Pattern Matching Algorithm Research Articles

Time Complexity of Knuth Morris Algorithm and Rejang Algorithm in Rejang-Indonesian Translator

Among the pattern-matching algorithms is the Knuth-Morris algorithm. In order to minimize the number of comparisons required and, in the worst scenario, achieve an ideal O(n+m) running time, the Knuth-Morris search algorithm skips unneeded comparisons. Every character in the text and every character in the pattern must be checked at least once by the pattern-matching algorithm. The Knuth-Morris algorithm's primary goal is to preprocess the pattern string P in order to determine the failure function f, which displays P's precise shift, so that earlier comparisons can be reused. In order to extract the fundamental word of the attached sentence, words containing affixes are separated using the Rejang stemming method. The purpose of this research is to determine the time complexity of the Rejang method and the Knuth-Morris algorithm based on affix groups. The Rapid Application Development (RAD) approach, which entails planning, designing, building, and implementing, is used during the research stages. The research results have produced efficient and effective Knuth Morris algorithm and Rejang algorithm, where efficiency is indicated by the algorithm time complexity of O (log n), and effectiveness is indicated by the accuracy results of 99% against testing 6000 affixed words.

Order-Preserving Pattern Matching with Partition

Order-preserving pattern matching, which considers the relative orders of strings, can be applied to time-series data analysis. To perform a more meaningful analysis of time-series data, approximate criteria for the order-isomorphism are necessary, considering diverse types of errors. In this paper, we introduce a novel approximation criterion for the order-isomorphism, called the partitioned order-isomorphism. We then propose an efficient O(n+sort(m))-time algorithm for the order-preserving pattern matching problem considering the criterion of partition. A comparative experiment demonstrates that the proposed algorithm is more effective than the exact order-preserving pattern matching algorithm.

Preliminary analysis of acoustic detection of the Red-throated Caracara in northern Costa Rica

The noisy Red-Throated Caracara (Ibycter americanus) is a species whose population has inexplicably declined across much of its range and is now rare in the Pacific and Caribbean slopes of Costa Rica. Advances in automatic acoustic detection have transformed bird ecology, allowing researchers to analyze bird populations using pattern matching algorithms, machine learning, and random forest models. Although these studies are limited in the country, it represents an area with great interdisciplinary potential for technological advances. This study focused on the use of Pattern Matching to detect the presence of the Red-Throated Caracara in northern Costa Rica using a large number of sound recordings and its validation with metrics such as Accuracy, Precision Negative predictive value, Sensitivity, Specificity and Unweighted average recall. The results showed a moderate performance of the model by obtaining accuracy and precision values of 0.71 compared to the values obtained in other investigations in which the reported model was used. Therefore, we suggest exploring new techniques and methods to improve the detection of the species, considering the particular acoustic structure, the repertoire of sounds of the species and similarities with vocalizations of other species. This similarity could indicate a supposed anti-predator defense behavior by “imitating” the sounds of other species with which it shares habitat. To optimize this acoustic detection, we recommend using complementary techniques such as noise filters that improve the quality and precision of the data.

Aerial Map-Based Navigation by Ground Object Pattern Matching

This paper proposes a novel approach to map-based navigation for unmanned aircraft. The proposed approach employs pattern matching of ground objects, not feature-to-feature or image-to-image matching, between an aerial image and a map database. Deep learning-based object detection converts the ground objects into labeled points, and the objects’ configuration is used to find the corresponding location in the map database. Using the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern-matching problem. The pattern-matching algorithm proposed in this paper does not require altitude information or a camera model to estimate the horizontal geographical coordinates of the vehicle. Moreover, it requires significantly less storage because the map database is represented as a set of tuples, each consisting of a label, latitude, and longitude. Probabilistic data fusion with the inertial measurements by the Kalman filter is incorporated to deliver a comprehensive navigational solution. Flight experiments demonstrate the effectiveness of the proposed system in real-world environments. The map-based navigation system successfully provides the position estimates with RMSEs within 3.5 m at heights over 90 m without the aid of the GNSS.

