scholarly journals Autonomous Gateway Architecture for Security Using OpenCV

2021 ◽  
Vol 9 (11) ◽  
pp. 1488-1493
Author(s):  
Dhruv Piyush Parikh
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Exit Time ◽  
Machine Intelligence ◽  
License Plate ◽  
Entry And Exit ◽  
Image Contour ◽  
The Cost ◽  
Cctv Camera ◽  
Day By Day

Abstract: Today as we can see security for anything is considered to be a very important part of our livelihood and we need to seek more and more security every day in this fast growing world. As the security of public parking lots increases day by day and to ensure safety, many people are required in this job that increases the cost of security So we have looked into the process and came up with a plan to use computer vision for the security purpose which will reduce the manpower required for work instead with machine intelligence. We are going to use Computer Vision to mask the license plate and save it with the entry and exit time. This research paper will enhance the security provided by a CCTV camera in any public parking and will also keep the record of every car entering and exiting the parking area. Keywords: OpenCV, Machine Learning, EasyOCR, SQLite, Image Contour Processing

scholarly journals Automated parking management system for identifying vehicle number plate

2019 ◽  
Vol 13 (1) ◽  
pp. 77 ◽  
Author(s):  
Asha Singh ◽  
Prasanth Vaidya
Keyword(s):  
Character Recognition ◽  
Management System ◽  
Exit Time ◽  
License Plate ◽  
Real Time System ◽  
Efficient Management ◽  
Plate Number ◽  
Entry Time ◽  
Parking Management ◽  
The Cost

<p>By using image processing, the Automated parking management system (APMS) to recognize the license plate number for efficient management of vehicle parking and vehicle billing. It is an independent real-time system, reduces number of people involvement in parking areas. The main aim of this system is to automated payment collection. This (APMS) system extract and recognize license plate numbers from the vehicles, then that image is being processed and used to generate an electronic bill. Generally in the parking lots heavy labor work is needed. This system used to decrease the cost of the labor and also enhance the performance of the APMS. This system is composed of vehicles license plate number extraction, character segmentation and character recognition. A proper pre-processing is done before extracting the license plate and it also generates the entry time and exit time of the vehicle and finally generates the electronic bill.</p>

scholarly journals A Comparative Study of Bitcoin Price Prediction Using Machine Learning Algorithms

2020 ◽  
pp. 160-163
Author(s):  
M. Akhil Sai ◽  
K. Sarath Chandra Sai ◽  
M. Manu Koushik ◽  
K. Gowri Raghavendra Narayan
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Price Prediction ◽  
The Cost ◽  
Day By Day

ML and AI-helped exchanging have pulled in developing enthusiasm for as far back as not many years.We examine day-by-day information for different digital currencies over some stretch of time. We show that straightforward exchanging methodologies helped by innovative AI calculations outflank standard benchmarks. We have picked two Machine Learning Algorithms to play out a Comparative Study to foresee cost of a Bitcoin; we have utilized Decision tree regressor and LSTM Algorithms and watched execution of every calculation as far as anticipating the cost of Bitcoin. We saw that Decision tree regressor gives progressively effective and precise outcomes when contrasted with others.

scholarly journals Proficient Mining of Informative Gene from Microarray Gene Expression Dataset Using Machine Intelligence

2021 ◽  
Author(s):  
Priya Ravindran ◽  
S. Jayanthi ◽  
Arun Kumar Sivaraman ◽  
Dhanalakshmi R ◽  
A. Muralidhar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Microarray ◽  
Pearson Correlation ◽  
Machine Intelligence ◽  
Selection Strategy ◽  
Microarray Gene Expression ◽  
Genomic Expression ◽  
Expression Levels ◽  
The Mind ◽  
The Cost

