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Seamless Service Evolution: Enhancing Customer Satisfaction Using AWS-Driven AI Chatbots for Restaurant Ordering 4

Seamless Service Evolution: Enhancing Customer Satisfaction ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ali, Mohammed Jaffer Vyshnavi, M.R.V. Kumar, Konda V.S. Harshith Vishnubhatla, Sravya Rajagopal, Shinu M. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331540364

In the current restaurant business environment, factors have emerged in the manner in which consumers are attended to and the ways orders are placed. As good as these traditional systems may be, they do not offer the optimum flexibility and consumer convergence that today's client demands for fast and convenient services. Recent approaches using only basic form of automation and rule-based chatbots do offer some enhancements but lack adequate capabilities to deal with huge volumes of orders, custom recommendations and scalability according to demand. Further, the existing systems have problems with scalability, versatility to different customers' trends and needs, and real-time interactions. To overcome these challenges our work, will utilize modern advances in artificial intelligence and cloud technology. The proposed system uses Amazon Lex for NLU, A WS Lambda for processing multiple booking requests simultaneously without any intervention from the users and Amazon Polly for making interaction more interactive with personalized audio. In addition, Amazon Rekognition and A WS S3 for images will be used for analysis of images and proper storage of data respectively, enhancing the customer satisfaction by recommending products based on a past order. All these technologies integrated with A WS ecosystem in our solution will provide convenience, efficiency and passion to restaurant business proprietors to fulfill the increasing demands of modern customer. The system also supports linkage with some commonly used applications such as WhatsApp, guaranteeing that the customers receive timely updates. Thus, this solution is designed to create an improved status of restaurant ordering and eliminate the problematic experiences placed in current schemes. © 2024 IEEE.

关键词： Scalability

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A Graph Based Approach For Detecting Sybil Node In Online Social Networks 15

A Graph Based Approach For Detecting Sybil Node In Online So...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chandra, Sucheta Das, Priyanka Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350370249

Sybil attacks pose a significant threat to the security and integrity of online social networks by allowing malicious actors to create multiple fake identities to manipulate network behavior. The abstract outlines a method for detecting Sybil attacks in online social networks using graph analysis. It involves constructing a graph representation of the network, calculating node centrality measures such as in-degree and out-degree, and setting a degree threshold to identify potentially malicious nodes. Additional parameters like clustering coefficient and community structure are also considered to improve detection accuracy. The proposed approach aims to enhance the security of online social networks by identifying and isolating Sybil identities, thus preserving network integrity and trustworthiness. The authors' firmly assert that employing graph-based theory for Sybil node detection in online social networks stands out as one of the most effective methods. This approach not only offers robustness but also scalability, allowing for efficient detection even in large-scale networks. With its ability to unveil intricate relationships, anomalies, and patterns within the network, graph-based theory emerges as a cornerstone in the fight against Sybil attacks. © 2024 IEEE.

关键词： Cyber attacks

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Securely Transmit Data over Long Distances Using Quantum Key Distribution Based on E91 Protocol 1

Securely Transmit Data over Long Distances Using Quantum Key...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： RajKumar, V. Priyadharshini, G. Dept. of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350336009

At its core, quantum computing is a fastdeveloping technology that has the potential to process massive volumes of data at high speeds. Some factoring issues may be difficult for the classical computer to solve because of the nature of the factoring. Quantum computing data connections are carried out using optical fiber at a distance as short as possible. While using the E91 protocol for quantum key distribution in a wireless network communication channel, we may extend the distance over which keys are sent. How to design a wireless communication channel for use in quantum computing applications. Using this technology, we are now investigating the development of a broadcast channel that will enhance the data transmission range while maintaining high security and minimizing time consumption. In our technique, we employed polarization multiplexing to communicate across a broadcast channel satellite connection, which allowed us to share data between the transmitter and receiver at the same time. The transfer key is protected by the use of polarization multiplexing and the E91 protocol, both of which are implemented. In a security network, it is used to create an extended communication channel distance between two nodes. In the end, we can meet our goal in this study by comparing the existing method to the performance analysis shown in a graph. © 2023 IEEE.

