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20th EAI international conference on Mobile and Ubiquitous Systems: computing, Networking and Services, MobiQuitous 2023

20th EAI International Conference on Mobile and Ubiquitous S...

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20th EAI international conference on Mobile and Ubiquitous Systems: computing, Networking and Services, MobiQuitous 2023

ISBN: (纸本)9783031639883

The proceedings contain 65 papers. The special focus in this conference is on Mobile and Ubiquitous Systems: computing, Networking and Services. The topics include: High-Performance Features in Generalizable Fingerprint-Based Indoor Positioning;SCORE: Scalable Contact Tracing over Uncertain Trajectories;faultBit: Generic and Efficient Wireless Fault Detection Using the Internet of Things;deepHeteroIoT: Deep Local and Global learning over Heterogeneous IoT Sensor Data;Federated Reinforcement learning for Automated LoRaWAN Management in Industrial IoT;a Hybrid Approach to Monitor Context Parameters for Optimising Caching for Context-Aware IoT Applications;LOADHoC: Towards the Automatic Local Distribution of Computation Using Existing IoT Devices;e-Go Bicycle Intelligent Speed Adaptation System for Catching the Green Light;FedGCS: Addressing Class Imbalance in Long-Tail Federated learning;FedRC: Representational Consistency Guided Model Uploading Mechanism for Asynchronous Federated learning;RADEAN: A Resource Allocation Model Based on Deep Reinforcement learning and Generative Adversarial Networks in Edge computing;a Stream Data Service Framework for Real-Time Vehicle Companion Discovery;KS-Autoformer: An Autoformer-Based SOC Prediction Framework for Electric Vehicles;deep Reinforcement learning-Based Multi-node Collaborative Task Offloading Optimization in 6G Space-Air-Ground Integrated Networks;securing Wireless Communication in Critical Infrastructure: Challenges and Opportunities;cross-User Activity Recognition via Temporal Relation Optimal Transport;selfAct: Personalized Activity Recognition Based on Self-Supervised and Active learning;a Novel Method for Wearable Activity Recognition with Feature Evolvable Streams;let’s Vibrate with Vibration: Augmenting Structural engineering with Low-Cost Vibration Sensing;research on Data Drift and Class Imbalance in Android Malware Detection;Reputation-Based Dissemination of Trustworthy Information in VANETs;reputation System

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Utilizing Deep learning Algorithms for Real-Time Language Translation and Breaking Down Communication Barriers

Utilizing Deep Learning Algorithms for Real-Time Language Tr...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Saurabh, Vivek Ramesh, B. Anandhi, R.J. Singla, Atul Chandra, Pradeep Kumar Faris, Najlaa Nasrulaah University of the Cumberlands WilliamsburgKY United States Information Technology Walmart India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India LLloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

In today's globalized society, individuals must overcome linguistic hurdles to collaborate. Language translation helps individuals, companies, and governments communicate, exchange information, and understand one other across language boundaries. Deep learning algorithms have transformed language translation. It now considers context in real time, is quicker, and more accurate. This research investigates how deep learning can solve linguistic difficulties in real-time translation. Context-Aware Real-Time Language Translation (CARLTT) is a novel method that combines deep learning to improve translation accuracy while preserving context. Translation using the Contextual Transformer Network (CTN) considers the present circumstances. The BMA reduces bias in versions. Background is maintained via the Contextual Language Model (CLM). We prove CARLTT is the best translation technique by exhibiting its superiority. CARLTT is more accurate, keeps context, and finds less bias than other translation models after several testing. CTN ensures linguistic and context accuracy in translations. By reducing biases significantly, BMA offers culturally sensitive and fair versions. With CLM, context is better preserved, therefore the translated content will retain all details and slang. Finally, CARLTT illustrates a new era in real time. Deep learning has ushered in language translation. It fosters diversity, educates individuals about various cultures, and helps companies collaborate when they don't speak the same language. Advanced deep learning approaches in language translation may become increasingly relevant as the globe gets more interconnected. This might help multilingual individuals converse and have more fruitful discussions. © 2024 IEEE.

