Data Science Archives | Adamas University

AI and ML for a better future

Have you ever considered about what motivates artificial intelligence programmes like Tony Stark’s JARVIS or the common man’s Alexa, Google Assistant, or Siri? These programmes can answer your calls and help you make decisions, but have you ever wondered what motivates them? In what ways does their brain operate? The application of Artificial Intelligence (AI) and Machine Learning is the straightforward response to each and every one of your questions (ML). The mechanical brains are controlled by artificial intelligence, which attempts to simulate human intellect so that they can perform like a human brain. With more and more research being done on AI and ML, there is the potential for AI and ML to assist in training computers to make decisions on their own, which will eventually make our lives easier by reducing the amount of work we have to perform.

This article, which was meticulously researched and penned with the intention of depicting the future reach of AI and ML in India, was created with the goal of assisting students in understanding how this subject will be advantageous to them if they decide to pursue it.

The potential applications of artificial intelligence in India are still in the process of being adopted, but the technology is gradually being put to use to find intelligent solutions to modern problems in almost all of the country’s most important industries, including agriculture, healthcare, education and infrastructure, transportation, cyber security, banking, manufacturing, business, hospitality, and entertainment. Readers who are interested in pursuing a course in artificial intelligence can find helpful information in this article. Candidates will gain some insight into the potential of artificial intelligence in India if they read this article and consider its contents.

Scope of AI in India

Both artificial intelligence and machine learning have a promising future in India and an immense potential to alter every area of the economy for the benefit of society. Artificial intelligence and machine learning have a bright future in India. AI is an umbrella term that incorporates a variety of helpful technologies, such as self-improving algorithms, machine learning, pattern recognition, and large amounts of data. Soon, there will not be a single business or market segment in India that is immune to the effects of this powerful instrument. This is one of the reasons why there is a growing demand for online courses in artificial intelligence in India.

Scope of AI and ML in Education Sector

By utilizing a variety of AI applications such as text translation systems, real-time message to speech, automating mundane and also repeated tasks such as taking presence, automating grading, and also customizing the learning journey based on ability, comprehension, and also experience, artificial intelligence can help our instructors be more effective. Within the purview of Artificial Intelligence education and learning is the consideration of the prospect of utilizing AI-powered rating machines that are able to evaluate solutions in an objective manner. This is being carried out in college and university settings in a step-by-step fashion. Real-time text-to-speech synthesis and text translation are two further AI-based applications in the educational sector.

The Role of AI and ML in the Development of Chatbots

The combination of chatbots in the digital framework or availability via the IVRS system education domain can be transformative in a country as diverse as India. They can be educated on the subject matter, and a large percentage of the students’ doubts can be answered quickly. This reduces the current workload of educators, allowing them to focus on more creative tasks.

The Integration of AI and ML into the Automated Grading System

On a more global scale, methods of machine learning such as Natural Language Processing could be used for automated grading of assessments on systems such as E-PATHSHALA, SWAYAM (Study Webs of Active Learning for Young Aspiring Minds), and DIKSHA. This would apply not only to inquiries that are subjective but also to those that are objective. This is because of the National Education Policy 2019, which places an emphasis on computer and internet literacy.

The Role of AI and ML in the Healthcare Industry

The healthcare industry in India is one of the most rapidly developing and competitive markets in the world. There is a dearth of doctors and services, including competent nurses and technicians, as well as infrastructure. This is one of the primary issues, but there are many others as well, including affordability and accessibility. As a result of the majority of high-quality medical facilities in India being situated in close proximity to tier 1 and tier 2 cities, access to healthcare in India is not uniformly distributed across the country physically. Aside from that, as Artificial Intelligence develops, there will be an increase in efficiency, which will lead to a reduction in the overall cost of medical treatment.

Because AI is able to process vast volumes of data in a short amount of time, it can be of assistance in the creation of medical equipment, as well as in design and innovation. Having a system that is enabled with AI helps to eliminate medical errors and increases overall productivity. Artificial intelligence has the potential to both circumvent access barriers and provide a solution to the accessibility problem by applying early detection followed by suitable diagnostic conclusions.

AI and ML in the Agriculture Sector

In India, agriculture is a major source of income for many people. Traditional farming methods pose a slight challenge for Indian farmers. Thermal imaging cameras can be used to continuously monitor agricultural land to ensure that plants receive adequate water. When it comes to selecting the right crop and the optimum method of sowing, this tool can help you get the most out of your land and save money.

As a result, improved insect control preparation can benefit from the application of artificial intelligence to predict behaviour and investigate parasites. Artificial intelligence-assisted anticipating modelling can be effective for delivering more detailed demand-supply details and for predicting the needs of farmers in terms of agricultural produce.

Automated Vehicles Using AI and ML

In the transportation industry, artificial intelligence offers a lot of potential. Artificial intelligence (AI) has the potential to be useful in a few specific contexts. Since its invention in 1922, autopilot has been used to keep ships, planes, and spacecraft on course. Self-driving cars are another field of research. Self-driving automobiles are being researched by companies around the world, including India. The use of artificial intelligence and machine learning has been prevalent in the design of these automobiles from the beginning. Self-driving cars, according to experts, will have various advantages, such as reducing pollution and eliminating human error from driving.

Artificial Intelligence and Machine Learning for a Smart Home

We are surrounded by artificial intelligence. Most of the time, we don’t even realize we’re interacting with devices powered by artificial intelligence. As an example, we routinely use OK GOOGLE, ALEXA, or CORTANA to execute a variety of chores by simply speaking to them. Artificial Intelligence and Machine Learning are used by these intelligent assistants for voice recognition. Learning from the user’s commands improves their productivity. You may ask a question, play a song, and buy anything online all with the help of this clever assistant.

Applied Artificial Intelligence and Machine Learning in Cybersecurity

Cybersecurity is another area where AI is being applied. Many companies have to deal with a lot of data. A security system is required, for example, in the banking industry or government entities that maintain vast databases containing the personal information of citizens. An good example of this topic is Cognitive Artificial Intelligence (CAI). Additionally, it helps analysts make better judgments by detecting, analysing, and reporting on hazards.

