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1.
Elif Nur Haner Kırğıl;Çağatay Berke Erdaş; 《International journal of imaging systems and technology》2024,34(4):e23148
Skin cancer occurs when abnormal cells in the top layer of the skin, known as the epidermis, undergo uncontrolled growth due to unrepaired DNA damage, leading to the development of mutations. These mutations lead to rapid cell growth and development of cancerous tumors. The type of cancerous tumor depends on the cells of origin. Overexposure to ultraviolet rays from the sun, tanning beds, or sunlamps is a primary factor in the occurrence of skin cancer. Since skin cancer is one of the most common types of cancer and has a high mortality, early diagnosis is extremely important. The dermatology literature has many studies of computer-aided diagnosis for early and highly accurate skin cancer detection. In this study, the classification of skin cancer was provided by Regnet x006, EfficientNetv2 B0, and InceptionResnetv2 deep learning methods. To increase the classification performance, hairs and black pixels in the corners due to the nature of dermoscopic images, which could create noise for deep learning, were eliminated in the preprocessing step. Preprocessing was done by hair removal, cropping, segmentation, and applying a median filter to dermoscopic images. To measure the performance of the proposed preprocessing technique, the results were obtained with both raw images and preprocessed images. The model developed to provide a solution to the classification problem is based on deep learning architectures. In the four experiments carried out within the scope of the study, classification was made for the eight classes in the dataset, squamous cell carcinoma and basal cell carcinoma classification, benign keratosis and actinic keratosis classification, and finally benign and malignant disease classification. According to the results obtained, the best accuracy values of the experiments were obtained as 0.858, 0.929, 0.917, and 0.906, respectively. The study underscores the significance of early and accurate diagnosis in addressing skin cancer, a prevalent and potentially fatal condition. The primary aim of the preprocessing procedures was to attain enhanced performance results by concentrating solely on the area spanning the lesion instead of analyzing the complete image. Combining the suggested preprocessing strategy with deep learning techniques shows potential for enhancing skin cancer diagnosis, particularly in terms of sensitivity and specificity. 相似文献
2.
As the amount of online video content is increasing, consumers are becoming increasingly interested in various product names appearing in videos, particularly in cosmetic-product names in videos related to fashion, beauty, and style. Thus, the identification of such products by using image recognition technology may aid in the identification of current commercial trends. In this paper, we propose a two-stage deep-learning detection and classification method for cosmetic products. Specifically, variants of the YOLO network are used for detection, where the bounding box for each given input product is predicted and subsequently cropped for classification. We use four state-of-the-art classification networks, namely ResNet, InceptionResNetV2, DenseNet, and EfficientNet, and compare their performance. Furthermore, we employ dilated convolution in these networks to obtain better feature representations and improve performance. Extensive experiments demonstrate that YOLOv3 and its tiny version achieve higher speed and accuracy. Moreover, the dilated networks marginally outperform the base models, or achieve similar performance in the worst case. We conclude that the proposed method can effectively detect and classify cosmetic products. 相似文献
3.
Active learning has been widely utilized to reduce the labeling cost of supervised learning. By selecting specific instances to train the model, the performance of the model was improved within limited steps. However, rare work paid attention to the effectiveness of active learning on it. In this paper, we proposed a deep active learning model with bidirectional encoder representations from transformers (BERT) for text classification. BERT takes advantage of the self-attention mechanism to integrate contextual information, which is beneficial to accelerate the convergence of training. As for the process of active learning, we design an instance selection strategy based on posterior probabilities Margin, Intra-correlation and Inter-correlation (MII). Selected instances are characterized by small margin, low intra-cohesion and high inter-cohesion. We conduct extensive experiments and analytics with our methods. The effect of learner is compared while the effect of sampling strategy and text classification is assessed from three real datasets. The results show that our method outperforms the baselines in terms of accuracy. 相似文献
4.
目的 为实现虚拟人物形象与用户情感偏好的匹配,提高虚拟人物形象设计效率,提出基于深度学习方法的虚拟人物图像生成方法。方法 首先收集相关风格图片并制作数据集,用于初始模型的微调;其次确定用户对虚拟形象的情感偏好与细节要求,得到生成所需的文本提示词;再次通过文本生成的方式迭代生成目标形象并初步筛选符合要求的形象;最后通过图像生成对形象进行局部优化和调整,使其符合用户情感目标。结果 以虚拟人物形象为研究对象,运用基于Stable diffusion模型的文本和图像两种生成方法得到符合用户情感需求与偏好的虚拟人物形象产品,并可快速调整人物形象的细节特征。结论 通过实例验证,所提方法可快速、高质量生成符合用户情感目标的虚拟人物形象,极大地提高虚拟人物形象设计的效率,丰富设计方法。 相似文献
5.
