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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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Research on the identification of polymer interface wear types based on machine learning algorithms 4

Research on the identification of polymer interface wear typ...

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

作者： Wen, Xiaohao Wang, Shuwen Macau Institute of Systems Engineering Macau University of Science and Technology China Glastonbury High School HartfordCT06033 United States

ISBN: (纸本)9781510672505

To monitor the in-situ wear state of friction pairs, a friction noise signal processing method based on polymer interface wear performance prediction was introduced. Four types of friction noise signal datasets were collected from four different friction surfaces consisting of three constant load-speed points of six temperature points ranging from -120 °C to 25 °C and seven types of metal counterparts. three wear states, including adhesive wear, wear, and a combination of both, were defined using field emission scanning electron microscopy for polymer wear surfaces. Six friction noise features were extracted from the time-domain signal of the sound using three machine learning algorithms, including random forest, deep forest, and XGBoost, to classify the three wear states. the results indicate that the deep forest algorithm exhibited the most effective classification performance. As a result, this method holds promising potential for on-site and real-time condition monitoring, as well as wear classification in the field of tribology applications. © 2023 SPIE.

关键词： Scanning electron microscopy

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Toward Improving Internet of things (IoT) Networks Security Using machine learning Based Intrusion Detection System 4

Toward Improving Internet of Things (IoT) Networks Security ...

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4th international conference on Digital Age and Technological Advances for Sustainable Development, ICDATA 2023

作者： Mouiti, Mohammed Elhariri, Ayyoub Habibi, Omar Lazaar, Mohamed Mohammed v University Ensias Rabat Morocco

ISBN: (纸本)9798350311112

As the Internet of things (IoT) expands quickly, network monitoring will confront new security challenges. By 2030, it is expected that fifty billion physical devices will be interconnected to the Internet. However, most IoT devices are vulnerable due to end users and device manufacturers' lack of security awareness. As a result, designing and implementing specialized security measures and mechanisms suitable for the network environment of the IoT is required. therefore, how to improve IoT network security challenges remains a critical computer security issue. this paper proposes a machine learning (ML) based network intrusion detection system (NIDS) to identify IoT network threats. In the first step of this research methodology, four machine-learning algorithms were implemented and compared using the UNSW-NB15 dataset. In the next step, an oversampling approach was performed with ADASYN (Adaptive Synthetic) to tackle imbalanced datasets. Lastly, hyperparameter tuning was proposed for different models to improve performance and enhance efficiency. the experimental results were analyzed and compared in terms of precision, accuracy, F1-score, area under the curve (AUC), and recall. the results were competitive compared with existing works, with an accuracy of 99%. © 2023 IEEE.

关键词： Internet of things

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An eve teasing prohibitor application with SOS services: A systematic review 4th

An eve teasing prohibitor application with SOS services: A s...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Tomar, Harsh Srivastav, Ishan Fahad, Mohammed Lamba, Sonia Department of CSE ABES Engineering College U.P. Ghaziabad India

ISBN: (纸本)9781032911571

Although, the constitution of India and IPC special laws pledge to provide gender equality, safety and dignity at all workplaces, educational institutions and other places for everyone in India. But still crimes like Eve –teasing is one of the profound and silent forms of crime, majorly in colleges as it is not reported in the capacity it happening now a days. this indicates a need of an emergency system for providing them help. this SOS application for android and iOS users provides support to students and help in situations of eve-teasing. It signals with the Morse code, short and long sounds, allowing students to transmit alert signals to higher authorities, thus responding swiftly to the location of the incident. It is a model based on SOS Emergency where a student can inform the officials by just giving some signs (Morse code, Short and long sounds). Hence, the officials get alert of eve-teasing along with the location of concerned student so they can reach out to help the student as soon as possible. therefore, we are developing this software that makes use Artificial Intelligence and machine learning to assist students and restrict eve teasing. We are adding features of location tracking, voice recognition and individual login features. Personalized and efficient emergency alert notification system is one of the most efficient tools for personal security and safety. Now a days, the most used and effective emergency alert notification system. © 2025 the Author(s).

