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International Journal of Interactive Mobile Technologies 2023年第16期17卷 116-128页

作者： Wildan, Muhammad Alkirom Widyaningrum, Mahmudah Enny Padmapriya, T. Sah, Basant Pani, Niroj Kumar Department of Management Faculty of Economics and Business University of Trunojoyo Madura Jawa Timur Bangkalan Indonesia Faculty of Economics and Business Universitas Bhayangkara Surabaya Surabaya Indonesia Melange Publications Puducherry India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Science Engineering and Applications Indira Gandhi Institute of Technology Odisha Sarang India

As more and more mobile devices rely on cloud services since the introduction of cloud computing, data privacy has emerged as one of the most pressing security concerns. Users typically encrypt their important data before uploading it to cloud servers to safeguard data privacy, which makes data usage challenging. On the other side, this also increases the possibility of brand-new issues in cities. A clever, effective and efficient urban monitoring system is required to address possible challenges that may arise in urban settings. In the smart city concept, which makes use of sensors, one strategy that might be used in IoT and cloud computing is to monitor and gather data on problems that develop in cities in real-time. However, it will take a while and be rather expensive to install IoT and sensors throughout the city. The Mobile Crowd-Sensing (MCS) method is proposed to be used in this study to retrieve and gather data on issues that arise in metropolitan areas from citizen reports made using mobile devices. And we suggest a budget-constrained, reputation-based collaborative user recruitment (RCUR) procedure for a MCS system. To construct an edge-assisted MCS system in urban situations, we first integrate edge computing into MCS. We also examine how user reputation affects user recruitment. Finally, we create a collaborative sensing approach using the edge nodes’ sensing capabilities. © 2023 by the authors of this article. Published under CC-BY.

关键词： Data privacy

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To infinity and beyond: A general framework for scaling economic theories

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Theoretical economics 2025年第2期20卷 511-542页

作者： Yannai A. Gonczarowski Scott Duke Kominers Ran I. Shorrer Department of Economics Department of Computer Science Harvard University Entrepreneurial Management Unit Harvard Business School Department of Economics Center of Mathematical Sciences and Applications Harvard University a16z crypto Department of Economics Penn State University

Many economic models incorporate finiteness assumptions that, while introduced for simplicity, play a real role in the analysis. We provide a principled framework for scaling results from such models by removing these finiteness assumptions. Our sufficient conditions are on the theorem statement only, and not on its proof. This results in short proofs, and even allows us to use the same argument to scale similar theorems that were proven using distinctly different tools. We demonstrate the versatility of our approach via an array of examples from revealed-preference theory.

关键词： Revealed preferences infinite models C02 D00

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A NEW APPROACH ON CONDUCTING FLEXIBILITY STUDIES BASED ON NEURAL NETWORKS - APPLICATION ON THE HELLENIC TRANSMISSION SYSTEM 14

A NEW APPROACH ON CONDUCTING FLEXIBILITY STUDIES BASED ON NE...

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

作者： Kaskouras, Christos D. Krommydas, Konstantinos F. Baltas, Ioannis Papaioannou, George P. Papayiannis, Georgios I. Yannacopoulos, Athanasios N. Department of Statistics Athens University of Economics and Business Athens Greece Stochastic Modelling and Applications Laboratory Athens University of Economics and Business Athens Greece Research Technology and Development Department Independent Power Transmission Operator S.A. Athens Greece Department of Electrical and Computer Engineering University of Patras Rion Greece Department of Financial and Management Engineering University of Aegean Chios Greece Center for Research and Applications of Nonlinear Systems CRANS University of Patras Rion Greece Department of Statistics and Insurance Science University of Piraeus Piraeus Greece

