Detection of exoplanets: exploiting each property of light by Rouan, Daniel, Lagrange, Anne-Marie

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Individual yeast cells signal at different levels but each with good precision by Steven S. Andrews, Roger Brent

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Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element by Francisco Maciá Pérez, Jose Vicente Berna Martienz, Alberto Fernández Oliva, Miguel Abreu Ortega

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Analysis of the Microbial Community Structure of <i>Ixodes persulcatus</i> at Each Developmental Stage by Yonghong Liu, Xiaonan Dong, Lianyang Sun, Hao Cui, Jiamei Kang, Nan Bu, Yishuai Zhang, Zehao Qi, Zixuan Li, Zilong Zhang, Li Zhao

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Framework for autonomous inspection of PV plants using IoT electronics on each PV panel and UAV collaboration by Miguel Tradacete-Ágreda, Carlos Santos-Pérez, Pablo José Hueros-Barrios, Francisco Javier Rodríguez-Sánchez, Rafael Pérez-Seguí, Javier Melero-Deza, Pascual Campoy

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Combination treatment with MEK and AKT inhibitors is more effective than each drug alone in human non-small cell lung cancer in vitro and in vivo. by Jieru Meng, Bingbing Dai, Bingliang Fang, B Nebiyou Bekele, William G Bornmann, Duoli Sun, Zhenghong Peng, Roy S Herbst, Vassiliki Papadimitrakopoulou, John D Minna, Michael Peyton, Jack A Roth

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Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection by Jayakumar Kaliappan, Revathi Thiagarajan, Karpagam Sundararajan

“…This paper is designed on the idea that each IDS is efficient in detecting a specific type of attack. …”
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Smoke Detection Transformer: An Improved Real-Time Detection Transformer Smoke Detection Model for Early Fire Warning by Baoshan Sun, Xin Cheng

“…However, the features of smoke are not apparent; the shape of smoke is not fixed, and it is easy to be confused with the background outdoors, which leads to difficulties in detecting smoke. Therefore, this study proposes a model called Smoke Detection Transformer (Smoke-DETR) for smoke detection, which is based on a Real-Time Detection Transformer (RT-DETR). …”
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Image forgery detection algorithm based on U-shaped detection network by Zhuzhu WANG

“…Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.…”
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Image forgery detection algorithm based on U-shaped detection network by Zhuzhu WANG

“…Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.…”
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GRADIENT-BASED VEHICLE DETECTION USING A TWO-SEGMENT DETECTION FIELD by Zbigniew CZAPLA

“…In the area of each segment, the sum of the edge values is calculated. …”
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Assessing the Detection Capabilities of RGB and Infrared Models for Robust Occluded and Unoccluded Pedestrian Detection by Muhammad Habibullah Abdulfattah, Usman Ullah Sheikh, Muhammad Idrees Masud, Mohd Afzan Othman, Nurulaqilla Khamis, Mohammed Aman, Zeeshan Ahmad Arfeen

“…This study presents a comparative analysis of RGB (visible spectrum) and infrared (IR) modalities, each employed independently to detect pedestrians under various conditions. …”
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Detect material volume by fusing heterogeneous camera target detection and depth estimation information by Wei Tian, Xuecong Cheng, Yipeng Zhang, Huazhi Lin

“…First, the improved DeepLabV3+ is used to detect the edge of the material pile in the monocular camera target detection, and the CREStereo cascade network is used in the binocular camera to calculate the depth map; then, SIFT is combined with FLANN to map the edge of the material pile into the depth map and separate the depth of the material pile; finally, the three-dimensional coordinates of each point in the material pile are calculated, and the volume is calculated using the microelement method. …”
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Classifying and Detecting Live Insects with Computationally Effective Deep Learning Object Detection Models by Arumuga Arun Rajeswaran, Karthik Katara, Yoganand Selvaraj, Ranjithkumar Sundarasamy

“…Abstract A crucial part of agriculture is detecting insects that increase yield productivity. …”
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PTSD Case Detection with Boosting by Vu Nguyen, Minh Phan, Tiantian Wang, Payam Norouzzadeh, Eli Snir, Salih Tutun, Brett McKinney, Bahareh Rahmani

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Tamper Detection in Color Image by Baghdad Science Journal

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Detecting Vietnamese fake news by Duc Vinh Vo, Phuc Do

“…Additionally, these three models evaluate the contribution of deep learning techniques for fake news detection and emphasize the potential for exploring interconnections between neural networks in addressing automatic Vietnamese fake news detection. …”
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Detecting Vietnamese fake news by Duc Vinh Vo, Phuc Do

“…Additionally, these three models evaluate the contribution of deep learning techniques for fake news detection and emphasize the potential for exploring interconnections between neural networks in addressing automatic Vietnamese fake news detection. …”
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Tamper Detection in Text Document by Baghdad Science Journal

“…Any modification, addition or deletion in a letter, word, or line in the document will be detected.…”
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Detection and Localization of Stationary Waves on Venus Using a Self‐Supervised Anomaly Detection Model by Husnu Baris Baydargil, Jose Eduardo Oliveira Silva, Yeon Joo Lee, Meeyoung Cha

“…However, manual detection is time‐consuming, especially with the increasing volume of new images. …”
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