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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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Design of College Students' Innovation and Entrepreneurship Management System Based on Deep learning 4

Design of College Students' Innovation and Entrepreneurship ...

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4th international conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Wang, Xin Hankou University Wuhan China

ISBN: (纸本)9798350341690

In order to strengthen the application, evaluation, mid-term inspection, final acceptance and fund management of college students' innovation and entrepreneurship training programs, it is necessary to develop a project management system for college students' innovation and entrepreneurship training programs by using modern computer technology. the content of this paper is to develop a management system for college students' innovation and entrepreneurship based on deep learning, including reasonable database design and friendly interface design, which mainly realizes the functions of information release, student registration, information inquiry, project review, project conclusion and system background management. the system is a B/S structure and a Web server. the application included in this system architecture can be divided into three parts, namely, data acquisition, application server and data server. Predictive project process mining can help managers identify abnormal activities that are not in compliance with the project process. BiLSTM is combined with self-attention mechanism by using deep learning technology, and then the event log in the project process is used as input to realize accurate multi-task activity and time prediction. the test results all meet the expectations, indicating that the system is relatively stable and reliable. And it can solve some problems faced by students through a management platform that can meet the daily management functions for all teachers and students, such as project teaming. © 2023 IEEE.

关键词： Students

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TDLAS gas concentration retrieval application via SPFE-WV with deep learning 4

TDLAS gas concentration retrieval application via SPFE-WV wi...

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4th international conference on Testing Technology and Automation Engineering, TTAE 2024

作者： Liu, Haixu Li, Zhengzhuo Li, Yawen Wang, Chenxi Zhu, Cunguang School of Physics Science and Information Technology Liaocheng University Liaocheng252000 China

ISBN: (数字)9781510686656

ISBN: (纸本)9781510686649

In this study, a deep multilayer perceptron machine network (DMLP) framework that integrates feature engineering and weighted voting integration methods, called spectral profile feature engineering and weighted voting method (SPFE-WV), is proposed. In order to accurately predict the gas concentration in an experimental environment where various noises and background baseline interferences are present, through the preprocessing of the second-harmonic data time series, a feature engineering and weighted voting integration method to successfully extract key features such as the height and area of the main peak and the height difference between different concentrations. © 2024 SPIE.

关键词： Voting machines

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Skin Cancer Detection using machine learning Framework with Mobile application 7

Skin Cancer Detection using Machine Learning Framework with ...

引用

7th international conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Emam Ananna, Mariam Nayeem, Jannatul Jahangir Alam, Mohammad Islam, Saiful Daffodil International University Computer Science and Engineering Faculty of Science and Information Technology Bangladesh

ISBN: (纸本)9798350397284

A notable increase in skin cancer mortality, one of the most lethal kinds of cancer, has been caused by a lack of awareness of warning signals and preventative measures. the need for early skin cancer diagnosis has increased because of the fast development rate of melanoma skin cancer, its high cost of treatment, and its mortality risk. Treatment of cancer cells usually requires perseverance and manual identification. this study recommends using image synthesis and machine learning techniques to develop a system for diagnosing skin cancer. thermoscopic pictures are the input for the pre-processing phase. Following the segmentation of the thermoscopic images, the attributes of the injured skin cells are obtained using a feature extraction approach. Utilizing a convolutional neural network classifier with deep learning, the collected characteristics are stratified. An accuracy of 89% has been discovered using the publicly available dataset. © 2023 IEEE.

关键词： Feature extraction

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Early Prediction of Diabetes using Several machine learning Algorithms 7

Early Prediction of Diabetes using Several Machine Learning ...

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7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Rexie, J.A.M. Santhosh, P. Solomon, P. Nikhil Vishnu, P. Atul Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

One of the main issues diabetes poses to the medical profession globally is that its consequences are escalating swiftly. Elevated blood glucose levels cause diabetes, also referred to as diabetes mellitus or simply diabetes. On the basis of physical and chemical exams, a number of standard approaches can be used to diagnose diabetes. But, Doctors face a difficult task in predicting diabetes that affects the kidneys, eyes, heart, nerves, feet and other parts of the *** diagnosis analyses illness prognosis and diagnosis using a doctor's training and expertise, although this might be vulnerable to error. machine learning and data science approaches have the potential to enrich other scientific disciplines by providing new perspectives on well-known issues. One such initiative is to assist in making predictions based on medical data. 'machine learning' describes the processes by which computers tend to learn from experience, and is a recent area of data science. the aim of this research is to develop a system to accurately diagnose diabetes in patients at an early stage by comparing the results of several machine learning algorithms. Use four different supervised machine learning techniques: Random Forests RF, Logistic LR Regression,Decision DT Trees and (SVM) Support Vector machines. the main objective of this research project is to develop a prognostic tool for early detection and prediction of *** model is also deployed into a web application using Python Flask, and the web application is built using HTML and CSS. Anyone can input features into the web application, and the model - which was previously developed using machine learning techniques - will then predict whether or not they will be diagnosed with diabetes. © 2023 IEEE.

