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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Ray Titus Tina Babu Rekha R Nair Rajesh Sharma R Chinnaiyan R Akey Sungheetha Alliance University Bengaluru India Department of Computer Science and Engineering Alliance School of Engineering and Design Alliance University Bengaluru India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

This article investigates customer behaviour towards meal delivery apps, focusing on factors such as restaurant diversity, food packaging quality, application design, and user interface. According to the study, having a varied choice of eateries increases consumer happiness, which leads to more app usage. However, the quality of food packaging has no meaningful impact on overall satisfaction. Fortunately the study emphasises the importance of application design and user interface in influencing consumer behaviour. Both factors have a substantial impact on overall happiness, with user-friendly interfaces attracting new users and encouraging frequent app usage. The findings highlight the necessity of organisations addressing these characteristics in order to improve customer satisfaction, increase app engagement, and create long-term client loyalty. Understanding and adapting to client preferences in these areas can help food delivery applications compete successfully.

关键词： Consumer behavior Statistical analysis Customer satisfaction Focusing User interfaces Food packaging

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Block chain-Based on Real Estate market

Block chain-Based on Real Estate market

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： R Radha Soumi Dhar R Tamilarasi Department of Computer Science Alliance School of Engineering and Design Alliance University Bengaluru

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Global investments in real estate (RE) surpass those in the stock market by more than double. Despite this, there are far less investors in real estate due to its global accessibility and liquidity. Owners, investors, and tenants are hardly happy with the current setup. The purpose of this paper is to demonstrate the benefits that block chain. Technology can offer the real estate industry while also testing its application in this space. To date, investigations have yielded the following findings: Conclusively, block chain technology and smart contracts offer solutions to the conventional challenges encountered by renewable energy, proving to be significantly more advantageous. They serve as effective tools for establishing a game-theoretic, stable-priced market.

关键词： Industries Renewable energy sources Automation Smart contracts Blockchains Security Stock markets

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An Evaluation of Machine Learning Techniques for Detecting Banking Frauds

An Evaluation of Machine Learning Techniques for Detecting B...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： R Rajagopal M Senbagavalli Saswati Debnath Deepu K Darshan K K S Varun Tejas Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India

Credit cards are an essential part of every person's daily life in the modern world. Customers use their credit cards and online transactions to make purchases for their requirements. To reduce the risk of defaulters, banks and other financial institutions consider customer credit applications. Credit risk is the increase in debt owed by a customer who repeatedly misses payments on their bills. The precision and recall measures received greater attention than the metrics for accuracy. Based on the False Negative value of the confusion metrics, logistic regression is the best model after comparison with the precision-recall curve. Additionally, a GUI (Graphical User Interface) was built after altering the logistic regression's threshold value and it successfully forecasted whether a customer was a defaulter or not. It is a problem to categorize clients' credit payment defaulters and non-defaulters. The basic objective of risk prediction is to evaluate each customer's credit risk and reduce it using information from financial statements, customer transactions, and repayment history. The damage and unpredictable nature. Different methods, including logistic regression and decision trees, are used to create risk prediction models.

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Single-input single-output multi-touch soft sensor systems using band-pass filters

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npj Flexible Electronics 2022年第1期6卷 681-690页

作者： Junhyung Kim Suhan Kim Yong-Lae Park Department of Mechanical Engineering Institute of Advanced Machines and DesignInstitute of Engineering ResearchSeoul National UniversitySeoul 08826Republic of Korea Department of Electrical Engineering and Computer Science Research Laboratory of ElectronicsMassachusetts Institute of Technology77 Massachusetts AvenueCambridgeMA 02139USA

In many soft sensor systems,external wires for connecting the sensors to control circuits have posed practical problems,in terms of compact form factors and physical *** study proposes a design of soft sensor arrays that can operate with a drastically reduced number of wires without degrading the original *** proposed concept is an array of soft sensing modules,each of which consists of an inductor and a capacitor embedded next to the resistive sensing module,constructing a resistorinductor-capacitor band-pass *** adjusting the values of the capacitance and the inductance,unique frequency band is assigned to each sensing module,enabling the distinctive detection using only two external wires regardless of the number of *** multi-touch sensing functionality of the system is analytically modeled and experimentally *** advantage of the design,a fingertip tactile sensor and an insole foot pressure sensor are demonstrated as practical applications.

