Computer Science is an ever-changing field with new inventions each day. Here are the latest trends in the field of computer science which are making their mark in this era of digitization.
Source: http://www.techsparks.co.in
Emerging trends in computer science and related technologiesSidraAfreen
This document discusses emerging trends in computer science and technology, including artificial intelligence, robotics, big data, cloud computing, cyber security, blockchain, bioinformatics, flying cars, and autonomous vehicles. It provides examples of each trend, such as how AI can be used for automated transportation and solving climate change. Robotics integrates computing, sensors, materials and AI to perform complex tasks. Big data deals with storing, processing, and analyzing massive amounts of data. Ensuring cyber security requires coordinating security efforts across information systems. Blockchain creates a distributed digital ledger to securely record transactions. Autonomous vehicles use sensors like radar and computer vision to navigate without human input.
Latest trends in information technologyAtifa Aqueel
This ppt includes the latest trends in information technology such as big data analytics, cloud computing, virtual reality, 5G wireless technology etc.
This document provides an overview of data science including what is big data and data science, applications of data science, and system infrastructure. It then discusses recommendation systems in more detail, describing them as systems that predict user preferences for items. A case study on recommendation systems follows, outlining collaborative filtering and content-based recommendation algorithms, and diving deeper into collaborative filtering approaches of user-based and item-based filtering. Challenges with collaborative filtering are also noted.
AI and its applications are not going away and will cause a significant amount of change to everyday life over the next decade. Whilst there has been a lot of buzz in the past that has not been fulfilled, advances in skills, computing power and modelling and ensuring that the hype is finally being realised. To some extent, we don’t even know what AI is capable of yet which is both exciting and scary!
THIS IS AN INTRODUCTORY PPT OF EMERGING TECHNOLOGIES AND NEED IN REAL LIFE. THIS WIL EXPLAIN BSICS ABOUT ALL EMERGING TECHNOLOGY AND THEIR APPLICATION IN VARIOUS SECTOR
Introduction to artificial intelligenceRajkumarVara
This document provides an overview of artificial intelligence, including its history, creators, types, and current applications. It defines AI as concerned with building intelligent machines that can perform human tasks. The modern history of AI began in 1956 when John McCarthy proposed the term. Alan Turing invented the Turing machine in the 1940s. There are three main types of AI: artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. Currently, AI is used in applications like chatbots, healthcare, data security, social media, and Tesla's self-driving cars. The document concludes that while AI is not yet as intelligent as depicted in films, its development will significantly change the world.
Artificial intelligence is the study of computer systems that attempt to model and apply human intelligence. The document discusses the early history of AI beginning in 1950 with Alan Turing's paper asking if machines can think. Current applications of AI include digital assistants like Siri, video game characters, and robotics. Challenges to further developing AI include computing power, intuitive thinking, and common sense. The future of AI is promising in areas like self-driving cars, improved healthcare, and space exploration, but concerns include lack of human qualities like creativity and unemployment.
The document describes a 10 module data science course covering topics such as introduction to data science, machine learning techniques using R, Hadoop architecture, and Mahout algorithms. The course includes live online classes, recorded lectures, quizzes, projects, and a certificate. Each module covers specific data science topics and techniques. The document provides details on the course content, objectives, and topics covered in module 1 which includes an introduction to data science, its components, use cases, and how to integrate R and Hadoop. Examples of data science applications in various domains like healthcare, retail, and social media are also presented.
Emerging trends in computer science and related technologiesSidraAfreen
This document discusses emerging trends in computer science and technology, including artificial intelligence, robotics, big data, cloud computing, cyber security, blockchain, bioinformatics, flying cars, and autonomous vehicles. It provides examples of each trend, such as how AI can be used for automated transportation and solving climate change. Robotics integrates computing, sensors, materials and AI to perform complex tasks. Big data deals with storing, processing, and analyzing massive amounts of data. Ensuring cyber security requires coordinating security efforts across information systems. Blockchain creates a distributed digital ledger to securely record transactions. Autonomous vehicles use sensors like radar and computer vision to navigate without human input.
Latest trends in information technologyAtifa Aqueel
This ppt includes the latest trends in information technology such as big data analytics, cloud computing, virtual reality, 5G wireless technology etc.
This document provides an overview of data science including what is big data and data science, applications of data science, and system infrastructure. It then discusses recommendation systems in more detail, describing them as systems that predict user preferences for items. A case study on recommendation systems follows, outlining collaborative filtering and content-based recommendation algorithms, and diving deeper into collaborative filtering approaches of user-based and item-based filtering. Challenges with collaborative filtering are also noted.
