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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Suresh Babu, Rakesh Thoppaen Narasimhan Krishnamoorthy, Lakshmi Lakshmi, G. Prasanna Ashok Tingare, Bhagyashree Diwan, Tarun Dhar Khatkale, Pravin B HEXAWARE TECHNOLOGIES INC 101 Wood Avenue South IselinNJ08830 United States HCL America Solutions Inc 330 POTRERO AVE Sunnyvale SunnyvaleCA94085 United States Sandip University School of Computer Science and Engineering Maharashtra Nashik India D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Atal Bihari Vajpayee University Bilaspur India Sanjivani University Maharashtra Kopargaon India

ISBN: (纸本)9798350390049

The integration of Artificial Intelligence (AI) into the financial technology (FinTech) and financial services (FinServ) sectors has catalyzed a profound transformation, reshaping traditional practices and driving substantial advancements in operational efficiency, customer experience, and market innovation. This paper examines the pivotal role of AI in revolutionizing these sectors through a detailed analysis of AI applications such as fraud detection, personalized banking, automated customer support, and algorithmic trading. Employing a comprehensive research methodology that includes quantitative and qualitative assessments, the study reveals how AI enhances financial performance metrics, including revenue growth, cost savings, and customer satisfaction. The findings underscore the growing significance of AI in addressing challenges and driving growth within the FinTech and FinServ industries. By examining market trends, AI integration, and regulatory dynamics, this research highlights the transformative impact of AI and its strategic importance for future development in financial services. © 2024 IEEE.

关键词： Fintech

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Revolutionizing H2M Interaction: Telepresence System Enabling Sign Language Expansion for Individuals with Disabilities 1

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26th International Conference on Distributed computer and Communication Networks: Control, Computation, Communications, DCCN 2023

作者： Kuznetsov, Konstantin Muthanna, Ammar Ateya, Abdelhamied A. Koucheryavy, Andrey The Bonch-Bruevich Saint-Petersburg State University of Telecommunications St. Petersburg193232 Russia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Communications Engineering Zagazig University Sharqia Zagazig44519 Egypt

ISBN: (数字)9783031504822

ISBN: (纸本)9783031504815

The modern network technology ecosystem is expanding rapidly in multiple dimensions. Due to the demands of the Tactile Internet and fifth-generation (5G) networks, conventional approaches are no longer producing significant advancements. The imminent integration of immersive technologies holds the potential to revolutionize human existence, ushering in a new era of fully realized and enhanced human-to-machine interactions (H2M). This paradigm shift is poised to offer many benefits and adopt wireless technologies to enable real-time remote control. Concurrently, introducing the Tactile Internet necessitates a fundamental reevaluation of the foundational principles governing current and prospective communication networks. This article delves into an experimental endeavor that seeks to seamlessly integrate immersive technologies within 5G networks. A microservice application was developed for this purpose, simulating the nuanced movements of a person’s hand in space and facilitating the expression of intricate gestures. The article also delves into the strategies employed to minimize latency, enhancing the overall experience. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： 5G mobile communication systems

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Comments on "A Lightweight Privacy Preserving Authentication Protocol for VANETs"

Comments on "A Lightweight Privacy Preserving Authentication...

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International Conference on computer and Communications (ICCC)

作者： Syed Muhammad Awais Wu Yucheng Khalid Mahmood Rupak Kharel School of Microelectronics and Communication Engineering Chongqing University Chongqing China School of Psychology and Computer Science and Graduate School of Intelligent Data Science University of Central Lancashire Preston United Kingdom School of Psychology and Computer Science and Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Taiwan School of Psychology and Computer Science and Faculty of Electrical and Electronics Engineering University of Central Lancashire Preston United Kingdom School of Psychology and Computer Science and Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Vietnam

