Assignment On Artificial Intelligence.pdf

ASSIGNMENT OF ARTIFICIAL INTELLIGENCE

TOPIC: COMPREHENSIVE RESEARCH CHART ON ARTIFICIAL INTELLIGENCE. SUBMITTED TO: DR. GOPAL KRISHNA SHARMA Computer Science DepartmentDev Sanskriti Vishwavidyalaya SUBMITTED BY: NIDHI B. SATIJA BCA VI Sem.

CONTENTS: 1. Definition of Artificial Intelligence (AI). 2. Research Fields in AI. 3. Table of the AI Research Papers. 4. The Top 5 Research Papers in the Field of AI 5. Major Use-Cases and Applications of AI 6. Pros and Cons of AI 7. Futuristic Scope of AI 8. Conclusion 9. Personal Review and Feedback (PRF) 10.References

In the Era of Technology and Innovation, in the Era of Industry 4.0 where technology is Revolutionizing the World in every Aspect and Dimension whether it is in Healthcare Sector or in Business Sector or in Manufacturing sector or in Online Social Media Platforms every field is being enhanced and drastically affected by the Colours of AI (The leading Technological Phenomenon). Everything is being done by the machine’s Intelligence and Robotics as we have experienced it with the Virtual Voice Assistants like Apple’s SIRI, Amazon’s ALEXA , Google’s GOOGLE ASSISTANT, Window’s CORTANA, Samsung’s BIXBY etc, all are the AI based Device Services(CHATBOTS) that are adding Quality to the Customers’ Experiences & Services nextly, we have Great ROBOTS that can be named as Roomba by iRobot , Sophia the incredibly advanced Learning Humanoid by Hanson Robotics, ASIMO world’s one of the most advanced Social Robot introduced by Honda such developments and Innovations are leading the Path towards the AI: The World of Superintelligence . The Intelligent tasks like the Learning, Power of Decision Making, the Sense of Discrimination, Problem Solving Approach, Analysing and identifying Patterns, Visioning, Understanding, Thinking Logically, Reasoning, and generating the outputs based on the Circumstances all such Abilities are now being mimicked by the Artificially Synthesized Intelligence known to be as Artificial Intelligence. So, further in the journey of this Assignment we are about to dig in depth the Areas and Fields being Revolutionized and Enhanced by the Human Synthesized Intelligence named to be as Artificial Intelligence THE AI.

1. DEFINITION OF ARTIFICIAL INTELLIGENCE: A. Howard Gardner’s definition of AI: “Intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings”. B. John McCarthy’s definition of AI: “Artificial Intelligence is the science and engineering of making intelligent machines”. C. Herbert A. Simon defines AI: “A branch of computer science that studies the properties of intelligence by synthesizing intelligence.”

D. Thomas Malone defines AI: “Machines acting in ways that seem intelligent”.

E. “Artificial Intelligence is use of science and engineering (software or hardware) to create intelligent machines that can make and/or act on decisions that usually require organic intelligence”.

TYPES OF ARTIFICIAL INTELLIGENCE (AI): AI can be classified in many ways. However, there are two most popular ways of classification based on their capabilities and functionality.

AI TYPE-1: BASED ON ABILITIES 1. Artificial Narrow Intelligence (ANI): ANI achieves human-level performance in one task characteristic of human intelligence. Narrow AI cannot perform beyond its field or limitations, as it is only trained for one specific task. Hence it is also termed as WEAK AI. Example: Alpha Go is an ANI built by Google’s DeepMind division. Alpha Go can outplay human world champions at the ancient Chinese game of Go. Although superhuman at Go, Alpha Go can’t do much else. It can’t play chess for example.

2. Artificial General Intelligence (AGI): AGI has general ability to solve, and in some cases exceed, human level performance in multiple (if not all) tasks considered markers of intelligence. Also defined as STRONG AI. Example – Replicants, Blade Runner AGI frequently features in science fiction. Given its theoretical human-level intelligence, it is often personified via the android or cyborg human trope.

