Connectionist Expert System to Diagnose Neck and Arm Pain

A connectionist expert system (CES) called BIONET aimed at assisting physicians in the diagnosis of diseases, such as neck and arm pain has been proposed. BIONET is an artificial network or connectionist network model capable of classifying diseases. Need for the development of CES for defence personnel has been discussed: BIONET is a feedforward three layer neural network with one hidden layer. The input, layer has been designated as stimulus layer, the hidden layer as receptor layer and output layer ag cortical layer. The sequential connections with spatial orientation have been maintained between stimulus layer and receptor layer for each specific factor. Parallel connections are established only at the cortical layer. Direct firing and facilitatory and inhibitory mechanisms are adhered to the neurophysiology of human nervous system. An algorithm for training on BIONET is also given. BIONET is simulated on a digital computer with training samples of patients collected from various hospitals in Tamil Nadu to diagnose neck and arm pain,diseases for testing purpose

Solving Battle Management/Command Control and Communication Problem using Modified BIONET

This paper proposes and implements a neural architecture to solve the weapon allocationproblem in the multi-layer defense scenario using modified BIONET neural network architecture.The presynaptic layer of the modified BIONET reduces the dimensionality of the principal stateequation by partitioning the state space. The post-synaptic layer of the modified BIONET includesthe perceptron Q-learning rule. The cortical layer incorporates L-learning scheme to providebetter exploration over action space. Thus, action selection is effectively made with quickerconvergence of training. The reward scheme in the reinforcement learning is obtained bycalculating the measure of probability of survival. The decision module has been enhanced byincorporating the features corresponding to the battle weapons for effective representation ofthe environment. Thus, the modified BIONET neural architecture is used to increase the efficiencyof assets saved in the simulation and the time complexity is reduced due to the state-spacepartitioning scheme involved in the neural network. The proposed modified BIONET isimplemented in MATLAB and the percentage of assets saved is increased. Also, the trainingtime is drastically reduced. Thus, the modified BIONET resulted in saving more assets with fasterconvergence of learning

Missile Defence and Interceptor Allocation by LVQ-RBFMulti-agent Hybrid Architecture

This paper proposes a solution methodology for a missile defence problem using theatremissile defence (TMD) concept. In the missile defence scenario, the concept of TMD is generallyused for the optimal allocation of interceptors to counter the attack missiles. The problem iscomputationally complex due to the presence of enormous state space. The Learning vectorquantiser–Radial basis function (LVQ-RBF) multi-agent hybrid neural architecture is used as thelearning structure, and Q-learning as the learning method. The LVQ-RBF multi-agent hybridneural architecture overcomes the complex state space issue using the partitioning and weightedlearning approach. The proposed LVQ-RBF multi- agent hybrid architecture improvises thelearning performance by the local and global error criterion. The state space is explored withinitial coarse partitioning by LVQ neural network. The fine partitioning of the state space isperformed using the multi-agent RBF neural network. The discrete reward scheme is used forLVQ-RBF multi-agent hybrid neural architecture. It has a hierarchical architecture which enablesquicker convergence without the loss of accuracy. The simulation of the TMD is performed with500 assets and six priority of assets

User centric web search using ontology

Semantic information retrieval systems query the World Wide Web based on context information, and are intended to provide more pertinent search results. However, most of the existing systems overlook one important aspect ‘the user’. They are more focused on eliminating the obscure results that a conventional or non-semantic search engine would throw up and hence, they are pretty much static. On the other hand, our effort would channel its focus more towards providing a more user-centric service using ontology and involving learning and prediction. By studying the usage statistics of the user, context information can be built and used effectively to produce better search results. Such an approach also entails that the knowledge that is accrued, be organized such that the relationships between the data elements can be elicited easily and unambiguously. The ontology would be described using OWL. Latent semantic indexing algorithm is used for context analysis and retrieva

A case report of recurrent achondroplasia in fetuses of normal parents

Achondroplasia, a skeletal dysplasia has an incidence of 1 in 15000 to 1 in 30000 live births. It is inherited in an autosomal dominant manner. The occurrence of recurrent achondroplasia in babies born to normal parents is rare. The present case report is one such type. A female fetus of 27 weeks gestational age was brought to the Department of Anatomy, Karpaga Vinayaga Institute of Medical Sciences, Maduranthagam. There was frontal bossing of forehead, rhizomelic type of limb shortening with limitation of elbow extension in the fetus. The mother of the fetus, who is 26 years old, gave history of recurrence of such condition. Her first pregnancy was a twin pregnancy, conceived by natural methods, where one of the twins was a male baby who also had achondroplasia and died 2 hours after delivery. The other twin is a girl and the child has delayed developmental milestones. Her second pregnancy was uneventful. The present fetus under study is from her third pregnancy. Her marriage is of second degree consanguineous type. The age of her husband is 36 years old. Germinal mosaicism has been attributed for the causation of recurrent achondroplasia in children, whose parents are normal. 80% of achondroplasia is due to a new mutation. Only 20% of achondroplasia is inherited. Increased paternal age is a risk factor for new mutations to occur. The other investigations of the case and the genetic analysis are described further in the article

Chemical examination of the resinous exudate from Azadirachta indica. A. Juss

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Least Squares Support Vector Machine Based Classification of Abnormalities in Brain MR Images

The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed. This research paper proposes an intelligent classification technique to the problem of classifying four types of brain abnormalities viz. Metastases, Meningiomas, Gliomas, and Astrocytomas. The abnormalities are classified based on Two/Three/ Four class classification using statistical and textural features. In this work, classification techniques based on Least Squares Support Vector Machine (LS-SVM) using textural features computed from the MR images of patient are developed. LS-SVM classifier using non-linear radial basis function (RBF) kernels is compared with other techniques such as SVM classifier and K-Nearest Neighbor (K-NN) classifier. It has been observed that the method proposed using LS-SVM classifier outperforms all the other classifiers tested

Microwave-assisted solvent-free synthesis of 4-methyl-2-hydroxy- and 2-methyl-4-hydroxyquinolines

1203-1207Rapid and efficient microwave-assisted synthesis of 4-methyl-2-hydroxy- and 2-methyl-4-hydroxyquinolines from anilines and ethyl acetoacetate under different conditions is described