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An Effective Deep Learning based Recommender System with user and item embedding

An Effective Deep Learning based Recommender System with use...

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Nareshkumar, R. Agalya, K. Arunpandiyan, A. Vijayalakshmi, M. Ranjani, V. Ramya, A. SRM Institute of Science and Technology Department of Networking and Communication Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India A. v C. College of Engineering Department of Computer Science and Engineering Mayiladuthurai India Care College of Engineering Artificial Intelligence and Data Science Trichy India Care College of Engineering Department of Computer Science and Engineering Trichy India Indra Ganesan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350334357

The success of deep learning in a variety of fields has spurred a rush of desire to develop new novel recommender systems. As a result, our research introduces new RecDNNing, a ground-breaking technique that combines integrated people and products with a deep learning model. There are two phases to the suggested suggestion method. We begin by constructing a unique mathematical version for each human and item, which we refer to as user and item embedding. The embeddings of the goods and users then are aggregated, concatenated, and sent into the deep learning model. In the second phase, the spliced people and items implants are utilised as sources, and the ratings scores are forecasted using the forward propagation technique. The suggested new RecDNNing algorithm outperforms state-of-the-art techniques on MovieLens, according to the results. © 2023 IEEE.

关键词： Recommender systems

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Market Basket Analysis for Retail Sales Optimization 2

Market Basket Analysis for Retail Sales Optimization

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

作者： Kumar, P. Manisha, K.N. Nivetha, M. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328202

Supermarkets and grocery stores aim to improve customer experience and maximize sales. Recommending frequently bought items and optimizing the layout of products can enhance customer satisfaction and sales revenue. The proposed system 'Market basket analysis for retail sales optimization' introduces a data-driven solution that leverages association rule mining algorithms, namely Eclat, FP-growth, and Apriori to recommend frequently purchased grocery items and optimize their placement within the store layout. The paper starts by utilizing a rich dataset of daily purchased grocery items, including historical transaction data. By employing the algorithms, the system uncovers hidden patterns, associations, and correlations among the purchased items. These algorithms efficiently mine frequent itemsets, allowing the system to recommend items that are often bought together. In addition to personalized recommendations, the project optimizes the store layout. Items that are frequently purchased together are strategically positioned in proximity to enhance customer convenience and encourage additional purchases. Simultaneously, it helps store owners to maximize sales by strategically placing items based on their association with one another. This paper bridges the gap between data analytics and retail optimization, offering a solution that benefits both customers and store operators. © 2024 IEEE.

关键词： Sales

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Customer Segmentation and Retention Strategies for Bank Using Machine Learning

Customer Segmentation and Retention Strategies for Bank Usin...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Beedhi Reddy, Hemanth Kumar Vijay Anand, M. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309140

Customer churn is a pressing problem faced by banks, affecting their revenues and customer satisfaction. To solve this problem, this study explores the use of machine learning, specifically Naive Bayes, Decision Trees, and AdaBoost classifiers, to predict and reduce customer churn. By leveraging these complex algorithms, banks can gain insight into customer behaviour and take proactive steps to retain valuable products as customers. The process starts with pre- processed data, model design and coding. This optimization can solve many problems such as excessive priority, missing data, categorical features, etc. Additionally, data Analytics (EDA) and visualizations to gain a deeper understanding of customer segmentation;This allows to uncover important trends and good standards. When evaluating performance in the model, Focused on simple metrics such as recall and ROC- AUC scores. These metrics provide insight into the model's ability to accurately identify past customers while minimizing false positives. To improve the performance of model, hyperparameter tuning was performed to ensure that we achieved the highest accuracy. The Model adopt ROC-AUC curve to see the balance between true positive and negative value. This analysis provides a better understanding of the performance model, allowing to make informed decisions and strategies to manage the customer's quality. Actual and estimated loss status is displayed as an estimate of Customer's final model. This valuable information provides banks with the strategies necessary to identify potential credit losers and take the necessary steps to maintain their trust. © 2024 IEEE.

