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SD-TS-RF (Sales Data-Time Series-Random Forest) Hybrid Machine Learning Model for Enhanced Next-Day Sales Prediction in Supermarkets 16

SD-TS-RF (Sales Data-Time Series-Random Forest) Hybrid Machi...

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Balaji, S. Manikavelan, D. Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331505264

Accurate sales prediction is critical for optimizing inventory management and resource allocation in supermarkets. This study presents an innovative hybrid model, SD-TS-RF, designed to improve the precision of next-day sales forecasting. The model combines Time Series (TS) analysis, Support Vector Regression (SVR)-based Feature Selection (FS), and Random Forest (RF) to deliver superior predictive performance. By analyzing historical sales data alongside contextual factors such as holidays, promotions, and weather conditions, the FS component isolates feature that have the most significant impact on sales outcomes. Empirical evaluations demonstrate the SD-TS-RF model's robustness, achieving a remarkably low Mean Absolute Percentage Error (MAPE) of 2.5%. When benchmarked against conventional Time Series methods, the SD-TS-RF model consistently outperforms, thanks to its strategic feature selection and ensemble learning approach. Beyond enhancing sales forecast accuracy, this model offers a flexible and scalable framework applicable across diverse forecasting scenarios in the retail sector. The findings underscore the SD-TS-RF model's potential as a transformative tool in predictive analytics, enabling smarter, data-driven decision-making in retail demand forecasting. © 2024 IEEE.

关键词： Resource allocation

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Sentiment analysis of product-based reviews using machine learning

Sentiment analysis of product-based reviews using machine le...

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Loganathan, V. Ganesh, S. Rakesh, N. Harish, Mamilla Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

Sentiment analysis is one of the technique which is also called as opinion mining referred with the utilisation of Natural Language Processing text analysis in order to identify the quality of text written in the public media systematically and subjective and lesses. Sentiment analysis applied in various field for analysing the text reviews return related to the products from the consumer side. Online social media Healthcare materials marketing documents are all analysed for sentiment class in order to identify the quality of feedback conveyed to the product. The presented system is focused on developing a robust sentiment analysis Framework for product based reviews using machine learning algorithm. The presented system access comparative analysis of various machine learning algorithms to classify the relevant sentiment class on product reviews. The novel algorithms such as multinomial Navy Base algorithm, linear regression algorithm linear SVM algorithm and random forest algorithm are comparatively validator in terms of performance using accuracy Precision record and F1. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Sentiment analysis

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Enhancing Accuracy in Image Recognition for Deep Faked Images using Convolutional Networks Compared with Decision Tree Algorithm 4

Enhancing Accuracy in Image Recognition for Deep Faked Image...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Chanakya, A.S. Surendran, R. Krishnan, Sumathy Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science Engineering Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331540364

This study's main goal is to improve deep fake image identification accuracy by using convolutional neural networks (CNN) and contrasting the outcomes with decision tree algorithms. Examining methods like feature extraction, transfer learning, and model evaluation can help deep fake detection systems become more reliable and effective. A dataset from Kaggle. For training and assessment, a combination of real and deep faked images is used. The dataset contains samples produced using different circumstances and methods for deep fake production. In this work, two different groups were implemented, each using a different algorithmic strategy for image identification. Group 1 utilized CNNs, or convolutional neural networks. Group 2 used the Decision Tree Algorithm, a hierarchical tree-like topology. With 20 samples in each group, there were 40 samples in total. In order to provide sufficient statistical power for further analyses, the sample size was determined. Using ***, an a priori power analysis was performed with the statistical power (G-power) set at 80%. Behavioural science conventions were followed while choosing the significance level (alpha, a) and type II error rate (beta, ß). The values selected were 0.2 and 0.05, respectively. A 95% confidence interval was chosen to assess the precision of the estimated effectiveness of the two algorithmic approaches. The accuracy of the Decision Tree was 79.2%, while the CNNs outperformed it with a 97.36% accuracy. A two-tailed significance test result showing a statistically significant p-value of 0.001 (p © 2024 IEEE.

关键词： Convolutional neural networks

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Computational Approaches for Identifying Potato Plant Pathogens 5

Computational Approaches for Identifying Potato Plant Pathog...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Vignesh, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Potato cultivation is a vital component of global agriculture, providing a significant source of nutrition and income. However, the threat of diseases affecting potato crops remains a persistent challenge for farmers. In our study, we present a novel approach to automatically classify diseases in potato plants using advanced deep learning methods. The proposed approach makes use of Convolutional Neural Networks (CNNs), that perform efficiently for image processing applications. The dataset utilized in our research comprises high-resolution Images depicting diverse disease symptoms in potato plants, including late blight, early blight, and the oomycete pathogen Phytophthora infestans, a destructive ailment that impacts potato crops. To automatically discern between healthy and diseased plants, we implement a multi-layered CNN architecture that learns discriminative characteristics of images. This research significantly contributes to precision agriculture by providing an automated and efficient solution for identifying potato diseases. The proposed method in disease classification not only facilitates prompt disease management but also enables precise treatments, thereby reducing reliance on chemical interventions. The practical implications of these findings extend to farmers, agronomists, and researchers involved in sustainable and resilient potato cultivation. © 2024 IEEE.

