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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Milad Mohseni Budesh Kanwer Navdeep Singh M Hari Krishna B Rajalakshmi Mohammed Ayad Alkhafaji Department of Computer and Engineering Islamic Azad University Tabriz Iran Department of Artificial Intelligence & Data Science Poornima Institute of Engineering & Technology Lovely Professional University Phagwara Institute of Aeronautical Engineering Hyderabad Department of Computer Science New Horizon College of Engineering Bangalore College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

The fast development of 5G wireless technologies (IoT) has been a major contributor to the rapid growth and expansion of the Internet of Things! This in-depth study investigates the mutually advantageous relationship between 5G and the Internet of Things (IoT), focusing on how the combination assists in the development of massive device networks, enhances connectivity, and enables the development of real-time applications. There are now new opportunities for innovative applications in smart cities, industrial automation, healthcare, and other industries owing to 5G, which enables Internet of Things devices to communicate with one another at a speed and efficiency that has never been seen before. In spite of the benefits, however, there are challenges to be faced, such as the construction of infrastructure to support the expansion of the Internet of Things and the assurance of robust security. This article provides an analysis of these components and a blueprint for leveraging the advantages of Internet of Things (IoT) with 5G while minimizing any potential downsides.

关键词： Wireless communication 5G mobile communication Smart cities Navigation Real-time systems Internet of Things Communication system security

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Ghost-in-Wave: How Speaker-Irrelative Features Interfere DeepFake Voice Detectors

Ghost-in-Wave: How Speaker-Irrelative Features Interfere Dee...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xuan Hai Xin Liu Zhaorun Chen Yuan Tan Song Li Weina Niu Gang Liu Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou University Lanzhou China Elmore Family School of Electrical and Computer Engineering Purdue University IN USA The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Recent speech synthesis technology can generate high-quality speech indistinguishable from human speech, thus introducing various security and privacy risks. Numerous recent studies have focused on fake voice detection to address these risks, with many claiming to achieve ideal performance. However, is this really the case? A recent research work introduced Speaker-Irrelative-Features (SiFs), unrelated to the information in speech files but capable of influencing fake detectors. This means that existing detectors may rely on SiFs to a certain extent to distinguish real and fake speech. In this paper, we introduce an evaluation framework to evaluate the influence of SiFs in existing fake voice detectors in depth. We evaluate three SiFs which include background noise, the mute parts before and after voice, and the sampling rate on ASVspoof2019 and FoR. Our results confirm the substantial influence of SiFs on fake voice detection performance, and we delve into the analysis of the underlying mechanisms.

关键词： Privacy Deepfakes Detectors Feature extraction Background noise Speech synthesis Security

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EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control

arXiv

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arXiv 2024年

作者： Li, Xiaochen Liu, Sicong Zhou, Zimu Guo, Bin Xu, Yuan Yu, Zhiwen Northwestern Polytechnical University School of Computer Science Xi’an China City University of Hong Kong School of Data Science Hong Kong Harbin Engineering University Harbin China

The rise of mobile devices with abundant sensory data and local computing capabilities has driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) emerges to train specific deep models for each mobile device to address data heterogeneity and varying performance preferences. However, mobile training times vary significantly, resulting in either delay (when waiting for slower devices for aggregation) or accuracy decline (when aggregation proceeds without waiting). In response, we propose a shift towards asynchronous PFL, where the server aggregates updates as soon as they are available. Nevertheless, existing asynchronous protocols are unfit for PFL because they are devised for federated training of a single global model. They suffer from slow convergence and decreased accuracy when confronted with severe data heterogeneity prevalent in PFL. Furthermore, they often exclude slower devices for staleness control, which notably compromises accuracy when these devices possess critical personalized data. Therefore, we propose EchoPFL, a coordination mechanism for asynchronous PFL. Central to EchoPFL is to include updates from all mobile devices regardless of their latency. To cope with the inevitable staleness from slow devices, EchoPFL revisits model broadcasting. It intelligently converts the unscalable broadcast to on-demand broadcast, leveraging the asymmetrical bandwidth in wireless networks and the dynamic clustering-based PFL. Experiments show that compared to status quo approaches, EchoPFL achieves a reduction of up to 88.2% in convergence time, an improvement of up to 46% in accuracy, and a decrease of 37% in communication costs. © 2024, CC BY-NC-ND.

