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Öğe Attention-based end-to-end CNN framework for content-based X-ray image retrieval(Tubitak Scientific & Technical Research Council Turkey, 2021) Öztürk, Şaban; Alhudhaif, Adi; Polat, KemalThe widespread use of medical imaging devices allows deep analysis of diseases. However, the task of examining medical images increases the burden of specialist doctors. Computer-assisted systems provide an effective management tool that enables these images to be analyzed automatically. Although these tools are used for various purposes, today, they are moving towards retrieval systems to access increasing data quickly. In hospitals, the need for content-based image retrieval systems is seriously evident in order to store all images effectively and access them quickly when necessary. In this study, an attention-based end-to-end convolutional neural network (CNN)framework that can provide effective access to similar images from a large X-ray dataset is presented. In the first part of the proposed framework, a fully convolutional network architecture with attention structures is presented. This section contains several layers for determining the saliency points of X-ray images. In the second part of the framework, the modified image with X-ray saliency map is converted to representative codes in Euclidean space by the ResNet-18 architecture. Finally, hash codes are obtained by transforming these codes into hamming spaces. The proposed study is superior in terms of high performance and customized layers compared to current state-of-the-art X-ray image retrieval methods in the literature. Extensive experimental studies reveal that the proposed framework can increase the current precision performance by up to 13Öğe Automated COVID-19 detection in chest X-ray images usingfine-tuned deep learning architectures(Wiley, 2022) Aggarwal, Sonam; Gupta, Sheifali; Alhudhaif, Adi; Koundal, Deepika; Gupta, Rupesh; Polat, KemalThe COVID-19 pandemic has a significant impact on human health globally. The illness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breathlessness, fever, and other symptoms. Although, the reverse transcription polymerase-chain reaction (RT-PCR) test has been used for COVID-19 testing its reliability is very low. Therefore, computed tomography and X-ray images have been widely used. Artificial intelligence coupled with X-ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine-tuned deep learning architectures has been made to speed up the detection and classification of COVID-19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine-tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset-1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset-2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumonia and viral pneumonia. The research was conducted on 959 X-ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%.Öğe Automatic arrhythmia detection based on the probabilistic neural network with FPGA implementation(Hindawi Ltd, 2022) Srivastava, Rohini; Kumar, Basant; Alenezi, Fayadh; Alhudhaif, Adi; Althubiti, Sara A.; Polat, KemalThis paper presents a prototype implementation of arrhythmia classification using Probabilistic neural network (PNN). Arrhythmia is an irregular heartbeat, resulting in severe heart problems if not diagnosed early. Therefore, accurate and robust arrhythmia classification is a vital task for cardiac patients. The classification of ECG has been performed using PNN into eight ECG classes using a unique combination of six ECG features: heart rate, spectral entropy, and 4th order of autoregressive coefficients. In addition, FPGA implementation has been proposed to prototype the complete system of arrhythmia classification. Artix-7 board has been used for the FPGA implementation for easy and fast execution of the proposed arrhythmia classification. As a result, the average accuracy for ECG classification is found to be 98.27%, and the time consumed in the classification is found to be 17 seconds.Öğe Brain tumor detection with multi-scale fractal feature network and fractal residual learning(Elsevier, 2024) Jakhar, Shyo Prakash; Nandal, Amita; Dhaka, Arvind; Alhudhaif, Adi; Polat, KemalDeep learning has enabled the creation of several approaches for segmenting brain tumors using convolutional neural networks. These methods have come about as a direct result of the advancement of the field of machine learning. The proposed pixel-level segmentation is based on fractal residual deep learning; provide an insufficient degree of sensitivity when used for tumor segmentation. This is achieved due to fractal feature extraction and multi-scale approach used for segmentation. If multi-level segmentation is used, it is possible to effectively increase the sensitivity