Title: UNIFIED PREDICTIVE MODEL FOR PREDICTING THE CLOSING PRICE OF VARIOUS CRYPTOCURRENCIES
<br>Cover Date: 2025-06-01
<br>Cover Display Date: June 2025
<br>DOI: 10.24507/icicelb.16.06.589
<br>Description: This study introduces a unified predictive model for forecasting cryptocurrency closing prices, utilizing advanced machine learning techniques such as Support Vector Regression (SVR), Random Forest, and Long Short-Term Memory (LSTM) net-works. By integrating multiple cryptocurrency datasets through feature-level data fusion via concatenation, the model effectively captures the complex, nonlinear, and dynamic relationships characteristic of cryptocurrency markets. The experimental results reveal that the proposed model outperforms baseline models developed for individual cryptocurrencies, particularly with SVR. This enhanced performance is due to SVR’s ability to manage high-dimensional data and model intricate nonlinear patterns. While Random Forest and LSTM also demonstrate strong predictive capabilities, their effectiveness is more dependent on specific data characteristics and configurations. The integration of diverse data sources and the application of Min-Max normalization play a crucial role in enhancing prediction accuracy and model robustness. This approach allows the model to account for broader market dynamics, providing valuable insights for short-term and medium-term trading strategies and supporting informed decision-making for investors, traders, and analysts.
<br>Citations: 0
<br>Aggregation Type: Journal
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<br><br><br>Title: MULTICLASS CLASSIFICATION APPROACH FOR DETECTING SOFTWARE BUG SEVERITY LEVEL FROM BUG REPORTS
<br>Cover Date: 2025-05-01
<br>Cover Display Date: May 2025
<br>DOI: 10.24507/icicelb.16.05.567
<br>Description: This study focuses on developing multiclass classifiers to predict the severity levels of bug reports using three machine learning algorithms: Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) with an RBF kernel. The research utilizes three datasets from the Mozilla bug tracking system – Core, Firefox, and Thunderbird – categorizing bug severity into five levels: blocker, critical, major, minor, and low. To address class imbalance and enhance model performance, a domain expertbased data augmentation method was applied, generating synthetic summaries from bug descriptions using cosine similarity. The augmented datasets, combined with undersampling techniques, ensure balanced class distributions, improving classifier robustness. The study leverages unigram and CamelCase features to build and evaluate the classifiers. Performance metrics, including accuracy, F1 score, and Matthews Correlation Coefficient (MCC), were used to assess model efficacy. The results demonstrate that LR outperforms RF and SVM, offering superior accuracy and interpretability, particularly for high-dimensional text data. LR’s efficiency, reduced overfitting risk, and effective handling of linear relationships make it well-suited for bug severity classification. This research provides a robust framework for improving bug triage processes, enhancing the prioritization and resolution of critical software issues.
<br>Citations: 0
<br>Aggregation Type: Journal
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<br><br><br>Title: The Development of a Unified Predictive Model to Predict Closed Price for a Variety of Cryptocurrencies
<br>Cover Date: 2024-01-01
<br>Cover Display Date: 2024
<br>DOI: 10.1109/RI2C64012.2024.10784451
<br>Description: Predicting the closing price of a cryptocurrency typically involves utilizing an individual predictive model for each particular cryptocurrency. However, a unified model has the potential to predict the closing price of many cryptocurrencies, providing convenience and facilitating comparative research. Thus, this study utilized feature-based data fusion to integrate historical data of cryptocurrencies from the same time period using a data fusion, called as the average method. The predictive models were developed using two machine learning algorithms, i.e. Support Vector Regression (SVR) with RBF kernel and Random Forest (RF). This study used three types of cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). After evaluating the effectiveness of several predictive models utilizing MAE, RMSE, and MAPE for short-term (5-day) predictions, it was found that the unified predictive model yielded similar outcomes to the models specifically d eveloped f or the given particular cryptocurrency. This method offers the ability to help investors in identifying general price movements among multiple cryptocurrencies and decreasing the amount of time needed for observations. However, the unified p redictive models developed by the random forest algorithm surpass other models in successfully predicting the short-term (5-day) closing prices of cryptocurrencies.
