scholarly journals Robust Drown Alerting, Preventing and Autonomous Rescue System

2021 ◽  
Vol 21 (1) ◽  
pp. 22-25
Author(s):  
Pillalamarri Laxman ◽  
Anuj Jain
Keyword(s):  
Artificial Intelligence ◽  
Life Saving ◽  
Rescue System ◽  
Novel Method

Abstract This paper introduces a novel method of automatic life-saving mechanism to save a drowning victim in a helpless condition. The realization of this method may be done by an Arduino Board or an embedded microcontroller. This method utilizes artificial intelligence to save drowning victims from fatal death and rings a loudspeaker.

scholarly journals Drown Alerting, Preventing And Autonomous Rescue System using Ardiuno, Tacticle switche (weight sensors) and Artificial Intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 762-766
Keyword(s):  
Artificial Intelligence ◽  
Swimming Pool ◽  
Human Intervention ◽  
Present Design ◽  
Rescue System ◽  
Novel Method ◽  
Processor Board

This paper introduces a novel method of automatic lifesaving swimming pool design to save a drowning victim in a helpless condition by using latest ardiuno(IoT) processor board and the framework. This method utilizes artificial intelligence to save drowning victims without the need of a life-guard(human intervention) from fatal death and also associated specific alerting mechanisms like a loudspeaker. In the present paper, a swimming pool framework is proposed with a responsive elevator assebly surface which is covering entire pool bottom, housing several weight sensitive waterproofed tactical switches arranged to sense any individual or any object incidence(falling) on bottom of the pool ,and associated alerting devices like loud speakers, and a drain motor control.The automatic rescue designs have not been tried succesfull so far in literature effectively so far. The present design delt with simplest algorithm and latest mechanical supporting strucures, and fast responding ardiuno processor. The finished prototype has given promising results in solving defined problem.

scholarly journals A novel method for dried chili pepper classification using artificial intelligence

2021 ◽  
Vol 3 ◽  
pp. 100099
Author(s):  
O. Cruz-Domínguez ◽  
J.L. Carrera-Escobedo ◽  
C.H. Guzmán-Valdivia ◽  
A. Ortiz-Rivera ◽  
M. García-Ruiz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Chili Pepper ◽  
Novel Method

scholarly journals 195 The Application of Radiomics in Laryngeal Cancer: The Forgotten Cohort

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
A Rajgor ◽  
A McQueen ◽  
T Ali ◽  
E Aboagye ◽  
B Obara ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Head And Neck ◽  
Laryngeal Cancer ◽  
Prognostic Value ◽  
Head And Neck Cancers ◽  
High Dimensional ◽  
Naked Eye ◽  
Online Databases ◽  
Novel Method

Abstract Background Radiomics is a novel method of extracting data from medical images that is difficult to visualise through the naked eye. This technique transforms digital images that hold information on pathology into high-dimensional-data for analysis. Radiomics has the potential to enhance laryngeal cancer care and to date, has shown promise in various other specialties. Aim The aim of this review is to summarise the applications of this technique to laryngeal cancer and potential future benefits. Method A comprehensive systematic review-informed search of the MEDLINE and EMBASE online databases was undertaken. Keywords ‘laryngeal cancer’ OR ‘larynx’ OR ‘larynx cancer’ OR ‘head and neck cancer’ were combined with ‘radiomic’ OR ‘signature’ OR ‘machine learning’ OR ‘artificial intelligence’. Additional articles were obtained from bibliographies using the ‘snowball method’. Results Seventeen articles were identified that evaluated the role of radiomics in laryngeal cancer. Two studies affirmed the value of radiomics in improving the accuracy of staging, whilst fifteen studies highlighted the potential prognostic value of radiomics in laryngeal cancer. Twelve (of thirteen) studies incorporated an array of different head and neck cancers in the analysis and only one study assessed laryngeal cancer exclusively. Conclusions Literature to date has various limitations including, small and heterogeneous cohorts incorporating patients with head and neck cancers of distinct anatomical subsites and stages. The lack of uniform data on solely laryngeal cancer and radiomics means drawing conclusions is difficult, although these studies have affirmed its value. Further large prospective studies exclusively in laryngeal cancer are required to unlock its true potential.

