Pattern Matching Process Research Articles

A supervised machine learning approach to generate the auto rule for clinical decision support system

This paper illustrates a prototype for a Clinical Decision Support System (CDSS), using Supervised Machine Learning (SML) to derive rules from pre-constructed cases or to automatically generate rules. We propose an integrated architecture invoking two main components - Rule Pattern Matching Process (RPMP) and Auto Rule Generation Process (ARGP). The RPMP searches for and matches rules from a clinically derived reference set, successful discovery resulting in continued processing through the system. If no rule is found, the AGRP is automatically activated. The AGRP has been designed based on the SML approach. A Decision Tree Algorithm has been used and nested If-else statements applied to transform the decision tree algorithm to generate rules. For experimental purposes, we have developed a prototype and implemented a learning algorithm for generating auto rules for the diagnosis of Acute Rheumatic Fever (ARF). Based on results, the prototype can successfully generate the auto rules for ARF diagnosis. The prototype was designed to classify the ARF stages into “Detected”, “Suspected” and “Not detected”, in addition, it has classifiers capable of classifying the severity levels of detected stage into Severe, Moderate or Mild case. We simulated a set of 104 cases of ARF and observed the rules. The prototype successfully generated the new rule and classified it with the appropriate category (stage). In summary, the applied approach performed extremely well and the developed prototype provided reliable rules for ARF diagnosis. This prototype therefore reduces the task of manually creating ARF diagnosis rules. This approach could be applied in other clinical diagnosis processes.

Designing a Chatbot Application for Student Information Centers on Telegram Messenger Using Fulltext Search Boolean

Chatbot can be used as a solution of human resource issues in term of answering user’s questions about STMIK PGRI Tangerang. A chatbot application can have a conversation or answer user’s questions automatically. Knowledge is needed for chatbot to work properly. The general scheme of chatbot’s knowledge is the existence of patterns and responses. This research will implement the Fulltext Search Boolean Mode for the pattern-matching process with the input received by the chatbot application based on Telegram Messenger Bot. Chatbot application which created in this research can provide answers to information that is relevant to the input or questions provided by users.

An Efficient Framework for Multiple Subgraph Pattern Matching Models

With the popularity of storing large data graph in cloud, the emergence of subgraph pattern matching on a remote cloud has been inspired. Typically, subgraph pattern matching is defined in terms of subgraph isomorphism, which is an NP-complete problem and sometimes too strict to find useful matches in certain applications. And how to protect the privacy of data graphs in subgraph pattern matching without undermining matching results is an important concern. Thus, we propose a novel framework to achieve the privacy-preserving subgraph pattern matching in cloud. In order to protect the structural privacy in data graphs, we firstly develop a k-automorphism model based method. Additionally, we use a cost-model based label generalization method to protect label privacy in both data graphs and pattern graphs. During the generation of the k-automorphic graph, a large number of noise edges or vertices might be introduced to the original data graph. Thus, we use the outsourced graph, which is only a subset of a k-automorphic graph, to answer the subgraph pattern matching. The efficiency of the pattern matching process can be greatly improved in this way. Extensive experiments on real-world datasets demonstrate the high efficiency of our framework.

Secure Collaborative Key Management System for Mobile Cloud Data Storage

Mobile Cloud Computing (MCC) is the combination of mobile computing, cloud computing and wireless networks to make mobile thin client devices resource-rich in terms of storage, memory computational power and battery power by remotely executing the wide range of mobile application’s data in a pay-per-use cloud computing environment. In MCC, one of the primary concern is the security and privacy of data stored in cloud. The existing techniques are not efficient to manage secret keys during key generation and key distribution processes. The objective of this project work is to develop a secure collaborative key management system (SCKMS) for mobile cloud data storage by implementing by the cryptographic techniques for file encryption and file decryption, key generation, key encryption, key distribution and key decryption processes. In our proposed methodology, DriverHQ public cloud infrastructure is used for accessing the secure file as Storage as a Service (SaaS) mechanism. For generating the secret key, the proposed work implemented with Pseudo Random Number Generator (PRNG) algorithm, it produces the sequence of random numbers for every time. The keys are distributed using general Secret key Sharing Scheme (SSS). The key pattern matching process is implemented to spilt the secret key into three partitions and sent it to client (mobile devices), cloud server and decryption server. The decryption server key and cloud sever key are mapped with client key. The key shares are grouped together using key-lock pair mechanism and it achieves key integrity during untrusted medium communication. The proposed work also eliminates key escrow and key exposure problems. The files are encrypted and decrypted using Rivest-Shamir-Adleman (RSA) algorithm. The RSA algorithm is more vulnerable against the brute force attack, because of using larger key size. Thus, the proposed SCKMS achieves data confidentiality and data integrity in mobile cloud storage data when compared to existing Key Management System (KMS). The work also reduces encryption & decryption computation and storage overhead in client mobile devices, and minimizes the energy consumption of the mobile devices efficiently.

