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1st International Conference on Applied Artificial Intelligence and computing, ICAAIC 2022

作者： Nagendra Kumar, V.V. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665497107

Chronic Obstructive Pulmonary Disorder (COPD) is a significant challenge encountered by healthcare professionals. With millions of people being affected by COPD every year, there is a need for better diagnosis conditions. Many of the earlier studies on the facets of the machine learning model's application to COPD have discussed the success in terms of accuracy, the optimal classifiers resulting in success, etc. This study, with the objective of a contemporary literature review, has focused on the earlier contributions of critical models of machine learning for COPD detection. However, based on the information collated from the models, while some classifiers are resourceful in training the system and improved accuracy is a need to address the feature selection issues. Some of the non-technical and purely medical-related studies reviewed in this study have impressed the facts about diagnosis state as false positives or false negatives (misdiagnosis) of COPD conditions. Both conditions are challenging for offering the right treatment to the patients. Thus, considering the existing patterns and the dynamics, this study has discussed three significant gaps in feature selection, the need for multiple layers of diagnosis, and the lifestyle conditions based on COPD detection models as a prospective domain for future research. © 2022 IEEE.

关键词： Computer aided diagnosis

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Hypergraphic partitioning of quantum circuits for distributed quantum computing

arXiv

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arXiv 2023年

作者： Cambiucci, Waldemir Silveira, Regina Melo Ruggiero, Wilson Vicente Department of Computing Engineering and Digital Systems University of Sao Paulo SP Brazil

- In the context of NISQ computers - Noise Intermediate Scale Quantum, it is a consensus that the distribution of circuits among processing agents is a viable approach to get greater scalability with small machines. This approach can increase the combined computational power at the cost of dedicating qubits to get the communication between partitions. Then comes the challenge of reducing this cost of communication-based on efficient circuit partitioning strategies. In the literature, we find numerous works exploring different partitioning approaches that need a consensus on the best method for distribution scenarios. In this context, this work aims to reduce the consumption of communication qubits generated during the physical partitioning of quantum circuits in the distributed quantum computing scenario. We present a new method for partitioning quantum circuits in a hypergraphic representation. The proposed approach uses a heuristic partitioning algorithm on the hypergraphic representation to reduce the number of communication qubits between the partitions, generating a lower cost of communication in distributed quantum systems. For this, we follow a three-step approach: first, a logical segmentation and grouping of gates for optimization and reuse of communication qubits between future partitions, considering the dimensions of width (total qubits), size (total of gates), and depth (total time steps for execution) of the circuit to be distributed;second, a translation of the quantum circuit into a hypergraphic representation, with vertices and edges representing qubits and multi-qubit gates, respectively;finally, use of a variation of the FIDUCCIA-MATTHEYSES heuristic method of hypergraphic partitioning, with recursive bipartite and direct k-way approaches, to generate partitions in balanced and unbalanced scenarios of distributed quantum systems. With this approach, we obtained partial results with a more than 50% reduction in the communication cost generated for

关键词： Timing circuits

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Optimizing Patient Satisfaction Surveys and Feedback with Cloud computing and NLP Techniques 8

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

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TWAFR-GRU: An Integrated Model for Real-time Charging Station Occupancy Prediction 19

TWAFR-GRU: An Integrated Model for Real-time Charging Statio...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Chen, Qiyang Liu, Sheng Qu, Haohao Zhu, Rui You, Linlin Sun Yat-Sen University School of Intelligent Systems Engineering Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Systems Science Department Singapore

ISBN: (纸本)9798350346558

The fast-growing number of electric vehicles has resulted in a variety of charging-related issues, such as the shortage of public charging piles, the inconvenience of finding available charging piles, the additional traffic congestion caused by unnecessary cruising to search for available charging piles, etc. Since only constructing new charging piles may not significantly improve the charging efficiency, methods of predicting charging station occupancy are widely discussed as an effective means to address these issues. However, several challenges are encountered in training an efficient and effective model by utilizing distributed data, improving convergence speed, and enhancing model generalization ability. To address these issues, this paper proposes a novel mechanism, named TWAFR-GRU, which integrates Temporally Weighted Asynchronous Federated Learning (TWAFL) with Reptile and Gated Recurrent Unit (GRU). As shown by the holistic evaluation based on the charging station occupancy dataset, compared with other state-of-the-art baselines, TWAFR-GRU can 1) decrease MAE, RMSE and RAE by 19%, 15% and 17% separately and improve R2, by 67%;2) cut rounds for the model to converge by 75%;3) save training time by 44%;and 4) reduce 17% in forecasting error after the personalization of the initial model to serve a specific charging station. © 2022 IEEE.

