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A Bibliometric and Social Media Analysis of the Nexus Between Blockchain technology and Finance 2nd

A Bibliometric and Social Media Analysis of the Nexus Betwee...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ayodele, Olamide Emmanuel Ajibade, Temiloluwa Iyanuoluwa Oyebode, Oluwadare Joshua Akintoye, Kayode Akinlekan Ajayi, Babatunde Adedotun Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Management Scholar School System Birmingham United Kingdom Civil and Environmental Engineering Afe Babalola University Ekiti State Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya KL Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This study explores Blockchain technology in the global financial industry using trends analysis, bibliometric analysis, and a literature appraisal using Elsevier Scopus data from 2016 to 2023. The Blockchain in Finance (BCF) study produced 185 publications: 53.5% papers, 45.4% conference proceedings, and 1.1% reviews. Analysis of the subject area revealed three important Computer science, engineering, and Decision sciences articles. However, source title analysis showed that ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences publish BCF research most often. According to the stakeholders study, Jon M. Truby and Qatar University (Qatar) are the top BCFin researchers and affiliations with 4% and % publications, respectively. China produces the most research on the topic. Due to active support from funding sources like the Chinese National Natural science Foundation (NNSF), 18 publications have been funded. Co-occurrence of keywords The investigation indicated that BCFin’s top keywords include Blockchain, Finance, and Supply Chain Finance. Three clusters with 4–8 keywords, 132 linkages, and 866 TLS were found via cluster analysis. Keywords classify the clusters as Integrated business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is a dynamic and influential multidisciplinary study field. BCFin has transformed the global financial sector by improving trust, transaction security, and communication. It still struggles with interoperability, system integration, and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Emergency Vehicle Detection Using Deep ConvNet2D and Computer Vision

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： Elumalaivasan Poongavanam Prathik. A Munirathinam. T Kayalvizhi. V Rajesh. R Mahavishnu. C Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Sona College of Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Shreenivasa Engineering College Dharmapuri India

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and Computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases.

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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DCVCP: Experimental Evaluation of Leukaemia Disease Prediction Using Deep Convoluted Vector Classification Principle

DCVCP: Experimental Evaluation of Leukaemia Disease Predicti...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Bhuvaneswari Balachander S. Sivamurugan M Gomathi J. Jayanthi Adkane R.V M. Kanan Department of Electronics and Communication Engineering Dr.M.G.R Educational and Research Institute Chennai Department of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai Department of Cyber Security Dhanalakshmi College of Engineering Chennai Department of Computer Science and Engineering Sona College of Technology Salem Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

A wide variety of cancers, including skin, breast, lung, and blood malignancies such as lymphoma and leukemia, are defined by the uncontrolled growth of cells within the body. Acquired lymphoblastic leukemia is among the most prominent cancers. Blood cancer diagnoses are notoriously time-consuming because hematologists often make mistakes. With the use of cutting-edge tools like Deep Learning and Machine Learning, this study considers a fresh approach to leukemia classification. The precise diagnosis of blood cancer is a crucial medical process, but it is also one of the most costly and challenging for clinical staff to do. Blood cancer is one of many severe forms of the disease. The devastating and deadly blood cancers, lymphoma and leukemia, impact people of all ages and genders, and they are a leading cause of other illnesses and an elevated survival rate. Damage to and an increase in immature monocytes, neutrophils, eosinophils, and lymphocytes are hallmarks of both leukemia and lymphoma. Predicting and treating blood cancer early is crucial for improving survival rates in the health industry. These days, a number of manual methods exist for analyzing and predicting blood cancer based on microscopic medical reports of white blood cell images; this method is both reliable and deadly. It takes a lot of time and effort to manually forecast and analyze eosinophils, lymphocytes, monocytes, and neutrophils. The Deep Convoluted Vector Classification Principle (DCVCP) is a new deep learning technique for leukemia illness detection that was described in this study. To test how well it works, it was cross-validated with the current method, the Convolutional Neural Network (CNN). findings from this study show that DCVCP can effectively leverage data from network sensors to identify blood cancer, which might lead to faster findings and lower costs in clinical settings.

关键词： Deep learning Lymphocytes Leukemia Cells (biology) Vectors Convolutional neural networks Medical diagnostic imaging Eosinophils Tumors systematic literature review

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Evaluating Deep Learning Models for Customer Lifetime Value Forecasting Based on Hybrid DT and Naive Bayes Model

Evaluating Deep Learning Models for Customer Lifetime Value ...

