Natural Language Processing

• Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

• Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken. NLP is a component of artificial intelligence (AI).

• Challenges in natural language processing frequently involve speech recognition, natural language understanding and natural language generation.

• The development of NLP applications is challenging because computers traditionally require humans to "speak" to them in a programming language that is precise, unambiguous and highly structured, or through a limited number of clearly enunciated voice commands. Human speech, however, is not always precise -- it is often ambiguous and the linguistic structure can depend on many complex variables, including slang, regional dialects and social context.

• This involves allowing users to query data sets in the form of a question that they might pose to another person. The machine interprets the important elements of the human language sentence, such as those that might correspond to specific features in a data set, and returns an answer.

• NLP can be used to interpret free text and make it analyzable. There is a tremendous amount of information stored in free text files, like patients' medical records, for example. Prior to deep learning-based NLP models, this information was inaccessible to computer-assisted analysis and could not be analyzed in any kind of systematic way. But NLP allows analysts to sift through massive troves of free text to find relevant information in the files.

• Google and other search engines base their machine translation technology on NLP deep learning models. This allows algorithms to read text on a webpage, interpret its meaning and translate it to another language.

• Earlier approaches to NLP involved a more rules-based approach, where simpler machine learning algorithms were told what words and phrases to look for in text and given specific responses when those phrases appeared. But deep learning is a more flexible, intuitive approach in which algorithms learn to identify speakers' intent from many examples, almost like how a child would learn human language.

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Machine Learning

• Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.

• Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities^[and then used to find meaningful anomalies.

     "Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it and make a determination or  prediction about something in the world.” 

• Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists,engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.

• Machine learning algorithms are often categorized as supervised or unsupervised. Supervised algorithms require a data scientist or data analyst with machine learning skills to provide both input and desired output, in addition to furnishing feedback about the accuracy of predictions during algorithm training. Data scientists determine which variables, or features, the model should analyze and use to develop predictions. Once training is complete, the algorithm will apply what was learned to new data.

• Machine learning is being used in a wide range of applications today. One of the most well-known examples is Facebook's News Feed. The News Feed uses machine learning to personalize each member's feed. If a member frequently stops scrolling to read or like a particular friend's posts, the News Feed will start to show more of that friend's activity earlier in the feed.

• Machine learning also plays an important role in self-driving cars. Deep learning neural networks are used to identify objects and determine optimal actions for safely steering a vehicle down the road.

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Biometrics

• Biometrics is the technical term for body measurements and calculations. It refers to metrics related to human characteristics. Biometrics authentication (or realistic authentication) is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance.

• Biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals and a group. Biometric identifiers are often categorized as physiological versus behavioral characteristics. Physiological characteristics are related to the shape of the body. Examples include, but are not limited to fingerprint, palm veins, face recognition, DNA, palm print, hand geometry, iris recognition, retina and odour/scent. Behavioral characteristics are related to the pattern of behavior of a person, including but not limited to typing rhythm, gait, and voice.Some researchers have coined the term behaviometrics to describe the latter class of biometrics.

• Biometrics is the science of analyzing physical or behavioral characteristics specific to each individual in order to be able to authenticate their identity.

      

      How biometrics work

• Authentication by biometric verification is becoming increasingly common in corporate and public security systems, consumer electronics, and point-of-sale applications. In addition to security, the driving force behind biometric verification has been convenience, as there are no passwords  to remember or security tokens carry. Some biometric methods, such as measuring a person's gait, can operate with no direct contact with the person being authenticated.

   Components of biometric devices include:

• A reader or scanning device to record the biometric factor being authenticated.
• Software to convert the scanned biometric data into a standardized digital format and to compare match points of the observed data with stored data.
• A database to securely store biometric data for comparison.

• India's national ID program called Aadhaar is the largest biometric database in the world. It is a biometrics-based digital identity assigned for a person's lifetime, verifiable online instantly in the public domain, at any time, from anywhere, in a paperless way. It is designed to enable government agencies to deliver a retail public service, securely based on biometric data (fingerprint, iris scan and face photo), along with demographic data (name, age, gender, address, parent/spouse name, mobile phone number) of a person. The data is transmitted in encrypted form over the internet for authentication, aiming to free it from the limitations of physical presence of a person at a given place.

• Biometrics is a emerging area with many opportunities for growth.

• Possibly in the near future,you will not have to remember PINs and passwords in your pockets will be things of the last.

          

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Google Speech Recognition

• Speech recognition is the ability of a machine or program to identify words and phrases and convert them to a machine-readable format. 

• Speech recognition basically means talking to a computer, having it recognize what we are saying.

• This process fundamentally functions as a pipeline that converts PCM (Pulse Code Modulation) digital audio from a sound card into recognized speech. Speech recognition technology has evolved for more than 40 years, spurred on by advances in signal processing, algorithms, architectures, and hardware. During that time it has gone from a laboratory curiosity to an art, and eventually to a full-fledged technology that is practiced and understood by a wide range of engineers, scientists, linguists, psychologists, and systems designers.

