BREAST-RANKNet: a fuzzy rank-based ensemble of CNNs with residual learning for enhanced breast cancer detection from ultrasound and mammogram images by Sohaib Asif, Lingying Zhu, Dane Yan, Luman Xu, Zhengqiu Huang, Haimin Xu, Ruxuan Yan, Linghong Cai, Changfu Zheng, Jiamei Lin, Enyu Wang

“…This method dynamically combines the decision scores of three state-of-the-art pre-trained CNN models—DenseNet169, MobileNetV1, and InceptionResNetV2—while accounting for the confidence in the predictions of each model. …”
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Explainable Artificial Intelligence for Diagnosis and Staging of Liver Cirrhosis Using Stacked Ensemble and Multi-Task Learning by Serkan Savaş

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Prediction of EGFR mutation status in non-small cell lung cancer based on CT radiomic features combined with clinical characteristics by YANG Taotao, WANG Xianqi, CHEN Cancan

“…The radiomic and clinical features were subsequently combined to develop a comprehensive model. All the 3 classification models were built using random forest (RF) machine learning. …”
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MRI-based deep learning with clinical and imaging features to differentiate medulloblastoma and ependymoma in children by Yasen Yimit, Yasen Yimit, Parhat Yasin, Yue Hao, Abudouresuli Tuersun, Abudouresuli Tuersun, Chencui Huang, Xiaoguang Zou, Xiaoguang Zou, Ya Qiu, Ya Qiu, Yunling Wang, Mayidili Nijiati, Mayidili Nijiati

“…The model performance was assessed using a 7:3 random split of the dataset for training and validation, respectively. …”
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An integrated deep convolutional neural networks framework for the automatic segmentation and grading of glioma tumors using multimodal MRI scans by Otung John Peter Odong, Mohammed Abo-Zahhad, Moataz Abdelwahab

“…These results highlight the potential of the proposed model to aid radiologists in achieving accurate and reliable diagnoses, improving patient outcomes, and supporting clinical decision-making.…”
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Enhanced Magnetic Wireless Sensor Network Algorithm for Traffic Flow Monitoring in Low-Speed Congested Traffic by Haji Said Fimbombaya, Nerey H. Mvungi, Ndyetabura Y. Hamisi, Hashimu U. Iddi

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MoSViT: a lightweight vision transformer framework for efficient disease detection via precision attention mechanism by Yuanqi Chen, Aiping Wang, Ziyang Liu, Jie Yue, Enxu Zhang, Fei Li, Ning Zhang

“…This study introduces MoSViT, an innovative classification model leveraging advanced machine learning and computer vision technologies. …”
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Identification of the pathological subtypes of lung cancer brain metastases with multiparametric MRI radiomics: A feasibility study by Lian-Yu Sui, Shuai Quan, Li-Hong Xing, Yu Zhang, Huan Meng, Jia-Liang Ren, Jia-Ning Wang, Xiao-Ping Yin

“…In the training and test datasets, the AUCs of the model for the classification of SCLC and NSCLC BMs were 0.765 (95% CI 0.711, 0.822) and 0.762 (95% CI 0.671, 0.845), respectively, whereas the AUCs of the prediction models combining the three sequences in differentiating AD from NAD BMs were 0.861 (95% CI 0.756, 0.951) and 0.851 (95% CI 0.649, 0.984), respectively. …”
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Explainable and cognitive attention evoked learning framework for mitigating the large-scale real time cyber attacks by Ragipani Sowmya, Bhagavan Konduri

“…The X-DLF not only detects intrusions but also provides interpretability, offering insights into the rationale behind each classification decision. Extensive experiments were conducted using a variety of benchmark datasets, and performance metrics like specificity, recall, accuracy, F1-score and precision were computed and examined with existing learning models. …”
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NIRS identification of cashmere and wool fibers based on spare representation and improved AdaBoost algorithm by Zhu Yaolin, Li Zheng, Chen Xin, Chen Jinni, Zhang Hongsong

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Reframing individual roles in collaboration: digital identity construction and adaptive mechanisms for resistance-based professional skills in AI-human intelligence symbiosis by Weizheng Jiang, Weizheng Jiang, Yongzhou Li, Xiaoling Hu, Dongling Ma

“…Furthermore, this study develops a digital identity recognition and classification framework that identifies three distinct groups: core innovators, marginal experts, and low performers. …”
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Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions by Daniel Voipan, Andreea Elena Voipan, Marian Barbu

“…We thus focus our study on three ML models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer. …”
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Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation by Seyma Yasar, Rauf Melekoglu

“…From an initial set of 97 differentially expressed proteins, 18 significant proteins were selected using the BORUTA feature selection method. Three machine learning models-Random Forest (RF), Support Vector Machine (SVM), and Bootstrap Aggregation with Classification and Regression Trees (BaggedCART)-were developed and evaluated.ResultsThe Random Forest model achieved the best performance with an AUC of 0.955, accuracy of 0.830, sensitivity of 0.904, specificity of 0.763, and F1-score of 0.839. …”
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Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basin by Gil Marcio Avelino Silva, Fernando Pellon de Miranda, Jarbas Vicente Poley Guzzo, Wagner Leonel Bastos, Ygor Rocha, Igor Viegas Alves Fernandes de Souza, Italo Oliveira Matias, Sarah Barron Torres, Francisco Fabio de Araujo Ponte

“…Seven machine learning algorithms were evaluated, with the random forest model achieving the highest classification accuracy of 91%. …”
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DASH Framework Using Machine Learning Techniques and Security Controls by Aref Shaheed, Haisam Al-radwan

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Modification of the WaldBoost algorithm to improve the efficiency of solving pattern recognition problems in real-time by A. N. Chesalin, S. Ya. Grodzenskiy, M. Yu. Nilov, A. N. Agafonov

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Presenting an Innovative Method Based on Ensemble Learning for a Credit Approval System by Eshonkulov Uchkun, Elmurodov Tulkin, Ravshanov Zavqiddin, Каramanov Asqar

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The integration of explainable AI in Educational Data Mining for student academic performance prediction and support system by Md. Mahmudul Islam, Farhad Hossain Sojib, Md. Fazle Hasan Mihad, Mahmudul Hasan, Mahfujur Rahman

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EFFECT OF WIDTH CROWN ON THE TAPER OF Populus nigra STEM IN ZAKHO REGION by Muzahim .S. younis

“…By using simple linear regression , four mathematical Models were established. In order to find the best fit model for these four Models , coefficient of determination, standard error of estimate were used and the following model give the best result d0 = b0+b1 cw (b2 (-b3hi) ) R2=0.87 S.E%=0.6369 By using above model , tapering tables was prepared for Populus nigra trees grown in Zakho at different stand density . …”
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Machine learning in dentistry: a scoping review. by Shrey Lakhotia, Hormazd Godrej, Amandeep Kaur, Chaitanya Sai Nutakki, Michelle Mun, Pascal Eber, Leo Anthony Celi

“…Most models focused on classification (59.6%), whereas generative applications were relatively rare (1.4%). …”
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