Technology has tremendously contributed to improving communication and facilitating daily activities.Brain-Computer Interface(BCI)study particularly emerged from the need to serve people with disabilities such as Amyo...Technology has tremendously contributed to improving communication and facilitating daily activities.Brain-Computer Interface(BCI)study particularly emerged from the need to serve people with disabilities such as Amyotrophic Lateral Sclerosis(ALS).However,with the advancements in cost-effective electronics and computer interface equipment,the BCI study is flourishing,and the exploration of BCI applications for people without disabilities,to enhance normal functioning,is increasing.Particularly,the P300-based spellers are among the most promising applications of the BCI technology.In this context,the region-based paradigm for P300 BCI spellers was introduced in an effort to reduce the crowding effect and adjacency problem that might affect the detection of P300 peak.This study extends this line of research by investigating the effect,in terms of accuracy and usability,of the letters’distribution among the speller’s regions.For this purpose,a clustering algorithm is proposed,and two region-based layouts were generated by redistributing the letters based on their dissimilarity or their similarity.A pilot usability evaluation was also conducted in order to assess the usability of the different layouts in terms of effectiveness,efficiency,and satisfaction.The results indicate that the distribution of the letters has an effect on the classification accuracy as well as the user experience.Particularly,when considering short-term accuracy and cognitive effort,the original region-based layout outperforms other layouts.展开更多
This paper addresses an interesting security problem in wireless ad hoc networks: the dynamic group key agreement key establishment. For secure group communication in an ad hoc network, a group key shared by all group...This paper addresses an interesting security problem in wireless ad hoc networks: the dynamic group key agreement key establishment. For secure group communication in an ad hoc network, a group key shared by all group members is required. This group key should be updated when there are membership changes (when the new member joins or current member leaves) in the group. In this paper, we propose a novel, secure, scalable and efficient region-based group key agreement protocol for ad hoc networks. This is implemented by a two-level structure and a new scheme of group key update. The idea is to divide the group into subgroups, each maintaining its subgroup keys using group elliptic curve diffie-hellman (GECDH) Protocol and links with other subgroups in a tree structure using tree-based group elliptic curve diffie-hellman (TGECDH) protocol. By introducing region-based approach, messages and key updates will be limited within subgroup and outer group;hence computation load is distributed to many hosts. Both theoretical analysis and experimental results show that this Region-based key agreement protocol performs well for the key establishment problem in ad hoc network in terms of memory cost, computation cost and communication cost.展开更多
The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place i...The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.展开更多
To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model（ISRCS） is presented...To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model（ISRCS） is presented. This scheme is region-based and mainly focuses on two issues. Firstly, an appropriate segmentation algorithm is developed to partition an image into some irregular regions and tidy contours, where the crucial regions corresponding to objects are retained and a lot of tiny parts are eliminated. The irregular regions and contours are coded using different methods respectively in the next step. The other issue is the coding method of contours where an efficient and novel chain code is employed. This scheme tries to find a compromise between the quality of reconstructed images and the compression ratio. Some principles and experiments are conducted and the results show its higher performance compared with other compression technologies, in terms of higher quality of reconstructed images, higher compression ratio and less time consuming.展开更多
In this paper, we present a novel region-based active contour model based on global in-tensity fitting energy in a variational level set framework. Meanwhile, an internal energy term is in-troduced, and it forces the ...In this paper, we present a novel region-based active contour model based on global in-tensity fitting energy in a variational level set framework. Meanwhile, an internal energy term is in-troduced, and it forces the level set function to be close to a signed distance function. Image global information utilized efficiently makes the proposed model insensitive to noise, and the introduced penalty term can avoid the costly re-initialization for the evolving level set function, which not only speeds up the contour evolvement, but also improves accuracy of the final contour. Comparisons with other classical region-based models, such as Chan-Vese model and Region-Scalable Fitting (RSF) model, show the advantages of our model in terms of efficiency and accuracy. Moreover, the model is robust to noise.展开更多
Background:Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer.This study was performed to develop an automatic and accurate imaging processing technique sys...Background:Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer.This study was performed to develop an automatic and accurate imaging processing technique system,allowing this system to read computed tomography(CT)images correctly and make diagnosis of pancreatic cancer faster.Methods:The establishment of the artificial intelligence(AI)system for pancreatic cancer diagnosis based on sequential contrastenhanced CT images were composed of two processes:training and verification.During training process,our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set.Additionally,we used VGG16,which was pretrained in ImageNet and contained 13 convolutional layers and three fully connected layers,to initialize the feature extraction network.In the verification experiment,we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolution network(Faster R-CNN)model that had completed training.Totally,1699 images from 100 pancreatic cancer patients were included for clinical verification.Results:A total of 338 patients with pancreatic cancer were included in the study.The clinical characteristics(sex,age,tumor location,differentiation grade,and tumor-node-metastasis stage)between the two training and verification groups were insignificant.The mean average precision was 0.7664,indicating a good training ejffect of the Faster R-CNN.Sequential contrastenhanced CT images of 100 pancreatic cancer patients were used for clinical verification.The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632.It took approximately 0.2 s for the Faster R-CNN AI to automatically process one CT image,which is much faster than the time required for diagnosis by an imaging specialist.Conclusions:Faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of pancreatic cancer.展开更多