ISBN: (纸本)9781837242689

In order to meet the environmental targets, a large scale integration of renewable energy sources (RESs) has been taking place in power systems. Although those sources contribute to more clean energy and help to reduce the green house gasses emissions, they pose significant challenges to the system's stability due to the intermittency and the high level of dependence to the weather conditions. One of the main challenges is to sustain the system's balance and to guarantee the flexibility adequacy. For this purpose, the need for more comprehensive flexibility studies will increase in the future, since different scenarios should be examined according to various level of RESs, demand and weather conditions. In this work, we propose a novel approach for the unit commitment (UC) algorithm that is implemented in the conventional flexibility studies that aims to reduce the computational time and simultaneously sustain the results' accuracy in a satisfactory level. For this scope, we designed a nested-neural network system of two recurrent neural networks with long short-term memory, which are integrated and aim to reproduce the results of the complex conventional UC algorithm. Results show that the predicted insufficient ramping resource expectation of the two solutions are similar, while the computational time is being reduced by almost 99%. © The Institution of Engineering & Technology 2024.

关键词： FLEXIBILITY RECURRENT NEURAL NETWORKS TRANSMISSION SYSTEM

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AI-Powered Business Intelligence for Smarter Decision-Making and Growth

AI-Powered Business Intelligence for Smarter Decision-Making...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Alijoyo, Franciskus Antonius Pradhan, Rahul Vats, Satvik Rani, V.Kanpur Kholmukhamedov, Timur Karthik, M. School of Business and Information Technology STMIK LIKMI Center for Risk Management and Sustainability Indonesia Bandung Indonesia GLA University Department of Computer Engineering and Applications Mathura India Graphic Era Hill University Department of CSE Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India Hindustan Institute of Technology and Science Department of EEE Chennai India Turin Polytechnic University in Tashkent Management Economics and Humanitarian Disciplines Department Tashkent Uzbekistan K.Ramakrishnan College of Engineering Department of AI & DS Trichy India

ISBN: (纸本)9798331517953

Business intelligence (BI) has become a cornerstone for data-driven decision-making in modern enterprises. With the increasing complexity of data, traditional methods often fall short in providing actionable insights. Machine learning techniques, such as Gradient Boosting Machines (GBMs), offer advanced predictive capabilities that can significantly enhance BI outcomes. This paper explores the application of Gradient Boosting Machines (GBMs) in enhancing BI for better decision-making and operational efficiency. GBMs, a powerful ensemble learning technique, are utilized to predict customer churn based on a comprehensive retail dataset encompassing customer demographics, purchase history, engagement scores, and monetary values. The Proposed Method is implemented using Python Software and demonstrates that GBMs achieve exceptional predictive accuracy of 99.2%, significantly outperforming traditional methods like Decision Tree, SVM, BDMS. Feature importance analysis reveals that 'Recency' (days since last purchase) is the most influential predictor of churn, followed by 'Engagement Score' and 'Monetary Value'. The findings emphasize the model's capability to enhance customer retention strategies, optimize resource allocation, and improve overall business outcomes. Utilizing GBMs, high-risk customers (top 10% predicted probabilities of churn) were targeted with a specialized retention campaign, which led to a 15% reduction in churn rates among this group. The effectiveness of the campaign validates the model's predictions and highlights the practical benefits of integrating GBMs into BI systems. This research underscores the transformative potential of AI-driven techniques in business intelligence, offering actionable insights for businesses seeking to leverage predictive analytics for competitive advantage. © 2024 IEEE.

关键词： Predictive analytics

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Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

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computers, Materials & Continua 2022年第7期72卷 1157-1172页

作者： Ashit Kumar Dutta Jenyfal Sampson Sultan Ahmad T.Avudaiappan Kanagaraj Narayanasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Department of Computer Applications J.J College of Arts and Science(Autonomous)Affiliated to Bharathidasan UniversityPudukkottai622422India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Intelligent Transportation Systems(ITS)have become a vital part in improving human lives and modern *** aims at enhancing road safety and environmental *** is a tremendous increase observed in the number of vehicles in recent years,owing to increasing *** vehicle has its own individual emission rate;however,the issue arises when the emission rate crosses a standard *** to the technological advances made in Artificial Intelligence(AI)techniques,it is easy to leverage it to develop prediction approaches so as to monitor and control air *** current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring(OSSOHDLAPM)in ITS *** proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of *** addition,hybridized Convolution Neural Network with Long Short-Term Memory(HCNN-LSTM)model is used to predict pollutant level based on the data attained earlier by the *** HCNN-LSTM model,the hyperparameters are selected and optimized using OSSO *** order to validate the performance of the proposed OSSO-HDLAPM technique,a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

关键词： Deep learning air pollution environment monitoring internet of things intelligent transportation systems oppositional learning LSTM model

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The Impact of Perceived Ease of Use on Intention to Use Mobile Payment Services for Data Security applications 2

The Impact of Perceived Ease of Use on Intention to Use Mobi...

