SOS: A multimedia recommender System for Online Social networks

Amato, Flora; Moscato, Vincenzo; Picariello, Antonio; Piccialli, Francesco

doi:10.1016/j.future.2017.04.028

Cited by 83 publications

(35 citation statements)

References 40 publications

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“…• Promoting marketing activities. Obtaining useful information of MSNs, we make better marketing decisions , promote product advertisements [58] and enhance recommendation systems [59]. • Analyzing social influence.…”

Section: Mobile Social Networkmentioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

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The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large scale wireless networks. Big data of large scale wireless networks has the key features of wide variety, high volume, real-time velocity and huge value leading to the unique research challenges that are different from existing computing systems. In this paper, we present a survey of the state-of-art big data analytics (BDA) approaches for large scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.

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Section: Mobile Social Networkmentioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

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“…A 2016 study predicts that in 2020, more than half of the business tools and processes will make use of the Internet of Things. [11][12][13][14][15] In the field of health, sensor networks and IoT would monitor the health of patients remotely, facilitate the life of the elderly, and allow them to remain independent.…”

Section: The Role Of Wireless Sensor Network and Iot In E-healthmentioning

confidence: 99%

Internet of Things ‐ integrated IR‐UWB technology for healthcare applications

Chehri

Mouftah

2019

Concurrency and Computation

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Summary Recent technology developments have produced small and smart biomedical sensors, which can be worn or implanted in the human body. These biosensors create closed wireless networks named Wireless Body Area Networks (WBAN). The WBAN will continuously observe the physiological state of patients for both diagnosis and prevention. Those include on‐body measurements such as the Electrocardiogram (ECG), Electroencephalogram (EEG), temperature, and blood pressure. Ultra‐Wide‐Band (UWB) is a technology that has received a lot of attention due to several unique features such as secure transmission, low noise, and low energy consumption. Given the fact that the patients' well‐being might be dependent on the accurate realization of such networks, a high level of design and implementation accuracy are maintained throughout the system. In this paper, we proposed an Impulse‐Radio Ultra‐Wideband system, which is composed of static biomedical nodes mounted on a patient's body to collect vital data and send it wirelessly to a central node or subsequent analysis by healthcare professionals. The performance of this network, such as the effect of node location, the number of transmitted symbols, multiuser interference, and intersymbol interference, is evaluated. We also study the physical layer and quality of service of this proposed architecture.

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“…It takes a lot of time to classify the data, and storing the data consumes a large amount of hardware resources. In order to solve this problem, an Internet of Things (IoT) sea clutter intelligent management system can be established to meet the different needs of sea clutter study by learning from other management architecture [39][40][41] in the future. The extracted data of the fixed range cells are normalized to improve training speed and prediction accuracy.…”

Section: The Proposed Sea Clutter Prediction System Based On Lstm Nnmentioning

confidence: 99%

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

Zhang

et al. 2019

Remote Sensing

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In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.

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SOS: A multimedia recommender System for Online Social networks

Cited by 83 publications

References 40 publications

Big Data Analytics for Large-scale Wireless Networks

Big Data Analytics for Large-scale Wireless Networks

Internet of Things ‐ integrated IR‐UWB technology for healthcare applications

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

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