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Real-Time Customer Emotion Analysis in E-Commerce based on Social Media Data: Insights and Opportunities

Real-Time Customer Emotion Analysis in E-Commerce based on S...

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Innovation and Intelligence for Informatics, computing, and Technologies (3ICT), International Conference o

作者： Madhav Manohar Suresh Nitish Chooramun Mhd Saeed Sharif Computer Science and Digital Technologies Department School of Architecture Computing and Engineering UEL London UK

In this era of social media, understanding consumer emotions in real-time is crucial for businesses to adapt and enhance their offerings promptly. This study delves into real-time consumer emotion analysis in the e-commerce industry using social media data, propelled by the publicly available GoEmotion dataset sourced from Reddit. This dataset encompasses 58,000 comments meticulously labeled for 28 emotion categories, providing an abundant source for sentiment analysis and emotion classification, especially relevant to the e-commerce sector. Released in 2020, this dataset offers a contemporary view of consumer emotions on social media. Human annotators diligently labeled each comment, resulting in a dataset that accurately mirrors users' emotions across diverse Reddit topics. Advanced machine learning techniques, including convolutional neural networks and Bidirectional Encoder Representations from Transformers (BERT), were employed to categorise the emotions expressed in the comments. The practical result of this research is a machine learning model integrated with a user interface, delivering actionable insights derived from real-time Twitter data to e-commerce companies. This integration allows businesses to tailor their products and services in alignment with consumer sentiments expressed on Twitter. Notably, the model achieved a substantial improvement in macro-average precision (0.54) compared to previous work (0.40). The BERT model demonstrated exceptional proficiency in predicting specific emotions like gratitude, love, and admiration, boasting F1-scores of 0.82, 0.57, and 0.48, respectively. Additionally, the CNN model excelled in predicting “gratitude,” demonstrating impressive precision, recall, and F1-scores. The study aims to identify the most prevalent emotions and empower businesses to customise their products and services accordingly.

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Implementing a Chatbot Music Recommender System based on User Emotion

Implementing a Chatbot Music Recommender System based on Use...

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Innovation and Intelligence for Informatics, computing, and Technologies (3ICT), International Conference o

作者： Neenumol Mathew Nitish Chooramun Mhd Saeed Sharif Computer Science and Digital Technologies Department School of Architecture Computing and Engineering UEL London UK

The use of chatbots has become increasingly popular in recent years, as more organisations try to improve and streamline their customer service operations. One area which has been gaining momentum is the use of chatbots for music recommendation. Such systems utilise AI technologies to deliver personalised music recommendations to users via conversational interfaces. Chatbot music recommender systems present several benefits namely, they provide a personalised and natural experience which can be engaging for the users. Moreover, the users can engage in a dialogue whereby the system can better interpret the user context and preferences. This work presents the development of a chatbot personalised music recommender system, based on Natural Language Processing (NLP) techniques, coupled with a web interface that can provide song recommendations based on the user’s emotions.

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Unravelling the Impact of Random Dopant Fluctuations on Si-Based 3nm NSFET: A NEGF Analysis

Unravelling the Impact of Random Dopant Fluctuations on Si-B...

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IEEE Conference on Nanotechnology

作者： Ankit Dixit Ali Rezaei Nikolas Xeni Naveen Kumar Tapas Dutta Ismail Topaloglu Preslav Aleksandrov Asen Asenov Vihar Georgiev James Watt School of Engineering University of Glasgow Scotland UK Institute for Computing Systems Architecture University of Edinburgh Scotland UK

ISBN: (数字)9798350386240

ISBN: (纸本)9798350386257

The utilization of Technology Computer-Aided Design (TCAD) tools for the simulation of both conventional and new electronic devices has become a crucial aspect within the semiconductor industry, as well as in academic research. This paper presents a comprehensive overview of our research investigating the impact of random dopant fluctuations on “3nm node” silicon nanosheet field effect transistors (NSFETs) fabricated. Our study employs NESS (Nano-Electronic Software Simulator), the in-house device simulator developed by the Device Modelling Group at the University of Glasgow. The first principle study was performed to calculate the electrical parameter of the material, and later a fully 3-D real Non-equilibrium Green's function (NEGF) simulation was performed to capture the device characteristics.

关键词： Performance evaluation Fluctuations Field effect transistors Software Silicon Nanoscale devices Semiconductor process modeling

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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A Frequency Bin Analysis of Distinctive Ranges Between Human and Deepfake Generated Voices

A Frequency Bin Analysis of Distinctive Ranges Between Human...