关键词： connecting constructing touch

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Machine learning with Monarch Butterfly Optimization for Prediction of Emergency Patient Admission Status 5

Machine learning with Monarch Butterfly Optimization for Pre...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Vijayarangam, S. Vasundhara, S. Beherac, Nihar Ranjan Das, Shyamali Chandre, Shanker Rajagopal, R. Sri Indu College of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India G.Narayanamma Institute of Technology and Science Department of Humanities and Mathematics Telangana Hyderabad India Swiss School of Business and Management Geneva Petit-Lancy1213 Switzerland CMR Institute of Technology AECS Layout Department of Artificial Intelligence and Machine Learning Karnataka Bangalore India SR University Department of Computer Science & Artificial Intelligence Telangana Warangal India Alliance College of Engineering & Design Department of Computer Science & Engineering Alliance University Karnataka Bengaluru India

ISBN: (纸本)9781665493604

Emergency admission is one of the most important resources of healthcare expenditure. The accessibility of predictive methods is support to recognize the admission status of arriving patients and the patient mix that supports managing downstream resources and decreasing overpopulation at emergency department (ED) by restriction boarding delays. In recent times, machine learning (ML) techniques are well enhanced and executed in several applications like health protection, cancer detection, and hospital operations. But, most difficult problem with ML is that each technique has parameter and without optimizing the parameter, attaining a higher accuracy method develops very complex. This study introduces Machine learning with Monarch Butterfly Optimization for Prediction of Emergency Patient Admission Status (MLMBO-PEP A) technique. The presented MLMBO-PEP A technique can predict the admission status of the emergency patient. In the presented MLMBO-PEP A technique, data normalization is initially performed to scale the medical data. For predictive process, the MLMBO-PEP A technique uses Dendritic Neuron Model (DRN). The MBO technique is used to optimize the forecasting outcomes of the DRN model. A widespread simulation study is performed for ensuring the better performance of the MLMBO-PEP A technique. The resultant values implied the improved efficacy of the MLMBO-PEP A technique under different evaluation metrics. © 2023 IEEE.

关键词： Forecasting

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Investigation of Task Scheduling in Cloud Computing by using Imperialist Competitive and Crow Search Algorithms

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Procedia computer science 2023年 230卷 879-889页

作者： Jayavadivel Ravi Rajkumar N Viji C Loganathan D K S N Sushma Stalin M Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance University Bangalore Karnataka India Department of Information Science & Engineering Cambridge Institute of Technology Bangalore Karnataka India Research Scholar School of Computer Science and Engineering REVA University Bangalore Karnataka India

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner.... 详细信息

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner. The main function of the data processing server is to show how many users are being investigated and provide accurate, efficient and efficient information. Important Algorithm Editing Players in the Cloud Defines the virtual machine (VM) required for this purpose. The role of editing the algorithm reduces the effect of the schedule. Naturally affected algorithms have recently been used to quickly comply with the recent traditional algorithms. Given many consumers of many cloud computing services, many researchers may have a serious explanation that many researchers take and discuss the complex themes of NP. Some sites use imperialist algorithms (ICA) and birds. The purpose of the proposed project is to develop intelligent scientific algorithms that focus on the integration of ICA and CSA to obtain data. CSA is concentrated on the corner of food habits. The crow is looking for his friends to get enough food for today's food. This will help the CSA find suitable VMs for these machines and complete the equipment. Cloud Sim is used to calculate CSA output with minmin and ant algorithms. The simulation results show that the CSA is extra powerful than the MinMin and Ant procedures .

关键词： Crow search algorithm Imperialist competitive algorithm. Task scheduling Nature-inspired and Cloud computing

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Towards Understanding and Applying Security Assurance Cases for Automotive Systems

arXiv

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arXiv 2024年

作者： Mohamad, Mazen Department of Computer Science & Engineering Division of Interaction Design and Software Engineering Chalmers University of Technology Gothenburg University Gothenburg Sweden

