Machine Learning in Mobile Crowd Sourcing: A Behavior-Based Recruitment Model

2022 ◽  
Vol 22 (1) ◽  
pp. 1-28
Author(s):  
Menatalla Abououf ◽  
Shakti Singh ◽  
Hadi Otrok ◽  
Rabeb Mizouni ◽  
Ernesto Damiani
Keyword(s):  
Machine Learning ◽  
Real Life ◽  
Task Completion ◽  
Behavioral Models ◽  
Crowd Sourcing ◽  
Behavioral Profiling ◽  
The Right ◽  
Mobile Crowd ◽  
Selection Of

With the advent of mobile crowd sourcing (MCS) systems and its applications, the selection of the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection frameworks rely primarily on reputation-based feedback mechanisms to assess the level of commitment of potential workers. Such frameworks select workers having high reputation scores but without any contextual awareness of the workers, at the time of selection, or the task. This may lead to an unfair selection of workers who will not perform the task. Hence, reputation on its own only gives an approximation of workers’ behaviors since it assumes that workers always behave consistently regardless of the situational context. However, following the concept of cross-situational consistency, where people tend to show similar behavior in similar situations and behave differently in disparate ones, this work proposes a novel recruitment system in MCS based on behavioral profiling. The proposed approach uses machine learning to predict the probability of the workers performing a given task, based on their learned behavioral models. Subsequently, a group-based selection mechanism, based on the genetic algorithm, uses these behavioral models in complementation with a reputation-based model to recruit a group of workers that maximizes the quality of recruitment of the tasks. Simulations based on a real-life dataset show that considering human behavior in varying situations improves the quality of recruitment achieved by the tasks and their completion confidence when compared with a benchmark that relies solely on reputation.

scholarly journals Solving the "right" problems for effective machine learning driven in vitro fertilization

2021 ◽  
Author(s):  
Itay Erlich ◽  
Assaf Ben-Meir ◽  
Iris Har-Vardi ◽  
James A Grifo ◽  
Assaf Zaritsky
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Clinical Factors ◽  
Vitro Fertilization ◽  
Data Intensive ◽  
Live Embryo ◽  
The Right ◽  
Visual Properties ◽  
Selection Of

Automated live embryo imaging has transformed in-vitro fertilization (IVF) into a data-intensive field. Unlike clinicians who rank embryos from the same IVF cycle cohort based on the embryos visual quality and determine how many embryos to transfer based on clinical factors, machine learning solutions usually combine these steps by optimizing for implantation prediction and using the same model for ranking the embryos within a cohort. Here we establish that this strategy can lead to sub-optimal selection of embryos. We reveal that despite enhancing implantation prediction, inclusion of clinical properties hampers ranking. Moreover, we find that ambiguous labels of failed implantations, due to either low quality embryos or poor clinical factors, confound both the optimal ranking and even implantation prediction. To overcome these limitations, we propose conceptual and practical steps to enhance machine-learning driven IVF solutions. These consist of separating the optimizing of implantation from ranking by focusing on visual properties for ranking, and reducing label ambiguity.

A Survey on Intelligence Tools for Data Analytics

2021 ◽  
pp. 73-95
Author(s):  
Shatakshi Singh ◽  
Kanika Gautam ◽  
Prachi Singhal ◽  
Sunil Kumar Jangir ◽  
Manish Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Language Processing ◽  
Real Life ◽  
Learning Tools ◽  
The Core ◽  
Training Mode ◽  
Real Life Situation ◽  
Selection Of

The recent development in artificial intelligence is quite astounding in this decade. Especially, machine learning is one of the core subareas of AI. Also, ML field is an incessantly growing along with evolution and becomes a rise in its demand and importance. It transmogrified the way data is extracted, analyzed, and interpreted. Computers are trained to get in a self-training mode so that when new data is fed they can learn, grow, change, and develop themselves without explicit programming. It helps to make useful predictions that can guide better decisions in a real-life situation without human interference. Selection of ML tool is always a challenging task, since choosing an appropriate tool can end up saving time as well as making it faster and easier to provide any solution. This chapter provides a classification of various machine learning tools on the following aspects: for non-programmers, for model deployment, for Computer vision, natural language processing, and audio for reinforcement learning and data mining.

