Relevant bibliographies by topics / Modified convolutional neural network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Modified convolutional neural network'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Modified convolutional neural network"

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Sapunov, V. V., S. A. Botman, G. V. Kamyshov, and N. N. Shusharina. "Application of Convolution with Periodic Boundary Condition for Processing Data from Cylindrical Electrode Arrays." INFORMACIONNYE TEHNOLOGII 27, no. 3 (2021): 125–31. http://dx.doi.org/10.17587/it.27.125-131.

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In this paper, modification of convolutional neural networks for purposes of processing electromyographic data obtained from cylindrical arrays of electrodes was proposed. Taking into account the spatial symmetry of the array, convolution operation was redefined using periodic boundary conditions, which allowed to construct a neural network that is invariant to rotations of electrodes array around its axis. Applicability of the proposed approach was evaluated by constructing a neural network containing a new type of convolutional layer and training it on the open UC2018 DualMyo dataset in orde
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Wasim Khan. "Image Classification using modified Convolutional Neural Network." Journal of Electrical Systems 20, no. 3 (2024): 3465–72. https://doi.org/10.52783/jes.4982.

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Image classification is the field of research since decades. With evaluation of new technologies, the performance of image classification has been improved and this is evident by it’s us in routine life. However there are scopes to use the deep learning networks to further improve the complex image classification problems. In this paper, the Convolution neural network based(CNN) image classification is evaluated by changing the parameters of CNN like number of layers, number of neurons, block size of convolution operation etc. The parametric analysis in terms of accuracy number of iteration fo
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Iatsenko, D. V., and B. B. Zhmaylov. "IMPROVING THE EFFICIENCY OF THE CONVOLUTIONAL NEURAL NETWORK USING THE METHOD OF INCREASING THE RECEPTIVE FIELD." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 18–24. http://dx.doi.org/10.14489/vkit.2020.09.pp.018-024.

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In many pattern recognition problems solved using convolutional neural networks (CNN), one of the important characteristics of network architecture is the size of the convolution kernel, since it coincides with the size of the maximum element that can act as a recognition sign. However, increasing the size of the convolution kernel greatly increases the number of tunable network parameters. The method of effective receptive field was first applied on AlexNet in 2012. The practical application of the method of increasing the effective receptive field without increasing convolution kernel size i
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Iatsenko, D. V., and B. B. Zhmaylov. "IMPROVING THE EFFICIENCY OF THE CONVOLUTIONAL NEURAL NETWORK USING THE METHOD OF INCREASING THE RECEPTIVE FIELD." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 18–24. http://dx.doi.org/10.14489/vkit.2020.09.pp.018-024.

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In many pattern recognition problems solved using convolutional neural networks (CNN), one of the important characteristics of network architecture is the size of the convolution kernel, since it coincides with the size of the maximum element that can act as a recognition sign. However, increasing the size of the convolution kernel greatly increases the number of tunable network parameters. The method of effective receptive field was first applied on AlexNet in 2012. The practical application of the method of increasing the effective receptive field without increasing convolution kernel size i
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Murinto, Murinto, and Sri Winiarti. "Modified particle swarm optimization (MPSO) optimized CNN’s hyperparameters for classification." International Journal of Advances in Intelligent Informatics 11, no. 1 (2025): 133. https://doi.org/10.26555/ijain.v11i1.1303.

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This paper proposes a convolutional neural network architectural design approach using the modified particle swarm optimization (MPSO) algorithm. Adjusting hyper-parameters and searching for optimal network architecture from convolutional neural networks (CNN) is an interesting challenge. Network performance and increasing the efficiency of learning models on certain problems depend on setting hyperparameter values, resulting in large and complex search spaces in their exploration. The use of heuristic-based searches allows for this type of problem, where the main contribution in this research
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Sun, Kai, Jiangshe Zhang, Junmin Liu, Shuang Xu, Xiangyong Cao, and Rongrong Fei. "Modified Dynamic Routing Convolutional Neural Network for Pan-Sharpening." Remote Sensing 15, no. 11 (2023): 2869. http://dx.doi.org/10.3390/rs15112869.