目的 交通标志识别作为智能驾驶、交通系统研究中的一项重要内容,具有较大的理论价值和应用前景.尤其是文本型交通标志,其含有丰富的高层语义信息,能够提供极其丰富的道路信息.因此通过设计并实现一套新的端到端交通标志文本识别系统,达到有效缓解交通拥堵、提高道路安全的目的.方法 系统主要包括文本区域检测和文字识别两个视觉任务,并基于卷积神经网络的深度学习技术实现.首先以ResNet-50为骨干网络提取特征,并采用类FPN结构进行多层特征融合,将融合后的特征作为文本检测和识别的共享特征.文本检测定位文本区域并输出候选文本框的坐标,文字识别输出词条对应的文本字符串.结果 通过实验验证,系统在Traffic Guide Panel Dataset上取得了令人满意的结果,行识别准确率为71.08％.结论 端到端交通标志文本识别非常具有现实意义.通过卷积神经网络的深度学习技术,提出了一套端到端交通标志文本识别系统,并在开源的Traffic Guide Panel Dataset上证明了该系统的优越性. 相似文献
6.
Rahul Sharma Amar Singh Kavita N. Z. Jhanjhi Mehedi Masud Emad Sami Jaha Sahil Verma 《计算机、材料和连续体（英文）》2022,71(2):2125-2140
Indian agriculture is striving to achieve sustainable intensification, the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem. Modern farming employs technology to improve productivity. Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity. Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost, approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert's opinion. Deep learning-based computer vision techniques like Convolutional Neural Network (CNN) and traditional machine learning-based image classification approaches are being applied to identify plant diseases. In this paper, the CNN model is proposed for the classification of rice and potato plant leaf diseases. Rice leaves are diagnosed with bacterial blight, blast, brown spot and tungro diseases. Potato leaf images are classified into three classes: healthy leaves, early blight and late blight diseases. Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study. The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58% accuracy and potato leaves with 97.66% accuracy. The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree and Random Forest. 相似文献
7.
Badriyya B. Al-onazi Saud S. Alotaib Saeed Masoud Alshahrani Najm Alotaibi Mrim M. Alnfiai Ahmed S. Salama Manar Ahmed Hamza 《计算机、材料和连续体（英文）》2023,74(3):5447-5465
The text classification process has been extensively investigated in various languages, especially English. Text classification models are vital in several Natural Language Processing (NLP) applications. The Arabic language has a lot of significance. For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations. However, there are few studies on the text classification process in Arabic. A few text classification studies have been published earlier in the Arabic language. In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality of the features. In this study, an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning (AATC-HTHDL) model is proposed. The major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic text. The first step in the proposed model is to pre-process the input data to transform it into a useful format. The Term Frequency-Inverse Document Frequency (TF-IDF) model is applied to extract the feature vectors. Next, the Convolutional Neural Network with Recurrent Neural Network (CRNN) model is utilized to classify the Arabic text. In the final stage, the Crow Search Algorithm (CSA) is applied to fine-tune the CRNN model’s hyperparameters, showing the work’s novelty. The proposed AATC-HTHDL model was experimentally validated under different parameters and the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches. 相似文献
8.
Uma Yadav Ashish K. Sharma 《International journal of imaging systems and technology》2023,33(1):108-122
Depression is one of the most common mental illnesses, impacting billions of people worldwide. The lack of existing resources is impeding the country's economic prosperity. As a result, new approaches for detecting and treating mental diseases as well as reaching out to individuals are required so that people can overcome their daily challenges and become more productive. An automated depression detection system can greatly aid in clinical findings and early treatment of depression. Automatic detection, like in a clinical interview can be derived from various modalities that include video, audio, and text. Among these modalities, audio characteristics are the most commonly researched while text elements are seldom investigated. In the light of the above, this paper proposes a novel automated depression identification approach based on linguistic material gathered from patient interviews. The focus is to enhance both the accuracy and efficiency of detection. The proposed approach is made up of two parts: a Bidirectional Gated Recurrent Unit (BGRU) network for dealing with linguistic information and a fully coupled network that integrates the model outputs to measure the depressed state. The proposed approach is validated using Distress Analysis Interview Corpus-Wizard-of-Oz interviews dataset. To evaluate the performance precision, recall, and F1 score are computed using the proposed approach and then the comparative analysis is done with the existing approaches. The proposed approach yielded an F1 score of 0.92, indicating the existence of depression as well as the projected severity level. It is realized from the generated results that the proposed approach has outperformed the previous ones. The proposed approach can not only automatically assess the severity of depression but also enhances both the accuracy and efficiency of detection. The proposed approach indicates the feasibility of BGRU over Long Short Term Memory by achieving exceptional results for recognition of depression. 相似文献
9.
