Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses

Informative nature of the electronic nose output signals based on the piezoelectric sensors

The purpose of this research was assessing the influence of the various factors on the output signals of the static “electronic nose” based on the piezoelectric sensors, and determining the informative nature of these signals for the identification and determination of the marker-substances related to the pathogenic processes in the equilibrium gas phase over the aqueous solutions. Individual substances contained in bio samples in the presence of pathogenic and neoplastic processes, such as ammonia, amines, carboxylic acids, ethanol, 1-butanol, acetone, ethyl acetate, phenol, hydrogen sulfide and water were selected as the marker-substances. The selective coating of sensors was chosen based on the results of the numerous studies for the living systems of different nature in order to determine the deviations from the norm, which included standard chromatographic phases and specific sorbents (indicators, crown ethers). It was shown that the analytical information of the electronic nose based on the piezoelectric sensors no more dependent on the experimental conditions than other popular, widely used methods of analysis. The informative value of the sensors array’ output signals which were used to identify the substances was described. The array set of piezoelectric sensors identification parameters was established in order to detect amines, organic acids, alcohols, ethyl acetate, acetone in the equilibrium gas phase over the aqueous solutions. The influence of the sensors order in the array on the values of three-element identification parameters has been demonstrated. The scheme of the identification parameters application, including nonselective ones, has been proposed for detecting the organic substances coincidentally at least two parameters. The possibility of an application of these parameters to identify amines, acids, alcohols, ketones in the equilibrium gas phase over the aqueous solutions of mixtures from these substances has been proven. This approach was characterized by high sensitivity and specificity, and may be used for the identification of substances in equilibrium gas phase over the samples with high water content (blood, urine, lymph, perspiration, juices, beverages).Обсуждается влияние температуры, влажности, режимов измерения, собственных характеристик пьезорезонаторов, природы и массы сорбента, природы и содержания аналита, типа проб на выходные данные массива пьезосенсоров, в том числе на параметры, используемые для идентификации веществ в смесях, и пути устранения или минимизации этого влияния. Показано, что аналитическая информация «электронного носа» на пьезосенсорах не более зависима от условий эксперимента, чем популярные, широко распространенные методы анализа. Описана информативность выходных сигналов массива сенсоров, используемых для идентификации веществ. Установлены идентификационные параметры массива пьезосенсоров для обнаружения аминов, органических кислот, спиртов, этилацетата, ацетона в равновесной газовой фазе над водными растворами. Продемонстрировано влияние порядка расположения сенсоров в массиве на значения трехэлементных идентификационных параметров. Предложена схема применения идентификационных параметров, в том числе неселективных, для обнаружения органических веществ по совпадению не менее двух параметров. Доказана возможность применения данных параметров для идентификации аминов, кислот, спиртов, кетонов в равновесной газовой фазе над водными растворами их смесей. Данный подход характеризуется высокой чувствительностью и специфичностью и может быть использован для идентификации веществ в равновесной газовой фазе над пробами с большим содержанием воды (кровь, моча, лимфа, пот, соки, напитки)

Early detection of lung cancer - A challenge

Lung cancer or lung carcinoma, is a common and serious type of cancer caused by rapid cell growth in tissues of the lung. Lung cancer detection at its earlier stage is very difficult because of the structure of the cell alignment which makes it very challenging. Computed tomography (CT) scan is used to detect the presence of cancer and its spread. Visual analysis of CT scan can lead to late treatment of cancer; therefore, different steps of image processing can be used to solve this issue. A comprehensive framework is used for the classification of pulmonary nodules by combining appearance and shape feature descriptors, which helps in the early diagnosis of lung cancer. 3D Histogram of Oriented Gradient (HOG), Resolved Ambiguity Local Binary Pattern (RALBP) and Higher Order Markov Gibbs Random Field (MGRF) are the feature descriptors used to explain the nodule’s appearance and compared their performance. Lung cancer screening methods, image processing techniques and nodule classification using radiomic-based framework are discussed in this paper which proves to be very effective in lung cancer prediction. Good performance is shown by using RALBP descriptor

Development of an Electrostatic Air Filtration System Using Fuzzy Logic Control

Particulate matter is one of the factors that can affect air quality. The air quality can be determined by the Air Pollution Index, which has several parameters including PM10 and ozone (O3). Air pollution can be overcome by using a filtration system based on electrostatic precipitation when particles are attached to the static charges. In this study, we have developed a prototype of electrostatic filter based on fuzzy logic control to reduce air pollution of particulate matters. The electrostatic filter is an ozone generator consisting of plate-type corona discharge and a high voltage dc generator. The experimental results showed that the more ozone generators used as electrostatic filters, the faster the particulate concentration decreases. However, the use of ozone generators may increase the concentration of ozone in the air that can be harmful to human health. Therefore, we have developed an electrostatic precipitation-based air pollution control using fuzzy logic. Semiconductor gas sensor and laser dust sensor are used as feedback signals for the system in regulating the amount of charges released by ozone generators. Implementation of this control system can reduce the particulates of PM10 by 80% within 10 minutes while maintaining a low level of ozone during air purification process

An investigation into spike-based neuromorphic approaches for artificial olfactory systems

The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses

Diagnosis of Smear-Negative Pulmonary Tuberculosis using Ensemble Method: A Preliminary Research

Indonesia is one of 22 countries with the highest burden of Tuberculosis in the world. According to WHO’s 2015 report, Indonesia was estimated to have one million new tuberculosis (TB) cases per year. Unfortunately, only one-third of new TB cases are detected. Diagnosis of TB is difficult, especially in the case of smear-negative pulmonary tuberculosis (SNPT). The SNPT is diagnosed by TB trained doctors based on physical and laboratory examinations. This study is preliminary research that aims to determine the ensemble method with the highest level of accuracy in the diagnosis model of SNPT. This model is expected to be a reference in the development of the diagnosis of new pulmonary tuberculosis cases using input in the form of symptoms and physical examination in accordance with the guidelines for tuberculosis management in Indonesia. The proposed SNPT diagnosis model can be used as a cost-effective tool in conditions of limited resources. Data were obtained from medical records of tuberculosis patients from the Jakarta Respiratory Center. The results show that the Random Forest has the best accuracy, which is 90.59%, then Adaboost of 90.54% and Bagging of 86.91%

Diabetes Prediction Using Artificial Neural Network

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3

IQ Classification via Brainwave Features: Review on Artificial Intelligence Techniques

Intelligence study is one of keystone to distinguish individual differences in cognitive psychology. Conventional psychometric tests are limited in terms of assessment time, and existence of biasness issues. Apart from that, there is still lack in knowledge to classify IQ based on EEG signals and intelligent signal processing (ISP) technique. ISP purpose is to extract as much information as possible from signal and noise data using learning and/or other smart techniques. Therefore, as a first attempt in classifying IQ feature via scientific approach, it is important to identify a relevant technique with prominent paradigm that is suitable for this area of application. Thus, this article reviews several ISP approaches to provide consolidated source of information. This in particular focuses on prominent paradigm that suitable for pattern classification in biomedical area. The review leads to selection of ANN since it has been widely implemented for pattern classification in biomedical engineering