We use electronic nose data of odor measurements to build machine learning clas- sification models. The presented analysis focused on determining the optimal time of measurement, leading to the best model performance. We observe that the most valuable information for classification is available in data collected at the beginning of adsorption and the beginning of the desorption phase of measurement. We demonstrated that the usage of complex features extracted from the sensors’ response gives better classification performance than use as features only raw values of sensors’ response, normalized by baseline. We use a group shuffling cross-validation approach for determining the reported models’ average accuracy and standard deviation.
An approach to assess the quality of honey using partial least square method IJECEIAES
The objective of the present study is to obtain the quantity of honey components such as moisture, glucose, fructose and sucrose in order to access the quality of honey. The tested honey samples are authenticated if the characteristics of a pure honey. The average ratio of 56% fructose to 44% glucose, but the ratios in the individual honeys ranged from a high of 64% fructose and 36% glucose to a low of 50% fructose and 50% glucose. The contents such as fructose and sucrose in honey is due to the presence of invertase enzymes. The organic acids present in the honey is responsible for the flavor and stability against the contamination of honey due to microorganisms. The natural food items are adulterated intentionally to increase the quantity and there by the quality gets affected. The main adulterants added in honey are sucrose, corn syrup, sugar syrup and jaggery syrup. The quantification deals in finding out the amount of basic constituents present in pure honey and adulterated honey using Fourier transform infrared (FTIR) spectrometer with the multivariate analysis and validating the same using chemical analysis method. The partial least square model is used in predicting the constituents of the samples.
Primary Odor on Consideration of Reducing the Number of Compositionsinventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
ROLE OF SUBSTANTIAL CHARACTERISTICS IN ELECTRONIC NOSE SENSOR SELECTION FOR D...IAEME Publication
Electronic Nose has widen up the possibilities of detection and analysis of certain factors for environmental, medical, narcotic, military and many more application areas. A systematic approach for the parameter selection and choice of proper sensor arrays determines the quality of the expected output. The main motto behind the approach is to obtain certain outcomes as the final result of analysis carried out on the basis of data acquired. Certain characteristics and properties play a vital role in the entire process of odor recognition, ranging from sensor selection to odor measurement and analysis
Multiple Federal and State Agencies (e.g. EPA, DOD, DEQs and DEPs) in the United States as well as international organizations (e.g. ASTM) are quickly publishing new analytical methodologies for PFAS monitoring and establishing more stringent limits. Liquid Chromatography with Mass Spectrometry-based detection is established as the most suitable technology for meeting the requirements from official methods released up to date for monitoring PFAS. A comparison of instruments’ performance was conducted in this work.
Hyphenated techniques have received ever-increasing attention as the principal means to solve complex analytical problems.
Hyphenated techniques are widely used in chemistry and biochemistry and used for both quantitative and qualitative analysis of unknown compounds in complex natural product extracts or fraction and estimation of protein samples also.
An approach to assess the quality of honey using partial least square method IJECEIAES
The objective of the present study is to obtain the quantity of honey components such as moisture, glucose, fructose and sucrose in order to access the quality of honey. The tested honey samples are authenticated if the characteristics of a pure honey. The average ratio of 56% fructose to 44% glucose, but the ratios in the individual honeys ranged from a high of 64% fructose and 36% glucose to a low of 50% fructose and 50% glucose. The contents such as fructose and sucrose in honey is due to the presence of invertase enzymes. The organic acids present in the honey is responsible for the flavor and stability against the contamination of honey due to microorganisms. The natural food items are adulterated intentionally to increase the quantity and there by the quality gets affected. The main adulterants added in honey are sucrose, corn syrup, sugar syrup and jaggery syrup. The quantification deals in finding out the amount of basic constituents present in pure honey and adulterated honey using Fourier transform infrared (FTIR) spectrometer with the multivariate analysis and validating the same using chemical analysis method. The partial least square model is used in predicting the constituents of the samples.
Primary Odor on Consideration of Reducing the Number of Compositionsinventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
ROLE OF SUBSTANTIAL CHARACTERISTICS IN ELECTRONIC NOSE SENSOR SELECTION FOR D...IAEME Publication
Electronic Nose has widen up the possibilities of detection and analysis of certain factors for environmental, medical, narcotic, military and many more application areas. A systematic approach for the parameter selection and choice of proper sensor arrays determines the quality of the expected output. The main motto behind the approach is to obtain certain outcomes as the final result of analysis carried out on the basis of data acquired. Certain characteristics and properties play a vital role in the entire process of odor recognition, ranging from sensor selection to odor measurement and analysis
Multiple Federal and State Agencies (e.g. EPA, DOD, DEQs and DEPs) in the United States as well as international organizations (e.g. ASTM) are quickly publishing new analytical methodologies for PFAS monitoring and establishing more stringent limits. Liquid Chromatography with Mass Spectrometry-based detection is established as the most suitable technology for meeting the requirements from official methods released up to date for monitoring PFAS. A comparison of instruments’ performance was conducted in this work.
Hyphenated techniques have received ever-increasing attention as the principal means to solve complex analytical problems.
Hyphenated techniques are widely used in chemistry and biochemistry and used for both quantitative and qualitative analysis of unknown compounds in complex natural product extracts or fraction and estimation of protein samples also.
RECOMMENDER SYSTEM FOR DETECTION OF DENGUE USING FUZZY LOGICIAEME Publication
The recommender System involved in health care is important since user can detect whether he has problem or not. A user will get whole information on the go. Today user doesn’t have much time and information about the dengue and it will be disclosed to the user at later stages. The dengue is deadly disease so its information should be disclosed at earlier stage. The proposed system works toward this aspect. The set of parameters including fever, TLC, blood pressure, severe headache etc. are analysed in proposed system. The filtering mechanism is also utilised in the proposed system which is integral part of recommender system. The content based filtering will be utilised in proposed system.