关键词： disease identification early blight late blight potato disease

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Digital currency price prediction

Digital currency price prediction

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Julian, Anitha Rao, Bangale Sampath Kumar Chowdary, Pedavalli Mukesh Upendra, Narravula Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

Investments are one of the major sources of income in one’s life. The usual question that arises is which category one should invest in. In the market, there are many investment options such as stocks, cryptocurrencies, and mutual funds, but everyone who wants to invest normally expects high returns as well as security. That is what cryptocurrency does, since the future is not just for money, besides money, day-to-day operations and processes also depend on different factors. The proposed work uses machine learning algorithms for predicting the Crypto Currency price. A linear regression model is used to train the machine learning model with maximum efficiency and the model is implemented to predict Crypto Currency price. The model has been trained with a dataset containing the Crypto Currency exchanges which were provided by the user for the time with minute-by-minute updates. The proposed regression algorithm identifies with reasonable accuracy. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Python

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Career profiling based psychometric test for students

Career profiling based psychometric test for students

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Nagappan, G. Prasath Ravi, Gokul Prathipa, K. Saranya, V. Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

The decision of what career to pursue after graduation is one of the most important one for every student because it affects the growth and development of their future. Due to many uncertainties while picking a career, students often end up selecting the incorrect one. The purpose of this project is to create a psychometric test that will identify a student’s skills and suggest career possibilities for them. They gain a good idea of their personality and the appropriate employment for them. It aids in understanding a person’s personality, interests, and ability in both professionals and students. Students can better understand their strengths and limitations thanks to this assessment. We will be able to classify the student’s career with even greater accuracy thanks to the questions on this psychometric test that are based on the nine intelligences. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Students

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Analysis and Prediction of Crimes Against Women 3

Analysis and Prediction of Crimes Against Women

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Julian, Anitha Varshini, K. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350381931

The proposed work addresses the escalating concern of crimes against women in contemporary society. This comprehensive endeavor employs a data-driven approach to tackle this critical issue. The primary objectives encompass crime rate analysis, crime rate prediction, and the implementation of an emergency alert system. The crime rate analysis module employs the K-Means clustering algorithm to identify crime-prone areas and high-probability crime occurrences. In the crime rate prediction component, machine learning algorithms, including DBSCAN, Agglomerative Clustering, and Mean Shift, are used to forecast emerging crime trends. The emergency alert system offers women real-time alerts and emergency assistance, fostering a safer environment. The Silhouette Score, an evaluation metric, helps assess the efficiency of these algorithms in capturing inherent data patterns. Proposed work has accessed the performance metrices of K-Means © 2024 IEEE.

关键词： Forecasting

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The Accuracy and Efficiency in Facial Mask Detection: Comparing Resnet-50 Algorithm to Googlenet 2

The Accuracy and Efficiency in Facial Mask Detection: Compar...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Khaleelbasha, Shaik Kannan, K. Nattar Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

The purpose of this study is to evaluate the new Resnet-50 algorithm in comparison to Google Net in order to improve the accuracy of face mask identification via the use of two different machine learning techniques. Resnet50 and GoogleNet are used for the purpose of face mask detection, with distinct training and testing splits that are utilised. The parameters for the Gpower test, which are α=0.05 and power=0.85, result in an estimated success rate of 85%. The accuracy of Resnet50 (96.4020%) is higher than that of GoogleNet (95.4130%), with a value of 0.480 that is statistically insignificant (p>0.05). When compared to the accuracy of Google Net, the preciseness of Resnet 50 is much higher. © 2024 IEEE.

关键词： Adversarial machine learning

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Ornamental Portable Battlefield Surveillance using Generative Adversarial Networks 2

Ornamental Portable Battlefield Surveillance using Generativ...

引用

2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Kaviya, K. Bhavani, R. Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328202

This article investigates the sole use of generative adversarial networks (GANs) for portable battlefield surveillance equipment optimisation. High-resolution and clear pictures are produced by the strategic application of GANs for accurate object recognition, anomaly detection, and real-time video quality enhancement. In military applications, the resultant GAN-centric surveillance system exhibits increased accuracy and efficiency. We report major progress in object detection, anomalous activity detection, including video quality improvement. This GAN-centric approach significantly enhances situational awareness, allowing military personnel to make quick and accurate decisions in a variety of dynamic and varied operational settings. The study demonstrates how GANs may effectively advance military technology for surveillance on mobile platforms, strengthening national security standards in the process. The results highlight how GANs are uniquely adept in powerful improvements in military surveillance capabilities, which in turn improves their operational efficiency. In order to improve both the visual appeal and the efficacy of surveillance in a variety of military environments, this research paper suggests a novel way to improve portable battlefield surveillance systems through the integration of Generative Adversarial Networks (GANs). The technique emphasises discrete design and real-time processing capabilities for portable devices, with a focus on the creation and implementation of a GAN-based system. © 2024 IEEE.

关键词： Efficiency

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E-voting system with secure blockchain alert on data tampering

E-voting system with secure blockchain alert on data tamperi...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Julian, Anitha Priya, M.S. Lekshmi Chamundeeswari, V. Vijaya Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

With the growth of Blockchain technology in the second decade of the new millennium, numerous applications throughout the globe have been interested in intriguing new uses. The subject of electronic voting is one of the difficult uses of blockchain technology. Since the election is being conducted offline, security is of the utmost importance. An organization still has full access to the current electoral system’s centralized infrastructure. The entity that controls the database and has complete control over the system is mostly to blame for the issues because it is using antiquated election systems. A significant factor that seriously affects the system is database manipulation. The use of blockchain technology is one of the efficient solutions to this issue. Block chain technology is the best option because it has a decentralized structure and a shared database. This paper explores the potential for using Blockchain technology in e-voting systems to enhance the voting process by addressing issues of trustlessness, privacy, and security with effective face authentication techniques using Haar cascade and Support Vector Machine Algorithms. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Blockchain

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