Real-world pharmacovigilance of ofatumumab in multiple sclerosis: a comprehensive FAERS data analysis by Hui Chen

“…Statistical approaches used included the Reporting Odds Ratio, Proportional Reporting Ratio, Bayesian Confidence Propagation Neural Network, and Multi-item Gamma Poisson Shrinker. …”
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Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study. by Solange Amorim Nogueira, Fernanda Ambrogi B Luz, Thiago Fellipe O Camargo, Julio Cesar S Oliveira, Guilherme Carvalho Campos Neto, Felipe Brazao F Carvalhaes, Marcio Rodrigues C Reis, Paulo Victor Santos, Giovanna Souza Mendes, Rafael Maffei Loureiro, Daniel Tornieri, Viviane M Gomes Pacheco, Antonio Paulo Coimbra, Wesley Pacheco Calixto

“…This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. …”
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Sound recurrence analysis for acoustic scene classification by Jakob Abeßer, Zhiwei Liang, Bernhard Seeber

“…In the second part, we evaluate three strategies to incorporate self-similarity matrices as an additional input feature to a convolutional neural network architecture for ASC. We observe the characteristic repetition of transient sounds in recordings of “park” and “street traffic” as well as harmonic sound repetitions in acoustic scene classes related to public transportation. …”
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Small Object Detection with Multiscale Features by Guo X. Hu, Zhong Yang, Lei Hu, Li Huang, Jia M. Han

“…The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. …”
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Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review by Mohammad Faiz, Bakkanarappa Gari Mounika, Mohd Akbar, Swapnita Srivastava

“…This analysis covers both machine learning models (ML), such as support vector machine (SVM) & random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. …”
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Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine by Hang-cheong Wong, Pak-kin Wong, Chi-man Vong

“…This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN) and decremental least-squares support vector machine (DLSSVM). …”
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Sizing Control and Hardware Implementation of a Hybrid Wind-Solar Power System, Based on an ANN Approach, for Pumping Water by Ons Zarrad, Mohamed Ali Hajjaji, Aymen Jemaa, Mohamed Nejib Mansouri

“…The first contribution of our work is the utilization of an artificial neural network controller to command, at fixed atmospheric conditions, the maximum power point. …”
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A New Preprocessing Method for Diabetes and Biomedical Data Classification by Sarbast CHALO, İbrahim Berkan AYDİLEK

“…We present a method for the identification of diabetes that involves the training of the features of a deep neural network between five and 10 times using the cross-validation training mode. …”
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Loss Architecture Search for Few-Shot Object Recognition by Jun Yue, Zelang Miao, Yueguang He, Nianchun Du

“…In this paper, we investigate the problem of designing an optimal loss function for few-shot object recognition and propose a novel few-shot object recognition system that includes the following three steps: (1) generate a loss function architecture using a recurrent neural network (generator); (2) train a base embedding network with the generated loss function on a training set; (3) fine-tune the base embedding network using the few-shot instances from a validation set to obtain the accuracy and use it as a reward signal to update the generator. …”
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An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning by Néstor Rodríguez-Padial, Marta Marín, Rosario Domingo

“…In the proposed Custom Balance Scorecard design, an exploratory data phase is integrated with another analysis and prediction phase using Principal Component Analysis algorithms and Machine Learning that uses Artificial Neural Network algorithms. This new extension allows better control over the maintenance function of an industrial plant in the medium-term with a yearly horizon taken over monthly intervals which allows the measurement of the indicators of strategic productive areas and the discovery of hidden behavior patterns in work orders. …”
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Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm by Jinqing Zhang, Pengchao Zhang, Bin Xu

“…To solve this problem, this paper proposes a new way by using a questionnaire which covers most aspects of a student’s life to collect comprehensive information and feed the information into a neural network. With reliable prediction on students’ state of mind and awareness of feature importance, colleges can give students guidance associated with their own experience and make macroscopic policies more effective. …”
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SQL Injection Detection Based on Lightweight Multi-Head Self-Attention by Rui-Teng Lo, Wen-Jyi Hwang, Tsung-Ming Tai

“…This paper presents a novel neural network model for the detection of Structured Query Language (SQL) injection attacks for web applications. …”
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Coincidence Detection Using Spiking Neurons with Application to Face Recognition by Fadhlan Kamaruzaman, Amir Akramin Shafie, Yasir M. Mustafah

“…We elucidate the practical implementation of Spiking Neural Network (SNN) as local ensembles of classifiers. …”
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Unveiling the Complexity of Medical Imaging through Deep Learning Approaches by Javaid Iqbal Bhat, Novsheena Rasool

“…Specifically, an in-depth discussion is conducted on the Convolutional Neural Network (CNN) owing to its widespread adoption as a paramount tool in computer vision tasks. …”
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Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy by Huihui Li, Linfeng Gou, Hua Zheng, Huacong Li

“…A new intelligent fault diagnosis scheme combining improved pattern gradient spectrum entropy (IPGSE) and convolutional neural network (CNN) is proposed in this paper, aiming at the problem of poor fault diagnosis effect and real-time performance when CNN directly processes one-dimensional time series signals of aeroengine. …”
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TXtreme: transformer-based extreme value prediction framework for time series forecasting by Hemant Yadav, Amit Thakkar

“…This paper proposes a TXtreme framework that uses Long-Short memory network, feed-forward neural network, and transformer to improve time series forecasting under extreme values. …”
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Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods by Elaheh Konoz, Amir H. M. Sarrafi, Alireza Feizbakhsh, Zahra Dashtbozorgi

“…A 6-7-1 back propagation artificial neural network (ANN) was used to improve the accuracy of the constructed model. …”
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Finite volume modeling of neural communication: Exploring electrical signaling in biological systems by Muzammal Saleem, Muhammad Saqib, Badar Saad Alshammari, Shahid Hasnain, Amjad Ayesha

“…The model has been widely applied to study phenomena such as neural network behavior and the impact of drugs on neuronal function. …”
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Deep Multiscale Soft-Threshold Support Vector Data Description for Enhanced Heavy-Duty Gas Turbine Generator Sets’ Anomaly Detection by Zhang Kun, Li Hongren, Wang Xin, Xie Daxing, Sun Xiaokai

“…The model combines a support vector data description (SVDD) with a deep autoencoder backbone network framework, integrating a multiscale convolutional neural network (M) and soft-threshold activation network (S) into the Deep-SVDD framework. …”
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Monitoring and Simulation of Dynamic Spatiotemporal Land Use/Cover Changes by Andong Guo, Yuqing Zhang, Qing Hao

“…In this study, we preprocessed multiperiod land use and socioeconomic data, combined with spatial zoning, multilayer perception (MLP) artificial neural network, and Markov chain (MC), to construct a cellular automaton model of spatial zoning. …”
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