Enhancing the Role of Large-Scale Recommendation Systems in the IoT Context

Kashef, Rasha

doi:10.1109/access.2020.3026310

Cited by 27 publications

(8 citation statements)

References 35 publications

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“…This section discusses the performance of the proposed models on two real datasets. The performance is quantified by the accuracy and the F1-measure validation measures [32,39,40]. We compared the proposed models to individual CNN and LSTM, and GRU.…”

Section: Resultsmentioning

confidence: 99%

Detecting Shilling Attacks Using Hybrid Deep Learning Models

Ebrahimian

Kashef

2020

Symmetry

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Recommendation systems play a significant role in alleviating information overload in the digital world. They provide suggestions to users based on past symmetric activities or behaviors. Being heavily dependent on users’ behavior, they tend to be vulnerable to shilling attacks. Therefore, protecting them from attacks’ effects is highly important. As shilling attacks have features of a large number of ratings and increasing complexity in attack models, deep learning methods become proper alternatives for more accurate attack detections. This paper proposes a hybrid model of two different neural networks, convolutional and recurrent neural networks, to detect shilling attacks efficiently. The proposed deep learning model utilizes the transformed network architecture for undertaking the attributes derived from user-rated profiles. This architecture enables modeling of the temporal and spatial information in the recommendation system’s ratings. The hybrid model overcomes the limitations of the existing shilling attack deep-learning methods to enhance the recommendation systems’ efficiency and robustness. Experimental results show that the hybrid model results in better predictions on the Movie-Lens 100 K and Netflix datasets by accurately detecting most of the obfuscated attacks compared to the state-of-art deep learning algorithms used for investigation.

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Section: Resultsmentioning

confidence: 99%

Detecting Shilling Attacks Using Hybrid Deep Learning Models

Ebrahimian

Kashef

2020

Symmetry

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“…Reserved energy: Transmitting a single data packet through a non-clustered WSN allows the data to travel at a high communication cost; additionally, data loss or leakage may occur in this type of communication. 17 Clustering would alleviate the problem because the CH would be the next-hop destination for every sensor node in the cluster; thus, the data packet could identify the ideal destination for the next hop, preventing the looping problem. As a result, balanced energy utilization is impossible without a proper clustering process.…”

Section: Clustering In Iotmentioning

confidence: 99%

An opportunistic energy‐efficient dynamic self‐configuration clustering algorithm in WSN‐based IoT networks

Tumula,

Ramadevi,

Padmalatha

et al. 2023

Int J Communication

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SummaryThe demand for the Internet of Things (IoT) has significantly increased in the current scenario; specific sectors that require IoT include industrial automation, home control, health care applications, military and surveillance applications, habitat monitoring, and nanoscopic sensor applications. The use of optimal wireless sensor networks (WSNs) in transmission techniques has resulted in their involvement. A WSN is made up of thousands of randomly distributed sensor nodes that sense and transmit environmental data such as temperature, pressure, humidity, light, and sound. One of the most important requirements when using these sensor nodes is energy. As a result, it has become a major area of research in recent years; additionally, several design techniques and protocols have been presented in the last decade, particularly for IoT‐based applications. As a result, the systemization of an energy‐optimized WSN in dynamic functional conditions with automatic self‐configuration of sensor nodes is a critical goal. This paper proposed an opportunistic energy‐efficient dynamic self‐configuration routing (OEDSR) algorithm for IoT‐based applications. Initially, the optimal route to the base station (BS) is calculated by using the residual energy and mobility factor of the sensor nodes obtained through a routing tree model based on graph theory. To reduce the number of connections, an optimal path is determined based on dynamic cluster generation through a hierarchical tree architecture. Finally, the network‐related parameters, such as throughput, delay and packet delivery ratio (PDR), are compared with the peer existing routing protocols to depict the efficiency of the OEDSR protocol.

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“…Precision, Recall, F1 score, and Accuracy [49][50][51][52][53][54] the well-known evaluation metrics to assess the performance of a classifier. Precision finds pertinent instances among the gathered instances.…”

Section: Evaluation Metricsmentioning

confidence: 99%

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

Aljasim

Kashef

2022

Sensors

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The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this paper, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase in-car awareness for improved safety. This paper proposes E2DR, a new scalable model that uses stacking ensemble methods to combine two or more deep learning models to improve accuracy, enhance generalization, and reduce overfitting, with real-time recommendations. The highest performing E2DR variant, which included the ResNet50 and VGG16 models, achieved a test accuracy of 92% as applied to state-of-the-art datasets, including the State Farm Distracted Drivers dataset, using novel data splitting strategies.

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Enhancing the Role of Large-Scale Recommendation Systems in the IoT Context

Cited by 27 publications

References 35 publications

Detecting Shilling Attacks Using Hybrid Deep Learning Models

Detecting Shilling Attacks Using Hybrid Deep Learning Models

An opportunistic energy‐efficient dynamic self‐configuration clustering algorithm in WSN‐based IoT networks

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

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