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2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021

作者： Andavara, Venkatesh Sundaram, Barani Bacha, DIdha Dadi, Tolossa Karthika, P. College of Business and Economics Salale University Department of Management Fitche Ethiopia Kalasalingam Academy of Research and Education Research Department of Computer Applications Krishnankoil India

ISBN: (纸本)9781665428675

In emerging countries such as Ethiopia, the use of such mobile phones for the provision of banking services is becoming highly significant, especially for social groups that do not have easy access to public services. There seem to be a lot of factors that affect user individuals' mobile banking services, one of which is possible ways use and, which has piqued the interest of several researchers. As a result, the research goal of this paper is to investigate the positive and negative effects of facilitating conditions on users' intention to adopt mobile facilities. A questionnaire was developed to gather data from 406 effective M-BIRR and Hello Cash MPS users for the study. The information gathered meets the requirements for SEM. The researchers discovered that perceived usefulness (PU) and perceived ease of use (PEOU) both have a significant direct influence on the intention to use (ITU) mobile payment systems (MPS). The study results also show which PEOU does have a significant indirect effect on MPS intention via the mediating factor, PU. © 2021 IEEE.

关键词： Electronic money

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Optimal Confidential Mechanisms in Smart City Healthcare

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computers, Materials & Continua 2022年第3期70卷 4883-4896页

作者： R.Gopi P.Muthusamy P.Suresh C.G.Gabriel Santhosh Kumar Irina V.Pustokhina Denis A.Pustokhin K.Shankar Department of Computing Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India School of Computing Science and Engineering Galgotias UniversityNCRDelhi203201India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely *** system also concedes the freedom of a *** is a part of this system and it helps in providing care to the *** healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate *** methodologies exhibit few shortcomings in terms of computational complexity,cost and data ***,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT *** procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam *** key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box *** study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the *** methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing *** service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare *** proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access *** model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum ***,the level of the model and throughput were analyzed by maximum value i.e.,50Mbp

关键词： Smart city healthcare security block cipher LWC

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Assessing AI adoption for enhancing healthcare supply chain resilience: A novel hybrid interval-valued q-rung orthopair fuzzy MCDM

Assessing AI adoption for enhancing healthcare supply chain ...

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Digital Health (ICDH), IEEE International Conference on

作者： Huzhi Xue Haihua Xie Carl K. Chang School of Economics and Management Beihang University (BUAA) Beijing China Beijing Institute of Mathematical Sciences and Applications Beijing China Department of Computer Science Iowa State University Ames Iowa USA

ISBN: (数字)9798350368574

ISBN: (纸本)9798350368581

With ongoing market competitions, advancements in technology, and diverse products and services, supply chains (SC) have become increasingly sophisticated and complicated. The complexity of SC makes the ability to withstand the external uncertain changes critically important, which is known as SC resilience. Healthcare supply chains (HSC) are a critical subset facing similar or even more severe challenges. HSC resilience is the ability of the system to quickly adapt and recover from disruptions, thereby ensuring continuous healthcare product and service delivery. Recently various measures have been applied to enhance the resilience of SC. Among them, the adoption of artificial intelligence (AI) contributes most. However, the diversity of AI technologies requires an assessment of their contributions in enhancing SC resilience. To address this, this paper proposes a hybrid resilient assessment multi-criteria decision-making (MCDM) framework. The hybrid method combines the strength of interval-valued q rung orthopair fuzzy set (IVq-ROFS) in flexibly handling vagueness, the superiority of preference ranking organization method for enrichment evaluation (PROMETHEE) in ranking process, and the effectiveness of prospect theory in dealing with bounded rationality of decision-makers. Besides, a set of HSC resilience evaluation criteria are introduced from the perspective of technical contribution. In practice, the hybrid method is validated by identifying the impact of AI technologies on HSC resilience. Sensitivity analysis and comparison analysis are also conducted to prove the robustness and superiority of the method.

