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An Automatic Threshold Selection Using ALO for Healthcare Duplicate Record Detection with Reciprocal Neuro-Fuzzy Inference System

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computers, Materials & Continua 2023年第3期74卷 5821-5836页

作者： Ala Saleh Alluhaidan Pushparaj Anitha Subbappa Ved Prakash Mishra P.V.Chandrika Anurika Vaish Sarthak Sengupta Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhKSA.P.O.Box:8442811671 Department of Electronics and Communication Engineering National Institute of Technical Teacher Training and ResearchChandigarh160019India Department of Periodontology JSS Dental College&HospitalJSS Academy of Higher Education and ResearchMysuru570015India Department of Computer Science and Engineering Amity UniversityDubai345019United Arab Emirates Department of Management/Economics Prin LN Welingkar Institute of Management Development and ResearchMumbai400019India Department of Management Studies Indian Institute of Information TechnologyAllahabad211012India

ESystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt *** still have been pioneering collections of massive volumes of health *** detections involve discovering records referring to the same practical components,indicating tasks,which are generally dependent on several input parameters that experts *** linkage specifies the issue of finding identical records across various data *** similarity existing between two records is characterized based on domain-based similarity functions over different ***-duplication of one dataset or the linkage of multiple data sets has become a highly significant operation in the data processing stages of different data mining *** objective is to match all the records associated with the same *** measures have been in use for representing the quality and complexity about data linkage algorithms,and many other novel metrics have been *** outline of the problem existing in themeasurement of data linkage and de-duplication quality and complexity is *** article focuses on the reprocessing of health data that is horizontally divided among data custodians,with the purpose of custodians giving similar features to sets of *** first step in this technique is about an automatic selection of training examples with superior quality from the compared record pairs and the second step involves training the reciprocal neuro-fuzzy inference system(RANFIS)*** the Optimal Threshold classifier,it is presumed that there is information about the original match status for all compared record pairs(i.e.,Ant Lion Optimization),and therefore an optimal threshold can be computed based on the respective ***,Clinical Decision(CD),and Cork Open Research Archive(CORA)data repository help analyze the proposed method with evaluated benchmarks with current techniques.

关键词： Duplicate detection healthcare record linkage dataset pre-processing reciprocal neuro-fuzzy inference system and ant lion optimization fuzzy system

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Mining High Utility-probability Sequential Patterns in Bigdata Environments 14th

Mining High Utility-probability Sequential Patterns in Bigd...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Wu, Jimmy Ming-Tai Liu, Shuo Wu, Mu-En Lin, Jerry Chun-Wei College of Computer Science and Engineering Shandong University of Science and Technology Shandong China National Taipei University of Technology Taipei Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

ISBN: (纸本)9789811684296

High utility-probability sequential pattern mining (HUPSPM) has become a novel research errand realm in data mining. In practical application, most of the data are collected by a variety of sensors, forming an uncertain database. Therefore, the data collected in real life can be expressed in terms of probability. In previous research work, for the sake of heightening the performance of mining in small data sets and single machine, a great deal of algorithm have been posed. Unfortunately, these algorithms have loopholes in mining large databases with mickle records. Therefore, it is very urgent to construct an efficient model to deal with large uncertain dateabas data sets. In this article, a MapReduce framework is moved to mine effective information in multitudinous uncertain data sets and reduce the number of database scans. Experiments on a large number of uncertain data sets show that the posed Hadoop algorithm has a strong ability to mine the high utility-probability sequential pattern. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Probability

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The OCPT Mechanism: An Innovative Methodology to Erect the Language Understanding to Build the Enhanced STT Model

