Multiclass Incremental Learning for Fault Diagnosis in Induction Motors Using Fine-Tuning with a Memory of Exemplars and Nearest Centroid Classifier by Magdiel Jiménez-Guarneros, Jonas Grande-Barreto, Jose de Jesus Rangel-Magdaleno

“…Test samples are classified using nearest centroid classifier (NCC) in the feature space from 1D CNN. The proposed framework was evaluated and validated over two public datasets for fault detection in induction motors (IMs): asynchronous motor common fault (AMCF) and Case Western Reserve University (CWRU). …”
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Joint Optimal Production Planning for Complex Supply Chains Constrained by Carbon Emission Abatement Policies by Longfei He, Zhaoguang Xu, Zhanwen Niu

“…Furthermore, numerical studies by featuring exponentially distributed demand compare systemwide performances in various scenarios. …”
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Handwritten Geez Digit Recognition Using Deep Learning by Mukerem Ali Nur, Mesfin Abebe, Rajesh Sharma Rajendran

“…Convolutional neural network (CNN) is preferable for pattern recognition like in handwritten document recognition by extracting a feature from different styles of writing. …”
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Binary Classification of Pneumonia in Chest X-Ray Images Using Modified Contrast-Limited Adaptive Histogram Equalization Algorithm by Abror Shavkatovich Buriboev, Akmal Abduvaitov, Heung Seok Jeon

“…The model’s robustness is validated through five-fold cross-validation, and its feature extraction is visualized to ensure clinical relevance. …”
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Deformable detection transformers for domain adaptable ultrasound localization microscopy with robustness to point spread function variations by Sepideh K. Gharamaleki, Brandon Helfield, Hassan Rivaz

“…This object detection network tackles object deformations by utilizing multi-scale feature maps and incorporating a deformable attention module. …”
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Potential for using the datafree application Moya during civil unrest and displacement situations for continuity of education by Fazlyn Petersen

“…However, challenges related to message delivery, profile updates, account verification, feature access and network reliability were also noted. …”
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Towards a multi-modal Deep Learning Architecture for User Modeling by Ange Tato, Roger Nkambou

“…The architecture combines a Long Short-Term Memory, a Convolutional Neural Network, and multiple Deep Neu-ral Networks to handle the multi-modality of data. …”
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Visual Content Captioning and Audio Conversion using CNN-RNN with Attention Model by Aldy Agil Hermanto, Giat Karyono, Imam Tahyudin, Boby Sandityas Prahasto

“…The research design follows a systematic approach involving data collection, preprocessing, model development, training, evaluation, and implementation. The methodology utilizes CNN for visual feature extraction, RNN for language modeling, and an Attention Mechanism to enhance contextual relevance in caption generation. …”
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Early surface crack detection and localization in structures: an artificial intelligence approach by Biswarup Yogi, Sourav Kumar Das, Soham Modak, Aritra Biswas, Satyabrata Roy

“…It uses Convolutional Neural Networks (CNNs) and YOLOv5. CNNs help in extracting features from the images to identify cracks. …”
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Automatic detection of teacher behavior in classroom videos using AlphaPose and Faster R-CNN algorithms by Jing Huang, Harwati Hashim, Helmi Norman, Mohammad Hafiz Zaini, Xiaojun Zhang

“…This study proposes an automated classification framework for evaluating teacher behavior in classroom settings by integrating AlphaPose and Faster region-based convolutional neural networks (R-CNN) algorithms. …”
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Deep learning-based identification and localization of intracranial hemorrhage in patients using a large annotated head computed tomography dataset: A retrospective multicenter stu... by Jingjing Liu, Weijie Fan, Yi Yang, Qi Peng, Bingjun Ji, Luxing He, Yang Li, Jing Yuan, Wei Li, Xianqi Wang, Yi Wu, Chen Liu, Qingfang Gong, Mi He, Yeqin Fu, Dong Zhang, Si Zhang, Yongjian Nian

