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Design of Wireless Communication Base Station Monitoring System Based on Artificial intelligence and Network Security 3

Design of Wireless Communication Base Station Monitoring Sys...

引用

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

作者： Liu, Xiaojun Shandong Vocational College of Light Industry Shandong Zibo255300 China

With the rapid popularization of the network, under the increasingly complex network security situation and the increasingly prominent network security problems, network security occupies an important field in the wireless communication base station monitoring system, and has become a hot research direction. It is to design a wireless communication base station monitoring system based on artificial intelligence and network security. In the experiment, using the supervised machine learning algorithm, the program of the wireless communication base station monitoring system is designed by setting the working frequency of the GSM-based wireless communication system to the wireless communication base station monitoring system. © 2023 Elsevier B.V.. All rights reserved.

关键词： Network security

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Deep Learning based Chatbot Architecture for Medical Diagnosis and Treatment Recommendation 3

Deep Learning based Chatbot Architecture for Medical Diagnos...

引用

3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Rajani, Girish Ruparel, Khushi Thadomal Shahani Engineering College Dept. of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350348347

With the exponential growth and rapid development in the fields of deep learning and neural networks, chatbots have gained a lot of popularity and have become a proven, and efficient tool to interact and provide service to users. Healthcare is one of the most promising fields where chatbots can be used more efficiently. This has become important, especially in the current medical landscape, where there is a shortage of doctors, and patients often have to wait long periods before getting any medical guidance. By using the power of transformer models and machine learning algorithms chatbots can help patients with personalized diagnoses and treatment recommendations, efficiently at ease and convenience. This helps the patients to access medical services anywhere, at any point in time. This paper proposes a well-planned systematic architecture for a medical chatbot that utilizes the potential of transformers, classification algorithms and machine-learning models. The architecture includes three main components: a Naïve Bayes Classifier, a Binary Tree classifier along with a Support Vector Classifier, and a sequence-to-sequence model. These algorithms are used to classify symptoms and determine the severity of a medical condition to provide patients with accurate medical diagnoses and treatment recommendations. Overall, the proposed architecture is built and designed to bridge the gap between doctors and patients by providing immediate access to medical advice, making it a promising tool for improving the quality and accessibility of healthcare services. © 2023 IEEE.

关键词： Diagnosis

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Research on the Application of Artificial Emotional intelligence in the Border——Take iBorderCtrl as an Example 3

Research on the Application of Artificial Emotional Intellig...

引用

3rd international conference on Artificial intelligence and Electromechanical Automation, AIEA 2022

作者： Zhou, Xin Yang, Zenghui Han, Wenjun Kuang, Jie Liao, Donghua Key Laboratory of Psychology of TCM and Brain Science Jiangxi Administration of Traditional Chinese Medicine Jiangxi University of Chinese Medicine 1688 Meiling Avenue Nanchang China Guangdong Ocean University China Henan Normal University China

ISBN: (纸本)9781510657281

Artificial intelligence (AI) technologies are rapidly proliferating around the world. In this paper, we first present the current state of AI applications around the world, analyzing the current use of iBorderCtrl in border control and the problems that exist. The current state of domestic security against terrorist attacks and mass migration problems highlights the need for a better and more effective border management system. At the heart of the AI-based iBorderCtrl project is the ability to perform automated "deception detection" and "risk assessment" in border crossing security. iBorderCtrl's technology is built on the controversial discipline of "emotion recognition science". Systems that rely on machine learning, especially those involving facial recognition technology, have so far been problematic and biased. © 2022 SPIE.

关键词： Risk assessment

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Evaluation method of singing pronunciation quality based on artificial intelligence technology 3

Evaluation method of singing pronunciation quality based on ...

引用

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

作者： Tang, Hanzhang Southwest University of Science and Technology Mianyang621010 China

Since people have limited ability to judge the quality of their own singing and pronunciation, it is difficult to find problems on their own after reaching a certain level, so they need external help. However, external help also comes from human beings in the past and has many limitations. Therefore, in order to provide better help, this paper will carry out research from the perspective of artificial intelligence. In this paper, the basic concepts of artificial intelligence technology are discussed, and then the evaluation ideas of singing pronunciation quality on the technical level are put forward. Finally, the method system is designed. © 2023 The Authors. Published by Elsevier B.V.

