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Java 8 interview questions

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java interview questions
java interview questions

What are the new features released in Java 8?

Java party line, Standard Edition 8 (Java SE 8)” is liberated on 18th March 2014. accompanying the Java SE 8 party line, the outcome that contrivance the party line, “Java SE expansion Kit 8 (JDK 8)” and “Java SE Runtime Environment 8 (JRE 8)” is also liberate and accessible for initializing. From this liberate, the code name custom is discontinued and so no official code name for Java 8.Java SE 8 is one of the most feature-packed liberate in java history.
In this article, let us go through the highlights of the core new features of Java 8. The following list is a highlight of major features, there are other slight intensification, security features, bug fixes that are obtainable as part of Java 8.
  1. Lambda Expressions
  2. Pipelines and Streams
  3. Date and Time API
  4. Default Methods
  5. Type Annotations
  6. Nashorn JavaScript Engine
  7. Concurrent Accumulators
  8. Parallel operations
  9. PermGen Space Removed
  10. TLS SNI

                                                            

  • What is a Lambda expression in Java 8?                                                                                                                                                                                 
    Lambda expression is a new and major characteristic of Java which was included in Java SE 8. It supplies an intelligible and succinct way to constitute one procedure bond using an utterance.  It is very convenient in the collection library. It helps to iterate, filter, and extract data from the assemblage.

    The Lambda expression is used to supply the performance of a bond that has a functional bond. It saves a lot of code. In the case of the lambda expression, we don’t need to explicate the procedure again for providing the execution. Here, we just write the execution code.Java lambda expression is treated as a function, so the compiler does not create.

  • What are the three main parts of a Lambda expression in Java?                                                                                                                                         
    A list of parameters  A lambda expression can have zero (represented by empty departure), one or more parameters:

    () -> System.out.println("Hi");
    (String s) -> System.out.println(s);
    (String s1, String s2) -> System.out.println(s1 + s2); 
    The type of parameters can be proclaimed absolutely, or it can be deduced from the context:(s) -> System.out.println(s); If there is a single parameter, the type is inferred and it is not obligatory to use departure:  s -> System.out.println(s);If the lambda expression uses a limitation name which is the same as a changeable name of the surrounding context, a compose error is generated  :// This doesn't compile
                                                                                                                                                    
    String s = ""; s -> System.out.println(s);
    An arrow formed by the characters - and > to separate the restriction and the body. A body the body of the lambda expressions can hold one or more declaration.                                                                                                                                                                                                                                                                                                                                                                                                                    If the body has one declaration, curly brackets are not entailed and the worth of the expression (if any) is returned:() -> 4; (int a) -> a*6;If the body has more than one declaration, curly brackets are entailing, and if the expression returns a worth, it must be returned with a return declaration

    System.out.println("Hi");
    return 4;
    }
    (int a) -> {
    System.out.println(a);
    return a*6;
    }                                                                                                                                 
    If the lambda expression doesn’t return an outcome, a return the declaration is voluntary. For example, the following expressions are equivalent                                                                                                                                                                                                                               System.out.println("Hi");
    () -> {
    System.out.println("Hi");
    return;
    }                                                                                

 

 

 

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java data structures interview questions

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What is data Structure?

A data structure is a data administration, supervision, and storage format that enables efficient access and modification. Main goal is a  data Structure, assemble of data values, we can apply any types of data for the Data Structure.

Differentiate between file and structure storage structure. 

A file is a collection of data. All of the data structures are satisfy it. It usually assemble the records are stored in external storage in the computer’s.                                                                                                                                                                                                                                                                                     The major difference between file structure and storage structure is depend on memory area that is being accessed.
Storage structure is When we allocate with the structure that occupy in the main memory of the computer system, known as the storage structure.                                                        

When is a binary search best applied? 

A binary search is an algorithm that is elite petition to search a list when the component are previously in order or sorted.
The list is searched starting in the mid of binary, like that if that middle utility is not the quarry search key, it will inspect to see if it will pursue the seek on the bottom half of the list or the higher half. The split and search will then continue in the same process.

What is a linked list?   

A linked list is a succession of interchange in which severely interchange is fasten to the node following it. This forms a alternation link for data storage.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               How do you reference all the components in a one-dimension array?                                                                                                                                                                                                                                                                                                                                                                                                                            To mention all of the components in a  one-dimension array, you compulsory to use an indexed loop, in order that, the counter runs from 0 to the array size minus one.
In this demeanour, You can mention all of the components in progression by using the loop counter as the array appendix.

In what areas do data structures are applied?     

