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Real-Time Anomaly Detection in Telecommunications: Advanced data mining Techniques for Fraud Identification 3

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

引用

3rd international conference on Disruptive Technologies, ICDT 2025

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

ISBN: (纸本)9798331519582

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. © 2025 IEEE.

关键词： Telecommunication industry

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IoT-Based Driving pattern Analysis and Engine Sensor Damage Prediction Using Onboard Diagnostics 3rd

IoT-Based Driving Pattern Analysis and Engine Sensor Damage ...

引用

3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

作者： Abhinand Krishna, K.K. Bavos, Abhinav Thomas, Ashak Achankunju Joseph, Jerin John, Siju Kumar, S.N. Department of Computer Science and Engineering Amal Jyothi College of Engineering Kerala Kottayam India Department of Electrical and Electronics Engineering Amal Jyothi College of Engineering Kerala Kottayam India

ISBN: (纸本)9789811979811

Car diagnostic tools use sophisticated software to quickly and accurately identify problem areas in a car’s engine or elsewhere. The adaptation from mechanical systems to electrical paved the way for much research in the area of vehicle monitoring through a server. Although many studies focus on the tracking of vehicles, fault detection has also gained considerable attention. From our background study, we noticed that most of the systems currently available display the data from OBD and do not perform any functions using that data. We propose a machine learning-based driving pattern analysis and sensor damage prediction system using the data collected from an Onboard Diagnostics (OBD) port. Our system consists of an ELM327 adapter to read data from an OBD port, a mobile application, and a cloud backend. Using the time series data collected, we perform driving pattern analysis and sensor damage detection from the backend server. The smartphone application makes the findings of these analyses visible. The findings can be accessed by the drivers directly via the smartphone app. Drivers are notified of alerts created in the backend as a result of unfavorable conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Damage detection

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Developing Automated Optical Character recognition System Using machine learning Algorithm to Solve Payment Verification Issues 3

Developing Automated Optical Character Recognition System Us...

引用

3rd international conference on Cybernetics and Intelligent System (ICORIS)

作者： Siek, Michael Soeharto, Rafi Bina Nusantara Univ Business Informat Syst Program Informat Syst Dept Fac Comp & Media Jakarta 11480 Indonesia

ISBN: (纸本)9781665425803

One primary aspect in customer services is to provide immediate solution towards payment verification issues, such as a time delay of payment confirmation by the payment service provider or supplier. This paper presents a development of an accurate optical character recognition (OCR) system using convolutional neural network with deep learning algorithm, which can skip some steps in the workflow of manual payment approval to fasten the process of payment verification and confirmation. By using some machine learning frameworks of pyTorch utilizing Tensors and CUDA-GPU parallel computing, the machine learning based OCR system was developed and tested with the actual data. The real data sets used here cover the non-uniformity of the receipt bill's papers with various conditions (crumple, water drops, and folds) with some nature of the customer's overall camera noise, angle, and lighting. Several experiments associated with data preparation, deep learning parameter settings, and model performance comparison, were properly conducted to obtain a high quality of OCR system to detect trace number, approval codes, and nominals on the widely-used payment receipts. The resulting OCR system performed very satisfactory with 100% accuracy on testing data set. This promising results permit for the integration between this accurate and automated OCR system and chat environment with chatbot technology in order to provide better user experience and immediate and reliable solution toward payment verification issues.

关键词： Convolutional neural network deep learning algorithm electronic payment technology using text recognition

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Recognizing Sentiments In Regional Language Text From Social Media Using machine learning Approach: A comparative Study to understand the state of the art tools and associated functionalities available 3

Recognizing Sentiments In Regional Language Text From Social...

