Explore the vast range of titles available.

Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.

Alzheimer’s disease (AD) is a growing public health crisis, a very global health concern, and an irreversible progressive neurodegenerative disorder of the brain for which there is still no cure. Globally, it accounts for 60–80% of dementia cases, thereby raising the need for an accurate and effective early classification. The proposed work used a healthy aging dataset from the USA and focused on three transfer learning approaches: VGG16, VGG19, and Alex Net. This work leveraged how the convolutional model and pooling layers work to improve and reduce overfitting, despite challenges in training the numerical dataset. VGG was preferably chosen as a hidden layer as it has a more diverse, deeper, and simpler architecture with better performance when dealing with larger datasets. It consumes less memory and training time. A comparative analysis was performed using machine learning and neural network algorithm techniques. Performance metrics such as accuracy, error rate, precision, recall, F1 score, sensitivity, specificity, kappa statistics, ROC, and RMSE were experimented with and compared. The accuracy was 100% for VGG16 and VGG19 and 98.20% for Alex Net. The precision was 99.9% for VGG16, 96.6% for VGG19, and 100% for Alex Net; the recall values were 99.9% for all three cases of VGG16, VGG19, and Alex Net; and the sensitivity metric was 96.8% for VGG16, 97.9% for VGG19, and 98.7% for Alex Net, which has outperformed when compared with the existing approaches for the classification of Alzheimer’s disease. This research contributes to the advancement of predictive knowledge, leading to future empirical evaluation, experimentation, and testing in the biomedical field.

Parkinson’s disease (PD) is a neurodegenerative disorder which challenges the population due to its uncertainty in prediction of the disease. It is a progressive disorder of the nervous system marked by tremor, muscular rigidity and slow, imprecise movement, chiefly affecting middle-aged and elderly people. In our proposed work, we utilize the facial emotions of PD patients and normal person to identify their facial emotions like sad, happy, anger, and depression. For this predictive analysis, the datasets are acquired from Parkinson’s Progression Markers Initiative (PPMI) which consists of 188 PD patients and 50 normal people for testing and training process. By utilize this dataset, we applying the CNN architecture of Alex Net, and Vgg 16 to achieve their performance in terms of accuracy, sensitivity, specificity, F1 score and area under curve. Finally, it proven that Vgg 16 gives 10% more accurate results than Alex Net. This research outcome will be very useful in diagnosis of early-stage Parkinson’s disease in healthcare.

Parkinson’s disease (PD) is one of the main types of neurological disorders affected by progressive brain degeneration. Early detection and prior care may help patients to improve their quality of life, although this neurodegenerative disease has no known cure. Magnetic Resonance (MR) Imaging is capable of detecting the structural changes in the brain due to dopamine deficiency in Parkinson’s disease subjects. Deep learning algorithms provide cutting-edge results for various machine learning and computer vision tasks. We have proposed an approach to classify MR images of healthy and Parkinson’s disease patients using deep convolution neural network. However, these algorithms require a large training dataset to perform well on a particular task. To this effect, we have applied a deep convolution neural network classifier that incorporates transfer learning and data augmentation techniques to improve the classification. To increase the size of training data, GAN-based data augmentation is used. A total of 504 images are collected, and 360 images are used to augment data. The increased data set of this model is as many as 4200 images, and the produced images are of good quality by using this data set for the detection of peak signal-to-noise ratio (PSNR) having an innovative value in the norm of real images. The pre-trained Alex-Net architecture helps in refining the diagnosis process. The MR images are trained and tested to provide accuracy measures through the transfer learned Alex-Net Model. The results are addressed to demonstrate that the fine-tuning of the final layers corresponds to an average classification accuracy of 89.23%. The experimental findings show that the proposed method offers an improved diagnosis of Parkinson’s disease compared to state-of-the-art research.

Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and follow the epoch. We proposed and tested a convolutional neural network called SleepInceptionNet, which allows sleep classification of a single epoch using a single-channel electroencephalogram (EEG). SleepInceptionNet is based on our systematic evaluation of the effects of different EEG preprocessing methods, EEG channels, and convolutional neural networks on automatic sleep staging performance. The evaluation was performed using polysomnography data of 883 participants (937,975 thirty-second epochs). Raw data of individual EEG channels (ie, frontal, central, and occipital) and 3 specific transformations of the data, including power spectral density, continuous wavelet transform, and short-time Fourier transform, were used separately as the inputs of the convolutional neural network models. To classify sleep stages, 7 sequential deep neural networks were tested for the 1D data (ie, raw EEG and power spectral density), and 16 image classifier convolutional neural networks were tested for the 2D data (ie, continuous wavelet transform and short-time Fourier transform time-frequency images). The best model, SleepInceptionNet, which uses time-frequency images developed by the continuous wavelet transform method from central single-channel EEG data as input to the InceptionV3 image classifier algorithm, achieved a Cohen κ agreement of 0.705 (SD 0.077) in reference to the gold standard polysomnography. SleepInceptionNet may allow real-time automated sleep staging in free-living conditions using a single-channel EEG, which may be useful for on-demand intervention or treatment during specific sleep stages.

