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

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

引用

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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Deep learning-Based Plant Species Identification using VGG16 and Transfer learning: Enhancing Accuracy in Biodiversity and Agricultural Applications

Deep Learning-Based Plant Species Identification using VGG16...

引用

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

作者： K Kiran Arpanpreet Kaur School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In this paper, the method of deep learning is applied for plant species identification, with a special emphasis on Convolutional Neural Network VGG16 for feature extraction and classification. Identification of plants is important in matters concerning biological diversity, agriculture as well as management and utilization of resources but the conventional methods used in identification are time consuming and sometimes inaccurate. machine learning specifically deep learning is a more suitable replacement by offering a fast and accurate identity recognition. The research work works with a Kaggle dataset comprising of 24881 images of four plant species categorized into twenty-two with the objectives of increasing efficiency of the classification. We used transfer learning with weights from the ImageNet data set and obtained accuracy greater than 90% on the test data set, which shows the model's ability to identify plant species based on leaf structure. To counter overfitting and enhance model generality, data augmentation methods were employed where the input images were rotated by 30, 45 and 60 degrees, and flipped vertically. The performances suggest that deep learning models, such as VGG 16, yield improved recognition for plant classification than conventional methods provide higher reliability. This research makes a valuable input toward a number of applications related to automated systems for agriculture and conservation, discussing possible uses of AI technique for better penetration of plant species, enhancing conservation goals, and future advancements in the agrarian field. Future research will leverage on these discoveries to make improvements on classification frameworks.

关键词： Deep learning Accuracy Plants (biology) Biological system modeling Transfer learning data augmentation data models Convolutional neural networks Biodiversity Environmental management

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A Comprehensive Analysis of Hate Speech Detection and recognition Utilising Advanced machine learning Algorithms and Techniques 3

A Comprehensive Analysis of Hate Speech Detection and Recogn...

引用

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

作者： Mannepalli, Praveen Kumar Sharma, Saurabh Rajput, Neha Kalamdhad, Sanjay Chandigarh University Department of Computer Science & Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Punjab India St. Vincent Pallotti College of Engineering and Technology Department of Computer Science & Business System Nagpur India

ISBN: (纸本)9798331542375

Social networking sites have come into demand as a way of sharing ideas and opinions with other people. But unfortunately, people tend to misuse social media to write nasty comments. Victims' lives might be negatively impacted by behaviours that are abusive and humiliating. Social networks give people a great tool to share information on ideas on multiple topics and opinions. However, these platforms are often abused to announce toxic and offensive comments, which eventually negatively affect victims and demographics. This study offers a thorough method for identifying hate speech (HS) on social media by utilising cutting-edge ML algorithms. A publicly available Twitter dataset containing 159,571 tweets classified into six categories, including toxic and identity hatred, was utilised. The methodology comprises data collection, conditioning, and categorisation that employs BiLSTM, which is particularly appropriate for the tasks demanding contextual sensitivity. These operations include symbol removing, stemming and stop-word eliminating, feature extraction using TF-IDF, and using the Bag-of-Words model. For classification of the data between hate and non-hate, the dataset is then split into training and testing datasets and the BiLSTM model used to provide classifications for the text. The evaluation of the BiLSTM model demonstrates superior performance, achieving an accuracy86%, a precision90%, a recall86%, and an F1-score88% • A comparison between the model and other models such as SVM and CNN reveals the strength of the BiLSTM in contextual classification tasks. The results demonstrate that BiLSTM can tackle problems with hate speech detection, providing a strong answer to the problem of harmful content on social media and helping to make the internet a better place for everyone. © 2025 IEEE.

关键词： Tweets

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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...

