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Computer vision is a fascinating field of artificial intelligence that enables computers to interpret and understand the visual world. Here's a broad overview of how it works:

Basics of Computer Vision
1. Image Acquisition:

The first step in computer vision is capturing an image using cameras, scanners, or other imaging devices.

2. Image Processing:

Once the image is captured, it often needs to be preprocessed. This step might involve noise reduction, resizing, normalization, or converting the image to a different color space (e.g., grayscale).

3. Feature Extraction:

Key features from the image are identified and extracted. Features can include edges, corners, blobs, and textures. Techniques like edge detection and blob detection help in identifying these features.

4. Object Detection and Recognition:

The extracted features are used to detect and recognize objects within the image. This can involve identifying specific objects (e.g., recognizing a cat or a car) or more abstract patterns.

Techniques and Algorithms
1. Traditional Methods:

Image Filtering: Convolutional operations to detect features like edges.

Segmentation: Dividing an image into parts that are easier to analyze.

Template Matching: Comparing segments of the image to predefined templates.

2. Machine Learning Approaches:

Feature-based Methods: Using handcrafted features to train classifiers (e.g., Support Vector Machines or Random Forests) to recognize objects.

3. Deep Learning Approaches:

Convolutional Neural Networks (CNNs): These are currently the state-of-the-art models for most computer vision tasks. CNNs automatically learn features from raw images by using multiple layers of convolutions and pooling.

Object Detection Models: Like YOLO (You Only Look Once) and SSD (Single Shot Multibox Detector), which are used to detect objects in real time.

Semantic Segmentation: Identifying and classifying every pixel in an image (e.g., determining which pixels belong to a dog and which to the background).

Generative Models: Such as GANs (Generative Adversarial Networks) which can generate new images that resemble real ones.

Applications of Computer Vision
1. Autonomous Vehicles:

Computer vision is critical for self-driving cars to detect and interpret road signs, pedestrians, and other vehicles.

2. Medical Imaging:

Used for analyzing medical images like X-rays, MRIs, and CT scans to assist in diagnosis.

3. Facial Recognition:

Identifying and verifying individuals based on facial features. It's used in security systems, smartphones, and social media tagging.

4. Retail:

Enhancing the shopping experience with features like automated checkout and personalized recommendations based on visual recognition.

5. Agriculture:

Monitoring crop health, detecting pests, and managing livestock through drone imagery and computer vision.

Computer vision is an ever-evolving field with a wide range of applications that are continuously expanding.

Unleashing the Future: A Journey into Artificial Intelligence
Artificial Intelligence (AI) has swiftly transitioned from the realms of science fiction to a profound reality, impacting virtually every facet of our lives. From enhancing daily tasks to revolutionizing entire industries, AI's footprint is undeniable. But what exactly is AI, and how is it shaping our world?

Understanding AI
At its core, AI refers to the development of computer systems that can perform tasks typically requiring human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. AI systems can be broadly categorized into two types: Narrow AI, which is designed for a specific task, and General AI, which can perform any intellectual task that a human can.

The Evolution of AI
The journey of AI began in the mid-20th century with pioneers like Alan Turing and John McCarthy. The initial excitement led to the creation of early AI programs that could play chess and solve logical problems. However, limitations in computing power and data led to periods of stagnation, known as "AI winters."

Fast forward to the 21st century, the advent of big data, advanced algorithms, and powerful computing resources reignited AI research and applications. Today, AI technologies like machine learning (ML), natural language processing (NLP), and computer vision are making waves across various domains.

AI in Everyday Life
AI is seamlessly integrated into our daily lives, often in ways we might not even realize:

Personal Assistants: AI-powered assistants like Siri, Alexa, and Google Assistant help manage our schedules, control smart home devices, and provide instant information.

Healthcare: AI aids in diagnostics, personalized treatment plans, and predictive analytics to improve patient outcomes.

Transportation: Self-driving cars and intelligent traffic management systems are paving the way for safer and more efficient travel.

Finance: AI algorithms detect fraudulent activities, provide investment insights, and enhance customer service in the financial sector.

Entertainment: Recommendation systems on platforms like Netflix and Spotify personalize our media consumption experiences.

Challenges and Ethical Considerations
While AI offers immense benefits, it also poses significant challenges and ethical questions:

Bias and Fairness: AI systems can inadvertently perpetuate biases present in training data, leading to unfair outcomes. Ensuring fairness and transparency in AI decision-making is crucial.

