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Letteratura scientifica selezionata sul tema "MDLNN"

Autore: Grafiati

Pubblicato: 11 settembre 2023

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Articoli di riviste sul tema "MDLNN"

AL-Ghamdi, Abdullah S. AL-Malaise, e Mahmoud Ragab. "Tunicate swarm algorithm with deep convolutional neural network-driven colorectal cancer classification from histopathological imaging data". Electronic Research Archive 31, n. 5 (2023): 2793–812. http://dx.doi.org/10.3934/era.2023141.

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<abstract> <p>Colorectal cancer (CRC) is one of the most popular cancers among both men and women, with increasing incidence. The enhanced analytical load data from the pathology laboratory, integrated with described intra- and inter-variabilities through the calculation of biomarkers, has prompted the quest for robust machine-based approaches in combination with routine practice. In histopathology, deep learning (DL) techniques have been applied at large due to their potential for supporting the analysis and forecasting of medically appropriate molecular phenotypes and microsatellite instability. Considering this background, the current research work presents a metaheuristics technique with deep convolutional neural network-based colorectal cancer classification based on histopathological imaging data (MDCNN-C3HI). The presented MDCNN-C3HI technique majorly examines the histopathological images for the classification of colorectal cancer (CRC). At the initial stage, the MDCNN-C3HI technique applies a bilateral filtering approach to get rid of the noise. Then, the proposed MDCNN-C3HI technique uses an enhanced capsule network with the Adam optimizer for the extraction of feature vectors. For CRC classification, the MDCNN-C3HI technique uses a DL modified neural network classifier, whereas the tunicate swarm algorithm is used to fine-tune its hyperparameters. To demonstrate the enhanced performance of the proposed MDCNN-C3HI technique on CRC classification, a wide range of experiments was conducted. The outcomes from the extensive experimentation procedure confirmed the superior performance of the proposed MDCNN-C3HI technique over other existing techniques, achieving a maximum accuracy of 99.45%, a sensitivity of 99.45% and a specificity of 99.45%.</p> </abstract>

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Ly, Ngoc Q., Tuong K. Do e Binh X. Nguyen. "Large-Scale Coarse-to-Fine Object Retrieval Ontology and Deep Local Multitask Learning". Computational Intelligence and Neuroscience 2019 (18 luglio 2019): 1–40. http://dx.doi.org/10.1155/2019/1483294.

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Object retrieval plays an increasingly important role in video surveillance, digital marketing, e-commerce, etc. It is facing challenges such as large-scale datasets, imbalanced data, viewpoint, cluster background, and fine-grained details (attributes). This paper has proposed a model to integrate object ontology, a local multitask deep neural network (local MDNN), and an imbalanced data solver to take advantages and overcome the shortcomings of deep learning network models to improve the performance of the large-scale object retrieval system from the coarse-grained level (categories) to the fine-grained level (attributes). Our proposed coarse-to-fine object retrieval (CFOR) system can be robust and resistant to the challenges listed above. To the best of our knowledge, the new main point of our CFOR system is the power of mutual support of object ontology, a local MDNN, and an imbalanced data solver in a unified system. Object ontology supports the exploitation of the inner-group correlations to improve the system performance in category classification, attribute classification, and conducting training flow and retrieval flow to save computational costs in the training stage and retrieval stage on large-scale datasets, respectively. A local MDNN supports linking object ontology to the raw data, and an imbalanced data solver based on Matthews’ correlation coefficient (MCC) addresses that the imbalance of data has contributed effectively to increasing the quality of object ontology realization without adjusting network architecture and data augmentation. In order to evaluate the performance of the CFOR system, we experimented on the DeepFashion dataset. This paper has shown that our local MDNN framework based on the pretrained NASNet architecture has achieved better performance (14.2% higher in recall rate) compared to single-task learning (STL) in the attribute learning task; it has also shown that our model with an imbalanced data solver has achieved better performance (5.14% higher in recall rate for fewer data attributes) compared to models that do not take this into account. Moreover, MAP@30 hovers 0.815 in retrieval on an average of 35 imbalanced fashion attributes.

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Khan, Mohammad Ayoub. "An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier". IEEE Access 8 (2020): 34717–27. http://dx.doi.org/10.1109/access.2020.2974687.

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Praveena, Anto, e B. Bharathi. "An approach to remove duplication records in healthcare dataset based on Mimic Deep Neural Network (MDNN) and Chaotic Whale Optimization (CWO)". Concurrent Engineering 29, n. 1 (marzo 2021): 58–67. http://dx.doi.org/10.1177/1063293x21992014.

