machine learning 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.

Digitalization in Marketing Process-A New Skill in Marketing Specialization

Marketing is the process that satisfies human and social needs. It is nothing but a value-creation process. If we look into the marketing process, the job of most marketers is to design and develop the value in such a way that attracts customers and makes them happy buying. But this scenario is changing very rapidly due to the pandemic and the huge development of digital technology. Now market and marketing processes are more digitalized than the conventional marketing system. The job of a salesman is changing to the digital selling process. Marketers need not make a flow-up plan, it is automatically set up by the technology and responses are coming within a specific time. So, the process becomes more hybrid through embarked digitalization in the system. Therefore, it is imperative that using of digital technology in marketing and its associated function is a substitute for marketing success. Looking into this, concept marketers are focusing more on digital expert professionals than the salesman. Due to the huge demand for this, manpower is scarce. This is one part of the other way the process of marketing is also changing. Like the development of promotion strategy, communicate with the customers and find out the most effective methods for approaching customers. Though all these are experience stages, on the other side to get succeed in the fast-paced environment they always look into the audience’s requirements, it is difficult to stay ahead of the audience because market nature is monopolistic.

The recent trend in the Marketing Process:

Mass marketing converted into customized marketing and especially influencer marketing which is more common through digital technology like artificial intelligence, and machine learning, Marketers identify the preference of buying of customers and they try to influence them by offering more customized products. Therefore, targeting an individual is much easier than the conventional process. Development of user-generated content is another tool to identify prospects, it is a technique that allows the customer to design their product, and using digital space marketers publish those products on the web and counts the most effective design out of the available design and makes the product based on these design. Companies also do marketing through publicize the video content and using web analytics they publish it through various social media like various web pages, Facebook, Twitter, and LinkedIn, and personalizing the email. This video makes confidence the buyer about product information, brand, service, and other components associated with the product. It applies to business to business and business to consumer and both the process learn and evaluate the impact of promotion using video content in social media which makes their marketing promotion faster. The current market depends on millennials and Gen Z, they are more inclined toward digital process and most of them prefer digital buying process, not only digital buying but also other parts of marketing, they prefer digitalization. Therefore, to enhance their buying power, there is a need for mobile-optimized digital services which may be an important part for business owners and houses who are looking to attract fast-paced tech-savvy generations.

Ephemeral content is a new arena of digital marketing. Here company publishes its information through social media and they always stay on social media through standard posts, videos, and live events. Customer is not able to show the message if they do not save it or achieve it. Therefore, it makes curiosity customers give more concentration on the information. It is an effective platform for marketing campaigns.

Application of Digital Technology in Marketing

Artificial Intelligence in marketing is mainly developed with the help of three main marketing disciplines research, strategy, and action, and three levels of AI intelligence, that is, mechanical, thinking, and feeling AI. While mechanical AI entails automation of repetitive and routine tasks mainly covering market research, strategy, and standardization, thinking AI relates to processing data for new insights and decision-making, and feeling AI refers to interactions with humans or analyzing human feelings and emotions. Another, important techniques are big data analytics, using these techniques marketers predict the outcome of the customers and it also techniques which help marketers to identify the preference, maintain inventory management, and manage distribution and logistics system. Machine learning is another digitalized technique that helps marketers to do proper market segmentation, it processes customer data and analyzes it for discovering recurring patterns across various features. It helps to do proper clustering of different various demographic segments and helps to measure the preference difference between various demographic segments. Using blockchain analysis marketers maintain the logistics system of the firms and maintain a smooth and faster delivery system. One other important area used by blockchain technology like user verification, Blockchain can be employed through advertisement networks and reduce the interface of agents and middlemen and help those users who want the information by clicking through the ad system and combat fraud. It helps advertisers to identify the source of fraud and advertisers can make more user interface design.

Therefore, digital technology must be not only an effective tool for modern marketing but in the future, it should be the only way for business growth and survival. Most large-scale firms have started their marketing practices and maintain all the marketing processes from taking the order to supplying feedback through digital space. In India, most middle-order firms were trying to adopt the blended process, with a few parts of technology-based and another part traditional because of the nature of Indian consumers. It is a challenging task for a small firm because its market and investment level is low. Therefore, the marketing professionals need to develop some skills that small firms can be benefitted without much more investment. Last but not the list, it can be commented that the traditional skill of marketing will not work for long. Digitalized skills need to learn by the marketing professionals at the time of career selection. Few specialized skills need to be imparted to get a better market understanding.