The OCPT Mechanism: An Innovative Methodology to Erect the L...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： V. Saranya Devi T N. Deepa Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Generative Pretrained Transformers (GPT) predict the sequence of words when building the language model in speech recognition. The traditional model inherits the high range biases during training the data, this issue leads to inappropriate outputs, the proposed research, the Optimized Constructive Pretrained Transformers(OCPT) mechanism emphasizes tuning GPT models specific to domain language modeling, accentuating the value of an organized approach that involves data preprocessing, model initialization, fine-tuning, and assessment. This model helps to build a more efficient and effective Speech-to-text model. The model obtained 97.12% accuracy on the validation set, compared with the AuGPT model which produces 91% accuracy, indicating the proposed model's ability to understand and generate contextually relevant text. During the training phase, the model was hyperparameter-tuned to improve performance metrics including learning rate, batch size, and training epochs. The Adam optimizer was important in accurately adjusting the model parameters to ensure convergence and stability. To avoid overfitting and ensure the model's generalizability, accuracy, and loss have been evaluated throughout the training and validation phases. The findings assessed the model's learning trajectories and performance indicators, demonstrating its efficacy.

关键词： Training Accuracy Computational modeling Generative Pre-trainer transformer Transformers Ubiquitous computing Data models Trajectory Tuning Context modeling

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Real-Time Speech Emotion Recognition Using Hybrid Transformer Networks for Mental Health Monitoring

Real-Time Speech Emotion Recognition Using Hybrid Transforme...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Durga, P. Janani Basha, C. H. Hussaian Christopher, Nelson Kennedy Babu Senthilkumar, S. Kumar, N. Rathina Kavitha, M. E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India Sr University Department of Electrical and Electronics Engineering Telangana Hanumakonda50637 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Sns College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore641107 India

ISBN: (纸本)9798331509828

This study proposes a hybrid method to multi dialect speech recognition using the transfer learning with transformer-based architecture. The primary objective of this method is to enhance the automatic speech recognition (ASR) across the different dialectal variations of specific language. The transfer learning methods are utilized to modify the base model that was trained on high resource language to manage the multiple dialects with limited labelled data for each dialect. This is achieved by using pre trained transformer methods such as Wav2Vec2.0 and Whisper. An approach which employs dialect specific embeddings is presented. It enables the method to capture data and differentiate between prosodic elements and phonetic that are distinctive to a variety of dialects. Data augmentation technique are utilized in order to overcome the limited availability of labelled data for low resource dialects. These methods are utilized to increase the efficiency of training set and enhance the robustness of the method. Evaluation of the method is carried out using multi dialect speech datasets which include Arabic MGB-3 and common voice. These datasets represent a variety of dialects that are spoken in various linguistic regions. The result of this study indicates that the transformer-based method is effective in managing difficulties of dialectal variances while the reliability is reduced on different datasets. Different real-world applications are provided by the proposed method for improving voice enabled applications. Furthermore, this method leads the way for the development of ASR method that are more accurate and independent in locations that have a significant linguistic variation. © 2025 IEEE.

关键词： Transfer learning

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Intelligent Personalized Nutrition Guidance System Using IoT and Machine Learning Algorithm 2

Intelligent Personalized Nutrition Guidance System Using IoT...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Santhuja, P. Choudri, Subramani Roy Balaji, S. Reddy, Elangovan Guruva Muthulekshmi, M. Department of Computer Science Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Information Technology Sri Shanmugha College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Nowadays, dietary issues are increasing around the world. Numerous problems, such as weight gain, obesity, diabetes, etc., can arise from an unbalanced diet. By integrating image processing, the system can assess food images in new ways to provide personalized solutions for better eating habits. The system uses machine learning, IoT, and image processing techniques to evaluate food image data and extract useful information. IoT devices like smartphones or specialized cameras take images of food and send to the cloud for analysis. The Support Vector Machine (SVM) is a machine learning technique that is integrated with Internet of Things (IoT) technology in this study to propose a novel method for creating a system that uses personalized nutritional suggestions. The SVM algorithm is then used to analyze and analyze this data to find patterns, correlations, and individual dietary requirements. A cloud database is used to process and store all the data related to food consumption. The system analyzes food images and determines the nutrition and caloric content of the food using image processing and segmentation. The system takes up new abilities using an extensive collection of dietary records that have been labeled and include details about the sorts of foods, portion sizes, and nutritional value. The system can predict and identify users' nutritional needs, deficits, and personalized dietary objectives by using this trained SVM model to their nutritional data. © 2023 IEEE.