• Google Speech Recognition enables developers to convert audio to text by applying powerful neural network models in an easy-to-use API. The API recognizes 120 languages and variants to support your global user base. You can enable voice command-and-control, transcribe audio from call centers, and more. It can process real-time streaming or prerecorded audio, using Google’s machine learning technology.

• It applies the most advanced deep-learning neural network algorithms to audio for speech recognition with unparalleled accuracy. 

    APPLICATIONS:

• In Car Systems.

• Medical Care.

• Military.

• Smartphones.

• People with Disabilities.

         

  There are many Virtual Assistant for different    software companies:

• Alexa Echo (Amazon)

• Cortana(Microsoft)

• Google Assistant(Google)

• Siri (Apple Inc.)

• From the technology perspective, speech recognition has a long history with several waves of major innovations. Most recently, the field has benefited from advances in deep learning and big data. The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worldwide industry adoption of a variety of deep learning methods in designing and deploying speech recognition.

• Google, IBM, Baidu, Apple, Amazon, Nuance,SoundHound many of which have publicized the core technology in their speech recognition systems as being based on deep learning.

         

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Internet of Things!

• The Internet Of Things (IOT) is a computing concept that describes the idea of everyday physical objects being connected to the internet and being able to identify themselves to other devices. The term is closely identified with RFID (Radio Frequency Identification) as the method of communication, although it also may include other sensor technologies, wireless technologies.

• IOT involves extending internet connectivity beyond standard devices, such as desktops, laptops, smartphones and tablets, to any range of traditionally dumb or non-internet-enabled physical devices and everyday objects. Embedded with technology, these devices can communicate and interact over the internet, and they can be remotely monitored and controlled.

“The Internet of Things is not a concept; it is a network, the true technology-enabled Network of all networks". 

   

"IoT Based Humidity and Temperature Monitoring Using Arduino Uno"

INTRODUCTION:

Using Internet of Things (IOT), we can control any electronic equipment in homes and industries. Moreover, you can read a data from any sensor and analyse it graphically from anywhere in the world. Here, we can read temperature and humidity data from DHT11 sensor and upload it to a Thing Speak cloud using Arduino Uno and ESP8266-01 module. Arduino Uno is MCU, it fetch a data of humidity and temperature from DHT11 sensor and Process it and give it to a ESP8266 Module ESP8266 is a WiFi module, it is one of the leading platform for Internet of Things. It can transfer a data to IOT cloud.

HARDWARE REQUIREMENTS:

• Arduino Uno
• ESP8266-01
• DHT11
• AMS1117-3.3V
• 9V battery

SOFTWARE REQUIREMENTS:

• Arduino IDE

CIRCUIT AND WORKING:

First make the connection as shown in figure The 2nd pin is of  DHT11 is a data pin, it can send  a temperature and humidity value to the 5th pin of Arduino Uno. 1st and 4th pin of DHT11 is a Vcc and Gnd and 3rd pin is no connection. The Arduino Uno process a temperature and humidity value and send it to a ESP8266 WiFi module. The Tx and Rx pin of ESP8266 is connected to the 2nd (Rx) and 3rd (Tx) of Arduino Uno. Make sure that input voltage of ESP8266 must be 3.3V, not a 5V (otherwise it would damage a device).For that, we are using AMS1117 Voltage regulator circuit. It can regulate a voltage from 9V to 3.3V and will give it to Vcc pin of ESP8266. The Ch_Pd is a chip enable pin of ESP8266 and should be pullup to 3.3V through 3.3KΩ resistor. For reset the module pull down the RST pin of ESP8266 to Gnd. ESP8266 have 2 GPIO pins GPIO 0 and GPIO 2.

Fig: 1.1 Circuit diagram for monitoring Humidity and Temperature in IOT cloud

CONSTRUCTING AND TESTING:

ThingSpeak is an open source platform to store and retrieve a data for Internet of Things application. To use this, you need to register in ThingSpeak cloud and then login to your account. After create a new channel with temperature in one field and humidity in another field as shown in Fig: 1.2. Once you created a new channel, it will generate a two API keys, they are READ API keys and WRITE API keys. First, copy the WRITE API keys from ThingsSpeak and paste it into the line (String apiKey = "OX9T8Y9OL9HD0UBP";) of the program. Next, replace the Host_Name and Password with your WiFi name and WiFi password in the two lines given below in the program. (String Host_Name = "Pantech" and String Password = "pantech123")

The Arduino program Uses DHT library, if it is not presented in your arduino IDE, select SketchàInclude libraryàManage librariesàInstall DHT Sensor library. Then compile the program and upload to a Arduino Uno through Arduino IDE. Ensure that WiFi modem and internet connection in your Smartphone or PC are working properly. After uploaded a program, the Temperature and Humidity data is uploaded on ThingSpeak platform. You can see it graphically in the private view window of your channel as shown in Fig: 1.3. And you can able to see the uploaded data from serial port of Arduino IDE.

Fig: 1.2: Creating new channel on ThingSpeak cloud

Fig: 1.3: Graphical representation of Humidity and Temperature data

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• Next week I will post more about IOT..so stay tuned!