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检索条件"任意字段=IEEE International Conference on Computer Science and Automation Engineering"

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A Comprehensive Survey on Methods Used for Automatic Assessment of Subjective Answers

A Comprehensive Survey on Methods Used for Automatic Assessm...

引用

2024 ieee international Students' conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Joshi, Sudarshan Bachkar, Akshay Awaje, Omkar Bhoir, Rhutuj Savitribai Phule Pune University Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350348460

The paper explores various techniques for identifying multilingual text similarities and basic image similarities, covering a wide array of methods and opportunities for comparing and evaluating textual and diagrammatic content. By delving into these categories and studying the pros and cons of various methods, the survey equips researchers, practitioners, and enthusiasts in the field of NLP with a deeper understanding of text similarity *** paper delves into tokenization, vectorization, and similarity metrics, among other approaches. It also discusses modelling techniques, such as LSA and LDA, text alignment algorithms, cosine similarity, BERT, and optimization techniques. The survey categorizes these techniques into corpus-based, knowledge-based, semantic, term-based, and character-based similarities. The survey aims to provide a comprehensive overview of the methods available for checking multilingual text similarity identification and its significance in contemporary text analysis. Index Terms - Similarity Techniques, Machine learning, Natural Language Processing. © 2024 ieee.

关键词： Image Similarity Multilingualism Natural Language Processing Semantic Analysis Similarity Techniques

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Crime Scene Prediction for Unmanned Aerial Vehicles Investigation via Machine Learning 8

Crime Scene Prediction for Unmanned Aerial Vehicles Investig...

引用

8th international conference on automation, Control and Robotics engineering, CACRE 2023

作者： Ojo, Taiwo P. Chi, Hongmei Hilliard, Emmanuel Yan, Jie Florida AandM University Dept. of Computer and Info Science TallahasseeFL32307 United States Bowie State University Dept. of Computer Science BowieMD20715-9465 United States

ISBN: (纸本)9798350302776

Unmanned Aerial Vehicles have increased their applicability in evidence gathering for collecting digital evidence. Authenticating this evidence depends on the crime scenario and the type of drones used. With drones, the evidence type could be the GPS location, the communication signal, the flight log, and the multimedia evidence. These are forms of evidence that forensic experts use to finalize crime cases. Analyzing this evidence requires expensive forensic toolsets. This paper will investigate the level of accuracy and performance verification of machine learning algorithms such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) can reach in identifying vehicle information and the details of the people inside the vehicle at the crime scene and starting with the level of identifying the minute details for the investigation. The performance of the machine learning algorithms will analyze the evidence for deepfakes and possible manipulations in the evidence gathered. © 2023 ieee.

关键词： Support vector machines

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Experimental Research of Iterative Learning Control with Fourier Transform Filter for Electro-hydraulic Strength Test Bench 3

Experimental Research of Iterative Learning Control with Fou...

引用

3rd ieee international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Trubin, Maksim V. Yurkevich, Valery D. Novosibisk State Technical University Faculty of Automation Novosibisk Russia Institute of Automation and Electrometry Novosibisk State Technical University Russian Academy of Sciences Novosibisk Russia

ISBN: (纸本)9798331516321

The problem of synthesis of a control system for strength test benches for aircraft structures is considered. Due to the nonlinearity of the characteristics and the presence of a delay in the control channel, it is not possible to increase the accuracy of the implementation of a given force loading cyclogram by increasing the controller coefficients. The repeatability of the force loading process allows using a control algorithm with iterative learning to increase the accuracy of generating a given force loading cyclogram for aircraft structures. One of the problems of using an algorithm with iterative learning is the loss of stability of this algorithm in the presence of interference and irregular disturbances in the control loop. This paper considers one of the possible approaches to solving this problem by using a low-pass filter based o n t he Fourier transform. The paper presents the results of an experimental verification of the proposed control system on a test stand. The obtained experimental results showed the possibility of ensuring the stability of the iterative learning algorithm in strength testing control systems. © 2024 ieee.

关键词： Fourier transforms

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Transformative Technology in Poultry Management: 3D CNNs for Broiler Chicken Weight Prediction 1

Transformative Technology in Poultry Management: 3D CNNs for...

