Nowadays, computer networks and the Internet are unprotected from many security threats. Introducing adaptive and flexible security-related techniques is challenging because of the new types of frequently occurring attacks. An intrusion detection system (IDS) is a security device similar to other measures, including firewalls, antivirus software, and access control models devised to strengthen communication and information security. Network intrusion detection system (NIDS) plays a vital function in defending computer networks and systems. However, several issues concerning the sustainability and feasibility of existing techniques are faced with recent networks. These concerns are directly related to the rising levels of necessary human interactions and reducing the level of detection accuracy. Several approaches are designed to detect and manage various security threats in a network. This study uses Chimp Chicken Swarm Optimization-based Deep Long Short-Term Memory (ChCSO-driven Deep LSTM) for the intrusion detection process. A CNN feature extraction process is necessary for effective intrusion detection. Here, the Deep LSTM is applied for detecting network intrusion, and the Deep LSTM is trained using a designed optimization technique to enhance the detection performance.

Medical imaging means the methods and procedures used for creating pictures of various parts of the human body for numerous clinical objectives. These images are constantly gets dirtied by noise during picture acquisition and transmission, resulting in low quality images. Noise is the unwanted signal which corrupts the important and desirable information. The noises can be categorized into different types based on their nature and origin. e.g. Gaussian, the impulsive and speckle noise etc. The removal of noise is very necessary for proper analysis and diagnosis. Filtering noise helps to recreate a high-quality image in digital image processing for further image processing such as segmentation of images, identification, recognition and monitoring, etc. There are various approaches to denoise medical images based on transform approach, machine learning, filtering method and statistical method. These techniques or approaches is subject to noise type exist in the image. To evaluate the denoising performance, parameters like SNR, PSNR etc. are used. This paper takes a review of current denoising techniques.

Due to the increase in the use of the internet all over the world for business and education activates, cybercrime is increasing day by day in spite of the development of security protocols and algorithms. Recent research is based on the intrusion detection system. We attempt to develop is a secure protocol to detect malicious data, along with actual data, in the incoming data traffic. An intrusion detection system based on a recurrent neural network (RNN) classifier for feature reduction. Failure in intrusion detection apparatus results in a series of negatives ranging from loss of confidential data and thereof reducing reliability for the end-user. Hence, detection systems play an important role in the service end user. In the proposed work, an intelligent system is developed based on machine learning techniques specifically RNN wherein, a novel algorithm is developed for combining a correlation and information gain, feature reduction is achieved. A feed-forward neural network is then fed these reduced features for testing and training on the NSL-KDD dataset. Normally, pre-processing of the dataset is carried out before the training phase. It helps us to regularize instances of each class in the dataset. Attack and non-attack classes are formed which helps us to implement this developed algorithm for giving the best results in terms of Feature ranking. Our algorithms reduced the number of features, which in turn reduced in preprocessing time of extracting features related to information gain and correlation in the dataset.

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The Multi Input Multi Output (MIMO) radar waveform diversity Significantly improves parameter identifiably than phased-array radar performance. Precoding, combining and spatial multiplexing techniques improves the data throughput and reliability of the transmission in MIMO systems. But increment in transmit and receive elements in MIMO antenna array induces considerable increase in required power for hardware and computation cost. Hybrid beamforming employs fewer RF-to-baseband chains. With conscious selection of the weights for pre-coding and combining, hybrid beamforming establishes perfect trade-off between complexity, performance, cost, and power consumption in practical applications. Performance of MIMO radar system can be improved using newly developed bio inspired metaheuristic algorithms as compared to conventional and adaptive beamforming algorithms. In this work the Salp Swarm algorithm (SSA) is implemented to optimize the performance of hybrid beamforming using Raleigh channel and considering the bit error rate and normalized array power parameters. The swarming behavior of salps when navigating and foraging in oceans is the inspiration behind the SSA optimization algorithm. The obtained results are compared with the conventional phase-shift as well as adaptive linearly constrained minimum variance beamforming algorithms on simulation platform with standard considerations. It is observed that this new approach of Salp swarm algorithm is having improved and much better performance with the considered parameters.

An intrusion detection systems (IDS) detect and prevent network attacks. Due to the complicated network environment, the ID system merges a high number of samples into a small number of normal samples, resulting in inadequate samples to identify and train and a maximum false detection rate. External malicious attacks damage conventional IDS, which affects network activity. Adaptive Dolphin Atom Search Optimization overcomes this. Thus, the work aims to create an adaptive optimization-based network intrusion detection system that modifies the classifier for accurate prediction. The model selects feature and detects intrusions. Mutual information selects feature for further processing in the feature selection module. Deep RNNs detect intrusions. The novel Adaptive Dolphin Atom Search Optimization technique trains the deep RNN. Adaptive DASO combines the DASO algorithm with adaptive concepts. The DASO is the integration of the dolphin echolocation (DE) with the atom search optimization (ASO). Thus, the intrusions are detected using the adaptive DASO-based deep RNN. The developed adaptive DASO approach attains better detection performance based on several parameters such as specificity, accuracy, and sensitivity.

In the current times, cyber-attacks are becoming more sophisticated and modern; this has increased the threat in precisely detecting intrusion. Inability to restrict the intrusions can degrade the validity of security administrations and loss of data confidentiality, integrity, and availability. Hence, Detection is an important step in avoiding such attacks; once an issue is detected properly, effective countermeasures can be deployed. Intrusion Detection Systems (IDS) plays a very crucial role and help to detect incoming attacks. Network-based IDS is an important tool used to protect the computer network against malicious attacks and threats. An application of the Bayesian Information Gain concept for feature selection and Deep Recurrent neural network (Deep RNN) to model building is proposed in this paper to increase the efficiency of a network intrusion detection system. The Bayesian Information Gain concept is used to select important features, which have high predictive power. Deep RNN classifier successfully plays out the intrusion detection system measure utilizing the hidden layers dependent on the weight and bias-related with the classifier. Appropriately, the Adam optimization algorithm to build the precision of the model ideally tunes the weights and bias.

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