RETRACTED ARTICLE: Facial landmark detection using artificial intelligence techniques

Chen, Zhongshan; Xinning, Feng; Manickam, Adhiyaman; Sathishkumar, V E

doi:10.1007/s10479-021-04355-y

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…For the iterated regularization parameter of C=100, SVM outperforms the KNN and Logistic Regression models with an accuracy of 89.6 [14][15]. Numerous of the above-mentioned stateof-the-art prototype and classifier systems function on IoT and recognize animals using machine learning approaches [16]. This system, however, is still in its infancy for real-time use because it has yet to achieve the needed performance [17] [18].…”

Section: Related Workmentioning

confidence: 99%

Animal detection in farms using machine learning

Thylashri

Rajalakshmi

Karthikeyan

et al. 2023

ACE

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Agriculture is extremely important to a countrys development. Agricultural issues have consistently stymied the countrys progress. Farmers face a slew of problems, including a lack of water for irrigation, crops withering due to climatic changes, nutrient-depleted soils, and crop damage from pests and wildlife. In recent decades, innovation has developed to provide effective solutions to a large number of these difficulties. In any event, agricultural protection from wild animals has not been fully addressed until now. The fields have been destroyed by elephants, monkeys, and wild boars. The production of crops is diminished when wild animals trample them or eat them. The presence of wild animals on farmland has always been a contentious issue for farmers. Deer, wild boar, moles, elephants, and monkeys are just some of the animals that might ruin a harvest. When the farmer isn't there, these animals may eat the crops and wander the field, which is bad for the harvest. Because of this, yields might drop significantly, and supplementary financial protection could be necessary to cope with the fallout. This problem has to be solved as soon as possible, and a workable solution needs to be found and put into action. Accordingly, the goal of our programme is to address this problem. Wireless sensors might be used to set off an alarm in the event of an animal intrusion, communicating the situation to the proprietor and forest authorities with an attached photo. This provides an early warning so that measures may be taken according to the intruder type.

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Section: Related Workmentioning

confidence: 99%

Animal detection in farms using machine learning

Thylashri

Rajalakshmi

Karthikeyan

et al. 2023

ACE

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“…However, incorporating mammographic breast density into the Gail Model can improve its predictive accuracy. A study found that during 5.1 years, 9.5% of women suffered from invasive breast cancer, but the Gail Model's predictive accuracy was 95% [20].The use of Convolutional Neural Networks (CNNs) for the analysis of digital mammography images has shown promising results in terms of speed and accuracy in both image segmentation and tissue classification. The use of a median-sized database has also been found to improve the performance of the CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Blockchain and Classification of Mammograms and Histopathology Images in Breast Cancer Lesions

Barthwal

Joshi

Kumar

et al. 2023

ACE

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This work provides an overview of the research that has been done on using Computer-Aided Detection (CAD) for the diagnosis of breast cancer. The focus is on mammographic and histopathology images and the different techniques used for image pre-processing, segmentation, and classification. The accuracy of the different algorithms was evaluated on different datasets, including MIAS, IRMA, DDSM, and CBIS-DDSM, and the results showed that deep learning models such as Convolutional Neural Networks (CNNs) and Random Forest, along with Multi-Layer Perception and Nave Bayes, were effective in detecting and classifying breast cancer. The results of these studies show the potential of CAD in making the diagnosis of breast cancer easier and more accurate. In the segmentation stage, the U-Net model has been modified to perform better in terms of accuracy. The DDSM database has been found to have a higher accuracy percentage compared to the MIAS and CBIS-DDSM databases. This indicates that the modified U-Net model has performed well on the DDSM database in terms of accurately segmenting the images. The use of Computer Aided Diagnosis (CAD) has improved the accuracy of breast cancer diagnosis in mammography and histopathology images. The four-step process of preprocessing, segmentation, feature extraction, and classification has proven to be effective in detecting malignant and benign tumors. Different algorithms like Naive Bayes, Multilayer Perception, Random Forest, and Convolutional Neural Network (CNN) have been used with varying accuracy levels. The best results were obtained by using the modified U-Net model for segmentation and the Random Forest algorithm for classification, as they showed higher accuracy compared to other methods and databases. In this study it has been analyzed that the use of Computer-Aided Diagnosis (CAD) in mammography and histopathology images has shown promising results in the diagnosis of breast cancer. Different algorithms and techniques have been used to improve the accuracy of the classification of malignant and benign tumors. The MIAS database has shown the best results when using a Convolutional Neural Network approach. However, histopathology images have some limitations in comparison to radiologist images, but the use of CAD is still an important tool in cancer diagnosis.

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“…The fundamental target of face identification method is to recognize the face in the edge by lessening the outer clamours and different elements [9]. The means engaged with the FACE Identification Cycle are Facial feelings are considered as the most pivotal figure individuals' correspondence which empower us to see others' aims [10]. Normally, individuals conclude the others profound states like satisfaction, outrage, misery, and dread through two primary channels: voice tone and look.…”

Section: Face Capturing Using Webcammentioning

confidence: 99%

A heuristic technique for music recommendation using Haar cascade classifier through facial expressions

Karrothu¹,

Lavanya²,

Harish³

et al. 2023

ACE

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Human emotion assumes a fundamental part lately. Feeling depends on human sentiments which can be both communicated or not. Feelings communicate the human's singular way of behaving which can be in various structures. Extraction of the emotion states people individual condition of conduct. The goal of this task is to separate element from human face and identify feeling and to play music as indicated by the emotion identified. In this task, fostering a model to suggest dynamic music proposal framework in view of human feelings is our principal perspective. From the genuine face the emotion is recognized and when the mind-set is gotten from input picture, particular melodies for the state of mind would be played to hold the clients. In this methodology, the application gets associated with human sentiments accordingly giving an individualized touch to the clients. Our projected framework focuses on distinguishing the human face using Haar Cascade classifier. A few stages like face location utilizing webcam, facial feature extraction, look identification, with that interfacing with a music player will be followed. For exploratory outcomes, Haar cascade calculation and Computer vision along with CNN (Convolution Neural Network) for feeling discovery and music suggestion.

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RETRACTED ARTICLE: Facial landmark detection using artificial intelligence techniques

Cited by 4 publications

References 22 publications

Animal detection in farms using machine learning

Animal detection in farms using machine learning

Blockchain and Classification of Mammograms and Histopathology Images in Breast Cancer Lesions

A heuristic technique for music recommendation using Haar cascade classifier through facial expressions

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