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Based on deep learning, various pan-sharpening models have achieved excellent results. However, most of them adopt simple addition or concatenation operations to merge the information of low spatial resolution multi-spectral (LRMS) images and panchromatic (PAN) images, which may cause a loss of detailed information. To tackle this issue, inspired by capsule networks, we propose a plug-and-play layer named modified dynamic routing layer (MDRL), which modifies the information transmission mode of capsules to effectively fuse LRMS images and PAN images. Concretely, the lower-level capsules are ge
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Adhari, Firman Maulana, Taufik Fuadi Abidin, and Ridha Ferdhiana. "License Plate Character Recognition using Convolutional Neural Network." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (2022): 51–60. http://dx.doi.org/10.20473/jisebi.8.1.51-60.

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Background: In the last decade, the number of registered vehicles has grown exponentially. With more vehicles on the road, traffic jams, accidents, and violations also increase. A license plate plays a key role in solving such problems because it stores a vehicle’s historical information. Therefore, automated license-plate character recognition is needed. Objective: This study proposes a recognition system that uses convolutional neural network (CNN) architectures to recognize characters from a license plate’s images. We called it a modified LeNet-5 architecture. Methods: We used four differen
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Misko, Joshua, Shrikant S. Jadhav, and Youngsoo Kim. "Extensible Embedded Processor for Convolutional Neural Networks." Scientific Programming 2021 (April 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/6630552.

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Convolutional neural networks (CNNs) require significant computing power during inference. Smart phones, for example, may not run a facial recognition system or search algorithm smoothly due to the lack of resources and supporting hardware. Methods for reducing memory size and increasing execution speed have been explored, but choosing effective techniques for an application requires extensive knowledge of the network architecture. This paper proposes a general approach to preparing a compressed deep neural network processor for inference with minimal additions to existing microprocessor hardw
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Prochukhan, Dmytro. "IMPLEMENTATION OF TECHNOLOGY FOR IMPROVING THE QUALITY OF SEGMENTATION OF MEDICAL IMAGES BY SOFTWARE ADJUSTMENT OF CONVOLUTIONAL NEURAL NETWORK HYPERPARAMETERS." Information and Telecommunication Sciences, no. 1 (June 24, 2023): 59–63. http://dx.doi.org/10.20535/2411-2976.12023.59-63.

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Background. The scientists have built effective convolutional neural networks in their research, but the issue of optimal setting of the hyperparameters of these neural networks remains insufficiently researched. Hyperparameters affect model selection. They have the greatest impact on the number and size of hidden layers. Effective selection of hyperparameters improves the speed and quality of the learning algorithm. It is also necessary to pay attention to the fact that the hyperparameters of the convolutional neural network are interconnected. That is why it is very difficult to manually sel
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Luo, Guoliang, Bingqin He, Yanbo Xiong, et al. "An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression." Sensors 23, no. 4 (2023): 2250. http://dx.doi.org/10.3390/s23042250.

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Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compress
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Dissertations / Theses on the topic "Modified convolutional neural network"

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Ayoub, Issa. "Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39337.

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Affective computing has gained significant attention from researchers in the last decade due to the wide variety of applications that can benefit from this technology. Often, researchers describe affect using emotional dimensions such as arousal and valence. Valence refers to the spectrum of negative to positive emotions while arousal determines the level of excitement. Describing emotions through continuous dimensions (e.g. valence and arousal) allows us to encode subtle and complex affects as opposed to discrete emotions, such as the basic six emotions: happy, anger, fear, disgust, sad and n
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Long, Cameron E. "Quaternion Temporal Convolutional Neural Networks." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1565303216180597.

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Bylund, Andreas, Anton Erikssen, and Drazen Mazalica. "Hyperparameters impact in a convolutional neural network." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18670.

