Retrospective Frailty Assessment in Older Adults Using Inertial Measurement Unit-Based Deep Learning on Gait Spectrograms by Julius Griškevičius, Kristina Daunoravičienė, Liudvikas Petrauskas, Andrius Apšega, Vidmantas Alekna

“…The raw signals from accelerometers and gyroscopes were converted into time–frequency spectrograms. A convolutional neural network (CNN) trained solely on raw IMU-derived spectrograms achieved 71.4 % subject-wise accuracy in distinguishing frailty levels. …”
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AI-Based Architecture and Distributed Processing for the Detection and Mitigation of Spoofing Attacks in IoT Networks by William Villegas, Iván Ortiz-Garcés, Jaime Govea

“…The proposed approach integrates Convolutional Neural Networks with a distributed processing architecture based on Edge nodes, enabling real-time anomaly detection while reducing computational overhead on central servers. …”
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TFTformer: A novel transformer based model for short-term load forecasting by Ahmad Ahmad, Xun Xiao, Huadong Mo, Daoyi Dong

“…Additionally, a Temporal Convolutional Network is integrated within the Transformer’s encoder, employing causal convolutions and dilation to adapt to the sequential nature of data with an expanded receptive field. …”
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Identifying Bias in Deep Neural Networks Using Image Transforms by Sai Teja Erukude, Akhil Joshi, Lior Shamir

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Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images by Onur Sevli

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Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest by Gabriel R. Pardini, Paulo M. Tasinaffo, Elcio H. Shiguemori, Tahisa N. Kuck, Marcos R. O. A. Maximo, William R. Gyotoku

“…The initial algorithm utilized satellite images combined with the use of Convolutional Neural Networks (CNNs) to find the targets’ spatial locations (latitude and longitude). …”
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Mapping the Use of Artificial Intelligence–Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review by Wei Chen, Monisha Dhawan, Jonathan Liu, Damie Ing, Kruti Mehta, Daniel Tran, Daniel Lawrence, Max Ganhewa, Nicola Cirillo

“…Results Of the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). …”
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EA-CNN: Enhanced attention-CNN with explainable AI for fruit and vegetable classification by Zeshan Aslam Khan, Muhammad Waqar, Khalid Mehmood Cheema, Ali Abu Bakar Mahmood, Quratul Ain, Naveed Ishtiaq Chaudhary, Abdullah Alshehri, Sultan S. Alshamrani, Muhammad Asif Zahoor Raja

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PD Recognition for Typical Cardboard Insulation Defect with CNN by ZHAO Jing-he, XIU Da-yuan, WANG Jin-long, CHI Ming-he

“…The convolutional neural network， which is constructed and optimized， obtains the correct rate of about 96.5% and 89.9% respectively in the training set and the test set, showing that convolutional neural networks are suitable for local discharge recognition based on PRPD images.…”
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Relation extraction based on CNN and Bi-LSTM by Xiaobin ZHANG, Fucai CHEN, Ruiyang HUANG

“…Relation extraction aims to identify the entities in the Web text and extract the implicit relationships between entities in the text.Studies have shown that deep neural networks are feasible for relation extraction tasks and are superior to traditional methods.Most of the current relation extraction methods apply convolutional neural network (CNN) and long short-term memory neural network (LSTM) methods.However,CNN just considers the correlation between consecutive words and ignores the correlation between discontinuous words.On the other side,although LSTM takes correlation between long-distance words into account,the extraction features are not sufficiently extracted.In order to solve these problems,a relation extraction method that combining CNN and LSTM was proposed.three methods were used to carry out the experiments,and confirmed the effectiveness of these methods,which had some improvement in F1 score.…”
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Breast Cancer Detection Using Mammography: Image Processing to Deep Learning by Shahzad Ahmad Qureshi, Aziz-Ul-Rehman, Lal Hussain, Touseef Sadiq, Syed Taimoor Hussain Shah, Adil Aslam Mir, Muhammad Amin Nadim, Darnell K. Adrian Williams, Tim Q. Duong, Qurat-Ul-Ain Chaudhary, Natasha Habib, Asrar Ahmad, Syed Adil Hussain Shah

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Novel Gabor-Type Transform and Weighted Uncertainty Principles by Saifallah Ghobber, Hatem Mejjaoli

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Wavelet-Enhanced Desnowing: A Novel Single Image Restoration Approach for Traffic Surveillance Under Adverse Weather Conditions by Zihan Shen, Yu Xuan, Qingyu Yang

“…In this paper, we propose a wavelet-enhanced snow removal method that uses a Dual-Tree Complex Wavelet Transform feature enhancement module and a dynamic convolution acceleration module to address snow degradation in surveillance images. …”
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Enhancing the Accuracy of Image Classification for Degenerative Brain Diseases with CNN Ensemble Models Using Mel-Spectrograms by Sang-Ha Sung, Michael Pokojovy, Do-Young Kang, Woo-Yong Bae, Yeon-Jae Hong, Sangjin Kim

“…This study introduces a novel ensemble-based classification model that utilizes Mel spectrograms and Convolutional Neural Networks (CNNs) to distinguish between healthy individuals (NM), AD, and PD patients. …”
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A Contrastive Representation Learning Method for Event Classification in Φ-OTDR Systems by Tong Zhang, Xinjie Peng, Yifan Liu, Kaiyang Yin, Pengfei Li

“…Furthermore, CLWTNet incorporates wavelet transform convolution to enhance its capacity to capture intricate features of event signals. …”
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A Power Monitor System Cybersecurity Alarm-Tracing Method Based on Knowledge Graph and GCNN by Tianhao Ma, Juan Yu, Binquan Wang, Maosheng Gao, Zhifang Yang, Yajie Li, Mao Fan

“…To this end, this paper proposes a power monitor system cybersecurity alarm-tracing method based on the knowledge graph (KG) and graph convolutional neural networks (GCNN). Specifically, a cybersecurity KG is constituted based on the historical alert, accurately representing the entities and relationships in massive alerts. …”
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Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning by Anand Shankar, Samarendra Dandapat, Shovan Barma

“…For this purpose, the Markov transition field transformation technique has been employed for 2D image construction by preserving statistical dynamics characteristics of EEG signals, which are very important during the discrimination of different types of seizures. And, a convolution neural network (CNN) has been used for classification. …”
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CNN-based vane-type vortex generator modelling by Koldo Portal-Porras, Unai Fernandez-Gamiz, Ekaitz Zulueta, Roberto Garcia-Fernandez, Xabier Uralde-Guinea

“…The simplicity and accuracy of Computational Fluid Dynamics (CFD) tools have made them the most widely used method for solving fluid dynamics problems. …”
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Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants by H. M. S. S. Herath, S. L. P. Yasakethu, Nuwan Madusanka, Myunggi Yi, Byeong-Il Lee

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A Method of Fatigue Driving State Detection Based on Deep Learning by XIONG Qunfang, LIN Jun, YUE Wei

“…Current domestic and overseas fatigue recognition algorithms are implemented using fatigue features which are mostly singular and man-made. Most of those algorithms have complex structure, low efficiency and weak adaptability in face of driver’s individual behavior habit. …”
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