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DivLog: Log Parsing with Prompt Enhanced In-Context Learning

DivLog: Log Parsing with Prompt Enhanced In-Context Learning

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International Conference on Software engineering (ICSE)

作者： Junjielong Xu Ruichun Yang Yintong Huo Chengyu Zhang Pinjia He School of Data Science The Chinese University of Hong Kong Shenzhen China Dept. of Computer Science & Engineering The Chinese University of Hong Kong China ETH Zurich Switzerland Shenzhen Research Institute of Big Data China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing super-vised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose Di-vLog, an effective log parsing framework based on the in-context learning (ICL) ability of large language models (LLMs). Specifically, before log parsing, DivLog samples a small amount of offline logs as candidates by maximizing their diversity. Then, during log parsing, DivLog selects five appropriate labeled candidates as examples for each target log and constructs them into a prompt. By mining the semantics of examples in the prompt, DivLog generates a target log template in a training-free manner. In addition, we design a straightforward yet effective prompt format to extract the output and enhance the quality of the generated log templates. We conducted experiments on 16 widely-used public datasets. The results show that DivLog achieves (1) 98.1% Parsing Accuracy, (2) 92.1% Precision Template Accuracy, and (3) 92.9% Recall Template Accuracy on average, exhibiting state-of-the-art performance.

关键词： Training Measurement Analytical models Semantics Feature extraction Tuning Software engineering

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Dynamic Topology Management in Ad-Hoc Networks for Improved Performance

Dynamic Topology Management in Ad-Hoc Networks for Improved ...

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International Conference on Advances in Information Technology (ICAIT)

作者： A.S. Anakath R. Kannadasan G. Simi Margarat A. Pasumpon Pandian Antony Sibiya Varghese V Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Vellore Tamil Nadu India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy India Department of Artificial Intelligence and Data Science GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Optimization in ad hoc networks is a highly specialized task because the structure of the network is loosely formed, and each node is independently responsible for its operation. In order to obtain a better result from this solely formed networks this report offer extensive Dynamic Topology Management (DTM) method. Dynamic routing protocols lager lines, dividend balancing, Life-saving administration, guarantee of reliability, cross-layer design, security aspects and the on-time monitoring and discovery of topology changes are the main objectives and steps to be accomplished. Real-time Location monitoring and forest structure discovery are made possible by employing adaptive routing protocols, namely Dynamic Source Routing(DSR) and Ad-Hoc On-Demand Distance Vector(AODV). Also, the algorithms are capable of making advance adjustments to routing routes and resource allocations drawn from the mobility patterns forecasting which is vital. Load balancing methods, which distribute traffic in a network evenly, enable the system to run efficiently and avoid problems caused by overloading. Algorithms that are energy-efficient and make the best use of energy resources having their topology's optimized are introduced to the streamlined energy distribution systems. These algorithms are focused on the node's energy level. Through fault tolerance techniques which recognize and handle failures in nodes and partitioned networks, connectedness activity is a continuous one. Through QoS perception, the network topology might automatically be tailored to conform to the given quality criteria, thus not restricting the choice for differing applications. The proposed could have defenses against adversary nodes and their potential DDoS attacks which will enhance network protection. Yet another advantage of an architectural style that promotes cross-layer collaboration is enhanced topology management which is the goal around which the functions of various levels of the protocol stack revolve

关键词： Network topology Aerodynamics Routing Ad hoc networks Routing protocols Real-time systems Energy efficiency

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LSTM-ANN Based Price Hike Sentiment Analysis from Bangla Social Media Comments 25

LSTM-ANN Based Price Hike Sentiment Analysis from Bangla Soc...

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25th International Conference on computer and Information Technology, ICCIT 2022

作者： Chakraborty, Sovon Uddin Talukdar, Muhammad Borahn Yaseen Morshed Adib, Muhammed Mitra, Sowmen Rabiul Alam, Md. Golam Brac University School of Data and Sciences Department of Computer Scinece and Engineering Dhaka1212 Bangladesh Department of Computer Science and Engineering Dhaka1209 Bangladesh Hebei University of Technology Department of Artificial Intelligence Beijing065001 China

ISBN: (纸本)9798350346022

Price hike has always been a substantial concern for people all over the world. The crisis gets more conspicuous, and people find themselves more confounded when even the bare minimum of expenses still exceeds the amount they can get to earn. This tension tends to invite chaos in society as the number of people affected increases. Bangladesh is currently undergoing a formidable wave of price hikes. People have been expressing mixed reactions on social media regarding this issue. Hence, understanding the overall public sentiment can be crucial for policymaking and preventing chaos in society. This study utilizes social media comments for analyzing underlying sentiments. data were collected from the Facebook pages of some popular Bangladeshi media for this purpose, and thereby a specialized dataset was constructed. The dataset contains 2000 public comments annotated with three polarity values- positive, negative, and neutral. A hybrid LSTM-ANN deep architecture has been exploited in this research. The model outperforms other state-of- the-art models in terms of less trainable parameters along with an F1-score of 88.47%. © 2022 IEEE.

