Relevant bibliographies by topics / SLR; CNN; Artificial Neural Network

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'SLR; CNN; Artificial Neural Network'

Author: Grafiati
Published: 5 June 2025
Last updated: 11 July 2025

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Journal articles on the topic "SLR; CNN; Artificial Neural Network"

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Himanshu, Rai Goyal Savita. "Crop Yield Prediction Using Adam Optimizer and Machine Learning." Scandinavian Journal of Information Systems 34, no. 1 (2023): 138–43. https://doi.org/10.5281/zenodo.7885144.

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The data in its form generated from variable sensors incredibly impacts the structure of the functional structure utilizing Machine Learning (ML) calculations. Many of its components are utilized to work on all areas of the rice harvesting process in horticulture, which change customary rice cultivating tests into another period of smart rice horticulture or accuracy in rice farming. Here played out a study of the most recent examination on keen information handling innovation applied in farming, especially in crop yield forecast. Artificial Intelligence (AI) is a rule based unbiased calculation model for significant prediction on paddy and crop horticulture. This played out a Systematic Literature Review (SLR) to extricate and blend the calculations and elements that have been utilized in crop yield expectation studies. In view of pursuit standards, recovered 567 important examinations from six electronic information bases, of which have been chosen 50 investigations for additional investigation utilizing incorporation and avoidance measures. Examined these chose concentrates cautiously, investigated the techniques and elements utilized, and gave ideas to additional exploration. As indicated by examinations, the most utilized highlights are temperature, precipitation, and soil type, and the most applied calculation is Artificial Neural Networks in these models. As indicated by this extra examination, Convolutional Neural Networks (CNN) is the deep learning architecture that involves the immense layers of calculations on the investigation results and improving the forecast results.
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Ferreira, Pedro M., Diogo Pernes, Ana Rebelo, and Jaime S. Cardoso. "Signer-Independent Sign Language Recognition with Adversarial Neural Networks." International Journal of Machine Learning and Computing 11, no. 2 (2021): 121–29. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1024.

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Sign Language Recognition (SLR) has become an appealing topic in modern societies because such technology can ideally be used to bridge the gap between deaf and hearing people. Although important steps have been made towards the development of real-world SLR systems, signer-independent SLR is still one of the bottleneck problems of this research field. In this regard, we propose a deep neural network along with an adversarial training objective, specifically designed to address the signer-independent problem. Specifically, the proposed model consists of an encoder, mapping from input images to latent representations, and two classifiers operating on these underlying representations: (i) the sign-classifier, for predicting the class/sign labels, and (ii) the signer-classifier, for predicting their signer identities. During the learning stage, the encoder is simultaneously trained to help the sign-classifier as much as possible while trying to fool the signer-classifier. This adversarial training procedure allows learning signer-invariant latent representations that are in fact highly discriminative for sign recognition. Experimental results demonstrate the effectiveness of the proposed model and its capability of dealing with the large inter-signer variations.
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Zhang, Yong. "Seedlings Supplement Device and Seedling Recognition Based on Convolution Neural Network." Traitement du Signal 39, no. 5 (2022): 1567–75. http://dx.doi.org/10.18280/ts.390513.

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A fully automatic plug seedling device is designed, its structure and working principle are introduced, and a plug seedling hole identification method based on CNN is proposed to address the issue of adjacent holes in order to increase the automation and intelligence of the vegetable transplanting machine. The issue of low recognition accuracy of plug seedlings is brought on by intertwined stems and leaves. This study first grows tomato seedlings in an artificial greenhouse and then utilizes an SLR camera to take pictures of those plants. The photos are then subjected to the appropriate preprocessing, such as separating the complete hole plate image into several hole images in accordance with the hole plate standards to facilitate recognition. The CNN model is then finished being trained after receiving the processed image. Relu, which has a better ability for classification, is chosen as the activation function of the convolutional layer after the network is enlarged on the basis of LeNet-5CNN. In addition, the over-fitting issue of the model is resolved using data augmentation technology, resulting in a recognition accuracy of the test set of the model that is as high as 0.985. The automatic vegetable transplanting machine can greatly increase the automation and intelligence level of the plug seedling recognition model based on CNN, which has high recognition accuracy and generalization ability. This model also solves the main technical problems of the plug seedling device and improves the machine's ability to transplant.
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Nugroho, Erwin Setyo, Igi Ardiyanto, and Hanung Adi Nugroho. "Systematic literature review of dermoscopic pigmented skin lesions classification using convolutional neural network (CNN)." International Journal of Advances in Intelligent Informatics 9, no. 3 (2023): 363. http://dx.doi.org/10.26555/ijain.v9i3.961.

