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Research on intelligent aided design of architectural space based on deep learning 3

Research on intelligent aided design of architectural space ...

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3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Chen, Xi Jiangxi Science and Technology Normal University Nanchang330013 China

ISBN: (纸本)9781510668522

Information technology has been developing rapidly in recent years, the application of computer aided design (CAD) in architectural design is more and more. Based on the computer-Aided design method, designers can parameterize the design scheme in the software, and use a series of variables to control and transform the design scheme, which brings great convenience for the architect to optimize the process of architectural scheme. But the building performance optimization method based on intelligent algorithm is mainly used in scientific research and is difficult to put into practical engineering. Therefore, for the purpose of solving the above problems, this paper carries on the mathematical modeling to the building performance prediction problem which takes the building spatial parameters as the optimization variables. Based on BP neural network, this paper builds an accurate and efficient building performance prediction platform to assist designers to make decisions. The simulation results show that the prediction and optimization proposed here can give accurate and reliable optimal solutions, and the optimal construction scheme has strong rationality and high engineering application value. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： computer aided design

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The Prediction of DDoS Attack by machine learning 3

The Prediction of DDoS Attack by Machine Learning

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3rd international conference on Electronics and Communication;Network and computer Technology (ECNCT)

作者： Liu, Zhongbang Qian, Likun Tang, Shiwen GXUST Coll Software Dev Nanning Guangxi Peoples R China Changzhou Univ Coll Overseas Educ Changzhou Jiangsu Peoples R China Boston Univ Coll Arts & Sci Boston MA 02215 USA

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

Distributed Denial of Service (DDoS) attacks are malicious cyber-attacks that overwhelm the target server with traffic and cause the online service unavailable. With the wide-scale rollout of Internet of Things (IoT), the DDoS attack has threatened almost all walks of life, including business and government. The accurate prediction and early prevention of DDoS attacks are necessary. In this paper, two machine learning models, the Logistic Regression model and linear Support Vector machine (SVM), are introduced to make the DDoS attack prediction. Two dimensionality reduction methods, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) are tried in the data preprocessing. The mean cross-validation accuracy is used to evaluate models' performance. Experimental results indicate that the linear SVM model performs better than the Logistic Regression model on the DDoS Evaluation Dataset (CIC-DDoS2019). Besides, compared with accuracies of models using RFE, accuracies of models with PCA are higher and more stable. Overfitting is likely to occur in learning models with RFE, according to our observations of losses on the training set and the testing set.

关键词： DDoS machine learning SVM

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Recognition of Speech Emotion Using machine learning Techniques 3rd

Recognition of Speech Emotion Using Machine Learning Techniq...

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3rd international conference on Communication, Networks and Computing, CNC 2022

作者： Chaurasiya, Akash Garg, Govind Gaud, Rahul Chakraborty, Bodhi Gupta, Shashi Kant ITM University Gwalior MP Gwalior India

ISBN: (纸本)9783031431449

Speech emotion recognition (SER) is a crucial portion of interaction between computers and machines. In this venture, we aim to classify speech that is taken as fair, joyful, sorrowful, amazed, panic, dislike, fume. Speech emotion recognition frameworks recognize feelings from the human voice in the areas of hospitality, the automobile industry, telecom centers, programmed interpretation frameworks, and human-machine interaction like robots. The Audio dataset was created by combining sound recordings from the Ryerson Audiovisual Database of Emotional Speech and Song (RAVDESS) and the Toronto Speech Set Data (TEES).The relevance of these highlighted feeling classifications is contrasted using methodologies such as Long Short Term Memory (LSTM), Decision Tree, MLP Classifier and K-Nearest Neighbour (KNN). In this classification 7 feelings are present. The LSTM training model achieved 92.5% accuracy;the Decision Tree training model achieved 100% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Multi-scale Feature Fusion for Enhanced Person Re-identification 3rd

Multi-scale Feature Fusion for Enhanced Person Re-identific...