关键词： Contrastive learning

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Exploration of AI-Empowered Professional Skill Training Models for Normal School Students 4

Exploration of AI-Empowered Professional Skill Training Mode...

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4th international conference on Educational Technology, ICET 2024

作者： Ma, Wan Shi, Shanfei Zhu, Chuang Xing, Beiyuan Wang, Yaping Shu, Jiangbo Central China Normal University National Engineering Research Center of Educational Big Data Wuhan China Central China Normal University Faculty of Artificial Intelligence in Education Wuhan China

ISBN: (纸本)9798350376944

Addressing issues in traditional professional skill training for normal school students, such as insufficient teaching staff, high workload for instructors, low learning efficiency, and delayed feedback, an AI-empowered model has been proposed to enhance the training of professional skills for normal school students. The model relies on the Xiaoya Intelligent teaching Platform and integrates AI technologies like the intelligent evaluation engine for standardized Chinese handwriting, intelligent voice app, and ChatGPT. It reconstructs the training process in skills such as pen writing, chalk writing, Putonghua and teaching design. This approach enables personalized adaptive learning and effectively promotes the quality and efficiency of the training of normal school students. © 2024 IEEE.

关键词： Contrastive learning

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RecFlex: Enabling Feature Heterogeneity-Aware Optimization for Deep Recommendation Models with Flexible Schedules 24

RecFlex: Enabling Feature Heterogeneity-Aware Optimization f...

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2024 international conference for High Performance computing, Networking, Storage and Analysis

作者： Pan, Zaifeng Zheng, Zhen Zhang, Feng Xie, Bing Wu, Ruofan Smith, Shaden Liu, Chuanjie Ruwase, Olatunji Du, Xiaoyong Ding, Yufei Renmin Univ China Beijing Peoples R China Microsoft Redmond WA 98052 USA Univ Calif San Diego San Diego CA 92103 USA

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

Industrial recommendation models typically involve numerous feature fields. The embedding computation workloads are heterogeneous across these fields, thus requiring varied optimal code schedules. While existing solutions apply basic fusion optimization for embedding operations, they inefficiently treat all feature fields with identical schedules, leading to suboptimal performance. In this paper, we introduce RecFlex, which generates fused kernels with distinct schedules for different feature fields. RecFlex employs the interference-aware schedule tuner to tune schedules and the heterogeneous schedule fusion compiler to generate fused kernels, addressing two major challenges. To determine optimal schedules of different feature fields within the fused kernel, RecFlex proposes a two-stage interference-simulated tuning strategy. To handle dynamic workloads that challenge tuning and fusion, RecFlex combines compile-time schedule tuning with runtime kernel thread mapping. RecFlex surpasses state-of-the-art libraries and compilers, achieving average speedups of 2.64x, 20.77x, and 11.31x over TorchRec, HugeCTR, and RECom, respectively. RecFlex is publicly available at https://***/PanZaifeng/RecFlex.

关键词： recommender system machine learning compiler

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An Optimized Deep learning for Efficient Defect Inspection in Smart Industry with Fog computing 2

An Optimized Deep Learning for Efficient Defect Inspection i...

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2nd international conference on Data Science and Information System, ICDSIS 2024

作者： Narayana, Potu Keerthi, K. Adnan, Myasar Mundher Madhura, G.K. Ghamya, K. Stanley College Of Engineering And Technology For Women Department Of Cse Telangana Hyderabad India Methodist College Of Engineering And Technology Department Of Cse Telangana Hyderabad India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Artificial Intelligence And Machine Learning Bengaluru India Tirupati India

