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Review of Recent Datasets Used in Image Captioning Models 5

Review of Recent Datasets Used in Image Captioning Models

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Srivastava, Swati GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

Automatic image captioning is a complex research area whose objective is to generate descriptions in natural language from a query image. It has significantly advanced with deep learning with applications in humancomputer interaction, medical image captioning, traffic analysis, industrial quality control, and assistive systems for the visually impaired. Image captioning task includes visual features extraction and generate syntactically and semantically correct descriptions of the image. Deep learning techniques are proficient in handling the multifaceted relationships among image objects. This work explores 14 popular image captioning datasets used in the evaluation of deep learning models for automatic image captioning. The ultimate goal of this work is to provide better understanding of image captioning datasets which may benefit the related research communities. © 2024 IEEE.

关键词： Structured Query Language

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Optimizing SMS Spam Detection: An In-Depth Analysis of Machine Learning Approaches 5

Optimizing SMS Spam Detection: An In-Depth Analysis of Machi...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Jain, Vinod Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350389609

Since mobile messaging has become more popular, there is a rising worry in the digital communication environment about SMS spam detection because of the surge in unwanted and potentially hazardous communications. Beyond conventional rule-based approaches, machine learning (ML) provides a potent way to automate the detection and filtering of spam in SMS (Short Message Service) systems. By converting text data into numerical representations, these techniques enable models to identify trends in both spam and ham (non-spam) communications. SMS spam datasets are used to train the models, and measures including precision, recall, and F1-score are used to assess the *** outcomes show that machine learning models are capable of identifying spam. Because they work well in text classification tasks, Naive Bayes and Logistic Regression in particular perform well. According to the study's findings, machine learning provides a reliable, scalable, and adaptable method for detecting SMS spam that can handle big datasets and changing spam strategies. Advanced NLP (Natural Language Processing) techniques is integrated into SMS systems to greatly enhance the performance of ML models. Future research will include multilingual spam filtering and real-time detection. © 2024 IEEE.

关键词： Logistic regression

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Performance Enhancement of Machine Learning Algorithms for Predicting Stroke 7

Performance Enhancement of Machine Learning Algorithms for P...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Jain, Vinod Mangal, Anuj GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350372816

Stroke is very common heath problem now. Early prediction of stroke is very essential to decrease the impact of this disease. Machine Learning (ML) algorithms are very useful in making predictions for various diseases. In this paper, the power of machine learning algorithms is used for stroke prediction. Even after a lot of research the accuracy of machine learning algorithms is a challenging issue. Many researchers worked on improving the accuracy of ML models. Machine learning models worked on data sets. For stroke prediction using machine learning, a well-known data set is considered in this paper. The data set consist of many important factors which may affect the chances of stroke in a person. Logistic Regression, Decision Tree and Random Forest are three machine learning algorithms which are very popular and effective in prediction of stroke. In this work these three machine learning algorithms are applied for stroke prediction and their performance is checked and compared. From the results it is observed that the performance of Logistic Regression is best with 95.34% accuracy. © 2024 IEEE.

关键词： Prediction models

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CNN-DBO: Dung Beetle Optimization Based Urban Area Change Detection Using Satellite Images

CNN-DBO: Dung Beetle Optimization Based Urban Area Change De...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pujari, Jambukeshwar Wasim, Javed Tripathi, Aprna Mangalayatan University Department of Computer Engineering and Applications Aligarh20221385 India Mangalayatan University Department of Computer Engineering and Applications Aligarh India Manipal University Department of Data Science Engineering Jaipur India

ISBN: (纸本)9798331512965

In recent years, Geographic Information Systems (GIS) have garnered a significant deal of interest for their ability to detect changes in metropolitan areas. One of the uses of change detection in satellite photographs is in the field of urban planning. Other applications include environmental monitoring and remote surveillance. A number of different approaches have been suggested in recent times for the purpose of automatically identifying alterations in the land cover based on GIS photographs. However, due to poor texture, cluttered context, and considerable object scale disparities, the convolutional neural network (CNN) method is only partially successful in capturing the context from geographic information systems (GISs). This is despite the fact that it remains an effective method for detecting change in urban environments. The article presents the construction of a CNN-based change detection model using the utilization of Dung Beetle Optimization (DBO). The DBO algorithm is used to adjust the loss parameter in order to increase the overall performance. The approach that was proposed makes use of the Onera satellite data set and displays an overall accuracy of 97.50% percent. In light of this, it appears that the strategy that is proposed is both very efficient and effective. © 2025 IEEE.

