A Novel Approach for Identifying and Eliminating the Degradations in Real Time Images by S. Rajkumar, L. M. Jenila Livingston, Vansh Juneja, Shashwat Sharv, Pratik Dattatray Mahajan

“…The CNN model is trained on a dataset of images with various noises, learning to discriminate between different noise patterns. After classifying the type of noise, we apply a enhancement approach utilizes deep learning techniques by leveraging U-Net based CNN model to reduce noise levels in an image. …”
Get full text

Systematic Review on Automation of Central Tire Inflation System Based on Terrain Conditions by Carl Luis C. Ledesma, Charlothe John I. Tablizo, Marites B. Tabanao, Emmanuel A. Salcedo, Emmy Grace T. Requillo, John Paul T. Cruz

“…Incorrect vehicle tire pressure affects vehicle dynamics, fuel efficiency, and driver safety across different terrain conditions. The current central tire inflation system (CTIS) alleviates this issue by adjusting the tire pressure to a predetermined reference level. …”
Get full text

End-to-End Intelligent Fault Diagnosis of Transmission Bearings in Electric Vehicles Based on CNN by Yong Chen, Guangxin Li, Anhe Li, Bolin He

“…The original time-domain vibration signal numerical matrix of the bearing is trained and tested to extract and learn abstract fault features of different fault types, and then the fault classification of the bearing is achieved. …”
Get full text

DeepTransIDS: Transformer-Based Deep learning Model for Detecting DDoS Attacks on 5G NIDD by Kumar Harshdeep, Konatham Sumalatha, Rohit Mathur

“…The proposed model is trained on 5G-NIDD dataset with 1,215,890 network flows that include benign and a different type of malicious traffic. The transformer model achieves 99.79% multi-classification accuracy with better precision, recall, and F1-score, with an increase in accuracy of 0.10% compared to CNN-based IDS models, though the accuracy of RNN-based IDS model is 99.91% the computational time is significantly high. …”
Get full text

Multimodal rapid identification of growth stages and discrimination of growth status for Morchella by Ning Jia, Chunjun Zheng

“…By introducing multi-stage input embedding, enhanced position encoding, and optimized Transformer Encoder layers, the performance of the model in identifying different growth stages of Morchella mushrooms is significantly improved. …”
Get full text

Scenario-adaptive wireless fall detection system based on few-shot learning by Yuting ZENG, Suzhi BI, Lili ZHENG, Xiaohui LIN, Hui WANG

“…A scenario robust fall detection system based on few-shot learning (FDFL) in wireless environment was designed.The performance of existing fall detection methods based on Wi-Fi channel state information (CSI) degrades significantly across scenarios, which requires collecting and marking a large number of CSI samples in each application scenario, resulting in high cost for large-scale deployment.Therefore, the method of few-shot learning was introduced, which can maintain the performance of fall detection with high accuracy when the number of annotated samples in unfa-miliar scenes is insufficient.The proposed FDFL was mainly divided into two stages, source domain meta-training and target domain meta-learning.The meta training stage of the source domain consists of two parts: data preprocessing and classification training.In the data preprocessing stage, the collected original CSI amplitude and phase data were denoised and segmented.In the classification training stage, a large number of processed source domain data samples were used to train a CSI feature extractor based on convolutional neural network.In the meta-learning stage of the target domain, the limited labeled data sampled in the target domain was effectively extracted based on the feature extractor trained in the meta-training module, and then a lightweight machine learning classifier was trained to detect the fall behavior under the cross-scene.Through several experiments in different scenarios, FDFL can achieve an average accuracy of 95.52% for the four classification tasks of falling, sitting, walking and sit down with only a small number of samples in the target domain, and maintain robust detection accuracy for changes in test environment, personnel target and equipment location.…”
Get full text

Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2. by Paulina N Smaruj, Fahad Kamulegeya, David R Kelley, Geoffrey Fudenberg

“…Here, we update and utilize Akita, a convolutional neural network model, to extract the sequence preferences and grammar of CTCF contributing to genome folding. …”
Get full text

Speech Databases, Speech Features, and Classifiers in Speech Emotion Recognition: A Review by G. H. Mohmad Dar, Radhakrishnan Delhibabu

“…It also analyzes the efficacy of different speech features and classifiers in handling challenges such as data imbalance, limited data availability, and cross-lingual variations. …”
Get full text

Few-Shot Learning in Wi-Fi-Based Indoor Positioning by Feng Xie, Soi Hoi Lam, Ming Xie, Cheng Wang