Chemometrical Optimization for Fourier Transform Near Infrared Analysis of Su...IJERD Editor
Real-time evaluation of sugar quality requires determining the content of sugar brix in the steps of
the cane sugar process. Sugar brix is a key indicator for evaluating sugar quality. Fourier transform near infrared
(FTNIR) spectroscopy is a simple, rapid and non-destructive technology on the analysis of material contents. In
this study, the chemometric algorithm of parameter-combined tuning of Savitzky-Golay (SG) smoother and
Partial Least Squares (PLS) regression was utilized for FTNIR analysis of sugar brix content in sugarcane
clarified juice, an important intermediate product in cane sugar industry. The algorithms of combined
optimization of SG smoother and PLS regression was achieved and the calibration models were established
optimized by screening the expanded 540 SG smoothing modes and the 1-30 latent valuables (LV). The
optimized models have high predictive accuracy. These results confirm that the combined optimization of SG
smoothing modes and PLS LVs is effective in the quantitative determination of sugar brix contents in sugarcane
clarified juice, and that the FTNIR spectroscopic technology with its chemometric algorithms have the potential
in the analysis of cane sugar intermediates.
The aim of the coupling is to obtain an information-rich detection for both identification and quantification compared to that with a single analytical technique.
MEMORY EFFICIENT FREQUENT PATTERN MINING USING TRANSPOSITION OF DATABASEIAEME Publication
Frequent pattern can be extract from any dataset by using Apriori algorithm. Apriori algorithm is first choice of all researchers to find frequently occurs pattern from any binary dataset. Dataset contain record of user purchase item as transaction record. This paper improves existing apriori algorithm performance by extract frequent patterns from binary transaction data. New approach is applied for dataset implementation in form of transposed database of user’s record for fast data access. New work has done to mine frequent patterns using transposition of dataset, if database is large and contains thousands of attributes but having only some objects.
Improvement of bacterial estimation in urinalysis by iQ®200 ELITE®: capture i...kridsada31
Kridsada Sirisabhabhorn1 Anurak Choeymang2 Supaporn Pumpa1 Krim Kamyod1 and Palakorn Puttaruk1
1Department of Medical Technology Laboratory, Thammasat University hospital, Pathumthani, Thailand
2Pharmacology and Toxicology Unit, Faculty of Allied Health Sciences, Thammasat University, Pathumthani, Thailand
Non-invasive moisture content measurement system based on the ESP8266 microco...journalBEEI
Moisture content in the process of drying is often unknown when carrying out the drying process, especially the fluidized dryer. A lot of experimental designs are needed when observing the drying phenomenon more deeply. It is because to stop and repeat drying process from the beginning again when the sample is taken to test its moisture content needed more experiments. Therefore, this paper presents development of a non-intrusive moisture measurement system prepared for fluidization type dryers. The method used in to conduct this research consists of (i) structural design analysis and (ii) functional (mechanical and electrical systems) and (iii) simple testing of the water content measurement system of constructed material. Test parameters observed include errors in measuring and fluctuating sensor signals against vibration applied to the weighing system. The results showed that non-intrusive moisture content measurement system for fluidized dryers based on the ESP8266 microcontroller had been successfully developed and worked normally. The measurement system has been calibrated with a coefficient of determination (R2) close to one. Measurement error resulting from the effect of vibration on this system shows a very satisfactory value of 6.89%.
PROPOSED ONTOLOGY FRAMEWORK FOR DYNAMIC RESOURCE PROVISIONING ON PUBLIC CLOUDIAEME Publication
Cloud computing is an essential ingredient of today’s modern information technology. Cloud computing is totally based on internet. With the use of cloud computing resources can be shared from anywhere and anytime. In cloud computing there are multiple users simultaneously requests for the number of services and its important to provision all resources to user in efficient manner to satisfy their requirements. To come out this problem in this paper we had reviwed the different types of resource allocation strategies and proposed an ontology based resource management framwork for dynamic resource allocation. Ontology Framework contain four sections, each section equipped with functionality to collect information regarding all resources available in actual cloud deployment based on signed SLA agreement, and then replies to the user with appropriate allocation.
Made in Millersville: Determining the Concentration of Parabens in Personal C...Gloria Chung
This is my poster presentation for the 2017 Made in Millersville Conference at Millersville University. I had the opportunity to share my independent study on parabens in personal care products to the faculty, staff, administration, and students at Millersville, as well as get reviewed by professionals on my presentation.
UV spectrophotometric method development and validation for quantitative esti...Sagar Savale
UV Spectrophotometric Method Development and Validation for quantitative estimation of Ondansetron
Hydrochloride (HCL). U.V Spectrophotometric method have been widely employed in determination of
individual components in a mixture or fixed dose combination. Our aim is to develop spectroscopic method for
estimation of the Ondansetron HCL in ternary mixture by using U.V spectrophotometry. The method was
validated as per ICH guidelines. The recovery studies confirmed the accuracy and precision of the method. It was
successfully applied for the analysis of the drug in bulk and could be effectively used for the routine analysis.