Learning an effective object detector with little supervision is an essential but challenging problem in computer vision applications. In this paper, we consider the problem of learning a deep convolutional neural net...Learning an effective object detector with little supervision is an essential but challenging problem in computer vision applications. In this paper, we consider the problem of learning a deep convolutional neural network (CNN) based object detector using weakly-supervised and semi-supervised information in the framework of fast region-based CNN (Fast R-CNN). The target is to obtain an object detector as accurate as the fully-supervised Fast R-CNN, but it requires less image annotation effort. To solve this problem, we use weakly-supervised training images (i.e., only the image-level annotation is given) and a few proportions of fully-supervised training images (i.e., the bounding box level annotation is given), that is a weakly-and semi-supervised (WASS) object detection setting. The proposed solution is termed as WASS R-CNN, in which there are two main components. At first, a weakly-supervised R-CNN is firstly trained;after that semi-supervised data are used for finetuning the weakly-supervised detector. We perform object detection experiments on the PASCAL VOC 2007 dataset. The proposed WASS R-CNN achieves more than 85% of a fully-supervised Fast R-CNN's performance (measured using mean average precision) with only 10%of fully-supervised annotations together with weak supervision for all training images. The results show that the proposed learning framework can significantly reduce the labeling efforts for obtaining reliable object detectors.展开更多
Background:Distinguishing between primary clear cell carcinoma of the liver(PCCCL)and common hepatocellular carcinoma(CHCC)through traditional inspection methods before the operation is difficult.This study aimed to e...Background:Distinguishing between primary clear cell carcinoma of the liver(PCCCL)and common hepatocellular carcinoma(CHCC)through traditional inspection methods before the operation is difficult.This study aimed to establish a Faster region-based convolutional neural network(RCNN)model for the accurate differential diagnosis of PCCCL and CHCC.Methods:In this study,we collected the data of 62 patients with PCCCL and 1079 patients with CHCC in Beijing YouAn Hospital from June 2012 to May 2020.A total of 109 patients with CHCC and 42 patients with PCCCL were randomly divided into the training validation set and the test set in a ratio of 4:1.The Faster RCNN was used for deep learning of patients’data in the training validation set,and established a convolutional neural network model to distinguish PCCCL and CHCC.The accuracy,average precision,and the recall of the model for diagnosing PCCCL and CHCC were used to evaluate the detection performance of the Faster RCNN algorithm.Results:A total of 4392 images of 121 patients(1032 images of 33 patients with PCCCL and 3360 images of 88 patients with CHCC)were uesd in test set for deep learning and establishing the model,and 1072 images of 30 patients(320 images of nine patients with PCCCL and 752 images of 21 patients with CHCC)were used to test the model.The accuracy of the model for accurately diagnosing PCCCL and CHCC was 0.962(95%confidence interval[CI]:0.931-0.992).The average precision of the model for diagnosing PCCCL was 0.908(95%CI:0.823-0.993)and that for diagnosing CHCC was 0.907(95%CI:0.823-0.993).The recall of the model for diagnosing PCCCL was 0.951(95%CI:0.916-0.985)and that for diagnosing CHCC was 0.960(95%CI:0.854-0.962).The time to make a diagnosis using the model took an average of 4 s for each patient.Conclusion:The Faster RCNN model can accurately distinguish PCCCL and CHCC.This model could be important for clinicians to make appropriate treatment plans for patients with PCCCL or CHCC.展开更多
Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several...Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several approaches have been suggested for gait recognition;nevertheless,the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions,clothing changes,walking speed,and varying camera viewpoints.Furthermore,most existing research focuses on single-person gait recognition;however,counting,tracking,detecting,and recognizing individuals in dual-subject settings with occlusions remains a challenging task.Therefore,this research proposed a variant of an automated gait model for occluded dual-subject walk scenarios.More precisely,in the proposed method,we have designed a deep learning(DL)-based dual-subject gait model(DSG)involving three modules.The first module handles silhouette segmentation,localization,and counting(SLC)using Mask-RCNN with MobileNetV2.The next stage uses a Convolutional block attention module(CBAM)-based Siamese network for frame-level tracking with a modified gallery setting.Following the last,gait recognition based on regionbased deep learning is proposed for dual-subject gait recognition.The proposed method,tested on Shri Mata Vaishno Devi University(SMVDU)-Multi-Gait and Single-Gait datasets,shows strong performance with 94.00%segmentation,58.36%tracking,and 63.04%gait recognition accuracy in dual-subject walk scenarios.展开更多