关键词： Quantum cryptography

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Machine Learning-Powered Defense Against Phishing Websites

Machine Learning-Powered Defense Against Phishing Websites

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Dwivedi, Dipesh Ahmed, Sajjad Patel, Adarsh Azath, H. Vit University Dept. of Computer Science and Engineering Sehore466114 India

ISBN: (纸本)9798350348460

The proliferation of phishing URLs has experienced rapid growth in recent years, necessitating urgent attention to phishing attack detection in cybersecurity. In response, we introduce an improved predictive model that leverages both machine learning and a Deep Learning Model. Our dataset consists of 88,674 instances, encompassing 112 features, including the class level. Notably, this dataset comprises 58,000 legitimate instances and 30,647 phishing *** proposed method incorporates six distinct algorithms: Decision Tree, Support Vector Machine, K-nearest neighbors, Logistic Regression, Ensemble Classifier, and Neural Network algorithm. we systematically evaluated total 28 models performance with different types of feature changes. Following enhancements, every algorithm demonstrated an accuracy performance surpassing 93.16%. Among them, the Ensemble Classifier emerged as the most effective, boasting an accuracy 97.45%, MCC 94.37%, precision a96.35%, and an F1 Score 96.31%. © 2024 IEEE.

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Federated TD Learning in Heterogeneous Environments with Average Rewards: A Two-timescale Approach with Polyak-Ruppert Averaging 63

Federated TD Learning in Heterogeneous Environments with Ave...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Naskar, Ankur Thoppe, Gugan Koochakzadeh, Abbasali Gupta, Vijay Indian Institute of Science Dept. of Computer Science and Automation Bengaluru India Purdue University Dept. of Electrical and Computer Engineering IN United States

ISBN: (纸本)9798350316339

Federated Reinforcement Learning (FRL) provides a promising way to speedup training in reinforcement learning using multiple edge devices that can operate in parallel. Recently, it has been shown that even when these edge devices have access to different dynamic models, an optimal convergence rate that has a linear speedup proportional to the number of devices is achievable. However, this result requires that the stepsize in the algorithm be chosen in a manner dependent on the unknown model parameters. Also, it applies only to a discounted setting, which has been argued to fit episodic tasks better than continuing control tasks. In this paper, we obtain finite-time bounds for heterogeneous FRL with average rewards. We show that the optimal convergence rate with a linear speedup is possible even with a universal stepsize choice, independent of the underlying dynamics. To achieve our result, we modify the existing one-timescale FRL method to a novel two-timescale variant that additionally incorporates iterate averaging. © 2024 IEEE.

关键词： Federated learning

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Automated Explanation Quality Assessment based on Similarity Calculations of Representative Explanations of Different Qualities 7

Automated Explanation Quality Assessment based on Similarity...

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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Chou, Chih-Yueh Chou, Yu-Ting Huang, Yu-Cheng Yuan Ze University Dept. of Computer Science & Engineering Taoyuan Taiwan

ISBN: (纸本)9798350307207

Peer instruction is instructional in guiding students to learn by answering questions, and explaining and discussing their answers with peers. Researchers recommended asking students to write down their answers and explanations before discussion to prevent social loafing. In addition, text-based explanations can be recorded and analyzed. The quality of students' explanations varies, ranging from superficial and low-quality to detailed and in-dept. high-quality explanations. In tradition, the qualities of students' explanations were assessed by experts. Recently, machine learning classification models have been developed and applied to classify texts. However, the level of explanations of questions are question-dependent. Thus, each question needs its classification model. Therefore, a feature transformation was applied in this study so that the explanations of different questions could be combined and applied to train the same classification model. An automated explanation quality assessment mechanism was developed based on the similarity of representative explanations of different qualities. Students' text-based explanations were collected and assessed by experts into four levels, ranging from 0 (worst) to 3 (best). The four-level classifications were merged into binary classifications of low (0 and 1) and high (2 and 3). Different classification models, including Support Vector Machine (SVM), Naive Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Bidirectional Encoder Representations from Transformers (BERT) were applied to train models and evaluate the accuracy of the models. In addition, three ensemble learning algorithms, including voting, stacking, and boosting, were applied to combine models chosen from SVM, NB, KNN, LR, and RF. The results showed that RF and RF+KNN+NB with stacking model showed the best accuracy (75.3%) among all four-level classification models whereas RF with boosting model showed the best accuracy (9

关键词： Students

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Automated Classification of Chest CT Scans for Accurate Diagnosis and Risk Stratification: A Deep Learning Approach

Automated Classification of Chest CT Scans for Accurate Diag...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Gupta, Mridul Yadav, Devansh Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350388534

