The Effect of SMOTE and Optuna Hyperparameter Optimization on TabNet Performance for Heart Disease Classification by Danang Wijayanto, Robert Marco, Acihmah Sidauruk, Mulia Sulistiyono

“…While numerous studies have explored various approaches for heart disease classification, challenges related to data imbalance and improper parameter settings remain persistent issues that affect model performance. …”
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An innovative approach to advanced voice classification of sacred Quranic recitations through multimodal fusion by Esraa Hassan, Abeer Saber, Omar Alqahtani, Nora El-Rashidy, Samar Elbedwehy

“…Compared with the traditional voice classification strategies, VSCF aims at solving issues regarding limitations of the adopted datasets and variations among different reciters. …”
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Remote Sensing Image Scene Classification Based on Mutual Learning With Complementary Multi-Features by Anzhi Chen, Mengyang Xu

“…A novel neural network model based on mutual learning, with complementary multi-features (MLCMFNet), is proposed for scene classification, addressing common issues with insufficient extraction to more effectively learn target features from remote sensing images. …”
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ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images by Jing Zhang, Hao Zhou, Kunyu Liu, Yuguang Xu

“…Although low-altitude drone technology offers advantages such as high resolution and strong timeliness, it faces dual challenges in the field of disease identification, such as complex background interference and irregular disease morphology. To address these issues, this study proposes an intelligent classification method for cassava diseases based on drone imagery and an ED-Swin Transformer. …”
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Waveshift Augmentation: A Physics-Driven Strategy in Fine-Grained Plant Disease Classification by Gent Imeraj, Hitoshi Iyatomi

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Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks by Suzhao Bi, Rongjian Lu, Qiang Xu, Peiwen Zhang

“…To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN). …”
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Utilization of Deep Learning YOLO V9 for Identification and Classification of Toraja Buffalo Breeds by Abdul Rachman Manga', Herawati Herawati, Purnawansyah Purnawansyah

“…The YOLOv9 model was trained across 90 epochs, aiming to achieve high accuracy in breed detection and classification. The evaluation results demonstrate the model's strong performance, achieving a precision of approximately 0.9 and a recall of 0.8. …”
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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

“…Before executing the classification process, issues frequently arise when data cannot be directly processed due to class imbalance. …”
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Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data by Haniyeh Rafiepoor, Alireza Ghorbankhanloo, Kazem Zendehdel, Zahra Zangeneh Madar, Sepideh Hajivalizadeh, Zeinab Hasani, Ali Sarmadi, Behzad Amanpour‐Gharaei, Mohammad Amin Barati, Mozafar Saadat, Seyed‐Ali Sadegh‐Zadeh, Saeid Amanpour

“…Furthermore, it is crucial to address modeling issues in models with a restricted number of features for future research.…”
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XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones by Qian Sun, Jie Zeng, Lulu Dai, Yangliu Hu, Lin Tian

“…The internal capture processes and matching results of DL models are useful for addressing these issues. The key challenges involve obtaining this internal information from DL-based anomaly detection methods, using this internal information to establish new classifications for uncovered protocol attacks and tracing the input back to the anomalous protocol sequences. …”
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UEF-HOCUrdu: Unified Embeddings Ensemble Framework for Hate and Offensive Text Classification in Urdu by Kifayat Ullah, Muhammad Aslam, Muhammad Usman Ghani Khan, Faten S. Alamri, Amjad Rehman Khan

“…As a result, the most efficient models, namely FastText, XLM-RoBERTa, ULMFiT, and XGBoost were incorporated in the proposed ensemble approach to achieve the best results in both classification and mitigation of NLP issues. To further enhance the confidence in proposed model, a stratified 5-fold cross-validation technique has been utilized. …”
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Class-Discrepancy Dynamic Weighting for Cross-Domain Few-Shot Hyperspectral Image Classification by Chen Ding, Jiahao Yue, Sirui Zheng, Yizhuo Dong, Wenqiang Hua, Xueling Chen, Yu Xie, Song Yan, Wei Wei, Lei Zhang

“…In recent years, cross-domain few-shot learning (CDFSL) has demonstrated remarkable performance in hyperspectral image classification (HSIC), partially alleviating the distribution shift problem. …”
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A fine-tuned convolutional neural network model for accurate Alzheimer’s disease classification by Muhammad Zahid Hussain, Tariq Shahzad, Shahid Mehmood, Kainat Akram, Muhammad Adnan Khan, Muhammad Usman Tariq, Arfan Ahmed

“…Our model achieved impressive classification results of 99.4% on the Kaggle MRI dataset as well as 98.2% on the Open Access Series of Imaging Studies (OASIS) database. …”
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DermViT: Diagnosis-Guided Vision Transformer for Robust and Efficient Skin Lesion Classification by Xuejun Zhang, Yehui Liu, Ganxin Ouyang, Wenkang Chen, Aobo Xu, Takeshi Hara, Xiangrong Zhou, Dongbo Wu

“…Currently, skin lesion classification faces challenges such as lesion–background semantic entanglement, high intra-class variability, artifactual interference, and more, while existing classification models lack modeling of physicians’ diagnostic paradigms. …”
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Preprocessing-Free Convolutional Neural Network Model for Arrhythmia Classification Using ECG Images by Chotirose Prathom, Ryuhi Fukuda, Yuto Yokoyanagi, Yoshifumi Okada

“…Machine learning models have been developed to classify arrhythmia using electrocardiogram (ECG) data, which effectively capture the patterns associated with different abnormalities and achieve high classification performance. However, these models face challenges in terms of input coverage and robustness against data imbalance issues. …”
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IoT-Enhanced Smart Parking Management With IncepDenseMobileNet for Improved Classification by Xiaoxia Zheng, Wenxi Feng, Ning Wang, Huhemandula

“…The model demonstrated a log loss of 0.18 and outstanding performance on new metrics, including Weighted Error Impact Score (WEIS), Dynamic Class Stability Index (DCSI), and Harmonized Classification Risk (HCR), signifying its effectiveness. …”
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Joint Classification of Hyperspectral and LiDAR Data via Multiprobability Decision Fusion Method by Tao Chen, Sizuo Chen, Luying Chen, Huayue Chen, Bochuan Zheng, Wu Deng

“…However, the process of joint use suffers from data redundancy, low classification accuracy and high time complexity. To address the aforementioned issues and improve feature recognition in classification tasks, this paper introduces a multiprobability decision fusion (PRDRMF) method for the combined classification of HSI and LiDAR data. …”
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AI-Based Performance Classification of Multi-Level System-of-Systems via Hypergraph Modeling by Jun Jiang, Abdeslem Smahi, Yiwen Chen, Othman Lakhal, Rochdi Merzouki

“…Accurately classifying the performance of System-of-Systems (SoS) is crucial for maintaining system reliability and resilience. Without clear classifications, engineers may struggle to assess the severity of malfunctions and implement timely corrective actions. …”
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Ransomware detection and family classification using fine-tuned BERT and RoBERTa models by Amjad Hussain, Ayesha Saadia, Faeiz M. Alserhani

“…The lack of standardization across IoT devices creates interoperability issues and complicates data transfer between medical devices and healthcare systems. …”
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Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification by Yong Zhou, Yizhou Tan, Shasha Wang, Hanshu Cai, Ying Gu

“…However, application of existing methods for evaluating alertness is limited due to issues such as high subjectivity, practice effect, susceptibility to interference, and complexity in data collection. …”
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