Journal articles: 'CNN ALGORITHM'

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Reddy, Y. Venkat Sai, G. Chandana, G. Chetan Redddy, Ayush Kumar, Suvarna Kumar, and Dr Syed Siraj Ahmed. "Lung Cancer Detection using YOLO CNN Algorithm." International Journal of Research Publication and Reviews 4, no. 5 (June 2023): 5297–300. http://dx.doi.org/10.55248/gengpi.4.523.43476.

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Diqi, Mohammad. "Waste Classification using CNN Algorithm." International Conference on Information Science and Technology Innovation (ICoSTEC) 1, no. 1 (February 26, 2022): 130–35. http://dx.doi.org/10.35842/icostec.v1i1.17.

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One of the cornerstones to efficient waste management is proper and accurate waste classification. However, people find it challenging to categorize such a big and diverse amount of waste. As a result, we employ deep learning to classify waste efficiently. This paper uses the CNN algorithm to provide a problem-solving strategy to waste classification. The model achieves an accuracy of 0.9969 and a loss of 0.0205. As a result, we argue that employing CNN algorithms to categorize waste yields better results and reduces losses efficiently.

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Tiancheng, Li, Ren Qing-dao-er-ji, and Qiu Ying. "Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia." Advances in Meteorology 2019 (December 6, 2019): 1–13. http://dx.doi.org/10.1155/2019/5176576.

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Hazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm occurrence, called improved naive Bayesian-CNN classification algorithm (INB-CNN classification algorithm). Firstly, we established a sandstorm prediction model based on the convolutional neural network algorithm, which considered atmospheric movement factors. Convolutional neural network (CNN) is a deep neural network with convolution structure, which can automatically learn features from massive data. Then, we established a sandstorm prediction model based on the Naive Bayesian algorithm, which considered ground factors. Finally, we established a sandstorm prediction model based on the improved naive Bayesian-CNN classification algorithm. Experimental results showed that the prediction accuracy of the sandstorm prediction model based on INB-CNN classification algorithm is higher than that of others and the model can better reflect the law of sandstorm occurrence. This paper used two algorithms, naive Bayesian algorithm and CNN algorithm, to identify and diagnose the strength of sandstorm in Inner Mongolia and found that combining the two algorithms, INB-CNN classification algorithm had the greatest success in predicting the occurrence of sandstorms.

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Bahaa, Ahmed, Abdalla Sayed, Laila Elfangary, and Hanan Fahmy. "A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach." PLOS ONE 17, no. 12 (December 1, 2022): e0278493. http://dx.doi.org/10.1371/journal.pone.0278493.

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Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle swarm optimization–whale optimizer algorithm (APSO-WOA) for optimization of the hyperparameters of a convolutional neural network (APSO-WOA-CNN). The APSO–WOA optimization algorithm’s fitness value is defined as the validation set’s cross-entropy loss function during CNN model training. In this study, we compare our optimization algorithm with other optimization algorithms, such as the APSO algorithm, for optimization of the hyperparameters of CNN. In model training, the APSO–WOA–CNN algorithm achieved the best performance compared to the FNN algorithm, which used manual parameter settings. We evaluated the APSO–WOA–CNN algorithm against APSO–CNN, SVM, and FNN. The simulation results suggest that APSO–WOA–CNf[N is effective and can reliably detect multi-type IoT network attacks. The results show that the APSO–WOA–CNN algorithm improves accuracy by 1.25%, average precision by 1%, the kappa coefficient by 11%, Hamming loss by 1.2%, and the Jaccard similarity coefficient by 2%, as compared to the APSO–CNN algorithm, and the APSO–CNN algorithm achieves the best performance, as compared to other algorithms.

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Ramekar, Aditya Dhanraj, Pooja Rajendra Sanas, Akshay Rajendra Ghodekar, Shailesh Ramesh, and Prof S. S. Bhagat. "Crop Prediction Using CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2714–19. http://dx.doi.org/10.22214/ijraset.2022.41873.

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Abstract: In general, agriculture is the backbone of India and also plays an important role in Indian economy by providing a certain percentage of domestic product to ensure the food security. But now-a-days, food production and prediction is getting depleted due to unnatural climatic changes, which will adversely affect the economy of farmers by getting a poor yield and also help the farmers to remain less familiar in forecasting the future crops. This research work helps the beginner farmer in such a way to guide them for sowing the reasonable crops by deploying machine learning, one of the advanced technologies in crop prediction. Convolution Neural Network one of the most popular deep neural networks puts forth in the way to achieve it. The soil type image is taken here like alluvial soil, black soil, red soil, sandy soil. Etc. to start the prediction process. Keywords: Crop prediction, Machine Learning, Convolution Neural Network, Supervised Machine Learning.

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N, Krishnamoorthy. "TV Shows Popularity and Performance Prediction Using CNN Algorithm." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1541–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202257.

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Qin, Jiangping, Yan Zhang, Huan Zhou, Feng Yu, Bo Sun, and Qisheng Wang. "Protein Crystal Instance Segmentation Based on Mask R-CNN." Crystals 11, no. 2 (February 4, 2021): 157. http://dx.doi.org/10.3390/cryst11020157.

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Protein crystallization is the bottleneck in macromolecular crystallography, and crystal recognition is a very important step in the experiment. To improve the recognition accuracy by image classification algorithms further, the Mask R-CNN model is introduced for the detection of protein crystals in this paper. Because the protein crystal image is greatly affected by backlight and precipitate, the contrast limit adaptive histogram equalization (CLAHE) is applied with Mask R-CNN. Meanwhile, the Transfer Learning method is used to optimize the parameters in Mask R-CNN. Through the comparison experiments between this combined algorithm and the original algorithm, it shows that the improved algorithm can effectively improve the accuracy of segmentation.

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HUANG, Jiawei, Caixia BI, Jiayue LIU, and Shaohua DONG. "Research on CNN-based intelligent recognition method for negative images of weld defects." Journal of Physics: Conference Series 2093, no. 1 (November 1, 2021): 012020. http://dx.doi.org/10.1088/1742-6596/2093/1/012020.

