Myriads O' Technology

This protocol for WSN is a data centric query-based protocol where sink floods a query into the network through several routes between the sink and source. The sink supports one of those routes and receives data from that path within shorter time interval. Therefore, multipath delivery can be realized and significant achievement can be attained by adapting subset of network.  The four features of directed diffusion routing protocol include Interests, data, gradients and reinforcement. The query that determines the user’s requirements is the Interest. Processed information is the data. The direction state of node that gets the Interest is the gradient. Multiple gradient paths are used to transmit events from originators of interest. The attribute value pairs that are used to name task descriptions are  example:             type=wheeled vehicle                     //vehicle...

 A wireless sensor network is formed using multiple small sensors. Such a network is a self- organized infrastructure-less wireless network that monitors environmental conditions on its own and transmit data in a synchronized manner to main location called sink which analyses the data. Sink addresses the queries generated by users by collecting results and providing required information. Routing protocols play an important role in producing efficient and interruption less communication within the nodes of the WSN. Applications using WSN demand quick data transmission with minimum interruption. The service, performance and reliability of the WSN depends on the choice of its routing protocol. This article discusses Sensor protocol for information via negotiation (SPIN) which allow exchange of data effectively over a WSN. Sensor Protocol for Information via Negotiation (SPIN) This routing protocol is basically for exchanging data about the sensor data in network operation. It is a neg...

 Machine Learning is the next trending concept in various fields related to education. It involves the study of learning processes and computer modelling in different contexts. The authors in (Carbonell, 1983) rightly stated that ML can be used in task-oriented studies, theoretical analysis and cognitive simulation. Machine Learning in Education field falls in the category of task-oriented studies since it involves developing and analyzing learning systems to improve performance of education institutions through predetermined set of actions. Classification Techniques in Education The main classification techniques used in education include Artificial Neural Networks, Decision Trees, Logical Regression and Support Vector Machine. As stated by Nieto et. al. in the paper, the decision making model offers support in decision making on various aspects in educational industry. ML can be useful in increasing student retention and mitigating dropout rate, strategic planning using the kno...

Cyberattacks are growing in volume as well as complexity. Firewalls and access controls can be bypassed in much intelligent ways by attackers to enter highly secured networks. The level of complexity surpasses the human capability to handle the attacks themselves. AI offers solutions to tackle such security risks at present and for future. Security analysts use AI to identify and counteract complex criminal activity and intentions without human intervention. Cyberattack vectors are better explored using AI and machine learning. They provide improved responses to incidents related to security by learning and updating algorithms on the basis of data received. They can predict threats and observe any inconsistency or discrepancy with high accuracy beyond the capacity of humans.  Certain advantages of AI in cybersecurity are discussed here. AI detects malicious activities from the beginning and prevent full-fledged attack on network or business. It removes zero-day vulnerabilities, ide...

This article discusses the threats and vulnerabilities researched over the internet related to information security in business operations. Sources of information for vulnerabilities and threats have been included with a clear description on which threats are prevalent and how they affect the business, organizations and governments. Security threat is a malicious act that targets an organization’s systems or the whole organization to steal or corrupt data and disrupt the operations. Cyber security attack may be aimed by one organization to breach the systems of another organization with motivations of information theft, espionage, sabotage or financial gain, all with malicious intentions. Company data or its network may get exposed resulting in network or data breach. The threats have been classified into internal and external threats by Loch et. al (1992). The first category includes employee activities, administrative procedures, software problems and mechanical and electrical fail...

  Autonomous systems are developed to emulate certain characteristics of intelligent biological systems. The technology is swiftly gaining pace for significant advances in various fields. The systems are changing the perspective towards self-governance and decision-making abilities of machines with system enhancements in real cases. There is a large scope of research in ethics related to autonomous systems and their technical implementation which still remains unexplored in depth. I am influenced by the approach taken by the authors of the article ‘Ethical Framework for Designing Autonomous Intelligent Systems’ where concept of principlism, casuistry and importance of stakeholders are discussed. Ethical issues are identified on the basis of research and assessed whether they should be included in the system design. These can be considered on the basis of three approaches such as, value-based design, life-based design and responsible research and innovation. The first approach de...

Deep Learning is a division of Machine Learning where machine learns how to classify tasks that the humans do naturally. DL uses text, audio and visuals to accomplish accuracy in decision making capability. It is the DL technology which achieves high level of accuracy in recognition of objects within images equivalent or better than humans. DL analyses large sets of data which are labeled datasets using substantial amount of computing power. DL further explores several hidden layers of neural networks as shown in the diagram below in figure 1. Nodes are interconnected deeply which are explored in DL to extract the feature from the data or image without extracting it manually. Figure 1: Neural network with thousands of hidden layers of interconnected nodes, explored through deep learning to recognize unexplored feature. How DL Works? Deep Learning uses neural network architecture and large sets of data are fed to DL models in order to learn directly from the labeled data without human i...