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共 34544 条记录，以下是641-650 订阅

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Detection and Localization of Car Damages Using Deep Learning

Detection and Localization of Car Damages Using Deep Learnin...

引用

2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Parihar, Prachi Sachdeva, Manas Gupta, Deepak Pranveer Singh Institute of Technology Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350369175

In the automotive industry, accurate and efficient vehicle damage detection is crucial for reducing the cost of claims and improving assessment procedures. This paper presents an automated method for vehicle damage detection using deep learning based You Only Look Once models, specifically YOLOv3, YOLOv5, and YOLOv8, in conjunction with two optimizers, AdamW and SGD. The COCOtoYOLO dataset contains pictures with six damage kinds. Gaussian Blur, Median Blur, Grayscale conversion, and Contrast Limited Adaptive Histogram Equalization (CLAHE) improved picture quality and feature visibility. The results reveal that YOLOv8, optimized with SGD over 200 epochs, achieved the highest mean Average Precision (mAP-50) of $72 \%$, surpassing other configurations. This study demonstrates the effectiveness of YOLOv8 and SGD in vehicle damage detection. The highest F1-score of ${9 3 \%}$ for glass shutter class claims the model's robustness for different types of car damages. © 2024 IEEE.

关键词： Automotive industry

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An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural Network Technique for Event Detection in Video Sequences 1

An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural N...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Sachdeva, Kumud Sandhu, Jasminder Kaur Sahu, Rakesh Chandigarh University Dept. of Computer Science Engineering Punjab Gharuan140413 India

ISBN: (纸本)9798350361780

Classifying and detecting difficult video events based on visual modalities remains an uncertain problem. Conventional video presentation approaches ineffectively extract each modality, hindering video event detection (VED) rates. The research methodology involves pre-processing steps: uploading video data samples, extracting video frames, converting to grayscale (rgb2gray), enhancing, and calculating smooth frames from the UCF -101 dataset. This pre-processing phase aims to deliver high-quality frames without data loss. Next, the feature extraction process employs the HoG method to extract feature vectors from refined video frames, facilitating the training process and efficiently reducing dimensions. An adaptive, nature-inspired PBC method is then implemented to select reliable and optimized feature sets from the extracted ones. These selected optimized feature vectors are input into different events of the deep neural network (DNN) classifier. Finally, reliable feature sets are identified through feature matching, and experimental outcomes demonstrate a significant 21.3% enhancement in VED and classification, assessed through accuracy rate, specificity (SP), sensitivity (SN), etc. Compared with traditional approaches using manually designed feature sets, the proposed approach proves more effective. Simulation outcomes on publicly available VED databases consistently outperform state-of-the-art video representation methods such as EFS-linear multi-support vector machine (MSVM), convolutional neural network (CNN), etc. © 2024 IEEE.

关键词： Deep neural networks

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A Deep Learning Approach to Enhance Air Quality Prediction: Comparative Analysis of LSTM, LSTM with Attention Mechanism and BiLSTM

A Deep Learning Approach to Enhance Air Quality Prediction: ...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Bhardwaj, Deepanshu Ragiri, Prakash Rao Netaji Subhas University of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350364866

In this study, we address the challenge of predicting air quality indices (AQIs) by leveraging Long Short-Term Memory (LSTM) networks. Recognizing the dynamic nature of air quality data, we explore the efficacy of three LSTM variants - standard LSTM, LSTM with an attention mechanism, and Bidirectional LSTM (BiLSTM) - in capturing temporal dependencies and improving forecast accuracy. Our methodology involved preprocessing a comprehensive dataset of air quality measures, followed by an experimental design that tested each model variant under different sequence lengths (30 and 365 days). The models were evaluated based on their mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) across three response variables: Nitrogen Oxides (NO), Particulate Matter up to 10 micrometers in size (PM10), and Particulate Matter up to 2.5 micrometers in size (PM2.5). Notably, when comparing the RMSE values across the three models, the LSTM with the attention mechanism consistently outperformed the standard LSTM and BiLSTM models, particularly with a sequence length of 30 days, indicating a superior ability to capture the intricacies of temporal dependencies in environmental data. Moreover, the shorter sequence length generally yielded better predictive performance, suggesting that longer sequences might introduce unnecessary complexity or overfitting. These indications of our study suggest a promising direction for the deployment of attention-based models in real-time AQI prediction systems, which could enhance the accuracy of environmental forecasts and inform public health decisions more effectively. Future work will explore the integration of additional environmental factors, the refinement of model frameworks, and the expansion of the dataset to include multi-city comparisons. © 2024 IEEE.

