Telecommunication/ICT devices that do not comply with a country’s applicable national conformity processes and regulatory requirements or other applicable legal requirements should be considered unauthorized for sale and/or activation on telecommunication networks of that country.
The global number of stolen phones, counterfeit phones and illegal imports is increasing drastically. For the same reason awareness of this distressing situation grows within the worldwide Regulators’ domain. Considering the enormous and various impact within all the stakeholders as well as country specific situations, there is no “best solution, one size fits all”. It requisites thorough investigation case by case, gathering information on the current footprint as well as the targeted future goals.
Implementing a central solution like CEIR (MDMS, DIRBS etc.) would decrease the number of trading stolen, illegal and counterfeit phones and in short-term would result in public safety and awareness by registering and checking the status of mobile phones.
One of many effective solutions that introducing IMEI blacklisting will enhance is that it discourages mobile theft worldwide. This can only be effective beyond borders when global coverage is in place. Not participating as a country will pave the way for criminals and terrorists.
A call detail record (CDR) is a data record produced by a telephone exchange or other telecommunications equipment that documents the details of a telephone call or other telecommunications transaction (e.g., text message) that passes through that facility or device. The record contains various attributes of the call, such as time, duration, completion status, source number, and destination number. It is the automated equivalent of the paper toll tickets that were written and timed by operators for long-distance calls in a manual telephone exchange.
CDRs can also include SMS messaging metadata and any other official communications transmission. However, the contents of the messages/calls are not revealed through the CDR. The call detail record simply shows that the calls or messages took place, and measures basic call properties.
Artificial intelligence in healthcare is an overarching term used to describe the utilization of machine-learning algorithms and software, or artificial intelligence (AI), to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.
What distinguishes AI technology from traditional technologies in health care is the ability to gather data, process it and give a well-defined output to the end-user. AI does this through machine learning algorithms and deep learning. These algorithms can recognize patterns in behavior and create their own logic. To gain useful insights and predictions, machine learning models must be trained using extensive amounts of input data. AI algorithms behave differently from humans in two ways: (1) algorithms are literal: once a goal is set, the algorithm learns exclusively from the input data and can only understand what it has been programmed to do, (2) and some deep learning algorithms are black boxes; algorithms can predict with extreme precision, but offer little to no comprehensible explanation to the logic behind its decisions aside from the data and type of algorithm used.
The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs are applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. AI algorithms can also be used to analyze large amounts of data through electronic health records for disease prevention and diagnosis.