Parkinson Disease P arkinson disease involves the nervous system, specifically, movement and control of muscles. Parkinson disease affects more than 1 million individuals in the United States. Because it is more common in older persons, the incidence (number of new cases) of Parkinson disease is increasing as the population grows older. Parkinson disease was first described in 1817 and was originally called " shaking palsy. " Medical researchers later discovered that parkinsonian symptoms were due to degeneration of nerve cells in an area of the brain called the substantia nigra. These cells supply dopamine, a chemical that modulates movement, to other areas of the brain called the basal ganglia. Parkinson disease is progressive and leads to severe limitations in activity and quality of life if the disorder is not treated. Neurologists (doctors who specialize in treating diseases of the nervous system) and neurosurgeons (doctors who specialize in surgery of the nervous system) individualize each person' s treatment to manage the symptoms and slow the progress of Parkinson disease. The January 21, 2004, issue of JAMA includes an article about treatment of Parkinson disease. The JAMA Patient Page is a public service of JAMA. The information and recommendations appearing on this page are appropriate in most instances, but they are not a substitute for medical diagnosis. For specific information concerning your personal medical condition, JAMA suggests that you consult your physician. This page may be photocopied noncommercially by physicians and other health care professionals to share with patients. Any other print or online reproduction is subject to AMA approval. To purchase bulk reprints, call 718/946-7424. • Levodopa (a synthetic medication) replaces dopamine for patients with Parkinson disease, improving symptoms of tremor and rigidity. • Carbidopa helps reduce adverse effects of levodopa and increases the amount of dopamine in the brain (and not in the rest of the body). • Several other medications are available to decrease symptoms or slow the progression of Parkinson disease. • Antidepressants may be prescribed if depression occurs. • Pallidotomy—a probe delivering electrical current is used to create a permanent lesion (scar) in a specific area of the brain (the globus pallidus) to lessen rigidity, tremor, and abnormal movements (dyskinesias). • Thalamotomy—electrical current creates a small, permanent lesion in the area of the brain called the thalamus. This helps reduce tremor and rigidity. • Deep brain stimulation—an electrode is placed into a specific deep brain structure. Electrical …

Journal of the American Medical Association

/pdf/ensemble-deep-learning-a-review-16rga6gz.pdf

Ensemble deep learning: A review

With the growth of online social network platforms and applications, large amounts of textual user-generated content are created daily in the form of comments, reviews, and short-text messages. As a result, users often find it challenging to discover useful information or more on the topic being discussed from such content. Machine learning and natural language processing algorithms are used to analyze the massive amount of textual social media data available online, including topic modeling techniques that have gained popularity in recent years. This paper investigates the topic modeling subject and its common application areas, methods, and tools. Also, we examine and compare five frequently used topic modeling methods, as applied to short textual social data, to show their benefits practically in detecting important topics. These methods are latent semantic analysis, latent Dirichlet allocation, non-negative matrix factorization, random projection, and principal component analysis. Two textual datasets were selected to evaluate the performance of included topic modeling methods based on the topic quality and some standard statistical evaluation metrics, like recall, precision, F-score, and topic coherence. As a result, latent Dirichlet allocation and non-negative matrix factorization methods delivered more meaningful extracted topics and obtained good results. The paper sheds light on some common topic modeling methods in a short-text context and provides direction for researchers who seek to apply these methods.

/pdf/using-topic-modeling-methods-for-short-text-data-a-15j608gync.pdf

Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis.

Feature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other fields. For this purpose, some studies have introduced tools and softwares such as WEKA. Meanwhile, these tools or softwares are based on filter methods which have lower performance relative to wrapper methods. In this paper, we address this limitation and introduce a software application called FeatureSelect. In addition to filter methods, FeatureSelect consists of optimisation algorithms and three types of learners. It provides a user-friendly and straightforward method of feature selection for use in any kind of research, and can easily be applied to any type of balanced and unbalanced data based on several score functions like accuracy, sensitivity, specificity, etc. In addition to our previously introduced optimisation algorithm (WCC), a total of 10 efficient, well-known and recently developed algorithms have been implemented in FeatureSelect. We applied our software to a range of different datasets and evaluated the performance of its algorithms. Acquired results show that the performances of algorithms are varying on different datasets, but WCC, LCA, FOA, and LA are suitable than others in the overall state. The results also show that wrapper methods are better than filter methods. FeatureSelect is a feature or gene selection software application which is based on wrapper methods. Furthermore, it includes some popular filter methods and generates various comparison diagrams and statistical measurements. It is available from GitHub (
 https://github.com/LBBSoft/FeatureSelect
 ) and is free open source software under an MIT license.