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3rd international conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Wang, XiaoHui Sun, Yulin Zou, Zhipeng Liang, Xiaoyang Sun, Zhenyu Liu, Wei Shandong Business Institute Yantai China Shandong Xuewen Information Technology Co. Ltd. Jining China Technology of Computer Application Yantai Engineering and Technology College Yantai China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (纸本)9789819624676

In the realm of surveillance and security, person re-identification (Re-ID) stands as a critical challenge, particularly across non-overlapping camera networks. The essence of this challenge lies in the accurate and efficient identification of individuals by leveraging features captured from multiple perspectives. Addressing this, we introduce the Contextual Multi-Scale Network (Asy-CMSNet), a novel framework designed to enhance the Re-ID process through the integration of asymmetrical convolution and an innovative loss function, the Enhanced TriHard (E-TriHard). Unlike traditional approaches that rely on the stacking of convolutional layers, Asy-CMSNet employs a sophisticated mechanism that allows for the nuanced extraction and integration of multi-scale features, thereby optimizing the identification process across diverse pedestrian appearances. Central to our framework is the notion that effective person Re-ID necessitates the consideration of both global and localized pedestrian details, ranging from broad clothing characteristics to subtle accessories, which our model adeptly captures and utilizes. Through extensive experimentation across several benchmark datasets, Asy-CMSNet demonstrates superior performance in person Re-ID challenges, highlighting a substantial progression in the area. Our findings not only set new performance benchmarks but also pave the way for future research into more efficient, scalable, and robust person Re-ID systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Benchmarking

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Dueling DQN-Rollout for Collision Avoidance Path Planning with Vehicle Speed Location 3

Dueling DQN-Rollout for Collision Avoidance Path Planning wi...

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3rd international Symposium on computer Technology and Information Science, ISCTIS 2023

作者： Nian, Gujiayin Xiao, Jingzhong Zhou, Xuchuan School of Computer Science and Engineering Southwest Minzu University Chengdu China

ISBN: (纸本)9798350325386

The rapid progress of artificial intelligence has led to significant advancements in the field of autonomous driving, yet effective collision avoidance path planning remains a challenging task. In response, deep reinforcement learning offers an efficient and modern alternative to traditional navigation strategies. This paper proposes a novel approach that incorporates vehicle speed location into the deep reinforcement learning process, utilizing the Dueling DQN-Rollout framework to consider both the distance of the road and obstacles ahead. The agent interacts with the environment to learn a policy, with a reward function that accounts for deviations from the intended path and collisions with obstacles. The training process focuses on imparting human-like driving skills to the autonomous vehicle. By employing the rollout algorithm, the rough Q-value is optimized to reduce training costs. Experimental results demonstrate that this approach can successfully plan a collision-free path for autonomous driving from origin to destination on a simulation platform. © 2023 IEEE.

关键词： Reinforcement learning

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Covid-19: Sentiment Analysis of Students Readiness on Open Distance learning (ODL) via Twitter 3

Covid-19: Sentiment Analysis of Students Readiness on Open D...

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3rd international conference on Artificial Intelligence and Data Sciences, AiDAS 2022

作者： Ahmad, Samsiah Nizam, Zaki Hadri Mohd Zulkifli, Zalikha Latif, Lily Marlia Abdul Ariff, Mohamed Imran Mohamed Universiti Teknologi Mara Perak Branch Tapah Campus Faculty of Computer and Mathematical Sciences Perak35400 Malaysia Sdn Bhd.56 A & 56 B Jalan SS 21/62 Damansara Utama Selangor Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

The widespread of Corona virus in Malaysia has led to open distance learning (ODL) for every student in continuing their study. However, the student readiness in ODL is unknown for the university. Thus, university institutions must always monitor what the university says about social media and the student readiness on ODL. This project aimed at developing a sentiment classification by means of a Naïve Bayes algorithm in displaying the readiness index of students by extracting Twitter tweets. A standard sentiment analysis performance measurement was used in evaluating the developed sentiment analysis model. About 98.8% of the extracted tweet are negative tweet about the online learning which indicate that the students are not ready for the ODL. The classifier model's precision, recall and f1-measure for each category on online learning readiness are obtained. The best f1-score of the classifier model is in online category with 29.0% for negative tweets and 31.7% for positive tweets compare to the other category with a precision of 27.9% for negative tweets and 30.7% for positive tweets while for the recall value is 29.6% for negative tweets and 32.2% for positive tweets. However, there are room for improvements by implement of the autocorrect function for the sentiment analysis model, implementation of a live update function in dynamically for student readiness index live when new tweets appear, implement a machine classification model, providing dynamic database application and used higher performance computer for data scrapping. © 2022 IEEE.

关键词： Sentiment analysis

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Perspective of signal processing-based on brain-computer interfaces using machine learning methods

Perspective of signal processing-based on brain-computer int...

