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Early stage detection of osteoarthritis of the joints (hip and knee) using machine learning
This study explores the developing relationship between health care and technology, with a special emphasis on the use of machine learning (ML) algorithms to detect early stage osteoarthritis (OA) in the hip and knee joints. OA, a substantial worldwide health problem, requires improved diagnosis techniques. In this analysis, we illuminate the limitations of traditional methods, emphasizing the inherent subjectivity of clinical assessments and the delay in detection using routine imaging techniques. The research investigates the potential of ML to bring about significant changes. It focuses on combining various algorithms with extensive datasets and highlights the need to select relevant features and prepare the data to improve the accuracy of the models. The use of ML is closely connected to ethical issues, which include the protection of data privacy and the capacity to comprehend the models used. To bridge the gap between theory and practice, the chapter presents concrete examples of ML's practical use in detecting OA, opening possibilities for customized therapy and enhanced patient results. The chapter also highlights potential areas for future study, emphasizing the urgent requirement for additional progress in ML-based early detection techniques to alleviate the worldwide impact of OA. 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Early Prediction of Plant Disease Using AI Enabled IOT
India is an industrialized country, and about 70% of the residents rely on agriculture. Leaves are damaged by chemicals, and climates issues. An unknown illness is found on plants leads to the lowering of quality of produced. Internet of Things is a practice of reinventing the wheel agriculture by enabling farmers to tackle the problems in the industry with practical farming techniques. IoT helps to inform knowledge about factors like weather, and moisture condition. We proposed IoT, ML, and image processing based method to identify the infection. IOT enabled camera to capture the image then required region of interest is extracted. After ROI extraction, image is enhanced to remove the unwanted details form the image and to improve image quality. We compute image features. At the end we do the classification which is a twostep process training and testing and done by SVM. Our proposed method gives 92% accuracy. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Early prediction of lungs cancer by deep learning algorithms from the CT images with LBP features
The early prediction of the any type of cancer can save the lives of many especially if it is lung cancer which is one of the deadly diseases in the world. Thus the early prediction is implemented we can increase life expectancy and bring the mortality level low. Although there are various methods to detect the lung cancer cells by X-ray and CT scans, however the CT images are more preferred. The 2D images like CT scans are used to get medical results more accurate. The proposed method here will discuss how the LBP features are used to analyze the CT images with the support of Deep Learning methods. In this research work we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. LBP features helps in estimating the distribution of local binary pattern of an image. A final result with 93% is achieved after the training of the processed images by LBP features. 2020 SERSC. -
Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning
Drunkenness or exhaustion is a leading cause of car accidents, with severe implications for road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of time. Several drowsiness detection technologies to monitor for signs of inattention while driving and notifying the driver can be adopted. Sensors in self-driving cars must detect if a driver is sleepy, angry, or experiencing extreme changes in their emotions, such as anger. These sensors must constantly monitor the driver's facial expressions and detect facial landmarks in order to extract the driver's state of expression presentation and determine whether they are driving safely. As soon as the system detects such changes, it takes control of the vehicle, immediately slows it down, and alerts the driver by sounding an alarm to make them aware of the situation. The proposed system will be integrated with the vehicle's electronics, tracking the vehicle's statistics and providing more accurate results. In this paper, we have implemented real-time image segmentation and drowsiness using machine learning methodologies. In the proposed work, an emotion detection method based on Support Vector Machines (SVM) has been implemented using facial expressions. The algorithm was tested under variable luminance conditions and outperformed current research in terms of accuracy. We have achieved 83.25 % to detect the facial expression change. 2013 IEEE. -
Early diagnosis of COVID-19 patients using deep learning-based deep forest model
Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Early Detection of Cervical Cancer using Machine Learning Classifiers for Improved Diagnosis in Underserved Regions
