Relevant bibliographies by topics / Deep neural decision forest

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Deep neural decision forest'

Author: Grafiati
Published: 5 June 2025
Last updated: 8 July 2025

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Journal articles on the topic "Deep neural decision forest"

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Zhou, Zhi-Hua, and Ji Feng. "Deep forest." National Science Review 6, no. 1 (2018): 74–86. http://dx.doi.org/10.1093/nsr/nwy108.

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Abstract Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized differentiable non-linear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules such as decision trees. After a discussion about the mystery behind deep neural networks, particularly by contrasting them with shallow neural networks and traditional machine-learning techniques such as decision trees and boosting machines, we conjecture that the success of deep neural networks owes much to three characteristics, i.e. layer-by-layer processing, in-model feature transformation and sufficient model complexity. On one hand, our conjecture may offer inspiration for theoretical understanding of deep learning; on the other hand, to verify the conjecture, we propose an approach that generates deep forest holding these characteristics. This is a decision-tree ensemble approach, with fewer hyper-parameters than deep neural networks, and its model complexity can be automatically determined in a data-dependent way. Experiments show that its performance is quite robust to hyper-parameter settings, such that in most cases, even across different data from different domains, it is able to achieve excellent performance by using the same default setting. This study opens the door to deep learning based on non-differentiable modules without gradient-based adjustment, and exhibits the possibility of constructing deep models without backpropagation.
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Kumano, So, and Tatsuya Akutsu. "Comparison of the Representational Power of Random Forests, Binary Decision Diagrams, and Neural Networks." Neural Computation 34, no. 4 (2022): 1019–44. http://dx.doi.org/10.1162/neco_a_01486.

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Abstract In this letter, we compare the representational power of random forests, binary decision diagrams (BDDs), and neural networks in terms of the number of nodes. We assume that an axis-aligned function on a single variable is assigned to each edge in random forests and BDDs, and the activation functions of neural networks are sigmoid, rectified linear unit, or similar functions. Based on existing studies, we show that for any random forest, there exists an equivalent depth-3 neural network with a linear number of nodes. We also show that for any BDD with balanced width, there exists an equivalent shallow depth neural network with a polynomial number of nodes. These results suggest that even shallow neural networks have the same or higher representation power than deep random forests and deep BDDs. We also show that in some cases, an exponential number of nodes are required to express a given random forest by a random forest with a much fewer number of trees, which suggests that many trees are required for random forests to represent some specific knowledge efficiently.
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Nandi Tultul, Ahana, Romana Afroz, and Md Alomgir Hossain. "Comparison of the efficiency of machine learning algorithms for phishing detection from uniform resource locator." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1640. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1640-1648.

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We are using cyberspace for completing our daily life activities because of the growth of Internet. Attackers use some approachs, such as phishing, with the use of false websites to collect personal information of users. Although, software companies launch products to prevent phishing attacks, identifying a webpage as legitimate or phishing, is a very defficult and these products cannot protect from attacks. In this paper, an anti-phishing system has been introduced that can extract feature from website’s URL as instant basis and use four classification algorithms named as K-Nearest neighbor, decision tree, support vector machine, random forest on these features. According to the comparison of the experimental results from these algorithms, random forest algorithm with the selected features gives the highest performance with the 95.67% accuracy rate. Then we have used one deep learning algorithm as enhanced of our experiment named as deep neural decision forests which have given performance with the 92.67% accuracy rate. Then we have created a system which can extract the features from raw URL and pass the features to our deep neural decision forest trained model and can classify the URL as Phishing or legitimate.
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Tultul, Ahana Nandi, Romana Afroz, and Md Alomgir Hossain. "Comparison of the efficiency of machine learning algorithms for phishing detection from uniform resource locator." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1640–48. https://doi.org/10.11591/ijeecs.v28.i3.pp1640-1648.

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We are using cyberspace for completing our daily life activities because of the growth of Internet. Attackers use some approachs, such as phishing, with the use of false websites to collect personal information of users. Although, software companies launch products to prevent phishing attacks, identifying a webpage as legitimate or phishing, is a very defficult and these products cannot protect from attacks. In this paper, an anti-phishing system has been introduced that can extract feature from website’s URL as instant basis and use four classification algorithms named as K-Nearest neighbor, decision tree, support vector machine, random forest on these features. According to the comparison of the experimental results from these algorithms, random forest algorithm with the selected features gives the highest performance with the 95.67% accuracy rate. Then we have used one deep learning algorithm as enhanced of our experiment named as deep neural decision forests which have given performance with the 92.67% accuracy rate. Then we have created a system which can extract the features from raw URL and pass the features to our deep neural decision forest trained model and can classify the URL as Phishing or legitimate.
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M, Sudharshan. "Pneumonia Prediction and Decision Support System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47649.