Artificial Intelligence in Health Care: A New opportunity to build Career

In lockdown era of Covid-19 outbreak, creates a recession in different working sectors and numerous people lost their jobs in different areas. After pandemic, new job opportunities have been opened up. Among them, AI in health care is considered as one of the promising one. In the context of covid-19 pandemic there exist shortage of health care personnel and this not fulfilling the diagnosis response at the emergency stage. Integration of AI in health care can be considered as a promising option to overcome the shortage of health care personnel. Now question is that how a computer Engineer can incorporate AI in health care. The applications of AI into health care have been categorized into three groups.

Patient-oriented AI
Clinical oriented AI
Administration and operational Oriented AI

The Patient oriented AI system can directly improve the patient care. According to the report of UK Govt., if the AI-enabled symptoms checker is coupled with the telemedicine technology, reduced number of physicians visits in hospitals. Different Machine Learning and deep learning-based (ML/DL) algorithms have been considered to train the aforementioned AI-enabled symptoms checker system where the several symptoms of the common diseases have been considered as the training data.

Apart from this, several organizations adopted the chatbot system to improve the patient care. Chatbot is a software program that automatically chat with the patients through text or voice messages. A chatbot system, initially collects information from patients. After analyzing this information using different Computer vision techniques, provides the information regarding the present conditions of the disease as well as, what he will do. In some places, the chatbot systems are not capable of collecting the patients’ information, a wearable device can play an important role. These wearable devices sense the patient’s disease information through some sensors and AI-based methodologies provide the actual conditions of the disease. Another noteworthy fact is that AI can improve the accuracy in disease detection.

In developing countries like India, the doctor and patient are low and an individual clinician works nearly 14-18 hours in a day. Due to this extensive workload, clinicians may overlook the early sign of the disease. A computer aided diagnosis system (CAD) can assist doctors to detect these symptoms at the early stages. The researchers from University of Calcutta said that their implemented CAD system is capable of detecting lung nodules at early stages which may indicate lung cancer if it is detected at later stages.

Furthermore, AI can also increase the efficacy of the targeted therapy. AI is capable of identifying the accurate effected area of the abnormal tissue. By supervising the effected area through computers, a clinician can provide the drug to the patients.

Apart from the computer vision techniques, the natural language processing (NLP) also improves the clinical outcomes. In daily clinical practice, clinicians often required previous disease history, medications doses and the family history of the disease to prepare appropriate diagnose plan. In health sector, the data are stored in an unstructured manner i.e., the health sector-maintained paper-based work. Due to extensive workload these data may lost. The Electronic Medical Record or EMR is a software where the NLP techniques can store large number of clinical text data in a structured form. In present context, the existing EMR software is very costly. This necessitates the AI-based health care industry to implement a low cost and more accurate EMR tool for improved diagnosis procedures. The Norway based Globus.ai’s AI enabled EMR system shows that it fills clinical data 90% more faster than the human work. Another interesting application of AI in health care is robotic surgery. In this application, different computer algorithms have been automated for different surgeries. However, the general decisions are still taken by the surgeon.

Beside the clinical outcomes, AI can also improve the patient safety. It has been observed that, several patients suffer from adverse drug effects i.e., the drug is not suited for the patient body. Israel’s MedAware’s patient safety platform considered different ML algorithms to detect and reduce the risk of medication error.

This discussion reveals that to provide improved health care, participation of AI engineers in heal care industry become an inevitable option. This creates huge job opportunities to the engineers.

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.

Post-pandemic professions in Cyber Security

The epidemic caused significant employment losses and layoffs across a variety of sectors, with few or no new positions being filled. However, recruiting has started up again in some industries, which is a sign that the world has moved on from the pandemic.

