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Intelligent Financial Fraud Detection Using Artificial Bee Colony Optimization Based Recurrent Neural Network

Intelligent Automation & Soft Computing 2023年第8期37卷 1483-1498页

作者： T.Karthikeyan M.Govindarajan V.Vijayakumar Department of Computer Science and Engineering Annamalai UniversityAnnamalai NagarTamil NaduIndia Department of Computer Science and Engineering Sri Manakula Vinayagar Engineering CollegePuducherryIndia

Frauds don’t follow any recurring *** require the use of unsupervised learning since their behaviour is continually ***-sters have access to the most recent technology,which gives them the ability to defraud people through online *** make assumptions about consumers’routine behaviour,and fraud develops *** learning must be used by fraud detection systems to recognize online payments since some fraudsters start out using online channels before moving on to other *** a deep convolutional neural network model to identify anomalies from conventional competitive swarm optimization pat-terns with a focus on fraud situations that cannot be identified using historical data or supervised learning is the aim of this paper Artificial Bee Colony(ABC).Using real-time data and other datasets that are readily available,the ABC-Recurrent Neural Network(RNN)categorizes fraud behaviour and compares it to the current *** compared to the current approach,the findings demonstrate that the accuracy is high and the training error is minimal in ABC_*** this paper,we measure the Accuracy,F1 score,Mean Square Error(MSE)and Mean Absolute Error(MAE).Our system achieves 97%accuracy,92%precision rate and F1 score 97%.Also we compare the simulation results with existing methods.

关键词： Fraud activity optimization deep learning classification online transaction neural network credit card

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Optimized Visual Recognition Through a Deep Convolutional Neural Network With Hierarchical Modular Organization

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IEEE Access 2024年 12卷 95517-95528页

作者： Clinton Sitepu, Ade Liu, Chuan-Ming National Taipei University of Technology College of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often require access to high-performance hardware, such as GPUs and cloud-based computing resources, to perform tasks efficiently. Visual recognition requires DCNNs with numerous layers. Fully connected layers in DCNNs are often the most computationally intensive. These layers connect every neuron in one layer to every neuron in the next layer, resulting in a large number of parameters to compute. To mitigate redundancy and make DCNNs more efficient, this article implements and demonstrates the concept to identifying and removing redundant or low-impact connections from fully connected layers using convolution neural network with hierarchical modular organization. The modularity of the DCNNs is built based on the cluster hierarchy of the similar image. These clusters are created based on a novel similarity metric, which measures how closely related images are to each other. The architecture uses multiple smaller DCNNs, referred to as modules, designed to progressively classify images into super clusters according to their similarity. Experimental results using popular image datasets show that the proposed DCNNs model to optimized number of operations by 49% to 99% and keeps its performance comparable. © 2024 The Authors.

关键词： Convolution

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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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CNN and Fuzzy Rules Based Text Detection and Recognition from Natural Scenes

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computer Systems science & engineering 2022年第9期42卷 1165-1179页

作者： T.Mithila R.Arunprakash A.Ramachandran Department of Computer Science and Engineering University College EngineeringBIT CampusAnna UniversityTrichy620021India Department of Computer Science and Engineering University College of EngineeringAriyalur621704India Department of Computer Science and Engineering University College of EngineeringPanruti607106India

In today’s real world, an important research part in image processing isscene text detection and recognition. Scene text can be in different languages,fonts, sizes, colours, orientations and structures. Moreover, the aspect ratios andlayouts of a scene text may differ significantly. All these variations appear assignificant challenges for the detection and recognition algorithms that are consideredfor the text in natural scenes. In this paper, a new intelligent text detection andrecognition method for detectingthe text from natural scenes and forrecognizingthe text by applying the newly proposed Conditional Random Field-based fuzzyrules incorporated Convolutional Neural Network (CR-CNN) has been ***, we have recommended a new text detection method for detecting theexact text from the input natural scene images. For enhancing the presentation ofthe edge detection process, image pre-processing activities such as edge detectionand color modeling have beenapplied in this work. In addition, we have generatednew fuzzy rules for making effective decisions on the processes of text detectionand recognition. The experiments have been directedusing the standard benchmark datasets such as the ICDAR 2003, the ICDAR 2011, the ICDAR2005 and the SVT and have achieved better detection accuracy intext detectionand recognition. By using these three datasets, five different experiments havebeen conducted for evaluating the proposed model. And also, we have comparedthe proposed system with the other classifiers such as the SVM, the MLP and theCNN. In these comparisons, the proposed model has achieved better classificationaccuracywhen compared with the other existing works.

