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检索条件"任意字段=4th International Conference on Soft Computing in Data Science, SCDS 2018"

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ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System 4

ANFIS Model for Cost Analysis in a Dual Source Multi-Destina...

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4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Okwu, M.O. Tartibu, L.K. Ojo, E.O. Adume, S. Gidiagba, J.O. Fadeyi, J. Department of Mechanical Industrial Engineering Tech. University of Johannesburg South Africa Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Mathematics and Data Science Dept. Computing Science and Mathematics Division Faculty of Natural Science University of Stirling United Kingdom Faculty of Engineering and Applied Sc. Memorial University Newfoundland Canada Department of Industrial and Systems Engineering University of Buffalo United States

Managers face uncertainties while making allocation decisions, especially in dual or multi-source multi-destination inventory systems. Regrettably, many studies focus on classical methods as a technique for product distribution, which has never guaranteed a satisfactory solution. Real-life problems are non-deterministic polynomial-time hard (NP-hard), and solving such problems is relatively challenging. Such complicated problems need an efficient and robust computational hybrid algorithm. this study emphasises the need for a hybrid intelligent technique for effective product distribution. soft computing hybrid algorithm, ANFIS was applied to product distribution in a double source multi-destination system. Rules were developed from available input datasets. Distributing products from dual manufacturing plants to fifteen available depots using the creative algorithm resulted in an overall 13.5% decrease in cost compared to the existing method adopted by the company. the result showed that the proposed method is relatively satisfactory and adequate for cost modelling. In addition, it is easy to use and outperforms the classical approach. © 2022 the Authors. Published by ELSEVIER B.V.

关键词： soft computing

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Rainfall Prediction in Flood Prone Area Using Deep Learning Approach 6th

Rainfall Prediction in Flood Prone Area Using Deep Learning ...

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

作者： Ramlan, Siti Zuhairah Deni, Sayang Mohd Univ Teknol MARA Fac Comp & Math Sci Shah Alam 40450 Selangor Malaysia Univ Teknol MARA Ctr Stat & Decis Sci Studies Inst Big Data Analyt & Artificial Intelligence IB Fac Comp & Math Sci Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9789811673344;9789811673337

Flood is a catastrophic event that contributes to the impact on socioeconomic of a developing country. Flood prone area is also known as the location at risk as heavy rainfall attribute to flood events. this circumstance leads to the effective flood mitigation phase. One of the critical problems facing by responsible government agencies is to minimize future uncertainties. this study explores four deep learning methods with univariate rainfall temporal data in gauging station near to flood prone area. Four models tested are Multi-layer Perceptron MLP, Long Short Term Memory LSTM, Stacked-LSTM, and hybrid model Convolutional Neural Network CNN-LSTM. the aim of this paper is to compare and determine the best method for rainfall prediction of next day event. Model comparison is conducted by comparing the correlation coefficient, Root Mean Square error (RMSE) and Mean Absolute Error (MAE). Based on the selected locations, the results showed at Kuantan station generally underfitting, meanwhile the Kuala Krai station does not showed discrepancies between training and testing dataset. It could be concluded that by adding the complexity to the model, will not significantly improved the model prediction. the LSTM model with 16 memory blocks was outperformed in both locations. the potential of deep learning methods should be considered for rainfall amount prediction due to less complexity and much easier to be applied into dataset for model fitting purposes. It may assist to accommodate strategic precautions for flood mitigation phases in flood prone areas.

关键词： Floods mitigation Long Short Term Memory (LSTM) Rainfall prediction Univariate time series

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A Systematic Review of data Augmentation Techniques in Natural Language Processing for Sentiment Analysis using Machine learning Techniques

A Systematic Review of Data Augmentation Techniques in Natur...

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

作者： Uday Dhanaji Patil Pradeep Sudhakaran Department of Computer Science & Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu India KIT’s College of Engineering (Autonomous) Kolhapur Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Due to the enormous growth of mobile technology, Social media has created a crucial platform to express the feelings and opinions of people. Based on these opinion considerations, it is very beneficial for making strategic decisions in organizations, businesses, trading, political entities, and so on. To achieve effective improvement, sentiment analysis is taken into account, and the public opinion polarity for further reference. In this perspective, the main contribution of the study is to explore the benefits and limitations of various learning techniques under the evaluation of sentiment analysis. Generally, Sentiment analysis is evaluated by Natural language processing tasks with various learning techniques, to achieve effective generalization ability and efficiency, data augmentation techniques are integrated, which reduces the overfitting problem effectively. To explore the limitations of the traditional methods, 25 literary papers were taken into account to analyze various approaches, and methods under the utilization of distinct datasets. Together with this, the achieved evaluation metrics along with the achievements are also discussed briefly in the study. Additionally, the study provides the shortcomings and future scope of the conventional methods with a detailed description. the main objective of the survey includes knowledgeable categorization, which aims to explore a new model with the illustration of recent trends and achieve effective data augmentation in future analysis.

