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

Seamless Service Evolution: Enhancing Customer Satisfaction ...

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Sustainable Expert Systems (ICSES), International Conference on

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

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

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.

关键词： Scalability Customer satisfaction Chatbots Market research Real-time systems Image storage Artificial intelligence Internet telephony Optimization Business

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Medlet-Block: Secured Health-Based Cloudlet Platform Using Blockchain

Medlet-Block: Secured Health-Based Cloudlet Platform Using B...

引用

International Communication Systems and Networks and Workshops, COMSNETS

作者： Atrayee Gupta Varun Agarwal Dept. of Computer Science and Engineering Techno Main Saltlake Kolkata India

Medical information cannot be easily shared and exchanged between various healthcare systems and providers due to a lack of interoperability, delays, inefficiencies, and above all lack of security. This paper provides a design of an edge-based blockchain for a secured health-care system which takes care of confidentiality, integrity, non-repudiation and other security challenges.

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Securing Layers: The Synergy of Mix Networks and Shamir's Secret Sharing in Onion Routing

Securing Layers: The Synergy of Mix Networks and Shamir's Se...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Aniketh Vasudev Jambha Dutta Swetchana Karru Sushrutha Shinu M. Rajagopal Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

In the contemporary digital landscape, where online privacy and security face escalating challenges due to evolving cyber threats, this investigation uncovers vulnerabilities within traditional security measures. The study presents a pioneering integrated cryptographic approach leveraging Mix Networks, Shamir's Secret Sharing, and Onion Routing, reinforcing the framework supporting secure digital communication. By seamlessly blending attributes such as anonymity, data dispersion, and layered encryption, these methods signify a monumental shift in securing online interactions. Overcoming inherent limitations and adept.y counters threats like single points of compromise and amplifies the anonymity in online communication, while enhancing resilience and offering adaptable solutions for diverse communication scenarios. Through meticulous technical analyses and empirical investigations, the study substantiates the pivotal role of the integrated cryptographic approach in reinforcing the fabric of secure digital communication. Validation of the system's functionality and precision through successful implementations of secure node-to-node connections lays the groundwork for a proactive defense against the spectrum of cyber threats.

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

Preliminary Design of Hybrid Simulation Framework for Worklo...

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International Conference on Information and Communication Technology Convergence (ICTC)

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

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

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.

关键词： Geometry Cloud computing Roads Hybrid power systems Hardware Information and communication technology Servers Edge computing Automotive engineering Convergence

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Introduction to Quantum Systems and Security Vulnerabilities

Introduction to Quantum Systems and Security Vulnerabilities

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Ignacio Astaburuaga Shamik Sengupta Dept. of Computer Science & Engineering University of Nevada Reno Nevada USA

The objective of this paper is to propose and establish a new area of research and increase awareness of key cybersecurity aspects relating to the manufacturing, creation, development, and maintenance of quantum systems and identify key challenges in this domain. It aims to present areas of focus of cybersecurity in the quantum field. Through research, we have established a new emerging gap in the security of quantum systems. This paper introduces the basics of quantum systems, circuits, and computers. Then, it continues to introduce the current state of quantum technologies. Finally, it introduces cybersecurity and quantum and establishes new avenues of attacks for such systems.

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Machine Learning based Malware Detection in Android: A Practical Approach

Machine Learning based Malware Detection in Android: A Pract...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Toshi Tyagi Madhu Gautam Dept. of Computer Science and Engineering KIET Group of Institutions Ghaziabad India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Today's society heavily depends on online communities, which host a wide variety of social networks and public and private services. These demands led to the creation of Android, the world's most popular mobile operating system. The wide range of features and capabilities offered by freely installable third-party apps is largely responsible for this success. While focusing on the positive aspects of technology use, it is vital to keep in mind the potential negative consequences like privacy breaches, data theft, and malware infections that could compromise personal information and device security, especially if negligence or overuse of technology is present. This article discusses a systematic review of ML-based Android malware detection techniques. It provides a critical analysis of a number of carefully selected articles, highlighting their methods and conclusions. To support this study, we also present our own analysis and findings, for which an evaluation of diverse ML models with 123,453 applications and 5,560 malware samples from the DREBIN dataset was done to classify apps as malicious and benign. Preprocessing was done on the dataset to address missing values and perform label encoding. To cope with the issue of class imbalance, we used SMOTE an efficient ML technique that handles imbalance issues efficiently. We have applied five different ML algorithms, The results show that among all the techniques, Decision Tree (DT), Support Vector Machines (SVM) with various kernels (linear, polynomial, RBF), Sequential Neural Network (NN), K-Nearest Neighbours (KNN), and Logistic Regression (LR), the sequential neural network model outperformed other methods with 99% accuracy. Additionally, the process identifies areas for future research and improvement in ML-based Android malware detection techniques.