One of the incurable diseases that affect women is cervical cancer. It is brought on by a protracted infection of the skin and the vaginal mucous membrane cells. The Human Papilloma Virus (HPV), is the main factor causing aberrant cell proliferation in the area around the cervix. There are no symptoms present when the illness first appears. Early detection of this malignancy may be used to prevent death. People in less developed countries cannot afford to periodically examine themselves due to a lack of awareness, poor medical infrastructure, and expensive medication. The EDA technique is applied to examine the data and understand its characteristics. Machine Learning algorithm has been used to diagnose cervical cancer. In order to spot the existence of cervical cancer, five machine learning classifiers are utilized, the algorithms to begin earlier. The Logistic Regression classifier's results validate the correct stage prediction. 2023 IEEE. -
Early detection of breast cancer using ER specific novel NIR fluorescent dye conjugate: A phantom study using FD-f-DOT system
Fluorescence diffuse optical tomography (f-DOT) is an imaging technique that can quantify the spatial distribution of fluorescent tracers in small animals and human soft tissues. Efficacy of f-DOT imaging can be improved by tagging a functional group to the dye. A novel estrogen receptor (ER) specific near-infrared (NIR) fluorescent dye conjugate was synthesized which can be effectively used for detecting breast cancer tissues at an early stage. Our novel dye, Near Infrared Dye Conjugate-2 (NIRDC-2), is a conjugate of 17?-estradiol with an analogue of Indocyanine Green dye, bis1,1-(4-sulfobutyl) indotricarbocyanine-5-carboxylic acid, sodium salt. Our present study focuses on imaging cylindrical silicone phantoms using Frequency Domain f-DOT system. Background absorption and scattering coefficients were 0.01mm-1 and 1mm-1 respectively. 10?M concentration of NIRDC-2 and Indocyanine Green (ICG) were administered separately into a cylindrical hole (target) of size 8mm diameter in the phantom. In-silico studies were performed to analyze the properties of dyes using experimental data. Absorption coefficient of 0.0002 mm-1 was recovered for the background. Fluorophore absorption coefficient at the target recovered were 0.000173 mm-1 and 0.000408 mm-1 for ICG and NIRDC-2 respectively. In comparison with ICG, our novel dye had a two fold higher target to background contrast. Recovered target position was accurate but size altered. In concurrence with the recovered fluorescent property and the cell lines studies carried out earlier, binding properties of NIRDC-2 makes it a potential probe for the early tumor detection using f-DOT system. COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. -
Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging
The most frequent kind of damage to strawberries is bruising. However, most of the bruises are so barely perceptible at an early stage on the surface, that detection of them with the human eye is quite challenging. This study proposes a method for accurately detecting and classifying the damage using reflectance imaging spectroscopy. In order to carry out the study, an experiment was devised to artificially induce bruises and a dataset was generated at different bruise intervals. A model for detecting and classifying bruises at their latent stage was developed using machine learning classifiers, including support vector machines (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), to investigate the changes over time after bruise occurrence on the detection performance. Regression models for the prediction of bruising time were developed using partial least square regression (PLSR), RF, gradient boosting (GB), support vector regression (SVR), and DT. Among the compared models, both SVM and LDA could achieve 99.99 % classification accuracy. RF was regarded as being the most advisable for detection and prediction jobs due to its high performance. It achieved MSE of 0.052 and R2 of 0.989 for prediction. 2024 Elsevier Ltd -
Earliness of SME internationalizationand performance: Analyzing the role of CEO attributes
Purpose: The purpose of this paper is to understand the mediating effects of Chief Executive officer (CEO) attributes on the earliness of internationalization and performance in context of Indian small and medium enterprises (SMEs). Design/methodology/approach: The proposed framework is tested through analysis of a sample of 102 internationalized SMEs of the engineering industry in the Bangalore city region of India. Findings: Results highlight that CEOs age and educational background moderates between early internationalization and performance in the Indian SME context. Practical implications: Overall results facilitate in leveraging the decision-makers capabilities to successfully formulate and strategize their international marketing efforts to achieve higher performance. Originality/value: The study enriches the importance of CEO attributes in influencing the early internationalization and degree of internationalization in the context of an emerging economy where studies are limited. 2019, Emerald Publishing Limited. -
