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Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture
Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days. To overcome such issues in e-Healthcare systems, Internet of Things (IoT) applications have been deployed using cloud computing (CC) approaches. There are still a number of disadvantages to using CC, including latency, bandwidth, energy usage, and security and privacy concerns. Fog computing (FC), a CC development, may be able to overcome these obstacles. DiaFog enabling remote users for real-time diagnosis of diabetic mellitus disease (DMD) has been proposed in this study, which is based on the combined ideas of IoT, cloud, and fog computing, as well as an ensemble deep learning (EDL) technique. The proposed system is trained with EDL approaches on the integrated dataset of two diabetes mellitus disease datasets (DMDDs), namely, Pima Indians Diabetes Dataset (PIDD) and Hospital Frankfurt Germany Diabetes Dataset (HFGDD), obtained from the UCI-ML and Kaggle repository, respectively, and the integrated dataset of these two. The suggested system has been used to demonstrate accuracy, precision, recall, F-measure, latency, arbitration time, jitter, processing time, throughput, energy consumption, bandwidth utilization, network utilization, scalability, and more. In the remote instantaneous diagnosis of diabetic patients, the integration of IoT-fog-cloud is useful. The results of the trials show the value of employing FC principles and their applicability for speedy diabetic patient remote diagnosis. PACS-key is describing text of that key PACS-key describing text of that key. 2022 Abhilash Pati et al. -
Diabetic Retinopathy Diagnosis Using Retinal Fundus Images through MobileNetV3
Diabetic Retinopathy (DR) is a major disease throughoutthe world. Diagnosis of diabetes at an early stage is so critical and could help save several lifestyles. One out of two individuals experiencing diabetes has been determined to have some phase of DR. Recognition of DR symptoms in time can turn away the vision weakness inmost the cases, nonetheless, such disclosure is troublesome with present devices and strategies. Existingmethods for determining whether a person is suffering from diabetes or maybe the chances of acquiring diabetesrely heavily on examiners. Most of the time, it can be treated if caught during the early stages. There is a need for creating models that are efficient and robust to detect DR holistically. In recent times the advent of Deep learning models has been used extensively in various Bio medical applications. In this work, we utilize a Hyper parameter tuned MobileNet-V3 model based on a multi-stage Convolutional Neural Network (CNN) to efficiently classify images from the IRDID dataset. A Multiclass classification model involving images collated from various sources were trained, validated and tested for classification accuracy. The network was evaluated based on parameters and the network was able to achieve an accuracy of 88.6% 2023 IEEE. -
Diabetic retinopathy detection via deep learning based dual features integrated classification model
Background: The primary recognition of diabetic retinopathy (DR) is a pivotal requirement to prevent blindness and vision impairment. This deadly condition is identified by highly qualified professionals by examining colored retinal images. Objective: The physical diagnostics for this condition was time-consuming and prone to fault. The development of computer-vision based intelligent systems has develop a main research area to effectually diagnosis the pathologies from an image. Methods: In this research, a novel Deep learning based Dual Features Integrated classification (DD-FIC) framework is designed to detect the DR from a color retinal image. Initially, the fundus images are denoised by Wavelet integrated Retinex (WIR) algorithm to remove the noise artifacts which provide high contrast image. This DD-FIC model contains two phases of feature extraction module to evaluation of several retinal areas. Initially, global features of the fundus image are retrieved by the assist of attention fused efficient model, whereas the attention module dynamically highlights the important features. Afterwards, the segmented retinal vessels data is converted into features for learning the local features. Results: Finally, the collective of features is processed into the Random Forest based feature selection model for the optimal prediction with five different classes using multi-class support vector machine (MCSVM). The efficacy of the proposed DD-FIC framework is estimated by Kaggle dataset with the detection accuracy of 98.6%. Conclusions: The proposed framework rises the accuracy of 1.54%, 3.65%, 13.79% and 6.28% for Multi-channel CNN, CNN, VGG NiN and Shallow CNN respectively. The Author(s) 2024. -
Diabetic Retinopathy Detection Using Various Machine Learning Algorithms
