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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Legapriyadharshini, N. Thirumalaikumari, T. Senbagam, K. Sarasu, R. Basha, H. Anwer Malathi, P. Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science Saveetha College of Liberal Arts and Sciences Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350309140

In today's rapidly evolving digital landscape, using Artificial Intelligence (AI) and Cloud-Native Technologies is essential for improving personalized mobile banking experiences. The limitations and errors of existing financial systems necessitate such integration. Existing systems often struggle to adapt to modern consumers dynamic tastes and expectations, resulting in unsatisfactory customer experiences. Identifying the gap, the proposed system revolutionizes mobile banking applications by leveraging AI algorithms such as machine learning and natural language processing within a microservices-based cloud infrastructure. As a result, the proposed system solves the limitations of outdated architectures by providing personalized and efficient services while addressing security issues. The study indicates considerable gains in terms of user pleasure, system scalability, and security robustness through an in-depth evaluation of the results. The results highlight AI's and Cloud-Native Technologies transformational potential in revolutionizing the landscape of tailored mobile banking experiences. The study provides helpful information to financial institutions looking to adopt innovation and fulfill the evolving needs of modern consumers. © 2024 IEEE.

关键词： Legacy systems

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Mining fuzzy local periodic activity pattern for Smart home applications

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Knowledge-Based Systems 2024年 293卷

作者： Iqbal, Mohammad Prayitno, Adila Sekarrati Dwi Pao, Hsing-Kuo Mukhlash, Imam Department of Mathematics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo East Java Surabaya60111 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Perceiving resident movements from ubiquitous sensor devices can aid health and safety management in smart home environment. One can find resident movement patterns to get notification about what should be aware of to prepare for possible accidents. The goal of this work is to propose a method to search for periodic residents’ activity patterns given smart home applications. Specifically, the proposed method is aimed to capture fuzzy local periodic activity patterns (FLPAP) from the smart home data. A FLPAP is an activity pattern that periodically occurs for a particular duration in fuzzy time intervals. We construct an algorithm to help to find the FLPAP called Fuzzy Local Periodic Activity Pattern Miner (FLoPMiner). On the side, a parameter called k-scale period is well-chosen to reorganize the data to make it suitable for the need of periodic activity pattern mining. The proposed method was demonstrated for its effectiveness on a public smart home dataset. After all, we successfully retrieved periodic resident activity patterns, which can be applied to self-health management systems, especially when focusing on a precise scale period. Overall, the proposed method shows descent pattern representation against the state-of-the-art methods of the same category. © 2024 Elsevier B.V.

关键词： Automation

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Tackling System-Induced Bias in Federated Learning: A Pricing-Based Incentive Mechanism

Tackling System-Induced Bias in Federated Learning: A Pricin...

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International Conference on Distributed Computing Systems

作者： Shuo Wang Bing Luo Ming Tang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Data Science Research Center Duke Kunshan University Suzhou China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

In federated learning (FL), distributed users collaboratively train a neural network model under the coordination of a central server. However, during the training process, clients often exhibit time-varying availability and have non-independent and non-identically distributed (non-IID) datasets. This results in system-induced bias, as models trained by the available clients do not accurately represent the entire population, which includes both available and unavailable clients. To address this bias, we propose a pricing-based incentive mechanism to encourage clients to adjust their availability. First, we model the strategic interaction among a large number of FL clients as a non-cooperative game under an arbitrary pricing scheme. We demonstrate that this game is a potential game, and its equilibrium can be found by solving an optimization problem. Second, based on equilibrium analysis, we derive an optimal pricing scheme for scenarios with a large client population. For general scenarios with any number of clients, we propose a bi-level optimization algorithm that utilizes Particle Swarm Optimization (PSO) to determine the optimal pricing scheme. This algorithm can effectively handles the intricate correlation between the equilibrium and pricing scheme. Our experimental results, based on real-world client availability datasets, highlight the effectiveness of our proposed incentive mechanism in mitigating system-induced bias, with improvements of up to 99.5% compared to the uniform pricing benchmark. Furthermore, this mechanism enhances the FL convergence rate by up to 3.43 times.

关键词： Training Federated learning Sociology Pricing Games Transforms Servers

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A systematic literature review of machine learning application in COVID-19 medical image classification

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Procedia computer science 2023年 216卷 749-756页

作者： Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia. Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia. Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia.

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance.

关键词： COVID-19 classification disease machine learning medical images

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： J. Uma Mageswaran G. Indumathi A.V. Kalpana G. Elangovan S.D. Lalitha S. Nalini Mayank Kumar Sohanda Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur Dist Tamil Nadu SRM Institute of Science and Technology Ramapuram Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Department of Data Science and Business Systems SRM Institute of Science and Technology Department of Computer Science and Engineering R.M.K Engineering College Department of Computing Technologies SRM Institute of Science and Technolog Kattankulathur

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus.

关键词： Sociology Telecommunication computing Internet of Things statistics RFID tags Monitoring Accidents

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A Transfer Learning based Deep Learning Framework for Lung Disease Detection Utilizing CXR Images

A Transfer Learning based Deep Learning Framework for Lung D...

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

作者： Kansal, Kajal Kansal, Kanika School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Abes Engineering College Department of Computer Science and Engineering - Data Science Uttar Pradesh Ghaziabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527495

The classification of various lung diseases with the help of chest X-ray images is one of the significant and challenging issues in medical imaging applications. This work proposes a transfer learning-based deep neural network architecture to classify lung diseases using a publicly available dataset. The proposed DenseNet201 model uses the interconnected structure to optimize feature transmission, minimize computation redundancy, and improve the gradient flow. The proposed model achieved a better classification accuracy of 98.34% compared to the other existing methods. These results show that DenseNet201 effectively learns discriminative features from the input medical image data. The proposed framework is suitable for large-scale implementation of lung disease classification. It helps develop more efficient computer-aided diagnostic systems, assisting clinicians in decision-making. © 2025 IEEE.