of the segmentation process which is the additional benefit from the proposed method. In this work, the production of tumor region is based on multi-scale pixel segmentation. This approach protects the integrity of tumor information while simultaneously improving the detection accuracy by cutting down on the total number of tumor regions. When compared to the information about the brain found in tumor locations, the proposed strategy has the potential to enhance the percentage of brain tumor information. This work proposes a novel network structure known as the Mutli-scale fractal feature network (MFFN) to increase the accuracy of the network's classification as well as its sensitivity when it comes to the segmentation of brain tumors. The proposed method with overall feature results in 94.66% accuracy, 94.42% sensitivity and 92.81% specificity using 5fold cross validation. In this paper the Cancer Imaging Archive (TCIA) dataset has been used in order to evaluate performance evaluation metrics and segmentation results to quantify the superiority of proposed brain tumor detection approach in comparison to existing methods.Öğe Classification of imbalanced hyperspectral images using SMOTE-based deep learning methods(PERGAMON-ELSEVIER SCIENCE LTD, 2021) Özdemir, Akın; Polat, Kemal; Alhudhaif, AdiHyperspectral imaging (HSI) is one of the most advanced methods of digital imaging. This technique differs from RGB images with its wide range of the electromagnetic spectrum. Imbalanced data sets are frequently encountered in machine learning. As a result, the classifier performance may be poor. To avoid this problem, the data set must be balanced. The main motivation in this study is to reveal the difference and effects on the classifier performance between the original imbalanced dataset and the data set modified by balancing methods. In the proposed method, hyperspectral image classification study carried out on Xuzhou Hyspex dataset includes nineclasses including bareland-1, bareland-2, crops-1, crops-2, lake, coals, cement, trees, house-roofs of elements, by using the convolutional neural networks (CNN) and dataset balancing methods comprising the Smote, Adasyn, KMeans, and Cluster. This dataset has been taken from IEEE-Dataport Machine Learning Repository. To classify the hyperspectral image, the convolutional neural networks having different multiclass classification approaches like One-vs-All, One-vs-One. Dataset was splitted in two different ways: %50-%50 Hold-out and 5-Fold Crossvalidation. In order to evaluate the performance of the proposed models, the confusion matrix, classification accuracy, precision, recall, and F-Measure have been used. Without the dataset balancing, the obtained classification accuracies are 93.63%, 92.33%, 88.36% for %50-%50 train-test split, and 94.46%, 94%, 92.24% for 5Fold cross-validation using multi-class classification, One-vs-All, and One-vs-One respectively. After Smote balancing, the obtained classification accuracies are 96.41%, 95.6%, 92.53% for %50-%50 train-test split and 96.49%, 95.64%, 93.38% for 5-Fold cross-validation using multi-class classification, One-vs-All and One-vs-One respectively. After Adasyn balancing, the obtained classification accuracies are 95.86%, 93.62%, 87.05% for % 50-%50 train-test split and 96.38%, 95.09%, 91.55% for 5-Fold cross-validation using multi-class classification, One-vs-All and One-vs-One respectively. After K-Means balancing, the obtained classification accuracies are 95.23%, 93.36%, 90.6% for %50-%50 train-test split and 95.74%, 94.72%, 91.94% for 5-Fold cross-validation using multi-class classification, One-vs-All and One-vs-One respectively. After Cluster balancing, the obtained classification accuracies are 94.83%, 94.1%, 90.07% for %50-%50 train-test split and 96.28%, 95.88%, 92.5% for 5-Fold cross-validation using multi-class classification, One-vs-All and One-vs-One respectively. The obtained results have shown that the best model is Smote Balanced 5-CV multiclass classification.Öğe Common-Unique Decomposition Driven Diffusion Model for Contrast-Enhanced Liver MR Images Multi-Phase Interconversion(Institute of Electrical and Electronics Engineers Inc., 2024) Xu, Chenchu; Tian, Shijie; Wang, Boyan; Zhang, Jie; Polat, Kemal; Alhudhaif, Adi; Li, ShuoAll three contrast-enhanced (CE) phases (e.g., Arterial, Portal Venous, and Delay) are crucial for diagnosing liver tumors. However, acquiring all three phases is constrained due to contrast agents (CAs) risks, long imaging time, and strict imaging criteria. In this paper, we propose a novel Common-Unique Decomposition Driven Diffusion Model (CUDD-DM), capable of converting any two input phases in three phases into the remaining one, thereby reducing patient wait time, conserving medical resources, and reducing the use of CAs. 1) The Common-Unique Feature Decomposition Module, by utilizing spectral decomposition to capture both common and unique features among different inputs, not only learns correlations in highly similar areas between two input phases but also learns differences in different areas, thereby laying a foundation for the synthesis of remaining phase. 