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A Bibliometric Analysis of Location-Based Social Networks and applications: Research trends and Future Challenges 1

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23rd Wuhan International conference on E-business (WHICEB)

作者： Liu, Lin Yao, Xiaoyu Anhui Agr Univ Coll Resources & Environm 130 Changjiangxilu Hefei Anhui Peoples R China Univ Sci & Technol China Sch Management Jinzhai Rd 96 Hefei Anhui Peoples R China

ISBN: (数字)9783031602641

ISBN: (纸本)9783031602634;9783031602641

Location-based social networks (LBSN) have undergone rapid development over the past decade, garnering extensive attention from scholars in various research domains. However, the current status and overarching trends in the field of LBSN and relevant applications remain unclear, and key research hotspots and keywords are yet to be distinctly identified. This bibliometric study delves into the expansive realm of LBSN, uncovering research trends and future challenges. This study aims to address these issues by analyzing 1,099 high-quality articles from the Web of science database between 2010 and 2023, and the analytical tools are CiteSpace and VOSviewer. The study unveiled pressing challenges confronting LBSN research, such as privacy concerns, the integration of emerging technologies, and the imperative for interdisciplinary collaboration. These challenges present promising opportunities for future exploration and innovation within the LBSN domain. As a roadmap for future research endeavors, this analysis guides researchers to Supporting socially marginalized communities, discovering and preventing of public health issues, and integrating and organizing multimodal geographic information.

关键词： bibliometric analysis Location-based social network Geo-social network Geo-privacy smart city

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Image Processing: Unlocking the Secrets of Visual Information 2

Image Processing: Unlocking the Secrets of Visual Informatio...

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2nd IEEE International conference on trends in Quantum Computing and emerging Business Technologies, TQCEBT 2024

作者： Lavanya, D. Bhushan, Kavi Kant, Gur Sharan Sountharraj, A.C. Srivastav, Akanksha Begum, M. Sharmila Sri Venkateswara College of Engineering Computer Science and Engineering Department Andhra Pradesh Tirupati India Ccs University Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science U.P Meerut India Dr. Ngp Art's and Science College Department of Computer Science with Data Analytics Coimbatore-48 India Tula's Institute Department of Computer Science and Engineering Dehradun India Maharaja Institute of Technology Department of Computer Science Mysore India

ISBN: (纸本)9798350384277

In the age of image processing, the hidden ability can be encoded with visual information that is not only observed but also painstakingly carried out;in this way, the complex secrets that are encoded in each pixel have been made clear. Image processing is the magic that has increased the amount of clarity, significance, and utility while transforming stationary arrays of images into dynamic narratives. The image's quality is typically influenced by several variables, such as temperature, light, and noise. Due to its persistent presence in various digital photographs, noise plays a crucial function in the image. Throughout the image coding, transmission, and development procedure, noise consequences could arise. The field of noise removal in image processing has grown to be quite interesting and vibrant. Recently, there has been a notable advancement regarding the filtering of various noise types for image processing in digital format. This research paper aims to discuss image processing and denoising various noises in images to improve overall quality. In terms of methodology systematic qualitative research has been conducted that helps the research gather information from a wide range of sources. Moreover, this paper finds that image processing has significant potential in this digital age. By denoising images, this phenomenon can be applied in different applications in every sector. © 2024 IEEE.

关键词： Image denoising

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Two-Pronged Intrusion Detection System for MANET

Two-Pronged Intrusion Detection System for MANET

引用

2024 IEEE International conference on emerging Technologies in computer science for Interdisciplinary applications, ICETCS 2024

作者： Rajan, Nandana Vamsi Kiran, M. Vigneshwari, K. Jaiswal, Sameeksha Nithya, B. National Institute of Technology Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350372502

Mobile Ad hoc Networks (MANETs) provide a flexible wireless infrastructure for various applications. Still, their decentralized and dynamic nature makes them susceptible to security threats. The topology of a MANET changes frequently due to node mobility. This dynamic nature can result in frequent network disruptions, packet losses, and increased latency. This paper introduces a Two-Pronged Intrusion Detection System (TP-IDS) designed for MANET, focusing on blackhole, hello flooding, and UDP flooding attacks. These attacks take advantage of the dynamic and self-organizing features of MANETs, making the network perform poorly. A dataset with eight features is created, capturing mobility, received, sent, and dropped packets, forwarding difference, energy, and transmission power. We have used SMOTE to overcome the class imbalance. Hyperparameter Optimization is performed using Bayesian Optimization and Optuna across Decision Tree, Random Forest, and XGBoost classifiers showcasing strengths and weaknesses for optimal selection. We conclude that the XGBoost classifier with Grid-initialized Bayesian hyperparameter optimization provides the best overall performance to create a robust TP-IDS. © 2024 IEEE.