关键词： CRF rules text detection text recognition natural scene images CR-CNN

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Deep Learning Based Tuberculosis Classification from Chest X-ray Images 15

Deep Learning Based Tuberculosis Classification from Chest X...

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

作者： Kavitha, S. Mahalakshmi, A. Prakash, P. Thambi Raj, V. Computer Science and Engineering Kongu Engineering College Erode India Computer Science and Engineering Sri Eshwar college of Engineering Coimbatore India Medical Electronics Velalar College of Engineering and Technology Erode India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370249

Tuberculosis (TB) is a serious health issue that kills a lot of people these days. TB is completely curable if it is discovered early enough. One way of diagnosing tuberculosis (TB) early on is to undergo a chest X-ray (CXR), which can show if a person has active TB. Chest x-ray images (CXR) can be analyzed to diagnose the deadly TB disease more accurately owing to deep learning (DL), a significant area of artificial intelligence. To automate tuberculosis (TB) identification, this study used Convolutional Neural Networks (CNNs), a form of deep learning models designed specifically for image processing. This architecture needs a lot of datasets to learn features, which makes the learning process take longer. Transfer Learning (TL), which uses a large number of pre-trained models to perform disease classification, is suggested as a solution to this issue. In this work, four different CNN architecturesEfficientNetB0, AlexNet, GoogleNet, and Xception-are trained and evaluated. The results demonstrated that GoogleNet could diagnose tuberculosis with an astounding 97% accuracy, even after extended training periods. By comparison, AlexNet trained faster due to its simpler architecture, but having a little lower accuracy of 96%. © 2024 IEEE.

关键词： Deep learning Accuracy Tuberculosis Computational modeling Transfer learning Predictive models Feature extraction Data models X-ray imaging Medical diagnostic imaging

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Utilizing Artificial Intelligence for Advance Data science and Analysis 2

Utilizing Artificial Intelligence for Advance Data Science a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Deepak Chaudhary, Gaurav Rajput, Kapil Singhal, Amit Sharma, Tarun Kumar Srm Institute of Science and Technology Department of Computer Applications UPModinagar Delhi India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Hill University Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Applications UP Ghaziabad India

ISBN: (纸本)9798350371055

Data technology has experienced an explosion in popularity and scope over the past few years, inflicting several companies to try to capitalize on the sizeable possibilities the technology has to provide. Agencies are utilizing AI to develop records science studies, allowing them to go beyond conventional strategies and discover new insights and innovations within the discipline. With AI, statistics scientists can identify styles in massive datasets, optimize algorithms to clear up complicated issues and create predictive fashions to discover developments and correlations. In addition, AI may be used to automate specific components of fact processing, making data technological know-how simpler and quicker. To make the most of this generation, organizations should have personnel knowledgeable in the basics of facts science and AI and the knowledge to recognize the complexities of the algorithms and applications they use. With the right group and the proper equipment, AI may make data technological know-how studies extra effective and more beneficial than ever. © 2024 IEEE.

关键词： Algorithms Correlations Fundamentals Technology Understand

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Classifying Fake News Detection Using SVM, Naive Bayes and LSTM 12

Classifying Fake News Detection Using SVM, Naive Bayes and L...

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12th International Conference on Cloud Computing, Data science and engineering, Confluence 2022

作者： Jain, Parita Sharma, Swati Monica Aggarwal, Puneet Kumar Kiet Group Of Institutions Computer Science And Engineering India Abes Engineering College Information Technology

ISBN: (纸本)9781665437011

Web-primarily based media is a stage to state one's perspectives and viewpoints unreservedly and has made correspondence simpler than it became previously. This moreover opens up a risk for people to get out counterfeit phrase intentionally. The trustworthy entry to an collection of statistics sources on the net likewise brings the issue of people being provided to counterfeit facts and doubtlessly accepting such information. This makes it considerable for us to understand furthermore, banner such substance through on-line media. With the modem-day tempo of information created via online media, it's far hard to split between certifiable information and fabrications without knowing the wellspring of the news. In this paper, we propose various techniques to verify that the collected news is fake or not. For this, the approach named Natural Language Processing (NLP) is used. Various other methodologies like text classification, classification modeling is also used, and analysis of results has been done. Data from various sources was collected and to verify that the news is correct or not various techniques like SVM, Naive Bayes LSTM are used. © 2022 IEEE.

