A Database for Counterfeit Electronics and Automatic Defect Detection Based on Image Processing and Machine Learning

2016 ◽  
Author(s):  
Navid Asadizanjani ◽  
Sachin Gattigowda ◽  
Mark Tehranipoor ◽  
Domenic Forte ◽  
Nathan Dunn
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Integrated Circuits ◽  
Web Application ◽  
The United States ◽  
Global Market ◽  
Online Database ◽  
Ongoing Effort ◽  
Time Period ◽  
Physical Defects

Abstract Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.

scholarly journals Export Competitiveness of Bangladesh Readymade Garments Sector

2019 ◽  
Vol 8 (3) ◽  
pp. 45-63
Author(s):  
Md. Sajib Hossian ◽  
Rashedul Kabir ◽  
Enamul Hafiz Latifee
Keyword(s):  
Market Share ◽  
The United States ◽  
Global Market ◽  
Export Market ◽  
Actual Condition ◽  
Percentage Points ◽  
Time Period ◽  
Export Competitiveness ◽  
Geographical Dispersion ◽  
Geographical Diversification

The paper evaluates the competitiveness of Bangladesh Readymade Garments (RMG) sector in the global RMG market through Market share analysis using two-digit classifications of Harmonized System (HS)data for the period covering 2012 to 2016 to understand the actual condition of sector’s export comparison with its competitors. While Trade Entropy index has also been calculated to make an attempt to understand the geographical dispersion of export market scenario using two-digit classifications of HS data for the time period between 2006 and 2016. The findings of the paper based on market share analysis illustrate that Bangladesh’s RMG export share in the global market reached 7.50percentin 2016, a shot up by 2.73 percentage points, indicating an increase in export competitiveness, whereas China’s share declined to 33.75 percentin 2016, with an exception to 2013. During this period, Vietnam and India’s market share increased by 2.24, and 0.72 percentage points respectively, making them the 3rd and 6th largest RMG exporter in the world. Trade Entropyindex calculationindicates that the geographical diversification of Bangladesh’s RMG export has increased considering the value of the index, it has increased more than two times during this time period. But, in spite of increasing geographical diversification of the RMG sector market, itsexport is still heavily concentrated on two markets: the European UnionEU (28), and the United States of America (USA). The paper also provides a set of policyrecommendations that wouldbe helpful to both RMG sector stakeholders and policymakers to move forward the sector towards more export competitiveness.

Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

Pricing Initial Audit Engagements: A Test of Competing Theories

1999 ◽  
Vol 74 (2) ◽  
pp. 201-216 ◽  
Author(s):  
Allen T. Craswell ◽  
Jere R. Francis
Keyword(s):  
United States ◽  
Product Quality ◽  
Audit Fees ◽  
The United States ◽  
Public Disclosure ◽  
Audit Pricing ◽  
Experience Goods ◽  
Economic Theories ◽  
Time Period

Two competing theories of initial engagement audit pricing are examined empirically. DeAngelo's (1981a) model predicts initial engagement discounts in all settings, while Dye's (1991) model specifically predicts discounting will not occur in settings where audit fees are publicly disclosed. Unlike the United States and most countries, audit fees are publicly disclosed in Australia. Our study examines initial engagement pricing in Australia during a time period when comparable U.S. studies report discounts of 25 percent (Ettredge and Greenberg 1990; Simon and Francis 1988). The Australian evidence finds initial engagement discounting only for upgrades from non-Big 8 to Big 8 auditors. Discounting for upgrades to Big 8 auditors is consistent with economic theories of discount pricing by sellers of higher-priced, higher-quality experience goods as an inducement to purchase when uncertainty about product quality is resolved through buying (experiencing) the goods. The evidence in our study is generally consistent with Dye's (1991) conclusion that public disclosure of audit fees precludes initial engagement discounting and the potential independence problems arising from such discounting.

The 1980s to the Present

2018 ◽  
Author(s):  
Gregory A. Barton
Keyword(s):  
United States ◽  
United Nations ◽  
Organic Farming ◽  
The United States ◽  
Global Market ◽  
Health Food ◽  
Organic Certification ◽  
Organic Farmers ◽  
The United Nations ◽  
Mainstream Press

While a few positive stories on organic farming appeared in the 1970s most mainstream press coverage mocked or dismissed organic farmers and consumers. Nevertheless, the growing army of consumer shoppers at health food stores in the United States made the movement impossible to ignore. The Washington Post and other newspapers shifted from negative caricatures of organic farming to a supportive position, particularly after the USDA launched an organic certification scheme in the United States under the leadership of Robert Bergland. Certification schemes in Europe and other major markets followed, leading to initiatives by the United Nations for the harmonization of organic certification through multilateral agencies. As organic standards proliferated in the 1990s the United Nations stepped in to resolve the regulatory fragmentation creating a global market for organic goods.

Race and Gender

2020 ◽  
pp. 251-269 ◽  
Author(s):  
Timnit Gebru
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
The United States ◽  
Error Rates ◽  
Political Factors ◽  
Recidivism Rates ◽  
Race And Gender ◽  
Decision Tools ◽  
And Gender ◽  
Technical Solutions

This chapter discusses the role of race and gender in artificial intelligence (AI). The rapid permeation of AI into society has not been accompanied by a thorough investigation of the sociopolitical issues that cause certain groups of people to be harmed rather than advantaged by it. For instance, recent studies have shown that commercial automated facial analysis systems have much higher error rates for dark-skinned women, while having minimal errors on light-skinned men. Moreover, a 2016 ProPublica investigation uncovered that machine learning–based tools that assess crime recidivism rates in the United States are biased against African Americans. Other studies show that natural language–processing tools trained on news articles exhibit societal biases. While many technical solutions have been proposed to alleviate bias in machine learning systems, a holistic and multifaceted approach must be taken. This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.

Fat as a Floating Signifier

2019 ◽  
pp. 143-163
Author(s):  
Sabrina Strings
Keyword(s):  
United States ◽  
Discourse Analysis ◽  
The United States ◽  
Anglo Saxon ◽  
National Belonging ◽  
Time Period ◽  
Irish Women ◽  
Stuart Hall ◽  
Fat Phobia ◽  
Fat Stigma

Studies on the development of fat stigma in the United States often consider gender, but not race. This chapter adds to the literature on the significance of race in the propagation of fat phobia. I investigate representations of voluptuousness among “white” Anglo-Saxon and German women, as well as “black” Irish women between 1830 and 1890—a time period during which the value of a curvy physique was hotly contested—performing a discourse analysis of thirty-three articles from top newspapers and magazines. I found that the rounded forms of Anglo-Saxon and German women were generally praised as signs of health and beauty. The fat Irish, by contrast, were depicted as grotesque. Building on the work of Stuart Hall, I conclude that fat was a “floating signifier” of race and national belonging. That is, rather than being universally lauded or condemned, the value attached to fatness was related to the race of its possessor.

scholarly journals Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

2021 ◽  
Vol 14 (5) ◽  
pp. 472
Author(s):  
Tyler C. Beck ◽  
Kyle R. Beck ◽  
Jordan Morningstar ◽  
Menny M. Benjamin ◽  
Russell A. Norris
Keyword(s):  
United States ◽  
Machine Learning ◽  
Drug Interactions ◽  
The United States ◽  
Structural Features ◽  
Physiochemical Properties ◽  
Drug Dosing ◽  
Therapeutic Outcomes ◽  
Cyp Inhibition ◽  
Cyp Inhibitors

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

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