关键词： Diagnosis

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Crime Hotspot Detection using Optimized K-means Clustering and machine learning Techniques 4

Crime Hotspot Detection using Optimized K-means Clustering a...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Bonam, Janakiramaiah Burra, Lakshmi Ramani Susheel, G Sai Venkata Naga Sudarshan Narendra, Kadiyala Sandeep, MacHarla Nagamani, Gali Pvp Siddhartha Institute of Technology Department of CSE APVijayawada Kanuru India Koneru Lakshmaiah Education Foundation Department of CSE AP Vaddeswaram India

ISBN: (纸本)9798350300093

Over the world, crimes are becoming increasingly complicated and technologically advanced. It has become essential to prioritize actions targeting crime categories within every area. Nowadays, people are more concerned and stressed by the unprecedented increase in city crimes and violations. Large numbers of crimes are perpetrated frequently each day. Crime data analysts can aid law enforcement officials in discovering criminals more quickly. Crime analysis is an organized method of categorizing locations into crime hotspots and non-hotspot zones. the dataset utilized is the Kaggle-obtained UCI Crime and Communities Dataset. the algorithm can categorize the areas into hotspots and non-hotspot areas by examining crime records and associated variables. the suggested system uses data mining and machine learning techniques, such as optimized K-means clustering and the elbow approach, to explore datasets and analyze crimes committed. three classification algorithms - Decision Tree Algorithm, Support Vector machine, and Random Forest Algorithm are included in the model. they are trained on the dataset used to determine if an area is a hotspot based on the criteria that influence the occurrence of crimes in a region. © 2023 IEEE.

关键词： Support vector machines

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A Continual learning-Based Approach for Multi-source Image Enhancement and Reconstruction over Different Categorical Scenes 4

A Continual Learning-Based Approach for Multi-source Image E...

引用

4th international conference on computer Vision and Data Mining, ICCVDM 2023

作者： Gong, Xuyuan Qian, Longyue Yang, Yu Song, Ran School of Control Science and Engineering Shandong University Shandong Jinan250012 China

ISBN: (纸本)9781510674448

Real environments exhibit higher frequencies of complex scene conditions, characterized by their inherent variability. the loss of detail, loss of contrast, blurred picture and other degradation phenomena in the captured images, which may affect visual perception and the performance of computer vision tasks. this paper studies the image enhancement problem under complex scenes to improve the performance of computer vision tasks. this paper proposes a continuous learning-based deep neural network for enhancing images over different categorical scenes. the proposed model incorporated a Cross-Attention Module (CAM) into the image enhancement model and introduced an image compression reconstruction model within the continuous learning framework. the experimental results demonstrate that the proposed model demonstrates its accurate performance and its advantages than the baseline methods. © 2024 SPIE.

关键词： Image reconstruction

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Hybrid Approaches for Intracerebral Hemorrhage Stroke Classification Using Deep learning Techniques 4

Hybrid Approaches for Intracerebral Hemorrhage Stroke Classi...

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

作者： Sundaram, M.Sathya Karthick, S. thiyagarajan, P. Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Paavai Engineering College Department of Information Technology India

ISBN: (纸本)9798350300888

Intracerebral Hemorrhage (ICH) is a type of stroke and it is a second leading fatal condition. Due to ICH, most of the people are losing their individuality in life. the mortality rate of ICH is also increasing every year. this issue needs an emergency treatment and timely classification. Radiologists are now using the computer Tomography (CT) images for the analysis and identification of ICH but it is a time-consuming process. So, this research study concentrates on the analysis of deep learning models to develop a better solution for solving the existing problem. this research study utilizes the hybrid approaches of VGGNet and ResNet based deep learning approach. Here, small size filters of 3×3 are used to perform the better feature map. Definitely, this deep learning approaches will produce best accuracy and develop an effective model for the classification of ICH when compared to other tools. Here, the dataset is collected from Kalinga Institute of Medical Science, Bhubaneswar. Totally, 1164 CT images are collected from the 62 patients. © 2023 IEEE.

关键词： computerized tomography

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Ctransnet: A Modern Deep learning Model to Estimate Tropical Cyclone Intensity 4

Ctransnet: A Modern Deep Learning Model to Estimate Tropical...