引用

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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Cybersecurity Threat Intelligence Automated via machine learning: A System for Analyzing and Responding to data in Real-Time

Cybersecurity Threat Intelligence Automated via Machine Lear...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Shanmugam Muthu P. Deepalakshmi Shipt Inc Birmingham AL USA School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Businesses are relentlessly bombarded with advanced and ever-evolving attacks in today's rapidly evolving cyber threat landscape. Predefined signatures and rule-based methods to traditional security approaches cannot keep pace with threats of today's magnitude and diversity. The focus of this study is an ML-based real-time threat assessment and response cybersecurity threat intelligence solution. Advanced machine learning algorithms, anomaly detection, and data mining are employed to analyze vast amounts of security information, identify new threats, and respond with instantaneous, effective defense. The system detects intrusion attempts, data exfiltration, and ransomware attacks by analyzing diverse streams of data from security appliances like network logs, endpoint behavior, and traffic patterns. Supervised and unsupervised learning discovers known attack patterns, and anomaly detection discovers new threats. The system learns from new data to detect new attack pathways without rule updates. An automatic response method can isolate compromised devices, ban malicious IP addresses, and warn security personnel in real time, decreasing reaction time and human interaction. Threat intelligence feeds add external context to the system's ability to identify advanced persistent threats (APTs) and other high-level cyberattacks, according to the research. Integration with threat-sharing systems keeps the system updated on threat intelligence, ensuring defensive measures are effective against developing TTPs. The suggested ML-based threat intelligence system speeds up and improves cyber threat detection and response, improving security and allowing organisations to scale their defences in a more complex cyber environment. This paper analyses the system's architecture, performance, and potential for future improvements, such as deep learning models, federated learning for privacy-preserving intelligence sharing, and complex decision-making automation. machine learning and aut

关键词： Automation Federated learning Decision making Traffic control Real-time systems Threat assessment Feeds Computer security Anomaly detection Unsupervised learning

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A Multi-View Feature Fusion-Based Recommendation Model with User-Side data mining 25

A Multi-View Feature Fusion-Based Recommendation Model with ...

引用

Proceedings of the 2025 3rd international conference on Communication Networks and machine learning

作者： Runnan Zhou Ping Lou School of Information Engineering Wuhan University of Technology Wuhan Hubei China

ISBN: (纸本)9798400713231

Traditional recommender systems typically rely on collaborative filtering (CF) methods based on user-item interactions. However, these methods often face data sparsity issues, which negatively impact recommendation performance and degrade user experience. Recent research has focused on methods that incorporate side information to enhance representation learning and improve data utilization. To this end, this paper proposes a recommendation model named RMSI, aimed at mining and fusing user-side information. To construct this model, we utilize multi-faceted information to build user-item interaction graphs (including view, click, add to cart, etc.), user-user social network graphs, and item-category information graphs. An encoder is used to capture different behaviour relationship between users and items, and a graph neural network is used to capture the strength of user-user social relationship. Finally, an attention mechanism is used to weight and integrate the user representations extracted from these different graphs. Experimental results on synthetic and real-world datasets demonstrate that RMSI achieves better recommendation performance.

关键词： Attention mechanism

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Semi-supervised Lightweight Object Detection Method Based on Improved YOLOv7-tiny 25

Semi-supervised Lightweight Object Detection Method Based on...

引用

Proceedings of the 2025 3rd international conference on Communication Networks and machine learning

作者： Zhilong Gao Chunsheng Hua Institute of Intelligent Robots and Pattern Recognition Liaoning University Shenyang Liaoning China College of Information Liaoning University Shenyang Liaoning China

ISBN: (纸本)9798400713231

Challenges in target detection include significant variations in target size, insufficient detection accuracy on small, resource-constrained devices, and the high cost of fully annotating large datasets. To address these issues, a semi-supervised lightweight target detection method based on the improved YOLOv7-tiny algorithm is proposed. First, a new spatial pyramid pooling network, MSPP, is designed to address the problem of significant variations in target size. Then, a lightweight convolutional attention module, GC-SAM, is introduced, which generates stronger feature representations by combining channel and spatial attention mechanisms. Finally, a multi feature semi-supervised data filtering method is proposed, which enhances the model's ability to learn valuable information from unlabeled data, effectively improving detection accuracy. Experimental results show that the improved algorithm achieves a detection accuracy of 82.2% and 63.6% for mAP@50 on 30% of the KITTI and SODA1000 datasets, which is 5.4% and 2.7% higher than the original model, respectively. The model size is only 14.4 MB and the detection speed is 82.1 frames per second, confirming the effectiveness of the algorithm.