关键词： Students

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Customer Segment Classification Prediction in the Australian Retail Based on machine learning Algorithms 4

Customer Segment Classification Prediction in the Australian...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Luo, Yiming Zhang, Rui Wang, Fei Wei, Tao Business Schoo The University of Sydney Sydney Australia Computer and Information Sciences Program Harrisburg University of Science and Technology Harrisburg United States Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China School of Chemical and Environmental Engineering Sichuan University of Science and Engineering Zigong China

ISBN: (纸本)9798400709449

Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. this study employs multiple machine learning methods to classify Australian Retail company BIGW's customer segments and first to apply multiple model explanation methods to find insights related to customer segmentation identification. After rigorous comparison and hyperparameter fine-tuning, XGBoost is the most adept for this dataset. We derive three key insights through the model results and model interpretive methods. First, BIGW's primary clientele comprises young families in urban areas who prefer cost-effective products, establishing the foundation of their consumer base. Second, the model result indicates a notable gap in BIGW's understanding of its high-end customers, suggesting an area for immediate attention. third, a specific customer segment emerges from the data: individuals favoring online shopping, demonstrating high total expenditure but low interest in promotions, representing a high-value segment. © 2023 ACM.

关键词： Customer satisfaction

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Prediction of Diseases in Potato Plant using Pre-trained and Traditional machine learning Models 4

Prediction of Diseases in Potato Plant using Pre-trained and...

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4th international conference for Emerging Technology, INCET 2023

作者： Sahu, Swati Laxmi Chintala Bhargavi, Renta Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335750

Potato, among the most vegetables is commercially significant and well-known vegetable which is known for its high nutritional content and delicious flavor. India is one of the world's leading producers of potato. Unfortunately, plant diseases in potato have been one of the causes of decreased production. So, it is necessary to detect them. Collecting images of plants diseases is a big challenge as it is a very time-consuming process. Often, we do not have sufficient data to train our deep learning models, so data augmentation techniques are used for increasing the dataset which lead to poor generalization. this study focuses on detecting whether the plant is healthy or diseased. In this proposed method, limited dataset is used for potato plant disease classification without using any data augmentation techniques. Popular pre-trained models - VGG16, InceptionResNetV2, ResNet50V2 are used for feature extraction and traditional machine learning algorithms - XGBoost, Support Vector machine (SVM), K-Nearest Neighbor (KNN), Random Forest are used as classifiers. From the study, it is observed that the combination of VGG16 model as a feature extractor and SVM as a classifier achieved the highest accuracy of 93% compared to rest of the combination of models and algorithms. the method proposed in this study can be used for potato plant disease detection with limited dataset. © 2023 IEEE.

关键词： Vegetables

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Early Landslide Detection using Wireless Sensor Network 4

Early Landslide Detection using Wireless Sensor Network

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4th international conference on Artificial Intelligence and Signal Processing

作者： Singh, Aditi Dhoriyani, Urva Maiti, Rusha Panpalia, Gaurang Dalal, Vansh Dash, Debashish Vellore Inst Technol Sch Comp Sci & Engn SCOPE Vellore Tamil Nadu India Vellore Inst Technol Sch Elect Engn SENSE Vellore Tamil Nadu India

ISBN: (纸本)9798350350661;9798350350654

this paper aims at detecting and warning of possible landslides well in advance, preventing a significant threat to both human life and infrastructure. Hence the development of effective early detection systems is no longer an option but a necessity. Here, we performed a comprehensive study on implementing a machine learning (ML) based early landslide detection system. the primary objective is to investigate the feasibility and accuracy of an ML algorithm in predicting landslides before they occur and providing a warning using a system of wireless sensors. We considered a dataset comprising of diverse geological and meteorological features, compiled to train and evaluate the ML algorithm. Here, we also explored the application of a supervised learning algorithm, to classify regions susceptible to landslides based on historical data and update its dataset based on data from landslides which it itself predicts, to be used for future reference. Additionally, the research investigates the integration of remote sensing data, including ground-based sensors, to provide real-time inputs periodically, such as the moisture level of the soil nearby as well as any change in the inclination of the land, for the ML models. the findings contribute to the growing body of knowledge in landslide detection and offer insights into the practical implementation of ML algorithms for early warning systems. the results highlight the potentials of ML to significantly enhance the accuracy and timeliness of landslide predictions, ultimately contributing to improved disaster preparedness and risk reduction strategies.

