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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Chinnaiah, Valliyammai Dhayanithi, Manikandan Patturaj, Santhosh Ranganathan, Ramanujan Mohan, Vishnu B. A. Department of Computer Technology Madras Institute of Technology Anna University Chennai India

ISBN: (纸本)9789819726707

Social media plays a major part in everyone’s lives in which it aids the people to interact and connect with them. Social media has created large communities to influence what everyone reads, does, or even thinks. The influencing mechanism of Twitter trends is exploited using bots and spam accounts where several tweets on a topic are boosted into trends based in an artificial way. The fake trends are common in the social media domain which is discovered through promoting gambling, information phishing, disinformation campaigns, political slogans, and hate speech. The manipulation of Twitter trends has great implications because Twitter trends get more attention. The broader media channels cover trends through utilizing as a stand-in for what people are talking about. Unfortunately, this stand-in is manipulated, clouding the public’s perception of the topics being discussed. It is important to detect these fake trends and ensure that the public is not exposed to fake trends. The proposed fake trend detection system classifies the Twitter accounts into bot and human accounts. TwiBot-20 benchmark dataset is trained to classify the bot accounts using Random Forest model which achieves 94% accuracy. The metadata features and influence entropies are extracted from bot accounts. The influence entropy is measured by constructing an influence graph using igraph and NetworkX through analyzing the retweet count and user mentions in each tweet of an account. The trending topic of the tweets is analyzed and the authenticity of the trend is categorized using one-dimensional clustering. A web application is developed to facilitate the proposed fake trend detection system for the users without technical knowledge. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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Deepfake Video Detection: A Comprehensive Survey of Advanced machine learning and Deep learning Techniques to Combat Synthetic Video Manipulation

Deepfake Video Detection: A Comprehensive Survey of Advanced...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), international conference on

作者： Tenali Anusha A. Srinagesh Department of CSE Acharya Nagarjuna University Guntur Andhra Pradesh INDIA Department of CSBS R.V.R. & J.C College of Engineering Guntur Andhra Pradesh INDIA

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The detection of deepfake videos has become a critical area of research due to the widespread use of manipulated content across digital platforms. With the increasing sophistication of generative models deepfake videos have reached a level of realism that makes them challenging to detect. This survey paper provides an extensive review of 70 research papers published from 2021 to 2024, categorizing the existing detection techniques into three primary groups such as supervised learning techniques, unsupervised learning techniques, and hybrid methods. These methods leverage labelled datasets to train models to identify subtle artefacts in manipulated videos. Notable advancements have been made by integrating CNNs with RNNs to capture both spatial and temporal patterns, enhancing detection accuracy. Unsupervised learning techniques, such as clustering algorithms and anomaly detection methods, are increasingly being explored for deepfake detection. Hybrid methods combine the strengths of both supervised and unsupervised techniques, improving detection performance. CNN-RNN architectures integrated with attention mechanisms are an example, as they enhance the focus on manipulated regions within videos. Additionally, multimodal approaches that combine both visual and audio cues have been introduced to address the complexity of detecting deepfakes across various media types. This review identifies the challenges faced by current detection systems, such as limitations in datasets, high computational costs, and a lack of robustness against evolving manipulation techniques. To address these challenges, future research should focus on developing more diverse datasets, enhancing the generalizability of models, and improving computational efficiency to support real-time applications. The most promising approach for advancing deepfake detection systems involves integrating multi-modal techniques, advanced deep learning architectures, and scalable designs.

