of an Eye for a Priori Prediction of Glaucoma;a Hybrid Deep learning Framework for Uncertain Supply Chains: An Optimization Approach;a Comprehensive Review of machine learning Techniques in Recommender System for E-Commerce Platform;Ethics of AI in the Educational Sector - Navigating the Moral Landscape;international Law and AI Interface.

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Autoencoder-Lstm Framework for Accurate Foreign Exchange Volatility Prediction

Autoencoder-Lstm Framework for Accurate Foreign Exchange Vol...

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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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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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Generalization of pattern-growth methods for sequential pattern mining with gap constraints

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3rd international conference on machine learning and data mining, mldm 2003

作者： Antunes, Cláudia Oliveira, Arlindo L. Department of Information Systems and Computer Science Instituto Superior Técnico / INESC-ID R. Alves Redol 9 Lisboa1000 Portugal

ISBN: (纸本)3540405046

The problem of sequential pattern mining is one of the several that has deserved particular attention on the general area of data mining. Despite the important developments in the last years, the best algorithm in the area (Prefix- Span) does not deal with gap constraints and consequently doesn't allow for the introduction of background knowledge into the process. In this paper we present the generalization of the PrefixSpan algorithm to deal with gap constraints, using a new method to generate projected databases. Studies on performance and scalability were conducted in synthetic and real-life datasets, and the respective results are presented. © Springer-Verlag Berlin Heidelberg 2003.

关键词： data mining

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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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A learning autonomous driver system on the basis of image classification and evolutional learning

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3rd international conference on machine learning and data mining, mldm 2003

作者： Kuhnert, Klaus-Dieter Krödel, Michael University of Siegen Institute for Real-Time-Systems Hölderlinstrasse 3 SiegenD-57068 Germany

ISBN: (纸本)3540405046

Driver Systems for autonomous vehicles are the nucleus of many studies done so far. In this light, they mainly consist of two major parts: the recognition of the environment (usually based on image processing) as well as any learning aspects for the driving behaviour. The latter is the nucleus of this research whereby learning aspects are understood that way that the driving behaviour should be optimised over time, therefore the most appropriate actions for each possible situation should be self-created and lastly offered for selection. The current research bases the learning aspects on means of Reinforcement learning which is in sharp contrast to other research studies done before being mainly based on explicit modelling or neural nets. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Image classification

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data mining -: New challenges for statisticians

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SOCIAL SCIENCE COMPUTER REVIEW 2000年第4期18卷 442-449页

作者： Hand, DJ Univ London Imperial Coll Sci Technol & Med Dept Math London SW7 2BZ England

data mining is the science of finding unexpected, valuable, or interesting structures in large data sets. It is an interdisciplinary activity, taking ideas and methods from statistics, machine learning, database technology, and other areas. It poses novel challenges, in part arising from the sheer size of modem data sets. Although there is no doubt that it addresses important questions, there are deep issues to be resolved relating to data quality and the nature of inference. Statisticians have an important role to play in resolving these issues.

关键词： data mining large data sets machine learning patterns models data quality

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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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Work Accident Analysis with machine learning Techniques 3

Work Accident Analysis with Machine Learning Techniques

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3rd international conference on Computer Science and Engineering (UBMK)

作者： Sahin, Durmus Ozkan Sirin, Burce Akleylek, Sedat Kilic, Erdal Ondokuz Mayis Univ Bilgisayar Muhendisligi Bolumu Samsun Turkey Ronesans Holding Ankara Turkey

ISBN: (纸本)9781538678930

All over the world, serious investments have been made in recent years on workers' health and safety. With the importance given to health and safety of workers, new studies have been performed. In this study, data mining and machine learning techniques are applied to the real worker accident data. Firstly, data cleaning and feature selection are performed to use machine learning algorithms, then the classification result obtained by using K-nearest neighbors (KNN) and Naive Bayes (NB) classification algorithms. Accuracy and F-measure metrics were used to measure classification success. The highest success rate was obtained with the KNN algorithm by 10 cross-validation. These values are 0.994075 and 0.993257 for the accuracy and F measure respectively.

关键词： accident of employment worker health job security machine learning data mining knn na ve bayes

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