Artificial Neural Network Classification Method Research Articles

Retweet Prediction Based on User-Based, Content-Based, Time-Based Features Using ANN Classification Optimized with the Bat Algorithm

Twitter is one of the most popular social media platforms today for information dissemination. It is favored by the public due to its real-time information sharing capabilities. Twitter provides two important features for information dissemination: Tweets and Retweets. Tweets allow users to write messages that can be instantly shared. Each tweet can contain text, media such as images, videos, or URLs. Retweets allow users to repost someone else's tweet and distribute it to their own followers. The Retweet feature is considered an effective way to spread information, as a high number of retweets indicates that the information in the tweet is spreading quickly and widely. This research aims to predict retweets based on several features: User-Based Feature, Content-Based Feature, and Time-Based Feature. The classification method used is Artificial Neural Network, which is optimized using a Nature-Inspired Algorithm called Bat Algorithm. The evaluation results of this study show an accuracy of 86%, precision of 87.8%, recall of 93.6%, and F1-score of 90.6% without imbalance class handling. Under Undersampling condition, the accuracy is 80.8%, precision is 91.0%, recall is 81.4%, and F1-score is 85.9%. Under Oversampling condition, the accuracy is 82.4%, precision is 89.6%, recall is 85.6%, and F1-score is 87.5%. These results indicate that using user-based, content-based, and time-based features, applying Artificial Neural Network classification method, and optimizing hyperparameters using Bat Algorithm are effective in predicting retweets.

An artificial neural network classification method employing longitudinally monitored immune biomarkers to predict the clinical outcome of critically ill COVID-19 patients.

The severe form of COVID-19 can cause a dysregulated host immune syndrome that might lead patients to death. To understand the underlying immune mechanisms that contribute to COVID-19 disease we have examined 28 different biomarkers in two cohorts of COVID-19 patients, aiming to systematically capture, quantify, and algorithmize how immune signals might be associated to the clinical outcome of COVID-19 patients. The longitudinal concentration of 28 biomarkers of 95 COVID-19 patients was measured. We performed a dimensionality reduction analysis to determine meaningful biomarkers for explaining the data variability. The biomarkers were used as input of artificial neural network, random forest, classification and regression trees, k-nearest neighbors and support vector machines. Two different clinical cohorts were used to grant validity to the findings. We benchmarked the classification capacity of two COVID-19 clinicals studies with different models and found that artificial neural networks was the best classifier. From it, we could employ different sets of biomarkers to predict the clinical outcome of COVID-19 patients. First, all the biomarkers available yielded a satisfactory classification. Next, we assessed the prediction capacity of each protein separated. With a reduced set of biomarkers, our model presented 94% accuracy, 96.6% precision, 91.6% recall, and 95% of specificity upon the testing data. We used the same model to predict 83% and 87% (recovered and deceased) of unseen data, granting validity to the results obtained. In this work, using state-of-the-art computational techniques, we systematically identified an optimal set of biomarkers that are related to a prediction capacity of COVID-19 patients. The screening of such biomarkers might assist in understanding the underlying immune response towards inflammatory diseases.

Artificial Neural Network in Classification of Multisource Remote Sensing Images

How to solve multi-category image recognition and meet a certain accuracy is a key issue in the research of high-resolution remote sensing images, and it is of great significance. This article mainly studies artificial neural network in the classification of multi-source remote sensing images. This paper improves the efficiency and accuracy of image segmentation by studying the principle and implementation process of image segmentation algorithm from the two aspects of initial segmentation and region merging; secondly, it studies the method of object feature quantization and the image of different object features on the classification results; and finally, it selects BP neural network. The network classification method classifies the image objects and realizes the extraction and classification of high-resolution remote sensing images. Experiments in this paper show that for multi-source remote sensing image data, the overall accuracy of the two parallel classification algorithms is very similar, and both are close to 85%, which has a good classification effect. When performing large-scale image classification, the terrain types in the image will be more complicated. In this case, the extraction accuracy relative to the artificial neural network classification method will decrease, and the classification time will also become longer. This paper proves through experiments that the classification method of multi-source remote sensing images based on artificial neural network is feasible and has certain advantages.

