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JournalISSN: 2327-3305

Advances in wireless technologies and telecommunication book series 

IGI Global
About: Advances in wireless technologies and telecommunication book series is an academic journal published by IGI Global. The journal publishes majorly in the area(s): Computer science & Blockchain. It has an ISSN identifier of 2327-3305. Over the lifetime, 4 publications have been published receiving 4 citations. The journal is also known as: Advances in wireless technologies and telecommunication (AWTT) book series.

Papers published on a yearly basis

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Journal ArticleDOI
TL;DR: In this paper , an intermediate device is used to combine all the individual sensor data and deliver it to the sink in a single packet, which helps to extend the life of a node while also reducing network transmission.
Abstract: Because privacy concerns in IoT devices are the most sensitive of all the difficulties, such an extreme growth in IoT usage has an impact on the privacy and life spans of IoT devices, because until now, all devices communicated one to one, resulting in high traffic that may shorten the life of unit nodes. In addition, delivering data repeatedly increases the likelihood of an attacker attacking the system. Such traffic may exacerbate security concerns. The employment of an aggregator in the system as an intermediary between end nodes and the sink may overcome these problems. In any system with numerous sensors or nodes and a common controller or sink, we can use an intermediate device to combine all of the individual sensor data and deliver it to the sink in a single packet. Aggregator is the name given to such a device or component. Data aggregation is carried out to decrease traffic or communication overhead. In general, this strategy helps to extend the life of a node while also reducing network transmission.

18 citations

Journal ArticleDOI
TL;DR: In this article , the authors introduced the novel approach in deep learning for diabetes prediction and described the various ML algorithms in the field of diabetic prediction that has been used for early detection and post examination of the diabetic prediction.
Abstract: This chapter introduces the novel approach in deep learning for diabetes prediction. The related work described the various ML algorithms in the field of diabetic prediction that has been used for early detection and post examination of the diabetic prediction. It proposed the Jaya-Tree algorithm, which is updated as per the existing random forest algorithm, and it is used to classify the two parameters named as the ‘Jaya' and ‘Apajaya'. The results described that Pima Indian diabetes dataset 2020 (PIS) predicts diabetes and obtained 97% accuracy.

3 citations

Journal ArticleDOI
TL;DR: In this article , a combination of ML and DL approaches to predict the house price with the updated regression algorithm is introduced. And the results of the model tested with the different datasets existing in the Kaggle data source using Python libraries with the Jupyter platform and continuation using the Android OS to develop the smart home web-based application.
Abstract: House price predictions are a crucial reflection of the economy; sometimes house prices include the land prices and demand of the place and location. The house price and land price are two different things, but both are important for both buyers and sellers. This chapter introduced the combination of ML and DL approaches to predict the house price with the updated regression algorithm. The algorithm named as ‘Mopuri algorithm' reads the 14 attributes like crime rate, population density, rooms, etc. and produces the cost estimation result as a prediction. The proposed model accurately estimates the worth of the house as per the given features. The results of the model tested with the different datasets existing in the Kaggle data source using Python libraries with the Jupyter platform and continuation of the model using the Android OS to develop the smart home web-based application.

2 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202368
202214