Machine Learning formulae and Deep Learning networks are used to improve and strengthen the AI over time. As a framework and central point of control for safety and security responses, IBM Resilient is an open and agnostic platform.

In the Manufacturing Industry, AI and ML

The industrial sector is a popular target market for AI-based firms from India. In order to assist the manufacturing industry flourish, these companies are developing AI-based solutions. Various types of robots are controlled by artificial intelligence in the workplace. The ability to examine data and forecast the future is a unique AI technology.

Using this AI capabilities, companies may estimate future supply and demand based on data from prior years’ sales or market surveys, allowing them to make faster decisions and better use of existing products. Artificial intelligence (AI) will be widely used in manufacturing in the future years.

Is Operations Research useful in Data Science?

“Operations research (OR) is defined as the scientific process of transforming data into insights to making better decisions.”

–The Institute for Operations Research and the Management Sciences (INFORMS)

Introduction:

In the twenty-first century, especially in the last decade, the most trending domain of study is may be Data Science and Data Analytics. In this domain of study, people work with data from different fields and they use different tools and techniques from the domain of Mathematics, Statistics, and Computer Science to study and analyze the data. Then make some conclusion from the data and use them to predict the future of the phenomenon under study. Before the rise of data science as a domain of study, Operations Research analyst and Statisticians are used to do the similar kind of job. Due to these facts, the overlap between the domain of Data Science and the domain of OR is misunderstood. Also, there is a common perception that OR is not useful in for Data Science or Data Analytics. Actually, the marketing of OR products and services which are applied to solve the real world problems leads to this kind of misconception, as most of the time the end-users do not have an understanding or background of OR and data science. Another possible reason may be that the availability of machine learning models which are available as packages of several platforms like Python and do not really contain specific any OR models. In practical, OR tools and techniques are applicable to data science. In fact, a lot of ideas which are used in Artificial Intelligence (AI) and data science problem solving, have cross-pollinated from OR due to the large overlap in the techniques and methods used. In this blog, I try to explore these relations of OR with Data Science and Data Analytics.

Operations Research and Data Science:

Before going to the discussion on the role and relation between Data Science and OR, let us try to understand another very important term called Analytics. According to INFORMS, Analytics is the application of scientific & mathematical methods to the study & analysis of problems involving complex systems. There are three distinct types of analytics:

i) Descriptive Analytics gives insight into past events, using historical data;

ii) Predictive Analytics provides insight on what will happen in the future; and

iii) Prescriptive Analytics helps with decision making by providing actionable advice [https://www.informs.org/Explore/Operations-Research-Analytics]. In an INFORMS podcast, depending on organizational backgrounds, Glenn Wegryn divides Analytics into two distinct camps: Data Centric Analytics where data is used to find interesting insights and information to predict or anticipate what might happen; and Decision Centric or Problem Centric Analytics which is used to understand the problem, then determine the specific methodologies and information needed to solve the specific problem. This data centric analytics are done by using Data Science whereas problem centric analytics are done by Operations Research. The above mention figure clearly give an idea about this. From the figure, it is very clear that there is a common point of interest from both the domain. Hence OR plays a very important role in Data Science domain.

Operations Research and Machine Learning:

Machine learning is the area of data science where most of the OR tools and techniques are used. Linear programming and Optimization techniques are fundamental part of the overall machine learning lifecycle. Some of the examples of OR are:

Enabling smart human resource management by forecasting human resource requirements and optimizing daily schedule for resource persons (linear programming model)
Increasing TV program viewership by optimal scheduling of programs’ promotion (linear programming model)
Enabling supply chain transformation by providing AI/machine learning-based recommendations for optimized product utilization
AI-enabled forecasting for retail and eCommerce applications to optimize funnel and customer traffic
Data-driven optimization models for automated inventory management where we need to do warehouse management, inspection and quality control

Operations Research and Artificial Intelligence:

Another important area of data science is Artificial Intelligence where we can observe the use of OR algorithm. AI is used to build an automated system. Now, any real-life system have many decision variables and parameters, so if we want to build an automated system then we have to deal with a lot of decision variables. That’s why operations research algorithm must be a core engine in the system.

An Artificial Intelligence development lifecycle consists of the following steps: (Link)

Descriptive and Predictive steps:

In the first step, we need to define the problem to be solved
In the next step, we need to understand the current state of the problem and accordingly we have to define the work scope
Next we need to develop a Machine Learning model, where the machine learning solution is developed and tested.

Prescriptive steps:

Machine learning outputs or the predictions obtained using machine learning are given as OR inputs. Here, the OR techniques are used to make recommendations based on the outputs from the ML model. This is a critical step for the entire life cycle.
Finally, the solution output is delivered to the client.

Covid-19 impact:

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale ‘big data’ generated and harnessed for combating the COVID-19 pandemic. (Zhang, Qingpeng, et al. “Data science approaches to confronting the COVID-19 pandemic: a narrative review.” Philosophical Transactions of the Royal Society A 380, No. 2214 (2022)). Covid-19 has a big impact on supply chain strategies also. People from data science community are analyzing “lessons learned” from the pandemic to better prepare and more efficiently and effectively respond to the next disaster, interested people can visit the following for a discussion on it (Link1).

Conclusion:

From the above discussion, it is very much clear that Data Science and Operations Research have some overlapping objectives with clear line of difference between these two domains of study. Also, we observe that there are several OR techniques and algorithms which have important role to play in different topics of data science. In my opinion, operations research together with data science and analytics is going to play a very important role to build the future of us.

