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Scenic buildings Recognition Based on MobileNetV1 Network and Transfer learning 4

Scenic buildings Recognition Based on MobileNetV1 Network an...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Feng, Haiwen Fan, Fan Shenyang University of Technology Shenyang China

ISBN: (纸本)9798350373233

the recognition of images in natural scenes based on mobile devices has become mainstream. the cost of recognizing images through mobile phone photography is low and the operation is simple. A method based on MobileNetV1 and transfer learning is proposed to identify scenic buildings in China due to the small sample size and low recognition accuracy. Firstly, collect a dataset of buildings from famous scenic spots in China, and use methods such as flipping, flipping, scaling, brightness adjustment, adding noise, and pixel translation to improve the diversity of the data samples and avoid overfitting the model. Use MobileNetV1 to pre train the model on the ImageNet dataset;then, based on the pre trained results, a MobileNetV1 network for scenic spot building recognition was constructed. the weights of the pre trained MobileNetV1 network model were transferred to the constructed MobileNetV1 network for model weight initialization, thereby reducing training costs. the outcomes of the experiment demonstrate that using transfer learning enables the model to complete training quickly, and the accuracy is greatly improved compared to non-transfer learning methods. Research has shown that using methods based on MobileNetV1 and transfer learning effectively solves the problems of small sample size and low accuracy in scenic building recognition, providing a new method for scenic building recognition. © 2023 IEEE.

关键词： Buildings

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Indian Music Instrument Classification Using Deep learning on Embedded Platforms 4th

Indian Music Instrument Classification Using Deep Learning o...

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and applications, ICMISC 2023

作者： Chikkamath, Satish Kulkarni, Uday Jain, Mahek Kamat, Guruprasad Bachari, Rochan Mainale, Sangamesh School of Electronics and Communication Engineering KLE Technological University Hubballi Hubballi India School of Computer Science and Engineering KLE Technological University Hubballi Hubballi India

ISBN: (纸本)9789819994410

Indian musical instruments are an integral part of the rich cultural heritage of India. these instruments have evolved over centuries and are unique in their sound, construction, and playing technique. the popularity of Indian music in Western countries has given rise to the need of identifying Indian musical instruments. In this study, we aim to classify Indian musical instruments by extracting Mel-Frequency Cepstral Coefficients (MFCCs) from the audio signals and training the Recurrent Neural Networks’ (RNNs) architecture on these features. We compare the performance of the RNN with various machine learning (ML) techniques and find out that RNN outperforms all the other techniques. We quantize the trained RNN model to decrease latency by 13% and model size by 87.2% with negligible change in the model inference accuracy compared to the baseline model and deploy it on an Android device making a user-friendly application. this application allows users to record audio clips and classifies the audio into five different Indian musical instruments and other sounds as background noise. the app helps users to recognize Indian musical instruments in a convenient manner. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Music

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A Novel Reinforcement learning Based Optimization Approach for Supply Chain Management 4

A Novel Reinforcement Learning Based Optimization Approach f...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Sai, D Gowtham Sri Mani Venkatraman, K. Chellammal, P. Natarajan, B. Sridevi, R. Chennai601103 India J J College of Engineering and Technology Department of Computer Science and Engineering India Samayapuram Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350306118

In this research, a method for improving supply chain management transport routing decisions is developed. It made use of reinforcement learning, more especially the Advan-tage Actor-Critic (A2C) model. the A2C approach integrated value-based and policy-based learning to successfully handle the dynamic and unpredictable nature of transport routing difficulties. the A2C model could choose the best route, allocate the load, and schedule by learning from previous experiences. the goal of this research to create a simulation environment that fully accounted for the complexities of transport routing in supply chain networks, taking into account variables like traffic conditions, delivery deadlines, vehicle capacities, and resource availability. In order to increase the efficacy and efficiency of transport routing decisions, the A2C model was trained in this context. To assess the effectiveness of the A2C model in achieving optimal transport routing solutions, a thorough performance study and comparison with baseline techniques were performed. the findings and understandings from this study advanced the subject of supply chain management and gave organizations looking to improve their transport routing plans invaluable advice. © 2023 IEEE.

关键词： Reinforcement learning

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Deep learning based Analysis for Automated Detection and Classification of Brain Tumor 4

Deep Learning based Analysis for Automated Detection and Cla...

