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Artificial Intelligence (AI) Assisted Literature Review 3

Artificial Intelligence (AI) Assisted Literature Review

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Hilda, Jabanjalin Jeevaa, S.V. Bhadhrinath, U. Solanki, Darshini Sr. School of Computer Science Engineering Vellore Institute of Technology Vellore India Vellore Institue of Tehnology Vellore Vellore India

ISBN: (纸本)9798331527549

This research aims to address the inefficiencies and inaccuracies in traditional literature review methodologies by integrating advanced Artificial Intelligence (AI) technologies, specifically transformer-based models. The study focuses on optimizing PDF-to-text conversion through image and character recognition, thereby minimizing resource wastage and errors associated with manual transcription. Additionally, the research employs advanced Natural Language Processing (NLP) techniques to automate the identification of critical phrases such as keywords and organizational entities, significantly reducing manual errors and improving accuracy. The methodology also includes the use of sophisticated summarization algorithms and sentiment analysis, streamlining the process of summarizing extensive literature and identifying study limitations. By leveraging state-of-the-art transformer models and industry-standard libraries, this research introduces a novel, efficient framework for literature analysis. The outcome is a comprehensive, AI-powered toolkit that enhances the speed, accuracy, and precision of literature reviews, ultimately providing researchers with a more reliable and resource-efficient approach to navigating scholarly content. © 2025 ieee.

关键词： Natural language processing systems

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Anomaly Detection in Meteorological data Using Machine Learning Techniques

Anomaly Detection in Meteorological Data Using Machine Learn...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Rawal, Uddhav Patel, Subhas Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331529833

Anomaly detection is a very important component in weather forecasting, disaster preparedness and climate modeling in meteorology. We use Density Based Spatial Clustering of Applications with Noise, Isolation Forest, Local Outlier Factor, Elliptic Envelope and One Class Support Vector Machine to detect anomalies in Tunisian meteorological data in this study. Key meteorological parameters, such as wind speed, rainfall and temperature, was analyzed as the important indicators of atmospheric conditions. DBSCAN detected local density changes very well and is therefore a good choice for anomaly detection in areas with complex spatial patterns from the models used. Isolation Forest algorithm was able to find global anomalies with robust performance, giving very high true positive rates with minimum false positives. The use of these machine learning techniques is complementary and this dual approach shows how they can be used to detect different kinds of anomalies. The results show how machine learning can be used to make environmental monitoring and weather prediction much more accurate and reliable, with the ability to detect anomalies precisely and reliably. Having such capabilities is critical for taking proactive actions in response to extreme weather events, and to be able to intervene at the right time to minimize the impacts of the events. Additionally, these models could be integrated into real time monitoring systems to provide additional utility in optimizing their use and provide actionable insight for disaster management and climate resilience. Further research could involve the fusion of these algorithms with deep learning methods, larger datasets, and real time deployment to build intelligent systems that can learn and adapt to changing environmental conditions. This would be a large advance in applying machine learning in meteorology. © 2025 ieee.

关键词： intelligent systems

来源：评论

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Vehicle Pseudonym Change Mechanism Based on Dynamic Game

Vehicle Pseudonym Change Mechanism Based on Dynamic Game

引用

2024 international conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhao, Zheng Wang, Junren Mao, Qian Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Liaoning University Shenyang China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

intelligent Transportation systems (ITS), particularly vehicular ad-hoc networks, play a crucial role in improving road safety and traffic efficiency. However, transmitting vehicle location and identity data poses significant privacy risks, potentially allowing attackers to exploit this information. In this study, we developed a user-centric privacy model based on information entropy, enabling vehicles to monitor real-time location privacy levels. We also proposed a decision algorithm for pseudonym changes, dynamically identifying a cooperative set of vehicles for pseudonym alteration. Comparative experiments using SUMO and NS-3 simulations show that our scheme enhances location privacy while reducing pseudonym change frequency. This research provides valuable theoretical and technical foundations for ITS. © 2025 SPIE.