In today’s era, vegetables are considered a very important part of many foods. Even though every individual can harvest their vegetables in the home kitchen garden, in vegetable crops, Tomatoes are the most popular and can be used normally in every kind of food item. Tomato plants get affected by various diseases during their growing season, like many other crops. Normally, in tomato plants, 40–60% may be damaged due to leaf diseases in the field if the cultivators do not focus on control measures. In tomato production, these diseases can bring a great loss. Therefore, a proper mechanism is needed for the detection of these problems. Different techniques were proposed by researchers for detecting these plant diseases and these mechanisms are vector machines, artificial neural networks, and Convolutional Neural Network (CNN) models. In earlier times, a technique was used for detecting diseases called the benchmark feature extraction technique. In this area of study for detecting tomato plant diseases, another model was proposed, which was known as the real-time faster region convolutional neural network (RTF-RCNN) model, using both images and real-time video streaming. For the RTF-RCNN, we used different parameters like precision, accuracy, and recall while comparing them with the Alex net and CNN models. Hence the final result shows that the accuracy of the proposed RTF-RCNN is 97.42%, which is higher than the rate of the Alex net and CNN models, which were respectively 96.32% and 92.21%.

Human bones are the hard organs that protect vital organs such as the heart, lungs, and other internal organs. Fractures of the bones are a prevalent issue among humans. Bone fractures may develop from an accident or another circumstance when there is great pressure on the bones. It may be difficult and time-consuming to determine the site of a fracture in a patient who is suffering discomfort. The manual examination of fractures during radiological interpretation is a time-consuming and error-prone process. This may result in erroneous detection, poor fracture healing, and an extensive procedure. So, this research proposed an effective approach to rectifying bone fractures with the inclusion of the latest technologies. The solution is proposed by employing a Deep learning model. Moreover, a novel concept of classification is also incorporated. Firstly; the MURA dataset was collected from Stanford. Secondly; The proposed model used techniques like DCNN (Deep Convolution Neural Network) and use Alex Net model. Bones are classified into fractured or non-fractured through a classification approach. The proposed model was created using Google Colab. The proposed model was trained by repeating several experiments. The performance was evaluated based on accuracy. The suggested model results were compared with baseline algorithms as well. Consequently, the findings of this work will be useful for the medical industry.

Tailoring a deep convolutional neural network (DCNN) is a tedious and time-consuming task in the field of medical image analysis. In this research paper, Deep-generative adversial neural network (Deep-GAN) based model is proposed using grid search optimization (GSO) technique for identification and classification of thyroid nodule. The main objective of this work is to propose a deep learning (DL) model for the identification and classification of thyroid nodules without user or specialist intervention. The proposed model has gone through four phases namely (i) data acquisition, (ii) pre-processing (iii) data augmentation using GAN technique and (iv) optimization and classification using Deep-GAN model. Two pre-trained architectures namely Alex-Net and Visual Geometry Group (VGG-16) are considered for the identification and classification of thyroid nodule in ultrasonography (USG) images. From the experiment, it is found that Alex-GAN model has shown an improvement of 2 to 4 percentage points in comparison with VGG-GAN model and reported literature on Thyroid digital image database (TDID) public and collected dataset.

Diabetic Retinopathy is a major threat to cause vision loss in people suffering from Diabetes Mellitus. Many machine learning algorithms were proposed to detect Diabetic Retinopathy (DR) at an early stage, and with proper treatment vision loss may be reduced. This paper proposes a novel method to detect DR through severity scale by observing the abnormalities through ensemble methods. Deep learning based models are gaining focus to construct automated tools for medical image analysis. This paper uses Alex Net based DNN (Deep Neural Network) which functions on the basis of Convolution Neural Network (CNN) and is applied to have an optimal solution for automated DR detection with Random Forest Classifier (RFC). Recursively Separated and Weighted Histogram Equalisation (RSHWE) is used to preserve brightness, ensemble of segmentation algorithms to the identify Region of Interest (ROI). Feature map constructed using Gaussian and Gabor filter coefficients and Grey Level Co occurrence Matrix (GLCM) features and these features are applied to Random Forest Classifier (RFC) to classify the diseased images. The performance of RFC is also compared with and without Gradient features with Enhanced RFC (E-RFC). The accuracy of various classifiers is compared with our proposed method. In this paper, the considered performance metrics are accuracy, sensitivity, specificity. This method experimented on publicly available fundus image data sets for DR and shows good results with an accuracy (94.8%), specificity (93%), sensitivity (96%).

The Automatic Number Plate Recognition (ANPR) technology can automatically photograph and identify vehicle license plates. It uses advanced computer vision technologies to analyse video surveillance footage. Automatic License Plate Recognition (ALPR) is used to identify cars in car components, stolen vehicles, and traffic management. Many Automatic Number Plate Recognition (ANPR) research have practical obstacles. These challenges include being limited to predefined borders, interior spaces, and vehicle velocities on designated driveways. We introduce the Black Window evolutionary Transfer Moth fly (BWETM) with Alex. This research aims to extract number plate attributes and improve detection. The Preprocessing approach improves the median construct, while Morphological Methods help identify the License Plate. The Enhanced Sliding Contract Window aids segmentation. Finally, the hybrid Black Window and Evolutionary Transfer Moth fly (BW-ETMFO), and Alex net architecture achieve optimum image characteristics and boost classification accuracy. The method’s Sensitivity, Specificity, F-measure, Recall, and Precision are compared to traditional approaches to determine its accuracy. The suggested solution surpasses other conventional methods in identifying license plate numbers with 98.54% accuracy, regardless of image quality degradation.