Sign language is used all over the world by the hearing-impaired and disabled to communicate with each other and the rest of the world.Sign language is more than just moving fingers or hands; it is a viable and visible language in which gestures and facial expressions play a very important role.These signs can be effectively used as far as communication with humans is concerned but with respect to communication with machines better methodologies and algorithms have to be developed.This paper presents a system which can be used by disabled people (who can communicate only with sign languages) to communicate with machines in order to employ it to their daily life.The system focuses on pose estimation of gestures used by physically disabled people to give suitable signals to the machines.Pose is generated using silhouettes followed by gesture recognition using Mahalanobis distance metric.The system further was to control a wireless robot from different gestures used by disabled people.The basic American sign language symbols I Hand, II Hand, Aboard, All Gone, etc were recognized and a specific signal with respect to each gesture is transmitted which controls the robot.The robot is made to perform forward, backward, left, right and stop actions for each gesture presented to the system by the disabled person.The present system can also be modeled to send wireless SMS or emails in worse situations.The system demonstrated potency in the estimation of motion and pose for interpreting the sign language by using the silhouettes of the pose.The proposed system recognizes sign language, thus providing disabled people a medium to communicate with machines, leading to simplicity in their day to day work.

In the past years, microarray technologies have become a central tool in biological research. The extraction or identification of gene groups with similar expression pattern plays an important role in the analysis of genes. Besides traditional clustering methods, biclustering is also being used to analyze biological datasets due to its ability to group both genes across conditions simultaneously. The paper presents a comparison of advanced with the traditional tools for biological data extraction. This paper compares different clustering and biclustering approaches used to analyze DLBCL (diffuse large B-cell lymphoma) microarray dataset. The algorithms were compared on the grounds of enrichment values with support from runtime analysis. Typical annotations for the analyzed list of genes can be well understood using the BicAT toolbox. The paper explains in detail the intellects affecting the enrichment values, leading to the best technique for the dataset mentioned above.

A number of different classifiers have been used to improve the precision and accuracy and give better classification results. Machine learning classifiers have proven to be the most successful techniques in majority of the fields. This paper presents a comparison of the three most successful machine learning classification techniques SVM, boosting and Local SVM applied to a cancer dataset. The comparison is made on the basis of precision and accuracy along with the training time analysis. Finally, the efficacy of the classifiers is found.