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Duplication of data in an application will become an expensive factor. These replication of data need to be checked and if it is needed it has to be removed from the dataset as it occupies huge volume of data in the storage space. The cloud is the main source of data storage and all organizations are already started to move their dataset into the cloud since it is cost effective, storage space, data security and data Privacy. In the healthcare sector, storing the duplicated records leads to wrong prediction. Also uploading same files by many users, data storage demand will be occurred. To address those issues, this paper proposes an Optimal Removal of Deduplication (ORD) in heart disease data using hybrid trust based neural network algorithm. In ORD scheme, the Chaotic Whale Optimization (CWO) algorithm is used for trust computation of data using multiple decision metrics. The computed trust values and the nature of the data’s are sequentially applied to the training process by the Mimic Deep Neural Network (MDNN). It classify the data is a duplicate or not. Hence the duplicates files are identified and they were removed from the data storage. Finally, the simulation evaluates to examine the proposed MDNN based model and simulation results show the effectiveness of ORD scheme in terms of data duplication removal. From the simulation result it is found that the model’s accuracy, sensitivity and specificity was good.

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Lee, Jandee, Chan Hee Kim, In Kyung Min, Seonhyang Jeong, Hyunji Kim, Moon Jung Choi, Hyeong Ju Kwon, Sang Geun Jung e Young Suk Jo. "Detailed characterization of metastatic lymph nodes improves the prediction accuracy of currently used risk stratification systems in N1 stage papillary thyroid cancer". European Journal of Endocrinology 183, n. 1 (luglio 2020): 83–93. http://dx.doi.org/10.1530/eje-20-0131.

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Objective The characteristics of metastatic lymph nodes (MLNs) have been investigated as important predictors of recurrence and progression in papillary thyroid cancer (PTC). However, clinically applicable risk stratification systems are limited to the assessment of size and number of MLNs. This study investigated the predictive value of detailed characteristics of MLNs in combination with currently used risk stratification systems. Design and methods We retrospectively characterized 2811 MLNs from 9014 harvested LNs of 286 patients with N1 PTC according to the maximum diameter of MLN (MDLN), maximum diameter of metastatic focus (MDMF), ratio of both diameters (MDMFR), lymph node ratio (LNR, number of MLNs/number of total harvested LNs), presence of extranodal extension (ENE), desmoplastic reaction (DR), cystic component, and psammoma body. Results Factors related to the size and number of MLNs were associated with increased risk of recurrence and progression. Extensive presence of ENE (>40%) and DR (≥50%) increased the risk of recurrence/progression. The combination of MDLN, LNR, ENE, and DR had the highest predictive value among MLN characteristics. Combination of these parameters with ATA risk stratification or 1-year response to therapy improved the predictive power for recurrence/progression from a Harrell’s C-index of 0.781 to 0.936 and 0.867 to 0.960, respectively. Conclusions The combination of currently used risk stratification systems with detailed characterization of MLNs may improve the predictive accuracy for recurrence/progression in N1 PTC patients.

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Sukardi, Hadi Ahmad. "ANALISIS INVESTASI SAHAM DENGAN MENGGUNAKAN CAPITAL ASSET PRICING MODEL". Jurnal SIKAP (Sistem Informasi, Keuangan, Auditing Dan Perpajakan) 5, n. 1 (8 febbraio 2021): 18. http://dx.doi.org/10.32897/jsikap.v5i1.251.

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Analisis menunjukkan bahwa dari 23 saham perusahaan yang terpilih sebagai sampel penelitian, terdapat 5 saham efisien dan 18 saham tidak efisien berdasarkan metode CAPM. Saham-saham efisien adalah saham undervalued dengan nilai R_i > nilai E(R_i ). Saham-saham efisien yaitu PPRO, EMDE, JSPT, DMAS dan PWON. Sedangkan saham tidak efisien adalah saham yang memiliki nilai R_i < nilai E(R_i ) dan dikatakan sebagai saham overvalued. Saham-saham tidak efisien tersebut yaitu APLN, DILD, ELTY, KIJA, SMDM, LPKR, BSDE, SMRA, CTRA, MDLN, COWL, ASRI, MTLA, GPRA, RODA, GWSA, LPCK dan BEST.

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Chen, Yi, Jin Zhou, Qianting Gao, Jing Gao e Wei Zhang. "MDNN: Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning". Computer Modeling in Engineering & Sciences 136, n. 1 (2023): 381–401. http://dx.doi.org/10.32604/cmes.2023.023234.