AI and its applications are not going away and will cause a significant amount of change to everyday life over the next decade. Whilst there has been a lot of buzz in the past that has not been fulfilled, advances in skills, computing power and modelling and ensuring that the hype is finally being realised. To some extent, we don’t even know what AI is capable of yet which is both exciting and scary!
THIS IS AN INTRODUCTORY PPT OF EMERGING TECHNOLOGIES AND NEED IN REAL LIFE. THIS WIL EXPLAIN BSICS ABOUT ALL EMERGING TECHNOLOGY AND THEIR APPLICATION IN VARIOUS SECTOR
Introduction to artificial intelligenceRajkumarVara
This document provides an overview of artificial intelligence, including its history, creators, types, and current applications. It defines AI as concerned with building intelligent machines that can perform human tasks. The modern history of AI began in 1956 when John McCarthy proposed the term. Alan Turing invented the Turing machine in the 1940s. There are three main types of AI: artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. Currently, AI is used in applications like chatbots, healthcare, data security, social media, and Tesla's self-driving cars. The document concludes that while AI is not yet as intelligent as depicted in films, its development will significantly change the world.
Artificial intelligence is the study of computer systems that attempt to model and apply human intelligence. The document discusses the early history of AI beginning in 1950 with Alan Turing's paper asking if machines can think. Current applications of AI include digital assistants like Siri, video game characters, and robotics. Challenges to further developing AI include computing power, intuitive thinking, and common sense. The future of AI is promising in areas like self-driving cars, improved healthcare, and space exploration, but concerns include lack of human qualities like creativity and unemployment.
The document describes a 10 module data science course covering topics such as introduction to data science, machine learning techniques using R, Hadoop architecture, and Mahout algorithms. The course includes live online classes, recorded lectures, quizzes, projects, and a certificate. Each module covers specific data science topics and techniques. The document provides details on the course content, objectives, and topics covered in module 1 which includes an introduction to data science, its components, use cases, and how to integrate R and Hadoop. Examples of data science applications in various domains like healthcare, retail, and social media are also presented.
The document discusses opportunities in IT careers. It notes that demand for IT workers is higher than the supply as businesses increasingly rely on technology. There are many growing areas of IT like software development, cybersecurity, and data analysis. While some basic IT jobs may move overseas, jobs requiring business knowledge and close collaboration will remain. To succeed in IT, one needs relevant degrees, certifications, experience through internships or projects, and strong technical and business skills. Overall, IT careers offer high growth, pay, and flexibility for creative problem-solvers interested in emerging technologies.
This PPT provides you the essential information about the emerging technologies in the field of computer science.
Data Mining,Cloud Computing, Artificial Intelligence,Internet of Things and many more.
Computer vision is a field that uses techniques to electronically perceive and understand images. It involves acquiring, processing, analyzing and understanding images and can take forms like video sequences. Computer vision aims to duplicate human vision abilities through artificial systems. It has applications in areas like manufacturing inspection, medical imaging, robotics, traffic monitoring and more. Some techniques used in computer vision include image acquisition, preprocessing, feature extraction, detection, recognition and interpretation.
The document discusses artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
Data science uses data to find solutions and predict outcomes. It involves blending mathematics, business knowledge, tools, algorithms, and machine learning techniques to uncover hidden patterns in raw data. This helps with making major business decisions. Data science is used across many industries like manufacturing, e-commerce, banking, transportation, and healthcare for tasks like predicting problems, recommending products, detecting fraud, and discovering drugs. Real-world examples of data science applications include identifying online consumers, monitoring cars, and assisting in entertainment and retail brands.
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
what is the role of AI in this world..
Introducing the Artificial Intelligence to the world...
How the technology takes place in world to make the world better...
AI introduce the new world with the help of new technology...
Digital world Digital Networking..
Big data is large amounts of unstructured data that require new techniques and tools to analyze. Key drivers of big data growth are increased storage capacity, processing power, and data availability. Big data analytics can uncover hidden patterns to provide competitive advantages and better business decisions. Applications include healthcare, homeland security, finance, manufacturing, and retail. The global big data market is expected to grow significantly, with India's market projected to reach $1 billion by 2015. This growth will increase demand for data scientists and analysts to support big data solutions and technologies like Hadoop and NoSQL databases.
