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2nd International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Mayuranathan, M. Akilandasowmya, G. Jayaram, B. Velrani, K. Sundara Kumar, M Jogendra Vidhya, R.G. Srm Valliammai Engineering College Department of Computer Science Engineering India Sri Sairam Institute of Technology Department of Computer Communication and Engineering India Rmk Engineering College Department: Computer Science and Business Systems Tamilnadu Gummidipoondi Taluk Tiruvallur District Kavaraipettai India School of Computing Sathyabama Institute of Technology Information Technology India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350325799

Recently, AI and NLP have been used to improve customer care. Customer support relies on event extraction and relevant data. In customer support applications, AI-based algorithms extract events. Event extraction identifies and extracts specific events or activities from textual data like customer service chats, emails, and social media interactions. Customer support teams may discover issues, trends, and improve the customer experience by automatically extracting events. Customer support event extraction can be done using AI-based models. Event extraction relies on named entity recognition (NER), sentiment analysis, and text classification. NER classifies customer names, product names, and problem keywords. Text classification classifies client issues into preset categories like billing, technical support, or product feedback, while sentiment analysis assesses the customer's emotional tone. Event extraction AI models use machine learning algorithms. Support vector machines (SVM) and random forests can be trained on annotated customer support data to reliably classify events. Recurrent neural networks (RNNs) and transformers can capture complicated patterns in textual data, making them promising event extraction methods. Customer support applications benefit from AI-based event extraction algorithms. It frees up support teams to focus on client issues by automating evaluation and categorization. It also helps firms identify emerging trends and enhance their products and services to answer client complaints. AI-based customer support event extraction models face hurdles. Labeled training data is costly and time-consuming to obtain. Ensuring model generalizability across multiple domains or languages is ongoing research. In conclusion, AI-based event extraction in customer support systems can improve customer experiences and support team efficiency. © 2023 IEEE.

关键词： Support vector machines

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A Review Analysis on Smart Blind Stick Using IoT 4

A Review Analysis on Smart Blind Stick Using IoT

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Asha, P.K. Shanmugha Priya, R.K. Nagaraj, D. Aswini, S. Wilson, G Preethi Vinoth Kumar, K. New Horizon College of Engineering Department of Chemistry Bengaluru India Jai Shriram Engineering College Avinashipalayam Department of Information Technology Tirupur India Dhanalakshmi College of Engineering Department of Cse Chennai India Bangalore Technological Institute Department of Cse Bengaluru India Jerusalem College of Engineering Department of Computer Science & Business Systems Chennai India New Horizon College of Engineering Department of Eee Bengaluru India

ISBN: (纸本)9798331523923

Millions of individuals worldwide struggle with blindness and other forms of visual impairment. 940 million individuals will suffer some degree of vision loss by 2015, according to the WHO (World Health Organization). 246 million people experience fleeting apparitions, while 39 million people are blind. Most people with bad vision are older than 50 and live in industrialized cultures. The main challenge that blind individuals encounter is mobility. They are cut off from their environment. Since they are oblivious to what is going on around them, they require assistance. Technological advancements have been crucial in increasing the mobility and freedom of those who are blind. One such innovation is the Smart Blind Stick, a high-tech assistance gadget made to improve the safety and navigation of the blind. The goal of this review article is to examine the many facets of Smart Blind Stick technology, including its functionality, benefits, drawbacks, and overall influence on users' life. This review aims to shed light on the current status of Smart Blind Stick technology and its potential future advances by reviewing existing literature and advancements. © 2025 IEEE.

关键词： Obstacle detectors

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications 4

Adaptive Deep Reinforcement Learning-based Resource Manageme...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karne, Niharika Ramagundam, Chandra Shekar Patnala, Ranga Rao Ramagundam, Shashishekhar Tadiboina, Sai Nitisha Sr. Principal Systems Engineer in C&RS Platform Services University of South Alabama Department of Electrical Engineering Masters in Electrical Engineering Amtrak DT United States Software Engineer in Cloud Platform Services City of Philadelphia Pennsylvania United States Vice President Neuberger Berman United States Software Development & Engineering in Content Discovery & AI Troy University Department of Computer Science Masters in Computer Science AL Comcast Montgomery Campus United States Department of Information Technology GEICO - Chevy Chase Chevy ChaseMD20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Deep reinforcement learning

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Challenges and Initial Measurements on Communication and Localization for Mountain Bike Safety Applications

Challenges and Initial Measurements on Communication and Loc...

