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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Keerthika, S. Krishnasamy, Lalitha Priyanka, S. Sanjai, R. Sathyaa, S. Pooja, K. Kongu Engineering College Department of Artificial Intelligence TamilNadu Perundurai India Nandha Engineering College Department of Artificial Intelligence and Data Science TamilNadu Perundurai India Department of Artificial Intelligence Kongu Engineering College TamilNadu Perundurai India

ISBN: (纸本)9798331541217

Air pollution is described as the contains of dangerous substances in atmosphere that are emitted by natural sources or human activities and can harm humans, animals, and the environment. Sulfurous anhydride, carbon monoxide, Dioxide of nitrogen, trioxygen, carbon monoxide, and volatile organic compounds are some of the most common air contaminants. The development of tools for tracking and reducing air pollution is greatly supported by this analysis, which is important for both public health and environmental sustainability. To improve model performance, particular attention is paid to feature engineering and data pre-treatment methods. The study employs machine learning models, including Decision Tree Classifier, Logistic Regression, Naive Bayes, K-Nearest Neighbors, Random Forest, and SVM, to ascertain the most effective approach for accurate air quality forecasting. The key contribution of this work lies in its ability to provide a practical decision-making framework that can guide authorities in implementing focused interventions and optimizing resource allocation for pollution control. The hybrid approach utilized in this study surpasses traditional methods, offering a more precise tool for air quality forecasting. This model not only addresses the need for improved predictive performance but also acts as a useful tool for public health and environmental agencies seeking to minimize the harmful effects of air pollution. © 2024 IEEE.

关键词： Logistic regression

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Pinnacle of Steganography Security with Fernet

Pinnacle of Steganography Security with Fernet

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Kumar, Senthil Linett Sophia, D. Ashok Kumar, P. Kathirvel, R. Gokulnath, M. Sri, Roja Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode638057 India

ISBN: (纸本)9798331510299

A novel web-based platform that securely conceals encrypted communications within digital photos by utilizing Least Significant Bit (LSB) steganography in conjunction with Fernet encryption is presented. The technology guarantees that not only is the message's content kept private, but that its mere existence is invisible to unauthorized persons by combining these two strategies. With a React frontend and Flask-based backend, the platform has a solid architecture that produces a seamless and user-friendly interface. For maximum scalability and dependability, the system is hosted on Firebase for the frontend and Render for the backend. In the future, the platform intends to incorporate Fernet encryption for improved cryptographic security, strengthening the system against potential dangers like quantum computing. In sensitive settings where integrity and privacy are crucial, like the government, business, or personal life, this technology is especially well-suited for secure communications. © 2024 IEEE.

关键词： Steganography

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Plant Disease Detection with artificial intelligence Algorithms for Smart Agriculture 14

Plant Disease Detection with Artificial Intelligence Algorit...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Sudeepthi, R. Akshitha, V. Navya, K. Shalini, K. Karthikeyan, R. Shanmugasundaram, Hariharan Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9798331531935

Overseeing plant leaf diseases is vital for sustaining crop health, preventing extensive agricultural losses, safeguarding farmers livelihoods and ensuring a stable food supply. The health of the plant leaf is severely harmed by diseases such as early and late blight. Detecting leaf diseases is a time-consuming operation, and many farmers lack understanding about it, resulting in crop failure. Thus, early disease detection is crucial since it affects the quality and quantity of the potatoes. A type of artificial intelligence called deep learning has many uses in several domains and enables accurate and efficient data processing and decision-making. In this study, Leaf illnesses like early blight and late blight are detected and predicted using DL architectures like MobileNetV2, ResNet50, and EfficientNetB0. A comprehensive dataset is employed, containing images of healthy and infected leaves, which aids in model training and evaluation. The results show that MobileNetV2 achieves the highest accuracy of 91% in identifying leaf diseases. © 2025 IEEE.

关键词： Seed

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Importance-Based Pruning for Genetic Programming Based Symbolic Regression 37th

Importance-Based Pruning for Genetic Programming Based Symb...