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3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Sirsat, Sudarshan Zulpe, Nitish K J Somaiya Institute of Management Somaiaya Vidyavihar University Department. of Data Science and Technology Mumbai India Swami Ramanand Teerth Marathwada Univesity College of Computer Science and Information Technology Latur India

ISBN: (纸本)9798350348347

Regional language contents gain the highest traction in today's social media network marketing and from users' perspectives since nearly 90% of users consume contents in regional language. Those who are only focusing on English as the major language of social media or website contents are clearly missing the majority. Social media currently is touching the untouched digital population of the world through creative contents, easy to use convincing features and infinite possibilities of enhancing personal social networks using regional language like Hindi, Marathi, and Gujarati and many other state and regional languages. This has created immense opportunities in the area of text mining, text analysis and text generation too many scholars and researchers to visualize and analyses the contents to gain insights into what are the multidimensional perspectives of the authors of any such contents. This research work is going to understand the current work done in the area of regional language sentiment analysis in India and all across the globe to understand the status and research gap in the domain to work further in the Maharashtra state regional language contents available on social media pages. This research work will try to explore all possible tools and technologies available and used by the recent research scholars and industry leaders to decide the way ahead to work in the Marathi regional language domain in near future. © 2023 IEEE.

关键词： Sentiment analysis

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Speaker Gender recognition Based on Semi-Supervised learning

Speaker Gender Recognition Based on Semi-Supervised Learning

引用

Computing, Communication, Perception and Quantum Technology (CCPQT), international conference on

作者： Zheyan Zhang Renwei Li Kewei Chen Faculty of Mechanical Engineering & Mechanics Ningbo University Ningbo China

ISBN: (数字)9798331528386

ISBN: (纸本)9798331528393

Determining a speaker's gender from speech signals is essential for intelligent human-machine interaction. Significant progress has been made in supervised learning for speaker gender recognition. However, effective performance relies heavily on large labeled datasets. To address this, a semi-supervised learning framework is introduced to fully leverage vast amounts of unlabeled speech data for gender recognition. First, contrastive learning is employed to enhance and augment a relatively small set of labeled audio data, training an encoder capable of distinguishing speech genders. Next, a fuzzy clustering algorithm is utilized to generate gender pseudo-labels from a large pool of unlabeled data. Finally, the speaker gender recognition model is iteratively trained by using these pseudo-labels. Experiments and analyses on the Common Voice English dataset demonstrate that the model attains a mean accuracy of 97.4% in gender recognition tasks.

关键词： Training Accuracy Computational modeling Supervised learning Noise Speech recognition Contrastive learning Semisupervised learning Feature extraction data models

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Achieving Energy Efficiency in Life-Logging Applications of Internet of Things Using data Compression Through Incorporation of machine learning and Edge-Cloud Architecture 1

引用

3rd international conference on Computing Science, Communication and Security (COMS)

作者： Barot, Vishal Patel, Ritesh LDRP Inst Technol & Res Gandhinagar Gujarat India CSPIT U & PU Patel Dept Comp Engn Anand Gujarat India

ISBN: (数字)9783031105517

ISBN: (纸本)9783031105517;9783031105500

Life logging applications for physiological and behavioral analysis of humans make use of smart wearable devices for capturing the various biosignals of a human. These devices have a combination of sensors that capture the respective biosignals and report the recorded data to the edge device (usually a mobile phone) which in turn acts as a primary communicator delivering the data over cloud for storage and future analysis. As this huge amount of data is transmitted from edge device to the cloud, IoT devices are forced early exhaustion, due to higher energy consumption. We propose a SZ based lossy compression technique on edge devices. To evaluate our proposal we implemented the algorithm over automobile drivers' stress recognition dataset from PhysioNet that resulted in a reduction of the dataset size by 103 times after compression. As information loss is a primary constraint while compressing data, we performed stress prediction over the decompressed data using a fully connected neural network with an accuracy of 98.01% which is more than the accuracy achieved for predictions made on the original - uncompressed dataset.

关键词： Internet of Things machine learning Stress level prediction data compression Energy consumption optimization

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Traditional Work Station System with AI based Crypto mining

Traditional Work Station System with AI based Crypto Mining

引用

Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： M. Vanitha Harivanth A Govardhan K Electronics and Communication Engineering Saveetha Engineering College India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research explores the feasibility of AI-powered cryptocurrency mining on traditional workstations. Traditional workstations, while widely available, face challenges in terms of computational efficiency and energy consumption compared to specialized hardware like ASICs and GPUs. This project aims to address these challenges by leveraging machine learning techniques to optimize mining performance. By analyzing real-time data such as hash rates, power usage, and network difficulty, the system will dynamically adjust mining parameters to maximize profitability while minimizing resource consumption. Furthermore, the project will investigate the use of reinforcement learning to enable the system to continuously adapt to changing mining conditions and optimize its performance over time. This approach aims to democratize cryptocurrency mining by making it accessible to users with traditional workstations, enabling them to participate in a sustainable and efficient manner.

