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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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A Study on Sustainable Tourism and application of Sentiment Analysis in the Tourism Industry 4

A Study on Sustainable Tourism and Application of Sentiment ...

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4th IEEE international conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Gupta, Chandra Prakash Kumar, V. V. Ravi Pune India

ISBN: (纸本)9798350387063

Sentiment analysis has emerged as one of the most promising tools for analyzing the travelers review about the various facilities related to the tourism industry, be it a choice of right destination, hotels, travel agencies, airlines or any other mode of transportation etc., helping the tourist to opt for the sustainable and green tourism initiatives. Sentiment analysis facilitates the identification and extraction of subjective data from texts, including attitudes, sentiments, andfeelings. Some of the approaches used by sentiment analysis are lexicon-based approaches, machine learning algorithms, and deep learning models for checking the polarity of the comments. the authors explore the multifaceted applications of sentiment analysis in the tourism industry. through the examination of traveler reviews, social media exchanges, and online conversations, sentiment analysis offers priceless insights into travelers' opinions, preferences, and degrees of satisfaction to the stake holders in the tourism industry. the study also focused on how sentiment analysis can help in implementing the sustainable practices. the findings of the study will enable stakeholders in the tourism sector to make informed decisions, enhance customer experiences, and foster a culture of sustainability within the industry. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of High Blood Pressure using machine learning Techniques 4

Prediction of High Blood Pressure using Machine Learning Tec...

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4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Taher, Fatma Prakash, Neema AlShamsi, Alyazia College of Technological Innovation Zayed University Dubai United Arab Emirates College of Engineering and IT American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839539954

Hypertension or high blood pressure is the major cause of several health complications and increases the chances of heart disorders, stroke, and sometimes death. therefore, early detection of hypertension will save many lives. this paper discusses some of the most preferred machine learning techniques namely: Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Naive Bayes, Support vector machine (SVM), Random Forest, multi-layer perceptron (MLP), and Logistic Regression which helps in the pre-diagnosis of hypertension. this study will help the researchers in finding out the datasets and understanding the performances of the machine learning algorithms in terms of accuracy, sensitivity, and specificity. © the Institution of Engineering & Technology 2023.

关键词： Convolutional neural networks

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Optimization Model for machine learning Based Intrusion Detection System (OML-IDS) in Mobile-Edge Computing 4

Optimization Model for Machine Learning Based Intrusion Dete...

引用

4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kiruthiga, R. Jeyalaksshmi, S. Vanitha, V. Khan, Abdul Azeem Pal, Souvik Kumar, Rahul SRM Institute of Science & Technology Department of Computer Applications Kattankulathur Chennai India Depart. of IT Vels Institute of Science Technology & Advanced Studies Chennai India Sri Sankara Arts and Science College Department of Computer Applications Kanchipuram India Faculty of Islamic Technology Brunei Sister Nivedita University Department of CS and Engineering Kolkata India Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

the Internet of things (IoT) and 5G communications now primarily use mobile edge computing (MEC). the fundamental concept behind MEC is the deployment of a hierarchy of edge servers with more computational power to handle the computing duties that are being offloaded by low-end IoT edge devices. Some of the security risks in MEC pertain to the application layer, the network layer, the data, and the node. Out main aim is to protect MEC from different cyber-attacks and continuous monitoring of digital assets. the network layer security for MEC is protected by intrusion detection systems (IDSs). We need an updated IDS system capable of detecting modern cyber-attacks as well as monitoring the devices. thus, this paper proposes an optimization model for MEC settings that functions in conjunction with an OML-IDS, a machine learning-based intrusion detection system. To begin identifying attack patterns in a MEC environment, this model first develops an optimization-based feature selection strategy for selecting an ideal subset of characteristics. For the purpose of intrusion categorization and detection, a hybrid ML algorithm is also used. Enhancing the offloading latency, synchronization time, and turnaround time is necessary if you want to be able to recognize assaults in real time. © 2024 IEEE.