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Abstract The rising prevalence of pneumonia demands automated diagnostic systems to enhance clinical efficiency and accuracy. Traditional diagnosis, reliant on manual chest X-ray and blood test analysis, is time-consuming and error-prone. This project introduces a deep learning-based system for pneumonia prediction, integrating chest X-ray images and blood test biomarkers to classify patients as healthy, viral, or bacterial pneumonia. It employs Convolutional Neural Networks (CNNs) for X-ray feature extraction, Random Forests for biomarker classification, and a heuristic-based fusion model for accurate predictions. Preprocessing includes image normalization and biomarker validation, with Grad-CAM enhancing interpretability. The system achieves 93.4% fusion accuracy, processes cases in 3.8 seconds, and generates structured clinical reports. Scalable and web-based, it supports paperless healthcare and hospital integration. Future enhancements include multilingual support and cloud deployment, advancing digital transformation in medical diagnostics. Keywords— Pneumonia Prediction, Deep Learning, Multimodal Fusion, CNN, Random Forest, Healthcare Automation
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Liu, Xiaobo, Xu Yin, Min Wang, Yaoming Cai, and Guang Qi. "Emotion Recognition Based on Multi-Composition Deep Forest and Transferred Convolutional Neural Network." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 5 (2019): 883–90. http://dx.doi.org/10.20965/jaciii.2019.p0883.

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In human-machine interaction, facial emotion recognition plays an important role in recognizing the psychological state of humans. In this study, we propose a novel emotion recognition framework based on using a knowledge transfer approach to capture features and employ an improved deep forest model to determine the final emotion types. The structure of a very deep convolutional network is learned from ImageNet and is utilized to extract face and emotion features from other data sets, solving the problem of insufficiently labeled samples. Then, these features are input into a classifier called multi-composition deep forest, which consists of 16 types of forests for facial emotion recognition, to enhance the diversity of the framework. The proposed method does not need require to train a network with a complex structure, and the decision tree-based classifier can achieve accurate results with very few parameters, making it easier to implement, train, and apply in practice. Moreover, the classifier can adaptively decide its model complexity without iteratively updating parameters. The experimental results for two emotion recognition problems demonstrate the superiority of the proposed method over several well-known methods in facial emotion recognition.
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Lee, Sang-Hyun. "Performance Evaluation of Machine Learning and Deep Learning-Based Models for Predicting Remaining Capacity of Lithium-Ion Batteries." Applied Sciences 13, no. 16 (2023): 9127. http://dx.doi.org/10.3390/app13169127.

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Lithium-ion batteries are widely used in electric vehicles, smartphones, and energy storage devices due to their high power and light weight. The goal of this study is to predict the remaining capacity of a lithium-ion battery and evaluate its performance through three machine learning models: linear regression, decision tree, and random forest, and two deep learning models: neural network and ensemble model. Mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R-squared), and root mean squared error (RMSE) were used to measure prediction accuracy. For the evaluation of the artificial intelligence model, the dataset was downloaded and integrated with measurement data of the CS2 lithium-ion battery provided by the University of Maryland College of Engineering. As a result of the study, the RMSE of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. According to the measured values, the ensemble model showed the best predictive performance, followed by the neural network model. Decision tree and random forest models also showed very good performance, and the linear regression model showed relatively poor predictive performance compared to the other models.
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Naderpour, Mohsen, Hossein Mojaddadi Rizeei, and Fahimeh Ramezani. "Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework." Remote Sensing 13, no. 13 (2021): 2513. http://dx.doi.org/10.3390/rs13132513.

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Forest fire is one of the foremost environmental disasters that threatens the Australian community. Recognition of the occurrence patterns of fires and the identification of fire risk is beneficial to mitigate probable fire threats. Machine learning techniques are recognized as well-known approaches to solving non-linearity problems such as forest fire risk. However, assessing such environmental multivariate disasters has always been challenging as modelling may be biased from multiple uncertainty sources such as the quality and quantity of input parameters, training processes, and a default setup for hyper-parameters. In this study, we propose a spatial framework to quantify the forest fire risk in the Northern Beaches area of Sydney. Thirty-six significant key indicators contributing to forest fire risk were selected and spatially mapped from different contexts such as topography, morphology, climate, human-induced, social, and physical perspectives as input to our model. Optimized deep neural networks were developed to maximize the capability of the multilayer perceptron for forest fire susceptibility assessment. The results show high precision of developed model against accuracy assessment metrics of ROC = 95.1%, PRC = 93.8%, and k coefficient = 94.3%. The proposed framework follows a stepwise procedure to run multiple scenarios to calculate the probability of forest risk with new input contributing parameters. This model improves adaptability and decision-making as it can be adapted to different regions of Australia with a minor localization adoption requirement of the weighting procedure.
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Du, Lei, Haifeng Song, Yingying Xu, and Songsong Dai. "An Architecture as an Alternative to Gradient Boosted Decision Trees for Multiple Machine Learning Tasks." Electronics 13, no. 12 (2024): 2291. http://dx.doi.org/10.3390/electronics13122291.

