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Proceedings - 2021 7th international conference on Mechanical Engineering and Automation, ICMEAS 2021

Proceedings - 2021 7th International Conference on Mechanica...

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7th international conference on Mechanical Engineering and Automation, ICMEAS 2021

ISBN: (纸本)9781728197685

the proceedings contain 42 papers. the topics discussed include: real-time forward kinematics algorithm for redundant parallel mechanism;multi-factor based analysis and statistical process control for body-in-white dimension status;adaptive backstepping terminal sliding mode attitude stabilization control for moving mass rocket-propelled CubeSat;a vision-based relative navigation sensor for on-orbit servicing of CubeSats;attitude control of the low earth orbit satellite with moving masses under strong aerodynamic disturbance;mathematical model on the visualization of coaxiality error;decomposition of a periodic perturbed signal with unknown perturbation frequency by the method of variable projection;simulation verification of parts processing based on virtual numerical control machine;and transient numerical investigation of a thermal-driving water pipe cooling system.

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Research on Environment Perception and Path Planning Modeling of UAV Navigation Guided by Deep Learning

Research on Environment Perception and Path Planning Modelin...

引用

international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Wenyuan Yang School of Software Henan University Kaifeng China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

this paper proposes a deep learning-based environment perception and path planning modeling method for UAV navigation. First, this paper uses binocular vision system to obtain environmental information, and uses deep learning algorithm for image processing and feature extraction to realize real-time perception of the surrounding environment. Secondly, aiming at the local path planning problem, this paper designs an algorithm based on deep reinforcement learning to improve the computing efficiency and achieve fast and accurate path planning through parallel computing technology. In addition, this paper also proposes an autonomous navigation strategy based on deep learning to enable drones to navigate autonomously in complex environments. Finally, simulation experiments are carried out to verify the effectiveness and superiority of the proposed algorithm. the experimental results show that the proposed method can realize efficient and accurate environment perception and path planning in various scenarios, and significantly improve the navigation performance of UAVs. the research in this paper not only enriches the application of deep learning in the field of UAV navigation, but also provides a valuable reference for the research in related fields.

关键词： Deep learning Accuracy Navigation Machine vision parallel processing Autonomous aerial vehicles Path planning Real-time systems Autonomous robots Information systems

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VLSI Secure communication: Advancements in Blowfish Algorithm for Secure Data Transmission

VLSI Secure communication: Advancements in Blowfish Algorith...

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international conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： R. Anusha Padmavathi K. S. Dhanalakshmi K. Kalai Selvi Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamilnadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

In the rapidly evolving field of Very Large-Scale Integration (VLSI) systems, securing communication channels is of paramount importance due to the increasing prevalence of cyber threats. this paper presents Modified Blowfish (MBF) algorithm employed for secured communication in VLSI systems. While the traditional Blowfish algorithm delivers a superior balance between security and performance, the improved version focuses on further improving encoding speed, reducing power consumption and reducing hardware footprint, which are important factors for VLSI implementations. the novel modifications are introduced in key expansion and encryption processes to increase the throughput and efficiency of the algorithm when implemented in hardware. the MBF algorithm leverages parallel processing capabilities and optimized data paths to achieve higher performance metrics compared to the traditional Blowfish implementation. Extensive simulations and hardware synthesis results demonstrate that the improved algorithm outperforms existing encryption standards in terms of speed and energy efficiency, making it ideal for real-time secure communications in resource-constrained VLSI environments.

关键词： Power demand Very large scale integration parallel processing throughput Hardware Real-time systems Energy efficiency

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Optimization Focused on parallel Fuzzy Deep Belief Neural Network for Opinion Mining 7th

Optimization Focused on Parallel Fuzzy Deep Belief Neural N...

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7th international conference on Business Intelligence, CBI 2022

作者： Es-sabery, Fatima Es-sabery, Khadija El Akraoui, Bouchra Hair, Abdellatif Department of Computer Science Faculty of Sciences and Technology Sultan Moulay Slimane University Beni Mellal23000 Morocco Department of Mathematics Faculty of Sciences and Technology Sultan Moulay Slimane University Beni Mellal23000 Morocco

ISBN: (纸本)9783031064579

In this work, we propose a new parallel fuzzy deep belief neural network for sentiment analysis. We have applied several preprocessing tasks to enhance data quality and remove noisy data. then, we have applied a semi-automatic data labeling over the dataset by combining two techniques: Vader lexicon and Mamdani’s fuzzy system. In addition, we have used four extraction techniques, which are: TFIDF (Unigram), TFIDF (Bigram), TFIDF (Trigram) and GloVe in order to represent each tweet by numerical vector. Further, we have implemented three feature selection techniques which are: the mutual information approach, the chi-square method and the ANOVA technique. Finally, we have applied the deep belief network as classifier in order to classify each tweet into a neutral, negative or positive and our hybrid parallel deep-fuzzy belief neural network is deployed in a parallel design employing the Hadoop framework to overcome the issue of long runtime of huge data sets. Also, a comparisons of the proposed model’s effectiveness with other existing models in the literature is carried out and the experimental results shown that our suggested parallel fuzzy model surpasses the baseline models by a considerable margin in terms of recall, runtime, F1 score, accuracy, error rate and precision. © 2022, Springer Nature Switzerland AG.

