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data mining for Fraud Detection in Telecommunications: Detecting Anomalous Behaviors in Real-Time

Data Mining for Fraud Detection in Telecommunications: Detec...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Mishra, Nidhi Shivaji, Ghorpade Bipin Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798331542375

In the recent past, telecom experts have been confronted with so many problems emanating from fraudulent deeds that apart from causing loss of money discredits organizations. To mitigate these problems, real-time fraud detection systems are very essential in detected those unusual and unauthorized activities. This study aims at examining how data mining can be used in the identification of the actual fraud in the telecommunication area through users' interactions in the course of which a huge amount of data is produced. In this study, the case uses different machine learning algorithms including clustering, decision trees, and anomaly detection for mapping out unusual call pattern, unauthorized access and Identity theft. To capture anomalous behavior, the research uses both time-domain and frequency domain data from different telecom networks for training and testing models. The research also intends to extend existing theories and practices of Naive Bayes, Support Vector machines (SVM), and deep learning networks to improve accuracy and effectiveness of the sort of systems used in the detection of fraud. The performance of these models is measured in terms of accuracy in classification and prediction of fraudulent actions thereby reducing potentials false negative and false positive cases. Further, the paper outlines how these models can be integrated into existing network security architecture to assess the prospects for scalability of these solutions within a maturing fraud environment. The findings also affirm that data mining is effective in real-time monitoring and prevention of frauds as such highlighting the importance of frequent updates of the fraud detection solutions within the telecommunication sector. © 2025 IEEE.

关键词： Anomalous data mining Fraud Detection SVM Telecommunication

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Real-Time Anomaly Detection in Telecommunications: Advanced data mining Techniques for Fraud Identification

Real-Time Anomaly Detection in Telecommunications: Advanced ...

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Disruptive Technologies (ICDT), international conference on

作者： Khalid Hafiz Mir Ravin Kumar Sangeeta Rai Abass Hassan Tiyas Sarkar Bhimasen Moharana School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Recently, the telecom fraud issues are so intense that it has become difficult for industry professionals, not just because of monetary loss but because it tarnishes the reputation of the firm as well. In any case, real time analysis and monitoring systems are critical in curbing such transactions and activities. The main purpose of this paper is to analyze how mining processes can help in detecting telecommunication fraud from the interactions of the users, which generate significant amounts of data. In this paper, different methods of machine learning are applied: clustering, decision trees and anomaly detection to identify unusual call patterns, unauthorized use, and impersonation. To detect suspicious actions, a combination of time and frequency data from various mobile operators is used for model training and testing. The goal of this study is to improve the accuracy of fraud detection systems by applying Naive Bayes, Support Vector machines (SVM), and deep learning networks. Furthermore, this paper indicates the possibilities of incorporating these models into the current framework of network security architecture to measure the level of scalability of such solutions in an advanced fraud environment. Along with many other findings, these results reveal that data mining can handle monitoring and prevention of frauds in real-time. This observation shows the importance of updating the fraud detection mechanisms within the telecommunication industry.

关键词： Support vector machines Industries Training Prevention and mitigation Network security Real-time systems Communications technology Fraud Anomaly detection Monitoring

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The Analysis and Prediction Mode of Students' Academic Performance and Social Behavior in the Background of Big data 24

The Analysis and Prediction Mode of Students' Academic Perfo...

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Proceedings of the 2024 3rd international conference on Artificial Intelligence and Education

作者： Shaoping Qiao Humanities and Design College Chengdu Technological University Chengdu Sichuan China

ISBN: (纸本)9798400712692

The popularization of digital campus brings convenience to the life of teachers and students, and provides rich data for educational data mining (EDM). EDM uses data mining and machine learning techniques to analyze student data, aiming to provide quality services. This paper uses big data to analyze students' behavior, constructs the academic performance prediction model, and provides a reference for decision-making for teaching assistants. The Dis_GAT network based on graph neural network is proposed and compared with traditional methods to prove its effectiveness. By preprocessing and analyzing student data, the behavioral characteristics were extracted, and identifying abnormal students using K-means and isolated forest algorithms to help the counselors fully understand the student situation. Finally, the Dis_GAT network prediction framework is proposed, transforming the academic performance prediction problem into the node classification problem in the graph structure, introduces the concept of graph neural network, improves the graph attention network, and considers the influence of early performance similarity on later academic performance.

关键词： Academic performance

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ClipXpert: Automated Clip mining from Video data for High-Demand Content

ClipXpert: Automated Clip Mining from Video Data for High-De...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Rajdeep Chatterjee Sudip Chakrabarty Pappu Bishwas School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

ClipXpert is a new system that automates the extraction of relevant clips from YouTube videos using user-defined keywords or frequently used word or comment analysis. The system uses advanced transcription models to search a pre-existing database for lines associated with the provided keywords. If the database doesn’t contain the keywords, it dynamically identifies and stores relevant content for future use. ClipXpert also automates identifying frequently used nouns, adjectives, and adverbs to generate focused content highlights without user intervention. It also performs sentiment analysis on YouTube comments to understand audience engagement and reception. ClipXpert enhances video content extraction efficiency and accuracy, catering to the growing demand for targeted, high-value clips in the digital media landscape.