2) The Multi-scale Temporal Reset Gates Module, by bidirectional comparing lesions in current and multiple historical slices, maximizes reliance on previous slices when no lesions and minimizes this reliance when lesions are present, thereby preventing interference between consecutive slices. 3) The Diffusion Model-Driven Lesion Detail Synthesis Module, by employing a continuous and progressive generation process, accurately captures detailed features between data distributions, thereby avoiding the loss of detail caused by traditional methods (e.g., GAN) that overfocus on global distributions. Extensive experiments on a generalized CE liver tumor dataset have demonstrated that our CUDD-DM achieves state-of-the-art performance (improved the SSIM by at least 2.2% (lesions area 5.3%) comparing the seven leading methods). These results demonstrate that CUDD-DM advances CE liver tumor imaging technology. IEEEÖğe Consistency- and dependence-guided knowledge distillation for object detection in remote sensing images(Pergamon-Elsevier Science Ltd, 2023) Chen, Yixia; Lin, Mingwei; He, Zhu; Polat, Kemal; Alhudhaif, Adi; Alenezi, FayadhAs one of the challenging tasks in the remote sensing (RS), object detection has been successfully applied in many fields. Convolution neural network (CNN) has recently attracted extensive attention and is widely used in the natural image processing. Nevertheless, RS images have cluttered scenes compared with natural images. As a result, the existing detectors perform poorly in RS images, especially with the complicated backgrounds. Moreover, the detection inference time and model volume of detectors in RS images often go unrecognized. To address the above issues, this study proposes a novel method for object detection in RS images, which is called the consistency- and dependence-guided knowledge distillation (CDKD). To this end, the spatial- and channeloriented structure discriminative modules (SCSDM) are put forward to extract the discriminative spatial locations and channels to which the teacher model pays attention. SCSDM improves the feature representation of the student model by effectively eliminating the influence of noises and the complicated backgrounds. Then, the consistency and dependence of the features between the teacher model and the student model are constructed under the guidance of SCSDM. Experimental results over public datasets for RS images demonstrate that our CDKD method surpasses the state-of-the-art methods effectively. Most of all, on the RSOD dataset, our CDKD method achieves 92% mean average precision with 3.3 M model volume and 588.2 frames per second.Öğe Convolution smoothing and non-convex regularization for support vector machine in high dimensions(Elsevier, 2024) Wang, Kangning; Yang, Junning; Polat, Kemal; Alhudhaif, Adi; Sun, XiaofeiThe support vector machine (SVM) is a well-known statistical learning tool for binary classification. One serious drawback of SVM is that it can be adversely affected by redundant variables, and research has shown that variable selection is crucial and necessary for achieving good classification accuracy. Hence some SVM variable selection studies have been devoted, and they have an unified empirical hinge loss plus sparse penaltyformulation. However, a noteworthy issue is the computational complexity of existing methods is high especially for large-scale problems, due to the non-smoothness of the hinge loss. To solve this issue, we first propose a convolution smoothing approach, which turns the non-smooth hinge loss into a smooth surrogate one, and they are asymptotically equivalent. Moreover, we construct computationally more efficient SVM variable selection procedure by implementing non-convex penalized convolution smooth hinge loss. In theory, we prove that the resulting variable selection possesses the oracle property when the number of predictors is diverging. Numerical experiments also confirm the good performance of the new method.