The increasing elderly population has heightened the need for accurate and reliable fall detection systems,as falls can lead to severe health complications.Existing systems often suffer from high false positive and fa...The increasing elderly population has heightened the need for accurate and reliable fall detection systems,as falls can lead to severe health complications.Existing systems often suffer from high false positive and false negative rates due to insufficient training data and suboptimal detection techniques.This study introduces an advanced fall detection model integrating YOLOv8,Faster R-CNN,and Generative Adversarial Networks(GANs)to enhance accuracy and robustness.A modified YOLOv8 architecture serves as the core,utilizing spatial attention mechanisms to improve critical image regions’detection.Faster R-CNN is employed for fine-grained human posture analysis,while GANs generate synthetic fall scenarios to expand and diversify the training dataset.Experimental evaluations on the DiverseFALL10500 and CAUCAFall datasets demonstrate that the proposed model significantly outperforms state-of-the-art methods.The model achieves a mean Average Precision(mAP)of 0.9507 on DiverseFALL10500 and 0.996 on CAUCAFall,surpassing conventional YOLO and R-CNN-based models.Precision and recall metrics also indicate superior detection performance,with a recall of 0.929 on DiverseFALL10500 and 0.9993 on CAUCAFall,ensuring minimal false negatives.Real-time deployment tests on the Xilinx Kria™K26 System-on-Module confirm an average inference time of 43ms per frame,making it suitable for real-time monitoring applications.These results establish the proposed R-CNN_GAN_YOLOv8 model as a benchmark in fall detection,offering a reliable and efficient solution for healthcare applications.By integrating attention mechanisms and GAN-based data augmentation,this approach significantly enhances detection accuracy while reducing false alarms,improving safety for elderly individuals and high-risk environments.展开更多
Soil salinization poses a threat to maize production worldwide,but the genetic mechanism of salt tolerance in maize is not well understood.Therefore,identifying the genetic components underlying salt tolerance in maiz...Soil salinization poses a threat to maize production worldwide,but the genetic mechanism of salt tolerance in maize is not well understood.Therefore,identifying the genetic components underlying salt tolerance in maize is of great importance.In the current study,a teosinte-maize BC2F7 population was used to investigate the genetic basis of 21 salt tolerance-related traits.In total,125 QTLs were detected using a high-density genetic bin map,with one to five QTLs explaining 6.05–32.02%of the phenotypic variation for each trait.The total phenotypic variation explained(PVE)by all detected QTLs ranged from 6.84 to 63.88%for each trait.Of all 125 QTLs,only three were major QTLs distributed in two genomic regions on chromosome 6,which were involved in three salt tolerance-related traits.In addition,10 pairs of epistatic QTLs with additive effects were detected for eight traits,explaining 0.9 to 4.44%of the phenotypic variation.Furthermore,18 QTL hotspots affecting 3–7 traits were identified.In one hotspot(L5),a gene cluster consisting of four genes(ZmNSA1,SAG6,ZmCLCg,and ZmHKT1;2)was found,suggesting the involvement of multiple pleiotropic genes.Finally,two important candidate genes,Zm00001d002090 and Zm00001d002391,were found to be associated with salt tolerance-related traits by a combination of linkage and marker-trait association analyses.Zm00001d002090 encodes a calcium-dependent lipid-binding(CaLB domain)family protein,which may function as a Ca^(2+)sensor for transmitting the salt stress signal downstream,while Zm00001d002391 encodes a ubiquitin-specific protease belonging to the C19-related subfamily.Our findings provide valuable insights into the genetic basis of salt tolerance-related traits in maize and a theoretical foundation for breeders to develop enhanced salt-tolerant maize varieties.展开更多
Alzheimer’s disease(AD)is a neurological disorder that predominantly affects the brain.In the coming years,it is expected to spread rapidly,with limited progress in diagnostic techniques.Various machine learning(ML)a...Alzheimer’s disease(AD)is a neurological disorder that predominantly affects the brain.In the coming years,it is expected to spread rapidly,with limited progress in diagnostic techniques.Various machine learning(ML)and artificial intelligence(AI)algorithms have been employed to detect AD using single-modality data.However,recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD prediction.In this study,we developed a framework that utilizes multimodal data(tabular data,magnetic resonance imaging(MRI)images,and genetic information)to classify AD.As part of the pre-processing phase,we generated a knowledge graph from the tabular data and MRI images.We employed graph neural networks for knowledge graph creation,and region-based convolutional neural network approach for image-to-knowledge graph generation.Additionally,we integrated various explainable AI(XAI)techniques to interpret and elucidate the prediction outcomes derived from multimodal data.Layer-wise relevance propagation was used to explain the layer-wise outcomes in the MRI images.We also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data provided.Genetic expression values play a crucial role in AD analysis.We used a graphical gene tree to identify genes associated with the disease.Moreover,a dashboard was designed to display XAI outcomes,enabling experts and medical professionals to easily comprehend the predic-tion results.展开更多
This paper proposes a solution to localization and classification of rice grains in an image.All existing related works rely on conventional based machine learning approaches.However,those techniques do not do well fo...This paper proposes a solution to localization and classification of rice grains in an image.All existing related works rely on conventional based machine learning approaches.However,those techniques do not do well for the problem designed in this paper,due to the high similarities between different types of rice grains.The deep learning based solution is developed in the proposed solution.It contains pre-processing steps of data annotation using the watershed algorithm,auto-alignment using the major axis orientation,and image enhancement using the contrast-limited adaptive histogram equalization(CLAHE)technique.Then,the mask region-based convolutional neural networks(R-CNN)is trained to localize and classify rice grains in an input image.The performance is enhanced by using the transfer learning and the dropout regularization for overfitting prevention.The proposed method is validated using many scenarios of experiments,reported in the forms of mean average precision(mAP)and a confusion matrix.It achieves above 80%mAP for main scenarios in the experiments.It is also shown to perform outstanding,when compared to human experts.展开更多