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Deep networks-based models have achieved excellent performances in various applications for extracting discriminative feature representations by convolutional neural networks (CNN) or recurrent neural networks (RNN). However, CNN or RNN may not work when handling data without temporal/spatial structures. Therefore, finding a new technique to extract features instead of CNN or RNN is a necessity. Gradient Boosted Decision Trees (GBDT) can select the features with the largest information gain when building trees. In this paper, we propose an architecture based on the ensemble of decision trees and neural network (NN) for multiple machine learning tasks, e.g., classification, regression, and ranking. It can be regarded as an extension of the widely used deep-networks-based model, in which we use GBDT instead of CNN or RNN. This architecture consists of two main parts: (1) the decision forest layers, which focus on learning features from the input data, (2) the fully connected layers, which focus on distilling knowledge from the decision forest layers. Powered by these two parts, the proposed model could handle data without temporal/spatial structures. This model can be efficiently trained by stochastic gradient descent via back-propagation. The empirical evaluation results of different machine learning tasks demonstrate the the effectiveness of the proposed method.
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Alrayes, Fatma S., Mohammed Zakariah, Maha Driss, and Wadii Boulila. "Deep Neural Decision Forest (DNDF): A Novel Approach for Enhancing Intrusion Detection Systems in Network Traffic Analysis." Sensors 23, no. 20 (2023): 8362. http://dx.doi.org/10.3390/s23208362.

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Intrusion detection systems, also known as IDSs, are widely regarded as one of the most essential components of an organization’s network security. This is because IDSs serve as the organization’s first line of defense against several cyberattacks and are accountable for accurately detecting any possible network intrusions. Several implementations of IDSs accomplish the detection of potential threats throughout flow-based network traffic analysis. Traditional IDSs frequently struggle to provide accurate real-time intrusion detection while keeping up with the changing landscape of threat. Innovative methods used to improve IDSs’ performance in network traffic analysis are urgently needed to overcome these drawbacks. In this study, we introduced a model called a deep neural decision forest (DNDF), which allows the enhancement of classification trees with the power of deep networks to learn data representations. We essentially utilized the CICIDS 2017 dataset for network traffic analysis and extended our experiments to evaluate the DNDF model’s performance on two additional datasets: CICIDS 2018 and a custom network traffic dataset. Our findings showed that DNDF, a combination of deep neural networks and decision forests, outperformed reference approaches with a remarkable precision of 99.96% by using the CICIDS 2017 dataset while creating latent representations in deep layers. This success can be attributed to improved feature representation, model optimization, and resilience to noisy and unbalanced input data, emphasizing DNDF’s capabilities in intrusion detection and network security solutions.
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Dissertations / Theses on the topic "Deep neural decision forest"

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Granström, Daria, and Johan Abrahamsson. "Loan Default Prediction using Supervised Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252312.

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It is essential for a bank to estimate the credit risk it carries and the magnitude of exposure it has in case of non-performing customers. Estimation of this kind of risk has been done by statistical methods through decades and with respect to recent development in the field of machine learning, there has been an interest in investigating if machine learning techniques can perform better quantification of the risk. The aim of this thesis is to examine which method from a chosen set of machine learning techniques exhibits the best performance in default prediction with regards to chosen model evaluation parameters. The investigated techniques were Logistic Regression, Random Forest, Decision Tree, AdaBoost, XGBoost, Artificial Neural Network and Support Vector Machine. An oversampling technique called SMOTE was implemented in order to treat the imbalance between classes for the response variable. The results showed that XGBoost without implementation of SMOTE obtained the best result with respect to the chosen model evaluation metric.<br>Det är nödvändigt för en bank att ha en bra uppskattning på hur stor risk den bär med avseende på kunders fallissemang. Olika statistiska metoder har använts för att estimera denna risk, men med den nuvarande utvecklingen inom maskininlärningsområdet har det väckt ett intesse att utforska om maskininlärningsmetoder kan förbättra kvaliteten på riskuppskattningen. Syftet med denna avhandling är att undersöka vilken metod av de implementerade maskininlärningsmetoderna presterar bäst för modellering av fallissemangprediktion med avseende på valda modelvaldieringsparametrar. De implementerade metoderna var Logistisk Regression, Random Forest, Decision Tree, AdaBoost, XGBoost, Artificiella neurala nätverk och Stödvektormaskin. En översamplingsteknik, SMOTE, användes för att behandla obalansen i klassfördelningen för svarsvariabeln. Resultatet blev följande: XGBoost utan implementering av SMOTE visade bäst resultat med avseende på den valda metriken.
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Bengana, M. (Mohamed). "Land cover and forest segmentation using deep neural networks." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201905101715.