The quick improvement of DNA microarray innovation empowers analysts to quantify the expression levels of thousands of genomic data and permits scientists effortlessly pick up and understanding the mind-boggling prediction in tumors based on genomic expression levels. The application in malignancy has been demonstrated and extraordinary achievement has been performed in both conclusion and clarification using the neurotic methodologies. In many cases, DNA microarray information about gene contains a large number of qualities and the majority of them are turned out to be uninformative and excess. In the interim, little size of tests of microarray information undermines the determination precision of factual models. In this way, choosing profoundly discriminative qualities from crude quality genetic expression can enhance the execution of genetic prediction and chopped down the cost of medicinal analysis. Pearson Correlation based Feature Selection strategy with machine learning methodologies is effective to locate a conspicuous arrangement of components which can be utilized to anticipate and idealize the blend of quality to analyze the disease. As conflicting to the customary cross approval, filter one cross approval technique is connected for the analyses. As needs be, the proposed blend between the PCBFS and Machine Learning methodology is an effective apparatus for disease grouping and can be actualized as a genuine clinical supportive system.

A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite

2020 ◽  
Vol 26 (26) ◽  
pp. 3049-3058
Author(s):  
Ting Liu ◽  
Hua Tang
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Future Development ◽  
Malaria Parasite ◽  
Mitochondrial Proteins ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Effective Drugs ◽  
Construction Strategies ◽  
Day By Day

The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

scholarly journals Quality Classification of Injection-Molded Components by Using Quality Indices, Grading, and Machine Learning

Polymers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 353
Author(s):  
Kun-Cheng Ke ◽  
Ming-Shyan Huang
Keyword(s):  
Machine Learning ◽  
Integrated Circuit ◽  
Quality Indices ◽  
Statistical Process ◽  
Suitable Alternative ◽  
Quality Grading ◽  
Quality Of Products ◽  
The Cost ◽  
Mlp Model

Conventional methods for assessing the quality of components mass produced using injection molding are expensive and time-consuming or involve imprecise statistical process control parameters. A suitable alternative would be to employ machine learning to classify the quality of parts by using quality indices and quality grading. In this study, we used a multilayer perceptron (MLP) neural network along with a few quality indices to accurately predict the quality of “qualified” and “unqualified” geometric shapes of a finished product. These quality indices, which exhibited a strong correlation with part quality, were extracted from pressure curves and input into the MLP model for learning and prediction. By filtering outliers from the input data and converting the measured quality into quality grades used as output data, we increased the prediction accuracy of the MLP model and classified the quality of finished parts into various quality levels. The MLP model may misjudge datapoints in the “to-be-confirmed” area, which is located between the “qualified” and “unqualified” areas. We classified the “to-be-confirmed” area, and only the quality of products in this area were evaluated further, which reduced the cost of quality control considerably. An integrated circuit tray was manufactured to experimentally demonstrate the feasibility of the proposed method.

scholarly journals Smooth input preparation for quantum and quantum-inspired machine learning

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhikuan Zhao ◽  
Jack K. Fitzsimons ◽  
Patrick Rebentrost ◽  
Vedran Dunjko ◽  
Joseph F. Fitzsimons
Keyword(s):  
Machine Learning ◽  
Quantum Algorithms ◽  
Machine Learning Algorithms ◽  
Low Rank ◽  
Smoothed Analysis ◽  
Machine Learning Applications ◽  
Data Points ◽  
Input Model ◽  
The Cost ◽  
Input Perturbation

AbstractMachine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoothed analysis that if the data analysis algorithm is robust against small entry-wise input perturbation, state preparation can always be achieved with constant queries. This criterion is typically satisfied in realistic machine learning applications, where input data is subjective to moderate noise. Our results are equally applicable to the recent seminal progress in quantum-inspired algorithms, where specially constructed databases suffice for polylogarithmic classical algorithm in low-rank cases. The consequence of our finding is that for the purpose of practical machine learning, polylogarithmic processing time is possible under a general and flexible input model with quantum algorithms or quantum-inspired classical algorithms in the low-rank cases.

scholarly journals A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Helder F. Castro ◽  
Jaime S. Cardoso ◽  
Maria T. Andrade
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Qualitative Analysis ◽  
Continuous Growth ◽  
Scientific Databases ◽  
Extensive Survey ◽  
Research Areas ◽  
Current State ◽  
Global View ◽  
Cooperative Production

The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

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