Application of Machine Learning in Google Services- A Case Study

Pulluparambil, Siji Jose; Bhat, Subrahmanya

doi:10.47992/ijcsbe.2581.6942.0117

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…As discussed in company analysis methodologies by Aithal et al [30], technologies used in various companies like data sciences, IoT, mobility, and Artificial Intelligence (AI) will widely change the way the business (insurance companies) operates and provides areas for innovation in products and services. Automation of underwriting and fraud controls could be enabled using AI and analytics [31].…”

Section: Discussionmentioning

confidence: 99%

Digital Transformation in Indian Insurance Industry – A Case Study

Desikan

Devi

2021

IJCSBE

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Purpose: In India, the insurance industry has grown rapidly in the last decade, introducing many innovative products. India's insurance industry is vital to the country's economy. Digital Transformation have a drastic impact on the Insurance sector. Digitization results in future innovative designs and launch innovative products which help insurance companies and the customers. Digital innovation is transforming the way how the insurance companies work with industries by integrating IoT devices with health insurance which will also benefit the customers. In this paper, we will analyze and understand how HDFC ERGO has implemented digital transformation that has enhanced operational efficiencies and completely transformed service deliveries and customer experience in the insurance industry. Objectives: To do analysis and review on the digital transformation in the insurance company and how it has impacted the operational efficiencies, service deliveries and customer experience. Design/Methodology/Approach: This company analysis was done by analyzing and referring different sources like online sources, such as websites, blogs, scholarly articles, web articles, and using Technology Analysis as a framework. Findings/Result: Digital transformation and how it impacts insurance company in terms of its operational efficiency, service deliveries and customer experience are discussed. Analysis done to find how the organization should stay ahead in implementing the digital technologies and how digital transformation helps the insurance industry to explore new technologies and provides innovative ideas to improve organizational efficiency Originality/Value: Based on the information and the data available, digital transformation and its impact in the insurance company in the current state is analyzed. Paper Type: A Case study analysis done on the digital transformation in the HDFC ERGO general insurance company.

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Section: Discussionmentioning

confidence: 99%

Digital Transformation in Indian Insurance Industry – A Case Study

Desikan

Devi

2021

IJCSBE

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“…So, the classification of HFIs into HFWMIs and HFWOMIs types is a crucial requirement in this situation. ML [24][25][26][27][28][29] can be considered as a solution for the classification of HFIs into HFWMIs and HFWOMIs categories. The ML based methods can be broadly classified as supervised and unsupervised.…”

Section: Introductionmentioning

confidence: 99%

A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask

Jena¹,

K²,

Thinakaran³

2022

STAIQC

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The importance of face mask (FM) is a major concern for the entire human society in the current circumstances. All peopleshould wear FM in order to lower the chance of infection due to several diseases. It is very much essential to track the peoplewho have not worn the FM in different crowded places, so that warning can be given to them to wear FM in order to lower thespread of infection of different diseases. So, the classification of human face images (HFIs) into human face with mask images(HFWMIs) and human face without mask images (HFWOMIs) types is an essential requirement in this situation. In this work, amachine intelligent (MI) based approach is proposed for the classification of HFIs into HFWMIs and HFWOMIs types. Theproposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN),Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is comparedwith other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB),Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Theproposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 200HFWMIs and 200 HFWOMIs are taken from the Kaggle source. The performance of all the methods is assessed using theperformance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA,F1,PR and RC ascompared to other ML based methods such as LRG, SVMN,RFS, NNT,DTR,ADB, NBY, KNNHand SGD.

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“…so that the diagnosis process can be initiated accordingly at the earliest in order to make attempt for increasing the survivability of the patient. 20,22] based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, etc. play a significant role to accomplish the classification mechanism.…”

Section: Introductionmentioning

confidence: 99%

Classification of Breast Ultrasound Images: An Analysis Using Machine Intelligent Based Approach

Jena

K.²

2022

IJMTS

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Purpose: Breast Cancer (BC) is considered as one of the most dangerous diseases, especially in women. The survivability of the patient is a challenging task if the breast cancer is in severe stage. It is very much essential for the early classification of breast ultrasound images (BUIs) into several categories such as benign (BN), malignant (MG) and normal (NL), etc. so that preventive measures can be taken accordingly at the earliest. Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of BUIs into the BN, MG and NL types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 750 TLDIs having 250 numbers of each type such as BN, MG and NL are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD. Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of BUIs into several types such as BN, MG and NL. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods. Paper Type: Conceptual Research.

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Application of Machine Learning in Google Services- A Case Study

Cited by 7 publications

References 12 publications

Digital Transformation in Indian Insurance Industry – A Case Study

Digital Transformation in Indian Insurance Industry – A Case Study

A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask

Classification of Breast Ultrasound Images: An Analysis Using Machine Intelligent Based Approach

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