关键词： Network intrusion

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Pneumonia Image Recognition Based On Deep learning 4

Pneumonia Image Recognition Based On Deep Learning

引用

4th IEEE international conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Jiang, Zhengyu Toe, Teoh Teik University of Electronic Science and Technology of China Glasgow College Hn China Nanyang Technological University Ntu Business Ai Lab Singapore

ISBN: (纸本)9798350363586

this article introduces a comprehensive method for diagnosing pneumonia using chest X-ray (CXR) images. By using deep learning models for feature extraction and machine learning methods for classification, the effectiveness of feature extraction has been evaluated using CNN, ResNet.50, and ViT models. the performance of random forest and logistic regression classifiers based on these models were compared, conducting a comprehensive evaluation of accuracy and F1 score to determine the most effective model. this study addresses the challenges of diagnosing artificial pneumonia and emphasizes the urgency of automated solutions. In the end, the combination of ResNet.50 and logistic regression achieved higher accuracy (78.68%), higher precision (99.99%), and more ideal F1 score (0.6239). these results demonstrate that the application of deep learning in clinical diagnosis can improve the clinical efficiency and accuracy of pneumonia detection, which is beneficial for the subsequent treatment of patients. © 2024 IEEE.

关键词： Logistic regression

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Research on physical feature analysis of integrated circuits based on machine learning 4

Research on physical feature analysis of integrated circuits...

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

作者： Yuan, An He, Zhaoquan Chen, Yanlin School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9781510666412

Based on the application requirements of physical feature recognition of integrated circuits, this study will use YOLOv5, a representative of the YOLO series algorithms, to improve and optimize them from the theoretical and experimental perspectives, in order to obtain a faster and more accurate detection model in precision-type target detection tasks. this experiment will detail the basic architecture of the YOLOv5 model, and make up for the shortcomings of the YOLOv5 series model in terms of detection accuracy and break through the model bottleneck by appropriately replacing core components, introducing spatial attention mechanism, channel attention mechanism, and improving the loss function and nonlinear function. © 2023 SPIE.

关键词： Integrated circuits

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A Health Monitoring System with Multi-Sensor Information Fusion (HMS-MSIF) using a Deep learning Approach 4

A Health Monitoring System with Multi-Sensor Information Fus...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Guru Prasath, S. Santhosh, R. Mekala, N. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

In recent years, the Health Monitoring System (HMS) is a complicated technology that offers numerous benefits over conventional patient health management systems. these systems comprise a wearable wireless device, such as a bracelet, with sensors, incoming sensors, EEG sensors, and other features linked to a smartphone application that allows a doctor to obtain medical data remotely through a cloud platform. the cloud platform handles the information storage and retrieval management. Multi-sensor management involves managing or coordinating various sensor resources to govern environment perception activities. the innovative study on multi-sensor fusion is becoming the most significant factor in creating and researching modern multi-sensor-supported healthcare systems. therefore, this research finds a solution by designing a health monitoring system with multi-sensor information fusion (HMS-MSIF) using a deep neural network. this model designs a deep neural network for learning sensor representations from the raw sensor-based health data. the simulation results show the highest prediction accuracy of 97.58%, reliability ratio of 97.23% and signal-to-noise ratio of 98.64%. the proposed model gives the lowest error rate of 19.76%, thus suggesting real-time implementation. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient Hybrid Model for Botnet Detection Using machine learning 4

Efficient Hybrid Model for Botnet Detection Using Machine Le...

引用

4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Saxena, Priti Patel, R.B. Uttarakhand Technical University Computer Science and Engineering Dehradun India Chandigarh College of Engineering and Technology Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350393248

In recent years, with the advancements in technology, threats to internet security have also been increased and botnets are among those threats. Botnet is a very critical and important area of research in the field of cybersecurity. Botnets are basically a network of computers or other internet-connected devices that are infected by malware and controlled by a cybercriminal. they are used for various malicious purposes like spamming, phishing, DDoS (Distributed Denial of Service) attacks, and so on. One of the most dangerous uses of botnets involves perpetrating fraud which can even lead to financial losses for individuals as well as organizations. Detecting botnet fraud is a challenging task because of its complexity and sophistication. therefore, it requires a high level of research for its detection. In the current research paper, ML (machine learning) algorithms have been used for botnet fraud detection. A hybrid model (KNN, Random Forest, and Logistic regression) is built on the dataset which has yielded 86.97 percent accuracy, 81.76 percent precision, 98.25 percent recall, and 89.25 percent F1-score in detecting botnet attacks. We have also discussed the challenges associated with botnet detection and have identified potential future directions for research in this area. © 2023 IEEE.