Computer Aided Diagnosis: A Spectacular Achievement in Health Care

Artificial Intelligence has the great impact in health care. It can assist doctors to detect or diagnosis a disease at early stages. In developing countries like India has very low patient and doctor ratio. As a consequence, the performance of the manual detection of the disease often degrades i.e., doctors may overlook the early sign of the disease and patients can suffer death. In order to decrease the mortality rate of several diseases, Computer aided detection or diagnosis may be a potential solution. It is a computer-based program which analyse different radiological image modalities and predict the presence of the disease. Consequently, it can be said that these types of technology can provide some treatment facility to the patients where minimum treatment facility is available. In early 1980’s, based on the symptoms of the patient researchers have proposed several algorithms to predict the presence of the disease. However, these methodologies were not acceptable to the medical community. In 2005, International association of Computer Aided Diagnosis established and they first approved the prediction of breast cancer from mammography in clinical practice. After that several researchers have proposed several CAD methodologies for early diagnosis. They give emphasis mostly on early detection of different types of cancer from different organs. The basic challenges of implementing such system are present of sufficient amount of annotated patient data. The collection of patient data from different hospital is a tedious job.

However, several researchers have proposed different medical image datasets by collaborating with different hospitals. In order to predict lung cancer at early stages, LIDC-IRDI dataset is introduced which consisted of 1081 Lung CT images and these data are taken from 7 different hospitals of United States of America. This dataset has been released in 2008 and the CT scans are taken from a 32 slice CT scanner i.e., the images are taken from the old CT scanner machine. In present context, most of the hospitals use a 64 slice CT scanner machines and the implemented models are not provided satisfactory results if researchers have considered this dataset. Moreover, this dataset only provides the information about the presence of the abnormalities but not confirm the presence of cancer in it. These necessitates a new benchmarking dataset that consists of the CT scan of a 64 slice CT scanner and also have the information about the disease. The researchers of University of Calcutta have introduced a new public dataset “Swash” for the lung cancer researchers which consisted of 289 CT scan machines and all the data are biopsy proven. Like lung cancer, researchers have also introduced BRAST datasets for Brain tumor detection, MIAS dataset for breast cancer detection and diagnosis from digital mammography, DRIVE dataset and DRISTI dataset for diabetic retinopathy detection from FUNDUS images. These aforementioned datasets have been used for implementing several CAD methodologies for early detection of the disease by considering the different algorithms of machine learning (ML) and deep learning. The researchers of United states of America, have designed a ML-based methodology that is capable of detecting breast lump from digital mammography, after that the computer-based technique is also capable of grading the stage of cancer. The researchers of Redbound University and University of Calcutta proposed a fully automated software tool that is capable of predicting lung cancer from Computed tomography images. Instead of having higher accuracy in brain tumor detection from MRI images, the Machine Intelligence Unit of Indian Statistical Institute have proposed a novel methodology that can assist clinicians about the post-surgery survival of brain tumor patients. Apart from cancer detection, the researchers also tried to propose several CAD methodologies of other diseases. At the middle of the pandemic era, researchers have proposed several methodologies that are capable of detecting COVID-19, after analysing the digital chest X-ray and thoracic CT images. The published literature also reveals that their exist several ML and DL-based algorithms that are capable of detecting Alzheimer, Parkinson’s, Strokes, fractures, cysts from different modalities of medical images. Furthermore, the researchers are also capable of measuring the changes of abnormalities after several drugs are induced on the patients. However, the accuracy of these methods is quite satisfactory, but these models are implemented by considering several data which are taken from old scanning technology. As the precision of the scanning technology has been improved, the characteristics of the data has been changed and this requires advancement in existing algorithms or implementation of new model that can provide higher accuracy and these methodologies can use in clinical practice.

Post COVID Career Prospects of M.Sc. Tech (Statistics and Data Science)

In this current scenario i.e. post COVID period data science becomes a new era. Data science has played a vital role in making the policies or decision making in real life world. It is one of the trendiest jobs across the globe in terms of future scope and career stability. Data science is an interdisciplinary subject that includes the use of statistics, big data analytics, machine learning and related aspects in order to understand the problem or phenomena with respect to a set of real-world data. The thrust areas of data science are fraud and risk detection, healthcare, internet search, targeted advertising, advanced image recognition, speech recognition, airline route planning etc. Under health care sector it is having different applications such as medical image analysis, genetics & genomics, drug development, virtual assistance for patients and customer support. Thus, data science has major demand in many organization around the globe. In today’s career-oriented world, students are confused on choosing the right subject after completing graduation that will help them to get a good placement in the job enterprise. After graduation, numerous options like master degree in the general subjects, or in various professional courses confuse the students to take the right decision. Today, both students and their parents are seeking for job-centric programs, though general study programs are mostly preferred as their first choice. A good choice can be a program that is a combination of both general and professional courses. It is always better to choose a program that is a natural progression of the existing skills and qualifications along with some professional development skills.

The Role of Statistics and Data Science in Today’s World:
The pursuit of a career in Statistics is in high demand today. With a degree in Statistics, career opportunities are boundless. Statisticians have been known as Economists, Scientists, Mathematicians, Field Investigator or Qualitative Researcher. The ‘data-hungry’ modern world now calls them data analyst, business analysts, data scientists, quality and risk analysts. Data Science has become an integral contributor to success in career opportunities. Data Scientists and Data Analytics are in high demand in today’s job world. Data Science based enterprises are the largest companies in the entire world. The famous websites like Google, Amazon, and Facebook, use data science to create algorithms that improve customer satisfaction, which in turn maximizes the profit. Thus, with a degree in Data Science, one can work with high-tech companies like Google, Amazon, LinkedIn, Facebook, banking and financial companies like ICICI Bank, Axis Bank, or research firms like McKenzie, Deloitte.

So, according to the trend of the modern job world, the best option is to choose a program in Statistics or Data Science. But, can one pursue both Statistics and Data Science at the same time? Yes, the Department of Mathematics, Adamas University is offering such a program which is a combination of both Statistics and Data Science. The program name is ‘M.Sc. (Tech.) in Statistics and Data Science’. This program is also a combination of both general and professional courses, Statistics, being a general subject and Data Science, a professional course.