Research of electrical impedance tomography based on multilayer artificial neural network optimized by Hadamard product for human-chest models

Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.

GPU-accelerated relaxed graph pattern matching algorithms

GPU-accelerated relaxed graph pattern matching algorithms

Electrospray deposition of physical unclonable functions for drug anti-counterfeiting

In recent years, pharmaceutical counterfeiting has become an increasingly dangerous situation. A patient who unknowingly consumes a counterfeit drug is at a serious health risk. To address this problem, a low-cost and robust approach for authentication that can be administered at the point-of-care is required. Our proposed solution uses Optical Physical Unclonable Functions (PUFs); patterns formed by a stochastic process that can be used for authentication. We create edible PUFs (ePUFs) using electrospray deposition, which utilizes strong electric fields to atomize a liquid suspension into a plume of micro-scale droplets that are delivered to the target. The ePUFs are electrospray-deposited from an edible ink directly onto the surface of the drug tablets. The process parameters (flow rate, translation speed, and suspension concentration) govern the characteristics of the ePUF to provide highly stochastic patterns. To evaluate our approach, 200 ePUFs were deposited onto tablets at various conditions, followed by imaging and storage of the patterns in a database. For ePUF authentication, a machine vision approach was created using the open source SIFT pattern matching algorithm. Using optimized pattern-matching constraints, our algorithm was shown to be 100% successful in authenticating the cellphone images of the ePUFs to the database. Additionally, the algorithm was found to be robust against changes in illumination and orientation of the cellphone images.

Multi pattern matching algorithm for embedded computer network engineering intrusion detection system

In computer networks, security issues persist, and addressing hidden security risks is pivotal for ensuring network security. However, traditional single pattern matching algorithms like BM (Boyer-Moore) lack efficiency for network intrusion detection. This study employs multiple pattern matching algorithms to bolster the security of computer network engineering intrusion detection systems (IDS). A computer network intrusion detection system (NIDS) is designed using embedded technology to collect network logs and other pertinent data, subsequently comparing log data packets. The study delves into a multi pattern matching algorithm, AC (Aho-Corasick), which incorporates the SUNDAY algorithm to optimize unnecessary string matching jumps. Furthermore, the AC algorithm and BM algorithm are fused as control methods. Randomly generated 48M text data is utilized for testing purposes, comparing the AC algorithm, AC-BM algorithm, and AC-SUNDAY algorithm. For instance, when the pattern string length is 20 bytes, the memory consumption of the AC algorithm, AC-BM algorithm, and AC-SUNDAY algorithm is 12.2 MB, 9.8 MB, and 6.2 MB respectively. The findings indicate that applying the AC-SUNDAY algorithm in NIDS effectively reduces memory consumption and enhances the efficacy of network intrusion detection.

Deep magnetic resonance fingerprinting based on Local and Global Vision Transformer

To mitigate systematic errors in magnetic resonance fingerprinting (MRF), the precomputed dictionary is usually computed with minimal granularity across the entire range of tissue parameters. However, the dictionary grows exponentially with the number of parameters increase, posing significant challenges to the computational efficiency and matching accuracy of pattern-matching algorithms. Existing works, primarily based on convolutional neural networks (CNN), focus solely on local information to reconstruct multiple parameter maps, lacking in-depth investigations on the MRF mechanism. These methods may not exploit long-distance redundancies and the contextual information within voxel fingerprints introduced by the Bloch equation dynamics, leading to limited reconstruction speed and accuracy. To overcome these limitations, we propose a novel end-to-end neural network called the Local and Global Vision Transformer (LG-ViT) for MRF parameter reconstruction. Our proposed LG-ViT employs a multi-stage architecture that effectively reduces the computational overhead associated with the high-dimensional MRF data and the transformer model. Specifically, a local Transformer encoder is proposed to capture contextual information embedded within voxel fingerprints and local correlations introduced by the interconnected human tissues. Additionally, a global Transformer encoder is proposed to leverage long-distance dependencies arising from shared characteristics among different tissues across various spatial regions. By incorporating MRF physics-based data priors and effectively capturing local and global correlations, our proposed LG-ViT can achieve fast and accurate MRF parameter reconstruction. Experiments on both simulation and in vivo data demonstrate that the proposed method enables faster and more accurate MRF parameter reconstruction compared to state-of-the-art deep learning-based methods.

