Artificial Neural Networks For Detection Research Articles

Surface-Enhanced Raman Spectroscopy and Artificial Neural Networks for Detection of MXene Flakes' Surface Terminations.

The properties of MXene flakes, a new class of two-dimensional materials, are strictly determined by their surface termination. The most common termination groups are oxygen-containing (=O or -OH) and fluorine (-F), and their relative ratio is closely related to flake stability and catalytic activity. The surface termination can vary significantly among MXene flakes depending on the preparation route and is commonly determined after flake preparation by using X-ray photoelectron spectroscopy (XPS). In this paper, as an alternative approach, we propose the combination of surface-enhanced Raman spectroscopy (SERS) and artificial neural networks (ANN) for the precise and reliable determination of MXene flakes' (Ti3C2Tx) surface chemistry. Ti3C2Tx flakes were independently prepared by three scientific groups and subsequently measured using three different Raman spectrometers, employing resonant excitation wavelengths. Manual analysis of the SERS spectra did not enable accurate determination of the flake surface termination. However, the combined SERS-ANN approach allowed us to determine the surface termination with a high accuracy. The reliability of the method was verified by using a series of independently prepared samples. We also paid special attention to how the results of the SERS-ANN method are affected by the flake stability and differences in the conditions of flake preparation and Raman measurements. This way, we have developed a universal technique that is independent of the above-mentioned parameters, providing the results with accuracy similar to XPS, but enhanced in terms of analysis time and simplicity.

Use of cellular automata-based artificial neural networks for detection and prediction of land use changes in North-Western Dhaka City.

The purpose of this study was to analyze the trend of change in land use land cover (LULC) and land surface temperature (LST) in Mirpur and its surrounding area over the last 30years using Landsat satellite images and remote sensing indices, and to develop relationships between LULC types and LST, as well as to analyze their impact on local warming. Using this analyzed data, a further projection of LULC and LST change over the next two decades was made. From 1989 to 2019, 5-year intervals of Landsat 4-5 TM and Landsat 8 OLI images were utilized to track the relationship between LULC changes and LST. The modeled LST was validated with MODIS-derived LST within the study area. Cellular automata-based artificial neural network (CA-ANN) algorithm was used to model the LULC and LST maps for the year 2039. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were analyzed to determine their link with LST. The relation between LST and LULC types indicates that built-up area raises LST by substituting non-evaporating surfaces for natural vegetation. The average surface temperature was increasing steadily for the last 30years. For the year 2019, it was determined that roughly 86% of total land area has been converted to built-up area and that 89% of land area had an LST greater than 28°C. According to the study, if the current trend continues, 72% of the Mirpur area is predicted to see temperatures near 32°C in 2039. Additionally, LST had a significant positive association with NDBI and a negative correlation with NDVI. The overall accuracy of LULC was greater than 90%, with a kappa coefficient of 0.83. The study may assist urban planners and environmental engineers in comprehending and recommending effective policy measures and plans to mitigate the consequences of LULC.

Retraction Note to: Application of back propagation artificial neural network in detection and analysis of diabetes mellitus

Retraction Note to: Application of back propagation artificial neural network in detection and analysis of diabetes mellitus

Detection and Isolation of DoS and Integrity Cyber Attacks in Cyber-Physical Systems with a Neural Network-Based Architecture

New applications of industrial automation request great flexibility in the systems, supported by the increase in the interconnection between its components, allowing access to all the information of the system and its reconfiguration based on the changes that occur during its operations, with the purpose of reaching optimum points of operation. These aspects promote the Smart Factory paradigm, integrating physical and digital systems to create smarts products and processes capable of transforming conventional value chains, forming the Cyber-Physical Systems (CPSs). This flexibility opens a large gap that affects the security of control systems since the new communication links can be used by people to generate attacks that produce risk in these applications. This is a recent problem in the control systems, which originally were centralized and later were implemented as interconnected systems through isolated networks. To protect these systems, strategies that have presented acceptable results in other environments, such as office environments, have been chosen. However, the characteristics of these applications are not the same, and the results achieved are not as expected. This problem has motivated several efforts in order to contribute from different approaches to increase the security of control systems. Based on the above, this work proposes an architecture based on artificial neural networks for detection and isolation of cyber attacks Denial of Service (DoS) and integrity in CPS. Simulation results of two test benches, the Secure Water Treatment (SWaT) dataset, and a tanks system, show the effectiveness of the proposal. Regarding the SWaT dataset, the scores obtained from the recall and F1 score metrics was 0.95 and was higher than other reported works, while, in terms of precision and accuracy, it obtained a score of 0.95 which is close to other proposed methods. With respect to the interconnected tank system, scores of 0.96,0.83,0.81, and 0.83 were obtained for the accuracy, precision, F1 score, and recall metrics, respectively. The high true negatives rate in both cases is noteworthy. In general terms, the proposal has a high effectiveness in detecting and locating the proposed attacks.

Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors.

As a form of artificial intelligence, artificial neural networks (ANNs) have the advantages of adaptability, parallel processing capabilities, and non-linear processing. They have been widely used in the early detection and diagnosis of tumors. In this article, we introduce the development, working principle, and characteristics of ANNs and review the research progress on the application of ANNs in the detection and diagnosis of gastrointestinal and liver tumors.

RETRACTED ARTICLE: Application of back propagation artificial neural network in detection and analysis of diabetes mellitus

Diabetes Mellitus affects adults and children, causing changes in lifestyle. The diabetic affected person count has increased drastically worldwide over the last few years; about 425 million people have diabetes. By 2030, it is predicted that diabetic disorder will be the seventh leading cause of human death. Diabetes mellitus is measured invasively. This method has limitations such as patient’s preparation, piercing of the skin, which causes infection and needs for skilled technicians. In order to avoid the limitations of invasive methods, vibrations from the pancreas are acquired using a smartphone accelerometer sensor and detecting the value of diabetes. The human body has a unique energy signature for every organ, which leads to vibrations with different frequencies. The frequency of the vibration signal from the pancreas is proportional to insulin secretion and dynamics. The signals obtained from the accelerometer sensor are trained and analyzed with the Levenberg–Marquardt algorithm for obtaining the relation between the excess insulin secretion and clinical value of the diabetic level of the person. The accelerometer signals and clinical values are modeled with Regression analysis for the diabetic and non-diabetic persons. The results show the correlation between the fluid dynamics of insulin and clinical value at about 95% in prediction.

Application of Image Processing Techniques and Artificial Neural Network for Detection of Diseases on Brinjal Leaf

The plant diseases cause damage and monetary losses in crops. The farmers monitor the crops in the field at regular intervals in order to detect various diseases. Instead of manual diagnosis, computers have been used to provide automatic detection and classification of different types of diseases. In this paper, the lesion areas affected by diseases are segmented. Further, discrete wavelet transform based image fusion technique is used to improve the imaging quality reducing redundancy. The colour, texture and structural based features of the fused images are extracted and given as an input to the ANN for classification which provides better performance.

Application of an Artificial Neural Network for Detection of Attacks in VANETs

This work contains results of developing an approach for detecting routing attacks in VANET networks using an artificial neural network. In the course of this work, such research methods as analysis and modeling were used to select the most promising approach for identifying routing attacks, as well as to develop a mock-up of a software system that detects Gray Hole attacks in VANETs. According to the results of experimental studies, the effectiveness of the developed software was evaluated.

Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network

This paper presents a condition monitoring and combustion fault detection technique for a 12-cylinder 588 kW trainset diesel engine based on vibration signature analysis using fast Fourier transform, discrete wavelet transform, and artificial neural network. Most of the conventional fault diagnosis techniques in diesel engines are mainly based on analyzing the difference of vibration signals amplitude in the time domain or frequency spectrum. Unfortunately, for complex engines, the time- or frequency-domain approaches do not provide appropriate features solely. In the present study, vibration signals are captured from both intake manifold and cylinder heads of the engine and were analyzed in time-, frequency-, and time–frequency domains. In addition, experimental data of a 12-cylinder 588 kW diesel engine (of a trainset) are captured and the proposed method is verified via these data. Results show that power spectra of vibration signals in the low-frequency range reliably distinguish between normal and faulty conditions. However, they cannot identify the fault location. Hence, a feature extraction method based on discrete wavelet transform and energy spectrum is proposed. The extracted features from discrete wavelet transform are used as inputs in a neural network for classification purposes according to the location of sensors and faults. The experimental results verified that vibration signals acquired from intake manifold have more potential in fault detection. In addition, the capacity of discrete wavelet transform and artificial neural network in detection and diagnosis of faulty cylinders subjected to the abnormal fuel injection was revealed in a complex diesel engine. Beside condition monitoring of the engine, a two-step fault detection method is proposed, which is more reliable than other one-step methods for complex engines. The average condition monitoring performance is from 93.89% up to 99.17%, based on fault location and sensor placement, and the minimum classification performance is 98.34%.

