A good understanding of the population dynamics of algal communities is crucial in several ecological and pollution studies of freshwater and oceanic systems. This paper reviews the subsequent introduction to the automatic identification of the algal communities using image processing techniques from microscope images. The diverse techniques of image preprocessing, segmentation, feature extraction and recognition are considered one by one and their parameters are summarized. Automatic identification and classification of algal community are very difficult due to various factors such as change in size and shape with climatic changes, various growth periods, and the presence of other microbes. Therefore, the significance, uniqueness, and various approaches are discussed and the analyses in image processing methods are evaluated. Algal identification and associated problems in water organisms have been projected as challenges in image processing application. Various image processing approaches based on textures, shapes, and an object boundary, as well as some segmentation methods like, edge detection and color segmentations, are highlighted. Finally, artificial neural networks and some machine learning algorithms were used to classify and identifying the algae. Further, some of the benefits and drawbacks of schemes are examined.

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Edge detection is one of the fundamental tool in image processing, machine vision and computer vision, which aim at identifying points in a digital image. It is an important tool for medical image segmentation and 3D reconstruction. Generally, edge has detected according to some early brought forward algorithms such as gradient-based algorithm and templatebased algorithm, but they are not so good for noisy medical image edge detection. In order to overcome this problem, adaptive threshold using ACO has proposed. Ant colony optimization technique is used for computing an optimal threshold value used by adaptive threshold for edge detection. The various edge detection algorithms are compared with the proposed algorithm and their performance are evaluated using the evaluation metrics. From the experimental results, the proposed algorithm was better than the adaptive threshold method.

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Cyber security comprises of technologies, architecture, infrastructure, and software applications that are designed to protect computational resources against cyber-attacks. Cyber security concentrates on four main areas such as application security, disaster security, information security, and network security. Numerous cyber security algorithms and computational methods are introduced by researchers to protect cyberspace from undesirable invaders and susceptibilities. But, the performance of traditional cyber security algorithms suffers due to different types of offensive actions that target computer infrastructures, architectures and computer networks. The implementation of intelligent algorithms in encountering the wide range of cyber security problems is surveyed, namely, nature-inspired computing (NIC) paradigms, machine learning algorithms, and deep learning algorithms, based on exploratory analyses to identify the advantages of employing in enhancing cyber security techniques.

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