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AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques

AADS: An Automated Accident Detection and Nighttime Surveill...

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Intelligent Control, Computing and Communications (IC3), international conference on

作者： M. Karthikeyan Pravin Ramdas Kshirsagar Tan Kuan Tak Keshav Lalit Kamal Upreti Rituraj Jain Dept. of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Electronics Engg. J.D College of Engg & Mgmt. Nagpur MS India Engineering Cluster Singapore Institute of Technology Singapore Dept. of Computer Science & Engineering Manipal Institute of Technology Manipal Karnataka India Department of Computer Science Christ University Delhi NCR Ghaziabad India Department of Info. Tech. Marwadi University Rajkot Gujarat India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

computer vision-based surveillance is very important today's security systems to detect, track and regulate the security much better than standard cameras. However, like any other performance measurement systems they have potential pitfalls and technical, ethical, and legal implications must be well understood. The continuous rise in connection and interaction implies that safety of the public especially when navigating roads or operating in public domains is paramount. The conventional approaches to accident identification include observation or reporting from witnesses and always record slow and imprecise outcomes. With the improvement of AI and computer visions, especially with deep learning models such as YOLO, accident detection is changing. YOLO v10 which is incorporated in the surveillance systems, performs real time video analysis to provide object and pattern recognition of accidents including car accidents and incidents involving the pedestrians. When applied to the initial set of annotated accident images, the fine-tuning of the YOLO v10 model enhances its detection capability. The system is in watching the video frames that contain aberrations and issues and alarms are issued when the accidents happen and relayed to the monitoring stations or emergency departments for proper response. The optimized YOLOv10 here delivers a meaningful testing score of 72.3% mAP to outperform the regular YOLOv10 efficiency in incident detection.

关键词： YOLO Deep learning computer vision Surveillance Computational modeling Streaming media Cameras Real-time systems Time factors Accidents

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GRU in Anomaly Detection for IoT: A Comparative Study

GRU in Anomaly Detection for IoT: A Comparative Study

引用

IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： V Sivakumar Aenugu Harshith Reddy Allam Nehal Varma School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

In this paper, we present a comparative analysis of Gated Recurrent Unit (GRU) models against other deep learning architectures, particularly focusing on anomaly detection and failure prediction within IoT-based industrial systems. GRU’s simplified structure, which accelerates training and reduces memory usage, makes it well-suited for real-time applications in IoT, outperforming its Long Short-Term Memory (LSTM) counterparts in many cases. However, its limitations in handling long-term dependencies, noisy data, and larger datasets can impede performance in more complex scenarios. Several studies have explored hybrid models that combine GRU with architectures like Transformers and CNNs to improve accuracy, especially in environments with irregular and noisy data. This paper discusses the computational efficiency of GRU and its application in edge computing while addressing its weaknesses through hybrid approaches. By leveraging insights from recent literature and projects, we evaluate GRU’s effectiveness in IoT applications and propose a hybrid GRU-Transformer model to enhance anomaly detection capabilities.

关键词： Training Computational modeling Predictive models Transformers Real-time systems Noise measurement Anomaly detection Long short term memory Industrial Internet of Things Predictive maintenance

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AI-Enhanced Cyber Security Proactive Threat Detection and Response systems

AI-Enhanced Cyber Security Proactive Threat Detection and Re...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Venkata Sai Swaroop Reddy Nallapareddy Siva Koteswara Rao Katta Department of Software Engineering Sony Interactive Entertainment Austin Department of Mechanical Engineering KIETW India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The introduction of AI-Enhanced Cyber Threat Detection, which aids in improving organizational security through sophisticated algorithms, has caused a paradigm change in cybersecurity. The proactive posture of AI-driven detection systems that autonomously sift through massive data sets to identify patterns suggestive of cyber intrusions and attacks is explored in this research study. These tools relieve human analysts of routine tasks like threat identification and incident response, freeing them to focus on more strategic matters like making decisions and developing mitigation measures. Artificial intelligence (AI) based detection also makes monitoring network activities easier in real-time, increasing operational efficiency and decreasing dwell time. Another approach to accomplishing this in cybersecurity is combining different technologies, including threat intelligence platforms, with AI or SOAR systems. Further research and improved collaboration are needed to strengthen AI-enabled cyber threat identification in the face of obstacles like adversarial attacks and ethical concerns. Given the prevalence of modern e-crimes, this paper stresses the need for joint defence strategies and proactive cybersecurity measures to safeguard digital assets.

