Diabetic retinopathy (DR) is a disease that damages retinal blood vessels and leads to blindness. Usually, colored fundus shots are used to diagnose this irreversible disease. The manual analysis (by clinicians) of the mentioned images is monotonous and error-prone. Hence, various computer vision hands-on engineering techniques are applied to predict the occurrences of the DR and its stages automatically. However, these methods are computationally expensive and lack to extract highly nonlinear features and, hence, fail to classify DR's different stages effectively. This paper focuses on classifying the DR's different stages with the lowest possible learnable parameters to speed up the training and model convergence. The VGG16, spatial pyramid pooling layer (SPP) and network-in-network (NiN) are stacked to make a highly nonlinear scale-invariant deep model called the VGG-NiN model. The proposed VGG-NiN model can process a DR image at any scale due to the SPP layer's virtue. Moreover, the stacking of NiN adds extra nonlinearity to the model and tends to better classification. The experimental results show that the proposed model performs better in terms of accuracy, computational resource utilization compared to state-of-the-art methods.

Cardiovascular disease (CVD) is a prevalent and serious condition causing a significant global mortality rate. According to the World Health Organization (WHO), in 2022, CVD claimed the lives of approximately 19.1 million people, accounting for 33% of global fatalities. ECG is widely used for automatic detection of CVD using traditional machine learning; however, it is usually difficult to select optimal features. Addressing this issue, a scalable machine learning-based architecture is proposed for early CVD detection based optimal feature selection. This architecture aims to revolutionize healthcare by enabling timely diagnosis and treatment, reducing CVD-related fatalities. Comprising data collection, storage, and processing components, the system employs machine learning classifiers to predict patients’ heart conditions. Initially feature are extracted from ECG signals then feature selection techniques like FCBF, MrMr, and relief, along with PSO-optimization are used to select optimal features. Extra Tree and Random Forest classifiers trained on the selected features have achieved notable performance rates with accuracy of 100% respectively. Furthermore, a comparison of the proposed method with state of the art on both small and large dataset is provided. The proposed system holds potential to revolutionize patient care and substantially lower CVD-related mortality, enhancing the quality of life for affected individuals. In summary, this architecture offers a promising solution to the pressing issue of CVD and paves the way for advanced healthcare systems.

Parkinson’s disease (PD) Dysgraphia is a disorder that affects most PD patients and is characterized by handwriting anomalies caused mostly by motor dysfunctions. Several effective ways to quantify PD dysgraphia analysis have been used, including online handwriting processing. In this research, an integrated approach, using a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) layers along with a Random Forest (RF) classifier, is proposed for dysgraphia classification. The proposed approach uses uniform and normal distributions to randomly initialize the weights and biases of the CNN and LSTM layers. The CNN-LSTM model predictions are paired with the RF classifier to enhance the model’s accuracy and endurance. The suggested method shows promise in identifying handwriting symbols for those with dysgraphia, with the CNN-LSTM model’s accuracy being improved by the RF classifier. The suggested strategy may assist people with dysgraphia in writing duties and enhance their general writing skills. The experimental results indicate that the suggested approach achieves higher accuracy.

The ever-growing Internet of Things (IoT) data traffic is one of the primary research focuses of future mobile networks. 3rd Generation Partnership Project (3GPP) standards like Long Term Evolution-Advanced (LTE-A) have been designed for broadband services. However, IoT devices are mainly based on narrowband applications. Standards like LTE-A might not provide efficient spectrum utilization when serving IoT applications. The aggregation of IoT data at an intermediate node before transmission can answer the issues of spectral efficiency. The objective of this work is to utilize the low cost 3GPP fixed, inband, layer-3 Relay Node (RN) for integrating IoT traffic into 5G network by multiplexing data packets at the RN before transmission to the Base Station (BS) in the form of large multiplexed packets. Frequency resource blocks can be shared among several devices with this method. An analytical model for this scheme, developed as an r-stage Coxian process, determines the radio resource utilization and system gain achieved. The model is validated by comparing the obtained results with simulation results.

There are several infections and diseases that can affect plants. Diagnosis of plant diseases is a challenging task. A fascinating method for identifying plant diseases is computer-based diagnosis using digital images of plant’s leaves. Most of the earlier research in this field has been devoted to feature engineering and traditional machine learning (ML) techniques. Based on hand-crafted features taken from digital photographs of the plant’s leaves, these methods identify various plant diseases. It can be challenging to use feature engineering to extract high-quality features from these digital images. Deep learning (DL) algorithms, have relieved feature engineering by automatically extracting and learning resilient features. However, the high number of parameters in conventional DL models typically leads to overfitting. Therefore, gradient vanishing problems in vast networks intensify learning failure and generalization errors. Additionally, getting a big dataset for starting from scratch to train a deep learning model is also difficult. To solve these issues to detect Dome Galls in Cordia dichotoma G. Forst . early, this research suggests quick and efficient deep transfer learning (DTL) models. In the proposed method, a custom dataset of Cordia dichotoma leaf images is created. 1784 images of Cordia dichotoma leaf are collected in real world environment, and offline augmentation techniques are applied for obtaining the final training dataset of 5400 images. Further, image preprocessing techniques are used to enhance the images. A DL model, Yolov4, has been modified and is trained for the early detection of dome galls in the leaves of Cordia. The model is trained using pre-trained weights, a process called transfer learning (TL). The model is tested on another set of 200 images, and the results show an accuracy of 95% and an F1-score of 95.8%. Experimental results show that the modified Yolov4 performs 3% more accurately than the original Yolov4.