关键词： MapReduce

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Rose Plant Disease Detection using Deep Learning

Rose Plant Disease Detection using Deep Learning

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Md. Ali- Al - Alvy Golam Kibria Khan Mohammad Jahangir Alam Saiful Islam Mokhlesur Rahman Mirza Shahriyar Rahman Dept.of CSE Daffodil International University Dhaka Bangladesh

the detection and identification of rose plant disease is the focus of this investigation. Identification and detection are essential components of contemporary agro technology. In this case, AI technology was utilized to identify a disease in rose plants, although plant disease detection is difficult for sustainable agriculture. there are several instances of rose plant disease, and as a result, fascinating decoration is being lost. Due to this situation, which is getting worse every day in Bangladesh, the economy of agricultural sector is suffering. Bangladesh's population relies heavily on agriculture industry for their revenue. this study includes some disease detection of rose plants, albeit not all plants are affected equally by the illness. the plant leaf provides the plant with vital sustenance. When a leaf is ill, the plant is at its most vulnerable. Due to the accessibility of the sick leaf, disease identification is difficult. Agriculture field must be properly assessed to see significant improvements in proposed work. the best resource for creating this kind of disease detection model is deep learning technology. Image pre-processing and model analysis are steps in the disease detection construction process. Few CNN architectures are used in this study, including ResNet50, VGG-16 (Visual Geometry Group), MobileNetV2, and Inception V3. Four diseases have been identified in rose plant leaves. Here, image processing is investigated using a discovered approach and obtain a MobileNetV2 model accuracy of 96.11%.

关键词： Deep learning Plant diseases Visualization Plants (biology) Sociology Agriculture Internet of things

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Enhancing Object Detection in Low Light Environments Using Image Enhancement Techniques and YOLO architectures

Enhancing Object Detection in Low Light Environments Using I...

引用

IEEE international conference on Multi Topic

作者： Syed Muhammad Ammar Ali Jaffri Munim ul Haq Muhammad Farhan Dhanani School of Science and Engineering Habib University Karachi Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

this study analyzes how YOLO structures and image enhancement techniques can improve object detection in low light conditions. In order to overcome the difficulties caused by glare, noise, and dim lighting, the study makes use of the Exclusively Dark (ExDark) dataset, which covers a wide variety of low light situations. the research showed significant enhancements in object detection precision through the utilization of Convolutional Neural Networks (CNN) and pre-processing techniques. A comparative analysis of YOLO versions v7, v8, and v9 is included in the paper, highlighting the superior performance of YOLO v7 when combined with Enlighten GAN, achieving a mAP 0.5 of 73.7%. the results demonstrate how useful preprocessing methods are when used with YOLO algorithms to provide reliable object detection in difficult low-light situations.

关键词： YOLO Noise Lighting Generative adversarial networks Convolutional neural networks Reliability Image enhancement

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Accelerating GNN Inference by Soft Channel Pruning

Accelerating GNN Inference by Soft Channel Pruning

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international Symposium on parallel architectures, algorithms and Programming (PAAP)

作者： Wenbo Zhang Jingwei Sun Guangzhong Sun University of Science and Technology of China

ISBN: (纸本)9781665452199

Graph Neural Networks (GNNs) are effective models for processing graph-structured data. With the continuous growth of graph data scale and the deepening of graph neural network layers, the heavy cost of GNN inference has greatly limited its application in real-time tasks. this paper focus on accelerating the performance of GNN inference. We first measures the execution time ratio of each stage in the inference process for commonly used GNN models, and analyzes the performance characteristics of different stages. We find out that the critical performance factor of GNN inference is the feature dimension, which is different to CNN and NLP models. therefore, we propose a soft channel pruning method with a ladder pruning pattern. It reduces the calculation from unimportant graph node features and achieve performance acceleration. Meanwhile, it reserves inference accuracy of GNNs. According to experimental validation on graph datasets of different scales, our method can effectively reduce the inference latency and achieve 2×–7× speedup. Also, compared with existing pruning methods, higher inference accuracy can be obtained with comparable speedup ratio.