Communication network security situation analysis based on time series data mining technology

AbstractCommunication network security situation analysis prevents unauthorized users from accessing and stealing sensitive information. Network security analysis aims to monitor, discover, and eradicate security flaws by carefully examining the network’s architecture, data, and traffic to ensure safety. In time series data mining analysis by cyber terrorism, specialists must pay attention to cyber security, which involves identifying the elements contributing to long-term trends or systemic patterns via pattern-matching algorithms and other types of inferential processing on large datasets. The challenging characteristics of communication network security situation analysis are data loss, security breaches, hacking, and viruses. Hence, in this research, attention mechanism-based convolutional neural network-enabled practical byzantine fault tolerant (AMBCNN-PBFT) has been designed to improve communication network security situation analysis in time series data mining. AMBCNN-PBFT helps to increase communication network security usage and support the expansion during the evaluation system by optimizing the time series data mining. AMBCNN-PBFT effectively predicts the rise in the communication network, associated with faster times series benefits data mining approach. The study concludes that the AMBCNN-PBFT efficiently indicates and validates the communication network security in time series data mining during the evaluation system. The experimental analysis of AMBCNN-PBFT outperforms the data mining time series in terms of accuracy, efficiency, performance, and prediction.

Pattern matching algorithms in blockchain for network fees reduction

Blockchain received a vast amount of attention in recent years and is still growing. The second generation of blockchain, such as Ethereum, allows execution of almost any program in EVM, making it a global protocol for distributed applications. The code deployment and each operation performed in EVM cost the network fee called gas, whose price varies and can be significant. That is why code optimization and well-chosen algorithms are crucial in programming on the blockchain. This paper evaluates the gas usage of several exact pattern matching algorithms on the EVM. We also propose an efficient implementation of the algorithms in the Solidity/YUL language. We evaluate the gas fees of all the algorithms for different parameters (such as pattern length, alphabet size, and text size). We show a significant gas fee and execution time reduction with up to 22-fold lower gas usage and 55-fold speed-up compared to StringUtils (a popular Solidity string library).

Forward linearised tree pattern matching using tree pattern border array

We define a tree pattern border array as a property of linearised trees analogous to border arrays from the string domain. We consider tree patterns with node wildcards and subtree wildcards. Then we use the tree pattern border array in a new forward tree pattern matching algorithm for ordered ranked trees. The algorithm finds all occurrences of a single linearised tree pattern in a linearised input tree. As with the classical Morris–Pratt algorithm for searching in strings, the tree pattern border array is used to determine shift lengths in the searching phase of the tree pattern matching algorithm. We compare this new algorithm with the best performing previously existing algorithms based on backward linearised tree pattern matching algorithms, (non-)linearised tree pattern matching algorithms using finite tree automata or stringpath matchers. We show that our new algorithm outperforms other tree pattern matching algorithms in single tree pattern matching.

Multi-Pattern GPU Accelerated Collision-Less Rabin-Karp for NIDS

In the domain of network communication, network intrusion detection systems (NIDS) play a crucial role in maintaining security by identifying potential threats. NIDS relies on packet inspection, often using rule-based databases to scan for malicious patterns. However, the expanding scale of internet connections hampers the rate of packet inspection. To address this, some systems employ GPU accelerated pattern matching algorithms. Yet, this approach is susceptible to denial of service (DOS) attacks, inducing hashing collisions and slowing inspection. This research introduces a GPU-optimized variation of the Rabin-Karp algorithm, achieving scalability on GPUs while resisting DOS attacks. Our open-source solution (https://github.com/AnasAbbas1/NIDS) combines six polynomial hashing functions, eliminating the need for false-positive validation. This leads to a substantial improvement in inspection speed and accuracy. The proposed system ensures minimal packet misclassification rates, solidifying its role as a robust tool for real-time network security.