3. Artificial Super Intelligence (ASI): Super AI is a hypothetical concept in AI research. It is an AI form that completely supersedes all AI systems with greater memory, faster data processing and analysis, and decisionmaking capabilities, exceeding human capabilities. Example – Deep Thought, A Hitchiker’s Guide to the Galaxy (Album on Youtube). Deep Thought, an ASI from a Hithchiker’s Guide to the Galaxy, is asked the ultimate question – “what is the meaning of Life, the

Universe and Everything“? Deep Thought is both quantitatively and qualitatively superior at thought vs. humans, hence being delegated this question.

AI TYPE-2: BASED ON FUNCTIONALITIES 1. Reactive Machines: Reactive machines are the most basic type of artificial intelligence. This type of AI looks at the world around it and responds based on its observation. They cannot learn anything from past experiences and cannot form any memories. Example: IBM’s chess-playing supercomputer, Deep Blue is a good example of a reactive machine. Alpha GO is a Computer Program that defeated many professional Go players. “Alpha Go master” has beaten Ke Jie in 2017, the world’s number 1 ranked “Go” player at that time.

2. Limited Memory: Limited memory AI is similar to reactive machines except they have a small memory that they can use to make observations over a period of time to judge the situation and give a response based on that. Generally, this type of AI is used in Autonomous Vehicles. Example: Limited memory example AI would be self-driving cars. In these autonomous vehicles some memories are pre-programmed like: • • •

If there is a car in-front then push the break. Push the break, if the traffic light is red. Take the turn, if the path is clear. Etc……

3. Theory of Mind: This type of AI understand how other objects and entities will react to their actions and act accordingly. Theory of mind researchers are trying to build machines that exhibit human features like: • • • • • •

Responding to emotions. Taking decisions by itself. Capable of holding a conversation with humans. Behaving like a human. Replicating the emotions based on the situations. Learning from past experiences.

4. Self-Awareness: Self-aware AI is an AI with an idea of self. This type of AI has consciousness and sentiments as well. These machines will be Smarter than Human Mind. Currently, this type of AI is purely

hypothetical. Self-Awareness AI involves a machine that can take care of itself. Means machines that are self-aware.

“AI and its offshoot, machine learning, will be a foundational tool for creating social good as well as business success.” ~Mark Hurd

2. RESEARCH FIELDS IN ARTIFICIAL INTELLIGENCE (AI):

SUB-DOMAINS IN THE FILED OF AI: 1. MACHINE LEARNING: Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Further it consists of the following categories:

2. DEEP LEARNING: Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabelled. Also known as deep neural learning or deep neural network.

3. ARTIFICIAL NEURAL NETWORK: An Artificial Neural Network (ANN) is a computational nonlinear model based on the neural structure of the brain that is able to learn to perform tasks like classification, prediction, decision-making, visualization, and others just by considering examples. An artificial neural network consists of artificial neurons or processing elements and is organized in three

interconnected layers: input, hidden that may include more than one layer, and output. The neural network is made up many perceptron. Perceptron is a single layer neural network. It is a binary classifier and part of supervised learning. A simple model of the biological neuron in an artificial neural network is known as the perceptron.

4. NATURAL LANGUAGE PROCESSING (NLP): (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics. The goal is for computers to process or “understand” natural language in order to perform tasks like Language Translation and Question Answering. The field of NLP involves making computers to perform useful tasks with the natural language’s humans use. The input and output of an NLP system can be − • •

Speech Written Text

Components of NLP There are two components of NLP as given − Natural Language Understanding (NLU): Mapping the given input in natural language into useful representations. • Analyzing different aspects of the language. Natural Language Generation (NLG) •

It is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation. It involves − •

Text planning − It includes retrieving the relevant content from knowledge base.

•

Sentence planning − It includes choosing required words, forming meaningful phrases, setting tone of the sentence.