Ellák Somfai Benjámin Baffy Kristian Fenech Rita Hosszú Dorina Korózs Marcell Pólik Miklós Sárdy András Lőrincz 《International journal of imaging systems and technology》2023,33(2):556-571
Dataset dependence affects many real-life applications of machine learning: the performance of a model trained on a dataset is significantly worse on samples from another dataset than on new, unseen samples from the original one. This issue is particularly acute for small and somewhat specific databases in medical applications; the automated recognition of melanoma from skin lesion images is a prime example. We document dataset dependence in dermoscopic skin lesion image classification using three publicly available medium size datasets. Standard machine learning techniques aimed at improving the predictive power of a model might enhance performance slightly, but the gain is small, the dataset dependence is not reduced, and the best combination depends on model details. We demonstrate that simple differences in image statistics account for only 5% of the dataset dependence. We suggest a solution with two essential ingredients: using an ensemble of heterogeneous models, and training on a heterogeneous dataset. Our ensemble consists of 29 convolutional networks, some of which are trained on features considered important by dermatologists; the networks' output is fused by a trained committee machine. The combined International Skin Imaging Collaboration dataset is suitable for training, as it is multi-source, produced by a collaboration of a number of clinics over the world. Building on the strengths of the ensemble, it is applied to a related problem as well: recognizing melanoma based on clinical (non-dermoscopic) images. This is a harder problem as both the image quality is lower than those of the dermoscopic ones and the available public datasets are smaller and scarcer. We explored various training strategies and showed that 79% balanced accuracy can be achieved for binary classification averaged over three clinical datasets. 相似文献
10.
Gestational Diabetes Mellitus (GDM) is one of the commonly occurring diseases among women during pregnancy. Oral Glucose Tolerance Test (OGTT) is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective. So, there is a need to design an effective and automated GDM diagnosis and classification model. The recent developments in the field of Deep Learning (DL) are useful in diagnosing different diseases. In this view, the current research article presents a new outlier detection with deep-stacked Autoencoder (OD-DSAE) model for GDM diagnosis and classification. The goal of the proposed OD-DSAE model is to find out those mothers with high risks and make them undergo earlier diagnosis, monitoring, and treatment compared to low-risk women. The presented OD-DSAE model involves three major processes namely, preprocessing, outlier detection, and classification. In the first step i.e., data preprocessing, there exists three stages namely, format conversion, class labelling, and missing value replacement using k-nearest neighbors (KNN) model. Outliers are superior values which considerably varies from other data observations. So, it might represent the variability in measurement, experimental errors or novelty too. So, Hierarchical Clustering (HC)-based outlier detection technique is incorporated in OD-DSAE model, and thereby classification performance can be improved. The proposed model was simulated using Python 3.6.5 on a dataset collected by the researcher themselves. A series of experiments was conducted and the results were investigated under different aspects. The experimental outcomes inferred that the OD-DSAE model has outperformed the compared methods and achieved high precision of 96.17%, recall of 98.69%, specificity of 89.50%, accuracy of 96.18%, and F-score of 97.41%. 相似文献
11.
Şevket Ay;Ekin Ekinci;Zeynep Garip; 《International journal of imaging systems and technology》2024,34(1):e23018
The healthcare industry has found it challenging to build a powerful global classification model due to the scarcity and diversity of medical data. The leading cause is privacy, which restricts data sharing among healthcare providers. Federated learning (FL) can contribute to developing classification models by protecting data privacy. This study has tested various federated techniques in a peer-to-peer setting to classify brain Magnetic Resonance Images (MRI). The authors propose various aggregation strategies for FL, including Federated Averaging (FedAvg), Quantum FL with FedAVG (QFedAvg) and Fault Tolerant FedAvg (Ft-FedAvg) and FedAvg with differential privacy (Dp-FedAvg). In each approach, a custom Convolutional Neural Network (CNN) model is applied to compute locally run nodes with different parts of the same brain MRI dataset for 10, 20 and 30 training and test rounds. A central server and CNN-based three federated clients are included in the FL-based brain tumour classification model to exchange data and combine the model weights on the server, which are sent from local devices to the server. The superiority of the performance of the proposed model is demonstrated by comparing it with traditional methods on various performance metrics. Experimental results show that in brain MRI dataset classification using FL approaches, FedAVg showed the best performance with 85.55% and 84.60% success for 10 and 20 rounds, respectively, while Ft-FedAvg showed the best performance with 85.80% success for 30 rounds for test set. Statistical results obtained from FL approaches showed that FedAvg and Ft-FedAvg have superior performance with regard to accuracy and robustness in comparison with the others. 相似文献
12.
Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis. The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease. This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification (GOFED-RBVSC) model. The proposed GOFED-RBVSC model initially employs contrast enhancement process. Besides, GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions. The ORB (Oriented FAST and Rotated BRIEF) feature extractor is exploited to generate feature vectors. Finally, Improved Conditional Variational Auto Encoder (ICAVE) is utilized for retinal image classification, shows the novelty of the work. The performance validation of the GOFED-RBVSC model is tested using benchmark dataset, and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches. 相似文献
13.
Shabir Hussain Muhammad Ayoub Yang Yu Junaid Abdul Wahid Akmal Khan Dietmar P. F. Moller Hou Weiyan 《计算机、材料和连续体（英文）》2023,75(3):5355-5377
As the COVID-19 pandemic swept the globe, social media platforms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the proposed aspect2class annotation algorithm, we labeled bulk unlabeled tweets according to their contained aspect terms. However, we also recognized the challenges of reducing data’s high dimensionality and sparsity to improve performance and annotation on unlabeled datasets. To address this issue, we proposed the Volatile Stopwords Filtering (VSF) technique to reduce sparsity and enhance classifier performance. The resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21% when using the random forest as a meta-classifier. Through testing on three benchmark datasets, we found that the SABAC ensemble framework performed exceptionally well. Our findings showed that international students during the pandemic faced various issues, including stress, uncertainty, health concerns, financial stress, and difficulties with online classes and returning to school. By analyzing and summarizing these annotated tweets, decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic. 相似文献
14.
传统的语音情感识别方式采用的语音特征具有数据量大且无关特征多的特点,因此选择出与情感相关的语音特征具有重要意义。通过提出将注意力机制结合长短时记忆网络(Long Short Term Memory, LSTM),根据注意力权重进行特征选择,在两个数据集上进行了实验。结果发现:(1)基于注意力机制的LSTM相比于单独的LSTM模型,识别率提高了5.4%,可见此算法有效提高了模型的识别效果;(2)注意力机制是一种有效的特征选择方法。采用注意力机制选择出了具有实际物理意义的声学特征子集,此特征集相比于原有公用特征集在降低了维数的情况下,提高了识别准确率;(3)根据选择结果对声学特征进行分析,发现有声片段长度特征、无声片段长度特征、梅尔倒谱系数(Mel-Frequency Cepstral Coefficient, MFCC)、F0基频等特征与情感识别具有较大相关性。 相似文献
15.
Fast recognition of elevator buttons is a key step for service robots to ride elevators automatically. Although there are some studies in this field, none of them can achieve real-time application due to problems such as recognition speed and algorithm complexity. Elevator button recognition is a comprehensive problem. Not only does it need to detect the position of multiple buttons at the same time, but also needs to accurately identify the characters on each button. The latest version 5 of you only look once algorithm (YOLOv5) has the fastest reasoning speed and can be used for detecting multiple objects in real-time. The advantages of YOLOv5 make it an ideal choice for detecting the position of multiple buttons in an elevator, but it’s not good at specific word recognition. Optical character recognition (OCR) is a well-known technique for character recognition. This paper innovatively improved the YOLOv5 network, integrated OCR technology, and applied them to the elevator button recognition process. First, we changed the detection scale in the YOLOv5 network and only maintained the detection scales of 40 * 40 and 80 * 80, thus improving the overall object detection speed. Then, we put a modified OCR branch after the YOLOv5 network to identify the numbers on the buttons. Finally, we verified this method on different datasets and compared it with other typical methods. The results show that the average recall and precision of this method are 81.2% and 92.4%. Compared with others, the accuracy of this method has reached a very high level, but the recognition speed has reached 0.056 s, which is far higher than other methods. 相似文献
16.
目的 针对人工分拣组成的零件包装盒常常会出现缺少部分零件的问题,开发一套集训练、识别、分选于一体的智能分拣系统.方法 在设计过程中,提出一种基于深度学习的改进Yolov3算法,针对工业现场光照、业零件形状和质地等实际因素,对Yolo算法的训练和检测进行改进,通过对包装盒产品的一次拍摄,检测出画面中出现的预设物体,并与标准设置相比对,从而判断出该盒内产品是否有缺料、多料的情况,以此分选出合格与否的包装盒.结果 在物体摆放相互重叠不超过20％的情况下,物体检测的准确率为98.2％,召回率为99.5％.结论 通过文中提出的改进算法,设计的检测系统能够在复杂的工业现场环境下正常工作,并能对包装的完整性进行准确的检测. 相似文献
17.