关键词： Surveys Sentiment analysis Analytical models Social networking (online) Organizations data augmentation Real-time systems Security Overfitting Systematic literature review

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Predictive Analysis of Machine Learning Frameworks in the review of Multivariate datasets for Crop Yield Prediction

Predictive Analysis of Machine Learning Frameworks in the re...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Kutlwano Sedimo Venumadhav Kuthadi Othapile Dinakenyane Rajalakshmi Selvaraj Department of Computing and Informatics Botswana International University of Science and Technology Palapye Botswana

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Crop Yield Prediction (CYP) is crucial for optimizing agricultural practices globally. this study conducts an in-depth review of Machine Learning (ML) techniques applied to multivariate datasets for crop yield forecasting. We examine the performance of models including deep learning, ensemble classifiers, and hybrid approaches, emphasizing their ability to integrate diverse data sources and adapt to varying climatic conditions. Despite significant progress, challenges such as sparse data handling and generalization across regions persist. this paper not only identifies these challenges but also proposes advanced solutions for improving prediction accuracy, offering a roadmap for future research in agricultural ML frameworks.

关键词： Deep learning Adaptation models Technological innovation Accuracy Reviews soft sensors data models Reliability Predictive analytics Crop yield

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Comparison Performance of Long Short-Term Memory and Convolution Neural Network Variants on Online Learning Tweet Sentiment Analysis 6th

Comparison Performance of Long Short-Term Memory and Convolu...

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

作者： Ali, Muhammad Syamil Yusoff, Marina Univ Teknol MARA Fac Comp & Math Sci Shah Alam Selangor Malaysia Univ Teknol MARA Inst Big Data Analyt & Artificial Intelligence IB Al Khawarizmi Complex Shah Alam Selangor Malaysia

ISBN: (纸本)9789811673344;9789811673337

Sentiment analysis can be act as an assisted tool in improving the quality of online teaching and learning between teachers and students. Twitter social media platform currently more than 500 million tweets sent each day which is equal to 5787 tweets per second. therefore, it is hard to track users' overall opinions on the topics contained in social media. To catch up with the feedback on online learning, it is crucial to detect the topic being discussed and classify users' sentiments towards those topics. Even though there are many approaches in developing sentiment analysis models, DL models prove to provide the best performance in the sentiment analysis field. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are two mainstream models in DL used for sentiment analysis classification. therefore, we evaluate CNN, LSTM, and its hybrids to classify sentiment or an online learning tweet from 2020 until 2021 of 23168 tweets. CNN-LSTM, LSTM-CNN, Bidirectional LSTM, CNN-Bidirectional LSTM models were designed and evaluated based on random hyper-parameter tuning. We explain the proposed methodology and model design illustration. the outcome assesses the superiority of all models with a remarkable improvement of accuracy and a reduction loss when applying the random oversampling technique. Specifically, the LSTM-CNN model with random oversampling technique outperformed the other six models with an accuracy of 87.40% and loss value of 0.3432. However, the computational time has resulted increased when with random oversampling technique. thus, in the future, the performance can be improved on computational time and hyperparameter selection with the employment of nature-inspired computing for fast and optimal results.

关键词： CNN LSTM Online learning Random oversampling Sentiment analysis

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Applying Machine Learning to Improve Weather Forecasting in a Mediterranean Area: Preliminary Experiments 1

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

作者： Yousaf, Umair De Rango, Alessio Furnari, Luca D’Ambrosio, Donato Senatore, Alfonso Department of Mathematics and Computer Science University of Calabria Ponte Pietro Bucci Rende87036 Italy Department of Environmental Engineering University of Calabria Ponte Pietro Bucci Rende87036 Italy

ISBN: (数字)9783031812446

ISBN: (纸本)9783031812439

We investigate the effectiveness of Machine Learning (ML) as a complementary tool in enhancing the accuracy of precipitation forecasting in the Calabria region (southern Italy), a Mediterranean area frequently affected by extreme rainfall events, with significant material damage and potential loss of human lives. the operational weather forecasting system developed at the University of Calabria, based on the Weather Research and Forecasting (WRF) model, is considered to this end. We test two ML models, namely an artificial neural network and a random forest, to improve the accuracy of WRF forecasting. We provide several WRF output variables to the ML models. After a proper calibration (learning and test phases) over a two-year-long extended dataset, we assess the results to the original WRF outcomes (validation phase). Preliminary experiments resulted in a significant improvement by the WRF-ML coupled systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Fuzzy-Enhanced XGBoost Model for Classifying Kidney Disease Severity

Fuzzy-Enhanced XGBoost Model for Classifying Kidney Disease ...