M.Sc. Tech (Statistics and Data Science) program is a two years (four semesters) post-graduate degree course which combines Statistics, Mathematics and Computer Science with applications to Data Science and Data Analysis to meet the demand of today’s job world. From Probability Theory and Statistics to Statistical Inference, from Applied Statistics to Statistical Modeling, from Problem Solving to Number Theory, from Computer Programming to Data Mining, the program is also offering a number of optional papers, a few of which are Big Data Analytics, Cryptography and Network Security, and Artificial Intelligence. Besides these, the program also offers summer internship and Project/Dissertation. In summer internship, a student may choose to visit relevant institute or industry according to the availability. The project/Dissertation helps the students to explore and strengthen the understanding of fundamentals through practical application of theoretical concepts.
On completion of the program, a student will
• Be acquainted with the various Statistical tools useful for Data Analysis
• Develop programming skills
• Acquire knowledge on Data Analytics and Data Mining
• Learn the concepts of Data Structures
• Develop a conceptual understanding on Network Security
Eligibility Criteria for the Program:
Graduate student having Statistics/Mathematics/Economics/Physics as compulsory subject, or graduate students in Data Science, or students having a B.Tech. degree in IT/CSE/ECE or BCA or other relevant stream with at least 50% marks are eligible to apply for this course.

Career Prospects:
From careers in IT sector to technological companies, Data Science professionals can choose their career in a numerous field including business, industry, agriculture, government and private sectors, computer science, and software development.
A few job roles available for a student after completion of the program are:
(i) Data Scientist: Data scientists also called analytical experts utilize their skills in both social science and technology to manage all kinds of data. A data scientist involves in arranging and analyzing disorganized and unstructured data, from numerous sources like smart devices, social media feeds, emails, industry, health science, environmental data.
(ii) Data Analyst: The role of a Data Analyst is to figure out a market trend. The data analyst serves as a caretaker for an organization’s data and as such shareholders are able to understand data and use it to make tactical business decisions.
(iii) Statistician: A Statistician deals with gathering, analyzing and interpreting to aid in many businesses and decision-making process. The Statisticians apply statistical models and methods to real-world problems. They analyse, gather and interpret data to help draw valid conclusions.
(iv) Forecasting Analyst: The task of a Forecasting Analyst includes tracking, analyzing, and evaluating operations in order to provide accurate forecasts. Forecasting analysts use current data of the company to predict future level production and sales. By examining inventory levels, demand for products or services, and speed of production, they ascertain a company’s optimal production levels and possible future sales.
(v) Data Manager: Data Manager are involved in making and implementing policies for effective data management, framing management techniques for quality data collection to confirm adequacy, accuracy and validity of data. They are also involved in planning and executing efficient and secure procedures for data management and data analysis with attention to all procedural aspects

Conclusion:
From above discussion we can see that a student with master degree in Statistics and Data Science has numerous career opportunities and so this program is recommended to graduate students seeking for a good career opportunity in the present scenario of the job world.

Bright Career Opportunities with specialization in the field of Economics

In today’s scenario of growing income inequality where technology is already displacing jobs at a fast pace, the shock created post-pandemic on health and the economy ground has put the economies into freefall, and labor markets are very much disrupted. There is huge unemployment created worldwide due to the global recession, and the recession has influenced mainly those communities which are already in a vulnerable position. The speed of technology adoption is expected to even enhance in near future and the adoption of cloud computing, big data, and e-commerce remain high priorities for business leaders, following a trend established in previous years. However, there has also been a significant rise in interest in encryption, non-humanoid robots, and artificial intelligence. In this scenario, this blog discusses career opportunities in the field of economics.

In today’s world of huge competition, it’s the field of specialization studies that are in high demand in all sectors. So, it is very important to opt for a career option where there is the core domain knowledge and subject specialization to fetch a good job in all aspects. Having specialization in the subject of Economics has bright career opportunity options in various sectors (both government and private) and it is expected to grow further in the post-pandemic scenario. A background in economics carries huge weightage and impression on the chances of selection for a bright career option in many sectors. The beauty of studying this subject is that the student can choose from many varied career options after finishing their graduation with economics honours.

Since every moment of our daily life, data is created everywhere, and a proper analysis of data can answer lots of questions. Hence, in the given scenario in the field of economics, there is a huge demand for professionals who are trained in handling Big Data.

Thus, after pursuing a Masters’s in Economics with a specialization in Econometrics where they learn data handling techniques and usages of software like EViews, Stata, and R, there are opportunities to work in MNCs working with handling Big Data. Since the subject of Economics always has a research inclination, there is a huge demand for Economics postgraduates professionals trained in analysing data and preparing reports. They are hired not only by corporates but also by various government agencies and organizations which are engaged in analysing data and publishing reports. It’s a win-win position for the postgraduates who have specialization in financial economics as well since there is a huge demand for profiles such as investment portfolio management which requires knowledge in finance and data handling. Online content development and content writing on the basis of reports and data analysis are in huge demand in the field of economics. Lots of start-ups are engaging people having skills in data analytics. Typically, Data analysts are promoted to higher positions like senior data analysts, data scientists, data analytics managers, business analysts, etc. So if somebody enjoys working with numbers, and thinks analytically the career option as a data analyst is extremely in demand. There is a huge scope in the academic research field also for social scientists like economists. Graduates in economics also have huge opportunities to incorporate MNCs and companies engaged in research-related activities. Many students also pursue MBA after their graduation in Economics, since it adds a feather in the cap of the candidate. They are in high demand in the corporate sector.

Besides the corporate sector, students with Masters in Economics can appear for the examination for various specialist officer posts in Economics which are conducted by various Government Banks and the Reserve Bank of India. These are specialist officer posts that require domain knowledge of economics. There are also examinations conducted by Public Insurance companies like Oriental insurance for specialist officer posts in Economics Those students having a research inclination and wish to come in the field of academics can appear for the NET examination for lecturership and NET-JRF examination for Ph.D. scholarship. There are various government organizations preparing research reports, they also hire Economics postgraduates for research purposes and various other officer posts.

