Relevant bibliographies by topics / Deep learning CNN model

Contents

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

Academic literature on the topic 'Deep learning CNN model'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Deep learning CNN model"

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Dr., Rekha Patil, Kumar Katrabad Vidya, Mahantappa, and Kumar Sunil. "Image Classification Using CNN Model Based on Deep Learning." Journal Of Scientific Research And Technology (JSRT) 1, no. 2 (2023): 60–71. https://doi.org/10.5281/zenodo.7965526.

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In this work, we will use a convolutional neural network to classify images. In the field of visual image analysis, CNNs (a subset of deep neural networks) are the norm. Multilayer perceptron is used to develop CNN; it is based on a hierarchical model that works on network construction and then delivers to a fully linked layer. All the neurons are linked together and their output is processed in this layer. Here, we demonstrate how our system can get the job done in challenging domains like computer vision by using a deep learning approach. Convolutional Neural Networks (CNNs) are a machine le
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Srinivas, Dr Kalyanapu, and Reddy Dr.B.R.S. "Deep Learning based CNN Optimization Model for MR Braing Image Segmentation." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11 (2019): 213–20. http://dx.doi.org/10.5373/jardcs/v11i11/20193190.

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Aysuh, Jaggi, and Vinod Sharma Prof. "Classification of Healthy Seeds Using Deep Learning." Journal of Scientific Research and Technology (JSRT) 1, no. 4 (2023): 10–23. https://doi.org/10.5281/zenodo.8222793.

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With the increasing demand for healthy and high-quality seeds in agriculture, accurate and efficient seed classification methods are essential for seed quality control and optimisation of crop production. This work utilises a deep learning-based approach for healthy seed classification. It proposes a deep learning-based approach for beneficial seed classification, leveraging the power of neural networks to learn discriminative features from seed images automatically. The proposed method involves a multi-step pipeline that includes Image preprocessing, and Classification. The seed images are in
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Santosh, Giri1 and Basanta Joshi. "TRANSFER LEARNING BASED IMAGE VISUALIZATION USING CNN." International Journal of Artificial Intelligence and Applications (IJAIA) 10, July (2019): 47–55. https://doi.org/10.5281/zenodo.3371299.

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Image classification is a popular machine learning based applications of deep learning. Deep learning techniques are very popular because they can be effectively used in performing operations on image data in large-scale. In this paper CNN model was designed to better classify images. We make use of featureextraction part of inception v3 model for feature vector calculation and retrained the classification layer with these feature vector. By using the transfer learning mechanism the classification layer of the CNN model was trained with 20 classes of Caltech101 image dataset and 17 classes of
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K, Gayathri, and Thangavelu S. "Novel deep learning model for vehicle and pothole detection." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1576–82. https://doi.org/10.11591/ijeecs.v23.i3.pp1576-1582.

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The most important aspect of automatic driving and traffic surveillance is vehicle detection. In addition, poor road conditions caused by potholes are the cause of traffic accidents and vehicle damage. The proposed work uses deep learning models. The proposed method can detect vehicles and potholes using images. The faster region-based convolutional neural network (CNN) and the inception network V2 model are used to implement the model. The proposed work compares the performance, accuracy numbers, detection time, and advantages and disadvantages of the faster region-based convolution neural ne
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Zahraa, Najm Abdullah, Abdulridha Abutiheen Zinah, A. Abdulmunem Ashwan, and A. Harjan Zahraa. "Official logo recognition based on multilayer convolutional neural network model." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 5 (2022): 1083–90. https://doi.org/10.12928/telkomnika.v20i5.23464.

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Deep learning has gained high popularity in the field of image processing and computer vision applications due to its unique feature extraction property. For this characteristic, deep learning networks used to solve different issues in computer vision applications. In this paper the issue has been raised is classification of logo of formal directors in Iraqi government. The paper proposes a multi-layer convolutional neural network (CNN) to classify and recognize these official logos by train the CNN model on several logos. The experimental show the effectiveness of the proposed method to recog
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Swetha, NG, Himasri Allu, Chandana KP Hari, Ujwal Kumar, Naga Sushwar, and BL Swathi. "Emotion Detection using Deep Learning CNN Model." International Journal of Microsystems and IoT 2, no. 9 (2024): 1187–96. https://doi.org/10.5281/zenodo.14099594.