The performance assessment metrics of the topic of classification models, such as recall, accuracy, and F1-score were considered in this work. We examined how many types of events our model categorizes well and view the more detailed metrics for every class. A special account of the precision-recall trade-off even shows that there is an undeniable advantage, and there may be a negative side. The outcomes also show how good the model is. For example, a perfect accuracy score in the 2nd Class demonstrates that the algorithm can differentiate occurrences of such a category. The weighted average metrics consider the fundamental dissimilarity in a group better. The model has an average 0.91 F1-score, indicating that it makes a balanced decision between accuracy and recall. While I can be proud of these results, I understand that the presented weaknesses realized that additional study of potential feature engineering and other model architectures remain. This experiment will be valuable for future research and usage in real-life. © 2024 IEEE.

关键词： Contrastive Learning

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A Bengali Physics Chatbot Utilizing Sequence-to-Sequence Model with Beam Search

A Bengali Physics Chatbot Utilizing Sequence-to-Sequence Mod...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Hossain, Javed City University Dept. of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331529765

Chatbots, also known as talkbots or interactive agents, are software applications designed to facilitate communication between humans and machines. While most students in Bangladesh currently waste their valuable time engaging in casual conversations with each other, using a Bengali educational chatbot for such interactions would provide them with a more beneficial use of their time by providing hand note-wise answers for physics. The challenge faced by a Bengali chatbot lies in the necessity of creating a dataset in Bengali, given the scarcity of available Bengali datasets online. We have developed a physics-focused educational chatbot, akin to personalized hand notes, offering comprehensive answers to aid students in learning and revising complex physics concepts. The development of chatbots involves diverse techniques, including rule-based systems, machine learning-based approaches, and generative models. Our proposed system implements the seq2seq model with a beam search decoding algorithm, featuring BiLSTM layers for enhanced context understanding and efficient exploration of potential output sequences. This approach outperforms traditional seq2seq models, ensuring higher accuracy and delivering high-quality responses. Finally, we obtained the highest BLEU1, BLEU2, BLEU3, and BLEU4 scores, respectively, of 0.52, 0.41, 0.29, and 0.23. © 2024 IEEE.

关键词： Chatbots

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Preliminary Design of Hybrid Simulation Framework for Workload Analysis in Automotive Edge Computing 15

Preliminary Design of Hybrid Simulation Framework for Worklo...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kwon, JiHyun Park, SangEun Chwa, Hoon Sung Kwak, Jeongho Choi, Ji-Woong Kim, BaekGyu Dgist Dept. of Electrical Engineering and Computer Science Daegu Korea Republic of

ISBN: (纸本)9798350364637

Many vehicular tasks shall be completed in a timely manner as they move. In automotive edge computing, a vehicle can offload such tasks to an edge server in proximity to process them faster than using the cloud computing model. However, such an edge server has limited communication coverage and relatively smaller computing power compared to the general cloud server, it is important to distribute the offloaded workloads appropriately. One needs to understand such workload patterns and quantitatively analyze them for such workload distribution. We propose a preliminary hybrid simulation framework that generates compute workloads via a combination of the virtual vehicular traffic and the physical edge-server platforms. © 2024 IEEE.

关键词： Computation offloading

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Implementation of Pruned and Quantized Semantic Segmentation Neural Network Using Cambridge-Driving Labeled Video Database (CamVid) Dataset 2

Implementation of Pruned and Quantized Semantic Segmentation...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Upadhyay, Deepak Malhotra, Sonal Gupta, Mridul Mishra, Somesh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372847

In This research investigates the streamlined implementation of Semantic Segmentation Neural Networks through advanced techniques: pruning and quantization. Leveraging the CamVid dataset, our study achieved remarkable reductions in computational complexity and memory usage. Pruning removed redundant connections, effectively reducing learnable parameters, while quantization significantly minimized memory footprint. Despite these optimizations, the networks maintained high semantic segmentation accuracy. The implications of our findings are pivotal, particularly in resource-constrained applications like autonomous driving and image analysis. By enhancing efficiency without compromising accuracy, this research facilitates the seamless integration of deep learning models into real-time systems. Our study not only advances the field of computer vision but also underscores the practical feasibility of deploying sophisticated neural networks in practical, resource-efficient contexts. © 2024 IEEE.

关键词： Semantic Segmentation

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