引用

1st ieee international conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Anuprabha, K. Kaliappan, Vishnu Kumar Baniekal Hiremath, Gangadhar Kpr Institute of Engineering and Technology Department of Computer Science Engineering Tamilnadu Coimbatore641407 India Wasp Autonomous Systems Pvt Ltd Karnataka Ballari India

ISBN: (纸本)9798350372748

In global food production, broiler chickens hold significant importance, which are traditionally managed through manual labour for both meat and egg production. However, the poultry farming industry has increasingly turned to automation to address challenges associated with manual labour and enhance efficiency. This research introduces a novel methodology for estimating chicken weight utilizing top-view videos as input and leveraging 3D Convolutional Neural Networks (CNNs). Accurate weight assessment is a critical aspect of poultry farming, influencing feeding strategies and resource allocation. Traditional manual weighing methods are laborious and time-intensive, necessitating the exploration of automated alternatives. The methodology involves dataset collection, preprocessing, and employing 3D CNNs for effective feature extraction. 3D CNN architecture, adept at capturing spatial and temporal features, consists of convolutional and pooling layers, followed by flatten and dense layers for weight prediction. The model achieves a remarkable accuracy of 95%, as evidenced in the experimental results. Experimental validation showcases the potential of this method as a non-invasive and efficient tool for estimating chicken weight. It contributes to the growing field of automated livestock management in agriculture. © 2024 ieee.

关键词： Convolutional neural networks

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A High-Performance Deep Learning Approach for Real-Time Detection of Rice Leaf Diseases Using CNN Model 2

A High-Performance Deep Learning Approach for Real-Time Dete...

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2nd ieee international conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Kumar, Manish Kumar, Vivek Sabitha, Burra Singh, Jagendra Al-Jawahry, Hassan M. Vani, M. Sree Ajay Kumar Garg Engineering College Department of CSE Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India CVR College of Engineering Department of CSE-DS Mangalpally India The Islamic University Najaf Iraq BVRIT Hyderabad College of Engineering for Women Department of CSE Hyderabad India

ISBN: (纸本)9798350368109

The present research evaluates and compares the performance of four convolutional neural network models, VGG19, VGG16, Inception V3, and ResNet50, in classifying diseases affecting rice plants, such as brown spot, rice blast, bacterial blight, and healthy. After training and testing each model with a comprehensive dataset, the researcher analyses the performance metrics, which include precision, recall, F1 Score, and confusion matrix. The results show that VGG19 consistently outperforms other models in all evaluated metrics. In terms of precision, VGG19 also demonstrates higher results in disease classification, in particular, correctly identifying brown spot, rice blasts, and bacterial blight and misclassifying fewer plants as healthy. To be more precise, those figures for VGG19 equal 96%, 97%, and 96.5%, respectively, illustrating the high capacity of this model for disease detection. At the same time, VGG16, Inception V3, and ResNet50 deliver the same good results, although these models consistently demonstrate lower precision and recall rates. The confusion matrix proves that VGG19 reliably identifies disease and requires fewer data points to mispredict the plant as healthy. Using a unique and extensive dataset, the author proves an essential role in selecting an appropriate model for specific tasks. In the context of agriculture, a model should efficiently address disease detection tasks. At the same time, VGG19 also demonstrates high rates of false prediction in classifying healthy plants as infected, which has to be improved using other models. © 2024 ieee.

关键词： Convolutional neural networks

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Intelligent Live Broadcast Shot Switching System Based on Semantics 24

Intelligent Live Broadcast Shot Switching System Based on Se...

引用

24th ieee/ACIS international conference on computer and Information science, ICIS 2024

作者： Luo, Xinquan Yang, Nan Cao, Wanting School of Information and Communication Engineering Communication University of China Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350376647

In modern society, live broadcasting has become a popular real-time interactive medium, covering a wide range of fields such as news conferences, sports events, concerts and education. In the traditional multi-camera live broadcast, the director needs to switch between different shots in real time according to the site conditions. This process is highly dependent on the director's individual judgment and reaction speed, which is under great pressure when facing complex and changing application scenarios. Therefore, this paper proposes a semantic based intelligent shot switching system. It combines real-time face recognition technology with keyword matching to achieve more accurate and smarter shot switching. First of all, we collected face photos of different genders and age levels as datasets, and preprocessed them. Then, we use the YOLOv8 model for face recognition. After training our customized datasets, we loaded the model to extract the character features from different camera screens. By comparing the similarity between face features and keywords, we calculated a matching score for each picture, and then selected the picture with the highest score as the main screen. In addition, this paper also designed a visual interface for the intelligent switching system based on PyQt5, which will provide users with a convenient use experience. © 2024 ieee.