关键词： Lung cancer

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Switching autoregressive low-rank tensor models 23

Switching autoregressive low-rank tensor models

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Hyun Dong Lee Andrew Warrington Joshua I. Glaser Scott W. Linderman Computer Science Department Stanford University Department of Statistics Stanford University Department of Neurology Northwestern University

An important problem in time-series analysis is modeling systems with time-varying dynamics. Probabilistic models with joint continuous and discrete latent states offer interpretable, efficient, and experimentally useful descriptions of such data. Commonly used models include autoregressive hidden Markov models (ARHMMs) and switching linear dynamical systems (SLDSs), each with its own advantages and disadvantages. ARHMMs permit exact inference and easy parameter estimation, but are parameter intensive when modeling long dependencies, and hence are prone to overfitting. In contrast, SLDSs can capture long-range dependencies in a parameter efficient way through Markovian latent dynamics, but present an intractable likelihood and a challenging parameter estimation task. In this paper, we propose switching autoregressive low-rank tensor (SALT) models, which retain the advantages of both approaches while ameliorating the weaknesses. SALT parameterizes the tensor of an ARHMM with a low-rank factorization to control the number of parameters and allow longer range dependencies without overfitting. We prove theoretical and discuss practical connections between SALT, linear dynamical systems, and SLDSs. We empirically demonstrate quantitative advantages of SALT models on a range of simulated and real prediction tasks, including behavioral and neural datasets. Furthermore, the learned low-rank tensor provides novel insights into temporal dependencies within each discrete state.

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APRCOIE: A Open Information Extraction System for Chinese

SSRN

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SSRN 2023年

作者： Liao, Yan Luo, Liangqing Ping, Weiying Lu, Xuewen Zhong, Yuansheng Hua, Jialin Key Laboratory of Data Science in Finance and Economics School of Statistics and Data Science Jiangxi University of Finance and Economics Nanchang China Department of Mathematics and Statistics University of Calgary Calgary Canada

Open information extraction (OIE) is a key task in natural language processing. Compared to the flourishing development of OIE systems in English, there are very few high-quality Chinese OIE systems that are publicly available. APRCOIE is a system to conduct open information extraction for Chinese text data. Differing from both traditional rule-based approaches and learning-based methods, the new system innovatively employs automated methods to explore the nature of complex Chinse literal characteristics, and thus generate a large number of extraction rules and subsequently establish the extraction model. The feature of low resource demanding makes APRCOIE is easy to be deployed in various real applications. © 2023, The Authors. All rights reserved.

关键词： Information retrieval

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Diverse acoustic wave propagation to confirmable time–space fractional KP equation arising in dusty plasma

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Communications in Theoretical Physics 2021年第11期73卷 47-64页

作者： Aly R Seadawy Muhammad Younis Muhammad Z Baber Syed T R Rizvi Muhammad S Iqbal Mathematics Department Faculty of ScienceTaibah UniversityAl-Madinah Al-MunawarahSaudi Arabia Department of Computer Science University of the PunjabLahorePakistan Department of Mathematics and Statistics The University of LahoreLahorePakistan Department of Mathematics COMSATS University IslamabadLahore CampusPakistan

In this study,the(3+1)-dimensional fractional time–space Kadomtsev–Petviashivili(FTSKP)equation is considered and analyzed analytically,which propagates the acoustic waves in an unmagnetized dusty *** fractional derivatives are studied in a confirmable *** new modified extended direct algebraic(MEDA)approach is adopted to investigate the diverse nonlinear wave structures.A variety of new families of hyperbolic and trigonometric solutions are obtained in single and different *** obtained results are also constructed graphically with the different parametric choices.

关键词： exact solutions fractional calculus new MEDA technique unmagnetized dusty plasma

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Enhancing Fake Review Detection: A Hierarchical Graph Attention Network Approach Using Text and Ratings 2

Enhancing Fake Review Detection: A Hierarchical Graph Attent...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Anuprathibha, T. Praveen, R.V.S. Jayanth, H. Sukumar, P. Suganthi, G. Ravichandran, T. V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Department of Digital Engineering and Assurance Skyview Building 20 Sarelingampally Telangana Hyderabad India St. Claret College Autonomous Department of Commerce and Management Karnataka Bengaluru India Department of Computer Science and Engineering Tamilnadu Erode India Sona College of Technology Department of Mathematics Tamil Nadu Salem India Akshaya College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350388916

The establishment of trusting connections between buyers and sellers is essential in electronic commerce (EC) since it paves the way for mutual benefit. Users can research a business's reputation before deciding to do business with them. There are some aspects of the reputation system that are unflattering to both vendors and clients. For instance, as a consequence of their dishonesty or lack of effort while posting reviews, some users may create phantom feedback, which seems as fabricated assessments meant to boost their *** proposed approach incorporates stage preprocessing, feature extraction, and model training. In order to prepare the data for the SA job, the pre-processing phase incorporates initial procedures that aid in data transformation. One way to improve the model's accuracy is to use features extraction to remove irrelevant data points. We kept using the HGAN model throughout training. This state-of-the-art method outperforms SVM and LSTM with an average accuracy of 91.26%. © 2024 IEEE.

关键词： data accuracy

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