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The occurrence of pigmented skin lesions (PSL), including melanoma, are rising, and early detection is crucial for reducing mortality. To assist Pigmented skin lesions, including melanoma, are rising, and early detection is crucial in reducing mortality. To aid dermatologists in early detection, computational techniques have been developed. This research conducted a systematic literature review (SLR) to identify research goals, datasets, methodologies, and performance evaluation methods used in categorizing dermoscopic lesions. This review focuses on using convolutional neural networks (CNNs) in analyzing PSL. Based on specific inclusion and exclusion criteria, the review included 54 primary studies published on Scopus and PubMed between 2018 and 2022. The results showed that ResNet and self-developed CNN were used in 22% of the studies, followed by Ensemble at 20% and DenseNet at 9%. Public datasets such as ISIC 2019 were predominantly used, and 85% of the classifiers used were softmax. The findings suggest that the input, architecture, and output/feature modifications can enhance the model's performance, although improving sensitivity in multiclass classification remains a challenge. While there is no specific model approach to solve the problem in this area, we recommend simultaneously modifying the three clusters to improve the model's performance.
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Colantonio, Lorenzo, Lucas Equeter, Pierre Dehombreux, and François Ducobu. "A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques." Machines 9, no. 12 (2021): 351. http://dx.doi.org/10.3390/machines9120351.

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In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance.
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Mustaqim, Adi Zaenul, Nurdana Ahmad Fadil, and Dyah Aruming Tyas. "Artificial Neural Network for Classification Task in Tabular Datasets and Image Processing: A Systematic Literature Review." Jurnal Online Informatika 8, no. 2 (2023): 158–68. http://dx.doi.org/10.15575/join.v8i2.1002.

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Artificial Neural Network (ANN) is one of the machine learning algorithms that is widely used for classification cases. Some examples of classification cases that can be handled with ANN include classifications in the health sector, banking, and classification in image processing. This study presents a systematic literature review (SLR) of the ANN algorithm to find a research gap that can be used in future research. There are 3 phases used in preparing the SLR. Those are planning, conducting, and reporting. Formulation of research questions and establishing a review protocol is carried out in the planning phase. The second phase is conducted. In this phase, searching for relevant articles is carried out, determining the quality of the literature found and selecting particles according to what has been formulated in the planning phase. The selected literature is then carried out by the process of extracting data and information and then synthesizing the data. Writing SLR articles based on existing findings is carried out in the last phase, namely reporting. The results of data and information extraction from the 13 reviewed articles show that the ANN algorithm is powerful enough with satisfactory results to handle classification cases that use tabular datasets or image datasets. The challenges faced are the need for extensive training data so that ANN performance can be better, the use of appropriate evaluation measures based on the cases studied does not only rely on accuracy scores, and the determination of the correct hyperparameters to get better performance in the case of image processing.
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Alluhaidan, Ala Saleh, Oumaima Saidani, Rashid Jahangir, Muhammad Asif Nauman, and Omnia Saidani Neffati. "Speech Emotion Recognition through Hybrid Features and Convolutional Neural Network." Applied Sciences 13, no. 8 (2023): 4750. http://dx.doi.org/10.3390/app13084750.

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Speech emotion recognition (SER) is the process of predicting human emotions from audio signals using artificial intelligence (AI) techniques. SER technologies have a wide range of applications in areas such as psychology, medicine, education, and entertainment. Extracting relevant features from audio signals is a crucial task in the SER process to correctly identify emotions. Several studies on SER have employed short-time features such as Mel frequency cepstral coefficients (MFCCs), due to their efficiency in capturing the periodic nature of audio signals. However, these features are limited in their ability to correctly identify emotion representations. To solve this issue, this research combined MFCCs and time-domain features (MFCCT) to enhance the performance of SER systems. The proposed hybrid features were given to a convolutional neural network (CNN) to build the SER model. The hybrid MFCCT features together with CNN outperformed both MFCCs and time-domain (t-domain) features on the Emo-DB, SAVEE, and RAVDESS datasets by achieving an accuracy of 97%, 93%, and 92% respectively. Additionally, CNN achieved better performance compared to the machine learning (ML) classifiers that were recently used in SER. The proposed features have the potential to be widely utilized to several types of SER datasets for identifying emotions.
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Frieyadie, Frieyadie. "SYSTEMATIC LITERATURE REVIEW (SLR): DISEASE DETECTION IN MELONS USING DIGITAL IMAGE PROCESSING." Jurnal Riset Informatika 3, no. 1 (2021): 75–80. http://dx.doi.org/10.34288/jri.v3i1.178.