The advances in technologies have paved the way to generate huge amounts of data in a variety of forms. Machine learning techniques, accompanied by Artificial Intelligence with its challenging nature help in extracting meaningful information from such data. This will have a great impact on many sectors, such as social media analytics, construction and healthcare, etc. Computer-aided clinical decision-making plays a vital role in todays medical field. Hence, a high degree of accuracy with which machine learning algorithms can detect diabetic retinopathy is really in demand. Convolutional neural networks, a deep learning technique, have been used to recognize pathological lesions from images. Image processing and analytics methods are used and have been trained to recognize the significant complications of diabetes, which cause damage to the retina, diabetic retinopathy (DR). Though this condition does not show any symptoms in its early stages, it has to be screened, diagnosed and treated at the earliest or it may lead to blindness. Deep neural networks have proved successful in screening DR from retinal images and handling the risks that may arise due to the disease. This chapter focuses on detecting diabetic retinopathy in retinal images by using efficient image processing and deep learning techniques. It also attempts to investigate the requirements of image pre-processing techniques for diabetic retinopathy. Experiments are carried out by taking a set of retinal images and predicting the level of diabetic retinopathy on a scale of 0 to 4. Deep learning techniques like CNN and DenseNet are applied and tested. 2024 Taylor & Francis Group, LLC. -
Diabetic retinopathy detection using convolutional neural networka study
Detection and classification of Diabetic Retinopathy (DR) is a challenging task. Automation of the detection is an active research area in image processing and machine learning. Conventional preprocessing and feature extraction methods followed by classification of a suitable classifier algorithm are the common approaches followed by DR detection. With the advancement in deep learning and the evolution of Convolutional Neural Network (CNN), conventional preprocessing and feature extraction steps are rapidly being replaced by CNN. This paper reviews some of the recent contributions in diabetic retinopathy detection using deep architectures. Further, two architectures are implemented with minor modifications. Experiments are carried out with different sample sizes, and the detection accuracies of the two architectures are compared. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Diabetes mellitus prediction using machine learning within the scope of a generic framework
Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Diabetes Mellitus Classification Using Machine Learning Algorithms with Hyperparameter Tuning
Diabetes Mellitus is a prevalent condition globally, marked by elevated blood sugar levels resulting from either insufficient production of insulin or the body cells' inability to respond appropriately to released insulin. For people with diabetes to lead healthy, normal lives, early identification and treatment of the condition are essential. With the need to move away from current traditional procedures, towards a noninvasive methodology, machine learning and data mining technologies can be very useful in the classification of diabetes. Creating an effective machine learning model for the classification of diabetes mellitus was the primary goal of this research. This work is primarily carried out on combined Pima Indian diabetes dataset and German Frankfurt diabetes dataset. The class imbalance issue has been resolved using Synthetic Minority Oversampling Technique. One-hot encoding is applied to convert categorial features to numerical and various single and ensemble classifiers with the best hyperparameters obtained using GridSearchCV method were employed on the pre-processed dataset. With an AUC of 0.98 and maximum accuracy of 98.79%, the Random Forest ensemble technique outperformed the other models, according to the experimental results. As a result, the algorithm might be used to predict diabetes and alert doctors to serious cases that call for emergency care. 2024 IEEE. -
Diabe Maigre and Diabe Gras Revisited
[No abstract available] -
Di-cationic ionic liquid catalyzed synthesis of 1,5-benzothiazepines
A simple and elegant method for the synthesis of 1,5-benzothiazepines has been developed using di-cationic liquid as a solvent cum catalyst by the reaction of o-aminothiophenol with a variety of chalcones under mild reaction conditions. Furthermore the reusability of the catalyst has also been studied for three cycles. All the reactions are proposed to proceed through a 1,4-conjugate Michael addition followed by a cyclo-condensation reaction. 2018, Chemical Publishing Co. All rights reserved. -
Dhirubhai H.Ambani
Dhirubhai H.Ambani commemorative postage stamp was originally issued by the Department of Posts on 28 December, 2002. The stamp was chosen in honor of his contributions in economic growth and regeneration of the country. -
Dhat syndrome and its perceived impact on psychological well-being
