scholarly journals Light Monitoring System using Z-Score Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 196-204
Author(s):  
Banala Krishna Gopal
Keyword(s):  
Machine Learning ◽  
Light Intensity ◽  
Monitoring System ◽  
Personal Space ◽  
Modern World ◽  
Z Score ◽  
Work Efficiency ◽  
Alert System ◽  
Score Analysis ◽  
The Moment

In today’s modern world where everything is being automated and security is a growing concern, we made an automated module to live-monitor the anomalies in any provided space at all times to ensure security in our personal space. By implementing our project, we can monitor anything important which would be out of our reach at the moment with a live alert system through which we can identify any anomalies. In our proposed system we integrated Machine Learning to work with an IoT system by using Bolt Wi-Fi module which also uses an LDR sensor to detect the light intensity, here LDR is used specifically to better understand the Z-Score analysis. We are using ML to do an analysis known as Z-Score, which processes a math equation to detect anomalies. This analysis is done to predict a frame of upper and lower boundaries for the light intensity. Eventually, when the LDR sensor value i.e., light intensity goes out of range in a room, it generates Real-Time alerts in the form of an SMS alert which will be directed to the user's mobile phone through Twilio. This alert system is an advanced way to increase the work efficiency of any live monitoring system as the ML is always working to increase accuracy. In our project, this system specifically uses Light Dependent Resistor to detect changes in light intensities, but this can be implemented for any sensor to detect.

scholarly journals ANALISIS LAPORAN KEUANGAN GUNA MEMPREDIKSI KEBANGKRUTAN DENGAN MENGGUNAKAN MODEL ALTMAN Z-SCORE PADA PERUSAHAAN TELEKOMUNIKASI YANG TERDAFTAR DI BURSA EFEK INDONESIA ( PT XL Axiata Tbk dan PT Indosat Tbk )

2017 ◽  
Vol 2 (02) ◽  
Author(s):  
Putri Fitria Ridwan ◽  
Meiril Hardi
Keyword(s):  
Stock Exchange ◽  
Financial Statements ◽  
Z Score ◽  
Analysis Model ◽  
Financial Condition ◽  
Financial Difficulties ◽  
Score Analysis ◽  
Calculation Results ◽  
The Moment ◽  
Telecommunications Company

ABSTRACTThis study aims to determine the potential bankruptcy using the Altman Z-Score in the telecommunications company listed on the Indonesia Stock Exchange (BEI). Because telecommunications taking an important role in everyday life, both in terms of day-to-day telecommunications as well as in terms of business. Selected objects to do this research is PT XL Axiata Tbk and PT Indosat Tbk, which is two telecommunications company that is developing and steal the attention of consumers at the moment. The method of the research is to analyze the company's financial statements using analysis tools Altman Z-Score. Use of Altman Z-Score aims to be able to know or predict the financial condition perusahaa, because of the financial statements and the calculation of Altman Z-Score can be known, whether the company experienced financial difficulties in the future or not. Z-Score analysis model using five variables that represent liquidity ratio (X1), profitability (X2 and X3), activity (X4 and X5) and the data used are the financial statements for 2011-2013 were obtained through the site Indonesian Stock Exchange (BEI ) is www.idx.co.id. With the formulation of the Z-Score = 1.2 1.4 X1 + X2 + X3 3.3 +0.6 + 1.0 X4 X5 with the assessment criteria Z-Score> 2.99 categorized as a very healthy company, 1.81 <Z-Score <2.99 were in gray area so the chances saved and the possibility of bankruptcy. From the analysis, based on calculations that have been made to the financial statements that PT XL Axiata Tbk and PT Indosat Tbk during the period 2011-2013 experienced financial condition is said to be bankrupt due to the calculation results Z-Score PT XL Axiata Tbk in 2011 1.23, year in 2012 and 1.29 in 2013 to 0.87 while for PT Indosat Tbk in 2011 was 0.46, in 2012 and 0.47 in 2013 to 0.25. With the results of the Z-Score <1.81, which indicates that the company entered into the category bangkut or companies experiencing very serious financial difficulties. Then the company should improve financial performance by reducing the debt and increase equity by utilizing available assets. Keywords: Financial Statements, Bankruptcy and Z-score

scholarly journals EXPRESS: Overcoming the Cold Start Problem of CRM using a Probabilistic Machine Learning Approach

2021 ◽  
pp. 002224372110329
Author(s):  
Nicolas Padilla ◽  
Eva Ascarza
Keyword(s):  
Machine Learning ◽  
Cold Start ◽  
Customer Relationship ◽  
Exponential Families ◽  
Modeling Framework ◽  
Wide Range ◽  
The Moment ◽  
Probabilistic Machine Learning ◽  
Marketing Actions ◽  
Cold Start Problem

The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to identify and leverage differences across customers — a very diffcult task when firms attempt to manage new customers, for whom only the first purchase has been observed. For those customers, the lack of repeated observations poses a structural challenge to inferring unobserved differences across them. This is what we call the “cold start” problem of CRM, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. We propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it exibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is exible enough to capture a wide range of heterogeneity structures. We validate our approach in a retail context and empirically demonstrate the model's ability at identifying high-value customers as well as those most sensitive to marketing actions, right after their first purchase.

scholarly journals Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 374 ◽  
Author(s):  
Sudhanshu Kumar ◽  
Monika Gahalawat ◽  
Partha Pratim Roy ◽  
Debi Prosad Dogra ◽  
Byung-Gyu Kim
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Age Groups ◽  
Modern World ◽  
Support Vector ◽  
Digital Information ◽  
Age And Gender ◽  
And Gender ◽  
The Impact

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

scholarly journals Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110135
Author(s):  
Florian Jaton
Keyword(s):  
Machine Learning ◽  
William James ◽  
A Priori ◽  
Learning Algorithms ◽  
Three Dimensional ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Ground Truthing ◽  
Set Up ◽  
The Moment

This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: (I) the definition of the problem to be solved (problematization), (II) the identification of the data to be collected and set up (databasing), and (III) the qualification of the targets to be learned (labeling). I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems.

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