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Systematic Literature Review (SLR) is a technique used in this study which is used to study techniques for identifying leaf diseases using digital images as a basis for obtaining an understanding of disease identification techniques in melon leaves with digital images. Based on data from the Central Statistics Agency for the last 3 years from 2017-2019, melon production has increased considerably. Melon production data in 2017 was 92.43 tons, in 2018 was 118,708 and in 2019, overall melon production was 122,105 tons collected from 34 provinces in Indonesia. The problem that is often encountered in melon cultivation is the presence of plant pests that can harm and not maximize the yields of farmers. Several viruses cause mosaic disease that infects Cucurbitaceae plants, namely Cucumber aphid borne yellows virus (CABYV), Cucumber green mottle mosaic virus (CGMMV), Cucumber mosaic virus (CMV), Papaya ringspot virus (PRSV), Squash mosaic virus (SqMV), Squash leaf curl virus (SLCV), Watermelon mosaic virus (WMV). Information technology has now developed to be able to manage digital image data to identify problems faced by farmers. Several classification methods that can be used to answer problems include SVM, Artificial Neural Network, Decision Tree, Convolutional Neural Network.
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Jayavardhana, Arya, and Samuel Ady Sanjaya. "A Systematic Literature Review: A Comparison Of Available Approaches In Chatbot And Dialogue Manager Development." International Journal of Science, Technology & Management 4, no. 6 (2023): 1441–50. http://dx.doi.org/10.46729/ijstm.v4i6.983.

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The present study reviewed a number of articles chosen from a screening and selecting process on the various different methods that can be used in the context of chatbot development and dialogue managers. Since chatbots have seen a significant rise in popularity and have played an important role in helping humans complete daily tasks, this systematic literature review (SLR) aims to act as a guidance for future research. During the process of analyzing and extracting data from the 13 articles chosen, it has been identified that Artificial Neural Network (ANN), Ensemble Learning, Recurrent Neural Network (RNN), and Long-Short Term Memory (LSTM) is among some of the most popular algorithms used for developing a chatbot. Where all of these algorithms are suitable for each unique use case where it offers different advantages when implemented. Other than that, dialogue managers lean more towards the field of Deep Reinforcement Learning (DRL), where Deep Q-Networks (DQN) and its variants such as Double Deep-Q Networks (DDQN) and DDQN with Personalized Experience Replay (DDQN-PER) is commonly used. All these variants have different averages on episodic reward and dialogue length, along with different training time needed which indicates the computational power needed. This SLR aims to identify the methods that can be used and identify the best proven method to be applied in future research.
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Kamara, Alhassan A. "SARSNet—A Novel CNN Approach for SARWater Body Segmentation." International Journal of Electrical and Electronic Engineering & Telecommunications 13, no. 5 (2024): 323–30. http://dx.doi.org/10.18178/ijeetc.13.4.323-330.

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This paper presents the SARSNet architecture, developed to address the growing challenges in Synthetic Aperture Radar (SAR) deep learning-based automatic water body extraction. Such a task is riddled with significant challenges, encompassing issues like cloud interference, scarcity of annotated dataset, and the intricacies associated with varied topography. Recent strides in Convolutional Neural Networks (CNNs) and multispectral segmentation techniques offer a promising avenue to address these predicaments. In our research, we propose a series of solutions to elevate the process of water body segmentation. Our proposed solutions span several domains, including image resolution enhancement, refined extraction techniques tailored for narrow water bodies, self-balancing of the class pixel level, and minority class-influenced loss function, all aimed at amplifying prediction precision and streamlining computational complexity inherent in deep neural networks. The framework of our approach includes the introduction of a multichannel Data-Fusion Register, the incorporation of a CNN-based Patch Adaptive Network augmentation method, and the integration of class pixel level balancing and the Tversky loss function. We evaluated the performance of the model using the Sentinel-1 SAR electromagnetic signal dataset from the Earth flood water body extraction competition organized by the artificial intelligence department of Microsoft. In our analysis, our suggested SARSNet was compared to well-known semantic segmentation models, and a comprehensive assessment demonstrates that SARSNet consistently outperforms these models in all data subsets, including training, validation, and testing sets.
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Dissertations / Theses on the topic "SLR; CNN; Artificial Neural Network"

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Lind, Benjamin. "Artificial Neural Networks for Image Improvement." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-137661.

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After a digital photo has been taken by a camera, it can be manipulated to be more appealing. Two ways of doing that are to reduce noise and to increase the saturation. With time and skills in an image manipulating program, this is usually done by hand. In this thesis, automatic image improvement based on artificial neural networks is explored and evaluated qualitatively and quantitatively. A new approach, which builds on an existing method for colorizing gray scale images is presented and its performance compared both to simpler methods and the state of the art in image denoising. Saturation is lowered and noise added to original images, which the methods receive as inputs to improve upon. The new method is shown to improve in some cases but not all, depending on the image and how it was modified before given to the method.
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Hodges, Jonathan Lee. "Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/86364.

