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1 Jul 01, 2024
An Efficient Machine Learning Technique for Rating Prediction Of Google Play Store Apps

The vast landscape of mobile applications, user ratings play a crucial role in determining an app's success and popularity. Predicting these ratings accurately not only aids users in making informed decisions but also assists developers in improving their apps. This paper presents an efficient machine learning technique tailored for rating prediction of Google Play Store apps. Our approach leverages a combination of advanced machine learning algorithms, feature analysis, and data pre-processing methods to achieve robust performance. Simulation results shows that the effectiveness of proposed technique through extensive experimentation on datasets, showcasing its ability to accurately forecast app ratings....

Authors: Veena Vadinee Raikwar, Prof. Mahendra Sahare, Prof. Anurag Shrivastava.

2 Jul 01, 2024
ENHANCED PREDICTION OF DIABETES USING MACHINE LEARNING TECHNIQUE

Due to its intricate dependencies on numerous factors, diabetes diagnosis is a very difficult task in the early stages. In order to assist medical experts in the demonstration method, it is necessary to establish restorative symptomatic emotionally supportive networks. Neural system functions have been successfully linked to the diagnosis of many medical conditions. Gradient boosting machine learning is used in this thesis to train the diabetes diagnosis and classify diabetic patients into two groups based on their class values. To attain an accuracy of 81.95% in the suggested strategy, we employed an ensemble of gradient boosting techniques.For diabetic disease dataset, the majority vote-based model, which includes Naïve Bayes, Decision Tree, and Support Vector Machine classifiers, achieved an accuracy of 76.56%, sensitivity of 79.16%, and specificity of 77.476%. ...

Authors: Prachi Patel, Manoj Yadav.

3 Jul 01, 2024
Redefining Channel Dimensioning in FMCG Industry: A Technological Perspective

In recent years, the Fast-Moving Consumer Goods (FMCG) sector has experienced remarkable growth. Extensive and intricate distribution networks remain a characteristic of the FMCG sector. With the introduction of technologies like artificial intelligence, big data analytics, and the Internet of Things, channel dimensioning in the FMCG sector has undergone significant transformation. This study examines how channel dimensioning in the FMCG sector is changing as a result of technology breakthroughs. The study, which is based on secondary data, examines the tools and approaches used by top companies, analyses the measurable effects of these innovations, and examines any difficulties that may have arisen. The potential transformation of AI-based demand forecasting, IoT-based tracking, and data analytics in supply chain optimisation for cost reduction and customer happiness were among the main conclusions. The article concludes with practical advice and views on using technology to solve inefficiencies and prepare distribution networks for the future....

Authors: Jyotika James, Nishant Shrivastava.

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  • Calling Papers For Volume 13, Issue 4 Last Deadline For Paper Submission 22-July-2023 Posted by Admin Posted by Admin.