There are career opportunities for students after finishing their graduation in Economics as well. The students can compete for prestigious government services like the Indian Economic services examination which is conducted by UPSC. The syllabus of this examination comprises all the topics which an Economics graduate learns in his/her three years of Economics Honours (BA/ B.Sc) course. They can also appear for various other government examinations like Staff Service Commission and Public Services Commission where a background in Economics is given good preference. There are various NGOs and Research organizations also engaging students of economics in preparing analytical reports on topics of social issues.

The above description gives us a clear picture that there is a huge demand for Economics graduates and post-graduates in many sectors for well-paid and prestigious positions.

Overall, the subject of economics is highly in demand in the world of Big Data especially in the post-pandemic world with data centers in growing demand. So, if students select a program with a specialization in the field of Economics, it will surely give them a competitive advantage in their career growth and opportunities.

Applications of Machine Learning in Geography: Present and future trends

Machine learning is one word at present time machine learning can be defined as a system where computers can perform tasks that require human intervention and intelligence. Machine learning is part of artificial intelligence which is growing rapidly in the present era. Artificial intelligence (AI) is the broader umbrella of computer-driven applications where Human support is hardly required. Deep learning is a subset of machine learning. AI is also associated with the development and design of algorithms. Artificial intelligence and machine learning have reduced the role of humans in many activities Like in industry public affairs research and academia. Geography as a discipline has also benefited from the applications of Artificial Intelligence and machine learning. Geographical information system Remote Sensing and GIScience as a whole get the benefit of machine learning which is termed GeoAI. Different organizations like the American Association of Geographers (AAG) in Environment System Research Institute (ESRI) and Microsoft are collaborating to develop a GeoAI-based platform for the analysis and visualization of different data.

The advantage of machine learning algorithms lies in the fact that the algorithm can use the data pattern to train itself and come up with better predictive or classification solutions. Machine learning is classified into different groups supervised learning, unsupervised learning, Semi-supervised learning, and reinforced learning.

In recent years automation in geography and location-based study is taking place at a quick pace. Mechanisms to monitor different spheres (terrestrial, atmospheric, and aquatic) are growing at high speed. This leads to a large quantum of data availability of different types and formats. Machine learning is helping to analyze this data using different advanced algorithms like Support vector machine, Random Forest, Artificial neural network (ANN), or even one step ahead deep learning methods like Convolution Neural network (CNN) and dynamic learning methods like Q- Learning.

In the area of human geography, machine learning has been used to explore the distribution of products and goods, population genetics, detecting, characterizing, and evaluating human epidemiology, tourism projections, detecting military patterns, international politics, and agricultural production systems. Within physical geography, ML is used to advance research on environmental diversity, biogeography, and abundance, water resource management, climate modeling, climate change, etc.

In the Geographical Information System (GIS), users can use it to comprehend the spatial elements of their work and connect it to data such as population data. The GIS data can be utilized for a variety of reasons, including transportation, drought analysis, agricultural, disease outbreak analysis, land occupation, and so on. At the same time, GIS enables the storing of a large volume of data in a secure manner, as well as access to it at any time and on a rapid basis. So, the purpose of this chapter is to examine previous works and research in this area to better understand the current status and capabilities; also, it will be an attempt to prepare for future improvements in the field of GIS.

There are also many challenges faced by machine learning in the part of lack of data and as well as the quality of data. Quality issues in machine learning lead to improper predictions and classification. As the technology is still evolving real-world complexities are sometimes mishandled by the algorithms. Ethical issues are very much prominent in countering human geographical contents. They’re also voids left for spatially explicit machine learning and other spatiotemporal models. This vacuum of Spatio-temporal models hinders the growth of machine learning applications in geography.

In the future, machine learning will be more robust with advanced GeoAI algorithms which can encounter issues like handling large and complex data. More spatial explicit models and algorithms will be available to provide solutions to complex and multifaceted real-world problems. The emergence of automatic machine learning (AutoML) is showing the path for such a solution. In solving geographical problems of the future integration is required between the conceptualisation and implementation of models. As artificial intelligence has started touching down nearly all spheres of life, machine learning is going to play a key role in unlocking the different mysteries of nature near future.

Career as a Machine Learning Engineer in the post-pandemic world

The entire globe has been facing an unprecedented challenge from the Novel Coronavirus, which has made the physical world come to a standstill and the world economy has been holding onto a thread due to this fatal pandemic. But the brighter side of this gloomy situation is that companies are finally appreciating and understanding the significance of Machine Learning and Artificial Intelligence in the practical world. More and more brands are now taking up Machine Learning solutions for their business problems. Not only Machine Learning is used to combat the global pandemic but it has also come out as an important tool in constructing a better world post-COVID. Machine Learning has the capability of providing an understanding and early analysis of problems and prompt resolutions. This technology is used by the doctors and health practitioners to track the virus, identify potential threat to patients and predict the possible cure from disease. These reasons indicate that Machine Learning and Artificial Intelligence, both are here to rule and this can be an interesting career option for the aspirants who are passionate about data and numbers. Machine Learning has been at the forefront for all the advanced programmers who intend to develop intelligent systems that learn and apply knowledge. These programmers, better known as Machine Learning (ML) engineers, train systems with the help of complex datasets and algorithms.

Machine learning brief explanation

Machine Learning is a subset of Artificial Intelligence, which combines Statistics and Computer Science to predict using different mathematical models. The predictive model can be based on like whether an image contains cat or dog, predicting credit card fraud detection etc. The main objective of Machine Learning is to take decisions based on predictive modelling. Hidden patterns across the datasets are extracted and useful insights about data are found out to drive important decisions, improve customer relationship based on feedback patterns or launch new business.

What are the pre-requisites for becoming a Machine Learning engineer?

A Machine Learning engineer requires to be proficient in a bunch of technical skills for building predictive models.

