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4th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Senthil, G.A. Prabha, R. Rajesh Kanna, R. Umadevi Venkat, G. Deepa, R. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Science and Engineering Vels Institute of Science and Technology and Advanced Studies Chennai India

ISBN: (纸本)9789819965854

Evolving new diseases demand the need for technology to identify the disease in an effective way. Medical imaging in the field of disease identification helps to identify the disease by scanning the human parts, thereby preventing the increased rate of deaths. Deep learning algorithms make it easier to identify and analyze disease efficiently through medical imaging. The high performance of these models is needed for the disease to be predicted with accurate results. The prediction rate of the disease can be increased by the efficient use of deep learning modules and algorithms. This research involves the use of deep learning models in identifying brain hemorrhage and retinopathy diseases through deep learning algorithms. The deep learning algorithms AlexNet and convolutional neural network (CNN) with the accuracy of 90% and 96%, respectively, are employed for the detection of brain hemorrhage, and ResNet-50 and CNN with accuracy of 70% and 92%, respectively, are used for the identification of retinopathy. The output of the model is displayed using augmented reality (AR), which makes it interactive for the user to analyze the results. The AR display is achieved using the unity engine along with the Vuforia package and using the barracuda package for importing the deep learning model into unity. Thus, by increasing the accuracy rate of the system, this research demonstrates the high performance of the intelligent system. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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Wide and fault diameter in Kneser graphs for enhanced fault tolerance in parallel computing

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International Journal of Networking and Virtual Organisations 2024年第3期31卷 169-190页

作者： Sundara Rajan, R. Kirithiga Nandini, G. Lin, Yuqing Reji, Remi Mariam Department of Mathematics Hindustan Institute of Technology and Science Chennai603 103 India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai603 103 India School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia Department of Mathematics Christian College Kerala Chengannur689 122 India

A system’s fault tolerance is its capacity to function even if one or more of its components fail. Implementing a fault-tolerant network becomes an important criterion for reliable computing. Reliability measures play a significant part in recognising the role of faulty and non-faulty processors in a parallel computing system. Parallel computing is used primarily for saving time, solving big problems, and doing multiple tasks at once at the same time. Various reliability measures have been introduced to evaluate a network’s fault-tolerance capability. We have measured the wide diameter and fault diameter of the Kneser graphs in this study. Also, we have verified the fault diameter obtained using an experimental study. Further, we have described some applications of wide diameter and fault diameter in parallel and distributed computing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Fault tolerant computer systems

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Video Recommendation System Using Machine-Learning Techniques

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Journal of Harbin institute of technology(New Series) 2024年第4期31卷 24-33页

作者： Meesala Sravani Ch Vidyadhari S Anjali Devi Department of Computer Science and Engineering GMR Institute of TechnologyRajam 532127Andhra PradeshIndia Department of Information Technology Gokaraju Rangaraju Institute of Engineering and TechnologyKukatpallyHydarabad 500090TelanganaIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522302Andhra PradeshIndia

In the realm of contemporary artificial intelligence,machine learning enables automation,allowing systems to naturally acquire and enhance their capabilities through *** this cycle,Video recommendation is finished by utilizing machine learning strategies.A suggestion framework is an interaction of data sifting framework,which is utilized to foresee the“rating”or“inclination”given by the different *** expectation depends on past evaluations,history,interest,IMDB rating,and so *** can be carried out by utilizing collective and substance-based separating approaches which utilize the data given by the different clients,examine them,and afterward suggest the video that suits the client at that specific *** required datasets for the video are taken from *** recommender framework is executed by utilizing Python Programming *** building this video recommender framework,two calculations are utilized,for example,K-implies Clustering and KNN grouping.K-implies is one of the unaided AI calculations and the fundamental goal is to bunch comparable sort of information focuses together and discover the *** that K-implies searches for a steady‘k'of bunches in a dataset.A group is an assortment of information focuses collected due to specific similitudes.K-Nearest Neighbor is an administered learning calculation utilized for characterization,with the given information;KNN can group new information by examination of the‘k'number of the closest information *** last qualities acquired are through bunching qualities and root mean squared mistake,by using this algorithm we can recommend videos more appropriately based on user previous records and ratings.