关键词： Sentiment analysis Video on demand Accuracy machine learning Media Real-time systems Web sites data mining Intelligent systems Videos

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Multiple Manifold Regularization Based on Non-Negative Matrix Factorization for Multi-View Clustering

Multiple Manifold Regularization Based on Non-Negative Matri...

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Disruptive Technologies (ICDT), international conference on

作者： Mutyala Sirisha Ghufran Ahmad Khan Jalaluddin Khan Hemlata Pant Kaneez Zainab Zulfikar Ali Ansari Shamshul Haq Ansari Department of Computer and Applications Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur Uttar Pradesh India Department of Computer Science and Engineering B.N. College of Engineering and Technology Lucknow Uttar Pradesh India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) (SIU) Lavale Pune India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In recent years, non-negative matrix factorization (NMF) cluster analysis of multi-view data has shown outstanding results in data mining and machine learning. Multi-view data typically encompasses complementary elements from multiple perspectives. Finding a consensus solution that works for all the different views is a difficult task. This study proposes a novel NMF-based clustering method that utilizes various manifold regularizations for multi-view data to address the previously mentioned problem. In this method, the NMF will decompose the input data into the two non-negative matrices. Next, we employ the manifold scenario to preserve the geometric structure within the data. Lastly, we design the novel objective function by combining all the earlier mentioned terms and then optimize it using the iterative strategy method to achieve the optimal solution. The empirical analysis of real-world datasets indicates that the proposed method outperforms numerous existing techniques in clustering efficiency.

关键词： Manifolds Encyclopedias machine learning Linear programming Internet Performance analysis Online services Iterative methods Matrix decomposition Optimization

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machine learning Based Fault Distance Estimation of Underground Transmission Using Impedance Relay

Machine Learning Based Fault Distance Estimation of Undergro...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sri Chaithanya Mathi Pooja Naidu Tammisettti Shaik Rohid Akthar Manitha P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Fast development of electric transmission infrastructure necessitates efficient methods of transmission and distribution for reliable cost effectiveness. Between the two main types of transmission, Overhead Transmission Lines involve extensive maintenance cost, use of extensive protection schemes, earthing, and lightning protection. In contrast, Underground Transmission Lines with their insulated and securely laid design are highly resistant to external factors such as lightning, wind, and open conductor faults, showing higher reliability and efficiency. However, faults such as symmetrical and unsymmetrical faults can occur due to conductors being bound together. The traditional fault distance estimation techniques are often inefficient for high-power transmission when using impedance-based fault distance relays. The constant variation in fault characteristics leads to impedance fluctuations, and therefore, fault distance calculations become inaccurate. The current research introduces a machine learning-based Fault Distance Estimation system for Underground Transmission Lines using Impedance Relays to address the above-mentioned challenges. Through data fed into the training machine learning model based on various fault scenarios, data mining is extracted for predicting precise impedance in faulty states and accurately determining fault locations by utilizing trained models with high-performance K-Nearest Neighbour (KNN), Decision Trees (DT), Support Vector Regression (SVR), Random Forest (RF), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANNs). Through these algorithms and approaches, implementations that emphasize optimal preprocessing, effective feature selection and tuning of specific hyperparameters determine their respective modeling efficiencies. The results show that the models generate very accurate distance estimations, allowing for more accurate fault locations and quick rectification. Not only does it bypass the deficiency of the method

关键词： Power transmission lines Costs Estimation Predictive models Fault location Conductors data models Impedance data mining Long short term memory

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Optimized Management of English Distance Education database Based on WEB Content Recommendation Algorithm 24

Optimized Management of English Distance Education Database ...

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Proceedings of the 2024 3rd international conference on Artificial Intelligence and Education

作者： Xuefei Fan Jingwen Feng BinHai School of Foreign Affairs of Tianjin Foreign Studies University Tianjin China Qiqihar University Qiqihar Heilongjiang China

ISBN: (纸本)9798400712692

Current age with rapid developments in network technology has led to rapid changes in distance education, which has now begun to take center stage in the academic sector. Nevertheless, the gap between the wide availability of online resources and personalized, individual, differentiated, and multi-faceted learning needs has come to light. This fact, however, increasingly shows the inadequacy of conventional approaches to satisfactorily meet the multifaceted, various, and constantly evolving needs of users. To this effect, the academicians have been assiduously engaged in searching for newer approaches based on individualized web-published content recommendation algorithms to improve the English distance education framework. Emerging as one of the best learning systems mainly by blending advanced data-mining methods with cutting-edge machine-learning technologies, these algorithms can specifically provide resources according to the particular interests and level of proficiency of learners while raising overall educational efficiency and quality of instruction. Besides this, database query optimization algorithms play an especially important role in this model. By availing efficient data management and timely, swift queries that are clean and effective, this new technology boosts the overall system operational efficiency tremendously.