Below are given some of the primary components of the Machine Learning engineer role:

Data: The Machine Learning engineer has to understand the importance of data in predictive modelling. The data pre-processing is one of the important steps for constructing a Machine Learning model. Data has to be analysed and described in terms of the problem requirement. Good quality data is a necessary requirement in building efficient Machine Learning model.
Predictive Models: Machine Learning engineers need to construct the models designed by the data scientists, understand the model validation in order to get an essence of the estimation of value addition to the business and understand how to fine-tune these models to optimize them for the consumption by end users.
Software Engineering: They need to be efficient in coding back-end so that the models can be made available to users through a user-friendly API.
Efficient Scaling of Infrastructure: They need to keep the system prepared for scaling of infrastructure so that the system may not collapse when multiple number of users start operating their models.

Where is Machine Learning used in real life?

Machine Learning is being used in real life in many fields, industries or domains. Some of the application areas of Machine Learning are listed below:

Image Recognition: It is a popular and widely used application area of Machine Learning. This is used to identify an object from a digital image.

Some of the use-cases of Image Recognition:

Photograph-tagging in social media
Hand-writing recognition by segregating a single letter into component images
Speech Recognition: Machine Learning has the capability of translating speech into text.

Some of the use-cases of Speech Recognition:

Voice-based digital assistants like Amazon Alexa or Google Home etc.
Search based on voice
Dialling based on voice
Symptom analysis in healthcare domain: Machine Learning can help the medical practitioners to ascertain symptoms in diseases by leveraging the capability of chat bots. This is called symptom analysis which utilises the power of Natural Language Processing and text mining etc. to analyse the disease symptoms and predict the next steps to be taken as precautionary measures or remedial measures.

Primary objectives of a Machine Learning Engineer

Primary responsibilities of a Machine Learning engineer lies in creation of Machine Learning models and re-training models as and when required. Some of the common responsibilities of the role relate to:

Machine Learning system design
Implementation of Machine Learning algorithms and tools
Dataset selection and dataset representation methods
Verification of data quality
Accomplishing statistical analysis
Executing Machine Learning tests
Improving Machine Learning models by tuning of models by proper selection of hyper-parameters
Constructing Machine Learning apps as per requirement

Now, let us see the skill sets that are important for a Machine Learning Engineer.

Skill set of a Machine Learning Engineer

For becoming a Machine Learning Engineer, an aspirant should have the following skills:

Mathematical and statistical skills relating to subjects such as Calculus, Linear Algebra, Statistics etc.
Advanced degree in Computer Science, Mathematics, Statistics or a related degree
Master’s degree is desirable in Machine Learning, Deep Learning or related fields
Coding in programming languages like Python, R etc.
Skills pertaining to Software Engineering, Computer Architecture, Data Science and the like
Working experience with Machine Learning packages and libraries etc.

References:

Rise in the demand for Machine Learning & AI skills in the post-COVID world, https://timesofindia.indiatimes.com/spotlight/rise-in-the-demand-for-machine-learning-ai-skills-in-the-post-covid-world/articleshow/75464397.cms
Machine learning engineer (ML engineer), https://www.techtarget.com/searchenterpriseai/definition/machine-learning-engineer-ML-engineer#:~:text=Machine%20learning%20engineers%20design%20and,data%20engineers%20and%20data%20architects.
AI/ML Remains The Most In-Demand Tech Skill Post COVID, https://analyticsindiamag.com/ai-ml-remains-the-most-in-demand-tech-skill-post-covid/
AI, Automation and In-Demand Skills for a Post-Pandemic World, https://www.sigconsult.com/blog/2021/03/ai-automation-and-in-demand-skills-for-a-post-pandemic-world?source=google.co.in
Artificial Intelligence in a post-pandemic world of work and skills, https://www.cedefop.europa.eu/en/news/artificial-intelligence-post-pandemic-world-work-and-skills

5G Implementation to 6G Evolution in IoT

The change from landline phones to the movability that 1G distant advancement offered was an immense leap. However, in our view, the leap from 4G to 5G is in much the same way enormous, as 5G is the vessel that will broaden the Internet of Things (IoT) climate decisively. We expect the creating number of related contraptions passing on at higher multi-gigabit data speeds and dare to increase network capacity to work with 5G’s gathering rate. Besides, as individual and corporate clients experience 5G’s benefits, they will clear a path for 6G’s augmentation — which history proposes is most likely going to come sooner than later.

Key Points

Broadband IoT, including 4G and 5G far off headways, is ready to outperform 2G besides, 3G as the part that engages the greatest piece of IoT applications from one side of the planet to the other by 2027.
The amount of related IoT contraptions generally extended by around 9% in 2021 to 2.3 billion unique endpoints. Moreover, that number should pass twofold to more than 27billion when 2025.
The energy for 5G continues to grow with associations beforehand executing the advancement. 5G should make 4G old by 2030, along these lines, with everything taken into account 6G could be Again ready to change IoT.

5G Network Expected to Widespread in 2025

As with each new alliance, a mass get-together of 5G will take time, yet not as much time as its forerunners. The 3G social affair wasn’t fast due to purchasers floundering towards change and 3G’s more extreme expense networks. The clearest system for drawing in an agreed data network development combines exploring the speed at which past cell networks were made. Shipped off in 2001, 3G cell networks didn’t change into the norm until 2007, when clients embraced 3Gconnected phones — four years after 3G opened up and 16 years after 2G moderate accessibility opened up. Generally, 4G cell networks were conveyed in 2009 and changed into the standard four years eventually later in 2013 — two years faster than the 2G to 3G change and simply a brief timeframe after 3G shipped off.

Cell Information Network Ages as The Years Progressed

As per these certain examples, we expect 5G’s gathering rate to go on in the steps of 4G’s fast move to standard. After a restricted scope course of action in 2015, as attempts began testing 5G advancement, current appraisals suggest an overall inevitable gathering of 5G occurs in 2025 4G flexible enrolments are projected to top at 4.7 billion in Quarter 4 2021, and a short time later constantly decline to 3.3 billion around the completion of 2027, as 5G transforms into the fundamental participation choice.