Data structures are indispensable in as good as every feature data is complicated. In circulation, algorithms that assume effective data structure is petition in many ways.

What is LIFO?       

 LIFO is a short form of Last In First Out. It mention how data is acquire, stored and repossess. Using this strategy, data that was stored last should be the one to be deracinate first.
This also means that in order to gain ebullition to the first data, all the other data that was stored before this first data must first be repossess and withdraw.

What is a queue?                                                                                                     

A queue is a data structure that can imitate a list or rivulet of data. In this structure, new component are position at one end and subsist component are withdraw from the other end.

                                                                                                                                  

What are binary trees?                                                                                              

  A binary tree is one type of data structure that has two confluence, a left confluence, and a right confluence. In programming, binary trees are an expansion of the linked list structures.

 

Which data structures are applied when transactions with a recursive function? 

Repetitive, is a function that calls itself position on a terminating condition, makes use of the mound. Using LIFO, a call to a repetitive function saves the submit address so that it knows how to return to the calling function after the call terminates.

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Top 5 Machine Learning Frameworks

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Top 10 Machine Learning Frameworks
Top 10 Machine Learning Frameworks
Basically machine learning is a computer ability to pick up something that was not expressly customized or programmed into. Simply a scientific study of statistics of perform tasks without given instructions. The machine learning framework is a significant part of this field.
The Machine Learning field incorporates numerous particular methodologies such as probability, logical functionalities, statistics & analysis, reinforcement and combinatoric optimization. Machine learning is a convenient field offering numerous answers for issues in our life. Because of this way, make it so helpful today.
Machine learning is an emerging field. So discovering applications in progressively diverse paths, such as Organizations in e-commerce, defense, Betting, and so on. In this article, we are going to discuss the top 5 machine learning frameworks.

1. CAFFE (Convolutional Architecture for Fast Feature Embedding) machine learning framework

CAFFE can recognize as a popular machine learning framework presented by the Berkeley Vision and Learning Center (BVLC). It decreases the processing works of the client.
With the support of enabling the client to define complex, profound neural systems in a primary CSS type language.
CAFFE utilizes the BLAS libraries, Nvidia’s CUDA, and OpenCV library to produce exceptionally advanced code in C++.
Caffe machine learning framework is compatible with different info types. Such as  raw image records, LevelDB, multidimensional information, LMDB, and much more. Also, it gives a MATLAB library and a Python library for interfacing with different conditions.
CAFFE’s significant downside is the absence of a user-friendly user interfaces. And also it only supports Linux.
Windows isn’t directly support for the Caffe. But there are ports cross compile 64bit libraries.

2. Scikit-Learn machine learning framework

Scikit-learn is a famous python based machine learning framework. This intends to be user-friendly and practical.
Also available from non-specialists, to experts in different settings. This is a Python module coordinating a wide range of advanced machine learning algorithms.
Those are for medium-scale administered and solo issues. The Python programming language is setting up itself as a one of the most well-known dialects for logical registering. This machine learning framework libraries center around bringing machine learning from non-experts to specialists.
Main focus is put on convenience, execution, API consistency, and documentation. It has negligible dependency conditions. Also circulated under the improved BSD permit. This empower its utilization in both scholarly and business.

3. TensorFlow machine learning framework

TensorFlow is a machine learning framework. This works for comprehensive environments. TensorFlow uses data flow charts to show calculations, common states, and functions that modify this state.
It maps the hubs of a data flow diagram. Those crosswise over numerous machines in a cluster. Also inside a machine over various specific computational gadgets. I.e. multicore CPUs, useful GPUs, and custom ASICs called Tensor Processing Units (TPUs). This architecture provides adaptability to the application developer.
Based on previous records, over 150 Google teams have utilized TensorFlow.

4. Microsoft CNTK (Cognitive Toolkit) machine learning framework

CNTK, Microsoft’s advance open source based machine learning framework. This supports for Windows and Linux.
CNTK is a fantastic machine learning framework. This based on computational graphs for evaluating preparing and assessing intelligent neural systems.
Microsoft users use CNTK,  to make the Cortana speech models and web positioning. CNTK underpins convolution, intermittent systems, picture, combinations, and outstanding content tasks.
Also Mainstream network types are native or can depict as a CNTK sequence to sequence setup. CNTK compatible with different GPU servers and is structured effectively. Complete set of CNTK’s consistent with the general architecture.
Such as calculations utilized for automatic differentiation, repetitive circle deduction (recurrent-loop), execution, multi-server parallelization, and memory sharing.