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Abstract The existing technology of automatic classification and recognition of welding negative images by computer is difficult to achieve a multiple classification defect recognition while maintaining a high recognition accuracy, and the developed automatic recognition model of negative image defect cannot meet the actual needs of the field. Therefore, the convolutional neural network (CNN)-based intelligent recognition algorithm for negative image of weld defects is proposed, and a B/S (Browser/Server) architecture of weld defect feature image database combined with CNN is established subsequently, which converted from the existing CNN by the migration learning method. It makes full use of the negative big data and simplifies the algorithm development process, so that the recognition algorithm has a better generalization ability and the training algorithm accuracy of 97.18% achieved after training. The results of the comparison experiments with traditional recognition algorithms show that the CNN-based intelligent recognition algorithm for defective weld negatives has an accuracy of 92.31% for dichotomous defects, which is significantly better than the traditional recognition algorithm, the established recognition algorithm effectively improving the recognition accuracy and achieving multi-category defect recognition. At the same time, the CNN-based defect recognition method was established by combining the image segmentation algorithm and the defect intelligent recognition algorithm, which was applied to the actual negative images in the field with good results, further verifying the feasibility of CNN-based intelligent recognition algorithm in the field of defect recognition of welding negative images.

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Vitale, S., G. Ferraioli, and V. Pascazio. "EDGE PRESERVING CNN SAR DESPECKLING ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 4, 2020): 97–100. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-97-2020.

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Abstract. SAR despeckling is a key tool for Earth Observation. Interpretation of SAR images are impaired by speckle, a multiplicative noise related to interference of backscattering from the illuminated scene towards the sensor. Reducing the noise is a crucial task for the understanding of the scene. Based on the results of our previous solution KL-DNN, in this work we define a new cost function for training a convolutional neural network for despeckling. The aim is to control the edge preservation and to better filter man-made structures and urban areas that are very challenging for KL-DNN. The results show a very good improvement on the not homogeneous areas keeping the good results in the homogeneous ones. Result on both simulated and real data are shown in the paper.

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Mehta, Jahangir Jepee, Furqaan Ahmad Wani, Aamir Ashraf Ahangar, Kanwaljeet Kaur, and Najmusher H. "Leaf Disease Remedy Using CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1148–51. http://dx.doi.org/10.22214/ijraset.2022.41468.

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Abstract: The proposed method aids in the diagnosis of plant diseases as well as the provision of medicines that may be employed as a defensive machine against them. The file collected from the web is correctly separated, and the various plant types are recognized and named again to produce a suitable record. A test file including several plant ailments is then obtained, which is used to assess the project's accuracy and confidence level. We'll next train our classifier with training data, and the result will be expected with maximum accuracy. We employ a Deep Convolutional Neuronic network (CNN), which consists of many layers for an estimate. A newly designed drone prototypical is also being developed that can be used to provide live updates of huge farming lands. The drone will be equipped with a highresolution photographic camera that will capture the image of the plants, which will be used as a contribution to the software, which will determine whether the plant is healthy or not. We reached a 78 percent accuracy level with our programming and training model. Our programmer provides us with the identity of the plant species, as well as the confidence level of the species and the medicine that may be used to treat it. Keywords: Machine Learning, Leaf Disease, Remedy, CNN

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Kozek, T., T. Roska, and L. O. Chua. "Genetic algorithm for CNN template learning." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 40, no. 6 (June 1993): 392–402. http://dx.doi.org/10.1109/81.238343.

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Gazawy, Qusai, Selim Buyrukoğlu, and Yıldıran Yılmaz. "Convolutional neural network for pothole detection in different road and weather conditions." Journal of Computer & Electrical and Electronics Engineering Sciences 1, no. 1 (April 28, 2023): 1–4. http://dx.doi.org/10.51271/jceees-0001.

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Aims: To propose a deep learning algorithm for pothole detection and compare the performance of Sigmoid and Softmax activation functions in the creation of Convolutional Neural Network (CNN) algorithms. Methods: Three different datasets were used to justify the robustness of the CNN model in detecting dry and wet potholes. The CNN algorithms were created separately using the Sigmoid and Softmax activation functions. Results: The CNN algorithm using the Sigmoid function achieved higher accuracy scores than the CNN algorithm using the Softmax function. Specifically, the Sigmoid algorithm achieved accuracy scores of 91%, 96%, and 83% over datasets 1, 2, and 3, respectively, while the Softmax algorithm achieved scores of 81%, 96%, and 85% over the same datasets. Conclusion: The results of this study suggest that the CNN algorithm using the Sigmoid activation function is more robust and effective in detecting pothole images compared to the CNN algorithm using the Softmax activation function.

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Zaheer, M. M., and P. Nirmala. "An Effective Approach to Detect Liver Disorder using CNN Algorithm in Comparison with Random Forest Algorithm to Measure Accuracy." CARDIOMETRY, no. 25 (February 14, 2023): 1031–37. http://dx.doi.org/10.18137/cardiometry.2022.25.10311037.

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Aim: The ultimate aim of this work is to show the better mean accuracy of CNN algorithm in comparison with random forest algorithm on detection of liver disorder. Materials & Methods: For identification of effective approaches to detect liver disorder, the conventional neural network algorithm is used comparatively with the random forest algorithm which is an existing algorithm. For each group sample size is taken as 20 and total sample size is taken as 40. Sample size calculation was done using clincalc. com by keeping g-power at 80%, confidence interval at 95 % and threshold at 0.05 %. Result: By this innovative liver disorder detection method, it is concluded that the random forest algorithm is much lower than CNN algorithm in predicting the liver disorder. Each sample shows different accuracy values, the overall mean accuracy values shows that the CNN algorithm is having greater accuracy of 95.86% than the random forest algorithm which is having 93.88% on analysis of liver disorder approach. For CNN and random forest algorithms the statistical significance of different values is p=0.001 i.e., p<0.05 (Independent sample T-test) provided by statistical results. Conclusion: On analysis of liver disorder it is clearly known that the CNN algorithm shows better mean accuracy value then random forest algorithm. The CNN algorithm shows the accuracy of 95.86% and random forest shows the accuracy of 93.88%.

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Liu, Yuepeng, Xingyu Zhou, and Hongwei Han. "Lightweight CNN-Based Method for Spacecraft Component Detection." Aerospace 9, no. 12 (November 27, 2022): 761. http://dx.doi.org/10.3390/aerospace9120761.

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Spacecraft component detection is essential for space missions, such as for rendezvous and on-orbit assembly. Traditional intelligent detection algorithms suffer from drawbacks related to high computational burden, and are not applicable for on-board use. This paper proposes a convolutional neural network (CNN)-based lightweight algorithm for spacecraft component detection. A lightweight approach based on the Ghost module and channel compression is first presented to decrease the amount of processing and data storage required by the detection algorithm. To improve feature extraction, we analyze the characteristics of spacecraft imagery, and multi-head self-attention is used. In addition, a weighted bidirectional feature pyramid network is incorporated into the algorithm to increase precision. Numerical simulations show that the proposed method can drastically reduce the computational overhead while still guaranteeing good detection precision.