A pattern matching identification method of cracks on cantilever beams through their bending modes measured by magnetoelastic sensors

This work introduces a simple method on identification of single transverse cracks on cantilever beams, having as an input the first 8 bending modes of the beam. The beam modeling part of the method was developed based on a finite element analysis and fracture mechanics theory, and implemented using Matlab programming. Bending mode normalized frequencies were extracted from the model, versus crack location (CL) and crack depth (CD), and all possible normalized frequency ratios (NFRs) were calculated in order to be stored in a database. This database information was used to find the optimum CL and CD values from input data, through a pattern matching process. Two different approaches were presented to validate the method, one experimental and one numerical. In the case of the experimental approach a number of 19 beam specimens of aluminum alloy 6063 were used, with various fixed crack locations and depths, and the extracted CL and CD errors were 1.7% and 11.4%, respectively. The measurement of the 8 bending modes was accomplished non-invasively by the use of magnetoelastic vibration sensors, which were composed of 29 μm thin ribbons of magnetoelastic material Metglas 2826MB3. On the other hand, the numerical approach was performed using ANSYS 2016 Workbench software on beam designs with the same physical and geometrical characteristics as the beam specimens used in the experiment. The extracted CL and CD errors are found to be 0.7% and 8.5%, respectively. The above errors show that the proposed method is extremely capable of predicting the crack location and quite capable of predicting the crack depth. This work sets the basic prospects for the design of a new Structural Health Monitoring (SHM) technique combining the efficiency of the magnetoelastic sensors together with the proposed method.

Privacy-Preserving Subgraph Matching Protocol for Two Parties

Graph data structure has been widely used across many application areas, such as web data, social network, and cheminformatics. The main benefit of storing data as graphs is there exists a rich set of graph algorithms and operations that can be used to solve various computing problems, including pattern matching, data mining, and image processing. Among these graph algorithms, the subgraph isomorphism problem is one of the most fundamental algorithms that can be utilized by many higher level applications. The subgraph isomorphism problem is defined as, given two graphs [Formula: see text] and [Formula: see text], whether [Formula: see text] contains a subgraph that is isomorphic to [Formula: see text]. In this paper, we consider a special case of the subgraph isomorphism problem called the subgraph matching problem, which tests whether [Formula: see text] is a subgraph of [Formula: see text]. We propose a protocol that solve the subgraph matching problem in a privacy-preserving manner. The protocol allows two parties to jointly compute whether one graph is a subgraph of the other, while protecting the private information about the input graphs. The protocol is secure under the semi-honest setting, where each party performs the protocol faithfully.

A Information Retrieval Based on Question and Answering and NER for Unstructured Information Without Using SQL

In today’s world, the availability of information in the form of unstructured data is in abundance. The unstructured information received is more often than not in the form of natural language text. For any defense establishment, the spy data or any sensitive information received may be best utilized when the information can be extracted efficiently and easily. The proposed model is applicable wherever the influx of text-heavy (unstructured data) is high like the information from the world wide web, documents related to a particular domain, or any other source where the information is in the form of natural language. The proposed Natural Language Information Interpretation and Representation System (NLIIRS) accepts the information in the form of natural language text, processes the information and allows the user to retrieve information by rendering questions in natural language. The questions thus asked by the user are responded by NLIIRS in the form of factoid or phrase based answers. In comparison to the conventional question and answering systems the proposed NLIIRS uses the advantages of both named entity recognition as well as sequential pattern matching based answer search technique. The proposed technique helps us to avoid the use of structured query language (SQL) at the back-end for information processing, storage and extraction. The conversion of user query to SQL statements and also storing the unstructured text in the form of relation tables can be avoided by using NLIIRS. By using this approach in our novel text processing algorithm, after every execution step, the pattern matching and extraction process of the answers to the queries becomes concise and faster. The whole system has been designed on natural language tool kit of Stanford University which helped us to generate parts of speech tag, tokenize the data, and forming tree structure. The novel text processing algorithm utilizes the lemmatizer, stemmer and ne_chunker to prepare the text for information retrieval via Q&A. The advantage of this system is that it does not need training. This system will enable the user to retrieve any information of his/her choice from the available unstructured information.