“The pandemic helped us understand how important it is to digitise our records. Everything, from the job roles itself to the hiring process itself, has been shifted into the virtual platform. According to Ashutosh Seth, founder of Risebird, an edtech company that assists recruiting teams in completing the technical interviewing process, as a consequence, positions have evolved to deal and manage the enormous amount of data that has transferred on cloud platforms.

“There is a significant shortage of qualified candidates for tech positions such as artificial intelligence (AI), machine learning (ML), cyber security (CS), data analyst (DA), and coding developers (coders).” In addition to this, there is also an increase in the demand for people who work in the medical field as well as pharmacists, says he.

During the epidemic, there was a halt to any new employment, and there were even reductions in workforce size and layoffs. It was anticipated that once the pandemic was declared over, there would be an increase in the number of people getting jobs. According to Kamlesh Vyas, Partner at Deloitte India, “unfortunately, we have not seen that happening.” [Citation needed]

“This could be due to a number of factors since a number of businesses have incurred damages that are beyond repair and are unable to backfill their positions.” There aren’t many sectors that are still operating in the watch-and-wait mentality before investing in people. Because of the epidemic, many organisations have gained the ability to function with fewer employees as a result of automation, rationalisation, restructuring, multi-skilling, and other such practises, and thus do not see the need for aggressive hiring. However, according to Vyas, occupations in high-end technologies, such as artificial intelligence, analytics, cyber security, augmented reality/virtual reality, robots, cloud computing, and so on, would continue to be in demand during this period.

The epidemic has also brought to light the significance of developing automated systems. As a direct consequence of this, there is a greater demand for hardware engineers to automate the gear and devices that are already in use. According to Balasubramanian A, Business Head, Consumer and Healthcare, TeamLease Services, the professionals who will be working in the world after the pandemic would need to get themselves ready for the digital world and the more automated sector.

In addition to the obvious desire for IT expertise or occupations driven by technology, he notes that there is a demand for entry-level positions in field sales. These individuals are responsible for bringing the meal to the table. During the shutdown, a large number of businesses were severely disrupted, and many found it difficult to reach their ultimate goals. Now that a lot of businesses are attempting to get back on their feet, make up for any losses, and enhance their market share, those businesses are placing a strong emphasis on employing frontline sales employees.

According to a survey compiled by TeamLease, the average growth in salaries for sales profiles was found to be 7.41 percent, while the growth in salaries for R & D Analyst positions in the Healthcare & Pharmaceuticals industry was reported as 9.39 percent. The report went on to state that the increase in pay for the position of Automation Engineer in Information Technology and Knowledge Services was registered at 10.71 percent.

According to Balasubramanian A., in the world that has been left behind by the epidemic, businesses are now delivering professionals concrete benefits in the form of flexibility in regard to both time and location. Compared to a couple of years ago, when it came to giving in to the expectations of the employers, the businesses have been a lot more accommodating in recent times.

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

Significance of Computer Science Engineering in a COVID-19 infested Era

Ever since the outbreak of COVID-19 or the novel coronavirus from December 2019 till date, the whole world is adversely impacted by the induced pandemic which has forced on every form of the established system, a drastic change in the day-to-day lives of the people belonging to different strata of the society. The socio-economic setup that was prevalent throughout the world, is already significantly shattered, and hence calls for the conceiving as well as establishing a new system wherein the various functionalities may ensure the smooth and orderly execution of different aspects of the human society can be reinstated keeping in mind the robustness of this system against a pandemic like situation such as these.

This COVID-19 infested pandemic has exposed major loopholes and faulty perceptions regarding the various domains like, healthcare sector, financial institutions & bodies, educational establishments, research & developments bodies as well as public welfare organizations, as a result of which the whole world community stands not just clueless but also helpless in grappling with the current catastrophe.

a) Impact of Covid on the domain

The viral outbreak at a massive global scale has forced governments across the world to impose hard lockdowns and travel restrictions thereby compelling the shutdown of offices that offer varied services related to several sectors. Furthermore, at an individual level, people need to maintain a safe distance and be physically covered (especially facial areas) at maximum times when outside. This has invariably impacted interpersonal communication greatly.