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Fire dynamics is a complex process involving multi-mode heat transfer, reacting fluid flow, and the reaction of combustible materials. High-fidelity predictions of fire behavior using computational fluid dynamics (CFD) models come at a significant computational cost where simulation times are often measured in hours, days, or even weeks. A new simulation method is to use a machine learning approach which uses artificial neural networks (ANNs) to represent underlying connections between data to make predictions of new inputs. The field of image analysis has seen significant advancements in ANN performance by using feature based layers in the network architecture. Inspired by these advancements, a generalized procedure to design ANNs to make spatially resolved predictions in multi-physics applications is presented and applied to different fire applications. A deep convolutional inverse graphics network (DCIGN) was developed to predict the two-dimensional spatially resolved spread of a wildland fire. The network uses an image stack corresponding to the spatially resolved landscape, weather, and current fire perimeter (which can be obtained from measurements) to predict the fire perimeter six hours in the future. A transpose convolutional neural network (TCNN) was developed to predict the spatially resolved thermal flow field in a compartment fire from coarse zone fire model predictions. The network uses thirty-five parameters describing the geometry of the room and the ventilation conditions to predict the full-field temperature and velocity throughout the room. The data for use in training and testing both networks was generated using high-fidelity CFD fire simulations. Overall, the ANN predictions in each network agree with simulation predictions for validation scenarios. The computational time to evaluate the ANNs is 10,000x faster than the high-fidelity fire simulations. This work represents a first step in developing super real-time full-field fire predictions for different applications.<br>Ph. D.<br>The National Fire Protection Agency estimates the total cost of fire in the United States at $300 billion annually. In 2017 alone, there were 3,400 civilian fire fatalities, 14,670 civilian fire injuries, and an estimated $23 billion direct property loss in the United States. Large scale fires in the wildland urban interface (WUI) and in large buildings still represent a significant hazard to life, property, and the environment. Researchers and fire safety engineers often use computer simulations to predict the behavior of a fire to assist in reducing the hazard of fire. Unfortunately, typical simulations of fire scenarios may take hours, days, or even weeks to run which limits their use to small areas or sections of buildings. A new method is to use a machine learning approach which uses artificial neural networks (ANNs) to represent underlying connections between data to make new predictions of fire behavior. Inspired by advancements in the field of image processing, this research developed a procedure to use machine learning to make rapid high resolution predictions of fire behavior. An ANN was developed to predict the perimeter of a wildland fire six hours in the future based on a set of images corresponding to the landscape, weather, and current fire perimeter, all of which can be obtained directly from measurements (US Geological Survey, Automated Surface Observation System, and satellites). In addition, an ANN was developed to predict high-resolution temperature and velocity fields within a floor of a building based on predictions from a coarse model. The data for use in training and testing these networks was generated using high-resolution fire simulations. Overall, the network predictions agree well with simulation predictions for new scenarios. In addition, the time to run the model is 10,000x faster than the typical simulations. The work presented herein represents a first step in developing high resolution computer simulations for different fire scenarios that run very quickly.
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Garbay, Thomas. "Zip-CNN." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS210.pdf.