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Machine learning and image recognition is a big and growing subject in today's society. Therefore the aim of this thesis is to compare convolutional neural networks with different hyperparameter settings and see how the hyperparameters affect the networks test accuracy in identifying images of traffic signs. The reason why traffic signs are chosen as objects to evaluate hyperparameters is due to the author's previous experience in the domain. The object itself that is used for image recognition does not matter. Any dataset with images can be used to see the hyperparameters affect. Grid search
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Reiling, Anthony J. "Convolutional Neural Network Optimization Using Genetic Algorithms." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512662981172387.

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DiMascio, Michelle Augustine. "Convolutional Neural Network Optimization for Homography Estimation." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1544214038882564.

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Embretsén, Niklas. "Representing Voices Using Convolutional Neural Network Embeddings." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261415.

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In today’s society services centered around voices are gaining popularity. Being able to provide the users with voices they like, to obtain and sustain their attention, is of importance for enhancing the overall experience of the service. Finding an efficient way of representing voices such that similarity comparisons can be performed is therefore of great use. In the field of Natural Language Processing great progress has been made using embeddings from Deep Learning models to represent words in an unsupervised fashion. These representations managed to capture the semantics of the words. This
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Tawfique, Ziring. "Tool-Mediated Texture Recognition Using Convolutional Neural Network." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-303774.

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Vibration patterns can be captured by an accelerometer sensor attached to a hand-held device when it is scratched on various type of surface textures. These acceleration signals can carry relevant information for surface texture classification. Typically, methods rely on hand crafted feature engineering but with the use of Convolutional Neural Network manual feature engineering can be eliminated. A proposed method using modern machine learning techniques such as Dropout is introduced by training a Convolutional Neural Network to distinguish between 69 and 100 various surface textures. EHapNet,
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Winicki, Elliott. "ELECTRICITY PRICE FORECASTING USING A CONVOLUTIONAL NEURAL NETWORK." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2126.

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Many methods have been used to forecast real-time electricity prices in various regions around the world. The problem is difficult because of market volatility affected by a wide range of exogenous variables from weather to natural gas prices, and accurate price forecasting could help both suppliers and consumers plan effective business strategies. Statistical analysis with autoregressive moving average methods and computational intelligence approaches using artificial neural networks dominate the landscape. With the rise in popularity of convolutional neural networks to handle problems with l
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Cui, Chen. "Convolutional Polynomial Neural Network for Improved Face Recognition." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1497628776210369.

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Li, Chao. "WELD PENETRATION IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK." UKnowledge, 2019. https://uknowledge.uky.edu/ece_etds/133.

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Weld joint penetration determination is the key factor in welding process control area. Not only has it directly affected the weld joint mechanical properties, like fatigue for example. It also requires much of human intelligence, which either complex modeling or rich of welding experience. Therefore, weld penetration status identification has become the obstacle for intelligent welding system. In this dissertation, an innovative method has been proposed to detect the weld joint penetration status using machine-learning algorithms. A GTAW welding system is firstly built. Project a dot-structur
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Books on the topic "Modified convolutional neural network"

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Ally, Afshan. A Hopfield neural network decoder for convolutional codes. National Library of Canada = Bibliothèque nationale du Canada, 1991.

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Shanthini, A., Gunasekaran Manogaran, and G. Vadivu. Deep Convolutional Neural Network for The Prognosis of Diabetic Retinopathy. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-3877-1.

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Doan, Tai. Convolutional Neural Network in Classifying Scanned Documents. GRIN Verlag GmbH, 2017.

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Journey from Artificial to Convolutional Neural Network. Central West Publishing, 2023.

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Deep Convolutional Neural Network for the Prognosis of Diabetic Retinopathy. Springer, 2023.

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Manogaran, Gunasekaran, G. Vadivu, and A. Shanthini. Deep Convolutional Neural Network for the Prognosis of Diabetic Retinopathy. Springer, 2022.

