Rule Based Methods Research Papers

Identification of Multi-Focal Questions in Question and Answer Reports

2023, Lecture Notes in Computer Science

A significant amount of business and scientific data is collected via question and answer reports. However, these reports often suffer from various data quality issues. In many cases, questionnaires contain a number of questions that... more

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Voltage Variation Signals Source Identification and Diagnosis Method

by Muhamad Faizal

2023, International Journal of Advanced Computer Science and Applications

Power Quality (PQ) problem has become an important issue for generating bad impact to the users nowadays. It is important to detect and identify the source of the PQ problem. This paper presents a voltage variation signals source... more

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Crane monitoring system based on internet of things using long range

by Fakrulradzi Idris

2023, International Journal of Power Electronics and Drive Systems

The four main causes of crane accidents are overturned, falls, mechanical failure, and contact with power lines. It is important to keep track of the crane's health and condition as it is always too late when a failure of the crane was... more

Figure | shows the block diagram of the system designed. There are two parts which are the transmitter and receiver parts. The sensors will evoke when the crane starts to work. The value of sensors wil be sent to Arduino for processing before being passed to LoRa for transmission. The received data will be sent to the designed database using a Wi-Fi connection from the NodeMCU ESP8266 internet module Finally, the user will be able to view and monitor the crane by running the developed application on the mobile phone. Figure 2 shows the hardware setup for the transmitter side. In this project, ACS712 was used as the current sensor where TS90a servo motor was connected to the sensor to act as the current sensor’s load; to allow current data measurement. An Arduino Nano is used to supply power and program the servo motor to rotate occasionally for every 15 seconds. Besides, the load cell sensor needs a load for measuring the weight data. Thus, an object is added and placed on the load cell sensor to become the weight sensor load. There is also a real time clock (RTC) module used to record the date and time for calculating crane utilization rate. An HC-SR04 Ultrasonic sensor was used to detect the crane’s fuel consumption. DHT22 is used as the temperature sensor for detecting any abnormality of the crane. The crane data mentioned in this project are only simulation data, these data are not tested on the real crane, but the data type can be used to monitor the real crane. For example, the temperature data in this project refers to the temperature of the crane, the weight

Figure 2. Transmitter side hardware setup data refers to the load of the crane, and data from the ultrasonic sensor refer to the fuel level of the crane. The close-up figure for current and weight sensors and how there were connected is shown in Figure 3. As mentioned earlier, the current sensor needs a load to show readings. Therefore, a servo motor is used tc mimic the real condition. In a real situation, the current sensor will be connected to the crane motor fo current measurement. The weight sensor used in this project can detect weight until 5 kg. A robust anc precise weight sensor needs to be used for measuring real crane data as it carries a bigger load. Figure 4 shows the receiver side of this project. The receiver side is simpler, it is consisting of a LoRa receiver being connected to ESP8266 for Wi-Fi connections.

Figure 3. Current and weight sensor setup

Figure 6. Receiver’s serials monitor display Figure 5. Transmitter’s serials monitor display Figure 5 shows the display from the serial monitor of Arduino software when a transmitter sends data dan Figure 6 shows the serial monitor from the Arduino software when a receiver receives data. The measured items are current, power, temperature, weight, date and time, a distance that will be used to detect the fuel oil in the crane, and total working time for the crane. In addition, the signal to noise ratio (SNR) and received signal strength indication (RSSI) values of the LoRa module is also displayed in the serial monitor of the receiver side. SNR and RSSI indicate the signal strength for LoRa.

Figure 10. Display after “Past Record” icon was Fi clicked Figure 8. On launch interface

Figure 11. Display after data was entered in specific format and “Submit Date” icon was clicked Figure 9. Display after icon “Realtime monitoring” was clicked

Crane monitoring system based on internet of things using long range (Ng Wen Jun) Figure 13. Zoom in option Figure 12. Spinner’s sensor option

work easier. Besides that, the crane’s temperature is observed for its motor and hydraulic part. If the temperature of the motor keeps increasing, it may cause the system of the crane to fail and stop working normally. Figure 18 shows the weight data in the per hour graph view. In this project, 5 grams of load is being used although the system able to detect up to 5 kg. The weight sensors allow the user to monitor and control the maximum capacity that a crane can lift. Whenever a crane is overloaded, it causes irreversible damage and may cause hazards. The most common case will be the drop of the load or crane is swinging and hard to be controlled.