SAT-Based Algorithms for Regular Graph Pattern Matching

Graph matching is a fundamental problem in pattern recognition, with many applications such as software analysis and computational biology. One well-known type of graph matching problem is graph isomorphism, which consists of deciding if two graphs are identical. Despite its usefulness, the properties that one may check using graph isomorphism are rather limited, since it only allows strict equality checks between two graphs. For example, it does not allow one to check complex structural properties such as if the target graph is an arbitrary length sequence followed by an arbitrary size loop. We propose a generalization of graph isomorphism that allows one to check such properties through a declarative specification. This specification is given in the form of a Regular Graph Pattern (ReGaP), a special type of graph, inspired by regular expressions, that may contain wildcard nodes that represent arbitrary structures such as variable-sized sequences or subgraphs. We propose a SAT-based algorithm for checking if a target graph matches a given ReGaP. We also propose a preprocessing technique for improving the performance of the algorithm and evaluate it through an extensive experimental evaluation on benchmarks from the CodeSearchNet dataset.

Application-Oriented Pattern Analysis Utilizing the SPTA Algorithm for Efficient Targeted Sequential Pattern Mining

In the gaming business, esports, also known as competitive gaming, has ushered in a new age that has brought up some innovative issues. One of these challenges is determining the level of player competency based on their plans and talents. Using game traces from Rocket League, a one-of-a-kind "soccer with rocket-powered cars" game, this investigation focuses on the automatic recognition of skill shots, an essential component of player performance. Challenges arise for classic pattern matching algorithms due to the unique characteristics of each skill performance. A Player Skill Modeling through Data-Centric Patterns (PSM-DCP) strategy that uses pattern mining and supervised learning techniques offers a solution to this issue. To highlight the potential of our system for player modeling shows that it can efficiently detect a wide variety of Rocket League abilities and shots. Also confirm the efficacy of this technique by conducting a comprehensive set of tests, which reveals that it is able to effectively differentiate and evaluate skillshots. The study results have a wide range of implications for various applications within esports. These applications include the improvement of match-making algorithms, the provision of assistance to game commentators in the form of insightful assessments, and the development of learning systems that are intended to improve player abilities. This study not only tackles the unique context of Rocket League, but it also offers a framework for exploiting data-centric techniques in esports analytics. This allows for additional developments and applications in competitive gaming, which opens up new paths for possibilities.

Accuracy assessment of crystal orientation indexations by EBSD

Accuracy and uncertainty analyses are essential for every measurement technology. In crystal orientation indexation by electron backscatter diffraction (EBSD), a series of accuracy estimations have been made for the Hough transform and dictionary indexation methods. The mean angular deviation is a standard parameter to indicate orientation accuracy, but this criterion is indirect and closely related to the accuracy of the projection center coordinates. Precise known orientation relationships (OR) are necessary to evaluate orientation accuracy without the ground truth. The current work uses the natural crystal twins and hardware OR to assess the orientation accuracy directly. The accuracy level for different EBSD analysis methods is compared through four experimental data sets of varying pattern definitions and noise levels. It is found that the full pattern match (FPM) algorithms improve the accuracy as compared to Hough indexation, and the gain varies greatly between 14% for fast acquisitions and 20 times for high-quality patterns. Depending on the resolution and quality of diffraction patterns, FPM results in an accuracy of crystal orientation between 0.04° and 0.9°. Comparing the two FPM variants, matching the gradients of diffraction patterns performs better in the case of high-to-median quality acquisitions while matching the pattern itself is more accurate for more noisy and low-definition patterns.