关键词： Renewable energy sources Energy consumption Profitability Reinforcement learning Hardware Regulation Real-time systems Cryptocurrency Workstations data mining

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machine learning Technique based Trip Planning System - TripMa 3

Machine Learning Technique based Trip Planning System - Trip...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Vishwajith, Lochana Attanayake, Hasitha Rangana, Savith Ushara, Thimal Bandara, Pradeepa Rupasinghe, Shalini Sri Lanka Institue of Information Technology Department of Software Engineering Malabe Sri Lanka

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Travel planning is a tedious task to engage from the perspective of travelers since it involves crucial and critical decision-making due to a lack of foreign geography and cultural knowledge. Initially, the concept of traveling through travel agencies was used and it was slowly diminishing due to the fact of freedom and exploration are too constant and bounded according to the organizer's perspective. The proposed model overcomes almost all existing challenges and removes the traveler's local knowledge gap by using the dynamic prediction and recommendation features. The proposed system has identified the importance of grouping or the sharing expectation of a traveler through mapping out the best matching companions along with identification of tourist attractions and the best time to visit those places, which also included safety precaution methodologies to the travelers, which provide them the capabilities of preparing and mitigating the potential risks along with the traveling areas. The inclusion of the user feedback analysis model will impact identifying the user's perspective of the system and ensures users' trust towards the system, The system has iteratively used location-based knowledge through third-party APIs such as GOOGLE MAP API. The below sections will describe and illustrate the modes of machine learning techniques used, weighted models, and the NLP data mining methodologies used to acquire the systems architecture. © 2022 IEEE.

关键词： Decision making

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machine learning Based Impostor Detection by Invariant Features from Nir Finger Vein Imaging 3

Machine Learning Based Impostor Detection by Invariant Featu...

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3rd international conference on Innovations in Computer Science and Software Engineering, ICONICS 2022

作者： Rasool, Akhtar Rehman, Faisal Sarfaraz, Nadeem Sharif, Hanan Khan, Rashid Khan, Abdul Manan Lahore Leads University Department of Computer Science and Information Technology Lahore Pakistan

ISBN: (纸本)9781665461412

Biometrics mostly used as personal identification accordingly securing a client against the unapproved utilization of his or her identity. Acquiring biometric information is getting to be simpler. Smartphones and other advanced technologies exist from which biometric data can collect easily without the knowledge of others. Finger vein authentication is a method for biometric verification that depends on a vein pattern, which is located beneath the human finger's skin. Veins are covered with skin that cannot be copied by others. In this research, our focus is on these invariant features of finger veins. We have collected invariant features from various recent features extraction techniques and then classified with state-of-The-Art machine learning classifiers. For this purpose, we have used publicly available finger vein image databases. The performance has been evaluated by various evaluation metrics and comparative analysis of various machine learning classifiers has been presented to describe the performance of these classifiers on the said data set. © 2022 IEEE.

关键词： Biometrics

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Research on the Evaluation of Chinese Articulation Using machine learning and Algorithm Optimization

Research on the Evaluation of Chinese Articulation Using Mac...

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Electrical Engineering, Big data and Algorithms (EEBDA), IEEE international conference on

作者： Meng Tang Sichuan Technology & Business College Chengdu China

This design uses SPCE061A, SPLC501 and keyboard module to realize speech recognition system. The system realizes the functions of adding, deleting and searching basic information. The data saved in SPR4096 was recognized by voice. A steady state interference noise for Chinese speech audibility detection based on pink noise is proposed, and its frequency is consistent with the speech features recorded in anechoic chamber. The corresponding training samples were obtained by convolution of the Chinese speech audibility test word list with the equivalent binaural chamber impulse response. It is then tuned to a specific signal strength. The objective evaluation of the intelligibility of Chinese speech is done by audiologists. It is found that factors such as reverberation time, signal-to-noise ratio and pronunciation speed play an important role in the intelligibility of Chinese speech. Compared with reverberation time and speech frequency, SNR plays a more significant role in the intelligibility of Chinese speech. Compared with conventional methods, this method has better recognition performance and speed.

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