Expert System of Dengue Disease Using Artificial Neural Network Classifier

Abstract – Expert systems can be applied to the classification of dengue fever. Dengue is a serious disease that can be fatal if not diagnosed and treated properly. Headache, muscle aches, fever, and rash are some of the most prevalent symptoms. Dengue fever is a disease that is endemic in various South Asian and Southeast Asian nations. Dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome are the three types of dengue (DSS). Currently, these diseases may be classified using a machine learning approach with dengue symptoms as the input data. This study proposes implementing an Artificial Neural Network (ANN) with the Backpropagation (BPNN) algorithm as the classifier to categorize dengue types, divided into three categories: DF, DHF, and DSS. The dengue symptoms are represented by 21 attributes in the dataset. It was gathered from 110 patients. Cross-validation with k-fold 3, 5, and 10 were applied as the evaluation method. Three parameters were obtained to evaluate the ANN classification method: precision, recall, and accuracy. These were used to justify the most optimal performance. Cross-validation using k-fold 3 produced the best evaluation results, with precision, recall, and accuracy values of 97.3%, 97.3%, and 97.27%, respectively.

Research on fan vibration fault diagnosis based on image recognition

The conventional methods for vibration fault detection and diagnosis relies on feature extraction from the waveforms of the vibration signals. This article exploits the scope of image recognition application for the detection and diagnosis of fan vibration faults. In this paper, a novel image recognition technique is proposed for vibration-based fault diagnosis using the spectrum images of the vibration signals. 1D vibration signal spectrum is initially computed using Fast Fourier Transform (FFT) and the FFT frequencies are adjusted such that it captures a vibration spectrum diagram as 2D image representation. FFT based vibration analysis is done and the image recognition concept is utilized for feature extraction and a machine learning classification module is used for fault analysis and diagnosis. Effective feature generation is done using Principal Component Analysis (PCA) by removing the redundancy from the feature vectors and machine learning classifiers are used to obtain improved image recognition and classification performance. Artificial Neural Network (ANN) classifier yields better performance in terms of various performance parameters and percentage improvement in terms of accuracy for ANN classification methods over Support Vector Machine (SVM), k-Nearest Neighbours (kNN) and Random Forest Ensemble (RFE) methods are 10.01 %, 4.51 % and 2.01 % respectively. Comparative scenarios are considered in this work for fan vibration fault detection as well as diagnosis based on the image features for various realistic vibration fault conditions. Effectiveness of the proposed image recognition-based technique is compared with the state-of-the-art methods, justifying its outperformance for fan fault detection and diagnosis using the combination of spectrum adjustment, PCA and ANN classification method.

Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively.

Klasifikasi American Sign Language Menggunakan Ekstraksi Fitur Histogram of Oriented Gradients dan Jaringan Syaraf Tiruan

Sign languages have various types, one of which is American Sign Language (ASL). In this study, ASL images from the handshape alphabet were extracted using Histogram of Oriented Gradient (HOG) then these features were used for the classification of Artificial Neural Networks (ANN) with various training functions using 3 variations of multi-layer network architecture where ANN architecture consists of one hidden layer. Based on ANN training, trainbr test results have a higher success rate than other training functions. In architecture with 15 neurons in the hidden layer get an accuracy value of 99.29%, a precision of 91.84%, and a recall of 91.47%. The test results show that using the HOG feature and ANN classification method for ASL recognition gives a good level of accuracy, with an overall accuracy of 5 neurons 95.38%, 10 neurons 96.64%, and 15 neurons with 97.32%.
 
 Keywords— Artificial Neural Network; American Sign Language; Histogram of Oriented Gradient; Training Function

Artificial Intelligence-Based Fault Diagnosis for Condition Monitoring of Electric Motors

In the era of globalization, manufacturing industries are facing intense pressure to prevent unexpected breakdowns, reduce maintenance cost and increase plant availability. Induction motors are the most sought-after prime movers in modern-day industries due to their robustness. Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. This paper presents the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN)-based system to diagnose the vibration and Instantaneous Power (IP)-based responses of rolling element bearings and broken rotor bars in an induction motor. The dimensionality of the extracted features was reduced using Principal Component Analysis (PCA) and thereafter the selected features were ranked in order of relevance using the Sequential Floating Forward Selection (SFFS) method for reducing the size of input features and finding the most optimal feature set. A comparative analysis of the effectiveness of SVM and ANN is carried out using statistical parameters extracted from vibration and IP signals. The highest accuracy of 92.5% and 98.2% was achieved for vibration and IP signatures, respectively, using the proposed SFFS-based feature selection technique and ANN classification method. The results reveal that ANN has better performance than SVM and the proposed strategy can be used for automatic recognition of machine faults. The use of this type of intelligent system helps in avoiding unwanted and unplanned system shutdowns due to the failure of the motor.