Considering all the above factors, a computer/smartphone device, internet connectivity and a relatively stable power supply has come to the rescue of people in many ways. Professionals associated with the computer science & engineering domain are able to maintain various sorts of data in large bulks securely and meticulously with the help of different technologies. Furthermore, with the advent and development of the internet infrastructure, an abundance of virtual space can be created and managed for warehousing these data. Various applications are these days widely used via handheld devices to serve day-to-day purposes like product or food delivery, cab or train booking, hotel booking, seat reservation at any event, online academic courses, online mails, messaging and meeting and so on.

With this current pandemic prevailing all over, the usage of the above online applications is greatly amplified as these software-based utility tools are proving to be beneficial in running the various sectors simply while sitting at home. More and more people are continually seeking online services and demanding applications that will make it possible in every way to handle various business or commercial activities, smoothen the functioning of the education and healthcare sector and providing real-time support to travel as well as transport services. Even in the entertainment industry and also crime investigation & forensics, complex application development with easy user-friendly outlooks are ever-growing.

In short, the computer science & information technology domain has invariably become even more lucrative, prospective and ever-growing field as the pandemic has forced majority of the services to go “online”. Interestingly, a number of professions has by this time become obsolete and many individuals lost their jobs except for some cases such as this domain. These days the most important and valued element is data on various things and this data is available in such large quantities that it needs organized storage and management. While attempting to find a robust and effective solution to the COVID-19 virus, several amounts of data are being recorded and used. Efficient handling and accessing of such data is thus necessitated which is realized by the applications developed by various professionals who are experts in the computer science field.

b) New trends

As mentioned earlier, several sectors across the world are either closed down or grappling to survive by somehow managing to lift their face up the troubled waters of the pandemic crisis. An interesting trend of relying on online services is being noticed among such sectors.

Education: With almost all academic institutions physically shut down, this sector has shifted to online mode of admission, lecture delivery, examination conduction, assignment delivery, subject-wise note sharing and student evaluation. These activities require the support of some multimedia-enabled devices having a camera, microphone and speaker, online document preparing web services, and virtual (cloud-based) storage space. There has emerged a vogue of online coaching and academic preparation via the use of mobile apps like Byju’s Classes, Unacademy and so on which have reduced the dependency on availing tutors. Altogether, all these necessitate the development of software-based applications that require the expertise of domain professionals.

Healthcare: Currently as the majority of the professionals in this sector are the frontline ‘warriors’ in this battle against the pandemic, naturally, they are highly exposed and vulnerable to the COVID-19 virus attack. Despite the extreme precautions taken, most of them even are losing their lives to this deadly virus given the highly contagious environment they are working in. This has led to a fear of psychosis among the general public to avoid visiting the doctors and other medical professionals for a routine health checkup. As a result, the growing demand for online health checkup from home has led many software and app developers to build such dedicated applications that aid in routine healthcare and delivery of medicines. Some of the prominent apps operating in India are DoctorOnDemand and PharmEasy.

Business, Commerce, Banking & Industry: Several commercial and industrial workplaces are physically shut thereby causing losses and job cuts. A growing need has propped up for automation of industrial machinery and online support to various financial services as well as transactions, remote ordering, selling and purchasing, delivery of items. Some of these applications are realized by popular app services like Yono app (State bank of India), PolicyBazaar, Amazon, Flipkart, Paytm, Myntra.

c) Areas of research

Unlike other domains where research and development include extensive needs for laboratory or enterprise-level facilities and field works, pursuing research in the computer science domain requires just three things at home: a good quality computer system/workstation, internet connection and access to research-level online repositories. There are several open-source development tools that can be downloaded and utilized for effective research. Some of the major domains where computer science has pervaded are Medical science, genetics & genomics, microbiology, transport technologies, core industrial & engineering, basic sciences. Researchers under this domain by sheer knowledge of Artificial Intelligence, Data Science, Machine Learning and Deep Learning are gradually succeeding in presenting automated models for determining the nature and spread of the COVID-19 virus. Although the concept of Artificial Intelligence is not new (coined in 1956 by John McCarthy), the extensive development and the need for massive automation across several fields has uplifted the status of this concept that has led to enhanced research in Artificial Intelligence, Data Science, Machine Learning and Deep Learning. In short, a professional equipped with such domain knowledge easily feels valued and almost indispensable across several multi-disciplinary fields.