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Comparative Evaluation of Machine Learning Algorithms for Detecting Credit Card Fraud 8

Comparative Evaluation of Machine Learning Algorithms for De...

引用

8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Prashanth, Banagani Gangadhar, Suddapalli Bhardwaj, Vivek Reddy, Kovvuru Diwakar Reddy, Puli Shiva Mehra, Manik Lovely Professional University Department of Computer Science and Engineering Phagwara India Manipal University Jaipur Department of Computer Science and Engineering Jaipur India Lovely Professional University School of Computer Application Phagwara India

ISBN: (纸本)9798350396638

Detecting credit card fraud is an extremely important issue in the financial sector, as it results in significant financial losses and negatively impacts customer trust. The main motive of CCFD is to develop methods to identify fraudulent transactions accurately and efficiently. This research typically involves the use of statistical and machine-learning algorithms to analyze large amounts of transaction data. Utilizing machine learning is a potential solution for addressing credit card fraud. Studies suggest that machine learning techniques can help overcome the challenges of identifying fraudulent transactions with high detection rates, both directly and indirectly. While supervised ML algorithm and unsupervised ML algorithms have been proposed, the limitations of each approach underscore the need for hybrid methods. From the results, it is evident that Logistic Regression has the highest accuracy of 94.86%, highest recall value of 98.97% and highest F1-score of 95.09%. © 2023 IEEE.

关键词： Feature extraction

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Potato Leaf Disease Detection Method is Based on the Support Vector Machines 2

Potato Leaf Disease Detection Method is Based on the Support...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Srivastava, Animesh Singh Rawat, Bhupender Sawan, Vikash Vishal Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India College of Smart Computing COER University Roorkee India GLA University Department of Computer Engineering & Applications Uttar Pradesh Mathura India School of Computer Application Lovely Professional University Punjab Jalandhar India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798331519056

Potato production is challenged by plant diseases whose adverse effects are a reduction in the yield of the crops, and which have undesirable economic implications. Recognizing these diseases is of utmost importance since control should be applied. This paper describes a method for diagnosing diseases of the potato using Support Vector Machine (SVM) classification. A SVM is an ML algorithm that is effective in classification tasks such as classifying potato leaves in terms of their diseases. The results show that the proposed method is useful and works rather well for detecting potato plant leaf diseases. In this paper, the SVM method emphasizes detecting various potato plant leaf diseases for efficient intervention before the late stage of visual assessment for microbial-introduced disease. The main aim of this research is to assist in overcoming the limitations of existing agricultural practices in speeding up the diagnosis and control of diseases affecting potato plants. So the SVM-based methods can help farmers in making timely decisions, reducing economic losses, and increasing agricultural productivity. With linear SVM, the accuracy stands at 93.1%. Nevertheless, when non-linear SVM is employed, the accuracy rises to 95.6%. © 2024 IEEE.

关键词： Agricultural economics

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application of 3D Magnetic Resonance Imaging in the Characterization of Brain Tumors 2

Application of 3D Magnetic Resonance Imaging in the Characte...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Samender Kishore, Lalit Badhoutiya, Arti Sagar, Pramod Kumar Saraswat, Laxmi Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India SRMIST Delhi Ncr Department of Computer Application Modinagar UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India Raj Kumar Goel Institute of Technology Srmist Department of Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350371055

MRI has revolutionized the analysis and remedy of disorder by using imparting precise and accurate images of soft tissue. This technology has been similarly superior by means of the improvement of 3-D MRI, which permits improved detection and characterization of tumors and improves accuracy of diagnosis and remedy. 3D MRI has been found to be in particular beneficial within the characterization of brain tumors as it offers a greater detailed assessment of tumor size, shape and composition, in comparison to traditional imaging techniques including X-ray or CT. This information can help in identifying the nature of the tumor that may impact its remedy and analysis. Furthermore, 3D MRI is useful for figuring out the chance of malignant invasion, which can provide important information for affected person control. Additionally, the advanced decision of 3-D MRI permits better delineation of areas of healthy and diseased tissue that can assist clinicians in making plans complex surgeries. (3-D) MRI has emerged as an increasingly more critical tool inside the characterization of brain tumors. The three-D imaging affords distinct anatomical visualization of the tumor and it's courting to surrounding systems, allowing for extra effective remedy making plans. Furthermore, the potential to visualize tumor tissue parameters together with edema, necrosis, and perfusion may be used to decide whether a tumor is malignant or no longer. 3D MRI has additionally enabled the evaluation of tumor vascularization, which serves as a vital prognostic indicator. The statistics received from 3-D imaging can then be used to be expecting the recurrence of a tumor following treatment, supporting to manual decisions on comply with-up remedies. The reduction in test time, decrease value, and better resolution of 3-D MRI in contrast to standard x-ray imaging also makes the technology more and more attractive for brain tumor management. © 2024 IEEE.

