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AIxHMI 2024 - Proceedings of the 3rd Workshop on Artificial Intelligence for Human-machine Interaction 2024, co-located with the 23rd international conference of the Italian Association for Artificial Intelligence, AIxIA 2024

AIxHMI 2024 - Proceedings of the 3rd Workshop on Artificial ...

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3rd Workshop on Artificial Intelligence for Human-machine Interaction, AIxHMI 2024

The proceedings contain 11 papers. The topics discussed include: assessing appropriate reliance: a framework for evaluating AI influence on user decision-making;AI to detect Parkinson’s disease symptoms via wearables: from detection to management to treatment;AI personalized models based on subjective data;the role of physiological signals in human-machine interaction;co-design of scenarios for interacting with a NAO robot in treating autism spectrum condition;digital biomarkers of mood states from speech in bipolar disorder;a protocol to evaluate the impact of visual distractors on driving attention using a virtual reality simulator;rule enforcement in LLMs: a parameter efficient fine-tuning approach with self-generated training dataset;and a multi-source deep learning model for music emotion recognition.

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Detection of Phishing Link and QR Code of UPI Transaction using machine learning 3

Detection of Phishing Link and QR Code of UPI Transaction us...

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

作者： Charan, Gangisetty Raj Thilak, K Deepa SRM Institute of Science and Technology Dept. of Networking and Communications Chennai India

ISBN: (纸本)9798350343632

Development of digital payment systems and the evolution of technology give rise to new frauds. With bogus URLs and QR Codes, phishing poses a major danger to transaction security and integrity. The proposed detection of phishing link and QR code of UPI transaction using machine learning delves into the complexity of this important topic, providing information about fraud and enhancing the exploitation of UPI ecosystems. This study intends to overcome this problem by leveraging machine learning and AI techniques to prevent fraud and improve security mechanisms. Phishing is a malicious attempt to pose as a reliable source in order to fool people into disclosing private information, including passwords, usernames, or bank account information. Phishing attacks can also be carried out with QR codes, which are a kind of data storage that can be swiftly accessed by a smartphone's camera. User awareness and resistance against phishing attacks. The Universal Payment Interface (UPI) is at the forefront of this disruptive wave due to its ease and efficiency in financial operations. This study investigates not just the historical context of phishing efforts but also the evolving techniques employed by fraudsters to exploit the UPI ecosystem. © 2023 IEEE.

关键词： Artificial Intelligence data mining Football machine learning Predictions

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A Study on machine learning-Based Methods for Early Gastric Cancer Screening 3rd

A Study on Machine Learning-Based Methods for Early Gastric ...

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

作者： Xu, Jialun Wu, Xiayang Xu, Zhenyuan School of Statistics Nanjing Audit University Nanjing China School of Arts Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9789819624676

machine learning (ML), deep learning (DL), and artificial intelligence (AI) are increasingly employed in medical assistance and statistical analysis. These technologies enhance medical diagnostics by enabling precise image segmentation, feature extraction, and lesion localization. Such capabilities significantly improve the accuracy of auxiliary diagnoses and facilitate more effective treatment planning. In the field of medical statistics, machine learning (ML), deep learning (DL), and artificial intelligence (AI) can aid in large-scale analysis and mining of medical data to identify potential disease patterns and predict patient risks. For instance, doctors use ML and AI to integrate and analyze patients’ genomic and imaging data for personalized treatment plans. However, extensive research has not been carried out concerning physical examination data in this field. However, the early stages of numerous diseases are not objectively reflected in the current physical examination evaluation system. For instance, with gastric cancer, the significance of routine physical examination detection indexes is limited, making it challenging to diagnose precisely during consultations. This factor significantly complicates the early detection of gastric cancer. In our study, we introduce a method based on PSO and Stacking learning (PSOS) for predicting early-stage gastric cancer. This method leverages basic personal bioinformation and standard physical examination data to construct a predictive model. Designed to assist in the pre-gastroscopy clinical screening of gastric cancer, this model aims to identify potential cases before the need for invasive procedures such as gastroscopy and tissue biopsies. We trained the model using data from 22,304 actual patients and achieved a high success rate in early detection, demonstrating the model’s robust capability to identify gastric cancer at early stages. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2

关键词： Diseases

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A Bengali Word Identification and Verification Using machine learning Approach 3

A Bengali Word Identification and Verification Using Machine...