In order to solve the problem of small objects detection in unmanned aerial vehicle(UAV)aerial images with complex background,a general detection method for multi-scale small objects based on Faster region-based convo...In order to solve the problem of small objects detection in unmanned aerial vehicle(UAV)aerial images with complex background,a general detection method for multi-scale small objects based on Faster region-based convolutional neural network(Faster R-CNN)is proposed.The bird’s nest on the high-voltage tower is taken as the research object.Firstly,we use the improved convolutional neural network ResNet101 to extract object features,and then use multi-scale sliding windows to obtain the object region proposals on the convolution feature maps with different resolutions.Finally,a deconvolution operation is added to further enhance the selected feature map with higher resolution,and then it taken as a feature mapping layer of the region proposals passing to the object detection sub-network.The detection results of the bird’s nest in UAV aerial images show that the proposed method can precisely detect small objects in aerial images.展开更多
In order to improve the accuracy of threaded hole object detection,combining a dual camera vision system with the Hough transform circle detection,we propose an object detection method of artifact threaded hole based ...In order to improve the accuracy of threaded hole object detection,combining a dual camera vision system with the Hough transform circle detection,we propose an object detection method of artifact threaded hole based on Faster region-ased convolutional neural network(Faster R-CNN).First,a dual camera image acquisition system is established.One industrial camera placed at a high position is responsible for collecting the whole image of the workpiece,and the suspected screw hole position on the workpiece can be preliminarily selected by Hough transform detection algorithm.Then,the other industrial camera is responsible for collecting the local images of the suspected screw holes that have been detected by Hough transform one by one.After that,ResNet50-based Faster R-CNN object detection model is trained on the self-built screw hole data set.Finally,the local image of the threaded hole is input into the trained Faster R-CNN object detection model for further identification and location.The experimental results show that the proposed method can effectively avoid small object detection of threaded holes,and compared with the method that only uses Hough transform or Faster RCNN object detection alone,it has high recognition and positioning accuracy.展开更多
In this paper, we present a novel and efficient scheme for extracting, indexing and retrieving color images. Our motivation was to reduce the space overhead of partition-based approaches taking advantage of the fact t...In this paper, we present a novel and efficient scheme for extracting, indexing and retrieving color images. Our motivation was to reduce the space overhead of partition-based approaches taking advantage of the fact that only a relatively low number of distinct values of a particular visual feature is present in most images. To extract color feature and build indices into our image database we take into consideration factors such as human color perception and perceptual range, and the image is partitioned into a set of regions by using a simple classifying scheme. The compact color feature vector and the spatial color histogram, which are extracted from the seqmented image region, are used for representing the color and spatial information in the image. We have also developed the region-based distance measures to compare the similarity of two images. Extensive tests on a large image collection were conducted to demonstrate the effectiveness of the proposed approach.展开更多
A novel segmentation method for medical image with intensity inhomogeneity is introduced.In the proposed active contour model,both region and gradient information are taken into consideration.The former,i.e.,region-ba...A novel segmentation method for medical image with intensity inhomogeneity is introduced.In the proposed active contour model,both region and gradient information are taken into consideration.The former,i.e.,region-based fitting energy,draws upon the region information and guarantees the accurate extraction of inhomogeneous image's local information.The latter,i.e.,an edge indicator,weights the length penalizing term to consider the gradient constrain.Moreover,signed distance penalizing term is also added to ensure accurate computation and avoid the time-consuming re-initialization of evolving level set function.Experiments for synthetic and real images demonstrate the feasibility and superiority of the proposed model.展开更多
An approach to detection of moving objects in video sequences, with application to video surveillance is presented. The algorithm combines two kinds of change points, which are detected from the region-based frame dif...An approach to detection of moving objects in video sequences, with application to video surveillance is presented. The algorithm combines two kinds of change points, which are detected from the region-based frame difference and adjusted background subtraction. An adaptive threshold technique is employed to automatically choose the threshold value to segment the moving objects from the still background. And experiment results show that the algorithm is effective and efficient in practical situations. Furthermore, the algorithm is robust to the effects of the changing of lighting condition and can be applied for video surveillance system.展开更多