“…The experiments were conducted across various scenarios, evaluating the performance of the models with different numbers of samples per class (K) after filtering by cosine similarity (FCS) during both the stages of data preprocessing and meta-learning. …”
Get full text

Legal Perspectives for Explainable Artificial Intelligence in Medicine - Quo Vadis? by Cătălin-Mihai PESECAN, Lăcrămioara STOICU-TIVADAR

“…Grad-CAM will generate heatmaps based on the gradient from the last layer (because it contains the most information) of a convolutional neural network. Explainable Artificial Intelligence methods come in multiple flavors and options and can offer different perspectives. …”
Get full text

DANC-Net: Dual-Attention and Negative Constraint Network for Point Cloud Classification by Hang Sun, Yuanyue Zhang, Jinmei Shi, Shuifa Sun, Guanqun Sheng, Yirong Wu

“…Convolutional neural networks, as a branch of deep neural networks, have been widely used in multidimensional signal processing, especially in point cloud signal processing. …”
Get full text

Salient object detection dataset with adversarial attacks for genetic programming and neural networksMendeley Data by Matthieu Olague, Gustavo Olague, Roberto Pineda, Gerardo Ibarra-Vazquez

“…This dataset is an image repository containing five different image databases to evaluate adversarial robustness by introducing 12 adversarial examples, each leveraging a known adversarial attack or noise perturbation. …”
Get full text

Scmaskgan: masked multi-scale CNN and attention-enhanced GAN for scRNA-seq dropout imputation by You Wu, Li Xu, Xiaohong Cong, Hanxiao Li, Yanli Li

“…Finally, multiple experiments were conducted to evaluate the methods’ performance using seven different data types and scRNA-seq data from ten neuroblastoma samples. …”
Get full text

Detection of the Pin Defects of Power Transmission Lines Based on Improved TPH-MobileNetv3 by Mengxuan Li, Jingshan Han, Zhi Yang, Bin Zhao, Peng Liu

“…A feature fusion structure with layers of self-attention and a convolutional block attention module (CBAM) is added to the neck network, and a transformer prediction head are added to the head network so that different scale characteristics can be fused and focused from space and channels to strengthen the detection of small targets. …”
Get full text

A Hybrid Approach for Color Face Recognition Based on Image Quality Using Multiple Color Spaces by Mahdi Hosseinzadeh, Mohammad Mehdi Pazouki, Önsen Toygar

“…Additionally, the proposed system is designed to serve as a secure anti-spoofing mechanism, tested against different attack scenarios, including print attacks, mobile attacks, and high-definition attacks. …”
Get full text

Multi-Function Working Mode Recognition Based on Multi-Feature Joint Learning by Lei Liu, Minghua Wu, Dongyang Cheng, Wei Wang

“…This hybrid model leverages the local convolution operations of the CNN module to extract local characters from radar pulse sequences, capturing the dynamic patterns of radar waveforms across different modes. …”
Get full text

Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images by Avigyan Roy, Priyam Saha, Nandita Gautam, Friedhelm Schwenker, Ram Sarkar

“…Early detection and accurate classification of cancer types are crucial for effective treatment. Imaging tests on different image modalities such as Histopathology images, provide valuable insights into the cellular and architectural features of tissues, allowing pathologists to make diagnosis, determine disease stages, and guide treatment decisions. …”
Get full text

CNN-Based Medical Ultrasound Image Quality Assessment by Siyuan Zhang, Yifan Wang, Jiayao Jiang, Jingxian Dong, Weiwei Yi, Wenguang Hou

“…The label of each example is obtained by averaging the scores of different doctors. Afterwards, a deep CNN network and a residuals network are taken to establish the IQA models. …”
Get full text

FROM PIXELS TO DIAGNOSIS: A DEEP LEARNING FRAMEWORK FOR HISTOPATHOLOGICAL IMAGE ANALYSIS IN CANINE TESTICULAR PATHOLOGY

“…We propose an artificial intelligence-based computational pathology approach to automate the discrimination of different testicular developmental, inflammatory or degenerative pathologies and the main testicular neoplasms (Seminoma, Sertolioma, Leydigoma). …”
Get full text

Quality Judgment of 3D Face Point Cloud Based on Feature Fusion by Gong Gao, Hong Liu, Hongyu Yang

“…The experimental results show that concat depth map features and point cloud features can achieve the complementary effect between different features.…”
Get full text