关键词： Intrusion detection

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emerging trends in AI-Powered IoT: Innovations, Key Challenges, and Future Directions

Emerging Trends in AI-Powered IoT: Innovations, Key Challeng...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Abdelraouf, Hussien Abouyoussef, Mahmoud Fouda, Mostafa M. Fadlullah, Zubair Md Ibrahem, Mohamed I. School of Computer and Cyber Sciences Augusta University AugustaGA30912 United States North Carlina A&T State University Department of Computer Systems Technology GreensboroNC27411 United States Idaho State University Department of Electrical and Computer Engineering PocatelloID83209 United States Western University Department of Computer Science LondonON Canada

ISBN: (纸本)9798331504847

This survey provides a comprehensive overview of recent emerging technologies in artificial intelligence (AI) applied to the Internet of Things (IoT), highlighting their significance and applications across various domains. Our study covers key advancements, including Concept Drift, Transformers, TinyML, Explainable AI (XAI), and Federated Learning (FL). Concept Drift addresses changes in data patterns to maintain the accuracy of machine learning models in dynamic environments. Transformers revolutionize Natural language processing (NLP) by efficiently capturing long-range dependencies. TinyML enables smart IoT applications on low-power devices, while XAI promotes transparency in AI decisions. FL facilitates decentralized model training while preserving data privacy. Our discussion also covers challenges, limitations, and future research directions to improve AI and IoT integration. © 2025 IEEE.

关键词： Federated learning

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Tracing the Progression: Networking applications From Conventional Methods to Incremental AI/ML Integration

Tracing the Progression: Networking Applications From Conven...

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2024 International conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Singh, Sushma Rawat, Varsha Sharma, Dolly Amity University Department Of Computer Science & Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350366846

The swift progression of networking technologies and the escalating intricacy of contemporary network environments have stimulated a burgeoning interest in utilizing advanced techniques to tackle various networking hurdles. This paper offers an overview of the evolution of applications within networking, tracing the progression of fundamental technologies, methodologies, and approaches over time. Through an examination of existing literature, we scrutinize pivotal works, recent progressions, and emerging patterns in the field, encompassing areas such as network optimization, anomaly detection, security, and automation. Our observations underscore the profound impact of these techniques on networking, facilitating more effective utilization of resources, improved security measures, and heightened adaptability to fluctuating network dynamics. By exploring ongoing trends and deliberating on future avenues of research, this paper furnishes insights into the current landscape of applications within networking and provides guidance for researchers and professionals endeavouring to navigate the complexities of contemporary networking environments. © 2024 IEEE.

关键词： Machine learning

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Price Comparison Using Web Scraping and Machine Learning 1

Price Comparison Using Web Scraping and Machine Learning

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1st International conference on computer science and emerging Technologies, CSET 2023

作者： Singh, Neelam Rana, Aryan Chaudhary, Anmol Graphic Era University Department of Computer Science and Engineering Dehradun India Graphic Era University Department of Computer Applications Dehradun India

ISBN: (纸本)9798350341737

This work aims to create a robust and user-friendly web scraping-based price comparison system tailored for e-commerce websites. By leveraging advanced web scraping techniques, the system extracts and processes product prices from various platforms. It employs data analysis methods to uncover patterns and trends, enabling users to make well-informed decisions when searching for the most competitive deals across multiple e-commerce platforms. Through its intuitive interface and visualization capabilities, the system empowers users to effortlessly navigate the vast online market and discover the best prices for their desired products. © 2023 IEEE.

关键词： Visualization

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Artificial Neural Network-Based Steganography: Techniques, applications, and Challenges 3rd

Artificial Neural Network-Based Steganography: Techniques, A...

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3rd International conference on emerging trends in Expert applications and Security, ICE-TEAS 2024

作者： Rajput, Prashant Vaisla, Kunwar Toyesh Singh Department of Computer Science Amramali Institute of Management and Computer Applications Shiksha Nagar Lamachaur Uttarakhand Haldwani India Uttarakhand Pauri India

ISBN: (纸本)9789819739905

This paper aims to the understanding and advancement of ANN-based steganography by providing a comprehensive overview of its techniques, applications, and challenges in modern secure communication systems and digital media processing. Artificial neural network (ANN)-based steganography has emerged as a promising approach for embedding secret information into digital media while preserving perceptual quality and resisting detection. This paper presents a comprehensive review of ANN-based steganography techniques, covering various aspects including methodologies, applications, and challenges. Through an in-depth analysis of recent research and advancements, this paper aims to provide insights into the state-of-the-art techniques, applications, and future directions of ANN-based steganography. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Steganography

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A Review of Blockchain based Decentralised Authentication Solutions and their improvement through Metamask 2

A Review of Blockchain based Decentralised Authentication So...

