scholarly journals Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6031
Author(s):  
Farhad Ahamed ◽  
Seyed Shahrestani ◽  
Hon Cheung
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Internet Of Things ◽  
Cognitive Status ◽  
Classification Model ◽  
Learning Approaches ◽  
Home Setting ◽  
Distinctive Features ◽  
Daily Life Activities ◽  
Nonintrusive Monitoring

Identifying the symptoms of the early stages of dementia is a difficult task, particularly for older adults living in residential care. Internet of Things (IoT) and smart environments can assist with the early detection of dementia, by nonintrusive monitoring of the daily activities of the older adults. In this work, we focus on the daily life activities of adults in a smart home setting to discover their potential cognitive anomalies using a public dataset. After analysing the dataset, extracting the features, and selecting distinctive features based on dynamic ranking, a classification model is built. We compare and contrast several machine learning approaches for developing a reliable and efficient model to identify the cognitive status of monitored adults. Using our predictive model and our approach of distinctive feature selection, we have achieved 90.74% accuracy in detecting the onset of dementia.

A Novel ECG Automatic Detection Using LongShort-Term Memory Network and Internet of Things Technology

2021 ◽  
Vol 11 (6) ◽  
pp. 1592-1598
Author(s):  
Xufei Liu
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Internet Of Things ◽  
Network Structure ◽  
Recognition Rate ◽  
Classification Model ◽  
Data Set ◽  
Public Data ◽  
Lstm Network ◽  
Internet Of Things Technology

The early detection of cardiovascular diseases based on electrocardiogram (ECG) is very important for the timely treatment of cardiovascular patients, which increases the survival rate of patients. ECG is a visual representation that describes changes in cardiac bioelectricity and is the basis for detecting heart health. With the rise of edge machine learning and Internet of Things (IoT) technologies, small machine learning models have received attention. This study proposes an ECG automatic classification method based on Internet of Things technology and LSTM network to achieve early monitoring and early prevention of cardiovascular diseases. Specifically, this paper first proposes a single-layer bidirectional LSTM network structure. Make full use of the timing-dependent features of the sampling points before and after to automatically extract features. The network structure is more lightweight and the calculation complexity is lower. In order to verify the effectiveness of the proposed classification model, the relevant comparison algorithm is used to verify on the MIT-BIH public data set. Secondly, the model is embedded in a wearable device to automatically classify the collected ECG. Finally, when an abnormality is detected, the user is alerted by an alarm. The experimental results show that the proposed model has a simple structure and a high classification and recognition rate, which can meet the needs of wearable devices for monitoring ECG of patients.

scholarly journals A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1955
Author(s):  
Ikram Sumaiya Thaseen ◽  
Vanitha Mohanraj ◽  
Sakthivel Ramachandran ◽  
Kishore Sanapala ◽  
Sang-Soo Yeo
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Experimental Analysis ◽  
Parameter Tuning ◽  
Computational Time ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

In recent years, different variants of the botnet are targeting government, private organizations and there is a crucial need to develop a robust framework for securing the IoT (Internet of Things) network. In this paper, a Hadoop based framework is proposed to identify the malicious IoT traffic using a modified Tomek-link under-sampling integrated with automated Hyper-parameter tuning of machine learning classifiers. The novelty of this paper is to utilize a big data platform for benchmark IoT datasets to minimize computational time. The IoT benchmark datasets are loaded in the Hadoop Distributed File System (HDFS) environment. Three machine learning approaches namely naive Bayes (NB), K-nearest neighbor (KNN), and support vector machine (SVM) are used for categorizing IoT traffic. Artificial immune network optimization is deployed during cross-validation to obtain the best classifier parameters. Experimental analysis is performed on the Hadoop platform. The average accuracy of 99% and 90% is obtained for BoT_IoT and ToN_IoT datasets. The accuracy difference in ToN-IoT dataset is due to the huge number of data samples captured at the edge layer and fog layer. However, in BoT-IoT dataset only 5% of the training and test samples from the complete dataset are considered for experimental analysis as released by the dataset developers. The overall accuracy is improved by 19% in comparison with state-of-the-art techniques. The computational times for the huge datasets are reduced by 3–4 hours through Map Reduce in HDFS.