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

作者： Satyanarayana Nimmala Maragoni Mahendar Pinnapureddy Manasa H. N. Lakshmi Medikonda Asha Kiran C Raghavendra Department of CSE (Data Science) CVR College of Engineering Hyderabad India Department of CSE Neil Gogte Institute of Technology Hyderabad India Department of CSE (AIML) CVR College of Engineering Hyderabad India Department of AIML Chaitanya Bharathi Institute of Technology Hyderabad India Department of CSE(CS) CVR College of Engineering Hyderabad India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

this research presents a novel diagnostic method for determining the severity of kidney disease by combining fuzzy logic with the Extreme Gradient Boosting (XGBoost) algorithm. the model makes use of patient data from the UCI ML Repository, which includes clinical measures and demographic data. Before the XGBoost algorithm receives the data, fuzzy logic preprocesses it, turning unclear inputs into organized information. With an accuracy of 93.65%, the Fuzzy-Enhanced XGBoost model outperforms the SVM, Random Forest, Decision Tree, and Naive Bayes algorithms as well as standalone XGBoost. the incorporation of fuzzy logic enhances the model’s capacity to manage imprecise and noisy data, resulting in enhanced robustness and generalization. this study improves interpretability and reliability by addressing the shortcomings of conventional methods in managing ambiguous medical data. A comparative examination demonstrates that the Fuzzy-Enhanced XGBoost model provides superior management of uncertainty in addition to increasing classification accuracy. To sum up, this model offers a powerful and understandable diagnostic tool that represents considerable progress in the classification of renal disease severity.

关键词： Fuzzy logic Accuracy Uncertainty Computational modeling data preprocessing Boosting Turning data models Kidney Diseases

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Machine Learning for Real-Time Language Translation: A Server-Side Approach to Overcome Cross-Cultural Communication Challenges

Machine Learning for Real-Time Language Translation: A Serve...

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

作者： S. Elango Mohanaprakash T A D. Siva Gowtham A Sasikumar A N Sunitha T Department of Computer Science and Engineering SOET CMR University Bengaluru Department of Computer Science and Applications SRM Institute of Science and Technology Chennai Department of CSE (Cyber Security) Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

the evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-the-art results in language translation, a universal solution remains elusive. this research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. the process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). this approach aims to enhance real-time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers.

关键词： Translation Machine learning algorithms Speech recognition Machine learning Speech enhancement Cross-cultural communication Real-time systems Speech synthesis Machine translation Security

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An Intelligent Group Recommender System using Henry Gas Solubility Optimization Algorithm-based Adaptive Deep Belief Network

An Intelligent Group Recommender System using Henry Gas Solu...

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computing and Communication Systems (I3CS), international conference on

作者： Deepjyoti Roy Mala Dutta Computer Science & Engineering Assam Down Town University Guwahati India

Group Recommender Systems (GRS) have recently gained attention for use in various applications. the GRS model is useful in any scenario that involves a decision-making process for a group of users. However, existing approaches are not able to provide high quality recommendations due to their inability to handle traditional problems such as cold start, sparsity, and dealing with large dimensional information. To address these issues, a novel deep learning-based adaptive model is proposed. Raw data is first collected from four different data sources and cleaned using stemming, stop word and punctuation removal methods. data preprocessing is followed by the feature extraction phase. A Text Convolutional Neural Network (TCNN) is employed to extract efficient features from group reviews. the efficient features are fed into an Adaptive Deep Belief Network (ADBN) to provide the recommendation output. It is further optimized using the Henry Gas Solubility optimization (HGSO) algorithm. through extensive experiments it is found that the performance of the proposed system outperforms existing standard algorithms, hence, solving the traditional GRS issues.

关键词： Deep learning Adaptation models Adaptive systems soft sensors Feature extraction Classification algorithms Convolutional neural networks

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Fine-grained classification network for fish based on the attention mechanism and EfficientNet 4

Fine-grained classification network for fish based on the at...

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4th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Gong, Yaohan Gu, Shenming Guan, Linting School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China

ISBN: (纸本)9781450398343

Many countries have been interested in the sustainable development of marine resources. Countries can protect the sustainable development of fishery resources by joining international fisheries organizations such as CCSBT, IATTC, IOTC, ICCAT, and others, and by adhering to the Code of Conduct for Responsible Fisheries. Traditional fishing supervision methods, on the other hand, frequently have the issue that the amount of original data is cumbersome and expensive, which cannot be handled manually and wastes a lot of manpower. this paper investigates fine-grained fish classification methods based on deep learning and attention mechanisms and proposes one that can target complex background and actual working scenarios, allowing countries to reallocate human capital to management and law enforcement activities. © 2022 ACM.

关键词： Fisheries

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