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Artykuły w czasopismach na temat „CNN ALGORITHM”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 25 lipca 2025

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1

Xu, Siyi, Wenwen Liu, Chengpei Wu, and Junli Li. "CNN-HT: A Two-Stage Algorithm Selection Framework." Entropy 26, no. 3 (2024): 262. http://dx.doi.org/10.3390/e26030262.

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The No Free Lunch Theorem tells us that no algorithm can beat other algorithms on all types of problems. The algorithm selection structure is proposed to select the most suitable algorithm from a set of algorithms for an unknown optimization problem. This paper introduces an innovative algorithm selection approach called the CNN-HT, which is a two-stage algorithm selection framework. In the first stage, a Convolutional Neural Network (CNN) is employed to classify problems. In the second stage, the Hypothesis Testing (HT) technique is used to suggest the best-performing algorithm based on the s

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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 (2023): 5297–300. http://dx.doi.org/10.55248/gengpi.4.523.43476.

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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

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Diqi, Mohammad. "Waste Classification using CNN Algorithm." International Conference on Information Science and Technology Innovation (ICoSTEC) 1, no. 1 (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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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 (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 stu

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Roopa, Sri Paladugu, Immadisetty Anusha, and Ramesh M. "Skin Cancer Detection using CNN Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 45–49. https://doi.org/10.35940/ijeat.E1079.089620.

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The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is w

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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 (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

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Jagtap, Prof Priyanka T. "Disease Prediction Using CNN Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35207.

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In this project, I have endeavored to revolutionize disease detection by harnessing the power of artificial intelligence, allowing users to effortlessly and accurately identify three specific diseases from the comfort of their own homes with just a few clicks. This innovative approach eliminates the need to endure days of anticipation for traditional diagnostic reports, ensuring that treatment can commence promptly. The cornerstone of this project lies in the application of Convolutional Neural Networks (CNNs), a cutting-edge deep learning technology. These networks are designed to take input

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Jin, Huang J., and Cathern Hibbins. "Effective Ransomware Attacks Detection Using CNN Algorithm." International Journal of Informatics and Computation 5, no. 2 (2023): 46. http://dx.doi.org/10.35842/ijicom.v5i2.62.

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This study identified ransomware threats in social media platforms by evaluating the performance of Assessing different machine-learning algorithms in various aspects of detecting and classifying ransomware content. The primary problem revolves around the need to enhance cybersecurity within the dynamic landscape of social media, where users are increasingly susceptible to malicious attacks. The research objectives involve assessing the effectiveness of different algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN),

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Bhalake, Prof. S. B., Aditya Borgave, Nitin Abhang, Nitin Shinde, and Navanath Thorat. "Predictive Maintenance Failure Analysis of Hacksaw Blade Using ML Algorithm." International Journal of Ingenious Research, Invention and Development 1, no. 4 (2023): 18–23. https://doi.org/10.5281/zenodo.8050907.

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<em>The In this Research Article we are going to study Predictive Maintenance Failure Analysis of Hacksaw Blade Using ML Algorithm, with the help of previous paper related to algorithm such as ANN, CNN, SVM, KNN, PCA, etc. Different Parameters are studied related Hacksaw operation and Collected Data of same for analyze collected data we are going to used ANN algorithms with the help of ANN algorithm we will find the fault in Hacksaw operation. By using this we will obtain efficiency of hacksaw operation.</em>

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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 (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 exp

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Anjani, Sarah, and Maria Yohana Jawa Betan. "Detecting Acute Liver Diseases Using CNN Algorithm." International Journal of Informatics Engineering and Computing 1, no. 2 (2024): 23–37. https://doi.org/10.70687/ijimatic.v1.i2.45.

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This study tackles the critical challenge of detecting Acute Liver Failure (ALF) using machine learning algorithms. The main goal is to assess the effectiveness of several algorithms, including Convolutional Neural Network (CNN), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting, in accurately classifying cases of ALF. For this purpose, a comprehensive dataset with 8,785 records and 30 features from Kaggle is utilized, involving thorough preprocessing steps like feature selection, data cleaning, and normalization. The rese

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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 (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 subse

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Budiono, Arvel Adelar, and Yulianto Tejo Putranto. "COMPARISON BUS PASSENGER COUNTING AND GENDER DETECTION USING YOLOV8, FASTER R-CNN, AND MASK R-CNN ALGORITHM." Proxies : Jurnal Informatika 8, no. 2 (2025): 107–17. https://doi.org/10.24167/proxies.v8i2.13022.

