Volume 12 | Issue 5 |

 Abstract

This research study presents a novel approach that integrates state-of-the-art Convolutional Neural Networks, algorithms to transform the management of women's health. This project intends to employ artificial intelligence to build a comprehensive manual that is unique to the health requirements of women, given the increasing prevalence of modern technology in healthcare. The suggested method covers a broad range of health-related topics, such as illness prevention, mental health, dietary counseling, and reproductive health, among others. CNN algorithms are used to provide real-time insights and recommendations based on lifestyle choices, medical history, and demographic details. This allows the guide to adapt to each user's individual health profile. The freedom of women to take proactive measures to optimize their health outcomes is at the heart of this research. Through intuitive design, the resource guide promotes self-care habits and educated decision-making. By giving users access to peer networks, professional guidance, and related information, it also helps users feel more supportive of one another and connected to each other.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CNN algorithm, personalized healthcare, disease prevention, wellness management, artificial intelligence, women's health, and community support

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Phishing, a prevalent cybercrime, involves deceiving individuals into disclosing personal or confidential information under the guise of legitimate websites or emails. Recognizing phishing websites is challenging due to their similarity to authentic ones. Our study focuses on employing machine learning techniques for efficient phishing website detection. We detail the approach encompassing data gathering, preprocessing steps, extracting features, and employing Support Vector Machine (SVM) for classification. SVM stands out due to its capacity to handle high-dimensional data and non-linear relationships, making it robust to overfitting. SVM offers understandable detection capabilities by optimizing the margin between classes to its maximum extent. Our research aligns with broader cybersecurity objectives, aiming to safeguard individuals and organizations against online deception. Through the development of robust detection systems, we contribute to enhancing cybersecurity measures and empowering users with more secure online experiences. This endeavor underscores the importance of proactive measures in combatting evolving cyber threats

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 Keywords

Phishing, Support Vector Machine, high- dimensional, Feature extraction

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Social media platforms like Twitter face a growing challenge with the proliferation of malicious Twitter bots. These bots spread disinformation, manipulate public opinion, and engage in fraudulent activities, undermining trust in online spaces. To address this issue, our project focuses on detecting malicious Twitter bots using advanced machine learning techniques and URL analysis. By examining various features, including URL characteristics, we've developed a robust framework. We've trained models like Logistic Regression, Random Forest, Naive Bayes, and Decision Trees to classify Twitter accounts as malicious or benign. Our work contributes to enhancing social media security, protecting user trust, and combating fake accounts. Keywords-- Malicious Twitter bots, Machine learning, URL analysis, Social media security, Classification, Logistic Regression, Random Forest, Naive Bayes, Decision Trees, User trust, Fake accounts.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Detecting Malicious Twitter Bot using Machine Learning and URL analysis

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The purpose of this paper is to develop and evaluate a breast cancer prediction system using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). Within the wake of a breast cancer diagnosis, the project presents a comprehensive web-based platform created to meet the interrelated demands of administrators, healthcare providers, and patients. Patients are empowered to contribute to their healthcare journey by seamlessly uploading mammographic images. Through the integration of AI-driven algorithms, including CNN and SVM, the system predicts whether an individual has breast cancer or not can be determined by using the features that were taken from the images. For patients, a dedicated dashboard provides insights into diagnostic results, alongside some precautions. This paper examines the intricate integration of CNN and SVM algorithms, alongside patient- centric features and administrative controls, highlighting the potential impact on enhancing breast cancer diagnosis accessibility, efficiency, and overall user experience.

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 Keywords

Support Vector Machine, Convolutional Neural Network, Machine Learning, Deep Learning, Cancer Prediction, Predictive Model.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In many domains, data duplication is a major problem that results in inefficient processing, storage, and analysis. In this work, we investigate the use of machine learning methods for tasks related to data duplication and deduplication, with a particular emphasis on textual and image data. This project provides preparation pipeline for textual data that includes tokenizing text, addressing missing values, and standardizing formats. Then, feature extraction techniques like word embeddings and TF-IDF are used to mathematically represent text. These attributes can be used to train machine learning models, such as Support Vector Machines (SVM) or clustering methods like K- means, which are used to efficiently identify and eliminate duplicate entries. These models are capable of identifying duplicate photos because they learn hierarchical representations of images. To successfully recognize duplicates, this entails creating hybrid models that integrate textual and visual information.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Data security, Deduplication, Authorization, Authentication, Access control, Cloud Security, Cipher Technology, Data synchronization