关键词： video recommendation system KNN algorithms collaborative filtering content⁃based filtering classification algorithms artificial intelligence

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Market Segmentation Using Machine Learning and Data Analysis: A Comprehensive Study 15

Market Segmentation Using Machine Learning and Data Analysis...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Ajay Dhanda, Namrata Tiwari, Sharda Verma, Rajat Amity University Uttar Pradesh Amity School of Engineering and Technology Lucknow India Babu Banarasi Das Northern India Institute of Technology Department of Computer Science and Engineering Lucknow India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350370249

This research explores the integration of machine learning into market segmentation, a strategy traditionally reliant on demographic, geographical, and psychographic traits. With the rise of big data, machine learning offers enhanced precision by uncovering intricate consumer behavior patterns often missed by conventional methods. The study involves reviewing relevant literature, preprocessing datasets, and developing machine learning models to evaluate their effectiveness in segmenting markets. Findings indicate that machine learning significantly improves segmentation accuracy, enabling businesses to predict behaviors and tailor marketing strategies more effectively. This research contributes by highlighting machine learning's potential to transform market segmentation and drive business growth. Future work should focus on real-world implementation of these models to validate their practicality and impact. © 2024 IEEE.

关键词： consumer behavior customer insights machine learning market segmentation predictive analytics targeted marketing

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Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load Forecasting

Differential Evolution Algorithm based Hyper-Parameters Sele...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Sen, Anuvab Rhik Mazumder, Arul Sen, Udayon Indian Institute of Engineering Science and Technology Electronics and Telecommunication Shibpur Howrah India School of Computer Science Computer Science Carnegie Mellon University Pittsburgh United States Indian Institute of Engineering Science and Technology Computer Science and Technology Shibpur Howrah India

ISBN: (纸本)9781665430654

Accurate load forecasting plays a vital role in numerous sectors, but accurately capturing the complex dynamics of dynamic power systems remains a challenge for traditional statistical models. For these reasons, time-series models (ARIMA) and deep-learning models (ANN, LSTM, GRU, etc.) are commonly deployed and often experience higher success. In this paper, we analyze the efficacy of the recently developed Transformer-based Neural Network model in load forecasting. Transformer models have the potential to improve load forecasting because of their ability to learn long-range dependencies derived from their Attention Mechanism. We apply several metaheuristics namely Differential Evolution to find the optimal hyperparameters of the Transformer-based Neural Network to produce accurate forecasts. Differential Evolution provides scalable, robust, global solutions to non-differentiable, multi-objective, or constrained optimization problems. Our work compares the proposed Transformer-based Neural Network model integrated with different metaheuristic algorithms by their performance in load forecasting based on numerical metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Our findings demonstrate the potential of metaheuristic-enhanced Transformer-based Neural Network models in load forecasting accuracy and provide optimal hyperparameters for each model. © 2023 IEEE.

关键词： Deep Learning Differential Evolution Genetic Algorithm Meta-heuristics Particle Swarm Optimization

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Comparative Study of Different Optical Character Recognition Models on Handwritten and Printed Medical Reports

Comparative Study of Different Optical Character Recognition...

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Kumar, Anand Singh, Pardeep Lata, Kusum National Institute of Technology Hamirpur Department of Computer Science and Engineering Hamirpur India Sharda University Department of Computer Science and Engineering School of Engineering and Technology India

ISBN: (纸本)9798350397208

Text may be extracted from an image using a method called optical character recognition (OCR). An OCR is mostly used to translate existing paper documents or image data into editable documents. To create an OCR, a large variety of algorithms are needed, and it essentially functions in two stages, such as character and word detection. To retain a document's structure while using a more advanced technique, an OCR additionally focuses. The advancement in OCR has been very promising lately. Different tools have arrived in the market ranging from open-source tools PyTesseract to big established companies releasing their own OCR tools like Amazon's AWS Textract and Google's Google Vision. The accuracy of modern OCR techniques like AWS Textract, Google Vision, and updated versions of Pytesseract haven't been analyzed for different datasets. For complex datasets like a CBC report, there are various parameters and fields in a tabular and unstructured fashion. Every algorithm has a different approach to extracting the text from the image, so it becomes difficult to determine how accurate these models are on such datasets. Based on 2 different datasets, one handwritten and the other printed CBC images, this study performed analysis using the above 3 models by building an end-to-end pipeline to retrieve the necessary fields, thus making it feasible to access the accuracy of these models. Upon testing these models' accuracy, the study concluded that the AWS Textract model was the highest-performing model for both datasets, achieving an accuracy of 95% for the printed dataset and 89.42% for the handwritten IAM dataset after preprocessing. © 2023 IEEE.