scholarly journals Niépce–Bell or Turing: how to test odour reproduction

2016 ◽  
Vol 13 (125) ◽  
pp. 20160587 ◽  
Author(s):  
David Harel
Keyword(s):  
Artificial Intelligence ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Alan Turing ◽  
Novel Method

Decades before the existence of anything resembling an artificial intelligence system, Alan Turing raised the question of how to test whether machines can think, or, in modern terminology, whether a computer claimed to exhibit intelligence indeed does so. This paper raises the analogous issue for olfaction: how to test the validity of a system claimed to reproduce arbitrary odours artificially, in a way recognizable to humans. Although odour reproduction systems are still far from being viable, the question of how to test candidates thereof is claimed to be interesting and non-trivial, and a novel method is proposed. Despite the similarity between the two questions and their surfacing long before the tested systems exist, the present question cannot be answered adequately by a Turing-like method. Instead, our test is very different: it is conditional, requiring from the artificial no more than is required from the original, and it employs a novel method of immersion that takes advantage of the availability of easily recognizable reproduction methods for sight and sound, a la Nicéphore Niépce and Alexander Graham Bell.

DIMENSIONALITY REDUCTION FOR SENSORY DATASETS BASED ON MASTER–SLAVE SYNCHRONIZATION OF LORENZ SYSTEM

2013 ◽  
Vol 23 (05) ◽  
pp. 1330013 ◽  
Author(s):  
REZA GHAFFARI ◽  
IOAN GROSU ◽  
DACIANA ILIESCU ◽  
EVOR HINES ◽  
MARK LEESON
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Electronic Nose ◽  
Lorenz System ◽  
Nearest Neighbor ◽  
Performance Testing ◽  
K Nearest Neighbor ◽  
Classification Rate ◽  
Benchmark Datasets ◽  
Novel Method

In this study, we propose a novel method for reducing the attributes of sensory datasets using Master–Slave Synchronization of chaotic Lorenz Systems (DPSMS). As part of the performance testing, three benchmark datasets and one Electronic Nose (EN) sensory dataset with 3 to 13 attributes were presented to our algorithm to be projected into two attributes. The DPSMS-processed datasets were then used as input vector to four artificial intelligence classifiers, namely Feed-Forward Artificial Neural Networks (FFANN), Multilayer Perceptron (MLP), Decision Tree (DT) and K-Nearest Neighbor (KNN). The performance of the classifiers was then evaluated using the original and reduced datasets. Classification rate of 94.5%, 89%, 94.5% and 82% were achieved when reduced Fishers iris, crab gender, breast cancer and electronic nose test datasets were presented to the above classifiers.

scholarly journals Review on the Application of Metalearning in Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Pengfei Ma ◽  
Zunqian Zhang ◽  
Jiahao Wang ◽  
Wei Zhang ◽  
Jiajia Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Reinforcement Learning ◽  
Research Direction ◽  
Development Trend ◽  
Research Process ◽  
Learning Ability ◽  
Novel Method ◽  
Accuracy Of Prediction ◽  
The Development Trend

In recent years, artificial intelligence supported by big data has gradually become more dependent on deep reinforcement learning. However, the application of deep reinforcement learning in artificial intelligence is limited by prior knowledge and model selection, which further affects the efficiency and accuracy of prediction, and also fails to realize the learning ability of autonomous learning and prediction. Metalearning came into being because of this. Through learning the information metaknowledge, the ability to autonomously judge and select the appropriate model can be formed, and the parameters can be adjusted independently to achieve further optimization. It is a novel method to solve big data problems in the current neural network model, and it adapts to the development trend of artificial intelligence. This article first briefly introduces the research process and basic theory of metalearning and discusses the differences between metalearning and machine learning and the research direction of metalearning in big data. Then, four typical applications of metalearning in the field of artificial intelligence are summarized: few-shot learning, robot learning, unsupervised learning, and intelligent medicine. Then, the challenges and solutions of metalearning are analyzed. Finally, a systematic summary of the full text is made, and the future development prospect of this field is assessed.

