scholarly journals Recapitulation of Machine Learning Algorithms in Diabetic Detection

2019 ◽  
Vol 8 (12) ◽  
pp. 3942-3947
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Predictive Models ◽  
Human Services ◽  
Human Life ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Work Culture ◽  
Computational Investigation ◽  
Way Of Life

Diabetes mellitus is one of the major nontransmittable infections, which have extraordinary impact on human life. Due to dynamic work culture and dormant way of life-style of 21st century, approximately 62 million Indian families are diabetic. By applying prescient examination on clinical enormous information, the gigantic volume of information is produced in the human services frameworks, and this will be utilized to make therapeutic insight, which drive medicinal expectation & anticipation. A lot of information is accessible with respect to the malady, manifestations and their impact on well-being. Since this information isn't legitimately investigated to foresee or to examine an infection. The objectives of paper is summarized as to give a point by point adaptation of prescient models for computational investigation from condition of workmanship, depicting different reasons for diabetes procedure, for extricating information from diabetes patients and describing different predictive models with their applications in Healthcare, particularly in the field of diabetes.

scholarly journals Detection of Diabetic Patterns using Supervised Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 1169-1173
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
World Health ◽  
Performance Parameters ◽  
Major Health ◽  
Major Increase ◽  
Boosted Tree

World Health Organization’s (WHO) report 2018, on diabetes has reported that the number of diabetic cases has increased from one hundred eight million to four hundred twenty-two million from the year 1980. The fact sheet shows that there is a major increase in diabetic cases from 4.7% to 8.5% among adults (18 years of age). Major health hazards caused due to diabetes include kidney function failure, heart disease, blindness, stroke, and lower limb dismembering. This article applies supervised machine learning algorithms on the Pima Indian Diabetic dataset to explore various patterns of risks involved using predictive models. Predictive model construction is based upon supervised machine learning algorithms: Naïve Bayes, Decision Tree, Random Forest, Gradient Boosted Tree, and Tree Ensemble. Further, the analytical patterns about these predictive models have been presented based on various performance parameters which include accuracy, precision, recall, and F-measure.

scholarly journals Internet of Things-Based Intelligent Smart Home Control System

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Olutosin Taiwo ◽  
Absalom E. Ezugwu
Keyword(s):  
Machine Learning ◽  
Mobile Application ◽  
Smart Home ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Automation System ◽  
Home Automation ◽  
Area Of Interest ◽  
Home Automation System

The smart home is now an established area of interest and research that contributes to comfort in modern homes. With the Internet being an essential part of broad communication in modern life, IoT has allowed homes to go beyond building to interactive abodes. In many spheres of human life, the IoT has grown exponentially, including monitoring ecological factors, controlling the home and its appliances, and storing data generated by devices in the house in the cloud. Smart home includes multiple components, technologies, and devices that generate valuable data for predicting home and environment activities. This work presents the design and development of a ubiquitous, cloud-based intelligent home automation system. The system controls, monitors, and oversees the security of a home and its environment via an Android mobile application. One module controls and monitors electrical appliances and environmental factors, while another module oversees the home’s security by detecting motion and capturing images. Our work uses a camera to capture images of objects triggered by their motion being detected. To avoid false alarms, we used the concept of machine learning to differentiate between images of regular home occupants and those of an intruder. The support vector machine algorithm is proposed in this study to classify the features of the image captured and determine if it is that of a regular home occupant or an intruder before sending an alarm to the user. The design of the mobile application allows a graphical display of the activities in the house. Our work proves that machine learning algorithms can improve home automation system functionality and enhance home security. The work’s prototype was implemented using an ESP8266 board, an ESP32-CAM board, a 5 V four-channel relay module, and sensors.

scholarly journals Social well-being of converted Muslims in different countries of the world

2016 ◽  
Vol 1 (19) ◽  
pp. 171-173
Author(s):  
Olga Maksymenko
Keyword(s):  
Human Life ◽  
Negative Attitude ◽  
Well Being ◽  
Islamic State ◽  
New Wave ◽  
Life Changes ◽  
Ordinary People ◽  
Way Of Life ◽  
The World ◽  
Hostile Attitude

The tendency to intensify Islamophobia in its various manifestations, from the hostile attitude towards the Muslims to open acts of aggression and calls for hatred and violence against the representatives of this religion - unfortunately, has recently been observed in many countries of the world. Some factors contribute to this: firstly, the inspiration by some unscrupulous media of identifying Muslims with terrorists and extremists, a new wave of fear, caused by reports of numerous crimes by militants of the self-proclaimed "Islamic State" (whose activities generally contradict the spirit of Islam as a peaceful and humanistic religion that recognizes human life of the highest value and equates the killing of one person to the murder of all mankind) and recent attacks with a large number of human victims (in particular, in France and Belgium); and secondly, the reluctance of ordinary people to see in their environment those who differ from them (rejection of "someone else", due to the imaginary division of the world into "we" and "they"). Bearers of another culture are perceived as a threat of violations of the usual way of life, changes in the established system of values. Hence, the sharply negative attitude towards refugees from Syria and other Islamic countries.

scholarly journals Machine Learning for Landslides Prevention: A Survey

2020 ◽  
Author(s):  
Zhengjing Ma ◽  
Gang Mei
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Warning Systems ◽  
Negative Effects ◽  
Relevant Research ◽  
Related Information ◽  
Urgent Task ◽  
Comprehensive Survey

Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

A new classification system for autism based on machine learning of artificial intelligence

2021 ◽  
pp. 1-18
Author(s):  
Seyed Reza Shahamiri ◽  
Fadi Thabtah ◽  
Neda Abdelhamid
Keyword(s):  
Machine Learning ◽  
Scoring Function ◽  
Autistic Traits ◽  
Well Being ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
The Social ◽  
Hidden Patterns ◽  
The Individual ◽  
Fold Cross Validation

BACKGROUND: Autistic Spectrum Disorder (ASD) is a neurodevelopment condition that is normally linked with substantial healthcare costs. Typical ASD screening techniques are time consuming, so the early detection of ASD could reduce such costs and help limit the development of the condition. OBJECTIVE: We propose an automated approach to detect autistic traits that replaces the scoring function used in current ASD screening with a more intelligent and less subjective approach. METHODS: The proposed approach employs deep neural networks (DNNs) to detect hidden patterns from previously labelled cases and controls, then applies the knowledge derived to classify the individual being screened. Specificity, sensitivity, and accuracy of the proposed approach are evaluated using ten-fold cross-validation. A comparative analysis has also been conducted to compare the DNNs’ performance with other prominent machine learning algorithms. RESULTS: Results indicate that deep learning technologies can be embedded within existing ASD screening to assist the stakeholders in the early identification of ASD traits. CONCLUSION: The proposed system will facilitate access to needed support for the social, physical, and educational well-being of the patient and family by making ASD screening more intelligent and accurate.

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