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Research on the application of artificial intelligence algorithms in drought prediction 22

Research on the application of artificial intelligence algor...

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

作者： Mengxi Liu Guangjun Zai Guofu Wang Weiwei Shi Guanqun Zhang School of Cyber Science and Engineering ZhengZhou University China National Climate Center China Beijing SuperMap Software Co. Ltd China

ISBN: (纸本)9781450397148

With the aggravation of global warming, drought has caused more and more serious impact on society and national economy, so the accurate prediction of drought is also the most important thing to deal with the frequent drought problem. On the other hand, artificial intelligence technology has shown its advantages in various fields and has a broad application prospects, so it is gradually being used in drought prediction. In this paper, drought prediction algorithms in recent years are reviewed, and existing cases of drought prediction using different arithmetic models for different drought indices are studied. the advantages and disadvantages of different algorithms are compared, the challenges of drought prediction are summarized, and the future of drought prediction is prospected.

关键词： Drought forecasting machine learning Time series forecasting

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Breaking Boundaries: Harnessing the Power of Deep learning Model Design to Predict Diabetes with Artificial Intelligence Assisted Prediction Strategy

Breaking Boundaries: Harnessing the Power of Deep Learning M...

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Intelligent Technologies (CONIT), international conference on

作者： N.K. Senthil Kumar S Kalpana K. Karthika A. Anandh Badepalli Anusha P Santhoshini Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer science BITS Pilani Dubai campus (BPDC) Dubai UAE Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Kamaraj College of Engineering and Technology Virudhunagar Tamil Nadu India Department of ECE MLR Institute of Technology Dundigal Hyderabad India Department of Electronics and Communications Engineering RMD Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Predicting the onset of diabetes at an early stage might greatly mitigate the disease’s devastating effects; the condition is currently incurable and results from chronically elevated blood sugar levels. Predicting the onset of diabetes has been the focus of current efforts within the deep learning community, building on decades of prior study. Nowadays, diabetes is a common and unfortunately incurable health problem. People can’t give their health the attention it needs because of their busy schedules. the food we eat is broken down into glucose, which is then released into our bloodstream. When blood sugar levels are too high, the pancreas secretes a hormone called insulin. A key function of insulin is to facilitate the uptake of glucose by cells for energy production. Identifying diabetes at an early stage can greatly improve the chances of leading a sustainable life. A fruitful way would be to use machine learning algorithms, which can improve themselves with future predictions after being taught and tested with massive amounts of data. this study presents AI-based Diabetes Prediction (AIDP), a model that explores diabetes data to make better predictions about the illness, with the severity of the condition in mind. the suggested rationale is tested against the traditional K-Nearest Neighbor (kNN) learning paradigm to see how well it works. Results from the planned study demonstrate that the model can accurately forecast the onset of diabetes, if not better. When compared to another deep learning method known as kNN, our results reveal that the suggested AIDP attained superior accuracy.

关键词： Deep learning Schedules Accuracy Production Predictive models Prediction algorithms Diabetes

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An Android Malware Detection and Malicious Code Location Method Based on Graph Neural Network 21

An Android Malware Detection and Malicious Code Location Met...

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4th international conference on machine learning and machine Intelligence, MLMI 2021

作者： Wu, Qing Sun, Peng Hong, Xueshu Zhu, Xueling Liu, Bo College of Computer National University of Defense Technology China

ISBN: (纸本)9781450384247

In recent years, enormously Android malware poses a significant threat to Android platform security. To detect malicious applications, researchers have done a lot of work, in which finding and locating malicious code segments is an important research content. In the previous research, most detection methods cannot directly locate malicious code, and some methods with the locate ability can only find some specific types of malicious operations. this paper proposed a graph convolution algorithm and weighted mechanism to find malicious nodes implied in the Android application function call graph and provided a general method for malicious code location. We analyzed the sub-graph structural differences between benign code and malicious payload in the function call graph, constructed graph convolution operation to make the nodes in the graph learn the surrounding sub-graph structure, designed the weighting mechanism to set the malicious score to every code node, and filtered out the nodes with the highest malicious score to locate the malicious code fragments. On the dataset composed of 2650 malicious and 2650 benign applications, the accuracy of malware detection is 92.6%, and the accuracy of malicious code location is between 72.6% and 88.1%, indicating that our method is accurate and efficient. © 2021 ACM.

