Damage Assessment and Recovery in Fog-based Computing Systems

Thoumi, Sergio; Haraty, Ramzi A.

doi:10.1109/3ict56508.2022.9990724

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…With the rapid development of artificial intelligence (Guebli & Belkhir, 2021;Jiao et al, 2022;Tan et al, 2022), the Internet of Things (Chamra & Harmanani, 2020;Madhu et al, 2022), big data (Thirumalaisamy et al, 2022), cloud-fog computing (Thoumi & Haraty, 2022;Vijayakumar et al, 2022), smart communication (Almomani et al, 2022;Dwivedi, 2022;Ling & Hao, 2022;Samir et al, 2020), and other fields, intelligent mobile devices have become a necessity for people's public lives, and the demand for location-based services is showing explosive growth. Location-based social networks (LBSNs) have attracted a large number of users (Werneck et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Personalized POI Recommendation Using CAGRU and Implicit Semantic Feature Extraction in LBSN

Ye,

Chen

2024

International Journal on Semantic Web and Information Systems

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With the intention of addressing the concern that existing point of interest recommendation methods fail to fully utilize the auxiliary information of the point of interest, from which it is challenging to extricate a substantial quantity of deeper feature information, a personalized point of interest (POI) recommendation model using Context-Aware Gated Recurrent Unit (CAGRU) and implicit semantic feature extraction was proposed. First, the check-in data is divided into five tags, and the continuous geographical location check-in data and time data are discretized. Then, the CAGRU was used to obtain the POI check-in features. Finally, the time sequence location information, user information and target location information are transformed through the nonlinear activation function to obtain the score of each location in the data set as the next POI location, and the Top-N recommendation is generated through the score. Experiments indicated that the results of the suggested method were better than the comparative methods.

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Section: Introductionmentioning

confidence: 99%

Personalized POI Recommendation Using CAGRU and Implicit Semantic Feature Extraction in LBSN

Ye,

Chen

2024

International Journal on Semantic Web and Information Systems

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“…They offer vast potential to address infrastructural, societal, and pandemic challenges. With smart technologies, communities can improve energy distribution, streamline trash collection, decrease traffic congestion, improve air quality, and more with help from smart, connected sensing systems (Tay & Mourad, 2020;Thoumi & Haraty, 2022).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities

Malik

Prabha

Soni

et al. 2023

International Journal on Semantic Web and Information Systems

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Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.

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“…, fog computing (Thoumi & Haraty, 2022), the Internet of Things (Chamra & Harmanani, 2020), smart homes (Madhu et al, 2022;Guebli & Belkhir, 2021), intelligent communication (Samir et al, 2020) and other fields, the application scope of human emotion recognition is becoming increasingly widespread. Therefore, conveniently, effectively, and accurately recognizing human emotions is significantly important for promoting the development of new eras such as artificial intelligence, Web 3.0, and the metaverse (Kouti et al, 2022).…”

mentioning

confidence: 99%

Channel Semantic Enhancement-Based Emotional Recognition Method Using SCLE-2D-CNN

Fu,

Yang,

Pan

2024

International Journal on Semantic Web and Information Systems

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The existing EEG emotion classification methods have some problems, such as insufficient emotion representation and lack of targeted channel enhancement module due to feature redundancy. To this end, a novel EEG emotion recognition method (SCLE-2D-CNN) combining scaled convolutional layer (SCLs), enhanced channel module and two-dimensional convolutional neural network (2D-CNN) is proposed. Firstly, the time-frequency features of multi-channel EEG emotional signals were extracted by stacking scl layer by layer. Secondly, channel enhancement module is used to reassign different importance to all EEG physical channels. Finally, 2D-CNN was used to obtain deep local spatiotemporal features and complete emotion classification. The experimental results show that the accuracy of SEED data set and F1 are 98.09% and 97.00%, respectively, and the binary classification accuracy of DEAP data set is 98.06% and 96.83%, respectively, which are superior to other comparison methods. The proposed method has a certain application prospect in the recognition of human mental state.

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Damage Assessment and Recovery in Fog-based Computing Systems

Cited by 4 publications

References 14 publications

Personalized POI Recommendation Using CAGRU and Implicit Semantic Feature Extraction in LBSN

Personalized POI Recommendation Using CAGRU and Implicit Semantic Feature Extraction in LBSN

Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities

Channel Semantic Enhancement-Based Emotional Recognition Method Using SCLE-2D-CNN

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