Machine learning framework for predicting reliability of solder joints

2019 ◽  
Vol 32 (2) ◽  
pp. 82-92 ◽  
Author(s):  
Sung Yi ◽  
Robert Jones
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Failure Modes ◽  
Solder Joints ◽  
Big Data Analytics ◽  
Content Type ◽  
Learning Framework ◽  
Surface Finishes ◽  
Reliability Of Solder Joints

Purpose This paper aims to present a machine learning framework for using big data analytics to predict the reliability of solder joints. The purpose of this study is to accurately predict the reliability of solder joints by using big data analytics. Design/methodology/approach A machine learning framework for using big data analytics is proposed to predict the reliability of solder joints accurately. Findings A machine learning framework for predicting the life of solder joints accurately has been developed in this study. To validate its accuracy and efficiency, it is applied to predict the long-term reliability of lead-free Sn96.5Ag3.0Cu0.5 (SAC305) for three commonly used surface finishes such OSP, ENIG and IAg. The obtained results show that the predicted failure based on the machine learning method is much more accurate than the Weibull method. In addition, solder ball/bump joint failure modes are identified based on various solder joint failures reported in the literature. Originality/value The ability to predict thermal fatigue life accurately is extremely valuable to the industry because it saves time and cost for product development and optimization.

scholarly journals Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Habeeb Balogun ◽  
Hafiz Alaka ◽  
Christian Nnaemeka Egwim
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Predictive Models ◽  
Data Analytics ◽  
Prediction Models ◽  
Big Data Analytics ◽  
Support Vector ◽  
Traffic Data ◽  
Content Type ◽  
Pollution Concentration

PurposeThis paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO2 prediction models. The purpose of this paper is to pre-process a relatively large data of NO2 from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO2 prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.Design/methodology/approachThis research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.FindingsThe results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO2 pollution.Practical implicationsThis paper's hybrid model provides a basis for giving an informed decision on the NO2 pollutant avoidance system.Originality/valueThis research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

Biases in machine learning models and big data analytics: The international criminal and humanitarian law implications

2020 ◽  
Vol 102 (913) ◽  
pp. 199-234
Author(s):  
Nema Milaninia
Keyword(s):  
Machine Learning ◽  
Human Rights ◽  
Big Data ◽  
Data Analytics ◽  
International Criminal ◽  
Big Data Analytics ◽  
International Criminal Law ◽  
Gender Inequalities ◽  
Humanitarian Law ◽  
Mass Graves

AbstractAdvances in mobile phone technology and social media have created a world where the volume of information generated and shared is outpacing the ability of humans to review and use that data. Machine learning (ML) models and “big data” analytical tools have the power to ease that burden by making sense of this information and providing insights that might not otherwise exist. In the context of international criminal and human rights law, ML is being used for a variety of purposes, including to uncover mass graves in Mexico, find evidence of homes and schools destroyed in Darfur, detect fake videos and doctored evidence, predict the outcomes of judicial hearings at the European Court of Human Rights, and gather evidence of war crimes in Syria. ML models are also increasingly being incorporated by States into weapon systems in order to better enable targeting systems to distinguish between civilians, allied soldiers and enemy combatants or even inform decision-making for military attacks.The same technology, however, also comes with significant risks. ML models and big data analytics are highly susceptible to common human biases. As a result of these biases, ML models have the potential to reinforce and even accelerate existing racial, political or gender inequalities, and can also paint a misleading and distorted picture of the facts on the ground. This article discusses how common human biases can impact ML models and big data analytics, and examines what legal implications these biases can have under international criminal law and international humanitarian law.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

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