关键词： Convolutional neural networks

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Spam Message Identification Using Machine Learning Approach 2

Spam Message Identification Using Machine Learning Approach

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Mohan, Narendra Jain, Vinod Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350368413

Spam message detection has become an essential task in modern digital communication systems due to the exponential growth of unsolicited messages. Traditional rule-based spam filters, though initially effective, have become inadequate in handling the sophisticated techniques employed by spammers. Consequently, machine learning algorithms are used for spam detection because of their ability to learn the data and these algorithms can improve their performance by providing new data. This research paper explores various ML algorithms utilized for spam detection. Supervised learning methods are predominantly highlighted, focusing on how these models are trained using labeled datasets where messages are classified as spam or non-spam. Additionally, the paper addresses challenges such as the evolving nature of spam, imbalanced datasets, and the need for real-time detection solutions. The paper concludes by exploring the outcome results of ML models for spam detection with Support Vector Classifier (SVC) as best ML model with 97.48% accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Energy Efficient Mobile Application for Solar Energy using Machine Learning 1

Energy Efficient Mobile Application for Solar Energy using M...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Rathee, Sonia Yadav, Amita Shalu Choudhary, Rhythm MSIT Computer Science and Engineering Delhi India Computer Applications MSI Delhi India

ISBN: (纸本)9798350361780

As individuals search for environmentally acceptable ways to preserve the environment, solar panels are a great option. However, the utility sector requires intelligent technologies that can effectively increase the integration of renewable energy. So, we have proposed building this solar estimation platform which culminates Artificial Intelligence, Machine Learning, and Software engineering to build the best solar estimation application, where professional companies can sell their solar devices and provide estimation for costs and potential solar production for the clients. On Earth the largest source of energy is Sun. Solar energy is more than many times of human needs. If solar power is utilized in a proper way, it can cater to all our needs in a sustainable way. Solar energy depends on several factors like geographic locations, altitude, weather conditions, diurnal variation, etc. We propose a model which identifies optimal space for harnessing solar energy using ANN for predicting the daily GSR of location in India and then using our traineddeep neural network model with all the factors affecting solar flux in consideration. © 2024 IEEE.

关键词： Solar energy

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Safeguarding Information in QR Codes Through Steganographic Technique 3

Safeguarding Information in QR Codes Through Steganographic ...

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Bhardwaj, Jyotsana Panwar, Ruby Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331528201

The act of concealing data within a different message or object in order to avoid detection is referred to as steganography. Steganography can hide various types of digital content such as text, images, audio, and video. Upon arrival, the hidden information is subsequently exposed. Employing a form of steganography called QR Image Steganography, in which a secret message is encoded into a QR code and subsequently hidden within an image to provide effective and efficient security. To safeguard the secret communication, encoding is carried out on the sender's end, and decoding is done on the recipient's end. This paper is focused on the idea that, in order to maintain confidentiality and prevent a third party from spotting the hidden message in the stegano image, the cover image should not be significantly altered when concealing the QR code containing the secret message. Additionally, the PSNR values of the cover picture and the stegano image were used to verify this;they were fairly high, showing that the cover image had slight distortion. In order to determine the pixel value change where the distortion is not apparent, the sample histograms of the cover and stegano picture were also plotted. © 2024 IEEE.

