Sentiment Analysis on Public Perception of the Nusantara Capital on Social Media X Using Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) Methods by Dinda Haliza, Muhammad Ikhsan

“…Based on the evaluation results, SVM and K-NN proved to be effective for sentiment analysis. …”
Get full text

A CHATBOT FOR POSTGRADUATE INFORMATION DISSEMINATION by Umar Emmanuel, Odulaja Godwin Oluwaseyi, Babatunde Akinbowale Nathanie

“…Incremental software development model was used for software development, Decision Tree Algorithm was used for training the system whose database was populated with relevant and likely questions and corresponding responses. …”
Get full text

Oversampling based on generative adversarial networks to overcome imbalance data in predicting fraud insurance claim by Ranu Agastya Nugraha, Hilman Ferdinandus Pardede, Agus Subekti

“…The new balanced data are used to train 17 classification algorithms. Our experiments show that our proposed method achieves better performance on several evaluation metrics: accuracy, precision score, F1-score, and also ROC than other referenced methods to deal imbalance data random over sampling (ROS), random under sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE), Borderline SMOTE (B-SMO), and adaptive synthetic (ADASYN) methods. …”
Get full text

Network Intrusion Detection Using Knapsack Optimization, Mutual Information Gain, and Machine Learning by Akindele S. Afolabi, Olubunmi A. Akinola

“…Next, we applied an information gain filter to the candidate features set to prune out redundant features, leaving only the features that possess the maximum information gain, which were used to train machine learning models. The proposed KOMIG IDS was applied to the UNSW-NB15 dataset, which is a well-known network intrusion evaluation dataset, and the resulting data, after optimization operation, were used to train four machine learning models, namely, logistic regression (LR), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN). …”
Get full text

IMPLEMENTATION OF BACKPROPAGATION AND HYBRID ARIMA-NN METHODS IN PREDICTING ACCURACY LEVELS OF RAINFALL IN MAKASSAR CITY by Hisyam Ihsan, Irwan Irwan, Andi Illa Erviani Nensi

“…Using a gradient descent algorithm, backpropagation adjusts synaptic weights based on the error between the network's prediction and actual training data values. …”
Get full text

Predicting temporomandibular disorders in adults using interpretable machine learning methods: a model development and validation study by Yuchen Cui, Fujia Kang, Xinpeng Li, Xinning Shi, Han Zhang, Xianchun Zhu

“…Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. …”
Get full text

SDGTrack: A Multi-Target Tracking Method for Pigs in Multiple Farming Scenarios by Tao Liu, Dengfei Jie, Junwei Zhuang, Dehui Zhang, Jincheng He

“…We only used a portion of the daytime scenes as the training set, while the remaining daytime and nighttime scenes were used as the validation set for evaluation. …”
Get full text

Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

“…The k-fold cross-validation technique is utilized in every algorithm to mitigate the overfitting problem during the training of models. …”
Get full text

Development and validation of a nomogram to predict bacterial blood stream infection by Yu Huan Jiang, Rui Zhao, Yun Xue Bai, Hui Ming Li, Jun Liu, Shi Xuan Wang, Xing Xie, Yang Liu, Qiang Chen

“…The study dataset was randomly divided into a 70% training set and a 30% validation set. Univariate logistic analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and random forest algorithms were utilized to identify the potential risk factors for BSI. …”
Get full text

A web-based prediction model for brain metastasis in non-small cell lung cancer patients by Jianing Chen, Li Wang, Li Liu, Qi Wang, Jing Zhao, Xin Yu, Shiji Zhang, Chunxia Su

“…Results Through a comprehensive evaluation incorporating metrics such as the area under the curve (AUC), accuracy, sensitivity, specificity, Kappa value, and calibration curve, the model generated by the Gradient Boosting Machine (GBM) algorithm demonstrated exceptional and near-perfect performance in both the validation set (AUC: 0.8276) and the test set (AUC: 0.8301), however, performance was lower in the SPH cohort (AUC: 0.6100). …”
Get full text

Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting by Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Leen Al‐Omran, Duminda N. Wijeysundera, Mohamad A. Hussain, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Graham Roche‐Nagle, Mohammed Al‐Omran