关键词： Surveys Measurement Deepfakes Reviews Supervised learning Neural networks Media Robustness Real-time systems Unsupervised learning

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AI-Powered Financial Fraud Detection System

AI-Powered Financial Fraud Detection System

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intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), international conference

作者： Prabha M Sowmiya Sunder Rajasreenithaa Kumarabaabu Rachel Davis Saraswathi P Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Credit card blackmail may be a major issue, and numerous innocents are mishandled with their private pieces of data. Since credit cards are both available and commendable, they have been a well-known outline installment elective. Their wide utilization comes with the natural issue of securing individual and installment information against blackmail or unauthorized get-to. This makes a distinction in keeping the belief and certainty of the clients while premonition security hones are crucial. To address this fundamental issue, we have utilized the display day concepts of credit card extortion disclosure and we'll propose coordinates metaphysics for a computer-aided handle in which property imprisonments are characterized by interior reification components by utilizing progressed twins as well as blockchain. That exploration besides prescribes that understanding and combatting credit card blackmail are basic for making strides in the welfare of buyers as well as budgetary teach. This consider gives a comprehensive see of credit card blackmail examination counting classification of unmistakable sorts of it to show the chance scene. It presents a new computerized twin strategy, which contrasts with prevalent extortion discovery. In the slightest complex terms, a computerized twin is the virtual adjustment of something honest to goodness. The foremost critical issues for financial specialists and clients are hacking, scamming, etc. People do not have appropriate mindfulness of this issue. In this issue, the most challenging task is recognizing. To overcome this issue afterward considers multi-source content information to overhaul the blackmail area. This investigates a more significant consideration of interior organization and outside media which contrasts recognizing the precision for location of cash-related blackmail revelation systems. This method of multi-source substance data procedure is utilized in the Chinese promotion system between 2017 and 2021.

关键词： Logistic regression Heuristic algorithms Finance Organizations machine learning Media Credit cards Fraud Security Particle swarm optimization

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Expanding conformal prediction to system identification

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Pattern Recognition 2025年 167卷

作者： Msaddi, Saleh Kumbasar, Tufan Artificial Intelligence and Intelligent Systems Laboratory Istanbul Technical University Istanbul34469 Turkey Control and Automation Engineering Department Istanbul Technical University Istanbul34469 Turkey

System Identification (SysID) is a cornerstone of control engineering, aimed at modeling system dynamics from input–output data. However, SysID approaches often overlook uncertainty quantification (UQ), which is crucial for robust decision-making. To address this gap, we propose Conformal System Identification (C-SysID), a model-agnostic framework for UQ in single-output and multiple-output SysID tasks. C-SysID transforms point predictions of any SysID model into High-Quality (HQ) Prediction Intervals (PIs) for single-output systems or Prediction Regions (PRs) for multiple-output systems, ensuring the true output is captured within the specified confidence level. For single-output systems, we use the Updated Residuals (UR) and Weighted Updated Residuals (WUR) methods to generate valid HQ PIs. In the case of multiple-output systems, we apply the Copula method and introduce the Updated Copula (UC) method, which captures output dependencies to generate HQ PRs. We empirically validate C-SysID on diverse single-output and multiple-output SysID datasets. The statistically significant comparative results show that the WUR method outperforms baseline methods, delivering tight PIs with the desired coverage. For multiple-output systems, our proposed UC method generates PRs that achieve accurate joint coverage while maintaining the compactness of PRs. The results highlight the effectiveness of C-SysID in UQ and enhancing uncertainty-aware predictions for SysID applications. © 2025 Elsevier Ltd

关键词： Parameter estimation

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Research on intelligent detection system and method for road surface damage utilizing tire noise

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Measurement: Journal of the international Measurement Confederation 2025年 253卷

作者： Li, Huixia Chen, Ruohan Ritha, Nyirandayisabye Wang, Jian School of Civil Engineering Fujian University of Technology Fuzhou350118 China Institute of Mountain Hazards and Environment University of Chinese Academy of Sciences Beijing100012 China CPNET Corporation Fuzhou Branch 350118 China

