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ICAAI 2020 - 2020 4th international conference on Advances in Artificial Intelligence

ICAAI 2020 - 2020 4th International Conference on Advances i...

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4th international conference on Advances in Artificial Intelligence, ICAAI 2020

ISBN: (纸本)9781450387842

the proceedings contain 16 papers. the topics discussed include: the application of machine learning in activity recognition with healthy older people using a batteryless wearable sensor;10k logo dataset for machine learning logo retrieval purposes;a comparative study of Parkinson disease diagnosis in machine learning;design of deep learning experiment teaching case based on EMG signal analysis;safety risk assessment for children’s products based on reinforcement learning;using multi-agent microservices for a better dynamic composition of semantic web services;increase the system utilization by adaptive queue management system with time restricted reservation;application of artificial neural network for daily evaporation forecasting using weather data;learning symbolic action definitions from unlabeled image pairs;and transitioning from traditional learning to blended learning at some public universities in Vietnam after the COVID-19 pandemic.

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Cloud Detection from a Hyperspectral Infrared Atmospheric Sounder Using a machine-learning Model 4

Cloud Detection from a Hyperspectral Infrared Atmospheric So...

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4th international conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Shi, Huaxiang Yu, Yi Zhang, Weimin Ma, Gang Zhang, Qi Luo, Tengling Huang, Qunbo National University of Defense Technology College of Meteorology and Oceanography Changsha China National Satellite Meteorological Center Beijing China Beijing100843 China

ISBN: (纸本)9781665406925

Cloud detection is an essential step in the application of hyperspectral infrared (HIR) data. In this paper, a new cloud detection method using a LightGBM algorithm based on principal component (PC) space is proposed for HIR data from the High Spectral Infrared Atmospheric Sounder (HIRAS). Considering the difference of infrared radiation between ocean and land, day and night, this paper respectively builds the LightGBM day-land, day-sea, night-land and night-sea models. the truth cloud fraction of the HIRAS field of view (FOV) is determined by the collocated cloud masks of the Medium Resolution Spectral Imager-II (MERSI) onboard the FY-3D satellite. the HIRAS infrared channels were transformed into the leading K PCs as predictors through principal component analysis (PCA), with the advantages of reducing the correlation of infrared channels, accelerating model convergence and prediction. the cloudy FOVs in global datasets are randomly down-sampling to alleviate the impact of dataset imbalance. the validation experiments have shown that the LightGBM model has high accuracy for the completely cloudy and completely clear-sky FOVs. However, the partially cloudy FOVs are sometimes detected as the clear-sky. It may be because these partially cloudy FOVs have prominent clear-sky radiation properties. the cloud detection performance of the LightGBM model (accuracy=0.93, HSS=0.85) for land HIR data is better than that of sea HIR data (accuracy=0.89, HSS=0.65). It may be because there are more partially cloudy FOVs in the ocean. In addition, the imbalance of the ocean dataset significantly reduced its Heidke skill score (HSS=0.65). Compared with the HIRAS Lie cloud cover product, the accuracy of the LightGBM model for land (sea) HIR data is increased by about 0.22 (0.07). the time complexities of the algorithms have shown that the cloud detection speed of the LightGBM model is approximately 670 times that of the HIRAS/MERSI collocation cloud detection method. the higher cloud

关键词： Principal component analysis

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Single object tracking based on OSTrack and NAS head 22

Single object tracking based on OSTrack and NAS head

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proceedings of the 2022 4th international conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Conghao Li Haofeng Wang Pengjie Liu Beijing Institute of Computer Technology and Application China

ISBN: (纸本)9781450398343

Neural architecture search (NAS) has attracted much attention in recent years. Especially, Differentiable Architecture Search (DARTS) has become a mainstream NAS algorithm because of its differentiable architecture hyper-parameter and low time complexity, but few people apply it in the field of object tracking due to its high memory complexity. this paper redesigns the cell structure according to the characteristics of the target tracking head network and defines a simplified search space. We propose a NASHT model to replace the three head networks of OSTrack with the modified DARTS cell, which automatically searches for a target structure suitable for its task. We use two benchmarks, PTB-TIR and LSOTB-TIR, to train and evaluate the model. the accuracy of the proposed model reached 86.5% and the success rate reached 69.0% on the PTB-TIR benchmark. Compared with OSTrack, the accuracy has increased by 1.5%, and the success rate has a 0.8% improvement. the accuracy of the proposed model on the LSOTB-TIR reached 84.9% and the success rate was 71.4%, which achieved comparable effects to OSTrack while performing better in situations such as motion blur, low resolution, and aspect ratio changes.

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Orthopantomographic X ray defocused image restoration with blind and nonblind deconvolution for diagnostic use in dental application 4

Orthopantomographic X ray defocused image restoration with b...