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Lackner, Thomas E. "Advances in Managing Overactive Bladder". Journal of Pharmacy Practice 13, n. 4 (1 agosto 2000): 277–89. http://dx.doi.org/10.1106/8843-q9gl-g9xc-mdln.

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Wang, Xingmei, Anhua Liu, Yu Zhang e Fuzhao Xue. "Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network". Remote Sensing 11, n. 16 (13 agosto 2019): 1888. http://dx.doi.org/10.3390/rs11161888.

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A method with a combination of multi-dimensional fusion features and a modified deep neural network (MFF-MDNN) is proposed to recognize underwater acoustic targets in this paper. Specifically, due to the complex and changeable underwater environment, it is difficult to describe underwater acoustic signals with a single feature. The Gammatone frequency cepstral coefficient (GFCC) and modified empirical mode decomposition (MEMD) are developed to extract multi-dimensional features in this paper. Moreover, to ensure the same time dimension, a dimension reduction method is proposed to obtain multi-dimensional fusion features in the original underwater acoustic signals. Then, to reduce redundant features and further improve recognition accuracy, the Gaussian mixture model (GMM) is used to modify the structure of a deep neural network (DNN). Finally, the proposed underwater acoustic target recognition method can obtain an accuracy of 94.3% under a maximum of 800 iterations when the dataset has underwater background noise with weak targets. Compared with other methods, the recognition results demonstrate that the proposed method has higher accuracy and strong adaptability.

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Huang, Xixian, Xiongjun Zeng, Qingxiang Wu, Yu Lu, Xi Huang e Hua Zheng. "Face Verification Based on Deep Learning for Person Tracking in Hazardous Goods Factories". Processes 10, n. 2 (17 febbraio 2022): 380. http://dx.doi.org/10.3390/pr10020380.

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Person tracking in hazardous goods factories can provide a significant improvement in security and safety. This article proposes a face verification model which can be used to record travel paths for staff or related persons in the factory. As face images are captured from the dynamic crowd at entrance–exit gates of workshops, face verification is challenged by polymorphic faces, poor illumination and changing of a person’s pose. To adapt to this situation, a new face verification model is proposed, which is composed of two advanced deep learning neural network models. Firstly, MTCNN (Multi-Task Cascaded Convolutional Neural Network) is used to construct a face detector. Based on the SphereFace-20 network model, we have reconstructed a convolutional network architecture with the embedded Batch Normalization elements and the optimized network parameters. The new model, which is called the MDCNN, is used to extract efficient face features. A set of specific processing algorithms is used in the model to process polymorphic face images. The multi-view faces and various types of face images are used to train the models. The experimental results have demonstrated that the proposed model outperforms most existing methods on benchmark datasets such as the Labeled Faces in the Wild (LFW) and YouTube Face (YTF) datasets without multi-view (accuracy is 99.38% and 94.30%, respectively) and the CNBC/FERET datasets with multi-view (accuracy is 94.69%).

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Più fonti

Tesi sul tema "MDLNN"

Terefe, Adisu Wagaw. "Handwritten Recognition for Ethiopic (Ge’ez) Ancient Manuscript Documents". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288145.

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The handwritten recognition system is a process of learning a pattern from a given image of text. The recognition process usually combines a computer vision task with sequence learning techniques. Transcribing texts from the scanned image remains a challenging problem, especially when the documents are highly degraded, or have excessive dusty noises. Nowadays, there are several handwritten recognition systems both commercially and in free versions, especially for Latin based languages. However, there is no prior study that has been built for Ge’ez handwritten ancient manuscript documents. In contrast, the language has many mysteries of the past, in human history of science, architecture, medicine and astronomy. In this thesis, we present two separate recognition systems. (1) A character-level recognition system which combines computer vision for character segmentation from ancient books and a vanilla Convolutional Neural Network (CNN) to recognize characters. (2) An end- to- end segmentation free handwritten recognition system using CNN, Multi-Dimensional Recurrent Neural Network (MDRNN) with Connectionist Temporal Classification (CTC) for the Ethiopic (Ge’ez) manuscript documents. The proposed character label recognition model outperforms 97.78% accuracy. In contrast, the second model provides an encouraging result which indicates to further study the language properties for better recognition of all the ancient books.
Det handskrivna igenkännings systemet är en process för att lära sig ett mönster från en viss bild av text. Erkännande Processen kombinerar vanligtvis en datorvisionsuppgift med sekvens inlärningstekniker. Transkribering av texter från den skannade bilden är fortfarande ett utmanande problem, särskilt när dokumenten är mycket försämrad eller har för omåttlig dammiga buller. Nuförtiden finns det flera handskrivna igenkänningar system både kommersiellt och i gratisversionen, särskilt för latin baserade språk. Det finns dock ingen tidigare studie som har byggts för Ge’ez handskrivna gamla manuskript dokument. I motsats till detta språk har många mysterier från det förflutna, i vetenskapens mänskliga historia, arkitektur, medicin och astronomi. I denna avhandling presenterar vi två separata igenkänningssystem. (1) Ett karaktärs nivå igenkänningssystem som kombinerar bildigenkänning för karaktär segmentering från forntida böcker och ett vanilj Convolutional Neural Network (CNN) för att erkänna karaktärer. (2) Ett änd-till-slut-segmentering fritt handskrivet igenkänningssystem som använder CNN, Multi-Dimensional Recurrent Neural Network (MDRNN) med Connectionist Temporal Classification (CTC) för etiopiska (Ge’ez) manuskript dokument. Den föreslagna karaktär igenkännings modellen överträffar 97,78% noggrannhet. Däremot ger den andra modellen ett uppmuntrande resultat som indikerar att ytterligare studera språk egenskaperna för bättre igenkänning av alla antika böcker.