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Qiu, Ningjia, Lin Cong, Sicheng Zhou, and Peng Wang. "Barrage Text Classification with Improved Active Learning and CNN." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 6 (November 20, 2019): 980–89. http://dx.doi.org/10.20965/jaciii.2019.p0980.

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Traditional convolutional neural networks (CNNs) use a pooling layer to reduce the dimensionality of texts, but lose semantic information. To solve this problem, this paper proposes a convolutional neural network model based on singular value decomposition algorithm (SVD-CNN). First, an improved density-based center point clustering active learning sampling algorithm (DBC-AL) is used to obtain a high-quality training set at a low labelling cost. Second, the method uses the singular value decomposition algorithm for feature extraction and dimensionality reduction instead of a pooling layer, fuses the dimensionality reduction matrix, and completes the barrage text classification task. Finally, the partial sampling gradient descent algorithm (PSGD) is applied to optimize the model parameters, which accelerates the convergence speed of the model while ensuring stability of the model training. To verify the effectiveness of the improved algorithm, several barrage datasets were used to compare the proposed model and common text classification models. The experimental results show that the improved algorithm preserves the semantic features of the text more successfully, ensures the stability of the training process, and improves the convergence speed of the model. Further, the model’s classification performance on different barrage texts is superior to traditional algorithms.

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O, Naveen kumar reddy, and Ramkumar G. "Hybrid Model for Detection of Corrosion in Water Pipeline Images Using CNN and Comparing Accuracy with SVM." ECS Transactions 107, no. 1 (April 24, 2022): 13861–71. http://dx.doi.org/10.1149/10701.13861ecst.

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The work aims at studying a hybrid model for novel corrosion detection in water pipeline images using two different machine learning algorithms in low resolution images. Methods and Material: Convolutional Neural Network (CNN) and Support Vector Machine (SVM) algorithm implemented to detect the corrosion in low resolution image dataset with 40 samples. Results: CNN Classifier model has an detection accuracy value of 93.18% and the SVM has an detection accuracy of 77.77%. Attained significance (p=0.001) through SPSS tool. Conclusion: CNN algorithm perform well compared to SVM algorithm.

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Zavala-Mondragon, Luis A., Bishal Lamichhane, Lu Zhang, and Gerard de Haan. "CNN-SkelPose: a CNN-based skeleton estimation algorithm for clinical applications." Journal of Ambient Intelligence and Humanized Computing 11, no. 6 (February 28, 2019): 2369–80. http://dx.doi.org/10.1007/s12652-019-01259-5.

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Chen, Dong-Hao, Yu-Dong Cao, and Jia Yan. "Towards Pedestrian Target Detection with Optimized Mask R-CNN." Complexity 2020 (December 22, 2020): 1–8. http://dx.doi.org/10.1155/2020/6662603.

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Aiming at the problem of low pedestrian target detection accuracy, we propose a detection algorithm based on optimized Mask R-CNN which uses the latest research results of deep learning to improve the accuracy and speed of detection results. Due to the influence of illumination, posture, background, and other factors on the human target in the natural scene image, the complexity of target information is high. SKNet is used to replace the part of the convolution module in the depth residual network model in order to extract features better so that the model can adaptively select the best convolution kernel during training. In addition, according to the statistical law, the length-width ratio of the anchor box is modified to make it more accord with the natural characteristics of the pedestrian target. Finally, a pedestrian target dataset is established by selecting suitable pedestrian images in the COCO dataset and expanded by adding noise and median filtering. The optimized algorithm is compared with the original algorithm and several other mainstream target detection algorithms on the dataset; the experimental results show that the detection accuracy and detection speed of the optimized algorithm are improved, and its detection accuracy is better than other mainstream target detection algorithms.

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Park, Sungwoo, Kyong-Ho Han, and Wooyoung Jang. "CNN Deep Learning Acceleration Algorithm for Mobile System." Journal of Korean Institute of Information Technology 16, no. 10 (October 31, 2018): 1–9. http://dx.doi.org/10.14801/jkiit.2018.16.10.1.

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An, Jianjing, Dezheng Zhang, Ke Xu, and Dong Wang. "An OpenCL-Based FPGA Accelerator for Faster R-CNN." Entropy 24, no. 10 (September 23, 2022): 1346. http://dx.doi.org/10.3390/e24101346.

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In recent years, convolutional neural network (CNN)-based object detection algorithms have made breakthroughs, and much of the research corresponds to hardware accelerator designs. Although many previous works have proposed efficient FPGA designs for one-stage detectors such as Yolo, there are still few accelerator designs for faster regions with CNN features (Faster R-CNN) algorithms. Moreover, CNN’s inherently high computational complexity and high memory complexity bring challenges to the design of efficient accelerators. This paper proposes a software-hardware co-design scheme based on OpenCL to implement a Faster R-CNN object detection algorithm on FPGA. First, we design an efficient, deep pipelined FPGA hardware accelerator that can implement Faster R-CNN algorithms for different backbone networks. Then, an optimized hardware-aware software algorithm was proposed, including fixed-point quantization, layer fusion, and a multi-batch Regions of interest (RoIs) detector. Finally, we present an end-to-end design space exploration scheme to comprehensively evaluate the performance and resource utilization of the proposed accelerator. Experimental results show that the proposed design achieves a peak throughput of 846.9 GOP/s at the working frequency of 172 MHz. Compared with the state-of-the-art Faster R-CNN accelerator and the one-stage YOLO accelerator, our method achieves 10× and 2.1× inference throughput improvements, respectively.

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Yuan, Yongjie, Yongjun Zhang, Junyuan Wang, and Ping Fang. "Classification of Electrocardiogram of Congenital Heart Disease Patients by Neural Network Algorithms." Scientific Programming 2021 (August 31, 2021): 1–8. http://dx.doi.org/10.1155/2021/3801675.

