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Detection of diabetic Retinopathy using Retinal Fundus Images

Detection of diabetic Retinopathy using Retinal Fundus Image...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Lalitha Krishnasamy Rajesh Kumar Dhanaraj Monika Gupta Priti Rai K. Sruthi Gopika T Department of IT Kongu Engineering College India School of Computing Science and Engineering Galgotias University India Chitkara Business School Chitkara University Rajpura Punjab India PGDM & MIB IMS Noida Uttar Pradesh

Diabetic retinopathy is one of the diabetes consequences that affects the eyes. This is caused by damage to the blood vessels in the retina, the light-sensitive tissue in the rear of the eye. It may create no symptoms at first, or it may cause minor eyesight difficulties. When the blood vessels become damaged, they may leak and this leakage can cause dark spots on our vision. The DR can be detected by finding the Hard Exudate present in it. The deep networks are becoming deeper and more complex. So that adding more number of layers to a neural network can make it stronger for image related tasks. But the main disadvantage in adding more layers is that, it may greatly reduces the accuracy of the image and also the data models are complex. In order to overcome this drawback, Recurrent Neural Network can be introduced. The fundamental benefit of using a recurrent neural network is that it can represent a collection of data in such a way that each pattern may be presumed to be reliant on the one before it. It can process inputs of any length. Even if the input size is large, the model size will not change. It makes the training process faster and attains more accuracy while compared to other neural networks. This method greatly reduces the loss of accuracy because each layer knows the information of the top layers while updating the weights. This Recurrent Neural Network has more number of parameters , so it is obvious that it can produce better result as compared to other net.

关键词： Training Recurrent neural networks Sensitivity Retinopathy Blood vessels Retina Diabetes

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Financial automatic management system based on artificial intelligence technology

Financial automatic management system based on artificial in...

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Electronics and Devices, Computational science (ICEDCS), international conference on

作者： Yanru Li Huanbin Xiao Departrnent of Management Chengyi University College Jimei University Fujian Xiamen China Quanzhou Harbour Group Co. Ltd. Fujian Quanzhou China

ISBN: (数字)9781665455411

ISBN: (纸本)9781665455428

Financial information needs more digital and intelligent transformation, which improves the efficiency of internal financial management of enterprises and ensures the accuracy of financial data. With the advent of the era of artificial intelligence, modern information technologies such as big data and cloud computing have received more and more attention. This paper studies a set of computer intelligent financial information systems, which innovatively proposes the operation and application of financial automation management system through the application research of computer artificial intelligence technology in financial information systems through the establishment of a multi-index decision-making entropy model. The algorithm has been verified on the financial information system and proved that it has obvious advantages, has good operability for financial personnel, and has significant practical significance for improving the efficiency of financial personnel.

关键词： Technological innovation Cloud computing Computational modeling Decision making Financial management Personnel Artificial intelligence

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(NMRNN-LSTM) - Novel Modified RNN with Long and Short-Term Memory Unit in Healthcare and Big data Applications

(NMRNN-LSTM) - Novel Modified RNN with Long and Short-Term M...

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Advancements in Smart, Secure and Intelligent computing (ASSIC), international conference on

作者： N. Deepa S. Prabakeran Devi. T Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Department of Networking and Communications School of Computing - SRM Institute of Science & Technology (SRMIST) Kattankulathur Chengalpattu District

In the modern world, people's expectations and needs are automatically supportive and easy to use such as voice messages, playing music, or movies automatically which may reduce the manual operations mostly. In past decades technological advances such as machine learning and its application over many data like structured and unstructured are very much tedious. Whereas the operations based on non-categorical data, and categorical data are working rapidly using Natural Language Processing (NLP) comparatively, existing ones were not very productive. Each process on the internet is carrying an enormous amount of information which can lag in storage as well as performance. When any CRUD operations such as create, modify, update and delete are being analyzed one at a time, complex data such as unstructured and structured data are used in any field. In such a way the location analysis, social media data, health organization information, etc are categorized in natural language processing (NLP). The proposed work is organized as i) managing the huge amount of data in healthcare and log files created due to electronic health record management(EHR), ii) Unstructured data that are generated from all electronic equipment such as monitoring heartbeat, brain waves, etc that can be interpreted to classify using machine learning algorithms. To overcome the complications and medical records access inefficiency due to the complex structure of the dataset, Natural language processing uses the recurrent neural network along with the novel modified long and short-term memory unit (NMRNN-LSTM). Using the big data types such as structured, unstructured, and reinforcement kind of databases which handle images such as CTs, X-rays, MRI, raw texts, video streaming medical history to have effective systems and clinical records for enhancing the technological Medical care.

