Divya Sharma

The objective of computer vision is extracting useful information by assessing snapshots. It can be used for radiography, industrial inspection, document processing, microscopy, robot navigation and remote sensing. Swarm intelligence is flattering a gradually applicable research topic for economists, computer geniuses, functional investigators and engineers. It is well-defined as the combined behaviour of isolated as well as self-organized groups. The distinguished models of swarm are communal insect colonies similar to ants, termites, bees, fish schools and bird flocks. The intellect of the swarm originates in the form of communication networks that happen amongst both elementary agents and the external environment. Computational models encouraged by natural swarm structures are identified as swarm intelligence models. This model consists of particle swarm optimization, cat swarm optimization, ant colony optimization, and artificial bee colony, glowworm swarm optimization and other algorithms. A natural swarm-based approach is being used to address complicated issues in a variety of fields, including engineering, medicine, and robotics. In this study, a review of computer vision approach using various nature inspired algorithms is discussed.

Sentiment, emotions, feelings, showing or giving judgments by face gestures, etc. can be communicated by text, speech or images. Analyzing image Polarity via images is now a burgeoning research field. An image can easily be interpreted by any human being and acts as a bridge to relate an image to the human’s thoughts. The paper proposed a model for analyzing the sentiments or emotions of an image. We have used a Data-Mining Orange Tool for executing the experiment for classification and sentiment analysis of an image. The main challenge is to acquire the polarity of the unlabeled data using optimal machine learning techniques. To overcome this challenge for getting the polarity and sentiment of an unlabeled data the proposed model used various noteworthy machine learning models like Neural Network, Naïve Bayes and random forest for classifying and analyzing the image sentiment and also provide a comparison based on certain parameters like Area Under Curve, Precision, Recall, classification Accuracy, etc. The tool provides a complete package for performing image analysis using the widgets available with orange tool and a data visualization tool to get a visible representation of the output. The proposed model shows that Random Forest performance the best from the three compared algorithms with classification Accuracy of 0.747, recall & precision as 0.747 & 0.752 and the accuracy based on confusion matrix is 74.72%.

Adapting innovative technology is always overwhelming to this agile developing world, but these technologies, IOT devices, systems robust enough to face daily security challenges. Penetration Testing plays a vital role for making sure daily usage of systems are not vulnerable and patched regularly. In this research paper we have discussed overview of penetration testing and various aspects that impact security of systems. Malwares are the major cyber threats since 19s, and cause billion of damages over a long period of time. The improvement in cryptography helps in maintaining Confidentiality and Integrity of data, alongside different techniques and methods were implemented in a hope of providing better security.

Security plays a major role in an individual’s life to win this world with highly secure and authentic lifestyle with the digital equipment’s. The paper proposed an encryption based secure face detection and recognition model which can be implemented in daily life to generate a more robust and efficient security bubble around the world. The most crucial problem encountered during face recognition is due to the variation in face direction of an individual, the model solves the mentioned pose variation problem. The proposed model takes the help of face recognition library to recognize the face and use HOG (Histogram of Oriented Gradients) & SVM for checking the face authentication by performing an image match, the model also applies the concept of HOG to generate the encoded features from the image. The system is divided into two modules first is to detect a face and then match the detected face from the authentic persons dataset available. The system uses the concept of OpenCV library for giving a support system for the real time image. For data encryption, proposed model used the concept of DES3 and RSA algorithm. The proposed model gets 83.33% accuracy while tested for three different image types and states that the RSA algorithm performs encryption in less computational time.