A Smart Device Integrated with an Android for Alerting a Person’s Health Condition: Internet of Things

Prakash, M.; Gowshika, U.; Ravichandran, T.

doi:10.17485/ijst/2016/v9i6/69545

Cited by 16 publications

(3 citation statements)

References 5 publications

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“…Android is the most used OS for researchers, with 87 documents. This OS is used for IoT gateways [238][239][240][241] or end sensing devices [242][243][244]. Contiki is a lightweight OS for memory constrained systems (like microcontroller-based systems) designed for low-power wireless devices [245].…”

Section: Device Operating Systems (Os) and Hardwarementioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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Abstract:Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author's country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.

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Section: Device Operating Systems (Os) and Hardwarementioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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“…In the proposed filter's detection phase, the two local maxima, Lsalt and Lpepper, are sought. The search will be oriented to the center of the picture matrix, and it will be responsive to direction [24][25][26][27]. In an 8-bit integer representation of an image, Lsalt is set to 255, and Lpepper is set to 0.…”

Section: Adaptive Fuzzy Filteringmentioning

confidence: 99%

Handcrafted Deep-Feature-Based Brain Tumor Detection and Classification Using MRI Images

Mohan

Sathishkumar

Neelakandan³

et al. 2022

Electronics

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An abnormal growth of cells in the brain, often known as a brain tumor, has the potential to develop into cancer. Carcinogenesis of glial cells in the brain and spinal cord is the root cause of gliomas, which are the most prevalent type of primary brain tumor. After receiving a diagnosis of glioblastoma, it is anticipated that the average patient will have a survival time of less than 14 months. Magnetic resonance imaging (MRI) is a well-known non-invasive imaging technology that can detect brain tumors and gives a variety of tissue contrasts in each imaging modality. Until recently, only neuroradiologists were capable of performing the tedious and time-consuming task of manually segmenting and analyzing structural MRI scans of brain tumors. This was because neuroradiologists have specialized training in this area. The development of comprehensive and automatic segmentation methods for brain tumors will have a significant impact on both the diagnosis and treatment of brain tumors. It is now possible to recognize tumors in photographs because of developments in computer-aided design (CAD), machine learning (ML), and deep learning (DL) approaches. The purpose of this study is to develop, through the application of MRI data, an automated model for the detection and classification of brain tumors based on deep learning (DLBTDC-MRI). Using the DLBTDC-MRI method, brain tumors can be detected and characterized at various stages of their progression. Preprocessing, segmentation, feature extraction, and classification are all included in the DLBTDC-MRI methodology that is supplied. The use of adaptive fuzzy filtering, often known as AFF, as a preprocessing technique for photos, results in less noise and higher-quality MRI scans. A method referred to as “chicken swarm optimization” (CSO) was used to segment MRI images. This method utilizes Tsallis entropy-based image segmentation to locate parts of the brain that have been injured. In addition to this, a Residual Network (ResNet) that combines handcrafted features with deep features was used to produce a meaningful collection of feature vectors. A classifier developed by combining DLBTDC-MRI and CSO can finally be used to diagnose brain tumors. To assess the enhanced performance of brain tumor categorization, a large number of simulations were run on the BRATS 2015 dataset. It would appear, based on the findings of these trials, that the DLBTDC-MRI method is superior to other contemporary procedures in many respects.

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“…They have become part of complex and critical categories, such as Medical, Health Monitoring, Banking, Education, Traveling, etc. Furthermore, each of these categories are discussed by several studies [6]- [11]. Android app development has special characteristics than traditional desktop, and web development.…”

Section: Introductionmentioning

confidence: 99%

A Static Analysis of Android Source Code for Design Patterns Usage

Rimawi¹

2020

IJETER

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Design patterns help software developers in building better software designs as well as fostering software maintainability and re-usability. Recently, mobile applications, apps hereinafter, have gained much ground in critical domains, such as banking, health, payments, and military, just to mention a few. Accordingly, it has become imperative to consider increasing the apps' code quality, which urges to better usage of design patterns. Although there is plethora of studies that discuss design patterns usage in object-oriented languages such as Java, C++, and C#, to our best of knowledge, no studies have discussed design patterns usage for Android apps. This study performs an exploratory research using static code analysis methods and a sample of more than 1400 Android apps toward finding design patterns implemented inside their source code. We extend our PatRoid framework, which detects all design patterns in Android app source code. Our initial results show that there is a variation in the extent in which design patterns are applied among different Android apps' categories. Overall, we argue that there is still a lack of proper usage of design patterns in Android apps development.

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A Smart Device Integrated with an Android for Alerting a Person’s Health Condition: Internet of Things

Cited by 16 publications

References 5 publications

Internet of Things: A Scientometric Review

Internet of Things: A Scientometric Review

Handcrafted Deep-Feature-Based Brain Tumor Detection and Classification Using MRI Images

A Static Analysis of Android Source Code for Design Patterns Usage

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