Tag-Aware Personalized Recommendation Using a Hybrid Deep Model

Recently, many efforts have been put into tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep neural network approach to solve this problem by mapping the tag-based user and item profiles to an abstract deep feature space, where the deep-semantic similarities between users and their target items (resp., irrelevant items) are maximized (resp., minimized). To ensure the scalability in practice, we further propose to improve this model's training efficiency by using hybrid deep learning and negative sampling. Experimental results show that our approach can significantly outperform the state-of-the-art baselines in tag-aware personalized recommendation (3.8 times better than the best baseline), and that using hybrid deep learning and negative sampling can dramatically enhance the model's training efficiency (hundreds of times quicker), while maintaining similar (and sometimes even better) training quality and recommendation performance.

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An Efficient Method for Detection of DDoS Attacks on the Web Using Deep Learning Algorithms

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/271042021 ◽

2021 ◽

Vol 10 (4) ◽

pp. 2821-2829

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

State Of The Art ◽

Ddos Attacks ◽

Problem Statement ◽

Neural Network Approach ◽

Learning Techniques ◽

Attack Data ◽

Deep Learning Neural Network

Recently, DDoS attacks is the most significant threat in network security. Both industry and academia are currently debating how to detect and protect against DDoS attacks. Many studies are provided to detect these types of attacks. Deep learning techniques are the most suitable and efficient algorithm for categorizing normal and attack data. Hence, a deep neural network approach is proposed in this study to mitigate DDoS attacks effectively. We used a deep learning neural network to identify and classify traffic as benign or one of four different DDoS attacks. We will concentrate on four different DDoS types: Slowloris, Slowhttptest, DDoS Hulk, and GoldenEye. The rest of the paper is organized as follow: Firstly, we introduce the work, Section 2 defines the related works, Section 3 presents the problem statement, Section 4 describes the proposed methodology, Section 5 illustrate the results of the proposed methodology and shows how the proposed methodology outperforms state-of-the-art work and finally Section VI concludes the paper.

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Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions

Future Internet ◽

10.3390/fi11010024 ◽

2019 ◽

Vol 11 (1) ◽

pp. 24

Author(s):

Emelia Opoku Aboagye ◽

Rajesh Kumar

Keyword(s):

Deep Learning ◽

Feature Space ◽

Cold Start ◽

Personalized Recommendation ◽

Tensor Factorization ◽

Scalable Systems ◽

Collaborative Recommendation ◽

Real World Datasets ◽

Low Dimensional ◽

Cold Start Problem

We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learning is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate approach for collaborative filtering in such environments. In this paper, we propose a hybrid-based system to address scalability problems in such environments. We propose to use a multi-task approach which represent multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a low dimensional feature space where item-user resemblance and their preferred items is maximized. The evaluation results from real world datasets show that, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem through explicit multi-task approach for optimal recommendation decision making.

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An Input-aware Factorization Machine for Sparse Prediction

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/203 ◽

2019 ◽

Cited By ~ 2

Author(s):

Yantao Yu ◽

Zhen Wang ◽

Bo Yuan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real World ◽

State Of The Art ◽

Overall Performance ◽

Factorization Machine ◽

The Impact ◽

Novel Model ◽

Individual Input ◽

Better Than

Factorization machines (FMs) are a class of general predictors working effectively with sparse data, which represents features using factorized parameters and weights. However, the accuracy of FMs can be adversely affected by the fixed representation trained for each feature, as the same feature is usually not equally predictive and useful in different instances. In fact, the inaccurate representation of features may even introduce noise and degrade the overall performance. In this work, we improve FMs by explicitly considering the impact of individual input upon the representation of features. We propose a novel model named \textit{Input-aware Factorization Machine} (IFM), which learns a unique input-aware factor for the same feature in different instances via a neural network. Comprehensive experiments on three real-world recommendation datasets are used to demonstrate the effectiveness and mechanism of IFM. Empirical results indicate that IFM is significantly better than the standard FM model and consistently outperforms four state-of-the-art deep learning based methods.

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Benchmarking Deep Learning for On-Board Space Applications

Remote Sensing ◽

10.3390/rs13193981 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3981

Author(s):

Maciej Ziaja ◽

Piotr Bosowski ◽

Michal Myller ◽

Grzegorz Gajoch ◽

Michal Gumiela ◽

...