Pallabi Sharma Dipankar Das Anmol Gautam Bunil Kumar Balabantaray 《International journal of imaging systems and technology》2023,33(2):495-510
The traditional process of disease diagnosis from medical images follows a manual process, which is tedious and arduous. A computer-aided diagnosis (CADs) system can work as an assistive tool to improve the diagnosis process. In this pursuit, this article introduces a unique architecture LPNet for classifying colon polyps from the colonoscopy video frames. Colon polyps are abnormal growth of cells in the colon wall. Over time, untreated colon polyps may cause colorectal cancer. Different convolutional neural networks (CNNs) based systems have been developed in recent years. However, CNN uses pooling to reduce the number of parameters and expand the receptive field. On the other hand, pooling results in data loss and is deleterious to subsequent processes. Pooling strategies based on discrete wavelet operations have been proposed in our architecture as a solution to this problem, with the promise of achieving a better trade-off between receptive field size and computing efficiency. The overall performance of this model is superior to the others, according to experimental results on a colonoscopy dataset. LPNet with bio-orthogonal wavelet achieved the highest performance with an accuracy of 93.55%. It outperforms the other state-of-the-art (SOTA) CNN models for the polyps classification task, and it is lightweight in terms of the number of learnable parameters compared with them, making the model easily deployable in edge devices. 相似文献
18.
The exponential increase in data over the past few years, particularly in images, has led to more complex content since visual representation became the new norm. E-commerce and similar platforms maintain large image catalogues of their products. In image databases, searching and retrieving similar images is still a challenge, even though several image retrieval techniques have been proposed over the decade. Most of these techniques work well when querying general image databases. However, they often fail in domain-specific image databases, especially for datasets with low intraclass variance. This paper proposes a domain-specific image similarity search engine based on a fused deep learning network. The network is comprised of an improved object localization module, a classification module to narrow down search options and finally a feature extraction and similarity calculation module. The network features both an offline stage for indexing the dataset and an online stage for querying. The dataset used to evaluate the performance of the proposed network is a custom domain-specific dataset related to cosmetics packaging gathered from various online platforms. The proposed method addresses the intraclass variance problem with more precise object localization and the introduction of top result reranking based on object contours. Finally, quantitative and qualitative experiment results are presented, showing improved image similarity search performance. 相似文献
19.
Awais Khan Muhammad Attique Khan Muhammad Younus Javed Majed Alhaisoni Usman Tariq Seifedine Kadry Jung-In Choi Yunyoung Nam 《计算机、材料和连续体（英文）》2022,70(2):2113-2130
Human gait recognition (HGR) has received a lot of attention in the last decade as an alternative biometric technique. The main challenges in gait recognition are the change in in-person view angle and covariant factors. The major covariant factors are walking while carrying a bag and walking while wearing a coat. Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are presented in the literature. The requirement of an efficient framework is always required for correct and quick gait recognition. We proposed a fully automated deep learning and improved ant colony optimization (IACO) framework for HGR using video sequences in this work. The proposed framework consists of four primary steps. In the first step, the database is normalized in a video frame. In the second step, two pre-trained models named ResNet101 and InceptionV3 are selected and modified according to the dataset's nature. After that, we trained both modified models using transfer learning and extracted the features. The IACO algorithm is used to improve the extracted features. IACO is used to select the best features, which are then passed to the Cubic SVM for final classification. The cubic SVM employs a multiclass method. The experiment was carried out on three angles (0, 18, and 180) of the CASIA B dataset, and the accuracy was 95.2, 93.9, and 98.2 percent, respectively. A comparison with existing techniques is also performed, and the proposed method outperforms in terms of accuracy and computational time. 相似文献
20.
Sitara Afzal Muazzam Maqsood Irfan Mehmood Muhammad Tabish Niaz Sanghyun Seo 《计算机、材料和连续体（英文）》2021,66(3):2301-2315
Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer’s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’s perspective for this research area. In this paper, an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images (SWI). The proposed framework consists of four main phases (i) making clusters of brain Magnetic Resonance Imaging (MRI) using k-mean classifier (ii) reduce false positives for better classification results (iii) discriminative feature extraction specific to CMBs (iv) classification using a five layers convolutional neural network (CNN). The proposed method is evaluated on a public dataset available for 20 subjects. The proposed system shows an accuracy of 98.9% and a 1.1% false-positive rate value. The results show the superiority of the proposed work as compared to existing states of the art methods. 相似文献