关键词： Explainable AI Organizations Telecommunication traffic Threat assessment Real-time systems Time factors Artificial intelligence computer security Stress Stress measurement

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Anomaly Detection on Gas Pipeline Operational Data Using Classic Seasonal Decomposition and Level Shift Anomaly Detection Architecture

Anomaly Detection on Gas Pipeline Operational Data Using Cla...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), international conference on

作者： Akmal Muzakki Bakir Aditya Firman Ihsan Widi Astuti School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Gas pipeline infrastructure is critical to energy distribution, but its operational existence is often threatened by anomalies such as leaks, malfunctions, and system disruptions. Early detection of these anomalies is critical to prevent safety hazards, operational inefficiencies, and economic losses. This paper proposes an anomaly detection framework using Classic Seasonal Decomposition and Level Shift Anomaly Detection methods for frequency selection and labeling processes, as well as LSTM and VAE-GAN algorithms for anomaly detection. The dataset consists of time-series sensor measurements, including pressure, temperature, energy flow rate, and gas composition. Classic Seasonal Decomposition isolates trend, seasonal, and residual components, enhancing periodic anomaly detection. Meanwhile, Level Shift Anomaly Detection identifies abrupt changes in data levels that signal critical events. LSTM leverages temporal dependencies to detect anomalies with high accuracy in data with strong sequential patterns, while VAE-GAN is effective in modeling complex data distributions and capturing anomalies in datasets with less obvious patterns. Experimental results demonstrate the effectiveness of the proposed approach, with model performance evaluated using F1-Score for each LSTM and VAE-GAN model. The proposed framework shows great potential for peering into real-time monitoring systems, improving operational efficiency, supporting data-driven decision making, and significantly reducing the risk of system failure.

关键词： Temperature measurement Computational modeling Pipelines Decision making computer architecture Market research Data models Real-time systems Anomaly detection Long short term memory

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Co-Design of Network Topology and Qubit Allocation for Distributed Quantum Computing

Co-Design of Network Topology and Qubit Allocation for Distr...

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Quantum Communications, Networking, and Computing (QCNC), international conference on

作者： Jiyao Liu Lei Fan Yuanxiong Guo Zhu Han Yu Wang Department of Computer and Information Sciences Temple University Philadelphia USA Dept. of Engineering Technology University of Houston Houston USA Dept. of Electrical and Computer Engineering University of Houston Houston USA Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs), but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.

关键词： Costs Network topology Computational modeling Qubit Topology Computational efficiency Resource management Quantum circuit Integrated circuit modeling Optimization

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Enhancing the Production of White Charcoal and Syngas from a Stove with an IoT Airflow Controller

Enhancing the Production of White Charcoal and Syngas from a...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Charnyut Karnjanapiboon Nattapon Wichan Chakkraphan Thawonngamyingsakul Kiattisak Sornjai Kanyaphorn Chaiwong Faculty of Engineering Rajamangala University of Technology Lanna Nan Thailand Faculty of Engineering Rajamangala University of Technology Lanna Tak Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

The aim of this study is to improve the production of white charcoal and syngas using a stove equipped with an IoT airflow controller. The airflow regulation system for a gasification furnace including a double-burner, utilized for syngas production alongside white charcoal. The temperature range for a double-burner gasification furnace, utilizing the prescribed IoT air flow control system, is between 800 and 1,250 °C. The gasification reaction at the stove's exterior generates syngas, while the pyrolysis process at the stove's core produces white charcoal. The experimental air flow rate ratio ranged from 2.5 to 4 m/s to examine the yield of syngas and the characteristics of white charcoal derived from rice husk. The implementation of an IoT air flow control system revealed that the air flow rate could be adjusted and the furnace temperature could be continually modulated in response to variations in reactivity within the furnace.

关键词： Temperature sensors Temperature measurement Temperature distribution Flow production systems Furnaces Production Regulation Temperature control Monitoring Syngas

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Enhancing Software Quality with Fault Detection and Prediction based on AI-Driven Model

Enhancing Software Quality with Fault Detection and Predicti...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Ramesh Bishukarma Sumeet Mathur Puneet Matapurkar Sandeep Gupta University of Waikato NZ – Joint Institute at Zhejiang University Hangzhou China Department of Mathematical Sciences & Computer Application Bundelkhand University Jhansi India Department of Computer Science Engineering SATI Vidisha India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Early defect detection and prediction are essential in software engineering to reduce costs and improve quality. This study presents an AI-driven approach for fault detection and prediction employing ML techniques on the CM1 dataset from NASA’s software metrics program. The data preprocessing steps include normalisation, feature selection, and the use of the ADASYN algorithm to enhance minority class detection. Faults are predicted using ML classifiers: Extra Trees (ET), Support Vector Machine (SVM), KNN, RF, MLP, and NB. F1 score, recall, accuracy, and precision are a metrics utilised to assess a models. When it comes to defect prediction, the Extra Trees Classifier (ETC) model outperforms all of the others with an accuracy91.51%, precision91.76%, and recall99%. Although the Naive Bayes model shows lower accuracy (77.61%) and efficiency, it offers a simpler approach for fault detection. Future research in this area should be guided by these findings, which highlight the promise of AI-driven approaches to enhance frameworks for software defect prediction and quality assurance.

