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Research on the development of artificial intelligence based on CiteSpace and SPSS technology 3

Research on the development of artificial intelligence based...

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3rd international conference on Education, Knowledge and Information Management, ICEKIM 2022

作者： Yang, XiaoNing Zhang, Min Dalian University School of Marxism Liaoning Dalian China

ISBN: (纸本)9781665416665

In recent years, artificial intelligence technology is booming, and artificial intelligence technology has become a research hotspot in academic circles. The author uses CiteSpace to conduct citation analysis on the research literature of knowledge map in the field of artificial intelligence. Through LLR cluster analysis, it is known that most of the research topics focus on basic theoretical research, method research and application research. This paper analyzes the proportion of artificial intelligence in various industries by using SPSS technology, and forecasts the trend of the scale of China's artificial intelligence market. On the basis of adhering to the principle of people-oriented and the unity of truth principle and value principle, we should overcome the problems caused by man-machine relationship and promote the harmonious coexistence of artificial intelligence and human intelligence. © 2022 IEEE.

关键词： Cluster analysis

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A Comparative Study of the Impact of Different First Order Optimizers on the Learning Process of UNet for Change Detection Task 3rd

A Comparative Study of the Impact of Different First Orde...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Dokkar, Basma Meddour, Bouthaina Bouanane, Khadra Allaoui, Mebarka Kherfi, Mohamed Lamine Computer Science and IT Department Laboratoire d’Intelligence Artificielle et Technologies de l’Information LINATI Kasdi Merbah University Ouargla Algeria The National Higher School of Artificial Intelligence ENSIA Algiers Algeria

ISBN: (纸本)9783031463372

UNet is an encoder-decoder neural network that has been used to detect changes in remote-sensing images. This paper provides a comparative study on the performance of UNet when trained with different optimizers for the Change Detection task. Although several previous works aim to compare different UNet models for change detection, this paper is, as far as we know, the first work that investigates UNet regarding the optimization method that is used in the learning process. This can help designing of more efficient UNet models, especially with limited training resources. We compare five common gradient-based optimization techniques: Gradient descent with Momentum (Momentum GD), Nesterov Accelerated Gradient (NAG), Adaptive Gradient (AdaGrad), Root Mean Square Propagation optimizer (RMSProp), and the adaptive moment estimation optimizer (Adam). For this purpose, UNet is trained over 200 epochs using ONERA dataset for the optimization of the binary cross entropy. The model is assessed using three metrics: Accuracy, Precision, and F1-score. According to the obtained results, RMSProp, NAG, and AdaGrad reached the highest validation accuracies: 0.976, 0.978, and 0.979 with 10- 2, 10- 3 and 10- 4 respectively. Adam was the fastest to converge and scored the lowest validation loss. Moreover, Adam scored the highest precision and F1-score across all learning rates, with 0.491 and 0.376 respectively. We also note that both Momentum-based algorithms and adaptive algorithms perform better with relatively small learning rate values. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Momentum

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Enhanced Pneumonia Detection Through Cough Analysis Techniques 3

Enhanced Pneumonia Detection Through Cough Analysis Techniqu...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Ramesh Babu, A. Sathvika, V. Junaid, Abdul Mannan Abhi Ram, S. Reddy, V.Akhila Shiva Shankar Reddy, K. SR University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350343632

Millions of individuals worldwide are afflicted with pneumonia, a potentially fatal respiratory infection that is an inflammatory disease that affects the air sacs in the lungs. For prompt intervention and successful treatment, pneumonia must be identified early. The proposed approach analyzes cough sounds and recognizes patterns linked to pneumonia by using 1D-CNN and sophisticated signal processing techniques. One promising non-invasive, low-cost method for detecting pneumonia may be cough sound analysis. A plethora of information about the respiratory system can be found in cough sounds, and recent advances in machine learning have made it possible to create accurate and trustworthy frameworks for the detection of pneumonia from cough sounds. Our results propose that cough sound analysis has the potential to be a valuable apparatus for pneumonia detection in resource-limited settings. Our framework is simple to utilize and does not require any specialized equipment, making it perfect for utilize in community health clinics and other inaccessible areas. The algorithm showed 91.35% test accuracy for the 82 cases of cough sound in pneumonia group and 91 cases in non-pneumonia group. © 2023 IEEE.