Privacy: The extensive use of personal data by AI systems raises concerns about privacy and data security.

Job Displacement: Automation driven by AI could displace certain job categories, necessitating strategies for workforce transition and reskilling.

Accountability: Determining responsibility for decisions made by AI systems, especially in critical areas like healthcare and autonomous driving, is complex.

The Future of AI
The future of AI is both exciting and unpredictable. As research continues to advance, we can anticipate AI becoming even more integrated into our lives. Here are some areas to watch:

AI and Creativity: AI is already aiding in the creation of art, music, and literature. Future developments could lead to even more sophisticated collaborations between humans and AI.

Human-Machine Collaboration: Instead of replacing humans, AI will augment human capabilities, leading to new forms of partnership and productivity.

AI in Education: Personalized learning experiences driven by AI can revolutionize education, making it more accessible and effective.

Ethical AI: Ongoing efforts to create ethical AI frameworks will be crucial in ensuring that AI developments align with societal values and benefit all of humanity.

Conclusion
AI is a transformative technology with the potential to reshape our world in unprecedented ways. Embracing its possibilities while addressing its challenges will be key to harnessing its full potential. As we stand on the brink of this technological revolution, the journey into AI promises to be an exciting adventure, filled with endless possibilities and profound implications for the future.

AI has become a trademarked technology used in multiple industries, causing both creativity and effectiveness. Whether it’s a recommendation system or virtual assistants, AI systems assist people in completing their day-to-day tasks more effectively. Do you want to know how to build AI from scratch? Whether you are a beginner or an experienced developer, this guide will outline the exact steps that you should take to build your own AI systems from scratch. This guide will also cover the programming languages used in building AI systems from scratch.

What is AI?

Before building AI systems, let us know what is AI and its fundamentals. AI is a machine or software program intelligence that adopts critical duties, which include problem-solving, choice-making, pattern popularity, and herbal language information. AI systems are mainly constructed and advanced to simulate human cognitive features, such as problem-solving and reasoning, by using unique algorithms.

Programming Languages Used in Building AI
The improvement of AI algorithms can involve different programming languages and a combination of tools. Below are some.

Python:

Python is the most popular language in AI. The language is well known for its simplicity, readability, and rich libraries and programs. It is suitable for various applications such as data analysis and machine learning. It is equipped with important AI libraries, including scikit-learn, TensorFlow, PyTorch, spaCy,

R:

R features include information and fact-checking. It is mainly used in AI due to its capabilities in record visualization, statistical modeling, and machine learning. The popular language also contains tools for analyzing and manipulating facts.

Java:

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Java is a very versatile programming language and is widely used for AI development. It is notably popular in areas such as coil processing, robotics, etc.

Steps to Build AI From Scratch

AI is a vast field that includes a number of subdomains, such as the study of natural language processing, computer vision, deep learning, etc. Here are the critical steps to developing AI systems: Here are the essential steps to developing AI systems:

Define Your AI Project Goals:

Before building your AI project, you need to determine your project goals. It would be best if you were clear on what problem you would like to address and solve. This is necessary because AI systems are specially trained to solve particular issues.

Choose your AI Project?

After setting goals for your project, you need to decide what to work on your AI project. Choosing your profession largely depends on your interests and objectives.

Here Are Some Major AI Project Ideas To Consider:
Image classification: This mainly involves the development of an AI model that classifies images into several predefined categories.

Sentiment analysis: Design a sentiment analysis system that can accurately understand text data such as reviews, tweets, and comments and filter it out into negative, positive, or neutral.

Chatbot development: Chatbot development has become a widely adopted process that draws on the competence of AI by implementing a bot that can communicate with and satisfy users’ information requests and answers. You can perfectly build it into your website’s messaging platform or online store.

Collect Your Data
Data collection is the phase that takes the most time and requires the most data to train an AI model adequately. Your project should consider text, audio, images, and other relevant data, so some of these will be necessary. With a lot of data in hand, you must perform the transformation step.

Integrate AI algorithm
It depends on the nature of the specific project and whether machine learning algorithms ought to power your project.

Get your AI trained
Training your AI algorithm involves tweaking, feeding your AI model, and maintaining the parameters to remove mistakes. Scholars indicate that 80% of data is used for AI training purposes. The last 20% will be directed to validating the model’s ability to offer predicted results. One of the most important things that you will learn to do while training is separating your data into sections that are used for the model’s evaluation and those that are used for training the model. In addition to this, you need to specify an appropriate system of metrics to establish whether or not the model works. Multiple criteria can measure accuracy, precision, recall, F1-score, and so on.