Background: Dhat syndrome is a culture-bound syndrome originating in the Indian subcontinent, primarily among men characterized by the fear of loss of semen. Objective: The article discusses the perceived impact of Dhat syndrome on the overall psychological well-being of the individual. Method: Four patients from hospitals in Kolkata, West Bengal, were screened using MINI and then interviewed using semi-structured interview to assess presenting concerns, interventions, psychological well-being, attitude toward sex and masturbation, and their sociodemographic details. The data were then categorized based on the dimensions of the questionnaire, which was then analyzed individually and separately based on the dimensions. The differences and commonalities between the dimensions as conveyed by the participants were then reported. Results: The analysis showed that the participants reported lower levels of psychological well-being based on the categories of Seligman's PERMA model and attributed it to the symptoms experienced by them. They traced the beginning of the hindrances to achieving optimal well-being to the onset of symptoms. Conclusion: This article proposes the incorporation of integrative therapeutic interventions and advocacy of sex education to address the psychological well-being over the current symptom reduction interventions used. 2019 Indian Journal of Social Psychiatry | Published by Wolters Kluwer - Medknow. -
Dharma in Manusmrti: Agent of social cohesion and equilibrium
Dharma, through its role as a moral ideal and through its manifestations that permeate every part of the society, binds all the individuals in Manu's society as envisaged in Manusmrti. It serves as a common value and dictates common goals for the people that could be attained by functioning for the good of the institutions they belong to and, ultimately, for the survival of the society. Social institutions like marriage, family, varna system, as?rama system, political system and legal system were structured with accurate positions and roles for their efficient and smooth functioning. They were made to function compatibly with each other to ensure the survival of the society. Manu carefully avoided conflicts and competitions in the society. Thus, by acting as a cohesive agent, Dharma, as a foundational moral principle, integrates both individuals and the institutions to maintain the equilibrium of Manu's state. 2013 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (Dharmaram Vidya Kshetram, Bangalore), ISSN: 0253-7222. -
Dharma as a binary identity
The idea of Dharma has different connotation in History from that of religion as is popularly understood. While it is accepted as righteousness, it transcends the notion that Dharma represents piety, spirituality, belief and nobility. On the contrary, History is replete with instances of how religion, an institutionalized aspect of Dharma, was constantly articulated as representing Authority, Power, Status and Hierarchy. Due to these interpretations Dharma often was projected as a tool for realization of the above by various institutions, be they, political, social, cultural or economic, and Dharma provided legitimacy and justified their identities. The present paper juxtaposes this articulation in the context of Ancient and Medieval India, spanning a period approximately from 3rd century BCE to 10th century CE. It argues that the different trajectories that flowed between Dharma and various other secular institutions constantly witnessed divergence as well as assimilation at various points of time. 2015 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bengaluru), ISSN: 0253-7222. -
DFT, spectroscopic studies, NBO, NLO and Fukui functional analysis of 1-(1-(2,4-difluorophenyl)-2-(1H-1,2,4-triazol-1-yl)ethylidene) thiosemicarbazide
A novel triazole derivative 1-(1-(2,4-difluorophenyl)-2-(1H-1,2,4-triazol-1-yl)ethylidene) thiosemicarbazide was synthesized and subjected to density functional theory (DFT) studies employing B3LYP/6-31+G(d,p) basis set. Characterization was done by FT-IR, Raman, mass, 1H NMR and 13C NMR spectroscopic analyses. The stability of the molecule was evaluated from NBO studies. Delocalization of electron charge density and hyper-conjugative interactions were accountable for the stability of the molecule. The dipole moment (?), mean polarizabilty (??) and first order hyperpolarizability (?) of the molecule were calculated. Molecular electrostatic potential studies, HOMO-LUMO and thermodynamic properties were also determined. HOMO and LUMO energies were experimentally determined by Cyclic Voltammetry. 2018 Elsevier B.V. -
DFT study of structural and electronic properties of [Fe(CO)4(PbX)] complexes (X = O, S, Se and Te): Influence of terminal lead chalcogenide ligands on bonding and stability
Density Functional Theory (DFT) calculations at the B3LYP level were performed to investigate the structural and electronic properties of axial and equatorial isomers of [Fe(CO)4(PbX)] complexes, where X = O, S, Se, and Te. Total energy evaluations indicate that equatorial isomers are generally more stable than their axial counterparts. Detailed bonding analysis was carried out using Natural Population Analysis (NPA) and Energy Decomposition Analysis (EDA), providing insight into the nature of the FePbX interactions. The FePbX bond strengths were further assessed through Wiberg Bond Index (WBI) calculations. Frontier Molecular Orbital (FMO) analysis revealed HOMOLUMO gaps ranging from 3.04 to 3.97 eV, all of which are narrower than the corresponding gap in Fe(CO)5, suggesting enhanced electronic reactivity due to PbX substitution. Natural Bond Orbital (NBO) analysis indicated a greater electron density contribution from the Pb atom to the FePb bond, whereas for FeC bonds, carbon atoms contributed more significantly than Pb. These results collectively highlight the influence of terminal lead chalcogenide ligands on both the geometric and electronic structure of iron carbonyl complexes. 2025 Elsevier Inc. -