scholarly journals How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques

Molecules ◽  
2020 ◽  
Vol 25 (6) ◽  
pp. 1452
Author(s):  
Igor Sieradzki ◽  
Damian Leśniak ◽  
Sabina Podlewska
Keyword(s):  
Machine Learning ◽  
Training Set ◽  
Activity Prediction ◽  
Prediction Uncertainty ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Model Training ◽  
Short Time ◽  
Selection Of

A great variety of computational approaches support drug design processes, helping in selection of new potentially active compounds, and optimization of their physicochemical and ADMET properties. Machine learning is a group of methods that are able to evaluate in relatively short time enormous amounts of data. However, the quality of machine-learning-based prediction depends on the data supplied for model training. In this study, we used deep neural networks for the task of compound activity prediction and developed dropout-based approaches for estimating prediction uncertainty. Several types of analyses were performed: the relationships between the prediction error, similarity to the training set, prediction uncertainty, number and standard deviation of activity values were examined. It was tested whether incorporation of information about prediction uncertainty influences compounds ranking based on predicted activity and prediction uncertainty was used to search for the potential errors in the ChEMBL database. The obtained outcome indicates that incorporation of information about uncertainty of compound activity prediction can be of great help during virtual screening experiments.

scholarly journals Web Based Decision Support System yo Determine yhe Appropriate Packaging of Ethnic and Traditional Indonesian Foods

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Latifah Nurbaiti ◽  
Kudang Boro Seminar ◽  
Nugraha Edhi Suyatma
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Food Packaging ◽  
Active Packaging ◽  
Web Based ◽  
The Right ◽  
Oxygen Barriers ◽  
Selection Of

Culinary efforts, especially ethnic and traditional snacks attract many people to Indonesia. Maintaining the quality of snacks for consumers requires a good packaging technique. Food packaging consists of a wide variety of packaging options that match the characteristics of each snack; this is no easy task. Decision support systems can help to facilitate decisions made regarding selection of the right packaging. This paper focuses on identifying snacks, types of packaging and active packaging parameters to build a decision support system in order to determine appropriate packaging. Types of packaging are determined using fuzzy Sugeno 4 parameters: fat, water activity, shelf-life and price. Active packaging of the snacks is done using the if-else rule with parameterised types of packaging, preservatives, oxygen barriers and water vapour barriers. The end result of this research is a web-based decision support system, which recommends types of packaging and active packaging for snacks.

scholarly journals Logistic Regression for Employability Prediction

2020 ◽  
Vol 9 (3) ◽  
pp. 2471-2478
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Weather Forecasting ◽  
Previous Knowledge ◽  
Recruitment Process ◽  
Proposed Model ◽  
Unknown Event ◽  
The Right

Prediction is a conjecture about something which may happen. Prediction need not be based upon the previous knowledge or experience on the unknown event of interest in the future. But it is a necessity for mankind to foresee and make the right decisions to live better. Every person does predictions but the quality of the predictions differs and that differentiates successful persons and unsuccessful persons. In order to automate the prediction process and to make quality predictions available to every person, machines are trained to make predictions and such field comes under machine learning and later on deep learning algorithms. Various fields such as health care, weather forecasting, natural calamities, and crime prediction are some of the applications of prediction. The researchers have applied the field of prediction to see whether a model can predict the employability of a candidate in a recruitment process. Organizations use human expertise to identify a skilled candidate for employment based on various factors and now these organizations are trying to migrate to automated systems by harnessing the benefits of the exponential growth in the area of machine learning and deep learning. This investigation presents the development of a model to predict the employability by using Logistic Regression. A set of candidates was tested in the proposed model and results are discussed in this paper.