To address the lack of efficient and economical intelligent detection systems in the field of pavement damage detection and the current research's neglect of pavement audio signals, this paper presents a tire noise-based pavement damage detection system. The system integrates GPS positioning, tire noise recording, image capture for audio annotation, and real-time data storage, facilitating the collection of extensive pavement condition data. The raw audio is processed with the Nussl algorithm to reduce noise, then marked and segmented into samples for damaged sections using GPS and images, and used for cluster analysis of pavement conditions. Mel-Frequency cepstral coefficients (MFCC) and their first-order derivatives from the samples serve as features for building a random forest classifier-based pavement damage detection model. To enhance predictive performance, the model is optimized using GridSearchCV and Gradient Boosted Decision Tree (GBDT) algorithms. Ultimately, the model achieved an 88.4% accuracy rate on an independent test set, confirming its effectiveness in pavement damage detection. © 2025 Elsevier Ltd

关键词： machine learning MFCC characteristics Pavement surface damage detection Random forest classifier Tire noise

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Research and practice of remote monitoring system for road test of intelligent connected vehicle

Research and practice of remote monitoring system for road t...

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2021 international conference on machine learning and intelligent systems engineering, mlise 2021

作者： Han, Yanyan Fang, Xiyu He, Kexun Co. Ltd Tianjin300300 China

ISBN: (纸本)9781665417365

Remote monitoring is an indispensable part for the road test of the intelligent connected vehicle. This paper studies and designs the overall architecture of the remote monitoring system for the road test of the intelligent connected vehicle, expounds the hierarchical model and function description of the system in detail, and finally realizes the design scheme of the system, The real vehicle verification shows that the remote monitoring system can accurately monitor and manage the running state of the intelligent connected vehicle in the actual road test, effectively guarantee the safety and controllability of the intelligent connected vehicle road test, and provide data support for the improvement and optimization of the vehicle. © 2021 IEEE.

关键词： Roads and streets

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8th Workshop on Software Architecture Erosion and Architectural Consistency, SAEroCon 2021, 1st international Workshop on Software Architecture and machine learning, SAML 2021, 4th international Workshop on Context-aware, Autonomous and Smart Architecture, CASA 2021, 5th Workshop on Formal Approaches for Advanced Computing systems, FAACS 2021, 2nd international Workshop on Model-Driven engineering for Software Architecture, MDE4SA 2021 and 1st international Workshop on Mining Software Repositories for Software Architecture, MSR4SA 2021 which complemented 15th European conference on Software Architecture, ECSA 2021

8th Workshop on Software Architecture Erosion and Architectu...

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8th Workshop on Software Architecture Erosion and Architectural Consistency, SAEroCon 2021, 1st international Workshop on Software Architecture and machine learning, SAML 2021, 4th international Workshop on Context-aware, Autonomous and Smart Architecture, CASA 2021, 5th Workshop on Formal Approaches for Advanced Computing systems, FAACS 2021, 2nd international Workshop on Model-Driven engineering for Software Architecture, MDE4SA 2021 and 1st international Workshop on Mining Software Repositories for Software Architecture, MSR4SA 2021 which complemented 15th European conference on Software Architecture, ECSA 2021

ISBN: (纸本)9783031151156

The proceedings contain 15 papers presendted at a virtual meeting. The special focus in this conference is on Software Architecture Erosion and Architectural Consistency. The topics include: Interactive Elicitation of Resilience Scenarios Based on Hazard Analysis Techniques;Towards an Extensible Approach for Generative Microservice Development and Deployment Using LEMMA;applying Knowledge-Driven Architecture Composition with Gabble;architectural Optimization for Confidentiality Under Structural Uncertainty;foundations and Research Agenda for Simulation of Smart Ecosystems Architectures;Blended Graphical and Textual Modelling of UML-RT State-machines: An Industrial Experience;toward Awareness Creation of Common Challenges Women are Facing in Academia: A Study from a German Perspective;mapping Source Code to Modular Architectures Using Keywords;hierarchical Code-to-Architecture Mapping;Building the MSR Tool Kaiaulu: Design Principles and Experiences;self-adaptive machine learning systems: Research Challenges and Opportunities;behavioral Maps: Identifying Architectural Smells in Self-adaptive systems at Runtime;an Architectural Approach for Enabling and Developing Cooperative Behaviour in Diverse Autonomous Robots.