Earlier Stage Identification of Bone Cancer with Regularized ELM
A major focus of current research in the field of image processing is the application of such methods to the field of medical imaging. While dealing with biological issues like fractures, canoers, ulcers, etc., image processing facilitated pinpointing the precise cause and tailoring a remedy. In the field of tumor identification, medical imaging has set a new standard by overcoming a number of challenges. Medical imaging is the practice of generating images of the human body for diagnostic or exploratory purposes. Because of its high image quality, MRI is the method of choice for detecting tumors. This research study proposes the integration of RLM to detect tumors and presents an automatic bone cancer detection system to assist oncologists in making early diagnosis of bone malignancies, which in turn allows patients to receive treatment as soon as possible. This research work also proposes to detect bone tumors by using a combination of the RELM based M3 filtering, Canny Edge segmentation, and the Enhanced Harris corner approach. When compared to other models like CNN, ELM, and RNN, the suggested technique achieves an accuracy of around 97.55%. 2023 IEEE. -
Ear Recognition Using ResNet50
Deep learning techniques have become increasingly common in biometrics over the last decade. However, due to a lack of large ear datasets, deep learning models in ear biometrics are limited. To address this drawback, researchers use transfer learning based on various pre-trained models. Conventional machine learning algorithms using traditional feature extraction techniques produce low recognition results for the unconstrained ear dataset AWE. In this paper, an ear recognition model based on the ResNet-50 pretrained architecture outperforms traditional methods in terms of recognition accuracy in AWE dataset. A new feature level fusion of ResNet50 and GLBP feature is also experimented to improve the recognition accuracy compared to traditional features. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Ear Recognition Using Rank Level Fusion of Classifiers Outputs
An individuals authentication plays a vital role in our daily life. In the last decade, biometric-based authentication has become more prevalent than traditional approaches like passwords and pins. Ear recognition has gained attention in the biometric community in recent years. Researchers defined several features for the identification of a person from ear image. The features play a vital role in the success of classification models. This paper considers an ensemble of features for designing a new classification model. The features are assessed in isolation as well as through feature-level fusion. Subsequently, a rank-level fusion for classification is introduced. The experiments are conducted on both constrained and unconstrained ear datasets. The results are promising and open up new possibilities in machine learning-based ear recognition 2023, International journal of online and biomedical engineering.All Rights Reserved. -
Ear Recognition Using Pretrained Convolutional Neural Networks
Ear biometrics, which involves the identification of a person from an ear image, is challenging under unconstrained image capturing scenarios. Studies in Ear biometrics reported that the Convolutional Neural Network is a better alternative to classical machine learning with handcrafted features. Two major concerns in CNN are the requirement of enormous computing resources and large datasets for training. The pretrained network concept helps to use CNN with smaller datasets and is less demanding on hardware. In this paper, three pre-trained CNN models, AlexNet, VGG16, and ResNet50 are used for ear recognition. The fully connected classification layers of the nets are trained with AWE, an unconstrained ear dataset. Alternatively, the CNN layers output (the CNN features) are extracted, and an SVM classification model is built. To improve the classification accuracy, the training dataset size is increased through data augmentation. Data augmentation improved the classification accuracy drastically. The results show that ResNet50, with the fully connected classification layer, results in higher accuracy. 2021, Springer Nature Switzerland AG. -
Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest Based FinTech Application for Hyper-automation
Hyper automation is the group of approaches and software companies utilised to automate manual procedures. Financial Technology (FinTech) was processed as a distinctive classification that highly inspects the financial technology sector from a broader group of functions for enterprises with utilise of Information Technology (IT) application. Financial crisis prediction (FCP) is the most essential FinTech technique, defining institutions financial status. This study proposes an Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest (ESAOA-ODCF) based FinTech Application for Hyperautomation. The ESAOA-ODCF technique has achieved exceptional performance with maximum accu y of 98.61%, and F score of 98.59%. Extensive experimental research revealed that the ESAOA-ODCF model beat more modern, cutting-edge approaches in terms of overall performance. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