关键词： Deep learning

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A study of code search technology 7

A study of code search technology

引用

7th international conference on computer Information science and Application Technology, CISAT 2024

作者： Zhao, Lingjun National University of Defence Technology School of Computer Science Changsha China

ISBN: (纸本)9798350375107

The expansion of open-source communities like Github and Stack Overflow has led to an abundance of high-quality code snippets and engineering projects, driving advancements in the domain of code search. This section specifically examines code search, which involves retrieving code that matches a given natural language query by effectively capturing semantic similarities between the query and the code. Deep learning, known for its ability to extract intricate semantic information, has emerged as a powerful tool in this domain. Recent advancements have seen the application of various deep learning techniques, such as graph neural networks and pre-trained models, to code search, resulting in significant progress. Deep learning has now become the predominant approach in code search. This paper thoroughly investigates code search techniques, akin to model search techniques, and provides a comprehensive overview of deep learning-based code search methodologies. © 2024 ieee.

关键词： Semantics

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Block chain-Based on Real Estate market

Block chain-Based on Real Estate market

引用

2023 international conference on New Frontiers in Communication, automation, Management and Security, ICCAMS 2023

作者： Radha, R. Dhar, Soumi Tamilarasi, R. Alliance University Alliance School of Engineering and Design Department of Computer Science Bengaluru India

ISBN: (纸本)9798350317060

Global investments in real estate (RE) surpass those in the stock market by more than double. Despite this, there are far less investors in real estate due to its global accessibility and liquidity. Owners, investors, and tenants are hardly happy with the current setup. The purpose of this paper is to demonstrate the benefits that block chain. Technology can offer the real estate industry while also testing its application in this space. To date, investigations have yielded the following findings: Conclusively, block chain technology and smart contracts offer solutions to the conventional challenges encountered by renewable energy, proving to be significantly more advantageous. They serve as effective tools for establishing a game-theoretic, stable-priced market. © 2023 ieee.

关键词： Smart contract

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Predicting Liver Disease with Machine Learning Methods 2

Predicting Liver Disease with Machine Learning Methods

引用

2nd ieee international conference on Advances in Information Technology, ICAIT 2024

作者： Dhondiyal, Shiv Ashish Luthra, Pooja Aparna Taluja, Tannu Uniyal, Ishita Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Chandigarh University Gharuan Punjab Mohali India Graphic Era Deemed to be University India Department of Electrical Engineering Graphic Era Hill University India

ISBN: (纸本)9798350383867

With time, illnesses pertaining to the heart and liver are getting more and more common. These will only rise in the future due to ongoing technological improvements. The issue will persist despite the fact that individuals are taking yoga and dancing classes and are becoming more health conscious, as well as continuing to introduce and improve their sedentary lifestyles and indulgences. The aging population combined with an increase in the frequency and prevalence of chronic illnesses raises the risk of liver disease-related hospitalization or mortality. Three algorithms are chosen: Random Forest, K-Nearest Neighbours (K-NN), and Support Vector Machine (SVM). Python is used to compare the accuracy and determine which categorization technique best predicts liver illness. Using these methods we find the KNN method gives maximum good result than SVM and Random Forest *** gives 0.993 Accurate value. © 2024 ieee.

关键词： Adversarial machine learning

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Studying the Fundamentals of Internet of Things (IoT) in the Healthcare Applications 1

Studying the Fundamentals of Internet of Things (IoT) in the...

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1st international conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Kadam Rangrao, Shrikant Patil Abhijit, Seema Sharma, Lakshay Rani, D Shobha Ukirade, Chaitali Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India Indraprastha Institute of Information Technology Delhi India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India Department of Obstetrics and Gynecology Karad India

ISBN: (纸本)9798350360707

The Internet of Things (IoT) has brought smart healthcare systems to the medical industry. These systems are typically made up of a network, a remote server, and sensors with smart capabilities. The primary goals of this system are to offer multidimensional feature monitoring and fundamental therapy recommendations. The primary goals of Internet of Things (IoT)-based healthcare services are to enhance quality of life and offer a great user experience at an affordable price. The primary purpose of IoT is to give elderly patients with chronic illnesses access to trustworthy, efficient, and intelligent healthcare services as well as connectivity to the medical resources that are readily available. Thus, many studies are developed on the different applications which worked on the IoT for the healthcare which includes wearable as well as non-wearable devices or machines. Thus, it makes the users to obtain the data from anywhere and wherever required for the study or analysis of the patients. In the meantime, the Internet of Things is expanding to include even the tiniest sensors and the fundamentals of automation and machine-to-machine communication. © 2024 ieee.

关键词： healthcare internet of things (IoT) network service

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