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Implementation of Artificial intelligence techniques is used as a medical diagnostic tool to increase the diagnostic accuracy and provide more additional knowledge. Muscular dystrophy is a disorder which diagnosed with Electromyography (EMG) signals. A Wavelet-based decomposition technique is proposed here to classified Healthy EMG signals (Normal) from abnormal muscular dystrophy EMG signals. In this work, a wavelet transform is applied to preprocessed EMG signals for decomposing it into different frequency sub-bands. Statistical analysis is carried out to these decomposed sub-bands to extract different statistical features. SVM and ANN classifier is proposed here to discriminate muscular dystrophy disorder from healthy Electromyography signals. Finally proposed methodology gives classification accuracy of 95% on publically available clinical EMG database. The results show better classification accuracy using an SVM classifier compare to ANN classifier on selected statically feature sets. The finding from the above method gave the best classifier for analysis and classification of EMG signals for recognition of muscular dystrophy disorders.

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Multi-hop Ad Hoc Networks are self-organizing networks characterized by dynamically changing topology due to node mobility and time varying characteristics of the wireless channel. Routing is a crucial issue in these networks. Several routing protocols have been proposed which fall into either of these categories: proactive, reactive or hybrid routing protocols. Proactive protocols have the advantage of less route establishment latency but suffer from heavy control overhead. Since routes maintained may never be used, system resources are unnecessarily wasted making proactive approaches less efficient. The reactive protocols overcome this drawback. These incur less overhead due to their "on demand" nature; nodes maintain routing information only when it is needed. Hence reactive protocols e.g AODV, DSR etc are preferred and widely adopted. But the on-demand behavior of these approaches itself leads to another problem, e.g the "broadcast storm" problem and thus challenges their usability. In this paper we highlight limitations and operational challenges of widely adopted reactive protocols and survey different optimization approaches suggested to overcome these challenges.

Abstract. Artificial intelligence techniques are being used effectively in medical diagnostic tools to increase the diagnostic accuracy and provide additional knowledge. Electromyography (EMG) signals are becoming increasingly important in clinical and biomedical applications. Detection, processing and classification of EMG signals are very desirable because it allows a more standardized evaluation to discriminate between different neuromuscular diseases. This paper reviews a brief explanation of the different features extraction and classification tech-