“…The algorithm with the best performance was further trained using intra‐ and postoperative features. …”
Get full text

Machine learning with hyperparameter optimization applied in facies-supported permeability modeling in carbonate oil reservoirs by Watheq J. Al-Mudhafar, Alqassim A. Hasan, Mohammed A. Abbas, David A. Wood

“…Results showed that the XGBoost algorithm with configuration of (RS as search algorithm, Box Cox as the normalization method, Z-score for outlier detection, without scale correction, old parameter space) delivered the best prediction performance for permeability with RMSE values of 6.9 md and 9.78 md for training and testing, respectively.…”
Get full text

Optimizing Artificial Neural Networks Using Mountain Gazelle Optimizer by Muhammed Abdulhamid Karabiyik, Bahaeddin Turkoglu, Tunc Asuroglu

“…While traditional training algorithms, such as gradient-based methods, have been widely used, they often face challenges like premature convergence and stagnation in local optima. …”
Get full text

Very-Large-Scale Integration-Friendly Method for Vital Activity Detection with Frequency-Modulated Continuous Wave Radars by Krzysztof Ślot, Piotr Łuczak, Paweł Kapusta, Sławomir Hausman, Arto Rantala, Jacek Flak

“…Experimental evaluation of the presented method, performed using the dataset of indoor recordings, indicates that the proposed simple, hardware implementation-friendly algorithm provides over 94% human detection accuracy and similar F1 scores.…”
Get full text

Research on RF Intensity Temperature Sensing based on 1D-CNN by DING Meiqi, GUI Lin, WANG Ziyi, SHANG Disen, QIAN Min, LI Qiankun

“…Finally, the Root Mean Square Error (RMSE) is used as the evaluation index, and the performance of 1D-CNN is compared with the traditional algorithms (maximum-value method, centroid method and Gaussian fitting method) to analyze its performance under different temperature conditions.…”
Get full text

Increment of Academic Performance Prediction of At-Risk Student by Dealing With Data Imbalance Problem by Nguyen Giap Cu, Thi Lich Nghiem, Thi Hoai Ngo, Manh Tuong Lam Nguyen, Hong Quan Phung

“…The proposal methods were designed and applied to education datasets, and they were tested on public datasets and a dataset collected from a Vietnamese university for evaluation. The experimental results on learning datasets showed the high potential of novel algorithms, I_SMOTE and I_ADASYN, for student academic performance problems in general and at-risk student groups especially. …”
Get full text

ADVERTISING CAMPAIGN OF HIGHER EDUCATION INSTITUTION DEVELOPMENT BASICS by Natela R. Kordzaia

“…The stages of this algorithm are as follows: determining the purpose and objectives of the advertising campaign; setting the objectives of the advertising campaign, determining the metrics (performance indicators) of the advertising campaign; characteristics of the target audience; research and analysis of advertising activities of competitors; research of the main channels and carriers of future advertising; conducting media planning (planning of advertising media, budget and deadlines); creating a creative idea of an advertising message; advertising production; control over the placement of advertising; evaluation of the effectiveness of the advertising campaign. …”
Get full text

Implementation of SMOTE to Improve the Performance of Random Forest Classification in Credit Risk Assessment in Banking by Nafa Nur Adifia Nanda, Yuniar Farida, Wika Dianita Utami

“…Objective: This study aims to evaluate an individual's creditworthiness by classifying and assessing their eligibility for credit. …”
Get full text

Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete by Abba Bashir, Esar Ahmad, Shashivendra Dulawat, Sani I. Abba

“…This study assesses nine machine learning models, integrating conventional AI algorithms, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF) with nature-inspired optimization techniques including chicken swarm optimization (CSO), moth flame optimization algorithm (MFO), and whale optimization algorithm (WOA). …”
Get full text

Analysis of Bridge Health Detection Based on Data Fusion by Ying Chen, Jiuhong Zhang, Yanfeng Li, Jialong Li

“…The neural network was then trained and the resulting trained network was applied to the safety evaluation of the cables of the cable-stayed bridge. …”
Get full text