4 10 33 https://repository.horizon.ac.id/files/original/961a91d4d3ccef3e2068842b247f20c3.pdf d2f677b2f84493cbc285ad9bef85a238 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Stacking Ensemble Learning Model for Intrusion Detection in Electrical Substation Subject The topic of the resource electrical substations; intrusion detection system; machine learning; stacking ensemble learning Description An account of the resource Electrical substations are crucial infrastructure in power transmission and distribution but are increasingly vulnerable to cyber threats. However, existing intrusion detection systems (IDS) face several limitations, such as high false positive rates,weak in anticipating new attack patterns, and imbalances in detecting different types of intrusions. This study proposes a Stacking Ensemble Learning model to enhance intrusion detection accuracy in electrical substations. The proposed model integrates Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost (XGB) as base models with XGB acting as the meta-model. A real-world electrical substation IEC 60870-5-104 network traffic dataset comprising 319,949 instances with multiple attacks, such as DoS, Port Scan, NTP DdoS, IEC 104 Starvation, Fuzzy Attack, Flood Attack, and MITM, was used in this study. The results demonstrate that the stacking model achieves the best performance, with accuracy (0.99990), precision (0.99990), recall (0.99990), and F1 score (0.99990), surpassing the base model, Bagging, and Boosting. T-test results further confirmed statistical significance, with p-values of 0.00428 (LR), 0.04237 (SVM), 0.00000 (XGB), 0.00057 (KNN), 0.00549 (Boosting), and 0.00000 (Bagging) reinforcing the superiority of the proposed methodapproach. These findings highlight the effectiveness of Stacking Ensemble Learning in enhancing the detection performanceof IDS for electrical substations and outperforming traditional models and other ensemble learning methods Creator An entity primarily responsible for making the resource Mohammad Mahruf Alam1, Feddy Setio Pribadi2, Rizky Ajie Aprilianto3, Arvina Rizqi Nurul’aini4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6502/1139 Publisher An entity responsible for making the resource available Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Semarang, Semarang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 11, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/9da6740c76ded7220b2837976ea8a603.pdf 1f7b4842f4f6c4b3afb8bf897934c73e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Breast Cancer Histopathological Image Classification with Convolutional Neural Networks Models Subject The topic of the resource breast cancer; histopathological image classification; deep learning; convolutional neural network; support vector machine Description An account of the resource Early diagnosis and treatment can reduce mortality rates by preventing the progression of breast cancer. Owing to convolutional neural networks (CNN),breast cancer diagnosis can be performed faster and more objectively than humans using thousands of histopathological images. This study aimed to evaluate and compare the rapid and effective diagnostic performance of CNN models on breast tumor images, utilizing transfer learning through pre-training and fine-tuning on novel datasets. The study was performed in two ways on BreakHis and BACH datasets. First, fine-tuned VGG16, VGG19, Xception, InceptionV3, ResNet50, and InceptionResNetV2 models were used for classification. Second, these CNN models were used as feature extractors and support vector machines (SVMs) as classifiers. The success of all models in tumor classification was interpreted using performance metrics, such as accuracy, precision, recall, F1 score, and AUC. The models showing the best performance as a result of the analyses were as follows: InceptionResNetV2+SVM model with an accuracy of 99.3%, precision of 99.0%, recall of 100.0%, F1 score of 99.5%, AUC of 98.9% for BreakHis dataset; and InceptionResNetV2 model with accuracy of 96.7%, precision of 93.8%, recall of 100.0%, F1 score of 96.8%, AUC of 96.7% for the BACH dataset. As a conclusion, it has been seen that the CNN methods have good generalization abilities and can respond to clinical needs Creator An entity primarily responsible for making the resource Isil Unaldi1, Leman Tomak2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6420/1130 Publisher An entity responsible for making the resource available Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ondokuz Mayis University, Samsun, Türkiye Date A point or period of time associated with an event in the lifecycle of the resource September29, 2025 Contributor An entity responsible for making contributions to the resource Isil Unaldi1, Leman Tomak Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource INDONESIA Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/21e42433a211ee46ec348000a9675eb0.pdf 1f7b4842f4f6c4b3afb8bf897934c73e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Breast Cancer Histopathological Image Classification with Convolutional Neural Networks Models Subject The topic of the resource breast cancer; histopathological image classification; deep learning; convolutional neural network; support vector machine Description An account of the resource Early diagnosis and treatment can reduce mortality rates by preventing the progression of breast cancer. Owing to convolutional neural networks (CNN),breast cancer diagnosis can be performed faster and more objectively than humans using thousands of histopathological images. This study aimed to evaluate and compare the rapid and effective diagnostic performance of CNN models on breast tumor images, utilizing transfer learning through pre-training and fine-tuning on novel datasets. The study was performed in two ways on BreakHis and BACH datasets. First, fine-tuned VGG16, VGG19, Xception, InceptionV3, ResNet50, and InceptionResNetV2 models were used for classification. Second, these CNN models were used as feature extractors and support vector machines (SVMs) as classifiers. The success of all models in tumor classification was interpreted using performance metrics, such as accuracy, precision, recall, F1 score, and AUC. The models showing the best performance as a result of the analyses were as follows: InceptionResNetV2+SVM model with an accuracy of 99.3%, precision of 99.0%, recall of 100.0%, F1 score of 99.5%, AUC of 98.9% for BreakHis dataset; and InceptionResNetV2 model with accuracy of 96.7%, precision of 93.8%, recall of 100.0%, F1 score of 96.8%, AUC of 96.7% for the BACH dataset. As a conclusion, it has been seen that the CNN methods have good generalization abilities and can respond to clinical needs. Creator An entity primarily responsible for making the resource sil Unaldi1, Leman Tomak Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6420/1130 Publisher An entity responsible for making the resource available Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ondokuz Mayis University, Samsun, Türkiye Date A point or period of time associated with an event in the lifecycle of the resource September29, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT