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检索条件"机构=Artificial Intelligence and Data Science Faculty of Engineering and Technology"

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Investigating the Utility of data Mining for Automated Credit Scoring 15

Investigating the Utility of Data Mining for Automated Credi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Yadav, Dhyan Garima Soni, Rudransh Anandhasilambarasan, D. Sasirekha, R. Prema, S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Karnataka Bangalore India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore641021 India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350370249

data mining is a powerful device for automatic credit scoring. This paper will look into the application of statistics mining for automated credit score scoring by discussing its advantages and downsides. The form may even look at cutting-edge research in the location and discuss the demanding situations that information mining techniques pose while used for automatic credit score scoring. Similarly, the paper will provide pointers on how statistics mining techniques can improve the effectiveness of computerized credit score scoring. Subsequently, a precis of the paper's conclusions and implications for future studies could be supplied. © 2024 IEEE.

关键词： Conclusions Implications Improve Information Scoring

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FedDGL: Federated Dynamic Graph Learning for Temporal Evolution and data Heterogeneity 16

FedDGL: Federated Dynamic Graph Learning for Temporal Evolut...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Xie, Zaipeng Li, Likun Chen, Xiangbin Yu, Hao Huang, Qian Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China College of Artificial Intelligence and Automation Hohai University Nanjing China

Federated graph learning enhances federated learning by enabling privacy-preserving collaborative training on distributed graph data. While traditional methods are effective in managing data heterogeneity, they typically assume static graph structures, overlooking the dynamic nature of real-world graphs. Integrating federated graph learning with dynamic graph neural networks addresses this issue but often fails to retain previously acquired knowledge, limiting generalization for both global and personalized models. This paper proposes FedDGL, a novel framework designed to address temporal evolution, and data heterogeneity in federated dynamic graph learning. Unlike conventional approaches, FedDGL captures temporal dynamics through a global knowledge distillation technique and manages client heterogeneity using a global prototype-based regularization method. The framework employs contrastive learning to generate global prototypes, enhancing feature representation while utilizing a prototype similarity-based personalized aggregation strategy for effective adaptation to local and global data distributions. Experiments on multiple benchmark datasets show that FedDGL achieves significant performance improvements over state-of-the-art methods, with up to 9.02% and 8.77% gains in local and global testing, respectively, compared to FedAvg. These results highlight FedDGL’s effectiveness in improving personalized and global model performance in dynamic, heterogeneous federated graph learning scenarios. © 2024 Z. Xie, L. Li, X. Chen, H. Yu & Q. Huang.

关键词： Dynamic Graph Learning Federated Graph Learning Knowledge Distillation Personalized Aggregation Prototype Learning Temporal Evolution

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Interpreting Doctors' Notes Using Handwriting Recognition and Deep Learning Techniques

Interpreting Doctors' Notes Using Handwriting Recognition an...

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International Conference on science technology engineering and Management (ICONSTEM)

作者： Maheswari G Ramya. R Babitha. S Sriram. R Jennis. M I RamPrasad. P Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

This research provides a deep learning approach and handwriting recognition to understand medical notes. The system processes and interprets textual medical notes using connectionist temporal classification (CTC) and optical character recognition (OCR) techniques. The written text is transcribed using the OCR component, and then the CTC algorithm is utilized to analyze the text and give significance to particular note portions. The suggested system's ability to correctly understand and arrange written medical information is demonstrated by training it on prescription notes from doctors taken from a dataset. According to the final forecast, the system can read medical notes with high accuracy, making medical professionals' usage and accessibility of these records easier. The suggested paradigm offers fast and precise access to patient information, which could increase the efficacy and efficiency of medical care.

关键词： Deep learning Training Handwriting recognition Accuracy Optical recording Optical character recognition Software algorithms

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Optimizing Resource Management and data Security in Cloud Computing Environments 3

Optimizing Resource Management and Data Security in Cloud Co...

引用

3rd International Conference for Advancement in technology, ICONAT 2024

作者： Shri, S.Jothi Karthiyayini, G. Vignesh, L.S. Upender, Talluri Shobana, D. Patra, Ladi Sandip Kumar Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Ramco Institute of Technology Department of CSE Tamil Nadu Rajapalayam India Nadar Saraswathi College of Engineering and Technology Department of Artificial Intelligence and Data Science Theni India CMR College of Engineering and Technology Medchal Department of CSE Telangana Hyderabad India Rajalakshmi Engineering College Department of Mechatronics Engineering Chennai India MLR Institute of Technology Department of ECE Hyderabad India

