Research on lightweight malware classification method based on image domain by SUN Jingzhang, CHENG Yinan, ZOU Binghui, QIAO Tonghua, FU Sizheng, ZHANG Qi, CAO Chunjie

“…To address the high deployment costs and long prediction times associated with traditional malware classification methods, a lightweight malware visualization classification method was proposed. …”
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HR Management Big Data Mining Based on Computational Intelligence and Deep Learning by Genliang Zhao, Zhe Xue

“…To this end, this paper proposes an end-to-end competency-aware job requirement generation framework to automate the job requirement generation, and the prediction based on competency themes can realize the skill prediction in job requirements. …”
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Deep-Learning-Based Computer-Aided Grading of Cervical Spinal Stenosis from MR Images: Accuracy and Clinical Alignment by Zhiling Wang, Xinquan Chen, Bin Liu, Jinjin Hai, Kai Qiao, Zhen Yuan, Lianjun Yang, Bin Yan, Zhihai Su, Hai Lu

“…<b>Objective:</b> This study aims to apply different deep learning convolutional neural network algorithms to assess the grading of cervical spinal stenosis and to evaluate their consistency with clinician grading results as well as clinical manifestations of patients. …”
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Research on Identification Technology of Explosive Vibration Based on EEMD Energy Entropy and Multiclassification SVM by Huayuan Ma, Xinghua Li, Qiang Liu, Xie Xingbo, Chong Ji, Changxiao Zhao

“…Taking eigenvector composed of CEE (components of energy entropy) as input, multiclassification SVM algorithm was used for training and prediction. Prediction accuracy was more than 80%. …”
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Energy-efficient strategy for data migration and merging in Storm by Yonglin PU, Jiong YU, Liang LU, Ziyang LI, Chen BIAN, Bin LIAO

“…Storm is suffering the problems of high energy consumption but low efficiency.Aiming at this problem,the resource constraint model,the optimal principle of data reorganization in executors and node voltage reduction principle were proposed based on the analysis of the architecture and topology of Storm,and further the energy-efficient strategy for data migration and merging was put forward in Storm(DMM-Storm),which was composed of resource constraint algorithm,data migration and merging algorithm as well as node voltage reduction algorithm.The resource constraint algorithm estimates whether work nodes are appropriate for data migration according to the resource constraint model.The data migration and merging algorithm designs an optimal method to migrate data according to the the optimal principle of data reorganization in executors.The node voltage reduction algorithm reduces voltage of work nodes according to node voltage reduction principle.The experimental results show that the DMM-Storm can reduce energy consumption efficiently without affecting the performance of cluster compared with the existing researches.…”
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Energy-efficient strategy for data migration and merging in Storm by Yonglin PU, Jiong YU, Liang LU, Ziyang LI, Chen BIAN, Bin LIAO

“…Storm is suffering the problems of high energy consumption but low efficiency.Aiming at this problem,the resource constraint model,the optimal principle of data reorganization in executors and node voltage reduction principle were proposed based on the analysis of the architecture and topology of Storm,and further the energy-efficient strategy for data migration and merging was put forward in Storm(DMM-Storm),which was composed of resource constraint algorithm,data migration and merging algorithm as well as node voltage reduction algorithm.The resource constraint algorithm estimates whether work nodes are appropriate for data migration according to the resource constraint model.The data migration and merging algorithm designs an optimal method to migrate data according to the the optimal principle of data reorganization in executors.The node voltage reduction algorithm reduces voltage of work nodes according to node voltage reduction principle.The experimental results show that the DMM-Storm can reduce energy consumption efficiently without affecting the performance of cluster compared with the existing researches.…”
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Constructing a tumor immune microenvironment-driven prognostic model in acute myeloid leukemia using bioinformatics and validation data by Amir Abbas Navidinia, Ali Keshavarz, Bentol Hoda Kuhestani Dehaghi, Reza Khayami, Najibe Karami, Vahid Amiri, Mehdi Allahbakhshian Farsani

“…ROC analysis demonstrated predictive accuracy (AUC: 63.38–68.5% for 1–5-year survival). …”
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Atrial fibrillation and chronic kidney disease: main clinical characteristics of patients in selected subjects of the Russian Federation by M. A. Druzhilov, T. Yu. Kuznetsova, O. Yu. Druzhilova, U. D. Arustamova, D. V. Gavrilov, A. V. Gusev

“…The information was taken from the Webiomed predictive analytics platform, including 80775 patients with AF (men, 42,5%, mean age, 70,0±14,3 years) who underwent outpatient and/or inpatient treatment in medical organizations in 6 Russian subjects in 2016-2019 with data on blood creatinine levels. …”
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Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification by Zhan Wang, Zhaokai Zhou, Shuai Yang, Zhengrui Li, Run Shi, Ruizhi Wang, Kui Liu, Xiaojuan Tang, Qi Li