High precision orientation mapping from 4D-STEM precession electron diffraction data through quantitative analysis of diffracted intensities

The association of scanning transmission electron microscopy (STEM) and detection of a diffraction pattern at each probe position (so-called 4D-STEM) represents one of the most promising approaches to analyze structural properties of materials with nanometric resolution and low irradiation levels. This is widely used for texture analysis of materials using automated crystal orientation mapping (ACOM). Herein, we perform orientation mapping in InP nanowires exploiting precession electron diffraction (PED) patterns acquired by an axial CMOS camera. Crystal orientation is determined at each probe position by the quantitative analysis of diffracted intensities minimizing a residue comparing experiments and simulations in analogy to x-ray structural refinement. Our simulations are based on the two-beam dynamical diffraction approximation and yield a high angular precision (∼0.03°), much lower than the traditional ACOM based on pattern matching algorithms (∼1°). We anticipate that simultaneous exploration of both spot positions and high precision crystal misorientation will allow the exploration of the whole potentiality provided by PED-based 4D-STEM for the characterization of deformation fields in nanomaterials.

Analysis of the Enhanced Boyer-Moore Search Algorithm for a Desktop PC Search Engine

In the digital era, the volume of digital contents stored on personal computers has been rapidly and continuously increasing. Finding documents among this bulk of files has become tedious and stressful. In this regard, string searching and pattern matching algorithms must be employed to alleviate the performance of search engines, making it easier to find the desired documents. This paper aims to analyze the performance of an enhanced Boyer-Moore (BM) search algorithm to assist in finding the intended information or files. A series of tests have been conducted in order to compare the enhanced BM algorithm with the traditional BM algorithm. The results have shown that the Enhanced Boyer-Moore Algorithm performed the search and managed to speed up the searching process by reducing the time duration and the accuracy of the overall performance as the input text increased.

A Rule‐Based Framework to Identify Severity of Dementia from Unstructured Electronic Health Record Data

AbstractBackgroundThe severity of Alzheimer’s disease and related dementias (ADRD) is mostly documented in unstructured textual data in electronic health records (EHR). This information is important for clinical decision‐making yet is often “hidden” in free text fields and then not as readily available as information in the structured fields for clinicians to act upon. This study assessed the feasibility and potential bias in using keywords and rules‐based matching for obtaining information about severity of ADRD from EHR.MethodWe used EHR data from a large academic healthcare system that included patients with a primary discharge diagnosis of ADRD based on ICD‐9/10 codes between 2014 and 2020. The severity of ADRD was determined using clinicians’ notes based on (1) scores from the Mini Mental State Examination and Montreal Cognitive Assessment, and (2) explicit terms for ADRD severity (e.g., “mild dementia”, “advanced AD”). A list of common ADRD symptoms, cognitive test names and diagnosis stage terms was compiled and iteratively refined based on prior literature and clinical expertise. We used the list together with rule‐based pattern matching to identify the context in which the word/phrase was mentioned. The algorithm was developed in python 3.8 using spaCy and pandas library. We assessed the prevalence of the documented ADRD severity and used logistic regression to examine whether the severity varies by patient characteristics.ResultA total of 9,115 patients with over 65k providers’ notes were evaluated. Overall, 16.85% (N = 1,536) of patients were documented with mild ADRD, 17.95% (N = 1,636) were documented with moderate or severe ADRD, and 65.20% (N = 5,942) did not have any documentation of the severity of their ADRD. Compared with patients with mild ADRD, those documented with more advanced ADRD were older, more likely to be female, black, and receive their diagnoses in a primary care or in‐hospital setting. Relative to patients with undocumented ADRD severity, those documented with ADRD severity had a similar distribution regarding sex, race, and rural/urban living environment.ConclusionThis study demonstrated the value of unstructured EHR data and the feasibility of using pattern matching algorithm in identifying severity of ADRD. Still, differences in the documentation may introduce bias in the algorithm.

An Improved Hashing Approach for Biological Sequence to Solve Exact Pattern Matching Problems

Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm’s effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns.