Leveraging an ensemble of EfficientNetV1 and EfficientNetV2 models for classification and interpretation of breast cancer histopathology images by Mahdi Azmoodeh-Kalati, Hasti Shabani, Mohammad Sadegh Maghareh, Zeynab Barzegar, Reza Lashgari

“…Additionally, we propose two ensemble architectures that integrate different trained EfficientNet models. Our framework achieves a classification accuracy of 99.58%, outperforming conventional CNN models on the BreakHis dataset. …”
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Alternate encoder and dual decoder CNN-Transformer networks for medical image segmentation by Lin Zhang, Xinyu Guo, Hongkun Sun, Weigang Wang, Liwei Yao

“…In recent years, methods based on convolutional neural networks and Transformer have achieved great success in the medical image segmentation field. …”
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Deep learning techniques for speech emotion recognition: A review by Silviana Widya Lestari, Saliyah Kahar, Trismayanti Dwi

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HEAT: Incorporating hierarchical enhanced attention transformation into urban road detection by Ting Han, Chuanmu Li, Siyu Chen, Zongyue Wang, Jinhe Su, Yundong Wu, Guorong Cai

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Enhancing Defect Detection on Surfaces Using Transfer Learning and Acoustic Non-Destructive Testing by Michele Lo Giudice, Francesca Mariani, Giosuè Caliano, Alessandro Salvini

“…This study presents a novel approach that leverages transfer learning techniques to improve the detection of debonding defects across different surface types using PICUS, an acoustic diagnostic device developed at Roma Tre University for the assessment of defects in heritage wall paintings. …”
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Towards scalable medical image compression using hybrid model analysis by Shunlei Li, Jiajie Lu, Yingbai Hu, Leonardo S. Mattos, Zheng Li

“…The performance of SDWTCNN is evaluated using different performance metrics, demonstrating its effectiveness in maintaining image quality at various compression rates. …”
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Biological Motion-Based Emotion Recognition Through a Deep Learning Approach by Amjaad T. Alotaibi, Suhare Solaiman

“…A model integrating convolutional neural networks (CNN) and long short-term memory (LSTM) layers was proposed to predict six distinct emotions: happiness, sadness, anger, fear, disgust, and neutral. …”
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DESIGN OF A SYSTEM FOR AUTOMATIC MONITORING OF MOBILE TERRITORIALLY SEPARATED OBJECTS by I.A. Kubasov, L.A. Lekar

A Federated Learning-Based Framework for Accurately Identifying Human Activity in the Environment by Nwadher Suliman Al-Blihed, Dina M. Ibrahim

“…Analysis of the data generated by HAR devices may involve deep learning models and algorithms of different kinds. These data are personal and may include some sensitive data. …”
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IT Equipment Automatic Configuration Method Based on Energy Consumption Prediction by CHEN Xiaojiang, HUANG Hongcong, CAI Xuelong, DING Bo

“…Next, an attention module is used to weight the features for different importance levels, and the final energy consumption prediction is obtained. …”
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Foreign Body Detection Method for Transmission Equipment Based on Edge Computing and Deep Learning by Yanqiao LU, Cuiying SUN, Hongwei CAO, Hongwei YAN

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Fault Diagnosis Method for Transformer Winding Based on the Load Normalized Lissajous Graphical Analysis of Leakage Magnetic Field by Bowen ZHANG, Jian FENG, Bowen WANG, Feiran YANG, Yitong XING

“…This study proposes a transformer winding fault diagnosis method based on Lissajous graphics and convolutional neural networks (CNN).Methods First, a simulation model consistent with an actual transformer is developed, and the simulation system is utilized to obtain magnetic flux leakage signal data from different measurement points outside the winding under both normal and fault conditions. …”
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Detection of Architectural Dysplastic Features from Histopathological Imagery of Oral Mucosa Using Neural Networks by Watchanan Chantapakul, Sirikanlaya Vetchaporn, Sansanee Auephanwiriyakul, Nipon Theera-Umpon, Ritipong Wongkhuenkaew, Uklid Yeesarapat, Nutchapon Chamusri, Mansuang Wongsapai

“…This paper proposes a neural network architecture for detecting dysplastic features of epithelial architecture, including irregular epithelial stratification and bulbous rete ridges. The different combinations of atrous convolution, batch normalization, global pooling, and dropout are discussed regarding their effects, along with an ablation study. …”
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The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…Additionally, this article analyzes contributions from different countries, highlighting temporal and methodological trends in this field. …”
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How Resilient Are Kolmogorov–Arnold Networks in Classification Tasks? A Robustness Investigation by Ahmed Dawod Mohammed Ibrahum, Zhengyu Shang, Jang-Eui Hong

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AirStrum: A virtual guitar using real-time hand gesture recognition and strumming technique by Beulah ARUL, Shashank PANDA, Tushar NAIR

“…Finally, the proposed system can play different sounds by inferring both the played chord and the strumming velocity from human actions. …”
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Deep Learning for Multi-Tissue Cancer Classification of Gene Expressions (GeneXNet) by Tarek Khorshed, Mohamed N. Moustafa, Ahmed Rafea

“…Our model achieves 98.9% classification accuracy on human samples representing 33 different cancer tumor types across 26 organ sites. …”
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Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning by Teng-Li Lin, Arvind Mukundan, Riya Karmakar, Praveen Avala, Wen-Yen Chang, Hsiang-Chen Wang

“…The current study investigates the use of ten different machine learning algorithms for the purpose of classification of AK, BCC, and SK, including convolutional neural network (CNN), random forest (RF), you only look once (YOLO) version 8, support vector machine (SVM), ResNet50, MobileNetV2, Logistic Regression, SVM with stochastic gradient descent (SGD) Classifier, SVM with logarithmic (LOG) Classifier and SVM- Polynomial Classifier, in assessing the capability of the system to differentiate AK from BCC and SK with heightened accuracy. …”
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Improving TMJ Diagnosis: A Deep Learning Approach for Detecting Mandibular Condyle Bone Changes by Kader Azlağ Pekince, Adem Pekince, Buse Yaren Kazangirler

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Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks by Yu Li, Patrick Matgen, Marco Chini

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