A New Hybrid Deep Learning Model based-Recommender System using Artificial Neural Network and Hidden Markov Model

Djellali, Choukri; Adda, Mehdi

doi:10.1016/j.procs.2020.07.032

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…An adapted system would utilize DL methds to support the processiing and analysis such as hetereogeneous data-rendering visualization [75]. Examples of adapted systems include the recommender systems [76,77]. Finally, in a fully auonomous system the human and the machine would make decisions collectively.…”

Section: Systems Engineering For Human-ai Teamingmentioning

confidence: 99%

Human-machine cooperative AI decision-making with heterogeneous data

Blasch,

Bastian,

Aved

et al. 2023

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

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Many techniques have been developed for sensor and information fusion, machine and deep learning, as well as data and machine analytics. Currently, many groups are exploring methods for human-machine teaming using saliency and heat maps, explainable and interpretable artificial intelligence, as well as user-defined interfaces. However, there is still a need for standard metrics for test and evaluation of systems utilizing artificial intelligence (AI), such as deep learning (DL), to support the AI principles. In this paper, we explore the elements associated with the opportunities and challenges emerging from designing, testing, and evaluating such future systems. The paper highlights the MAST (multi-attribute scorecard table), and more specifically the MAST criteria ―analysis of alternatives‖ by measuring the risk associated with an evidential DL-based decision. The concept of risk includes the probability of a decision as well as the severity of the choice, from which there is also a need for an uncertainty bound on the decision choice which the paper postulates a risk bound. Notional analysis for a cyber networked system is presented to guide to interactive process for test and evaluation to support the certification of AI systems as to the decision risk for a human-machine system that includes analysis from both the DL method and a user.

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Section: Systems Engineering For Human-ai Teamingmentioning

confidence: 99%

Human-machine cooperative AI decision-making with heterogeneous data

Blasch,

Bastian,

Aved

et al. 2023

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

View full text Add to dashboard Cite

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“…One of the typical deep learning methods is the convolutional neural network, which is a feedforward multilayer network and a class of feedforward neural networks that include convoluted computation and deep structure [11][12][13][14]. Convolutional neural networks can efficiently capture subtle local features through shallow networks, then pass low-level features to deep networks to obtain the global input features and use weight sharing to reduce model parameters, which are widely used in various industries for image feature extraction and classification [15].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Research on computer network information image data processing based on machine learning

Mei,

Sun

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, we aim to progress the efficiency and accuracy of information processing and security detection in computer networks by introducing convolutional neural networks in machine learning algorithms that are capable of multi-scaling from the channel attention module and spatial attention module in extracting image information. Global maximum pooling and global averaging are done for the feature maps generated by both modules to get the clearest feature maps by dimensionality reduction. The loss function is used to calculate the feature maps to reduce the data loss generated during data extraction and finally complete the image data processing. To verify the effectiveness of the proposed platform, network images containing different amounts of data are input into the platform, and the accuracy and loss of data extraction are obtained. The results show that the data extraction accuracy of the reduced platform is up to 100%, which is 6% higher than other platforms. The number of data losses in other platforms is more than twice of this paper, while the number of losses in this paper can be controlled within 5. It can be seen that convolutional neural network in machine learning improves the accuracy of data extraction from computer network information images and reduces the loss in data extraction.

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“…We evaluated and compared the performance of several recommendation algorithms. We conducted an experimental study on Databricks and Jupyter notebook, a publicly available election dataset, to analyse and evaluate several recommendation algorithms, namely: Term frequency-inverse document frequency [20], Rocchio [21], latent semantic analysis [22], Knearest neighbours [23], Naïve Bayes [24], decision tree classifier [25], support vector machine [26], Linear regression(LR) [27], K-means [28], Slope One [29], Non-matrix factorisation [30], Singular decomposition vector [10], Co-clustering [31], Pearson correlation [32] and Artificial neural network5(ANN) [33].…”

Section: End Proceduresmentioning

confidence: 99%

“…analyse and evaluate several recommendation algorithms, namely: Term frequency-inverse document frequency [20], Rocchio [21], latent semantic analysis [22], K-nearest neighbours [23], Naïve Bayes [24], decision tree classifier [25], support vector machine [26], Linear regression(LR) [27], Kmeans [28], Slope One [29], Non-matrix factorisation [30], Singular decomposition vector [10], Co-clustering [31], Pearson correlation [32] and Artificial neural network5(ANN) [33]. The evaluation of the algorithms is based on precision, recall, f1 score, accuracy, area under the curve, RMSE, MSE, and MAE.…”

Section: True Negative (Tn) = Number Of Instances Correctlymentioning

confidence: 99%

Resource Optimisation in Cloud Computing: Comparative Study of Algorithms Applied to Recommendations in a Big Data Analysis Architecture

Ndayikengurukiye

Ez-Zahout

Aboubakr

et al. 2022

JAMRIS

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Recommender systems (RS) have emerged as a means of providing relevant content to users, whether in social networking, health, education, or elections. Furthermore, with the rapid development of cloud computing, Big Data, and the Internet of Things (IoT), the component of all this is that elections are controlled by open and accountable, neutral, and autonomous election management bodies. The use of technology in voting procedures can make them faster, more efficient, and less susceptible to security breaches. Technology can ensure the security of every vote, better and faster automatic counting and tallying, and much greater accuracy. The election data were combined by different websites and applications. In addition, it was interpreted using many recommendation algorithms such as Machine Learning Algorithms, Vector Representation Algorithms, Latent Factor Model Algorithms, and Neighbourhood Methods and shared with the election management bodies to provide appropriate recommendations. In this paper, we conduct a comparative study of the algorithms applied in the recommendations of Big data architectures. The results show us that the K-NN model works best with an accuracy of 96%. In addition, we provided the best recommendation system is the hybrid recommendation combined by content-based filtering and collaborative filtering uses similarities between users and items.

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A New Hybrid Deep Learning Model based-Recommender System using Artificial Neural Network and Hidden Markov Model

Cited by 10 publications

References 10 publications

Human-machine cooperative AI decision-making with heterogeneous data

Human-machine cooperative AI decision-making with heterogeneous data

Research on computer network information image data processing based on machine learning

Resource Optimisation in Cloud Computing: Comparative Study of Algorithms Applied to Recommendations in a Big Data Analysis Architecture

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