/pdf/featureselect-a-software-for-feature-selection-based-on-r1op9mue64.pdf

FeatureSelect: a software for feature selection based on machine learning approaches

A ranking-based feature selection approach for handwritten character recognition

COVID-19 continues to have catastrophic effects on the lives of human beings throughout the world. To combat this disease it is necessary to screen the affected patients in a fast and inexpensive way. One of the most viable steps towards achieving this goal is through radiological examination, Chest X-Ray being the most easily available and least expensive option. In this paper, we have proposed a Deep Convolutional Neural Network-based solution which can detect the COVID-19 +ve patients using chest X-Ray images. Multiple state-of-the-art CNN models—DenseNet201, Resnet50V2 and Inceptionv3, have been adopted in the proposed work. They have been trained individually to make independent predictions. Then the models are combined, using a new method of weighted average ensembling technique, to predict a class value. To test the efficacy of the solution we have used publicly available chest X-ray images of COVID +ve and –ve cases. 538 images of COVID +ve patients and 468 images of COVID –ve patients have been divided into training, test and validation sets. The proposed approach gave a classification accuracy of 91.62% which is higher than the state-of-the-art CNN models as well the compared benchmark algorithm. We have developed a GUI-based application for public use. This application can be used on any computer by any medical personnel to detect COVID +ve patients using Chest X-Ray images within a few seconds.

/pdf/automatic-covid-19-detection-from-x-ray-images-using-3q1i22dqrj.pdf

Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network

Feature selection, both for supervised as well as for unsupervised classification is a relevant problem pursued by researchers for decades. There are multiple benchmark algorithms based on filter, wrapper and hybrid methods. These algorithms adopt different techniques which vary from traditional search-based techniques to more advanced nature inspired algorithm based techniques. In this paper, a hybrid feature selection algorithm using graph-based technique has been proposed. The proposed algorithm has used the concept of Feature Association Map (FAM) as an underlying foundation. It has used graph-theoretic principles of minimal vertex cover and maximal independent set to derive feature subset. This algorithm applies to both supervised and unsupervised classification. The performance of the proposed algorithm has been compared with several benchmark supervised and unsupervised feature selection algorithms and found to be better than them. Also, the proposed algorithm is less computationally expensive and hence has taken less execution time for the publicly available datasets used in the experiments, which include high-dimensional datasets.

A new hybrid feature selection approach using feature association map for supervised and unsupervised classification

In modern world, we have to deal with huge volumes of data which include image, video, text and web documents, DNA, microarray gene data, etc. Organizing such data into rational groups is a critical first step to draw inferences. Data clustering analysis has emerged as an effective technique to accurately accomplish the task of categorizing data into sensible groups. Clustering has a rich association with researches in various scientific domains. One of the most popular clustering algorithms, k-means algorithm was proposed as early as 1957. Since then, many clustering algorithms have been developed and used, to group data in various commercial and non-commercial sectors alike. In this paper, we have given concise description of the existing types of clustering approaches followed by a survey of the fields where clustering analytics has been effectively employed in pattern recognition and knowledge discovery.

A Short Review on Different Clustering Techniques and Their Applications

FCTFS works in both autonomous and user guided mode.The defined taxonomy helps in arriving at optimal number of good quality clusters.Feature elimination due to irrelevance and redundancy is clearly isolated.It is faster than traditional search based methods.Yields superior results compared to some state of the art methods over 24 data sets. Feature subset selection is basically an optimization problem for choosing the most important features from various alternatives in order to facilitate classification or mining problems. Though lots of algorithms have been developed so far, none is considered to be the best for all situations and researchers are still trying to come up with better solutions. In this work, a flexible and user-guided feature subset selection algorithm, named as FCTFS (Feature Cluster Taxonomy based Feature Selection) has been proposed for selecting suitable feature subset from a large feature set. The proposed algorithm falls under the genre of clustering based feature selection techniques in which features are initially clustered according to their intrinsic characteristics following the filter approach. In the second step the most suitable feature is selected from each cluster to form the final subset following a wrapper approach. The two stage hybrid process lowers the computational cost of subset selection, especially for large feature data sets. One of the main novelty of the proposed approach lies in the process of determining optimal number of feature clusters. Unlike currently available methods, which mostly employ a trial and error approach, the proposed method characterises and quantifies the feature clusters according to the quality of the features inside the clusters and defines a taxonomy of the feature clusters. The selection of individual features from a feature cluster can be done judiciously considering both the relevancy and redundancy according to users intention and requirement. The algorithm has been verified by simulation experiments with different bench mark data set containing features ranging from 10 to more than 800 and compared with other currently used feature selection algorithms. The simulation results prove the superiority of our proposal in terms of model performance, flexibility of use in practical problems and extendibility to large feature sets. Though the current proposal is verified in the domain of unsupervised classification, it can be easily used in case of supervised classification.

A feature cluster taxonomy based feature selection technique

The importance of agriculture in modern society need not be overstated. In order to meet the huge requirements of food and to mitigate, the conventional problems of cropping smart and sustainable agriculture have emerged over the conventional agriculture. From computational perspective, computer vision and machine learning techniques have been applied in many aspects of human and social life, and agriculture is not also an exception. This review paper gives an overview of machine learning and computer vision techniques which are inherently associated with this domain. A summary of the works highlighting different seeds, crops, fruits with the country is also enclosed. The paper also tries to give an analysis, which can help researchers to look at some relevant problems in the context of India.

Amit Kumar Das

Papers

Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network

A new hybrid feature selection approach using feature association map for supervised and unsupervised classification

A Short Review on Different Clustering Techniques and Their Applications

A feature cluster taxonomy based feature selection technique

A Review on Agricultural Advancement Based on Computer Vision and Machine Learning