Deploy Your AI
Having completed the training and am pleased with the results, you can now begin using the deployed model for real-time applications. AI deployment ultimately circumvents the wave of technology only if the project is web-based or tightly integrated with your existing system. Even while utilizing AI systems, you want to prioritize security, scalability, and performance. You should also track and observe your model on the production side and ensure that there is enough data to maintain accuracy.

Overview of Software Testing Training in Durgapur.

Software testing is a highly skilled area in the IT business, and one of the most sought-after careers. As a result of global competition, many IT businesses are under pressure to produce accurate software at the outset. As a result, software testing specialists who are capable of doing exact testing on such applications are in high demand.

Additional Info
Who are software testers and what do they do?
Testing plays a crucial part in the development process of a program created by a Software Developer for a client. As a quality assurance employee, the tester tests products. Software programs are provided by the professional at various stages of production, and the professional tests these programs and documents the results.

How to Understand Software Testing:
Tests are needed to ensure the effectiveness of software; we need to talk about how easy they are to run. We should incorporate more unit tests into our testing process since they are simpler to write and execute. We should have fewer integration tests than unit tests as integration tests are comparatively more costly. In addition, end-to-end tests are one of the most complex; therefore, they should be few and far between.

1. Unit Testing:- A unit test is performed by software developers to check that individual module developed by them are functioning properly. Throughout the application, every aspect of the stability is tested separately. An inexpensive and quick test, it takes the least amount of time.

2. Integration Testing:- Testing two or more modules together to see if they are compatible and how data is transferred between them is called integration testing. In the case of complex software integration, this level of testing is performed by the developer and/or the QA tester.

3. System Testing (End to end testing):- System testing tests fully integrated applications as opposed to unit and integration testing. Software is designed to ensure that all intended target systems work with it as a whole. QA testers perform this testing and it is done from the perspective of the users. Performing this type of testing takes the most time and effort.

4. Acceptance Testing:- The acceptance test is used to determine whether the system is acceptable. Our software compatibility test determines whether or not the software is suitable for delivery based on the requirements we have. The customer sign-off is done in order to deliver the software and receive payment for it. We have already looked at the different Test Levels, so let's look at the different Test Approaches for Software Testing. Testing can be approached in two basic ways: a white-box approach and a black-box approach.

White box Testing:- This test examines the source code to determine if it works as it is expected to. To design white-box test cases, programmers and insiders of the system look at details of the system. Tests are typically conducted at the unit level.

Black box Testing:- We test the interface created by the code using automated tools, but we do not have access to the source code. Software testers examine the functionality and not the code structure of the software under test.

Software testing: Why is it important?
The importance of software testing arises from the possibility of costly and even dangerous software bugs. Human and monetary losses can be caused by them. The airbag sensor in Nissan cars failed and Nissan had to recall over a million cars from the market. A software failure has been reported to have caused two accidents.

Further reasons for why testing is necessary are as follows:
Testing software reduces the risk of software failure by catching bugs before they become functional. Integrating an application ensures that the application will work correctly and its performance won't suffer.
The testing of software takes into account the scenarios that may be encountered by the end-user. Conformance with any technical standard or certification can measure the reliability of software. In this way, business relationships are made healthy and confidence is built. By keeping the end-user in mind, Testing can improve the efficiency of the products when it is done at the right time.

What are the skills needed?
You should have or plan to develop the following skills no matter how you prepare for a testing career:
Test plans and procedures should be created and documented, and they should be executed, tested, analyzed, and checked for bugs.

Ensure software quality at all stages of the development, testing, release, and maintenance cycles , Develop an understanding of various development methodologies, such as Agile and Scrum , Have thorough knowledge of a variety of testing tools. You will also need to have extensive experience with scripting and command line tools in UNIX, Linux, and Windows.

Write and speak well (verbal and written)
It's sometimes necessary to know how to program and understand databases for certain positions. As a tester, you don't need extensive programming experience, but getting familiar with the basics wouldn't hurt.

Software Testing Career Path
Career paths include the following:
1. Non Technical skills:- Analytical skills are useful for understanding a complex system, dividing it up into smaller systems, and developing test cases. Ability to communicate verbally and in writing. The ideal software tester should be able to communicate well verbally and in writing. An effective tester should write test conditions, cases, test strategies, and bug reports in an easy-to-read format that anyone could understand. In order to work with a developer during a defect or other follow-up, the tester will need to communicate well.

2. Technical skills:- Several databases such as Oracle, My SQL, etc., store data in the software application in the background. If this data needs to be validated, then SQL queries are required. Although we previously mentioned dozens of potential paths, there are still plenty more. You can specialize in agile, explore different testing methods, build tools and expertise in different areas, conduct research in one's sector, and inspire others to become testers by saying keynote speeches, joining meetups, following blogs (like this one! ), using social media, and giving lectures. As a software tester, there are various career paths to choose from.

Career Benefits
In order to meet the expectations of both professionals and students who are curious about software testing, the software testing training certification has been designed and developed carefully. Various open opportunities for software testers are currently available on the job market. Some organizations are looking for the testers with hands-on experience of manual testing, and some are looking for testers with hands-on experience of automation testing. With this Online Software Testing Course, you will receive training on both manual and automation approaches to testing. You will be able to handle the entire software testing process after you complete the software testing training certification. The salary package you will receive will be competitive, as testing professionals are in high demand these days. You will be preferred over other testers after finishing the course since you will know several testing methodologies.

India's average salary for software testers
Software Tester salaries in India vary according to several factors. It is the skills of the professionals that matter most among them. It is possible for someone with an entry-level career path to get paid well, even if they have a high set of skills and good knowledge of software testing. Having some experience is imperative if a Software Tester hopes to earn more than the basic pay. Additionally, the location of employment and working for a top-tier software company can affect Software Tester salaries. The Average Salary of a Software Tester. Software Testers can expect an annual salary of 3.5 LPA, based on PayScale. Based on the various factors mentioned above, this figure lies between 1.80 LPA and 7.81 LPA. A top software company in a high cost of the living area will offer you a higher salary with less experience.

Industry 4.0 is the next step in the evolution of manufacturing, based on the idea that machines should be able to communicate with each other and work together to create new products and services. Also known as the fourth industrial revolution, Industry 4.0 solutions are already changing the way we live and work forever—and making a huge impact on the world economy. Industry 4.0 is the application of technology to digitally transform how industrial companies operate. These technologies include the industrial IoT, automation and robotics, predictive maintenance, simulation, additive manufacturing, and IoT analytics. Industry 4.0 is driven by a need to boost efficiency, become more agile to respond to market unpredictability, improve quality, and to enable new business models.

The term was coined by Klaus Schwab, founder and executive chairman of the World Economic Forum. Our manufacturing solutions today have advanced far past where the industrial revolution got its start—and companies like yours are reaping the benefits of digital transformation initiatives today from this evolution.
Benefits of Industry 4.0
Industry 4.0 is the new era of manufacturing, combining technology, robotics, artificial intelligence, and automation to create an efficient and effective manufacturing process. Investing in new technologies—where digital solutions transform your physical operations—means keeping the benefits for the organization at the forefront of your strategy. When it comes to Industry 4.0 digital transformation in your factory, your business can achieve increased productivity and efficiency, higher quality and output, and improved safety across your lines, assemblies, plants, and factories.

The easiest takeaway for understanding the difference between machine learning and deep learning is to know that deep learning is machine learning.
Machine learning is an application of AI that includes algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions.
Deep learning is a subfield of machine learning that structures algorithms in layers to create an "artificial neural network” that can learn and make intelligent decisions on its own.

NPA in terms of RBI regulations result out of non-payment of interest for a period of 90 days or non-payment of principle amount for 90 days or more. So beyond that point, it is called Non-Performing Asset. The loan is taken by the company on its assets from the bank. When the asset is not performing because they become doubtful and NPAs from doubtful become bad loans.
Before the period of 90 days, they are calledStressed Assets.Stressed assets= NPAs + restructured loans + Written Off Assets.
Types of stressed Assets:
Sub-standard Assets-If borrower fails to repay the installment, interest on principal or principal for 90 days the loan becomes NPA and it is termed as Special Mention Account (SMA). If it remains SMA for a period less than or equal to 12 months it is termed as Substandard Assets.
Doubtful Assets-If the Sub-standard assets remains so for 12 months or more, then it would be termed as Doubtful Asset.
Loss Assets-If the loan is not repaid even after it remains substandard for more than three years it would be called as loss Asset.
Written Off Assets-Written off assets are those on which the bank or lender doesn’t count the money borrower owes to it.