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The study intended to explore the effect of different neural network algorithms in the electrocardiogram (ECG) classification of patients with congenital heart disease (CHD). Based on the single convolutional neural network (CNN) ECG algorithm and the recurrent neural network (RNN) ECG algorithm, a multimodal neural network (MNN) ECG algorithm was constructed utilizing the MIT-BIH database as training set and test set. Furthermore, the MNN ECG algorithm was optimized to establish an improved MNN (IMNN) algorithm, which was applied to the diagnosis of CHD patients. The CHD patients admitted between August 2016 and August 2019 were selected for analysis to compare the classification effect and accuracy rate of IMNN, MNN, CNN ECG, and RNN ECG algorithms. It was found that the RNN ECG algorithm had higher classification sensitivity and true positive rate in terms of normal or bundle (NB) branch block beat, supraventricular abnormal (SA) rhythm, abnormal ventricular (AV) beat, and fusion beat (FB) than the CNN ECG algorithm ( P < 0.05 ), and the classification sensitivity and true positive rate of IMNN algorithm in the four aspects were significantly higher than those of MNN algorithm ( P < 0.05 ). The classification accuracy of CNN ECG algorithm and RNN ECG algorithm was above 98%, while that of MNN algorithm and IMNN algorithm was better than that of CNN ECG algorithm and RNN ECG algorithm, and the accuracy rate can reach 98.5% or more. Moreover, the accuracy rate of the IMNN algorithm can reach more than 98%. In conclusion, IMNN not only has a good classification ability in the simulated environment but also performs well in the actual environment, which is worthy of clinical promotion.

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Gu, Han-Qing, Xia-Xia Liu, Lu Xu, Yi-Jia Zhang, and Zhe-Ming Lu. "DSSS Signal Detection Based on CNN." Sensors 23, no. 15 (July 26, 2023): 6691. http://dx.doi.org/10.3390/s23156691.

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With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB.

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U, Chaitanya, Emmanuel Alisetti, Harsitha Ballam, and Maneesha Dodda. "Text Recognition from Images using CNN and MSER Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 889–94. http://dx.doi.org/10.22214/ijraset.2023.53777.

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Abstract: The ability to recognize text from images is a great importance in a range of applications, including document analysis, images captioning and augmented reality. The reliability and accuracy of text extraction from images have been completely transformed by text recognition models using Optical Character Recognition (OCR) and Maximally Stable Extremal Regions (MSER) algorithms. In our study, we propose a text recognition model that leverages the advantages of both OCR and MSER algorithms to enhance the reliability and accuracy of the text extraction process. OCR algorithm serve as the fundamental basis for text recognition by utilizing advanced methods to separate and identify individual characters. None the less these algorithms can encounter difficulties when confronted with intricated backgrounds, images of poor quality, or text arranged in irregular layouts. To address these limitations, we integrate the MSER algorithm, which excels in detecting text regions by identifying maximally stable regions across different scales and intensities. Our proposed model follows a multi-stage approach. First, the input image, the MSER method is used to extract probable text locations. These regions are then refined using pre-processing techniques, such as noise removal and image enhancement, to improve OCR performance. Next, the refined regions are passed through the OCR algorithm, which utilizes machine learning and pattern recognition techniques to recognize the text within each regions. The recognized text is subsequently post-processed to refine the results and improve overall accuracy. The text recognition model is implemented using CNN (OCR which is part of CNN) and the Maximally stable Extremal regions (MSER) algorithms

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Mirunalini, K., and Dr Vasantha Kalyani David. "Traffic sign Detection using CNN." International Journal of Engineering and Advanced Technology 10, no. 3 (February 28, 2021): 129–35. http://dx.doi.org/10.35940/ijeat.c2245.0210321.

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Lane Detection and Traffic sign detection are the essential components in ADAS .Although there has been significant quantity of analysis dedicated to the detection of lane detection and sign detection in the past, there is still need robustness in the system. An important challenge in the current algorithm is to cope with the bad weather and illumination. In this paper proposes an improved Hough transform algorithm in order to achieve detection of straight line while for the detection of curved sections, the tracking algorithm is studied. The proposed method uses Hybrid KSVD for removing the noise and Hybrid Lane Detection Algorithm is used for identifying the lanes and CNN based approach is used for the Traffic sign Detection. The proposed method offers better Peak Signal to Noise Ratio (PSNR) and Root Mean Square (RMS) in contrast to the existing methods.

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Gao, Zefu, Yiwen Jiao, Wenge Yang, Xuejian Li, and Yuxin Wang. "A Method for UWB Localization Based on CNN-SVM and Hybrid Locating Algorithm." Information 14, no. 1 (January 12, 2023): 46. http://dx.doi.org/10.3390/info14010046.

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In this paper, aiming at the severe problems of UWB positioning in NLOS-interference circumstances, a complete method is proposed for NLOS/LOS classification, NLOS identification and mitigation, and a final accurate UWB coordinate solution through the integration of two machine learning algorithms and a hybrid localization algorithm, which is called the C-T-CNN-SVM algorithm. This algorithm consists of three basic processes: an LOS/NLOS signal classification method based on SVM, an NLOS signal recognition and error elimination method based on CNN, and an accurate coordinate solution based on the hybrid weighting of the Chan–Taylor method. Finally, the validity and accuracy of the C-T-CNN-SVM algorithm are proved through a comparison with traditional and state-of-the-art methods. (i) Focusing on four main prediction errors (range measurements, maxNoise, stdNoise and rangeError), the standard deviation decreases from 13.65 cm to 4.35 cm, while the mean error decreases from 3.65 cm to 0.27 cm, and the errors are practically distributed normally, demonstrating that after training a SVM for LOS/NLOS signal classification and a CNN for NLOS recognition and mitigation, the accuracy of UWB range measurements may be greatly increased. (ii) After target positioning, the proposed method can realize a one-dimensional X-axis and Y-axis accuracy within 175 mm, and a Z-axis accuracy within 200 mm; a 2D (X,Y) accuracy within 200 mm; and a 3D accuracy within 200 mm, most of which fall within (100 mm, 100 mm, 100 mm). (iii) Compared with the traditional algorithms, the proposed C-T-CNN-SVM algorithm performs better in location accuracy, cumulative error probability (CDF), and root-mean-square difference (RMSE): the 1D, 2D, and 3D accuracy of the proposed method is 2.5 times that of the traditional methods. When the location error is less than 10 cm, the CDF of the proposed algorithm only reaches a value of 0.17; when the positioning error reaches 30 cm, only the CDF of the proposed algorithm remains in an acceptable range. The RMSE of the proposed algorithm remains ideal when the distance error is greater than 30 cm. The results of this paper and the idea of a combination of machine learning methods with the classical locating algorithms for improved UWB positioning under NLOS interference could meet the growing need for wireless indoor locating and communication, which indicates the possibility for the practical deployment of such a method in the future.