关键词： Steganography

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Enhancement of Logistic Regression to Detect Breast Cancer using Cross Validation 8

Enhancement of Logistic Regression to Detect Breast Cancer u...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Jain, Vinod Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350367904

Many women in the world are suffering from cancer. Breast cancer is among one of the cancer which is affecting women all over the globe. The machine learning algorithms are found good for detecting breast cancers. Logistic Regression is among the best supervised machine learning algorithms which is found very good in detecting breast cancer. Many hospitals provided their data of females which comes for breast cancer detection. WDBC is one of the best known data sets for breast cancer. K-fold cross-validation reduces the possibility of overfitting and enhances generalization by repeating the process across all folds and computing the average performance. By applying the Logistic Regression model with cross validation we can more effectively generalize to new data. Through the identification of the most significant predictors, feature selection further enhances the model by lowering computational complexity and dimensionality. The findings show that good accuracy for breast cancer prediction is obtained when using Logistic Regression in conjunction with cross-validation. The model is a good fit for clinical applications because of its robust performance, ease of interpretation, and simplicity. The findings of this study demonstrate the validity of logistic regression as a tool for the diagnosis of breast cancer and imply that its combination with cross validation improves diagnostic accuracy and aid in clinical decision-making. © 2024 IEEE.

关键词： Logistic regression

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Cybersecurity Enabled Improved BigData Privacy Management Measures to Preserve Information With Privacy Concerns 9

Cybersecurity Enabled Improved BigData Privacy Management Me...

引用

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

作者： Sankar, R. Alwin Christopher, T. Daden, Wilson A Miruthula, M. Vaishali, M. Rajan Kirthy, G. S.A. Engineering College Department of Master of Computer Applications Chennai India S. A. Engineering College Department of Master of Computer Applications Chennai India

ISBN: (纸本)9798350365092

In today's data-driven landscape, preserving the privacy and security of sensitive information within Big Data environments, particularly in sectors like healthcare, is of paramount importance. This paper introduces a comprehensive framework for Cybersecurity Enabled Improved Big Data Privacy Management Measures (IBPMM) tailored to address the challenges of safeguarding privacy while harnessing the potential of vast datasets. The framework integrates advanced cybersecurity measures with privacy management strategies, incorporating key components such as data encryption, access controls, anonymization techniques, threat detection, and incident response protocols. Regulatory compliance requirements, including GDPR and HIPAA, are carefully considered throughout the framework's development. Additionally, methodologies for data classification, risk assessment, and continuous monitoring ensure that privacy risks are systematically identified and mitigated. Privacy-preserving technologies such as differential privacy and homomorphic encryption enable secure data analysis while protecting individual privacy. Employee training and awareness initiatives foster a culture of privacy and security within organizations. The framework also includes robust incident response procedures to address security breaches promptly. By implementing the IBPMM framework, organizations can effectively balance the benefits of Big Data analytics with the imperative to preserve information with privacy concerns. © 2024 IEEE.

关键词： Regulatory compliance

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Prediction of Cryptocurrency Price Using Deep Learning

Prediction of Cryptocurrency Price Using Deep Learning

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Rahunathan, L. Suhana Nafais, A. Rivitha, S. Saran, K. Kumari, Swetha Kongu Engineering College Department of Computer Applications Tamil Nadu Perundurai638060 India Sona College of Technology Department of Computer Applications Salem India

ISBN: (纸本)9798331509828

Cryptocurrencies have gained popularity since the launch of virtual currencies like Bitcoin, Ethereum, and many more. Because of their extreme volatility, the cryptocurrency markets present both opportunities and challenges for traders and investors. In this volatile market, accurate cryptocurrency price prediction is essential for making wise investment choices. In this research, created a ground-breaking deep learning-based technique for forecasting bitcoin values. LSTM, GRU, Random Forest Regressor (RFR), and Super Vector Regressor (SVR) are a few of the deep learning algorithms that are used. The proposed model considers a number of elements that may have an impact on the value of cryptocurrencies, including historical price information, trading volume, market mood, and technical signals. In this research cross model is suggested that combines LSTM and GRU for the accurate prediction. Based on the results of evaluation measures such RMSE, MAE, MSE, R-Square Error, and Variance Regression Score, the optimal model is selected. In comparison to previous deep learning models, the hybrid model of LSTM+GRU has performed well in price prediction, according to the results of evaluation metrics. © 2025 IEEE.

关键词： Prediction models

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