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Les systèmes numériques utilisés pour l'Internet des Objets (IoT) et les Systèmes Embarqués ont connu une utilisation croissante ces dernières décennies. Les systèmes embarqués basés sur des microcontrôleurs (MCU) permettent de résoudre des problématiques variées, en récoltant de nombreuses données. Aujourd'hui, environ 250 milliards de MCU sont utilisés. Les projections d'utilisation de ces systèmes pour les années à venir annoncent une croissance très forte. L'intelligence artificielle a connu un regain d'intérêt dans les années 2012. L'utilisation de réseaux de neurones convolutifs (CNN) a permis de résoudre de nombreuses problématiques de vision par ordinateur ou de traitement du langage naturel. L'utilisation de ces algorithmes d'intelligence artificielle au sein de systèmes embarqués permettrait d'améliorer grandement l'exploitation des données récoltées. Cependant le coût d'exécution des CNN rend leur implémentation complexe au sein de systèmes embarqués. Ces travaux de thèse se concentrent sur l'exploration de l'espace des solutions pour guider l'intégration des CNN au sein de systèmes embarqués basés sur des microcontrôleurs. Pour cela, la méthodologie ZIP-CNN est définie. Elle tient compte du système embarqué et du CNN à implémenter. Elle fournit à un concepteur des informations sur l'impact de l'exécution du CNN sur le système. Un modèle fourni quantitativement une estimation de la latence, de la consommation énergétique et de l'espace mémoire nécessaire à une inférence d'un CNN au sein d'une cible embarquée, quelle que soit la topologie du CNN. Ce modèle tient compte des éventuelles réductions algorithmiques telles que la distillation de connaissances, l'élagage ou la quantification. L'implémentation de CNN de l'état de l'art au sein de MCU a permis la validation expérimentale de la justesse de l'approche. L'utilisation des modèles développés durant ces travaux de thèse démocratise l'implémentation de CNN au sein de MCU, en guidant les concepteurs de systèmes embarqués. De plus, les résultats obtenus ouvrent une voie d'exploration pour appliquer les modèles développés à d'autres matériels cibles, comme les architectures multi-cœur ou les FPGA. Les résultats d'estimations sont également exploitables dans l'utilisation d'algorithmes de recherche de réseaux de neurones (NAS)<br>Digital systems used for the Internet of Things (IoT) and Embedded Systems have seen an increasing use in recent decades. Embedded systems based on Microcontroller Unit (MCU) solve various problems by collecting a lot of data. Today, about 250 billion MCU are in use. Projections in the coming years point to very strong growth. Artificial intelligence has seen a resurgence of interest in 2012. The use of Convolutional Neural Networks (CNN) has helped to solve many problems in computer vision or natural language processing. The implementation of CNN within embedded systems would greatly improve the exploitation of the collected data. However, the inference cost of a CNN makes their implementation within embedded systems challenging. This thesis focuses on exploring the solution space, in order to assist the implementation of CNN within embedded systems based on microcontrollers. For this purpose, the ZIP-CNN methodology is defined. It takes into account the embedded system and the CNN to be implemented. It provides an embedded designer with information regarding the impact of the CNN inference on the system. A designer can explore the impact of design choices, with the objective of respecting the constraints of the targeted application. A model is defined to quantitatively provide an estimation of the latency, the energy consumption and the memory space required to infer a CNN within an embedded target, whatever the topology of the CNN is. This model takes into account algorithmic reductions such as knowledge distillation, pruning or quantization. The implementation of state-of-the-art CNN within MCU verified the accuracy of the different estimations through an experimental process. This thesis democratize the implementation of CNN within MCU, assisting the designers of embedded systems. Moreover, the results open a way of exploration to apply the developed models to other target hardware, such as multi-core architectures or FPGA. The estimation results are also exploitable in the Neural Architecture Search (NAS)
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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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Knutsson, Magnus, and Linus Lindahl. "A COMPARATIVE STUDY OF FFN AND CNN WITHIN IMAGE RECOGNITION : The effects of training and accuracy of different artificial neural network designs." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17214.

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Image recognition and -classification is becoming more important as the need to be able to process large amounts of images is becoming more common. The aim of this thesis is to compare two types of artificial neural networks, FeedForward Network and Convolutional Neural Network, to see how these compare when performing the task of image recognition. Six models of each type of neural network was created that differed in terms of width, depth and which activation function they used in order to learn. This enabled the experiment to also see if these parameters had any effect on the rate which a network learn and how the network design affected the validation accuracy of the models. The models were implemented using the API Keras, and trained and tested using the dataset CIFAR-10. The results showed that within the scope of this experiment the CNN models were always preferable as they achieved a statistically higher validation accuracy compared to their FFN counterparts.
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Ďuriš, Denis. "Detekce ohně a kouře z obrazového signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412968.

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This diploma thesis deals with the detection of fire and smoke from the image signal. The approach of this work uses a combination of convolutional and recurrent neural network. Machine learning models created in this work contain inception modules and blocks of long short-term memory. The research part describes selected models of machine learning used in solving the problem of fire detection in static and dynamic image data. As part of the solution, a data set containing videos and still images used to train the designed neural networks was created. The results of this approach are evaluated in conclusion.
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Andersson, Viktor. "Semantic Segmentation : Using Convolutional Neural Networks and Sparse dictionaries." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139367.

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The two main bottlenecks using deep neural networks are data dependency and training time. This thesis proposes a novel method for weight initialization of the convolutional layers in a convolutional neural network. This thesis introduces the usage of sparse dictionaries. A sparse dictionary optimized on domain specific data can be seen as a set of intelligent feature extracting filters. This thesis investigates the effect of using such filters as kernels in the convolutional layers in the neural network. How do they affect the training time and final performance? The dataset used here is the Cityscapes-dataset which is a library of 25000 labeled road scene images.The sparse dictionary was acquired using the K-SVD method. The filters were added to two different networks whose performance was tested individually. One of the architectures is much deeper than the other. The results have been presented for both networks. The results show that filter initialization is an important aspect which should be taken into consideration while training the deep networks for semantic segmentation.
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Wilson, Brittany Michelle. "Evaluating and Improving the SEU Reliability of Artificial Neural Networks Implemented in SRAM-Based FPGAs with TMR." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8619.