Web based Water Turbidity Monitoring and Automated Filtration System: IoT App...IJECEIAES
Water supplied to residential areas is prone to contaminants due to pipe residues and silt, and therefore resulted in cloudiness, unfavorable taste, and odor in water. Turbidity, a measure of water cloudiness, is one of the important factors for assessing water quality. This paper proposes a low-cost turbidity system based on a light detection unit to measure the cloudiness in water. The automated system uses Intel Galileo 2 as the microprocessor and a server for a web-based monitoring system. The turbidity detection unit consists of a Light Dependent Resistor (LDR) and a Light Emitting Diode (LED) inside a polyvinyl chloride (PVC) pipe. Turbidity readings were recorded for two different positionings; 90° and 180° between the detector (LDR) and the incident light (LED). Once the turbidity level reached a threshold level, the system will trigger the filtration process to clean the water. The voltage output captured from the designed system versus total suspended solid (TSS) in sample water is graphed and analyzed in two different conditions; in total darkness and in the present of ambient light. This paper also discusses and compares the results from the above-mentioned conditions when the system is submerged in still and flowing water. It was found that the trends of the plotted graph decline when the total suspended solid increased for both 90° and 180° detector turbidimeter in all conditions which imitate the trends of a commercial turbidimeter. By taking the consideration of the above findings, the design can be recommended for a low-cost real-time web-based monitoring system of the water quality in an IOT environment.
Design of Electronic Nose System Using Gas Chromatography Principle and Surfa...TELKOMNIKA JOURNAL
Most gases are odorless, colorless and also hazard to be sensed by the human olfactory system.
Hence, an electronic nose system is required for the gas classification process. This study presents the
design of electronic nose system using a combination of Gas Chromatography Column and a Surface
Acoustic Wave (SAW). The Gas Chromatography Column is a technique based on the compound partition
at a certain temperature. Whereas, the SAW sensor works based on the resonant frequency change. In
this study, gas samples including methanol, acetonitrile, and benzene are used for system performance
measurement. Each gas sample generates a specific acoustic signal data in the form of a frequency
change recorded by the SAW sensor. Then, the acoustic signal data is analyzed to obtain the acoustic
features, i.e. the peak amplitude, the negative slope, the positive slope, and the length. The Support
Vector Machine (SVM) method using the acoustic feature as its input parameters are applied to classify
the gas sample. Radial Basis Function is used to build the optimal hyperplane model which devided into
two processes i.e., the training process and the external validation process. According to the result
performance, the training process has the accuracy of 98.7% and the external validation process has the
accuracy of 93.3%. Our electronic nose system has the average sensitivity of 51.43 Hz/mL to sense the
gas samples.
Asthma Identification Using Gas Sensors and Support Vector MachineTELKOMNIKA JOURNAL
The exhaled breath analysis is a procedure of measuring several types of gases that aim to
identify various diseases in the human body. The purpose of this study is to analyze the gases contained
in the exhaled breath in order to recognize healthy and asthma subjects with varying severity. An
electronic nose consisting of seven gas sensors equipped with the Support Vector Machine classification
method is used to analyze the gases to determine the patient's condition. Non-linear binary classification is
used to identify healthy and asthma subjects, whereas the multiclass classification is applied to recognize
the subjects of asthma with different severity. The result of this study showed that the system provided a
low accuracy to distinguish the subjects of asthma with varying severity. This system can only differentiate
between partially controlled and uncontrolled asthma subjects with good accuracy. However, this system
can provide high sensitivity, specificity, and accuracy to distinguish between healthy and asthma subjects.
The use of five gas sensors in the electronic nose system has the best accuracy in the classification
results of 89.5%. The gases of carbon monoxide, nitric oxide, volatile organic compounds, hydrogen, and
carbon dioxide contained in the exhaled breath are the dominant indications as biomarkers of asthma.The
performance of electronic nose was highly dependent on the ability of sensor array to analyze gas type in
the sample. Therefore, in further study we will employ the sensors having higher sensitivity to detect lower
concentration of the marker gases.
RECOMMENDER SYSTEM FOR DETECTION OF DENGUE USING FUZZY LOGICIAEME Publication
The recommender System involved in health care is important since user can detect whether he has problem or not. A user will get whole information on the go. Today user doesn’t have much time and information about the dengue and it will be disclosed to the user at later stages. The dengue is deadly disease so its information should be disclosed at earlier stage. The proposed system works toward this aspect. The set of parameters including fever, TLC, blood pressure, severe headache etc. are analysed in proposed system. The filtering mechanism is also utilised in the proposed system which is integral part of recommender system. The content based filtering will be utilised in proposed system.
Chemometrical Optimization for Fourier Transform Near Infrared Analysis of Su...IJERD Editor
Real-time evaluation of sugar quality requires determining the content of sugar brix in the steps of
the cane sugar process. Sugar brix is a key indicator for evaluating sugar quality. Fourier transform near infrared
(FTNIR) spectroscopy is a simple, rapid and non-destructive technology on the analysis of material contents. In
this study, the chemometric algorithm of parameter-combined tuning of Savitzky-Golay (SG) smoother and
Partial Least Squares (PLS) regression was utilized for FTNIR analysis of sugar brix content in sugarcane
clarified juice, an important intermediate product in cane sugar industry. The algorithms of combined
optimization of SG smoother and PLS regression was achieved and the calibration models were established
optimized by screening the expanded 540 SG smoothing modes and the 1-30 latent valuables (LV). The
optimized models have high predictive accuracy. These results confirm that the combined optimization of SG
smoothing modes and PLS LVs is effective in the quantitative determination of sugar brix contents in sugarcane
clarified juice, and that the FTNIR spectroscopic technology with its chemometric algorithms have the potential
in the analysis of cane sugar intermediates.
The aim of the coupling is to obtain an information-rich detection for both identification and quantification compared to that with a single analytical technique.