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Bus passengers' data are crucial for the bus agents, especially in Indonesia. With this data, bus agents could identify the traffic for each route of the bus. To handle this problem, many researchers have made a system to count and detect the public transportation passengers with different algorithms. Many researchers defined that You Look Only Once (YOLO) has best performance to overcome the object detection problem that has similarity in this research. The Convolutional Neural Network algorithm is also not inferior in implementing object detection either. In this research, it will investigat

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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 (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 achieve

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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 %. R

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H, Romanah, and Juwita Sampe Ruru. "Enhancing Cardiovascular Diseases Classification using CNN Algorithm." International Journal of Informatics and Computation 5, no. 2 (2024): 16. http://dx.doi.org/10.35842/ijicom.v5i2.60.

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This study focuses on using machine learning algorithms to detect cardiovascular diseases, addressing the critical need for accurate and timely diagnosis of these conditions, which are significant contributors to global morbidity and mortality. The research aims to evaluate the performance of various machine learning algorithms such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting in categorizing patients into 'yes' or 'no' groups for cardiovascular diseases based on a thorough datas

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Karo Karo, Ichwanul Muslim, Justaman Arifin Karo Karo, Manan Ginting, et al. "Comparison of Activation Functions on Convolutional Neural Networks (CNN) to Identify Mung Bean Quality." Sinkron 8, no. 4 (2023): 2757–64. http://dx.doi.org/10.33395/sinkron.v8i4.13107.

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Mung bean production levels by farmers in Indonesia are not stable. When there is a surplus, the stock of mung beans in the warehouse will accumulate, the storage factor affects the quality of mung beans. Indicators of quality mung beans can be seen from the color and size through direct observation. However, the aspect of view and assessment and the level of health of each observer is a human error in the classification of mung bean quality so that the results are less than optimal. One alternative way to identify object quality is to use deep learning algorithms. One of the popular deep lear

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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 (2020): 1541–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202257.

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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 (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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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 (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, fus

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Liu, Yuepeng, Xingyu Zhou, and Hongwei Han. "Lightweight CNN-Based Method for Spacecraft Component Detection." Aerospace 9, no. 12 (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 analyz

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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 convol

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Rawal, Purvi. "Skin Cancer Diagnosis: Integrating CNN and Machine Learning Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29407.

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Skin cancer is serious health concern that arises from abnormal growth of skin cells. It encompasses of three types, melanoma being the most aggressive form of cancer. The skin cancer starts to arise globally so accordingly there is being necessary public awareness, prevention for this, and strategies for early detection. Accurate diagnosis treatment and Early detection are better for effective management which will improve accuracy and efficiency for diagnostic process. Also, the Machine learning and deep learning algorithms we research and analyse for better outcomes and for innovation in fi

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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. T

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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 (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 (C

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Nisha N, Shrieya S, R Shreyas, Keerti Kulkarni, and Jyoti Raghavendra Munavalli. "Kidney Stone Detection using CNN Algorithm." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1814–23. https://doi.org/10.32628/cseit241061204.

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Kidney stones are a prevalent condition that can cause intense pain, with early detection being critical for effective treatment. Traditionally, radiologists rely on CT scans or X-rays to identify kidney stones, but this manual process can be time-consuming and subject to human error. Convolutional Neural Networks (CNNs) are emerging as a valuable tool in medical imaging, offering the potential to automate the kidney stone detection process and improve diagnostic accuracy. By training on extensive datasets of kidney images, CNNs can learn to detect stones and reduce inconsistencies between dif

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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 (1993): 392–402. http://dx.doi.org/10.1109/81.238343.

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Bai, Ms P. Annapurna, Yeruva Bala Tejaswini, Vana Kameswara Rao, and Ponnapalli Omkareaswari Bhavana. "Parkinson Disease Detection Using CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 2342–46. http://dx.doi.org/10.22214/ijraset.2024.62084.

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Abstract: Parkinson's disease, a progressive neurological disorder, results from the depletion of dopamine-producing neurons in the brain, leading to diminished motor function. Common symptoms include tremors, rigidity, bradykinesia, shivering, and impaired balance. This study introduces two neural network architectures: the Voice Impairment Classifier, designed for early disease detection. The research conducted a thorough assessment of convolutional neural networks (CNNs) for classifying gait signals transformed into spectrogram images, and deep dense networks for analyzing voice recordings.