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Artificial neural networks (ANNs) are used in many types of computing applications. Traditionally, ANNs have been implemented in software, executing on CPUs and even GPUs, which capitalize on the parallelizable nature of ANNs. More recently, FPGAs have become a target platform for ANN implementations due to their relatively low cost, low power, and flexibility. Some safety-critical applications could benefit from ANNs, but these applications require a certain level of reliability. SRAM-based FPGAs are sensitive to single-event upsets (SEUs), which can lead to faults and errors in execution. However there are techniques that can mask such SEUs and thereby improve the overall design reliability. This thesis evaluates the SEU reliability of neural networks implemented in SRAM-based FPGAs and investigates mitigation techniques against upsets for two case studies. The first was based on the LeNet-5 convolutional neural network and was used to test an implementation with both fault injection and neutron radiation experiments, demonstrating that our fault injection experiments could accurately evaluate SEU reliability of the networks. SEU reliability was improved by selectively applying TMR to the most critical layers of the design, achieving a 35% improvement reliability at an increase in 6.6% resources. The second was an existing neural network called BNN-PYNQ. While the base design was more sensitive to upsets than the CNN previous tested, the TMR technique improved the reliability by approximately 7× in fault injection experiments.
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Mele, Matteo. "Convolutional Neural Networks for the Classification of Olive Oil Geographical Origin." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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This work proposed a deep learning approach to a multi-class classification problem. In particular, our project goal is to establish whether there is a connection between olive oil molecular composition and its geographical origin. To accomplish this, we implement a method to transform structured data into meaningful images (exploring the existing literature) and developed a fine-tuned Convolutional Neural Network able to perform the classification. We implement a series of tailored techniques to improve the model.
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Bianchi, Eric Loran. "COCO-Bridge: Common Objects in Context Dataset and Benchmark for Structural Detail Detection of Bridges." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87588.

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Common Objects in Context for bridge inspection (COCO-Bridge) was introduced for use by unmanned aircraft systems (UAS) to assist in GPS denied environments, flight-planning, and detail identification and contextualization, but has far-reaching applications such as augmented reality (AR) and other artificial intelligence (AI) platforms. COCO-Bridge is an annotated dataset which can be trained using a convolutional neural network (CNN) to identify specific structural details. Many annotated datasets have been developed to detect regions of interest in images for a wide variety of applications and industries. While some annotated datasets of structural defects (primarily cracks) have been developed, most efforts are individualized and focus on a small niche of the industry. This effort initiated a benchmark dataset with a focus on structural details. This research investigated the required parameters for detail identification and evaluated performance enhancements on the annotation process. The image dataset consisted of four structural details which are commonly reviewed and rated during bridge inspections: bearings, cover plate terminations, gusset plate connections, and out of plane stiffeners. This initial version of COCO-Bridge includes a total of 774 images; 10% for evaluation and 90% for training. Several models were used with the dataset to evaluate model overfitting and performance enhancements from augmentation and number of iteration steps. Methods to economize the predictive capabilities of the model without the addition of unique data were investigated to reduce the required number of training images. Results from model tests indicated the following: additional images, mirrored along the vertical-axis, provided precision and accuracy enhancements; increasing computational step iterations improved predictive precision and accuracy, and the optimal confidence threshold for operation was 25%. Annotation recommendations and improvements were also discovered and documented as a result of the research.<br>MS<br>Common Objects in Context for bridge inspection (COCO-Bridge) was introduced to improve a drone-conducted bridge inspection process. Drones are a great tool for bridge inspectors because they bring flexibility and access to the inspection. However, drones have a notoriously difficult time operating near bridges, because the signal can be lost between the operator and the drone. COCO-Bridge is an imagebased dataset that uses Artificial Intelligence (AI) as a solution to this particular problem, but has applications in other facets of the inspection as well. This effort initiated a dataset with a focus on identifying specific parts of a bridge or structural bridge elements. This would allow a drone to fly without explicit direction if the signal was lost, and also has the potential to extend its flight time. Extending flight time and operating autonomously are great advantagesfor drone operators and bridge inspectors. The output from COCO-Bridge would also help the inspectors identify areas that are prone to defects by highlighting regions that require inspection. The image dataset consisted of 774 images to detect four structural bridge elements which are commonly reviewed and rated during bridge inspections. The goal is to continue to increase the number of images and encompass more structural bridge elements in the dataset so that it may be used for all types of bridges. Methods to reduce the required number of images were investigated, because gathering images of structural bridge elements is challenging,. The results from model tests helped build a roadmap for the expansion and best-practices for developing a dataset of this type.
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Book chapters on the topic "SLR; CNN; Artificial Neural Network"

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Zacarias, Abel, and Luís A. Alexandre. "SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation." In Artificial Neural Networks in Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99978-4_8.

