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application of Genetic Algorithm in Satellite Communication Task Scheduling 4

Application of Genetic Algorithm in Satellite Communication ...

引用

4th international conference on Signal Processing and computer Science, SPCS 2023

作者： He, Ying Zheng, Jianping Department of Service Technology China Mobile Research Institute Beijing100053 China

ISBN: (纸本)9781510672505

With the development of satellite communication technology, the demand for satellite resources for various communication tasks is increasingly complex. In order to further improve the utilization efficiency of satellite resources and better deal with the resource competition between different types of services, this paper proposes a satellite communication task scheduling algorithm based on genetic algorithm. By matching the satellite task scheduling problem with the utility optimization problem, and designing the chromosome coding method and genetic operator matching with this problem, the convergence performance of genetic algorithm is effectively improved. Simulation results show that the algorithm is a fast convergence and efficient satellite communication task scheduling algorithm. © 2023 SPIE.

关键词： Genetic algorithms

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machine learning-Based Power Consumption Prediction for Unmanned Aerial Vehicles in Dynamic Environments 56

Machine Learning-Based Power Consumption Prediction for Unma...

引用

56th Annual Hawaii international conference on System Sciences, HICSS 2023

作者： Gatscher, Julian Breitenbach, Johannes Buettner, Ricardo University of Bayreuth Germany University of Bayreuth Fraunhofer FIT Germany

ISBN: (纸本)9780998133164

Unmanned aerial vehicles are becoming integrated into a wide range of modern IoT and CPS environments for various industrial, military, and entertainment applications. With growing estimations for this market in the future, the problem of energy consumption and its prediction is becoming increasingly important for optimal battery-saving, as well as the safety of the application and thus protection of surrounding persons near the drone flight. this paper presents a machine learning-based approach for the prediction of the power consumption of unmanned aerial vehicles at certain times of the flight. Instead of predicting the power consumption in prescribed environments with complex, time-consuming measurement techniques, our approach is fast, easy to implement, and predicts real-world power consumption in five classes, with a balanced accuracy of 66.7 percent. © 2023 IEEE computer Society. All rights reserved.

关键词： Drones

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PT-MVSNet: Overlapping Attention Multi-view Stereo Network with Transformers 4

PT-MVSNet: Overlapping Attention Multi-view Stereo Network w...

引用

4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

作者： Zhang, Song Li, Lin Qiao, Jiangxuan School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Qinghai Normal University Academy of Plateau Science and Sustainability Xining China National Qinghai-Tibet Plateau Scientific Data Center Qinghai Sub-center Xining China

ISBN: (纸本)9798350326444

In this paper, we propose a new multi-view stereo vision model PT -MVSNet based on multi-view stereo (MVS). Multi-view stereo is a successful reconstruction method that uses multiple images to reconstruct a 3D scene. It has been applied in many practical scenes such as architecture, cultural heritage protection, and map making. MVS still faces a lot of challenges, including inaccurate feature matching, excessive image noise, and overly complex computation. To solve the feature-matching inaccuracy problem, we take the Transformer model as the main structure in the feature-matching and add a patch-based overlap attention module (POLA). In this paper, we proposed PT-MVSNet can solve the image feature extraction problem more effectively. To validate the effectiveness of the model, we conducted experiments on the DTU dataset and evaluated its performance by two evaluation metrics. the experiment results show that our method outperforms the latest methods, whose accuracy and completeness reach 0.386 and 0.271 respectively. © 2023 IEEE.

关键词： Feature extraction

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Fashion Recommendation System Using machine learning 4

Fashion Recommendation System Using Machine Learning

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Sivaranjani, Lingala Rachamadugu, Sandeep Kumar Reddy, B.V. Suresh Basi Reddy, A. Sakthivel, M. Depuru, Sivakumar School of Computing Mohan Babu University Tirupati India G. Pulla Reddy Engineering College Dep. of Cse A.P Kurnool India Koneru Lakshmaiah Education Foundation Dept. Of. Cse A.P Vaddeswaram India Tirupati India

