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AI Sentries: Evaluating Machine Learning Models for Superior Phishing Email Detection 8

AI Sentries: Evaluating Machine Learning Models for Superior...

引用

8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Sahit, S. Vaishnavi, S. Vaibhav Reddy, A. Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505462

Email Phishing Detection focuses on studying how to process emails and spot threats using machine learning and advanced natural language processing. It aims to shield people and businesses from real email misuse. This study tries to fix some problems that earlier research had faced. It does this by making better machine learning detectors to spot fake emails. These detectors use different data sets and look at how to pull out key info. They also try to adapt to new tricks scammers use. The solutions put forward use new methods. These include tweaking features, combining different learning models, and using deep learning to boost accuracy while checking emails in real-time. The study also looks at how to use natural language processing (NLP) to understand what emails mean. This helps catch more fake emails. By looking at both how emails are written and what they mean, the system can spot scams more. This cuts down on false alarms and makes the system more reliable. This well-rounded approach not pushes the boundaries of spotting fake emails but also creates a strong system that can change as cyber threats do. This ensures protection that lasts and can bounce back from attacks. © 2024 IEEE.

关键词： Phishing

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HAR-CLET: A Novel Conv-LSTM with Extra Tree Classifier for Human Activity Recognition

HAR-CLET: A Novel Conv-LSTM with Extra Tree Classifier for H...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Choudhury, Nurul Amin Soni, Badal National Institute of Technology Silchar Dept. of Computer Science and Engineering Assam Silchar India

ISBN: (纸本)9798350350821

In this paper, we propose a novel ensemble of Convolutional Neural Network-Long Short-Term Memory with an Extra Tree Classifier for automatic feature engineering in the spatiotemporal domain and classification of different human activities collected using inbuilt smartphone sensors. Prominent sensor-based HAR systems are trained and validated in extracted features of datasets collected in a controlled and simulated environment, which can adversely impact the reliability in real-world scenarios. Our approach utilizes unsimulated data from diverse users collected using inbuilt smartphone sensors in real world environment. Also, the proposed model was effectively trained and tested with multiple standard public datasets for validation and benchmarking with diverse evaluation metrics. With effective parameter tuning and lightweight feature extraction architecture, the proposed model achieved an average performance accuracy of 99 % with their own generated dataset, mHealth and MotionSense dataset, in comparatively optimized computational time compared to the benchmark models. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Athletic Performance:2D Human Pose Estimation Using Deep Neural Networks for Movement Analysis 7

Enhancing Athletic Performance:2D Human Pose Estimation Usin...

引用

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

作者： Devi, Pothreddypally Jhansi Sumani, Aleshwaram Chandra, Chenna Mukesh Thrisha, Battu Bhanu Vamshi, Arkatala Sai B V Raju Institute of Technology Dept. of Computer Science Engineering Telangana India

ISBN: (纸本)9798350372816

computer vision is a field that is used to build many neural network-based prototypes efficiently. It analyses images and videos effectively and processes them digitally. Human Pose estimation (HPE) is one of the computer vision applications which additionally uses neural networks for prediction. Human pose estimation is used widely in many fields which includes action recognition, fitness, gaming, and motion capture. HPE identifies and locates the key points on the body joints of a person's body. These body points are connected to form a Skeletal Structure. This skeletal representation helps to easily visualize the image in 2-D. Focusing on Sports domain, HPE plays major role in estimating athlete movements. It helps in analyzing the feedback on the posture which allows to make adjustments during training. It also helps viewers to analyse the insights of athlete performance. To achieve the model that contains all these features, requires involvement of few frameworks. OpenPose is utilised for fast processing of videos and can detect the multiple people in one frame. OpenPose has the capability of handling the occulsion. On the Other hand, the model also import the capabilities of HRNet, which is used for analyzing athlete movements and tracks the gestures. After detecting the keypoints on the body joints, these points must be integrated to form 2-D images. To achieve the spatial relationship between the bodypoints Joint Graph Convolutional Neural Networks (JGCN) framework is used. The proposed model is estimated to generate approximately (4- 5)percent more efficiency and accuracy in its output 2-D image compared with the previous models. © 2024 IEEE.