关键词： climate conditions real-time data analysis supervised learning random forest geological features

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Deep learning Model for Simulating Self Driving Car 4

Deep Learning Model for Simulating Self Driving Car

引用

4th international conference on Communication Systems, Computing and IT applications, CSCITA 2023

作者： Bhujbal, Kunal Pawar, Mahendra Department of Computer Engineering Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Mumbai India

ISBN: (纸本)9781665459877

Self-driving cars have become a trending subject with a significant improvement in the technologies in the last decade. the project purpose is to train a convolutional neural network to drive an autonomous car agent on the tracks of Udacity's Car Simulator environment. Udacity has released the simulator as an open source software. Driving a car in an autonomous manner requires learning to control steering angle, throttle and brakes. Behavioral cloning technique is used to mimic human driving behavior in the training mode on the track. that means a dataset is generated in the simulator by a user driven car in training mode, and the NVIDIA's convolutional neural network model then drives the car in autonomous mode. Augmentation and image pre-processing are used to increase the accuracy of CNN model. © 2023 IEEE.

关键词： Autonomous vehicles

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Efficient and Unique learning of the Complex Receiver Structure of Galileo E5 AltBOC Using an Educational Software in Matlab 4

Efficient and Unique Learning of The Complex Receiver Struct...

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4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Ali, Irfan Khan, Subhan Ali, Mumtaz Mujeeb, Kashif Sukkur Iba University Computer Systems Engineering Department Sindh Sukkur Pakistan School of Electrical & Information Engineering University of Sydney Sydney Australia

ISBN: (纸本)9798350335316

In this paper, a unique and efficient method for learning the complex receiver structure of Galileo E5 AltBOC (Alternative Binary Offset Carrier) is presented. the methodology involves a software application designed in Matlab, which demonstrates the steps required to design a software receiver for Galileo E5 signal. the software application covers key concepts related to the Galileo E5 signal, such as signal acquisition, signal tracking, navigation data extraction, power spectral density (PSD) of the AltBOC (15,10), Fast Fourier Transform (FFT) of the E5a and E5b signals, and implementation of the subcarrier used for the AltBOC (15,10). Additionally, this paper presents a novel approach for extracting navigation data using the prompt channel of carrier tracking from the code loop discriminator. © 2023 IEEE.

关键词： MATLAB

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ML&AI_IIIT Ranchi@DravidianLangTech: Leveraging Transfer learning for the Discernment of Fake News within the Linguistic Domain of Dravidian Language 3

ML&AI_IIIT Ranchi@DravidianLangTech: Leveraging Transfer Lea...

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3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023

作者： Kumari, Kirti Jha, Shirish Shekhar Dayanand, Zarikunte Kunal Sharma, Praneesh IIIT Ranchi Ranchi India IISER Bhopal Bhopal India KIIT Bhubaneswar India

ISBN: (纸本)9789544520854

the primary focus of this research endeavor lies in detecting and mitigating misinformation within the intricate framework of the Dravidian language. A notable feat was achieved by employing fine-tuning methodologies on the highly acclaimed Indic-BERT model, securing a commendable 4th rank in a prestigious competition organized by DravidianLangTech 2023 while attaining a noteworthy macro F1-Score of 0.78. To facilitate this undertaking, a diverse and comprehensive dataset was meticulously gathered from prominent social media platforms, including but not limited to Facebook and Twitter. the overarching objective of this collaborative initiative was to proficiently discern and categorize news articles into either the realm of veracity or deceit through the astute application of advanced machine learning techniques, coupled with the astute exploitation of the distinctive linguistic idiosyncrasies inherent to the Dravidian language. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023 - proceedings.

关键词： Linguistics

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Deep learning-Based Solution for Differently-Abled Persons in the Society 4

Deep Learning-Based Solution for Differently-Abled Persons i...

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4th international conference for Emerging Technology, INCET 2023

作者： Rahul, M. Ravichandra, P. Yakoobi, Mohammed Mubashir Gowda, Naveen Chandra REVA University Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335750

this paper intends to furnish support to individuals grappling with visual impairments through the employment of YOLOV4, an advanced object detection model. the proposed system employs a mobile application to generate audio feedback in real-time contingent upon object detection in the user's immediate surroundings. YOLOV4's remarkable precision in detecting objects renders it an ideal contender for this application. the effectiveness of the system will be evaluated through a series of experiments, and its potential impact on the visually impaired community will be analyzed. this paper represents a breakthrough in assistive technology, and can potentially transform the lives of millions of people worldwide. Overall, this paper aims to harness cutting-edge technology to enhance the quality of life for individuals with visual impairments by enabling them to better navigate their surroundings through the use of audio feedback. © 2023 IEEE.

关键词： Object detection

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