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GAUTAM, AJAI KUMAR. "BIOMETRIC RECOGNITION". Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19630.

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Biometric Recognition is the essential process of authenticating an individual and has a very wide application area starting from one’s essential need like phone or laptop access to high end applications like in, Airport, Border control, surveillance, forensic applications etc. It is implemented almost in every system that require some sort of authentication for establishing the identity of a person. Biometrics recognition is based on the Biometric traits, which are the physical or behavioral characteristics of a person that are physically or behaviorally linked to the person. Since Biometric traits are physically linked to the user therefore very difficult to steal or forge and person do not require to remember the login or password for accessing any system or premises. There are various biometric traits like Face, Finger Print, Finger veins, Iris, Scalera, etc which are extensively utilized in several recognition applications. Some recognition applications use number of traits of a person to have high recognition accuracy. Some even take physical as well as behavioral along with soft biometric traits like hand shape etc. to have robust recognition system that works in unconstrained environment with higher accuracy. Purpose of this research work is to improve the accuracy of the recognition system by taking number of traits of a person together called Multi-Modal Biometric (MMB) Recognition and by focusing on the finger vein trait, which is one of the current research areas as it can be captured only of a live person. A new method is proposed, the MMB recognition system centered on the Features Level and Scores Level (FLSL) fusion method and Modified Deep Learning Neural Network (MDLNN) classifier in order to enhance the performance. The face, ear, retina, fingerprint, and front hand image traits are considered by the proposed method. iii The unique patterns of finger veins (FV) are utilized by Finger vein recognition (FVR) for detecting individuals at a high-level accuracy. However, on account of the existence of artifacts, irregular shading, distortions, etc, precise FV detection is a difficult task. A framework for identifying FV is created by the work to offer a precise biometric authorization utilizing Enhanced Sigmoid Reweighted based Convolutional Neural Network (ES-RwCNN).

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Libri sul tema "MDLNN"

Studer, J. A. GEOMECH MDLNG IN ENGNG PRACTICE. Taylor & Francis, 1986.

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Spsht Mdlng and Dec Analysis. 4^a ed. South-Western, Div of Thomson Learning, 2003.

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Petroleum Reservoir Mdlng Simulation Geol Geostatistics Perf. McGraw-Hill Education, 2019.

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Capitoli di libri sul tema "MDLNN"

Bezerra, Byron Leite Dantas, Cleber Zanchettin e Vinícius Braga de Andrade. "A MDRNN-SVM Hybrid Model for Cursive Offline Handwriting Recognition". In Artificial Neural Networks and Machine Learning – ICANN 2012, 246–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33266-1_31.

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Sharma, Swati, e Varun Prakash Saxena. "Hybrid Sign Language Learning Approach Using Multi-scale Hierarchical Deep Convolutional Neural Network (MDCnn)". In Advances in Intelligent Systems and Computing, 663–77. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5443-6_51.

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"Optimizing MDLNS Implementations". In Multiple-Base Number System, 203–26. CRC Press, 2017. http://dx.doi.org/10.1201/b11652-8.

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"The Multidimensional Logarithmic Number System (MDLNS)". In Multiple-Base Number System, 109–34. CRC Press, 2017. http://dx.doi.org/10.1201/b11652-5.

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Singh, Pooja, Usha Chauhan, S. P. S. Chauhan e Harshit Singh. "Advanced Detection". In Advances in Electronic Government, Digital Divide, and Regional Development, 248–68. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6418-2.ch014.

