In this paper, authors have proposed a technique which uses the existing database of chess games and machine learning algorithms to predict the game results. Authors have also developed various relationships among different combinations of attributes like half-moves, move sequence, chess engine evaluated score, opening sequence and the game result. The database of 10,000 actual chess games, imported and processed using Shane’s Chess Information Database (SCID), is annotated with evaluation score for each half-move using Stockfish chess engine running constantly on depth 17. This provided us with a total of 8,40,289 board evaluations. The idea is to make the Multi-Variate Linear Regression algorithm learn from these evaluation scores for same sequence of opening moves and game outcome, then using it to calculate the winning score of a side for each possible move and thus suggesting the move with highest score. The output is also tested with including move details. Game attributes are also classified into classes. Using Naïve Bayes classification, the data result is classified into three classes namely move preferable to white, black or a tie and then the data is validated on 20% of the dataset to determine accuracies for different combinations of considered attributes.

Accurate prediction of cancer can play a crucial role in its treatment. The procedure of cancer detection is incumbent upon the doctor, which at times can be subjected to human error and therefore leading to erroneous decisions. Using machine learning techniques for the same can prove to be beneficial. Many classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are proven to produce good classification accuracies. The following study models data sets for breast, liver, ovarian and prostate cancer using the aforementioned algorithms and compares them. The study covers data from condition of organs, which is called standard data and from gene expression data as well. This research has shown that SVM classifier can obtain better performance for classification in comparison to the ANN classifier.

Opportunistic routing (OR) for multi-hop wireless networks was first proposed by Biswas and Morris in 2004, but again as a modified version in 2005 as Extremely Opportunistic Routing (ExOR). A few other variants of the same were also proposed in the meanwhile time. In this paper we propose a Dynamic Opportunistic Routing (DOR) protocol which depends on network density and also provides spatial diversity. Our routing protocol is distributed in nature and provides partial 802.11 MAC layer abstraction. To verify the results of our protocol we took a network with light-density of nodes and bigger in size (as OR performs better in higher node density). A wireless mesh network in “QualNet network simulator” was created, where the average end-to-end delay and throughput at every node are compared with that of other standard routing protocol OLSR-INRIA.