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Tiivistelmä. Land Use and Land Cover (LULC) information is important for a variety of applications notably ones related to forestry. The segmentation of remotely sensed images has attracted various research subjects. However this is no easy task, with various challenges to face including the complexity of satellite images, the difficulty to get hold of them, and lack of ready datasets. It has become clear that trying to classify on multiple classes requires more elaborate methods such as Deep Learning (DL). Deep Neural Networks (DNNs) have a promising potential to be a good candidate for the task. However DNNs require a huge amount of data to train including the Ground Truth (GT) data. In this thesis a DL pixel-based approach backed by the state of the art semantic segmentation methods is followed to tackle the problem of LULC mapping. The DNN used is based on DeepLabv3 network with an encoder-decoder architecture. To tackle the issue of lack of data the Sentinel-2 satellite whose data is provided for free by Copernicus was used with the GT mapping from Corine Land Cover (CLC) provided by Copernicus and modified by Tyke to a higher resolution. From the multispectral images in Sentinel-2 Red Green Blue (RGB), and Near Infra Red (NIR) channels were extracted, the 4th channel being extremely useful in the detection of vegetation. This ended up achieving quite good accuracy on a DNN based on ResNet-50 which was calculated using the Mean Intersection over Union (MIoU) metric reaching 0.53MIoU. It was possible to use this data to transfer the learning to a data from Pleiades-1 satellite with much better resolution, Very High Resolution (VHR) in fact. The results were excellent especially when compared on training right away on that data reaching an accuracy of 0.98 and 0.85MIoU.
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Kunz, Jenny. "Neural Language Models with Explicit Coreference Decision." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-371827.

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Coreference is an important and frequent concept in any form of discourse, and Coreference Resolution (CR) a widely used task in Natural Language Understanding (NLU). In this thesis, we implement and explore two recent models that include the concept of coreference in Recurrent Neural Network (RNN)-based Language Models (LM). Entity and reference decisions are modeled explicitly in these models using attention mechanisms. Both models learn to save the previously observed entities in a set and to decide if the next token created by the LM is a mention of one of the entities in the set, an entity that has not been observed yet, or not an entity. After a theoretical analysis where we compare the two LMs to each other and to a state of the art Coreference Resolution system, we perform an extensive quantitative and qualitative analysis. For this purpose, we train the two models and a classical RNN-LM as the baseline model on the OntoNotes 5.0 corpus with coreference annotation. While we do not reach the baseline in the perplexity metric, we show that the models’ relative performance on entity tokens has the potential to improve when including the explicit entity modeling. We show that the most challenging point in the systems is the decision if the next token is an entity token, while the decision which entity the next token refers to performs comparatively well. Our analysis in the context of a text generation task shows that a wide-spread error source for the mention creation process is the confusion of tokens that refer to related but different entities in the real world, presumably a result of the context-based word representations in the models. Our re-implementation of the DeepMind model by Yang et al. 2016 performs notably better than the re-implementation of the EntityNLM model by Ji et al. 2017 with a perplexity of 107 compared to a perplexity of 131.
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Lind, Sebastian. "Ensemble approach to prediction of initial velocity centered around random forest regression and feed forward deep neural networks." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-79956.

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Prediction of initial velocity of artillery system is a feature that is hard to determine with statistical and analytical models. Machine learning is therefore to be tested, in order to achieve a higher accuracy than the current method (baseline). An ensemble approach will be explored in this paper, centered around feed forward deep neural network and random forest regression. Furthermore, collinearity of features and their importance will be investigated. The impact of the measured error on the range of the projectile will also be derived by finding a numerical solution with Newton Raphsons method. For the five systemstest data was used on, the mean absolute errors were 26, 9.33, 8.72 and 9.06 for deep neural networks,random forest regression, ensemble learning and conventional method, respectively. For future works,more models should be tested with ensemble learning, as well as investigation on the feature space for the input data.
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Nylund, Andreas. "To be, or not to be Melanoma : Convolutional neural networks in skin lesion classification." Thesis, KTH, Medicinsk teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190000.

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Machine learning methods provide an opportunity to improve the classification of skin lesions and the early diagnosis of melanoma by providing decision support for general practitioners. So far most studies have been looking at the creation of features that best indicate melanoma. Representation learning methods such as neural networks have outperformed hand-crafted features in many areas. This work aims to evaluate the performance of convolutional neural networks in relation to earlier machine learning algorithms and expert diagnosis. In this work, convolutional neural networks were trained on datasets of dermoscopy images using weights initialized from a random distribution, a network trained on the ImageNet dataset and a network trained on Dermnet, a skin disease atlas.  The ensemble sum prediction of the networks achieved an accuracy of 89.3% with a sensitivity of 77.1% and a specificity of 93.0% when based on the weights learned from the ImageNet dataset and the Dermnet skin disease atlas and trained on non-polarized light dermoscopy images.  The results from the different networks trained on little or no prior data confirms the idea that certain features are transferable between different data. Similar classification accuracies to that of the highest scoring network are achieved by expert dermatologists and slightly higher results are achieved by referenced hand-crafted classifiers.  The trained networks are found to be comparable to practicing dermatologists and state-of-the-art machine learning methods in binary classification accuracy, benign – melanoma, with only little pre-processing and tuning.
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Landmér, Pedersen Jesper. "Weighing Machine Learning Algorithms for Accounting RWISs Characteristics in METRo : A comparison of Random Forest, Deep Learning & kNN." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-85586.