Figure 16. Power per hour graph display Figure 17. Temperature per hour graph display

Figure 18. Weight per hour graph display

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Location tracking using LoRa

by Fakrulradzi Idris

2023, International Journal of Electrical and Computer Engineering (IJECE)

Local area network (LAN) as Bluetooth, WiFi and ZigBee are well established technology. The biggest problem with many LAN is the battery consumption and short ranges link budgets. LoRa is a new, private, unlicensed and spread spectrum... more

Figure 1. Block diagram of the proposed diagram Figure 2. Transmitter ei a wai LoRa or global positioning system (GPS) shield with the part number of SX1276/SX1278 was used in the project. In this project, Arduino Mega was chosen as the microcontroller as it has more pins that can be used as an input or output compared to other version of Arduino. Yun shield was stacked between LoRa and Arduino, where the purpose of Yun shield is to solve the storage issue for Arduino board as this project require more storage to store GPS data. The transmitter and receiver parts were shown as Figures 2 and 3. Arduino IDE was used to develop the project.

Figure 3. Receiver Figure 4. Location of field test

Figure 7. Signal received and RSSI versus distances Figure 6. Serial data display the GPS reading at receiver

Figure 5. Serial data display the GPS reading at transmitter Figure 8 shows the analysis of LoRa transceiver battery lifetime. Each LoRa hardware was supplied with alkaline battery AA which is equivalent to 6 volts power supply. It is observed that after 24 hours, the signal transmission still occurred from transmitter to receiver. LoRa proven to be the preferred technology for long hours of operating time.

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Reservoir Operation: Principles, Objectives, Methods, and Challenges

by C. P. Kumar

2023

This article provides an overview of reservoir operation, which involves managing the storage and release of water from reservoirs to meet various demands while ensuring the safety of the dam and downstream areas. The article discusses... more

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Crane monitoring system based on internet of things using long range

by International Journal of Electrical and Computer Engineering (IJECE) and

2023, International Journal of Electrical and Computer Engineering (IJECE)

The four main causes of crane accidents are overturned, falls, mechanical failure, and contact with power lines. It is important to keep track of the crane's health and condition as it is always too late when a failure of the crane was... more

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Muscle activities segmentation of EMG signal for manual lifting

by Tengku Nor Shuhada Tengku Zawawi

2023

This paper presents the segmentation process using spectrogram for electromyography (EMG) signal analysis. Manual lifting activities are repeated to five times with the different load mass and lifting height are performed until achieve... more

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Exploring the Relation Between EMG Pattern Recognition and Sampling Rate Using Spectrogram

by Tengku Nor Shuhada Tengku Zawawi

2023, Journal of Electrical Engineering & Technology

The application of electromyography (EMG) has shown great success in rehabilitation engineering. With the existing multiple-channel EMG recording system, the detection and classification of EMG pattern have become viable. The purpose of... more

Fig. 2 Hand movement tasks Fig. 1 Electrodes configuration

Fig.3 Spectrogram of 1024 Hz sampling rate at 30, 25 and 20 dB SNR

Table 4 Result of F-measure for SVM and k-NN cost, 1024 Hz sampling rate enhances 0.0073 s compare to 512 Hz. The major drawback of higher sampling rate is longer computation time. In sum, it can be inferred that 1024 Hz sampling rate is more suitable for EMG pattern recognition. In a real system, down-samples the sampling rate lower than 1000 Hz is not recommended.