Öğe Detection of atrial fibrillation from variable-duration ECG signal based on time-adaptive densely network and feature enhancement strategy(IEEE-Institute Electrical Electronics Engineers Inc, 2023) Zhang, Xianbin; Jiang, Mingzhe; Polat, Kemal; Alhudhaif, Adi; Hemanth, Jude; Wu, WanqingAtrial fibrillation (AF) is one of the clinic's most common arrhythmias with high morbidity and mortality. Developing an intelligent auxiliary diagnostic model of AF based on a body surface electrocardiogram (ECG) is necessary. Convolutional neural network (CNN) is one of the most commonly used models for AF recognition. However, typical CNN is not compatible with variable-duration ECG, so it is hard to demonstrate its universality and generalization in practical applications. Hence, this paper proposes a novel Time-adaptive densely network named MP-DLNet-F. The MP-DLNet module solves the problem of incompatibility between variable-duration ECG and 1D-CNN. In addition, the feature enhancement module and data imbalance processing module are respectively used to enhance the perception of temporal-quality information and decrease the sensitivity to data imbalance. The experimental results indicate that the proposed MP-DLNet-F achieved 87.98% classification accuracy, and F1-score of 0.847 on the CinC2017 database for 10-second cropped/padded single-lead ECG fragments. Furthermore, we deploy transfer learning techniques to test heterogeneous datasets, and in the CPSC2018 12-lead dataset, the method improved the average accuracy and F1-score by 21.81% and 16.14%, respectively. Experimental results indicate that our method can update the constructed model's parameters and precisely forecast AF with different duration distributions and lead distributions. Combining these advantages, MP-DLNet-F can exemplify all kinds of varied-duration or imbalance medical signal processing problems such as Electroencephalogram (EEG) and Photoplethysmography (PPG).Öğe Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images(PERGAMON-ELSEVIER SCIENCE LTD, 2021) Alhudhaif, Adi; Polat, Kemal; Karaman, OnurX-ray units have become one of the most advantageous candidates for triaging the new Coronavirus disease COVID-19 infected patients thanks to its relatively low radiation dose, ease of access, practical, reduced prices, and quick imaging process. This research intended to develop a reliable convolutional-neural-network (CNN) model for the classification of COVID-19 from chest X-ray views. Moreover, it is aimed to prevent bias issues due to the database. Transfer learning-based CNN model was developed by using a sum of 1,218 chest X-ray images (CXIs) consisting of 368 COVID-19 pneumonia and 850 other pneumonia cases by pre-trained architectures, including DenseNet-201, ResNet-18, and SqueezeNet. The chest X-ray images were acquired from publicly available databases, and each individual image was carefully selected to prevent any bias problem. A stratified 5fold cross-validation approach was utilized with a ratio of 90% for training and 10% for the testing (unseen folds), in which 20% of training data was used as a validation set to prevent overfitting problems. The binary classification performances of the proposed CNN models were evaluated by the testing data. The activation mapping approach was implemented to improve the causality and visuality of the radiograph. The outcomes demonstrated that the proposed CNN model built on DenseNet-201 architecture outperformed amongst the others with the highest accuracy, precision, recall, and F1-scores of 94.96%, 89.74%, 94.59%, and 92.11%, respectively. The results indicated that the reliable diagnosis of COVID-19 pneumonia from CXIs based on the CNN model opens the door to accelerate triage, save critical time, and prioritize resources besides assisting the radiologists.Öğe Development of smart camera systems based on artificial intelligence network for social distance detection to fight against COVID-19(Elsevier, 2021) Karaman, Onur; Alhudhaif, Adi; Polat, KemalIn this work, an artificial intelligence network-based smart camera system prototype, which tracks social distance using a bird’s-eye perspective, has been developed. ‘‘MobileNet SSD-v3’’, ‘‘Faster-RCNN Inception-v2’’, ‘‘Faster-R-CNN ResNet-50’’ models have been utilized to identify people in video sequences. The final prototype based on the Faster R-CNN model is an integrated embedded system that detects social distance with the camera. The software developed using the ‘‘Nvidia Jetson Nano’’ development kit and Raspberry Pi camera module calculates all necessary actions in itself, detects social distance violations, makes audible and light warnings, and reports the results to the server. It is predicted that the developed smart camera prototype can be integrated into public spaces within the ‘‘sustainable smart cities,’’ the scope that the world is on the verge of a changeÖğe Dynamic trajectory partition optimization method based on historical trajectory data(Elsevier, 2024) Yu, Xiang; Zhai, Huawei; Tian, Ruijie; Guan, Yao; Polat, Kemal; Alhudhaif, AdiPartitioning dynamic trajectory data can improve the efficiency and accuracy of trajectory data processing, provide a foundation for trajectory