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37th Australasian Joint Conference on artificial intelligence, AJCAI 2024

作者： Rimas, Mohamad Chen, Qi Zhang, Mengjie Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9789819603503

Pruning is a major research field in neural networks, enhancing their efficiency and generalization. The field of pruning approaches in genetic programming (GP) is continually evolving, with researchers actively exploring new techniques and approaches to optimise the performance of GP models. This research introduces a novel pruning algorithm for Genetic Programming-based Symbolic Regression (GPSR). The proposed method employs a weighting mechanism to identify and filter out unimportant subtrees in each generation. To achieve this, the method arranges all subtrees linearly and assigns weights to each subtree and terminal. It then uses Ordinary Least Squares (OLS) to optimize these weights, enabling the identification of unimportant subtrees and terminals for effective pruning. The algorithm’s effectiveness was evaluated using ten regression datasets, including high-dimensional and complex feature sets. Furthermore, comparisons were made with two other algorithms to evaluate its performance. The results indicate that the proposed approach not only achieves better learning and generalisation performance but also generates smaller trees compared to standard GP, thereby improving interpretability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic Programming Ordinary Least Squares Pruning Regression Subtrees

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Utility Poles Identification using Google Street View Images 1

Utility Poles Identification using Google Street View Images

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Vasavi, S. Sunkara, Chathurya Gullapalli, Aarya Sri Bolla, Jayadeep Sai Tiwari, Sudeer Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350352931

Accurate detection of utility poles is essential for effective infrastructure management in power systems, but traditional methods often fall short in delivering the needed precision. Previous versions of You Only Look Once (YOLO) showed promise but lacked the accuracy required for reliable utility pole identification. To address these shortcomings, an enhanced YOLOv9 model was developed to improve detection performance in street view images. By incorporating advanced feature extraction techniques, the YOLOv9 model achieves better detection accuracy. Tested on Google Street View images, the model reached a 95% detection accuracy while reducing the False Positive Rate to 9. 0 1 %. These results demonstrate the model's ability to accurately identify utility poles with fewer false alarms, offering a reliable tool for infrastructure supervision. The enhanced YOLOv9 marks a significant improvement, making maintenance and management more efficient. © 2024 IEEE.

关键词： Poles and zeros

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Medical Insurance Predictive Modelling: An Analysis of Machine Learning Methods 2

Medical Insurance Predictive Modelling: An Analysis of Machi...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Kandula, Ashok Reddy Kalyanapu, Srinivas Rayapalli, Sai Nithin Rao Veerabathina, Kondala Modugumudi, Venkatesh Kanikella, Sai Ram Seshadri Rao Gudlavalleru Engineering College Department of Artificial Intelligence and Data Science Gudlavalleru India

ISBN: (纸本)9798350360523

Predicting healthcare expenses accurately is crucial for a wide range of stakeholders and healthcare institutions. The healthcare industry generates massive volumes of data about patients, symptoms, and diagnoses, but this data has yet to be thoroughly studied to provide insight into patient healthcare costs. Machine learning (ML) may help enhance policy wording efficiency in the insurance industry, and ML algorithms are especially beneficial in healthcare for projecting high-cost, high-need patient bills. A computational intelligence technique is used to estimate healthcare insurance expenditures using machine learning algorithms. It is difficult to choose a single regression model for predicting health-care costs, thus we build an algorithm that uses multiple regression models and calculates the accuracy. The stochastic gradient boosting technique achieves an accuracy of 88.98%. © 2024 IEEE.

关键词： Multiple linear regression

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AI-Powered Personalization in E-Commerce 2

AI-Powered Personalization in E-Commerce

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Chaudhari, Aarti Hajare, Shital Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Sawangi442001 India

ISBN: (纸本)9798331528713

The nature of e-commerce is constantly changing how the business is carried out and how things are bought. It has been made possible to do so because of technological advancements and the capacity of the internet that goes as far as allowing the customers to view, buy and get products from the ease of their homes. The idea of e-commerce has developed to cut through both the retail environment and the interface of customer expectations. This is the outcome of artificial intelligence being adopted by the e-commerce sector, namely in the area of customization. More specifically, when AI algorithms are applied, machine learning is becoming more and more significant in e-commerce customization. AI technologies keep developing further and further which enables to analyse the detailed information about the shopping habits, interests and preferences of every client so that every e-commerce unit may be able to cater to every need of a customer. This document focuses on the AI methods related to the e-commerce personalization processes: machine learning, deep learning, and natural language processing (NLP) methods. It focuses on the procedures of gathering and analysing data because these are crucial in understanding the buying patterns of consumers and what they prefer. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid Design for Privacy Preserved Image Representation in a Cloud Environment 12th