Fig.4 Mean classification accuracy according to the change in win- dow size (16, 32, 64 and 128) of ten subjects using SVM

Fig.5 Mean classification accuracy of three different sampling rates across ten subjects

Table 2 Classification accuracy (mean+SD) of 256, 512, and 1024 Hz sampling rate at different SNR for SVM

Table 3 Classification accuracy (mean+SD) of 256, 512, and 1024 Hz sampling rate at different SNR for k-NN

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A Comparative Modeling and Analysis of Voltage Variation by Using Spectrogram

by Mustafa Manap

2022, TELKOMNIKA (Telecommunication Computing Electronics and Control)

In this paper, the power quality (PQ) disturbance which is the voltage variations consist of voltage swell, sag and interruption are model and analyze. Different types of voltage variations PQ disturbances models are developed and created... more

5.3. Voltage Swell Simulink Model eS ee IE IO OE EE EET The developed huge load shutdown Simulink model is shown in Figure 2 below. The huge load closedown model is used to simulate the voltage swell situation by eliminating the load. This model consists of 25kV, 50Hz 3-phase source block connected to a 25kV/0.6kV, 10MVA star/delta transformer. There is a 3-phase external load clossure upon clearance of three-phase breaker at 0.6kV feeder line which brings the occurrence of voltage swell.

5.4. Voltage Sag Simulink Model ROSES? Weve SE VM cear Ene eee ris Erne Pee nck The Simulink 1 model of F restorative of huge load is used to simulate the voltage sag situation is shown as in Figure 3 below. This model consists of 25kV, 50Hz 3-phase source block connected to a 25kV/0.6kV, 10MVA star/delta transformer. A fault block is applied at the load and energizing large load occurs.

Figure 4. Simulation model of Voltage Interruption The model for voltage interruption is developed in Figure 4. This model is used tc represent the actual condition of interruption due to fault. It consists of 25kV, 50Hz 3-phase source block connected to a 25kV/0.6kV, 10MVA star/delta transformer. A fault block is appliec at the load which acts as permanent fault to interrupt the voltage magnitude within the syster for interruption simulation.

The power quality disturbances are generated by using parametric equations ir MATLAB software. Figure 5 shows the voltage variations waveforms produced by using the quations stated. There are a lot of advantages of voltage variation signals modeling by using the mathematical equation in MATLAB.The signal waveforms and parameter are almost identical to the realpower quality signals occur in power system. The voltage variation signals simulated by this model can be easily applied in analysis to extract their distinctive features and characteristics.

Figure 10. (a) TFR of mathematical models, (b) TFR of Simulink models voltage sag. When the magnitude is no longer can be presented at 0.05s to 0.15s, a dropping of 1.0pu to Opu, indicating the occurrence of voltage interruption. The Vrms and power values are estimated and calculated from each of the TFR presented in each variation. The THD values are being extracted at fundamental frequency of the signals which is 50Hz. The parameters obtained and extracted from the spectrogram of each variation are as tabulated in Table 2.

Figure 11. (a) Vpu graph of mathematical models, (b) Vpu graph of Simulink models

y(t)=PQ signal, A=Amplitude (constant), w=angular frequency, t=time, ox=time duration of event occurrence (constant), T=time duration ee iit In the research of voltage variations, the signals are usually simulated in software to get a view of the actual condition. In this paper, 4 types of voltage variations are built by using the mathematical models as shown in Table 1. 5.1. Parametric Equations Model Simulation models include mathematical model and voltage variation models to represent the occurrence of power quality disturbances [13], [36], [37].

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EMG Signal Analysis Of Fatigue Muscle Activity In Manual Lifting

by saleha saleh

2022

In manufacturing industries, manual lifting is commonly practiced by workers in their routine to move or transport the objects to a desired place. Manual lifting with high repetition and loading on the arm will contribute the effects of... more

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Analysis of harmonics using wavelet technique

by Thangaraj Thangam

2022, International Journal of Electrical and Computer Engineering (IJECE)

This paper develops an approach based on wavelet technique for the estimation of harmonic presents in power system signals. The proposed technique divides the power system signals into different frequency sub-bands corresponding to the... more

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Selection of Spectrogram’s Best Window Size in EMG Signal during Core Lifting Task

by Abdul Rahim Abdullah

2022, Journal of Telecommunication, Electronic and Computer Engineering

Electromyography (EMG) is one of the most commonly used tools to study human muscle condition. Past researchers have introduced various techniques from time distribution (TD), frequency distribution (FD) and timefrequency distribution... more

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A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and Classification Analysis

by Abdul Rahim Abdullah

2022, International Journal of Electrical and Computer Engineering (IJECE)