关键词： Audio Processing Cough Analysis Deep learning Feature Extraction machine learning Pneumonia Detection

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Optimization of Load Balancing Algorithm in Cloud Computing 3

Optimization of Load Balancing Algorithm in Cloud Computing

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

作者： Walia, Ranjan Kansal, Lavish Singh, Mahipal Kumar, Komuravelly Sudheer Mastan Shareef, R.M. Talwar, Suresh UCRD Chandigarh University Punjab Mohali India Lovely Professional University Punjab Phagwara India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India St. Marin's Engineering College Department of Science & Humanities Telangana Secunderabad India St. Martin's Engineering College Department of IT Telangana Secunderabad India

ISBN: (纸本)9798350399264

A rapidly expanding service, cloud computing (CC) uses a pay-per-use business model. As far as capacity, organization, web administrations, and so forth, innovation offers various administrations. Regardless, the expansion of these organizations and the enormous flood in user demand has made it trying to stay aware of execution as per QoS assessment and SLA chronicles that cloud suppliers make available to businesses. This growth brought about difficulties like load balancing. In addition, it became challenging to meet customer expectations for response speed and work scheduling deadlines. This research suggests an optimal approach based on schedule limitations using the machine Learning Classification technique to overcome these issues. The main goals of the suggested technique are to increase productivity, optimise server resources by taking into account the importance of various users' tasks, and prevent server failure. Based on the most recent literature, our suggested method will address the aforementioned problems and the existing research gap. © 2023 IEEE.

关键词： machine learning

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Crowd-Sourced Supervisors for the Automatic Invigilation of Online Assessments 3rd

Crowd-Sourced Supervisors for the Automatic Invigilation of ...

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3rd international conference on Artificial intelligence and Knowledge Processing (AIKP)

作者： Visentin, Nicholas Angelo Mhlongo, Siyabonga Ade-Ibijola, Abejide Univ Johannesburg Dept Appl Informat Syst Johannesburg South Africa Univ Johannesburg Johannesburg Business Sch Res Grp Data Artificial Intelligence & Innovat Di JBS Innovat Lab Johannesburg South Africa

ISBN: (纸本)9783031686160;9783031686177

An increase in digitalisation and the compounded effects of the COVID-19 pandemic have forced educational institutions to adopt digital solutions for supervising online assessments. Misconduct in online assessments is increasing as institutions compromise academic integrity to remain operational. Implementing the right tools to mitigate the risk of academic dishonesty has become the priority in ensuring academic integrity. Existing proctoring tools are intrusive, less privacy-conscious and operate in a space that has limited to no standards. Due to the state of current proctoring tools, there is a lack of adequate supervision solutions, novel enough to deal with the issues of academic misconduct. This paper proposes an algorithm called Crowd-Vision, encapsulated in a web-based tool and powered by crowd-sourced supervisors, to decrease levels of academic dishonesty in online assessments. Crowd-Vision uses various configurable assessment parameters to simulate an assessment environment balanced with both real and generated invigilators. The evaluation of the web-based tool revealed that the tool has the potential to mitigate academic dishonesty.

关键词： Crowd-sourced invigilation Automated supervision Online assessment Online proctoring Proctoring algorithm

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CNN-KNN Model for Assessing Hair Health in Telogen Effluvium 3

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Gotte Ranjith Shubham Banerjee, Deepak School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

The abstract provides a concise overview of a study that focuses on predicting hair breakage levels using a CNN-KNN hybrid model. The study evaluates the model's performance across five Breakage Degrees (BDs) by utilizing precision, recall, and F1-score metrics. Among these degrees, BD2 stands out with precision, recall, and F1-score values of 94.20%, indicating the model's ability to accurately classify hair damage levels between 21% and 40%. The support values, which indicate the number and proportion of instances in each BD, demonstrate a well-balanced distribution across all categories, ensuring the model's robust training. Researchers used a dataset of 10,050 images to reach an impressive 94.02% accuracy in predicting hair breakage levels caused by Telogen Effluvium in the research. Moreover, the model achieves an impressive overall accuracy of 98%, showcasing its effectiveness in distinguishing between various degrees of hair breakage severity. This high accuracy level highlights the model's potential application in clinical settings for dermatological diagnostics and personalized treatment planning in hair care management. The abstract underscores the CNN-KNN hybrid approach as a promising methodology for automated hair health assessment, providing healthcare professionals with a dependable tool to improve diagnostic accuracy and treatment efficacy. By incorporating machine learning techniques, specifically convolutional and k-nearest neighbor algorithms, the study contributes to the advancement of computational methods in dermatology and cosmetology. The results suggest practical implications for enhancing patient care through early detection and intervention of hair-related conditions, ultimately supporting better health outcomes and quality of life for individuals affected by hair breakage concerns. © 2025 IEEE.

关键词： Nearest neighbor search

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recognition and classification of ceramic sculpture styles based on pattern recognition and intelligent systems 3

Recognition and classification of ceramic sculpture styles b...

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3rd international conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Feng, Henan Liu, Jinfang College of Plastic Arts and Design Kunsan University Korea Republic of Yunnan National University College of Arts Kunming China

ISBN: (纸本)9781510680449

So far, pottery to create art has a history of thousands of years. Ceramics, sculpture art not only conforms to the trend of the times, but also into the characteristics of the times, in their own creation and development of the brand of the times. This paper summarizes the style of Chinese contemporary ceramic sculpture by analyzing the development history and current situation of Chinese ceramic sculpture, and giving in-depth thoughts to the future development trend. Combining factor analysis and K-mean algorithm, this paper can comprehensively identify and categorize the styles of ceramic sculpture. Firstly, the factor analysis method is used to find out the potential correlation between different styles, and then the K-mean algorithm is used to classify the sculptures of different styles, so as to realize the comprehensive identification and classification of ceramic sculpture styles, and to provide more accurate and comprehensive data support for the research of culture and art. The experimental results proved that the accuracy, checking rate and F1 value of K-mean algorithm are 98%, 87% and 56% respectively, which are better than the classification effect of SVM (support vector machines) algorithm. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： K-means clustering

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Renal Tumor Classification and Detection Based on Artificial intelligence 3

Renal Tumor Classification and Detection Based on Artificial...

