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Global food security can be influenced by the diseases in crop plants as several diseases straightforwardly influence the quality of the grains, vegetables, fruits, etc., which also results in affecting of agricultural productivity. Like other plants, the mango tree is also affected by several diseases, and also the identification of multi-disease classification with a single leaf is more complex, and also it is impossible to detect diseases with bare eyes. Based on the other plants, the mango tree is also affected by various diseases, which is more difficult to detect the disorders with bare eyes. It is error-prone, inconsistent, and unreliable. Here, the mango trees are affected during the production, and also affect the plant health regarding multi-diseases. When the plants are affected by the diseases, it may cause fewer amounts of productivity, as a result, impacting the economy. However, it is more critical to detect plant diseases with the large varieties of trees and plants. Various research tasks on deep learning approaches focus on identifying the diseases in plants including leaves and fruits. Thus, the main objective of this paper is to implement an effective and appropriate technique for diagnosing mango tree diseases and their symptoms through fruit and leaf images, and thus, there is a need for an appropriate system for cost-effective and early solutions to this problem. Hence, the main intention of this work is to implement an efficient and suitable technique for diagnosing mango tree diseases and also identify the symptoms through fruit and leaf images. Intending to overcome the existing challenges, there is a need for an appropriate system for achieving cost-effectiveness and also creating an early solution to resolve this problem. This paper intends to present novel deep learning models for mango tree multi-disease classification. Initially, the data collection is done for gathering the diseased parts of the mango tree in terms of leaf and fruit images. Then, the contrast enhancement of the images is performed by the "Contrast-Limited Adaptive Histogram Equalization (CLAHE)". For the deep feature extraction of leaf images, and fruit images, Convolutional Neural Network (CNN) is employed, and the features from both inputs are concatenated for further processing. Further, the weighted feature selection is adopted for selecting the most significant features by the Adaptive Squirrel-Grey Wolf Search Optimization (AS-GWSO). Enhanced "Long Short Term Memory (LSTM)" is applied in the classification part with parameter optimization using the same AS-GWSO for enhancing classification accuracy. At last, the results of the designed system on various mango tree diseases verify that the designed approach has yielded the highest accuracy by evaluating conventional approaches. Therefore, it would also alleviate and treat the affected mango leaf diseases accurately. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Cardiac Vascular Disease (CVD) is determined to be the most prevailing disease all over the globe specifically in the case of elderly persons. Among various cardiac disease, CVD account for major mortality all over the globe. Diagnosis of cardiac disease at an early stage is mandatory to reduce the rate of mortality. Still, there is no availability of skilled specialists even in case of developed countries for accurate diagnosis. OBJECTIVES: Achieving automated and accurate diagnosis, computer vision based methods that functions with the help of AI techniques are focused on by researchers. In this current research automated CVD prediction model is designed using a deep learning approach. METHODS: ECG image dataset is utilized in this proposed CVD prediction model. Initially, the Non-ST-elevation myocardial infarction (NSTEMI) ECG data collected from the healthcare centre is taken as input. This input ECG image is converted into a signal and further, it is segmented using the sliding window segmentation technique. Then, using segmented signal QRS peak detection is achieved using Elephant Herd Optimization (EHO) algorithm. From the peak, detected signal features are extracted using Heart Rate Variability (HRV) analysis. Following that the extracted features are sent as input into the Deep Belief Network (DBN) classifier to predict CVD patients. RESULTS: The proposed CVD prediction model is implemented and some of the performance metrics are calculated. Accuracy, error, precision, sensitivity and specificity attained by the proposed model using the second dataset are 95%, 5%. 96%, 94% and 96%. Results showed that the functioning of proposed CVD prediction model is better when compared with other existing techniques. CONCLUSION: Based on this analysis it can be revealed that accurate and timely CVD prediction can be achieved with a lessor error rate. Further, this proposed model can be used in real time healthcare application by collecting NSTEMI ECG signal from patients. [ABSTRACT FROM AUTHOR]

Brain tumor is a serious disease leading to a life-threatening condition when worsen resulting in brain cancer. With a rapid fall in 5-year survival rate, brain cancer is the root cause for a large number of deaths worldwide. Diagnosis of brain tumor and detection of nature of cancer with higher accuracy at earliest possible stage is very important for proper treatment that will help patients have a better chance of surviving the disease. As per our study in this paper we conducted comparative research on various machine learning methods for detection of brain tumor at an early stage using Magnetic Resonance Images (MRI). MRIs is one of the most preferable diagnostic techniques for Brain Tumor image diagnosing as it gives more detailed images of the brain structures and blood vessels without using ionized radiations and any physical movement of the patient. This paper majorly focuses on a comparative analysis of various approaches for classification and detection of brain tumor and at last analysing the recent best technique. The process of detection followed in research till date follows Image Pre-Processing, Image Segmentation, Feature Extraction, and Support Vector Machine Classification. Various sources of datasets have been used that includes limited amount of MRI Images. Our analysis is also focused on the limitations and shortcomings of current algorithms. Thus, by comparing various researches, we summarized that when support vector machine (SVM) was used to classify brain tumor images, accuracy was trustworthy for patients but usage of limited amount of dataset reduced the essence of the prediction as using larger dataset can surely bring worthy conversion. We sum up that CNN with Transfer Learning model when used for brain tumor detection increase the reliability of prediction as this model when used with Paediatric Pneumonia detection and Covid-19 detection gave impressive results with increased dataset volume, resulting in early detection of disease and rapid treatment. Thus, for better results, classification and detection of Brain Tumor using CNN with Transfer Learning will be a good option. [ABSTRACT FROM AUTHOR]