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Facial Emotion Recognition (FER) is crucial in domains like human-computer interaction, mental health assessment, and marketing. This paper details the design and implementation of a FER model using Deep Convolutional Neural Networks (DCNNs) on the FER2013 dataset, which contains grayscale images labeled with seven emotions. Data augmentation and feature extraction are employed to enhance dataset diversity and reduce dimensionality. The DCNN architecture includes ReLU and Softmax activations for efficient non-linearity and multiclass classification, respectively, with Tanh and LeakyReLU showin
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Mohebbanaaz, Mohebbanaaz, Y. Padma Sai, and L. V. Rajani Kumari. "Detection of cardiac arrhythmia using deep CNN and optimized SVM." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 217–25. https://doi.org/10.11591/ijeecs.v24.i1.pp217-225.

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Deep learning (DL) has become a topic of study in various applications, including healthcare. Detection of abnormalities in an electrocardiogram (ECG) plays a significant role in patient monitoring. It is noted that a deep neural network when trained on huge data, can easily detect cardiac arrhythmia. This may help cardiologists to start treatment as early as possible. This paper proposes a new deep learning model adapting the concept of transfer learning to extract deep-CNN features and facilitates automated classification of electrocardiogram (ECG) into sixteen types of ECG beats using an op
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Anbarasi., A., and S. Ravi. "Optimal Deep Learning based Classification Model for Mitral Valve Diagnosis System." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 315–21. https://doi.org/10.35940/ijeat.C6530.049420.

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In present days, the domain of mitral valve (MV) diagnosis so common due to the changing lifestyle in day to day life. The increased number of MV disease necessitates the development of automated disease diagnosis model based on segmentation and classification. This paper makes use of deep learning (DL) model to develop a MV classification model to diagnose the severity level. For the accurate classification of ML, this paper applies the DL model called convolution neural network (CNN-MV) model. And, an edge detection based segmentation model is also applied which will helps to further enhance
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Yue, Wang, and Li Lei. "Sentiment Analysis using a CNN-BiLSTM Deep Model Based on Attention Classification." Information 26, no. 3 (2023): 117–62. http://dx.doi.org/10.47880/inf2603-02.

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With the rapid development of the Internet, the number of social media and e-commerce platforms increased dramatically. Users from all over world share their comments and sentiments on the Internet become a new tradition. Applying natural language processing technology to analyze the text on the Internet for mining the emotional tendencies has become the main way in the social public opinion monitoring and the after-sale feedback of manufactory. Thus, the study on text sentiment analysis has shown important social significance and commercial value. Sentiment analysis is a hot research topic in
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Dissertations / Theses on the topic "Deep learning CNN model"

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Meng, Zhaoxin. "A deep learning model for scene recognition." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36491.

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Scene recognition is a hot research topic in the field of image recognition. It is necessary that we focus on the research on scene recognition, because it is helpful to the scene understanding topic, and can provide important contextual information for object recognition. The traditional approaches for scene recognition still have a lot of shortcomings. In these years, the deep learning method, which uses convolutional neural network, has got state-of-the-art results in this area. This thesis constructs a model based on multi-layer feature extraction of CNN and transfer learning for scene rec
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Suresh, Sreerag. "An Analysis of Short-Term Load Forecasting on Residential Buildings Using Deep Learning Models." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99287.

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Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since the residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting at the building level. Although, other studies have looked at using deep learning models for building energy f
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Viebke, André. "Accelerated Deep Learning using Intel Xeon Phi." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-45491.

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Deep learning, a sub-topic of machine learning inspired by biology, have achieved wide attention in the industry and research community recently. State-of-the-art applications in the area of computer vision and speech recognition (among others) are built using deep learning algorithms. In contrast to traditional algorithms, where the developer fully instructs the application what to do, deep learning algorithms instead learn from experience when performing a task. However, for the algorithm to learn require training, which is a high computational challenge. High Performance Computing can help
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Lind, Johan. "Evaluating CNN-based models for unsupervised image denoising." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176092.

