Informatics Publishing Limited

K. Meenakshi Sundaram ¹, C. S. Ravichandra ¹

Affiliations
1 Coimbatore, Tamil Nadu, IN

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a name that refers to two lung diseases; they are chronic bronchitis and emphysema. The name COPD is used since both diseases are characterized by impediment to airflow that interferes with normal breathing and the two frequently co-exist with each other. Many researchers have developed different techniques to improve the performance of automatic screening process. In this paper, first the input image is pre-processed; the lung region is segmented from that image, segmented the cavity region in that lung region, extracted some features for training the classifier and used the FRB classifier to identify the COPD affected lung. The preprocessing is done by using the gaussian filter and the lung segmentation is done by comparing the region growing technique and the Local Gabor XOR pattern (LGXP) based region growing technique. The cavity segmentation is done by evaluating the pixel range in the segmented lung region and setting a threshold value from that evaluated pixels and comparing every pixel with that threshold value. After the lung and cavity segmentation, some parameters are chosen to train the classifier to identify whether an x-ray image is a normal or affected. The classifier used in proposed technique is FRB classifier. The FRB Classifier is then trained using the parameters chosen from the sample chest CT scan images to identify the normal lung and tuberculosis affected lung.

Keywords

Chronic Obstructive Pulmonary Disease (COPD), Fuzzy Rule Based Classifier (FRB), Local Gabor Xor Pattern (LGXP), Medical Imaging, Region Growing Technique

Full Text

     

M. Pitchiah Kumar ¹, K. Meenakshi Sundaram ², M. S. Ramasamy ²

Affiliations
1 State Licensing Authority, Directorate of Indian Medicine (Government of Tamilnadu), Arumbakkam, Chennai − 600106, IN
2 Sanjeev Biomedical Research Centre, 1/15, Kumaran Street, Keelkatalai, Chennai − 600117, Tamilnadu, IN

Abstract

COVID19 is the prime threat to the human race now. Currently the world faces Covid-19 out-break across all continents and it is characterized by its membrane proteins due to mutations. Siddha Medicine is one of the oldest medical system in the world which is believed to be originated more than 10,000 years ago which is prevalent in the ancient Tamil land. Siddha medicine classifies disease and disorders into 4448 types and has remedy for more than 64 types of fever. Among these Kabasura Kudineer and Thonthasura Kudineer are two siddha formulations used against fevers due to respiratory infections. The present study was carried out to evaluate these two formulations against COVID 19 using in silico docking methods. For that the active principles/phytocompounds from the ingredients of the formulations were docked against coronavirus spike glycoprotein trimmer (PDB ID: 3JCL) using iGEMDOCK software. 10 phytocompounds showed promising activity against COVID spike glycoprotein. This study showed that 10 phytocompounds which act as ligands to bind with viral proteins to prevent the binding of host receptors. Of these Cucurbitacin B (-112.09), Cardiofoliolide (-111.5), Apigenin (-98.84) and Pyrethrin (-92.98) were observed as more effective with less bind energies required for binding with spike proteins to prevent the fusion lead viral replication. Since Kabasura Kudineer contains more active phyto constituents, the higher activity was observed than Thontha sura Kudineer. The study demonstrated that Kabasura Kudineer could be a potential siddha medicine for COVID 19 provided further preclinical and clinical confirmatory studies

Keywords

COVID19, Kabasura Kudineer, Siddha Medicines, Thonthasura Kudineer.

Full Text

   

S. Karthigai ¹, K. Meenakshi Sundaram ¹

Affiliations
1 Department of Computer Science, Erode Arts and Science College, IN

Abstract

Data mining techniques used in many applications as there is an incredible growth in records and it is not feasible to find a solution manually. Amongst them, the medical records in data mining gains more popularity and have many missed values due to emergency cases or complicated situation etc. These missing values have a great influence in the desired output. The traditional mining procedure has to be enhanced to handle that between them and adjust the parameters to minimize the errors. The activation function in the neuron performs the non-linear transformation function making it capable to learn and perform more complex tasks. This function plays a vital role in the output process. This work focus on this function and made some enhancement by applying multi logit regression with Maximum A posteriori method in activation function to handle multi-class classification The proposed Enhanced Activation function in Multi- Layer Perceptron is implemented in Weka 3.9.6 and it is compared with traditional MLP with suitable evaluation metrics.

Keywords

Data Mining, Neural Network, Multi Layer Perceptron, Multi Logit Regression, Maximum APosteriori.

Full Text

     

K. Meenakshi Sundaram ¹, T. Parimalam ¹

Affiliations
1 Department of Computer Science, Erode Arts and Science College, Erode, Tamilnadu, IN

Abstract

Today many companies publish their applications on the Internet. Web Services play a vital role in business environment. Many services around the web, when we take a single web service alone had limited functionality. So single service is not sufficient to respond the user’s request and often services should be combined through services composition to achieve a specific goal. A large number of approaches exist to resolve multi-objective optimization problems. In this work PSO-inspired Clustering algorithms are proposed to reduce the processing time and increase the performance of composite services.

Keywords

Composite Services, PSO, Multi-Objective Optimization.

Full Text