Privacy as Protection of the Incomputable Self: From Agnostic to Agonistic Machine Learning

2019 ◽  
Vol 20 (1) ◽  
pp. 83-121 ◽  
Author(s):  
Mireille Hildebrandt
Keyword(s):  
Machine Learning ◽  
Research Design ◽  
Real Life ◽  
Big Data Analytics ◽  
Validity And Reliability ◽  
Human Identity ◽  
The Right To Privacy ◽  
Trade Offs ◽  
Life Problems ◽  
The Right

Abstract This Article takes the perspective of law and philosophy, integrating insights from computer science. First, I will argue that in the era of big data analytics we need an understanding of privacy that is capable of protecting what is uncountable, incalculable or incomputable about individual persons. To instigate this new dimension of the right to privacy, I expand previous work on the relational nature of privacy, and the productive indeterminacy of human identity it implies, into an ecological understanding of privacy, taking into account the technological environment that mediates the constitution of human identity. Second, I will investigate how machine learning actually works, detecting a series of design choices that inform the accuracy of the outcome, each entailing trade-offs that determine the relevance, validity and reliability of the algorithm’s accuracy for real life problems. I argue that incomputability does not call for a rejection of machine learning per se but calls for a research design that enables those who will be affected by the algorithms to become involved and to learn how machines learn — resulting in a better understanding of their potential and limitations. A better understanding of the limitations that are inherent in machine learning will deflate some of the eschatological expectations, and provide for better decision-making about whether and if so how to implement machine learning in specific domains or contexts. I will highlight how a reliable research design aligns with purpose limitation as core to its methodological integrity. This Article, then, advocates a practice of “agonistic machine learning” that will contribute to responsible decisions about the integration of data-driven applications into our environments while simultaneously bringing them under the Rule of Law. This should also provide the best means to achieve effective protection against overdetermination of individuals by machine inferences.

MODIFIKASI MODEL PEMBELAJARAN MIND MAPPING DENGAN STRATEGI PEMBELAJARAN TUGAS DAN PAKSA

2019 ◽  
Vol 2 ◽  
Author(s):  
Dini Yuliani ◽  
Mutiara Bhayangkari ◽  
Maria Ulfah
Keyword(s):  
Learning Strategies ◽  
Learning Process ◽  
Intellectual Development ◽  
Learning Strategy ◽  
Student Discipline ◽  
Learning Models ◽  
Mind Mapping ◽  
The Right ◽  
Selection Of

This writing aims to improve students' understanding by modifying learning models with appropriate learning strategies that are considered based on the characteristics and intellectual development of students to improve learning outcomes. The learning process in Indonesia is currently still centered on teachers which causes students' memory and understanding to be still low. This is the background for the purpose of mind mapping learning models, namely making patterned visual and graphical subject matter that can help strengthen and recall information that has been studied. The selection of the right learning strategy can improve the results that will be obtained from the application of learning models in the classroom. Forced and forced learning strategies are chosen to complement and perfect the implementation of mind mapping learning models in the classroom. This strategy aims to train students' independence and discipline in learning through assignments given with clear time limits and strict penalties if there are students not completing their assignments properly. The combination of mind mapping learning models with forced and task learning strategies can be an alternative to improve the quality of learning in schools. With the increase in thinking power accompanied by student discipline in learning, the learning process in the classroom will run well and get maximum results so that the objectives of the learning can be achieved. For educators it is recommended to implement mind mapping learning models with forced and forced learning strategies in schools, so that learning objectives can be achieved optimally.

scholarly journals REBRANDING PRODUK KERIPIK JAMUR TIRAM UNTUK PENINGKATAN PENJUALAN PADA UMKM SPORAMUSHROOM

2020 ◽  
Vol 4 (1) ◽  
pp. 77-83
Author(s):  
Andi Iva Mundiyah ◽  
Dudi Septiadi ◽  
Sharfina Nabila ◽  
Ni Made Wirastika Sari ◽  
Ni Made Zeamita
Keyword(s):  
Pearl Oyster ◽  
Oyster Mushroom ◽  
Brand Marketing ◽  
Medium Enterprise ◽  
Oyster Mushrooms ◽  
The Right ◽  
Almost All ◽  
Product Sales ◽  
Selection Of