5. Accord.NET machine learning framework

Accord.NET is a Machine Learning framework system for .Net platform. Accord.NET gives different libraries to a wide range of applications. I.e.
probability distributions, kernel functions, statistical data processing, hypothesis tests, pattern recognition, information preparing, image processing, linear algebra, and neural systems.
Simply Accord.net is a machine learning framework includs image processing and audio processing libraries written in C#.
Top 5 Machine Learning Frameworks
Top 5 Machine Learning Frameworks

 

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What is Machine Learning?

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What is Machine Learning?

SELF DRIVING Artificial intelligence

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Self driving vehicles
Self driving vehicles
Self-driving vehicles Many of us once dreamt of riding a flying car. It is brilliant inventions. This is made for get easier for our busy dailey life.One of these brilliant inventions is autonomous cars.

HOW SAFE ARE SELF DRIVING CARS?

A modern car contains more than 100 million lines of code. The software enables many different features — cruise control, speed assistance, and parking cameras. This system can gets more complex.
This trend will come to all over the world in the future.  Cars are increasingly dependent on technology. And they will progressively become more autonomous — and ultimately self-driving.
Self-driving cars are very  safer than human drivers, according to experts. We can see so many accidents but sllf driving is the best answer for that reason .Human drivers get into millions of accidents each year.
Less than one per cent of those accidents were fatal.
Self-driving cars, on the other hand, This invention is still tested. They have a long way to go before they become the norm.
In 2018 March, self-driving car failed to identify the biker as a human until it was too late to stop. It is failed notice the biker. It’s likely to take some time before we see widespread use of self-driving cars.
But vehicles driven by humans are using more and more technology in Advanced Driver Assistance Systems (ADAS).

HOW MUCH WILL A SELF DRIVING CAR COST?

Developing self-driving cars is of course, very expensive. Fully autonomous tech could add at least $100,000 to the price of a vehicle, while even semi-autonomous features like Tesla’s Autopilot and Cadillac’s Super Cruise already add $5,000 and $10,000 respectively, to the base vehicle cost.

ADVANTAGES OF A SELF DRIVING CARS

 Decreased the number of accidents

We can decreased accidents. It leaves no opportunity for distraction, not just like humans who are prone to interruptions. It also uses best secular.It can determine the correct stopping distance from one vehicle to another. Thereby, lessening the chances of accidents dramatically.

Lessens traffic jams

Driverless cars in a group participate in platooning. It can manage and controling fast. It help to identify the traffic problems early on.  It detects road fixing and detours instantly. It also picks up hand signals from the motorists and reacts to it accordingly.

Time-saving vehicle

As the system takes over the control, the driver has a spare time to continue work or spend this time catching up with their loved ones without having the fear about road safety.

 Accessibility to transportation

Autonomous vehicles assist them in safe and accessible transportation.

WHAT ARE THE DISADVANTAGED OF A SELF DRIVING CAR?

 Expensive

High-technology vehicles and equipment are expensive. It use to high cost for developing this technology. software updating is the main part in the self driving. modified vehicle parts, and sensors are another main part of this technology. Thus, the cost is very higher.

 Safety and security concerns

Technologies are always continuously update but it is  not properly and successfully done.

 Prone to Hacking

Autonomous vehicle’s next main target is identify the details of owner. This may lead to a possible collection of personal data.

 Fewer job opportunities for others

As artificial intelligence continues to overcome the roles and responsibilities of humans, taxi, trucks, or even co-pilots may be laid off as their services will no longer be needed. This may some impact in the job opportunities in the world.

 Non-functional sensors

Sensors failures often happened during drastic weather conditions. This may not work during a blizzard or a heavy snowfall.
HOW SAFE ARE SELF DRIVING CARS
HOW SAFE ARE SELF DRIVING CARS

