Rule-based Techniques Research Articles

A Decentralized Multi-Agent Approach for Service Restoration in Uncertain Environment

This paper proposes a decentralized multiagent system (MAS) approach to solve the service restoration problem considering the uncertainty of load demand and renewable distributed generators (RDGs). Service restoration is formulated as a multi-objective optimization problem and solved by implementing controlled DG islanding using the proposed MAS approach. First, the uncertainty of load demand and RDG generation is forecast by generating scenarios using Monte Carlo simulations. Next, the expected nodes to be restored in an island are determined using a heuristic rule-based technique. Finally, the maximum likelihood of the expected island ranges to be restored are estimated using the maximum likelihood estimation method to assist the utility in decision making. Extensive case studies are conducted on 38 bus and 119 bus distribution system to show the efficacy of the proposed restoration strategy. The use of electric vehicles and battery energy storage as flexible sources of energy to alleviate the uncertainties in the system is also highlighted.

A Novel approach for Video based Fire Detection system using Spatial and Texture analysis

Objectives: A novel video based fire detection algorithm based on rule base technique using RGB and HSV color space and spatial analysis based on wavelet analysis is proposed. Methods/Statistical Analysis: Rule base technique utilizing RGB and HSV color space for extraction of fire pixels in the frame was used. Threshold based spatial energy methodology is used for differentiating fire and fire like objects. Wavelet analysis is performed for calculation of spatial energy. Texture analysis using Local Binary Pattern (LBP) is also performed when fire or fire like candidate having spatial energy near to threshold of fire pixel. Findings: The usage of RGB color space alone for identification of fire in the video frames is not sufficient as they suffer from false detection. Two novel rules based on HSV plane is proposed which have improved the detection ability of system when compare to previous studies. But still suffers from false detection. Spatial energy methodology based on differentiating fire and fire like objects performs well and has achieved greater efficiency with low false detection rate of 4% on standard datasets. Texture analysis using Local Binary Pattern (LBP) is also performed in rare case when fire candidate is having spatial energy near to that of fire like object category. This has helped in reducing the computational complexity of the system. The system shows 100% accurate results. Improvements: The results obtained for different standard datasets using the proposed hybrid spatial and texture base analysis shows 100% accuracy with zero false positive and false negative rates which is not observed in any of the present articles. Keywords: Fire Detection, Local Binary Pattern (LBP), Spatial Analysis, Texture Analysis, Wavelet Analysis

Intrusion Detection System with Correlation Engine and Vulnerability Assessment

The proposed Intrusion Detection System (IDS) which is implemented with modern technologies to address certain prevailing problems in existing intrusion detection systems’ is capable of giving an advanced output to the security analyst. Even though the network of an organization has been secured internally as well as externally the intruders find ways to penetrate the network. With the system that is proposed activities of those intruders can be identified with a higher probability even if managed to bypass security controls of the network. The goal of this project is to give a reliable output to the system users where all the alerts are more accurate and correlated using HIDS alerts and NIDS alerts which is similar to the modern SIEM concept. The system will perform as a centralized IDS by getting inputs from both HIDS and NIDS which gives data regarding the activities of hosts and network traffic. With those implementations, the system is capable of monitoring host activities, monitoring network traffic with existing tools and give a correlated output which is more accurate, advanced and reliable prioritizing the possible attacks by using machine learning techniques and rule-based correlation techniques. With all these capabilities final product is a fully automated Intrusion Detection System which gives correlated alerts as outputs with a less rate of false positives compared to the existing systems.

Rule-Based Technique for Monitoring of Voltage Events in Power Systems

For the development of smart grid, the effective detection of voltage events is a very important task for the power system monitoring. Voltage sags, interruptions, and voltage swells which usually produce large losses in commercial and industrial consumers are the main events in power systems due to the sensitivity of equipments to these voltage variations. In this paper, a hybrid detection method for voltage events by combining the wavelet analysis and adaptive linear combiner (ADALINE) is proposed. The usefulness of the proposed algorithm is demonstrated by a simple laboratory setup with LabVIEW program and actual recorded waveforms. With the help of accurate time locating of the wavelet analysis and correct event classification of ADALINE, power system monitoring could provide accurate and useful information to power grids via the developing advanced metering infrastructure.