MEMORY EFFICIENT FREQUENT PATTERN MINING USING TRANSPOSITION OF DATABASEIAEME Publication
Frequent pattern can be extract from any dataset by using Apriori algorithm. Apriori algorithm is first choice of all researchers to find frequently occurs pattern from any binary dataset. Dataset contain record of user purchase item as transaction record. This paper improves existing apriori algorithm performance by extract frequent patterns from binary transaction data. New approach is applied for dataset implementation in form of transposed database of user’s record for fast data access. New work has done to mine frequent patterns using transposition of dataset, if database is large and contains thousands of attributes but having only some objects.
Improvement of bacterial estimation in urinalysis by iQ®200 ELITE®: capture i...kridsada31
Kridsada Sirisabhabhorn1 Anurak Choeymang2 Supaporn Pumpa1 Krim Kamyod1 and Palakorn Puttaruk1
1Department of Medical Technology Laboratory, Thammasat University hospital, Pathumthani, Thailand
2Pharmacology and Toxicology Unit, Faculty of Allied Health Sciences, Thammasat University, Pathumthani, Thailand
Non-invasive moisture content measurement system based on the ESP8266 microco...journalBEEI
Moisture content in the process of drying is often unknown when carrying out the drying process, especially the fluidized dryer. A lot of experimental designs are needed when observing the drying phenomenon more deeply. It is because to stop and repeat drying process from the beginning again when the sample is taken to test its moisture content needed more experiments. Therefore, this paper presents development of a non-intrusive moisture measurement system prepared for fluidization type dryers. The method used in to conduct this research consists of (i) structural design analysis and (ii) functional (mechanical and electrical systems) and (iii) simple testing of the water content measurement system of constructed material. Test parameters observed include errors in measuring and fluctuating sensor signals against vibration applied to the weighing system. The results showed that non-intrusive moisture content measurement system for fluidized dryers based on the ESP8266 microcontroller had been successfully developed and worked normally. The measurement system has been calibrated with a coefficient of determination (R2) close to one. Measurement error resulting from the effect of vibration on this system shows a very satisfactory value of 6.89%.
PROPOSED ONTOLOGY FRAMEWORK FOR DYNAMIC RESOURCE PROVISIONING ON PUBLIC CLOUDIAEME Publication
Cloud computing is an essential ingredient of today’s modern information technology. Cloud computing is totally based on internet. With the use of cloud computing resources can be shared from anywhere and anytime. In cloud computing there are multiple users simultaneously requests for the number of services and its important to provision all resources to user in efficient manner to satisfy their requirements. To come out this problem in this paper we had reviwed the different types of resource allocation strategies and proposed an ontology based resource management framwork for dynamic resource allocation. Ontology Framework contain four sections, each section equipped with functionality to collect information regarding all resources available in actual cloud deployment based on signed SLA agreement, and then replies to the user with appropriate allocation.
Made in Millersville: Determining the Concentration of Parabens in Personal C...Gloria Chung
This is my poster presentation for the 2017 Made in Millersville Conference at Millersville University. I had the opportunity to share my independent study on parabens in personal care products to the faculty, staff, administration, and students at Millersville, as well as get reviewed by professionals on my presentation.
UV spectrophotometric method development and validation for quantitative esti...Sagar Savale
UV Spectrophotometric Method Development and Validation for quantitative estimation of Ondansetron
Hydrochloride (HCL). U.V Spectrophotometric method have been widely employed in determination of
individual components in a mixture or fixed dose combination. Our aim is to develop spectroscopic method for
estimation of the Ondansetron HCL in ternary mixture by using U.V spectrophotometry. The method was
validated as per ICH guidelines. The recovery studies confirmed the accuracy and precision of the method. It was
successfully applied for the analysis of the drug in bulk and could be effectively used for the routine analysis.
Web based Water Turbidity Monitoring and Automated Filtration System: IoT App...IJECEIAES
Water supplied to residential areas is prone to contaminants due to pipe residues and silt, and therefore resulted in cloudiness, unfavorable taste, and odor in water. Turbidity, a measure of water cloudiness, is one of the important factors for assessing water quality. This paper proposes a low-cost turbidity system based on a light detection unit to measure the cloudiness in water. The automated system uses Intel Galileo 2 as the microprocessor and a server for a web-based monitoring system. The turbidity detection unit consists of a Light Dependent Resistor (LDR) and a Light Emitting Diode (LED) inside a polyvinyl chloride (PVC) pipe. Turbidity readings were recorded for two different positionings; 90° and 180° between the detector (LDR) and the incident light (LED). Once the turbidity level reached a threshold level, the system will trigger the filtration process to clean the water. The voltage output captured from the designed system versus total suspended solid (TSS) in sample water is graphed and analyzed in two different conditions; in total darkness and in the present of ambient light. This paper also discusses and compares the results from the above-mentioned conditions when the system is submerged in still and flowing water. It was found that the trends of the plotted graph decline when the total suspended solid increased for both 90° and 180° detector turbidimeter in all conditions which imitate the trends of a commercial turbidimeter. By taking the consideration of the above findings, the design can be recommended for a low-cost real-time web-based monitoring system of the water quality in an IOT environment.
Design of Electronic Nose System Using Gas Chromatography Principle and Surfa...TELKOMNIKA JOURNAL
Most gases are odorless, colorless and also hazard to be sensed by the human olfactory system.