data mining and analysis. However, with the continuous growth of trajectory data scales and the urgent demand for trajectory query efficiency and accuracy, partitioning methods have become crucial. The partitioning method of dynamic trajectory data faces significant challenges in terms of spatiotemporal trajectory locality, partition load balancing, and partition time. To address these challenges, we propose a method based on historical trajectory pre-partitioning, which can store data more effectively in distributed systems. We partition similar historical trajectory data to achieve preliminary partitioning of the data. In addition, we also construct a cost model to ensure that the workload of each partition is close to consistency. Extensive experiments have demonstrated the excellent partitioning efficiency and query efficiency achieved by our design compared to other partitioning methods.Öğe Emotional speaker identification using a novel capsule nets model(Pergamon-Elsevier Science Ltd, 2022) Nassif, Ali Bou; Shahin, Ismail; Elnagar, Ashraf; Velayudhan, Divya; Alhudhaif, Adi; Polat, KemalSpeaker recognition systems are widely used in various applications to identify a person by their voice; however, the high degree of variability in speech signals makes this a challenging task. Dealing with emotional variations is very difficult because emotions alter the voice characteristics of a person; thus, the acoustic features differ from those used to train models in a neutral environment. Therefore, speaker recognition models trained on neutral speech fail to correctly identify speakers under emotional stress. Although considerable advancements in speaker identification have been made using convolutional neural networks (CNN), CNNs cannot exploit the spatial association between low-level features. Inspired by the recent introduction of capsule networks (CapsNets), which are based on deep learning to overcome the inadequacy of CNNs in preserving the pose relationship between low-level features with their pooling technique, this study investigates the performance of using CapsNets in identifying speakers from emotional speech recordings. A CapsNet-based speaker identification model is proposed and evaluated using three distinct speech databases, i.e., the Emirati Speech Database, SUSAS Dataset, and RAVDESS (open-access). The proposed model is also compared to baseline systems. Experimental results demonstrate that the novel proposed CapsNet model trains faster and provides better results over current stateof-the-art schemes. The effect of the routing algorithm on speaker identification performance was also studied by varying the number of iterations, both with and without a decoder network.Öğe An ensemble learning approach for resampling forgery detection using Markov process(Elsevier, 2023) Mehta, Rachna; Kumar, Karan; Alhudhaif, Adi; Alenezi, Fayadh; Polat, KemalResampling is an extremely well-known technique performed for Image forgery detection. It includes the changes in the content of a picture in terms of rotation, stretching/zooming, and shrinking, to frame a forged picture that is a localized forgery in comparison to the original picture. With the wrong intention, resampling forgery has been increased day by day, and its negative impact has been increased in criminology, law enforcement, forensics, research etc. Accordingly, the interest in the algorithm of image resampling forgery detection is significantly developed in image forensics. In this paper, a novel image resampling forgery detection technique has been proposed. In the proposed technique, two types of Markov feature with spatial and Discrete Cosine Transform domains have been extracted to recognize the resampling operation. The spatial domain gives the information for the distribution of the pixels and DCT gives the edge information. Further, these Markov features are consolidated. Due to high dimensionality hard thresholding technique is used for reducing the dimensionality. Then, these Markov features are applied to the set of models of different classifiers. With the utilization of classifiers, weighted majority voting values have been calculated during the ensemble classification. Unlike the other techniques, these weighted voting boundaries have been consequently balanced during the training process until the best accuracy has been obtained. However, it is very difficult to get best accuracy so for getting best accuracy this research needs to do lots of iterations and trained the dataset. For the comparative study very few research has been found for this resampling forgery technique with different interpolation techniques and classifier. Still, comparison has been done with some latest research work. The comparative analysis shows that the proposed ensemble learning-based algorithm provides the best outcomes with the accuracy of 99.12% for bicubic, 98.89% for bilinear, and 98.23% for lanczos3 kernel with considerably less complexity and high speed in comparison to prior techniques which are using single support vector machine for classification. Moreover, the proposed algorithm also detects a very low probability of error of 0.44% and detects the type of interpolation kernel, size of the forgery, and the type of resampling, whether it is up sampling and down sampling, using Graphical User Interface which has not been detected previously with multiple forgery detection.