关键词： Support vector machines Analytical models Technological innovation Accuracy Social networking (online) Artificial neural networks Feature extraction Malware Robustness Security

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“Liver Disease Risk Prediction Using Regularized Deep Learning: A Novel Approach”

“Liver Disease Risk Prediction Using Regularized Deep Learn...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), International Conference on

作者： Parvez Rahi Sandeep Singh Kang Ajay Pal Singh Inderjeet Singh Dept. of Computer Science & Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

Liver diseases are common, thereby posing a significant quality-of-life impairment. They have required accurate and prompt assessments to be managed. Typically, conventional methods of evaluation often include subjectivity and laborious amounts of work. It introduces a DNN-based model with L2 regularization to predict risk categorization of liver diseases based on five categories of categorizations: Healthy_Liver, Low_Risk, Moderate_Risk, High_Risk, and Severe_Liver_Disease. The model includes the hidden layers with rectified linear unit (ReLU) activation functions, batch normalization, and dropout for regularizing to achieve robust learning. The DNN was trained in a very comprehensive Kaggle dataset with many physiological and biochemical features and has achieved an impressive 94.07% classification accuracy. Healthcare professionals will be equipped with a significant resource to develop personalized treatment strategies facilitated by this investigation, which highlights the capabilities of deep learning methodologies in the automation of liver disease assessment. Subsequent research will focus on integrating additional data sources to improve the model’s scalability and predictive precision, thus promoting the advancement of automated liver disease evaluation in clinical settings.

关键词： Green energy Accuracy Liver diseases Computational modeling Soft sensors Scalability Medical services Predictive models data models Physiology

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Image Recognition-Based Indoor Positioning System Using Federated Learning

Image Recognition-Based Indoor Positioning System Using Fede...

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IEEE Conference on Vehicular Technology (VTC)

作者： Heng-Shao Yu Yu-Chiao Jhuang Jenq-Shiou Leu Dept. of Electronic and Computer Engineering University of Science and Technology Taipei Taiwan

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

With the advancement of artificial intelligence (AI), indoor positioning systems have become increasingly important for various applications, leading to the development of diverse indoor positioning methods. Among various approaches, image-based indoor positioning methods have demonstrated relatively good positioning accuracy. However, maintaining user privacy continues to be a significant challenge. To tackle this issue, we propose a Federated Learning (FL) approach and develop an FL-based indoor image recognition positioning system. We conducted experiments to validate our method using two real-world datasets and compare the results to a Non-FL approach. Furthermore, we evaluated the accuracy of our method across various communication rounds, the number of client devices, and the amount of data per client device. Our experimental results indicate that the proposed method effectively preserves client privacy while achieving accuracy similar to the Non-FL approach. By employing FedAvg and FedOpt algorithms with the MobileNet model, our system ultimately reaches 94% accuracy.

关键词： Training Privacy Vehicular and wireless technologies Image recognition Accuracy Federated learning data models Servers Artificial intelligence Indoor positioning systems

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Efficient Road Status Monitoring in Vehicular Ad-Hoc Networks: An Optimization Framework 25

Efficient Road Status Monitoring in Vehicular Ad-Hoc Network...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Samara, Ghassan Turki, Hussain Mohammed Odeh, Mahmoud Al-Oraiqat, Anas Aljaidi, Mohammad Alazaidah, Raed Al-Mousa, Mohammad Rasmi Banimustafa, Ahmed Kanan, Mohammad Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Zarqa Jordan Higher Colleges of Technology Computer Information Science Dept. Abu Dhabi United Arab Emirates University of Business and Technology Department of Industrial Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The number of cars on the road in the world is currently increasing. With the widespread usage of vehicles, a slew of issues inevitably arises. The damage of the road can be annoying to the drivers, and congestion in traffic can waste both fuel and time. It's worth noting that one of the primary causes of car damage and aging is the state of the road surface. Driving comfort and vehicle controllability can be harmed by poor road conditions. As a result, the system of monitoring road status is crucial to reducing congestion in traffic, reducing accident rates, and protecting cars from hazardous road conditions. This paper proposes using a framework based on cloud-fog network architecture, The fog computing network architecture is a dispersed network that may process data at the fog layer to minimize cloud computing's computational load. In addition, using small and large threshold values ( ), Main Authority (MA) deals with the major conditions, and the Sub Authority (SA) deals with the minor conditions. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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A Random Forest Framework for Predicting Cardiovascular Disease in Diverse Populations“

A Random Forest Framework for Predicting Cardiovascular Dise...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Parvez Rahi Sandeep Singh Kang Ajay Pal Singh Inderjeet Singh Dept. of Computer Science & Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

According to the latest information from WHO, the World Health Organization, heart disease remains a major global health challenge, resulting in millions of deaths each year. Heart disease presents in various forms, including Hypertensive Heart Disease, Congestive Heart Failure (CHF), Coronary Artery Disease, Cardiomyopathy, and Arrhythmia. Accurate classification of these conditions is crucial for reducing mortality rates and improving patient outcomes. This study utilizes an enhanced Random Forest classifier to improve the accuracy of heart disease classification. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to rectify the disparities in class. and Polynomial Feature engineering was employed to capture complex interactions within the data. The dataset underwent comprehensive preprocessing, including feature normalization and label encoding, followed by extensive hyperparameter tuning using GridSearchCV. The optimized model achieved a high accuracy of 92% and a Mean Squared Error (MSE) of 0.05. Key performance indicators, including an F1 score of 0.87, recall of 0.88, and accuracy of 0.92, highlight the model's efficacy in differentiating between various forms of heart disease. Future research could explore incorporating additional clinical data to further enhance the model's accuracy and applicability across diverse patient populations.

关键词： Heart Accuracy Key performance indicator Mortality Organizations Predictive models Polynomials Cardiovascular diseases Tuning Random forests

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