Hence, an electronic nose system is required for the gas classification process. This study presents the
design of electronic nose system using a combination of Gas Chromatography Column and a Surface
Acoustic Wave (SAW). The Gas Chromatography Column is a technique based on the compound partition
at a certain temperature. Whereas, the SAW sensor works based on the resonant frequency change. In
this study, gas samples including methanol, acetonitrile, and benzene are used for system performance
measurement. Each gas sample generates a specific acoustic signal data in the form of a frequency
change recorded by the SAW sensor. Then, the acoustic signal data is analyzed to obtain the acoustic
features, i.e. the peak amplitude, the negative slope, the positive slope, and the length. The Support
Vector Machine (SVM) method using the acoustic feature as its input parameters are applied to classify
the gas sample. Radial Basis Function is used to build the optimal hyperplane model which devided into
two processes i.e., the training process and the external validation process. According to the result
performance, the training process has the accuracy of 98.7% and the external validation process has the
accuracy of 93.3%. Our electronic nose system has the average sensitivity of 51.43 Hz/mL to sense the
gas samples.
Asthma Identification Using Gas Sensors and Support Vector MachineTELKOMNIKA JOURNAL
The exhaled breath analysis is a procedure of measuring several types of gases that aim to
identify various diseases in the human body. The purpose of this study is to analyze the gases contained
in the exhaled breath in order to recognize healthy and asthma subjects with varying severity. An
electronic nose consisting of seven gas sensors equipped with the Support Vector Machine classification
method is used to analyze the gases to determine the patient's condition. Non-linear binary classification is
used to identify healthy and asthma subjects, whereas the multiclass classification is applied to recognize
the subjects of asthma with different severity. The result of this study showed that the system provided a
low accuracy to distinguish the subjects of asthma with varying severity. This system can only differentiate
between partially controlled and uncontrolled asthma subjects with good accuracy. However, this system
can provide high sensitivity, specificity, and accuracy to distinguish between healthy and asthma subjects.
The use of five gas sensors in the electronic nose system has the best accuracy in the classification
results of 89.5%. The gases of carbon monoxide, nitric oxide, volatile organic compounds, hydrogen, and
carbon dioxide contained in the exhaled breath are the dominant indications as biomarkers of asthma.The
performance of electronic nose was highly dependent on the ability of sensor array to analyze gas type in
the sample. Therefore, in further study we will employ the sensors having higher sensitivity to detect lower
concentration of the marker gases.
An evaluation of machine learning algorithms coupled to an electronic olfact...IJECEIAES
The aim of this investigatation is to compare the utility of machine learning algorithms in distinguishing between untreated and processed mint beside in predicting the spray day of the insecticide. Within seven days, mint treated samples with the malathion insecticide are collected, and their aromas are Studied using a laboratory-manufactured sensor array system based on commercial metallic semiconductor (MOS) gas sensors. To distinguish the mint type, some results of machine learning algorithms were compared to know the decision trees (DT), Naive Bayes, support vector machines (SVM), and ensemble classifier. Furthermore, to predict the treatment day support vector machines regression (SVMR) and partial least squares regression (PLSR) were compared. Regarding the best results, in the discrimination case, a success rate of 92.9% was achieved by the ensemble classifier while in the prediction case, a correlation coefficient of R=0.82 was reached by the SVMR. Good results are achieved if the right gas sensor array system is designed and realized coupled with a good choice of the appropriate machine learning algorithms.
Graphene/PSMA composite for gas sensing applicationjournalBEEI
This paper presents the fabrication of conducting polymer sensor which comprise of graphene-poly styrene-co-maleic acid (PSMA) composite that responds to volatile organic compound (VOC) via a change in the electrical resistance of the sensors. Five sensors composed of different material ratio were fabricated to find out the most prominent weight percentage ratio (wt %) for optimum sensor response. Those materials were deposited onto silver electrode using drop-casting method. NI Card PCIE-6323 measures the voltage obtained from the sensor circuit and the resistance of the gas sensor monitored using LabVIEW instrument. It was observed from the experiments that combination of 60% graphene and 40% PSMA gives the highest sensor response.
The high demand of meat causes the seller mix the fresh and not-fresh meat. Electronic nose was used to detect the quality of the meat quickly and accurately. This research is proposed to test and analyze the sensitivity of MOS sensor in the electronic nose and simulate it using Matlab to identify meat classification using neural network. Test parameters based on Indonesian National Standard (SNI 3932-2008) requirement on the quality of carcass and meat. In this simulation, the number of neurons in the hidden layer was varied to find the most accurate identification. The sensitivity analysis of the MOS sensor was conducted by testing the meat sample aroma, calculate the sensitivity, identify the formation of input, hidden layer, outputs, and simulate the result of the varied formation. Then, found the number of the most optimal neurons. The result of the data training will be applied to the real instrument.
Statistical Technique in Gas Dispersion Modeling Based on Linear InterpolationTELKOMNIKA JOURNAL
In this paper, we introduced statistical techniques in creating a gas dispersion model in an indoor with a controlled environment. The temperature, air-wind and humidity were constant throughout the experiment. The collected data were then treated as an image; which the pixel size is similar to the total data available for x and y axis. To predict the neighborhood value, linear interpolation technique was implemented. The result of the experiment is significantly applicable in extending the total amount of data if small data is available.
Reliable e-nose for air toxicity monitoring by filter diagonalization methodIJECEIAES
This paper introduces a compact, affordable electronic nose (e-nose) device devoted to detect the presence of toxic compounds that could affect human health, such as carbon monoxide, combustible gas, hydrogen, methane, and smoke, among others. Such artificial olfaction device consists of an array of six metal oxide semiconductor (MOS) sensors and a computer-based information system for signal acquisition, processing, and visualization. This study further proposes the use of the filter diagonalization method (FDM) to extract the spectral contents of the signals obtained from the sensors. Preliminary results show that the prototype is functional and that the FDM approach is suitable for a later classification stage. Example deployment scenarios of the proposed e-nose include indoor facilities (buildings and warehouses), compromised air quality places (mines and sanitary landfills), public transportation, mobile robots, and wireless sensor networks.