Using Landsat images to monitor changes in the snow-covered area of selected glaciers in northern Pakistan

Landsat satellite images were used to map and monitor the snow-covered areas of four glaciers with different aspects (Passu: 36.473°N, 74.766°E; Momhil: 36.394°N, 75.085°E; Trivor: 36.249°N, 74.968°E; and Kunyang: 36.083°N, 75.288°E) in the upper Indus basin, northern Pakistan, from 1990-2014. The snow-covered areas of the selected glaciers were identified and classified using supervised and rule-based image analysis techniques in three different seasons. Accuracy assessment of the classified images indicated that the supervised classification technique performed slightly better than the rule-based technique. Snow-covered areas on the selected glaciers were generally reduced during the study period but at different rates. Glaciers reached maximum areal snow coverage in winter and pre-monsoon seasons and minimum areal snow coverage in monsoon seasons, with the lowest snow-covered area occurring in August and September. The snow-covered area on Passu glacier decreased by 24.50%, 3.15% and 11.25% in the pre-monsoon, monsoon and post-monsoon seasons, respectively. Similarly, the other three glaciers showed notable decreases in snow-covered area during the pre- and post-monsoon seasons; however, no clear changes were observed during monsoon seasons. During pre-monsoon seasons, the eastward-facing glacier lost comparatively more snow-covered area than the westward-facing glacier. The average seasonal glacier surface temperature calculated from the Landsat thermal band showed negative correlations of -0.67, -0.89, -0.75 and -0.77 with the average seasonal snow-covered areas of the Passu, Momhil, Trivor and Kunyang glaciers, respectively, during pre-monsoon seasons. Similarly, the air temperature collected from a nearby meteorological station showed an increasing trend, indicating that the snow-covered area reduction in the region was largely due to climate warming.

Rule-based detection technique for ICMPv6 anomalous behaviour

The rapid growth of the Internet in the past few years has revealed the limitation of address space in the current Internet Protocol (IP), namely IPv4. Essentially, the increasing demand and consumption of IP addresses have led to the anticipated exhaustion of IPv4 addresses. In order to address this concern, the Internet Protocol version 6 (IPv6) has been developed to provide a sufficient address space. IPv6 is shipped with a new protocol, namely, the neighbour discovery protocol (NDP) which has vulnerabilities that can be used by attackers to launch attacks on IPv6 networks. Such vulnerabilities include the lack of exchange message authentication of NDP. Attacks targeting ICMPv6 protocol display ICMPv6 anomalies. As such, this paper proposes a rule-based technique for detecting ICMPv6 anomalous behaviours that negatively affect the network performance. The effectiveness of this technique is demonstrated by using substantial datasets obtained from the National Advance IPv6 Centre of Excellence (NAv6) laboratory. The experimental results have proved that the proposed technique is capable of detecting ICMPv6 anomalous behaviour s with a detection accuracy rate of 92%.

SHOTGUN: converting words into triplets

Software systems convert between graphemes and phonemes using lexicon-based, rule-based or data-driven techniques. SHOTGUN combines these techniques in a hybrid system which converts between graphemes and phonemes bi-directionally, adds linguistic and educational information about the relationships between graphemes and phonemes and provides estimates about the likelihood that the generated output is correct. We describe the components from which SHOTGUN is built and determine its accuracy by running tests on two data sources, the BasisSpellingBank and CELEX, comparing the results to Nunn’s (1998) rule-based conversion system. SHOTGUN converts phonemes to graphemes and vice versa with precision of 81% and 86% when tested on the BasisSpellingBank, and 80% and 81% when tested on CELEX. SHOTGUN proves to be a powerful new conversion tool.

New semi-automated mapping of asbestos cement roofs using rule-based object-based image analysis and Taguchi optimization technique from WorldView-2 images

ABSTRACTThe use of asbestos cement (AC) roofing materials is a significant concern because of their deleterious effects on human health and the environment. The main objective of this study was to map AC roofs from WorldView-2 (WV-2) images using object-based image analysis (OBIA). A robust Taguchi optimization technique was used to optimize segmentation parameters for WV-2 images in heterogeneous urban areas. In this research, two subsets of WV-2 satellite image sets were utilized to map AC roofs. Rule-based OBIA framework was developed on the first study area. Different supervised OBIA classifiers, such as Bayes, k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF), were tested on the first image of the study areas to evaluate the performance of a rule-based classifier. Results of the supervised classifiers showed confusion between AC roof class and some urban features, with overall accuracies of 72.21%, 77%, 81.75%, and 82.02% for Bayes, k-NN, SVM, and RF, respectively. To assess the transferability of the proposed method, the adopted classification framework was applied to larger subsets of WV-2 of the second study area. The results of the proposed approach showed outstanding performance, with overall accuracies of 93.10% and 90.74% for the first and second classified images, respectively. The McNemar test emphasized the statistical reliability of rule-based result (in the first site) compared with supervised classification results. Therefore, the proposed framework of using rule-based classification and Taguchi optimization technique provide an efficient and expeditious approach to mapping and monitoring the presence of AC roofs and help local authorities in their decision-making strategies and policies.

