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共 3244 条记录，以下是411-420 订阅

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Malicious Traffic Classification Algorithm Based on Multimodal Fusion 3rd

Malicious Traffic Classification Algorithm Based on Multimod...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Li, Xinhang He, Mingshu International School Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819616930

The application of deep learning in classifying malicious network traffic is prevalent. However, our paper highlights two frequently neglected issues in current methodologies: first, network traffic data need to be truncated and zero-filled to adapt to the training process of deep learning, which brings a certain degree of information loss;second, individual packets of network traffic will show different importance in the classification task due to their own data quality and location. To solve these two problems, a multimodal attention network is proposed to mitigate the information loss by fusing data from two different modalities in the network traffic and solving the imbalance of data from different modalities in the training process using a regularization method. Additionally, we incorporate a dual attention module to assess the significance of each packet, enabling the model to prioritize and emphasize the most crucial ones. To evaluate the model’s effectiveness, we test it on three openly accessible datasets: UNSW-NB15, ISCXIDS2012, and CIC-DoS2017. Our findings reveal that the model presented herein surpasses several conventional deep learning approaches in terms of classification outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： deep learning malicious behavior multimodal network traffic classification

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A Convolutional Neural Network-Based Web Prototype to Support Melanoma Skin Cancer Detection 3

A Convolutional Neural Network-Based Web Prototype to Suppor...

引用

3rd international conference on Information Systems and Software Technologies (ICI2ST)

作者： Rosas-Lara, Mauro Mendoza-Tello, Julio C. Flores, Aldrin Zumba-Acosta, Gema Univ Cent Ecuador Fac Ingn & Ciencias Aplicadas Quito Ecuador

ISBN: (纸本)9781665455176

The skin is the organ that protects the human body. However, factors such as solar radiation damage the texture and skin cells. Sometimes, the lack of timely diagnosis leads to skin cancer. In this line, melanoma is the most dangerous type of cancer and has caused the greatest number of deaths related to skin diseases. With this problem, collaborative efforts between different research areas are necessary to support early detection of the disease. In this way, the evolution of algorithms based on neural networks plays an important role for image processing, which is an essential activity for pattern detection and recognition in medical diagnosis. Faced with this challenge, this research proposes a web prototype based on convolutional neural networks to support melanoma detection. In this context, the reference framework suggested by the Cross Industry Standard Process for data mining (CRISP-DM) was used. For this, 18,000 high-quality images were compiled from the data science community. In addition, two learning models (based on convolutional neural network and Res-Net50) were created and evaluated. With these premises, a web application was developed using the waterfall model. Finally, conclusions and future work are suggested at the end of the document.

关键词： machine learning neural network melanoma data mining skin cancer

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A machine learning Based Melanoma Skin Cancer using Hybrid Texture Features 3

A Machine Learning Based Melanoma Skin Cancer using Hybrid T...

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Meenakshi, K. Adepu, Abhiram Nagandla, Venkata Vignesh Teja Agarwal, Siddhanth GRIET Dept. of ECE Hyderabad India

ISBN: (纸本)9798350338607

There is a great rise in patients afflicted with the disease Melanoma, the most unpredictable and potentially fatal cancer. If treated in time, the fatality rate can be reduced significantly in most cases. Active Melanoma discovery in the incipient stage is difficult to recognize. As the availability of qualified dermatologists is scarce, a computerized automated method of finding Melanoma accurately is urgently needed. The aforementioned research aims to accomplish this by using supervised machine learning algorithms that are capable of accurately identifying and classifying melanoma to separate skin lesions from the images collected from the combined dataset of the ISIC archive and the PH2 dermoscopic images. For segmenting the tumor portion, the Otsu Thresholding algorithm is employed. The segmented images are applied to a preprocessing network to extract features of ABCD, GLCM, LBP and Histogram of Gradients (HG). Later, these features are trained with different supervised classifiers to generate the model. Later the test data is fed to the model to obtain the predictive labels. The results of the proposed skin melanoma detection prove that skin melanoma detection with XGBoost provides superior results compared to the existing works in the literature. © 2023 IEEE.

关键词： Local binary pattern

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The spatiotemporal evolution of carbon emissions in China using machine learning methods: evidence from remote sensing data 3

The spatiotemporal evolution of carbon emissions in China us...

