Life cycle assessment and life cycle cost of tofu production and its extended recycling scenario by S. Hartini, A.N. Fatliana, N.U. Handayani, P.A. Wicaksono, B.S. Ramadan, T. Matsumoto

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The antibacterial and antifungal potential of marine natural ingredients from the symbiont bacteria of mangrove by D. Pringgenies, W. Ari Setyati, F. Feliatra, D. Ariyanto

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A Case Report of Leptomeningeal Myelomatosis and Rapid Improvement with Regimen Consisting of Daratumumab, Pomalidomide, Vincristine, Procarbazine, and Dexamethasone by Jew Win Kuan, Sing Ling Chai, Pathmanathan Rajadurai, Lee Gong Lau, Joseph Uchang, Sharifah Noor Akmal Syed Husain

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Solar parking lot capacity: an abundant dual-use alternative to meet demand for the renewable energy transition by Scott Markwith

“…Most utility-scale solar facilities (USSFs) in the United States (US) are planned or installed in grasslands, pastures, agriculture land, and timberland, with aggressive future expansion plans. …”
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Carbon sequestration rate in sediment mangroves from natural and rehabilitated mangroves by D. Ariyanto, D. Pringgenies

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Analysis Model of Giant Prawns Population (Macrobrachium rosenbergii) in Estuary Edge of Sembakung Waters, Nunukan, Indonesia by Agus Indarjo, Gazali Salim, Christine Dyta Nugraeni, Lukman Yudho Prakoso, Permana Ari Soejarwo, Rukisah Rukisah, Yen Thi Hong Pham, Achmad Daengs GS, Hariyadi Hariyadi, Abdul Jabarsyah

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Current Trends in Class Imbalance Learning for Software Defect Prediction by Somya R. Goyal

“…An analysis on the current trends addressing the Class Imbalanced Learning methods is essential in domain of SDP. …”
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Optimasi Data Tidak Seimbang pada Interaksi Drug Target dengan Sampling dan Ensemble Support Vector Machine by Nabila Sekar Ramadhanti, Wisnu Ananta Kusuma, Annisa Annisa

“…This unknown interaction led drug target interaction to become imbalanced data. A really imbalanced data may cause bias to prediction result. …”
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WGAN-DL-IDS: An Efficient Framework for Intrusion Detection System Using WGAN, Random Forest, and Deep Learning Approaches by Shehla Gul, Sobia Arshad, Sanay Muhammad Umar Saeed, Adeel Akram, Muhammad Awais Azam

“…In this research study, we propose a strategy for dealing with the problems of imbalanced datasets and high dimensionality in IDSs. …”
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CDR-Detector: a chronic disease risk prediction model combining pre-training with deep reinforcement learning by Shaofu Lin, Shiwei Zhou, Han Jiao, Mengzhen Wang, Haokang Yan, Peng Dou, Jianhui Chen

“…However, in real-world scenarios, people’s medical habits and low prevalence of diseases often lead to few-shot and imbalanced longitudinal EHR data. This has become an urgent challenge for chronic disease risk prediction based on EHR. …”
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Geographical Origin Identification of <italic>Panax notoginseng</italic> Using a Modified K-Nearest Neighbors Model With Near-Infrared Spectroscopy by Xuefeng Cheng, Min Liao, Juan Liu

“…The proposed model integrates distance and cosine similarity metrics to enhance classification performance, particularly for imbalanced datasets. Principal component analysis is employed to reduce dimensionality, significantly improving computational efficiency without sacrificing accuracy. …”
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FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining by K. R. Seeja, Masoumeh Zareapoor

“…This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. …”
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A Group Feature Screening Procedure Based on Pearson Chi-Square Statistic for Biology Data with Categorical Response by Hanji He, Jianfeng He, Guangming Deng

“…The performance of the proposed method is illustrated by simulation studies, where the proposed method performs well with imbalanced data and multicategorical responses. In the application of lung cancer diagnosis, the proposed method for imbalanced data categorization is impressive, and the dimension reduction using linear discriminant is still good.…”
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A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance by Nabil Belacel

“…Classifiers today face numerous challenges, including overfitting, high computational costs, low accuracy, imbalanced datasets, and lack of interpretability. Additionally, traditional methods often struggle with noisy or missing data. …”
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Application of Big Data Unbalanced Classification Algorithm in Credit Risk Analysis of Insurance Companies by Xian Wu, Huan Liu

“…Propose an integrated learning model based on imbalanced dataset resampling and apply it to UCI dataset (University of California Irvine). …”
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Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms by Sahar K. Hussin, Salah M. Abdelmageid, Adel Alkhalil, Yasser M. Omar, Mahmoud I. Marie, Rabie A. Ramadan

“…For a dataset identified without considering the data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…Challenges for credit card fraud monitoring include highly imbalanced datasets and the need for advanced models to detect fraud patterns. …”
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Mitigation of Harmonics and Unbalanced Voltage Disturbance Compensation by MSVPWM Based- DVR in the Distribution Net by Haider Muhamad Husen

“…Voltage dip, voltage rise, imbalanced voltage, line notching and distortion of harmonics are problems of power quality frequently take place. …”
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Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models by Ruinan Qiu, Yongfeng Yin, Qingran Su, Tianyi Guan

“…Therefore, func-bagging is particularly effective in scenarios with class imbalance and is applicable to classification tasks with imbalanced classes, such as anomaly detection.…”
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Adaptive Freeshape Clustering for Balanced Energy Saving in the WirelessHART Networks by Guangbing Xiao, Jiamin Shi, Ning Sun, Yong Chen, Yong Zhang

“…The frequent data convergecasting from the sensor nodes to the gateways may cause imbalanced energy consumption in the Wireless Highway Addressable Remote Transducer (WirelessHART) networks, yielding a short network lifetime and frequent failures in the data acquisition. …”
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