关键词： Temperature distribution Machine learning algorithms Recurrent neural networks databases Social networking (online) Memory management X-rays

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Implementation of Decision Tree with Pruning for Predicting the Outcomes of Myocardial Infarction

Implementation of Decision Tree with Pruning for Predicting ...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Akashdeep Singh Chaudhary Ravinder Saini Nitika Kapoor Department of Computer Science and Engineering Chandigarh University Mohali Punjab India

Over the last decade, the fast growing area of artificial intelligence and machine learning has made its vast impact in various fields such as healthcare sector, security, market, image processing, etc. Deep learning has also evolved these days for prediction and forecasting as the performance of the machine learning algorithms does not show good results with large amounts of data. Whereas, Deep learning shows better results and performance with large amounts of data but it requires large computational power. Nowadays, computational power is not the limitation, hence deep learning is at its boost. The healthcare sector is using deep learning techniques for human disease classification, detection, or even predictions related to disease. Our research is also based upon the technique which is known as Decision Trees. The objective of our research is to predict the various outcomes of Myocardial Infarction (commonly known as a heart attack) using the Decision Trees but also using one more technique to improve the accuracy of prediction and this technique is known as Pruning. This is a type of Cardiovascular disease which affects the blood vessels as well as the heart. Cardiovascular diseases cause deaths in large numbers globally. Due to Cardiovascular diseases, nearly 17.9 million people died in 2019 in which heart attack and stroke were the main reasons for 85% of deaths. A heart attack which is also known as Myocardial Infarction causes damage to the heart muscles because of the decrease in the flow of blood or when the flow of blood completely stops to the coronary artery of the heart.

关键词： Deep learning Heart Machine learning algorithms Systematics Medical services Cardiac arrest Myocardium

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advances in Terahertz Instrumentation and Technology for Cancer Applications

Advances in Terahertz Instrumentation and Technology for Can...

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international conference on Infrared and Millimeter Waves

作者： Mavis Gezimati Ghanshyam Singh Department of Electrical and Electronic Engineering Science Centre for Smart Information and Communication Systems Auckland Park Kingsway Campus P.O Box 524. University of Johannesburg Johannesburg South Africa

ISBN: (数字)9781728194271

ISBN: (纸本)9781728194288

There is an increasing demand for diagnostic techniques capable of label-free, noninvasive and early detection of cancer. In this work, an overview of the technological advancements in the terahertz (THz) technology towards miniaturized and optimized compact THz systems is presented. We focus on THz radiation generation and detection, THz techniques and their biomedical applications for cancer detection. The scope for future studies is envisaged by identifying challenges associated with THz technology and proposing the integration of THz systems with artificial intelligence (AI), internet-of-things (IoT), cloud computing, big data analytics etc. for more sophisticated systems.

关键词： Terahertz wave imaging Patient monitoring Cloud computing Instruments Big data Cancer detection Artificial intelligence

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An Expert System using Ensemble Learning for Crop Yield Prediction: EESCYP-I

An Expert System using Ensemble Learning for Crop Yield Pred...

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advances in computing, Communication & Materials (ICACCM), 2020 international conference on

作者： Deeksha Tripathi Saroj k. Biswas Department of Computer Science and Engineering National Institute of Technology Silchar India

ISBN: (纸本)9781665474405

Agriculture is the backbone of India which provides life to humans and animals. According to the survey of the past few years, over 70% of India's Gross domestic product (GDP) depends on the agriculture field. Most of the farmers are not educated in India and do not know which crop yield gives better production according to the agriculture land and environmental condition. In addition, agriculture requires a timely guideline that will enable crop yield predictions, allowing for the development of efficient agricultural yield-boosting measures. By analyzing the enormous amounts of data and interpreting the results, Machine Learning (ML) approaches provide a comprehensive understanding of the procedure. Therefore, this paper proposes a modal, named as Ensemble Expert System for Crop Yield Prediction-I (EESCYP-I) to explore the crop data to predict the crop name effectively. The proposed model executes the missing value treatment using mean data imputation technique as the crop dataset contains missing values. Classification is done using Extra Tree (ET) ensemble learning technique as Extra Tree is the best among Ensemble Learning (EL) techniques with 10-fold cross-validation due to highly excellent tree pruning and regularization which prevents the model from overfitting. EESCYP-I uses three different model training approaches: holdout, 5-fold cross-validation, and 10-fold cross-validation, to train and validate the model. Some researchers implement the EL techniques using holdout training approach (70-30%) without treating missing values. From the experimental results, it is observed that EESCYP-I with 10-fold cross-validation produces better performance than others training approaches. The performance improvement of EESCYP-I with missing value treatment and model training is observed, which is promising and significant.