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

作者： Lamba, Kamini Rani, Shalli Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350300093

Reproduction of quick and indefinite cells within brain cause a tissue which is generally known as a brain tumor. A number of individuals remain untreated as it does not show any hard symptoms at an initial stage. For identification of such disease, many neurologists suggest computer Tomography Scan, Magnetic Resonance Imaging etc. which can be time consuming process and expensive too. To avoid so, various computer assisted methods have been suggested by researchers to overcome the drawbacks of traditional approaches. Deep learning has been considered as one of the reliable approaches for identification and classification of brain tumor disease that can prevent an individual from death due to its strong features capability for providing quick and better results at an early stage as compared to the traditional approaches. this research study has considered 3264 images from kaggle having 2764 tumor images and 500 with healthy ones and proposed a model that comprises of Visual Geometry Group (VGG) having 16 layers in collaboration with the concept of transfer learning to perform the diagnosis and classification of brain tumor disease. the proposed model has delivered an accuracy of 98.16%, precision of 99.09%, recall of 98.73% and F1-score of 98.91% which is far better when compared to the existing approaches. © 2023 IEEE.

关键词： Feature extraction

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10th EAI international conference on Mobility, IoT and Smart Cities, Mobility IoT 2023

10th EAI International Conference on Mobility, IoT and Smart...

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10th EAI international conference on Mobility, IoT and Smart Cities, Mobility IoT 2023

ISBN: (纸本)9783031565328

the proceedings contain 29 papers. the special focus in this conference is on Mobility, IoT and Smart Cities. the topics include: Sustainable Mobility for the Sustainable Development of Modern Urban Agglomerations;mathematical Model of Transport Stream Sustainability at Road Network Areas During Traffic Jams;air Quality Improvement Based on the application of Emission Monitoring System with Triangulation;machine learning Regression Model in MATLAB for Predicting Gas Emissions During the Pellet Combustion;intelligent Devices Used in Healthcare;strategic Decision-Making for Evaluating Risks Significance in Degreasing and Pickling;methods of Choosing Technology for Improving the Environmental Indicators of Vehicles;an Engaging Mobile application for Tourists with Gamification Elements that Promotes Lesser-Known Coffee Shops in the Copenhagen Suburbs;Image Transmission in LoRaWAN: An Experimental Study on Transmission Time;the Effect of a Face Wall on a Cellular Structure During Bending;big Data Analytics: A Systematic Literature Review on Fields of application As Well As Costs and Risks in Management Reporting;sustainable Development of Rural Areas: machine learning for Semantic Segmentation of Agricultural Fields;Measurement of Data Transmission Speed in 5G and 4G LTE Mobile Networks Between a Moving Vehicle and the Environment;optimization of 3D computer Model Parameters for Spherical Elements Modeling;the Clean Energy as Solution for Global Challenges and Sustainable Development;virtual Influencer—A New Phenomenon in the Marketing Communication or Illusion?;tools for Creating Business Models in Smart Cities;current Challenges of Autonomous Vehicles in Relation to Regulatory Issues;use of Artificial Intelligence in Supply Chain Management.

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machine learning Techniques for the Prediction of Cyber-Attacks 4

Machine Learning Techniques for the Prediction of Cyber-Atta...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Verma, Rishabh thakur, Bharti Shoolini University of Bio-Technology and Management Sciences Himachal Pradesh Solan India

ISBN: (纸本)9798350306118

the aim of this paper to develop a machine-learning (ML) model for UNSW-NBI5 dataset cyberattack prediction. ML is a subset of AI which is a powerful tool for detection of the cyber-attacks. It uses existing data to identify the same pattern between old attacks and new attacks. ML helps to identify the features and characteristics of cyber-attacks. this paper employed four different classifiers: Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and ANN (Artificial Neural Network) on the UNSW-NB15 dataset. 9-fold cross-validation is used on the dataset to determine each classifier's best accuracy. For Ann, we used four layers which are the input layer, two hidden layers, and the output layer. In the input layer, we used 10 neurons. In the first hidden layer, we used 15 neurons, and 10 neurons for second layer. 1 neuron is used in the output layer. the classifiers are evaluated on the basis of accuracy, precision, recall, fl-score, mean square error(MSE), true positive rate (TPR), and false positive rate (FPR). Among all of the classifiers, RF overpowered other models and gave an accuracy of 0.95167% with a TPR of 0.96252% and a FPR of 0.06749%. © 2023 IEEE.

关键词： Neural networks

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Design of Modeling Elements of Luoshan Shadow Puppets Creative Goods Based on Deep learning 5

Design of Modeling Elements of Luoshan Shadow Puppets Creati...

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5th international conference on Applied machine learning, ICAML 2023

作者： Xu, Taotao Shanat, Musdi Bin Hj. Kah Le, Brendan Chan Zhang, Wei University Malaysia Faculty of Applied and Creative Arts Sarawak Sarawak94300 Malaysia University Malaysia Faculty of Computer Science and Information Technology Sarawak Sarawak94300 Malaysia

ISBN: (纸本)9798350341416

the application of Luoshan shadow puppet elements in creative goods can break through the single communication path of shadow play and make the communication process of Luoshan shadow play art more vivid and 3D. In this paper, based on the design of modeling elements of mountain shadow puppets, the characteristics of visual elements of shadow puppets creative goods are analyzed, and an intelligent design algorithm of shadow puppets creative goods based on deep learning (DL) is innovatively proposed. Based on the design of modeling elements of Luoshan shadow puppets, this paper analyzes the characteristics of visual elements of shadow puppet creative goods, puts forward an intelligent design algorithm of shadow puppet creative goods based on DL, and explores the computer-aided shadow puppets modeling design strategy driven by artificial intelligence. the results show that the F1 value of the algorithm is about 94%, and the efficiency of the algorithm is high. the validity and reliability of the method in this paper are effectively verified by the test, which provides a reliable and efficient method for the design of modeling elements of Luoshan shadow puppet creative goods. © 2023 IEEE.

关键词： Design

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Classification of Blood Cell Data using Deep learning Approach

Classification of Blood Cell Data using Deep Learning Approa...

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2023 IEEE international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Yepuri, Sravya Marandi, Ashapurna CSE PES University Bengaluru India CSIR-4th Paradigm Institute Bengaluru India

ISBN: (纸本)9781665462631

machine learning is an established branch of artificial intelligence that comprises of algorithms and mathematical relationships and is rapidly being used to clinical research. machine learning enables computers to be programmed without explicit information and to learn from it. the outcomes of using these technologies in medical data processing have been amazing, with great effectiveness in detecting illnesses. According to studies, machine learning techniques significantly improve complex medical decision-making processes in medical image processing by extracting and then evaluating the features of these pictures. An urgent demand for more sophisticated data analysis approaches has arisen as the number of medical diagnosis tools rose and a significant amount of high-quality data was produced. Traditional methodologies are incapable of examining such vast amounts of data. One of the critical diseases, Leukemia can be found in the blood cell data image of patients by applying machine learning algorithms. Here in this paper, the blood cell images available in the open literature are analyzed using the different algorithm networks for the refinement of images to identify the diseases like leukemia. © 2023 IEEE.

关键词： Diseases

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Exploring Drug Sentiment Analysis with machine learning Techniques 6

Exploring Drug Sentiment Analysis with Machine Learning Tech...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Rathod, Dharmendrasinh Patel, Kashyap Goswami, Akashgiri Jashvantgiri Degadwala, Sheshang Vyas, Dhairya Computer Science and Engineering Chandubhai S Patel Institute of Technology Charusat University India Department of Computer Engineering DEPSTAR Charotar University of Science and Technology India Silver Oak College of Computer Application Silver Oak Univercity Gota Gujarat Ahmedabad India Computer Engineering Department Sigma Institute of Engineering Gujarat Vadodara India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (纸本)9798350398496

this research investigates the use of machine learning techniques for sentiment analysis of drug re views, which is a crucial task for extracting valuable information from the vast amount of unstructured data available online. Drug reviews provide patients' opinions on various aspects of drugs, including their efficacy, side effects, and overall experience. the study explores the performance of different machine learning algorithms for sentiment analysis of drug reviews. Additionally, it examines the effectiveness of various feature engineering techniques, such as bag-of-words, n-grams, and word embeddings, in capturing the subtleties of drug reviews. the study evaluates the performance of these techniques on a publicly available dataset of drug reviews and compares the results based on accuracy, precision, recall, and F1-score. the experiments demonstrate that the appropriate use of machine learning algorithms and feature engineering techniques can accurately capture the sentiment of drug reviews, achieving high accuracy and F1-score. this study's findings can provide valuable insights for healthcare professionals and researchers to analyze patient opinions about drugs, identify potential side effects, and enhance drug development and regulatory processes. © 2023 IEEE.

关键词： Embeddings

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An Autonomous Driving Decision Making Method with Maximum Entropy Objective and Safety Constraints 4

An Autonomous Driving Decision Making Method with Maximum En...

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4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Shang, Junyou Zhu, Fenghua Lv, Yisheng Geng, Wendong Xiong, Gang Xi'an Jiaotong University Qian Xuesen College Xi'an China Qingdao Academy of Intelligent Industries Qingdao China Institute of Automation Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China

ISBN: (纸本)9798350349252

At present, the algorithm based on reinforcement learning has shown advantages over the rule-based algorithm in the field of building autonomous driving decision-making model, which has great potential and application prospects in the field of autonomous driving. However, reinforcement learning based algorithms often have disadvantages such as low learning efficiency, poor safety, and sensitivity to hyperparameters. To solve this problem, based on the Soft Actor-Critic algorithm with maximum entropy objective, the safety rule constraint is introduced to adjust the action sampling probability in the training phase, and the autonomous driving decision-making model under the conditions of 3 actions and 5 actions is trained and tested. It was compared with the value-based ssafety constraint algorithm and the rule-based safety constraint algorithm using DQN algorithm. the results show that the proposed algorithm is superior to the compared algorithms in terms of safety and training efficiency, and the algorithm based on SAC and safety constraints can improve the safety and learning efficiency of reinforcement learning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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