There are three main domains of artificial intelligence: data, computer vision, and natural language processing. Data domain involves collecting and analyzing various types of data like audio, video, text, and big data to derive insights. Computer vision allows machines to analyze visual information and make predictions. Natural language processing enables machines to understand and generate spoken and written human language through techniques like natural language understanding and generation. These domains power applications ranging from smart assistants and self-driving cars to emergency response systems. Artificial intelligence continues to be incorporated across many applications to improve user experience.
A little presentation/discussion about current and emerging technologies in libraries, as well as library/web 2.0., user generated content, and social media by robin fay, georgiawebgurl@gmail.com (Keynote address to GPLS Annual 2009)
What is Artificial Intelligence | Artificial Intelligence Tutorial For Beginn...Edureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This tutorial on Artificial Intelligence gives you a brief introduction to AI discussing how it can be a threat as well as useful. This tutorial covers the following topics:
1. AI as a threat
2. What is AI?
3. History of AI
4. Machine Learning & Deep Learning examples
5. Dependency on AI
6.Applications of AI
7. AI Course at Edureka - https://goo.gl/VWNeAu
For more information, please write back to us at sales@edureka.co
Call us at IN: 9606058406 / US: 18338555775
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document provides an overview of artificial intelligence, including its branches and fields of application. It discusses how AI aims to create intelligent machines through approaches like symbolic and statistical AI. The document also outlines key differences between human and artificial intelligence, noting that AI is non-creative, consistent, precise, and able to multitask, while humans are more creative but can contain errors or inconsistencies. It concludes by stating that combining knowledge from different fields including computer science, mathematics, psychology and more will benefit progress in creating intelligent artificial beings.
The document provides an overview of key concepts in data science including data types, the data value chain, and big data. It defines data science as extracting insights from large, diverse datasets using tools like machine learning. The data value chain involves acquiring, processing, analyzing and using data. Big data is characterized by its volume, velocity and variety. Common techniques for big data analytics include data mining, machine learning and visualization.
The document discusses human intelligence and artificial intelligence (AI). It defines human intelligence as comprising abilities such as learning, understanding language, perceiving, reasoning, and feeling. AI is defined as the science and engineering of making machines intelligent, especially computer programs. It involves developing systems that exhibit traits associated with human intelligence such as reasoning, learning, interacting with the environment, and problem solving. The document outlines the history of AI and discusses approaches to developing systems that think like humans or rationally. It also covers applications of AI such as natural language processing, expert systems, robotics, and more.
Artificial intelligence (AI) is the ability of computers and machines to think and learn. The document discusses different types of AI including strong AI which can think like humans and weak AI which responds to specific situations. The history of AI began in the 1950s with Alan Turing's work and the 1956 Dartmouth Conference where the term "artificial intelligence" was coined. Current applications of AI include mobile phones, games, GPS, robots and personal assistants. The future of AI may include military robots, self-driving cars, improved healthcare, and smart homes. While AI provides benefits like accuracy and space exploration, concerns include costs, lack of human touch, and potential job losses. Strong skills in math, programming, and machine learning
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
The document discusses the future of the Internet of Things (IoT). It covers key topics such as future applications and challenges of IoT, trends in IoT, future job roles, and vision for the future of IoT. Some of the main points discussed include how IoT will transform industries like healthcare and automotive through applications like remote health monitoring and connected vehicles. However, security is a major challenge as more devices are connected. Other trends discussed are growth of smart cities and use of data and artificial intelligence. The future of IoT is seen as limitless with potential in new areas like manufacturing and opportunities for new types of jobs and business models.
20 Latest Computer Science Seminar Topics on Emerging TechnologiesSeminar Links
A list of Top 20 technical seminar topics for computer science engineering (CSE) you should choose for seminars and presentations in 2019. The list also contains related seminar topics on the emerging technologies in computer science, IT, Networking, software branch. To download PDF, PPT Seminar Reports check the links.
Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. There are four main schools of thought in AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. Popular techniques used in AI include machine learning, deep learning, and natural language processing. The document then discusses the growth of AI and its applications in various domains like healthcare, law, education, and more. It also lists the top companies leading the development of AI like DeepMind, Google, Facebook, Microsoft, and others. Finally, it provides perspectives on the future impact and adoption of AI.
This document provides an introduction to artificial intelligence, including its history, applications, advantages, and future possibilities. It discusses how AI aims to help machines solve complex problems like humans by borrowing characteristics of human intelligence. The document outlines some key developments in AI's history from early computers in the 1940s to walking robots in 2000. It also describes common AI applications such as expert systems, natural language processing, speech recognition, computer vision, and robotics. Both advantages of medical uses and potential disadvantages like self-modifying computer viruses are mentioned. The future of AI having personal robots or potentially turning against humans is speculated.