ISBN: (纸本)9798350354171

The quick uptake of cloud computing has completely changed how companies run since it offers flexible and scalable resources that promote productivity and creativity. However, the dynamic and complex nature of cloud environments presents significant challenges in optimizing resource management and ensuring robust data security. This paper investigates advanced strategies and methodologies for enhancing resource allocation, virtualization, and scheduling to maximize performance and cost-efficiency in cloud infrastructures. Key techniques explored include dynamic scaling, load balancing, VM migration, containerization, and cost-effective resource usage through spot and reserved instances. The important facets of cloud computing data security, such as encryption, identity and access management (IAM), data loss prevention (DLP), and intrusion detection and prevention systems (IDPS), are also covered in tandem by this research. The study offers novel approaches for reducing security threats and guaranteeing data integrity by fusing cutting-edge technology like blockchain and machine intelligence. The comprehensive analysis aims to provide actionable insights and practical recommendations for IT professionals and organizations striving to optimize their cloud computing operations. By balancing resource management efficiency with stringent security measures, this research contributes to the development of a more secure and efficient cloud ecosystem. The results emphasise the significance of a comprehensive strategy for managing cloud systems, where data security and performance optimisation work together to provide durable and sustainable cloud computing environments. © 2024 IEEE.

关键词： Cloud security

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White Blood Cells Classification Model using Automatic Soft Computing

White Blood Cells Classification Model using Automatic Soft ...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Mahale, Pragati Prashant Deshmukh, Rakesh K. Pise, Priya Rangari, Sudhir Ramrao Kalinga University Raipur Department of Information Technology AISSMS's Institute of Information Technology Pune India Kalinga University Raipur India Indira College of Engineering and Management Artificial Intelligence and Data Science Pune India JSPM'S Jayawantrao Sawant College of Engineering Information Technology Department Pune411028 India

ISBN: (纸本)9798350361537

Accurate classification of blood cells, especially WBCs, is crucial for disease diagnosis and management. Traditional optical microscopic imaging provides limited information, but hyperspectral imaging (combining spatial and spectral data) offers richer insights. There are challenges and opportunities in white blood cell (WBC) classification using both Convolutional Neural Networks (CNN) including the following: (1) Important data, such as representative characteristics at various frequencies and orientations, may be lost due to the CNN model's usage of local receptive fields, shared weights, and subsamples. (2) To avoid over fitting, deep neural networks need a large pool of labeled training models. and (3) the higher computational overhead is yet another challenge. Therefore, these challenges motivate us the need of the proposed study. The scope of the proposed study is limited to the classification of WBC cells. Framework to classify and count the leukocytes in microscopic images and developed framework supports the pathologist and boosts the CAD system accuracy. The process of analyzing leukocytes in microscopic blood images is the major concern of this research. In this thesis, first we work on WBCs segmentation from microscopic blood images. © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Road Extraction using Scale Invariant Feature Transform-based Random Forest in Satellite Images 1

Automatic Road Extraction using Scale Invariant Feature Tran...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Rajesh, N. Alzubaidi, Laith H. Shivaprasad Yadav, S.G. Chembian, W.T. Prameela, Boddepalli Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India The Islamic University of Babylon Department of Computers Techniques Babylon Iraq Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bengaluru India Department of Computer Science and Engineering Chennai India Vignan's Institute of Information and Technology Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798350352931

Road Extraction from High-Resolution Satellite Images (HRSI) plays a significant role in the analysis of the remote sensor image field. The extraction of road automatically labels a road's pixels in a satellite image with semantic features depending on topographical meaningful features. However, manual road extraction inaccurately extracts road networks from satellite images which has significant difficulties like complex urban environments, varying road types, and diverse image conditions. Therefore, the Scale Invariant Feature Transform-based Random Forest(SIFT-RF) is proposed for automatic road extraction in satellite images. First, the image is obtained from DeepGlobe dataset and Z-score normalization is employed to maximize model performance. Then, SIFT is established to extract the road network in satellite images. Finally, the RF is utilized to extract and classify roads as road or non-road using Machine Learning (ML). The SIFT-RF achieves a greater f1-score of 98.24% compared to existing methods like Dual-Attention Road extraction Network (DA-RoadNet), High-Resolution U-Net architecture (HRU-Net), and Global Context-aware and Batch-independent Network (GCBNet) respectively. © 2024 IEEE.

关键词： Random forests

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A Structured Analysis on IPL 2022 matches by approaching various data Visualization and Analytics

A Structured Analysis on IPL 2022 matches by approaching var...