Detection and Prevention of Man-in-the-Middle Spoofing Attacks in MANETs Using Predictive Techniques in Artificial Neural Networks (ANN)

A Mobile Ad-Hoc Network (MANET) is a convenient wireless infrastructure which presents many advantages in network settings. With Mobile Ad-Hoc Network, there are many challenges. These networks are more susceptible to attacks such as black hole and man-in-the-middle (MITM) than their corresponding wired networks. This is due to the decentralized nature of their overall architecture. In this paper, ANN classification methods in intrusion detection for MANETs were developed and used with NS2 simulation platform for attack detection, identification, blacklisting, and node reconfiguration for control of nodes attacked. The ANN classification algorithm for intrusion detection was evaluated using several metrics. The performance of the ANN as a predictive technique for attack detection, isolation, and reconfiguration was measured on a dataset with network-varied traffic conditions and mobility patterns for multiple attacks. With a final detection rate of 88.235%, this work not only offered a productive and less expensive way to perform MITM attacks on simulation platforms but also identified time as a crucial factor in determining such attacks as well as isolating nodes and reconfiguring the network under attack. This work is intended to be an opening for future malicious software time signature creation, identification, isolation, and reconfiguration to supplement existing Intrusion Detection Systems (IDSs).

Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps

The extraction of artificial and natural features using light detection and ranging (Lidar) data is a fundamental task in many fields of research for environmental science. In this study, the possibility of using self-organising maps (SOM), which is an unsupervised artificial neural network classification method to extract the bare earth surface and features from airborne Lidar data, was investigated for two different urban areas. The effect of the enlargement of the study area was analysed using the proposed approach. The appropriate weights of SOM inputs, which are 3D coordinates and intensity, obtained from a Lidar point cloud were determined by using Pearson's chi-squared independence test. The weighted SOM feature extraction performance was better than that of the unweighted SOM. The filtering results of SOM to separate ground and non-ground data were also compared with those obtained by the adaptive TIN filtering algorithm. Most of the non-ground features could be removed by the weighted SOM.

An optimum ANN-based breast cancer diagnosis: Bridging gaps between ANN learning and decision-making goals

It is difficult to overestimate the importance of appropriate breast cancer diagnosis, as the disease ranks second among all cancers that lead to death in women. Many efforts propose data analytic tools that succeed in predicting breast cancer with high accuracy; the literature is abundant with studies that report close-to-perfect prediction rates. This paper shifts the focus of improvement from higher accuracy towards better decision-making. Quantitatively, we have shown more accuracy does not always lead to better decisions, and the process of Artificial Neural Networks (ANN) learning can benefit from the inculcation of decision-making goals. We have proposed a decision-oriented ANN classification method called Life-Sensitive Self-Organizing Error-Driven (LS-SOED), which enhances ANN’s performance in decision-making. LS-SOED combines the supervised and unsupervised learning power of ANN to handle the inconclusive nature of hidden patterns in the data in such way that the best possible decisions are made, i.e. the least misclassification cost (the minimum possible loosing of life) is achieved. The learning power of SOED matches, if not excels, the best performances reported in the literature when the objective is to achieve the highest accuracy. When the objective is to minimize misclassification costs, we have shown, on average, in one dataset more than 30 years of life for a group of 283 people, and in another more than 8 years of life for a group of 57 people can be saved collectively.

Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method

ABSTRACTThe problem of automated urban road network extraction is extremely complex because roads in urban scenes strongly interact with other objects. This problem can be simplified if road regions are first isolated using a classification procedure. The isolated road regions can be posteriorly used in tasks of refinement and reconstruction of the road network. This article addresses only the problem of road regions’ detection using Artificial Neural Network as classification method. However, in urban areas, the use of spectral data alone commonly leads to the confusion of the road class with other classes in RGB images, such as building roofs and concrete, because these objects may present similar spectral characteristics. To overcome this problem, it is proposed the integration of a high-resolution RGB aerial image with laser-derived images. The classification results showed that the integration of the geometric (height) and radiometric (laser pulse intensity) laser data significantly improved the classification accuracy, also contributing for the better detection of road pixel. The laser intensity data help to overcome the effects of road obstructions caused by shadows and trees. On the other hand, the laser height data help to separate the aboveground objects from those on the ground level.