•

Text Realization − It is mapping sentence plan into sentence structure.

The NLU is harder than NLG.

STEPS OF NATURAL LANGUAGE PROCESSING:

5. EXPERT SYSTEMS: In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code.

6. FUZZY LOGIC: Fuzzy logic algorithm helps to solve a problem after considering all available data. Then it takes the best possible decision for the given the

input. The FL method imitates the way of decision making in a human which consider all the possibilities between digital values T and F. Fuzzy logic is a computing approach based on the principles of “degrees of truth” instead of the usual modern computer logic i.e. Boolean in nature.

7. COMPUTER VISION: Computer vision is the field of study surrounding how computers see and understand digital images and videos. Computer vision spans all tasks performed by biological vision systems, including "seeing" or sensing a visual stimulus, understanding what is being seen, and extracting complex information into a form that can be used in other processes. This interdisciplinary field simulates and automates these elements of human vision systems using sensors, computers, and machine learning algorithms.

8. QUANTUM COMPUTING: Quantum Computing merges two great scientific revolutions of the 20th century: computer science and quantum physics. Quantum artificial intelligence (QAI) is an interdisciplinary field that focuses on building quantum algorithms for improving computational tasks within artificial intelligence, including sub-fields like machine learning. Quantum mechanics phenomena, superposition and entanglement, are allowing quantum computing to perform computations which are much

more efficient than classical AI algorithms used in computer vision, natural language processing and robotics.

9. PATTERN RECOGNITION: Pattern recognition is the process of recognizing patterns by using a Machine Learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. Pattern recognition is the ability to detect arrangements of characteristics or data that yield information about a given system or data set. PHASE 1:

• SENSING (Starts from Input) PHASE 2:

• SEGEMENTATION PHASE 3:

• FEATURE EXTRACTION PHASE 4:

• CLASSIFICATION PHASE 5:

• POST PROCESSING (Generates the Output)

Activity Cycle in Pattern Recognition:

10. PERCEPTION AND COGNITIVE ARTIFICIAL INTELLIGENCE (CAI): Perception: Perception is the process of acquiring, interpreting, selecting, and organizing sensory information. Perception can be seen as a special type of categorization (or classification, pattern recognition) where the inputs are sensory data, and the outputs are categorical judgments and conceptual relations.

CAI (Cognitive Artificial Intelligence): A Cognitive Artificial Intelligence has perception (sensory input like hearing, listening, reading, seeing, feeling). It can execute action (interpretation, reasoning, planning and communication) and it can react to unplanned situations.

11. ROBOTICS: AI Robots are artificial agents acting in a real-world environment to produce results by taking accountable actions. Robotics is a branch of AI, which is composed of Electrical Engineering, Mechanical Engineering, and Computer Science for designing, construction, and application of robots. Robots are the artificial agents acting in real world environment. They are aimed at manipulating the objects by perceiving, picking, moving, modifying the physical properties of object, destroying it, or to have an effect thereby freeing manpower from doing repetitive functions without getting bored, distracted, or exhausted.

PREPROGRAMMED ROBOTS

TYPES OF ROBOTS:

HUMANNOID ROBOTS

AUTONOMOUS ROBOT

TELEOPERATED ROBOTS

AUGMENTED ROBOTS

Sophia the humanoid is a good example of AI in robotics.

12. COMPUTATIONAL GAME THEORY: Game theory is the branch of economics that studies strategic decision making. Computational game theory looks at game theory through the lens of computation. On the one hand, it considers the application of game theoretic ideas in computing, and on the other hand, it considers how game theoretic ideas can be practically implemented. We define Game Theory as choosing from a set of rational choices in a multi-agent situation.

In Game Theory, we deal with deciding on a given set of options. An important thing to notice here is the phrase multi-agent situation- it means our choice affects the choices of the opponent in the game, and their decision affects our choices. Von Newman is credited for the invention of Game Theory. Development of the majority of popular games which we play in this digital world is with the help of AI and game theory. Also, game theory is not only restricted to games but is also relevant to other large applications of AI like GANs (Generative Adversarial Networks), Machine Learning Algorithms, manipulation-resistant systems, etc. Game theory is used in AI whenever there is more than one person involved in solving a logical problem.

