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Journal of Artificial Intelligence and Technology 2023年第4期3卷 187-194页

作者： Barnali Brahma Tusar Kanti Dash Ganapati Panda L V Narasimha Prasad Rajesh Kulkarni C V Raman Global University BhubaneswarIndia Institute of Aeronautical engineering HyderabadIndia mvsr engineering college nadargul HyderabadIndia

Cognitive-inspired computational systems play a crucial role in designing intelligent health monitoring systems which help both patients and *** also helps in early and consistent decision-making for various health issues including human psychological *** fountains built in parks and public spaces are used as decorative instruments which not only give appealing visuals but also provide a relaxing environment to the *** natural sounds have a direct effect on the psychological health of *** few research works are reported on developing the relationship between water sounds and their corresponding psychological *** assessment needs trained manpower and a lot of experimental time which is costly and may not be always *** this paper,to access the from the pleasantness from human health-friendly water fountain sounds,a perceptually weighted functional link artificial neural network(P-FLANN)model is *** reduce the computational complexity of training and for faster convergence,swam intelligence-based optimization algorithm is used for updating the *** is observed from the comparative simulation results that the proposed P-FLANN model can effectively perform prediction tasks which is not only cost-effective but also 95%accurate and can play a crucial role in designing human health-friendly water fountains in smart cities.

关键词： AI-ML automated speech recognition bio-inspired computing FLANN P-FLANN water sound

An Empirical Model for the Classification of Diabetes and Diabetes_Types Using Ensemble Approaches

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Journal of Artificial Intelligence and Technology 2023年第4期3卷 181-186页

作者： Sushma Jaiswal Priyanka Gupta L.V.Narasimha Prasad Rajesh Kulkarni Guru Ghasidas Vishwavidyalaya KoniBilaspur(C.G.)India Department of CSE Institute of Aeronautical EngineeringHyderabadIndia mvsr engineering college nadargul HyderabadIndia

Diabetes is a hereditary disorder that interferes with human life at all *** is challenging for cells to absorb glucose from the bloodstream when an individual has *** two main subtypes of diabetes are type 1 diabetes and type 2 *** 1 diabetes develops when the pancreas cannot make enough insulin,whereas type 2 diabetes spreads due to insulin *** is a recurrent,and chronic illness that is *** modern healthcare systems,disease detection technology is *** diabetes in its early stages is crucial for initiating timely treatment and halting disease *** proposed method has the potential not only to forecast the likelihood of future diabetes onset but also to identify the specific type of diabetes a person may *** paper investigates a potential solution for a diabetes prediction model in light of the continually rising prevalence of diabetes among *** proposed framework is designed using two datasets:the Pima Indian dataset,which is used to forecast diabetes,and the DiabetesType dataset,which is used to identify the type of diabetes mellitus an individual *** research aims to apply machine learning classifiers and ensemble models,such as Bagging,Voting,Averaging,and Stacking,for diabetes *** this context,SMOTE(synthetic minority oversampling technique)and hyperparameter adjustment of the algorithms are considered and have substantially improved the *** developed heterogeneous ensemble model offers enhanced prediction rates with different performance *** the bagging technique,random forest attains a 96%accuracy rate,resulting in better predictions in the PID *** the DiabetesType dataset,the voting ensemble model provides a 98.5%accuracy *** study highlights that ensemble learning models are effective in predicting diabetes and can outperform earlier relevant studies.

关键词： classification diabetes mellitus ensemble learning PID random forest

Rice Disease Diagnosis System (RDDS)

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Computers, Materials & Continua 2022年第10期73卷 1895-1914页

作者： Sandhya Venu Vasantha Shirina Samreen Yelganamoni Lakshmi Aparna Maturi Venkata Subba Rao(mvsr)engineering college HyderabadTelanganaIndia college of Computer and Information Sciences Majmaah UniversityKSA Indian Institute of Technology Indian School of Mines(ISM)DhanbadJharkhandIndia

Hitherto,Rice(Oryza Sativa)has been one of the most demanding food crops in the world,cultivated in larger quantities,but loss in both quality and quantity of yield due to abiotic and biotic stresses has become a major *** cultivation,the crops are most prone to biotic stresses such as bacterial,viral,fungal diseases and *** stresses can drastically damage the *** and erroneously recognized crop diseases can increase fertilizers costs and major yield loss which results in high financial loss and adverse impact on nation’s *** proven methods of molecular biology can provide accurate detection of pathogenic factors,but these methods are not accessible to the majority of the farmers,needs high costs or resources,and require domain knowledge to ***’s field inspection report provides precise crop diagnosis but continuous field inspection over the remotely placed agriculture fields is not ***,cost effective approach for early detection of diseases can help farmers to take necessary steps in time to boost up the crop *** agriculture makes use of decision support systems built using Machine Learning(ML)or Deep Learning(DL)approaches to cut down heavy *** crop diagnosis process can be automated with the involvement of Computer Vision,Image Processing and Deep Learning(DL)based methods for more precise prediction in less cost and *** research shows that more accurate image classification can be implemented using Deep Learning based Convolutional Neural Network(CNN)*** this paper,we have proposed an automated Rice Disease Diagnosis System(RDDS)for timely,more accurate and detailed crop disease diagnosis,which consisting of two modules,they are Leaf Disease Identification(LDI)module for disease detection and Infection Intensity Estimation(IIE)module for disease severity *** LDI module is based on the proposed novel RDD_CNN model that classified the eight most harmful a