ISBN: (纸本)9798350300888

For the past few years, fast fashion has become very popular, which has had a great impact on the textile and fashion industries. Fashion is an integral part of one's daily lives, and it has a significant impact on identity and self-expression. With the increasing availability of digital platforms and e-commerce websites, the fashion industry has transformed, and the way one shops for clothing has evolved. the rise of e-commerce has brought about a rise in the use of fashion recommendation systems. these systems aim to offer personalized product recommendations to users according to their interests and preferences. the field of machine learning has made significant strides in the development of image processing techniques, parsing techniques, image classification, segmentation, and networking, making it an ideal candidate for powering these recommendation systems. With these advancements in technology, the potential for fashion recommendation systems to provide even more accurate and individualized product recommendations is substantial. In this study, the dataset is used and the main technique is to use machine learning by splitting into training and testing data of the dataset. the convolutional neural network is used to produce similar items in the recommendation system. the CNN layers are upgraded by using RESNET50 and the filtering content is based on data provided for the product. the RESNET50 helps in overcome the problem of vanishing gradient. then KNN algorithm is used to recommend similar items. the main idea behind KNN is Euclidean distance and Cosine Similarity which helps in producing similar products. the user's past behavior is significant here and the convolutional neural networks (CNN) model was also utilized for picture categorization and recognition. the experimental results of the system achieved retrieval accuracy and outperformed the baseline. © 2023 IEEE.

关键词： Image recognition

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UNF-IDT: Automated Irony Detection in English Tweets 22

UNF-IDT: Automated Irony Detection in English Tweets

引用

22nd IEEE international conference on machine learning and applications, ICMLA 2023

作者： Jaganathan, Guna Sekaran Kilungeja, Grentina Kahanda, Indika School of Computing University of North Florida JacksonvilleFL United States

ISBN: (纸本)9798350345346

the usage of social media platforms has increased tremendously in the past decade. Twitter is one of the most popular platforms for sharing opinions and feelings on various topics, organizations rely on Twitter data to analyze and gather insights for their businesses using Natural Language Processing (NLP) techniques. However, there are various challenges in analyzing such a huge volume of tweets that are in text format. One such challenge is to identify irony in tweets, which has a significant impact on analyzing sentiments. To overcome this challenge, we propose a solution that automatically recognizes the presence of irony in text and classifies the type of irony. the solution consists of two tasks: Task A performs binary classification to annotate whether irony is expressed or not in each tweet, while Task B performs multi-class classification to classify the type of irony expressed in the tweets. We used the dataset from the 'SemEval-2018 Task 3: Irony detection in English tweets' challenge to train and test the proposed solution. the dataset contains 3,817 English tweets for training and 784 English tweets for testing both Tasks A and B. We developed different machine learning models by leveraging traditional classifiers, neural networks, and large language models. Our UNF-IDT (Irony Detector in Text) model developed using BERT (Bidirectional Encoder Representations from Transformers) was able to achieve an F1 score of 0.757 for Task A (Binary Classification) and an F1 score of 0.449 for Task B (Multi-class Classification), retrospectively obtaining 4th and 9th ranks, in the two tasks respectively. © 2023 IEEE.

关键词： BERT Irony Detection machine learning Text Mining Twitter

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Tomato Leaf Disease Detection based on Peer-to-Peer Federated learning in Wireless Networks 4

Tomato Leaf Disease Detection based on Peer-to-Peer Federate...

引用

4th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Liu, Ke Department of Asset and Equipment Xi’an Medical University Xi’an710021 China

ISBN: (纸本)9781510666412

As a research highlight of plant disease control, the tomato leaf disease is the one of the most important reasons resulting in tomato yield decline. Since the relevant studies not considering transmission errors in wireless environment and the privacy of datasets, a method based on P2P(Peer-to-Peer) federated learning in wireless networks to detect tomato leaf disease is proposed, which applies ARQ(Automatic Repeat-reQuest) protocols for reliability and ensures the privacy of datasets by transmitting model parameters. In this paper, the training/validation loss, precision and false alarm are analyzed for five different kinds of tomato leaf diseases images. Results show that accuracy and precision reach 90 percent and the average of false alarm is three percent for all kinds, where gap is below five percent compared to centralized learning method, thus proving the effectiveness and high performance of the proposed method and providing a reference to tomato leaf disease control in reality with machine learning. © 2023 SPIE.

关键词： Disease control

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Research on the control of overloading of muck vehicles based on morphological feature extraction from the perspective of deep learning 4

Research on the control of overloading of muck vehicles base...

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4th international conference on Image Processing and Intelligent Control, IPIC 2024

作者： Zhu, Shengkai Geng, Yiting Yang, Jiaqi Cao, Yiwen Shandong Xiehe University Shandong Jinan China

ISBN: (纸本)9781510682313

With the acceleration of urbanization, urban demolition, construction, reconstruction and other engineering projects to promote urban waste transportation activities increasingly frequent, but because of overload, overrun, dangerous driving and other non-standard operation problems, endangering social and traffic safety. Traditional police law enforcement has problems such as management and governance leakage, incomplete coverage, and weak law enforcement. In the application of the existing detection methods based on traditional loadometer weighing and road weighing, there are pain points such as high equipment investment, small detection range, and easy damage of equipment. In order to do a better job of muck vehicle management, this paper breaks the traditional muck vehicle management and loadometer weight measurement method, and creatively puts forward no gap dynamic real-time supervision and machine vision based on deep learning. through the accurate judgment of overload and limit of muck vehicle morphological detection, it effectively solves the problem of no blind spot, continuous and less investment in law enforcement. It provides an effective management scheme for the accurate law enforcement and violation remediation of traffic control departments and administrative departments. © 2024 SPIE.