3. RESEARCH PAPERS CHART IN AI (ARTIFICIAL INTELLIGENCE):

-

SR. NO

TITLE OF RESEARCH PAPER

FIELD OF RESEARCH

AUTHORS AND DETAILS

DOI NUMBER AND LINK

PUBLISHING YEAR

A. IMAGE PROCESSING IN AI 1.

Drowsiness Warning Neuro-Fuzzy Model, Nidhi Sharma, System Using Halstead Model, V. K. Banga. Artificial Intelligence Walston-Felix Model, Bailey-Basili Model, Doty Model, GA Based Model, Genetic Algorithm, EEG

Link:

January 2010.

https://citeseerx.ist .psu.edu/viewdoc/ download?doi=10. 1.1.933.9213&rep= rep1&type=pdf

(Electroencephalograp hy)

2.

3.

A Review on Image Segmentation Techniques with Remote Sensing Perspective.

Image, Segmentation, V. Dey, Model, Measurement, Y. Zhang, Optical. M. Zhong.

Link:

UAV-based forest fire detection and tracking using image processing techniques

UAV (Unmanned aerial vehicle), Median filtering, Segmentation, Morphological operations,

DOI: 10.1109/IC UAS.2015.71523 45

Chi Yuan , Zhixiang Liu,

Underwater Optical Image Processing: a Comprehensive Review

https://www.isprs. org/proceedings/xx xviii/part7/a/pdf/3 1_xxxviii-part7a.pdf

Link: https://ieeexplore.i eee.org/abstract/d ocument/7152345

De-scattering,

•

Huimin Lu

Link:

Ocean sensor networks,

•

Yujie Li Yudong Zhang Min Chen Seiichi Serikawa Hyoungseop Ki m

https://link.springe r.com/article/10.10 07/s11036-0170863-4

Underwater imaging, Ocean observing.

9 July 2015

Youmin Zhang.

Robot Vision, Realtime Analysis.

4.

7 July 2010

• • • •

26 April 2017

5.

Research of Animal’s Fully Convolutional image Semantic Network (FCN), Segmentation based Recurrent Neural on* Deep Learning. Networks (CRF‐ RNN), Image Semantic Segmentation,

Shouqiang Liu .

Link:

Miao Li ,

https://onlinelibrar y.wiley.com/doi/ab s/10.1002/cpe.489 2

Min Li, Qingzhen Xu.

25 September 2018

Deep Learning (DL).

B. MACHINE LEARNING

1.

2.

Learning Existing Social Conventions via Observationally Augmented Self-Play.

Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)

Adam Lerer, Alexande r Peysakhovich

Link:

13 Mar 2019

The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

ANN and Evolutionary Computing, Machine Learning, AI.

Jonathan Link: Frankle https://arxiv.org/a , Michael Carbin

24 Mar 2019

Machine Learning Multiagent Systems (cs.MA); Machine Learning (stat.ML).

Natasha Link: Jaques, Angeliki https://arxiv.org/a Lazaridou, Edw bs/1810.08647 ard Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

18 June 2019

Liyuan Liu, Haoming Jiang, Pengchen g He, Weizhu Chen, Xiaodong

8 Aug 2019

https://arxiv.org/a bs/1806.10071

bs/1803.03635

1. 3.

4.

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

On the Variance of 2. Image Classification, 3. Language Modelling, the Adaptive Neural machine Learning Rate and Translation, Beyond Algorithms like RAdam, Adam, RMSprop.

Link: https://arxiv.org/a bs/1908.03265v1

4.

5.

Liu, Jianfeng Gao, Jiawei Han

XL Net: Generalized Computation and Autoregressive Language, Pretraining for Machine Learning. Language Understanding 5.

