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A Framework for Structural Damage detection system in automobiles for flexible Insurance claim using IOT and Machine Learning

A Framework for Structural Damage detection system in automo...

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2022 International Mobile and Embedded Technology Conference, MECON 2022

作者： Agarwal, Deepshikha Tripathi, Khushboo Indian Institute of Information Technology Dept. of Information Technology Lucknow India Amity University Dept. of Computer Science and Engineering Haryana Gurgaon India

ISBN: (纸本)9781665420204

Automobiles are required in everyday life to commute from destination to other. But, due to faulty road conditions, heavy traffic and lack of adequate driver skills, crash incidents happen almost every hour of the day. With every vehicle, companies offer insurance which people can claim to get the damaged parts recovered. However, during the recovery, it solely depends on the insurance companies investigating officer to give an adequate claim to the vehicle owner. Due to inadequate visual precipitance, at times, the owner is not given the full insurance claim. The proposed framework offers automated damage detection system by which better and correct claim can be given to such an owner. The proposed framework is based on Machine Learning and IOT approach for transport network scenario. This paper presents the idea of implementing proposed framework to the researchers in future to detect damages and calculate the correct claim to the owner for Insurance claim. © 2022 IEEE.

关键词： Automobiles

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Detection of Hate Speech with Attention-Enhanced Deep Learning Techniques

Detection of Hate Speech with Attention-Enhanced Deep Learni...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： S Senthilkumar Kishore D Veeramalai Sankaradass A Merwin Blessy Yuvadarshan M R. Ponnusamy Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

One of the pressing issues on user-generated platforms and social media is hate speech, a threat to online communities. Conventional detection methods, such as keyword matching or basic classification, often miss hate speech's subtleties and contextual dependencies. This paper introduces a pioneering deep learning approach, leveraging transformer architectures and attention mechanisms, to advance the state-of-the-art in hate speech detection. Our model's unique feature is its ability to focus on words that carry more contextual relevance, thereby interpreting the intended meaning of the words more accurately. The multi-head self-attention mechanism allows for differential weighting of various input parts, thereby enhancing the model's ability to distinguish between hostile and hateful language, even in cases of word similarity. We explain the architecture of our model with further explanation of the calculation involved in queries, keys, and values, followed by a description of the mathematical foundation of the attention mechanism. In fine-tuning the pre-trained transformer model, say BERT, from the hate speech dataset, our optimised model uses the Adam optimiser. We run experiments to check the performance of the proposed model by using accuracy, precision, recall, and F1-score to prove that our proposed model outperforms the base models like CNNs and RNNs. Our attention-based approach successfully highlights hate speech in subtle, context-sensitive ways. As this is one of the first studies where attention has been explored in the context of hate speech detection, it provides a good platform for further work on hate speech and multimodal inputs in many other languages. The results of our experiments indicate the promise of attention-based models as a powerful and scalable solution to the real-world challenges of hate speech detection.

关键词： Deep learning Attention mechanisms Accuracy Sensitivity Social networking (online) Hate speech Transformers Mathematical models Robustness Context modeling

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Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models 10

Comparative Analysis of Epileptic Seizure Prediction: Explor...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Hasan Talukder, Md. Simul Sulaiman, Rejwan Bin Chhabra, Gunjan Yadav, Suman Avdhesh Haque, Rezaul Islam, Md Babul Kaushik, Keshav Nuclear Safety Security and Safegurad Division Bangladesh Atomic Energy Regulatory Authority Dhaka Bangladesh School of Computer Science and Technology Northumbria University Newcastle United Kingdom Graphic Era Hill University Dept. of Cse Dehradun India Amity School of Engineering & Technology Department of Computer Science and Engineering Noida India East West University Dept of Computer Science and Engineering Dhaka1212 Bangladesh Calabria Italy School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9798350382471

Precise prediction and detection of epilepsy, a common neurological condition marked by sporadic and repeated seizures, is necessary for efficient management and patient care. To do accurate and reliable seizure detection, machine learning (ML) on electroencephalogram (EEG) recordings is a good choice because of its capacity to provide insightful information about brain activity during seizures. In this work, we do a thorough comparative analysis of five machine learning methods for the prediction of epileptic seizures using EEG data: Random Forest (RF), Decision Tree (DT), Extra Trees (ET), Logistic Regression (LR), and Gradient Boosting (GB). Firstly, to assure the quality of the data and enable precise model training, the dataset underwent extensive preprocessing, which included cleaning, normalization, treatment of outliers, and oversampling. These preprocessing techniques significantly enhanced the models' performance. In our study, we evaluate the accuracy of each model;the LR classifier obtains 56.95% accuracy, while both GB and DT achieve 97. 17% accuracy. Outperforming the findings of previous investigations, the ET model showed the best performance with an accuracy of 99.29%. Achieving 98.99% accuracy was another accomplishment for the RF model. Our results imply that the ET model not only performed better than the other models in the comparison analysis but also more effectively than the most advanced outcomes from earlier research. Regarding the accurate and dependable prediction of epileptic seizures using EEG data, the ET model is a solid option due to its remarkable performance. © 2023 IEEE.

关键词： EEG epileptic seizure Extra Tree hyperparameter tuning machine learning

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Growth Rate Prediction on Fetal Brain Development using Deep Learning Techniques

Growth Rate Prediction on Fetal Brain Development using Deep...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Vellampalli Krishna Sai Mahesh Veldurthi Pavan Kumar Pandiselvam Pandiyarajan Vavila Raveendra Y. Mukesh Kumar Reddy Dept. of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India Dept. of Computer Science and Engineering Kalasalingam Academy of Research and Education Kalasalingam University Krishnankovil Tamilnadu India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

The development of the fetal brain is crucial for a child's cognitive and emotional maturation, highlighting the importance of early identification of neurological issues for effective prenatal care. This study introduces an innovative deep learning framework aimed at predicting fetal brain growth rates from 2D ultrasound images. We utilize a Convolutional Neural Network (CNN) that combines longitudinal data with various imaging modalities to enhance prediction accuracy. By incorporating transfer learning alongside sophisticated techniques like attention mechanisms, our model adept.y captures both spatial and temporal dynamics within the data. Results from our experiments demonstrate that this approach outperforms conventional methods, achieving an accuracy. The findings of this research hold significant promise for prenatal healthcare by enabling timely interventions for high-risk pregnancies. Ultimately, this study contributes to a deeper understanding of fetal brain development and illustrates the potential of integrating AI technologies into standard prenatal diagnostic practices.

关键词： Deep learning Pregnancy Accuracy Ultrasonic imaging Three-dimensional displays Transfer learning Imaging Brain modeling Data models Convolutional neural networks

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Communication-Avoiding Recursive Aggregation

Communication-Avoiding Recursive Aggregation

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IEEE International Conference on Cluster Computing

作者： Yihao Sun Sidharth Kumar Thomas Gilray Kristopher Micinski Dept. Electrical Engineering and Computer Science Syracuse University Syracuse USA Dept. Computer Science University of Illinois at Chicago Chicago USA Dept. Computer Science University of Alabama at Birmingham Birmingham USA Dept. of Electrical Engineering and Computer Science Syracuse University Syracuse USA

Recursive aggregation has been of considerable interest due to its unifying a wide range of deductive-analytic workloads, including social-media mining and graph analytics. For example, Single-Source Shortest Paths (SSSP), Connected Components (CC), and PageRank may all be expressed via recursive aggregates. Implementing recursive aggregation has posed a serious algorithmic challenge, with state-of-the-art work identifying sufficient conditions (e.g., pre-mappability) under which implementations may push aggregation within recursion, avoiding the serious materialization overhead inherent to traditional reachability-based methods (e.g., Datalog).State-of-the-art implementations of engines supporting recursive aggregates focus on large unified machines, due to the challenges posed by mixing semi-naïve evaluation with distribution. In this work, we present an approach to implementing recursive aggregates on high-performance clusters which avoids the communication overhead inhibiting current-generation distributed systems to scale recursive aggregates to extremely high process counts. Our approach leverages the observation that aggregators form functional dependencies, allowing us to implement recursive aggregates via a high-parallel local aggregation to ensure maximal throughput. Additionally, we present a dynamic join planning mechanism, which customizes join order per-iteration based on dynamic relation sizes. We implemented our approach in PARALAGG, a library which allows the declarative implementation of queries which utilize recursive aggregates and executes them using our MPI-based runtime. We evaluate PARALAGG on a large unified node and leadership-class supercomputers, demonstrating scalability up to 16,384 processes.