Computer science is an emerging field with new developments occurring frequently. Some of the latest trends discussed include big data, bioinformatics, cloud computing, artificial intelligence, deep learning, and methods of communication. Big data deals with processing and analyzing huge amounts of data from different sources. Bioinformatics is the interaction of biology and computer science to collect and process biological data. Cloud computing provides shared computing resources over the internet. Artificial intelligence aims to impart human thinking to computers, while deep learning is a subfield of machine learning using neural networks to solve complex problems. Cyber security and internet of things are also discussed.
In a world where the Internet of Things (IoT) produces massive amounts of data from mobile devices, vehicular systems and environmental sensors, data scientist will be tasked with what to do with all of this information. We sat down with Cristian Borcea, PhD from the New Jersey Institute of Technology to discuss the IoT and Big Data applications.
insideBIGDATA: It seems we can’t turn on the television or read a newspaper without encountering the phrase, the “Internet of Things”. Aside from being a marketing term, what does this really mean?
The document discusses opportunities in IT careers. It notes that demand for IT workers is higher than the supply as businesses increasingly rely on technology. There are many growing areas of IT like software development, cybersecurity, and data analysis. While some basic IT jobs may move overseas, jobs requiring business knowledge and close collaboration will remain. To succeed in IT, one needs relevant degrees, certifications, experience through internships or projects, and strong technical and business skills. Overall, IT careers offer high growth, pay, and flexibility for creative problem-solvers interested in emerging technologies.
This PPT provides you the essential information about the emerging technologies in the field of computer science.
Data Mining,Cloud Computing, Artificial Intelligence,Internet of Things and many more.
Computer vision is a field that uses techniques to electronically perceive and understand images. It involves acquiring, processing, analyzing and understanding images and can take forms like video sequences. Computer vision aims to duplicate human vision abilities through artificial systems. It has applications in areas like manufacturing inspection, medical imaging, robotics, traffic monitoring and more. Some techniques used in computer vision include image acquisition, preprocessing, feature extraction, detection, recognition and interpretation.
The document discusses artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
Data science uses data to find solutions and predict outcomes. It involves blending mathematics, business knowledge, tools, algorithms, and machine learning techniques to uncover hidden patterns in raw data. This helps with making major business decisions. Data science is used across many industries like manufacturing, e-commerce, banking, transportation, and healthcare for tasks like predicting problems, recommending products, detecting fraud, and discovering drugs. Real-world examples of data science applications include identifying online consumers, monitoring cars, and assisting in entertainment and retail brands.
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
what is the role of AI in this world..
Introducing the Artificial Intelligence to the world...
How the technology takes place in world to make the world better...
AI introduce the new world with the help of new technology...
Digital world Digital Networking..
Big data is large amounts of unstructured data that require new techniques and tools to analyze. Key drivers of big data growth are increased storage capacity, processing power, and data availability. Big data analytics can uncover hidden patterns to provide competitive advantages and better business decisions. Applications include healthcare, homeland security, finance, manufacturing, and retail. The global big data market is expected to grow significantly, with India's market projected to reach $1 billion by 2015. This growth will increase demand for data scientists and analysts to support big data solutions and technologies like Hadoop and NoSQL databases.
There are three main domains of artificial intelligence: data, computer vision, and natural language processing. Data domain involves collecting and analyzing various types of data like audio, video, text, and big data to derive insights. Computer vision allows machines to analyze visual information and make predictions. Natural language processing enables machines to understand and generate spoken and written human language through techniques like natural language understanding and generation. These domains power applications ranging from smart assistants and self-driving cars to emergency response systems. Artificial intelligence continues to be incorporated across many applications to improve user experience.
A little presentation/discussion about current and emerging technologies in libraries, as well as library/web 2.0., user generated content, and social media by robin fay, georgiawebgurl@gmail.com (Keynote address to GPLS Annual 2009)
What is Artificial Intelligence | Artificial Intelligence Tutorial For Beginn...Edureka!
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This tutorial on Artificial Intelligence gives you a brief introduction to AI discussing how it can be a threat as well as useful. This tutorial covers the following topics:
1. AI as a threat
2. What is AI?
3. History of AI
4. Machine Learning & Deep Learning examples
5. Dependency on AI
6.Applications of AI
7. AI Course at Edureka - https://goo.gl/VWNeAu
For more information, please write back to us at sales@edureka.co
Call us at IN: 9606058406 / US: 18338555775
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document provides an overview of artificial intelligence, including its branches and fields of application. It discusses how AI aims to create intelligent machines through approaches like symbolic and statistical AI. The document also outlines key differences between human and artificial intelligence, noting that AI is non-creative, consistent, precise, and able to multitask, while humans are more creative but can contain errors or inconsistencies. It concludes by stating that combining knowledge from different fields including computer science, mathematics, psychology and more will benefit progress in creating intelligent artificial beings.