Security Assurance Cases (SAC) are structured bodies of arguments and evidence used to reason about security properties of a certain artefact. SAC are gaining focus in the automotive domain as the need for security assurance is growing due to software becoming a main part of vehicles. Market demands for new services and products in the domain require connectivity, and hence, raise security concerns. Regulators and standardisation bodies started recently to require a structured for security assurance of products in the automotive domain, and automotive companies started, hence, to study ways to create and maintain these cases, as well as adopting them in their current way of working. In order to facilitate the adoption of SAC in the automotive domain, we created CASCADE, an approach for creating SAC which have integrated quality assurance and are compliant with the requirements of ISO/SAE-21434, the upcoming cybersecurity standard for automotive systems. CASCADE was created by conducting design science research study in two iterative cycles. The design decisions of CASCADE are based on insights from a qualitative research study which includes a workshop, a survey, and one-to-one interviews, done in collaboration with our industrial partners about the needs and drivers of work in SAC in industry, and a systematic literature review in which we identified gaps between the industrial needs and the state of the art. The evaluation of CASCADE was done with help of security experts from a large automotive OEM. It showed that CASCADE is suitable for integration in industrial product development processes. Additionally, our results show that the elements of CASCADE align well with respect to the way of working at the company, and has the potential to scale to cover the requirements and needs of the company with its large organization and complex products Copyright © 2024, The Authors. All rights reserved.

关键词： Automotive industry

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Different Scenarios of File Sharing

Different Scenarios of File Sharing

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Satyam Bhalerao Shravani S Vishal Halakude Rajani Sajjan Department of Computer Science and Engineering MIT Art Design and Technology Pune India

In the digital era, escalating data volumes necessitate secure and scalable file-sharing solutions. This research develops a private cloud architecture to address this need. It provides scalability through distributed servers, accommodating numerous users and data needs. Robust access controls, including authentication and encryption, ensure data security. The architecture supports diverse network configurations, enabling seamless file sharing. Synchronization and versioning facilitate collaboration, ensuring data integrity. In summary, our private cloud architecture offers secure, scalable, and efficient file sharing for organizations across various network scenarios.

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Comparative Analysis of the Use of Variational Autoencoders and Generative Adversarial Networks for Anomalous Image Detection Tasks 7

Comparative Analysis of the Use of Variational Autoencoders ...

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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Lytvynenko, Volodtmyr Olszewski, Serge Osypenko, Volodymyr Lurie, Iryna Demchenko, Violeta Dontsova, Dariia Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine Taras Shevchenko National University Department of Radio Engineering and Electronic Systems Kyiv Ukraine Kyiv National University of Technology and Design Department of Computer Science Kyiv Ukraine State Institution Kundiiev Institute of Occupational Health National Academy of Medical Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798331534141

This paper presents a comparative analysis of the application of Variational Autoencoders (VAE) and Wasserstein Generative Adversarial Networks (WGAN) for detecting anomalous images. The performance evaluation of the models is conducted using metrics such as AUC, Precision, Recall, and F1-Score. The results indicate that VAEs provide high Recall in anomaly detection but with low Precision, whereas WGANs demonstrate more balanced results with fewer false positives. Recommendations are proposed for selecting an appropriate model depending on the specific task requirements and the need to minimize false alarms or achieve high detection completeness. © 2024 IEEE.

关键词： Generative adversarial networks

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ROSS:RObust decentralized Stochastic learning based on Shapley values

arXiv

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arXiv 2024年

作者： Wang, Lina Yuan, Yunsheng Li, Feng Duan, Lingjie School of Computer Science and Technology Shandong University Qingdao266237 China The Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore

In the paradigm of decentralized learning, a group of agents collaborate to learn a global model using a distributed dataset without a central server;nevertheless, it is severely challenged by the heterogeneity of the data distribution across the agents. For example, the data may be distributed non-independently and identically, and even be noised or poisoned. To address these data challenges, we propose ROSS, a novel robust decentralized stochastic learning algorithm based on Shapley values, in this paper. Specifically, in each round, each agent aggregates the cross-gradient information from its neighbors (i.e., the derivatives of its local model with respect to its neighbors' datasets) to update its local model in a momentum-like manner, while we innovate in weighting the derivatives according to their contributions measured by Shapley values. We perform solid theoretical analysis to reveal the linear convergence speedup of our ROSS algorithm. We also verify the efficacy of our algorithm through extensive experiments on public datasets. Our results demonstrate that, in face of the above variety of data challenges, our ROSS algorithm have oblivious advantages over existing state-of-the-art proposals in terms of both convergence and prediction accuracy. © 2024, CC BY-NC-ND.

关键词： Contrastive Learning

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