关键词： Support vector machines

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DeepSwarm:towards swarm deep learning with bi-directional optimization of data acquisition and processing

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Frontiers of computer science 2025年第3期19卷 125-127页

作者： Sicong LIU Bin GUO Ziqi WANG Lehao WANG Zimu ZHOU Xiaochen LI Zhiwen YU School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Data Science City University of Hong KongHong Kong 999077China College Of Computer Science And Technology Harbin Engineering UniversityHarbin 150001China

1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.

关键词： drone swarm classification efficient local data processing data processing deep learning dl device deep learning bi-directional optimization iot devices swarm deep learning

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EMRChain: Electronic Medical Records Management System using Blockchain 3

EMRChain: Electronic Medical Records Management System using...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rajasekharan, Arjun Koshy, Rani College of Engineering Trivandrum Dept. of Computer Science and Engineering Kerala Thiruvananthapuram India College of Engineering Trivandrum Department of Computer Science and Engineering Kerala Thiruvananthapuram India

ISBN: (纸本)9798350354348

Electronic Medical Records (EMRs) are traditionally managed by central authorities, posing significant security risks such as data breaches, limited interoperability, and restricted patient control. This system leverages blockchain technology to address these issues, ensuring data remains immutable, transparent, and highly secure. Utilizing the Ethereum network and IPFS technology, our system provides a decentralized framework for storing and sharing medical records. Healthcare professionals, patients, and authorized parties can seamlessly exchange and access records through Ethereum's decentralized structure and smart contracts, enabling automated and auditable interactions. To enhance confidentiality, all data uploaded to IPFS undergoes AES 256-bit encryption, with the encrypted Content Identifier (CID) stored on the blockchain. This ensures that intercepted CIDs cannot be accessed without the decryption key. IPFS technology facilitates decentralized storage and retrieval, eliminating the need for a centralized server and improving scalability and resilience. Patients retain full control over their health information, granting or revoking access as necessary. This system ensures secure patient identification, precise access management, and data confidentiality while allowing authorized parties to track and trace record access and modifications. Our blockchain-based EMR management system addresses the inherent risks of traditional centralized systems by providing a secure, transparent, and decentralized solution. By leveraging Ethereum and IPFS, it enhances data security, patient control, and interoperability, thus paving the way for more robust and patient-centric healthcare information management. This approach ensures that healthcare data remains secure, patients maintain control over their information, and authorized parties can effectively manage and access the records. © 2024 IEEE.

关键词： Ethereum

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An In-Depth Exploration of Advanced Driver Drowsiness Detection Systems for Enhanced Road Safety 2

An In-Depth Exploration of Advanced Driver Drowsiness Detect...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Abirami, A. Bhuvaneswari, S. Vishnuvardhan, M. Karthik, Yadam Shankar, Saisuraj Easwari Engineering College Dept. of Computer Science and Engineering Chennai India Student of Easwari Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

Drowsiness, among drivers, plays a role in causing road accidents leading to loss of life, injuries, and financial setbacks. To address the issue of driver fatigue related accidents it is crucial to develop methods for detecting driver fatigue. This survey paper presents an overview of approaches used to identify driver fatigue. These approaches can be grouped into three categories: Behavioural, Vehicular and Physiological parameters. Behavioural parameters, such as eye closure percentage, blink rate patterns, yawning, head movements and facial expressions offer intrusive techniques that examine the physical state of the driver's body and provide information about their drowsiness level. Vehicular parameter-based techniques evaluate drowsiness by observing vehicle behaviour such as lane deviations, pedal usage, and unpredictable steering movements. Physiological indicators are also employed to detect tiredness. The paper also discusses the methodology employed in the research study including image processing techniques classifiers used and measurement metrics utilized, proposed methodology of fatigue detection with adding some extra features and Lastly the paper presents conclusions based on a study that can guide future researchers in identifying potential areas for further exploration in this field. © 2024 IEEE.

关键词： Physiological models

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