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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Sai Tejasree, K. Surendran, R. Rajakumar, B. Gomathi, R.M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India J.N.N Institute of Engineering Department of Artificial Intelligence and Data Science Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recognition. This method teaches the ML Model we clearly explained the Model using application's agent using PEAS and the application's task environment using the 6 dimensions by showing it tons of fruit images, so over time, it gets really good at spotting the differences and similarities between, say, a banana and a grape. We also used another technique called pattern recognition, which helps the computer pay attention to specific details like the fruit's color, shape, size and texture. Overcoming multiple obstacles in order to automatically identify the type of fruit from the picture. The variety of images is influencing the color, texture, and shape of many different types of fruits. When it came to fruit picture detection, Convolutional Neural Network (CNN) Algorithm outperformed standard support-vector-machine-based approaches using handcrafted features in terms of accuracy also, it is a lot faster to implement for new fruits. By integrating deep learning and pattern recognition techniques such as Convolutional Neural Network Algorithm we have got the accuracy of 84%, our system efficiently identified different fruit types from images, demonstrating the power and effectiveness of our methods. The goal of our project is to create a tool that can quickly and correctly identify many types of fruits in photos, which could be useful for things like sorting fruits in a grocery store or helping people learn about different fruits by using Convolutional Neural Network Algorithm. This is not just about teaching a computer to recognize fruits;it is about making technology that can understand and interact with the world in a way that is helpful to us. © 2025 IEEE.

关键词： Convolutional neural networks

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Observation points classifier ensemble for high-dimensional imbalanced classification

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CAAI Transactions on intelligence technology 2023年第2期8卷 500-517页

作者： Yulin He Xu Li Philippe Fournier‐Viger Joshua Zhexue Huang Mianjie Li Salman Salloum College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen UniversityShenzhenChina Faculty of Information Technology Macao University of Science and TechnologyMacaoChina School of Computing National University of SingaporeSingaporeSingapore

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction ***,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as ***,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data ***,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown *** experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data *** results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC *** demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

关键词： classifier ensemble feature transformation high-dimensional data classification imbalanced learning observation point mechanism

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Smart framework for enhancing security of IoT linked devices in Healthcare Systems 5

Smart framework for enhancing security of IoT linked devices...

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5th International Conference on Information Management and Machine intelligence, ICIMMI 2023

作者： Duvey, Anurag Anand Rahi, Pankaj Gowri Ganesh, N.S. Basa, Santi Swarup Dept. of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur India Health Information Technology Management Indian Institute of Health Management Research Bangalore India Dept. of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India Dept. of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha Baripada India

ISBN: (纸本)9798400709418

The adoption of blockchain technology is widespread across diverse industries due to its rapid advancement and high level of innovation. The technology in question is widely acknowledged for its ability to adapt and be utilized in diverse domains, thereby enabling the storage of data in interconnected fields through peer-to-peer networks. Blockchain technology demonstrates high performance in the storage of transactions that have minimal digital traces, as well as in situations where there is a need for immutability and transparency. The utilization of blockchain technology can potentially offer advantages in various domains such as supply chain management, the maintenance of dynamic patient authorization for data use, and the verification of patient, provider, or supplier identification in certain situations. The main objective of this research study is to investigate the application of blockchain technology in improving the security of Internet of Things (IoT) systems, particularly in the context of remote patient monitoring and its linked EHR system. This article explores the advantages of incorporating Internet of Things (IoT) devices into the remote patient monitoring practice, as well as the practical challenges that may arise during the installation phase. The proposed concept utilizes sophisticated and effective cryptographic methodologies, particularly the Advanced Encryption Standard (AES) scheme, to guarantee dependable data transmission across networks and safeguarded storage within cloud environments. © 2023 ACM.

关键词： Smart contract

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Multi-Modal LLM Driven Computer Interface 8

Multi-Modal LLM Driven Computer Interface

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sowmiya, R. Revathi, P. Ragunath, D. Sindhuja, S. Gokila, P. Kalaivani, T. Sns College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India Tamilnadu Coimbatore India Info Institute of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

This study presents a framework for multimodal models to control and interact with computers: The Multimodal-driven Computer Interface. A decision-making module is used by the framework to determine what actions are acceptable after it processes user input through a variety of modalities, such as speech, text, and gestures, and runs it through a multimodal fusion algorithm. Platform-specific APIs are then used to carry out these operations on the computer. It runs on Linux, Mac, and Windows platforms and is currently integrated with the multimodal LLM. Although the framework shows promising results, there are still issues with adjusting to different user demands and preferences and increasing the accuracy of mouse click position prediction. Potentially revolutionizing human-computer interaction, the Multimodal-driven Computer Interface offers new avenues for accessibility, productivity, and pleasure. © 2024 IEEE.

关键词： Windows operating system

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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data intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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Recommendations of Smartphone Applications According to Customer Reviews and Capabilities 2

Recommendations of Smartphone Applications According to Cust...

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2nd International Conference on Augmented intelligence and Sustainable Systems, ICAISS 2023

作者： Ramnath, M. Rubavathi, C. Yesubai Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Francis Xavier Engineering College Department of Computer Science and Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350325799

There are a plethora of new Android portable apps available now. As a result, making sure the apps you install on your mobile device are safe is a daunting undertaking for the average user. This study provides a popular and secure mobile App with a recommendation system to streamline the process. The design focus is on making recommendations for mobile apps by gauging their popularity and potential security issues. To index the apps and save the information in a database, a web crawler is employed. The programs are then sorted into groups according to their popularity and ratings. The suggested Android app provides a list of applications from the Google Play Store along with their respective security ratings whenever a query is executed. How popular an app is and what permissions it requires from the user significantly increase the app's potential security risks. This work aims to create such a recommendation system without sacrificing security or user engagement. © 2023 IEEE.