Intelligent Sensing Using Metal Oxide Semiconductor Based-on Support Vector M...IJECEIAES
Classifying odor in real experiment presents some challenges, especially the uncertainty of the odor concentration and dispersion that can lead to a difficulty in obtaining an accurate datasets. In this study, to enhance the accuracy, datasets arrangement based on MOS sensors parameters using SVM approach for odor classification is proposed. The sensors are tested to determine the sensors' time response, sensors' peak duration, sensors' sensitivity, and sensors' stability when applied to the various sources at different range. Three sources were used in experimental test, namely: ethanol, methanol, and acetone. The gas sensors characteristics are analyzed in open sampling method to see the sensors' performance in real situation. These performances are considered as the base of choosing the position in collecting the datasets. The sensors in dynamic experiment have average of precision of 93.8-97.0%, the accuracy 93.3-96.7%, and the recall 93.3-96.7%. This values indicates that the collected datasets can support the SVM in improving the intelligent sensing when conducting odor classification work.
Crimson Publishers -A Sensor Multiplatform for Non Invasive Diagnosis of Pros...CrimsonPublishers-SBB
Crimson Publishers -A Sensor Multiplatform for Non Invasive Diagnosis of Prostate Cancer By A D'Amico in Significances of Bioengineering & Biosciences
This work underlines the utility of a multiplatform based on the use of both artificial lactation and taste systems that show the ability to perform both gas and bio liquid chemical imaging. Preliminary results related to the investigation of prostate cancer suggest that high performance levels with diagnostic and screening purposes can be reached with non invasive experimental procedures.
This research introduces an instrument for performing quality control on aromatic rice by utilizing feature extraction of Principle Component Analysis (PCA) method. Our proposed system (DNose v0.2) uses the principle of electronic nose or enose. Enose is a detector instrument that work based on classification of the smell, like function of human nose. It has to be trained first for recognizing the smell before work in classification process. The aim of this research is to build an enose system for quality control instrument, especially on aromatic rice. The advantage of this system is easy to operate and not damaging the object of research. In this experiment, ATMega 328 and 6 gas sensors are involved in the electronic module and PCA method is used for classification process.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Final project report on grocery store management system..pdf
On data collection time by an electronic nose
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 6, December 2021, pp. 4767∼4773
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i6.pp4767-4773 r 4767
On data collection time by an electronic nose
Piotr Borowik1
, Leszek Adamowicz2
, Rafał Tarakowski3
, Krzysztof Siwek4
, Tomasz Grzywacz5
1,2,3
Faculty of Physics, Warsaw University of Technology, Warszawa, Poland
4,5
Faculty of Electrical Engineering, Institute of Theory of Electrical Engineering, Measurement and Information Systems,
Warsaw University of Technology, Warszawa, Poland
Article Info
Article history:
Received Oct 17, 2020
Revised May 12, 2021
Accepted Jun 12, 2021
Keywords:
Electronic nose
Measurement time
Multisensor measurement
Odor classification
ABSTRACT
We use electronic nose data of odor measurements to build machine learning clas-
sification models. The presented analysis focused on determining the optimal time
of measurement, leading to the best model performance. We observe that the most
valuable information for classification is available in data collected at the beginning
of adsorption and the beginning of the desorption phase of measurement. We demon-
strated that the usage of complex features extracted from the sensors’ response gives
better classification performance than use as features only raw values of sensors’ re-
sponse, normalized by baseline. We use a group shuffling cross-validation approach
for determining the reported models’ average accuracy and standard deviation.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Leszek Adamowicz
Faculty of Physics
Warsaw University of Technology
ul. Koszykowa 75, 00-662 Warszawa, Poland
Email: Leszek.Adamowicz@pw.edu.pl
1. INTRODUCTION
Electronic noses (e-noses) [1]-[3] are artificial devices that consist of an array of gas sensors supported
by machine learning pattern recognition techniques. One of the critical fields of application of e-nose is the food
industry [4]-[10] for which odor characteristics of the products are one of the essential indications of product
quality. Development and verification of the machine learning methods in application to the odors classification
by e-nose measurements data have a similar level of importance as the development of the sensors and sensors
arrays consisting of the e-nose hardware. Similar techniques can be employed regardless of the domain of
application of the e-nose. One of the main obstacles in such development is the relatively long time needed to
collect sufficient measurement data. To overcome this, one can use publicly available datasets, which became
famous as testbeds for machine learning modeling reported by multiple authors.
In the present studies, we use publicly available datasets [11] of odor measurements by electronic nose.
The same dataset was used in our previous research [12]. We focused on features extraction and selection,
optimization of the number of used sensors, and the possibility to use for classification only single-sensor
electronic nose. The original studies of the same dataset [13] were focused on the possibility of spoilage odor
detection after a very short exposure of the electronic nose to the odor sample, lasting a few seconds. Zhang
and coworkers [14] used this dataset to demonstrate proposed analytical algorithms’ performance.
In this report, we deal with the different subjects of optimization concerning the time of odor mea-
surement. We are interested in the analysis of the dependence of the classification accuracy on the odor mea-
surement time. Recently Rodriguez Gamboa and coworkers [15] examined several datasets and used deep
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learning and support vector machine models to demonstrate the potential of using only a part of electronic nose
measurement data for correct odor classification.
