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13 results for “Confusion Matrix”
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Research ArticleOpen accessIn the keywordsEnhancing Dam Safety and Management: Long Short-Term Memory Based Predictive Models for Accurate Alert ForecastingNisha C. M, N. ThangarasuDam management and early alert systems are critical for effective water resource management. Accurate prediction of dam alert signals facilitates proactive decision-making, thereby aiding in the effective management and reduction of potential risks linked to dam operations. Within this research, Long Short-Term Memory (LSTM) networks are utilized to forecast dam alert signals issued from the dam by leveraging daily parameters, … …Journal of Computer Science, Vol. 22, Issue 1 · 6 Feb 2026 · 1,626 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Comparative Study of Resampling, Cost-Sensitive, and Ensemble Techniques for Handling Class Imbalance in Indonesian Financial DataGunawan Kurnia, Ditdit Nugeraha UtamaHandling class imbalance is a critical challenge in machine learning applications, particularly in financial domains where minority instances often represent significant anomalies such as fraud or audit risks. Various oversampling and undersampling methods were tested, alongside cost-sensitive adjustments and ensemble models including Random Forest, AdaBoost, Gradient Boosting, and XGBoost. The evaluation, based on 10-fold stratified cross-validation and performance metrics such … …Journal of Computer Science, Vol. 22, Issue 2 · 27 Feb 2026 · 1,874 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdvanced Hierarchical Deep Learning Approach for Intrusion Detection: Hyperparameter Tuning and Performance Evaluation on the CICIDS DatasetAjeet Singh, Azath M, Shahazad Niwazi Qurashi et al.Intrusion Detection Systems (IDS) are essential in the communication security and integrity of contemporary network architectures. This study introduces a novel advanced hierarchical deep learning model for network intrusion detection using the CICIDS dataset-2019. To improve the detection accuracy, the proposed model architecture builds several deep learning layers embedded with hyperparameters' optimization. Additional preprocessing steps, including over-sampling and min-max scaling, … …Journal of Computer Science, Vol. 21, Issue 11 · 26 Dec 2025 · 2,148 accessesView PDFCite
Research ArticleOpen accessIn the abstractSecuring IoT Networks: Multi-Attack Detection of RPL Routing Threats Using Deep LearningAyoub Krari, Abdelmajid Hajami, Ayoub Toubi, Marouane Ait SaidThe growing frequency of cyber threats in Internet of Things (IoT) networks, including attacks on RPL routing, requires the creation of strong detection systems to safeguard network integrity and provide dependable communication. This study is driven by the pressing necessity to tackle the security weaknesses in IoT networks, where threats such as black holes, version number alteration, DIS flooding, and … …Journal of Computer Science, Vol. 21, Issue 4 · 6 Mar 2025 · 5,397 accessesView PDFCite
Research ArticleOpen accessIn the abstractStroke Risk Assessment with Classical ML MethodsMohammad Aref Alshraideh, Najwan Alshraideh, Abedalrahman Alshraideh et al.Stroke, often caused by a disruption in the supply of essential oxygen, blood, and nutrients to the brain, represents a significant global health challenge. Due to limited resources, developing countries like Ethiopia face unique obstacles in identifying and treating strokes. This study explores the potential of Machine Learning (ML) techniques to predict stroke risk and facilitate early detection and intervention. … …Journal of Computer Science, Vol. 21, Issue 1 · 18 Dec 2024 · 4,649 accessesView PDFCite
Research ArticleOpen accessIn the abstractMeasuring Uncertainty to Extract Fuzzy Membership Functions in Recommender SystemsHeersh Azeez Khorsheed, Sadegh AminifarNowadays, due to the high volume of choices for customers which causes confusion, the use of recommender systems is strongly growing. Of course, existing systems have two problems, one is complexity and the other is failure to consider uncertainty. In this article, we have reduced the complexity of the system by using a fuzzy innovative system and solved the problem … …Journal of Computer Science, Vol. 19, Issue 11 · 15 Oct 2023 · 5,491 accessesView PDFCite
Research ArticleOpen accessIn the abstractSentiment Analysis on User Reviews of Mutual Fund ApplicationsEvaristus Didik Madyatmadja, Shinta, Devi Susanti et al.The primary goal of this study is to compare the accuracyof the results of sentiment analysis using the Naive Bayes, Support VectorMachine (SVM), and Random Forest methods on one of the mutual fund application’s user reviews.The second goal is to identify user reviews of the mutual fund app to gaininsight into the topics covered by each sentiment. The user reviews … …Journal of Computer Science, Vol. 18, Issue 10 · 26 Sep 2022 · 6,441 accessesView PDFCite
Research ArticleOpen accessIn the abstractPhonocardiogram Classification Based on Machine Learning with Multiple Sound FeaturesKhalid M.O. Nahar, Obaida M. Al-Hazaimeh, Ashraf Abu-Ein, Nasr GharaibehIn this study the heartbeat sound signals were tackled by classifying them into heart disease categories such as normal, artifact, murmur and extrahals in an attempt for early detection of heart defects. Phonocardiogram (i.e., PCG) is used to obtain the digital recording dataset of the heart sounds using an electronic stethoscope or mobile device. Multiple features are extracted from the … …Journal of Computer Science, Vol. 16, Issue 11 · 26 Nov 2020 · 8,259 accessesView PDFCite
Research ArticleOpen accessIn the abstractStudy and Analysis of Prediction Model for Heart Disease Data Using Machine Learning TechniquesB. Santhi, K. RenukaHeart disease is the number one cause of death for all communities of individuals in advanced countries and a major problem for emerging nations too. Doctors’ availability to care for the general population could not catch up with the present demand for healthcare. So, there is a severe need for a support system to assist save individuals. With novel ML … …Journal of Computer Science, Vol. 16, Issue 3 · 26 Mar 2020 · 7,784 accessesView PDFCite
Research ArticleOpen accessIn the abstractMissing Values Treatment and Feature Reduction Analysis to Enhance ClassificationD. Muralidharan, K. Renuka, Mulagala Jaswant et al.Datasets may have large number of features which makes it hard and time consuming to classify. Additionally, they may have irrelevant and noise features too with missing values. The missing values should be treated in a proper way so that the classifier accuracy can be improved. There is also a need to reduce features and select only the features necessary … …Journal of Computer Science, Vol. 16, Issue 2 · 20 Feb 2020 · 8,114 accessesView PDFCite