“…Lastly, various machine learning algorithms were applied to identify novel potential targets of BLCA, following which their pro-tumorigenic effects were experimentally verified. …”
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A digital twin-enabled fog-edge-assisted IoAT framework for Oryza Sativa disease identification and classification by Goluguri N.V. Rajareddy, Kaushik Mishra, Satish Kumar Satti, Gurpreet Singh Chhabra, Kshira Sagar Sahoo, Amir H. Gandomi

“…To boost the model's predictive accuracy, the Chaotic Honey Badger Algorithm (CHBA) is employed to optimize the CNN hyperparameters, resulting in an impressive average accuracy of 93.5 %. …”
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Identification and validation of hub m7G-related genes and infiltrating immune cells in osteoarthritis based on integrated computational and bioinformatics analysis by Zhenhui Huo, Chongyi Fan, Kehan Li, Chenyue Xu, Yingzhen Niu, Fei Wang

“…Functional enrichment, drug target prediction, and target gene-related miRNA prediction were performed for these genes. …”
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A multi-objective master–slave methodology for optimally integrating and operating photovoltaic generators in urban and rural electrical networks by Jhony Andrés Guzmán-Henao, Rubén Iván Bolaños, Brandon Cortés-Caicedo, Luis Fernando Grisales-Noreña, Oscar Danilo Montoya, Jesús C. Hernández

“…The results demonstrated the effectiveness of these algorithms. NSGA-II achieved the best performance, with reductions of 32.84% in energy losses and 42.41% in operating costs (with standard deviations of 0.21% and 0.39%, respectively) for the urban system; and reductions of 21.87% in energy losses and 43.36% in operating costs (with standard deviations of 0.07% and 0.24%, respectively) for the rural system. …”
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Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…A range of sophisticated artificial intelligence methods, including One-Dimensional Convolutional Neural Network (1D-CNN), Artificial Neural Networks (ANN), Decision Tree (DT), Ensemble Learning (EL), Adaptive Boosting (AdaBoost), Random Forest (RF), and Least Squares Support Vector Machine (LSSVM), were utilized to model and predict pH variations with high accuracy. The Coupled Simulated Annealing (CSA) algorithm was employed to optimize the hyperparameters of these models, enhancing their predictive performance. …”
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Rapid Identification of Nine Easily Confused Mineral Traditional Chinese Medicines Using Raman Spectroscopy Based on Support Vector Machine by Jing Ming, Long Chen, Yan Cao, Chi Yu, Bi-Sheng Huang, Ke-Li Chen

“…The identification model was subsequently built by the SVM algorithm. The 3-fold cross validation (3-CV) accuracy of the SVM model established based on extracting characteristic intensity data from spectra pretreated by first derivation was 98.61%, and the prediction accuracies of the training set and validation set were 100%. …”
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Option Pricing Based on Modular Neural Network by Moslem Peymany Foroushani, mohamad ali dehghan dehnavi, Milad Kouhkan

“…In the neural network models, option prices were predicted using Python and its machine learning algorithms. …”
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Modeling of the Power Station Boiler Combustion Efficiency Considering Multiple Work Condition with Feature Selection by TANG Zhenhao, WU Xiaoyan, CAO Shengxian

“…It is difficult for power station boiler efficiency to measure precisely A datadriven modeling method is proposed to establish the boiler combustion efficiency model, according to the machine learning theories A classification and regression trees (CART) algorithm provides correlated variables which have significant relation with the boiler combustion efficiency by data analysis Then, a KNearest Neighbor (KNN) classifies the samples to distinguish the data from different work conditions Based on the classified data, a least square support vector machine (LSSVM) optimized by differential evolution (DE) algorithm is proposed to establish a datadriven model (DDMMF) The parameters of LSSVM are optimized dynamically by DE to improve the model accuracy Finally, the prediction model is corrected dynamically for further improvement of the prediction accuracy The experimental results based on actual production data illustrate that the proposed approach can predict the boiler combustion efficiency accurately, which meets the requirements of boiler control and optimization…”
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A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis by Seok-Woo Jang, Gye-Young Kim

“…The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. …”
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Innovation of Urban Circular Economy Growth Path Based on Neural Network by Weifeng Qiu, Yi Yang

“…Moreover, it has obvious advantages over the traditional algorithm in terms of error and recall rate. Compared with the actual economic data, the economic data predicted by the model is quite consistent, and the prediction of future data by the model basically accords with the development goal of the regional master plan. …”
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Preface to Special Issue on AI-Based Future Intelligent Networks and Communication Security by Sunil Kumar, Glenford Mapp, Abhay Bansal, Korhan Cengiz

“… Recent advancements in science focus on the study and development of algorithms that can learn from and make predictions and decisions based on data collected through intelligent devices. …”
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Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning. by Yatan Li, Wei Jia, Chen Chen, Cheng Chen, Jinchao Chen, Xinling Yang, Pei Liu

“…LASSO, SVM-RFE and Random Forest algorithms were used to screen biomarker genes. Then, ROC curves were drawn and PD risk predicting models were constructed on the basis of the biomarker genes. …”
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