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A Comparative Analysis of Customer Service Chatbots: Efficiency, Usability and Application

A Comparative Analysis of Customer Service Chatbots: Efficie...

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computer Applications & Technological Solutions (CATS)

作者： Kefaiat Lamia Ehsani Ehsanur Rahman Rhythm Md Humaion Kabir Mehedi Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

A computer programme that imitates and processes human interaction, either through the use of voice or text communication, is known as a chatbot. Its purpose is to be of assistance in the process of finding a solution to a problem. The transformation brought on by advances in technology has had an effect on every industry. The chatbot provides assistance with a wide variety of tasks, including Reservations, Customer Service, and a great number of other services. The fast development of technologies relating to artificial intelligence and natural language processing has resulted in an increase in the use of chatbots in a variety of fields, most notably in the field of customer service. Customers could receive advice that is prompt, accurate, and personalised through the use of chatbots, which has the potential to completely transform customer service. Because it can automate customer service and reduce the amount of work that needs to be done by humans, it has gained a lot of popularity in the business world. Which can help businesses improve the experience they provide for their customers. The purpose of this research is to undertake a comparative review of customer service chatbots, with a particular emphasis on their efficiency, usability, and application across a variety of business sectors. The research will uncover best practises, difficulties, and potential for improvement by analysing a variety of chatbot solutions.

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Deep Learning on Home Drone: Searching for the Optimal Architecture

Deep Learning on Home Drone: Searching for the Optimal Archi...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alaa Maalouf Yotam Gurfinkel Barak Diker Oren Gal Daniela Rus Dan Feldman Department of Computer Science Robotics and Big Data Labs University of Haifa Israel CSAIL MIT USA

We suggest the first system that runs real-time semantic segmentation via deep learning on the weak microcomputer Raspberry Pi Zero v2 (whose price was $15) attached to a toy drone. In particular, since the Raspberry Pi weighs less than 16 grams, and its size is half of a credit card, we could easily attach it to the common commercial DJI Tello toy-drone ($\times 92.5\times 41$ mm). The result is an autonomous drone (no laptop nor human in the loop) that can detect and classify objects in real-time from a video stream of an onboard monocular RGB camera (no GPS or LIDAR sensors). The companion videos demonstrate how this Tello drone scans the lab for people (e.g. for the use of firefighters or security forces) and for an empty parking slot outside the lab. Existing deep learning solutions are either much too slow for real-time computation on such IoT devices, or provide results of impractical quality. Our main challenge was to design a system that takes the best of all worlds among numerous combinations of networks, deep learning platforms/frameworks, compression techniques, and compression ratios. To this end, we provide an efficient searching algorithm that aims to find the optimal combination which results in the best tradeoff between the network running time and its accuracy/performance.

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A Hybrid Algorithm to Perform Dynamic Node Energy and Link Stability through invoking data from 5G Wireless Sensor based Network

A Hybrid Algorithm to Perform Dynamic Node Energy and Link S...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Dhinakaran, K. Elantamilan, D. Gnanavel, R. Vinod, D. Nalini, M.K. Dhanalakshmi College of Engineering Department of Artificial Intelligence and Data Science Chennai India VallalP. T. LeeChengalvarayaNaicker Arts and Science College Department of Computer Science Chennai112 India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of CSE Chennai India BMS College of Engineering Department of ISE Bangalore India

ISBN: (纸本)9781665495295

When it comes to WSN, routing is the most difficult and time-consuming part. For WSN, there are a variety of routing protocols. Routing techniques for mobile sensor networks include Ad hoc On-Demand Distance Vector (AODV). The use of less energy. NAODV is a new routing protocol that incorporates an energy-Aware algorithm and the shortest route into the current Ad hoc On-demand Distance Vector Routing (NAODV) protocol in order to save battery consumption and WSN lifespan. Research challenges including packet loss and energy consumption have been taken into consideration when developing new routing protocols for mobile nodes. The simulation results demonstrate that a combination of both bandwidth and energy management works better than the present methods. According to this study, the suggested cluster-based routing algorithms for mobile wireless sensor networks are compared among themselves. According to the simulation results, the suggested energy-efficient routing algorithm consumes very little energy and provides a high level of performance. A study of the AODV protocol's throughput, packet rate, coverage area, and the number of packets has been conducted in this work. With 11 nodes, our simulation findings demonstrate that the network's lifespan is increased greatly without affecting packet delivery speed. Cells, the smallest unit of geographic division in the 5G network, will be used to connect providers' service areas. There are still certain locations that don't yet have access to 5G. Thus new gadgets may still connect to 4G LTE networks. data aggregation methods are crucial to network efficiency. Accumulating and effectively condensing data packets is the fundamental goal of a data aggregation technique. This study created a hybrid clustering and routing strategy for data aggregation in 5G wireless sensor networks. © 2022 IEEE.