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Images are often corrupted by noise which reduces their visual quality and interferes with analysis. Convolutional Neural Networks (CNNs) have become a popular method for denoising images, but their training typically relies on access to thousands of pairs of noisy and clean versions of the same underlying picture. Unsupervised methods lack this requirement and can instead be trained purely using noisy images. This thesis evaluated two different unsupervised denoising algorithms: Noise2Self (N2S) and Parametric Probabilistic Noise2Void (PPN2V), both of which train an internal CNN to denoise im
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Wang, Zhihao. "Land Cover Classification on Satellite Image Time Series Using Deep Learning Models." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu159559249009195.

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Albert, Florea George, and Filip Weilid. "Deep Learning Models for Human Activity Recognition." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20201.

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AMI Meeting Corpus (AMI) -databasen används för att undersöka igenkännande av gruppaktivitet. AMI Meeting Corpus (AMI) -databasen ger forskare fjärrstyrda möten och naturliga möten i en kontorsmiljö; mötescenario i ett fyra personers stort kontorsrum. För attuppnågruppaktivitetsigenkänninganvändesbildsekvenserfrånvideosoch2-dimensionella audiospektrogram från AMI-databasen. Bildsekvenserna är RGB-färgade bilder och ljudspektrogram har en färgkanal. Bildsekvenserna producerades i batcher så att temporala funktioner kunde utvärderas tillsammans med ljudspektrogrammen. Det har visats att inkluder
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You, Yantian. "Sparsity Analysis of Deep Learning Models and Corresponding Accelerator Design on FPGA." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-204409.

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Machine learning has achieved great success in recent years, especially the deep learning algorithms based on Artificial Neural Network. However, high performance and large memories are needed for these models , which makes them not suitable for IoT device, as IoT devices have limited performance and should be low cost and less energy-consuming. Therefore, it is necessary to optimize the deep learning models to accommodate the resource-constrained IoT devices. This thesis is to seek for a possible solution of optimizing the ANN models to fit into the IoT devices and provide a hardware implemen
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Sievert, Rolf. "Instance Segmentation of Multiclass Litter and Imbalanced Dataset Handling : A Deep Learning Model Comparison." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175173.

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Instance segmentation has a great potential for improving the current state of littering by autonomously detecting and segmenting different categories of litter. With this information, litter could, for example, be geotagged to aid litter pickers or to give precise locational information to unmanned vehicles for autonomous litter collection. Land-based litter instance segmentation is a relatively unexplored field, and this study aims to give a comparison of the instance segmentation models Mask R-CNN and DetectoRS using the multiclass litter dataset called Trash Annotations in Context (TACO) i
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Dickens, James. "Depth-Aware Deep Learning Networks for Object Detection and Image Segmentation." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42619.

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The rise of convolutional neural networks (CNNs) in the context of computer vision has occurred in tandem with the advancement of depth sensing technology. Depth cameras are capable of yielding two-dimensional arrays storing at each pixel the distance from objects and surfaces in a scene from a given sensor, aligned with a regular color image, obtaining so-called RGBD images. Inspired by prior models in the literature, this work develops a suite of RGBD CNN models to tackle the challenging tasks of object detection, instance segmentation, and semantic segmentation. Prominent architectur
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Ghibellini, Alessandro. "Trend prediction in financial time series: a model and a software framework." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24708/.

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The research has the aim to build an autonomous support for traders which in future can be translated in an Active ETF. My thesis work is characterized for a huge focus on problem formulation and an accurate analysis on the impact of the input and the length of the future horizon on the results. I will demonstrate that using financial indicators already used by professional traders every day and considering a correct length of the future horizon, it is possible to reach interesting scores in the forecast of future market states, considering both accuracy, which is around 90% in all the experi
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Books on the topic "Deep learning CNN model"

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Cianci, Davio. A Deep-Learning-Based Muon Neutrino CCQE Selection for Searches Beyond the Standard Model with MicroBooNE. [publisher not identified], 2021.

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Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing - ebooks Account, 2017.

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Shlezinger, Nir. Model-Based Deep Learning. Now Publishers, 2023.

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El-Amir, Hisham, and Mahmoud Hamdy. Deep Learning Pipeline: Building a Deep Learning Model with TensorFlow. Apress, 2019.

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Intelligent System for COVID-19 , Machine Learning, Deep Learning, Intelligent System: COVID-19, Machine Learning, Deep Learning, Mathematical Model, Intelligent System. Independently Published, 2021.

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Lattery, Mark J. Deep Learning in Introductory Physics: Exploratory Studies of Model-Based Reasoning. Information Age Publishing, 2016.