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3rd international conference on Biomedicine and Bioinformatics Engineering, ICBBE 2023

作者： He, Ruyue Department of Electronics and Electrical Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781643684321

The application of artificial intelligence (AI) algorithms is an indispensable portion of developing brain-computer interfaces (BCI). With the continuous development of AI concepts and related technologies. AI algorithms such as neural networks play an increasingly powerful and extensive role in brain-computer interfaces. However, brain-computer interfaces are still facing many technical challenges. Due to the limitations of AI algorithms, brain-computer interfaces not only work with limited accuracy, but also can only be applied to certain simple scenarios. In order to explore the future directions and improvements of AI algorithms in the area of brain-computer interfaces, this paper will review and analyse the advanced applications of AI algorithms in the field of brain-computer interfaces in recent years and give possible future enhancements and development directions for the controversial parts of them. This review first presents the effects of different AI algorithms in BCI applications. A multi-objective classification method is compared with evolutionary algorithms in feature extraction of data. Then, a kind of supervised learning algorithm based on Event Related Potential (ERP) tags is presented to achieve a high accuracy in the process of pattern recognition. Finally, as an important experimental paradigm for BCI, a combined TFD-PSR-CSP feature extraction method, is explained for the problem of motor imagery. The 'Discussion' part comprehensively analyses the advantages and disadvantages of the above algorithms and proposes a deep learning-based artificial intelligence algorithm in order to solve the problems arising from the above algorithms. © 2023 IOS Press. All rights reserved.

关键词： Brain computer interface

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The application of incremental PID control algorithm in large tuning camera 3

The application of incremental PID control algorithm in larg...

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3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Zhang, Jie Liu, Shengchun Yang, Zhanshan Zhang, Yanan Guan, Lin Zhang, Guodong Electric Power Research Institute of State Grid Qinghai Electric Power Company Qinhai Xining81000 China Ultra-high Voltage Company of State Grid Qinghai Electric Power Company Qinhai Xining81000 China

Large adjusting camera is an important facility of modern power grid, its own operation must be stable enough, but manual can not control it comprehensively, so it needs to use incremental PID control algorithm to carry out automatic control. This paper will study the application of incremental PID control algorithm in large tuning camera, mainly introduces the basic concept of PID control algorithm, and compares the incremental PID control algorithm with the position PID control algorithm, demonstrates the advantages of the incremental algorithm, and then puts forward its application method, analyzes the application effect of the incremental algorithm. Through research, the incremental PID algorithm has advantages and has more outstanding application value in the control of large adjusting camera, which can better keep the large adjusting camera in stable operation state. © 2023 Elsevier B.V.. All rights reserved.

关键词： Cameras

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2022 3rd international conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

2022 3rd International Conference on Big Data, Artificial In...

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6th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

ISBN: (纸本)9781665469418

The proceedings contain 198 papers. The topics discussed include: automatic fish detection in underwater videos using machine learning;a prediction model for minimization of flood effects using machine learning algorithms;advanced deep learning-based controller for robust EV speed management;real time conversion of American sign language to text with emotion using machine learning;a survey on filter pruning techniques for optimization of deep neural networks;a review paper on the application of machine learning for ad-hoc network;neural network model structure and node optimization based on mathematical statistics;intelligent measurement information software for ai-enabled education based on Watson studio;artificial intelligence and advanced technology based bridge safety monitoring system;and an efficient object detection and classification from restored thermal images based on mask RCNN.

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IoT and machine learning Technology based Smart Shopping System 3

IoT and Machine Learning Technology based Smart Shopping Sys...

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3rd international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Arul, U. Prasath, A. Arul Mishra, Suman Shirisha, J. Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India CMR Engineering College Department of Electronics and Communication Engineering Telangana Hyderabad India Department of Electronics and Communication Engineering Telangana Hyderabad India

ISBN: (纸本)9781665462754

In today's environment, traditional shopping is a time-consuming activity. Customers must wait in huge lines at the pay counter in traditional shopping. As a result, both the client and the clerk waste a lot of time and energy. To combat this interaction, we're pushing a fantastic shopping framework built on an Android app. The user must first download and install the software on his or her Android phone in this framework. He then scans the item's QR code and enters the required amount. After that, the object is put onto the car. Customers buy the items and put them into the trucks in this manner. The client can add or remove items from the vehicle. The customer will be given the recommended items as well as the most convenient method of obtaining them. When the items are purchased, the bill is generated. This bill might be sent to the client's mobile device via internet banking, allowing the consumer to make a quick payment and leave the business sooner. © 2022 IEEE.

关键词： machine learning

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