关键词： 5G mobile communication systems

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Optimized Detection of Continuous Gravitational-Wave Signals using Convolutional Neural Network

Optimized Detection of Continuous Gravitational-Wave Signals...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Premkumar Duraisamy Yuvaraj Natarajan V. Niranjani K Parvathy Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore India

Identifying gravitational waves produced by binary black hole mergers has sparked an unheard-of revolution in physics and astronomy. However, due to the low magnitudes of gravitational wave signals and the inevitability of detector noise, proper processing of gravitational wave data is in high demand. In this research, we propose a deep learning technique termed convolution neural network based on a set of time-series data from “***/datasets/andradaolteanu/g2net-gravitational-wave-dataset” that contains simulated gravitational waves with noise. This work uses a convolution neural network and Fourier transformation-based gravitational wave signal recognition model (CNN). According to experimental findings, the frequency-domain signal graph describes the gravitational wave signal’s features more accurately than the time-domain signal. The signal recognition accuracy has been improved using CNN’s convolutional processing.

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Multi-Task Learning Frameworks to Classify Food and Estimate Weight From a Single Image

Multi-Task Learning Frameworks to Classify Food and Estimate...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Pakin Siwathammarat Panas Jesadaporn Jakarin Chawachat Data Science Research Center Faculty of Science Chiang Mai University Thailand Department of Internal Medicine Faculty of Medicine Chiang Mai University Thailand Department of Computer Science Faculty of Science Chiang Mai Thailand

Usually, elderly patients in hospitals suffer from malnutrition because they are unable to consume food as prescribed by doctors or nutritionists. Analyzing food intake is labor-intensive and time-consuming. Therefore, machine learning is used to analyze the food intake. Major food analysis tasks include food classification and food weight estimation. The basic machine learning approach to this problem is to combine a food classification model with a food weight estimate model sequentially. When we deployed it, we found that a large amount of memory and models were required. One solution is to use multi-task learning. In this study, we proposed multi-task learning frameworks that could recognize food and predict weight based on a single image. The performance of our frameworks was compared to the baseline models, which only utilized either regression or classification. Although baseline accuracy is higher, our framework has MAPE values that are lower than the baseline. To improve the performance, we explored different approaches for weighting loss, including manual weighting and auto weighting, using uncertainty and auxiliary tasks. From the experiment, our results showed that our multi-task learning framework that adjusted the weight of loss using auxiliary tasks outperformed the baseline models in terms of MAPE and Accuracy. Moreover, we demonstrate our framework when scaling up the backbone from ResNet50 to ResNet101 and ResNet152.

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Examining the Impact of data Mining on Decision-Making Processes 3

Examining the Impact of Data Mining on Decision-Making Proce...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Vaishali Pandey, Pratik Aggarwal, Prateek Reena, R. Bhagwat, Suvarna R. Sunil, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

Statistics mining has emerged as a crucial device for agencies in the past two decades that significantly impact selection-making methods. It can be used to gain insights from massive datasets and might assist with an employer's marketing approach, operational performance, customer segmentation, etc. This paper will explore the impact information mining has on decision-making approaches, especially regarding coming across new styles and relationships. It will discuss the blessings of using records mining and how it may help decorate choice-making. It will also look at various methods used to extract statistics from massive datasets and how they facilitate the selection-making method. Finally, it will discuss a number of the constraints of information mining and the ability of answers to overcome them. Standard, this paper aims to pick out the effect of information mining on the choice-making system and how it may be leveraged as a practical device. © 2023 IEEE.

关键词： discuss extract information overcome relationships

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Sentiment Analysis of Restaurant Reviews from Bangladeshi Food Delivery Apps

Sentiment Analysis of Restaurant Reviews from Bangladeshi Fo...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Ehsanur Rahman Rhythm Rajvir Ahmed Shuvo Md Sabbir Hossain Md. Farhadul Islam Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

In this study, we conducted sentiment analysis on restaurant reviews from Bangladeshi food delivery apps using natural language processing techniques. Food delivery apps have become increasingly popular in Bangladesh, and understanding the sentiment of customer reviews can provide valuable insights for restaurant owners and food delivery app companies. In this research, we have created a dataset named “Bangladeshi Restaurant Reviews” by gathering customer reviews of restau-rants available on Foodpanda and Hungrynaki, which are two popular food delivery apps in Bangladesh. We used Robustly Optimized BERT Pretraining Approach (RoBERTa), AFINN, and DistilBERT, a distilled version of Bidirectional Encoder Repre-sentations from Transformers (BERT) to perform the sentiment analysis. Overall, this research paper highlights the importance of sentiment analysis in the food delivery industry and demonstrates the effectiveness of different models in performing this task. It also provides insights for businesses looking to use sentiment analysis to improve their services and products. The accuracy of the models evaluated, RoBERTa, AFINN, and DistilBERT, were 74%, 73 %, and 77 % respectively.