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Annual Conference on Wireless On Demand Network systems and Services (WONS)

作者： Michele Zucchelli Marcus Marx Max Hörmann Frank Kargl Michele Segata Department of Information Engineering and Computer Science University of Trento Italy Institute of Distributed Systems Ulm University Germany

ISBN: (数字)9783903176713

ISBN: (纸本)9798331522025

As of today, research in vulnerable road users (VRUs) applications is mainly focused on safety in urban road scenarios. There is little to be found in the literature with respect to VRUs in mountain areas, where mountain biking and hiking also present risks of collision. Here, it is not yet clear whether existing localization and communication technologies would provide sufficient performance in such harsh environments. In this work, we start answering this question by presenting the results of a measurement campaign which took place in a mountain area in Northern Italy during Summer 2024. With respect to localization, we show that global navigation satellite system (GNSS)-based localization alone often provides unreliable results due to vegetation and terrain. Trilateration with Bluetooth Low Energy (BLE) and beacons mounted at fixed positions performs well in some circumstances and can be used to enhance GNSS, however, we also observed many unclear effects that require further investigations. Concerning communication, the results indicate that both direct short range communications (DSRC) and cellular V2X (C-V2X) works fairly well in most cases, but terrain characteristics might induce packet losses or low signal quality, whereas instabilities in GNSS fixes might also cause C-V2X outages.

关键词： Location awareness Wireless communication Global navigation satellite system Roads Vegetation mapping Packet loss Communications technology Safety Bluetooth Low Energy Network systems

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Involution symmetry quantification using recurrences

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Physical Review E 2024年第2期110卷 024203-024203页

作者： Gabriel Marghoti Thiago de Lima Prado Sergio Roberto Lopes Yoshito Hirata Physics Department Degree Program in Computer Science Interdisciplinary Center for Science Technology and Innovation CICTI Departament of Physics - Telegraphenberg 14473 Potsdam Germany Institute of Systems and Information Engineering

Symmetries are ubiquitous in science, aiding theoretical comprehension by discerning patterns in mathematical models and natural phenomena. This work introduces a method for assessing the extent of symmetry within a time series. We explore both microscopic and macroscopic features extracted from a recurrence plot. By analyzing the statistics of small recurrence matrices, our approach delves into microscale dynamics, facilitating the identification of symmetric time series segments through diagonal macroscale structures on a recurrence plot. We validate our approach by successfully quantifying involution symmetries for three-dimensional dynamical models, specifically, order-2 rotational symmetry in the Lorenz '63 model, and inversion symmetry in the Chua circuit. Our quantifier also detects symmetry breaking in the modified Lorenz model for El Niño phenomenon. The method can be applied in a versatile manner, not only to three-dimensional trajectories but also to univariate time series. Symmetry quantification in time series is promising for enhancing dynamical system modeling and profiling.

关键词： Chaos Dynamical systems Time series analysis

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FEAT: Towards Fast Environment-Adaptive Task Offloading and Power Allocation in MEC 42

FEAT: Towards Fast Environment-Adaptive Task Offloading and ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Ren, Tao Hu, Zheyuan He, Hang Niu, Jianwei Liu, Xuefeng Institute of Software Chinese Academy of Sciences Laboratory for Internet Software Technologies Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Mobile edge computing (MEC) has been proposed to provide mobile devices with both satisfactory computing resources and latency. Key issues in MEC include task offloading and power allocation (TOPA), for which deep reinforcement learning (DRL) is becoming a popular methodology. However, most DRL-based TOPA approaches are typically developed in a certain environment, lacking the adaptability to unseen environments. Motivated by this, this paper proposes a Fast Environment-Adaptive TOPA (FEAT) approach that could adapt to unseen environments with little fine-tuning. Specifically, we first split MEC states into the internal state and environmental state. Then, based on these two types of states, we develop two main components of FEAT - a group of internal state-dependent TOPA meta-policies and an environmental state-embedded steerer. Meta-policies learn TOPA skills within the internal state space (allowing reusing meta-policies in different environments), while the steerer learns to choose appropriate meta-policies according to embedded environmental states. When encountering an unseen environment with the same internal state space, FEAT only needs to fine-tune the steerer using the newly embedded environmental state with few internal state explorations. Extensive experimental results on simulation and testbed demonstrate that FEAT outperforms the state-of-the-art by more than 16.4% in terms of fine-tuning speeds. © 2023 IEEE.