引用

2nd International conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Khan, Andleeb Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Sharda School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Istinye University Department of Computer Engineering Istanbul Turkey National University of Computer and Emerging Sciences Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798331529529

With the advancing technology, it becomes difficult to cope up with novel trends and configurations. Similarly, it is difficult to secure the systems against each emerging threat. With this the loopholes in conventional forms of system security based on centralized format are now visible. Hence there are explorations in various alternatives of centralized system authentication and authorization. Blockchain technology has appeared as a means of securing the systems as it works on decentralization thereby by passing the loopholes that exist in centralized authentication. Hence, this study lays down the recent advancements in the field of blockchain authorization and authentication techniques. It further supplements the study with an explanation of MetaMask extension which connects the user to the blockchain network thereby enabling decentralization in system security. This study further proposes the future potentials of MetaMask and blockchain integration. © 2024 IEEE.

关键词： Authentication Protocol

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Impact of White-Box Adversarial Attacks on Convolutional Neural Networks 4

Impact of White-Box Adversarial Attacks on Convolutional Neu...

引用

4th International conference on emerging trends in Networks and computer Communications, ETNCC 2024

作者： Podder, Rakesh Ghosh, Sudipto Colorado State University Department of Computer Science Fort CollinsCO80523 United States

ISBN: (纸本)9798350353266

Autonomous vehicle navigation and healthcare diagnostics are among the many fields where the reliability and security of machine learning models for image data are critical. We conduct a comprehensive investigation into the susceptibility of Convolutional Neural Networks (CNNs), which are widely used for image data, to white-box adversarial attacks. We investigate the effects of various sophisticated attacks-Fast Gradient Sign Method, Basic Iterative Method, Jacobian-based Saliency Map Attack, Carlini & Wagner, Projected Gradient Descent, and DeepFool-on CNN performance metrics, (e.g., loss, accuracy), the differential efficacy of adversarial techniques in increasing error rates, the relationship between perceived image quality metrics (e.g., ERGAS, PSNR, SSIM, and SAM) and classification performance, and the comparative effectiveness of iterative versus single-step attacks. Using the MNIST, CIFAR-10, CIFAR-100, and Fashion_MNIST datasets, we explore the effect of different attacks on the CNNs performance metrics by varying the hyperparameters of CNNs. Our study provides insights into the robustness of CNNs against adversarial threats, pinpoints vulnerabilities, and underscores the urgent need for developing robust defense mechanisms to protect CNNs and ensuring their trustworthy deployment in real-world scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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Block Chain Technology: Anomaly Detection in Bitcoin Using RFMLPAlgorithm 4

Block Chain Technology: Anomaly Detection in Bitcoin Using R...

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4th International conference on emerging trends in Networks and computer Communications, ETNCC 2024

作者： Elangovan, V.R. Revathi, S. Nusrat Jabeen, T. Obaid, Ahmed J. Kumar, Rahul SRM Institute of Science and Technology Kattankulathur Faculty of Science and Humanities Department of Computer Applications Chennai India City University Faculty Cybersecurity Ajman United Arab Emirates University of Kufa College Al-Ayen University Computer Science and Mathematics Department of Computer Technical Engineering Technical Engineering Thi-Qar Iraq Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Block chain technology has been used in many different fields, including banking, logistics, healthcare, and government. However, billions have been lost to cyber attacks on block chain apps in the last several years. An efficient approach for identifying malicious behavior in block chain networks is urgently required. Anomaly detection is a well-studied issue with a lengthy history of research. Anomalies are, in a nutshell, unusual or improbable occurrences. Theft and other illicit activity in financial networks are often outliers. Participants in the network want to spot anomalies as soon as possible to safeguard the overall safety and security of the system. However, fraud and anomaly detection techniques are constantly developing along with the financial industry. Furthermore, the most secure approach being brought into money is block chain technology. The number of scams, however, increases every year alongside these innovative technology. This is why we suggested a safe Anomaly detection technique that combines Machine Learning with Deep Learning. For the purpose of classifying Anomaly transactions, we used a unique hybrid RFMLP approach. For Bitcoin transaction anomaly detection, the RFMLP combines Random Forest (RF) with multilayer perceptron (MLP). We found that our RFMLP was more accurate than competing algorithms. © 2024 IEEE.

关键词： Bitcoin

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