This paper presents a utilization of improved Gabor transform for harmonic signals detection and classification analysis in power distribution system. The Gabor transform is one of time frequency distribution technique with a capability... more

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Spectrogram Based Window Selection for the Detection of Voltage Variation

by Abdul Rahim Abdullah

2022, International Journal of Integrated Engineering

This paper presents the application of spectrogram with K-nearest neighbors (KNN) and Support Vector Machine (SVM) for window selection and voltage variation classification. The voltage variation signals such as voltage sag, swell and... more

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A Comparative Study on Spectrogram and S-Transform for Batteries Parameters Estimation

by Abdul Rahim Abdullah

2022, Jurnal Teknologi

This research presents the analysis of battery charging and discharging signals using spectrogram, and S-transform techniques. The analysed batteries are lead acid (LA), nickel-metal hydride (Ni-MH), and lithium-ion (Li-ion). From the... more

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Enhancing the Communication of Law: a cross-disciplinary investigation applying information technology

by Michael A Curtotti

2022

Law is pervasive in culture. It is a form of communication between government and citizens. When effective, it is a tool of government policy. If poorly designed, law results in unnecessary costs to society. Impediments to understanding... more

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Software tools for the visualization of definition networks in legal contracts

by Michael A Curtotti

2022, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law - ICAIL '13

This paper describes the development of prototype software-based tools for visualizing definitions within legal contracts. The tools demonstrate visualization techniques for enhancing the readability and comprehension of definitions and... more

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Determining Best Window Size for an Improved Gabor Transform in EMG Signal Analysis

by Azizi Abdullah

2022, TELKOMNIKA (Telecommunication Computing Electronics and Control)

Electromyography EMG is a standout amongst the most regularly utilized tools to study human muscle condition. But due to the intricate attributes of the EMG itself, time-frequency distributions such as Gabor transform and spectrogram are... more

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Power Quality Signals Detection and Classification Using Linear Time Frequency Distribution

by Mohd H A T T A Jopri

2022

Power quality has become a great concern to all electricity consumers. Poor quality can cause equipment failure, data and economical. An automated monitoring system is needed to ensure signal quality, reduces diagnostic time and rectifies... more

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A Comparative Modeling and Analysis of Voltage Variation by Using Spectrogram

by Wei Hown Tee

2022, TELKOMNIKA (Telecommunication Computing Electronics and Control)

In this paper, the power quality (PQ) disturbance which is the voltage variations consist of voltage swell, sag and interruption are model and analyze. Different types of voltage variations PQ disturbances models are developed and created... more

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Voltage Variation Signals Source Identification and Diagnosis Method

by Wei Hown Tee

2022, International Journal of Advanced Computer Science and Applications

Power Quality (PQ) problem has become an important issue for generating bad impact to the users nowadays. It is important to detect and identify the source of the PQ problem. This paper presents a voltage variation signals source... more

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Analysis of harmonics using wavelet technique

by Thangaraj Thangam

2022, International Journal of Electrical and Computer Engineering (IJECE)

This paper develops an approach based on wavelet technique for the estimation of harmonic presents in power system signals. The proposed technique divides the power system signals into different frequency sub-bands corresponding to the... more

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A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and Classification Analysis

by Mohd H A T T A Jopri

2022, International Journal of Electrical and Computer Engineering (IJECE)

This paper presents a utilization of improved Gabor transform for harmonic signals detection and classification analysis in power distribution system. The Gabor transform is one of time frequency distribution technique with a capability... more

Figure |. Implementation Chart for Harmonic and Inter-harmonic Signals Detection and Classification Time-frequency distribution (TFD) are superior methods that represent signals in time and frequency representation (TFR). Furthermore, from TFR, spectral parameters can be seen with the changes of time. In this manner, the TFD are significant to classify harmonic and inter-harmonic signals in the system. Linear TFD which is Gabor transform is proposed as in Figure | for the signal classification technique.