关键词： English distance learning

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Design and Application of English Pronunciation Correction Algorithm Based on SVM+LDA

Design and Application of English Pronunciation Correction A...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Huizhong Yu Ting Zhou Jiangsu College of Tourism Yangzhou Jiangsu China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

For the common pronunciation errors of English learners in the pronunciation process, an English pronunciation error correction algorithm based on SVM (Support Vector machine) and LDA (Latent Dirichlet Allocation) is introduced. The algorithm aims to accurately identify and correct the problems in learners' pronunciation, thereby improving their pronunciation accuracy and fluency. By combining the feature extraction of speech signals with machine learning methods, different types of pronunciation errors can be effectively distinguished and corrected. First, the pronunciation data of English learners are collected and preprocessed to extract speech features, including MFCCs (Mel-Frequency Cepstral Coefficients), formants, pitch, etc. Secondly, LDA is used to reduce the dimension of the extracted features to reduce the complexity of the feature space and enhance the ability to distinguish pronunciation errors. Then, SVM is used for classification training to train a pronunciation error model. Finally, the trained model is used to predict the learner's pronunciation, and corresponding pronunciation correction suggestions are made according to the classification results. This method can effectively identify different types of pronunciation errors and provide personalized correction feedback according to the nature of the error. When the pronunciation error correction algorithm based on SVM+LDA is used for testing, the learners' error rate is 31.9% on average before error correction, and drops to 26.3% after error correction. The English pronunciation error correction algorithm based on SVM+LDA introduced in this study can effectively improve the pronunciation accuracy of English learners. This method has strong practicality and scalability, and can provide strong support for English pronunciation teaching and speech recognition technology.

关键词： Support vector machines Training Accuracy machine learning algorithms Systematics Speech recognition Feature extraction Error correction Classification algorithms data mining

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Analyzing Handwriting Features for ADHD Detection in Children with Autism via machine learning

Analyzing Handwriting Features for ADHD Detection in Childre...

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Disruptive Technologies (ICDT), international conference on

作者： Ch. Prathima Anitha. T P Jayachandra reddy Yekkaluri Susmitha P Soni Reddy Nakkarasupalli Charitha Dept.of DS Mohan Babu University Tirupati India Department of CSE K.S.R.M College of Engineering Dept of CSE Annamacharya institute of technology & sciences Dept. of IT Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Attention deficit hyperactivity disorder (ADHD) develops in children's brains, portrayed by symptoms of inattentiveness, hyperactivity, and impulsivity. Early identification is very much essential, but traditional diagnostic methods might be subjective in practice and consuming time. Hence this work intends to introduce an end-to-end hybrid deep-learning system which fuses the techniques CNN and Bi-LSTM Networks to develop a system using handwriting to detect ADHD in children. The Bi-LSTM layers capture the sequential dependencies that exist in the handwriting data, capturing the temporal irregularities, whereas the CNN layers extract spatial features such as stroke patterns and pressure variations. The training and evaluation of the proposed model was done by synthetically balancing the dataset with accuracy, Confusion matrix, ROC curve and AUC for assessing performance. A method that has surpassed an accuracy of 90% in classification, this proposed system has been able to properly point out ADHD-related traits by hand. This method is scalable as it approaches to do fewer computations for the detection, thus making it a real data-driven alternative to traditional methods. Future work: Add attention mechanisms to increase the model's ability toward generalization, expand available datasets, and improve generalized capabilities.

关键词： Deep learning Training Hands Accuracy Attention mechanisms Fuses Feature extraction data models data mining Disruptive technologies

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Parallel Selective Meta-Heuristic Algorithms for Real-Time Non-Convex Optimization in Big data Analytics

Parallel Selective Meta-Heuristic Algorithms for Real-Time N...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Ghosal, Shreya Shankar, Amar Agarwal, Trapty Suchithra, R. Raj, Nimesh Hansda, Prity Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

With big data in modern analytics becoming more common, a lot of it is real-time optimization, which is an essential problem. This is because big data requires efficient and accurate optimization methods, leading to developing parallel selective viable meta-heuristic algorithms for nonconvex optimization in large analytics. They can be used to find optimal solutions rapidly by merging the complementary advantages of meta-heuristics and parallel computing. Algorithms do this using an optimization process, but not always for the whole data set used, saving computational performance. As information is updated in real-time, its approach will constantly be improved, resulting in a better and more accurate solution. A parallel implementation of these algorithms makes them effective for real-time optimization using large datasets. However, the performance of big data analytics may be significantly improved by implementing these algorithms because they have achieved satisfactory results in a number of applications, including machine learning, data mining, and pattern recognition. © 2025 IEEE.

关键词： Large datasets

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