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Development Of An Uncrewed Vehicle Prototype Using Deep learning And IOT 4

Development Of An Uncrewed Vehicle Prototype Using Deep Lear...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Sowmya, C.H. Varma, C. Sai Adarsh Akshay, B.R. Kumar, Chethan Sirdeshpande, Abhishek Reva University Electronics And Communication India

ISBN: (纸本)9798350384628

Driverless cars used to be something you'd only see in movies, but they're now becoming a reality in real life, not just in science fiction. Driver error is to blame for 95% of all road accidents. Most autonomous vehicles would be able to avoid human errors because to advances in deep learning technology. However, India's roadways are not as suitable to self-driving automobiles as their western counterparts. Roads in metropolitan areas are overburdened by traffic. Furthermore, in India, reckless driving is not pre-empted by a driverless automobile because it does not respect traffic rules, jump signals, or travel in allocated lanes. Using our prototype and several algorithms such as pothole detection, lane detection, object detection, and others that have been trained in a simulated environment, the probable solution to some portions of the problem can be obtained. A camera in the prototype takes real-time data, which is then analysed with the assistance of a computer utilising deep learning and image processing to achieve the best possible outcomes. After attaining ideal results, the algorithms can be put into practise for commercial application. If not for public use right away, autonomous cars must be allowed for private use first, then gradually integrated into public use, such as on-demand taxis and long hauling. Reduced road accidents, adherence to speed limits, and lane driving are just a few of the advantages that autonomous vehicles will provide. © 2023 IEEE.

关键词： Autonomous vehicles

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Research on Comprehensive Identification and Classification Technology for Latent Faults in High- Voltage Switchgear Based on machine learning 4

Research on Comprehensive Identification and Classification ...

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Tang, Bin Liang, Qinqin Han, Fangyuan Yu, Min Zhang, Longfei Luo, Zongchang Luo, Chuansheng Hu, Mengzhu Wang, Jialin Zhu, Liping Electric Power Research Institute of Guangxi Power Grid Co. Ltd Guangxi China Nanning Power Supply Bureau of Guangxi Power Grid Co. Ltd Nanning China Guangxi Power Grid Co. Ltd Guangxi China

ISBN: (纸本)9798350308082

With the development of ultra-high voltage projects in China, the scale of power grids has been expanding. High-voltage switchgear is widely utilized in various substations and the power industry, gradually becoming a critical component of the power system. In response to the issue of latent faults in high-voltage switchgear, this paper proposes a pulse current detection method based on machine learning to achieve detection and classification of latent faults in high-voltage switchgear. the Hilbert-Huang transform is employed along with the theory of energy entropy to extract discharge characteristic parameters, which serve as input vectors for the pattern recognition network. the discharge types are recognized using a Probabilistic Neural Network (PNN), and optimization algorithms are applied to enhance the performance of the PNN. the recognition accuracy before and after optimization is compared. the results indicate that although this method requires a longer recognition time, the recognition accuracy is relatively high, reaching 98.7%. this approach plays a significant role in achieving comprehensive identification and classification of latent faults in high-voltage switchgear. © 2023 IEEE.

关键词： Hilbert-Huang transform

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learning to Optimize Quantum Neural Networks Without Gradients 4

Learning to Optimize Quantum Neural Networks Without Gradien...

引用

4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Kulshrestha, Ankit Liu, Xiaoyuan Ushijima-Mwesigwa, Hayato Safro, Ilya Newark United States Fujitsu Research of America Sunnyvale United States

ISBN: (纸本)9798350343236

Quantum machine learning is an emerging sub-field in machine learning where one of the goals is to perform pattern recognition tasks by encoding data into quantum states. this extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimized using gradient based algorithms that run on a classical computer. the similarities in training of these hybrid algorithms and classical neural networks has further led to the development of Quantum Neural Networks (QNNs). However, in the current training regime for QNNs, the gradients w.r. t objective function have to be computed on the quantum device. this computation is highly non-scalable and is affected by hardware and sampling noise present in the current generation of quantum hardware. In this paper, we propose a training algorithm that does not rely on gradient information. Specifically, we introduce a novel meta-optimization algorithm that trains a meta-optimizer network to output parameters for the quantum circuit such that the objective function is minimized. We empirically and theoretically show that we achieve a better quality minima in fewer circuit evaluations than existing gradient based algorithms on different datasets. © 2023 IEEE.

关键词： Pattern recognition

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Malware behavior detection method based on reinforcement learning

Malware behavior detection method based on reinforcement lea...

