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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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Personal Finance Management and Prediction using ML Algorithms 8

Personal Finance Management and Prediction using ML Algorith...

引用

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

Software Fault Prediction Using Optimal Classifier Selection...

引用

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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Project Management and Evaluation system Using Node JS 3

Project Management and Evaluation system Using Node JS

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Kumar, Sanjay Umrao, Sandhya Gupta, Harsha Saxena, Kumud Greater Noida Institute of Technology Dept. of Computer Science Engineering Greater Noida India Noida Institute of Engineering and Technology Dept. of Computer Science Engineering Greater Noida India Noida Institute of Engineering and Technology Dept. of Information Technology Greater Noida India

ISBN: (纸本)9798350399264

Project Management is a social hub for students to interact and showcase their projects in a dynamic environment where other students and faculty can also put their thoughts. Major Project is the essential part of student's graduation, where they need to apply all the learning and build something innovative and inspiring. Students don't understand that what other students are developing because of no interaction. Thus with the help of this application, it is tried to solve this problem. An application is proposed to address about how student can go through an application to identify their capabilities and get an idea to develop a project accordingly. Students will be able to address a problem and find a solution to it, although through its inception organizing major projects can be a hassle therefore It is aptly called "Project Management"as students can join in a league with like-minded students for their major projects. Depending on factors like area of interest, the number of milestones covered and the grade received, the live popularity project tracking system provides a highly-standardized, automated technique for ranking. Top 10 projects are displayed on the homepage based on these *** are two key features in PMi)User Interactionii)Project Progress Tracking. © 2023 IEEE.

关键词： Students

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Graph Neural Network Guided Music Mashup Generation

Graph Neural Network Guided Music Mashup Generation

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Wu, Xinyang Horner, Andrew Hong Kong University of Science and Technology Dept. Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350362480

Music mashups integrate elements from different songs to create surprising and engaging listening experiences. Typically, a mashup combines the vocal track of a base song with the instrumental tracks of complementary songs. Automating the production of mashups has been an area of research for decades. Traditional approaches utilize rule-based methods, such as matching tempo and harmonic similarity, to select optimal segments for mashup generation. More recent techniques leverage neural networks to classify segment compatibility. However, both approaches primarily focus on layering segments that are generally compatible, without ensuring their detailed integration and alignment with the vocal track. In contrast, we introduce a novel approach using Graph Neural Networks (GNNs) that learns to rearrange instrumental segments to better align with the base vocal track, resulting in more surprising and accurate music blends for mashup generation. Additionally, we conducted subjective listening tests to evaluate our generated mashups against a baseline model using the same song pairs and the original base songs, assessing our model's performance. Generated mashups used for evaluation can be found in https://***/***/. © 2024 IEEE.

关键词： Graph neural networks

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Transformer based Compressed Data Recovery for Efficient Data Collection in Wireless Sensor Networks

Transformer based Compressed Data Recovery for Efficient Dat...

引用

2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Balaji, V.S. Sekar, K. Devendar Rao, B. Dheepa, T. Rajalakshmi Engineering College Dept. of Artificial Intelligence and Machine Learning Chennai India Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374711

Data collection is challenging in wireless sensor networks (WSNs) since energy consumption remains a significant constraint. Although energy consumption has increased, most data collection methods incur excessive computation overhead. Compressive sensing is used to minimize the number of sensing samples with a suitable sparse basis. A novel Transformer neural network-based compressed data recovery (TCDR) method has been proposed for extracting temporal correlation structures from compressed signals and reconstructing them with high accuracy. The compressed signal is forwarded to the base station through multi-hop communication. The base station uses an attentionbased Transformer Neural Network (TNN) to recover the compressed signal. This model provides greater energy saving performance than the other baseline models considered for evaluation, which are 5.23 percent, 6.82 percent, 10.26 percent, and 11.47 percent, respectively. © 2024 IEEE.

关键词： Spatio-temporal data

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Advancing Image Forgery Detection: A CNN-Based Approach 8

Advancing Image Forgery Detection: A CNN-Based Approach

引用

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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Robotics Process Automation Technologies for Data Collection and Analysis 15

Robotics Process Automation Technologies for Data Collection...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Usman, K. Hayath, T.M. Shafiulla, Mohammed Dadapeer Ballari Institute of Technology and Management Dept. of Computer Science and Engineering Karnataka Ballari India

ISBN: (纸本)9798350370249

Data collection and analysis are critical aspects of various business processes. However, these tasks can be time-consuming, prone to errors, and delays employee productivity when done manually, especially when we have to deal with huge data. Robotic Process Automation (RPA) technologies offer a promising solution in form of a BOT, by automating tedious tasks related with data handling. A survey by The Economist corporation believe that automation kick launches digital transformation, robotic process automation (RPA) has evolved from an emerging technology to a must-have digital transformation tool for enterprises to compete globally. An increasing number of companies are now using RPA to automate normal business processes in a non-intrusive and cost-effective way. On the other hand, decision-making, pattern recognition, and many other business-related tasks benefit greatly from the use of big data and analytics. This paper explores the application of RPA in data collection and analysis, examining its functionalities, benefits, and limitations. In practical, Bot produces error-free work and takes 94.44% less time for data collection compared to a manual process, which encourages all firms to adopt to RPA technologies. © 2024 IEEE.

关键词： Automation Data collection Process Robotics RPA

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Enhancing Arecanut Farming Profits through Technological Advancements: A CNN-based Approach for Efficient Grading and Sorting 5

Enhancing Arecanut Farming Profits through Technological Adv...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Dhanush Ghate, D. Pallavi, K.N. Poojari, Asmita NMAM Institute of Technology Dept. of Computer Science and Engineering Karnataka Nitte574110 India

ISBN: (纸本)9798350395235

Technological advancements can have powerful impact on the economic growth of agriculture in India. Areca nut farming is one such terrain of agriculture where investment in technology and automation can elevate the profit from farming by bringing down production cost. Areca nut is the major crop of Karnataka having roots in fifteen different districts. The grading and sorting of areca nuts is an important step in the production of areca nuts and is traditionally done by man power. People are trained to identify good and bad areca nuts, but the time taken for sorting areca nuts is more and it is not feasible for a large quantity of areca nuts. In order to minimize the time consumed, this method is introduced which classify the areca nuts into four different grades based on their quality. © 2024 IEEE.

关键词： Sorting

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ML based Text Summarization for Sentiment Analysis in Information Retrieval using Feature engineering 4

ML based Text Summarization for Sentiment Analysis in Inform...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Shirley, C.P. Berin Jeba Jingle, I. Venkatesan, R. Tamilarasan, R. Yashvanth Ram, R.V. Joyce Beryl Princess, P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Dept. of Ai & Ml Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Sentiment analysis plays a crucial role in understanding user preferences and opinions in the field of information retrieval. This research focuses on developing a system that combines text summarization and machine learning techniques to improve sentiment analysis accuracy. The proposed approach employs extractive and abstractive summarization techniques to reduce lengthy texts into concise summaries while preserving key information. Feature engineering is then applied to extract sentiment-related elements from the summary text, providing valuable insights for the SVM model. The SVM model, enhanced with feature engineering, is expected to effectively summarize text and accurately detect sentiments expressed in documents. This research contributes to the advancement of sentiment analysis techniques and provides valuable insights for decision-making and content development. © 2024 IEEE.

关键词： Support vector machines Measurement Sentiment analysis Accuracy Text summarization Machine learning Information retrieval Feature extraction Vectors Resilience

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