ISBN: (纸本)9798350381665

With the development of the constantly expanding Internet of Things (IoT) devices and communications infrastructure, the majority of sensors have been used, resulting in a vast amount of data. One of the most frequent instances is the manufacturer's inspection to precisely detect product flaws;which allows for adaptive optimization of the model's parameters. This research proposes a novel Q-learning Reinforcement learning (RL)-based Optimization Algorithm (ROA) for optimizing the Fog computing based Convolutional Neural Network (FC-CNN) that can discover probable faulty items precisely, to develop an effective inspection system. Given the possibility of several assembly lines operating in a single plant, one of the primary issues is the real-time processing of this data. Subsequently, adjusting the CNN model's hyperparameters significantly enhances its faults detection capabilities. The suggested FC-CNN-ROA transfers the burden from the central servers to the fog nodes, allowing it to handle very huge volumes of data. When compared with existing techniques such as You Only Look Once version4 combined with DL (YOLO v4-DL), MobileNet-V2, the proposed FC-CNNROA obtains a higher accuracy of 99.97%, 99.89%, 99.75%, 99.71%, and 99.16% for different sets of datasets such as 10,20, 30,40,50 respectively. © 2024 IEEE.

关键词： Internet of things

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Position Paper: Enhancing the learning and Mastery of Academic Writing in the Serbian Language Through an AI Tool with Adaptive Scaffolding 17

Position Paper: Enhancing the Learning and Mastery of Academ...

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17th international conference on Computer Supported Education, CSEDU 2025

作者： Francišković, Teodor Sakal Gajić, Dušan Luburić, Nikola Slivka, Jelena Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Trg Dositeja Obradovića 6 Novi Sad Serbia

ISBN: (纸本)9789897587467

Academic writing is a significant challenge for many learners striving for proficiency. Adaptive scaffolding techniques and AI tools in education have proven effective in addressing this challenge and supporting learners in improving their academic writing skills when used correctly. This position paper proposes combining adaptive scaffolding techniques with AI tools in a public university's final year academic writing course to enhance the learning experience and mastery of academic writing skills in Serbian. The proposed plan outlines the course structure and details how the AI-driven adaptive scaffolding techniques will be integrated to support the learning experience, focusing on summative and formative feedback from the AI tool. The proposed plan is a work in progress. It will be implemented in the next iteration of the course for evaluation, taking into account potential counter-arguments and their impact on the tool's development and the student's learning experience and outcomes. This study will analyse our plan's effectiveness in enhancing the learning experience and outcomes. The expected outcome is to assist students in their learning while contributing to the development of AI in education and the Serbian language. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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Analysis and Detection of Fake News Using Machine learning 3

Analysis and Detection of Fake News Using Machine Learning

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sathish Kumar, P. Suthanthiradevi, P. Arul Stephen, C. Ebenezer Abishek, B. Sivakumar, S. Mathiyarasu, M. Computer and Communication Engineering Rajalakshmi institute of technology Chennai124 India Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai603203 India Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai117 India Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai600062 India Electronics and Communication Engineering BIHER Chennai73 India Computer and Communication Engineering Rajalakshmi institute of technology Chennai India

ISBN: (纸本)9798350372120

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society. © 2024 IEEE.

关键词： Machine learning

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Automated Symptom Tracking And Prediction Of Angioedema Disease Using Machine learning

Automated Symptom Tracking And Prediction Of Angioedema Dise...

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2024 international conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Arunkumar, M. Palanisamy, M. Viknesh, K. Mugilarasan, M. Abinesh, B. Ifet College Of Engineering Dept. Of Information Technology Tamilnadu Villupuram India Dhanalakshmi Srinivasan Engineering College Dept. Of Information Technology Tamilnadu Perambalur India Ifet College Of Engineering Dept. Of Information Technology Tamilnadu Villupuram India