关键词： Blood

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MLP-Based Speech Emotion Recognition for Audio and Visual Features 1

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Kothai, G. Boora, Prabhas Bhanu Muzammil, S. Venkata Subhash, L. Naga Raju, B. KPR Institute of Engineering and Technology Tamil Nadu Uthupalayam Coimbatore India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India

ISBN: (数字)9789819995622

ISBN: (纸本)9789819995615

Due to its potential applications in domains involving psychology, social smart machines, and human–computer interaction, speech emotion recognition has become an important and growing topic field in the study. That article offers the service comparative analysis of strategies in artificial intelligence classifiers for speech emotion recognition, including MLP, decision tree, SVM, and random forest. Using visual analysis methods like wave plot and spectrogram analysis as well as audio feature extraction, we evaluate these classifiers. According to our observations, MLP achieves better performance than the other classifiers, obtaining an accuracy of 85% when identifying different emotions. Moreover, we illustrate how audio feature extraction and visual analysis help to improve emotion recognition efficiency. Our research has applications for creating speech emotion recognition algorithms that may be applied in real-life scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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Implementation of Machine Learning and Ensemble Learning Techniques to Proactively Predict Students' Academic Success in the Realm of Higher Education 4

Implementation of Machine Learning and Ensemble Learning Tec...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Devkishan, Taneja Sanjay Singh, Sanjay Kumar Bharti, Ajay Kumar Amity Institute of Information Technology Amity University Uttar Pradesh Lucknow India United College of Engineering and Research Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9798350387490

Predicting and analyzing student performance is a huge boon for both students and teachers. A number of analytical methodologies have been utilized in recent studies to forecast students' success;the CGPA is the end product of these analyses, which have been conducted for both the internal assessment as well as for the external assessment. Few well known machine learning algorithms have been suggested. Ensemble Learning along with Decision Trees, Support Vector Machine and Random Forest -obtained perfect 100s for Precision Value, Recall Value, as well as in F1 Scores in the evaluation of performances. Naive Bayes has worse performance in comparison, with F1 Scores of 66, Recall Value of 65, and Precision of 68. When contrasted with decision tree models, Support Vector Machines, Multi-Layer Perceptron, Ensemble Enabled Learning, and Random Forest, Naive Bayes, AdaBoost, and K-Nearest Neighbors all perform worse in terms of accuracy. The Naive Bayes classifier models the input distributions across classes and is very good in text categorization because it is a generative method. Since it fails to identify critical features for class differentiation, it cannot be distinguished from discriminative classifiers. © 2024 IEEE.

关键词： Adversarial machine learning

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Investigation of Ce/BEA as a passive NO_(x)adsorber:*** aging deactivation mechanism

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Journal of Rare Earths 2024年第8期42卷 1524-1530,I0004页

作者： Yiying Ding Minghui Yu Xiaojie Yu Meiqing Shen Jianqiang Wang Jun Wang Gurong Shen School of Chemical Engineering and Technology Tianjin UniversityTianjin300350China School of Materials Science and Engineering Tianjin UniversityTianjin300350China National Rare Earth Catalysis Research Institute Dongying257000China

Ce/BEA has the potential to be applied as a novel passive NO_(x)absorber(PNA)in the after-treatment of vehicles due to its considerable NO_(x)storage ***,as a vehicle exhaust after-treatment material,it must withstand the test of long-term hydrothermal *** work examined the deactivation mechanism of Ce/BEA during hydrothermal aging.3.0 wt%Ce/BEA was prepared using the ionexchange method,and then subjected to hydrothermal treatment at 650℃with 10%H_(2)O for 1-12 h to obtain samples with different aging *** comparison,the H-BEA support was aged under the same ***-Emmett-Teller(BET)method,X-ray diffraction(XRD),NH_(3)temperature programmed reduction(NH_(3)-TPD),^(27)Al MAS nuclear magnetic resonance(^(27)Al MAS NMR),H_(2)temperature programmed reduction(H_(2)-TPR),and high resolution-transmission electron microscopy(HR-TEM)were performed to characterize the changes in PNA performance,structure,Ce species,and *** HR-TEM and H_(2)-TPR results show that CeO_(x)particles appear after hydrothermal aging,which results from the detachment and aggregation of active Ce *** on the^(27)Al MAS NMR results,we conclude that BEA zeolite dealumination leads to the loss of acidic sites and the transformation of active Ce species on the acidic sites into the less active CeO_(x).This is the primary reason for the hydrothermal aging deactivation of Ce/BEA.

关键词： Passive NO_(x)adsorber Ce/BEA Hydrothermal aging CeO_(x) Dealumination Rare earths

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Harmonizing Healthcare Perspectives and In-Depth Strategy for Unifying Diverse Medical Big Data Sources 10

Harmonizing Healthcare Perspectives and In-Depth Strategy fo...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Manikandan, A. Thirumal, S. Kumar, N. Vels Institute of Science Technology &advanced Studies Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The healthcare sector has generated vast amounts of data, posing a significant challenge in terms of integration. Recently, deep learning frameworks have gained extensive utilization for forecasting relationships within healthcare systems. Specifically, the success of the Heterogeneous Multi-Layer Deep Learning Network (HMLDN) in seamlessly amalgamating diverse medical data for enhanced diagnostics and storage is explored in this paper. The initial discussion delves into capsule optimized networks and attention maps, laying the groundwork for their application in the formation of HMLDN to achieve efficient data integration. © 2024 IEEE.

关键词： Electronic health record

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