E-shopping orientation, trust and impulse buying in the online context a study based on female members of Generation Z in India
A large number of studies have attempted to understand consumer behaviour in the online context. One construct that has been of particular interest to marketers, retailers and researchers, is impulse buying behaviour. The number of studies attempting to understand the drivers of impulsive purchases has been on a rise. The current pandemic also saw a rise in impulsive purchases and the interest in the construct was renewed. The current study is based on the S-O-R model and evaluates the relationship between e-shopping orientation, trust and impulse buying behaviour. The findings are based on data collected from female members of Generation Z and suggest that frequent visits to e-retail stores and increased patronage can increase the level of trust in the retail partner and influence the number of impulsive purchases. The findings are particularly significant for retailers looking to drive sales through impulsive purchases. In addition, the findings provide empirical support for the application of the S-O-R model to online retail context. Copyright 2024 Inderscience Enterprises Ltd. -
E-service quality-impact on customer satisfaction
The paper aims to determine the impact of e-service quality on customer satisfaction. The study utilised data from 252 customers of private and public banks in India through questionnaires. It was found that the e-service quality has significant impact on customer satisfaction in public sector banks as well as private sector banks. 2019 SERSC. -
E-learning During COVID-19Challenges and Opportunities of the Education Institutions
As part of the COVID-19 lockdown, educational institutions were closed and adopted e-learning to keep the learning process going. Due to the COVID-19 pandemic, e-learning has become a required component of all educational institutions such as schools, colleges, and universities worldwide. This pandemic has thrown the offline teaching process into chaos. This chapter discusses the concept and role of e-learning during the pandemic and various challenges and opportunities of e-learning encountered by educational institutions. Three broad challenges identified in e-learning are inaccessibility, self-inefficacy, and technical incompetency. E-learning opportunities are no geographic barriers, flexibility, creativity, and critical learning incorporation increased utilization of online resources and reinforced distance learning. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
E-leadership, psychological contract and real-time performance management: Remotely working professionals
Using the model of E- Leadership in Virtual teams (Avolio et al.), this paper examines impact of Psychological Contract, mediated by E-leadership, on Real-time Performance Management of Remotely working professionals. Following a quantitative research method, data was collected from 57 remotely working professional across the world. Significant positive relationships were found among Leadership Effectiveness of E-leaders, Relational Psychological Contract and Efficiency of RPM. The results confirm the interaction of the given variables in Avolio's Model of E-leadership, highlighting technological aspects of human interactions and ways to optimise them. The results underline several important managerial implications for effective leadership, fulfilling psychological contract and effective performance management, of a type of workforce that is only virtually available. 2019 SCMS Group of Educational Institutions. All rights reserved. -
E-governance service quality and effective e-governance: A qualitative evaluation of telecentres of Karnataka /
Asian Journal of Research in Social Sciences and Humanities, Vol. 7, Issue 3, pp. 1272-1288, ISSN: 2249-7315. -
E-governance diffusion in the telecenters of Karnataka-a gender analysis
E-governance is the interaction between a government and its citizens to deliver services in an efficient manner by means of information technology and telecommunication. The current study takes into account three aspects namely-economical, governance and services that impact the e-governance diffusion in the telecenters set up at the hobli level of Karnataka state. A framework is created with these aspects and validated through the present study. The study explores whether gender differentials exist in the e-governance diffusion process. The research adds up to the literature in establishing that gender differentials disappear when the e-governance is in the stage of maturity. One-way ANOVA is used to identify the gender differentials in e-governance diffusion through the NadaKacheri centres of Karnataka. The study proposes policy changes by the government to render better services and governance to the citizens. 2017 Inderscience Enterprises Ltd.