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Kashyap, Dr Nikita, Dr Dharmendra Kumar Singh, Dr Girish Kumar Singh, and Dr Arun Kumar Kashyap, eds. Identification of Diabetic Retinopathy Stages Using Modified DWT and Artificial Neural Network. AkiNik Publications, 2021. http://dx.doi.org/10.22271/ed.book.1314.

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National Aeronautics and Space Administration (NASA) Staff. Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft. Independently Published, 2018.

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Kypraios, Ioannis. Performance Analysis of the Modified-Hybrid Optical Neural Network Object Recognition System Within Cluttered Scenes. INTECH Open Access Publisher, 2012.

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Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it
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Book chapters on the topic "Modified convolutional neural network"

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Vinotheni, C., S. Lakshmana Pandian, and G. Lakshmi. "Modified Convolutional Neural Network of Tamil Character Recognition." In Lecture Notes in Networks and Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4218-3_46.

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Kim, Ho-Joon, Joseph S. Lee, and Hyun-Seung Yang. "Human Action Recognition Using a Modified Convolutional Neural Network." In Advances in Neural Networks – ISNN 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72393-6_85.

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Sharma, Aditi, and D. Franklin Vinod. "Classification of Bacterial Skin Disease Images Using Modified Convolutional Neural Network." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0769-4_59.

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Parida, Prasanta Kumar, Lingraj Dora, Rutuparna Panda, and Sanjay Agrawal. "Multi-grade Brain Tumor Classification Using a Modified Convolutional Neural Network." In Intelligent Systems Design and Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64836-6_45.

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Brinthakumari, S., and P. M. Sivaraja. "mCNN: An Approach for Plant Disease Detection Using Modified Convolutional Neural Network." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8477-8_17.

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Gupta, Nidhi, Akhilesh Latoria, and Akash Goel. "Blood Cancer Classification with Gene Expression Using Modified Convolutional Neural Network Approach." In Artificial Intelligence in Cyber-Physical Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781003248750-11.

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Bhattacharya, Suchimita, Manas Ghosh, and Aniruddha Dey. "Face Detection in Unconstrained Environments Using Modified Multitask Cascade Convolutional Neural Network." In Proceedings of International Conference on Industrial Instrumentation and Control. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7011-4_29.

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Srinivasulu, Asadi, Umesh Neelakantan, Tarkeswar Barua, Srinivas Nowduri, and MM Subramanyam. "Early Prediction of COVID-19 Using Modified Convolutional Neural Networks." In Data Analytics, Computational Statistics, and Operations Research for Engineers. CRC Press, 2022. http://dx.doi.org/10.1201/9781003152392-2.

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Srinivasulu, Asadi, Tarkeshwar Barua, Umesh Neelakantan, and Srinivas Nowduri. "Early Prediction of COVID-19 Using Modified Convolutional Neural Networks." In Advanced Technologies and Societal Change. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5090-1_6.

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Li, Hui, Wenxin Liang, and Zihan Liao. "Detection of Spammers Using Modified Diffusion Convolution Neural Network." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60470-7_8.

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Conference papers on the topic "Modified convolutional neural network"

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Singh, Brahmjit, Poonam Jindal, Pankaj Verma, Vishal Sharma, and Chandra Prakash. "Automatic Modulation Recognition Using Modified Convolutional Neural Network." In 2025 3rd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT). IEEE, 2025. https://doi.org/10.1109/dicct64131.2025.10986502.

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Beeharry, Yogesh, and Didier Gael Daryl Emilien. "A Modified Convolutional Neural Network Model for Automatic Modulation Classification." In 2025 Emerging Technologies for Intelligent Systems (ETIS). IEEE, 2025. https://doi.org/10.1109/etis64005.2025.10961873.

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Nayak, Debasish Swapnesh Kumar, Arpita Priyadarshini, Pabani Mahanta, Soumyarashmi Panigrahi, Sushanta Meher, and Satyananda Swain. "Modified Deep Neural Network Approach to Identify Heart Disease using IoMT: Artificial Neural Networks or Convolutional Neural Networks!" In 2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT). IEEE, 2024. https://doi.org/10.1109/ic-sit63503.2024.10862075.