引用

2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Selva Birunda, S. Nagaraj, P. Jency A Jebamani, B. Revathi, B. Muneeswaran, V. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350348217

The main purpose of making this report is to give the information to the franchise to make their decision on buying the players by bidding in the auction based on their performances. There are many models of reports which describe the IPL seasons. This report's purpose is to give a brief analysis of the IPL 202215th league. In this report, much of the data from the datasets of matches is utilized and analysis is made by using Python packages specifically, Pandas, NumPy, and matplotlib. By making graphs and tables a brief analysis with complete knowledge of an IPL season is given. This analysis describes every good performance and gives good chance to the best players. © 2023 IEEE.

关键词： data visualization

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Dynamic AI Collaboration Frameworks Using Real-Time Multimodal Feedback Loops for Complex Task Automation

Dynamic AI Collaboration Frameworks Using Real-Time Multimod...

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data science, Agents & artificial intelligence (ICDSAAI), International Conference on

作者： K. Saranya Veeramalai Sankaradass V. Antony Suresh Sunitha. T Department of Artificial Intelligence and Data Science Panimalar Institute of Technology Chennai India Department of ComputerScience and Engineering Chennai Institute of Technology Chennai India Department of Information Technology St. Joseph College of Engineering Chennai India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

AI solutions are introduced in various complex, unstructured and perpetually evolving contexts, wherein multiple agents are often needed to cooperate in order to accomplish a given set of objectives. In such environment, flexibility is crucial because such systems are required to alter their operation in accordance with changes occurring in the environment. However, there are problems associated with traditional multi-agent systems to provide the wanted level of flexibility in order to react promptly to the constantly changing environment and handle a variety of sensory inputs. This paper describes a new Dynamic AI Collaboration Framework to address these issues by incorporating multi-modal feedback from vision, audio and text, haptic feedback streams. This framework is composed by a Dynamic Role Assigner based on reinforcement learning that will assign the roles of the agents according to the variations of the environment. This can be done to a much larger extent than in the earlier examples, and flexible adaptive AI agents can alter their roles without any problems according to their assessment of the ongoing task and its environment. This paper also presents an evaluation of the effectiveness of the proposed framework in a number of simulated disaster-response scenarios where our approach proves to be 25% more efficient and 40% more dynamic as compared to the conventional static role allocation based approaches. The findings show that adopting remote multimodal feedback as well as dynamic role allocation strengthen synergistic interactions among AI participants and guarantee optimised performance in critical, transient situations. In this work numerous applications are presented for dynamic AI systems for example in disaster response and industrial automation, in collaborative creative processes and health care showing that dynamic systems have a variety of uses in different fields.

关键词： Automation Collaboration Reinforcement learning Dynamic scheduling Real-time systems Resource management artificial intelligence Transient analysis Streams Multi-agent systems

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AI-Driven Approaches to Enhancing Mental Wellbeing and Stress Relief

AI-Driven Approaches to Enhancing Mental Wellbeing and Stres...

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

作者： Revathi K S. Priyanka Priyadharshini S Niranjan NN Visalachi N P. Sumathi Department of Information Technology SNS College of Engineering Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamil Nadu India Department of Information Technology & Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Mental health issues affected millions of peoples in worldwide, who often struggle with stigma, high costs, and low access to real life care. Conventional therapy methods faced lot of challenges to identify the issues. These challenges need to highlight for more accessible, affordable, and effective mental health solutions. Traditional models of care often fall short of providing adequate support to all those in need, leading to gaps in care and disparities in mental health outcomes. So, need for Innovation in Mental Health Support for effective mental health solutions to address these challenges. Introduction to “InnerPeaceAI” Aims to provide personalized, on-demand therapy using advanced AI technology, offering a confidential and user-friendly platform to improve mental health care accessibility. InnerPeaceAI aims to bridge the gap between the need for mental health care and its accessibility by leveraging advanced AI technology to deliver therapy sessions tailored to individual needs. The platform uses natural language processing and machine learning algorithms to create personalized treatment plans, identify mental health issues, and provide real-time support to users. This innovative approach allows for immediate access to therapy, without the constraints of time, location, or cost.

关键词： Technological innovation Costs Machine learning algorithms Medical treatment Mental health Depression Real-time systems Natural language processing Time factors artificial intelligence

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Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Yashas D M Shivani Kashyap Charan PR Hemanth Kumar GP Abhishek BK Deekshith C Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru Dept of Information Science and Engineering New Horizon College of Engineering Bengaluru Dept of Artificial Intelligence and Machine Learning BNM Institute of Technology Bengaluru Dept of Artificial Intelligence and Data Science Siddaganga Institute of Technology Tumkur Dept of Computer Science and Engineering Vidyavardhaka College of Engineering Mysore

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field.

关键词： Deep learning Dermatology Medical services Transforms Chatbots Skin Real-time systems Convolutional neural networks artificial intelligence Diseases

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