Zhilin Link: Yang, Zihang https://arxiv.org/a Dai, Yiming bs/1906.08237 Yang, Jaime Carbonell, Rusla n Salakhutdinov, Quoc V. Le

2 Jan 2020

C. DEEP LEARNING

1.

Deep learning in Agriculture: A survey

Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN),

2.

A robust human Activity recognition system using smartphone sensors and deep learning

Andreas Kamilaris, Francesc X. Prenafeta-Boldú

DOI: https://doi.org/10.1 016/j.compag.201 8.02.016

Smart farming

Link:

Food systems.

https://www.scien cedirect.com/scien ce/article/pii/S016 8169917308803

Human Computer Interaction (HCI), Activity recognition, Deep belief network (DBN), ANN, SVM (Support Vector Machine), LDA (Linear Discriminant analysis).

Mohammed Mehedi Hassana, Md. ZiaUddin,

DOI:

https://doi.org/10.1 016/j.future.2017.1 1.029

Amr Mohamed, Ahmad Almogrena.

April 2018

Link: https://www.scien cedirect.com/scien ce/article/pii/S016 7739X17317351

April 2018

3.

Deep learning in Medical Imaging and Radiation Therapy.

FCN (Fully Convolutional Network),

Berkman Sahiner,

HNN (Holistically Nested Network), CV (CrossValidation), SVM, CNN, RL (Reinforcement Learning)

DOI:

Aria Pezeshk ,

https://doi.org/10. 1002/mp.13264

Lubomir M. Hadjiiski ,

Link:

Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers,

26 October 2018

https://aapm.onlin elibrary.wiley.com/ doi/abs/10.1002/m [email protected]/ (ISSN)24734209.Reviewarticle s

Maryellen L. Giger.

4.

A Deep Learning Approach to Predict Football Match Result

Sports: Predictive Modelling, Match result, Machine learning (ML),

• • • •

Sasank Boro, Jatin Srivastava , Shyamu Singh, Dwijen Rudrap al.

Link:

Shuyun Ren

DOI:

Tsan-MingChoi

https://doi.org/10.1 016/j.tre.2019.101 834

https://link.springe r.com/chapter/10.1 007/978-981-138676-3_9

18 August 2019

Multi-layer Perception, Deep learning.

5.

Intelligent service capacity allocation for cross-border-Ecommerce related third-partyforwarding logistics operations: A deep learning approach

Cross-border Ecommerce, Multi-product newsvendor, Logistics service capacity (LSC) allocation, Third-party forwarding logistics (3PFL), Deep learning

Ka-ManLee •

LeiLind Link: https://www.scien cedirect.com/scien ce/article/pii/S136 6554519311688

February 2020

D. REINFORCEMENT LEARNING 1.

2.

Introduction of Fixed Mode States into Online Reinforcement Learning with Penalties and Rewards and its Application to Biped Robot Waist Trajectory Generation.

Biped robot, Exploitationoriented learning, improved PARP (Penalty Avoiding Rational Policy), Profit Sharing, Reinforcement learning.

Seiya Kuroda, Kazuteru Miyazaki, Hiroaki Kobayashi.

An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control.

ATSC (Adaptive Traffic Signal Control), Intelligent Transport Systems,

Patrick Mannion Link: ,

Multi-Agent Systems,

Enda Howley.

Jim Duggan,

Link: https://www.fujipr ess.jp/jaciii/jc/jacii 001600060758/

September 20, 2012

04 May 2016

https://link.springe r.com/chapter/10.1 007/978-3-31925808-9_4

Smart Cities 3.

4.

Quality of Experience in Cyber-Physical Social Systems Based on Reinforcement Learning and Game Theory.

Optimizing Quantum Error Correction Codes with Reinforcement Learning.

Quality of experience; (QoE) Congestion; Reinforcement Learning; Time management; Game Theory; Personalization and Recommendation.