Vehicle use and the concept of a "smart city" are developing quickly. As a result of this progression, the Vehicular Ad-Hoc Network (VANET) is a popular network for inter-vehicular communication. The data gathering about the state of the road, the whereabouts of vehicles, their speeds, and the description of traffic congestion are obtained using the VANET. The public nature of information flow on the VANET creates serious security risks. data security is typically one of the most important duties of VANET. As a result, it becomes one of the researchers’ top priorities. Li et al. [1] introduced a Lightweight Privacy Preserving Authentication Protocol for VANETs. They stated that several attacks could be resisted using their secure authentication devised protocol. However, after analyzing we discovered that their authentication protocol is susceptible to roadside impersonation attack and it does not provide vehicle anonymity. In the end, we have provided a few suggestions to cope with Li et al.’s devised protocol current shortcomings.

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FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

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Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation

arXiv

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arXiv 2023年

作者： Peng, Bo Parthasarathy, Srinivasan Ning, Xia The Department of Computer Science and Engineering The Ohio State University ColumbusOH43210 United States The Department of Computer Science and Engineering The Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States The Department of Biomedical Informatics The Department of Computer Science and Engineering The Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing sequential recommendation methods. Recently, pre-training-based methods have been developed to tackle this challenge. Though promising, in this paper, we show that existing methods suffer from the notorious negative transfer issue, where the model adapted from the pre-trained model results in worse performance compared to the model learned from scratch in the task of interest (i.e., target task). To address this issue, we develop a method, denoted as ANT, for transferable sequential recommendation. ANT mitigates negative transfer by 1) incorporating multi-modality item information, including item texts, images and prices, to effectively learn more transferable knowledge from related tasks (i.e., auxiliary tasks);and 2) better capturing task-specific knowledge in the target task using a re-learning-based adaptation strategy. We evaluate ANT against eight state-of-the-art baseline methods on five target tasks. Our experimental results demonstrate that ANT does not suffer from the negative transfer issue on any of the target tasks. The results also demonstrate that ANT substantially outperforms baseline methods in the target tasks with an improvement of as much as 15.2%. Our analysis highlights the superior effectiveness of our re-learning-based strategy compared to fine-tuning on the target tasks. © 2023, CC BY.

关键词： Recommender systems

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Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

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computers, Materials & Continua 2022年第1期70卷 715-738页

作者： Shidrokh Goudarzi Seyed Ahmad Soleymani Mohammad Hossein Anisi Domenico Ciuonzo Nazri Kama Salwani Abdullah Mohammad Abdollahi Azgomi Zenon Chaczko Azri Azmi Centre for Artificial Intelligent(CAIT) Universiti Kebangsaan MalaysiaBangi43600Malaysia School of Computer Engineering Iran University of Science and TechnologyResalat Sq.16846-13114TehranIran School of Computing Faculty of Engineering Universiti Teknologi MalaysiaJohorMalaysia School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUnited Kingdom Department of Electrical Engineering and Information Technologies University of Naples“Federico II”Naples80125Italy Razak Faculty of Technology and Informatics Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G ***,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network *** address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood *** light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river *** framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a ***,an algorithm hybridized with Group Method data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative *** proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River *** performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.

关键词： Unmanned aerial vehicles wireless sensor networks group method data handling particle swarm optimization river flow prediction

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Harnessing Large Language Models for Text-Rich Sequential Recommendation