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The numerical model to forecast road conditions, Model of the Environment and Temperature of Roads (METRo), laid the foundation of solving the energy balance and calculating the temperature evolution of roads. METRo does this by providing a numerical modelling system making use of Road Weather Information Stations (RWIS) and meteorological projections. While METRo accommodates tools for correcting errors at each station, such as regional differences or microclimates, this thesis proposes machine learning as a supplement to the METRo prognostications for accounting station characteristics. Controlled experiments were conducted by comparing four regression algorithms, that is, recurrent and dense neural network, random forest and k-nearest neighbour, to predict the squared deviation of METRo forecasted road surface temperatures. The results presented reveal that the models utilising the random forest algorithm yielded the most reliable predictions of METRo deviations. However, the study also presents the promise of neural networks and the ability and possible advantage of seasonal adjustments that the networks could offer.
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Hammarström, Tobias. "Towards Explainable Decision-making Strategies of Deep Convolutional Neural Networks : An exploration into explainable AI and potential applications within cancer detection." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424779.

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The influence of Artificial Intelligence (AI) on society is increasing, with applications in highly sensitive and complicated areas. Examples include using Deep Convolutional Neural Networks within healthcare for diagnosing cancer. However, the inner workings of such models are often unknown, limiting the much-needed trust in the models. To combat this, Explainable AI (XAI) methods aim to provide explanations of the models' decision-making. Two such methods, Spectral Relevance Analysis (SpRAy) and Testing with Concept Activation Methods (TCAV), were evaluated on a deep learning model classifying cat and dog images that contained introduced artificial noise. The task was to assess the methods' capabilities to explain the importance of the introduced noise for the learnt model. The task was constructed as an exploratory step, with the future aim of using the methods on models diagnosing oral cancer. In addition to using the TCAV method as introduced by its authors, this study also utilizes the CAV-sensitivity to introduce and perform a sensitivity magnitude analysis. Both methods proved useful in discerning between the model’s two decision-making strategies based on either the animal or the noise. However, greater insight into the intricacies of said strategies is desired. Additionally, the methods provided a deeper understanding of the model’s learning, as the model did not seem to properly distinguish between the noise and the animal conceptually. The methods thus accentuated the limitations of the model, thereby increasing our trust in its abilities. In conclusion, the methods show promise regarding the task of detecting visually distinctive noise in images, which could extend to other distinctive features present in more complex problems. Consequently, more research should be conducted on applying these methods on more complex areas with specialized models and tasks, e.g. oral cancer.
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Huatuco, Santos Gustavo. "Soccer Coach Decision Support System." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15136/.

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The savage essence and nature of sports means those who work on it hunt for the win. The sport enterprise is undergoing a gigantic digital transformation focused on imaging, real time and data analysis employed in the competitions. Conventional process methods in sports management such as fitness and health establishments, training, growth and match or game realisation are all being revolutionized by the sport digitization. In team sports it is well known that is needful an enough and simple digital methodology to organize and construct a feasible strategy. Digitization in sports is perpetually evolving and requires pervasive challenges. The sports and athletics digitization success is based on what is being done with collection of more data. Competitive advantages go to those who produce powerful operations using the data and acting on it in real time. The potential impact of these sport features in sport team operations is powerful. Data does not ride all decisions, but it empowers knowledgeable decisions. In these world circumstances, our vision with this system was born from a dream helping soccer sport management systems embrace and improve its contest success. Our perspective problem is how a decision support system for soccer coaches helps them to take enhancement decisions better. To face this problem we have created a soccer coach decision support system. This system is organised in two joined components; the first simulates the prediction of the soccer match winner through a data driven neural network. This component output activates the second to operate the logic rules learning and provides the stats, analysis, decision making and additionally plans improvements like drills and training procedures. This helps on the preparation towards upcoming matches as well as being aligned with their style and playing concepts. Future scalability and development, will analyse the mental and moral features of the teams by virtue of their athlete’s behavior changes.
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Mohamed, Abdelhack. "Top-down Modulation in Human Visual Cortex." Kyoto University, 2019. http://hdl.handle.net/2433/242434.

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Varatharajah, Thujeepan, and Eriksson Victor. "A comparative study on artificial neural networks and random forests for stock market prediction." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186452.

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This study investigates the predictive performance of two different machine learning (ML) models on the stock market and compare the results. The chosen models are based on artificial neural networks (ANN) and random forests (RF). The models are trained on two separate data sets and the predictions are made on the next day closing price. The input vectors of the models consist of 6 different financial indicators which are based on the closing prices of the past 5, 10 and 20 days. The performance evaluation are done by analyzing and comparing such values as the root mean squared error (RMSE) and mean average percentage error (MAPE) for the test period. Specific behavior in subsets of the test period is also analyzed to evaluate consistency of the models. The results showed that the ANN model performed better than the RF model as it throughout the test period had lower errors compared to the actual prices and thus overall made more accurate predictions.<br>Denna studie undersöker hur väl två olika modeller inom maskininlärning (ML) kan förutspå aktiemarknaden och jämför sedan resultaten av dessa. De valda modellerna baseras på artificiella neurala nätverk (ANN) samt random forests (RF). Modellerna tränas upp med två separata datamängder och prognoserna sker på nästföljande dags stängningskurs. Indatan för modellerna består av 6 olika finansiella nyckeltal som är baserade på stängningskursen för de senaste 5, 10 och 20 dagarna. Prestandan utvärderas genom att analysera och jämföra värden som root mean squared error (RMSE) samt mean average percentage error (MAPE) för testperioden. Även specifika trender i delmängder av testperioden undersöks för att utvärdera följdriktigheten av modellerna. Resultaten visade att ANN-modellen presterade bättre än RF-modellen då den sett över hela testperioden visade mindre fel jämfört med de faktiska värdena och gjorde därmed mer träffsäkra prognoser.
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Books on the topic "Deep neural decision forest"