关键词： Vehicular ad hoc networks

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Traffic Incident Detection Algorithm based on Deep Learning: A Statistical Optimization Approach 3

Traffic Incident Detection Algorithm based on Deep Learning:...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Jing, Zhao Heilongjiang Institute of Technology College of Automobile and Traffic Engineering Harbin150050 China

ISBN: (纸本)9798331509675

The continuous evolution of smart transportation has led to the vigorous development of research in the field of intelligent vehicle anomaly detection. This study proposes a new traffic incident detection algorithm based on deep learning and combines it with statistical optimization methods for real-time traffic incident monitoring. This model first uses the pre-trained YOLOv5m-c model to detect vehicles and outputs images with vehicle frames from the video stream. At the same time, from the data analysis level, it determines whether there is abnormal behavior between vehicles based on the characteristics of the vehicle frame. The proposed algorithm improves the effect of abnormal events by optimizing the network structure of the YOLOv5 model. Then, starting from the sensor level, the detection method based on the Internet of Vehicles collects and shares vehicle status information in real time. This analysis process fully considers the task offloading and migration strategy in the edge computing environment. Quantitative experimental results show that the proposed algorithm can effectively improve the detection accuracy of traffic incidents and reduce the false alarm rate in real-time traffic data. © 2025 ieee.

关键词： intelligent systems

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Decoding Voice Authenticity: An Exploration of Deep Learning and Audio Feature Techniques 3

Decoding Voice Authenticity: An Exploration of Deep Learning...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yalla, Swathi Tejah Raju, Morampudi Gopi Prashanth Nagaraju, D. Ali, Mohammad Farhaan Department of AI & Data Science CBIT Hyderabad India

ISBN: (纸本)9798331527549

The increasing sophistication of spoofing, mimicry, and deepfake technologies exposes critical vulnerabilities in voice authentication systems, including the inability to generalize across diverse attack types, reliance on narrow feature sets, and a lack of interpretability, which seriously limits their applicability in the real world. This review will analyze the state-of-the-art techniques for Logical Access, Presentation Attacks, and Deepfake manipulations. It points out the limitations of the traditional models and progress based on modern frameworks. It explores using advanced audio features Mel-Spectrogram and MFCC along with LightGBM classifiers and incorporates SHAP (Shapley Additive Explanations) for model transparency and cosine similarity matrices for mimicry detection. The study identifies a dual-layered framework that attains high accuracy and robustness across datasets while ensuring adaptability to emerging attack strategies. It addresses the critical need for scalable, interpretable, and adaptive voice authentication solutions and provides a foundation for effectively mitigating evolving audio spoofing threats. © 2025 ieee.

关键词： Deep learning

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Adaptive Learning systems for English Language Education based on AI-Driven System 1

Adaptive Learning Systems for English Language Education bas...

引用

1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Kumar, Arabati Pradeep Fatma, Gulnaz Sarwar, Sana Punithaasree, K.S. Sirisha Prema, S. Anurag University Department of English Hyderabad India University College Al Ardah Jazan University Jazan Saudi Arabia Jazan University College of Science for Girls Jazan Saudi Arabia Department of English Erode India Sri Venkateswara University Department of English Tirupati India Panimalar Engineering College Department of English Chennai India

ISBN: (纸本)9798331529246

Adaptive Learning systems (ALS) are transforming English language education by utilizing AI-driven techniques to provide personalized learning experiences that cater to each student's unique needs. Traditional strategies face difficulties rating the learners, thus skills covered, in that, there are challenges providing custom learning. This research presents an innovative NLP-BERT-based approach to learning English, providing customized feedback based on students' linguistic capabilities. Using the Student dataset provided by Kaggle, the system adjusts instruction content to fit a learner's level of proficiency dynamically. Using the Python-based platform, our model provides an outstanding accuracy of 95.23% for predicting the proficiency of any language. This adaptive system will not only enhance the experience of learning but also improve the shortcomings of current methods by making it individualistic. It will benefit the educators, students, and institutions as an efficient data-driven solution that encourages improved engagement and accelerated skill acquisition in English language education. © 2025 ieee.

关键词： Adversarial machine learning

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Empowering smart traffic avoidance using Natural language processing 3

Empowering smart traffic avoidance using Natural language pr...

引用

3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kalaiselvi, V.K.G. Vishnu Chandar, K.R. Vishwa, G. Tirunagari, Sharanya Hariharan, Shanmugasundaram Krishnamoorthy, Murugaperumal Sri Sairam Engineering College Department of Information Technology Tamilnadu India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798331515911

The integration of Natural Language Processing (NLP) into smart traffic systems offers a novel approach to enhancing real-time traffic management and optimization. This paper explores how NLP can be leveraged to interpret and analyze vast amounts of unstructured traffic-related data, such as social media updates, traffic reports, and incident logs. A key component of this approach is the use of a BERT (Bidirectional Encoder Representations from Transformers) classifier, which enables more accurate extraction and classification of relevant traffic information from textual data. By processing this data in real time, the BERT-based NLP system can provide more precise traffic predictions, identify congestion patterns, and optimize traffic flow dynamically. The study highlights the potential of NLP, particularly through the use of BERT, in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and enhanced urban mobility. The app also includes user-friendly interfaces that allow drivers and city planners to access real-time updates, personalized route recommendations, and predictive analytics. The system's adaptability ensures that it can respond quickly to unexpected incidents, such as accidents or road closures, further enhancing urban mobility. The study highlights the transformation potential of this app in improving the efficiency and responsiveness of traffic management systems, ultimately leading to reduced travel times, lower emissions, and a significant improvement in overall urban transportation experiences. © 2025 ieee.