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4th international conference on Communication, Information and Computing Technology, ICCICT 2021

作者： Shashikant, Goilkar Suhasini Manik, Yadav Dinkar JJTU Rajasthan India Electronics and Telecommunication Engineering Department Finolex Academy of Management and Technology Maharashtra Ratnagiri India GH Raisoni College of Engineering and Management Maharashtra Pune India

ISBN: (纸本)9781665404303

Quality enhanced digital images using deblurring and restoration techniques plays vital role in many fields of medical applications, security related surveillances, remote sensing and satellite communications and defense sector. Use of X ray images is done extensively in medical field for diagnostic purpose which has become a backbone for medical practitioners. Quality images without any blur or noise therefore becomes an important tool for diagnosis which leads to use of image processing for improvement of the captured X ray images but were defocused while actual capturing. this paper elaborates about the findings achieved after restoration of Orthopantomographic X ray defocused image used in dental application. this research work emphasizes two techniques as blind and non-blind deconvolution used for the restoration of Orthopantomographic X-ray which is captured for the diagnosis related to growth and development monitoring of pediatric teeth. the results after deconvolution are compared to understand one of the unaddressed problems normally present as no previous information of point spread function for blind technique as compared with non-blind technique. It was observed that deconvolution techniques applied to defocused image restoration are helpful in further usage in dental applications as well as in the field of machine learning and digital image processing. At the end, simulated results of this work are compared using different image parameters like structural similarity index (SSIM). Further the performance of these deconvolution methods is compared and results are represented graphically for better understanding and usage in the field of image processing. © 2021 IEEE.

关键词： Image reconstruction

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A Study on Effective analysis of machine learning algorithm towards the Women's safety in Social Media 4

A Study on Effective analysis of Machine Learning algorithm ...

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4th international conference on Electronics, Communication and Aerospace Technology, ICECA 2020

作者： Madhubala, D. Rajendiran, M. Elangovan, D. Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781728163871

Social media is the most challenging one for analyzing the text and the social media application is a tool for expressing the opinion without facing any fear. At the same time, there will be a lot of crime and violence occurs on the internet. Especially the women who are in a popular position like celebrities, politicians, and also the common people are facing the threats of violence are in social media. So they feel insecure in society. Still, there will be a problem for analyzing the text. It is very too difficult to classifying the text on social media. through the machine learning technique, it would be done effectively. It filtered the unwanted text and then it reports to the respective office. So it will help the government to protect the women in social media. In this paper, a survey on online abuse as well as a study about sentiment analysis. According to this paper, it helps many researchers to get an idea of the sentiment analysis in social media. Similarly, the survey is done in a combination of the different techniques and also drafted ***-Support vector machine (SVM), Naive Bayes classification, Sentiment analysis, Social networking site. © 2020 IEEE.

关键词： Sentiment analysis

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Heart Disease Prediction Using Ensemblers learning 4th

Heart Disease Prediction Using Ensemblers Learning

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4th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2020

作者： Bhatia, Meenu Motwani, Dilip Department of Computer Engineering Vidyalankar Institute of Technology Mumbai India

ISBN: (纸本)9789811570773

Heart disease describes a range of conditions that affect your heart. Worldwide increase in heart disease rates there is a need to detect the manifestations of heart stroke at the beginning period and therefore forestall it. It is illogical for a typical man to spend much on exorbitant tests like the ECG and in this manner, there should be a framework set up which is convenient and simultaneously dependable, in foreseeing the odds of coronary illness. thus, it will be useful to build up an application that can anticipate the weakness of a coronary illness given basic side effects like age, alcohol, smoke, cholesterol, lifestyle, gender, stress, etc. [3]. the machine learning algorithm has demonstrated to be the most accurate and solid algorithm and hence utilized in the recommended or the proposed framework. the main aim is to suggest a system/application which will consist of two modules: Doctor Login and Patient Login. Doctor can record the case details along with the case history of the patient. Whereas the Patient can view its entire medical history. the proposed model will work on Ensemblers learning that is Bagging, Boosting, and Stacking algorithms. Bagging, Boosting, and Stacking works on a hybrid algorithm which test and trains the dataset. Passing’s because of cardiovascular maladies increased from 13 lakh in 1990 to 28 lakhs in 2016. the number of common cases of cardiovascular illnesses has increased from 2.57 crore in 1990 to 5.45 crore in 2016. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heart

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Malware Intrusion Detection For System Security 4

Malware Intrusion Detection For System Security

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4th international conference on Communication, Information and Computing Technology, ICCICT 2021

作者： Katkar, Ashwini Shukla, Sakshi Shaikh, Danish Dange, Pradip Dept. Of Electronics & Telecommunication Engineering Vidyavardhini’s College of Engineering & Technology Vasai Mumbai India Electronics & Telecommunication Engineering Vidyavardhini’s College Of Engineering & Technology Vasai Mumbai India