ISBN: (纸本)9798350352689

The timely and precise identification of skin diseases is essential to optimizing therapy effectiveness and attaining favorable patient outcomes. However depending solely on visual examination and conventional diagnostic techniques adds subjectivity and might take a long time, which increases the risk of misdiagnosis or postponed treatment. This research aims to develop a machine-learning model to detect angioedema disease, a potentially fatal illness marked by sudden swelling of the skin and mucous membranes. Using several feature extraction techniques, the proposed model extracts texture, color, and intensity information from skin photographs. These extracted attributes are then integrated into a transfer learning framework to enhance overall performance, utilizing data from previously trained models. The shortcomings of traditional machine learning methods, such the need for huge datasets or protracted training cycles, are addressed by this innovative methodology The created approach has the potential to help medical practitioners make well-informed diagnoses, especially in areas with limited resources. The methodology has the potential to greatly improve patient outcomes and save healthcare costs by enabling early diagnosis and permitting proactive treatment actions. This development is especially helpful for increasing access to precise diagnoses in a variety of healthcare environments, guaranteeing that patients receive prompt and effective therapy. © 2024 IEEE.

关键词： Local binary pattern

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Analysis of Mathematical Modelling Deterministic and Stochastic Problems in Federated learning 3

Analysis of Mathematical Modelling Deterministic and Stochas...

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3rd international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2023

作者： Kumar, Birendra Singh, Saumya Grover, Reena Isabels, K Ruth Garg, Ashish Charudatta Dattatraya, Bele Iimt University Op School of Basic Sciences and Technology Department of Mathematics Meerut India

ISBN: (纸本)9798350399264

Federated learning is a distributed ma- chine learning method that empowers numerous gatherings to cooperate to get familiar with a model without sharing their information This paper presents a numerical examination of deterministic and stochastic issues in Federated learning. In deterministic Fed- erated learning, all the clients have the same data distribution, furthermore, the objective is to limit a worldwide goal capability. In stochastic Federated learning, the clients have different data distributions, what's more, the objective is to limit a worldwide goal capability. We talk about the difficulties of Federated learning, like client heterogeneity and non-IID information appropriations, and the techniques proposed to address these challenges. We also highlight open research questions, such as how to handle non-stationarity in Federated learning and how to handle adversarial clients in Federated learning. Finally, we suggest future research directions, including investigating the impact of Federated learning on model interpretability, fairness, and robustness, and exploring the potential of Federated learning for federated reinforcement learning, federated meta- learning, and federated unsupervised learning. © 2023 IEEE.

关键词： Unsupervised learning

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Efficient Data Sampling and Reduction Methods in Large-Scale Forensic Analysis

Efficient Data Sampling and Reduction Methods in Large-Scale...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Harika, Ala Sharma, Praveen Aravinda, K. Nagpal, Amandeep Praveen Albawi, Ali Institute of Aeronautical Engineering Hyderabad Dundigal India Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M. P Bhopal India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350373783

In this era of enormous, continuously changing datasets, forensic analysis needs innovative approaches that can adapt to changing data characteristics while maintaining data security. To satisfy this demand, our recommended system includes all algorithms for large forensic investigations. The adaptive stratified sampling algorithm sets the scene. It adjusts strata and sample quantities based on data attributes. The Feature Weighting with Machine learning Algorithm improves selection by weighting features using machine learning. Based on them, the dynamic cluster-based sampling algorithm organizes data points dynamically. By determining how much information each data point has, the entropy-guided data reduction algorithm improves the framework. The Temporal Correlation Analysis Algorithm finally allows you to view changing data. Tables3 and Tables4 indicate that our approach outperforms others in several key areas. The recommended technique outperforms others in accuracy, memory, F1 score, usability, stability, legal acceptability, ease of application, computing complexity, and freedom. The findings in Figures.6-10 indicate that the proposed framework can balance accuracy and memory, decrease data, and be highly usable, stable, and versatile. Finally, our technology represents a novel technique to conduct large-scale research. It is fast, adaptable, and data-safe. Machine learning, dynamic changes, and temporal variables make our platform robust and comprehensive for current forensic investigations. © 2024 IEEE.

关键词： Adaptive Stratified Sampling Algorithm Algorithm Optimization Data Reduction Data Sampling Dynamic Cluster-based Sampling Entropy-guided Data Reduction Feature Weighting Forensic Analysis Machine learning Temporal Correlation Analysis

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