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Design and Investigation of 8×10Gbps WDM system using Different Modulators

Design and Investigation of 8×10Gbps WDM System using Diffe...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Kumari, Meet Sharma, Neha Chauhan, Rahul Joshi, Kireet Kumar, Amit UIE Chandigarh University Dept. of ECE Mohali140413 India Chitkara University Chitkara University Institute of Engineering and Technology Punjab Rajpura India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350361537

A high speed 8×10Gbps wavelength division multiplexing (WDM) system over long-haul fiber is designed and investigated. The system performance is measured for district optical modulators or varied fiber range and throughput. Simulation results depict that faithful fiber range of 55km can be obtained by incorporating single drive mach-zehnder modulator than electroabsorption and MZM modulators. Besides, the fiber range can be extended upto 120km at 5Gbps throughput. The obtained eye patterns are also depict the superior performance of the system for varied fiber range using single drive MZM. Besides, this work illustrates best performance as compared to existing works. © 2024 IEEE.

关键词： Electroabsorption modulators

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Face Mask Detection Using MobileNetV2 and VGG16 11th

Face Mask Detection Using MobileNetV2 and VGG16

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Kumar, Ujjwal Arora, Deepak Sharma, Puneet Department of Computer Science and Engineering Amity University Lucknow Campus Uttar Pradesh Lucknow India Dept. of CSE Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789811988240

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Viruses

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Stacking Ensemble Framework for Hate Speech Detection in Bangla Videos

Stacking Ensemble Framework for Hate Speech Detection in Ban...

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Computing, Applications and systems (COMPAS), IEEE International Conference on

作者： Shamrin Jahan Mim Tanjim Mahmud Md Hasan Ali Mohammad Tarek Aziz Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

The proliferation of internet usage and social media platforms has significantly enhanced the ability of individuals to express their opinions on various topics. However, this freedom of expression sometimes morphs into a vehicle for disseminating hate speech, leading to increased incidents of cyberbullying, violations, and conflicts. Particularly on video-sharing websites, which have become a prominent stage for such activities due to their widespread use and the dynamic nature of video content. This study aims to address the issue of hate speech in video content by developing a robust method for detecting hate speech in Bangla language videos. The focus is on the spoken content within these videos, which is a primary vector for the transmission of harmful messages. We constructed a comprehensive dataset by extracting and converting audio from a collection of videos into text. Utilizing this dataset, we applied machine learning techniques and deep learning models to analyze and classify the content. Specifically, our approach involves a stacking ensemble model that combines the strengths of Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) with the analytical capabilities of customized feature extraction applied to Multinomial Naive Bayes and Random Forest classifiers serving as a meta-model. The proposed stacking ensemble model demonstrates a high level of efficacy, achieving an accuracy rate of 96% in detecting hate speech within the tested video content. This performance indicates a significant advancement over existing methods, underlining the effectiveness of our hybrid, multi-model approach.

关键词： Analytical models Computational modeling Hate speech Stacking Logic gates Vectors Bayes methods Convolutional neural networks Vehicle dynamics Random forests

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Real-Time Traffic Management system Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sumit Saklani Mahesh Manchanda Raman Sharma Devendra Singh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments.

关键词： Deep learning Accuracy Rain Smart cities Scalability Urban planning Traffic control Real-time systems Safety Internet of Things

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Comparative Study of Novel Convolutional Neural Network and Artificial Neural Network to Enhance the Accuracy for Flaw Detection in Animal Hide

Comparative Study of Novel Convolutional Neural Network and ...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Rajarajeswari Veeramalai Sankaradass Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This research explores how well Artificial Neural Networks (ANN) and Convolutional Neural Networks (NCNN) detect animal hide imperfections to improve quality of weather. Materials and Methods: The study extracted 3600 leather images from Kaggle and split them into two separate groups for this investigation. Group 1 used NCNN, and Group 2 used ANN. Statistical testing generated a sample size of 20 specimens at 95% confidence with 80% power in this experiment. Python became the chosen programming platform for data analysis and preprocessing. NCNN shows better results than ANN because it delivers 96.80% accuracy at 0.001 significance level (p<0.05). Conclusion: The analysis proves the Novel Convolutional Neural Network successfully finds defects in animal hides, but ANN cannot match its results.

关键词： Productivity Accuracy Data analysis Animals Statistical analysis Programming Convolutional neural networks Artificial intelligence Python Meteorology

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