关键词： Object Detection

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Skin Cancer Detection Using Image Classification in Deep learning

Skin Cancer Detection Using Image Classification in Deep Lea...

引用

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

作者： D. Prasanna R. Manishvarma Department of Computer science and Engineering Mahendra Engineering College Namakkal India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The understanding of deep learning in skin cancer results in a dependable and faster method to diagnose malignant skin lesions at an initial stage. To improve the accuracy of detection, this research uses a number of steps in its methodology. First, we apply the Weiner filter to smooth the input images, minimizing the noise effects and improving the overall image quality. The next step is to use Adaptive Histogram Equalization (AHE) because it enhances the contrast of the image and thus helps in feature extraction. The feature selection process is accomplished through an Autoencoder that decreases dimensionality while preserving essential data characteristics. Finally, classification is executed using a Convolutional Neural Network (CNN), which performs inadequately for pattern recognition in medical images. The model shows a high accuracy of 96.3%, reflecting its effectiveness in diagnosing skin cancer. Such high accuracy proves the effective applicability of the proposed method as a diagnostic aid to dermatologists for timely and efficient reactions. Future works will emphasize external validity and thereby set the system into real practice settings.

关键词： Deep learning Training Accuracy Wiener filters Transfer learning Feature extraction Skin Convolutional neural networks Medical diagnostic imaging Skin cancer

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A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial Intelligence to Quantum machine learning

A Comprehensive Survey on Breast Cancer Diagnostics: From Ar...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Manideep Raya V S R S Reddy Sunil Kumar Biswajit Bhowmik Dept. of Computer Science and Engineering Maharshi Kanad QC Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Maharshi Patanjali CPS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial Intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum machine learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis.

关键词： Surveys Accuracy Face recognition Biopsy machine learning Ultrasonography Sensitivity and specificity Breast cancer Artificial intelligence Medical diagnostic imaging

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AI Based Adaptive QoS Optimization for IoT Devices in Healthcare Networks

AI Based Adaptive QoS Optimization for IoT Devices in Health...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： S. Karthika K. Juliana Gnanaselvi K. Palanisamy S. Umamaheswari Department of Computer Science& Business Systems Dr. N. G. P Institute of Technology Coimbatore India Department of Computer Science Rathinam College of Arts and Science Coimbatore India Department of CSE Karpagam Academy of Higher Education Coimbatore India Department of ECE Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper proposes an artificial intelligence-based adaptive Quality of Service (QoS) optimization framework for Internet of Things (IoT) devices in healthcare networks. IoT devices, such wearable monitoring and remote diagnostic tools, struggle to maintain optimal performance due to fluctuating network circumstances and hardware limitations. The suggested system uses machine learning and deep learning techniques to dynamically update QoS settings in order to achieve consistent, low-latency, and energy-efficient data transport. The AI model forecasts congestion, distributes resources optimally, and enhances network performance by continuously learning from real-time data and network conditions. This customisable solution improves healthcare delivery by providing timely, continuous services, resulting in better patient outcomes and ensuring the long-term viability of IoT healthcare networks. The framework's adaptive nature addresses scalability and flexibility challenges in healthcare IoT systems by constantly learning and optimising performance as network conditions change. The simulation results demonstrate how well the proposed solution works to improve QoS, ensure seamless healthcare delivery, and improve patient outcomes in dynamic network situations. This study paves the way for AI-powered healthcare IoT networks that are more reliable, responsive, and intelligent.

关键词： Performance evaluation Adaptive systems Medical services Quality of service Throughput Real-time systems pattern recognition Internet of Things Convolutional neural networks Optimization

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