Determination of Electron Optical Properties for Aperture Zoom Lenses Using an Artificial Neural Network Method.

Multi-element electrostatic aperture lens systems are widely used to control electron or charged particle beams in many scientific instruments. By means of applied voltages, these lens systems can be operated for different purposes. In this context, numerous methods have been performed to calculate focal properties of these lenses. In this study, an artificial neural network (ANN) classification method is utilized to determine the focused/unfocused charged particle beam in the image point as a function of lens voltages for multi-element electrostatic aperture lenses. A data set for training and testing of ANN is taken from the SIMION 8.1 simulation program, which is a well known and proven accuracy program in charged particle optics. Mean squared error results of this study indicate that the ANN classification method provides notable performance characteristics for electrostatic aperture zoom lenses.

Detection of Melanoma Skin Lesions Using Phylogeny

Asymmetry, boundary irregularity, color variegation, and diameter are popular clinical guidelines used for early detection of melanoma. Several researchers have proposed and framed methodologies to detect the melanoma using combined and separate features with the help of statistical theory, fuzzy logic, artificial neural networks, and machine learning techniques. When we combine all the techniques to quantify the four attributes (recently derived) within a single interface to diagnose melanoma skin lesion, it will be much complex and time consuming task. Hence, this paper proposes a novel methodology to detect melanoma skin cancer through phylogeny of pigmented skin lesions and it is a pilot scale attempt using the images of skin lesions. The construction of phylogeny has the following steps namely, the construction of image matrix, construction of image blocks, phylogenetic tree, and classification. Classification is implemented through average mean rank method as well as artificial neural networks. Performance of the proposed methodology is tested with 110 different images of skin lesions (BC cancer agency) and it is shown to predict 87 % successfully (melanoma/non-melanoma). The advantage of the proposed methodology is to predict the classification of melanoma skin lesions with the help of phylogeny achieves accuracy of 87 % with less time complexity. Some additional analysis is required for the remaining 13 % of images. Finally, it is proved that both average rank method and artificial neural network classification method predict almost the same accuracy.

Modeling Land Use/Cover Changes by the Combination of Markov Chain and Cellular Automata Markov (CA-Markov) Models

Land use/cover changes modeling is essential for land use planning and management. Arak is one of several cities in Iran which have undergone swift urban expansion during recent decades due to rapid industrialization and population growth. In this study, aerial photos and Landsat TM and IRS-P6 LISS-III images were used to predict land use/cover changes in Arak. Land use/cover maps were generated with four classes from visual interpretation of aerial photos and an artificial neural network classification method for satellite images. Both classification methods resulted in land use/cover maps with overall accuracy over 95 %. In order to predict changes, Markov chain and Cellular Automata Markov models were applied and a land use/cover map for 2025 was simulated. The results showed that the combination of satellite remote sensing, GIS and Markov models provides useful information on land use/cover dynamics in future which could be consequently used for land use planning.

Comparison of pixel -based and artificial neural networks classification methods for detecting forest cover changes in Malaysia

According to the FAO (Food and Agriculture Organization), Malaysia lost 8.6% of its forest cover between 1990 and 2005. In forest cover change detection, remote sensing plays an important role. A lot of change detection methods have been developed, and most of them are semi-automated. These methods are time consuming and difficult to apply. One of the new and robust methods for change detection is artificial neural network (ANN). In this study, (ANN) classification scheme is used to detect the forest cover changes in the Johor state in Malaysia. Landsat Thematic Mapper images covering a period of 9 years (2000 and 2009) are used. Results obtained with ANN technique was compared with Maximum likelihood classification (MLC) to investigate whether ANN can perform better in the tropical environment. Overall accuracy of the ANN and MLC techniques are 75%, 68% (2000) and 80%, 75% (2009) respectively. Using the ANN method, it was found that forest area in Johor decreased as much as 1298 km2 between 2000 and 2009. The results also showed the potential and advantages of neural network in classification and change detection analysis.