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the era of information development, information transfer is far more practical. However, there is always a chance that the transmission method may be compromised, stolen, or assaulted, raising questions about the accuracy of the data source. Several academics suggested using passwords to secure sensitive data because of this. Proposed 3D-Playfair Cipher with Message Integrity Using MD5. The encryption used in this study is 3D-Playfair. Nevertheless, basic 3D-playfair encryption is unable to ensure the integrity of data while it is being sent, thus the author suggests In addition to using MD5 to guarantee data integrity, this study employs XOR calculation techniques to further confirm the data's legitimacy because there are concerns over the reliability of the data source.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Data security, Deduplication, Authorization, Authentication, Access control, Cloud Security, Cipher Technology, Data synchronization.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The diagnostic tropical cyclone intensity estimation system presented in this paper represents a novel approach to objective and automated intensity assessment using satellite imagery and deep learning techniques. Leveraging data from the National Hurricane Centre (NHC) and the National Oceanic and Atmospheric Administration (NOAA), the system integrates real- time storm outlooks with corresponding Geostationary Operational Environmental Satellite (GOES) imagery to provide accurate and timely predictions of cyclone intensity levels. The implementation involves web scraping for data retrieval, preprocessing of satellite imagery, and deployment of a convolutional neural network (CNN) model for intensity estimation. Results demonstrate the system's effectiveness in providing objective intensity assessments, with promising performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Furthermore, the system's scalability and potential impact on disaster preparedness and response efforts are discussed, highlighting its role as a valuable decision support tool for meteorological agencies and emergency responders. Overall, this implementation paper contributes to advancing the field of cyclone intensity estimation and underscores the importance of integrating technology and data-driven approaches in disaster management.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep learning, wind speed estimation, Convolutional neural network, Estimation, Cyclone, Disaster management

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Tropical cyclones (TCs) stand as dynamic and complex atmosphere-sea interaction phenomena, their behavior contingent upon a delicate interplay of oceanic and atmospheric conditions. Their emergence, dissipation, or intensification presents a challenge for accurate prediction, and as such, the development of precise diagnostic models holds immense potential for saving lives and safeguarding property. Existing techniques for diagnosing tropical cyclone wind speeds have displayed varying degrees of success, often limited by their reliance on specific points in time and satellite data. This paper introduces a groundbreaking paradigm shift by presenting a deeplearning-based objective diagnostic estimation of tropical cyclone intensity. Leveraging the power of deep learning, the model promises to transcend the limitations of traditional methods, offering a more nuanced and accurate representation of cyclonic behavior. A key innovation lies in the integration of an infrared satellite imagery-based diagnostic system. This method represents a new visualization gateway in addition to improving intensity estimation accuracy. This portal is not merely a scientific tool; it stands as one of the first systems to seamlessly translate deep learning results into a user-friendly interface, presenting not only raw data but also contextual information to end users. This move towards user accessibility marks a significant stride in bridging the gap between advanced scientific methodologies and practical, realworld applications

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep learning, wind speed estimation, Convolutional neural network, Estimation, Cyclone, Disaster management

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The behaviors of cryptocurrency markets are dynamic and complicated, with high volatility and a wide range of influencing factors. In order to anticipate bitcoin prices, this study investigates the use of deep learning techniques, particularly recurrent neural networks (RNNs) and long short- term memory networks (LSTMs). For well-known cryptocurrencies, the study makes use of past price data, trade volumes, and extra technical indicators. The methodology includes feature engineering, training the model, evaluation, and preprocessing of the data. Measures like Mean Absolute Error (MAE) and Mean Squared Error (MSE) are used to evaluate the performance of the deep learning model. The results shed light on how well deep learning captures patterns and temporal relationships in cryptocurrency price data. The conversation explores how the findings could affect real-world trading tactics, points out its shortcomings, and suggests directions for further study. This study adds to the expanding corpus of research on the prediction of cryptocurrency prices by providing a sophisticated knowledge of the potential benefits and difficulties of using deep learning in this field.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cryptocurrency, Price Prediction, Deep Learning, LSTM, Neural Networks, Financial Forecasting

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In this day and age the Mastercard extortion is the greatest issue and presently there is need to battle against the Visa misrepresentation. "Visa extortion is the most common way of cleaning messy cash, accordingly making the wellspring of assets as of now not recognizable." On consistent schedule, the monetary exchanges are made on gigantic sum in worldwide market and subsequently identifying charge card misrepresentation movement is testing task. As prior (Against Mastercard extortion Suite) is acquainted with distinguish the dubious exercises yet it is relevant just on individual exchange not for other financial balance exchange. To Conquers issues of we propose AI technique utilizing 'Underlying Closeness', to recognize normal credits and conduct with other financial balance exchange. Location of charge card misrepresentation exchange from huge volume dataset is troublesome, so we propose case decrease strategies to lessens the information dataset and afterward find sets of exchange with other financial balance with normal ascribes and conduct.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

credit card fraud, fraudulent activities, SVM (SUPPORT VECTOR MACHINE), Harr cascade Algorithm, Face Recognition.

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Creative Commons Attribution 4.0 and The Open Definition