关键词： Quality control

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Effect of Hyperparameter Tuning on Transfer Learning Models for Brain Tumor Detection and Classification 3

Effect of Hyperparameter Tuning on Transfer Learning Models ...

引用

3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Nazareth, Andre Isaac Pereira, Grace Prashant Nagarkatte, Trisha Nitin Save, Jagruti Artificial Intelligence and Data Science Fr.Conceicao Rodrigues College of Engineering Mumbai India

ISBN: (纸本)9798350348347

A mass of abnormal cell growth in the brain is known as a brain tumor, these are of two types benign and malignant. Currently, medical professionals use Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans to analyze and identify the presence of tumors. Finding brain tumors is an arduous but crucial endeavor. The emergence of Artificial intelligence techniques has led to the detection of brain tumors using various machine Learning algorithms and techniques, particularly Deep Learning. These algorithms are advantageous as there are fewer errors and greater precision which in turn which makes decision-making and selection of the patient's best course of action more accurate. In this paper, we have employed Magnetic Resonance Images (MRI) images that were applied to a variety of transfer learning models, including VGG19, InceptionV3, and DenseNet121. To determine the optimal values of the learning rate, batch size, optimizer, and the number of epochs we performed hyperparameter tuning for each model. The Wilcoxon-Mann-Whitney test was performed to assess whether the hyperparameters have made a significant impact on each metric used. After performing this test and determining the accuracy we discover that DenseNet121 gives us the highest accuracy of 96%, followed by InceptionV3 which displays an accuracy ranging from 89 to 91%, and VGG19 which provides an accuracy of 85% but with a rather high penalty on error. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Third international conference on Artificial intelligence, Virtual Reality, and Visualization, AIVRV 2023

Third International Conference on Artificial Intelligence, V...

引用

3rd international conference on Artificial intelligence, Virtual Reality, and Visualization, AIVRV 2023

ISBN: (纸本)9781510671485

The proceedings contain 92 papers. The topics discussed include: optimized scheduling of unmanned baggage transport vehicles in airports considering battery degradation;optimized scheduling of unmanned baggage transport vehicles in airports considering battery degradation;an efficient and privacy-preserving federal learning scheme;research on recommendation algorithm of private charging pile based on collaborative filtering;design of dormitory management system based on fingerprint recognition;adaptive genetic simulated annealing algorithm for project demonstration scheduling problem;research on the application of digital twin technology in smart highway real-time simulation;research on key technologies of smart security check in urban rail transit;an improved person re-identification method;and artificial intelligence-based intelligent elderly care system.

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Enhancing the Detection of Botnet Attacks in the Internet of Things Networks Through the Utilization of Hybrid Feature Selection 3

Enhancing the Detection of Botnet Attacks in the Internet of...

引用

3rd FORTEI-international conference on Electrical Engineering, FORTEI-ICEE 2024

作者： Kurniawan, Hartanto Samsudiat Nugroho, Cahyono Saputra, Andri Nursyahbani, Aditya Research Center for Artificial Intelligence and Cybersecurity National Research and Innovation Agency Jakarta Indonesia Center of Data and Information National Research and Innovation Agency Jakarta Indonesia

ISBN: (纸本)9798331542207

The proliferation of Internet of Things (IoT) technology is leading to an escalation in cyber risks. The Intrusion Detection System (IDS) is crucial in countering cyber threats, with botnets being the most commonly detected attack in IoT networks. Anomaly-based IDS is necessary to enhance their capability in identifying cyber threat attributes through the use of supervised machine learning. This work investigates an enhanced supervised machine learning algorithm technique that combines filter-based feature selection methods, specifically Low Variance and Pearson Correlation Coefficient, to detect a new type of botnet attack. The CICIoT2023 dataset, an updated benchmark intrusion dataset specifically designed for IoT networks, is employed to assess the model’s performance using several algorithms including Decision Tree, Random Forest, Logistic Regression, and Naive Bayes. The results demonstrate that the model effectively enhances the identification of botnet attacks by reducing the number of features from 46 to 14. This optimization leads to improved performance, including increased accuracy level and reduced computation time. © 2024 IEEE.