The document provides an overview of key concepts in data science including data types, the data value chain, and big data. It defines data science as extracting insights from large, diverse datasets using tools like machine learning. The data value chain involves acquiring, processing, analyzing and using data. Big data is characterized by its volume, velocity and variety. Common techniques for big data analytics include data mining, machine learning and visualization.
The document discusses human intelligence and artificial intelligence (AI). It defines human intelligence as comprising abilities such as learning, understanding language, perceiving, reasoning, and feeling. AI is defined as the science and engineering of making machines intelligent, especially computer programs. It involves developing systems that exhibit traits associated with human intelligence such as reasoning, learning, interacting with the environment, and problem solving. The document outlines the history of AI and discusses approaches to developing systems that think like humans or rationally. It also covers applications of AI such as natural language processing, expert systems, robotics, and more.
Artificial intelligence (AI) is the ability of computers and machines to think and learn. The document discusses different types of AI including strong AI which can think like humans and weak AI which responds to specific situations. The history of AI began in the 1950s with Alan Turing's work and the 1956 Dartmouth Conference where the term "artificial intelligence" was coined. Current applications of AI include mobile phones, games, GPS, robots and personal assistants. The future of AI may include military robots, self-driving cars, improved healthcare, and smart homes. While AI provides benefits like accuracy and space exploration, concerns include costs, lack of human touch, and potential job losses. Strong skills in math, programming, and machine learning
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
The document discusses the future of the Internet of Things (IoT). It covers key topics such as future applications and challenges of IoT, trends in IoT, future job roles, and vision for the future of IoT. Some of the main points discussed include how IoT will transform industries like healthcare and automotive through applications like remote health monitoring and connected vehicles. However, security is a major challenge as more devices are connected. Other trends discussed are growth of smart cities and use of data and artificial intelligence. The future of IoT is seen as limitless with potential in new areas like manufacturing and opportunities for new types of jobs and business models.
20 Latest Computer Science Seminar Topics on Emerging TechnologiesSeminar Links
A list of Top 20 technical seminar topics for computer science engineering (CSE) you should choose for seminars and presentations in 2019. The list also contains related seminar topics on the emerging technologies in computer science, IT, Networking, software branch. To download PDF, PPT Seminar Reports check the links.
Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. There are four main schools of thought in AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. Popular techniques used in AI include machine learning, deep learning, and natural language processing. The document then discusses the growth of AI and its applications in various domains like healthcare, law, education, and more. It also lists the top companies leading the development of AI like DeepMind, Google, Facebook, Microsoft, and others. Finally, it provides perspectives on the future impact and adoption of AI.
This document provides an introduction to artificial intelligence, including its history, applications, advantages, and future possibilities. It discusses how AI aims to help machines solve complex problems like humans by borrowing characteristics of human intelligence. The document outlines some key developments in AI's history from early computers in the 1940s to walking robots in 2000. It also describes common AI applications such as expert systems, natural language processing, speech recognition, computer vision, and robotics. Both advantages of medical uses and potential disadvantages like self-modifying computer viruses are mentioned. The future of AI having personal robots or potentially turning against humans is speculated.
Computer science is an emerging field with new developments occurring frequently. Some of the latest trends discussed include big data, bioinformatics, cloud computing, artificial intelligence, deep learning, and methods of communication. Big data deals with processing and analyzing huge amounts of data from different sources. Bioinformatics is the interaction of biology and computer science to collect and process biological data. Cloud computing provides shared computing resources over the internet. Artificial intelligence aims to impart human thinking to computers, while deep learning is a subfield of machine learning using neural networks to solve complex problems. Cyber security and internet of things are also discussed.
In a world where the Internet of Things (IoT) produces massive amounts of data from mobile devices, vehicular systems and environmental sensors, data scientist will be tasked with what to do with all of this information. We sat down with Cristian Borcea, PhD from the New Jersey Institute of Technology to discuss the IoT and Big Data applications.
insideBIGDATA: It seems we can’t turn on the television or read a newspaper without encountering the phrase, the “Internet of Things”. Aside from being a marketing term, what does this really mean?