The increasing capabilities of Artificial Intelligence(AI),has led researchers and visionaries to think in the direction of machines outperforming humans by gaining intelligence equal to or greater than humans,which m...The increasing capabilities of Artificial Intelligence(AI),has led researchers and visionaries to think in the direction of machines outperforming humans by gaining intelligence equal to or greater than humans,which may not always have a positive impact on the society.AI gone rogue,and Technological Singularity are major concerns in academia as well as the industry.It is necessary to identify the limitations of machines and analyze their incompetence,which could draw a line between human and machine intelligence.Internet memes are an amalgam of pictures,videos,underlying messages,ideas,sentiments,humor,and experiences,hence the way an internet meme is perceived by a human may not be entirely how a machine comprehends it.In this paper,we present experimental evidence on how comprehending Internet Memes is a challenge for AI.We use a combination of Optical Character Recognition techniques like Tesseract,Pixel Link,and East Detector to extract text from the memes,and machine learning algorithms like Convolutional Neural Networks(CNN),Region-based Convolutional Neural Networks(RCNN),and Transfer Learning with pre-trained denseNet for assessing the textual and facial emotions combined.We evaluate the performance using Sensitivity and Specificity.Our results show that comprehending memes is indeed a challenging task,and hence a major limitation of AI.This research would be of utmost interest to researchers working in the areas of Artificial General Intelligence and Technological Singularity.展开更多
基金This article contains results and findings from a research project that was supported by King Abdulaziz City for Science and Technology,http://www.kacst.edu.sa/,Grant No.827-37-11。
文摘Technology has tremendously contributed to improving communication and facilitating daily activities.Brain-Computer Interface(BCI)study particularly emerged from the need to serve people with disabilities such as Amyotrophic Lateral Sclerosis(ALS).However,with the advancements in cost-effective electronics and computer interface equipment,the BCI study is flourishing,and the exploration of BCI applications for people without disabilities,to enhance normal functioning,is increasing.Particularly,the P300-based spellers are among the most promising applications of the BCI technology.In this context,the region-based paradigm for P300 BCI spellers was introduced in an effort to reduce the crowding effect and adjacency problem that might affect the detection of P300 peak.This study extends this line of research by investigating the effect,in terms of accuracy and usability,of the letters’distribution among the speller’s regions.For this purpose,a clustering algorithm is proposed,and two region-based layouts were generated by redistributing the letters based on their dissimilarity or their similarity.A pilot usability evaluation was also conducted in order to assess the usability of the different layouts in terms of effectiveness,efficiency,and satisfaction.The results indicate that the distribution of the letters has an effect on the classification accuracy as well as the user experience.Particularly,when considering short-term accuracy and cognitive effort,the original region-based layout outperforms other layouts.
文摘This paper addresses an interesting security problem in wireless ad hoc networks: the dynamic group key agreement key establishment. For secure group communication in an ad hoc network, a group key shared by all group members is required. This group key should be updated when there are membership changes (when the new member joins or current member leaves) in the group. In this paper, we propose a novel, secure, scalable and efficient region-based group key agreement protocol for ad hoc networks. This is implemented by a two-level structure and a new scheme of group key update. The idea is to divide the group into subgroups, each maintaining its subgroup keys using group elliptic curve diffie-hellman (GECDH) Protocol and links with other subgroups in a tree structure using tree-based group elliptic curve diffie-hellman (TGECDH) protocol. By introducing region-based approach, messages and key updates will be limited within subgroup and outer group;hence computation load is distributed to many hosts. Both theoretical analysis and experimental results show that this Region-based key agreement protocol performs well for the key establishment problem in ad hoc network in terms of memory cost, computation cost and communication cost.
文摘The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.
基金supported by the National Science Foundation of China(60872109)the Program for New Century Excellent Talents in University(NCET-06-0900)
文摘To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model（ISRCS） is presented. This scheme is region-based and mainly focuses on two issues. Firstly, an appropriate segmentation algorithm is developed to partition an image into some irregular regions and tidy contours, where the crucial regions corresponding to objects are retained and a lot of tiny parts are eliminated. The irregular regions and contours are coded using different methods respectively in the next step. The other issue is the coding method of contours where an efficient and novel chain code is employed. This scheme tries to find a compromise between the quality of reconstructed images and the compression ratio. Some principles and experiments are conducted and the results show its higher performance compared with other compression technologies, in terms of higher quality of reconstructed images, higher compression ratio and less time consuming.
基金Supported by the State Key Program of National Natural Science of China (No. 61003134, 60736008)the National Natural Science Foundation of China (No. 60803082)the Key Program of Natural Science of Beijing (No.4081002)
文摘In this paper, we present a novel region-based active contour model based on global in-tensity fitting energy in a variational level set framework. Meanwhile, an internal energy term is in-troduced, and it forces the level set function to be close to a signed distance function. Image global information utilized efficiently makes the proposed model insensitive to noise, and the introduced penalty term can avoid the costly re-initialization for the evolving level set function, which not only speeds up the contour evolvement, but also improves accuracy of the final contour. Comparisons with other classical region-based models, such as Chan-Vese model and Region-Scalable Fitting (RSF) model, show the advantages of our model in terms of efficiency and accuracy. Moreover, the model is robust to noise.
基金This work was supported by grants from the National Natural Science Foundation of China(No.81802888)the Key Research and Development Project of Shandong Province(No.2018GSF118206 and No.2018GSF118088).