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Bessonov, Aleksey. The study of criminal activity using artificial intelligence. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2195488.

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The monograph describes the technology of building digital crime models, including the preparation of data on criminal acts for study using mathematical statistics and artificial intelligence methods, the features of studying such data through various artificial intelligence methods, including neural networks, gradient boosting, decision trees, random forest, clustering, etc. Special attention is paid to the use of mathematical statistics and artificial intelligence methods in the study of serial crimes in science and practice. It is intended for scientists and practitioners of law enforcement agencies, graduate students, adjuncts, students and students of higher educational institutions interested in methods of mathematical statistics and artificial intelligence in the criminalistic study of crimes. It may also be of interest to scientists who study criminal activity in the field of data science, criminology, criminology and legal psychology.
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Goyal, Dinesh, Karthikrajan Senthilnathan, Balamurugan Shanmugam, Iyswarya Annapoorani, and Ravi Samikannu. Deep Learning Applications and Intelligent Decision Making in Engineering. IGI Global, 2020.

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Goyal, Dinesh, Karthikrajan Senthilnathan, Balamurugan Shanmugam, Iyswarya Annapoorani, and Ravi Samikannu. Deep Learning Applications and Intelligent Decision Making in Engineering. IGI Global, 2020.

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Goyal, Dinesh, Karthikrajan Senthilnathan, Balamurugan Shanmugam, Iyswarya Annapoorani, and Ravi Samikannu. Deep Learning Applications and Intelligent Decision Making in Engineering. IGI Global, 2020.

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Goyal, Dinesh, Karthikrajan Senthilnathan, Balamurugan Shanmugam, Iyswarya Annapoorani, and Ravi Samikannu. Deep Learning Applications and Intelligent Decision Making in Engineering. IGI Global, 2020.

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Goyal, Dinesh, Karthikrajan Senthilnathan, Balamurugan Shanmugam, Iyswarya Annapoorani, and Ravi Samikannu. Deep Learning Applications and Intelligent Decision Making in Engineering. IGI Global, 2020.

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Alverio, Taryn. Machine Learning Guide : Instructing Neural Networks, Decision Trees, Random Forest, and Algorithms: Neural Network Definition. Independently Published, 2021.

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Glannon, Walter. Psychiatric Neuroethics I. Edited by John Z. Sadler, K. W. M. Fulford, and Werdie (C W. ). van Staden. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780198732372.013.30.

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Severe psychiatric disorders may be resistant to conventional pharmacological and psychotherapeutic treatments. Invasive interventions such as deep-brain stimulation (DBS) and neurosurgical ablation (lesioning) can modulate dysfunctional neural circuits implicated in these disorders. Yet these two forms of psychiatric neurosurgery are still experimental and investigational and thus their safety and efficacy have yet to be established. This chapter is an examination and discussion of the main ethical issues surrounding the experimental use of DBS and lesioning for treatment-refractory psychiatric disorders. I address questions regarding research subjects’ exposure to risk and informed consent to be enrolled in clinical trials testing these techniques for major depression and obsessive-compulsive disorder. These questions include whether or to what extent the therapeutic misconception influences decisions to enroll in these trials. I then explore similar questions about the use of DBS for schizophrenia and anorexia nervosa. Finally, I discuss the obligations of researchers conducting these studies to research subjects.
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Book chapters on the topic "Deep neural decision forest"

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Sjöberg, Anders, Emil Gustavsson, Ashok Chaitanya Koppisetty, and Mats Jirstrand. "Federated Learning of Deep Neural Decision Forests." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37599-7_58.

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Reinders, Christoph, Michael Ying Yang, and Bodo Rosenhahn. "Two Worlds in One Network: Fusing Deep Learning and Random Forests for Classification and Object Detection." In Volunteered Geographic Information. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35374-1_5.