关键词： Training Economic indicators Computational modeling Crops Production Predictive models Agriculture

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A Deep Learning based approach for MultiModal Sarcasm Detection

A Deep Learning based approach for MultiModal Sarcasm Detect...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Aruna Bhat Aditya Chauhan Department of Computer Science and Engineering Delhi Technological University(formerly DCE) New Delhi India

Sarcasm detection is used to single out natural language statements where intended meaning differs from what the surface meaning implies. A number of tasks in natural language processing areas like sentiment analysis and also mining of opinions use sarcasm detection underneath. Many of the key research in the area of sarcasm detection primarily focus on only text-based input. In the present day scenario , there has been a sudden explosion in the amount of multimodal data mainly due to social media. As a result of that, users these days are not just limited to text while expressing themselves , but also make heavy use of visuals like in images and videos. The objective of our paper is to incorporate multimodal data so as to enhance the performance of present sarcasm detection algorithms. Multiple methods that leverage data in the form of image and text, and also as a combination of both, have been presented thus *** present a unique architecture which works on the Robustly Optimized BERT pre-training approach(RoBERTa) which is a facebook modified version of well known model BERT with a co¬attention layer on the top for including the context incongruency between input text and attributes of the image. Using Gated Recurrent Unit(GRU), the text-based features are extracted and the features of the image are retrieved by conditioning the image through Feature-wise Linearly Modulated ResNet Blocks. These combined with CLS token from the model RoBERTa are used to obtain the final predictions. In the end, we show performance enhancements from our model when it is stacked against the other latest models with competitive results which make use of the same publicly available Twitter dataset.

关键词： Visualization Sentiment analysis Social networking (online) Computational modeling Bit error rate Predictive models Feature extraction

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Understanding Levels of Automation in Human-Machine Collaboration

Understanding Levels of Automation in Human-Machine Collabor...

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IEEE international conference on Big data

作者： Glaucia Melo Nathalia Nascimento Paulo Alencar Donald Cowan David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada

ISBN: (纸本)9781665480468

Recent advances in software and artificial intelligence technologies have led to the increasing need to support the collaboration between humans and systems in various application domains. The growing capacity for systems has leveraged the power of building applications that have higher autonomy. However, a proper understanding of allocating tasks to either humans or machines is still lacking, and no suggestions are provided to support this allocation. Current approaches do not consider knowledge about the appropriate level of automation (LOA) in this collaboration and do not support adaptive automation, especially task assignments during the system’s operation. The knowledge about which factors affect the variability in human-system interaction LOA has not been explicitly captured. This paper presents a preliminary study that identifies the factors that influence levels of automation in autonomous systems and present the identified factors as a list. Identifying the factors that influence the level of autonomy of systems advances research in the design of autonomous systems by introducing an adaptive automation approach that can recommend levels of automation to support human-computer interactions. Modern systems must be prepared to identify, capture and process the significant volume and variety of data related to the factors that might influence the variability of systems’ behaviours.

关键词： Automation Adaptive systems Autonomous systems Taxonomy Collaboration Big data Software

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An Effective Sentiment Analysis Model for Tobacco Consumption 22

An Effective Sentiment Analysis Model for Tobacco Consumptio...

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Proceedings of the 2022 11th international conference on computing and Pattern Recognition

作者： Yanru Hao Tianchi Yang Chuan Shi Rui Wang Ding Xiao Computer Science and Technology Beijing University of Posts and Telecommunications China China Tobacco Science and Technology Information Cente Zhengzhou Tobacco Research Institute of CNTC China

ISBN: (纸本)9781450397056

Analysis over product reviews has drawn much attention due to its wide application. Most of the sentiment analysis research focuses on entertainment and catering due to the limitation of existing public datasets. In order to promote the comprehensiveness of data in the field of sentiment analysis, we present a new large-scale multi-sentiment tobacco dataset by distilling effective consumer experience information from massive online reviews of tobacco consumption. The release of this dataset would push forward the research in tobacco field. With the goal of advancing and facilitating the research of the overall sentiment of sentences with multiple aspects, we propose simple yet effective EHCRNN model, which combines the strengths of recent NLP advances. Experiments on our new dataset and the public nlpcc2014 task dataset show that the proposed model significantly outperforms the state-of-the-art baseline methods.

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Detection of impurity content in wheat based on cone-beam CT

Detection of impurity content in wheat based on cone-beam CT

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Gangyang Wang Chunhua Zhu Jianhou Wang Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education College of Information Science and Engineering Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control. Henan University of Technology Zhengzhou China GCOM Technologies Inc Zhejiang China

Wheat impurity content is an essential index for evaluating the quality level and price of wheat or reserved grain. We obtain 2D slice images of the scanned sample by cone-beam CT, then a hybrid filtering algorithm is used to improve the quality of the 2D slice images. Finally, the 2D slice image is reconstructed in 3D to obtain volume data, so as to obtain the volume of each part, and then use the density formula to achieve the calculation of wheat impurity content. The experiments have shown that the proposed method can obtain the detection accuracy up to 90%, and can provide objective online detection of grain impurity content compared with the impurity detection of the national standard GT/T5494-2019.

关键词： Privacy Three-dimensional displays Impurities Computed tomography Filtering algorithms Security Indexes

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