Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs? by Ali Nazarizadeh, Touraj Banirostam, Taraneh Biglari, Mohammadreza Kalantarhormozi, Fatemeh Chichagi, Amir H Behnoush, Mohammad A Habibi, Ramin Shahidi

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Survey on explainable knowledge graph reasoning methods by Yi XIA, Mingjng LAN, Xiaohui CHEN, Junyong LUO, Gang ZHOU, Peng HE

“…In recent years, deep learning models have achieved remarkable progress in the prediction and classification tasks of artificial intelligence systems.However, most of the current deep learning models are black box, which means it is not conducive to human cognitive reasoning process.Meanwhile, with the continuous breakthroughs of artificial intelligence in the researches and applications, high-performance complex algorithms, models and systems generally lack the transparency and interpretability of decision making.This makes it difficult to apply the technologies in a wide range of fields requiring strict interpretability, such as national defense, medical care and cyber security.Therefore, the interpretability of artificial intelligence should be integrated into these algorithms and systems in the process of knowledge reasoning.By means of carrying out explicit explainable intelligence reasoning based on discrete symbolic representation and combining technologies in different fields, a behavior explanation mechanism can be formed which is an important way for artificial intelligence to realize data perception to intelligence perception.A comprehensive review of explainable knowledge graph reasoning was given.The concepts of explainable artificial intelligence and knowledge reasoning were introduced briefly.The latest research progress of explainable knowledge graph reasoning methods based on the three paradigms of artificial intelligence was introduced.Specifically, the ideas and improvement process of the algorithms in different scenarios of explainable knowledge graph reasoning were explained in detail.Moreover, the future research direction and the prospect of explainable knowledge graph reasoning were discussed.…”
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Modern Trends and Recent Applications of Hyperspectral Imaging: A Review by Ming-Fang Cheng, Arvind Mukundan, Riya Karmakar, Muhamed Adil Edavana Valappil, Jumana Jouhar, Hsiang-Chen Wang

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Computational intelligence for disease diagnosis: an approach based on neutrosophic logic by Omar Mar Cornelio, Barbara Bron Fonseca

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Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015 by Dandan Tang, Man Zhang, Jiabo Xu, Xueliang Zhang, Fang Yang, Huling Li, Li Feng, Kai Wang, Yujian Zheng

“…Then we used age, marital status, education level, and other variables as input variables and whether to infect HIV as output variables to establish four prediction models for the three datasets. We also used confusion matrix, accuracy, sensitivity, specificity, precision, recall, and the area under the receiver operating characteristic (ROC) curve (AUC) to evaluate classification performance and analyzed the importance of predictive variables. …”
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Unveiling the hidden effect of multi-morbidities on the severity of Covid-19: a latent class analysis approach by Sedigheh Akhavnnezhad, Seyedeh Solmaz Talebi, Ehsan Mosa Farkhani, Marzieh Rohani-Rasaf

“…Different comorbidities were grouped into three classes by the LCA model. Class 1 was patients without multi-morbidities, or 83% people., Class 2, which included 9% patients, was patients with hypertension, diabetes, respiratory diseases, and mental behavioral disorders (HRMD class). …”
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SkinSage XAI: An explainable deep learning solution for skin lesion diagnosis by Geetika Munjal, Paarth Bhardwaj, Vaibhav Bhargava, Shivendra Singh, Nimish Nagpal

“…A data set of around 50,000 images from the Customized HAM10000, selected for diversity, serves as the foundation. The Inception v3 model is used for classification, supported by gradient‐weighted class activation mapping and local interpretable model‐agnostic explanations algorithms, which provide clear visual explanations for model outputs. …”
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Market state transitions and crash early warning in the Chinese stock market by Wu-Yue Pang, Wu-Yue Pang, Li Lin, Li Lin

