The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence of efficient intrusion detection systems. The network attacks are also dynamic in nature, something which increases the importance of developing appropriate models for classification and predictions. Machine learning (ML) and deep learning algorithms have been prevalent choices in the analysis of intrusion detection systems (IDS) datasets. The issues pertaining to quality and quality of data and the handling of high dimensional data is managed by the use of nature inspired algorithms. The present study uses a NSL-KDD and KDD Cup 99 dataset collected from the Kaggle repository. The dataset was cleansed using the min-max normalization technique and passed through the 1-N encoding method for achieving homogeneity. A spider monkey optimization (SMO) algorithm was used for dimensionality reduction and the reduced dataset was fed into a deep neural network (DNN). The SMO based DNN model generated classification results with 99.4% and 92% accuracy, 99.5%and 92.7% of precision, 99.5% and 92.8% of recall and 99.6%and 92.7% of F1-score, utilizing minimal training time. The model was further compared with principal component analysis (PCA)-based DNN and the classical DNN models, wherein the results justified the advantage of implementing the proposed model over other approaches.

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Accurate analysis of ECG signals becomes difficult when a lot of noise such as AC (Power line) Interference, Electromyogram (EMG), Baseline wandering, channel noise, electrode motion, motion artifact, Gaussian noise & high frequency noise based on the frequency variation are present in the ECG signal. Thus, for better analysis and characterization of ECG, noise removal becomes an essential part. Denoising of ECG signals plays a very important role in diagnosis and detection of various cardiovascular diseases. The various methods available for denoising of ECG signals include linear filtering, Empirical Mode Decomposition (EMD), Independent and Principal Component Analysis, Neural networks, adaptive filtering etc. In recent studies by several researchers compared to the above mentioned denoising methods Discrete Wavelet Transform (DWT) and Ensemble Empirical Mode reducing noise from ECG signal. This paper presents the performance analysis on ECG denoising algorithms in EEMD and wavelet domains by evaluating Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE) in order to compare the effectiveness of these two methods in reducing the noise. 

The aim of this study is to design and develop an autonomous fire proof rescue robot. The robot is designed in such a way, that it can traverse through fire and hazardous situations. Further, it will sense and communicate information regarding these situations in real time with the server. The robot is fixed with multi-sensors and further, a driver circuit has been integrated for communication in these hazardous situations through Zigbee and a data acquisition system (DAQ). In mechanical design first, a 3D solid model is generated using Solid works software to understand the basic structure of robot which provides information regarding robotic platform, size and location of various components. The developed fire fighting robot is a predominately outdoor ground-based mobile robotic system with onboard subdual systems that can traverse autonomously in the hazardous environment. The robot is designed such that it can traverse into the fire and send information regarding the fire behaviour and also the images of the victim's location by using a camera. Further, a mathematical model which describes the kinematics and dynamic behaviour of robot motion are done. V-REP is used to create the simulation of the robot in a fire simulated fire environment. Finally, for the path planning, various techniques are discussed such as V-REPs inbuilt path planning module, A*, Fuzzy logic and artificial potential fields.

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