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International Joint Conference on Neural Networks (IJCNN)

作者： Harry Maltby Julie Wall Cornelius Glackin Mansour Moniri Nigel Cannings Iwa Salami School of Architecture Computing and Engineering University of East London London UK School of Computing and Engineering University of West London London UK Intelligent Voice Ltd London UK Royal Docks School of Business and Law University of East London London UK

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Deepfake technology has advanced rapidly in recent years. The widespread availability of deepfake audio technology has raised concerns about its potential misuse for malicious purposes, and a need for more robust countermeasure systems is becoming ever more important. Here we analyse the differences between human and deepfake audio and introduce a novel audio pre-processing approach. Our analysis aims to show the specific locations in the frequency spectrum where these artefacts and distinctions between human and deepfake audio can be found. Our approach emphasises specific frequency ranges that we show are transferable across synthetic speech datasets. In doing so, we explore the use of a bespoke filter bank derived from our analysis of the WaveFake dataset to exploit commonalities across algorithms. Our filter bank was constructed based on a frequency bin analysis of the WaveFake dataset, we apply this filter bank to adjust gain/attenuation to improve the effective signal-to-noise ratio, doing so we reduce the similarities while accentuating differences. We then take a baseline performing model and experiment with improving the performance using these frequency ranges to show where these artefacts lie and if this knowledge is transferable across mel-spectrum algorithms. We show that there exist exploitable commonalities between deepfake voice generation methods that generate audio in the mel-spectrum and that artefacts are left behind in similar frequency regions. Our approach is evaluated on the ASVSpoof 2019 Logical Access dataset of which the test set contains unseen generative methods to test the efficacy of our filter bank approach and transferability. Our experiments show that there is enhanced classification performance to be gained from utilizing these transferable frequency bands where there are more artefacts and distinctions. Our highest-performing model provided a 14.75% improvement in Equal Error Rate against our baseline model.

关键词： Deepfakes Vocoders Neural networks Filter banks Feature extraction Market research Speech synthesis

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Navigating the impact of huge electrical demand on grid through intelligently controlled renewables and emerging V2G technology

Navigating the impact of huge electrical demand on grid thro...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion (MEDPOWER 2024)

作者： Raheel Tariq Zunaib Ali Komal Saleem Nicholas Christofides School of Engineering London South Bank University London UK School of Architecture Computing and Engineering University of East London London UK Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (数字)9781837242689

Among the biggest initiatives to help the UK reach its Net Zero goal are the conversion of the power grid to renewable energy sources (RESs) and the switch to all-electric vehicles (EVs) by 2050. However, because of the advent of dispersed unconventional energy sources and electric vehicles (EVs), the full switch to renewable energy sources and EVs poses a possible risk to power system stability, power quality, and energy management. To intelligently regulate the flow of energy and enhance the current power system, it is crucial to employ appropriate energy management strategies. This paper aims to establish a microgrid that is grid-connected and utilises multiple energy sources, including solar, battery energy storage, and electric vehicles. The goal is to create an intelligent algorithm that efficiently controls these resources and mainly enable bi-directional EV operation for vehicle-to-grid (V2G) application, resulting in both financial and environmental advantages. The main objective is to maximise the microgrid's overall profitability while minimising carbon emissions and effectively managing energy demand and network stability. It has been shown that utilising EVs as storage units via beneficial V2G technology not only stabilises renewable energy generation but also provides economic advantages.

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Effectiveness of Bacteria Based Self-healing Concrete for Enhanced Durability 4th

Effectiveness of Bacteria Based Self-healing Concrete for En...

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4th fib International Conference on Concrete Sustainability, ICCS 2024

作者： Abbas, Ali Abdalla, Khalid Tann, David Joseph, Briston Daas, Vighnesh School of Architecture Computing and Engineering University of East London 4-6 University Way London16 2RD United Kingdom Sensicon Liverpool Science Park 131 Mount Pleasant LiverpoolL3 5TF United Kingdom

ISBN: (纸本)9783031807237

The present experimental investigation focuses on examining the effectiveness of the use of a bacteria-based self-healing concrete admixture, reducing the maintenance burden on concrete structures. Introducing such innovative solutions can enhance durability, resulting in maintenance savings, reduced need for additional repair materials and thereby reducing embodied carbon. In the present research work, an experimental investigation was carried out on a self-healing admixture containing bacteria that produces spores that remain dormant until exposed to water and oxygen, activating them to produce limestone that fills the cracks automatically. The experimental tests were conducted to assess the self-healing efficiency (of both control specimens and counterparts containing the self-healing admixtures) by initially inducing cracks in notched cube samples. The samples were left to heal in an incubation chamber and then the crack healing efficiency was evaluated using permeability tests and photogrammetry-assisted visual assessments of crack width before and after self healing. Quality standards were followed in the preparation of bacterial concrete, including common laboratory tests such as sieve analysis, slump tests, compressive strength tests, and setting time evaluations in order to assess the material characteristics. The results show that the self-healing admixtures are effective in reducing the crack widths of the RC samples tested. The mechanical properties of the samples were also improved with an increase in compressive strength and its influence on setting time. The study concludes that bacteria based self-healing agents are an effective method to enhance the durability of RC structures. A particular innovation in the current is the addition of anticorrosion inhibitor, thereby enabling a resilient concrete mix that has the potential to close cracks automatically and protect the steel reinforcement from corrosion. The findings have practical implications, as t