Two-Phase PFAC Algorithm for Multiple Patterns Matching on CUDA GPUs

The rapid advancement of high speed networks has resulted in a significantly increasing number of network packets per second nowadays, implying network intrusion detection systems (NIDSs) need to accelerate the inspection of packet content to protect the computer systems from attacks. On average, the pattern matching process in a NIDS consumes approximately 70% of the overall processing time. The conventional Aho–Corasick (AC) algorithm, adopting a finite state machine to identify attack patterns in NIDSs, is too slow to meet the requirement of high speed networks. In view of this, several studies have used the features of a graphics processing unit (GPU) to improve the core searching process of the AC algorithm. For instance, parallel failureless Aho-Corasick (PFAC) algorithm improves the process of pattern matching effectively by removing backward branches in the original finite state machine created using the AC algorithm. In this way, boundary detection can be avoided totally if we allocate an individual thread to each byte of an input stream to identify any pattern starting at the thread’s starting position. However, through analysis, we found that this algorithm experiences a serious load imbalance problem. Therefore, this paper proposes a two-phase PFAC algorithm to address the problem. A threshold is predefined to divide execution into two phases, and the failureless finite state machine is also decoupled into two parts accordingly. In the first phase, every thread identifies patterns by running the tiny part of the decoupled failureless finite state machine that are stored in fast shared memory. In the second phase, all the threads requiring further searching in a same block are regrouped into a few warps for less branch divergence. According to experimental results, the proposed algorithm shows a performance improvement of 50% compared to the PFAC algorithm.

Effect of spiral undersampling patterns on FISP MRF parameter maps

PurposeArtifacts arising from undersampling are not always treatable as incoherent noise for the pattern matching process in Magnetic Resonance Fingerprinting (MRF). To estimate the effect of undersampling artifacts on MRF quantitative results, spiral sampling trajectories and their temporal variation is examined. MethodsThe effect of sampling trajectories and their variation during the MRF experiment was assessed by characterizing aliasing artifacts. Temporal rearrangements of sampling trajectories were tested and evaluated in simulations and scans of phantoms and in a volunteer brain. ResultsResults show that some temporal variations of sampling patterns can lead to spatial biases in MRF parameter maps. Observed effects are consistent with derived performance indicators for different interleaving schemes, leading to substantially improved MRF sampling patterns. ConclusionWith the help of the presented simulation framework, MRF implementations can be investigated and improved. This was demonstrated for a spiral FISP (Fast imaging with steady-state free precession) MRF implementation, where a significantly improved interleaving scheme was identified, and confirmed by experiment.

Computing With Networks of Oscillatory Dynamical Systems

As we approach the end of the silicon road map, alternative computing models that can solve at-scale problems in the data-centric world are becoming important. This is accompanied by the realization that binary abstraction and Boolean logic, which have been the foundations of modern computing revolution, fall short of the desired performance and power efficiency. In particular, hard computing problems relevant to pattern matching, image and signal processing, optimizations, and neuromorphic applications require alternative approaches. In this paper, we review recent advances in oscillatory dynamical system-based models of computing and their implementations. We show that simple configurations of oscillators connected using simple electrical circuits can result in interesting phase and frequency dynamics of such coupled oscillatory systems. Such networks can be controlled, programmed, and observed to solve computationally hard problems. Although our discussion in this paper is limited to insulator-to-metal transition devices and spin-torque oscillators, the general philosophy of such a computing paradigm of “let physics do the computing” can be translated to other mediums as well, including micromechanical and optical systems. We present an overview of the mathematical treatments necessary to understand the time evolution of these systems and highlight the recent experimental results in this area that suggest the potential of such computational models.

ONLINE SIGNATURE RECOGNITION AND VERIFICATION USING (SURF) ALGORITHM WITH SVM KERNELS

One important way to identify the signer is the personal signature. The operation of recognize the signature and identify it, is very important. This process may be done either offline or online. This paper explains the online technique. Features extraction, pattern matching and Images processing are techniques used for signature confirmation. Speed up robust features (SURF) is an algorithm that uses the image of local feature with ability for matching images. SURF recognizes, describes and extracts the local feature of forged signature from the image. SURF algorithm provides fast and accurate comparison tool that can work under different lights, visions and rotation situations to check if the person signature is original or forged. The features extracted from the SURF algorithm are entered into Bag-of-word features. The features of bag-of-word are used inside multiclass Support Vector Machine (SVM) classifies. SURF with SVM kernels gives an accuracy of 98.75%.