关键词： Dietetics IoT Machine Learning Nutrition Guidance SVM Algorithm

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Efficient Adaptive Feedback Cancellation Method for Digital Hearing Aids Employing Short Processing Delays

Efficient Adaptive Feedback Cancellation Method for Digital ...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Muhammad Tahir Akhtar Department of Electrical and Computer Engineering School of Engineering and Digital Sciences Nazarbayev University Astana Republic of Kazakhstan

ISBN: (数字)9798350361759

ISBN: (纸本)9798350361766

The presence of acoustic feedback in digital hearing aids is almost an unavoidable phenomena, as the input microphone and loudspeaker are located very close to each other. The presence of strong acoustic feedback may result in howling effect, which degrades the performance of hearing aid. This paper presents an efficient solution for mitigating acoustic feedback in modern hearing aids equipped with a very short processing delays. The developed method employs two adaptive filters (AFs) and a delay-based strategy to monitor their convergence. The hearing aid received (amplified) signal acts as the input to the main adaptive filter (AF) which is adapted using lattice filtering-based algorithm with improved decorrelation properties. An internally generated random probe signal is input to the auxiliary AF which is adapted using a hybrid adaptive algorithm combining feature of normalized least mean square (NLMS) and maximum Versoria-criterion (MVC) cost functions. The computer simulation demonstrate that the proposed is quite efficient in mitigating the effect of the acoustic feedback in comparison with the other counter parts.

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AI generated route data pre-processing for faster decision making 8

AI generated route data pre-processing for faster decision m...

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8th International Young Engineers Forum on Electrical and computer engineering, YEF-ECE 2024

作者： Maas, Rene Shevtshenko, Eduard Karaulova, Tatjana TTK University of Applied Sciences Institute of Logistics Faculty of Science and Technology University of Tartu Institute of Computer Science Tallinn Estonia Tallinn University of Technology TTK University of Applied Sciences Department of Mechanical and Industrial Engineering Institute of Logistics Tallinn Estonia

ISBN: (纸本)9798350387643

The industry is constantly generating new data, allowing take advantage of their use to increase manufacturing efficiency through the reutilisation of operational *** such possibility is to use Artificial Intelligence (AI). While AI is heralded as a solution for industry efficiency and accuracy quandaries through process automation, its implementation grapples with the issue of inaccurate production data used for training, giving an erroneous *** article introduces a framework to facilitate the integration of AI tools within companies. The focus is on the accuracy of predicting normative times and selecting alternative routes when accuracy is low. This approach improves the price quotation prediction process, where using AI is practical due to time constraints. Making quick and accurate decisions is crucial for maintaining efficiency in *** elucidate the functionality, the authors have performed a case study in production for the framework's validation. The wood production sector was selected due to its lower operational complexity than machinery construction, resulting in time savings in the planning *** study revealed that the prediction accuracy of AI was 70% on average for operations normative times. To improve this accuracy, the t-Test method to classify operations was used. This approach eliminates operations with low AI prediction accuracy when making price quotations. Through the case study, AI module accuracy reached 80%, while the additional data collection volume decreased by one-Third. © 2024 IEEE.