关键词： Cracks

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An Effective Random Generalised Linear Model to Predict COPD

An Effective Random Generalised Linear Model to Predict COPD

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2022 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2022

作者： Saraireh, Linah Sharif, Mhd Saeed Alsallal, Muna Uel Cdt School of Architecture Computing and Engineering University way London United Kingdom College of Engineering Al-Muthanna University Electronics and Communication Dept Education Zone AL-Muthanna Iraq

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Chronic obstructive pulmonary disease (COPD) is a type of chronic lung illness that worsens with time and leads to a restriction in the outflow of air from the lungs. The World Health Organization ranks COPD as the third leading cause of death. Clinically, the diagnosis of this disease is relatively difficult;therefore, early identification of individuals at risk of developing COPD is vital for implementing preventative strategies. This research work has developed a generalised linear model (GLM) to predict the COPD status of the patients. A dataset of 1262 patients (688 COPD cases and 574 controls) was used. Exploratory data analysis (EDA) was utilised to observe how potential covariates were related to the response variable (COPD status), by employing rigorous model selection techniques (forward selection and backwards elimination). According to Akaike information criterion and Bayesian information criterion (BIC), a consensus was reached that the most suitable model is a binomial logistic regression model which includes the smoking history, gender, and age. The model was validated using an independent test set with an accuracy of 73%. Such a model, has the ability of predicting the risk of developing COPD in patients with existing lung conditions, including but not limited to, asthma. © 2022 IEEE.

关键词： Diagnosis

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Novel Self-healing Concrete with Anticorrosion Properties for High Durability 4th

Novel Self-healing Concrete with Anticorrosion Properties fo...

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4th fib International Conference on Concrete Sustainability, ICCS 2024

作者： Abbas, Ali Abdalla, Khalid Tann, David Jacob, Lijose Daas, Vighnesh School of Architecture Computing and Engineering University of East London 4-6 University Way London16 2RD United Kingdom Sensicon Liverpool Science Park 131 Mount Pleasant LiverpoolL3 5TF United Kingdom

ISBN: (纸本)9783031807237

An experimental study was carried out to investigate the effectiveness of a proposed concrete mix that enables self-healing of cracks and protects steel bars, creating a sustainable resilient concrete solution. Corrosion is a major problem that can significantly reduce the service life of reinforced-concrete (RC) structures, resulting in high maintenance and repair costs. The present study is conducted through accelerated corrosion laboratory experiments on RC samples that included a novel calcium nitrate based anticorrosion agent. Additionally, the effectiveness of the samples in resisting corrosion was also examined when a bacteria-based self-healing admixture was added to the mix. The corrosion inhibitor was added in two proportions by weight of cement, i.e. 4% and 8%. The samples were monitored for corrosion rate, setting time and mechanical properties before and after exposure to the corrosive environment. The results show that the selected corrosion inhibitors are effective in reducing the corrosion rate of the RC samples tested. The mechanical properties of the samples were also improved with the use of corrosion inhibitors, with an increase in compressive strength and enhanced setting time. The study concludes that calcium nitrate based corrosion inhibitors were effective in protecting RC structures and this was enhanced by using self-healing agents that help cracks to close automatically. The findings have practical implications as it can lead to improved durability, reduced maintenance costs and increased service life. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cracks

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Inventory Management System based on IoT and Microservices architecture Design

Inventory Management System based on IoT and Microservices A...

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2023 International Electrical engineering Congress, iEECON 2023

作者： Sithiyopasakul, Paisan Piyatananugoon, Chavinkorn Chaowalittawin, Vasutorn Krungseanmuang, Woranidtha Sathaporn, Posathip Kanjanasurat, Isoon Purahong, Boonchana Archevapanich, Tunjai Lasakul, Attasit King Mongkut's Institute of Technology Ladkrabang Computer Engineering Department Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand College of Computing Khon Kean University KhonKean Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Nonthaburi Thailand

ISBN: (纸本)9798350332360

This paper presents the inventory management system based on IoT and microservices architecture that synced between IoT (Internet of Things) and web application. There are two main parts consisting of IoT forklift vehicle and data transmitted system by MQTT protocol. Microservice can utilize data to process business logic and provides API. Inventory area includes a zone, a subzone and parking area for forklifts to scan QR code on each subzone. Data of forklifts is published to back-end service. The results of system microservice publish information events and processes business data to the admin client with a delay under a second. © 2023 IEEE.

关键词： Internet of things

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