Keyword(s):

Deep Learning ◽

Experimental Validation ◽

State Of The Art ◽

Learning Algorithms ◽

Real Life ◽

Space Applications ◽

Deep Model ◽

Learning Techniques ◽

Standard Tool ◽

Benchmark Datasets

Benchmarking deep learning algorithms before deploying them in hardware-constrained execution environments, such as imaging satellites, is pivotal in real-life applications. Although a thorough and consistent benchmarking procedure can allow us to estimate the expected operational abilities of the underlying deep model, this topic remains under-researched. This paper tackles this issue and presents an end-to-end benchmarking approach for quantifying the abilities of deep learning algorithms in virtually any kind of on-board space applications. The experimental validation, performed over several state-of-the-art deep models and benchmark datasets, showed that different deep learning techniques may be effectively benchmarked using the standardized approach, which delivers quantifiable performance measures and is highly configurable. We believe that such benchmarking is crucial in delivering ready-to-use on-board artificial intelligence in emerging space applications and should become a standard tool in the deployment chain.

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Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN

Applied Sciences ◽

10.3390/app9214656 ◽

2019 ◽

Vol 9 (21) ◽

pp. 4656 ◽

Cited By ~ 3

Author(s):

Haikel Alhichri ◽

Yakoub Bazi ◽

Naif Alajlan ◽

Bilel Bin Jdira

Keyword(s):

Deep Learning ◽

Visually Impaired ◽

Indoor Environment ◽

State Of The Art ◽

Activation Function ◽

Linear Functions ◽

Indoor Environments ◽

Scene Description ◽

Deep Cnn ◽

Better Than

This work presents a deep learning method for scene description. (1) Background: This method is part of a larger system, called BlindSys, that assists the visually impaired in an indoor environment. The method detects the presence of certain objects, regardless of their position in the scene. This problem is also known as image multi-labeling. (2) Methods: Our proposed deep learning solution is based on a light-weight pre-trained CNN called SqueezeNet. We improved the SqueezeNet architecture by resetting the last convolutional layer to free weights, replacing its activation function from a rectified linear unit (ReLU) to a LeakyReLU, and adding a BatchNormalization layer thereafter. We also replaced the activation functions at the output layer from softmax to linear functions. These adjustments make up the main contributions in this work. (3) Results: The proposed solution is tested on four image multi-labeling datasets representing different indoor environments. It has achieved results better than state-of-the-art solutions both in terms of accuracy and processing time. (4) Conclusions: The proposed deep CNN is an effective solution for predicting the presence of objects in a scene and can be successfully used as a module within BlindSys.

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Generative Image Inpainting with Submanifold Alignment

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/114 ◽

2019 ◽

Cited By ~ 2

Author(s):

Ang Li ◽

Jianzhong Qi ◽

Rui Zhang ◽

Xingjun Ma ◽

Kotagiri Ramamohanarao

Keyword(s):

State Of The Art ◽

Image Inpainting ◽

Feature Space ◽

Original Data ◽

Local Data ◽

Deep Feature ◽

Proposed Model ◽

Benchmark Datasets ◽

Structural Consistency ◽

Different Levels

Image inpainting aims at restoring missing regions of corrupted images, which has many applications such as image restoration and object removal. However, current GAN-based generative inpainting models do not explicitly exploit the structural or textural consistency between restored contents and their surrounding contexts. To address this limitation, we propose to enforce the alignment (or closeness) between the local data submanifolds (subspaces) around restored images and those around the original (uncorrupted) images during the learning process of GAN-based inpainting models. We exploit Local Intrinsic Dimensionality (LID) to measure, in deep feature space, the alignment between data submanifolds learned by a GAN model and those of the original data, from a perspective of both images (denoted as iLID) and local patches (denoted as pLID) of images. We then apply iLID and pLID as regularizations for GAN-based inpainting models to encourage two different levels of submanifold alignments: 1) an image-level alignment to improve structural consistency, and 2) a patch-level alignment to improve textural details. Experimental results on four benchmark datasets show that our proposed model can generate more accurate results than state-of-the-art models.

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Deep learning-based carotid plaque vulnerability classification with multicentre contrast-enhanced ultrasound video: a comparative diagnostic study

BMJ Open ◽

10.1136/bmjopen-2020-047528 ◽

2021 ◽

Vol 11 (8) ◽

pp. e047528

Author(s):

Yang Guang ◽

Wen He ◽

Bin Ning ◽

Hongxia Zhang ◽

Chen Yin ◽

...

Keyword(s):