关键词： Fuzzy sets Sensitivity analysis Supply chains Decision making Organizations Robustness Electronic healthcare

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Impact of Machine Learning on the Productivity of Employees in Workplace 4

Impact of Machine Learning on the Productivity of Employees ...

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4th International Conference on Inventive Research in Computing applications, ICIRCA 2022

作者： Wardhani, Rulyanti Susi Kant, Kamal Sreeram, Anusha Gupta, Monica Erwandy, Erwandy Bora, Pranjal Kumar Universitas Bangka Belitung Faculty of Economics Indonesia Indonesia Jai Narain Vyas University Department of Management Studies India India Chitkara Business School Chitkara University India Accounting Department Indonesia Dibrugarh University Centre for Computer Science and Applications India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Machine learning (ML) is the study of computer algorithms, which could enhance automatically through experiences and by use of data. This is a significant part of artificial intelligence (AI). This research paper aims to analyse the impact of machine learning on productivity of employees in the workplace. The research paper has conducted a literature review, where it has reviewed uses of ML and its impacts on organisational operation. Bring Your Own Device (BYOD) approach works as a tool of ML, which also marks a positive impact on employee productivity in global workplace. Additionally, it has been reviewed that by supporting BYOD programs, companies can make their employees feel more trusted towards the organisation. Implementation of a BYOD policy can increase transparency throughout organisations as well as enhance employee experience. Moreover, literature review has incorporated relevant information on use of ML in organizational standards. It also used proper theoretical framework for enhancing suitable discussion of the proper research context. It is important to note that this research incorporates a 'mixed data collection method' that will implement both primary and secondary data for bringing out suitable research outcomes. For primary data collection, it involves non-probability sampling (simple random sampling) and collect data through an online survey involving 50 employees from various sectors to understanding the impact of ML in workplace. For secondary data collection, this research will analyse relevant texts, journals, articles and annual reports of companies for creating an in-depth analysis of impacts of ML on productivity of staff within organizational standards. This research will analyse the primary data and assure suitable discussion on the context. For secondary data, it will assure proper interpretation and enhance a descriptive discussion of collected information. The conclusion part concludes the collected information and brings out suita

关键词： Personnel

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Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

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computers, Materials & Continua 2021年第9期68卷 2995-3013页

作者： M.S.Maharajan T.Abirami Irina V.Pustokhina Denis A.Pustokhin K.Shankar Department of Computer Science and Engineering GRT Institute of Engineering and TechnologyTiruttani631209India Department of Information Technology Kongu Engineering CollegeErode638060India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Wireless Sensor Networks(WSN)started gaining attention due to its wide application in the fields of data collection and information *** recent advancements in multimedia sensors demand the Quality of Service(QoS)be maintained up to certain *** restrictions and requirements in QoS management completely depend upon the nature of target *** of the major QoS parameters in WSN are energy efficiency,network lifetime,delay and *** this scenario,clustering and routing are considered as the most effective techniques to meet the demands of *** they are treated as NP(Non-deterministic Polynomial-time)hard problem,Swarm Intelligence(SI)techniques can be *** current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence(QoSCRSI)*** proposed QoSCRSI technique performs two-level clustering and proficient ***,the fuzzy is hybridized with Glowworm Swarm Optimization(GSO)-based clustering(HFGSOC)technique for optimal selection of Cluster Heads(CHs).Here,Quantum Salp Swarm optimization Algorithm(QSSA)-based routing technique(QSSAR)is utilized to select the possible routes in the *** order to evaluate the performance of the proposed QoSCRSI technique,the authors conducted extensive simulation analysis with varying node *** experimental outcomes,obtained from the proposed QoSCRSI technique,apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency,network lifetime,overhead,throughput,and delay.

关键词： Quality of service clustering routing energy efficiency wireless sensor networks swarm intelligence

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