Telecoms Report for 5G Adoption Evident Already

In its middle, 5G is planned to further develop the IoT’s buying experience. Purchasers will need to get to cloud organizations rolling from multiplayer cloud gaming, extended reality (AR)- filled shopping experiences, and permission to free robots for movements. As buyers experience 5G power, the more they’re likely going to research. Additionally, according to telecom providers, the word is getting out. Verizon itemized that 25% of its buyer distant clients used 5G-capable contraptions under a year after their conveyance, well before 4G’s 10% gathering rate a year after ship off. As a part of the 5G increment, telecoms are shutting down additional laid outages, including 3G, to reuse frequencies and work on their 4G and 5G associations. AT&T logically progressed away from the 3G relationship in February 2022 T-Mobile means to make a move as needs be in July, and Verizon before the ongoing year’s over by the completion of 2027, 40% of cell IoT affiliations should be broadband IoT (4G/5G).

5G’s Power on display

A couple of extraordinary IoT associations are early adopters of 5G advancement. From time to time, 5G made their more prepared models old. In various cases, 5G nudged the improvement of new advances. One model is Qualcomm’s Snapdragon, the association’s adaptable structures on a chip (SoC) thing suite. In handsets, the Snapdragon 8 Gen 1 is the world’s most significant 5G modem-radiofrequency reply for showing up at a 10-gigabit download speed or 10,000 megabits. At this speed, colossal data records can be downloaded rapidly. For setting, 4K spouting across different contraptions expects around 100 megabits or 1% of Snapdragon 8 Gen 1’s most extreme limit. Qualcomm checks that 5G-engaged handsets will show up at 750 million units sold in 2022. Cisco’s Catalyst 900X trading stage is another model. In light of Cisco’s Silicon One development, the 900X licenses the association to use quick Wi-Fi 6E sections to give 5G and cloud-based game plans. It expands the presence of existing cabling from 1 to 10 gigabits. Cisco hopes to offer 5G as an a-organization game plans commonly open to private endeavors, the advancement ability of which could be gigantic given the prerequisite for further developed data speeds, cloud accessibility, and flexibility in blend strategies. All around, attempts reliant upon security, similar to the Federal Emergency Management Agency, and organizations, such as programming as-expert association, Zendesk, relied upon Cisco’s switch advancement. Cisco expects spending on private LTE and 5G association establishment by relationship to outperform $5.7 billion by 2024. Likewise, mechanical innovation association ABB worked together with frameworks organization and telecom association Ericsson to make the keen handling plants address what might be on the horizon. ABB expects to include 5G advancement to motorize client help with utilities, present-day, transport, and structure pieces. 5G should allow more modern office robots to exploit the cloud for more unmistakable figuring power and discard comparatively inefficient and extreme in-house central dealing with units or plans taking care of units. All the while, robots can be controlled utilizing a 5G remote organization inside a distance of 1.5 kMS and in any case show steady control, given 5G’s super-low torpidity capacities. The overall market for 5G in cloud mechanical innovation should create at a 79.2% form yearly improvement rate to reach $10.6 billion by 2028.

5G Adoption to Surface the Way for 6G

We guess that fundamental tailwinds ought to result from 5G’s ability to relate, all things considered, everyone and everything speedier and more strongly than any time in late memory. Additionally, 5G’s accelerated gathering rate near to its predecessors will set up the overall economy for an altogether faster rollout representing things to come. 6G, which could hit the market by 2030, will offer more imperative use of circled radio access association (RAN), terahertz (THz) range for essentially more prominent cut off, lower dormancy, and better reach sharing so various classes of clients can safely have a comparative repeat gathering. With inventive work in progress starting around 2020, 6G will progress IoT impressively further towards a possible destiny of totally savvy and autonomous structures. Regardless, first, we’ll embrace 5G and its benefits as a general rule, which are currently starting to show up.

Advantages of 5G Communication

Fast Speed: The zenith speed of 5G correspondence can reach to 10Gbps up and 20Gbps down. Clients can truly experience speeds of 50Mbps up and 100Mbps down.

High cut off: 5G correspondence network is prepared for supporting high-thickness contraption affiliations and high breaking point data transmission. The presentation rundown of a 5G correspondence network is 1,000,000 contraption relationships for each square kilometer. Meanwhile, the display record of a 5G correspondence network is 10Mbps data transmission capacity per square meter.

High unfaltering quality: 5G correspondences support significantly strong data affiliations. Additionally, a show indication of high steadfastness tends to 0.001% package incident rate. This speed is like fiber optic trades. 5G correspondence network utilizes the consideration and convenience of low repeat band and the high information move limit and high speed of high repeat band to give high trustworthiness correspondence to clients through multi-affiliation advancement.

Low deferral: 5G correspondence has unimaginably low dormancy. Moreover, the typical show record of 5G correspondence’s beginning to end inaction is 1ms.

Low power use: 5G correspondence can figure out the traits of low power usage in unambiguous circumstances. 5G correspondence maintains high rest/development extent and broadened rest when no data is conveyed. In low power wide locale association (LPWAN), IoT has a remarkable application prospect.

6G Infrastructure Scenarios

Other than the utilization of new recurrence goes, an examination concerning new organizations is necessary. While certain uses of 5G will likewise keep on being conveyed in the current 5G groups, which, over the long run, perhaps rethought to 6G.

Industrial Networks

While 5G was inventive in presenting the chance of industry 4.0, we infer that 6G will take colossal steps in changing the social occasion and creation processes. The improvement of present-day affiliations will rely on useful social affairs of current and future radio access advances to the key business 4.0 and past use cases. Current associations are viewed as privatized, zeroing in on insane persevering through quality and ultralow idleness.

The critical strategy use cases are:

Correspondence among sensors and robots
Trades across different robots for coordination of attempts
Correspondence between human current office heads and robots.