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Sonata, I., Y. Heryadi, L. Lukas, and A. Wibowo. "Autonomous car using CNN deep learning algorithm." Journal of Physics: Conference Series 1869, no. 1 (April 1, 2021): 012071. http://dx.doi.org/10.1088/1742-6596/1869/1/012071.

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Deepalakshmi P., Prudhvi Krishna T., Siri Chandana S., Lavanya K., and Parvathaneni Naga Srinivasu. "Plant Leaf Disease Detection Using CNN Algorithm." International Journal of Information System Modeling and Design 12, no. 1 (January 2021): 1–21. http://dx.doi.org/10.4018/ijismd.2021010101.

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Agriculture is the primary source of economic development in India. The fertility of soil, weather conditions, and crop economic values make farmers select appropriate crops for every season. To meet the increasing population requirements, agricultural industries look for improved means of food production. Researchers are in search of new technologies that would reduce investment and significantly improve the yields. Precision is a new technology that helps in improving farming techniques. Pest and weed detection and plant leaf disease detection are the noteworthy applications of precision agriculture. The main aim of this paper is to identify the diseased and healthy leaves of distinct plants by extracting features from input images using CNN algorithm. These features extracted help in identifying the most relevant class for images from the datasets. The authors have observed that the proposed system consumes an average time of 3.8 seconds for identifying the image class with more than 94.5% accuracy.

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Shukla, Aayush, Chetan Parmar, and Ashwini Deshmukh. "Indian Sign Language Interpreter using CNN Algorithm." Journal of Signal Processing 8, no. 1 (April 25, 2022): 28–32. http://dx.doi.org/10.46610/josp.2022.v08i01.005.

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For quiet people, sign dialect could be an imperative implies of communication. Each movement in sign dialect features a unmistakable meaning. As a result, complex meanings may be portrayed employing a blend of a few crucial pieces. Sign language could be a gesture-based dialect utilized by hard of hearing and difficult of hearing people to communicate. It may be a non-verbal dialect that is utilized to assist hard of hearing and difficult of hearing people communicate more effectively with each other and with normal individuals. Sign dialect has its claim set of standards and sentence structure for communicating productively. The two essential sorts of sign dialect acknowledgment are image-based and sensor-based. Signal acknowledgment is getting to be more prevalent in an assortment of areas, counting human interface, communication, mixed media, and security. In most cases, sign acknowledgment is connected to visual comprehension. It is isolated into two stages: discovery and distinguishing proof of signs. The practice of obtaining a characteristic from certain objects based on specified parameters is known as sign detection. Sign identification is the process of recognizing a certain shape that differentiates a thing from other objects. Language is very useful when there are no other options for communicating.

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Sinnoor, Mala. "Front Desk Humanoid Officer Using CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 900–908. http://dx.doi.org/10.22214/ijraset.2021.37247.

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This paper presents an example of using front desk humanoid officer that is a receptionist at college. A Raspberry pi-based face recognition, face mask detection is provided. When this robot used in check in process reduces cost and can be used as a great alternative for human receptionist in this growing pandemic era. This system provides security by avoiding possible unauthorized people entering into college premises and also assist visitors with route map to department.

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Sumahasan, S. "Object Detection using Deep Learning Algorithm CNN." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (July 31, 2020): 1578–84. http://dx.doi.org/10.22214/ijraset.2020.30594.

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K.Narasimha Rao, Kesani Prudhvidhar Reddy, Gopavarapu Sai Satya Sreekar, and Gade Gopinath Reddy. "Retinal blood vessels segmentation using CNN algorithm." international journal of engineering technology and management sciences 7, no. 3 (2023): 499–504. http://dx.doi.org/10.46647/ijetms.2023.v07i03.70.

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The precise identification of blood vessels in fundus is crucial for diagnosing fundus diseases. In order to address the issues of inaccurate segmentation and low precision in conventional retinal image analysis for segmentation methods, a new approach was developed.The suggested method merges the U-Net and Dense-Net approaches and aims to enhance vascular feature information. To achieve this, the method employs several techniques such asHistogram equalization with limited contrast enhancement, median filtering, normalization of data, and morphological transformation. Furthermore, to correct artifacts, the method utilizes adaptive gamma correction. Next, randomly selected image blocks are utilized as training data to expand the data and enhance the generalization capability. The Dice loss function was optimized using stochastic gradient descent to improve the accuracy of segmentation, and ultimately, the Dense-U-net model was used for performing the segmentation. The algorithm achieved specificity, accuracy, sensitivity, and AUC of 0.9896, 0.9698, 0.7931, and 0.8946 respectively, indicating significant improvement in vessel segmentation accuracy, particularly in identifying small vessels.

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Luo, Wanli, and Jialiang Wang. "The Application of A-CNN in Crowd Counting of Scenic Spots." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 2 (March 20, 2019): 305–8. http://dx.doi.org/10.20965/jaciii.2019.p0305.

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In places where people are concentrated, such as scenic spots, the statistical accuracy of existing crowd statistics algorithms is not enough. In order to solve this problem, a crowd counting algorithm based on adaptive convolution neural network (A-CNN) is proposed, which is based on video monitoring technology. The process of its pooling is dynamically adjusted according to different feature graphs. Then the pooled weights are adjusted adaptively according to the contents of each pooled domain. Therefore, CNN can extract more accurate features when processing different pooled domains under different iteration times, so as to achieve adaptive effect finally. The experimental results show that the proposed A-CNN algorithm has improved the recognition accuracy.

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Ivanov, Egor Sergeevich, and Alexandr Vladimirovich Smirnov. "Acceleration of the Advanced Segmentation Algorithm for Multispectral Images Using CNN." Program Systems: Theory and Applications 13, no. 3 (September 21, 2022): 99–112. http://dx.doi.org/10.25209/2079-3316-2022-13-3-99-112.

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Proposed an improved approach to the segmentation of multispectral images using Convolutional Neural Networks (CNN). The original algorithm was described earlier. It took into account some errors that could arise during the processing of SNA images using a sliding window. The proposed modification uses the and indices, which have a high correlation coefficient with real objects present in the images, also images pyramids were used.

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Azamimi Abdullah, Azian, Aafion Fonetta Dickson Giong, and Nik Adilah Hanin Zahri. "Cervical cancer detection method using an improved cellular neural network (CNN) algorithm." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (April 1, 2019): 210. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp210-218.