关键词： Support vector machines Accuracy Fault detection Software algorithms Software quality Predictive models Nearest neighbor methods Software measurement Classification tree analysis Software engineering

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Advancing Bangla NLP: Transformer-Based Question Generation using Fine-Tuned LLM

Advancing Bangla NLP: Transformer-Based Question Generation ...

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international conference on Electrical, computer and Communication Engineering (ECCE)

作者： Washik Wali Faieaz Sayma Jannat Pronoy Kumar Mondal Sadman Sadik Khan Shuvo Karmaker Md Sadekur Rahman Dept. of CSE Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

This research explores the development of a question generation model for Bangla text using the Bangla T5 base model, a transformer-based architecture tailored for the language. The study addresses the underrepresentation of Bangla in natural language processing (NLP) by leveraging a carefully curated dataset of Bangla paragraphs and corresponding questions. The Bangla T5 model, pre-trained on a large Bangla corpus, is fine-tuned to generate contextually relevant and coherent questions from input paragraphs. The model’s performance is evaluated using standard metrics such as Exact Match (EM) and ROUGE, achieving a 28.65% Exact Match score. The ROUGE scores demonstrate strong alignment with the reference questions, with ROUGE-1 F1 at 73.64%, ROUGE-2 F1 at 57.86%, and ROUGE-L F1 at 70.77%. These results highlight the model’s ability to capture the linguistic nuances of Bangla and generate questions that are semantically and syntactically accurate. This work contributes to the advancement of Bangla NLP and underscores the potential for extending similar methodologies to other low-resource languages, promoting inclusivity and linguistic diversity in AI systems.

关键词： Training measurement Pipelines Linguistics Transformers Question generation Decoding Artificial intelligence Standards Context modeling

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Conv-TasNet Adaptive Noise Cancellation Model Enhanced by WaveNet

Conv-TasNet Adaptive Noise Cancellation Model Enhanced by Wa...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Kun Zhao Yaojie Hou College of Computer Science and Technology (College of Big Data) Taiyuan University of Technology Jinzhong China College of Electrical and Automation Henan Polytechnic University Jiaozuo China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Traditional speech denoising methods often struggle with low signal-to-noise ratios (SNR) and poor clarity in complex noise environments. To address these limitations, this study introduces an adaptive denoising model that integrates Conv-TasNet with WaveNet enhancements. The proposed model incorporates causal convolution within the Conv-TasNet separator to better capture long-term dependencies and employs WaveNet's gating activation mechanism to effectively handle nonlinear speech features. Additionally, a mask weighting strategy is implemented to emphasize target speech signals while suppressing background noise. Experimental results demonstrate that the enhanced model significantly outperforms the original Conv-TasNet, achieving an SNR improvement from –5 dB to 33.74 dB and reducing mean squared error (MSE) to nearly zero. These findings confirm the model's superior performance and robustness in complex noisy environments, highlighting its potential for advanced speech enhancement applications.

关键词： Adaptation models Convolution Computational modeling Particle separators Noise reduction Speech enhancement Feature extraction Computational efficiency Noise measurement Signal to noise ratio

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Advanced Deep Learning Framework for Early Breast Cancer Detection with Graph Convolutional Networks

Advanced Deep Learning Framework for Early Breast Cancer Det...

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

作者： A. Sadeeshkumar Seema Sharma Amshavalli M Asma Anjum Ashwani Gupta N Aparna Department of Electrical and Electronics Engineering St. Joseph's College of Engineering OMR Chennai India Department of Computer Science and Engineering JECRC University Jaipur India Department of Computer Science and Engineering Erode Sengunthar Engineering College Thudupathi India Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India Department of Computer Science and Information Technology MJP Rohilkhand University Bareilly India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Valley Campus Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Women with breast cancer are very common. Early signs are rare; hence the condition is often identified late. Later-stage treatment outcomes are less predictable and harder. Complementary screening programs and preventative checkups have considerably improved early breast cancer identification. Reducing the number of unscreened women and increasing early-stage breast cancer detection demands better screening methods. Iterative model training included preprocessing, segmentation, feature extraction, and detection. Preprocessing was necessary for well-read results. Corners were identified for segmentation using Shi-Tomasi. Feature extraction uses SIFT. Without modifying photo size, this approach discovers similar photos. The research models were all GCN-trained. CNNs and SVMs were employed to evaluate the GCN-based model. GCN's mean accuracy of 92.34 percent beat the competition. This shows that the GCN-based technique improves breast cancer diagnosis. Due to early discovery and treatment, this strategy may improve patient outcomes.

关键词： Training Temperature measurement Accuracy Graph convolutional networks Infrared imaging Transforms Feature extraction Breast cancer Integrated circuit modeling Tumors

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