关键词： Denial-of-service attack

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Construction of Financial Statement Analysis and Prediction Model Based on Deep learning Algorithm 24

Construction of Financial Statement Analysis and Prediction ...

引用

5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Liu, Jingjing Li, Yanwei Yang, Degang Weifang Engineering Vocational College Shandong Qingzhou China Shandong Lei Ao New Energy Co. Ltd. Shandong Weifang China

ISBN: (纸本)9798400709999

In order to understand the construction of financial statement analysis and prediction models based on deep learning algorithms, a research on the construction of financial statement analysis and prediction models based on deep learning algorithms is proposed. this paper first proposes a model for the intelligence of the financial information of enterprises based on deep learning. the next generation method transforms non-time indicators into real-time indicators and divides and evaluates financial data using neural networks with 4 hidden layers. Real financial data was used to evaluate the data and analysis, and the experimental results showed that the intelligent method focused on abnormal financial data based on deep learning is feasible. best and correct. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Prediction models

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Mental Healthcare Analysis using Power BI & machine learning 4

Mental Healthcare Analysis using Power BI & Machine Learning

引用

4th international conference on Signal Processing and Communication, ICSPC 2023

作者： Virani, Jash Daredi, Nikita Bhanushali, Aayush Shukla, Madhu Shah, Pooja Marwadi University Computer Engineering Big-Data Analytics Department Gujarat Rajkot India Marwadi University Computer Engineering Artificial Intelligence Department Gujarat Rajkot India Pandit Deendayal Energy University Cse Department Gujarat Gandhinagar India

ISBN: (纸本)9798350300772

the rise in mental health problems among people and the need for effective solutions for mental health have led to a lot of research in the machine learning domain for its applications in mental healthcare Many people's lives have been affected by the exponential rise in mental health and depression, and various factors must be revised. Different situations can have an impact on one's mental stability. So, taking the factors in mind like gender, age, insomnia, loneliness, depression level, aggressiveness, stress level etc. we are going to analyze the most affecting factors for poor mental health. Nowadays mostly people are facing some kind of mental health problems but very few people are giving attention to their mental health. So, our main purpose is to analyze the factors and find those factors which are responsible for a person's poor mental health. those factors are also anonymous by the person itself and they don't even know that they have weak mental health. For that we have taken the initiative to analyze and point out the main causes for poor mental health by circulating a google form Which has questions regarding regular lifestyle and from that we have collected data of different types of persons who are from different professions like students, employees, professors etc. After collecting the data, we had done preprocessing on that data and then we applied machine learning algorithms for classification and prediction. By applying machine learning algorithms, we found different types of outputs and by taking those outputs we can say which are more affecting factors on a person's mental health and we can suggest how people should improve their mental health and what type of things they should avoid for their good mental health. © 2023 IEEE.

关键词： machine learning

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Plant Image Disease Detection Using Deep learning 4

Plant Image Disease Detection Using Deep Learning

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

作者： Mishra, Devansh Pandey, Alok Deepanshu Sharma, Vivek Galgotias University Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350300888

Crop diseases present a significant challenge to food security, but the lack of infrastructure in many regions makes timely diagnosis difficult. However, the widespread adoption of smartphones and recent advancements in computer vision, powered by deep learning, have opened up new possibilities for disease detection with the help of smartphones. through the utilization of a deep convolutional neural network, trained on a publicly available dataset of 54,306 images featuring damaged and healthy plant leaves obtained under controlled conditions, it becomes possible to recognize 14 crop species and identify 26 different illnesses or determine if the plant is healthy. this deep learning model, boasting an impressive accuracy rate of 98.59%, proves to be the most effective artificial neural network topology for detecting plant diseases. By following a strategy that involves developing deep learning models on progressively larger image datasets and making them accessible to the public, the path is paved for widespread smartphone-assisted crop disease diagnosis on a global scale. Deep Convolutional Neural Networks (CNNs) have shown remarkable performance in image recognition tasks, including plant disease detection. these models are trained on large datasets of labeled images to learn patterns and features indicative of different diseases. © 2023 IEEE.

关键词： Smartphones

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