Hybrid Design for Privacy Preserved Image Representation in ...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Vijay, K. Sorna Shanthi, D. Jaeyalakshmi, M. Vignesh, P. Yuvaraja, M. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The digital era has made seamless sharing and keeping of media such as images on cloud platforms an integral part of our lives. Still, there is a big issue about user privacy and data security in these repositories. We have thus created a very strong system that is able to do image encryption and decryption so as to protect the privacy of users while at the same time making their pictures accessible. Our system uses hybrid encryption where we try to prevent even cloud service providers from accessing the original files hence ensuring that only the owner who has been authenticated through password based authentication can decrypt or access them. In this way, it allows maintaining high privacy standards without sacrificing user convenience. It starts with preparing photos for encryption so that they are kept safe during transmission and storage. When needed by a user, images are decrypted by our system and become available only to authorized users. Consequently, users can comfortably leave their private photo albums on cloud storage systems knowing too well that their friends cannot access them thereby increasing trust in using cloud services for storing sensitive information. The aim of this work is to increase user privacy and data security in cloud repositories through employing this encryption technique. The users are guaranteed that their private details are not exposed to others enabling them to use cloud storage efficiently without interfering with their privacy at all. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Enhancing Crop Prediction Accuracy Via Soil and Environmental Feature Selection Techniques in Machine Learning 1

Enhancing Crop Prediction Accuracy Via Soil and Environmenta...

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2023 1st DMIHER International Conference on artificial intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Kadu, Aishwarya Reddy, K.T.V. Artificial Intelligence and Data Science Faculty of Engineering and Technology Wardha India

ISBN: (纸本)9798350338423

Historically, farmers' practical knowledge was a significant factor in agricultural decisions. The start of climate change has, nevertheless, harmed crop output. Consequently, farmers face challenges when choosing the optimal crops considering the soil and environmental elements. Traditional manual methods of predicting the best crops to grow on particular types of terrain have typically yielded unsatisfactory results. Acquiring accuracy in crop forecasts is essential for increasing agricultural productivity. It is the precise point in the crop forecast process that machine learning plays a significant role. Crop forecast accuracy depends on several factors, including soil type, location, and regional weather variations. A fundamental aspect of prognostication is carefully identifying important qualities matched to particular crops, supported by feature selection methods. The current study orchestrates a comparison of several selection strategies for wrapper features. The experimental results show that the Recursive Feature Elimination method combined with the Adaptive Bagging classifier performs better than the alternative approaches. These procedures, which fall under the category of crop prediction and use categorization techniques, not only pinpoint the appropriate crops for specific plots of land but also plot the route for future development. © 2023 IEEE.

关键词： Crops

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A Multimodal Multiobjective Evolutionary Algorithm for Filter Feature Selection in Multilabel Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第9期5卷 4428-4442页

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multilabel learning is an emergent topic that addresses the challenge of associating multiple labels with a single instance simultaneously. Multilabel datasets often exhibit high dimensionality with noisy, irrelevant, and redundant features. In recent years, multilabel feature selection (MLFS) has gained prominence as a crucial and emerging machine learning task due to its ability to handle such data effectively. However, existing approaches for MLFS often prioritize top-ranked features based on intrinsic data criteria, disregarding relationships within the feature subset. Additionally, compared with conventional feature selection, multiobjective evolutionary algorithms (MOEAs) have not been widely explored in the context of MLFS. This study aims to address these gaps by proposing a multimodal multiobjective evolutionary algorithm (MMOEA) called MMDE_SICD which incorporates a preelimination scheme, an improved initialization scheme, an exploration scheme inspired by genetic operations and a statistically inspired crowding distance scheme. The results show that the proposed MMDE_SICD algorithm can outperform a variety of MOEAs and MMOEAs as well as conventional MLFS algorithms. Notably, this study is the first of its kind to consider MLFS as a multimodal multiobjective problem. © 2020 IEEE.

关键词： Multiobjective optimization

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