DFT studies on D?A substituted bis-1,3,4-oxadiazole for nonlinear optical application
In the present work, we have synthesized novel D?A substituted bis-1,3,4-oxadiazoles derivatives and studied nonlinear optical properties using density functional theory (DFT). The FT-IR and 1H NMR data confirmed the structure of the molecule. The HOMOLUMO, energy band gap, molecular electrostatic potential map, and global chemical reactivity descriptors were estimated using the DFT and TD-DFT with B3LYP, CAM-B3LYP and WB97XD using 6-31G (d) levels basis set and results show all synthesized molecules have excellent chemical hardness, chemical potential, excellent chemical strength, and excellent chemical stability. The static and dynamic linear polarizability, first hyperpolarizability and second hyperpolarizability components were estimated using time-dependent density functional theory. The first-order hyperpolarizability ? (2x; x, x) computed at a wavelength of 1064nm was found to be 55 times greater than the urea molecule. The dynamic molecular second-order hyperpolarizabilities ? (?3x;x,x,x) suggested good nonlinear properties for the designed molecule. The Author(s), under exclusive licence to The Optical Society of India 2024. -
DFT electronic structure calculations, spectroscopic studies, and normal coordinate analysis of 2-[(5-nitro-1,3-thiazol-2-yl)carbamoyl]phenyl acetate
The solid phase FTIR and FT-Raman spectra of 2-[(5-nitro-1,3-thiazol-2-yl)carbamoyl]phenyl acetate (25N2LCPA) have been recorded 450-4000 cm-1 and 100-4000 cm-1 respectively. The normal coordinate analysis was carried out to confirm the precision of the assignments. DFT calculations have been performed giving energies, optimized structures, harmonic vibrational frequencies and IR intensities. The structure of the molecule was optimized and the structural characteristics were determined by density functional theory (DFT) using B3LYP method with 6-31+G(d,p) basis set. The detailed interpretation of the vibrational spectra has been carried out with aid of normal coordinate analysis (NCA) following the scaled quantum mechanical force field methodology. The Vibrational frequencies are calculated in the above method and are compared with experimental frequencies which yield good agreement between observed and calculated frequencies. Stability of the molecule arising from hyper conjugative interactions, charge delocalization has been analyzed using natural bond orbital (NBO) analysis. In addition, Frontiers molecular orbital and molecular electrostatic potential were computed by using Density Functional Theory (DFT) B3LYP/6-31+G(d,p) basis set. The calculated HOMO and LUMO energies show that charge transfer occurs in the molecule. 2014 Elsevier B.V. All rights reserved. -
DFT Aware Test Architecture for Communication ICs: ATPG-Based Fault Detection on Lower Technology Node
In this research, hafnium dioxide (HfO2) and titanium dioxide (TiO2) are investigated as advanced gate dielectrics for GaN-based MISHEMTs on diamond substrates. AlGaN/GaN MISHEMTs, incorporating HfO2 and TiO2 as gate dielectrics, have been rigorously analyzed and optimized for RF and DC performance through ATLAS TCAD simulations. The MISHEMTs with HfO2 gate dielectrics exhibited impressive metrics: a high drain current density (IDS) of 3.62 A/mm, a breakdown voltage (VBR) of 998 V, a transconductance (gm) of 1.09 S/mm, and a cutoff frequency (fT) of 49 GHz. Conversely, the MISHEMTs utilizing TiO2 as the passivation layer demonstrated even superior performance, achieving an IDS of 3.7 A/mm, a V_B of 1168 V, a gm of 1.13 S/mm, and an fT of 48 GHz. Both dielectric materials contributed to a notably low on-resistance of 4.9 ?mm. The synergistic effect of the diamond substrate with high-performance HfO2 or TiO2 gate dielectrics positions these MISHEMTs as highly promising candidates for next-generation power switching and RF applications, due to their enhanced efficiency and robustness under high-power and high-frequency conditions. The proposed work improves the performance enhancement of Metal-Insulator-Semiconductor High Electron Mobility Transistors (MISHEMTs) with inclusion of diamond substrate. Diamond substrate to its wide energy bandgap ranges of 5.5 eV for used materials for both power electronics and RF applications electrical and Thermal properties are concerned in its high. 2025, Society for Communication and Computer Technologies. All rights reserved. -
Device, system and method for wireless control of medical devices /
Patent Number: 202121038822, Applicant: Dr K. Sampath Kumar.
The various embodiments of the present invention provide a device coupled with a medical apparatus for controlling a function of the said medical apparatus. The device comprises a processing unit, a communication coupler, an actuator, an accelerometer and a sensor package. The communication coupler provides a communication interface between the processing unit and a processor of a medical apparatus. The actuator is connected to the processing unit through an electromechanical mechanism. The accelerometer is connected to the processing unit through a bidirectional channel to control axial stability in a desirable position. The sensor package is installed over the medical apparatus and is connected to the processing unit.