Myriads O' Technology

In this era of new technologies emerging at an unprecedented fast pace, there is a binding need for collaboration between industry and academia. This article discusses the aspects of such alliance. Role of Academic I nstitut ions Academic I nstitut ions  serve both the industry and academia by providing a workforce, as well as furnishing innovative ideas to start new businesses. Therefore, the two are analogous to two banks of a river with their own independent sides. The two banks can be connected through bridges of science and engineering disciplines to contribute to the betterment of both—the industry and institutes. Therefore, institutes have to keep the curriculum in line with the changing needs of industry. Although it is not a quick and easy process but a continuous update in the syllabus shall keep the students and teachers at par with the demand in the market. The students learn the topics like testing, project leadership, management, professionalism and other skills withi...

Cybersecurity is a term with diverse definitions through multiple perspectives. The diversity in its definition satisfies the need of academia, industry and government and non-governmental organizations to manage the cybersecurity challenges. Simple said, cybersecurity is the practice of organizing and collecting resources, structures and processes to protect cyberspace and cyberspace-enabled systems for preventing occurrences of misalignment of true property rights. Cybersecurity is the protection of computer systems and networks from digital attacks, data theft and breach. Cybersecurity or IT security protects critical systems and sensitive information from cyberattacks. It is designed to fight threats against networked systems and applications. The threats may originate from inside or outside of an organization. Cybersecurity management is critical for small, medium and large organizations. In the current era of a plethora of technologies, it is crucial for organizations to hire...

Artificial Intelligence has played a disruptive role in traditional banking channels with exponential growth of technology. It has influenced the perception of people about banking and comfort of accomplishing banking activities. AI has emerged as an unavoidable and pioneering technology of the business world and AI applications are rigorously utilized in banking and financial services. This article discusses the transformation of banking system from traditional banking to modern digitized banking. It describes how financially excluded people are included into banking through AI-enabled operations. Lastly, the benefits of using AI in banking industry are outlined. Banking System Transformation Banking has evolved from traditional brick and mortar system to a completely transformed way of banking. The industry uses AI-enabled techniques like mobile banking, chatbots for customer service, robots for self-service in banking operations and customized services for customers. Banks use chatb...

Wearable IoT have broaden and improved the perspective of individuals about their surroundings. A successful IoT device has to be affordable as well as reliable. It should not interfere with other devices and should incorporate mechanisms to remain immune from interferences. It should further ensure effective battery life with perfect functionality and energy efficiency. However, the risks involved in attaining this ideal scenario are many. IoT include a plethora of devices or things like vehicles, smartphones, televisions, wearable devices like fitness tracking devices, smartwatch and Google Glass, etc. These devices are embedded with software applications, sensors and other components for receiving and sending data. A colossus of connected devices is created by inter-connectivity of those devices. This connectivity offers magnificent benefits to people’s lives but it entails certain security and privacy concerns. The most conspicuous among them are the wearable devices which are gain...

The internet of things in the current world needs a wide network to handle a great many nodes well connected to each other and accessing the internet. This requires a network which is capable of handling the huge interconnectivity. The sensors on wireless networks are dependent upon devices with limited energy. This has led the network industry to explore methods with which energy could be saved. Considering the plethora of nodes connected over the internet LoRaWAN is an optimum solution. LoRaWAN is a low range-low power wide area network protocol for internet of things. The Low range-low power technology is being adopted all over the world as it is a licensed, free, low power WAN ecosystem. This protocol can provide smooth connectivity among large numbers of nodes interconnected on the internet. It helps to address problems of bandwidth latency and coverage over large range. It is a feasible technology which allows communication for long-range apps. This review report illustrates th...

 Cloud computing is the most trending technology that provides access to computing services over the internet which is mostly based on pay per use model. The characteristics of cloud computing offer elasticity and cost efficiency to the adopting business sectors such as healthcare, supply chain, etc.  This paper discusses Cloud based services offered by Amazon Web Services. The functional and non-functional requirements that a company generally needs are documented. A scalable cloud solution from AWS [3] supporting those requirements is described along with its benefits and disadvantages. A.      Functional Requirements The system requires access to company records whenever needed irrespective of the location of the user. It should allow the users to access, update and manage records. The unique requirements of an organization for using cloud services are enlisted below. AWS Cloud services and resources are capable of fulfilling each of them. Intercloud Connecti...

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...