“…An improved YOLOv8 architecture with the bidirectional feature pyramid network was proposed and trained using the RSNA 2019+ training dataset and evaluated on the RSNA 2019+ test dataset. …”
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Enhancing Online Fraud Detection: Leveraging Machine Learning and Behavioral Indicators for Improved Accuracy and Real-Time Detection by Shaha Prasad, Gavekar Vidya

“…This study presents a comprehensive evaluation of machine learning (ML) models for fraud detection, emphasizing the role of behavioral indicators in enhancing model performance. …”
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Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects by Tariq Shahzad, Sunawar Khan, Tehseen Mazhar, Wasim Ahmad, Khmaies Ouahada, Habib Hamam

“…Software defect prediction is a critical task in software engineering, enabling organizations to proactively identify and address potential issues in software systems, thereby improving quality and reducing costs. In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. …”
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IoT intrusion detection method for unbalanced samples by ANTONG P, Wen CHEN, Lifa WU

“…In recent years, network traffic increases exponentially with the iteration of devices, while more and more attacks are launched against various applications.It is significant to identify and classify attacks at the traffic level.At the same time, with the explosion of Internet of Things (IoT) devices in recent years, attacks on IoT devices are also increasing, causing more and more damages.IoT intrusion detection is able to distinguish attack traffic from such a large volume of traffic, secure IoT devices at the traffic level, and stop the attack activity.In view of low detection accuracy of various attacks and sample imbalance at present, a random forest based intrusion detection method (Resample-RF) was proposed, which consisted of three specific methods: optimal sample selection algorithm, feature merging algorithm based on information entropy, and multi-classification greedy transformation algorithm.Aiming at the problem of unbalanced samples in the IoT environment, an optimal sample selection algorithm was proposed to increase the weight of small samples.Aiming at the low efficiency problem of random forest feature splitting, a feature merging method based on information entropy was proposed to improve the running efficiency.Aiming at the low accuracy problem of random forest multi-classification, a multi-classification greedy transformation method was proposed to further improve the accuracy.The method was evaluated on two public datasets.F1 reaches 0.99 on IoT-23 dataset and 1.0 on Kaggle dataset, both of which have good performance.The experimental results show that the proposed model can effectively identify the attack traffic from the massive traffic, better prevent the attack of hackers on the application, protect the IoT devices, and thus protect the related users.…”
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Trustworthy-constraint Deep Graph Learning for Enterprise Financial Risk Prediction by Wenting Ma

“…To this end, a trustworthy-constraint deep graph learning network (TDGL-net) is proposed to achieve the above goal, which includes the multi-view feature encoding, the heterogeneous graph information aggregation, the trustworthy decision-making mechanism. …”
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An Extended Firefly Algorithm for Enhanced Information Diffusion with Multi-Factor Considerations by Amjad Alloush, Ghaida Rebdawi:, Mohammad Saeed Abou Trab

“…Understanding and predicting how information spreads in online social networks is a crucial yet complex task, especially with the growing influence of content type, user engagement, and social dynamics. …”
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PCR and Bio-signature for data confidentiality and integrity in mobile cloud computing by Sreeja Cherillath Sukumaran, Mohammed Misbahuddin

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Analysis of signals from air conditioner compressors with ordinal patterns and machine learning by Keila Barbosa, Alejandro C Frery, George DC Cavalcanti

“…Furthermore, we incorporate machine learning algorithms, such as Artificial Neural Networks, Support Vector Machines, and Decision Trees, to evaluate and validate the effectiveness of Ordinal Patterns as discriminative features. …”
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Fault Diagnosis of Induction Motors Using Recurrence Quantification Analysis and LSTM with Weighted BN by Dengyu Xiao, Yixiang Huang, Chengjin Qin, Haotian Shi, Yanming Li

“…In addition, weighted batch normalization (BN), a modification of BN, is designed to evaluate the contributions of the three feature learning approaches. …”
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An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction by Josue Lopez-Cabrejos, Thuanne Paixão, Ana Beatriz Alvarez, Diodomiro Baldomero Luque

“…In this context, various architectures with different approaches exist, such as convolutional neural networks, diffusion networks, generative adversarial networks, and Transformer-based architectures, with the latter offering the best quality but at a high computational cost. …”
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