The Transmission Control Protocol.

Writing a faulty literature review is one of many ways to derail a dissertation. This article summarizes some pivotal information on how to write a high-quality dissertation literature review. It begins with a discussion of the purposes of a review, presents taxonomy of literature reviews, and then discusses the steps in conducting a quantitative or qualitative literature review. The article concludes with a discussion of common mistakes and a framework for the self-evaluation of a literature review.

A guide to writing the dissertation literature review.

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

Load-balancing algorithms in cloud computing

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.

/pdf/automatic-diagnosis-of-the-12-lead-ecg-using-a-deep-neural-3tivjfe75u.pdf

Automatic diagnosis of the 12-lead ECG using a deep neural network

Classification of electrocardiogram (ECG) signals plays an important role in diagnoses of heart diseases An accurate ECG classification is a challenging problem This paper presents a survey of ECG classification into arrhythmia types Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient Different classifiers are available for ECG classification Amongst all classifiers, artificial neural networks (ANNs) have become very popular and most widely used for ECG classification This paper discusses the issues involved in ECG classification and presents a detailed survey of preprocessing techniques, ECG databases, feature extraction techniques, ANN based classifiers, and performance measures to address the mentioned issues Furthermore, for each surveyed paper, our paper also presents detailed analysis of input beat selection and output of the classifiers

Classification of ECG signals using machine learning techniques: A survey

Detection and classification of rice plant diseases

Identifying disease from the images of the plant is one of the interesting research areas in computer and agriculture field. This paper presents a survey of different image processing and machine-learning techniques used in the identification of rice plant diseases based on images of disease infected rice plants. This paper presents not only survey of various techniques but also concisely discusses important concepts of image processing and machine learning applied to plant disease detection and classification. We carry out detailed study of 19 papers, covering the work on rice plant diseases and other different plants and fruits, and present a survey of these papers based on important criteria. These criteria include size of image dataset, no. of classes(diseases), preprocessing, segmentation techniques, types of classifiers, accuracy of classifiers etc. We utilize our survey and study to propose and design our work on detection and classification of rice plant diseases.

A survey on detection and classification of rice plant diseases

An accurate rainfall forecasting is very important for agriculture dependent countries like India For analyzing the crop productivity, use of water resources and pre-planning of water resources, rainfall prediction is important Statistical techniques for rainfall forecasting cannot perform well for long-term rainfall forecasting due to the dynamic nature of climate phenomena Artificial Neural Networks (ANNs) have become very popular, and prediction using ANN is one of the most widely used techniques for rainfall forecasting This paper provides a detailed survey and comparison of different neural network architectures used by researchers for rainfall forecasting The paper also discusses the issues while applying different neural networks for yearly/monthly/daily rainfall forecasting Moreover, the paper also presents different accuracy measures used by researchers for evaluating performance of ANN

Rainfall forecasting using neural network: A survey

Cotton is the most important cash crop in India. It is also known as “White Gold” or “The King of fibers” among all cash crops in the country. About 80–90% of the diseases which occur on the leaves of cotton are Alternaria leaf spot, Cercospora leaf spot, Bacterial blight and Red spot. This paper presents a survey on detection and classification of cotton leaf diseases. It is difficult for human eyes to identify the exact type of leaf disease which occurs on the leaf of plant. Thus, in order to identify the cotton leaf diseases accurately, the use of image processing and machine learning techniques can be helpful. The images used for this work were acquired from the cotton field using digital camera. In pre-processing step, background removal technique is applied on the image in order to remove background from the image. Then, the background removed images are further processed for image segmentation using otsu thresholding technique. Different segmented images will be used for extracting the features such as color, shape and texture from the images. At last, these extracted features will be used as inputs of classifier.

Harshadkumar B. Prajapati

Papers

Classification of ECG signals using machine learning techniques: A survey

Detection and classification of rice plant diseases

A survey on detection and classification of rice plant diseases

Rainfall forecasting using neural network: A survey

A survey on detection and classification of cotton leaf diseases