文摘Background:Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer.This study was performed to develop an automatic and accurate imaging processing technique system,allowing this system to read computed tomography(CT)images correctly and make diagnosis of pancreatic cancer faster.Methods:The establishment of the artificial intelligence(AI)system for pancreatic cancer diagnosis based on sequential contrastenhanced CT images were composed of two processes:training and verification.During training process,our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set.Additionally,we used VGG16,which was pretrained in ImageNet and contained 13 convolutional layers and three fully connected layers,to initialize the feature extraction network.In the verification experiment,we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolution network(Faster R-CNN)model that had completed training.Totally,1699 images from 100 pancreatic cancer patients were included for clinical verification.Results:A total of 338 patients with pancreatic cancer were included in the study.The clinical characteristics(sex,age,tumor location,differentiation grade,and tumor-node-metastasis stage)between the two training and verification groups were insignificant.The mean average precision was 0.7664,indicating a good training ejffect of the Faster R-CNN.Sequential contrastenhanced CT images of 100 pancreatic cancer patients were used for clinical verification.The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632.It took approximately 0.2 s for the Faster R-CNN AI to automatically process one CT image,which is much faster than the time required for diagnosis by an imaging specialist.Conclusions:Faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of pancreatic cancer.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.61876212,61733007,and 61572207the National Key Research and Development Program of China under Grant No.2018YFB1402604.
文摘Learning an effective object detector with little supervision is an essential but challenging problem in computer vision applications. In this paper, we consider the problem of learning a deep convolutional neural network (CNN) based object detector using weakly-supervised and semi-supervised information in the framework of fast region-based CNN (Fast R-CNN). The target is to obtain an object detector as accurate as the fully-supervised Fast R-CNN, but it requires less image annotation effort. To solve this problem, we use weakly-supervised training images (i.e., only the image-level annotation is given) and a few proportions of fully-supervised training images (i.e., the bounding box level annotation is given), that is a weakly-and semi-supervised (WASS) object detection setting. The proposed solution is termed as WASS R-CNN, in which there are two main components. At first, a weakly-supervised R-CNN is firstly trained;after that semi-supervised data are used for finetuning the weakly-supervised detector. We perform object detection experiments on the PASCAL VOC 2007 dataset. The proposed WASS R-CNN achieves more than 85% of a fully-supervised Fast R-CNN's performance (measured using mean average precision) with only 10%of fully-supervised annotations together with weak supervision for all training images. The results show that the proposed learning framework can significantly reduce the labeling efforts for obtaining reliable object detectors.
文摘Background:Distinguishing between primary clear cell carcinoma of the liver(PCCCL)and common hepatocellular carcinoma(CHCC)through traditional inspection methods before the operation is difficult.This study aimed to establish a Faster region-based convolutional neural network(RCNN)model for the accurate differential diagnosis of PCCCL and CHCC.Methods:In this study,we collected the data of 62 patients with PCCCL and 1079 patients with CHCC in Beijing YouAn Hospital from June 2012 to May 2020.A total of 109 patients with CHCC and 42 patients with PCCCL were randomly divided into the training validation set and the test set in a ratio of 4:1.The Faster RCNN was used for deep learning of patients’data in the training validation set,and established a convolutional neural network model to distinguish PCCCL and CHCC.The accuracy,average precision,and the recall of the model for diagnosing PCCCL and CHCC were used to evaluate the detection performance of the Faster RCNN algorithm.Results:A total of 4392 images of 121 patients(1032 images of 33 patients with PCCCL and 3360 images of 88 patients with CHCC)were uesd in test set for deep learning and establishing the model,and 1072 images of 30 patients(320 images of nine patients with PCCCL and 752 images of 21 patients with CHCC)were used to test the model.The accuracy of the model for accurately diagnosing PCCCL and CHCC was 0.962(95%confidence interval[CI]:0.931-0.992).The average precision of the model for diagnosing PCCCL was 0.908(95%CI:0.823-0.993)and that for diagnosing CHCC was 0.907(95%CI:0.823-0.993).The recall of the model for diagnosing PCCCL was 0.951(95%CI:0.916-0.985)and that for diagnosing CHCC was 0.960(95%CI:0.854-0.962).The time to make a diagnosis using the model took an average of 4 s for each patient.Conclusion:The Faster RCNN model can accurately distinguish PCCCL and CHCC.This model could be important for clinicians to make appropriate treatment plans for patients with PCCCL or CHCC.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Convergence Security Core Talent Training Business Support Program(IITP-2025-RS-2023-00266605)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several approaches have been suggested for gait recognition;nevertheless,the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions,clothing changes,walking speed,and varying camera viewpoints.Furthermore,most existing research focuses on single-person gait recognition;however,counting,tracking,detecting,and recognizing individuals in dual-subject settings with occlusions remains a challenging task.Therefore,this research proposed a variant of an automated gait model for occluded dual-subject walk scenarios.More precisely,in the proposed method,we have designed a deep learning(DL)-based dual-subject gait model(DSG)involving three modules.The first module handles silhouette segmentation,localization,and counting(SLC)using Mask-RCNN with MobileNetV2.The next stage uses a Convolutional block attention module(CBAM)-based Siamese network for frame-level tracking with a modified gallery setting.Following the last,gait recognition based on regionbased deep learning is proposed for dual-subject gait recognition.The proposed method,tested on Shri Mata Vaishno Devi University(SMVDU)-Multi-Gait and Single-Gait datasets,shows strong performance with 94.00%segmentation,58.36%tracking,and 63.04%gait recognition accuracy in dual-subject walk scenarios.