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In the present scenario, due to climate change, farmers crops get fungi due to bacteria since soil temperatures change very rapidly according to sudden climate changes for which the crop is getting spoiled. At this advanced era, disease can be detected early so that crops are safe. Different types of fungi-bacterial disease will be detected and prevented by machine learning-based predicted deterministic probabilistic and artificial technology-based CNN for colour changes in plants. This chapter described machine learning techniques and proposed modified algorithms to identify and classify plant diseases. Deep neural network (DNN) models and algorithms are used to improve object accuracy and entropy to reduce the complexity of computational processes and improve the features during deep learning processes (e.g., modified deep neural network [MDNN]). Additionally, they support dynamic feature extraction DSURF and classifier combinations for creating image clusters with the help of clustering and deterministic probability.

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Atti di convegni sul tema "MDLNN"

Reddy, Arikatla Venkata, Pasupuleti Sai Kumar, Pathan Asif Khan, Venkata Subba Reddy Karumudi, Pradeepini G e Sagar Imambi. "MDLNN Approach for Alcohol Detection using IRIS". In 2023 Second International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2023. http://dx.doi.org/10.1109/icears56392.2023.10085257.

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Rosaline, S., M. Ayeesha Nasreen, P. Suganthi, T. Manimegalai e G. Ramkumar. "Predicting Melancholy risk among IT professionals using Modified Deep Learning Neural Network (MDLNN)". In 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2022. http://dx.doi.org/10.1109/csnt54456.2022.9787571.

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Lyu, Tengfei, Jianliang Gao, Ling Tian, Zhao Li, Peng Zhang e Ji Zhang. "MDNN: A Multimodal Deep Neural Network for Predicting Drug-Drug Interaction Events". In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/487.

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The interaction of multiple drugs could lead to serious events, which causes injuries and huge medical costs. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. Recently, many AI-based techniques have been proposed for predicting DDI associated events. However, most existing methods pay less attention to the potential correlations between DDI events and other multimodal data such as targets and enzymes. To address this problem, we propose a Multimodal Deep Neural Network (MDNN) for DDI events prediction. In MDNN, we design a two-pathway framework including drug knowledge graph (DKG) based pathway and heterogeneous feature (HF) based pathway to obtain drug multimodal representations. Finally, a multimodal fusion neural layer is designed to explore the complementary among the drug multimodal representations. We conduct extensive experiments on real-world dataset. The results show that MDNN can accurately predict DDI events and outperform the state-of-the-art models.

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Martin, Patrick, Jean-Pierre de la Croix e Magnus Egersted. "MDLn: A Motion Description Language for networked systems". In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4739185.

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Zhang, Xulong, Jianzong Wang, Ning Cheng e Jing Xiao. "MDCNN-SID: Multi-scale Dilated Convolution Network for Singer Identification". In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892338.

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Francisco, Maxwell, Felipe Gouveia, Byron Bezerra e Mêuser Valença. "Reconhecimento de Escrita Cursiva Offline Utilizando um Modelo Composto por MDRNN-RC". In 11. Congresso Brasileiro de Inteligência Computacional. SBIC, 2016. http://dx.doi.org/10.21528/cbic2013-089.

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Salminen, Jukka, Rob Hindley e Sami Saarinen. "Mackenzie Delta LNG Transport and Ice Management Study". In Offshore Technology Conference. OTC, 2023. http://dx.doi.org/10.4043/32302-ms.

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Abstract This paper will study the LNG Transport opportunities from the Canadian Arctic to the Asian markets. Mackenzie Delta LNG (MDLNG) project is in the Canada's Northwest Territories (NWT) which contains publicly owned conventional natural gas reserves which could be developed for export that would provide immediate economic benefit to the Inuvialuit Settlement Region, NWT and Canada. This paper presents the results of a feasibility study undertaken to evaluate the shipping routes and ice conditions along the route from the Arctic to the Asian LNG markets. Arctic LNG carriers have been in use in the Russian Arctic for years already and we have been deeply involved in the design, development, and testing of those current LNG carriers. Russian rules are somewhat different and thus the operations would commence in Canada and US waters that would give some opportunities in the LNG Carrier design as well. In this paper we will go through the general differences in the LNG carriers design for MDLNG. Currently the plan is to build gravity-based structure (GBS) to the offshore MacKenzie Delta. The GBS would need additional ice management support and vessels. In this paper we would talk about ice management vessels needed to support the operations for loading the LNG carriers as well as talk about the recommended ice management operations. With modern technology, good design and planning, it can be shown that the LNG transportation by ships is a feasible solution compared to building pipelines across the Arctic.

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