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<span>Cervical cancer is the second most common in Malaysia and the fourth frequent cancer among women in worldwide. Pap smear test is often ignored although it is actually useful, beneficial and essential as screening tool for cervical cancer. However, Pap smear images have low sensitivity as well as specificity. Therefore, it is difficult to determine whether the abnormal cells are cancerous or not. Recently, computer-based algorithms are widely used in cervical cancer screening. In this study, an improved cellular neural network (CNN) algorithm is proposed as the solution to detect the cancerous cells in real-time by undergoing the image processing of Pap smear images. A few templates are combined and modified to form an ideal CNN algorithm to detect the cancerous cells in total of 115 Pap smear images. A MATLAB based CNN is developed for an automated detection of cervix cancerous cells where the templates segmented the nucleus of the cells. From the simulation results, our proposed CNN algorithm can detect the cervix cancer cells automatically with more than 88% accuracy.</span>

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Belurkar, Atharv, Ashish Waghmare, Sahil Mallick, Nikhil Waghamode, and Prof Reshma Totare. "Weapon Detection using Yolov4, CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2058–62. http://dx.doi.org/10.22214/ijraset.2022.41702.

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Abstract: A Weapon Detection software is a very efficient way to monitor the streets and alert the operator only when there is a situation where people carrying a weapon like a gun or knife can be detected and analyzed with the help of Yolov4 object detection algorithm. And the notification of the same will be given via email to the CCTV operator with the photo where people carrying a gun or knife can be seen and also the location of the CCTV. So this will enhance the safety and security of any city with the requirement of limited man power. The proposed paper focuses on different approaches and algorithms that could potentially help in Weapon detection. Keywords: Weapon Detection, Yolov4, Google Colab, CNN

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Belurkar, Atharv, Ashish Waghmare, Sahil Mallick, Nikhil Waghamode, and Prof Reshma Totare. "Weapon Detection using Yolov4, CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2058–62. http://dx.doi.org/10.22214/ijraset.2022.41702.

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Abstract: A Weapon Detection software is a very efficient way to monitor the streets and alert the operator only when there is a situation where people carrying a weapon like a gun or knife can be detected and analyzed with the help of Yolov4 object detection algorithm. And the notification of the same will be given via email to the CCTV operator with the photo where people carrying a gun or knife can be seen and also the location of the CCTV. So this will enhance the safety and security of any city with the requirement of limited man power. The proposed paper focuses on different approaches and algorithms that could potentially help in Weapon detection. Keywords: Weapon Detection, Yolov4, Google Colab, CNN

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Rautmare, Mayuri, Vinita Mahajan, Manisha Shinde, and Chetana Shinde. "Emotion Based Music Player Using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 3168–70. http://dx.doi.org/10.22214/ijraset.2023.52308.

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Abstract: This work presents a music recommendation system that suggests songs based on the user's emotional state. The system uses computer vision techniques to detect the user's emotions through facial expressions, and a suitable song is recommended accordingly. The proposed system automates the traditional manual process of selecting music based on mood, reducing time and effort. The algorithm uses Haar Cascade and CNN algorithms to detect facial expressions and select an appropriate music track. The inbuilt camera reduces system design costs. The system is based on recommender systems, convolutional neural networks, deep learning, image processing, artificial intelligence, and classification.

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Xia, Baizhan, Hao Luo, and Shiguang Shi. "Improved Faster R-CNN Based Surface Defect Detection Algorithm for Plates." Computational Intelligence and Neuroscience 2022 (May 17, 2022): 1–11. http://dx.doi.org/10.1155/2022/3248722.

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Defect recognition plays an important part of panel inspection, and most of the current manual inspection methods are used, but the recognition efficiency and recognition accuracy are low. The Fast-Convolutional Neural Network (Faster R-CNN) algorithm is improved, and a surface defect detection algorithm based on the improved Faster R-CNN is proposed. Firstly, the algorithm improves the bilateral filtering algorithm to smooth the image texture background. Subsequently, a feature pyramid network with a shape-variable convolutional ResNet50 network can be applied to acquire defect semantic feature maps to improve the network’s ability to express the features of multiscale defects while solving the difficulty problem of many types of defects and variable shapes. To obtain more accurate defect localization information, the algorithm in this paper uses the Region of Interest Align (ROI Align) algorithm instead of the crude Region of Interest Pooling (ROI Pooling) algorithm. Then, an improved attention region recommendation network is used to improve the focus of the model on plate defects and suppress the features of complex background. Finally, a K-means algorithm is added to cluster the defect data to derive anchor frames that are better adapted to the plate defects. In this paper, a dataset containing 3216 images of surface defects of plate metal is made by acquiring surface defect images from the production site of the plate metal factory, which mainly include various defect types. This dataset is used to train and test the algorithm model of this paper, and the results of detection accuracy and detection speed are compared with those of other algorithms, which prove that the algorithm of this paper can achieve real-time detection of plate defects with high detection accuracy.

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Dahou, Abdelghani, Mohamed Abd Elaziz, Junwei Zhou, and Shengwu Xiong. "Arabic Sentiment Classification Using Convolutional Neural Network and Differential Evolution Algorithm." Computational Intelligence and Neuroscience 2019 (February 26, 2019): 1–16. http://dx.doi.org/10.1155/2019/2537689.

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In recent years, convolutional neural network (CNN) has attracted considerable attention since its impressive performance in various applications, such as Arabic sentence classification. However, building a powerful CNN for Arabic sentiment classification can be highly complicated and time consuming. In this paper, we address this problem by combining differential evolution (DE) algorithm and CNN, where DE algorithm is used to automatically search the optimal configuration including CNN architecture and network parameters. In order to achieve the goal, five CNN parameters are searched by the DE algorithm which include convolution filter sizes that control the CNN architecture, number of filters per convolution filter size (NFCS), number of neurons in fully connected (FC) layer, initialization mode, and dropout rate. In addition, the effect of the mutation and crossover operators in DE algorithm were investigated. The performance of the proposed framework DE-CNN is evaluated on five Arabic sentiment datasets. Experiments’ results show that DE-CNN has higher accuracy and is less time consuming than the state-of-the-art algorithms.

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Saleh, Abdulrazak Yahya, and Lim Huey Chern. "Autism Spectrum Disorder Classification Using Deep Learning." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 08 (August 16, 2021): 103. http://dx.doi.org/10.3991/ijoe.v17i08.24603.