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3rd international conference on Remote Sensing, Surveying, and Mapping, RSSM 2024

作者： Zhang, Yuxin Sun, Chenghao Ming, Yitong Wang, Yuhao School of Management Shandong University of Traditional Chinese Medicine Jinan250355 China School of Economics and Trade Shandong Management University Jinan250357 China School of Energy and Power Engineering Qilu University of Technology Jinan250357 China

ISBN: (数字)9781510680081

ISBN: (纸本)9781510680074

The spatial-temporal distribution and heterogeneity of carbon emissions are important issues in ecological environmental protection and climate change monitoring. Based on DMSP/OLS night light remote sensing data and socioeconomic data, this paper uses machine learning methods to study the carbon dioxide emissions of 281 prefecture-level cities in China from 2005 to 2020, and reveals the characteristics of spatial-temporal distribution, growth trend and agglomeration of carbon emissions. The results show that in recent years, Chinese carbon emissions show an increasing trend, and regional carbon emissions have obvious characteristics of spatial agglomeration and unbalanced distribution. From the spatial dimension, the pattern of carbon emissions gradually evolves to "high in the east and low in the west" and "high in the north and low in the south". This study can provide scientific reference for carbon emission estimation and prediction, energy conservation and ecological environmental protection. © 2024 SPIE.

关键词： machine learning

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The Smart Handwritten Digits recognition Using machine learning Algorithm 3

The Smart Handwritten Digits Recognition Using Machine Learn...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Mohamed, Nachaat Josphineleela, R. Rakhamaji Madkar, Shankar Vasantha Sena, J. Alfurhood, Badria Sulaiman Pant, Bhasker University Sains Malaysia Rabdan Academy Homeland Security Abu Dhabi United Arab Emirates Panimalar Engineering College CSE Tamil Nadu India College of Engineering Maharashtra Pune India A.P.C.Mahalaxmi College for Women Thoothukudi India Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia CSE Graphic Era Deemed to be University Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

Never before have people relied so much on technology;now, machine learning and based on machine learning can do anything from classifying objects in photos to inserting sounds to old movies. Similar to this, handwritten word extraction is an important field of study and advancement with a wide range of potential outcomes. The capacity of a robot to accept and analyze understandable handwriting inputs via sources like paper files, pictures, smart phones, or other gadgets is called as handwriting, sometimes known at text recognition. Evidently, in this study, they have carried out handwritten digit recognition utilising Support Vector machines (SVM), Multi-Layer Number of neurons (MLP), and Deep Convolution Network (CNN) models with aid of MNIST dataset. To find the most accurate model for text detection, our major goal is to contrast the following models' execution speed and correctness. The MNIST registry identity, which will be scrawled in numerals, may be recognised by the software. In machine language, the natural handwritten form may be recognised. The deep neural network is a machines educational approach that we use in this case. The nine integers in the MNIST data range from 0 to 9. It could be used to organise number and composition into groups that computers can understand. The main objective of this work is to guarantee reliable methods for identifying written finger numbers and simplifying and eliminating errors in banking processes. The MNIST information will be correctly identified by the system. Comparing the model's precision and system performance is our main objective. © 2023 IEEE.

关键词： Support vector machines

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Abnormal Signal recognition Method of Wearable Sensor Based on machine learning 3rd

Abnormal Signal Recognition Method of Wearable Sensor Based ...

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3rd EAI international conference on IoT and Big data Technologies for Health Care, IotCARE 2022

作者： Li, Chao Zhang, Xuan Department of Information Engineering Tongling Polytechnic Anhui Tongling244000 China Monroe College New RochelleNY10801 United States

ISBN: (纸本)9783031335440

The recognition of abnormal signal of wearable sensor is of great significance to the application value of the device. In order to improve the accuracy of abnormal signal recognition of wearable sensors and indirectly ensure the safety of wearable sensor devices, a method of abnormal signal recognition of wearable sensors based on machine learning was proposed. According to the different abnormal types and principles of wearable sensors, the signal abnormal judgment criteria are set. The wearable sensor signal is collected, and the initial signal is preprocessed by Kalman filtering, normalization and weighted fusion. The machine learning algorithm is used to extract the features of sensor signals, and the recognition results of the abnormal type, abnormal semaphore and abnormal location of sensor signals are obtained through feature matching. Through the identification performance test experiment, it is obtained that the average abnormal type error detection rate of the optimization design identification method is 0.86%, and the average statistical error of abnormal semaphore is 0.22 db, lower than the preset value. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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Utilising Deep learning as a Law Enforcement Ally 3

Utilising Deep Learning as a Law Enforcement Ally

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3rd international conference on Smart data Intelligence, ICSMDI 2023

作者： Ramani, D. Roja Nirmala, M. Sourabh, V.N. Chaudhary, Shreyas Kumar, Deepesh New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665464871