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Ramteke, Arpit B. "Food Image Detection Using CNN Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30635.

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In the coming years, the fusion of deep learning with computer vision will usher in significant advancements in various domains, including food image analysis. A pioneering approach will be introduced to meet the rapidly increasing demand for automated recipe generation from food images, leveraging state-of-the-art deep learning techniques. The proliferation of social media platforms and the omnipresence of food-related content will necessitate efficient algorithms capable of understanding food images and generating coherent recipes. The proposed framework will integrate deep learning neural n

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Gunjal, Prof S. P. "Sign Language Analysis Using CNN Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem28223.

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Sign language is a rich and diverse mode of communication used by Deaf and hard of-hearing communities around the world. This paper presents a comprehensive analysis of sign language, focusing on its linguistic and gestural elements. Our study encompasses an exploration of the structural properties of sign languages, their historical evolution, and the cognitive and neural mechanisms underlying sign language processing. We begin by providing an overview of sign language as a natural and fully developed language with its own syntax, semantics, and phonological features. Drawing from various sig

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Sowmya, Sundari L. K., Mathapati Mallayya, Kumar K. Dilip, and Sachin. "SKIN DISEASES RECOGNITION USING CNN ALGORITHM." Advanced Innovations in Computer Programming Languages 5, no. 2 (2023): 36–43. https://doi.org/10.5281/zenodo.8149186.

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<em>The human skin is an extraordinary structure that frequently encounters both known and unknown diseases. Conducting regular and thorough skin examinations plays a vital role in the early detection of any detrimental or incipient changes in the skin, which could potentially lead to skin disease. This research paper is dedicated to addressing this challenge by proposing an effective solution using ML models, specifically a Convolutional Neural Network (CNN) system. The proposed CNN model exhibits superior accuracy in recognizing different types of skin diseases. By harnessing the power of ma

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Shang, Xiaofeng. "A Study of Deep Learning Neural Network Algorithms and Genetic Algorithms for FJSP." Journal of Applied Mathematics 2023 (October 25, 2023): 1–13. http://dx.doi.org/10.1155/2023/4573352.

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Flexible job-shop scheduling problem (FJSP) is a new research hotspot in the field of production scheduling. To solve the multiobjective FJSP problem, the production of flexible job shop can run normally and quickly. This research takes into account various characteristics of FJSP problems, such as the need to ensure the continuity and stability of processing, the existence of multiple objectives in the whole process, and the constant complexity of changes. It starts with deep learning neural networks and genetic algorithms. Long short-term memory (LSTM) and convolutional neural networks (CNN)

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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 (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 Ope

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Xin, Mingyong, Changbao Xu, Jipu Gao, Yu Wang, and Bo Wang. "High-Precision Recognition Algorithm for Equipment Defects Based on Mask R-CNN Algorithm Framework in Power System." Processes 12, no. 12 (2024): 2940. https://doi.org/10.3390/pr12122940.

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In current engineering applications, target detection based on power vision neural networks has problems with low accuracy and difficult defect recognition. Thus, this paper proposes a high-precision substation equipment defect recognition algorithm based on the Mask R-CNN algorithm framework to achieve high-precision substation equipment defect monitoring. The effectiveness of the Mask R-CNN algorithm is compared and analyzed in substation equipment defect recognition and the applicability of the Mask R-CNN algorithm in edge computing. According to different types of substation equipment defe

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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 bet

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Ahmed, Thamer Radhi, Hussein Zayer Wael, and Manaa Dakhil Adel. "Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 3 (2021): 1928–39. https://doi.org/10.11591/ijpeds.v12.i3.pp1928-1939.

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This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using onedimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents s

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Rizk, Faris H., Sofia Arkhstan, Ahmed Mohamed Zaki, Mohamed Ahmed Kandel, and S. K. Towfek. "Integrated CNN and Waterwheel Plant Algorithm for Enhanced Global Traffic Detection." Journal of Artificial Intelligence and Metaheuristics 6, no. 2 (2023): 36–45. http://dx.doi.org/10.54216/jaim.060204.