Supporting Vehicle Option Change Management Through a Graph-Based Visualization Tool

When implementing configuration management methods, the amount of data required can be problematic when validating changes to the database. This is especially true for rule-based configuration management techniques. This paper presents a graph visualization tool to assist in validating changes to the rule database. The development and implementation of the tool is presented, along with the execution and results of two user studies designed to test specific aspects of the support tool. The paper then presents how the visualization tool was implemented for four ongoing configuration changes at the original equipment manufacturer (OEM) to prove the effectiveness of the tool in assisting in validating configurations changes.

Adaptive residential demand-side management using rule-based techniques in smart grid environments

Programmable Communicating Thermostats (PCTs) as price-responsive thermostats are being used broadly for automatic control of residential HVAC systems in North America. The main advantage of existing PCTs is their capability to receive pricing signals from smart meters and shed HVAC load during high demand. In these cases; PCT automatically reduces the initialized set points to the levels pre-adjusted by user. However, there still exist neglected potentials for demand-side management that resides in the control and interaction of PCTs. Hence, it requires making PCTs more intelligence to learn and adapt to user’s preference changes which results in adaptive demand-side management. In this paper, an algorithm which is based on the integration of rule-based techniques and wireless sensors capabilities is presented. The proposed algorithm is embedded into exiting PCTs to improve their capabilities in learning and adapting to occupant’s pattern changes. The simulation results demonstrate that PCTs equipped with our approach performs better than existing PCTs with respect to energy saving and adapting to occupant’s pattern changes. To verify the feasibility of the algorithm; an embedded system using ZigBee wireless communication is built and applied to a conventional air conditioning (AC) system. The experimental results show the system precisely adapts to user’s preference changes while saving energy and cost.

Rule-Based Network Intrusion Detection System for Port Scanning with Efficient Port Scan Detection Rules Using Snort

In the field of network security, researchers have implemented different models to secure the network. Intrusion Detection System is also one of them and Snort is an open source tool for Intrusion Detection and Prevention System. Today intrusion Detection System is a growing technology in network security and mostly researchers have focused in this field, some of them used signature or rule-based technique and some are anomaly based techniques to improve security of network. In this paper we propose a rule-base Intrusion Detection System with our self generated new Efficient Port Scan Detection Rules (EPSDR). These rules will be used to detect naive port scan attacks in real time network using Snort and Basic Analysis Security Engine (BASE). BASE is used to view the snort results in font-end web page because Snort has no graphic user interface. In This rule-based Intrusion Detection System we will match the signature with our Efficient Port Scan Detection Rules (EPSDR) from captured packet. As a definition of signature based IDS this new EPSDR based IDS will be useful to reduce the false positive alarm.

Drug Side Effect Detection as Implicit Opinion from Medical Reviews (Research Article)

The enormous growth of online reviews in social media provides a valuable resource for human decision-making activities in diverse domains such as the medical domain. Extracting explicit and implicit opinions is one of the main tasks in the opinion mining area. As implicit opinion mining is a complicated task, limited work has been done on it, especially in the medical domain, as implicit opinion is a domain dependent task. Side effects are one of the critical concepts the recognition of which is a challenging task since it coincides with disease symptoms both lexically and syntactically. To the best of our knowledge, limited work has been done on side effect extraction from drug reviews. This study tries to extract drug side effects as implicit opinions from drug reviews of drugratingz.com by using the rule-based and SVM techniques. Due to the novelty of this issue, corpus construction is also carried out. The results proved that the combination of lexical, syntactical, contextual and semantic features leads to the best results in the SVM technique in comparison with the rule-based algorithm in terms of side effect detection. In this study, we develop a system to detect side effects in the drug reviews as a subtask of detecting implicit opinions in medical sources and discriminate between side effects and disease symptoms. The proposed technique, as an implicit opinion mining system, can help patients to investigate the drug before taking it and help physicians and drug producers to consider user feedback in their decision-making.

Sustainability assessment using fuzzy-inference technique (SAFT): A methodology toward green products

Green products are increasingly becoming the center of attention for policy and decision makers worldwide not only because of environmental and eco-systems crisis but also to satisfy the current competitiveness in the markets. With this aim, it is highly attractive to count with mathematical tools that allow assessing the sustainability of the products. In this regard, fuzzy techniques have been broadly used in different studies due to uncertainty and vagueness associated with sustainability problems. However, these studies are mostly based on fuzzy rules generation which is time consuming and also can lead to redundancy and inaccuracy. In this study, we introduced a fuzzy-inference system to evaluate product/process sustainability (SAFT). The proposed method does not require generation of rules which simplifies the procedure and makes it more precise. Furthermore, fuzzy analytic hierarchy process accompanied by Shannon's entropy formula was employed to determine the relative importance of each element in the hierarchy. The methodology SAFT was compared with fuzzy rule-base technique and impressively pretty the same results were obtained. The method introduced in this paper was built as a user interface platform which can be used as a fuzzy expert system to facilitate the sustainability assessment of products/processes in different manufacturing industries.