关键词： Machine Learning Normative Times Wood Industry

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The Comparative Analysis among RF,MLP and LWL Methods in Forecasting uses 4

The Comparative Analysis among RF,MLP and LWL Methods in For...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Sudha, I. Arunprasath, R. Rajeswari, R. Ramesh, P.S. Saud, Aseel Muhammad Hameed, Hind Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering TamilNadu Chennai India Sri Krishna College of Engineering Department of Eee Tamilnadu arakkonam India Peri Institute of Technology Department of Ece Chennai India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Computer Science and Engineering India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

An creative multi-time scale vitality control system is optimized for control buys, apparatus utilization, and battery execution, and it was made particularly for estimating the improvement of savvy PV (photovoltaic) light or sun oriented control. The savvy sun powered cluster delineates a neighborhood imperativeness a commonality of numerous residences residences and structures with sun based vitality boards and batteries. However, this makes it hard for the energy balance, and so reduces the expenses of the energy system. Our novel system uses an MPC-based technique with nonlinear functions and model-based predictions, and the framework allows a DRL-based controller to provide system design and deployment. The dual-scale method proposed here has a significant advantage over typical single-time-scale schemes because it can deal with both fast and slow dynamics of the system with enough computing overhead while still providing reasonably good accuracy. The effectiveness of the proposed framework is demonstrated through simulation results. In addition, this also highlights the need for system description and the necessity for a detailed system model for optimal performance in both PV forecasting and battery characterization. © 2024 IEEE.

关键词： Solar energy

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Pothole Detection in Asphalt Roads: A Comprehensive Approach for Enhanced Road Maintenance and Safety with AlexNet Model

Pothole Detection in Asphalt Roads: A Comprehensive Approach...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Abdelmalak, Mark Emad Sobhi Khodadadi, Nima Zaki, Ahmed Mohamed Eid, Marwa M. Rizk, Faris H. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt University of Miami Department of Civil and Architectural Engineering Coral GablesFL United States Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Bahrain Polytechnic Faculty of Engineering Isa Town Bahrain Ain Shams University Faculty of Computer and Information Sciences Department of Computer Science Cairo11566 Egypt Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Jadara University Jadara Research Center Irbid Jordan

ISBN: (纸本)9798350351408

The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection Dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15% accuracy, 91.38 % sensitivity (TPR), and a surprisingly high F-score, which reached 96.52%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies. © 2024 IEEE.

关键词： Digital storage

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Enhanced Electronic Diagnosis System by CCNNViT Model for Kidney Cyst Detection

Enhanced Electronic Diagnosis System by CCNNViT Model for Ki...

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International Conference on Circuits, Communication, Control and Computing

作者： S.K. Aruna N. Deepa Devi. T Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

A major health issue for kidney failure is becoming worse due to changes in diet, obesity, underlying medical conditions, mineral deficiencies. The kidneys calculus, also known as kidney cysts, is a hard accumulation of nutrients from the stool that develops in the urinary tract. One method of imaging that medical professionals use to diagnose kidney cysts is computerized tomography (CT). Kidney cysts are sometimes difficult to identify with the unaided eye owing to the poor quality that produces these images, which could result in false alarms. In order to help clinicians with diagnosis, an advanced deep learning technique-based electronic diagnosis system has been built throughout this work. Kidney cysts can be identified by CNN based deep learning algorithms. until now, it has issues with the conventional implementation and the efficiency of the convolution operations performed in the convolution layers. The suggested deep learning architecture uses a hybrid approach as a combination of CNN and pooling vision transformer (CCNNViT) technique that is incorporated in the deep learning architecture to decrease characteristic map redundant employment without the convolution overlap. We propose a method that extracts stated and abstract characteristics from the input pictures, thereby improving the network's efficiency. The suggested architecture is developed using the kidney cysts CT scans that are made accessible to anyone on Kaggle. Using CT scans, our automated simulation identified kidney cysts with a 98.9% accuracy rate. Our method outperforms some of the most recent and modern techniques for detecting kidney cysts of any form, even the tiniest previous ones. The present research proposes an innovative method for detecting cysts in the kidneys that uses Pooling-based Vision Transformers (PiT) and excludes the uterus from the evaluation.

关键词： Deep learning computer vision Obesity Convolution Computed tomography Uterus Transformers Nutrients Minerals Kidney

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