A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks

A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here. The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditions. The time-domain vibration signals were divided into 40 segments and simple features such as peaks in time domain and spectrum along with statistical features such as standard deviation, skewness, kurtosis etc. were extracted. Effectiveness of SVM classifier was compared with the performance of Artificial Neural Network (ANN) classifier and it was found that the performance of SVM classifier is superior to that of ANN. The effect of pre-processing of the vibration signal by Discreet Wavelet Transform (DWT) prior to feature extraction is also studied and it is shown that pre-processing of vibration signal with DWT enhances the effectiveness of both ANN and SVM classifiers. It has been demonstrated from experiment results that performance of SVM classifier is better than ANN in detection of bearing condition and pre-processing the vibration signal with DWT improves the performance of SVM classifier.

Monitoring of nanodiamonds in human urine using artificial neural networks

This paper presents the results of application of the elaborated methods for monitoring of nanodiamonds in human urine using fluorescence spectroscopy. High efficiency of artificial neural networks applied for recognition and estimation of the concentration of nanodiamonds in urine with a strong autofluorescence background is demonstrated. It was found that minimal concentration of nanodiamonds with strong fluorescence containing nitrogen‐vacancy (NV) centers can be detected in urine by fluorescence spectroscopy at a level of 3.06 × 10−4 g L−1; while use of artificial neural networks for detection of weakly fluorescent detonation nanodiamonds provides reasonably high accuracy of detection, not worse than 6.8 × 10−3 g L−1.

Neural Network Detector of ECG Signal Distortions

The use of artificial neural networks for detection of ECG signal distortions was discussed. Training and test databases were compiled. A technique for analysis of training samples based on the k-means clustering method was suggested. The effect of the number of hidden layer neurons on the neural network efficiency was studied. A method for testing the neural network efficiency based on the receiver operating characteristic (ROC) curve was developed. The structural principle of the neural network detector of ECG signal distortions was also developed. Testing of the system demonstrated high values of sensitivity and specificity (94.5%), as well as a high mean value of AUC (0.97).

A microsensor array for quantification of lubricant contaminants using a back propagation artificial neural network

We present a method based on an electrochemical sensor array and a back propagation artificial neural network for detection and quantification of four properties of lubrication oil, namely water (0, 500 ppm, 1000 ppm), total acid number (TAN) (13.1, 13.7, 14.4, 15.6 mg KOH g−1), soot (0, 1%, 2%, 3%) and sulfur content (1.3%, 1.37%, 1.44%, 1.51%). The sensor array, consisting of four micromachined electrochemical sensors, detects the four properties with overlapping sensitivities. A total set of 36 oil samples containing mixtures of water, soot, and sulfuric acid with different concentrations were prepared for testing. The sensor array’s responses were then divided to three sets: training sets (80% data), validation sets (10%) and testing sets (10%). Several back propagation artificial neural network architectures were trained with the training and validation sets; one architecture with four input neurons, 50 and 5 neurons in the first and second hidden layer, and four neurons in the output layer was selected. The selected neural network was then tested using the four sets of testing data (10%). Test results demonstrated that the developed artificial neural network is able to quantitatively determine the four lubrication properties (water, TAN, soot, and sulfur content) with a maximum prediction error of 18.8%, 6.0%, 6.7%, and 5.4%, respectively, indicting a good match between the target and predicted values. With the developed network, the sensor array could be potentially used for online lubricant oil condition monitoring.

An Artificial Neural Network for Detection of Biological Early Brain Cancer

Human analysis on medical images is a difficult task due to very minute variations. Due to co-resemblance between affected & original biological part &due to larger data set for analysis. This makes the biological analysis for prediction of affects. The problem goes more complicated under cancer predictions basically in brain cancer. It is a challenging task to develop an automated recognition system which could process on a large information of patient and provide a correct estimation. So we are going to develop an automated cancer recognition system for MRI images. We implement the neuro fuzzy logic for the classification and estimation of cancer affect on given MRI image.