& COPY; 2023 Elsevier B.V. All rights reserved.Öğe Exposing low-quality deepfake videos of Social Network Service using Spatial Restored Detection Framework(Pergamon-Elsevier Science Ltd, 2023) Li, Ying; Bian, Shan; Wang, Chuntao; Polat, Kemal; Alhudhaif, Adi; Alenezi, FayadhThe increasing abuse of facial manipulation methods, such as FaceSwap, Deepfakes etc., seriously threatens the authenticity of digital images/videos on the Internet. Therefore, it is of great importance to identify the facial videos to confirm the contents and avoid fake news or rumors. Many researchers have paid great attention to the detection of deepfakes and put forward a number of deep-learning-based detection models. The existing approaches mostly face the performance degradation in detecting low-quality(LQ) videos, i.e. heavily compressed or low-resolution videos through some SNS (Social Network Service), resulting in the limitation in real applications. To address this issue, in this paper, a novel Spatial Restore Detection Framework(SRDF) is proposed for improving the detection performance for LQ videos by restoring spatial features. We designed a feature extraction-enhancement block and a mapping block inspired by super-resolution methods, to restore and enhance texture features. An attention module was introduced to guide the texture features restoration and enhancement stage attending to different local areas and restoring the texture features. Besides, an improved isolated loss was put forward to prevent the expansion of a single area concerned. Moreover, we adopted a regional data augmentation strategy to prompt feature restore and enhancement in the region attended. Extensive experiments conducted on two deepfake datasets have validated the superiority of the proposed method compared to the state-of-the-art, especially in the scenarios of detecting low-quality deepfake videos.Öğe Graph-based link prediction between human phenotypes and genes(Hindawi Ltd, 2022) Patel, Rushabh; Guo, Yanhui; Alhudhaif, Adi; Alenezi, Fayadh; Althubiti, Sara A.; Polat, KemalDeep phenotyping is defined as learning about genotype-phenotype associations and the history of human illness by analyzing phenotypic anomalies. It is significant to investigate the association between phenotype and genotype. Machine learning approaches are good at predicting the associations between abnormal human phenotypes and genes. A novel framework based on machine learning is proposed to estimate the links between human phenotype ontology (HPO) and genes. The Orphanet's annotation parses the human phenotype-gene associations. An algorithm node2vec generates the embeddings for the nodes (HPO and genes). It performs node sampling on the graph using random walks and learns features on these sampled nodes for embedding. These embeddings were used downstream to predict the link between these nodes by supervised classifiers. Results show the gradient boosting decision tree model (LightGBM) has achieved an optimal AUROC of 0.904 and an AUCPR of 0.784, an optimal weighted F1 score of 0.87. LightGBM can detect more accurate interactions and links between human phenotypes and gene pairs.Öğe An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network(Elsevier, 2024) Yuan, Yue; Chen, Jichi; Polat, Kemal; Alhudhaif, AdiFruit and vegetable freshness testing can improve the efficiency of agricultural product management, reduce resource waste and economic losses, and plays a vital role in increasing the added value of fruit and vegetable agricultural products. At present, the detection of fruit and vegetable freshness mainly relies on manual feature extraction combined with machine learning. However, manual extraction of features has the problem of poor adaptability, resulting in low efficiency in fruit and vegetable freshness detection. Although exist some studies that have introduced deep learning methods to automatically learn deep features that characterize the freshness of fruits and vegetables to cope with the diversity and variability in complex scenes. However, the performance of these studies on fruit and vegetable freshness detection needs to be further improved. Based on