scholarly journals A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 219 ◽  
Author(s):  
Sweta Bhattacharya ◽  
Siva Rama Krishnan S ◽  
Praveen Kumar Reddy Maddikunta ◽  
Rajesh Kaluri ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Comprehensive Evaluation ◽  
Detection System ◽  
Human Life ◽  
Principal Component ◽  
Cyber Attacks ◽  
Classification Model ◽  
Learning Approaches ◽  
Machine Learning Model

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

scholarly journals Review on Data Securing Techniques for Internet of Medical Things

2021 ◽  
Vol 3 (3) ◽  
pp. 177-191
Author(s):  
R. Kanthavel
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Internet Of Things ◽  
Healthcare System ◽  
System Performance ◽  
Learning Approaches ◽  
Wide Range ◽  
Security Problem ◽  
Internet Of Medical Things ◽  
Privacy Issues

In recent days Internet of Things (IOT) has grown up dramatically. It has wide range of applications. One of its applications is Health care system. IOT helps in managing and optimizing of healthcare system. Though it helps in all ways it also brings security problem in account. There is lot of privacy issues aroused due to IOT. In some cases it leads to risk the patient’s life. To overcome this issue we need an architecture named Internet of Medical Things (IOMT). In this paper we have discussed the problems faced by healthcare system and the authentication approaches used by Internet of Medical Things. Machine learning approaches are used to improvise the system performance.

Internet of Things in Healthcare

2020 ◽  
pp. 23-39
Author(s):  
Rajasekaran Thangaraj ◽  
Sivaramakrishnan Rajendar ◽  
Vidhya Kandasamy
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Deep Learning ◽  
Internet Of Things ◽  
Healthcare Systems ◽  
Wearable Devices ◽  
Healthcare Research ◽  
Learning Technologies ◽  
Learning Approaches ◽  
And Performance

Healthcare motoring has become a popular research in recent years. The evolution of electronic devices brings out numerous wearable devices that can be used for a variety of healthcare motoring systems. These devices measure the patient's health parameters and send them for further processing, where the acquired data is analyzed. The analysis provides the patients or their relatives with the medical support required or predictions based on the acquired data. Cloud computing, deep learning, and machine learning technologies play a prominent role in processing and analyzing the data respectively. This chapter aims to provide a detailed study of IoT-based healthcare systems, a variety of sensors used to measure parameters of health, and various deep learning and machine learning approaches introduced for the diagnosis of different diseases. The chapter also highlights the challenges, open issues, and performance considerations for future IoT-based healthcare research.

scholarly journals Identifying patterns of health care utilisation among physical elder abuse victims using Medicare data and legally adjudicated cases: protocol for case–control study using data linkage and machine learning

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e044768
Author(s):  
Tony Rosen ◽  
Yuhua Bao ◽  
Yiye Zhang ◽  
Sunday Clark ◽  
Katherine Wen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Elder Abuse ◽  
Healthcare Providers ◽  
Healthcare Utilisation ◽  
Health Care Utilisation ◽  
Learning Approaches ◽  
Medicare Claims ◽  
Care Utilisation ◽  
Home Placement

IntroductionPhysical elder abuse is common and has serious health consequences but is under-recognised and under-reported. As assessment by healthcare providers may represent the only contact outside family for many older adults, clinicians have a unique opportunity to identify suspected abuse and initiate intervention. Preliminary research suggests elder abuse victims may have different patterns of healthcare utilisation than other older adults, with increased rates of emergency department use, hospitalisation and nursing home placement. Little is known, however, about the patterns of this increased utilisation and associated costs. To help fill this gap, we describe here the protocol for a study exploring patterns of healthcare utilisation and associated costs for known physical elder abuse victims compared with non-victims.Methods and analysisWe hypothesise that various aspects of healthcare utilisation are differentially affected by physical elder abuse victimisation, increasing ED/hospital utilisation and reducing outpatient/primary care utilisation. We will obtain Medicare claims data for a series of well-characterised, legally adjudicated cases of physical elder abuse to examine victims’ healthcare utilisation before and after the date of abuse detection. We will also compare these physical elder abuse victims to a matched comparison group of non-victimised older adults using Medicare claims. We will use machine learning approaches to extend our ability to identify patterns suggestive of potential physical elder abuse exposure. Describing unique patterns and associated costs of healthcare utilisation among elder abuse victims may improve the ability of healthcare providers to identify and, ultimately, intervene and prevent victimisation.Ethics and disseminationThis project has been reviewed and approved by the Weill Cornell Medicine Institutional Review Board, protocol #1807019417, with initial approval on 1 August 2018. We aim to disseminate our results in peer-reviewed journals at national and international conferences and among interested patient groups and the public.

