Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering

Purwono, Purwono; Ma’arif, Alfian; Wulandari, Amanah Tri

doi:10.23919/eecsi53397.2021.9624262

Cited by 3 publications

(3 citation statements)

References 46 publications

(33 reference statements)

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“…Purwono et al (Purwono, Ma'Arif, and Wulandari 2021) used LinkedIn profile photos to predict face shapes. In addition, using publicly available data, Nguyen et al (Nguyen, Naguib, and Loo 2021) explored the LinkedIn profiles of Australians.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Deep Journalism to Discover Age Dynamics in Multi-Generational Labour Markets from LinkedIn Media

Alaql¹,

Alqurashi²,

Mehmood³

2022

Preprint

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We live in the information age and, ironically, meeting the core function of journalism – i.e., to provide people access to unbiased information – has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach to study how the LinkedIn media could be useful for journalism. Specifically, we apply our deep journalism approach to LinkedIn to automatically extract and analyse big data to provide the public with information about labour markets, people’s skills and education, and businesses and industries from multi-generational perspectives. The Great Resignation and Quiet Quitting phenomena coupled with rapidly changing generational attitudes are bringing unprecedented and uncertain changes to labour markets and our economies and societies, and hence the need for journalistic investigations into these topics is highly significant. We combine big data and machine learning to create a whole machine learning pipeline and a software tool for journalism that allows discovering parameters for age dynamics in labour markets using LinkedIn data. We collect a total of 57,000 posts from LinkedIn and use it to discover 15 parameters by Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-parameters, namely Generations-Specific Issues, Skills & Qualifications, Employment Sectors, Consumer Industries, and Employment Issues. The journalism approach used in this paper can automatically discover and make objective, cross-sectional, and multi-perspective information available to all. It can bring rigour to journalism by making it easy to generate information using machine learning and can make tools and information available so that anyone can uncover information about matters of public importance. This work is novel since none of the earlier works have reported such an approach and tool and leveraged it to use LinkedIn media for journalism and to discover multigenerational perspectives (parameters) for age dynamics in labour markets. The approach could be extended with additional AI tools and other media.

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Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Deep Journalism to Discover Age Dynamics in Multi-Generational Labour Markets from LinkedIn Media

Alaql¹,

Alqurashi²,

Mehmood³

2022

Preprint

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“…Haar-like feature, also known as Haar Cascade Classifier, is a rectangular (square) feature that gives a specific indication of an image [21]. The Haar cascade classifier comes from the idea of Paul Viola and Michael Jhon, hence the name Viola & Jhon method [22].…”

Section: B Haar Cascade Classifiermentioning

confidence: 99%

Traffic Density Prediction using IoT-based Double Exponential Smoothing

Asmara

Noprianto

Ilmy

et al. 2022

Knowl. Eng. Data Sci.

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The number of vehicles and currents that tend to increase causes traffic density. A system is proposed to calculate the number of vehicles and predict real-time traffic density. This research uses Haar Cascade to detect the number of cars and motorcycles and the Double Exponential Smoothing (DES) for forecasting the number of vehicles on the road. MAPE describes forecasting accuracy as a base for selecting the best smoothing constant (Alpha). The best test results from June 13 to 20, 2020, are cars on June 14, 2020 (alpha 0.5, MAPE 0%) and Motorcylecycles on June 18, 2020 (alpha 0.5, MAPE 0.1134% ). The most significant MAPE results of the car were on June 15, 2020, with alpha 0.5 and MAPE 2.1073%. The 3 minutes haar cascade detects 72.58% of cars and 81.90% of motorcycles.

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“…39,40 Research in social media and LinkedIn mining has focused on revealing demographic insights, 41 employee skills 42,43 and job 44 and profile classifications. [45][46][47][48][49][50][51] Additionally, Twitter data has been harnessed for appraising transit service quality, 52 detecting traffic-related events, 53 emergency management 54 and conducting temporal trend analysis. 55 See also.…”

Section: Introductionmentioning

confidence: 99%

Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine learning

Alaql,

Alqurashi,

Mehmood

2023

Science Progress

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The impact of aggressive capitalist approaches on social, economic and planet sustainability is significant. Economic issues such as inflation, energy costs, taxes and interest rates persist and are further exacerbated by global events such as wars, pandemics and environmental disasters. A sustained history of financial crises exposes weaknesses in modern economies. The Great Attrition, with many quitting jobs, adds to concerns. The diversity of the workforce poses new challenges. Transformative approaches are essential to safeguard societies, economies and the planet. In this work, we use big data and machine learning methods to discover multi-perspective parameters for multi-generational labour markets. The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of Science for the period 1958–2022 and for the professionals’ perspective using 57,000 LinkedIn posts from 2022. We discover a total of 28 parameters and categorized them into five macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues and Generations-specific Issues. A complete machine learning software tool is developed for data-driven parameter discovery. A variety of quantitative and visualization methods are applied and multiple taxonomies are extracted to explore multi-generational labour markets. A knowledge structure and literature review of multi-generational labour markets using over 100 research articles is provided. It is expected that this work will enhance the theory and practice of artificial intelligence-based methods for knowledge discovery and system parameter discovery to develop autonomous capabilities and systems and promote novel approaches to labour economics and markets, leading to the development of sustainable societies and economies.

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Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering

Cited by 3 publications

References 46 publications

Data-Driven Deep Journalism to Discover Age Dynamics in Multi-Generational Labour Markets from LinkedIn Media

Data-Driven Deep Journalism to Discover Age Dynamics in Multi-Generational Labour Markets from LinkedIn Media

Traffic Density Prediction using IoT-based Double Exponential Smoothing

Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine learning

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