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Ramya, Medıkonda, T. Kishore Babu, P. Hussaın Basha, and Vikruthi Srıharsha. "Detection of Heart Failure Using a Convolutional Neural Network (CNN) via ECG Signals." In Proceedings of 4th International Conference on Artificial Intelligence and Smart Energy. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61475-0_37.

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Muhathir, Muhammad Farhan Dwi Ryandra, Rahmad B. Y. Syah, Nurul Khairina, and Rizki Muliono. "Convolutional Neural Network (CNN) of Resnet-50 with Inceptionv3 Architecture in Classification on X-Ray Image." In Artificial Intelligence Application in Networks and Systems. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35314-7_20.

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Gao, Lunrui, Hongbin Yan, Tingzhang Liu, Shaoyou Zhang, and Xinrui Xu. "Prediction of Environmental Parameters of Yungang Grottoes Based on BO-CNN-LSTM Artificial Neural Network." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-96-0294-0_12.

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Liu, Zhiyu, Wenhao Jiang, Kit-Hang Lee, et al. "A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network." In Artificial Intelligence in Radiation Therapy. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32486-5_6.

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Ji, Jinbao, Zongxiang Hu, Weiqi Zhang, and Sen Yang. "Development of Deep Learning Algorithms, Frameworks and Hardwares." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_71.

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AbstractAs the core algorithm of artificial intelligence, deep learning has brought new breakthroughs and opportunities to all walks of life. This paper summarizes the principles of deep learning algorithms such as Autoencoder (AE), Boltzmann Machine (BM), Deep Belief Network (DBM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Recursive Neural Network (RNN). The characteristics and differences of deep learning frameworks such as Tensorflow, Caffe, Theano and PyTorch are compared and analyzed. Finally, the application and performance of hardware platforms such as CPU and GPU in deep learning acceleration are introduced. In this paper, the development and application of deep learning algorithm, framework and hardware technology can provide reference and basis for the selection of deep learning technology.
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Eunice R, Jennifer, and D. Jude Hemanth. "Deep CNN for Static Indian Sign Language Digits Recognition." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220050.

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Sign language recognition (SLR) is a significant solution for the hearing and speech disabled to connect with the people. However, SLR system faces complexities such as low accuracy, overfitting, hand occlusions, and high interclass similarities. In this paper, a deep learning-based Convolution Neural Network model is proposed for Sign language recognition to address the issues. Our model uses Indian Sign Language dataset which comprises 10 class with a total of 2072 static digit gestures ranging between 0 to 9. Each class has 207 images. The proposed model generated desired outcome and the results are evaluated with varied optimizers such as Adam, RMS Prop, Stochastic gradient descent (SGD) optimizers. CNN model with SGD achieved training and validation accuracy of 99.72% and 98.97% respectively. The training and validation loss were comparatively minimum for our model. Further, the performance evaluation of the proposed model was analyzed based on precision, recall, F-score value. Our method shows its effectiveness over other machine learning models with a recognition rate of 99%.
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Kumar, Shalin, Parul Verma, Hoshiyar Singh Kanyal, Praveen Chandra Jha, and Jyoti Rai. "Initiatives for Challenges Faced By Developed Countries and India on Green Growth and Sustainable Development in the World." In Demystifying Emerging Trends in Green Technology. BENTHAM SCIENCE PUBLISHERS, 2025. https://doi.org/10.2174/9789815324099125030022.

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This review article analyzes various sign language recognition methodologies that rely on sign acquisition techniques and sign identification methods. Artificial neural networks are well suited for analyzing gestures, employing visionbased methods, and identifying signs. The Sign Language Recognition (SLR) system is a method for understanding a set of generated signs and converting them into text or speech while preserving the necessary context. The application of gesture recognition can exemplify effective human-machine interactions. Our objective in this study was to develop a model using a convolutional neural network. Consequently, the precision rate stands at approximately 85%. Subsequent efforts should enhance the Image Processing module to facilitate bidirectional communication, namely enabling the system to seamlessly convert between sign language and conventional language in both directions.
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Echim, Sebastian-Vasile, Răzvan-Alexandru Smădu, and Dumitru-Clementin Cercel. "Benchmarking Adversarial Robustness in Speech Emotion Recognition: Insights into Low-Resource Romanian and German Languages." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240774.