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Hasan, Shakibul Nadif, Mohammad Abu Bin Rahman, Nasim Rana, Masud Computer Science & Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering American International University Dhaka Bangladesh

ISBN: (纸本)9781665494007

The research project is mainly based on image classification. We also showed a path to Optical Character recognition. Optical Character recognition (OCR) is basically a technology which can change over a computerized image of content to editable content i.e. it can change digital image to editable text. An OCR by using image processing and classification for Bangla dialect is proposed here. The primary step of this project is that to recognize and detect 'text', the usage of image classification and Convolutional Neural Network (CNN) is implemented. Characters are separated from the Corpus and then the most decisive and challenging of the project step is implemented which is the verification of the word or corpus. The Bengali word or corpus is then extracted from the digital image and then it is converted to digital font and then the digital font is compared with our accumulated data set to verify whether the word is absolute or not. To enrich the diversity of Bengali language in the field of technology the project plays a very significant role from its own view. © 2023 IEEE.

关键词： machine learning

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A Hybrid Deep learning Scheme for Intrusion Detection in the Internet of Things 3rd

A Hybrid Deep Learning Scheme for Intrusion Detection in t...

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

作者： Momand, Asadullah Jan, Sana Ullah Ramzan, Naeem School of computing Engineering and Physical Sciences University of the West of Scotland PaisleyPA1 2BE United Kingdom School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom

ISBN: (纸本)9783031463372

The Internet of Things (IoT) is the connection of smart devices and objects to the internet, allowing them to share and analyze data, communicate with each other, and be controlled remotely. Several IoT devices are designed to collect, process, and store confidential data in order to perform their intended function. This information can be sensitive such as location, health, military, financial information, and biometric data. The efficient implementation of IoT networks has become increasingly reliant on security. In IoT networks, several researchers used intrusion detection systems (IDS) for the identification of cyberattacks where machine learning (ML) and deep learning (DL) are significant components. The existing IDS still needs improvements for the detection of multiclass detection to identify each category of attack separately. To improve the detection performance of IDS, this study proposes a hybrid scheme of convolutional neural networks (CNN) and gated recurrent units (GRU). The proposed hybrid scheme integrates two CNN layers and three GRU layers. The proposed scheme was assessed using the IoTID20 dataset. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Intrusion detection

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Tomato Leaf Disease Detection and Identification using machine learning 3

Tomato Leaf Disease Detection and Identification using Machi...

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3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Kumari, Neeraj Kumar, Vivek Pandey, Neeraj Kumar Mishra, Amit Kumar Kholiya, Deepak Computer Science and Engineering Teerthanker Mahaveer University Moradabad India School of Computing DIT University Dehradun India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India School of Agriculture Graphic Era Hill University Dehradun India

ISBN: (纸本)9798350303360

Auto-learning machines-based detection and recognition of tomato leaf disease images are a rapidly-growing field. CNN architecture based ResNet50 model used to demonstrate machine learning-based image detection and recognition. Indicator dataused in this study use image pre-processing techniques. Image analysis uses morphological techniques and the Hough transform. The fundamental objective of this work is to detectdiseases in images using the Resnet50 Architecture. The data set used for training the ResNet50 model identifies the leaf disease. Performance measures the efficiency evaluation of the modelfrom the perspectives of exactness, detail, andsensitivity. The findings reveals an overall accuracy of 97.6%, specificity of 95.63, and sensitivity of 98.21, which is an improvement over the existing approaches. © 2023 IEEE.

关键词： Hough transforms

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Research on the Application of YOLO Algorithm in Nuclide Identification 24

Research on the Application of YOLO Algorithm in Nuclide Ide...

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3rd international conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Xu, Yulong Chu, Chiawei Liu, Yueming Guo, Te Wei, Chunglun Faculty of Data Science City University of Macau China

ISBN: (纸本)9798400716942

In response to the challenges of poor accuracy and slow identification speed in traditional nuclide identification, this paper seeks to enhance the efficiency of nuclide recognition by integrating the YOLO (You Only Look Once) algorithm into the process. A nuclide identification method based on YOLOv5s is studied. The YOLOv5s network autonomously extracts the complete feature information of the energy spectrum images. Subsequently, the trained model is tested using simulated energy spectrum data generated by MCNP software. The experimental results demonstrate that the model is effective for identifying both single nuclides and mixed nuclides, achieving an overall mAP (mean Average Precision) of 0.995. Moreover, the recognition time per image is as low as 0.01 seconds. These findings corroborate the feasibility and effectiveness of applying the YOLO algorithm to nuclide identification. © 2024 Owner/Author.