关键词： Mean square error

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Architectural Design for AdhaarAssist - A Forward-Looking Lock-Free STM Solution for Adhaar

Architectural Design for AdhaarAssist - A Forward-Looking Lo...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Jafar, Sana Pranveer Singh Institute of Technology Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350385199

Any parallel program is judged based on its efficiency in doing multiple tasks at the same time without getting stuck due to an underlying problem. The main purpose of a parallel program is to effectively and quickly solve a large and complicated problem at hand. This is why;parallel programs form the basis of many powerful supercomputers where parallel computing is supported through special hardware which is complemented with parallel software achieving hundreds of petaflop/s of performance. The biggest challenge for a parallel programmer is to build a program that has a minimum tradeoff for increased parallelism and good concurrency control among the parallel tasks. One of the biggest applications of parallelism is the huge, ever-increasing database of Adhaar biometric. Its crucial to have a robust, fast and efficient way of accessing and manipulating this data so that along with faster results, consistency of the database is also maintained. This paper proposes a schema AdhaarAssist which is a lock-free STM based algorithm that will work on a shared in-memory data structure. Some of its features are increased concurrency by lazy conflict detection, partial aborts that optimize the algorithm for maximum throughput when complex and lengthy transactions are employed, memory barriers and timestamping. The algorithm proposed in this paper will open the way for leveraging it for distributed-shared in-memory databases like the one used by Adhaar. © 2024 IEEE.

关键词： Concurrency control

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Enhanced Machine Learning Approach for Detecting Spammers in Industrial Mobile Cloud Computing Environment 5

Enhanced Machine Learning Approach for Detecting Spammers in...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bhavana, K. Sai Abhinandu, B. Sriyesh, P. Sai Rama, K. Satheesh Kumar, S. Institute of Aeronautical Engineering Dept. Computer Science & Information Technology Hyderabad India

ISBN: (纸本)9798331518097

This research study proposes an innovative machine learning algorithm, Spammer Identification Novel K-Means Extension (SINKEX), to fortify IoT mobile networks against commands and potential disruptions in industrial production. Utilizing an industrial mobile cloud dataset, the algorithm employs basic, content, and network features, including follower count, post frequency, message content, and hyperlink usage, for spam detection. After employing Pearson Correlation and Euclidean Distance to eliminate dissimilar records, Principal Component Analysis (PCA) is applied to remove irrelevant features. The SINKEX algorithm then labels unclassified records, designating an account as a SPAMMER or NON-SPAMMER based on features like follower count and post frequency. Comparisons with a hybrid fuzzy c-means clustering algorithm demonstrate SINKEX's superior performance, achieving higher accuracy, precision, and recall. The results are presented through an intuitive graphical user interface, showcasing the algorithm's effectiveness in securing industrial IoT networks against malicious activities. © 2024 IEEE.

关键词： Adversarial machine learning

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Classification of Epileptic Seizure Using 2L- LSTM Architecture

Classification of Epileptic Seizure Using 2L- LSTM Architect...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Dhondiyal, Shiv Ashish Dimri, Sushil Chandra Malik, Preeti Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350373783

Epilepsy is a long-term neurological problem that makes it hard for people to function their daily lives normally because their seizures can happen at any time. People who have epileptic seizures often hurt their brains, which can lead to memory loss, mental decline, and other problems. Because of this, it is important to find epileptic seizures quickly and get medical help for them. This paper presents the optimized two-layer long short-term memory model (2L-LSTM), and it can automatically expand retrospectively and for each patient. The program automatically creates distinctive characteristics that help identify seizure patients as either epileptic or not epileptic in real time. The proposed two layer Long Short-Term Memory (2L-LSTM) classifier classifies Epileptic or non-Epileptic signals with up to 98% accuracy. It also gives the sensitivity, specificity and F1-score of 95%, 94% and 96% respectively. The Simulation results shows that proposed 2L-LSTM model perform far better as compared to 1L-LSTM. © 2024 IEEE.