引用

Communication & Computing (WCONF), World Conference on

作者： Nawab Akram Shobana M Harsh R Kathiriya Prasanthi Gottumukkala S. Rathika Rajasree R Magadh Professional Institute Patna Bihar India Department of Computer Science and Business Systems MKumarasamy College of Engineering Karur India Data Scientist at Big Analytixs Atlanta USA Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Science (Artificial Intelligence & Data Science) Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Customer lifetime value, a core concept in direct response marketing for quite some time, is now receiving increasing attention from management at Boston University's School of Management. However, the majority of published works on the topic have mainly concentrated on lauding its usefulness as a decision-making metric, offering only one numerical example of how it is calculated or determined, incorporating it into broader discussions of profitability, and examining its function in decisions regarding customer acquisition and the tensions between acquiring and retaining customers. There are three steps to the suggested method: preprocessing, feature extraction, and model training. After archiving duplicate and missing data, data preparation is employed to eradicate missing values. The feature selection process incorporates K-means, principal component analysis (PCA), and customer lifetime value measurements. An increase in the model training procedure's accuracy was achieved by utilizing the Hybrid DT and naive bayes. The recommended approach achieves a higher accuracy rate of 96.03 % compared to its rivals, which includes naive bayes and Hybrid DT.

关键词： Training Accuracy Profitability Computational modeling Predictive models Feature extraction Bayes methods Numerical models Forecasting Principal component analysis

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Intelligent Network Optimisation for Beyond 5G Networks Considering Packet Drop Rate 25

Intelligent Network Optimisation for Beyond 5G Networks Cons...

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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Mahmoud, Haitham Aneiba, Adel He, Ziming Tong, Fei Guo, Liucheng Asyhari, Taufiq Wang, Ziwei Gao, Zhen College Of Computing Birmingham City University Birmingham United Kingdom Samsung Cambridge Solution Centre System Lsi Samsung Electronics Cambridge United Kingdom London United Kingdom Monash University Data Science Department Tangerang Indonesia School Of Engineering Lancaster University Lancaster United Kingdom School Of Information And Electronics Beijing Institute Of Technology Beijing China

ISBN: (纸本)9798350340266

To meet the growing expectations for fast and dependable connectivity, Novel approaches such as reinforcement learning-based resource allocation and network slicing are essential to consider. Enhancing network intelligence through the use of deep reinforcement learning and machine learning may increase capacity, lower latency and congestion, improve energy efficiency, and open up new revenue opportunities and business models. However, the current body of research falls short in terms of thoroughly exploring network slicing datasets and taking packet drop likelihood into account when allocating resources. With reference to current benchmarks, this paper presents a network slicing strategy and illustrates its efficacy using seven machine learning algorithms (ANN, SVM, RF, DT, GNG, LDA, and RT). To prioritize packets that run the risk of being dropped, a priority algorithm is also developed. To improve network performance, a resource allocation technique is used that is based on the mathematical study of packet drop rate. Utilizing deep reinforcement learning and genetic algorithms, the distribution of tasks across Cloud and Edge resources is further improved according to network slice characteristics. In spite of high network traffic, this guarantees constant service availability. © 2024 IEEE.

关键词： Resource allocation

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Deep Learning Based Intrusion Detection system with Integration of Blockchain technology and Internet of Things

Deep Learning Based Intrusion Detection System with Integrat...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： G. Uganya D. Rajalakshmi K. Ashok Kumar S. Ravi Neeraj Sunheriya Mohammad Kanan Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Department of Information Technology St Joseph's college of Engineering OMR Chennai Department of VLSI Microeelctronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The number of people and objects linked to the Internet of Things (IoT) has expanded dramatically due to its fast development, giving consumers more opportunities to interact with and manage their physical surroundings. IoT is susceptible to a number of security risks, because of its openness, connectedness, and constrained device resources. It is essential to guarantee the dependability and security of data transmitted in IoT networks. In order to solve these security problems, this work provides intrusion detection system that combines IoT with and Blockchain technology. As well as, deep learning method is integrated to improve the security. This work uses the SHA-256 algorithm to secure the data, the Adaptive Optimization algorithm for generating the keys, Rivest, Shamir, and Adleman (RSA) algorithm for encryption. and Deep Neural Network (DNN) for identifying intrusions in Internet of Things. Through identification and mitigation of possible risks, this framework successfully protects IoT networks and ensures the integrity of communications, as demonstrated by a comparison analysis with existing models.

关键词： Deep learning Sensitivity Adaptive systems Accuracy Intrusion detection Artificial neural networks Blockchains Internet of Things Security Optimization

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Boosting Anomaly Detection in Financial Transactions: Leveraging Deep Learning with Isolation Forest for Enhanced Accuracy

Boosting Anomaly Detection in Financial Transactions: Levera...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Kumar Bansal, Prateek Nimma, Divya Das, Nripendra Narayan Paruchuri, Bvn Prasad Anandaram, Harishchander Karthik, M. Institute of Business Management GLA University Mathura India University of Southern Mississippi PhD in Computational Science Data Analyst in UMMC United States Manipal University Department of Information Technology Jaipur India Department of CSE Vijayawada India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India K.Ramakrishnan College of Engineering Department of AI & DS Trichy India