关键词： Efficiency

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Keyframe Extraction Approaches for intelligent Video Analysis 4

Keyframe Extraction Approaches for Intelligent Video Analysi...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sabarivasan, N. Thangavel, Senthil Kumar Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

Real-time violence detection is essential for protecting the safety and security of people, especially in college campuses that are dynamic and have crowds. Manual surveillance systems are popular but inefficient as they require human attention all the time and are not capable of quickly identifying and responding to violent *** the exsisting violence detection model takes quite a time for processing the entire video. The proposed model extracts features as keyframes from the video using MobileNet V3 and K-medoids clustering. The redundancy of video data can be avoided and significant violent activities identified by isolating and analyzing critical frames from video data. This enhances the efficiency and responsiveness of surveillance systems. This work primarily contributes a customized violent activities dataset and implementing keyframe extraction algorithm for optimizing the usage of computational resources, and incorporating lightweight algorithms for seamless deployment with existing CCTV networks. The experimental results clearly demonstrate the ability of this approach to detect keyframes, thereby offering a scalable and practical solution. © 2025 ieee.

关键词： Security systems

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Enhancing Customer Personality Prediction using advanced Machine Learning Techniques and data Balancing Strategies: A Comprehensive Approach to Addressing Imbalanced datasets in Marketing Analytics 1

Enhancing Customer Personality Prediction using Advanced Mac...

引用

1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Selvalakshmi, V. Sree, T. Manju Usha Saranya, S. Devi, A. Uma Basha, Md Shaik Amzad Velammal College of Engineering and Technology Department of Master of Business Administration Madurai India Department of Master of Business Administration Hyderabad India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Alpha College of Engineering Department of Management Studies Chennai India Hyderabad India

ISBN: (纸本)9798331529246

This study tackles imbalanced customer response prediction by integrating advanced resampling methods with diverse machine learning and ensemble techniques. Novel resampling approaches (SMOTE, Borderline-SMOTE, ADASYN, and SMOTEENN) were systematically applied alongside Logistic Regression, Random Forest, Gradient Boosting, LightGBM, SVM, and Neural Networks. Stacking and voting ensembles further consolidated individual model strengths. Results show that coupling resampling with ensembles significantly boosts minority class metrics. For example, ADASYN combined with LightGBM achieved a 95% accuracy and substantially improved minority class recall and precision, while SMOTEENN with a voting ensemble reached a 99% recall, albeit with a slight precision trade-off. These findings underscore that employing sophisticated data rebalancing techniques in conjunction with hybrid ensembles can surpass traditional models, offering a balanced improvement in metrics such as accuracy, precision, recall, and F1 score. The practical significance lies in enabling more reliable predictions in highly imbalanced scenarios, ultimately enhancing decision-making processes. © 2025 ieee.

关键词： Adversarial machine learning

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Review on Advancements in Coal Mine Safety and Comprehensive Overview of IoT-based Monitoring systems 3

Review on Advancements in Coal Mine Safety and Comprehensive...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Manonmani, A. Nalina, A. Seethaladevi, S. Yashika, S. Sri Saveetha Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Ensuring the safety of miners is a critical priority due to the inherently hazardous conditions in underground mining environments. This research presents a Zigbee-based wireless data acquisition system designed to enhance real-time monitoring of essential environmental parameters such as air quality, temperature, humidity, and toxic gases. Moving beyond conventional methods, the system incorporates an adaptive sensor deployment strategy tailored to the complex and dynamic underground landscape, greatly enhancing data reliability and network resilience. advanced RSSI modeling techniques were applied to validate packet delivery and optimize network performance under changing environmental conditions. Field trials demonstrated that this system achieves exceptional accuracy and efficiency in detecting hazardous conditions, providing a proactive approach to hazard mitigation. The findings highlight the system's ability to transform safety protocols and operational practices in mining with a scalable, robust, and innovative wireless monitoring solution. © 2025 ieee.

关键词： Zigbee

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