关键词： Piles

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IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

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Computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： Saeed Masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.Alzahrani Mohammed Maray Sana Alazwari Mohamed A.Shamseldin Mesfer Al Duhayyim Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)*** presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RSO algorithm is exploited for effective selection of subset of ***,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of ***,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

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Hybrid Visual Verification 14

Hybrid Visual Verification

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14th International Conference on Cloud computing, Data Science and engineering, Confluence 2024

作者： Bushra, S.Nikkath Syed Ismail, A. Sasigresa, P. Edwin Singh, C. Srm Institute of Scienece and Technology Department of Computing Technologies Chengalpattu Kattankulathur India Srm Institute of Scienece and Technology Department of Data Science and Business Systems Chengalpattu Kattankulathur India Veltech University Department: Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department: Computer Science and Engineering India

ISBN: (纸本)9798350344837

Data security is becoming increasingly important as cloud computing advances. Data security is the fundamental problem of all distributed computing systems. Cloud computing enables access to distributed applications and services belonging to various organizations spread across different locations. Cloud computing raises security concerns, because it shares data with distributed users via network in an open terrain. In this system, some of the most important security services like image-based security are handled by the cloud computing system. The proposed scheme takes care of several attacks like botnets, malware and guessing attacks. This scheme is contrasted withseveral established schemesand a new form of authentication is used for the purpose of storing files in a secure manner. © 2024 IEEE.

关键词： Authentication

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Integrating digital twins and deep learning for medical image analysis in the era of COVID-19

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Virtual Reality & Intelligent Hardware 2022年第4期4卷 292-305页

作者： Imran AHMED Misbah AHMAD Gwanggil JEON School of Computing and Information Science Faculty of Science and EngineeringAnglia Ruskin UniversityEast RoadCambridgeEnglandUK Center of Excellence in IT Institute of Management SciencesHayatabadPeshawarPakistan Department of Embedded Systems Engineering Incheon National UniversityIncheonKorea

Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and *** twins may allow healthcare organizations to determine methods of improving medical processes,enhancing patient experience,lowering operating expenses,and extending the value of *** the present COVID-19 pandemic,various medical devices,such as X-rays and CT scan machines and processes,are constantly being used to collect and analyze medical *** collecting and processing an extensive volume of data in the form of images,machines and processes sometimes suffer from system failures,creating critical issues for hospitals and *** To address this,we introduce a digital-twin-based smart healthcare system in-tegrated with medical devices to collect information regarding the current health condition,configuration,and maintenance history of the device/machine/***,medical images,that is,X-rays,are analyzed by using a deep-learning model to detect the infection of *** designed system is based on the cascade recurrent convolution neural network(RCNN)*** this architecture,the detector stages are deeper and more sequentially selective against small and close false *** architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the *** each stage,the bounding boxes are adjusted to locate a suitable value of the nearest false positives during the training of the different *** this manner,the arrangement of detectors is adjusted to increase the intersection over union,overcoming the problem of *** train the model by using X-ray images as the model was previously trained on another *** The developed system achieves good accuracy during the detection phase of *** experimental o

关键词： Digital twins Deep learning Healthcare COVID-19 Chest X-rays Artificial intelligence

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Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

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2024 International Conference on Advances in Data engineering and Intelligent computing systems, ADICS 2024

作者： Vasanth Kumar, V. Tamizhselvan, C. Manoharan, L. Vishnu Sekhar, R. Vinoth Kumar, V. Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Chennai India School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India School Of Bioengineering Srm Institute Of Science And Technology Integrated Bioprocessing Laboratory Department Of Biotechnology Kattankulathur India

ISBN: (纸本)9798350364828

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric. © 2024 IEEE.

关键词： Decision trees

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A Generative Prediction Model Based on TimeVAE and Crossformer for Predicting Multichannel Data of Transformer Bushing

A Generative Prediction Model Based on TimeVAE and Crossform...

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2024 China Automation Congress, CAC 2024

作者： Huang, Xiangjie Lang, Xun Li, Peng Guo, Tao Yu, Li Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China Honghe Power Supply Bureau Yunnan Power Grid Corporation Honghe Yunnan China

ISBN: (纸本)9798350368604

Transformer bushings play a crucial role in power systems. Despite their importance, there has been limited research specifically on the state prediction of transformer bushings. Moreover, currently available models only predict univariate data of bushings without considering the physical connection to other relevant data, which would give rise to a compromised prediction. To achieve a more accurate joint prediction of three critical transformer states: dielectric loss, end shield current, and equivalent capacitance, this work builds upon the state-of-the-art predictive network Crossformer. In addition to Crossformer, a data generation model named TimeVAE is introduced to enlarge the scale of the training dataset. This combination enabled us to achieve accurate multivariate predictions for transformer bushings. To evaluate the effectiveness of the proposed predictor, we conducted comparative experiments with attention-based frameworks (Autoformer, Pyraformer, Informer, Reformer, Transformer), which are considered efficient for multivariable prediction tasks. The results show that our proposed model outperforms these models in four key metrics: mean square error, mean absolute error, root mean squared error, and coefficient of determination. © 2024 IEEE.

关键词： Prediction models

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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