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On Predicting Emotion From Commercial Smartwatch - A Big Data Narrative

On Predicting Emotion From Commercial Smartwatch - A Big Dat...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Partha Pratim Sarmah Partha Sarathi Paul Krishnandu Hazra Dept. of CSE IIITG Assam India School of Computer Engineering KIIT University Bhubaneswar India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Smart wearable devices such as smartwatches and smart bands are all-pervasive nowadays. They are accompanied by various sensors like heart rate monitors, oximetry sensors, ambient light and temperature sensors, etc. that collect different physiological data about the individuals while wearing. Such data can effectively be used to detect the activities of the user. Here, one relevant intriguing question arises: Is it feasible to predict the emotional status of an individual by utilizing such physiological data from such devices? To address this question, no such work utilizes commercial smartwatches. Therefore, to fill this gap, in this work, we analyze and predict emotions by utilizing the physiological data collected by the commercial Fitbit smartwatch. Here, we have utilized the Lifesnaps Fitbit dataset, a large public dataset of size 9.72 GB that contains both physiological sensor data and psychological survey data collected from 71 volunteers from different demographic areas using commercially available Fitbit smartwatches between May 2021 and January 2022. Here we investigate the correlation between emotional state and physiological parameters using data collected from the Lifesnaps dataset. We perform comprehensive data pre-processing such as data cleaning, data imputation, and scaling to ensure the data integration and usability of the dataset. We apply various non-neural machine learning algorithms (Support Vector machine, Logistic Regression, Decision Trees, LightGBM, XGBoost, and Random Forest) and achieve an accuracy of up to 78.75% in predicting emotions. We further enhanced the performance of the prediction through efficiently hyper-tuning two neural network approaches (i.e., Feedforward neural network, and BiLSTM) and achieved a maximum accuracy of 97% in the Feedforward neural network. Our experimental evaluation shows a significant potential in commercial smartwatches for mental health and emotion detection.

关键词： Temperature sensors Temperature measurement Wearable Health Monitoring systems Correlation Accuracy Big Data Feedforward neural networks Biomedical monitoring Usability intelligent sensors

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Film CTR Prediction Based on Deep learning

Film CTR Prediction Based on Deep Learning

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2021 international conference on machine learning and intelligent systems engineering, mlise 2021

作者： Shi, Pian Shi, Mingze Michigan State University Michigan United States University of Denver Colorado United States

ISBN: (纸本)9781665417365

A series of behavioral characteristics are often accompanied by users when purchasing goods, and these operational behaviors can provide a great basis for predicting users' next behaviors to a great extent. By analyzing these massive data, we can effectively predict users' behavior, and help different institutions to improve transaction efficiency to a great extent. In this paper, based on big data technology, from the perspective of machine learning and deep learning, based on GroupLens Data, which is open source by researchers from the University of Minnesota, different machine learning algorithms are used to learn the internal models of data to obtain the prediction model of consumers' purchase behavior. Finally, through model fusion, the movie purchase behavior can be predicted by using the measurement indicators related to movie purchase. © 2021 IEEE.

关键词： Forecasting

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Personal credit risk identification based on combined machine learning model

Personal credit risk identification based on combined machin...

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2021 international conference on machine learning and intelligent systems engineering, mlise 2021

作者： Ruan, Xiao Ding, Ning Public Safety Behavioral Science Lab People's Public Security University of China Beijing China

ISBN: (纸本)9781665417365

Personal credit risk assessment is essentially based on the classification of indicators or characteristic variables related to personal credit. Currently, a single model is often used to classify normal customers and defaulting customers. However, the single model may face the risk of a higher type II error rate, that is, defaulting customers are misjudged as normal customers. From the perspective of the combined model, this paper uses the confidence-weighted voting strategy to give a single model different weights to construct a C5.0-SVM combined model. The study finds that the total classification accuracy of the C5.0-SVM combined model is higher than that of the single model, and the type II error rate is lower than that of the single model. © 2021 IEEE.

关键词： Risk assessment

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