Working in the MERN stack delivers powerful results simply and efficiently. Knowing your way around the stack is an important skill set since building and deploying solid MERN apps is likely to gain importance in the future.
Node.js was built on Chrome’s JavaScript runtime to make it more conducive to building fast-operating network applications with easy scalability. The platform operates using a nonblocking, event-driven I/O model that is incredibly efficient.
Node.js’s efficiency and simplicity make it an ideal platform for real-time applications running across distributed devices, especially those with intense data requirements. But Node.js has far more potential in conjunction with the MERN stack.
In this Node.js tutorial, we will show you how Node.js is integrated into the MERN stack and its significance for building front end applications. This knowledge will help you as you continue your web development journey.

What is internet of things (IoT)?
The internet of things, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.
How does IoT work?
An IoT ecosystem consists of web-enabled smart devices that use embedded systems, such as processors, sensors and communication hardware, to collect, send and act on data they acquire from their environments. IoT devices share the sensor data they collect by connecting to an IoT gateway or other edge device where data is either sent to the cloud to be analyzed or analyzed locally. IoT can also make use of artificial intelligence (AI) and machine learning to aid in making data collecting processes easier and more dynamic.

How to Make a Successful Career in Banking?
How to start a career in Banking? There are numerous lucrative jobs in Banking which offer high salary and job security but preparing for a career in Banking generally means getting graduated with a degree in Finance/Economics/Banking/Commerce and cracking bank exams. The minimum qualification needed for a job in Banking is a bachelor’s degree in Commerce or Management-related specialisation. Further for banking jobs, you will have to study quantitative aptitude, general awareness, reasoning, English and basic computer skills to ace bank exams! Read this blog to know all about how to to start a career in banking, top courses, jobs, salary and more!
Banks are institutions that are responsible for driving funds into the economy which further leads to the development of the country. Banking has been amongst the sought-after careers for ages, especially in India and still the mindset of the population has not changed. No matter the ongoing changes in the technology and the revolution taking place in the business industry, the only thing that has remained constant over centuries is having a career in the banking sector. Obviously one might not feel motivated to work in a sector if they are not aware about the present and future benefits. That is why we have provided a list of few benefits that will tell you why you should opt for a career in banking.
• Handsome salaries in India and abroad
• Competitive compensation and pension if you have worked in a government bank.
• Numerous work opportunities
• No limit for promotions
• A perfect sector for highly ambitious individuals and professionals
• Community and public service motive
• One of the important benefits as to why people opt for a career in banking is because of job security.
• Everyone having a basic education in the field of commerce is welcomed as fresher’s in this sector.
• The banking sectors are the backbone of any economy and hence they are not affected during the recession period in the economy.
• Job satisfaction and overall skill development
• A highly regarded/reputed job profile in the eyes of the society
List of Careers in Banking
Banking offers many rewarding and lucrative career opportunities, be it the public sector or the private sector. Let’s look at the top 10 most trending jobs and careers in banking:
Career in Banking Salary
Bank Manager 7 Lakhs p.a.
Financial Accountant 4 Lakhs p.a.
Financial Risk Manager 11 Lakhs p.a.
Certified Public Accountant (CPA) 7 Lakhs p.a.
Investment Banker 10 Lakhs p.a.
Equity Analyst 4 Lakhs p.a.
Apart from these, some of the most popular careers in Banking are:
• Asset Manager
• Financial Risk Manager
• Business Analyst
• Internal Auditor
• Foreign Exchange Trader
• Credit Analyst
• Loan Officer
• Bank Teller
• Financial Manager
• Financial Service Representatives
• Book-Keeper
• Audit Clerks
The following skills will help you to build a career in banking sector:
• Customer dealing
• Customer satisfaction
• Analytical skills
• Good knowledge of numbers and accounts
• Mental maths
• Communication skills
• Patience
• Attention to minute details
• Confidence
• Critical thinking
• Technical skills
• Stress management
• Resilience
For those planning to build a career in Banking, there are numerous degree programmes available at the undergraduate and postgraduate level. In India, the basic qualification you need to apply for Banking jobs is a degree in Commerce, Science or Arts and they must get a good score in the requisite Bank exams to be eligible for their chosen posts. Here are the prominent Banking courses you must consider:
• Bachelor’s in Banking and Finance
• MBA in Banking and Finance
• Diploma in Banking and Finance

HOW ARTIFICIAL INTELLIGENCE WILL CHANGE THE FUTURE:
Artificial intelligence is impacting the future of virtually every industry and every human being. Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future.