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3rd international conference on Communications, Information System, and Data Science, CISDS 2024

作者： Al-Taie, Raflaa Hilmi Hussein, Nashwan J. Physiology and Organ Functions Department College of Medicine University of Babylon Iraq Department of Electrical Power Engineering AL-Hussain University College Department of Programming Collage of Information Technology University of Babylon Iraq

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

The kidney is a crucial organ in the human body, co-operating billions of pipelines to cleanse the body's water. Kidney failure, renal tumor happens when cells divide uncontrollably and form an aberrant collection of cells surrounding or within the kidney. This cell type has the ability to disrupt regular kidney activity and destroy healthy cells. The prompt diagnosis of renal tumor is critical since they can be fatal if left untreated. Because it is dependent on the skill of the person analyzing the images, the traditional approach of manually checking the MR image may not be very accurate. The study concentrated on the diagnosis of normal and normal renal tumor. To enhance accuracy and expedite diagnosis, using publicly accessible individual records, the present research used methodologies for machine learning, involving support vector machine learning (SVM), adaptive optimization (AO), as well as gradient enhancement (GE). It uses an approach for making the dataset reduced multidimensional. Image feature extraction is a data preprocessing method that minimizes the time required to train the proposed algorithm. The accuracy rates of the algorithms for diagnosing normal and up normal are reported to be 88.8 % for GB, 83.8 % for ADA, 86.1 % for SVM and 93.3% for KNN and for up normal diagnosis tumor are reported to be 55.3 % for GB, 55.4 % for SVM, 55.3 % for ADA and 51.0% for KNN. © 2025 SPIE.

关键词： Image enhancement

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The Employment of AI and machine Learning methods for Mushroom Images Classification using Sequential CNN Model 3

The Employment of AI and Machine Learning methods for Mushro...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Chauhan, Rahul Kukreti, Sanjeev Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350319125

The employment of AI and machine learning methods for mushroom classification has several potential uses and applications in the area of AI and related disciplines. It is possible to learn more about biodiversity and ecological health with the use of mushroom taxonomy. The use of AI models for mushroom identification and classification may help us better understand the ecology of a specific area. The work of mushroom picture classification may be both difficult and fascinating. Classifying mushrooms is crucial for a number of reasons, including determining which species are edible and which are dangerous, researching biodiversity, and ecological issues. This study uses a proposed Sequential Convolutional Neural Network (CNN) model for mushroom image classification. Pre-processing of data is done using the graphics processing unit (GPU). The Loss and Accuracy curves are then analysed to provide a visual representation of the Deep Learning model. The proposed model has an expected accuracy of 80%, which would facilitate further study into the categorization of mushrooms. © 2023 IEEE.

关键词： Artificial intelligence Classification CNN Computer Vision Deep Learning Image Processing Model Training Mushroom Image Remote Sensing Sequential Model

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Role of Explainable Artificial intelligence Approaches in Cybersecurity 3rd

Role of Explainable Artificial Intelligence Approaches in Cy...

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3rd international conference on machine Vision and Augmented intelligence, MAI 2023

作者： Bawane, Nishant Nandan, Durgesh Pune411042 India School of CS & AI S R University Warangal India

ISBN: (纸本)9789819743582

The increasing use of Deep Learning (DL) algorithms in cybersecurity, for example in identifying dangerous source code or Android malware, has made Artificial intelligence (AI) and machine Learning (ML) more crucial. AI-based cybersecurity solutions, like those in other DL application domains like Computer Vision (CV) and Natural Language Processing (NLP), are not able to clearly communicate to people the implications (which might range from detection and prediction to reasoning and decision-making). Explainable AI, or XAI, has emerged as a key area of research to address the related problems of providing human users with an interpretable or comprehensible AI model. Security operators in the cybersecurity industry are overworked due to the tens of thousands of security notifications that are received every day, the majority of which are false positives. They might be better able to recognize potential threats and have less alert weariness thanks to XAI. We do a thorough literature assessment on the relationship between cybersecurity and XAI. The security of XAI and its applications to cybersecurity (such as intrusion detection and malware classification) are especially examined from two angles in the current literature (e.g., attacks on XAI pipelines, and potential countermeasures). To characterize the security of XAI, we make use of a variety of security traits that have been researched in the literature. We list open issues that have not been resolved or have been addressed properly while also highlighting potential study opportunities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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