关键词： Sentiment analysis

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TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter

arXiv

引用

arXiv 2023年

作者： Chen, Yiqun T. Zou, James Department of Biomedical Data Science Stanford University StanfordCA94305 United States Departments of Biomedical Data Science Electrical Engineering and Computer Science Stanford University StanfordCA94305 United States

Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos. In this work, we introduce TWIGMA (TWItter Generative-ai images with Metadata), a comprehensive dataset encompassing over 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text, creation date, number of likes), available at https://***/records/8031785. Through a comparative analysis of TWIGMA with natural images and human artwork, we find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. Additionally, we find that the similarity between a gen-AI image and natural images is inversely correlated with the number of likes. Finally, we observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content such as intricate human portraits, whereas their interest in simple subjects such as natural scenes and animals has decreased. Our findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images. © 2023, CC BY.

关键词： Metadata

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An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gaussian Mixture and RCNN

An Advanced and Ideal Method for Tumor Detection and Classif...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Samrat Ray K. Ramkumar Ch. V Anupama P. Misba Marybai K Anguraju P. Preethi Dean and Head of International Relations IIMS Pune India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamilnadu Department: Information Technology Malla Reddy Engineering College Hyderabad Telengana Artificial Intelligence and Data Science department Panimalar Engineering College Chennai Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Trichy Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Extensive research and methodological improvements over time attest to the necessity of brain tumour detection and analysis for any indication system. Accurate tumour detection is essential for this study; hence an efficient automated method must be implemented Numerous segmentation algorithms have been developed to classify brain tumors with greater precision. Medical image processing experts agree that brain picture segmentation presents unique difficulties. A novel automatic detection and classification system is proposed in this work. The suggested method is broken down into several stages, including preprocessing of MRI images, segmentation of those images, extraction of features, and classification of those features. The noise in the MRI picture is reduced during the preprocessing stage by applying an adaptive filter. The grey level co-occurrence matrix (GLCM) is used for feature extraction, while the improved Means clustering (IMC) technique is used for image segmentation. To categorize MRI scans, we used a deep learning algorithm scan after features were extracted from the scans them as benign lesions, gliomas, meningiomas, or pituitary tumours. In order to accomplish the classification task, recurrent convolutional neural networks (RCNN) were utilised. For a given input dataset containing photographs of the brain, the proposed technique yields superior classification results. The investigations made use of MRI pictures from Kaggle's data sets, which included 464 testing sets and 3264 training sets. The outcomes show that the proposed strategy outperforms its predecessors. The suggested RCNN method is then compared to BP, VG-Net, and RCNN, three of the most popular classification techniques currently in use. The suggested classifier successfully identified 96.21 percent of brain tumour tissues in magnetic resonance imaging scans.

关键词： Deep learning Training Image segmentation Magnetic resonance imaging Noise Feature extraction Classification algorithms Convolutional neural networks Tumors Testing

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big data Analytics 1

A Comparative Analysis of Artificial Intelligence and Busine...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Marella, Bhagath Chandra Chowdari Vegineni, Gopi Chand Addanki, Sireesha Ellahi, Ehsan Arun Kumar, K. Mandal, Rishabh Insights & Data Department of Financial Services Capgemini India Enrollment and Eligibility Team India System soft Technologies LLC India Cleveland State University Department of Information Systems OH United States Tula's Institute Department of Computer Science and Engineering UK Dehradun India India

ISBN: (纸本)9798331518578

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system (92.6%) and the neural network (NNs) models (92.7%). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans. © 2025 IEEE.

关键词： data accuracy

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Enhancing Kidney Stone Detection in CT scans with Image Processing and the DCNN-RELM Model

Enhancing Kidney Stone Detection in CT scans with Image Proc...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Manjula Prabakaran Jesmin Zakaria S Kannan Santhiya P Rajasekar P M V Rathnamma Department of CSE- (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Electronics & Communication Engineering (ECE) NSHM Knowledge Campus Durgapur West Bengal India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy Tamilnadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India Department of CSE KLM College of Engineering for Women Kadapa Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

CT of the urinary system, abdomen, and kidneys appears to improve kidney stone screening and identification. Segmentation and categorization are essential for kidney stone identification and management. This work developed a semi-automatic kidney stone detection system using geometric concepts and image processing. The main steps include model training, feature extraction, segmentation, embossing, and preprocessing. Preprocessing involves trimming the input image to remove unnecessary kidney regions to focus on critical CT scan locations. Relief embossing enhanced image details. Segmentation accurately classified veins and blood arteries to define kidney stone boundaries. CNN-RELM was our training model. Compared to CNN and ELM models, the recommended system performed better. Its 93.87% kidney stone detection and localization accuracy prove its reliability. The semi-automatic system's reliable kidney stone detection and segmentation method may improve clinical decision-making. Combining CNN-RELM modeling and enhanced image processing could lead to medical imaging applications.

关键词： Training Image segmentation Accuracy Three-dimensional displays Computed tomography Embossing data preprocessing Reliability Kidney Biomedical imaging

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