The used dataset is collected by custom-made e-nose consisting of Taguchi type MQ-series gas sen-
sors. In recent years one can find many suggestions for constructing low-cost electronic noses, and several
groups propose devices based on similar sensors [13], [16]-[22]. The findings presented in this report can be
relevant to other applications of similar devices.
Considerable research concerning e-nose data is focused on the extraction of the complex features
describing curves of sensors’ response to the gas exposure. However, there are also other reports, especially
applying deep learning neural networks, in which raw measurement data are used. It is interesting to compare
both approaches and demonstrate the influence of the dimensionality reduction by the principal components
method.
2. METHODS AND PROCEDURES
2.1. Odor measurement
Rodriguez Gamboa and coworkers [13] presented the measurement of odor at various spoilage stages.
Twenty-two samples of bottles of commercially available wines of different varieties and vintages from four
producers from the São Francisco valley (Pernambuco-Brazil) were used. Thirteen randomly selected bottles
were left open for six months, which gave the population of low-quality wines. Four randomly selected bottles
were left open for two weeks before measurement, and they are considered as average quality wines. The
remaining five bottles are labeled as high-quality wines. Except for these samples, samples of ethanol diluted
in distilled water in six different concentrations were used, which may be considered additional six measured
bottles. That gives four categories of odor that are classified. In total, the dataset consists of measurements of
300 samples as collections of sensors’ response of 3 300 points for each sensor.
The e-nose developed at Universidade Federal Rural de Pernambuco [13] consists of six commercially
available metal-oxide gas sensors produced by Hanwei Sensors (www.hwsensor.com). Two sensors of each
type (MQ-3, MQ-4, MQ-6) have been used in the presented construction. During the measurement, the first
10 seconds were used to collect baselines of sensors’ response when e-nose was exposed to pure air. Then, the
odor’s prepared sample was pumped into the sensor chamber, and 80 seconds of sensors’ response during the
adsorption phase was collected. After that, the sensors’ response during 90 seconds of the desorption phase was
collected when pure air was pumped to the sensors’ chamber. After the measurement, the e-nose was exposed
to pure air for 10 minutes to purge the experimental setup.
2.2. Classification modeling
The measurement data are a series of sensors’ responses expressed as their resistance R over time. As
the first step of data processing, the measurement data are divided by the sensor resistance’s baseline value R0
collected just before electronic nose exposure to the measured odor sample. In Figure 1, we present an example
of sensor signals collected during the measurement of the odor sample, with a schematic representation of the
time span used for the extraction of modeling features. The signal collection is performed with a frequency of
18.5 Hz. As a first step of the data processing to reduce the measurement noise, the average response with 20
observations is calculated.
Then two approaches of extraction of features used for classification within machine learning model
training have been employed. First, we decided to use modeling features, just the magnitudes of sensors’ re-
sponse relative to the baselines: R/R0 and inversion of these values representing sensors’ conductance G/G0.
We employed the second approach to extract the complex features describing sensors’ response curves, e.g.,
average value, maximum value, and maximum slope. The complete list of the features that we have used for
training classification models is presented in our previous report [12]. Since our studies are focused on the de-
pendence of the classification performance on the measurement time by the e-nose, the modeling features are
calculated using only part of available data, in the range from the beginning of gas exposure until the considered
time. We represent this by the dashed region in Figure 1.
The odor samples were prepared from 28 bottles, and each of them was used for about ten measure-
ments. It should be noted that such an experimental procedure leads to a correlation between training obser-
vations. Hence, to obtain a reliable estimation of the classification models’ performance, we applied a group
shuffle cross-validation procedure, assuring that all observations from a given bottle’s odor measurements are
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3. Int J Elec & Comp Eng ISSN: 2088-8708 r 4769
attributed either to training or testing dataset. The cross-validation procedure has been performed 200 times in
a loop, and model performance results averaged.
Figure 1. Example of sensors signals of odor measurement, (a) normalized resistance R/R0 and
(b) normalized conductance G/G0; the dashed rectangle schematically represents an example of the time span
of data used to extract the modeling features
Two types of modeling techniques have been applied: logistic regression with multinomial classi-
fication (LogReg) and support vector machine classification (SVC) with radial basis functions kernel and
one-vs-one multi-class scheme. For both algorithms, we performed two types of tests. In the first case, the
modeling features, as described above, were used. In the second case, these input variables were transformed
using the principal component analysis method. Only the six most important components were used as the
modeling features (PCAReg, PCASVC). The prepared features dataset was transformed using the standard
scaller method.
We decided to use only these classical modeling techniques [23] Moreover, we disregarded more
complex algorithms such as multilayer neural networks since the number of observations available for modeling
is quite limited. In total, the used datasets [11] contain measurements of 300 odor samples. Even though more
flexible modeling techniques can provide more expressive classification models, the number of fitted parameters
is much higher than in the applied methods. Aggarwal [24] (page 25) indicate that the total number of training
data points should be at least 2 to 3 times larger than the number of parameters in the neural network. However,
the precise number of data instances depends on the specific model at hand. Hence, the simpler models that
we applied in principle should be less prone to over-fitting. The modeling has been performed using computer
codes in Python 3.7 language with a scikit-learn module [25].
3. RESULTS AND DISCUSSION
In Figure 2, we present a comparison of the average cross-validation accuracy of various types of
models as a function of time from the beginning of sensors’ exposure to examined odor, from which data
have been used for model building. Besides, in two subfigures, we would like to distinguish between various
approaches to the extraction of modeling features. Figure 2(a) shows the raw data of sensors’ response relative
to the baseline. While in Figure 2(b), models are built using an extensive set of complex features [12].
