GDSL: A Universal Toolkit for Giving Semantics to Machine Language

BACKGROUND: DNA sequence alignment is one of the most fundamental and important operation to identify which gene family may contain this sequence, pattern matching for DNA sequence has been a fundamental issue in biomedical engineering, biotechnology and health informatics. OBJECTIVE: To solve this problem, this study proposes an optimal multi pattern matching with wildcards for DNA sequence. METHODS: This proposed method packs the patterns and a sliding window of texts, and the window slides along the given packed text, matching against stored packed patterns. RESULTS: Three data sets are used to test the performance of the proposed algorithm, and the algorithm was seen to be more efficient than the competitors because its operation is close to machine language. CONCLUSIONS: Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.

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Interactive machine-language programming

10.1145/1464013.1464036 ◽

1965 ◽

Cited By ~ 1

Author(s):

Butler W. Lampson

Keyword(s):

Machine Language

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An on-line machine language debugger for OS/360

10.1145/1478559.1478580 ◽

1969 ◽

Cited By ~ 2

Author(s):

William H. Josephs

Keyword(s):

Machine Language ◽

On Line

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Predicting their past: Machine language learning can discriminate the brains of chimpanzees with different early‐life social rearing experiences

Developmental Science ◽

10.1111/desc.13114 ◽

2021 ◽

Author(s):

Allyson J. Bennett ◽

Peter J. Pierre ◽

Michael J. Wesley ◽

Robert Latzman ◽

Steven J. Schapiro ◽

...

Keyword(s):

Language Learning ◽

Early Life ◽

Machine Language

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A hypothetical computer to simulate microprogramming and conventional machine language

ACM SIGMICRO Newsletter ◽

10.1145/378818.378829 ◽

1989 ◽

Vol 19-20 (4-1) ◽

pp. 4-10 ◽

Cited By ~ 1

Author(s):

Jerry E. Sayers ◽

David E. Martin

Keyword(s):

Machine Language ◽

Conventional Machine

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A methodology for machine language decompilation

10.1145/800182.810410 ◽

1974 ◽

Cited By ~ 5

Author(s):

Barron C. Housel ◽

Maurice H. Halstead

Keyword(s):

Machine Language

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An automatic seismic signal detection algorithm based on the Walsh transform

Bulletin of the Seismological Society of America ◽

10.1785/bssa0710041351 ◽

1981 ◽

Vol 71 (4) ◽

pp. 1351-1360

Author(s):

Tom Goforth ◽

Eugene Herrin

Keyword(s):

Signal Detection ◽

Real Time ◽

Seismic Signal ◽

Detection Algorithm ◽

Machine Language ◽

Walsh Transform ◽

Time Signal ◽

Data Systems ◽

Detection Capability ◽

Short Period

abstract An automatic seismic signal detection algorithm based on the Walsh transform has been developed for short-period data sampled at 20 samples/sec. Since the amplitude of Walsh function is either +1 or −1, the Walsh transform can be accomplished in a computer with a series of shifts and fixed-point additions. The savings in computation time makes it possible to compute the Walsh transform and to perform prewhitening and band-pass filtering in the Walsh domain with a microcomputer for use in real-time signal detection. The algorithm was initially programmed in FORTRAN on a Raytheon Data Systems 500 minicomputer. Tests utilizing seismic data recorded in Dallas, Albuquerque, and Norway indicate that the algorithm has a detection capability comparable to a human analyst. Programming of the detection algorithm in machine language on a Z80 microprocessor-based computer has been accomplished; run time on the microcomputer is approximately 110 real time. The detection capability of the Z80 version of the algorithm is not degraded relative to the FORTRAN version.

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Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.28.12952 ◽

2018 ◽

Vol 7 (2.28) ◽

pp. 312

Author(s):

Manu Kohli

Keyword(s):

Data Extraction ◽

Indirect Costs ◽

True Positive Rate ◽

Spare Parts ◽

Machine Learning Algorithms ◽

Machine Language ◽

Support Vector ◽

Data Sets ◽

Equipment Failure ◽

Using Data

Asset intensive Organizations have searched long for a framework model that would timely predict equipment failure. Timely prediction of equipment failure substantially reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk. In this paper, the author proposes a model that can predict equipment failure by using data from SAP Plant Maintenance module. To achieve that author has applied data extraction algorithm and numerous data manipulations to prepare a classification data model consisting of maintenance records parameters such as spare parts usage, time elapsed since last completed maintenance and the period to the next scheduled maintained and so on. By using unsupervised learning technique of clustering, the author observed a class to cluster evaluation of 80% accuracy. After that classifier model was trained using various machine language (ML) algorithms and subsequently tested on mutually exclusive data sets with an objective to predict equipment breakdown. The classifier model using ML algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) returned an accuracy and true positive rate (TPR) of greater than 95% to predict equipment failure. The proposed model acts as an Advanced Intelligent Control system contributing to the Cyber-Physical Systems for asset intensive organizations.

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