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<p class="0abstract">The goal of this paper is to evaluate the deep learning algorithm for people placed in the Autism Spectrum Disorder (ASD) classification. ASD is a developmental disability that causes the affected people to have significant communication, social, and behavioural challenges. People with autism are saddled with communication problems, difficulties in social interaction and displaying repetitive behaviours. Several methods have been used to classify the ASD from non-ASD people. However, there is a need to explore more algorithms that can yield better classification performance. Recently, deep learning methods have significantly sharpened the cutting edge of learning algorithms in a wide range of artificial intelligence tasks. These artificial intelligence tasks refer to object detection, speech recognition, and machine translation. In this research, the convolutional neural network (CNN) is employed. This algorithm is used to find processes that can classify ASD with a higher level of accuracy. The image data is pre-processed; the CNN algorithm is then applied to classify the ASD and non-ASD, and the steps of implementing the CNN algorithm are clearly stated. Finally, the effectiveness of the algorithm is evaluated based on the accuracy performance. The support vector machine (SVM) is utilised for the purpose of comparison. The CNN algorithm produces better results with an accuracy of 97.07%, compared with the SVM algorithm. In the future, different types of deep learning algorithms need to be applied, and different datasets can be tested with different hyper-parameters to produce more accurate ASD classifications.</p>

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He, Haitao, Zhifu Shang, Mingjie Wu, and Yuling Zhang. "Movie Recommendation System Based on Traditional Recommendation Algorithm and CNN Model." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 255–61. http://dx.doi.org/10.54097/hset.v34i.5481.

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As streaming services have expanded in recent years, an excellent recommendation algorithm, as one of the core technologies in movie service, brings huge benefits. Although the current application research of recommendation algorithms is mature, most systems or products usually rely on only one main algorithm. This makes it difficult for the system to overcome the shortcomings of various algorithms and cannot benefit from the combination of multiple recommendation algorithms. In addition, the widely used recommendation models are found to be unable to extract the finer features of users and movies, and the calculation time is very long. Meanwhile, the recommendation results are inaccurate, and they are not user-friendly. In this research, we design and construct a system to recommend movies which is consisted by a model based on a convolutional neural network consisting. By extracting the features of users and movies, we can calculate the direct similarity of different users' movies and then predict movie ratings to give recommendations. When extracting features of users and movies, we refer to traditional algorithms based on content and content. Through the "cosine similarity" and "user movie score matrix", the Item-Based Collaborative Filtering and User-Based Collaborative Filtering can be well implemented. To sum up, our movie recommendation system is based on Convolutional Neural Network (CNN) model and the performance of the system is improved by adopting multiple recommendation algorithms such as content-based recommendation and system filtering. We efficiently trained the neural network and finally built a movie recommendation system with a faster operation speed, although some parts are not perfect.

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M., Sivaram Chowdary, and Puviarasi R. "Accuracy Improvement in Disease Identification of Mango Leaf Using CNN Algorithm Compared with Fuzzy Algorithm." ECS Transactions 107, no. 1 (April 24, 2022): 11889–903. http://dx.doi.org/10.1149/10701.11889ecst.

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Aim: The main aim of this work is to measure the accuracy in the identification of mango leaf diseases using Convolutional Neural Networks (CNN) compared with Fuzzy Logic. Materials and methods: The data set contains 10 images collected from the seed buzz website and these images are used for training and testing the predictive model in MATLAB. Statistical analysis is done using SPSS software. In the SPSS tool, the measured accuracy of CNN is compared with the Fuzzy model accuracy. Result: The proposed system using CNN achieved high accuracy of 95.2%, whereas the fuzzy mean algorithm gives an accuracy of 93.5 with the significance value 0.038 for accuracy and 0.073 for sensitivity. Conclusion: The outcome of the study confirms that the CNN-based model provides better results in enhancing the accuracy of disease identification in mango leaves.

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Lu, Wentao, Gengxin Hua, Yunfu Zhao, and Jiantao Zhou. "Design and Optimization of Star Recognition Algorithm Based on Hierarchical CNN." Journal of Physics: Conference Series 2132, no. 1 (December 1, 2021): 012009. http://dx.doi.org/10.1088/1742-6596/2132/1/012009.

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Abstract With the rapid-development of AI technology, artificial intelligence algorithms for the aerospace applications have shown very good simulation performance in many areas. Among the spaceborne application fields, star identification can be seen as a typical pattern recognition process. It’s also the key part of attitude determination of the satellites, which requires the algorithm to be robust and efficient due to the limited computing and storing resources of the spaceborne computers. Nevertheless, most of the previous algorithms are not possible to be applied in practical due to the reasons above. This article proposes a strategy of constructing ‘net-structure’ images of stars to build the datasets for training and testing. Besides, a hierarchical convolutional neural network(CNN) with a small size is also designed. It performs good results on robustness and efficiency in the experiments. In the end, a method of fusing the Conv layers and the batch normalization (BN)layers is also adopted to further accelerate the algorithm.

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Wei, Xiaoqin, Xiaowen Chen, Ce Lai, Yuanzhong Zhu, Hanfeng Yang, and Yong Du. "Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures." BioMed Research International 2021 (December 16, 2021): 1–11. http://dx.doi.org/10.1155/2021/9956983.

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Liver image segmentation has been increasingly employed for key medical purposes, including liver functional assessment, disease diagnosis, and treatment. In this work, we introduce a liver image segmentation method based on generative adversarial networks (GANs) and mask region-based convolutional neural networks (Mask R-CNN). Firstly, since most resulting images have noisy features, we further explored the combination of Mask R-CNN and GANs in order to enhance the pixel-wise classification. Secondly, k -means clustering was used to lock the image aspect ratio, in order to get more essential anchors which can help boost the segmentation performance. Finally, we proposed a GAN Mask R-CNN algorithm which achieved superior performance in comparison with the conventional Mask R-CNN, Mask-CNN, and k -means algorithms in terms of the Dice similarity coefficient (DSC) and the MICCAI metrics. The proposed algorithm also achieved superior performance in comparison with ten state-of-the-art algorithms in terms of six Boolean indicators. We hope that our work can be effectively used to optimize the segmentation and classification of liver anomalies.

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İni̇k, Özkan, Mustafa Altıok, Erkan Ülker, and Barış Koçer. "MODE-CNN: A fast converging multi-objective optimization algorithm for CNN-based models." Applied Soft Computing 109 (September 2021): 107582. http://dx.doi.org/10.1016/j.asoc.2021.107582.

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K, Vishnu Swaroop, and Saravanan M S. "Prediction of Human Facial Expression with Machine Learning Classifier Using Convolutional Neural Network Instead of Traditional Pixel Value Algorithm for Better Accuracy." ECS Transactions 107, no. 1 (April 24, 2022): 13937–49. http://dx.doi.org/10.1149/10701.13937ecst.

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This study is to detect facial expressions using facial images for machine learning algorithms. The objective of this research is to improve the accuracy of human facial expressions. Materials and methods: The study used 71 samples with two groups of algorithms with the g-power value of 80 percent and the novel heart images were collected from various web sources with recent study findings. To predict facial expression in humans already, the Traditional Pixel Value (TPV) algorithm has found 82% of accuracy, therefore this study needs to find better accuracy for facial detection with the Convolutional Neural Network (CNN) machine learning algorithm. Results: This research study found 93% of accuracy for facial detection using the CNN algorithm. Conclusion: This study concludes that the CNN algorithm on facial images is significantly better than the TPV algorithm.

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Zhang, Chun Yang, and Dae-Hyun Kim. "Analysis of Impact Position based on Deep Learning CNN Algorithm." Transactions of the Korean Society of Mechanical Engineers - A 44, no. 6 (June 30, 2020): 405–12. http://dx.doi.org/10.3795/ksme-a.2020.44.6.405.

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Yang, Rui, Ying Zhang, Miao Xu, and Jing Ma. "Image Features of Magnetic Resonance Angiography under Deep Learning in Exploring the Effect of Comprehensive Rehabilitation Nursing on the Neurological Function Recovery of Patients with Acute Stroke." Contrast Media & Molecular Imaging 2021 (September 10, 2021): 1–9. http://dx.doi.org/10.1155/2021/1197728.

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This study was to explore the effects of imaging characteristics of magnetic resonance angiography (MRA) based on deep learning on the comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke. In this study, 84 patients with acute stroke who were treated in hospital were selected as the research objects, and they were rolled into a control group (routine care) and an experimental group (comprehensive rehabilitation care). The dense dilated block-convolution neural network (DD-CNN) algorithm under deep learning for cerebrovascular was adopted to assess the effect of comprehensive rehabilitation care on the neurological recovery of patients with acute stroke. The results showed that the Berg scale scores, Fugl-Meyer scores, and Functional Independence Measure (FIM) scores of the experimental group of patients after 6 weeks and 12 weeks of comprehensive rehabilitation nursing were greatly different from those before treatment, showing statistical differences ( P < 0.05 ). Compared with conventional magnetic resonance imaging (MRI) images, MRA images based on CNN algorithm, Dense Net algorithm, and DD-CNN algorithm can more clearly show the patient’s cerebral artery occlusion. The average dice similarity coefficient (DSC) values of CNN algorithm, Dense Net algorithm, and DD-CNN algorithm were determined to be 84.3%, 95.7%, and 97.8%, respectively; the average sensitivity (Sen) values of the three algorithms were 76.1%, 95.4%, and 96.8%, respectively; and the average accuracy (Acc) values were 87.9%, 96.3%, and 97.9%, respectively. Thus, there were statistically obvious differences among the three algorithms in terms of average values of DSC, Sen, and Acc ( P < 0.05 ). The MRA images processed by the DD-CNN algorithm showed that the degree of neurological recovery of the experimental group was observably greater than that of the control group, and the difference was statistically obvious ( P < 0.05 ). In short, the image features of MRA based on the deep learning DD-CNN algorithm showed good application value in studying the effect of comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke, and it was worthy of promotion.

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Zhao, Hangxing, Jingbin Li, Jing Nie, Jianbing Ge, Shuo Yang, Longhui Yu, Yuhai Pu, and Kang Wang. "Identification Method for Cone Yarn Based on the Improved Faster R-CNN Model." Processes 10, no. 4 (March 24, 2022): 634. http://dx.doi.org/10.3390/pr10040634.

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To solve the problems of high labor intensity, low efficiency, and frequent errors in the manual identification of cone yarn types, in this study five kinds of cone yarn were taken as the research objects, and an identification method for cone yarn based on the improved Faster R-CNN model was proposed. In total, 2750 images were collected of cone yarn samples in real of textile industry environments, then data enhancement was performed after marking the targets. The ResNet50 model with strong representation ability was used as the feature network to replace the VGG16 backbone network in the original Faster R-CNN model to extract the features of the cone yarn dataset. Training was performed with a stochastic gradient descent approach to obtain an optimally weighted file to predict the categories of cone yarn. Using the same training samples and environmental settings, we compared the method proposed in this paper with two mainstream target detection algorithms, YOLOv3 + DarkNet-53 and Faster R-CNN + VGG16. The results showed that the Faster R-CNN + ResNet50 algorithm had the highest mean average precision rate for the five types of cone yarn at 99.95%, as compared with the YOLOv3 + DarkNet-53 algorithm with a mean average precision rate that was 2.24% higher and the Faster R-CNN + VGG16 algorithm with a mean average precision that was 1.19% higher. Regarding cone yarn defects, shielding, and wear, the Faster R-CNN + ResNet50 algorithm can correctly identify these issues without misdetection occurring, with an average precision rate greater than 99.91%.

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Wagaa, Nesrine, Hichem Kallel, and Nédra Mellouli. "Improved Arabic Alphabet Characters Classification Using Convolutional Neural Networks (CNN)." Computational Intelligence and Neuroscience 2022 (January 11, 2022): 1–16. http://dx.doi.org/10.1155/2022/9965426.

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Handwritten characters recognition is a challenging research topic. A lot of works have been present to recognize letters of different languages. The availability of Arabic handwritten characters databases is limited. Motivated by this topic of research, we propose a convolution neural network for the classification of Arabic handwritten letters. Also, seven optimization algorithms are performed, and the best algorithm is reported. Faced with few available Arabic handwritten datasets, various data augmentation techniques are implemented to improve the robustness needed for the convolution neural network model. The proposed model is improved by using the dropout regularization method to avoid data overfitting problems. Moreover, suitable change is presented in the choice of optimization algorithms and data augmentation approaches to achieve a good performance. The model has been trained on two Arabic handwritten characters datasets AHCD and Hijja. The proposed algorithm achieved high recognition accuracy of 98.48% and 91.24% on AHCD and Hijja, respectively, outperforming other state-of-the-art models.

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Journal articles on the topic 'CNN ALGORITHM'

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