Artificial Intelligence Research Topics for PhD Manuscripts 2021 - PhdassistancePhD Assistance
This document discusses several artificial intelligence research topics that could be explored for a PhD thesis. It begins by introducing the rapid growth of AI in recent years. It then outlines topics such as machine learning, deep learning, reinforcement learning, robotics, natural language processing, computer vision, recommender systems, and the internet of things. For each topic, it provides a brief overview and lists some recent research papers as potential thesis ideas. In conclusion, the document aims to help PhD students interested in AI research by surveying the current state of the field and highlighting subtopics that could be investigated further.
Internet of Things (IoT) - Hafedh Alyahmadi - May 29, 2015.pdfImXaib
The document discusses the Internet of Things (IoT). It defines IoT as connecting physical objects through wireless networks and sensors, allowing communication between people and things and between things themselves. The document outlines the history and timeline of IoT development. It discusses enabling technologies like sensors and RFID, applications in areas like healthcare, transportation and smart homes, and challenges around standardization, privacy, and security. The future of IoT is predicted to include growth across enterprise, home and government sectors, with potential issues around autonomy, control and privacy requiring policy frameworks and consideration of technology's role beyond a human tool.
In this presentation, Vani introduces IoT and associated trends. Vani is interested in Big Data analytics of data generated in the IoT space to help solve real life problems.
This document summarizes a research paper on using big data methodologies with IoT and its applications. It discusses how big data analytics is being used across various fields like engineering, data management, and more. It also discusses how IoT enables the collection of massive amounts of data from sensors and devices. Machine learning techniques are used to analyze this big data from IoT and enable communication between devices. The document provides examples of domains where big data and IoT are being applied, such as healthcare, energy, transportation, and others. It analyzes the similarities and differences in how big data techniques are used across these IoT domains.
How Artificial Intelligence Will Kickstart the Internet of Thnigs Ahmed Banafa
The possibilities that IoT brings to the table are endless.
IoT continues its run as one of the most popular technology buzzwords of the year, and now the new phase of IoT is pushing everyone to ask hard questions about the data collected by all devices and sensors of IoT.
In this presentation, Praneeth introduces the topic of IoT and associated trends. His interest area lies in data analytics associated with all the data that is generated by these systems.
Comparative Study of Security Issue and Challenges in IoTijtsrd
In the past few years, Internet of things IoT has been a focal point of research. The Internet of Things IoT hold up an expansive scope of uses including keen urban areas, waste management, auxiliary wellbeing, security, crisis administrations, coordinations, retails, mechanical control, and wellbeing care. Privacy and Security are the key issues for IoT applications, and still face some colossal challenges. In late years, the Internet of Things IoT has increased calculable research consideration. Now days, the IoT is considered as eventual fate of the web. In future, IoT will assume a significant job and will change our gauges, plan of action just as living styles. Right now give a similar report on security issue and difficulties in iot just as a short depiction on utilizations of iot. Sayali Vishwanath Pawar "Comparative Study of Security Issue and Challenges in IoT" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30653.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30653/comparative-study-of-security-issue-and-challenges-in-iot/sayali-vishwanath-pawar
The Revolutionary Progress of Artificial Inteligence (AI) in Health CareSindhBiotech
This Lecture is presented by our 2k23 volunteer Hina Nawaz, she is from Karachi, Pakistan, and she is covering "The Revolutionary Progress of Artificial Inteligence (AI) in Health Care".
Youtube: https://youtu.be/vhJRCj5ZgJc
This document provides an overview of the Internet of Things (IoT). It defines IoT as the network of physical objects embedded with sensors that can collect and exchange data. It describes how IoT works through technologies like RFID, sensors, and embedded processing. It also outlines current and future applications of IoT such as smart homes, healthcare, and more. The document discusses both the potential benefits of IoT as well as challenges and criticisms around issues like privacy, security, and environmental impact.
The document discusses AI and IoT, highlighting several use cases and challenges. It notes that AI and IoT are transforming how people, devices, and data interact across many domains. Specifically, it provides examples of how Philips analyzes 15PB of patient data and how AI can connect disparate IoT data. Additionally, it outlines several common use cases for applying AI to IoT in various industries like manufacturing, energy, healthcare, and more. Finally, it contrasts bare IoT with AIoT, noting that AIoT involves intelligent data processing, self-learning, autonomous decision making that enhances IoT.