ABSTRACT Small-Medium Enterprise (SME) “Sporamushroom” which processes pearl-oyster mushrooms into pearl-oyster mushroom chips is located on Jalan Pelita, Makassar City. Pearl-oyster mushrooms are rich in nutrition and have savory taste and chicken-like texture, so that almost all people like it. The problem faced by SME “Sporamushroom” lies in the packaging of the mushroom chips that are not attractive and are not able to preserve the quality of the products contained therein. In addition, the mushroom chips brand have not been determined. The results of the activities carried out indicate the need for assistance and information sharing about the types of packaging for processed chips, so that the packaging will be produced accordingly, which is aluminum plastic packaging that is suitable for processed chip products. From the brand aspect, the selection of the right and easy-to-remember brand has an effect on product sales. The JAMBUL brand was chosen as the brand of pearl-oyster mushroom chips because it is easy to be remembered and has appropriate philosophy behind it. Key words: brand, marketing, packaging, SME

scholarly journals PENETAPAN BRAND AMBASSADOR SEBAGAI UPAYA STRATEGI KOMUNIKASI PEMASARAN “RUANGGURU” DI ERA PANDEMI COVID-19

2021 ◽  
Vol 5 (1) ◽  
pp. 66
Author(s):  
Ni Putu Stiti Ayuningtyas ◽  
Ni Luh Putu Sariani ◽  
Desak Made Sukarnasih
Keyword(s):  
Communication Strategies ◽  
Communication Strategy ◽  
Marketing Communication ◽  
The Future ◽  
The Right ◽  
Brand Ambassadors ◽  
Selection Of

ABSTRAKKegiatan pembelajaran saat pandemi di Indonesia dilakukan secara daring. Perlu adanya dukungan media pembelajaran seperti yang disediakan oleh Ruang guru. Ruang guru merupakan penyedia media pembejaran yang bisa diakses menggunakan internet, sehingga memudahkan guru dan siswa saat proses belajar. Dengan persaingan yang terus bertumbuh, Ruang guru perlu memilih strategi komunikasi pemasaran yang tepat, salah satunya dengan menunjuk brand ambassador. Tahun 2021, telah dipilih lima brand ambassadors baru yang berasal dari kalangan artis. Pengabdian dilakukan untuk mengetahui perkembangan strategi komunikasi pemasaran Ruang guru melalui pemilihan brand ambassadors dan seperti apa respon masyarakat terkait hal tersebut. Metode pengabdian melalui proses tahapan, pelaksanaan, dan evaluasi. Diketahui bahwa masyarakat setuju akan terpilihnya brand ambassador Ruangguru 2021 dan dinilai mampu meningkatkan positif perusahaan. Teori strategi komunikasi pemasaran yang dilakukan adalah pull, push, dan profle strategies. Hasil akhir kegiatan PKL menunjukkan sebanyak 40 responden setuju atas pemilihan brand ambassador, diantaranya 28% memilih Prilly Latuconsina sebagai “ikon” Ruang guru paling sesuai. Selain itu, banyak pengguna yang setuju atas terpilihnya kelima brand ambassadors karena prestasi serta rekam jejak yang baik dari masing-masing brand ambassador. Saran yang dapat diberikan adalah kedepannya Ruang guru harus lebih meningkatkan kualitas konten dan lebih selektif memilih brand ambassador agar sesuai dengan visi dan misi perusahaan. Kata kunci: brand ambassador; pemasaran; ruang guru. ABSTRACTLearning activities during the pandemic in Indonesia are carried out online. There needs to be support for learning media such as those provided by Ruang guru. With the high rivalry in this industry, Ruangguru needs to choose the right marketing communication strategy, one of which is by appointing a brand ambassador. In 2021, five new brand ambassadors have been selected from among the artists. This community dedication held to know the effect of Ruang guru’s brand ambassadors and what’s public responses to it. The method is through a process of stages, implementation, and evaluation. The marketing communication strategies used are pull, push, and profile strategies. The final result showed that 40 respondents agreed with the selection of brand ambassadors and they be able to increase the company's positivity and of which 28% chose Prilly Latuconsina as the most compatible Ruang guru "icon". In addition, many users agree with the selection of the five brand ambassadors because of the achievements and good track records of each brand ambassador. Recommendations that can be given are that in the future Ruang guru should further improve the quality of content and be more selective in choosing brand ambassadors to fit in the company's vision and mission. Keywords: brand ambassador; marketing; ruang guru.

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