this, this paper proposes a novel method that fusion of different deep learning models to extract the features of fruit and vegetable images and the correlation between various areas in the image, so as to detect the freshness of fruits and vegetables more objectively and accurately. First, the image size in the dataset is resized to meet the input requirements of the deep learning model. Then, deep features characterizing the freshness of fruits and vegetables are extracted by the fused deep learning model. Finally, the parameters of the fusion model were optimized based on the detection performance of the fused deep learning model, and the performance of fruit and vegetable freshness detection was evaluated. Experimental results show that the CNN_BiLSTM deep learning model, which fusion convolutional neural network (CNN) and bidirectional long -short term memory neural network (BiLSTM), is combined with parameter optimization processing to achieve an accuracy of 97.76% in detecting the freshness of fruits and vegetables. The research results show that this method is promising to improve the performance of fruit and vegetable freshness detection.Öğe A knowledge-driven graph convolutional network for abnormal electrocardiogram diagnosis(Elsevier, 2024) Ge, Zhaoyang; Cheng, Huiqing; Tong, Zhuang; He, Ziyang; Alhudhaif, Adi; Polat, Kemal; Xu, MingliangThe electrocardiogram (ECG) signal comprising P-, Q-, R-, S-, and T -waves is an indispensable noninvasive diagnostic tool for analyzing physiological conditions of the heart. In general, traditional ECG intelligent diagnosis methods gradually extract features of the signal from input data until they can classify the ECG signal. However, the decision -making process of ECG intelligence models is implicit to clinicians. Clinical experts rely on clear and specific features extracted from ECG data to diagnose cardiac diseases effectively. Inspired by this clinical diagnosis mechanism, we propose an ECG knowledge graph (ECG -KG) framework primarily to improve ECG classification by presenting knowledge of ECG clinical diagnosis. In particular, the ECG -KG framework contains an ECG semantic feature extraction module, a knowledge graph construction module, and an ECG classification module. First, the ECG semantic feature extraction module locates the key points using the difference value method and further calculates the ECG attribute features. Further, the knowledge graph construction module utilizes attribute features to design entities and relationships for constructing abnormal ECG triples. The triples vectorize ECG abnormalities through the strategy of knowledge graph embedding strategy. Finally, the ECG classification module combines the ECG knowledge graph with the graph convolutional network model and adequately integrates expert knowledge to identify ECG abnormalities. Experiments conducted on the benchmark QT, the CPSC-2018, and the ZZU-ECG datasets show that the ECG -KG framework considerably outperforms other ECG diagnosis models, indicating the effectiveness of the ECG -KG framework for ECG abnormality diagnosis.Öğe Machine learning and electrocardiography signal-based minimum calculation time detection for blood pressure detection(Hindawi Ltd, 2022) Nour, Majid; Kandaz, Derya; Uçar, Muhammed Kürşad; Polat, Kemal; Alhudhaif, AdiObjective. Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The study is aimed at determining the minimum ECG time required for calculating systolic and diastolic blood pressure based on the Electrocardiography (ECG) signal. Methodology. The study includes ECG recordings of five individuals taken from the IEEE database, measured during daily activity. For the study, each signal was divided into epochs of 2-4-6-8-10-12-14-16-18-20 seconds. Twenty-five features were extracted from each epoched signal. The dimension of the dataset was reduced by using Spearman's feature selection algorithm. Analysis based on metrics was carried out by applying machine learning algorithms to the obtained dataset. Gaussian process regression exponential (GPR) machine learning algorithm was preferred because it is easy to integrate into embedded systems. Results. The MAPE estimation performance values for diastolic and systolic blood pressure values for 16-second epochs were 2.44 mmHg and 1.92 mmHg, respectively. Conclusion. According to the study results, it is evaluated that systolic and diastolic blood pressure values can be calculated with a high-performance ratio with 16-second ECG signals.Öğe Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network(Elsevier Sci Ltd, 2024) Zhang, Dongran; Yan, Jiangnan; Polat, Kemal; Alhudhaif, Adi; Li, JunTraffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spatial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance.