arXiv

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arXiv 2024年

作者： Zheng, Zhi Chao, Wenshuo Qiu, Zhaopeng Zhu, Hengshu Xiong, Hui School of Data Science University of Science and Technology of China China Career Science Lab BOSS Zhipin China Hong Kong University of Science and Technology Guangzhou China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS). However, when items in the recommendation scenarios contain rich textual information, such as product descriptions in online shopping or news headlines on social media, LLMs require longer texts to comprehensively depict the historical user behavior sequence. This poses significant challenges to LLM-based recommenders, such as over-length limitations, extensive time and space overheads, and suboptimal model performance. To this end, in this paper, we design a novel framework for harnessing Large Language Models for Text-Rich Sequential Recommendation (LLM-TRSR). Specifically, we first propose to segment the user historical behaviors and subsequently employ an LLM-based summarizer for summarizing these user behavior blocks. Particularly, drawing inspiration from the successful application of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) models in user modeling, we introduce two unique summarization techniques in this paper, respectively hierarchical summarization and recurrent summarization. Then, we construct a prompt text encompassing the user preference summary, recent user interactions, and candidate item information into an LLM-based recommender, which is subsequently fine-tuned using Supervised Fine-Tuning (SFT) techniques to yield our final recommendation model. We also use Low-Rank Adaptation (LoRA) for Parameter-Efficient Fine-Tuning (PEFT). We conduct experiments on two public datasets, and the results clearly demonstrate the effectiveness of our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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Alzheimer's Disease Prediction Using Convolutional Neural Networks Pre Trained Architecture

Alzheimer's Disease Prediction Using Convolutional Neural Ne...

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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： P Sam Sahil Bhawesh K Sinha Thirumala Akash K Mohammed Riyaz Ahmed Computer Science and Engineering HKBK College of Engineering Bangalore India Data Science and Engineering Swami Vivekananda University Kolkata West Bengal India Analyst 1 Infrastructure Service DXC Technology Bangalore India Electronics and Communication Engineering HKBK College of Engineering Bangalore India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Alzheimer's Disease (AD) is a progressive neu-rodegenerative disorder that significantly impacts memory and cognitive abilities. Accurate early-stage diagnosis is critical for implementing preventive measures before irreversible brain damage occurs. This paper presents a novel approach for AD prediction using convolutional neural networks (CNN) with pre-trained architectures, leveraging transfer learning to enhance classification accuracy. MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were used, categorized into three classes: AD (Alzheimer's Disease), CI (Mild Cognitive Impaired) and CN (Common Normal). The proposed method em-ploys pre-trained models such as VGG16, DenseNet121, Xception, ResNet152V2, NasNetLarge and InceptionResNetV2, fine-tuned for AD detection. Experimental results demonstrate that our approach achieved classification exceeding 90 % for precision and recall, highlighting the effectiveness of CNNs in handling large-scale medical data. These findings underscore the potential of deep learning models for supporting clinical diagnosis, reducing time and cost, and aiding in the early detection of Alzheimer's Disease

关键词： Neuroimaging Deep learning Accuracy Magnetic resonance imaging Transfer learning Signal processing Predictive models Convolutional neural networks Alzheimer's disease Medical diagnostic imaging

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Exploring the Tangible Impact of Artificial Intelligence and Machine Learning: Bridging the Gap between Hype and Reality 1

Exploring the Tangible Impact of Artificial Intelligence and...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dattangire, Rahul Vaidya, Ruchika Biradar, Divya Joon, Ashish Publicis Sapient Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India University of Texas at Arlington Computer Science ArlingtonTX76019 United States Andhus Technologies Inc Software Developer PensacolaFL United States

ISBN: (纸本)9798350367720

Beyond being merely trendy terms in technology, machine learning (ML) and artificial intelligence (AI) have directly influenced every industry. This article thus demystifies the true meaning of these technologies by sifting through the sultry descriptions. Here, we explore how AI and ML enhance efficiency, decision-making, and problem-solving in various sectors including health, Agriculture, finance, and production as we discover real-world applications. Continuing with the discussion of implementation challenges, the aspects of bias, ethical pitfalls, and report cards are examined. In addition, the article also covered how the integration of AI as well as ML remains a way of improving processes, creating better user experiences, and spurring innovation. This paper, which is also based on case studies, discusses the relationship between social welfare and AI and the importance of AI's development and use being done responsibly. Considering all the discussed aspects, this volume provides a rather nuanced perspective on AI and ML, unmasking the myths around these concepts to reveal the fact that they have a highly significant impact on the current reality and its projected evolutions. © 2024 IEEE.

关键词： Adversarial machine learning

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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