The document discusses computerized accounting systems and their benefits compared to manual accounting systems. It provides examples of popular accounting software like Microsoft Excel, Sage, QuickBooks, and EFTPOS. These computerized systems allow for automatic summing, list autofill, easy organization of financial information, online banking capabilities, and electronic funds transfer at point of sale. They make accounting easier and less prone to errors compared to manual systems. The document also discusses career opportunities and benefits of working in management information systems or health care information management fields, such as high salaries, long-term career growth, and strong job market demand.
The Internet Of Things ( Iot And The InternetMichelle Singh
The document discusses the Internet of Things (IoT), which connects everyday devices to the internet. IoT presents many security challenges as connected devices have vulnerabilities and expose data. Most current IoT devices have limited functionality and cannot implement standard security strategies. This leaves networks and the internet open to exploits and attacks. Improved security frameworks are needed to address these issues as more devices connect. The rapid growth of IoT also raises privacy concerns that major companies and governments are working to address.
The Impact of Internet of Things (IoT) on Software Development.pdfBahaa Al Zubaidi
The Internet of Things (IoT) has transformed how we interact with technology. By connecting physical devices worldwide through the internet, the IoT has made it possible to gather and exchange data on an unprecedented scale.
The document provides an overview of the Internet of Things (IoT) in 3 sentences:
The Internet of Things (IoT) connects physical objects through sensors, software and network connectivity which allows these "things" to collect and exchange data between other devices. The document outlines what IoT is, how it works, current applications and challenges, and the future potential of a world where many everyday objects are connected to the internet and able to send and receive data. The increasing interconnectivity of physical objects through technologies like RFID, sensors and networking promises both benefits and risks relating to privacy, security, and how IoT may influence human behavior.
The future of IoT and Digital Twins.pdfRiley Claire
Unlock the potential of tomorrow's technology with a deep dive into the convergence of IoT and Digital Twin. Discover how these two revolutionary concepts are shaping industries, enhancing efficiency, and paving the way for smarter, more interconnected systems. Stay ahead of the curve as we delve into the limitless possibilities and applications that await in the future of IoT and Digital Twin technology.
Artificial intelligence and Internet of Things.pptxSriLakshmi643165
The document discusses artificial intelligence (AI) and the Internet of Things (IoT). It defines AI as using machines to simulate human intelligence through learning, reasoning and self-correction. IoT is defined as the network of physical devices connected through software and sensors to exchange data. The document outlines key applications of AI in healthcare, retail, education and more. It also discusses applications of IoT in healthcare, traffic monitoring, agriculture and fleet management. Finally, it discusses the future integration of AI and IoT, noting their potential to optimize systems, provide personalized recommendations and enable predictive maintenance through analysis of data collected by IoT devices.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of
such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards sustainable development. In the
information era, enormous amounts of data have become available on hand to decision makers. Big data
refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to
handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be
studied and provided in order to handle and extract value and knowledge from these datasets for different
industries and business operations. Numerous use cases have shown that AI can ensure an effective supply
of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the
different methods and scenario which can be applied to AI and big data, as well as the opportunities
provided by the application in various business operations and crisis management domains.
Available Research Topics in Machine LearningTechsparks
Due to the continuous the development in IT sector, research students have good chance in preparing their research papers in the field of the computer science. Although there are many subject areas that students opt for preparing their research papers, the most leading one is machine learning. What is the Machine Learning and why it is a leading subject area? Machine learning is an approach to analyzing the data. It is the applicable to automate construction of an analytical system. Considered one of the best sub-fields of artificial intelligence, machine learning allows systems to gain knowledge from the given data, recognize the patterns, and act accordingly without any human interference. Basically, machines are trained on how to learn and recognize various patterns in a given dataset, hence its name-'machine learning'. Both-small and big companies are using set of rules to develop models for getting better at the decision-making process without any human interference.
The document provides tips for completing a thesis fast, including studying submission guidelines, creating a realistic timeline and schedule, keeping the topic clear with specific details, finding support from a study group in addition to an advisor, and getting an initial draft written to seek feedback rather than pursuing perfection which takes more time.
The document provides guidelines for formatting a research paper to publish in IEEE format. This style is commonly used in technical fields like computer science. Key requirements include formatting the paper in a two-column layout with the title centered at the top in 24-point type. The abstract should be a single paragraph of 200 words that precisely summarizes the paper's contents. Sections and subsections can increase readability, and elements like equations, figures and tables should be numbered separately but centered in their columns.