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AbstractNeural networks have demonstrated great success; however, large amounts of labeled data are usually required for training the networks. In this work, a framework for analyzing the road and traffic situations for cyclists and pedestrians is presented, which only requires very few labeled examples. We address this problem by combining convolutional neural networks and random forests, transforming the random forest into a neural network, and generating a fully convolutional network for detecting objects. Because existing methods for transforming random forests into neural networks propose a direct mapping and produce inefficient architectures, we present neural random forest imitation—an imitation learning approach by generating training data from a random forest and learning a neural network that imitates its behavior. This implicit transformation creates very efficient neural networks that learn the decision boundaries of a random forest. The generated model is differentiable, can be used as a warm start for fine-tuning, and enables end-to-end optimization. Experiments on several real-world benchmark datasets demonstrate superior performance, especially when training with very few training examples. Compared to state-of-the-art methods, we significantly reduce the number of network parameters while achieving the same or even improved accuracy due to better generalization.
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Li, Ruiguang, Ming Liu, Dawei Xu, Jiaqi Gao, Fudong Wu, and Liehuang Zhu. "A Review of Machine Learning Algorithms for Text Classification." In Communications in Computer and Information Science. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9229-1_14.

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AbstractText classification is a basic task in the field of natural language processing, and it is a basic technology for information retrieval, questioning and answering system, emotion analysis and other advanced tasks. It is one of the earliest application of machine learning algorithm, and has achieved good results. In this paper, we made a review of the traditional and state-of-the-art machine learning algorithms for text classification, such as Naive Bayes, Supporting Vector Machine, Decision Tree, K Nearest Neighbor, Random Forest and neural networks. Then, we discussed the advantages and disadvantages of all kinds of machine learning algorithms in depth. Finally, we made a summary that neural networks and deep learning will become the main research topic in the future.
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Holzinger, Andreas, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, and Wojciech Samek. "xxAI - Beyond Explainable Artificial Intelligence." In xxAI - Beyond Explainable AI. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_1.

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AbstractThe success of statistical machine learning from big data, especially of deep learning, has made artificial intelligence (AI) very popular. Unfortunately, especially with the most successful methods, the results are very difficult to comprehend by human experts. The application of AI in areas that impact human life (e.g., agriculture, climate, forestry, health, etc.) has therefore led to an demand for trust, which can be fostered if the methods can be interpreted and thus explained to humans. The research field of explainable artificial intelligence (XAI) provides the necessary foundations and methods. Historically, XAI has focused on the development of methods to explain the decisions and internal mechanisms of complex AI systems, with much initial research concentrating on explaining how convolutional neural networks produce image classification predictions by producing visualizations which highlight what input patterns are most influential in activating hidden units, or are most responsible for a model’s decision. In this volume, we summarize research that outlines and takes next steps towards a broader vision for explainable AI in moving beyond explaining classifiers via such methods, to include explaining other kinds of models (e.g., unsupervised and reinforcement learning models) via a diverse array of XAI techniques (e.g., question-and-answering systems, structured explanations). In addition, we also intend to move beyond simply providing model explanations to directly improving the transparency, efficiency and generalization ability of models. We hope this volume presents not only exciting research developments in explainable AI but also a guide for what next areas to focus on within this fascinating and highly relevant research field as we enter the second decade of the deep learning revolution. This volume is an outcome of the ICML 2020 workshop on “XXAI: Extending Explainable AI Beyond Deep Models and Classifiers.”
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Zhai, Yikui, Peilun Lv, Wenbo Deng, Qirui Ke, Cuilin Yu, and Junying Gan. "Deep Cascaded Forest-Based Facial Beauty Prediction." In Recent Trends in Decision Science and Management. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3588-8_18.

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Lozano, Ricardo, Ivan Montoya Sanchez, and Vladik Kreinovich. "Why Deep Neural Networks: Yet Another Explanation." In Uncertainty, Constraints, and Decision Making. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36394-8_33.

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Unnisa, Sarwath, A. Vijayalakshmi, and Zainab Toyin Jagun. "Deep Neural Network Architecture and Applications in Healthcare." In Deep Learning for Healthcare Decision Making. River Publishers, 2023. http://dx.doi.org/10.1201/9781003373261-2.

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Drousiotis, Efthyvoulos, Lei Shi, Paul G. Spirakis, and Simon Maskell. "Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods." In Engineering Applications of Neural Networks. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08223-8_8.

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Young, Kyle, Gareth Booth, Becks Simpson, Reuben Dutton, and Sally Shrapnel. "Deep Neural Network or Dermatologist?" In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33850-3_6.

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Baral, Chitta, Olac Fuentes, and Vladik Kreinovich. "Why Deep Neural Networks: A Possible Theoretical Explanation." In Studies in Systems, Decision and Control. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61753-4_1.

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Conference papers on the topic "Deep neural decision forest"

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Kumar Reddy, Sana Pavan, M. Mounika, Jonnadula Harikiran, and Bolem Sai Chandana. "Design of an Improved Model for Pothole Detection Using Multiple Scale CNNs and Deep Neural Decision Forest Ensemble Process." In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2024. https://doi.org/10.1109/scopes64467.2024.10991311.

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Baldovino, Renann G., and Aldrin Joshua C. Tolentino. "Enhancing Forest Cover Type Classification Through Deep Learning Neural Networks." In 2024 9th International Conference on Mechatronics Engineering (ICOM). IEEE, 2024. http://dx.doi.org/10.1109/icom61675.2024.10652531.