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4th IEEE international conference on Pattern Recognition and machine learning, PRML 2023

作者： Shan, Xingchen Li, Jiagen Ren, Kaijun University of National Defense Technology College of Computer Science ChangSha China University of National Defense Technology College of Meteorology and Oceanography ChangSha China

ISBN: (纸本)9798350324303

Tropical cyclone (TC) intensity estimation is a crucial factor in disaster protection. Fast and accurate intensity estimation can provide data support for TC forecasts. With the popularization and development of deep learning networks, massive meteorological and oceanic data, such as remote sensing satellite images and multidimensional reanalysis data, have been fully utilized, providing new methods and ideas for estimating TC intensity. However, challenges still exist in the current state of deep learning in TC estimation: the research area is mainly in the Northwest Pacific Ocean, and there is a lack of universal deep learning models for the global region. In this study, we combine the CBAM module's adaptive attention mechanism with the Swin Transformer's excellent global modeling ability in computer vision tasks and propose a new deep learning network named CtransNet. this network consists of two main branches: feature extraction net, fine-grained intensity classification and numerical estimation net. the first branch is used to extract high-order semantic vectors from remote sensing images, the second is to output categorical intensity probability distribution and intensity estimation. Massive experiments were carried out on the TCIR dataset, which includes multi-channel remote sensing satellite images of TC in the Northwest Pacific Ocean, the Eastern Pacific Ocean (including the Central Pacific Ocean), the Indian Ocean, the Atlantic Ocean and the Southern Hemisphere. the root mean squared error (RMSE) in the intensity estimation task reached 11.85kt. Compared with the traditional method, the performance is improved by about 31.5%. © 2023 IEEE.

关键词： Probability distributions

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Driver Drowsiness Detection Using Deep learning 4

Driver Drowsiness Detection Using Deep Learning

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Sree Varshitha, V. Mohamed Iqbal, M. Amrutha, V. Pujitha, P. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravathi India

ISBN: (纸本)9798350306118

Every day many road accidents are happening across theworld and many people are losing their lives in these accidents. the majority of these incidents are causedby drowsy drivers. Drivers with Sleepiness and exhaustion are the critical issues contributing to road accidents. So, a real-Time drowsiness detection model using deep learning algorithms is used to avoid these incidents before they occur. By these algorithms, the sleepiness of the driver is detected by identifying the facial landmarks. Convolutional neural networks (CNNs) are used in the proposed system to extract essential features from images of faces taken with an onboard camera. A buzzer alarm is activated when drowsiness is detected to alert the driver and remind them to take the appropriate safety precautions. the accuracy of our well-Trained model is 95.5% which helps in easy detection of drowsiness. © 2023 IEEE.

关键词： Eye tracking

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9th international conference on machine learning, Optimization, and Data Science, LOD 2023

9th International Conference on Machine Learning, Optimizati...

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9th international conference on machine learning, Optimization, and Data Science, LOD 2023

ISBN: (纸本)9783031539657

the proceedings contain 34 papers. the special focus in this conference is on machine learning, Optimization, and Data Science. the topics include: Alternating Mixed-Integer Programming and Neural Network Training for Approximating Stochastic Two-Stage Problems;heaviest and Densest Subgraph Computation for Binary Classification. A Case Study;SMBOX: A Scalable and Efficient Method for Sequential Model-Based Parameter Optimization;accelerated Graph Integration with Approximation of Combining Parameters;improving Reinforcement learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison;a Hybrid Steady-State Genetic Algorithm for the Minimum Conflict Spanning Tree Problem;reinforcement learning for Multi-Neighborhood Local Search in Combinatorial Optimization;evaluation of Selected Autoencoders in the Context of End-User Experience Management;application of Multi-agent Reinforcement learning to the Dynamic Scheduling Problem in Manufacturing Systems;solving Mixed Influence Diagrams by Reinforcement learning;exploring Image Transformations with Diffusion Models: A Survey of applications and Implementation Code;multi-scale Heat Kernel Graph Network for Graph Classification;PROS-C: Accelerating Random Orthogonal Search for Global Optimization Using Crossover;a Multiclass Robust Twin Parametric Margin Support Vector machine with an application to Vehicles Emissions;LSTM Noise Robustness: A Case Study for Heavy Vehicles;ensemble Clustering for Boundary Detection in High-Dimensional Data;learning Graph Configuration Spaces with Graph Embedding in Engineering Domains;towards an Interpretable Functional Image-Based Classifier: Dimensionality Reduction of High-Density Diffuse Optical Tomography Data;on Ensemble learning for Mental Workload Classification;decision-Making over Compact Preference Structures;user-Like Bots for Cognitive Automation: A Survey;geolocation Risk Scores for Credit Scoring Models;On Channel Selection for EEG-Based Mental Workload Clas

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