ISBN: (纸本)9798331517953

Anomaly detection in financial transactions is paramount for safeguarding against fraudulent activities that pose significant risks to financial institutions and customers alike. Traditional methods often struggle to accurately identify complex and evolving patterns of fraud, necessitating innovative approaches that leverage advanced techniques such as deep learning and isolation forest. In this study, we propose a novel framework for boosting anomaly detection in financial transactions by integrating deep learning with isolation forest to achieve enhanced accuracy. Firstly, it employ an autoencoder, a type of neural network, to learn complex representations of normal transaction patterns and reconstruct input data. The autoencoder's ability to capture subtle variations in transaction attributes enables it to effectively distinguish between normal and anomalous instances based on the reconstruction error. Furthermore, we augment the anomaly detection process by incorporating isolation forest, a tree-based algorithm that isolates anomalies in the feature space by recursively partitioning data subsets. By combining the representation learning capabilities of deep learning with the outlier detection prowess of isolation forest, our framework offers a comprehensive solution for detecting fraudulent activities in financial transactions. Through experimentation on real-world financial datasets, we demonstrate the superior performance of our proposed framework compared to existing methods. The proposed method is implemented in Python software and has an accuracy of about 99.12% which is 1.49% higher than other existing methods like Conv-LSTM, Convolutional Neural Network (CNN)-LSTM, and CNN-GRU (Gated Recurrent Unit). Moreover, the integration of deep learning with isolation forest enables our framework to adapt to evolving patterns of fraud, ensuring robust and reliable anomaly detection in dynamic financial environments. Overall, our study contributes to the advancement

关键词： Convolutional neural networks

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Leveraging data Analytics to Enhance Smart Home Efficiency and Security

Leveraging Data Analytics to Enhance Smart Home Efficiency a...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Karthi Vivekanandan B. Kaleeswari P L. Natchiammai Arun Anthonisamy R. Vijayabharathi Manjunathan Alagarsamy Department of Electrical and Electronics Engineering Builders Engineering College Tirupur India Department of Electronics and Communication Engineering Easwari Engineering College (Autonomous) Chennai India Department of Electronics and Communication Engineering Mookambigai College of Engineering Pudukkottai India Department of Computer Science and Business System Panimalar Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Trichy India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Smart home technology has made daily life easier, automated, and managed. IoT devices like smart speakers, cameras, thermostats, and security systems are increasingly integrated, raising security and privacy concerns. This study examines smart home devices’ energy consumption, use trends, device failures, and system efficiency. This research uses smart home devices to identify consumption trends and risks. To better understand smart home ecosystems, the study will evaluate energy consumption, device usage hours, and malfunctions that can affect functionality and security. Using predictive analytics and machine learning, the researchers predict excessive energy usage and device problems. These methods identify trends and irregularities in smart home device operation to improve performance, efficiency, and security. This study investigates how user decisions, usage hours, and device age affect energy consumption, as well as how to identify optimization opportunities. The data analysis study contains statistical summaries and visualisations of device type, usage hours, energy consumption, user preferences, malfunction incidents, and device age. Machine learning algorithms such as linear regression and anomaly detection predict energy usage and locate anomalies. The data illustrate energy and device efficiency trends and recommend improvements to smart home security and effectiveness. This study addresses smart home device vulnerabilities and inefficiencies to improve smart home system optimization. The results offer smart home technology manufacturers and customers ways to improve energy efficiency, device performance, and system security.

关键词： Performance evaluation Energy consumption data analysis Smart homes Machine learning Market research Energy efficiency Security Object recognition Optimization

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A Hybrid Deep Learning Algorithm for Forecasting of Global Currency Market 2nd

A Hybrid Deep Learning Algorithm for Forecasting of Global C...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： San, Tan Jun Ajibade, Samuel-Soma M. Yong, Yoke Leng Ajibade, Temiloluwa Iyanuoluwa Ayodele, Olamide Emmanuel Oyewopo, Abdulgafar Olorunleke Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Faculty of Computing and Digital Technology Help University Bukit Damansara Kuala Lumpur Malaysia Department of Business Management Scholar School System Birmingham United Kingdom Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Administration Kwara State University Malete Nigeria Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The global currency market, which has millions of active participants and is the largest financial market in the world, has been greatly affected by the emergence of deep learning techniques. There is widespread recognition of deep learning’s effectiveness for price movement predictions, particularly in financial markets where its effectiveness has been shown. When compared to other machine learning methods, hybrid models like CNN-LSTM have shown to be the most effective and efficient. Nevertheless, there is a lack of thorough comparative research across deep learning hybrids, which makes it difficult to validate these claims empirically, especially in the diversified and extremely unpredictable FX market. To fill that void, this research builds a generic CNN-LSTM model to predict key currency pairs and compares it to another hybrid deep learning model. The objective is to evaluate and strengthen the reliability of deep learning techniques for FOREX forecasting. In order to maximize the generalizability of the model, this study mimics a categorization scenario using stringent cross-validation procedures. Important measures, including accuracy, precision, recall, and F1-score, were used to assess the models’ performance. The results show that there is a performance hierarchy, with CNN-LSTM, CNN-RNN. These findings highlight the need to evaluate deep learning models in different real-world contexts. In conclusion, this study highlights the difficulties of FOREX forecasting and the necessity for further evaluations of the predictive power of machine learning models in an ever-changing market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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