关键词： Urban transportation

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Empirical Analysis for Investigating the Effect of machine learning Techniques on Malware Prediction 18

Empirical Analysis for Investigating the Effect of Machine L...

引用

18th international conference on Evaluation of Novel Approaches to Software Engineering (ENASE)

作者： Vijayvargiya, Sanidhya Kumar, Lov Murthy, Lalita Bhanu Misra, Sanjay Krishna, Aneesh Padmanabhuni, Srinivas BITS Pilani Hyderabad India Hyderabad India NIT Kurukshetra Kurukshetra India Ostfold Univ Coll Halden Norway Curtin Univ Bentley WA Australia Testaing Com Gurgaon India

ISBN: (纸本)9789897586477

Malware is used to attack computer systems and network infrastructure. therefore, classifying malware is essential for stopping hostile attacks. From money transactions to personal information, everything is shared and stored in cyberspace. this has led to increased and more innovative malware attacks. Advanced packing and obfuscation methods are being used by malware variants to get access to private information for profit. there is an urgent need for better software security. In this paper, we identify the best ML techniques that can be used in combination with various ML and ensemble classifiers for malware classification. the goal of this work is to identify the ideal ML pipeline for detecting the family of malware. the best tools for describing malware activity are application programming interfaces (APIs). However, creating API call attributes for classification algorithms to achieve high accuracy is challenging. the experimental results demonstrate that the proposed ML pipeline may effectively and accurately categorize malware, producing state-of-the-art results.

关键词： SMOTE ANOVA Genetic Algorithm Ensemble learning Malware Family

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Hybrid machine learning Method for Product Sales Forecasting in E-Commerce 4

Hybrid Machine Learning Method for Product Sales Forecasting...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kumar, Ravi Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350300888

the term 'e-commerce' refers to the practice of buying and selling products using the internet. E-commerce was created so that consumers wouldn't have to leave the comfort of their homes to purchase something. So, they can buy it online and have it delivered to their house in a few days. Online retailing has recently developed in a favorable environment because of the robust backing of national legislation. this year's pandemic has made the e-commerce sector's contribution to the growth of the country's economy more noticeable. the quantity and effectiveness of e-commerce networks and e-commerce businesses are growing under such circumstances. A network must've been able to better match user demands and perform admirably in all areas of collaboration and administration if it hopes to keep its competitive edge. Accurately predicting the sales volume of e-commerce networks at this time is crucial. this article presents a novel hybrid machine learning (ML) algorithm to accurately predict revenue growth for an online store. A hybrid cat-XGBoost (HC-XG) algorithm is suggested to predict the sale. the main goal of the proposed work is to forecast e-commerce sales, thus i the final selling value is included in the solution since it has a significant impact on sales in extremely competitive and price-sensitive contexts like e-commerce. We next continue with the development of the suggested solution and evaluate it for accuracy, error, and overall efficiency, and compare it to some competitor solutions. thus, the proposed approach is compared with other existing models for sales forecasting and demonstrate that it provides the best overall performance in product sales forecasting. © 2023 IEEE.

关键词： Sales

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the application of machine learning in Deception Detection : a study based on the effect of empathy on deception detection 4

The Application of Machine Learning in Deception Detection :...

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4th international conference on machine learning, Big Data and Business Intelligence, MLBDBI 2022

作者： Wang, Buxue Shen, Xunbing Jiangxi University of Chinese Medicine Key Laboratory of Psychology of Tcm and Brain Science Jiangxi Administration of Traditional Chinese Medicine 1688 Meiling Avenue Nanchang China

ISBN: (纸本)9798350333947

Accurate deception detection is crucial for national security, judicial interrogation, clinical diagnosis, etc. However, humans have difficulties in deception detection. Shifting the perspective to machine learning to build models for automatic deception detection by feature extraction and feature classification of misleading information such as verbal, nonverbal, or physiological indicators will open up new prospects in the field of deception detection. Many deception detection algorithms have been developed, but machine learning algorithms still need to be continuously optimized to make it possible to achieve fully automated deception detection for more effective applications in national security and interrogation. © 2022 IEEE.

关键词： Feature extraction

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