HISTORY

This Experiments started in 1920. The first trials began in the 1950s. The first semi-automated car was developed in 1977, The vehicle reached speeds up to 30 kilometres per hour (19 mph) with the support of an elevated rail.
The first truly autonomous cars appeared in the 1980s, with Carnegie Mellon University’s Navlab and ALV projects funded by the United States’ Defense Advanced Research Projects Agency (DARPA) starting in 1984.
In 1985, the ALV had demonstrated self-driving speeds on two-lane roads of 31 kilometres per hour. A main milestone was achieved in 1995, Of the 2,849 mi (4,585 km) between Pittsburgh, Pennsylvania and San Diego, California, 2,797 mi (4,501 km) were autonomous (98.2%), completed with an average speed of 63.8 mph (102.7 km/h).
From the 1960s through the second DARPA Grand Challenge in 2005, automated vehicle research in the United States was primarily funded by DARPA, the US Army, and the US Navy, yielding incremental advances in speeds, driving competence in more complex conditions, controls, and sensor systems.
From 2016 to 2018, the European Commission funded the innovation strategy development for connected and automated driving through the Coordination Actions CARTER and SCOUT. Moreover, Roadmap for Connected and Automated Transport was published in 2019.
In 2017, Audi was joined the technology. It’s speeds of up to 60 kilometres per hour (37 mph) using its “Audi AI”. The driver wouldn’t  have to do something to safety. Audi is the first manufacturer to use laser scanners and ultrasonic sensors for their system.
In November 2017,  testing driverless cars without a safety driver in the driver position. however, there was still an employee in the car.

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ENVIRONMENTAL POLLUTION

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ENVIRONMENTAL POLLUTION
ENVIRONMENTAL POLLUTION

This situation has become a major problem today.

This pathetic situation due to man’s Selfish activities. We can see many types of Environmental pollution. Such as,
  1. Air pollution
  2. Water pollution
  3. Land and Soil pollution

WATER POLLUTION

what is water pollution?
  • We can see garbage near the waterways.
  • This has become a common sight today.
  • Most of factories used to release their industrial chemical waste into waterways.
  • Such as lakes , rivers , seas and  the oceans.
  • Water pollution is a problem for the all living beings and ecosystems.
  • There agriculture is one of the major source of water pollution.
  • Many companies use plastics and people throw it in the waterways.
  • In developing countries around 70% of their solid waste is dumping directly into the ocean or seas.
  • Water pollution is serious problems including the harming and killing of sea creatures, which ultimately affects humans.
  • Ships used to take much garbage with them and they release them into the oceans.
  • Ships release oil into the ocean.
  • Water is valuable source of our life.We need it to live.                                                                                                                                                                             

    Major causes of water pollution 

  •  Domestic Sewage
  • Industrial Wastewater
  • Agricultural Waste
  • Acid rain
  • Global warming                                              

AIR POLLUTION                  

  • Industries are necessary for develop countries but there should be a proper emission system of smoke from the factories to air and they release from burning of garbages also contributing to the air pollution.
  • There are two types of air pollution,
  •  (1). primary     (2). secondary
  • Primary pollutants are emitting directly from their source, while secondary pollutants are forming when primary pollutants react in the atmosphere.
  • Fossil burning fuels is on of the source of air pollution.
  • It transportation and electricity produces both primary and secondary pollutants and is one of the biggest sources of air pollution.
  • The fumes from car such as hydrocarbons, nitrogen oxides, and carbon monoxide.
  • These are very dangerous gases.
  • These gases  react with other atmospheric gases creating even more toxic gases.
  • According to The Earth Institute, the heavy use of fertilizer for agriculture is a major contributor to fine-particulate air pollution, with most of Europe, Russia, China, and the United States being affected.
  • Agricultural activities are a major problem today.
  • Ammonia is the primary air pollutant.
  • It comes from agricultural activities.
  • Ammonia enters the air as gases from concentrating livestock waste and fields that are over-fertilizing.

      Major causes of air pollution

  • The burning of fossil fuels.
  • Agricultural activities.
  • Exhaust from factories and industries.
  • Mining operations.
  • Indoor air pollution.

 LAND  AND SOIL POLLUTION

 what is Land and soil pollution

  • Land pollution is become a major problem today
  • It is very big threat to the environment.
  • It is growing by the increasing population as well as increasing of the population forests are being cut for houses and buildings, factories.
  • Forests will turn into industrial and residential areas.
  • They increase in the number of industries has added to the industrial and chemical waste.
  • This type of waste is extremely hard to dispose and it contributes to the worst type of land pollution.
  • Mining activities also cause harm to the land pollution.
  • These toxic substances come into contact with the human body directly through eating fruits and vegetables.
  • Those are growing in polluted soils, being consumed through drinking water that has been contaminated, direct contact with the skin, and breathing in air polluted with particles and dust.
  • Deforestation is the biggest concern when it comes to land degradation and soil erosion.
  • Clear cutting of vegetation and tree cover creates harsh conditions that destroy ecosystems and habitats.
  • Deforestation also creates an imbalance in atmospheric conditions, reducing the amount of carbon that is naturally taken out of the atmosphere.

 Major causes of air pollution 

  • Deforestation
  • Agriculture
  • Industry
  • Mining
  • Landfills and Waste
  • Urbanization

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