Email services have to put through a lot of effort in fighting spam emails. Most of the efforts go in for detecting and filtering spam emails from benign emails. On the other front, people are educated by banks and other organizations on the awareness of such emails. These approaches are essentially passive in nature, in countering spam attacks where the attacker is not directly engaged by the adversary. Despite all these efforts, many innocent people fall for such attacks leading them to share their account details or lose a large sum of money. We propose an AI based system, that is self-aware and self-defending, which sends coherent replies to spammers with the aim of consuming their time. To make it more difficult for spammers we reply from algorithmically generated mail servers. Also, to avoid simple match filtering of mails by spammers, we make the replies different from each other and genuine, by using a language model trained by LSTM to form sentences in natural language depending upon the context of the email.

Biotechnology and biology education has been known to show declining student interest due to classroom environments and instructor teaching styles, hence we introduced virtual labs as an interactive self-learning material in a blended environment. With ICT-based education becoming ubiquitous, virtual and remote triggered labs have become a novel platform that helps users to engage in a proactive learning process. A promisingly new trend in virtual labs-based education is the development of remote laboratories that are available over the internet and can be accessed by students and teachers. We implemented and deployed a low-cost light microscope using a simple front-end to enable users to have anytime-anywhere access. This paper reports the implementation, deployment and user-case studies on the learning and usage based on the remote-triggered virtual lab. This study also focuses on the analysis of using remote-triggered experiments as supplementary laboratory resources for overcoming the problems faced in a traditional lab environment. The study used online feedback surveys for evaluating the learning outcome and the flexibility of user-interactions with the remote labs and reports the status of usage of remote triggered techniques in biology courses. The statistical analysis suggests that remote labs are an easy learning and interactive platform for users from different places.

Articulation via target-oriented approaches have been commonly used in robotics. Movement of a robotic arm can involve targeting via a forward or inverse kinematics approach to reach the target. We attempted to transform the task of controlling the motor articulation to a machine learning approach. Towards this goal, we built an online robotic arm to extract articulation datasets and have used SVM and Naive Bayes techniques to predict multi-joint articulation. For control- ling the preciseness and efficiency, we developed pick and place tasks based on pre-marked positions and extracted training datasets which were then used for learning. We have used classification as a scheme to replace prediction-correction approach as usually attempted in traditional robotics. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests SVM and Naive Bayes algorithms as alterna- tives for computational intensive prediction-correction learning schemes for articulator movement in laboratory environ- ments.

Target-oriented approaches have been commonly used in robotics. In 3D space, movement of a robotic arm depends on the target position which can either follow a forward or inverse kinematics approach to reach the target. Predicting the movement of a robotic arm requires prior learning through methods such as transformation matrices or other machine learning techniques. In this paper, we built an online robotic arm to extract movement datasets and have used machine learning algorithms to predict robotic arm articulation. For efficient training, small training datasets were used for learning purpose. Classification is used as a scheme to replace prediction-correction approach and to test whether the method can function as a replacement of usual forward kinematics schemes or predictor-corrector methods in directing a remotely controlled robotic articulator. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests linear SVM and Naïve Bayes algorithms as alternatives for computational intensive learning schemes while predicting articulator movement in laboratory environments.