关键词： Sales

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A Novel Approach for Classification of Crops and Weeds using Deep Learning

A Novel Approach for Classification of Crops and Weeds using...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Ram Prakash, L. Pravinesh, H. Ariyamala, V. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

Weed detection is very important in modern agriculture, and timely and fast identification of weeds helps to reduce crop losses that have a positive effect on the yield. The classical methods of weed management involve manual weeding or broadcast spraying with herbicide, and both have laborious, expensive, and dismaying effects on the environment. The demand has never been higher for automatic weed detection systems in agriculture. Abstract: This project aims to build an automated weed detection system with YOLOv8, one of the most state-of-the-art object detection algorithms that excels in real-time accuracy. The system utilizes high-resolution drone and camera images of fields to pinpoint the specific plants growing in a crop, enabling more selective herbicide application and minimizing the need for manual labor from cropping operators. The design of YOLOv8 ensures high-speed and high-precision performance, making it ideal for use as a real-time detection system in the field. We train our model on a home-grown dataset of approximately 8k crop and weed images (gm), incorporating additional data augmentation for enhanced robustness. We will assess performance using evaluation metrics like precision, recall, F1-score, and mean average precision (mAP). © 2024 IEEE.

关键词： Deep learning

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A Survey on Tea Plant Disease Prediction for Blister Blight 2

A Survey on Tea Plant Disease Prediction for Blister Blight

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

作者： Gowthami, M. Nila Baaradhy, I. Nithin, V. Subhash, J. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328202

This survey seeks to give a general overview of the current state of Tea Plant disease prediction for the Blister Blight disease. To construct this system, IoT and machine learning approaches are applied. The goal of the research is to improve sustainable farming methods through early detection and prevention. The climatic conditions of the farmlands are analysed with a set of sensors. Real time input from the IoT setup is processed by the Machine Learning algorithms for detection of the Blister Blight disease. © 2024 IEEE.

关键词： Internet of things

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Performance Evaluation of Two-Tier Hybrid Cryptographic Models for Secure data Transactions in Cloud Computing 2

Performance Evaluation of Two-Tier Hybrid Cryptographic Mode...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Debnath, Pankoj Kar, Tonoy Chandra Ashraf, Din Md Arif, Redwanul Islam Lysuzzaman, Md Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350383652

Cloud computing is a trending and enormous technology. With the improvements in the field of Cloud computing, the main challenge arises in the transaction of data securely from one platform to another. Various cryptographic algorithms are used to provide a secure transaction of data. However, conventional cryptographic algorithms might not be sufficient enough for modern threats. To face these potential vulnerabilities, hybrid cryptographic algorithms can be the solution. Hybrid cryptography integrates multiple cryptographic algorithms to enhance performance and overall safety. RSA(Rivest-Shamir-Adleman) is the most common public key algorithm for encryption and decryption. This paper analyzes a modified form of RSA known as Modified-RSA that enhances security in hybrid cryptographic algorithms by incorporating three prime numbers instead of two. The Modified-RSA(MRSA) hybrid models are compared with the original RSA hybrid models with some theoretical and practical aspects. In this research, a comparative study of two-tier hybrid cryptographic models applied to secure data on the cloud is presented. Practical implementations were conducted with hybrid models of RSA and Modified-RSA using Python simulation. Encryption time, decryption time, average throughput, and efficiency were found using relatively more extensive data. The main motive was to find out the performance of every cryptographic hybrid and see which hybrid model is more efficient. © 2024 IEEE.