<br>Citations: 0
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Extracting product features and opinions from product reviews using dependency analysis
<br>Cover Date: 2010-11-29
<br>Cover Display Date: 2010
<br>DOI: 10.1109/FSKD.2010.5569865
<br>Description: In web pages, the reviews are written in natural language and are unstructured-free-texts scheme. Online product reviews is considered as a significant informative resource which is useful for both potential customers and product manufacturers. The task of manually scanning through large amounts of review one by one is computational burden and is not practically implemented with respect to businesses and customer perspectives. Therefore it is more efficient to automatically process the various reviews and provide the necessary information in a suitable form. The task of product feature and opinion is to find product features that customers refer to their topic reviews. It would be useful to characterize the opinions about product. In this paper, we propose an approach to extract product features and to identify the opinions associated with these features from reviews through syntactic information based on dependency analysis. ©2010 IEEE.
<br>Citations: 12
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Mining feature-opinion in online customer reviews for opinion summarization
<br>Cover Date: 2010-06-18
<br>Cover Display Date: 2010
<br>DOI: N/A
<br>Description: Online customer reviews is considered as a significant informative resource which is useful for both potential customers and product manufacturers. In web pages, the reviews are written in natural language and are unstructured-free-texts scheme. The task of manually scanning through large amounts of review one by one is computational burden and is not practically implemented with respect to businesses and customer perspectives. Therefore it is more efficient to automatically process the various reviews and provide the necessary information in a suitable form. The high-level problem of opinion summarization addresses how to determine the sentiment, attitude or opinion that an author expressed in natural language text with respect to a certain feature. In this paper, we dedicate our work to the main subtask of opinion summarization. The task of product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the performance of opinion orientation identification. It is important to properly identify the semantic relationships between product features and opinions. We proposed an approach for mining product feature and opinion based on the consideration of syntactic information and semantic information. By applying dependency relations and ontological knowledge with probabilistic based model, the result of our experiments shows that our approach is more flexible and effective. © J.UCS.
<br>Citations: 126
<br>Aggregation Type: Journal
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<br><br><br>Title: Automatic product feature extraction from online product reviews using maximum entropy with lexical and syntactic features
<br>Cover Date: 2008-09-23
<br>Cover Display Date: 2008
<br>DOI: 10.1109/IRI.2008.4583038
<br>Description: The task of product feature extraction is to find product features that customers refer to their topic reviews. It would be useful to characterize the opinions about the products. We propose an approach for product feature extraction by combining lexical and syntactic features with a maximum entropy model. For the underlying principle of maximum entropy, it prefers the uniform distributions if there is no external knowledge. Using a maximum entropy approach, firstly we extract the learning features from the annotated corpus, secondly we train the maximum entropy model, thirdly we use trained model to extract product features, and finally we apply a natural language processing technique in postprocessing step to discover the remaining product features. Our experimental results show that this approach is suitable for automatic product feature extraction. ©2008 IEEE.
<br>Citations: 32
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: A maximum entropy model for product feature extraction in online customer reviews
<br>Cover Date: 2008-01-01
<br>Cover Display Date: 2008
<br>DOI: 10.1109/ICCIS.2008.4670882
<br>Description: Product feature extraction is an important task of review mining and summarization. The task of product feature extraction is to find product features that customers refer to in their topic reviews. It would be useful to characterize the opinions which they review or express about the products. In this paper, we propose an approach to product feature extraction using a maximum entropy model. Maximum entropy is a probability distribution estimation technique. It is widely used for classification problems in natural language processing, such as question answering, information extraction, and part-of-speech tagging. The underlying principle of maximum entropy is that without external knowledge, one should prefer distributions that are uniform. Using a maximum entropy approach, at first we extract features from the corpus, train maximum entropy model with an annotated corpus, and then use it with additional product feature discovery to extract product features from customer reviews. Our experimental results show that this approach can work effectively for product feature extraction with 71.88% precision and 75.23% recall. © 2008 IEEE.
<br>Citations: 13
<br>Aggregation Type: Conference Proceeding
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