关键词： machine learning

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Developing an Effective Model for Skin Disease Detection: Techniques and Approaches

Developing an Effective Model for Skin Disease Detection: Te...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Nikhil Kumar Yadav Amit Tiwari Gaurav Harsh Kumar Verma Arpna Saxena Ajay Kumar Garg Engineering College Varanasi India Ajay Kumar Garg Engineering College Muzaffarpur India Ajay Kumar Garg Engineering College Meerut India Ajay Kumar Garg Engineering College Ghaziabad India Master of Computer Application Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

this research highlights the critical role of the skin as the body's primary defense against damage and infection. Despite its importance, the skin can be vulnerable to serious infections caused by fungi, viruses, or even dust, leading to conditions ranging from acne to eczema. Some skin diseases are highly contagious and can be spread by simple contact, underscoring the need for proper diagnosis and treatment to alleviate suffering and prevent transmission. the study focuses on developing a prototype for identifying skin diseases using convolutional neural networks (CNNs), a choice based on their effectiveness in image processing tasks. this research uses CNNs to classify specific skin conditions, including hand dermatitis, hand eczema, subacute eczema, lichen simplex, stasis dermatitis, and ulcers are all different skin conditions.. the methodology integrates image processing techniques with machine learning and uses the processed images to train a CNN model. the training data consists of five classes of skin conditions from the Dermnet dataset, which includes 19600 images. the prototype achieved an accuracy of 92%, highlighting the system's potential. Research suggests that with further improvements and a larger data set, the system could significantly improve early detection and treatment of skin ailments representing a substantial advance in dermatology care.

关键词： Deep learning Adaptation models Accuracy Computational modeling Dermatology Eczema Transformers Skin Convolutional neural networks Diseases

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Research on the application of Business Data Fusion in the Engineering Management of Oil and Gas Storage and Transportation Station 23

Research on the Application of Business Data Fusion in the E...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Rui Xie Bo Fan Jianlin Ma Meng Pan Southwest Pipeline Ltd. China School of Computing and Artificial Intelligence Southwest Jiaotong University China Chengdu Aerospace Science & Industry Big Data Institute Ltd. China

ISBN: (纸本)9798400709517

Due to challenges such as complex contractor personnel, a large amount of scattered form data, a cumbersome business implementation process, low levels of digitalization and security, and difficult management in the engineering management business of oil and gas storage and transportation stations, this paper proposes a study on business data fusion application in the engineering management of oil and gas storage and transportation stations. Focused on the business processes and data of engineering management in oil and gas storage and transportation stations, this research establishes and analyzes an architecture for engineering management business in these stations. Using an Artifact-centric data fusion method, the research constructs an information model for the engineering management business, identifying the data topics for this area. Finally, the research applies and verifies the study of business data fusion through real-world business management scenarios. As researchers in the field of business data fusion, this study utilizes digital technologies and methods to enhance the overall safety management of engineering operations, thereby promoting the transformation and upgrading of engineering management business in oil and gas storage and transportation stations

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Peer to Peer Social Lending Default Prediction with Convolutional Neural Networks 4

Peer to Peer Social Lending Default Prediction with Convolut...

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4th international conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2021

作者： Chengeta, Kennedy Mabika, Elizabeth Rutendo Artificial Intelligence Research KaribuTechs Ai Research Institute Pretoria South Africa National University of Science and Technology Department of Applied Mathematics Bulawayo Zimbabwe

ISBN: (纸本)9781728185927

Social lending or peer to peer lending (P2P lending) is a social digital lending marketplace where borrowers and lenders are interlinked. With the emergency of coronavirus medical risks and travel limitations, this has come as a lucrative business. However with huge industry lay-offs and small companies going under, the verification of a potential borrower's creditworthiness is important. the objective of peer to peer lending was to eliminate middlemen or banks and create a personal touch of the borrower and lender, but results have shown that those running away from traditional banks do so because of unpaid loans, bad debts or other slow payment behaviour. Information asymmetry, lack of demand and money supply, poor income verification, inaccurate credit reports and employment details, high debt to income ratios as well as inadequate anti fraud systems are some of the challenges in peer to peer lending. To mitigate these myriad of problems, machine learning algorithms are used to predict default, slow payments and fraudulent application behaviour. the measurement by classification of such creditworthiness of peer to peer lending marketplaces is done with new frameworks like deep learning convolution neural networks, XGBoost, CatBoost and LightGBM. these new class of classifiers performed better than the c4.5 decision tree, ensemble voting classifier, random forest and k nearest neighbor classifiers. the deep learning algorithms were also optimized for overfitting leading to even better performance. the implementation was done using Weka, Keras and Tensorflow. the datasets used include Prosper and Lending Club peer to peer datasets managed by their respective online lending houses. the study also finds that purpose, employment status, age and credit grade or prosper score are the best predictors for defaulting of loans. © 2021 IEEE.

关键词： Deep learning

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Sentiment Analysis on Amazon Alexa Reviews Using NLP & Classification

Sentiment Analysis on Amazon Alexa Reviews Using NLP & Class...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Sanjay Kumar Sahil Kanshal Vishnu Sharma Rajneesh Talwar Department of Computer Science Engineering Chandigarh Group of Colleges Jhanjeri Mohali India Department of Information Technology JIMS Delhi India Department of Computer Science Engineering Galgotia’s College of Engineering and Technology Greater Noida India Department of Electronic & Communication Engineering Chandigarh Group of Colleges Jhanjeri Mohali India

many firms and manufacturers, knowledge on public opinion is critical. they frequently want to know what consumers and the general public think of their products and services in real time. However, personally reading every post on the website and extracting important viewpoint information is not feasible. the frequency of data is very high to do it manually. Sentiment analysis enables for efficient and cost-effective data processing on a wide scale. We investigated the application of sentiment analysis in business to better understand its strengths and limits in order to learn more about it. Using Python and machine learning Algorithms, this project created a representation to forecast the response of a remark given the observation declaration using a dataset of Amazon Alexa reviews.