关键词： Cloud platforms

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Reversible data hiding in encrypted images with block-based bit-plane reallocation

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Multimedia Tools and Applications 2024年第37期83卷 84911-84932页

作者： Liu, Li Chen, Chaofan Wu, Yingchun Chang, Chin-Chen Wang, Anhong College of Electronic Information and Engineering Taiyuan University of Science and Technology Taiyuan030024 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

As cloud storage and multimedia communication continue to evolve, the preservation of image privacy is becoming increasingly important. Reversible data hiding in encrypted images (RDHEI) is an effective method for enhancing the protection of personal privacy. To improve the embedding capacity, this paper introduces a novel scheme for RDHEI using block-based bit-plane reallocation. First, an effective optimized prediction method is utilized to predict the value of each pixel, followed by the calculation of prediction errors (PEs). Then, block-based bit-planes of PEs are extracted, and ten different bit-plane types are categorized to facilitate bit-plane reallocation, thereby freeing up redundant space. Finally, an adaptive encryption procedure is employed to encrypt the reallocated bit-plane, excluding the flag-bits. Experimental results demonstrate that the proposed scheme achieves average embedding rates (ER) of 3.584 bpp, 3.501 bpp, and 2.985 bpp on the three experimental datasets, all of which are higher than those of other state-of-the-art RDHEI schemes while ensuring both reversibility and security. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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Mood Melody Matchmaker System Using Deep Learning Model 9

Mood Melody Matchmaker System Using Deep Learning Model

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Mishra, Susmita Pradhan, Chittaranjan Jananee, V. Rajalakshmi Engineering College Department of Computer Science And Engineering Chennai India School of Computer Engineering KIlT Deemed To Be University Bhubaneswar India Rajalakshmi Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

Accessing a wide variety of music is made simple by modern music services. Many users rely on recommendation algorithms to select the ideal song for any given circumstance. Due to its frequent use for relaxation, mood regulation, stress, and illness management, as well as support for mental and physical activities, music has a profound influence on individuals. Music therapy employs a range of therapeutic environments and methods to enhance overall well-being. The article suggests a mood melody matching related to the listener's feelings. Machine learning and deep learning methods are employed to create a recommendation system designed to assist individuals in choosing music suitable for various life circumstances. Efficiently recognizing the ideal track for the user's current mood, the system delivers songs tailored to their emotional state, eliminating the need for users to spend time searching or seeking out music themselves. © 2024 IEEE.

关键词： Python

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Integrating the Internet of Things to Protect Electric Vehicle Control Systems from Cyber Attacks

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IAENG International Journal of Applied Mathematics 2024年第3期54卷 433-440页

作者： Bhukya, Baddu Naik Venkataiah, V. Kuchibhatla, S. Mani Koteswari, S. Kumari, R.V.S. Lakshmi Raju, Yallapragada Ravi Department of Electrical and Electronics Engineering Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Department of Computer Science Engineering CMR College of Engineering & Technology Telangana Hyderabad India Department of Electrical and Electronics Engineering ACE Engineering College Telangana Hyderabad India Department of Electronics and Communication Engineering Pragati Engineering College Kakinada India Andhra Pradesh Surampalem India Department of Electrical and Electronics Engineering Gayatri Vidya Parishad College of Engineering for Women Andhra Pradesh Visakhapatnam India Department of Computer Science and Technology Madanapalle Institute of Technology and Science Andhra Pradesh India

The Internet of Things (IoT) facilitates the delivery of intelligent services by sensing, gathering, processing, and exchanging data from millions of linked smart devices. The Internet of Things (IoT), which is based on communication infrastructure, provides real-time cyber-physical device monitoring, control, and sensing, such as electric autos. Traditional communication infrastructure's vulnerability to cyberattacks hampered IoT's capacity to investigate these potential applications. This study proposes an algorithm for monitoring and controlling electric vehicles through the Internet of Things communication network to prevent fake data injection attacks. A state-space model depicts a fully autonomous electric car equipped with a vision system. Intelligent sensors and actuators from the Internet of Things are utilized to monitor and change system conditions, compensating for the large distance between the electric car and the control center. Sensing data is delivered from the vehicle to the central command center via an insecure, attack-prone communication path. The mean square error approach is used to derive states in the most effective state estimation system for comprehending and showing autos. A semi-definite programming approach is used to create an optimal control algorithm to manage the vehicle states. The simulation results show how precisely and successfully the proposed algorithms can foresee and regulate a vehicle's state. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Internet of things

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