Figure 2. Resolution of Gabor Transform For Gabor transform, hanning window is selected same as spectrogram but different in terms of window length which is 480 samples. The time and frequency resolution for Gabor transform is fixed for all frequency shown in Figure 2. The vertical axis (x-axis) shows the time resolution and horizontal axis (y-axis) shows the frequency resolution. 2.2. Signal Parameters

A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and.... (M. H Jopri, Parameters of harmonic and inter-harmonic are assessed from the TFR. The spectral parameter: consist of RMS fundamental voltage, momentary RMS voltage, instantaneous total inter-harmonic distortiot (TnHD), instantaneous total harmonic distortion (THD) and instantaneous total waveform distortion (TWD) Below are the signal parameters that been estimated from TFR [8].

Figure 3. Rule-based Classifier Flow Chart TnHDaye >= TNHDpnves And THDaye < TNHD hres THD ave > TNHD hres aNd TNH Daye < TNHD eres

Figure 4. (a) Harmonic Signal from Simulation and its, (b) TFR using Gabor Transform, (c) Instantaneous of RMS and Fundamental RMS Voltage, (d) Instantaneous of total Harmonic Distortion, Total Nonharmonic Distortion and Total Waveform Distortion

Table 1. MAPE Simulation Result for Gabor Transform Analysis 3.3. Classification of Harmonic and Inter-harmonic Signals

Table 2. Performance of Harmonic and Inter-Harmonic Signals Classification CONCLUSION

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Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

by Mustafa Manap

2022, Indonesian Journal of Electrical Engineering and Computer Science

A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and... more

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An Analysis of Harmonic and Interharmonic Contribution of Electric Arc Furnace by Using Periodogram

by Mustafa Manap

2022, International Journal of Electrical and Computer Engineering (IJECE)

Fast and accurate detection of the harmonic and interharmonic contribution of electric arc furnace (EAF) is crucial in identifying and to mitigate the undesired effects to the system. In this paper, periodogram, a fast and accurate... more

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Tools for the Visualization of Definition Networks in Legal Contracts Research

by Michael A Curtotti

2021

This paper describes the development of prototype software-based tools for visualizing definitions within legal contracts. The tools demonstrate visualization techniques for enhancing the readability and comprehension of definitions and... more

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Naïve bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification

by Mohd H A T T A Jopri

2021, Indonesian Journal of Electrical Engineering and Computer Science

The diagnostic analytic type of harmonic source is a vital research due to diagnose and identify type of harmonic source that exist in the power system. This paper presents a comparison of machine learning (ML) algorithm namely as the... more

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Support-vector machine and Naïve Bayes based diagnostic analytic of harmonic source identification

by Mohd H A T T A Jopri

2021, Indonesian Journal of Electrical Engineering and Computer Science

A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and... more

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Support-vector machine and Naïve Bayes based diagnostic analytic of harmonic source identification

by Mohd H A T T A Jopri

2021

A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and... more

Figure 2. IEEE 4-bus test feeders for case 2, 3 and 4 In most recent studies, suggest the execution of the proposed technique can be realized as depicts in Figures 1 and 2 using IEEE 4-bus test feeders, while the harmonic sources group into harmonic current source (HC) and harmonic voltage source (HV) type-load [34]. Four specific cases, which are case 1: N-N, case 2: N-H, case 3: H-H and case 4: H-N [16, 35, 36]. The main goal of this research is to diagnose and identify which case and type of the harmonic source in the power system.

TT De Oe eh Figure 3 shows the overview of identification and diagnosis of harmonic sources. Initially, the power quality signals which are current and voltage signals are measured at the PCC. In the next step, the S-transform is applied to transform the voltage and current signals into time-frequency representation (TFR). The signal parameters are then extracted from the TFR to form two feature sets: (1) current feature set, (2) voltage feature set. Furthermore the features are normalized and then fed into the machine learning for the identification and diagnosis of harmonic sources. Besides, two popular and powerful machine learning algorithms, namely, support vector machine (SVM) and Naive Bayes are applied to diagnose the NN, NH, HH, and HN cases for both He and Hy.

Figure 6. Confusion matrix of SVM using current feature set Figure 4. Confusion matrix of SVM using voltage feature set Figure 5. Confusion matrix of NB using voltage feature set

SVM, one can see that five types of sources (Hc-Hc, Hc-N, N-N, Hv-N, and N-Hv) were perfectly identified, in which a 100% class-wise accuracy was achieved. The harmonic source that most difficult to identify was N-He (89.29%). This is because SVM has misclassified the N-Hc as Hv-Hyv, thus leading to worst performance.

