CART Versus CHAID Behavioral Biometric Parameter Segmentation Analysis

The author describes the results of segmentation and pattern analyses of postprandial plasma glucose levels (PPG) and carbs/sugar intake amount (carbs), which are associated with his three daily meals. In this paper, there are three consistent ranges of low, medium, and high for PPG values and carbs/sugar amounts that are used for each meal but with different units. One of the final objectives for this analysis is to calculate the most reasonable and effective conversion ratio between measured PPG in mg/dL and carbs/sugar intake amount in grams, by discovering how much PPG amount would be generated from 1 gram of carbs/sugar intake. This investigation utilized the PPG data and carbs/sugar amount collected during a period of 2+ years from 5/5/2018 to 9/6/2020 with a breakdown of 855 days, including 2,565 meals, 33,345 glucose data, and 33,345 carbs/sugar data. By using the segmentation analysis of his 33,345 PPG data and 2,565 carbs/sugar data, the author has conducted a pattern recognition and segmentation analysis from his PPG profiles with its associated carbs/sugar intake of his food and meals in the past 855 days. Since 12/8/2015, he ceased taking any diabetes medications. In other words, his diabetes control is 100% dependent on his lifestyle management program with no chemical intervention from any medications. Subsequently, he has maintained a stringent exercise program after each meal; therefore, the development of his simplified PPG prediction model, excluding the exercise factor, can be expressed solely with carbs/sugar intake amount. Predicted PPG = (baseline glucose) + (conversion ratio * carbs/sugar amount) In his research work, he found the reasonable and effective conversion ratio between PPG and carbs that ranges from 1.8 mg/dL per gram to 2.5 mg/dL per gram. This simple equation could assist many type 2 diabetes (T2D) patients in controlling their diabetes via carbs/sugar intake amount. During this particular time period, his PPG control via a stringent lifestyle management without medication is highly successful. His estimated mathematically derived HbA1C values should be between 5.56% to 6.05%, which is a satisfactory HbA1C level for a 73-year-old male with a 25-year history of severe diabetes. It should be mentioned that he had an average daily glucose of 280 mg/dL and HbA1C of 11% in 2010. This segmented pattern analyses based on his PPG data and carbs/sugar intake amount offer a useful tool for analyzing other types of biomarkers in a deeper investigation with a wider entry point of research.

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Are atheist consumers less ethical? Investigating the role of religiosity and atheism on consumer ethics

Journal of Consumer Marketing ◽

10.1108/jcm-04-2020-3755 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Denni Arli ◽

Tuyet-Mai Nguyen ◽

Phong Tuan Nham

Keyword(s):

Consumer Ethics ◽

Intrinsic Religiosity ◽

Content Type ◽

Ethical Behaviors ◽

Segmentation Analysis ◽

Study Results ◽

Unethical Behaviors ◽

The Usa ◽

Using Data ◽

The Impact

Purpose There is a perception that non-religious consumers are less ethical than religious consumers. Studies found prejudices against atheists around the world and assumed that those who committed unethical behavior were more likely to be atheists. Hence, first, the purpose of this study is to investigate the effect of consumers’ intrinsic religiosity, extrinsic religiosity and atheism on consumers’ ethical beliefs. Second, this study attempts to segment consumers and identify differences between these segments. Design/methodology/approach Using data from 235 study participants in the USA and 531 in Vietnam. Subsequently, a two-step cluster approach was used to identify segments within these samples. Findings The study results show consumers’ intrinsic religiosity negatively influences all consumers’ unethical beliefs. Similarly, atheism also negatively influences all consumers’ unethical beliefs. This study also complements other studies exploring consumer ethics in developing countries. In addition, the segmentation analysis produced unique segments. The results from both samples (USA and Vietnam) indicated that non-religious consumers are less likely to accept various unethical behaviors compared to religious consumers. Religious consumers are not necessarily more ethical and atheism consumers are not necessarily less ethical. In the end, are implications for business ethics, religious and non-religious leaders on how to view the impact of beliefs on consumer ethical behaviors. Originality/value This is one of the first few studies investigating the impact of atheism on consumer ethics. The results of this study further extend the knowledge of study in consumer ethics by comparing consumers’ religiosity and atheism.

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A parallel algorithm for structure detection based on wavelet and segmentation analysis

Parallel Computing ◽

10.1016/0167-8191(94)00076-m ◽

1995 ◽

Vol 21 (2) ◽

pp. 265-285 ◽

Cited By ~ 17

Author(s):

E. Lega ◽

H. Scholl ◽

J.-M. Alimi ◽

A. Bijaoui ◽

P. Bury

Keyword(s):

Parallel Algorithm ◽

Structure Detection ◽

Segmentation Analysis

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Segmentation analysis in the volumetric measurement and tissue characterization of MS lesions over time

Journal of Neuroimmunology ◽

10.1016/0165-5728(95)98985-k ◽

1995 ◽

Vol 56-63 ◽

pp. 40-40

Author(s):

CF Gonzalez ◽

S. Vlnltski ◽

S. Shehagiri ◽

F.D. Lublin ◽

R.L. Knobler

Keyword(s):

Tissue Characterization ◽

Volumetric Measurement ◽

Segmentation Analysis ◽

Over Time

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Customer Segmentation Analysis and Visualization