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Djeddou, Messaoud, Jehad Al Dallal, Aouatef Hellal, Ibrahim A. Hameed, and Xingang Zhao. "Particle Swarm Optimization-Based Deep Neural Network vs Whale Optimization Algorithm-Based Deep Convolutional Neural Networks for Critical Heat Flux Prediction." In 2024 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2024. https://doi.org/10.1109/dasa63652.2024.10836649.

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Jegan, D., R. Surendran, and N. Madhusundar. "Hydroponic using Deep Water Culture for Lettuce Farming using Random Forest Compared with Decision Tree Algorithm." In 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2024. https://doi.org/10.1109/iceca63461.2024.10800972.

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Hua, Hongzhi, Guixuan Wen, and Kaigui Wu. "Building Decision Forest via Deep Reinforcement Learning." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191160.

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Sun, Jianyuan, Xubo Liu, Xinhao Mei, et al. "Deep Neural Decision Forest for Acoustic Scene Classification." In 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909575.

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Kontschieder, Peter, Madalina Fiterau, Antonio Criminisi, and Samuel Rota Bulo. "Deep Neural Decision Forests." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.172.

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Zhou, Zhi-Hua, and Ji Feng. "Deep Forest: Towards An Alternative to Deep Neural Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/497.

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In this paper, we propose gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks. In contrast to deep neural networks which require great effort in hyper-parameter tuning, gcForest is much easier to train; even when it is applied to different data across different domains in our experiments, excellent performance can be achieved by almost same settings of hyper-parameters. The training process of gcForest is efficient, and users can control training cost according to computational resource available. The efficiency may be further enhanced because gcForest is naturally apt to parallel implementation. Furthermore, in contrast to deep neural networks which require large-scale training data, gcForest can work well even when there are only small-scale training data.
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Nguyen, Khanh-Vinh, Quoc-An Nguyen, Hoang-Quynh Le, and Duy-Cat Can. "FOREcaST: Improving Extreme Weather Forecasts with Deep Neural Decision Forest for Climate Change Adaptation." In 2023 15th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2023. http://dx.doi.org/10.1109/kse59128.2023.10299427.

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Ajila, Samuel A., and Nidheesh Vijay. "Evaluating Deep Neural Nets and Optimized Hyperparameters Random Forest for Decision Support Systems." In 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2021. http://dx.doi.org/10.1109/iri51335.2021.00009.

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Reports on the topic "Deep neural decision forest"

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capabilities of deep learning architectures. Additionally, it explores biologically plausible learning mechanisms such as Hebbian learning, spike-timing-dependent plasticity (STDP), and neuromodulation, drawing insights from neuroscience and cognitive computing. The role of spiking neural networks (SNNs) and neuromorphic computing in low-power AI and real-time decision-making is also analyzed, with applications in robotics, brain-computer interfaces, edge AI, and cognitive computing. Case studies highlight the industrial adoption of biologically inspired AI, focusing on adaptive neural controllers, neuromorphic vision, and memory-based architectures. This research underscores the importance of integrating theoretical learning principles with biologically motivated AI models to develop more interpretable, generalizable, and scalable intelligent systems. Keywords Neural computation, learning theory, expressivity, deep learning, recurrent neural networks, spiking neural networks, biologically inspired AI, infinite-width networks, kernel methods, attractor networks, synaptic plasticity, STDP, neuromodulation, cognitive computing, dynamical systems, function approximation, generalization, AI stability, neuromorphic computing, robotics, brain-computer interfaces, edge AI, biologically plausible learning.
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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41302.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p &lt; 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.
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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Kendall Niles, Ken Pathak, and Joe Tom. Widened attention-enhanced atrous convolutional network for efficient embedded vision applications under resource constraints. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49459.

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Onboard image analysis enables real-time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision-making critical for time-sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time-sensitive inference. This article introduces the widened attention-enhanced atrous convolution-based efficient network (WACEfNet), a new convolutional neural network designed specifically for real-time visual classification challenges using resource-constrained embedded devices. WACEfNet builds on EfficientNet and integrates innovative width-wise feature processing, atrous convolutions, and attention modules to improve representational power without excessive over-head. Extensive benchmarking confirms state-of-the-art performance from WACEfNet for aerial imaging applications while remaining suitable for embedded deployment. The improvements in accuracy and speed demonstrate the potential of customized deep learning advancements to unlock new capabilities for unmanned aerial vehicles and related embedded systems with tight size, weight, and power constraints. This research offers an optimized framework, combining widened residual learning and attention mechanisms, to meet the unique demands of high-fidelity real-time analytics across a variety of embedded perception paradigms.
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Lasko, Kristofer, Francis O’Neill, and Elena Sava. Automated mapping of land cover type within international heterogenous landscapes using Sentinel-2 imagery with ancillary geospatial data. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49367.

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A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global and regional adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class, six-class, and five-class schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies. The results highlight performance in line with an intensive deep learning approach, and reasonably accurate models created without a full annual time series of imagery.
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p&lt;0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p&lt;0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
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