关键词： Adversarial machine learning

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Soccer Match Prediction based on Big data mining and Multimodal Features 3

Soccer Match Prediction based on Big Data Mining and Multimo...

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3rd international Signal Processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Zheng, Jiacheng Hangzhou Xuejun High School Hangzhou Zhejiang China

ISBN: (纸本)9798350307023

The sports industry ushered in new development and opportunities in the context of the big data era, especially the big data mining in the sports industry provides technical support for industrial iteration, promotes the innovative development of the sports industry, and accelerates the process of sports industry technology. As a popular sport in the field of soccer, it has received extensive attention from domestic and foreign fans, while data analysis and data mining in the field of soccer are also developing rapidly. Soccer game prediction is due to its many influencing factors, and there are diversity, uncertainty, ambiguity and other problems among the factors. At present, there are various ways to analyze soccer match prediction, and the rating system is not uniform, such as odds judgment, blind guessing based on historical experience, and it is not yet possible to quantify the internal relationship between features according to a set of uniform data mining process. Therefore, this paper proposes a multimodal feature extraction network based on deep learning, which can effectively extract a fusion of low-dimensional feature representations from many different modal features provided in the raw data of soccer matches. Finally, on the basis of solving the unidimensional prediction problem through traditional machine learning models and integration models, an improved Stacking integration model oriented to multidimensional features is innovatively proposed, and ultimately achieves the goal of improving the prediction accuracy and stability of soccer matches. © 2023 IEEE.

关键词： Forecasting

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Research on the Relationship Between data Visualization and Visualization data mining: Using the Iris dataset as an Example 3rd

Research on the Relationship Between Data Visualization and ...

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

作者： Chen, Hongjun Liu, Yunxiang He, Xuansen Wang, Junhu Information Technology and Engineering School Guangzhou College of Commerce Guangzhou511363 China

ISBN: (纸本)9789819616978

data visualization and visual data mining are two crucial directions in data analysis domain, and both of which play pivotal roles in data exploring, patterns identifying, hypotheses validating, and interactive discovery. In this essay, we take the classic dataset Iris as an example to study the interconnectedness of the two. Firstly, the target of data mining is clearly analyzed, and then the Iris data set is visually explored for data dispersion distribution, data distribution frequency, data estimation probability density, and data key features and potential patterns. Secondly, research is focused on the data visualization of dispersion graph and KNN visual data mining, the interaction between Violin graph and decision tree, and the discovery of data mining algorithm models. Additionally, hypothesis validation for unsupervised clustering numbers in the Iris dataset is also explored. Through this essay, we aim to offer practical applications and insights into research methods pertinent to data visualization and visual data mining. We also hope to provide some application and reference for the research of data visualization and visualization mining. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Visual analytics

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Power Network Security Situation Analysis Based on machine learning 3

Power Network Security Situation Analysis Based on Machine L...

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3rd international conference on New Energy and Power Engineering, ICNEPE 2023

作者： Zhang, Xiaolu Shen, Wuqiang Cui, Lei Information Center of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou510030 China

ISBN: (纸本)9798350393224

This work is using machine learning technology to ensure the safety of power grid data connection. The significance of this study depends on the grid electronic information network. The purpose of this project is to study the basic theories and key technologies of the power grid information network security knowledge localization. To achieve this goal, the project will use machine learning technology to establish a comprehensive safety assessment model for power grid information systems. This method will be used to analyze the security situation of the power plant information network and predict the potential risk. In addition, this project will develop a power grid security awareness system framework under the power grid information environment. This framework will complement the monitoring and management of the power grid information network, allowing for the timely identification and mitigation of potential security threats. Overall, the project aims to achieve security situational recognition of the power grid information environment, enabling efficient and effective management of network security and ensuring the safety and reliability of power grid operations. © 2023 IEEE.

关键词： machine learning Network security Situation awareness

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