关键词： Industries Sentiment analysis Analytical models Computational modeling Bit error rate Pipelines Transformers

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ReSkipNet: Skip Connected Convolutional Autoencoder for Original Document Denoising

ReSkipNet: Skip Connected Convolutional Autoencoder for Orig...

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International Conference on computer and Information Technology (ICCIT)

作者： Mohammad Muhibur Rahman Anushua Ahmed Mohammad Rakibul Hasan Mahin Fahmid Bin Kibria Waheed Moonwar Ehsanur Rahman Rhythm Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

data pre-processing, data analysis, and Optical Character Recognition need a huge amount of clean data, and document images are usually a good source for this. However, document images frequently exhibit blurring and various other forms of noise, which can pose challenges in their manipulation and analysis. To denoise and deblur such document images, autoencoders have been used for a long time. For this task, we propose a novel Convolutional Autoencoder Network which is composed of multiple skip-connected residual blocks and other layers for supporting the encoder and decoder parts. This model not only uses less computational power to denoise existing document image datasets but also performs well. While prior research primarily concentrates on optimizing evaluation metrics, our approach additionally prioritizes larger resolution input sizes. This characteristic of using larger image sizes enhances its practicality and usability as real-world documents are typically characterized by a higher word density. Moreover, in order to further advance the development of our model, we produced an original dataset and proceeded to train our model on this dataset, resulting in satisfactory outcomes.

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Intelligent Posture Detection System for Improved Ergonomics

Intelligent Posture Detection System for Improved Ergonomics

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Patil, Jay Joshi, Suvarna Mulla, Rahesha Yadav, Kushal Mit School of Computing Mit Adt University Department of Computer Science and Eng. Pune India University Department of Artificial Intelligence and Machine Learning Pune India Indian Institute of Technology Madras Department of Data Science India

ISBN: (纸本)9798331527495

This paper introduces a cost-effective smart posture detection system aimed at promoting ergonomic sitting habits and preventing musculoskeletal disorders. The system integrates ultrasonic sensors for backrest proximity measurement and load cells for seat weight distribution analysis. By analyzing these inputs, it identifies posture irregularities and provides real-time feedback for corrective action. Testing results demonstrate the system's reliability, achieving a detection accuracy of 99% across users with varying body types and postures. The proposed hybrid approach ensures simplicity, affordability, and scalability, making it a viable solution for homes, workplaces, and schools. This research contributes to the growing field of smart health monitoring systems, offering a practical alternative to traditional posture correction methods. © 2025 IEEE.

关键词： Ultrasonic testing

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RETHINKING THE POWER OF GRAPH CANONIZATION IN GRAPH REPRESENTATION LEARNING WITH STABILITY 12

RETHINKING THE POWER OF GRAPH CANONIZATION IN GRAPH REPRESEN...

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12th International Conference on Learning Representations, ICLR 2024

作者： Dong, Zehao Zhang, Muhan Payne, Philip R.O. Province, Michael A. Cruchaga, Carlos Zhao, Tianyu Li, Fuhai Chen, Yixin Department of Computer Science & Engineering Washington University in St. Louis United States Institute for Artificial Intelligence Peking University China Institute for Informatics Data Science and Biostatistics Washington University School of Medicine United States Department of Genetics Washington University School of Medicine United States Department of Psychiatry Washington University School of Medicine United States Department of Radiation Oncology Washington University School of Medicine United States Department of Pediatrics Washington University School of Medicine United States

The expressivity of Graph Neural Networks (GNNs) has been studied broadly in recent years to reveal the design principles for more powerful GNNs. Graph canonization is known as a typical approach to distinguish non-isomorphic graphs, yet rarely adopted when developing expressive GNNs. This paper proposes to maximize the expressivity of GNNs by graph canonization, then the power of such GNNs is studies from the perspective of model stability. A stable GNN will map similar graphs to close graph representations in the vectorial space, and the stability of GNNs is critical to generalize their performance to unseen graphs. We theoretically reveal the trade-off of expressivity and stability in graph-canonization-enhanced GNNs. Then we introduce a notion of universal graph canonization as the general solution to address the trade-off and characterize a widely applicable sufficient condition to solve the universal graph canonization. A comprehensive set of experiments demonstrates the effectiveness of the proposed method. In many popular graph benchmark datasets, graph canonization successfully enhances GNNs and provides highly competitive performance, indicating the capability and great potential of proposed method in general graph representation learning. In graph datasets where the sufficient condition holds, GNNs enhanced by universal graph canonization consistently outperform GNN baselines and successfully improve the SOTA performance up to 31%, providing the optimal solution to numerous challenging real-world graph analytical tasks like gene network representation learning in bioinformatics. Our source code is available at https://***/zehao-dong/RethinkGraphCanonical. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Graph neural networks

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