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Traffic detection is critical in ensuring road safety and efficient traffic management, demanding deploying accurate and practical algorithms. This research explores the fusion of Convolutional Neural Networks (CNNs) and the Waterwheel Plant Algorithm to augment global traffic detection capabilities, utilizing a diverse dataset primarily collected from Turkey. A comprehensive evaluation of prominent CNN architectures, such as VGG19Net, AlexNet, ResNet-50, GoogLeNet, and a generic CNN, underscores substantial efficacy, with the CNN achieving an accuracy of 92.14%. Introducing the Waterwheel Pla

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Pramudya, Franciska Nugrahaeni Siwi, and Yonathan Purbo Santosa. "COMPARISON BETWEEN CNN AND RANDOM FOREST PERFORMANCE IN DETECTING HOAX INDONESIAN NEWS ARTICLES." Proxies : Jurnal Informatika 7, no. 1 (2024): 1–14. http://dx.doi.org/10.24167/proxies.v7i1.12463.

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Hoax news is a serious problem in this era. Many people are easily led by opinions made by certain people without seeing the truth or looking for existing facts. To overcome this, many researchers have conducted hoax news detection using various algorithms. In some studies, it is said that Random Forest has better performance to overcome this hoax news problem. In other studies, it is also said that CNN has the same level of performance as the Random Forest algorithm. In addition, the problem that is often found is the error in prediction due to improper preprocessing methods. Therefore, in th

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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 (2019): 2369–80. http://dx.doi.org/10.1007/s12652-019-01259-5.

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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 (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 t

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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 (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 th

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Natália, Neves, and Matos Fernando. "UTILIZAÇÃO DA TÉCNICA CNN EM VARIADOS EVENTOS E COMO PROPOSTA PARA PREDIÇÃO DE NLOS." Revistaft 28, no. 136 (2024): 6. https://doi.org/10.5281/zenodo.12768006.

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A CNN é considerada uma variação existente nas redes de percepções de múltiplas camadas. Foi desenvolvida a partir do processo biológico de processamentos de dados visuais. O objetivo central desse estudo é detectar a relevância das CNNs em variados eventos e de prevenir riscos da presença de NLoS no ambiente. A metodologia utilizada se baseou em uma produção bibliográfica com analise de materiais científicos publicados na revista cientifica ciências da computação e na plataform

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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 (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 wid

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Sahu, Alok. "EnviroSmart – A Waste Management System using CNN." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 3187–97. https://doi.org/10.22214/ijraset.2025.68067.

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EnviroSmart" is an intelligent waste management system designed to address the growing need for efficient waste disposal and recycling solutions. Utilizing advanced machine learning algorithms, the system detects and classifies waste items by analyzing their images. Once an image is uploaded, EnviroSmart identifies the type of waste, such as plastic, glass, paper, or organic materials, and provides users with detailed instructions on how to properly manage it. This may include guidance on recycling, composting, or proper disposal methods, promoting environmentally responsible behavior. To ensu

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Pradila, Rike, and Yuliana Aprillia Sahuburua. "Effective Seashell Image Classification Using CNN Algorithm." International Journal of Informatics Engineering and Computing 1, no. 2 (2024): 12–22. https://doi.org/10.70687/ijimatic.v1.i2.44.

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Seashell classification presents significant challenges in image processing, particularly in distinguishing between blood shells (Anadara granosa) and feather mussels (Anadara antiquata). This study leverages deep learning and computer vision techniques to develop a classification model for seashell images using Convolutional Neural Networks (CNN). Additionally, we propose the RunCNN method to compare its performance with CNN. The research involves collecting a large dataset of blood shells and feather mussels, preprocessing the data, training the models, and evaluating their performance. Expe

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Saoji, Dr Saurabh, Sakshi Mahadik, Dhanashri Gajare, Akanksha Kshatriya, and Vaishnavi Patil. "Face Mask Detection for Automated Entry System Using CNN Algorithm." International Journal of Research Publication and Reviews 4, no. 12 (2023): 2996–3000. http://dx.doi.org/10.55248/gengpi.4.1223.123529.

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Anant Taywade Amit, Richa. "Implementation of CNN Algorithm for Real Time Facial Expression Recognition." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 244–46. http://dx.doi.org/10.21275/sr23731115320.

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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 (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 n

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K.Mirunalini and Kalyani David Dr.Vasantha. "Traffic sign Detection using CNN." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 3 (2021): 129–35. https://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 n

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