Data Fusion Technique Using Wavelet Transform and Taguchi Methods for Automatic Landslide Detection From Airborne Laser Scanning Data and QuickBird Satellite Imagery

Landslide mapping is indispensable for efficient land use management and planning. Landslide inventory maps must be produced for various purposes, such as to record the landslide magnitude in an area and to examine the distribution, types, and forms of slope failures. The use of this information enables the study of landslide susceptibility, hazard, and risk, as well as of the evolution of landscapes affected by landslides. In tropical countries, precipitation during the monsoon season triggers hundreds of landslides in mountainous regions. The preparation of a landslide inventory in such regions is a challenging task because of rapid vegetation growth. Thus, enhancing the proficiency of landslide mapping using remote sensing skills is a vital task. Various techniques have been examined by researchers. This study uses a robust data fusion technique that integrates high-resolution airborne laser scanning data (LiDAR) with high-resolution QuickBird satellite imagery (2.6-m spatial resolution) to identify landslide locations in Bukit Antarabangsa, Ulu Klang, Malaysia. This idea is applied for the first time to identify landslide locations in an urban environment in tropical areas. A wavelet transform technique was employed to achieve data fusion between LiDAR and QuickBird imagery. An object-oriented classification method was used to differentiate the landslide locations from other land use/covers. The Taguchi technique was employed to optimize the segmentation parameters, whereas the rule-based technique was used for object-based classification. In addition, to assess the impact of fusion in classification and landslide analysis, the rule-based classification method was also applied on original QuickBird data which have not been fused. Landslide locations were detected, and the confusion matrix was used to examine the proficiency and reliability of the results. The achieved overall accuracy and kappa coefficient were 90.06% and 0.84, respectively, for fused data. Moreover, the acquired producer and user accuracies for landslide class were 95.86% and 95.32%, respectively. Results of the accuracy assessment for QuickBird data before fusion showed 65.65% and 0.59 for overall accuracy and kappa coefficient, respectively. It revealed that fusion made a significant improvement in classification results. The direction of mass movement was recognized by overlaying the final landslide classification map with LiDAR-derived slope and aspect factors. Results from the tested site in a hilly area showed that the proposed method is easy to implement, accurate, and appropriate for landslide mapping in a tropical country, such as Malaysia.

Modified Rule-Based Concatenative Technique for Intelligible Speech Synthesis in Indian Languages

Modified Rule-Based Concatenative Technique for Intelligible Speech Synthesis in Indian Languages

Rule Based Approach for Arabic Part of Speech Tagging and Name Entity Recognition

The aim of this study is to build a tool for Part of Speech (POS) tagging and Name Entity Recognition for Arabic Language, the approach used to build this tool is a rule base technique. The POS Tagger contains two phases:The first phase is to pass word into a lexicon phase, the second level is the morphological phase, and the tagset are (Noun, Verb and Determine). The Named-Entity detector will apply rules on the text and give the correct Labels for each word, the labels are Person(PERS), Location (LOC) and Organization (ORG)

An efficient multi relational framework using fuzzy rule-based classification technique

Multi relational classification is one of the rapidly rising subclasses of multi relational data mining. It focuses on identifying attractive patterns in an interconnected multiple database relations via primary key/foreign key relationship. Many conventional rule-based classification approaches produce the rules from single 'flat' relation and typically suffer from rule clashes. However, many real world data repositories are stored in multiple database relations. Owing to the increase in the quantity of relational data that are being accumulated and the limitation of propositional rule-based classifiers in relational domains, multi relational classification has turned out to be a field with significance. In this work, a multi relational fuzzy rule system (MRFRS) is proposed for classification across multiple database relations based on PART algorithm that generates crisp rules. These rules are used to formulate the multi relational fuzzy rule system that produces rules of mutually exclusive and exhaustive in nature. The empirical results show that the proposed method outperforms well on real datasets in terms of efficiency and accuracy.

An efficient multi relational framework using fuzzy rule-based classification technique

An efficient multi relational framework using fuzzy rule-based classification technique

ADAPTING A CONSTITUENCY PARSER TO USER-GENERATED CONTENT IN POLISH OPINION MINING

The paper focuses on the adjustment of NLP tools for Polish; e.g., morphological analyzers and parsers, to user-generated content (UGC). The authors discuss two rule-based techniques applied to improve their efficiency: pre-processing (text normalization) and parser adaptation (modified segmentation and parsing rules). A new solution to handle OOVs based on inflectional translation is also offered.

Service workload patterns for Qos-driven cloud resource management

Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support a continuous approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction technique that combines a workload pattern mining approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-performant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques by a hybrid prediction solution. Uncertainty and noise are additional challenges that emerge in multi-layered, often federated cloud architectures. We specifically add log smoothing combined with a fuzzy logic approach to make the prediction solution more robust in the context of these challenges.