关键词： Mobile edge computing

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Companion Recognition System for Amnesia Affected Persons

Companion Recognition System for Amnesia Affected Persons

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Ignisha Rajathi, G. Devin James, R. Raja Balaji, N. Subhash Hari Krishnan, R. Yogeeshwaran, G. Dept of Information Technology Manipal Institute of Technology Bengaluru Engineering Manipal India Dept of CSE Sri Krishna College of Engineering Coimbatore India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Dept of CSE and Business Systems Sri Krishna College of Engineeirng and Technology Coimbatore India

ISBN: (纸本)9798331510299

Recogni is an AI-based application designed for the purpose of enhancing the quality for individuals who are suffering from amnesia and other memory-related challenges. This application addresses key challenges in memory management, daily task organization, and social integration through features like geofencing reminders, family sharing, and interactive memory aids and turns out to be a good companion for them. To protect user data, recogni's progressive development integrates advanced functions such as location-based notes, voice-controlled reminders, and multi-language support which are all reinforced by robust security measures. Wearable device integration, predictive analytics, and emergency assistance, are promised to be added in the ongoing development, making Recogni a versatile tool for memory care [1]. © 2024 IEEE.

关键词： Memory management

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Unveiling Veracity: An Integrated Semi-Supervised Algorithm for Fake News Detection

Unveiling Veracity: An Integrated Semi-Supervised Algorithm ...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Aman Malik Dayal Kumar Behera Amulya Ratna Swain Jhalak Hota Department of Computer Science & Information Systems Birla Institute of Technology and Science Hyderabad School of Computer Engineering KIIT University Bhubaneswar India

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

In recent years, the pervasive dissemination of misinformation and deliberately falsified content, commonly referred to as “fake news,” has become a critical challenge in the realm of information dissemination. With the exponential growth of digital media and online platforms, the ease and speed at which misleading information can be generated and spread have increased substantially. Addressing the formidable task of detecting fake news has thus become an imperative within the field of information technology. The supervised model gives promising results. However, the availability of labelled data is critical in the realm of fake news detection. Leveraging unlabeled data alongside labelled data is an effective approach, but it requires careful handling. This paper explores the application of Semi-Supervised Learning for fake news detection by integrating Label Propagation and various ML models as base classifiers. By leveraging both labelled and unlabeled data, Label Propagation with CatBoost Classifier seeks to overcome traditional limitations, offering a promising avenue for bolstering the reliability of automated systems in distinguishing between authentic and deceptive information in the vast landscape of digital content.

关键词： Measurement Technological innovation Logistic regression Accuracy Computational modeling Refining Semisupervised learning

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Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing 42

Enabling Communication-Efficient Federated Learning via Dist...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Guan, Yixuan Liu, Xuefeng Ren, Tao Niu, Jianwei Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Sciences Laboratory for Internet Software Technologies Institute of Software Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach. © 2023 IEEE.

关键词： Compressed sensing

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Virtual Reality Semiconductor Laboratory: Usability Testing to Improve User Experience (UX) in Virtual Environments 3

Virtual Reality Semiconductor Laboratory: Usability Testing ...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Ahmed Alnagrat, Ahmed Jamah Ismail Alkhallas, Muhannad Imhmed Mohamed, Abdalslam S Che Ismail, Rizalafande Syed Idrus, Syed Zulkarnain Salem Saed, Halifah Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya Perlis Kangar01000 Malaysia Faculty of Engineering Technology in Zuwarah Department of Software Engineering Zuwarah Libya Aljufra University Faculty of Information Technology Aljufra Libya Arau02600 Malaysia Department of Communication Perlis Kangar01000 Malaysia Higher Institute of Science and Technology Wadi Al-Shati Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya

ISBN: (纸本)9798350319897

The Virtual Reality (VR) Semiconductor Laboratory, a computer-based virtual lab designed for training engineering students in semiconductor concepts, was evaluated to enhance user experience (UX) in virtual environments. A sample of 97 participants, including undergraduate, Master's, and PhD students, participated in the study. The User Experience Questionnaire (UEQ) was developed to collect data from users to evaluate their experience with the VR lab. The results of the study showed that the participants had a positive experience with the VR learning environment. They found the experience to be enjoyable, easy to understand, and helpful in completing learning tasks. The majority of participants rated the experience as very easy or easy in terms of learning and understanding the material, enjoyment, ease of using the interface, comfort with body movement during learning, and assistance with tasks. Additionally, most participants rated the experience as very easy in terms of task completion and comfort with the VR experiment. The study found high reliability in terms of usability, indicating that the UEQ is a valid and reliable tool for assessing virtual laboratory usability. These findings suggest that virtual laboratories can be effective learning and training tools in various fields, such as science and engineering. This research highlights the potential of VR technology in improving the training and learning experience for students. © 2023 IEEE.

关键词： Virtual reality

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