RDF-F: RDF Datatype inFerring Framework

In the context of RDF document matching/integration, the datatype information, which is related to literal objects, is an important aspect to be analyzed in order to better determine similar RDF documents. In this paper, we present an RDF Datatype in Ferring Framework, called RDF-F, which provides two independent datatype inference processes: 1) a four-step process consisting of (i) a predicate information analysis (i.e., deduce the datatype from existing range property), (ii) an analysis of the object value itself by a pattern-matching process (i.e., recognize the object lexical space), (iii) a semantic analysis of the predicate name and its context, and (iv) generalization of Numeric and Binary datatypes to ensure the integration; and 2) a non-ambiguous lexical-space-matching process, where literal values are inferred by the modification of their representation, following new lexical spaces. We evaluated the performance and the accuracy of both processes with datasets from DBpedia. Results show that the execution time of both indicators is linear and their accuracy can increase up to 97.10 and 99.30%, respectively.

Identifying enablers and barriers: shaping collaborative sport governance theory

ABSTRACTThis study explores the enablers and barriers present when attempting to adopt a collaborative governance system in a non-profit sport. An empirical qualitative study was employed to shed light on these enablers and barriers with a particular focus on the behaviours and actions of those charged to govern. Three sports from Australia took part in the study and 36 in-depth interviews were conducted with participants from national and state organizations operating within those networks. Data were analysed using a pattern matching process and a thematic structure was subsequently developed. The current state of collaborative behaviours and its impact for future collaboration, the institutional design of the federal model including board composition and leadership, and the ability of the strategic planning process to act as a catalyst to facilitate collaboration emerged as the key themes in the study. The results of this work advance our understanding of the enablers and barriers facing systemic governing structures and increase our ability to theorize in relation to collaborative sport governance. A number of implications for sport governance practice and possible extensions for sport governance research based on these findings conclude the paper.

Fast Full-Search-Equivalent Pattern Matching Using Asymmetric Haar Wavelet Packets

Pattern matching is widely used in signal processing, computer vision, and image and video processing. One efficient approach is to perform pattern matching in a transform domain that has good energy packing ability and so allows early rejection of most mismatched candidates. Calculating the transforms of pixels in sliding windows requires much computation, and so fast algorithms are employed. Existing methods require $O(u)$ additions per pixel for projecting input pixels onto $u~2\text{D}$ basis vectors. In this paper, we propose a new 2D transform, called asymmetric 2D Haar transform, and extend it to wavelet packets that contain exponentially large number of bases. A basis selection algorithm is then proposed to search for the optimal basis in the wavelet packets. A fast algorithm is also developed, which can compute $u$ projection coefficients with only $O(\log u)$ additions per pixel. The results of experiments show that the proposed fast algorithm and the proposed transform can significantly accelerate the full-search-equivalent pattern matching process and outperform state-of-the-art methods.

Cognitive Biases Within Decision Making During Fire Evacuations

During a fire evacuation, once an individual perceives cues from a fire event, they must interpret them to assess the new situation and determine whether action is required. It is proposed that this assessment and action selection can employ either an automatic or reflective processing system depending on the nature of the situation and the experiences of the individual involved. This decision-making process is bounded in terms of the information available, the time available, and an individual’s resources to process such information that influences which processing mechanism is adopted. To compensate for such limitations and manage the uncertainty and complexity associated with the decision-making process, people may employ heuristics that reduce decision-making from a cognitively effortful problem-solving task requiring mental reflection, to a less effortful pattern-matching process, where stored conditions and expectations are quickly scanned to identify relevant responses. During this decision-making process cognitive biases may occur which cause an individual to neglect or be biased towards certain information: this may potentially lead to an inappropriate and/or unexpected response. Cognitive biases affect performance without the individual being directly aware of them. This paper identifies cognitive biases from existing literature that may influence a person’s decision-making process during a fire evacuation, along with how these align with general decision-making in the process. The purpose of the article is to promote consideration of cognitive biases in the modeling of evacuee behavior, as well as during the fire safety design of buildings and evacuation procedures.