引用

2022 international conference on computer application and Information Security, ICCAIS 2022

作者： Cui, Jiajia Leng, Biao Wang, Xianggen Wang, Fuxi Yang, Jun The 15th Research Institute of China Electronics Technology Corporation Beijing China School of Computer Science Beijing University of Aeronautics and Astronautics Beijing China

ISBN: (纸本)9781510663459

Malware in the network environment is a serious threat to the security of industrial control systems. With the gradual increase of malware variants, it brings great challenges to the detection and security protection of industrial control system malware. the existing detection methods have limitations such as low intelligence in adaptive detection and recognition. In response to this problem, this paper designs a detection application method framework by combining the use of reinforcement learning, an advanced machine learning algorithm, around the malware objects that threaten the network security of industrial control systems. In the implementation process, according to the actual needs of malware behavior detection, fully combined with intelligent features such as sequential decision-making and dynamic feedback learning of reinforcement learning, the key application modules such as feature extraction network, policy network and classification network are discussed and designed in detail. the application experiments based on the actual malware test data set verify the effectiveness of the method in this paper, which can provide an intelligent decision-making aid for general malware behavior detection. © 2023 SPIE.

关键词： Feature extraction

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Deep learning with Conditional Generative Adversarial Network Based Intrusion Detection System on Balanced Data 4

Deep Learning with Conditional Generative Adversarial Networ...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Hemavathi, K. Latha, R. St. Peter's Institute of Higher Education and Research Dept. of Computer Science Chennai India

ISBN: (纸本)9798350300093

Networks have an important role to play in modern life, and cyber security is an active research area. An Intrusion Detection System (IDS) becomes a crucial cyber security method that monitored the state of hardware and software running in the network. IDS can find attacks in available environments. the machine learning (ML) method is one among the emerging approaches that have better performance in the situation they have encountered already, and enjoy a wide variety of applications in outlier analysis speech recognition, pattern detection, and so on. With unbalanced data, the predictive model established utilizing ML method might produce an unacceptable classifier that affects the accuracy of predicting intrusion. Conventionally, researcher workers applied oversampling and undersampling to balance data in the dataset for overcoming these problems. therefore, this article presents a Deep learning with Conditional Generative Adversarial Network-based Intrusion Detection System (DLCGAN-IDS) technique on Balanced Data. the goal of the DLCGAN-IDS technique lies in the proper balancing of the network samples and identify the intrusions accurately. To accomplish this, the presented DLCGAN-IDS approach primarily normalizes the input data by employing min-max normalization. For imbalanced data handling, the CGAN approach is used for balancing the sample numbers in the dataset. Finally, DL based Long Short-Term Memory (LS TM) method is enforced for detecting and classifying intrusions in the network. the results of the DLCGAN-IDS method execute on the IDS dataset. the comprehensive outcomes pointed out the superior achievement of the DLCGAN-IDS model over other current algorithms. © 2023 IEEE.

关键词： Network security

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Vehicular Propagation Velocity Forecasting Using Open CV 4

Vehicular Propagation Velocity Forecasting Using Open CV

引用

4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Das, Udayan Sharma, Vandana Das, Madhabananda Mishra, Sushruta Iwendi, Celestine Osamor, Jude Kalinga Institute of Industrial Technology Bhubaneswar India Department of Computational Sciences Delhi NCR India School of Creative Technologies University of Bolton United Kingdom Glasgow Caledonian University Department of Cybersecurity and Networks GlasgowG4 0BA United Kingdom

ISBN: (纸本)9798350393248

this work presents a predictive learning driven methodology for recognizing the vehicular velocity. the developed model uses machine vision models to trace and detect vehicular movement in timely manner. It further deploys a machine tested framework for estimation of its velocity on basis of the accumulated information. the technique depends upon a CNN model that is validated with a standardized instances of vehicular scans and corresponding velocity parameters. the proposed model generates good efficiency and robustness in determining velocities across test conditions which encompass various kinds of vehicles and lighting scenarios. An optimal vehicular frequency is noted with heavy-weight vehicles in place in comparison to other vehicles. A mean latency period of 1.25 seconds and an error rate of 0.05 is observed with less road traffic in place. the suggested approach can be of great help in transportation systems, traffic monitoring and enhancing road safety. © 2023 IEEE.

关键词： computer vision

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Stony Coral Species Recognition System Using Deep learning 4

Stony Coral Species Recognition System Using Deep Learning

引用

4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Rusli, Nur Nadhirah Mohtar, Itaza Afiani College of Computing Informatics and Mathematics Universiti Teknologi Mara Computer Science Studies Perak Malaysia

ISBN: (纸本)9798350318432

Existing applications for identifying coral species are difficult to find as compared to other artificial intelligence applications, for example, plant recognition applications. People usually recognize coral species by rough observation and identify the information by themselves. It is difficult to recognize the coral species because each species has a wide range of colors, textures, shapes and are very similar to each other, making it rather hard to differentiate unless by trained eyesight. therefore, a web application system utilizing Convolutional Neural Network (CNN), to identify the name, family name and other related information of a stony coral's species is proposed. 10 Stony coral species were selected for this project. A total of 3831 images were split to train (80%), test (10%) and validate (10%) the model. the model was able to achieve training accuracy of 99%, validation accuracy of 97% and testing accuracy of 91.9% after 50 epochs. the precision, recall and F1 score achieved were 0.88, 0.96, and 0.92 respectively. As a conclusion, the model can classify the stony coral species. this project can be integrated with other platforms in the future. such as mobile applications. © 2023 IEEE.