关键词： Bot (Internet)

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recognition of Traffic Police Gestures Based on Transfer Learning 3

Recognition of Traffic Police Gestures Based on Transfer Lea...

引用

3rd international conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Lian, Li Mariano, Vladimir Y. National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University College of Big Data and Artificial Intelligence Hefei China

ISBN: (纸本)9798350316674

At present, the auto-driving technology is becoming more and more mature, but how the auto-driving car recognizes the traffic police gesture information under special road conditions has become an urgent problem to be solved. In this paper, based on transfer learning, eight kinds of gesture signals of Chinese traffic police are recognized. The dataset is "Chinese Traffic Police Gesture Dataset"(CTPGD). The total loss value is 0.2174, and good results have been achieved. © 2023 IEEE.

关键词： CTPGD traffic police gesture transfer learning

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Utilization of Kubeflow for Deploying machine Learning Models Across Several Cloud Providers 3

Utilization of Kubeflow for Deploying Machine Learning Model...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Chauhan, Rahul Rawat, Ruchira Hsiung, Pao-Ann Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9798350319125

Kubeflow is a global machine learning platform that is open-source in nature. Its primary objective is to streamline the process of deploying, managing, and scaling machine learning (ML) and deep learning (DL) workflows. This platform is specifically built to operate on Kubernetes, which is a widely used container orchestration platform. Kubeflow offers a comprehensive platform for managing machine learning operations (MLOps), facilitating efficient collaboration across data scientists, ML engineers, and DevOps teams at every stage of the machine learning lifecycle. The use of Kubernetes' containerization capabilities facilitates the establishment of a portable and scalable environment specifically designed for innovative machine learning applications. The objective of this study is to investigate the procedure of installing machine Learning models on Kubernetes by using Kubeflow, an open-source technology recognised as an end-to-end ML Stack orchestration toolkit. In this study, development of comprehensive machine Learning models is done using Kubeflow in the form of pipelines. Then many aspects of the tool are analysed, including the simplicity of its setup, the deployment of models, their performance, restrictions, and features. This project aims to serve as a seminar or introduction report, providing assistance to vanilla cloud/Kubernetes users who possess no prior knowledge of Kubeflow. The objective is to guide them in using Kubeflow for the deployment of machine learning models. This research provides comprehensive information and analytics on the performance of Kubeflow, including many aspects such as the setup on multiple cloud platforms and the deployment of our trained model for serving over the internet. © 2023 IEEE.

关键词： Artificial intelligence Data Science Deep Learning Docker Kserve Kubeflow Kubernetes machine Learning

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An Efficient Approach of Drowsiness Detection Based on ResNet50 and Xception Architectures 3

An Efficient Approach of Drowsiness Detection Based on ResNe...

引用

3rd IEEE international conference on Electronic Engineering, ICEEM 2023

作者： El-Nabi, Samy Abd El-Rabaie, El-Sayed M. Emam, Ahmed Ramadan, Khalil F. Faculty of Computer Science and Engineering Dept. of Artificial Intelligence Engineering South Sinai46511 Egypt Menoufia University Faculty of Electronic Engineering Dept. of Electronics and Electrical Communications Menouf32952 Egypt Menoufia University Dept. of Computer Science and Math Faculty of Science Menoufia Egypt

ISBN: (纸本)9798350323511

This paper delves into a critical aspect of road safety by investigating a method to detect drowsiness in images. The accurate utilization of this technology by fatigued drivers holds the potential to prevent numerous fatal accidents, as the proposed models exhibit swift responsiveness in identifying instances of driver drowsiness. Within the realm of eye closure detection, two drowsiness models, namely ResNet50 and Xception, are employed to portray various eye state classifications. Nonetheless, the scarcity of readily available and reliable eye datasets poses a formidable challenge. Leveraging the power of ResNet50, deep facial features are extracted, resulting in an impressive accuracy of 98.06%. Similarly, the utilization of Xception yields a commendable accuracy of 95.69% when evaluated with the MRL dataset. These experimental outcomes firmly establish the higher accuracy and cost-effectiveness of eye closure estimation, thereby underscoring the efficacy of the proposed framework for drowsiness recognition. © 2023 IEEE.

关键词： Cost effectiveness

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