Anomaly Detection with Machine Learning Technique to Support Smart Logistics

2019 ◽  
pp. 461-472
Author(s):  
Nittaya Kerdprasop ◽  
Kacha Chansilp ◽  
Kittisak Kerdprasop ◽  
Paradee Chuaybamroong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Machine Learning Technique ◽  
Smart Logistics ◽  
Learning Technique

Anomaly Detection of Budgetary Allocations Using Machine-Learning-Based Techniques

2021 ◽  
Vol 107 ◽  
pp. 174-181
Author(s):  
Garba Aliyu ◽  
Ibrahim Enesi Umar ◽  
Irunokhai Eric Aghiomesi ◽  
Hassan Jimoh Onawola ◽  
Sandip Rakshit
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Unintended Consequences ◽  
Critical Examination ◽  
Machine Learning Technique ◽  
National Assembly ◽  
Substantial Modification ◽  
Short Period ◽  
Learning Technique ◽  
The Government

In Nigeria, a crucial responsibility of the executive arms of the government is to submit annual budgetary allocations to the national assembly for approval. Due to the diversity and complexity of the budget, the national assembly is mandated to carry out its constitutional duty of scrutinizing the budget to discover irregularity or anomaly, make recommendations, or substantial modification upon what it received. This is very challenging, particularly in Nigeria where there are many different ethnicities and regional, to ensure inclusiveness, the national assembly must carry out its constitutional duty diligently and carefully without fear or favor that often has unintended consequences. This might not be very easy to accomplish within a short period. Thus, this research aims at detecting an anomaly in the budget that will ease the legislative duty thereby facilitating the process of appropriation. The concept of Clustering for Machine learning technique was used for the detection of an anomaly, where the detected ones are noted and indicated for critical examination.

scholarly journals Anomaly detection in reconstructed quantum states using a machine-learning technique

2014 ◽  
Vol 89 (2) ◽  
Author(s):  
Satoshi Hara ◽  
Takafumi Ono ◽  
Ryo Okamoto ◽  
Takashi Washio ◽  
Shigeki Takeuchi
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Quantum States ◽  
Machine Learning Technique ◽  
Learning Technique

Quantum-state anomaly detection for arbitrary errors using a machine-learning technique

2016 ◽  
Vol 94 (4) ◽  
Author(s):  
Satoshi Hara ◽  
Takafumi Ono ◽  
Ryo Okamoto ◽  
Takashi Washio ◽  
Shigeki Takeuchi
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Quantum State ◽  
Machine Learning Technique ◽  
Learning Technique

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection
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