关键词： Image classification

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Research on Sitting Posture Recognition Based on Deep Fusion Neural Network 4

Research on Sitting Posture Recognition Based on Deep Fusion...

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4th international conference on computer Engineering and application, ICCEA 2023

作者： Yang, Tao Tao, Qing Wu, Bin Zhao, Zirui School of Mechanical Engineering Xinjiang University Urumqi Xinjiang China

ISBN: (纸本)9798350347548

To address the problems of current sitting recognition methods such as low recognition accuracy, susceptibility to environmental interference, and the inability to adapt to viewpoint changes, a deep fusion neural network model based on VGG, Resnet, and Vision Transformer is proposed. Firstly, the image data are enhanced by using random cropping and random flipping;secondly, shallow features are extracted by the VGG network, and constant mapping is added by the Resnet residual network to extract multi-dimensional features of the image;and finally, the fused feature information is input into the Vision Transformer network for sitting posture recognition. the experimental results show that the accuracy of the fusion model for sitting posture recognition reaches 98.3% and improves to 22.9%, 12.8%, 7.8%, 17.5%, 27.5%, and 20% compared with the traditional VGG, Resnet, Vision Transformer, MobileNet, EfficientNet, and Inception networks, respectively. the fusion model can adapt to viewpoint changes in a range of up to 45 and combines the advantages of individual models;the localization of key feature regions is more complete and accurate, which improves the recognition accuracy. © 2023 IEEE.

关键词： Deep learning

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Multi-Pedestrians Anomaly Detection via Conditional Random Field and Deep learning 4

Multi-Pedestrians Anomaly Detection via Conditional Random F...

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4th international conference on Advancements in Computational Sciences, ICACS 2023

作者： Abdullah, Faisal Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9781665463775

Automated video surveillance frameworks quickly distinguish surprising and basic circumstances in a packed climate that would help to pursue sufficient choices for security and crisis control. Hence, In this paper, an innovative method for automatically detect and localize anomalous objects among multi-pedestrian crowds via conditional random field and deep learning is introduced. Initially, necessary preprocessing is performed on extracted frames and then super-pixels are generated using improved watershed transform, the objects are then segmented using a conditional random field. the region of interests are localized using conditional probability and temporal association is implemented to locate the regions with a group of pedestrians and pedestrians with other objects. A deep learning feature pyramid network is then implemented to detect and categorized the objects in each region and finally, the anomalous objects are identified using Jaccard similarity. the effectiveness of proposed framework is assessed on openly accessible UCSD Ped 1 and Ped 2 datasets and it accomplishes an accuracy rate of 94.2% and 95.4% respectively. Extensive experimental data and comparative analysis show that our model outperformed current state-of-the-Art models in terms of accuracy. © 2023 IEEE.

关键词： Anomaly detection

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the Effect of a Weighted Mean Pooling on the Performance of Convolution Neural Network 4th

The Effect of a Weighted Mean Pooling on the Performance of ...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Shadoul, Inas Hossen, Abdulnasir Mesbah, Mostefa Al-Hmouz, Rami Sultan Qaboos Univ Dept Elect Engn Coll Engn Al Khoud Oman

ISBN: (纸本)9783031821493;9783031821509

Convolutional Neural Networks (CNNs), a deep learning application, are powerful tools particularly suited for image processing and classification applications. Pooling is a major component of CNNs and significantly influences learning. In this step, data is reduced in size through a specific algorithm or technique, resulting in the reduction of the computational load on the layers. the various pooling techniques in CNNs have specific uses, features, and effects, some desirable and others counterproductive to the training goal. Using one pooling technique can produce results that other techniques cannot. Some cases can benefit differently from different pooling techniques. this raises the question of whether combining these pooling techniques could achieve a collective positive impact, potentially leading to performance gains beyond those achievable by individual techniques used separately. A control parameter is added to optimize the selection of the pooling method or could be a weighted combination of more than one method. the results show that the presented method guarantees the same performance as a single pooling layer at least and could be improved when weighted pooling layers are involved in some datasets.

关键词： machine learning Convolution Neural Network (CNN) Pooling Mixed pooling Image classification

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