Detection method of spot welding based on multi-information fusion and fractal

A novel detection method of support vector machine (SVM) based on fractal dimension of signals is presented.And models of SVM are made based on nugget size defects of spot welding. Classification using these trained SVM models is done to signals of spot welding.It is shown from effect of different SVM models that these models with different inputs.In detection of defects,these models with inputs including sound signal have a high percentage of accuracy,the detection accuracy of these models with inputs including voltage signal will reduce.So the SVM models based on fractal dimensions of sound are some optimal nondestructive detection ones.At last a comparison between SVM detection model and ANNS detection model is researched which indicates that SVM is a more effective measure than Artificial neural networks in detection of nugget size defects during spot welding.

An Artificial Neural Network for Detection of Simulated Dental Caries

Objects A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard.

Electronic sensor array coupled with artificial neural network for detection of Salmonella Typhimurium

An electronic sensor array with 12 non-specific metal oxide sensors was evaluated for its ability to monitor volatile compounds in super broth, alone and in super broth inoculated with Salmonella Typhimurium at 37 °C for 2–14 h. Using discriminant function analysis (DFA), it was possible to differentiate super broth alone from that containing Salmonella Typhimurium. The potential to predict the number of Salmonella Typhimurium was investigated using an artificial neural network (ANN). The ANN was comprised of an input layer, one hidden layer and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. Good prediction was found as measured by a regression coefficient ( R 2 = 0.998) between actual and predicted data.

Computer technology in detection and staging of prostate carcinoma: A review

After two decades of increasing interest and research activity, computer-assisted diagnostic approaches are reaching the stage where more routine deployment in clinical practice is becoming a possibility [Kruppinski, E.A., 2004. Computer-aided detection in clinical environment: Benefits and challenges for radiologists. Radiology 231, 7–9]. This is particularly the case in the analysis of mammographic images [Helvie, M.A., Hadjiiski, L., Makariou, E., Chan, H.P., Petrick, N., Sahiner, B., Lo, S.C., Freedman, M., Adler, D., Bailey, J., Blane, C., Hoff, D., Hunt, K., Joynt, L., Klein, K., Paramagul, C., Patterson, S.K., Roubidoux, M.A., 2004. Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial. Radiology 231, 208–214] and in the detection of pulmonary nodules [Reeves, A.P., Kostis, W.J., 2000. Computer-aided diagnosis for lung cancer. Radiol. Clin. North Am. 38, 497–509]. However, similar approaches can be applied more widely with the promise of increasing clinical utility in other areas. We review how computer-aided approaches may be applied in the diagnosis and staging of prostatic cancer. The current status of computer technology is reviewed, covering artificial neural networks for detection and staging, computerised biopsy simulation and computer-assisted analysis of ultrasound and magnetic resonance images.

Application of artificial neural networks for detection of abnormal fetal heart rate pattern: a comparison with conventional algorithms

Cardiotocography signals were sampled during labour in 53 patients. A recurrent artificial neural network with hidden layer feedback was trained and performance was compared with that of several conventional systems. Correct and false positive rates of all systems tested were calculated. To ensure that the performance of neural networks was not just caused by using different cut-off levels, the threshold used for conventional methods were also adapted and optimised. The correct positives rate of neural networks was between 0.72 and 0.9, and the false positive rate between 0.2 and 0.4. Before optimising, conventional algorithms produced a very low correct positive (0.02-0.5) and a low false positive rate (0.0-0.08). After adjusting the parameters, the tested neural networks still performed better than optimised conventional systems.

The use of infrared spectroscopy and artificial neural networks for detection of uropathogenic Escherichia coli strains' susceptibility to cephalothin.

Infrared spectroscopy is an increasingly common method for bacterial strains' testing. For the analysis of bacterial IR spectra, advanced mathematical methods such as artificial neural networks must be used. The combination of these two methods has been used previously to analyze taxonomic affiliation of bacteria. The aim of this study was the classification of Escherichia coli strains in terms of susceptibility/resistance to cephalothin on the basis of their infrared spectra. The infrared spectra of 109 uropathogenic E. coli strains were measured. These data are used for classification of E. coli strains by using designed artificial neural networks. The most efficient artificial neural networks classify the E. coli sensitive/resistant strains with an error of 5%. Bacteria can be classified in terms of their antibiotic susceptibility by using infrared spectroscopy and artificial neural networks.