关键词： Attractive Characterization Superior Traditional

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Automated Detection of Abnormalities in Ultrasound Images Using Deep Learning

Automated Detection of Abnormalities in Ultrasound Images Us...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumar, Amit Bhuvana, J. Kharbas, Vikas Kumar College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Deep getting to know has been utilized in scientific imaging studies to explore challenges with diagnosing ailments and detecting anomalies. This examination focused on the automated detection of bizarre tissues in ultrasound pix. Pics must adhere to a homogeneous, properly-described, predictable anatomy for hit-deep getting-to-know applications. It looks at the proposed artificial neural network (ANN) and lengthy quick-time period memory (LSTM) based version referred to as ALunoNet to detect anomalies in ultrasound pictures. The models were skilled on a 10,000 M-mode and B-mode ultrasound pictures dataset. After ten epochs, their checking out accuracy was eighty three.16%. They look at comparing the performance of ALunoNet with other popular CNN and LSTM models. The observed effects demonstrate that ALunoNet performs higher than the same old fashions and that its overall performance is consistent even on datasets with constrained information. It has a look at highlights the capability of deep-gaining knowledge to automatize the detection of strange tissues in ultrasound pics. © 2024 IEEE.

关键词： abnormal automatized homogeneous performance ultrasound

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Millimeter Wave Beamforming for Massive MIMO in 6G Communication Systems

Millimeter Wave Beamforming for Massive MIMO in 6G Communica...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shree, Ritu Verma, Pradeep Kumar Kavitha, R. Vivekananda Global University Department of Computer Science & Application Jaipur India College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Millimeter wave beamforming for large MIMO in 6G verbal exchange systems is a promising era that could dramatically improve the overall performance and spectral efficiency of 6G networks. This generation entails the use of more than one antenna and antenna array to direct and recognize the electromagnetic power in a specific path. One of the core variations of millimeter wave beamforming in comparison with traditional more than one-antenna approaches is using fairly directional beams. This provides massive blessings in the communique link, together with improved bandwidth, improved user enjoyment, and stepped forward protection. Similarly, the use of a couple of antenna factors in this technology allows for beamforming, which could substantially lessen interference and enhance spectrum performance. Ultimately, the deployment of this era additionally calls for effective signal processing techniques, along with coding and modulation that can similarly boost the overall gadget capability. In conclusion, millimeter wave beamforming for huge MIMO in 6G verbal exchange systems can load up a huge range of possibilities and blessings, which could substantially enhance the user experience and warfare congestion in 6G networks. © 2024 IEEE.

关键词： Antenna Beamforming Dramatically Millimeter Networks Traditional

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Evaluation of Emotional Intelligence for Academic Stress Analysis 8

Evaluation of Emotional Intelligence for Academic Stress Ana...

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

作者： Khan, Asif Siddiqui, Mohammad Atif Integral University Integral Robotics Lab Department of Computer Application Lucknow India Integral University Integral Robotics Lab Department of Electrical Engineering Lucknow India

ISBN: (纸本)9798350376425

The research study on the "Design and Implementation of an AI-Enhanced Mental Health Tool for Academic Stress"addresses the growing mental health crisis among students, particularly concerning academic pressures. The study highlights alarming statistics, such as over 13,000 student suicides in India in 2021 - a 4.5% increase from the previous year and a 70% rise since 2011 - indicating an urgent need for targeted interventions. The academic environment, characterized by high expectations, intense pressure, and fierce competition, places considerable strain on students' mental well-being. Recognizing that one-size-fits-all solutions are inadequate, this research advocates for a personalized approach to address these unique challenges. Artificial Intelligence (AI) is positioned as a key solution in this effort. The proposed AI-enhanced mental health tool aims to provide students with real-time, personalized support. Through proactive interventions and individualized assistance, it seeks to revolutionize mental health care in academic settings. This study underscores the importance of innovative approaches to tackling the student mental health crisis. By fostering resilience and well-being, the AI tool aims to effectively address academic stress and improve mental health outcomes. © 2024 IEEE.