关键词： Sentiment analysis machine learning algorithms Text recognition Virtual assistants Process control Prediction algorithms Tag clouds

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Immersive Virtual Reality application for Total Hip Replacement Surgical Training 4

Immersive Virtual Reality Application for Total Hip Replacem...

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4th international conference on Bio-Engineering for Smart Technologies, BioSMART 2021

作者： Younis, Saad B. Al-Hemiary, Emad H. Al-Nahrain University Computer Networks Engineering/College of Information Engineering Baghdad Iraq

ISBN: (纸本)9781665408103

Virtual reality (VR) is a virtual environment that might be identical to or diametrically opposed to the actual world. the traditional learning method for surgical training such that Cadaver Surgery is an effective method proven in the medical field to understand human anatomy and to perform training surgery;when held in comparison to digital 3D models (i.e., VR), it tends to be more complex, more expensive, and other concerned safety concern it. To teach various medical groups, VR is a modern approach that helps residents, students, and professionals in various fields of medicine grasp complex operations such as total hip replacement surgery before carrying them out on a patient. the VR application used an Oculus Quest headset with two hand controller;in this VR application, the user performs total hip joint replacement surgery procedures using a two-stage skeleton and total body's organ. the virtual reality application received a System Usability Scale score of (85.444), indicating that the application is recommended and good according to the System Usability Scale range. Also, for the virtual reality application, some participants in quantitative assessment got improvements of more than 50%, which is a positive sign. according to the results of this study, the feedback was positive. Based on the final result of this paper, it is that incorporating virtual reality skills into various medical teams might help them perform better in general and more specifically in Surgical procedures. therefore, it is feasible to suggest that VR applications can train different medical groups to improve their skills in surgical procedures. © 2021 IEEE.

关键词： Virtual reality

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An Unsupervised HIV Lateral Flow Immunochromatography Detection Algorithm Based on K-Means++ 22

An Unsupervised HIV Lateral Flow Immunochromatography Detect...

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proceedings of the 2022 4th international conference on Video, Signal and Image Processing

作者： Guijun Miao Yifan Wu Xianbo Qiu Fang Li College of Information Science and Technology Beijing University of Chemical Technology China

ISBN: (纸本)9781450397810

As a convenient biochemical detection technology, colloidal gold strip lateral flow immunochromatography has been widely used in disease detection and diagnosis, food safety and other fields. In order to achieve high accuracy and efficient test strip detection, the artificial intelligence-based image classification method was applied to HIV colloidal gold test strip detection in this paper. An unsupervised HIV colloidal gold strip detection algorithm based on K-means++ and machine learning is proposed, and key technologies such as strip image preprocessing, K-means++ image segmentation method, data enhancement and K value selection are studied. three image classifiers KNN(K nearest neighbor classification algorithm), SVC(support vector machine) and GaussianNB(Gaussian Bayes classifier) were used to compare the classification effect. Experiments show that the classification effect of the proposed algorithm is better than that of the deep learning yolox network. the classification accuracy of the unsupervised detection algorithm based on the combination of K-means++ and KNN can reach 94%, the sensitivity is 98%, and the specificity is 80%, which can well solve the misjudgment problem caused by the insignificant T-line of weak positive test strips.

关键词： K-means++ segmentation Biological strip image Classification and evaluation machine learning

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Understanding Tourists Relationship Quality Using a machine learning ApproachUnderstanding Tourists Relationship Quality 23

Understanding Tourists Relationship Quality Using a Machine ...

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proceedings of the 2023 4th international conference on Big Data Economy and Information Management

作者： Dawei Shang Yufang Chi Zhengzhou University China Beijing Vocational College of Social Management (Ministry of Civil Affairs Training Centre) China

ISBN: (纸本)9798400716669

From the perspective of segmented industry sectors, the significance of big data economy and information management in the tourism industry cannot be underestimated. However, research on non-linear analysis of the relationship between tourists relationship quality and its influencing factors is still limited. this study examines the impact of tourist perceived value on relationship quality using neural network methods in the field of information management. Exploring the non-linear relationship used cross validation and independent variable importance analysis. the Root Mean Square Error (RMSE) test results show that the model evaluation in this study is effective and acceptable. the results show that perceived value plays the important nonlinear role in the relationship quality. the analysis of the importance of independent variables shows that results shows that perceived functional value, perceived cost effectiveness value, and perceived emotional value have the greatest nonlinear impact on relationship quality. Meanwhile, the non-linear impact of perceived social value and perceived environmental value on relationship quality is relatively small. this study contributes to improving the quality of tourist destinations, various tourism management themes, and tourist relationships, flourishing the tourism market, and promoting the sustainable development of the tourism industry in information management intelligence era.

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