关键词： Costs Graphics processing units Programming Graph neural networks Time measurement Real-time systems Inference algorithms

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Time-Decay Attribution Model Using Half-Life in Exponential Decay for E-Commerce Conversion Analysis

Time-Decay Attribution Model Using Half-Life in Exponential ...

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international conference on Signal processing and Information Security (ICSPIS)

作者： Debajyoty Banik Suman Kr. Dey Mahendra Kumar Gourisaria School of CS & AI SR University Dept. of Electrical Engineering NIT Rourkela School of Computer Engineering KIIT Deemed to be University

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

With the rapid growth of e-commerce platform users, it has become essential for companies to analyze the customer journey leading to a purchase. Users may interact with or be exposed to multiple devices and touchpoints/channels throughout this process. So now, to identify which of those touchpoints resulted in a positive conversion, the companies use various types of attribution models. By identifying this, the company then allocates its marketing spend accordingly in the future on the chosen channels to further optimize its sales and customized user experience. Over time, various types of attribution models have been developed, each model having its pros and cons. this study proposes a novel Time-Decay Attribution Model using Half-life in Exponential Decay (TD-HLED). We also provide a comparative analysis with six different Models: First-touch, Markov Chain Model, Linear-Touch, Last-touch, and our proposed model, TDHLED. Furthermore, extensive experiments have proved that the model attributions in TD-HLED have more accurately reflected how the users have interacted with the marketing than the existing simplistic rule-based architectures.

关键词： Analytical models Data conversion Information security Companies Predictive models Signal processing User experience Data models Delays Electronic commerce

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parallel Approaches in Deep Learning: Use parallel Computing 23

Parallel Approaches in Deep Learning: Use Parallel Computing

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Proceedings of the 7th international conference on Future Networks and Distributed Systems

作者： Mekhriddin Rakhimov Shakhzod Javliev Rashid Nasimov Department of Computer Systems Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Uzbekistan Department of Artificial Intelligence Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Uzbekistan Department of Artificial Intelligence Tashkent State University of Economics Uzbekistan

ISBN: (纸本)9798400709036

In the present context, the rise of artificial intelligence (AI) has brought to light the importance of expediting processes due to the advancement in AI. this issue holds significance across various domains of machine learning. Consequently, all sectors linked to deep learning, a crucial facet of artificial intelligence, are experiencing continuous advancements. To illustrate, tasks associated with training such as the multiplication of extensive matrices or the manipulation of images to extricate vital features can lead to an increase in time consumption. It is common knowledge that dealing with substantial data quantities demands a considerable amount of time. the primary focus of this research revolves around significantly enhancing the time efficiency of deep learning procedures. While it is a recognized fact that graphics processing units (GPUs) deliver notably quicker outcomes for specific data handling tasks in comparison to a computer's central processing unit (CPU), this study delves into heterogeneous computing systems in cases where GPUs are inaccessible. Herein, we investigate strategies for attaining elevated processing speed through the utilization of advanced technologies. Ultimately, this study culminates by presenting comparative findings derived from di-verse approaches, accompanied by crucial recommendations for future endeavors.

关键词： CUDA technology OpenCL OpenMP artificial intelligence deep learning heterogeneous computing systems parallel computing parallel processing

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TranskV: A Networking Support for Transaction processing in Distributed Key-value Stores 7

TranskV: A Networking Support for Transaction Processing in ...

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7th IEEE international conference on Big Data Computing Service and Applications, BigDataService 2021

作者： Eldakiky, Hebatalla Du, David Hung-Chang Department of Computer Science and Engineering University of Minnesota Twin Cities United States

ISBN: (纸本)9781665434836

through the massive use of mobile devices, data clouds, and the rise of Internet of things, enormous amount of data has been generated and analyzed for the benefit of society. NoSQL Databases and specially key-value stores become the backbone in managing these large amounts of data. Most of key-value stores ignore transactions due to their effect on degrading key-value store's performance. Meanwhile, programmable switches with the software-defined networks and the Programming Protocol-Independent Packet Processor (P4) lead to a programmable network where in-network computation can help accelerating the performance of applications. In this paper, we proposed a networking support for transaction processing in distributed key-value stores. Our system leverages the programmable switch to act as a transaction coordinator. Using a variation of the time stamp ordering concurrency control approach, the programmable switch can decide to proceed in transaction processing or abort the transaction directly from the network. Our experimental results on an initial prototype show that our proposed approach, while supporting transactions, improves the throughput by up to 4X and reduces the latency by 35% when compared to the existing architectures. © 2021 IEEE.

关键词： Concurrency control

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