关键词： Rice disease detection CNN Machine Learning Deep Learning image processing

Identifying the viscoelastic properties of soft matter from the indentation response of a hard film-soft substrate system

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Science China(Physics,Mechanics & Astronomy) 2020年第4期63卷 108-118页

作者： YanWei Liu YueGuang Wei Hao Long Department of Mechanics and engineering Science College of EngineeringPeking UniversityBeijing 100871China

The indentation technique is widely used in measuring the mechanical properties of soft matter at the microscale or nanoscale,but still faces challenges by these unique properties as well as the consequent strong surface adhesion, including the strong nonlinear effect, unclear judgment of the contact point, difficulties in estimating the contact area, and the risk of the indenter piercing the sample. Here we propose a two-step method to solve these problems: lay a hard film on a soft matter, and obtain the viscoelastic properties of this soft matter through the indentation response of this composite structure. We first establish a theoretical indentation model of the hard film-soft substrate system based on the theory of plates, elastic-viscoelastic correspondence principle and Boltzmann superposition principle. To verify the correctness of this method, we measure the mechanical properties of the methyl vinyl silicone rubber(mvsr) covered by a Cu nanofilm. Finally, we test the effectiveness and error sensitivity of this method with the finite element method(FEM). The results show that our method can accurately measure the mechanical properties of soft matter, while effectively circumventing the problems of the traditional indentation technique.

关键词： viscoelastic properties soft matter theoretical indentation model indentation technique finite element method

An Introduction to Information Sets with an Application to Iris Based Authentication

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Journal of Modern Physics 2020年第1期11卷 122-144页

作者： Madasu Hanmandlu Mamta Bansal Shantaram Vasikarla CSE Department MVSR Engineering College Hyderabad India Hella India Automotive Pvt. Ltd. Pune India CSE Department California State University Los Angels CA USA

This paper presents the information set which originates from a fuzzy set on applying the Hanman-Anirban entropy function to represent the uncertainty. Each element of the information set is called the information value which is a product of the information source value and its membership function value. The Hanman filter that modifies the information set is derived by using a filtering function. Adaptive Hanman-Anirban entropy is formulated and its properties are given. It paves the way for higher form of information sets called Hanman transforms that evaluate the information source based on the information obtained on it. Based on the information set six features, Effective Gaussian Information source value (EGI), Total Effective Gaussian Information (TEGI), Energy Feature (EF), Sigmoid Feature (SF), Hanman transform (HT) and Hanman Filter (HF) features are derived. The performance of the new features is evaluated on CASIA-IRIS-V3-Lamp database using both Inner Product Classifier (IPC) and Support Vector Machine (SVM). To tackle the problem of partially occluded eyes, majority voting method is applied on the iris strips and this enables better performance than that obtained when only a single iris strip is used.

关键词： Information Sets Energy Feature (EF) Sigmoid Feature (SF) Hanman Trans-form (HT) Hanman Filter (HF) Hanman-Anirbanentropy Function

Keystroke Dynamics Based Authentication Using Possibilistic Renyi Entropy Features and Composite Fuzzy Classifier

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Journal of Modern Physics 2018年第2期9卷 112-129页

作者： Aparna Bhatia Madasu Hanmandlu Department of Electrical engineering Indian Institute of Technology Delhi HauzKhas New Delhi India CSE Department MVSR Engg. College Nadergul Hyderabad Formally with EE Department IIT Delhi New Delhi

This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the information set features from keystroke dynamics for the authentication of users. A new composite fuzzy classifier is also proposed based on Mamta-Hanman entropy function and applied on the Information Set based features. A comparison of the results of the proposed approach with those of Support Vector Machine and Random Forest classifier shows that the new classifier outperforms the other two.

关键词： Keystroke Dynamics Information Set Renyi Entropy Function and Its Possibilistic Version Composite Fuzzy Classifier

Mammogram Classification with HanmanNets Using Hanman Transform Classifier

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Journal of Modern Physics 2024年第7期15卷 1045-1067页

作者： Jyoti Dabass Madasu Hanmandlu Rekha Vig Shantaram Vasikarla EECE Department The Northcap University Sector 23-A Gurugram India CSE Department MVSR Engineering College Nadergul Hyderabad India CSE Department The Northcap University Sector 23-A Gurugram India CS Department California State University Northridge CA USA

Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep information set features from ResNet by modifying its kernel functions to yield Type-1 HanmanNets and then AlexNet, GoogLeNet and VGG-16 by changing their feature maps to yield Type-2 HanmanNets. The two types of HanmanNets exploit the final feature maps of these architectures in the generation of deep information set features from mammograms for their classification using the Hanman Transform Classifier. In this work, the characteristics of the abnormality present in the mammograms are captured using the above network architectures that help derive the features of HanmanNets based on information set concept and their performance is compared via the classification accuracies. The highest accuracy of 100% is achieved for the multi-class classifications on the mini-MIAS database thus surpassing the results in the literature. Validation of the results is done by the expert radiologists to show their clinical relevance.

关键词： Mammograms ResNet 18 Hanman Transform Classifier Abnormality Diagnosis VGG-16 AlexNet GoogleNet HanmanNets