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Therapy, interviews, and emergency services assisted by artificial intelligence (AI) are applications where speech emotion recognition (SER) plays an essential role, for which performance and robustness are subject to improvement. Deep learning approaches have proven effective in SER; nevertheless, they can underperform when exposed to adversarial attacks. In this paper, we explore and enhance architectures, such as convolutional neural networks with long short-term memory (CNN-LSTM), AlexNet, VGG16, Convolutional Vision Transformer (CvT), Vision Transformer (ViT), and LeViT, by finding the suitable setup for SER models regarding speech processing, network hyperparameters, spectrogram augmentations, and adversarial examples. We apply our methodology to Romanian and German SER datasets and achieve state-of-the-art results, with 89.81% validation weighted accuracy and 98.09% average weighted accuracy on the trained models. Our highly robust models reach complete adversarial defense and up to 5.56% weighted accuracy improvement when attacked. We also show how adversarial attacks influence model behavior in SER through explainable AI techniques.
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Véstias, Mário Pereira. "Convolutional Neural Network." In Research Anthology on Artificial Neural Network Applications. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch077.

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Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.
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Conference papers on the topic "SLR; CNN; Artificial Neural Network"

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Emran, Nurul A., Nurul Izrin Md Saleh, and Muhamad Zaidi Mohd Ali. "Sports Video Classification Using Convolutional Neural Network (CNN) with Normalization Flow." In 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2024. http://dx.doi.org/10.1109/aidas63860.2024.10730229.

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Jaini, Siti Nurfadilah Binti, Deugwoo Lee, and Choong Wai Heng. "CNN-LSTM Neural Network-Based Short-Term PV Power Generation Forecaster." In 2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). IEEE, 2024. http://dx.doi.org/10.1109/iicaiet62352.2024.10729888.

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Kautsari, Nadillah Rahmatia, Rita Magdalena, Yunendah Nur Fuadah, Feni Nur Septiani, Rachma Zaffindra Amalia, and Yusril Saleh. "Convolutional Neural Network (CNN) for Quality of Coffee Beans Classification System." In 2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP). IEEE, 2023. https://doi.org/10.1109/airosip58759.2023.10873946.

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Chen, Junwei, Chengji Zhao, and Rongbang An. "MAE-CNN: A Multi-Scale Attention Enhanced Convolutional Neural Network for CU partition prediction." In 2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI). IEEE, 2024. http://dx.doi.org/10.1109/icecai62591.2024.10674906.

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Pardede, Erni Yanthy, Alvian Pandapotan Sitohang, Yunendah Nur Fuadah, et al. "Classification of Cataract Fundus Images using Convolutional Neural Network (CNN) Method EfficientNet-B0 Architecture." In 2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP). IEEE, 2023. https://doi.org/10.1109/airosip58759.2023.10873914.

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Fitri Amalia, Nur Wakhidah, and Erwin Budi Setiawan. "Cyberbullying Detection on Twitter using Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU)." In 2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP). IEEE, 2023. https://doi.org/10.1109/airosip58759.2023.10873879.

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Nishath, S. Ahamed, Suresh Rasappan, and Francis Saviour Devaraj. "Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) Architecture for Lung Cancer Subtype Classification." In 2025 International Conference for Artificial Intelligence, Applications, Innovation and Ethics (AI2E). IEEE, 2025. https://doi.org/10.1109/ai2e64943.2025.10983876.

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Ilhamdi, Divo, Yunendah Fuadah, Sofia Sa’idah, and Zhafeni Arif. "Brain Tumor Classification Based on MRI Image Processing using Convolutional Neural Network (CNN) with ResNet Architecture." In 2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP). IEEE, 2023. https://doi.org/10.1109/airosip58759.2023.10873939.

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Degadwala, Sheshang, Dhairya Vyas, and Harsh Dave. "Expression of Concern for: Classification of COVID-19 cases using Fine-Tune Convolution Neural Network (FT-CNN)." In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE, 2021. http://dx.doi.org/10.1109/icais50930.2021.10703002.

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Manik, Fuzy Yustika, Syahril Efendi, Jos Timanta Tarigan, and Maya Silvi Lydia. "Utilization of the Deep Learning Convolutional Neural Network (CNN) Method to Classify Pineapple Ripeness Levels." In 2024 IEEE International Conference on Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA). IEEE, 2024. https://doi.org/10.1109/ceria64726.2024.10915053.

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