Table 2 shows the results of accuracy, sensitivity, specificity, and F-measure for the identification of the harmonic sources using SVM and NB. As can be observed, by applying SVM and NB, different types of harmonic sources can be successfully identified. From Table 2, the overall performance of SVM was significantly better than NB in this work. The SVM gave an increment of 1.5% accuracy as compared to NB, which offered more accurate identification of harmonic sources. Moreover, the highest properties on sensitivity, specificity, and F-measure supported the results. As a result, it can infer that SVM is more appropriate to be applied in power quality system. As compared to voltage feature set, current feature set is more capable to identify the harmonic source effectively. Table | presents the results of accuracy, sensitivity, specificity, and F-measure for the identification of the harmonic sources using SVM and NB for voltage feature set. In Table 1, it is clearly shows that the performance of the identification of harmonic sources was very low, which was below 40% accuracy. This means that the algorithms fail to estimate the harmonic source with problem. Based on the results obtained, voltage features were not good in describing the target concept, thus resulting in worst performance.

Table 2. The performances of SVM and NB using current feature set Figure 4 and Figure 5 illustrate the confusion matrix of SVM and NB for the identification of harmonic sources using voltage feature set. In these Figures, it shows that only N-N can be perfectly identified. Even though NB can work better than SVM, however, the performance is still far from perfect, which fail to identify the harmonic sources very well. Figure 6 and Figure 7 demonstrate the confusion matrix of SVM and NB for the identification of harmonic sources using current feature set. By applying

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Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

by Mustafa Manap

2021, Bulletin of Electrical Engineering and Informatics

The diagnostic analytic of harmonic source is crucial research due to identify and diagnose the harmonic source in the power system. This paper presents a comparison of machine learning (ML) algorithm known as linear discriminate analysis... more

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Voltage variations identification using gabor transform and rule-based classification method

by Wei Hown Tee

2021, International Journal of Electrical and Computer Engineering (IJECE)

This paper presents a comparatively contemporary easy to use technique for the identification and classification of voltage variations. The technique was established based on the Gabor Transform and the rule-based classification method.... more

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Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

by beei iaes

2021, Bulletin of Electrical Engineering and Informatics

The diagnostic analytic of harmonic source is crucial research due to identify and diagnose the harmonic source in the power system. This paper presents a comparison of machine learning (ML) algorithm known as linear discriminate analysis... more

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Voltage variations identification using Gabor Transform and rule-based classification method

by International Journal of Electrical and Computer Engineering (IJECE)

2020, International Journal of Electrical and Computer Engineering (IJECE)

This paper presents a comparatively contemporary easy to use technique for the identification and classification of voltage variations. The technique was established based on the Gabor Transform and the rule-based classification method.... more

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A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and Classification Analysis

by International Journal of Electrical and Computer Engineering (IJECE)

2020

This paper presents a utilization of improved Gabor transform for harmonic signals detection and classification analysis in power distribution system. The Gabor transform is one of time frequency distribution technique with a capability... more

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An Improved Detection and Classification Technique of Harmonic Signals in Power Distribution by Utilizing Spectrogram

by International Journal of Electrical and Computer Engineering (IJECE)

2020

This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to... more

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Web Content Extraction Using Hybrid Approach

by International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT

2017

Wide Web has rich source of voluminous and heterogeneous information which The World continues to expand in size and complexity. Many Web pages are unstructured and semi structured, so it consists of noisy information like advertisement,... more

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Overview of Biomedical Relations Extraction using Hybrid Rule-based Approaches

by Behrouz Bokharaeian

2014

Unstructured text documents are the major source of knowledge in biomedical fields. These huge amounts of information cause very difficult task of extraction or classification. Therefore, there is a need for knowledge discovery and text... more

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Corpus Based Classification of Text in Australian Contracts (2010)

by Michael A Curtotti

2011

" Written contracts are a fundamental framework for commercial and cooperative transactions and relationships. Limited research has been published on the application of machine learning and natural language processing (NLP) to... more

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Rule Based Methods

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