关键词： Convolutional neural networks

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Personal Finance Management and Prediction using ML Algorithms 8

Personal Finance Management and Prediction using ML Algorith...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Kaushik, Sujay G. Kashyap, Sumukha S. Surabhi, B.V. Chaitra, M. Reddy, Santhosh Anitha, N. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Effective personal finance management is essential for achieving financial stability and long-term financial health. This paper presents a comprehensive approach to summarizing, analyzing, and show monthly expenses using machine learning models. By collecting user inputs such as age, place of stay, number of dependents, monthly expenses on transport, education, subscriptions, savings, medical expenses, and more, the system uses linear regression, decision trees and random forest algorithms to predict financial values. The system provides detailed information of expenses, savings, and overall financial health, along with personal health analysis based on dietary habits. Users have access to a wide range of financial and lifestyle information, allowing the system to provide personalized recommendations to increase financial security and achieve financial goals. The model also evaluates the impact of recurring expenses and notable investments, helping users understand their spending patterns and make informed financial decisions. The results demonstrate the system's capability to deliver accurate predictions and actionable advice, enable users to better manage their finances. This approach has led to significant advances in personal financial management by integrating machine learning techniques to provide insight into people's financial health. © 2024 IEEE.

关键词： Adversarial machine learning

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LSTM Based Approach to Detect Fake News 2

LSTM Based Approach to Detect Fake News

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Khare, Sakhi Singh, Pushpa Kumar, Pankaj Gl Bajaj Institute of Technology Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

In the age of technology, every day, we ingest a variety of news data, either willingly or accidentally. People will always use various social media apps and keep looking for information. The information passed through these social media apps is only sometimes valid. Many organizations or individuals spread the news to influence the mass public. This type of information is called fake news. Fake news is a common problem that naturally comes with social media. Detection of counterfeit news with ubiquitous data from social media is almost impossible. Fake news kills real journalism and spreads false information in society. Researchers are still looking for better solutions. In this paper, we find a better solution with Natural Language Processing (NLP). We used fake news challenge datasets for training and testing purposes. We aim to classify the news item into four stance labels: agree, disagree, discuss, and unrelated. We developed a Long Short-Term Memory (LSTM) based solution to organize the news article in the given stance label. We find that our approach classifies data effectively with 99% of accuracy © 2024 IEEE.

关键词： Fake News LSTM NLP Word Embedding

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Improved Bounds For Online Convex Optimization

Improved Bounds For Online Convex Optimization

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Nayak, Tanvi S. Bharath, B.N. Dept. of Computer Science and Engineering Indian Institute of Technology Dharwad Dharwad India Dept. of EECE Indian Institute of Technology Dharwad Dharwad India

ISBN: (纸本)9798350368741

In this paper, we consider the problem of online learning with convex objectives. Most of the existing work shows that static and dynamic regrets scale logarithmically or sub-linearly with the time horizon T. On the contrary, for the strongly convex setting, it is shown that the static and dynamic regrets scale with the variation term that measures the rate at which the function changes over time. In the convex setting, we show that a simple Online Gradient Descent (OGD) algorithm with single round at each time achieves a regret that is comparable with the strongly convex setting with an additional assumption of Quadratic Growth (QG) condition. More specifically, we show that the static and dynamic regrets scale as O(Var3(T)) and O(Var1(T)) + O(Var2(T)), respectively. Here, Vari, i = 1, 2, 3 represent variation terms that capture different aspects of changes in the optimal solution over time. We use experiments to corroborate some of our theoretical findings. © 2025 IEEE.

关键词： convex optimization Online learning QG condition regret bounds sparse signal processing

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Machine Learning Techniques for Predicting Ovarian Cancer in its Early Stages Using Biomarkers 6

Machine Learning Techniques for Predicting Ovarian Cancer in...

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6th International Conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Alam, Mir Jafikul Anawar, Jannat-E Mohi Uddin, Khandaker Mohammad Rahman, Md. Hasibur Rahman, Md. Mahbubur Dhaka International University Dept. of Computer Science and Engineering Dhaka Bangladesh Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

The deadliest gynecological cancer affecting women is ovarian cancer, currently incurable with no effective medication treatments. The key focus of this research is to assess insights for early diagnosis using statistical analysis and machine learning techniques on data from clinical trials obtained from 349 patients. Several techniques, including Random Forest, Decision Tree, Gaussian NB, AdaBoost, and Logistic regression, were applied to find the most reliable factor for ovarian cancer prediction. A clinically evaluated raw dataset of benign samples and malignant ovarian tumor patient data set is used to develop early-stage ovarian cancer predictions, and the effectiveness of ML models was examined utilizing metrics including F1-score, Accuracy, Precision, and Recall. The proposed study shows better outcomes, with the Random Forest classifier exhibiting the highest accuracy for validation at 99% based on the test data of ovarian cancer predictions. Even though early-stage ovarian cancer detection is generally unavailable, cancer diagnosis may be greatly aided by machine learning. © 2024 IEEE.