A New Malware Detection System Using Machine Learning Techniques for API Call Sequences

ABSTRACTThe detection and classification of malwares in windows executables is an important and demanding task in the field of data mining. The malwares can easily damage the system by creating harm in the user's system, so some of the existing techniques are developed in the traditional works for an accurate malware detection. But, it lacks some major drawbacks such as inaccurate detection, not highly efficient, requires a large amount of time to classify the malware type, and an increased computational complexity. To solve these issues, this article develops an efficient system for detecting the malwares in an Application Programmable Interfaces (APIs), and classifying its type as worms, virus, Trojans, or normal. Initially, the input dataset is preprocessed by normalizing the data, then its upper and lower boundaries are estimated during feature extraction. Furthermore, the Rete algorithm is implemented to generate the rules based on the pattern matching process. Here, the Multi-Dimensional Naïve Bayes Classification (MDNBS) is implemented to classify the malware that occurred in an API call sequences. In experiments, the performance results of the existing and proposed techniques are evaluated and compared based on the measures of True Positive Rate (TPR), False Positive Rate (FPR), precision, recall, f-measure and, accuracy.

Comparing clinician descriptions of frailty and geriatric syndromes using electronic health records: a retrospective cohort study

BackgroundGeriatric syndromes, including frailty, are common in older adults and associated with adverse outcomes. We compared patients described in clinical notes as “frail” to other older adults with respect to geriatric syndrome burden and healthcare utilization.MethodsWe conducted a retrospective cohort study on 18,341 Medicare Advantage enrollees aged 65+ (members of a large nonprofit medical group in Massachusetts), analyzing up to three years of administrative claims and structured and unstructured electronic health record (EHR) data. We determined the presence of ten geriatric syndromes (falls, malnutrition, dementia, severe urinary control issues, absence of fecal control, visual impairment, walking difficulty, pressure ulcers, lack of social support, and weight loss) from claims and EHR data, and the presence of frailty descriptions in clinical notes with a pattern-matching natural language processing (NLP) algorithm.ResultsOf the 18,341 patients, we found that 2202 (12%) were described as “frail” in clinical notes. “Frail” patients were older (82.3 ± 6.8 vs 75.9 ± 5.9, p < .001) and had higher rates of healthcare utilization, including number of inpatient hospitalizations and emergency department visits, than the rest of the population (p < .001). “Frail” patients had on average 4.85 ± 1.72 of the ten geriatric syndromes studied, while non-frail patients had 2.35 ± 1.71 (p = .013). Falls, walking difficulty, malnutrition, weight loss, lack of social support and dementia were more highly correlated with frailty descriptions. The most common geriatric syndrome pattern among “frail” patients was a combination of walking difficulty, lack of social support, falls, and weight loss.ConclusionsPatients identified as “frail” by providers in clinical notes have higher rates of healthcare utilization and more geriatric syndromes than other patients. Certain geriatric syndromes were more highly correlated with descriptions of frailty than others.

Anonymous subject identification and privacy information management in video surveillance

The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework.

A multi-level approach to highly efficient recognition of Chinese spam short messages

The problem of spam short message (SMS) recognition involves many aspects of natural language processing. A good solution to solving the problem can not only improve the quality of people experiencing the mobile life, but also has a positive role on promoting the analysis of short text occurring in current mobile applications, such as Webchat and microblog. As spam SMSes have characteristics of sparsity, transformation and real-timedness, we propose three methods at different levels, i.e., recognition based on symbolic features, recognition based on text similarity, and recognition based on pattern matching. By combining these methods, we obtain a multi-level approach to spam SMS recognition. In order to enrich the pattern base to reduce manual labor and time, we propose a quasi-pattern learning method, which utilizes quasi-pattern matching results in the pattern matching process. The method can learn many interesting and new patterns from the SMS corpus. Finally, a comprehensive analysis indicates that our spam SMS recognition approach achieves a precision rate as high as 95.18%, and a recall rate of 95.51%.

Application of character recognition to robot control on smartphone test system

In this study, three webcams are applied to a five-joint robotic arm system. One camera is applied to recognize commands from control panel, which consist of words and numbers. The letters of command can be obtained after image is transformed from red, green, and blue to hue, saturation, and lightness color space and using the optical character recognition and pattern matching process. The message will then be sent to robot system. Another two cameras are used for catching the left and right images of an object. Calibration procedure of the robot and cameras is performed first. The values from image plane can be compared and transformed to three-dimensional coordinates by the Q matrix. The coordinates are then translated to 4096 precision values in robotic arm system. Movements of the robotic arm are based on fuzzy logic theory that can drive the robotic arm to the relative point and position of a given object. Feedback values of arm movement are applied to correct the position error in real time. The robot system can catch the three-dimensional coordinates of the object and perform smartphone automatic testing operations by the commands from visual recognition system.