PyMerger: Detecting Binary Black Hole Mergers from the Einstein Telescope Using Deep Learning by Wathela Alhassan, T. Bulik, M. Suchenek

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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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Common gene-network signature of different neurological disorders and their potential implications to neuroAIDS. by Vidya Sagar, S Pilakka-Kanthikeel, Paola C Martinez, V S R Atluri, M Nair

“…Further, this unique gene network was compared with another in silico derived novel, convergent gene network which is shared by seven major neurological disorders (Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, Age Macular Degeneration, Amyotrophic Lateral Sclerosis, Vascular Dementia, and Restless Leg Syndrome). These networks differed in their gene circuits; however, in large, they involved innate immunity signaling pathways, which suggests commonalities in the immunological basis of different neuropathogenesis. …”
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A Lightweight Network with Domain Adaptation for Motor Imagery Recognition by Xinmin Ding, Zenghui Zhang, Kun Wang, Xiaolin Xiao, Minpeng Xu

“…This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. …”
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Short-Term Electricity Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Improved Sparrow Search Algorithm–Convolutional Neural Netwo... by Han Qiu, Rong Hu, Jiaqing Chen, Zihao Yuan

“…A series of simpler intrinsic mode functions (IMFs) with different frequency characteristics can be decomposed by CEEMDAN from data, then each IMF is reconstructed based on calculating the sample entropy of each IMF. …”
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Multi-view Fusion 3D Model Classification by GAO Yuan, DING Bo, HE Yong-jun

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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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Soil Porosity Detection Method Based on Ultrasound and Multi-Scale Feature Extraction by Hang Xing, Zeyang Zhong, Wenhao Zhang, Yu Jiang, Xinyu Jiang, Xiuli Yang, Weizi Cai, Shuanglong Wu, Long Qi

“…Since the collected ultrasonic signals belong to long-time series data and there are different frequency and sequence features, this study constructs a multi-scale CNN-LSTM deep neural network model using large convolution kernels based on the idea of multi-scale feature extraction, which uses multiple large convolution kernels of different sizes to downsize the collected ultra-long time series data and extract local features in the sequences, and combining the ability of LSTM to capture global and long-term dependent features enhances the feature expression ability of the model. …”
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Research on real-time monitoring method of mine personnel protective equipment with improved YOLOv8 by Lei ZHANG, Zhipeng SUN, Hongjing TAO, Shangkai HAO, Qianru YAN, Xiwei LI

“…Making convolution deformable, when sampling, it can more closely detect the true shape and size of the object, more robust, It effectively improves its feature acquisition ability for targets of different scales. …”
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An XNet-CNN Diabetic Retinal Image Classification Method by CHEN Yu, ZHOU Yujia, DING Hui

“…In this research,a retina image automatic recognition system based on Convolutional Neural Network (CNN) is proposed for the disadvantages of the traditional retina image processing process which is cumbersome and poor in robustness. …”
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ARO-GNN: Adaptive relation-optimized graph neural networks by Yong Lu, Zhengguo Lin

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Enhancing the quality of low-light images via the coefficient bounds derived for a subclass of Sakaguchi-type function by K. Sivagami Sundari, B. Srutha Keerthi

“…Our method is designed to adapt dynamically to different lighting conditions, ensuring effective image enhancement in both uniformly and non-uniformly illuminated environments. …”
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SER-DC YOLO for the Detection of Abnormal Cervical Cells by LI Chaowei, YANG Xiaona, ZHAO Siqi, HE Yongjun

“…To solve this challenge，a target detection network called SE-ResNet-Deformable Convolution You Only Look Once( SER-DC YOLO) is proposed. …”
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Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning by M. Vimala, SatheeshKumar Palanisamy, Sghaier Guizani, Habib Hamam

“…The problem of brain tumor classification (BTC) has been approached with several methods and uses different features obtained from MRI brain scans. However, they suffer from achieving higher performance in BTC and produce poor performance with a higher false ratio. …”
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MultiSEss: Automatic Sleep Staging Model Based on SE Attention Mechanism and State Space Model by Zhentao Huang, Yuyao Yang, Zhiyuan Wang, Yuan Li, Zuowen Chen, Yahong Ma, Shanwen Zhang

“…The MultiSEss architecture utilizes a multi-scale convolution module to capture signal features from different frequency bands and incorporates a Squeeze-and-Excitation attention mechanism to enhance the learning of channel feature weights. …”
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AHG-YOLO: multi-category detection for occluded pear fruits in complex orchard scenes by Na Ma, Na Ma, Na Ma, Yile Sun, Yile Sun, Chenfei Li, Chenfei Li, Zonglin Liu, Zonglin Liu, Haiyan Song, Haiyan Song

“…Next, shared weight parameters are introduced in the head network, and group convolution is applied to achieve a lightweight detection head. …”
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Handwritten Words Image Character Extraction Adaptive Algorithm Based on the Multi-branch Structure by GUO Xiaojing, ZHAO Xiaoyuan, ZOU Songlin

“…Deep convolution layers are known for learning features at different abstraction levels, while lower layers capture more localized details. …”
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Classification of melanoma skin Cancer based on Image Data Set using different neural networks by Rukhsar Sabir, Tahir Mehmood

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Performance Analysis of Eye Movement Event Detection Neural Network Models with Different Feature Combinations by Birtukan Adamu Birawo, Pawel Kasprowski

“…In this study, a combination of two-dimensional convolutional neural networks (2D-CNN) and long short-term memory (LSTM) layers is proposed to simultaneously classify input data into fixations, saccades, post-saccadic oscillations (PSOs), and smooth pursuits (SPs). …”
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Analysis of different IDS-based machine learning models for secure data transmission in IoT networks by Gladić Dejana, Petrovački Jelena, Sladojević Srdan, Arsenović Marko, Ristić Sonja

“…Through a comparative analysis of different algorithms, the study seeks to identify the model with the best performance, which could serve as a foundation for efficient IDS solutions tailored to the specific characteristics of IoT networks. …”
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