文摘The increasing elderly population has heightened the need for accurate and reliable fall detection systems,as falls can lead to severe health complications.Existing systems often suffer from high false positive and false negative rates due to insufficient training data and suboptimal detection techniques.This study introduces an advanced fall detection model integrating YOLOv8,Faster R-CNN,and Generative Adversarial Networks(GANs)to enhance accuracy and robustness.A modified YOLOv8 architecture serves as the core,utilizing spatial attention mechanisms to improve critical image regions’detection.Faster R-CNN is employed for fine-grained human posture analysis,while GANs generate synthetic fall scenarios to expand and diversify the training dataset.Experimental evaluations on the DiverseFALL10500 and CAUCAFall datasets demonstrate that the proposed model significantly outperforms state-of-the-art methods.The model achieves a mean Average Precision(mAP)of 0.9507 on DiverseFALL10500 and 0.996 on CAUCAFall,surpassing conventional YOLO and R-CNN-based models.Precision and recall metrics also indicate superior detection performance,with a recall of 0.929 on DiverseFALL10500 and 0.9993 on CAUCAFall,ensuring minimal false negatives.Real-time deployment tests on the Xilinx Kria™K26 System-on-Module confirm an average inference time of 43ms per frame,making it suitable for real-time monitoring applications.These results establish the proposed R-CNN_GAN_YOLOv8 model as a benchmark in fall detection,offering a reliable and efficient solution for healthcare applications.By integrating attention mechanisms and GAN-based data augmentation,this approach significantly enhances detection accuracy while reducing false alarms,improving safety for elderly individuals and high-risk environments.
基金supported by grants from the National Natural Science Foundation of China(32101730)the National Key R&D Program Projects,China(2021YFD1201005)+2 种基金the Beijing Academy of Agriculture and Forestry Sciences(BAAFS)Excellent Scientist Training Program,China(JKZX202202)the BAAFS Science and Technology Innovation Capability Improvement Project,China(KJCX20230433)。
文摘Soil salinization poses a threat to maize production worldwide,but the genetic mechanism of salt tolerance in maize is not well understood.Therefore,identifying the genetic components underlying salt tolerance in maize is of great importance.In the current study,a teosinte-maize BC2F7 population was used to investigate the genetic basis of 21 salt tolerance-related traits.In total,125 QTLs were detected using a high-density genetic bin map,with one to five QTLs explaining 6.05–32.02%of the phenotypic variation for each trait.The total phenotypic variation explained(PVE)by all detected QTLs ranged from 6.84 to 63.88%for each trait.Of all 125 QTLs,only three were major QTLs distributed in two genomic regions on chromosome 6,which were involved in three salt tolerance-related traits.In addition,10 pairs of epistatic QTLs with additive effects were detected for eight traits,explaining 0.9 to 4.44%of the phenotypic variation.Furthermore,18 QTL hotspots affecting 3–7 traits were identified.In one hotspot(L5),a gene cluster consisting of four genes(ZmNSA1,SAG6,ZmCLCg,and ZmHKT1;2)was found,suggesting the involvement of multiple pleiotropic genes.Finally,two important candidate genes,Zm00001d002090 and Zm00001d002391,were found to be associated with salt tolerance-related traits by a combination of linkage and marker-trait association analyses.Zm00001d002090 encodes a calcium-dependent lipid-binding(CaLB domain)family protein,which may function as a Ca^(2+)sensor for transmitting the salt stress signal downstream,while Zm00001d002391 encodes a ubiquitin-specific protease belonging to the C19-related subfamily.Our findings provide valuable insights into the genetic basis of salt tolerance-related traits in maize and a theoretical foundation for breeders to develop enhanced salt-tolerant maize varieties.
文摘Alzheimer’s disease(AD)is a neurological disorder that predominantly affects the brain.In the coming years,it is expected to spread rapidly,with limited progress in diagnostic techniques.Various machine learning(ML)and artificial intelligence(AI)algorithms have been employed to detect AD using single-modality data.However,recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD prediction.In this study,we developed a framework that utilizes multimodal data(tabular data,magnetic resonance imaging(MRI)images,and genetic information)to classify AD.As part of the pre-processing phase,we generated a knowledge graph from the tabular data and MRI images.We employed graph neural networks for knowledge graph creation,and region-based convolutional neural network approach for image-to-knowledge graph generation.Additionally,we integrated various explainable AI(XAI)techniques to interpret and elucidate the prediction outcomes derived from multimodal data.Layer-wise relevance propagation was used to explain the layer-wise outcomes in the MRI images.We also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data provided.Genetic expression values play a crucial role in AD analysis.We used a graphical gene tree to identify genes associated with the disease.Moreover,a dashboard was designed to display XAI outcomes,enabling experts and medical professionals to easily comprehend the predic-tion results.
文摘This paper proposes a solution to localization and classification of rice grains in an image.All existing related works rely on conventional based machine learning approaches.However,those techniques do not do well for the problem designed in this paper,due to the high similarities between different types of rice grains.The deep learning based solution is developed in the proposed solution.It contains pre-processing steps of data annotation using the watershed algorithm,auto-alignment using the major axis orientation,and image enhancement using the contrast-limited adaptive histogram equalization(CLAHE)technique.Then,the mask region-based convolutional neural networks(R-CNN)is trained to localize and classify rice grains in an input image.The performance is enhanced by using the transfer learning and the dropout regularization for overfitting prevention.The proposed method is validated using many scenarios of experiments,reported in the forms of mean average precision(mAP)and a confusion matrix.It achieves above 80%mAP for main scenarios in the experiments.It is also shown to perform outstanding,when compared to human experts.