关键词： Emotional intelligence

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Secure Sentinel Leveraging Machine Learning for Fraud Detection in Blockchain Transactions 5

Secure Sentinel Leveraging Machine Learning for Fraud Detect...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shirley, C.P. Thanga Helina, S. Berin Jeba Jingle, I. Saran, P. Absin, S.J. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Kpr College of Arts Science and Research Department of Commerce with Computer Application Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Blockchain technology's decentralized and transparent transaction platforms have changed a number of sectors. However, harmful activities like fraud and money laundering are also drawn to blockchain due to its distributed and unchangeable nature. The distinctive features of blockchain transactions frequently prove to be too much for conventional fraud detection techniques to handle. In order to identify fraud in blockchain transactions, this work suggests "Secure Sentinel,"a machine learning-based method based on the Isolation Forest concept. By separating anomalies in a binary tree structure, the Isolation Forest algorithm is highly effective in detecting abnormalities inside datasets. By utilizing this approach, our system is able to identify transactions on the blockchain that are suspicious or stray from the norm. The Isolation Forest model will be trained using attributes such transaction amount, frequency, source/destination addresses, and transaction time. The project's goal is to improve blockchain networks' security and integrity by offering a real-time fraud detection system. Users, miners, and regulators are among the blockchain stakeholders who can take proactive steps to reduce risks and preserve system confidence by automatically identifying potentially fraudulent transactions. We will use past blockchain data to test our model's performance and determine how well it detects different kinds of fraudulent activity in terms of accuracy, precision, recall, and efficiency. By using machine learning to protect blockchain ecosystems from new dangers, Secure Sentinel is a step in the right direction toward advancing the acceptance and sustainability of blockchain technology. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of Fetal Brain Anomalies Using Deep Learning Models 5

Prediction of Fetal Brain Anomalies Using Deep Learning Mode...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Subburaj, Venkateshwari Pandiyarajan, Pandiselvam School of Computing Department of Computer Application Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India School of Computing Department of Computer Science and Engineering Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India

ISBN: (纸本)9798331518097

Fetal brain anomaly prediction is important for fetal medicine, as well as for prenatal health care. Fetal anomalies are classified into two types, anomalies in the fetus' body parts including heart, lung, and kidney anomalies in facial features. Thus, to predict the anomalies in the fetal brain, various deep-learning models are used in this paper. The LeNet, AlexNet, ResNet, VGGNet, GoogLeNet and ZFNet were used. From the results obtained, the ResNet method achieved 99% accuracy with the given 3D ultrasound images of the fetal brain compared with other models and benchmark algorithms like SVM, Logistic regression, and KNN. The results of this paper are useful to identify fetal anomalies. Hence, this approach will be useful for improving outcomes for individuals and society as much as possible. © 2024 IEEE.

关键词： Prediction models

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Study of Adaptive Nonlinear Signal Processing Structures for Underwater Acoustic Systems

Study of Adaptive Nonlinear Signal Processing Structures for...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kaushik, Neeraj Prabhu, A. Kaushik, Harshita College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This takes a look at and explores the use of adaptive nonlinear sign processing structures for underwater acoustic systems. The focus of the research is on how such structures can improve the accuracy of acoustic alerts underwater by adapting to various environmental conditions. Particularly, the research will analyze various acoustic sign distortion additives caused by nonlinear distortion, as well as the effectiveness of diverse adaptive nonlinear signal processing systems for reducing these effects. Sign processing strategies will include both adaptive thresholding and adaptive nonlinear time-frequency transformations. The studies will examine the overall performance of these systems in phrases of signal fidelity, in addition to the impact on cognitive complexity. The effects of the studies will inform capability enhancements and packages of nonlinear sign processing systems for underwater acoustic structures. © 2024 IEEE.

关键词： Performance Research Thresholding Transformations Underwater

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Ethereum Decentralized peer-to-peer Contracts Mechanism 3

Ethereum Decentralized peer-to-peer Contracts Mechanism

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Singh, Rajender Bansal, Naveen Vivekananda Global University Department of Mechanical Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9798350399264

With its dedicated money Ether, Ethereum offers regionalized Ethernet simulated computers to manage as an open-source public blockchain, peer-to-peer agreementsability to perform clever contracts. And since Ethereum is the second largest blockchain, transaction data volumes are growing rapidly. However, at this point, most analyses of Ethereum data are focused on the statistical characteristics of Ethereum nodes and do not examine how transactions between them behave. In this research, leverage transaction data from existing blocks to categorize users and smart contracts into groups and Applies machine learning to Ethereum analytics for the first time. The identification data of the accessible Ethereum users and smart contracts is used to examine the clustering findings. © 2023 IEEE.

关键词： Ethereum

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