scholarly journals The Mobilisation of AI in Education: A Bourdieusean Field Analysis

Sociology ◽  
2020 ◽  
pp. 003803852096788
Author(s):  
Huw C Davies ◽  
Rebecca Eynon ◽  
Cory Salveson
Keyword(s):  
Artificial Intelligence ◽  
Educational Technology ◽  
Field Analysis ◽  
Knowledge Graph ◽  
Novel Method

Artificial Intelligence (AI) is currently hailed as a ‘solution’ to perceived problems in education. Though few sociologists of education would agree with its deterministic claims, this AI solutionist thinking is gaining significant currency. In this article, using a relatively novel method for sociology – a knowledge graph – together with Bourdieusean theory, we critically examine how and why different stakeholders in education, educational technology and policy are valorising AI, the main concepts, such as personalisation, they collectively endorse and their incentives for doing so. Drawing on this analysis, we argue that AI is currently being mobilised in education in problematic ways and advocate for more systematic sociological thinking and research to re-orientate the field to account for society’s structural conditions.

scholarly journals Design and Implementation of a Computer Aided Intelligent Examination System

2020 ◽  
Vol 15 (01) ◽  
pp. 30 ◽  
Author(s):  
Abdulrahman Abdullah Alghamdi ◽  
Mohammed Ateeq Alanezi ◽  
Faizal Khan
Keyword(s):  
Artificial Intelligence ◽  
Database Design ◽  
Examination System ◽  
Teaching Materials ◽  
User Friendliness ◽  
Design And Implementation ◽  
Computer Aided ◽  
E Learning ◽  
Computer Based ◽  
Novel Method

Developing a computer based system for examinations are the substitute for the current examination system based on paper. In recent days, the e-learning has become more popular because of its adaptability, integrity and user friendliness. In terms of the paper based examinations, major challenge is the proctoring techniques used. In this paper, a novel method to avoid the presence of a proctor throughout the examination is proposed by an intelligent based examination system. This method is proposed to improve the e-learning using intelligent question bank and examination system. The system is designed with different complexity levels among the questions and also it acts as a tool to assessing the understanding of student from the teaching materials. This system can be timesaving and more efficient with an adequate level of security. The proposed methodology can be classified in to two main phases such as the design of question bank along with its database, design of the Artificial Intelligence (AI) based system for examination and its evaluation in order to improve the e-learning. Future works in this system can be the addition of theory-based questions and the integration of biometric based systems for enhancing the level of security.

PREDICTION METHOD OF SDN CONTROLLERS LOADING BASED ON THE ARTIFICIAL INTELLIGENCE TECHNOLOGIES

2021 ◽  
Author(s):  
А.Н. ВОЛКОВ ◽  
А.Е. КУЧЕРЯВЫЙ
Keyword(s):  
Artificial Intelligence ◽  
Prediction Method ◽  
Channel Activity ◽  
Load Prediction ◽  
Novel Method

Предложен подход к прогнозированию нагрузки на контроллеры SDN, основанный на алгоритмах искусственного интеллекта и полном мониторинге активности служебных каналов OpenFlow. Дано обоснование возможности прогнозирования нагрузки на аппаратную часть контроллера с помощью анализа метаданных служебных потоков OpenFlow. A novel method for SDN controllers load prediction based on artificial intelligence algorithms and total monitoring of OpenFlow channel activity is proposed. The justification for predicting the load on the hardware part, with the help of OpenFlow flows metadata analytics is given.

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