Deep Learning ◽

Carotid Plaque ◽

State Of The Art ◽

Characteristic Curve ◽

Diagnostic Study ◽

Contrast Enhanced Ultrasound ◽

Plaque Vulnerability ◽

Contrast Enhanced ◽

Holdout Validation ◽

Better Than

ObjectivesThe aim of this study was to evaluate the performance of deep learning-based detection and classification of carotid plaque (DL-DCCP) in carotid plaque contrast-enhanced ultrasound (CEUS).Methods and analysisA prospective multicentre study was conducted to assess vulnerability in patients with carotid plaque. Data from 547 potentially eligible patients were prospectively enrolled from 10 hospitals, and 205 patients with CEUS video were finally enrolled for analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the effectiveness of DL-DCCP and two experienced radiologists who manually examined the CEUS video (RA-CEUS) in diagnosing and classifying carotid plaque vulnerability. To evaluate the influence of dynamic video input on the performance of the algorithm, a state-of-the-art deep convolutional neural network (CNN) model for static images (Xception) was compared with DL-DCCP for both training and holdout validation cohorts.ResultsThe AUCs of DL-DCCP were significantly better than those of the experienced radiologists for both the training and holdout validation cohorts (training, DL-DCCP vs RA-CEUS, AUC: 0.85 vs 0.69, p<0.01; holdout validation, DL-DCCP vs RA-CEUS, AUC: 0.87 vs 0.66, p<0.01), that is, also better than the best deep CNN model Xception we had performed, for both the training and holdout validation cohorts (training, DL-DCCP vs Xception, AUC:0.85 vs 0.82, p<0.01; holdout validation, DL-DCCP vs Xception, AUC: 0.87 vs 0.77, p<0.01).ConclusionDL-DCCP shows better overall performance in assessing the vulnerability of carotid atherosclerotic plaques than RA-CEUS. Moreover, with a more powerful network structure and better utilisation of video information, DL-DCCP provided greater diagnostic accuracy than a state-of-the-art static CNN model.Trial registration numberChiCTR1900021846,

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Multimodel Deep Learning for Person Detection in Aerial Images

Electronics ◽

10.3390/electronics9091459 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1459

Author(s):

Mirela Kundid Vasić ◽

Vladan Papić

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Contextual Information ◽

Geographical Area ◽

Aerial Images ◽

Person Detection ◽

Current State ◽

Aerial Vehicle ◽

Novel Method ◽

Better Than

In this paper, we propose a novel method for person detection in aerial images of nonurban terrain gathered by an Unmanned Aerial Vehicle (UAV), which plays an important role in Search And Rescue (SAR) missions. The UAV in SAR operations contributes significantly due to the ability to survey a larger geographical area from an aerial viewpoint. Because of the high altitude of recording, the object of interest (person) covers a small part of an image (around 0.1%), which makes this task quite challenging. To address this problem, a multimodel deep learning approach is proposed. The solution consists of two different convolutional neural networks in region proposal, as well as in the classification stage. Additionally, contextual information is used in the classification stage in order to improve the detection results. Experimental results tested on the HERIDAL dataset achieved precision of 68.89% and a recall of 94.65%, which is better than current state-of-the-art methods used for person detection in similar scenarios. Consequently, it may be concluded that this approach is suitable for usage as an auxiliary method in real SAR operations.

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Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Plants ◽

10.3390/plants9101319 ◽

2020 ◽

Vol 9 (10) ◽

pp. 1319

Author(s):

Muhammad Hammad Saleem ◽

Johan Potgieter ◽

Khalid Mahmood Arif

Keyword(s):

Deep Learning ◽

Comparative Evaluation ◽

Plant Disease ◽

Performance Metrics ◽

State Of The Art ◽

Disease Classification ◽

Agricultural Applications ◽

Learning Architectures ◽

Better Than

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes.

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ResMem-Net: memory based deep CNN for image memorability estimation

PeerJ Computer Science ◽

10.7717/peerj-cs.767 ◽

2021 ◽

Vol 7 ◽

pp. e767

Author(s):

Arockia Praveen ◽

Abdulfattah Noorwali ◽

Duraimurugan Samiayya ◽

Mohammad Zubair Khan ◽

Durai Raj Vincent P M ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Mean Squared Error ◽

State Of The Art ◽

Rank Correlation ◽

Current State ◽

Intermediate Layers ◽

Better Than ◽

Made In ◽

Memory Efficient

Image memorability is a very hard problem in image processing due to its subjective nature. But due to the introduction of Deep Learning and the large availability of data and GPUs, great strides have been made in predicting the memorability of an image. In this paper, we propose a novel deep learning architecture called ResMem-Net that is a hybrid of LSTM and CNN that uses information from the hidden layers of the CNN to compute the memorability score of an image. The intermediate layers are important for predicting the output because they contain information about the intrinsic properties of the image. The proposed architecture automatically learns visual emotions and saliency, shown by the heatmaps generated using the GradRAM technique. We have also used the heatmaps and results to analyze and answer one of the most important questions in image memorability: “What makes an image memorable?”. The model is trained and evaluated using the publicly available Large-scale Image Memorability dataset (LaMem) from MIT. The results show that the model achieves a rank correlation of 0.679 and a mean squared error of 0.011, which is better than the current state-of-the-art models and is close to human consistency (p = 0.68). The proposed architecture also has a significantly low number of parameters compared to the state-of-the-art architecture, making it memory efficient and suitable for production.

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