The Monthly Wrap

  Upgrading Technologies change living standards, living standards change daily life problems which need equally upgraded technically advanced solutions. With the increase in advancements, smarter and efficient solutions have come up to promote the safety and transparency of services.                    One such field is surveillance. Surveillance includes carefully watching somebody whose activity is fishy. Keeping a watch on suspicious people includes identifying unknown/blacklisted faces and raising alerts when needed. Any such encounter will be read from a video surveillance camera, CCTVs employed will send it to the software/app scanning the footage. Artificial Intelligence enhanced Deep Learning Models will scan this footage using Neural Networks, which will be fed with data of familiar faces and of blacklisted people(in certain cases). After an alert, the concerned person or operator may contact the police or concerned authorities.   Taking the technical point of view of the sam

Catastrophic interference, also known as catastrophic forgetting, is the tendency of an  artificial neural network  to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the  network approach and connectionist approach  to  cognitive science . With these networks, human capabilities such as memory and learning can be modeled using computer simulations. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ratcliff (1990). It is a radical manifestation of the 'sensitivity-stability' dilemma or the 'stability-plasticity' dilemma. Specifically, these problems refer to the challenge of making an artificial neural network that is sensitive to, but not disrupted by, new information(see the diagram).  Lookup tables  and connec

There are already more linked devices than people in the world, and this number is growing. According to IDC, there will be 41.6 billion connected IoT devices, or "things," in the world by 2025.It also says that industrial and automotive equipment provide the largest possibility for connected "things," but that smart home and wearable gadgets will see rapid adoption in the near future.  According to Gartner, the enterprise and automotive industries will account for 5.8 billion devices this year, up nearly a quarter from last year.Because of the ongoing installation of smart metres, utilities will be the largest users of IoT. The second most common usage of IoT devices will be security devices such as intrusion detection and web cameras.The fastest-growing area will be building automation, which includes linked lighting, followed by automotive (connected autos) and healthcare (monitoring of chronic conditions).  Figure 1: IoT Market scale by 2021 Industry-specific of

The Internet of things is the new and vast field of opportunities in today’s era. In this digital world the most important thing is data and this is the only platform every one need to work. Without this, nothing would be possible in digital world. As the use of technology and its advancement increase the quantity of data automatically. According to the predictions of data scientists tremendous amount of data will be increased in near future. To maintain and extract the important information from this data automation of these processes is required. According to various agencies like data fabric trends report, by the end of 2025 data automation market will be reached to $4.2 billion. So Iot is the one area for data collection and its automation. In the upcoming future working and job opportunities in this field is the hottest platform for the youth.  Figure 1: IoT Infrastructure and Technologies IoT platform provide the various services for the ease of life, business improvement, refine

  Machine Learning (ML) Machine learning is a branch of AI, used to process and analyse the data using various algorithms that mimics the learning behaviour of humans for analysing the data. These algorithms predict the outcomes without intervention of human. It provides the systems that automatically learn and improve based on their experience.  Fig 1 depicts the process of machine learning. It consists of five steps starting with collection of data and ends at model deployment. These are 5 basic steps: Collection of Data from various data source Data cleaning and Feature Engineering Model building and Selection of ML Algorithm Model Evaluation Model Deployment Fig. 1 Basic Process of Machine Learning Generally, Data collection is the key process in ML. Based on the addressed problem, we have to obtain the data from different sources. Data can be collected from social media, public opinion poll, or survey of the product success or failure.  Next step in ML is data cleaning.  Before st