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Utilizing machine learning to predict the risk factors of episiotomy in parturient womenAJOG Global Reports at a Glance by Mojdeh Banaei, PhD, Nasibeh Roozbeh, PhD, Fatemeh Darsareh, PhD, Vahid Mehrnoush, MD, Mohammad Sadegh Vahidi Farashah, PhD, Farideh Montazeri, BSc

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An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data by Huiyu Zhao, Chuang Miao, Yidi Zhu, Yijun Shu, Xiangsong Wu, Ziming Yin, Xiao Deng, Wei Gong, Ziyi Yang, Weiwen Zou

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Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing by Mo Chen, Chao Ge, Qirui Yang, Zhe Wei

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A hybrid ensemble framework with particle swarm optimization for network anomaly detection by Narinder Verma, Neerendra Kumar, Gourav Kumar, Kuljeet Singh

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Noninvasive prediction of fetal growth restriction using maternal plasma cell-free RNA: a case-control study by Yihong Huang, Ruizhi Wang, Lixia Shen, Lingyi Kong, Peisong Chen, Zilian Wang, Zhuyu Li

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Effectiveness of interventions for middle-aged and ageing population with neck pain: a systematic review and network meta-analysis protocol by Alison B Rushton, Nicola R Heneghan, Taweewat Wiangkham, Uchukarn Boonyapo, Piyameth Dilokthornsakul, Nattawan Phungwattanakul

“…Traditional pairwise meta-analysis using random-effect model will be performed for all direct comparisons, and NMA using a frequentist random-effect model then performed based on NP classification where possible. …”
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A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence by Emrah Aslan, Yildirim Ozupak, Feyyaz Alpsalaz, Zakaria M. S. Elbarbary

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Development and validation of a machine learning-based nomogram for survival prediction of patients with hilar cholangiocarcinoma after curative-intent resection by Yubo Ma, Qi Li, Zhenqi Tang, Kangpeng Li, Chen Chen, Jianjun Lei, Dong Zhang, Zhimin Geng

“…The calibration curves for the nomogram showed good concordance. Based on the decision curve analysis, the nomogram had a good clinical application value, outperforming both the TNM staging system and the Bismuth-Corlette classification. …”
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FPGA SoC Implementation of Adaptive Deep Neural Network-Based Multimodal Edge Intelligence for Internet of Medical Things by Nikhil B. Gaikwad, Smith K. Khare, Dinesh Mendhe, Hasan Mir, Sokol Kosta, U. Rajendra Acharya

“…The results show that our adaptive DNN model has obtained a software accuracy of 99.2% for ECG, 91.4% for PPG, 95% for activity classification, and 98.7% for smoke detection with a five-fold cross-validation strategy. …”
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Prevention of the Fracture Problem Occurring in Automotive Alternator Heatsink Blocks Using Artificial Intelligence by Dinçer Kökden, Adem Egi, Emre Bulut, Emre İsa Albak, İbrahim Korkmaz, Ferruh Öztürk

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Multi-sequence MRI based radiomics nomogram for prediction expression of programmed death ligand 1 in thymic epithelial tumor by Jie Shen, Lantian Zhang, Shuke Li, Xiaofei Mu, Tongfu Yu, Wei Zhang, Yue Yu, Jing He, Wen Gao

“…The calibration curve and decision curve analysis further confirmed the clinical usefulness of this combined model. …”
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Transfer learning from inorganic materials to ivory detection by Agil Aghasanli, Plamen Angelov, Dmitry Kangin, Jemma Kerns, Rebecca Shepherd

“…This novel work demonstrates that: (1) ML methods can provide highly accurate classification of ivory from different species of elephant using data obtained using Raman spectroscopy and providing insight into the decision making (2) TL enables re-purposing the models trained on larger mineral datasets of inorganic materials (such as MLROD) to discriminating between the classes of ivory, containing inorganic and organic biological components, for the first time transgressing between non-biological and biological samples (3) the proposed method allows both training from labelled samples of ivory and the identification of unknown ivory samples through prototype-based methods.…”
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