The first observation from these results is that the logistic regression model exhibits the best model
accuracy performance. It is also interesting to notice that this is confirmed in two considered modeling feature
sets. When we compare Figures 2(a) and (b), we can also observe that models trained on complex features
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exhibit better classification accuracy than the models with the sensors’ response’s raw values. The significance
of the feature extraction procedures developed by the e-nose research community is thus visible.
Figure 2. The average accuracy of various classification techniques, as a function of time of measurement,
from which data are used for model training and testing: (a) normalized sensors’ responses used as modeling
features (both R/R0 and G/G0) and (b) complex modeling features extracted from sensors’ response curves
(the first 10 seconds of measurement is not presented as it was the phase of baseline level collection)
Another important observation can be deduced from Figure 2(b). There is an abrupt increase in the
model performance just at the beginning of the sensors’ exposure to the studied odor. Similar behavior can be
noticed at the starting moment of desorption when the sensors are again exposed to the clean air. We deduce
that precise measurement can give the most relevant information that can be used for odor classification during
these moments.
Special care [26] in the design of an electronic nose is required to provide a rapid change of sensors’
exposure to different gases, remembering to ensure repeatability of measurement conditions. Szczurek et al.
[27] and Staymates et al. [28] reported measurements in ”sniffing” mode when frequent changes between
studied odor and pure air occur or in the initial time of the sensors’ action [29]. In Figure 3, we present another
comparison of models’ performance as a function of time of measurement from which data are available for
model building. We focus on logistic regression models and compare six types of modeling feature sets, which
are a combination of two cases, as we summarize in the Table 1.
As we already noticed, the results presented in Figure 3(a) confirm that better classification perfor-
mance can be achieved when complex features extracted from the sensors’ response curves are used compared
to models built on just raw values of normalized sensors’ response. Another interesting observation in this
figure is that the models in which features are based on sensors’ conductance G also exhibit better performance,
especially when the time of odor measurement by the electronic nose is reduced. Suppose the model is built on
the sensors’ resistance R data. In that case, this requires performing odor measurement for a longer time and
mainly includes measuring the desorption phase of the sensors’ response. The same observation concerning
models built on the resistance data is valid for both types of sets of features considered in the present studies. In
Figure 3(a), one can notice that for both ”R” curves, they exhibit a kind of saturation region. After the beginning
of the desorption phase, at 100 seconds, the models’ accuracy is again improved. Figure 3(a) suggests that it
may be enough to reduce the odor measurement time for about 30 seconds when complex features are extracted
from resistance or resistance and conductance response curves. In that case, the increase of measurement time
and measurement in the desorption regime does not lead to better classification performance.
More insights give examination of Figure 3(b), in which the standard deviation of the accuracy of 200
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models trained during group shuffle cross-validation procedure is presented. We can conclude that the odor
measurement time should be in the range of 70–90 seconds (including 10 seconds of baseline conditions mea-
surements), allowing us to obtain more stable classification results. When the data extracted from the sensors’
resistance values are included in the modeling, it introduces some additional noise, which only slightly reduces
the classification accuracy and leads to less stable models. The reduction of the classification performance on
new data may appear in this way.
Figure 3. Cross-validation estimation of logistic regression models classification performance: (a) average
accuracy and (b) standard deviation of accuracy (the first 10 seconds of measurement is not presented as it was
the phase of baseline level collection)
As one can notice, examining Figure 1 and the description of the measurement procedure in
section 2.1, such optimal time of data collection is shorter than half of the measurement time given in [11],
[13]. In other research, [15] similar results have been found for measurements of other types of odors. An
advantage of shortening the time of odor detection by an electronic nose is noticeable. However, one can keep
in mind that this time is not directly related to the number of odor samples measured by the electronic nose
device in a given time. After the measurement, there is still a need for device purging and sensors’ base state
recovery in clear air, much longer than the odor measurement time.
Table 1. Types of modeling feature sets
sensors response
(R/R0, G/G0) complex features
resistance values R feat R
conductance values G feat G
both values RG feat RG
4. CONCLUSION
In the paper, we presented machine learning classification models built on publicly available datasets
of e-nose measurement of spoilage odor. The research focused on verifying the optimal choice of odor mea-
surement time by e-nose to collect data for training a machine learning classification model with superior
performance. We presented a comparison of various modeling features based on sensors’ response resistance
and conductance. A group shuffling cross-validation approach was used for determining the reported models’
average accuracy and standard deviation. We demonstrate that most of the information used by the models for
classification is available firstly in the data from the beginning of the adsorption phase, which means sensors
On data collection time by an electronic nose (Piotr Borowik)
6. 4772 r ISSN: 2088-8708
exposure to the studied odor, and secondarily in the data from the beginning of the desorption phase, which
means sensors exposure to the clear air after exposure to the studied gas. The performed analysis leads us to
the conclusions: i) that for the considered case, only complex features extracted from the sensors’ conductance
curves G should be used for a classification model, ii) it is sufficient to use data of measurement performed
during gas adsorption phase only, iii) and that the logistic regression algorithm should be used. There is a
conclusion concerning the recommended machine learning classification method. In many reports, the support
vector machine is used as a gold standard for such applications. As we demonstrated, it may depend on the
considered application, and there are cases when the logistic regression algorithms prove superior performance.
ACKNOWLEDGEMENT
This work was supported by the National Centre for Research and Development by the grant agree-
ment BIOSTRATEG3/347105/9/NCBR/2017.
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