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1st, Kala K. U., and Nandhini M. 2nd. Deep Learning Model for Categorical Context Adaptation in Sequence-Aware Recommender Systems. INSC International Publisher (IIP), 2021.

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Urtāns, Ēvalds. Function shaping in deep learning. RTU Press, 2021. http://dx.doi.org/10.7250/9789934226854.

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This work describes the importance of loss functions and related methods for deep reinforcement learning and deep metric learning. A novel MDQN loss function outperformed DDQN loss function in PLE computer game environments, and a novel Exponential Triplet loss function outperformed the Triplet loss function in the face re-identification task with VGGFace2 dataset reaching 85,7 % accuracy using zero-shot setting. This work also presents a novel UNet-RNN-Skip model to improve the performance of the value function for path planning tasks.
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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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Jena, Om Prakash, Alok Ranjan Tripathy, Brojo Kishore Mishra, and Ahmed A. Elngar, eds. Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150404011220301.

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Blockchain, whether public or private, is capable enough to maintain the integrity of transactions by decentralizing the records for users. Many IoT companies are using blockchain technology to make the world a better-connected place. Businesses and researchers are exploring ways to make this technology increasingly efficient for IoT services. This volume presents the recent advances in these two technologies. Chapters explain the fundamentals of Blockchain and IoT, before explaining how these technologies, when merged together, provide a transparent, reliable, and secure model for data proces
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Book chapters on the topic "Deep learning CNN model"

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Karpagam, Guruvareddiyur Rangaraju, Abishek Ganapathy, Aadhavan Chellamuthu Kavin Raj, Saravanan Manigandan, J. R. Neeraj Julian, and S. Raaja Vignesh. "Leveraging CNN Deep Learning Model for Smart Parking." In Studies in Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65661-4_8.

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Bisong, Ekaba. "Convolutional Neural Networks (CNN)." In Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_35.

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Kaushik, Shweta, Prabhat Singh, Rishu Kumar, Mohd Tabrej, and Rizwan Ahmad. "Detecting Brain Tumor Using Deep Learning Through CNN Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3442-9_16.

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Gunasekaran, Hemalatha, K. Ramalakshmi, Shalini Ramanathan, and R. Venkatesan. "A Deep Learning CNN Model for Genome Sequence Classification." In Intelligent Computing Applications for COVID-19. CRC Press, 2021. http://dx.doi.org/10.1201/9781003141105-9.

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Ravikumaran, P., K. Vimala Devi, and K. Valarmathi. "Smart Diabetes System Using CNN in Health Data Analytics." In Object Detection with Deep Learning Models. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003206736-8.

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Dongre, Shital, Yash Chindhe, Mayur Dabade, Savani Bondre, and Anannya Chaudhary. "Detection of B-ALL Using CNN Model and Deep Learning." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8031-0_23.

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Al Hossain, Mirsad, Saiful Islam Akash, Sajid Faysal Fahim, Md Arifin Zaman, and Md Motaharul Islam. "E-waste Classification Using Pre-trained Deep Learning CNN Model." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4928-7_1.

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Chopra, Sonali, Parul Agarwal, Jawed Ahmed, Siddhartha Sankar Biswas, and Ahmed J. Obaid. "RNN-CNN Based Hybrid Deep Learning Model for Mental Healthcare." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8074-7_30.

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Bhattacharya, Somnath, and Padmalini Singh. "CNN–RNN Hybrid Deep Learning Model for Monthly Rainfall Prediction." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-7717-4_39.

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Biswas, Sitanath, Chirag Nahata, Snigdha Ghosh, Shubhashree Sahoo, and Dipanjana Biswas. "Fish Freshness Detection Via Hybrid CNN-LSTM: An Interpretable Deep Learning Model." In Learning and Analytics in Intelligent Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82706-8_37.

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Conference papers on the topic "Deep learning CNN model"

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Shetty, Nisha, Saritha Shetty, and Nikhil Shetty. "Deep Learning-Powered Signature Authentication: The SigVerify CNN Model." In 2025 Third International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2025. https://doi.org/10.1109/icaiss61471.2025.11042173.

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Li, Guixia, and Lei Pan. "Multispectral fusion and lightweight CNN model framework for nondestructive detection of grain quality." In International Conference on Machine Vision and Deep Learning (MVDL 2025), edited by Chengzhong Xu and Dickson K. W. Chiu. SPIE, 2025. https://doi.org/10.1117/12.3072075.