ISBN: (纸本)9781665404303

With the improvement of web innovation, network assaults are getting increasingly more sourced and muddled. this makes it hard for the conventional malware discovery frameworks to viably recognize strange traffic. Intrusion Detection System is an application which is utilized to investigate all organization traffic and caution the clients if there has been any unapproved access or endeavors. Dissimilar to firewalls which basically screen network traffic and decide if it ought to be permitted or not, Intrusion Detection System centers around traffic that is on the inner organization for distinguishing any dubious or harmful action. Modern malware applies a rich lot of techniques to analysis but it's still ineffective. these analysis tools take lot's of effort to find the hidden malware;typically this trying is as possible to the behavior of a real run. An approach to do malware analysis that turns the idea using an abstraction of the operating system that strays from real behavior. the key part is finding the presence of malicious behavior and having sufficient evidence of it's malicious intent. the investigation of an association's organized traffic supplements antivirus programs that sudden spikes in demand for customer PCs. It permits associations to implement a strategy to secure the whole network. this methodology for detecting malware is conceivable in its location into the system. Malware discovery frameworks can precisely recognize organization traffic, giving organization security. © 2021 IEEE.

关键词： machine learning

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machine learning Techniques and Frameworks for Heterogeneous Data Fusion in Big Data Analytics 4

Machine Learning Techniques and Frameworks for Heterogeneous...

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4th international conference on Electronics, Communication and Aerospace Technology, ICECA 2020

作者： Divya, G Manish, T I Adi Shankara Institute of Engineering and Technology Kalady Kerala India

ISBN: (纸本)9781728163871

Big Data systems are now facing many challenges. Variety of data or heterogeneous nature of the data is one of the main challenges associated with Big Data. It is observed that, the data fusion can efficiently deal with this challenge. Data fusion aims at optimizing and combining the data from different sources so that the data can provide more value for decision making. Many applications can gain from data fusion. As big data applications are on rise, data fusion should become an integral part of the big data application life cycle. In this paper a review of the different data fusion techniques and their applications is provided. the main focus is to review the machine learning techniques that are applied in data fusion. this paper also covers the various frameworks for implementing machine learning. Towards the end of the paper, the recent trends in data fusion are also discussed. © 2020 IEEE.

关键词： Big data

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machine learning based RecyClick: Recycle at A Click 4

Machine Learning based RecyClick: Recycle at A Click

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4th international conference on Trends in Electronics and Informatics, ICOEI 2020

作者： Acharekar, Kaustubh Khedekar, Pranav Dsouza, Joanna Vaidya, Sonali St. Francis Institute of Technology Dept. of Information Technology Mumbai India

ISBN: (纸本)9781728155180

Waste is generated by every single individual existing on Earth. Certain categories of wastes like E-waste, Plastic waste, and Metal waste can pose a serious threat to the ecosystem if not recycled properly using appropriate recycling methods. Nowadays, people constantly stay up to date in terms of technology by changing their existing devices with new ones. the old devices turn out to be obsolete. the objective of our project is to develop an application to curb the waste menace generated by identifying E-Waste, Metal waste, and plastic waste from user input with the help of machine learning techniques and handle it efficiently by encouraging users to recycle the waste to gain credit points. People usually turn a blind eye to recycling as they find it elusive to personally go to a certain place to deposit the recyclable items. the application intends to provide a promising on the go solution at the comfort of their doorstep. © 2020 IEEE.

关键词： Electronic Waste

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Detection of Marine Chemical Pollution Based on Image Processing and machine learning 22

Detection of Marine Chemical Pollution Based on Image Proces...

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proceedings of the 2022 6th international conference on Electronic Information Technology and computer Engineering

作者： Lingyun Chen Mingjie Gao Langyu Wang Chuhan Xue Department of Information Beijing University of Technology China Department of Electrical and Computer Engineering Queen's University Canada School of Rail Transportation Soochow University China School of Electric Power South China University of Technology China

ISBN: (纸本)9781450397148

the problem of oil spills is lethal to the ocean ecosystem. To solve the problem, one of the most important key steps is to detect the ocean surface and judge whether there are or not oil spills. Remote sensing provides the advantage of controlling and observing events remotely, and it can cover the areas that people cannot access, so we use it to build a database. Next, we choose to use Matlab for the pre-image processing and then use the neural network by Python to realize and there are five pre-processing methods: expanding the dynamic histogram range in the ‘Y’ channel (method 1), expanding the dynamic histogram range in three channels (method 2), contrast enhancement (method 3), expanding the dynamic histogram range and then contrast enhancement (method 4), and contrast enhancement, and then expanding the dynamic histogram range (method 5). Finally, we use a neural network to test accuracy, in comparison, method 1 is the best and we improve the accuracy from 72% to 82%.

关键词： Contrast Histogram Image processing Neural network

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