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit217144 ◽

2021 ◽

pp. 280-284

Author(s):

K. Keerthi ◽

G. Lakshmi Thirupathamma ◽

N. Vijayalakshmi ◽

D. Aparna ◽

U. Vineela

Keyword(s):

Customer Segmentation ◽

Machine Learning Techniques ◽

Business World ◽

Competitive Edge ◽

Financial Gain ◽

New Concepts ◽

Segmentation Analysis ◽

Information Set ◽

And Behavior ◽

Algorithmic Rule

Today’s world is all concerning Innovation and new concepts, where everybody desires to contend to measure higher than others. In the business world, it is crucial to know the client's desires and behavior patterns concerning buying merchandise. With the giant number of merchandise the businesses square measure confused to work out the potential customers to sell their merchandise to earn the large profits. To solve this real-time downside we tend to use machine learning techniques and algorithms. We can conclude the hidden patterns of knowledge. So that we can observe choices for earning a lot of profits. For this, we tend to take client information and divides the purchasers into totally different teams conjointly known as segmentation. segmentation permits businesses to create higher use of their selling budgets, gain a competitive edge over rival corporations, and, significantly, demonstrate much better information about your customer's desires and needs. In this project, we tend to square measure implementing k-means agglomeration algorithmic rule to analyze the results of clusters obtained from the algorithmic rule. A code is developed in python and it’s trained on an information set having 201 data samples that are taken from the native shopping center. All the offered data within the dataset is placed along to own a concept concerning client age, gender, annual financial gain, and outlay score(Expenditure) of mall customers dataset. Finally, this understanding information is analyzed to the simplest of our knowledge under the abled guidance of our mentor.

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Model of the "biometry-code" converter based on artificial neural networks for analysis of facial thermograms

Digital technology security ◽

10.17212/2782-2230-2021-2-154-165 ◽

2021 ◽

pp. 154-165

Author(s):

Pavel Lozhnikov ◽

◽

Samal Zhumazhanova ◽

Keyword(s):

Social Engineering ◽

New Approach ◽

Thermal Images ◽

Code Converter ◽

Biometric Security ◽

Psychophysiological State ◽

Biometric Parameter ◽

The Subject ◽

Biometric Cryptosystem ◽

Authorized Access

Existing asymmetric encryption algorithms involve the storage of a secret private key, authorized access to which, as a rule, is carried out upon presentation of a password. Passwords are vulnerable to social engineering and human factors. Combining biometric security techniques with cryptography is seen as a possible solution to this problem, but any biometric cryptosystem should be able to overcome the small differences that exist between two different implementations of the same biometric parameter. This is especially true for dynamic biometrics, when differences can be caused by a change in the psychophysiological state of the subject. The solution to the problems is the use of a system based on the "biometrics-code" converter, which is configured to issue a user key after presentation of his/her biometric image. In this case, the key is generated in advance in accordance with accepted standards without the use of biometric images. The work presents results on using thermal images of a user for reliable biometric authentication based on a neural network "biometrics-code" converter. Thermal images have recently been used as a new approach in biometric identification systems and are a special type of biometric images that allow us to solve the problem of both the authentication of the subject and the identification of his psychophysiological state. The advantages of thermal imaging are that this technology is now becoming available and mobile, allowing the user to be identified and authenticated in a non-contact and continuous manner. In this paper, an experiment was conducted to verify the images of thermograms of 84 subjects and the following indicators of erroneous decisions were obtained: EER = 0.85 % for users in the "normal"state.

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Assessing Firm Readiness to Adopt Cluster-Based Innovative Projects: A Segmentation Analysis

Sustainability ◽

10.3390/su14020947 ◽

2022 ◽

Vol 14 (2) ◽

pp. 947

Author(s):

Kaoutar Jamai ◽

Ali Abidar ◽

Hans De Steur ◽

Xavier Gellynck

Keyword(s):

Organizational Innovation ◽

Three Dimensions ◽

Food Sector ◽

Segmentation Analysis ◽

Determining Factors ◽

Segmentation Approach ◽

Readiness To Adopt ◽

Innovation Practices ◽

Medium Firms ◽

Organizational Preparedness

As innovation has garnered substantial attention on corporate success and sustainability, organizations must evaluate internal contexts to determine potential innovative practices and benefits. Firms need to investigate the determining factors of innovation preparedness as organizational innovation practices are catalyzed through internal elements. This study evaluates small and medium firms’ readiness to adopt and execute collaborative innovative projects within a future cluster and its impacts on organizational advantages, intentions, and attributes. Thereby, three dimensions were considered in examining organizational preparedness, namely, climate, culture, and motivation. A total of 70 firms operating in the labeled agri-food sector in Morocco were interviewed and homogenously classified using integrated hierarchical and non-hierarchical algorithms, following a segmentation approach. Three segments were identified, stressing the degree of organizational readiness to undertake innovative projects within future service clusters. The segments varied according to the firm’s sub-sector, experience, and resources. Considering the association of readiness with benefits and practical aims, the results broaden firm preparedness understanding to adopt innovative projects. The results also illustrate the relevance of adapting both innovative and beneficial project arrangements for firms with minor to moderate experience while addressing current issues across different segments.

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