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Vallileka, N., G. Vinoth Rajkumar, R. Santhana Krishnan, S. Vijay Shankar, J. Relin Francis Raj, and M. Saravana Karthikeyan. "Hybrid CNN-LSTM Model for Enhanced Weather Forecasting: Leveraging Spatial and Temporal Dependencies." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933281.

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Lu, Yufei. "CATLoc: Protein Subcellular Localization Based on Embedded Features and CNN-Transformer Model." In 2025 6th International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2025. https://doi.org/10.1109/cvidl65390.2025.11086019.

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Arora, Aditi, Yashaswi Upadhyay, Satvik Shukla, and Saket. "Forecasting Stock Price by LSTM-CNN Hybrid Model and Compares Deep Learning Models." In 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE). IEEE, 2024. http://dx.doi.org/10.1109/icspcre62303.2024.10675145.

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Tanwar, Vishesh, Vatsala Anand, Deepak Upadhyay, and Mukesh Singh. "Predicting Cervical Spine Fractures Using CNN Model: A Deep Learning Approach." In 2024 4th Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2024. https://doi.org/10.1109/asiancon62057.2024.10838135.

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Sharma, Ankita, and Sonam Mittal. "Deep Learning Approach –Improved CNN Model for the Breast Cancer Classification." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC). IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10730870.

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Pasaribu, Novalanza Grecea, Gelar Budiman, and Indrarini Dyah Irawati. "Image-Based Essay Scoring Deep Learning Using a CNN Model GoogLeNet." In 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2024. http://dx.doi.org/10.1109/icitisee63424.2024.10730128.

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Bhoomika, Mehul Manu, Jayapal Lande, and Goldy Verma. "Deep Learning for Alzheimer's Disease Classification: CNN Model Using MRI Images." In 2024 International Conference on Information Science and Communications Technologies (ICISCT). IEEE, 2024. https://doi.org/10.1109/icisct64202.2024.10956717.

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Tamanna, Sheeban E., Mohammed Ezhan, R. Mahesh, et al. "Musical Instrument Classification Using Deep Learning CNN Models." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10859695.

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Reports on the topic "Deep learning CNN model"

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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNe
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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
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Huang, Lei, Meng Song, Hui Shen, et al. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48221.

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One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or nonmonotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advan
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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-
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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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Shrestha, Tanuja, Mir A. Matin, Vishwas Chitale, and Samuel Thomas. Exploring the potential of deep learning for classifying camera trap data: A case study from Nepal - working paper. International Centre for Integrated Mountain Development (ICIMOD), 2023. http://dx.doi.org/10.53055/icimod.1016.

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Data from camera trap networks provide crucial information on various important aspects of wildlife presence, movement, and behaviour. However, manual processing of large volumes of images captured is time and resource intensive. This study explores three different approaches of deep learning methods to detect and classify images of key animal species collected from the ICIMOD Knowledge Park at Godavari, Nepal. It shows that transfer learning with ImageNet pretrained models (A1) can be used to detect animal species with minimal model training and testing. These methods when scaled up offer tre
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Cheng, DingXin. Development of the Roadway Pothole Management Program. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2306.

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Addressing the issue of potholes is a primary concern for maintaining urban infrastructure. The research team has developed a prototype pothole management program. The program includes a mobile application and two machine learning models. The mobile app enables users to upload images of potholes, report relevant information, and provide driving directions to the pothole location. With the help of this application, the user can seamlessly capture images of the potholes, record pertinent information, and submit the data for necessary action. The mobile application is an essential tool in the Pot
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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. no
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Jiménez Láinez, Andrés, and María Dolores Pérez Godoy. Experimentación con modelos de Deep Learning para la detección de objetos. Fundación Avanza, 2023. http://dx.doi.org/10.60096/fundacionavanza/2032022.

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Experimentaremos con un modelo de deep learning para detectar diferentes tipos de flores en una imagen, analizando varios parámetros para ver su correcto funcionamiento y explicar las posibles causas de los mismos.
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Zheng, Jian. Relational Patterns Discovery in Climate with Deep Learning Model. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2021. http://dx.doi.org/10.7546/crabs.2021.01.05.

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