关键词： Electroencephalography

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Eva: Python-based Desktop Virtual Assistant for Visually Impaired 7

Eva: Python-based Desktop Virtual Assistant for Visually Imp...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Abraham, Anishamol Mathew, Benita Susan Mathew, Dona Lisa Mohammad, Fiaz S Krishnan, Gokul Amal Jyothi College of Engineering Dept. of Computer Science Kottayam686518 India

ISBN: (纸本)9798350372816

Virtual assistants have become essential resources in the digital age, providing users with a wide range of functionalities to expedite work and increase productivity. In this research, we provide a complete examination of Eva, a virtual assistant created using Python, and compare it to other virtual assistants such as Cortana and Copilot. Through a meticulous examination of Eva's features, we demonstrate its superiority in versatility and effectiveness. By embracing Eva's strengths and capitalizing on its competitive advantages, we push for a future in which digital experiences are inclusive and empowering for all users. © 2024 IEEE.

关键词： Python

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Lip Reading Into Text Using Deep Learning 14

Lip Reading Into Text Using Deep Learning

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Manglani, Krishna Bahuguna, Utkarsh Gupta, Subhash Chand Amity School of Engineering and Technology Dept. of Computer Science Uttar Pradesh India

ISBN: (纸本)9798350344837

An essential part of computer vision, lip reading, has grown significantly and is now used in autonomous driving, public safety, and hearing-impaired communication. This work provides an in-dept. analysis of both conventional and deep learning-based lip reading systems, examining their development over time, difficulties encountered, and critical role in human communication. The suggested LipNet model, which leverages the complementary abilities of Long Short-Term Memory networks (LSTMs) for temporal modelling and Convolutional Neural Networks (CNNs) for spatial feature extraction, is a central focus of this research. When LipNet is tested using the GRID Corpus dataset, it outperforms baseline models in terms of performance. Saliency maps and viseme analysis highlight LipNet's ability to capture phonologically relevant areas in lip motions, providing insight into the network's attention processes. Apart from examining the importance of datasets such as OuluVs2and GRID Corpus, this study presents GhostNet, a lightweight model that tackles the requirement for effective network designs. The testing results highlight LipNet's competitive Character Error Rate (CER) and Word Error Rate (WER) in comparison to baseline models, proving that it is the best method for real-world use. The research ends with a summary of potential future developments in lip reading technology, such as modal integration, realtime applications, personalisation, ethical issues, and the goal of creating a communication environment that is more inclusive. This study highlights the effectiveness of LipNet and paves the way for future developments in the ever-evolving area of lip reading by examining a variety of methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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An Innovative Ensemble Approach to Detecting Distributed Denial of Service Attacks Using Machine Learning

An Innovative Ensemble Approach to Detecting Distributed Den...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Rajput, Deepak Singh Upadhyay, Arvind Kumar Amity University Dept. of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798331517953

In the present era marked by rapid Internet development, there is a growing emphasis on information security. Distributed Denial of Service (DDoS) attacks poses significant threats to servers globally. Various methods and services have emerged to prevent and mitigate damage caused by these attacks. The challenge in implementing defence lies in timely attack detection, requiring a highly accurate mechanism. DDoS attack methods are diverse and evolving, rendering traditional prevention methods less effective. This research explores the application of machine learning, utilizing the CICDDoS2019 dataset to train hybrid models. The proposed ensemble model combines unsupervised K-Means for dataset labelling and supervised classifiers (Random Forest, XGBoost, AdaBoost, SVM) with Artificial Neural Network (ANN). Achieving an overall accuracy of 99.46%, this study underscores the effectiveness of deep learning in DDoS attack detection. © 2024 IEEE.

关键词： Random forests

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Survey on Online Learning Applications in Multi-UAV Networks 15

Survey on Online Learning Applications in Multi-UAV Networks

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kim, Jaemin Kim, Hyosu Paek, Jeongyeup Son, Yongseok Lee, Hyung Tae Cho, Sungrae Chung-Ang University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

This paper provides a comprehensive review of the application of online learning techniques in multi-UAV systems, highlighting their role in enhancing autonomy and efficiency in dynamic environments. Key areas of focus include adaptive path planning, collision avoidance, and swarm behavior control, along with the associated technical challenges and future research directions. © 2024 IEEE.

关键词： Federated learning

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