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Verma, Sonia, Pooja Singhal, Ritu Gupta, Abhilasha Singh, and Arun Kumar. "Facial Keypoint Detection using a Modified Convolutional Neural Network with RESNET50." In 2024 2nd International Conference on Advancements and Key Challenges in Green Energy and Computing (AKGEC). IEEE, 2024. https://doi.org/10.1109/akgec62572.2024.10868470.

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Krishnamaneni, Ramesh, Muralidhar Kurni, Souptik Sen, and Ashwin Murthy. "Modified Convolutional Neural Network with Multiple Features for Multimodal Sarcasm Detection." In 2024 2nd International Conference on Recent Advances in Information Technology for Sustainable Development (ICRAIS). IEEE, 2024. https://doi.org/10.1109/icrais62903.2024.10811714.

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Sahoo, Parthasarathi, Aryadutta Khandual, Soumya Rath, Lipsarani Parida, Debendra Muduli, and Santosh Kumar Sharma. "Enhanced Brain Tumor Classification Using a Modified Xception Convolutional Neural Network." In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2024. https://doi.org/10.1109/scopes64467.2024.10990472.

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Wang, Yiwen, and Meiling Xu. "Modified BBO-based Graph Convolutional Recurrent Neural Network for Emotion Recognition." In 2025 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2025. https://doi.org/10.1109/cec65147.2025.11042952.

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Lad, Saket, Bhavisha Chafekar, and Pramod Bide. "Lung Cancer Classification Using Deep Learning: A Comprehensive Approach with Modified Convolutional Neural Networks." In 2024 International Conference on Computational Intelligence and Network Systems (CINS). IEEE, 2024. https://doi.org/10.1109/cins63881.2024.10864431.

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Kumar, Ajay, and Abhimanyu Singh Panwar. "Human Mental State Detection Using Modified Convolutional Neural Network with Leaky Rectified Linear Unit." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752185.

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Gu, Guangjuan, Ke Li, and Yalong Jiang. "A Modified Dwarf Mongoose Optimization Based Deep Convolutional Neural Network for Building Structural Damage Detection." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691168.

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Reports on the topic "Modified convolutional neural network"

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Guan, Hui, Xipeng Shen, Seung-Hwan Lim, and Robert M. Patton. Composability-Centered Convolutional Neural Network Pruning. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1427608.

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Tilton, Miranda. CoNNOR: Convolutional Neural Network for Outsole Recognition. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-416.

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Shao, Lu. Automatic Seizure Detection based on a Convolutional Neural Network-Recurrent Neural Network Model. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-269.

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Tarasenko, Andrii O., Yuriy V. Yakimov, and Vladimir N. Soloviev. Convolutional neural networks for image classification. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3682.

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This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculatio
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Rocco, Dominick Rosario. Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1294514.

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Zhang, Shu. Overcoming the reality gap: Studying synthetic image modalities for convolutional neural network training. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-1095.

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Cheniour, Amani, Amir Ziabari, Elena Tajuelo Rodriguez, Mohammed Alnaggar, Yann Le Pape, and T. M. Rosseel. Reconstruction of 3D Concrete Microstructures Combining High-Resolution Characterization and Convolutional Neural Network for Image Segmentation. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2311320.

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Debroux, Patrick. The Use of Adjacent Video Frames to Increase Convolutional Neural Network Classification Robustness in Stressed Environments. DEVCOM Analaysis Center, 2023. http://dx.doi.org/10.21236/ad1205367.

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa
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Eka Saputro, Widianto. PENGENALAN ALFABET BAHASA ISYARAT TANGAN PADA CITRA DIGITAL MENGGUNAKAN PENDEKATAN CONVEX HULL DAN CONVOLUTIONAL NEURAL NETWORK (CNN). ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.rwpbjj07.1.

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