Mo Intelligence

In this post we will explore the software functionalities for Adobe after effects CC.       In short, After Effects CC software is one of Adobe company softwares. Adobe is known to develop graphic design related programs and these programs are used by professionals to produce commercials for big companies. So, After effects is a professional video creator and editor program which a person can use to create or edit footages. For instance, graphic designers use this program to apply 3d effects to footages. In addition, after effects can be used to create 2d and 3d videos. Project window: In this window you can either open a previous project or create a new one. Recents projects window: In this window you can open recently edited projects on this specific computer. Tool bar: Just below the file menu is the tool bar. Selecting these tools will change what you can do in your composition (note: in After Effects, a composition is basically a sequence or a new video). We’ll go over the most im

          Google, in full Google LLC formerly Google Inc. (1998–2017), American search engine company, founded in 1998 by Sergey Brin and Larry Page, that is a subsidiary of the holding company Alphabet Inc. More than 70 percent of worldwide online search requests are handled by Google, placing it at the heart of most Internet users’ experience.  Google has always seen itself as more than a search engine and advertising company. Now it’s turning its focus to healthcare, betting that its AI prowess can create a powerful new paradigm for the detection, diagnosis, and treatment of disease. In short, Google seems to be going after the healthcare space from every possible angle. Google is betting that the future of healthcare is going to be structured data and AI. The company is applying AI to disease detection, new data infrastructure, and potentially insurance. "Tomorrow, if AI can shape healthcare, it has to work through the regulations of healthcare… In fact, I see that as one of t

    To sum up, this study illustrates that AI in healthcare is being developed by a lot of companies as well as it is rich of benefits in this field because it saves money, effort and time. Google health and DeepMind is a perfect example for developing the sense to manage many critical health related conditions. In my point of view, AI and ML must be utilised more in these fields to reach a level of high-tech support and services. Lastly, after doing this study, it is clear that the positive effects of utilising AI and ML in healthcare services overweight the negative effects.

     As Google enters healthcare, it’s leaning heavily on its expertise in AI. Health data is getting digitised  and structured, from a new electronic record standard to imaging to DNA sequencing. Google is both helping speed up this process by creating new means of ingesting health data and betting that it can use AI to make sense of the data quickly and potentially more accurately than current methods. Among the big 5 tech giants (Facebook, Apple, Microsoft, Google, Amazon), Google emphasis sizes its progress on machine learning much more than the rest. In my point of view, I strongly believe that Google has a big potential to develop a lot of fields in the future, especially healthcare. Unfortunately, there are limitations to this study. For instance, I was not able to contact the mentioned companies to gain more information to backup this study. In addition, the reviewed methods has not been put through clinical trials yet. So, there is no enough studies and secondary data about it

     Using AI to tackle disease, from monitoring, to detection, to lifestyle management. Google’s strategy involves an end-to-end approach to healthcare, including: Data generation — This includes digitising and ingesting data produced by wearables, imaging, and MRIs among other methods. This data stream is critical to AI-driven anomaly detection. Disease detection — Using AI to detect anomalies in a given dataset that might signal the presence of some disease. Disease/lifestyle management — These tools help people who have been diagnosed with a disease or are at risk of developing one go about their day-to-day lives and/or make positive lifestyle modifications DeepMind is involved in several parts of disease detection and Google itself holds several patents under the parent company.      Google's DeepMind artificial intelligence research company is developing a clinical decision support (CDS) tool for identifying eye diseases. Researchers from DeepMind are progressing towards a

      DeepMind, a UK-based company, is teaming up with Google Health to tap into expertise in areas like artificial intelligence, app development, data security, and cloud storage. For the last three years, DeepMind has built a team to tackle complex problems in healthcare. Alongside teams at Google, DeepMind will work to build products that support care teams and improve patient outcomes. "During my time working in the UK National Health Service (NHS) as a surgeon and researcher, I saw first-hand how technology could help, or hinder, the important work of nurses and doctors," said Dominic King, UK Site Lead at Google Health. DeepMind and Google Health will aim to develop tools that could potentially help prevent sepsis and acute kidney injury. By joining forces with Google Health, DeepMind will add to its many projects aimed at improving patient care. The company recently developed a clinical decision support tool that can accurately identify more than 50 eye diseases. With

        Google, in full Google LLC formerly Google Inc. (1998–2017), American search engine company, founded in 1998 by Sergey Brin and Larry Page, that is a subsidiary of the holding company Alphabet Inc. More than 70 percent of worldwide online search requests are handled by Google, placing it at the heart of most Internet users’ experience.  Google has always seen itself as more than a search engine and advertising company. Now it’s turning its focus to healthcare, betting that its AI prowess can create a powerful new paradigm for the detection, diagnosis, and treatment of disease. In short, Google seems to be going after the healthcare space from every possible angle. Google is betting that the future of healthcare is going to be structured data and AI. The company is applying AI to disease detection, new data infrastructure, and potentially insurance. "Tomorrow, if AI can shape healthcare, it has to work through the regulations of healthcare… In fact, I see that as one of the

Machine Learning Process,  is the first step in ML process to take the data from multiple sources and followed by a fine-tuned process of data, this data would be the feed for ML algorithms based on the problem statement, like predictive, classification and other models which are available in the space of ML world. Let us see the process steps. ( Author's work ) Machine Learning – Stages: We can split ML process stages into 5 as below, mentioned in the flow diagram. Collection of Data Data Cleaning Model Building Model Evaluation Model Deployment

( Author's work )    Face recognition security strategy is the most common method that is being used by a lot of tech companies all over the world (eg. Apple, Samsung, Microsoft, etc.). In addition, sensitive security systems are using this strategy, for instance, house security services and military. Because this technology uses artificial intelligence and machine learning methodologies, so it is nearly impossible to bypass it. Moreover, no one can fake someone's face to bypass this security system, because it uses a 3D technology to scan and analyse. The above diagram illustrates a simple example explanation to this strategy. For instance, a house door is secured by a face recognition system, so whenever any person tries to unlock it, it will not be unlocked because the face is not recognised in the database. After a recognised face appears in-front of the sensor, the door will be unlocked and security systems will be off for a temporary time. Same this strategy is used in ma

     Case Based Reasoning (CBR) is a general AI technique used for problem-solution, fault detection and diagnosis, reasoning, learning and decision support. CBR finds solutions to new problems by adapting previously good solutions to similar problems. Case studies features need to be specified to be helpful in retrieving other cases. At the same time, features have to be discriminative enough to avoid the retrieval of cases studies which could lead to wrong solutions because of being too different. CBR does not require an explicit domain model, but just to identify new cases with significant features, which is in fact the way CBR “learns.” (De and Chakraborty, 2021). ( Barman, Biswas, Sarkar, Soni, and Purkayastha, 2020)      CBR procedures are usually explained as the so called “CBR working cycle”, which includes five steps: (1) current problem description; (2) search for a successful solution of a similar case; (3) adaptation and reuse of the solution to the new problem; (4) evaluat