A Novel Poisoned Water Detection Method Using Smartphone Embedded Wi-Fi Technology and Machine Learning Algorithms

Water is a necessary fluid to the human body and automatic checking of its quality and cleanness is an ongoing area of research. One such approach is to present the liquid to various types of signals and make the amount of signal attenuation an indication of the liquid category. In this article, we have utilized the Wi-Fi signal to distinguish clean water from poisoned water via training different machine learning algorithms. The Wi-Fi access points (WAPs) signal is acquired via equivalent smartphone-embedded Wi-Fi chipsets, and then Channel-State-Information CSI measures are extracted and converted into feature vectors to be used as input for machine learning classification algorithms. The measured amplitude and phase of the CSI data are selected as input features into four classifiers k-NN, SVM, LSTM, and Ensemble. The experimental results show that the model is adequate to differentiate poison water from clean water with a classification accuracy of 89% when LSTM is applied, while 92% classification accuracy is achieved when the AdaBoost-Ensemble classifier is applied.

Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms

COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, China and spread across the globe with unprecedented effect and has now become the greatest crisis of the modern era. The COVID-19 has proved much more pervasive demands for diagnosis that has driven researchers to develop more intelligent, highly responsive and efficient detection methods. In this work, we focus on proposing AI tools that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images dataset. The result of the experiments shows that the utilized models can provide accuracy up to 98 % via pre-trained network and 94.1 % accuracy by using the modified CNN.

A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study

Coronaviruses are a famous family of viruses that causes illness in human or animals. The new type of corona virus COVID-19 disease was firstly discovered in Wuhan-China. However, recently, the virus has been widely spread in most of the world countries and is reported as a pandemic. Further, nowadays, all the world countries are striving to control the coronavirus disease COVID-19. There are many mechanisms to detect the coronavirus disease COVID-19 including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost and it takes time to install them and use. Therefore, in this paper, a new framework is proposed to detect coronavirus disease COVID-19 using onboard smartphone sensors. The proposal provides a low-cost solution, since most of the radiologists have already held smartphones for different daily-purposes. People can use the framework on their smartphones for the virus detection purpose. Nowadays, smartphones are powerful with existing computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.