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Software engineering - Topics and Research AreasTechsparks
This document provides an overview of key topics in software engineering including the software development life cycle (SDLC), common software development models, software testing, the unified modeling language (UML), software maintenance, and case tools. It also outlines potential thesis, research, and project topics such as data modeling, UML, SDLC methodologies, software quality, and software project management. The document introduces software engineering principles and describes why software engineering practices are required to manage large, complex software projects and products.
Cloud computing and Cloud Security - Basics and TerminologiesTechsparks
Cloud Computing is a new trending field these days and is an Internet-based service. It is based on the concept of virtualization.
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How to write a thesis - Guidelines to Thesis WritingTechsparks
A thesis is an important part of the academics of the master's students. Without the submission of the thesis, a degree is not conferred to a student. Follow the slides to know the procedure of thesis writing.
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Matlab is programming language developed by MathWorks that provides a computing environment for programming.
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Digital Communication simply means devices communicating with each other in through digital signals. The signals are digitized and then the information is transferred through these digitized signals from source to destination.
But why Digital Communication or Digitization is needed?
Techsparks is an ISO-certified company that provides thesis guidance and support for M.Tech and PhD students. They have a team of technical experts who specialize in various technologies and can deliver quality thesis work on time. Techsparks assists with the latest IEEE projects and provides training on technologies. They support thesis work in areas like VLSI, wireless communication, networking, and data mining using tools and software like MATLAB, NS2, and Android.
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Big Data refers to the bulk amount of data while Hadoop is a framework to process this data.
There are various technologies and fields under Big Data. Big Data finds its applications in various areas like healthcare, military and various other fields.
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Techsparks deals with Thesis guidance and research work for M.Tech , PhD Students.
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How to get published in Scopus/ IEEE journalsTechsparks
The document provides guidance on publishing research theses in scholarly journals. It discusses reasons for disseminating research, choosing the right journal, preparing articles for submission, and the editorial review process. The key steps are evaluating a journal's reputation and scope, structuring the article appropriately, and addressing any revisions requested during peer review to improve the work for resubmission or publication in another journal if rejected. Getting published involves persistence in responding constructively to reviewer feedback.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
1. Latest Trends in Computer Science
Computer Science is an ever-emerging field with new developments
every other day. People especially students from computer science and
IT streams are curious about these latest discoveries and they want to
know more about that. Ph.D. and research students from computer
science need to be aware of these things as their study is based on the
latest trends going on in the world. They should leave behind all the
obsolete topics and concentrate on the new ones to write their thesis or
research paper.
Following are the latest emerging trends in the field of computer
science:
Big Data
Big Data or Data Science is one of the emerging technologies these
days. This technology that deals with the study, processing, storage,
and analysis of the huge amount of data produced from different
sources all over the world. This field has huge scope mainly for
research. There are also a lot of career opportunities in this field.
Bio-informatics
Bio-informatics is the interaction of biology with computer science. It
is a field in which the biological data is collected and processed using
computer-based programs. The collected biological data is further
converted into a readable form for study and research. There are a
2. number of applications of bio-informatics including gene therapy,
biotechnology, medicines etc. This field also has a promising future.
Cloud Computing
Cloud Computing is the technology that provides an on-demand
shared pool of resources over the internet to users. These services are
provided by cloud providers like Microsoft Azure, IBM, Amazon web
services etc. It is another emerging field in computer science and a
good area for research.
Artificial Intelligence
Artificial Intelligence is the science of imparting human thinking and
intelligence to computers to create intelligent systems that can act and
work like human beings. It is the driving force behind robotics and
fuzzy systems. Machine Learning is one of the major application of
Artificial Intelligence.
Deep Learning
Deep Learning is also getting a lot of attention these days. Machine
learning and deep learning are often considered as one and the same
thing but they are different. Instead, deep learning is a sub-field of
machine learning. Deep artificial neural networks is an application of
deep learning. It includes set of algorithms to solve certain complex
problems.
Internet of Things(IoT)
3. Internet of Things(IoT) is a technology in which objects and devices
are connected with each other virtually through the internet. The
devices have sensors and actuators to act according to the surrounding
environment. Smart homes and smart cities are applications of the
Internet of Things(IoT).
Cyber Security
Cyber Security is required considering the fact that there has been a
sharp increase in the cyber crimes in recent times. Cyber Security is
needed in every area including public, private, and governmental
organizations. This will also help in controlling malware activities and
viruses.
Virtual Reality
The concept of Virtual Reality(VR) is gaining momentum with
applications in gaming, engineering. It is going to affect the way we
perceive the world. Through this concept, an artificial environment is
built, which appears as real, using software and presented to the user.
These were some of the latest emerging technologies in the field of
computer science for research.