An Artificial Neural Network Model for Predicting the Subcellular Localization of Photosensitisers for Photodynamic Therapy of Solid Tumours

Photodynamic therapy (PDT) is a promising modality for the treatment of tumours based on the combined action of a photosensitiser (PS), visible light and molecular oxygen, which generates a local oxidative damage that leads to cell death. The site where the primary photodynamic effect takes place depends on the subcellular localization of the PS and affects the mode of action and efficacy of PDT. It is therefore of prime interest to develop structure-subcellular localization prediction models for a PS from its molecular structure and physicochemical properties. Here we describe such a prediction method for the localization of macrocyclic PSs into cell organelles based on a wide set of physicochemical properties and processed through an artificial neural network (ANN). 128 2D-molecular descriptors related to lipophilicity/hydrophilicity, charge and structural features were calculated, then reduced to 76 by using Pearson's correlation coefficient, and finally to 5 using Guyon and Elisseeff's algorithm. The localization of 61 PSs was compiled from literature and distributed into 3 possible cell structures (mitochondria, lysosomes and "other organelles"). A non-linear ANN algorithm was used to process the information as a decision tree in order to solve PS-organelle assignment: first to identify PSs with mitochondrial and/or lysosomal localization from the rest, and to classify them in a second stage. This sequential ANN classification method has permitted to distinguish PSs located into two of the most important cell targets: lysosomes and mitochondria. The absence of false negatives in this assignation, combined with the rate of success in predicting PS localization in these organelles, permits the use of this ANN method to perform virtual screenings of drug candidates for PDT.

Recognition Among Three Kinds of Pteridophyte Plants Based on Fourier Transform Infrared-Discrete Wavelet Feature Extraction and Artificial Neural Network Classification Method

Fourier transform infrared(FT-IR) and horizontal attenuated total reflectance(HATR) techniques were used to obtain the FT-IR of three kinds of pteridophyte plants(the root of Cyrtomium fortunei J.Sm,Dryopteris championii(Bench) C.Chr.apud Ching and Dryopteris varia(L.) O.Ktze.).The similar features of FT-IR among the root of Cyrtomium fortunei J.Sm,Dryopteris championii(Bench) C.Chr.apud Ching and Dryopteris varia(L.) O.Ktze.were extracted by discrete wavelet transform.The scale 4 and 5 were used to extract the feature vectors,which were used to train the artificial neural network(ANN).The trained neural network was used to classify the root of Cyrtomium fortunei J.Sm,Dryopteris championii(Bench) C.Chr.apud Ching and Dryopteris varia(L.) O.Ktze.,which were collected from different places.According to 240 prediction samples,we could effectively identify the root of Cyrtomium fortunei J.Sm,Dryopteris championii(Bench) C.Chr.apud Ching and Dryopteris varia(L.) O.Ktze.by FT-IR with discrete wavelet feature extraction and artificial neural network classification.

Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks

A marine propulsion system is a very complicated system composed of many mechanical components. As a result, the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft. It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis. For this reason, a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems. To monitor the gear conditions, the bispectrum analysis was first employed to detect gear faults. The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique, which could be regarded as an index actualizing forepart gear faults diagnosis. Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox. The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum, and the ANN classification method has achieved high detection accuracy. Hence, the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases, and thus have application importance.

Neural network classification of infrared spectra of control and Alzheimer's diseased tissue

Artificial neural network classification methods were applied to infrared spectra of histopathologically confirmed Alzheimer's diseased and control brain tissue. Principal component analysis was used as a preprocessing technique for some of these artificial neural networks while others were trained using the original spectra. The leave-one-out method was used for cross-validation and linear discriminant analysis was used as a performance benchmark. In the cases where principal components were used, the artificial neural networks consistently outperformed their linear discriminant counterparts; 100% versus 98% correct classifications, respectively, for the two class problem, and 90% versus 81% for a more complex five class problem. Using the original spectra, only one of the three selected artificial neural network architectures (a variation of the back-propagation algorithm using fuzzy encoding) produced results comparable to the best corresponding principal component cases: 98% and 85% correct classifications for the two and five class problems, respectively.