Finding fugitive offenders after they have committed a crime or an illegal act takes time and effort. It is challenging for law enforcement authorities to complete this work on their own given the rising population density and the size of any nation's landmass. Thus, public participation becomes crucial, revolutionary, and beneficial. This cycle is both times and works seriously. In this paper, we tried to suggest a different framework for criminal Distinguishing & recognition using Deep learning and Heroku Cloud, i.e. Cloud Computing, which, assuming it is used by our Crime Control Organizations, would help them catch criminals from CCTV images or images uploaded by the public if seen anywhere. This system is in place to assist in capturing criminals and anyone who can upload information indicating that they saw the relevant individual at a specific location and time. In India, where conditions are always changing due to things like light, weather, and specific directions, existing solutions use conventional face acknowledgement computations, which might be problematic because there is no open public contribution. Our research paper employs LBPH, Deep learning, and Heroku Cloud technologies to construct the system. © 2023 IEEE.

关键词： Local binary pattern

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Detection of Venous Thromboembolism Using Recurrent Neural Networks with Time-Series data 24

Detection of Venous Thromboembolism Using Recurrent Neural N...

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3rd Asia conference on Algorithms, Computing and machine learning, CACML 2024

作者： Xu, Can Huang, Yaqin Xiang, Xinni Lei, Haike Yang, Jie Shanghai Jiao Tong University Shanghai China Chongqing University Cancer Hospital Chongqing China West China Hospital Sichuan University Chengdu China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition China Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment China West China School of Medicine China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center China

ISBN: (纸本)9798400716416

machine learning (ML) has been widely applied to medical science for decades. As common knowledge, the progress of many diseases is often chronic and dynamic. Longitudinal data, or time-series data, has better descriptions of certain diseases, as it naturally captures the dynamic over time. Venous thromboembolism (VTE), as a regular complication of cancer tumour treatments, has been emphasized in many studies. Recent research has shown that ML algorithms incredibly contribute to VTE risk estimation and prediction. However, existing methods rarely consider the dynamic changes during the development of VTE, and often make decisions based on time-point data, such as the latest blood test results of patients. In this paper, we propose a VTE detection model that takes advantage of capturing dynamic information using time-series data. The model consists of a Recurrent Neural Network (RNN) based framework with time embedding and Attention mechanism to encode longitudinal features of patients. We test our proposed model on a real-world dataset that contains multiple times of blood test results of patients together with other clinical features. The network is compared with several commonly used ML models in previous VTE studies. The results show that our model outperforms all baseline models, and achieved a state-of-art performance with 80.0% accuracy and 0.881 AUC. © 2024 ACM.

关键词： Risk assessment

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Diabetes Prediction based on Supervised and Unsupervised learning Techniques - A Review 3

Diabetes Prediction based on Supervised and Unsupervised Lea...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Sivaraman, M. Sumitha, J. Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Diabetes is a disorder that develops in the human body when blood glucose or sugar levels are extremely high. machine learning (ML) is subfield of Artificial Intelligence (AI) that is built on the idea that systems and machines will evaluate and interpret data, learn from it, and make a decision with no human participation. Medical fields that operate with massive amounts of data have recognized by the of machine learning technology. Health care systems can work more effectively and gain a competitive advantage by extracting insights from this data. The general goal of this study is to discover and observe the working and performance of various machine-learning algorithms. This paper work discusses the possibilities of employing machine-learning technology in healthcare. © 2022 IEEE.

关键词： data mining

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Research on Sales Forecasting Model Based on GRU Neural Network and machine learning Model 3

Research on Sales Forecasting Model Based on GRU Neural Netw...

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2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Zhang, Chunchang Shi, Ruoxuan Harbin Institute of Technology Weihai Weihai China Lanzhou University Lanzhou China

ISBN: (纸本)9798350341546

In the age of big data, the retail industry generates a vast amount of sales data daily. Analyzing and mining these data can facilitate management decision-making and provide great benefits to enterprises. With competition intensifying in the retail industry, precise sales forecasts are essential to assist companies in effectively managing their inventory of specific commodities, developing marketing plans, and formulating timely and effective strategic policies to reduce costs and increase efficiency. In this paper, the STL method is combined with SVR and GRU to build a daily sales forecasting model for retail enterprises. The trend, seasonal, and residual sub-series decomposed from past sales are used to directly predict future sales to achieve a better forecasting effect, and are compared with some traditional models and STL-ML combined models based on the decomposition integration idea. The results show that the method used in this paper has higher forecasting accuracy, and the forecasting ideas proposed in this paper have important reference significance for the sales forecasting practice of retail enterprises. © 2023 IEEE.

关键词： GRU neural network sales forecasting STL decomposition SVR time series

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