关键词： Recommender systems

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Tackling Real-world Complexity: Hierarchical Modeling and Dynamic Prompting for Multimodal Long Document Classification

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5776-5790页

作者： Liu, Tengfei Hu, Yongli Li, Mingjie Yi, Junfei Chang, Xiaojun Gao, Junbin Yin, Baocai Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence School of Information Science and Technology Beijing100124 China Stanford University Department of Radiation Oncology StanfordCA94305 United States Hunan University National Engineering Laboratory of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha410082 China University of Technology Sydney ReLER Laboratory Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology UltimoNSW2007 Australia The University of Sydney Business School The University of Sydney Discipline of Business Analytics CamperdownNSW2006 Australia

With the rapid growth of internet content, multimodal long document data has become increasingly prominent, drawing significant attention from researchers. However, most existing methods primarily focus on scenarios where all modalities are present, often overlooking more challenging and realistic cases involving missing image modality. To address this limitation, we propose a robust multimodal long document classification (MLDC) framework that integrates hierarchical modeling and dynamic prompting to handle complex multimodal long document data. Our approach begins by leveraging hierarchical modeling combined with an Adaptive Correlation Multimodal Transformer (ACMT) to effectively capture relationships between text and images at both section and sentence levels. We also introduce a Dynamic Prompt Generation (DPG) module at both levels to enhance the model's robustness in handling missing image data. By evaluating sample uncertainty, the DPG module dynamically adjusts both the number of prompts and the prompts themselves, allowing the model to better adapt to the varying needs of different samples. Finally, a Hierarchical Heterogeneous Graph (HHG) is introduced to enhance feature interactions across levels, further improving the coherence and accuracy of the model. Extensive experiments on four multi-modal long document datasets demonstrate that our model shows superior performance compared to existing state-of-the-art MLDC classification methods in various conditions. © 1991-2012 IEEE.

关键词： Adaptation models Transformers data models Correlation Circuits and systems Electronic mail Robustness Uncertainty Training Complexity theory

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Iterative Dichotomiser 3 (ID3) for Detecting Firmware Attacks on Gadgets (ID3-DFA)

Iterative Dichotomiser 3 (ID3) for Detecting Firmware Attack...

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International Conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Punidha, A. Arul, E. Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Information Technology Coimbatore Institute of Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9789819932498

Even while the wonderful new product's standard distribution condition still offers very little protection, receiving a cutting-edge device during the holiday season may be entertaining but does not pose any risks. This protects because anybody may openly link the commercial item to the Web despite efforts to prevent theft, industrial espionage, and infections all of which might endanger the assets and protection. In fact, malware may be detected using machine learning without the usage of signatures or behavioral analysis. In order to characterize such a software assault on devices, we use Iterative Dichotomiser 3 (ID3). By training a Firmware ID3 with several input clusters and a malicious or benign API, a single output unit is classified as malicious or benign. The unknown IoT firmware was then subjected to training by ID3 to find a harmful pattern. The result shows that the truth positive rating is 97.86%, and that the adware attack is 0.02% positive. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Malware

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Investigating customer churn in banking:a machine learning approach and visualization app for data science and management

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data science and Management 2024年第1期7卷 7-16页

作者： Pahul Preet Singh Fahim Islam Anik Rahul Senapati Arnav Sinha Nazmus Sakib Eklas Hossain Institute for Artificial Intelligence and Data Science University at BuffaloThe State University of New YorkAmherst14068United States Department of Mechanical Engineering Khulna University of Engineering&TechnologyKhulna9203Bangladesh Department of Information Technology Kennesaw State UniversityMarietta30067United States Department of Electrical and Computer Engineering Boise State UniversityBoise83725United States

Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and,after that,end their connection with the ***,customer retention is essential in today’s extremely competitive banking ***,having a solid customer base helps attract new consumers by fostering confidence and a referral from a current *** factors make reducing client attrition a crucial step that banks must *** our research,we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying *** use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation *** addition,we developed a data Visualization RShiny app for data science and management regarding customer churn *** this data will help the bank indicate the trend and then try to retain customers on the verge of attrition.

关键词： Bank customer attrition Churn prediction Machine learning XGboost Random forest

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Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

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Computers, Materials & Continua 2023年第1期74卷 19-36页

作者： Motasem S.Alsawadi El-Sayed M.El-kenawy Miguel Rio Electronic and Electrical Engineering Department University College LondonLondonWC1E 7JEEngland Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

The ever-growing available visual data(i.e.,uploaded videos and pictures by internet users)has attracted the research community’s attention in the computer vision ***,finding efficient solutions to extract knowledge from these sources is ***,the BlazePose system has been released for skeleton extraction from images oriented to mobile *** this skeleton graph representation in place,a Spatial-Temporal Graph Convolutional Network can be implemented to predict the *** hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest,it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR ***,in this study,we present the first implementation of the BlazePose skeleton topology upon this architecture for action ***,we propose the Enhanced-BlazePose topology that can achieve better results than its ***,we propose different skeleton detection thresholds that can improve the accuracy performance even *** reached a top-1 accuracy performance of 40.1%on the Kinetics *** the NTU-RGB+D dataset,we achieved 87.59%and 92.1%accuracy for Cross-Subject and Cross-View evaluation criteria,respectively.

关键词： Action recognition BlazePose graph neural network OpenPose skeleton spatial temporal graph convolution network

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