基金National Defense Pre-research Fund Project(No.KMGY318002531)。
文摘In order to solve the problem of small objects detection in unmanned aerial vehicle(UAV)aerial images with complex background,a general detection method for multi-scale small objects based on Faster region-based convolutional neural network(Faster R-CNN)is proposed.The bird’s nest on the high-voltage tower is taken as the research object.Firstly,we use the improved convolutional neural network ResNet101 to extract object features,and then use multi-scale sliding windows to obtain the object region proposals on the convolution feature maps with different resolutions.Finally,a deconvolution operation is added to further enhance the selected feature map with higher resolution,and then it taken as a feature mapping layer of the region proposals passing to the object detection sub-network.The detection results of the bird’s nest in UAV aerial images show that the proposed method can precisely detect small objects in aerial images.
文摘In order to improve the accuracy of threaded hole object detection,combining a dual camera vision system with the Hough transform circle detection,we propose an object detection method of artifact threaded hole based on Faster region-ased convolutional neural network(Faster R-CNN).First,a dual camera image acquisition system is established.One industrial camera placed at a high position is responsible for collecting the whole image of the workpiece,and the suspected screw hole position on the workpiece can be preliminarily selected by Hough transform detection algorithm.Then,the other industrial camera is responsible for collecting the local images of the suspected screw holes that have been detected by Hough transform one by one.After that,ResNet50-based Faster R-CNN object detection model is trained on the self-built screw hole data set.Finally,the local image of the threaded hole is input into the trained Faster R-CNN object detection model for further identification and location.The experimental results show that the proposed method can effectively avoid small object detection of threaded holes,and compared with the method that only uses Hough transform or Faster RCNN object detection alone,it has high recognition and positioning accuracy.
文摘In this paper, we present a novel and efficient scheme for extracting, indexing and retrieving color images. Our motivation was to reduce the space overhead of partition-based approaches taking advantage of the fact that only a relatively low number of distinct values of a particular visual feature is present in most images. To extract color feature and build indices into our image database we take into consideration factors such as human color perception and perceptual range, and the image is partitioned into a set of regions by using a simple classifying scheme. The compact color feature vector and the spatial color histogram, which are extracted from the seqmented image region, are used for representing the color and spatial information in the image. We have also developed the region-based distance measures to compare the similarity of two images. Extensive tests on a large image collection were conducted to demonstrate the effectiveness of the proposed approach.
基金the National Natural Science Foundation of China (Nos.30801302 and 30872906)the "Medical and Engineering Crossing" Foundation of Shanghai Jiaotong University (No.40YG2009ZD102)
文摘A novel segmentation method for medical image with intensity inhomogeneity is introduced.In the proposed active contour model,both region and gradient information are taken into consideration.The former,i.e.,region-based fitting energy,draws upon the region information and guarantees the accurate extraction of inhomogeneous image's local information.The latter,i.e.,an edge indicator,weights the length penalizing term to consider the gradient constrain.Moreover,signed distance penalizing term is also added to ensure accurate computation and avoid the time-consuming re-initialization of evolving level set function.Experiments for synthetic and real images demonstrate the feasibility and superiority of the proposed model.
文摘An approach to detection of moving objects in video sequences, with application to video surveillance is presented. The algorithm combines two kinds of change points, which are detected from the region-based frame difference and adjusted background subtraction. An adaptive threshold technique is employed to automatically choose the threshold value to segment the moving objects from the still background. And experiment results show that the algorithm is effective and efficient in practical situations. Furthermore, the algorithm is robust to the effects of the changing of lighting condition and can be applied for video surveillance system.
文摘The increasing capabilities of Artificial Intelligence(AI),has led researchers and visionaries to think in the direction of machines outperforming humans by gaining intelligence equal to or greater than humans,which may not always have a positive impact on the society.AI gone rogue,and Technological Singularity are major concerns in academia as well as the industry.It is necessary to identify the limitations of machines and analyze their incompetence,which could draw a line between human and machine intelligence.Internet memes are an amalgam of pictures,videos,underlying messages,ideas,sentiments,humor,and experiences,hence the way an internet meme is perceived by a human may not be entirely how a machine comprehends it.In this paper,we present experimental evidence on how comprehending Internet Memes is a challenge for AI.We use a combination of Optical Character Recognition techniques like Tesseract,Pixel Link,and East Detector to extract text from the memes,and machine learning algorithms like Convolutional Neural Networks(CNN),Region-based Convolutional Neural Networks(RCNN),and Transfer Learning with pre-trained denseNet for assessing the textual and facial emotions combined.We evaluate the performance using Sensitivity and Specificity.Our results show that comprehending memes is indeed a challenging task,and hence a major limitation of AI.This research would be of utmost interest to researchers working in the areas of Artificial General Intelligence and Technological Singularity.