关键词： Machine learning

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Advancing Image Forgery Detection: A CNN-Based Approach 8

Advancing Image Forgery Detection: A CNN-Based Approach

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Nalini, N. Rakshitha, Sai Nithyashree, H.V. Rakshitha, S.L. Shravan, Motamarri Sai Uttej, K Sai Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331505462

In the digital age, image forgery is a significant concern due to advanced editing tools. This study addresses the need for reliable forgery detection, focusing on copy-move, splicing, and retouching techniques. Using Convolutional Neural Networks (CNNs) known for learning hierarchical image features, the research developed a robust detection system. The CNN model, trained on a diverse dataset which has over 10,000+ images and it is split into test and train data for better classification results, detected subtle inconsistencies indicative of manipulation, achieving a peak accuracy of 94% over 50 training epochs. Test cases showed the system effectively detected forged images and identified forgery types, though it had shortcomings with invalid inputs. The CNN architecture employed convolutional and max-pooling layers with specific hyperparameters to enhance feature extraction. This paper offers a valuable tool for digital forensics experts, law enforcement, and journalists to ensure the authenticity of visual content. © 2024 IEEE.

关键词： Convolutional neural networks

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From Ranch to Worldwide:Observing Soil Welbeing Patterns

From Ranch to Worldwide:Observing Soil Welbeing Patterns

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Divakar, K.M. Ankith Gowrishankar Esturi, R. Anil, P.S. Namith, D. Chethan, A. S J C Institute of Technology Dept. of Computer Science Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Declining soil wellbeing represents a huge danger to worldwide food security and rural maintainability. Precisely checking soil wellbeing patterns and anticipating future land appropriateness at nearby and worldwide scales are essential for illuminating feasible administration practices and strategy intercessions. This paper proposes a clever structure that use the force of large information investigation to address this test. We expect to overcome any issues between ranch level checking and worldwide experiences, engaging partners at each level to come to informed conclusions about land use and management. Our structure depends on the mix of assorted information sources. High-goal remote detecting information, including satellite symbolism and aeronautical photography, will give basic experiences into soil properties, crop conditions, and land the board rehearses. We will additionally improve the examination by consolidating promptly accessible open-access datasets on environment, soil overviews, and financial data. This exhaustive information combination approach permits us to catch the diverse idea of soil wellbeing and land reasonableness dynamics. Extracting significant experiences from these tremendous datasets requires clever examination devices. We will use a setup of AI procedures, including regulated and solo learning calculations, to reveal stowed away examples and connections inside the information. Directed learning will empower us to foster prescient models for soil wellbeing appraisal and land appropriateness planning, while solo learning will assist with recognizing remarkable attributes and likely peculiarities in various farming *** system will create constant soil wellbeing maps, featuring areas of corruption, supplement lacks, and potential improvement open doors. This enables ranchers to come to informed conclusions about manure application, water the board, and harvest choice, advancing their activities for expanded yield and long ha

关键词： Fertilizers

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Software Fault Prediction Using Optimal Classifier Selection: An Ensemble Approach 2

Software Fault Prediction Using Optimal Classifier Selection...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Agrawalla, Bikash Reddy, B. Ramachandra Dept. of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur831014 India

Fault prediction is the process of using data analysis and machine learning models to anticipate potential defects or faults in the software system. Using only the base machine learning models for software fault prediction leads to limited performance, difficulty in handling non-linear relationships and imbalanced data, inadequate feature representation, and limited complexity handling. Hence, in order to overcome these challenges, this paper proposes a new technique for the selection of classifiers that forms a heterogeneous ensemble. The main goal is to remove or trim out the classifiers that show poor performance compared to the other base classifiers, which can result into a more effective ensemble and can produce better results. The algorithm proposed in this paper finds a set of classifiers that can perform better than using all the classifiers. The challenge that was faced was how to identify the poor-performing classifiers. This challenge is dealt with by performing an experiment using different threshold values to choose the trimmed set of classifiers. For evaluation of the proposed model, 8 different benchmark software fault datasets were used, which are taken from PROMISE and the Apache repository, and AUC is used as the performance measure. The results obtained after the experimental analysis demonstrate the effectiveness of our algorithm compared to the traditional approaches, which used all the base classifiers. There is a significant increase in the AUC values for 6 datasets out of 8, while using the average of probabilities and majority voting, it was seen that there is improvement in 7 out of 8 datasets used. The best-performing dataset by using the average of probabilities is ARC, where the AUC values increase from 0.6505 to 0.694, and while using majority voting, the best-performing dataset is XALAN, where the AUC values increase from 0.5455 to 0.679. From this, it can be seen that the proposed ensemble approach achieved higher AUC values for the

关键词： Decision trees

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