scholarly journals A SYSTEMATIC REVIEW METHOD FOR SECURITY ANALYSIS OF INTERNET OF THINGS ON HONEYPOT DETECTION

TEKNOKOM ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 16-20
Author(s):  
Andrie Yuswanto ◽  
Budi Wibowo
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Internet Of Things ◽  
Security Analysis ◽  
The Internet ◽  
Malware Analysis ◽  
Learning Approaches ◽  
Exclusion Criteria ◽  
Recent Approach ◽  
The Internet Of Things

A very significant increase in the spread of malware has resulted in malware analysis. A recent approach to using the internet of things has been put forward by many researchers. Iot tool learning approaches as a more effective and efficient approach to dealing with malware compared to conventional approaches. At the same time, the researchers transformed the honeypot as a device capable of gathering malware information. The honeypot is designed as a malware trap and is stored on the provided system. Then log the managed events and gather information about the activity and identity of the attacker. This paper aims to use a honeypot in machine learning to deal with malware The Systematic Literature Review (SLR) method was used to identify 207. Then 10 papers were selected to be investigated based on inclusion and exclusion criteria. . The technique used by most researchers is to utilize the available honeypot dataset. Meanwhile, based on the type of malware being analyzed, honeypot in machine learning is mostly used to collect IoT-based malware.

Using Machine Learning Approaches to Explore Non-Cognitive Variables Influencing Reading Proficiency in English among Filipino Learners

2021 ◽  
Vol 11 (10) ◽  
pp. 628
Author(s):  
Allan B. I. Bernardo ◽  
Macario O. Cordel ◽  
Rochelle Irene G. Lucas ◽  
Jude Michael M. Teves ◽  
Sashmir A. Yap ◽  
...  
Keyword(s):  
Machine Learning ◽  
School Environment ◽  
Binary Classification ◽  
Reading Proficiency ◽  
Classification Model ◽  
Support Vector ◽  
Test Accuracy ◽  
Learning Approaches ◽  
Classification Methods ◽  
Poor Reading

Filipino students ranked last in reading proficiency among all countries/territories in the PISA 2018, with only 19% meeting the minimum (Level 2) standard. It is imperative to understand the range of factors that contribute to low reading proficiency, specifically variables that can be the target of interventions to help students with poor reading proficiency. We used machine learning approaches, specifically binary classification methods, to identify the variables that best predict low (Level 1b and lower) vs. higher (Level 1a or better) reading proficiency using the Philippine PISA data from a nationally representative sample of 15-year-old students. Several binary classification methods were applied, and the best classification model was derived using support vector machines (SVM), with 81.2% average test accuracy. The 20 variables with the highest impact in the model were identified and interpreted using a socioecological perspective of development and learning. These variables included students’ home-related resources and socioeconomic constraints, learning motivation and mindsets, classroom reading experiences with teachers, reading self-beliefs, attitudes, and experiences, and social experiences in the school environment. The results were discussed with reference to the need for a systems perspective to addresses poor proficiency, requiring interconnected interventions that go beyond students’ classroom reading.

scholarly journals A lifelong spam emails classification model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rami Mustafa A. Mohammad
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Positive Impact ◽  
Concept Drift ◽  
Classification Model ◽  
Learning Approaches ◽  
Suggested Model ◽  
Sound Classification ◽  
Internet Users ◽  
Using Data

Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

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