Right now, to accomplish the necessities for ultrahigh unwavering quality, most of the business approaches are going on a few spots in the extent of 3.4 and 3.8 GHz, where the spread divert is decently rich to the degree that diffraction ability. Regardless, machines with immense openness in the 6G period will likewise request high information rates close by advancing control and Al to have the decision to convey and manage top-quality visual information, drawing in robotized twins of machines and activities, too s remote inspecting. To this end, we expect the utilization of millimeter Wave frequencies notwithstanding bundles under 6 GHz for the present-day relationship over the going 10 years.

Wireless Personal Area Networks(WPANs)

One more area of affiliation is WPANS and far-off areas (WLANs). These WLANs’ astoundingly short affiliations may be under 0.5-1 m for WPANS and up to 30 m for WLANs. All window WLANs sight be reasonable for this application, taking into account that the affiliation money-related course of action can meet how episode when the higher windows are utilized and where genuine execution advancements exist.

Autonomous Vehicles and Smart Vehicles say Networks

6G could be utilized for data splitting between free vehicles and V2I. In any case, there are questions are expecting traffic conditions and brief distances considering showing up at deterrents talked about before will make the THz packs appropriate for this application. Further, in more, the speedy adaptable relationship between getting wires on train ate roofs and foundations can be utilized for transmission of both thriving’s fundamental data and outright pilgrim information. Such incredibly high rate joins are fitting for THz, yet the high cover ability makes serious areas of strength for beamforming bungles and expected issues with the Doppler spread. While the speed of current fast trains is fundamentally solid, and thus, shafts can be controlled in the right course settled on supposition, the required beamforming gain (and related thin bar width) makes the design touchy to even little deviations from the measures. Besides, high-rehash designs can comparatively be utilized for access among UES and radio wires in the lodges that outright the traveller information, like a (moving) area of interest. Recalling the arising 6G use cases, explicit prerequisites, new recurrent social events, and key sending conditions, we examine the developments expected to be the course of action of 6G radio and centre affiliation plans.

Why Computer Science?

“The Computer was born to solve the problem that did not exist before” – Bill Gates.

As a machine reduces the work effort, so does a computer for a typical complex computation. Computer science forces you to deal with a problem in a slightly different way which is a skill that can be applied to life itself. In the era of data, a computer is the most indispensable thing we can think of. Computer science gives you an opportunity in working and understanding hands-on the aspect of data.

Computer Science is the study of principles and the use of computers. The classical areas for a computer science course include discrete mathematics, data structure, theory of computation, compiler design, analysis and designing of Algorithm. The advanced study includes artificial intelligence (AI), computer networks and security, database management systems, computer vision and graphics, numerical analysis, software engineering, bioinformatics.

Apart from this, a computer engineer is supposed to be known as a fluent coder. Coding is the ‘Bread and Butter’ for all computer engineers. Programming or coding is an intriguing sector as it gives us the superpower to regulate computer programs on the go. The main goal for a computer engineer is to make a problem understandable to a machine so that it could solve the problem obligingly. The fluency and smoothness of an application are solely based on the way you design the code. C, the mother of all programming languages, is a general-purpose programming language that is extremely popular, simple, and flexible to use. It helps a programmer to build the base of designing a program. It is a structured programming language that is machine-independent and extensively used to write various applications, Operating Systems like Windows, and many other complex programs like Oracle database, Git, Python interpreter, and more. Java, a high-level programming language, is also the most popular language for its design structure using object-oriented concepts (OOP). When it comes to the web, Java is unparallel. Most of the web-based applications are based on java. Apart from this, there are several programming languages like c++, python, Matlab, R to name a few.

There are innumerable and varied specialization and career options for a computer engineer. After completing his/her B.Tech degree a student may get absorbed in the software industry or may opt for higher studies. There are multiple sub-division and specializations in Computer Engineering which require applications in various sectors. Student can also pursue B.Tech degree in various applied fields:

AI/ML: Artificial Intelligence (AI) are emerging fields that will shape and dominate the future of this universe. Data is one of the most important assets of a company or government agency. It helps us to predict the future based on past experiences. AI has the potential to vastly change the way that humans interact, not only with the digital world but also with each other, through their work and other socioeconomic institutions – for better or for worse. A vast volume of data can be analysed by using a smart alternative, Machine Learning. It can produce an accurate result by designing a fast and efficient model for real-time data.
Blockchain: The duration of the course for B.Tech with specialization in blockchain is four years. Blockchain is currently booming and one of the most popular technologies that have invaded to almost every industry in the world. The world is changing its shape towards cryptocurrency. In near future, Bitcoin will be one of the popular transaction media. The main technology behind this is the blockchain. The technology will help the student to learn different algorithms and to curate the bitcoin on their own.
Cyber Security: Cybersecurity provides an expertise insight analysis on global security threats. In the world of web, to provide a secure web service is one of the main goals. The course on Cyber Security will help to learn different algorithms along with the expertization on ethical hacking, penetration testing, digital forensics. There are immense career avenues open for specialization in cybersecurity. This includes Security software developer, security analyst, security architect etc.
M.Tech Degree in Computer Science: Apart from doing graduation and going for an early job, a student may go for a master’s degree in computer science and engineering along with the specialized degree discussed above.

With the knowledge and concept in the domain, a computer science engineer can be eligible for an immense variety of job opportunities. Some of them are jotted below:

Computer Science engineers are the primary resource of the software industry. With a sound concept in the subject, a student can crack any of the big houses easily.
Government sectors such as I.S.R.O, B.H.E.L, etc are recruiting computer science engineers having a good GATE score.
Interdisciplinary research is a new trend for Computer Science students. Inter-disciplinary subjects such as IoT, Bioinformatics provides a huge contribution in research for computer science students.
Apart from interdisciplinary research, mainstream research in computer science also provides a huge scope for the student both nationally and internationally.

As a computer science engineer, there are plenty of opportunities in Government sectors as well as private sectors. A focused, sincere and conceptual student has all the possibilities to touch the sky.