Eirini Eleni Tsiropoulou ,

Quantum Control (QC),

Hendrik Poulsen DOI: Nautrup,

Perception,

Nicolas Delfosse,

Learning Agent, Quantum Error Correction (QEC).

George Kousis ,

DOI: https://doi.org/10. 3390/fi10110108

7 November 2018

Athina Thanou , Ioanna Lykourentzou , Symeon Papavassiliou .

Vedran Dunjko, Hans J. Briegel, Nicolai Friis.

Link: https://www.mdpi. com/19995903/10/11/108

https://doi.org/10.22 331/q-2019-12-16215

Link: https://quantumjournal.org/papers/ q-2019-12-16-215/

26 December 2019.

5.

Analyzing the robotic behavior in a smart city with deep enforcement and imitation learning using IoRT

Artificial intelligence (AI), Internet of things (IoT),

Yan Liu

DOI:

Wei Zhang

https://doi.org/10.1 016/j.comcom.201 9.11.031

Shuwen Pan,

15 January 2020.

Yanjun Li,

Internet of robotic things (IoRT),

Yangting Chen.

Deep Reinforcement Learning (DFL),

Link: https://www.scien cedirect.com/scien ce/article/pii/S014 0366419312149

Imitation Learning (IL), Smart city

E. COMPUTER VISION IN PERSPECTIVE OF AI 1.

Visual simultaneous localization and mapping: a survey

Visual SLAM (simultaneous localization and mapping), Salient Feature Selection, Image Matching, Data association,

Jorge Fuentesacheco, José RuizAscencio •

Link: https://link.springe r.com/article/10.10 07/s10462-0129365-8

13 November 2012.

Juan Manuel Re ndón-Mancha

Topological and Metric maps. 2.

Common sense 6. reasoning and common-sense 7. knowledge in artificial intelligence8.

Computing Methodologies, Human centered Computing, Human Computer Interaction (HCI), 9. Software Engineering.

Ernest Davis

DOI:

Gary Marcus

https://doi.org/10.1 145/2701413

August 2015

Link: https://dl.acm.org/ doi/abs/10.1145/2 701413

3.

Visual Turing Test for computer vision systems.

Scene Interpretation, Computer Vision, Turing Test, Binary Questions,

Donald Geman, Stuart Geman,

DOI:

March 9, 2015

Unpredictable Answers.

Neil Hallonquist,

https://doi.org/10. 1073/pnas.142295 3112

Laurent Younes Link: https://www.pnas. org/content/112/1 2/3618.short

4.

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

Precision agriculture, Systematic review, DPN (Deep Belief Networks),

Diego Inacio Patricio, Rafael Reider.

Computer vision, GPU.

Advances in Computer VisionBased Civil Infrastructure Inspection and Monitoring

October 2018

https://doi.org/10.1 016/j.compag.201 8.08.001

Link:

10.

5.

DOI:

https://www.scien cedirect.com/scien ce/article/pii/S016 8169918305829

Structural inspection and monitoring,

Billie F.SpencerJr.

Artificial intelligence,

VedhusHoskere a,

Computer vision, Machine learning, Optical flow.

YasutakaNaraza kia

DOI:

April 2019

https://doi.org/10.1 016/j.eng.2018.11. 030

Link: https://www.scien cedirect.com/scien ce/article/pii/S209 5809918308130

F. PATTERN RECOGNITION

1.

Neural network computation with DNA strand

Perception, Pattern Recognition,

Lulu Qian, -

DOI:

21 July 2011

displacement cascades.

Decision Making,

Erik Winfree.

Face Recognition,

https://doi.org/10 .1038/nature1026 2

Anomaly Detection, Complex Biomolecular Circuits, Robotic Vehicle Control.

2.

A Survey of Sequential Pattern Mining

Sequential pattern mining, Sequences, Frequent pattern mining, Itemset mining, Data Mining.

Link: https://www.natur e.com/articles/nat ure10262?page=2# article-info

Philippe Fournier-Viger, Email: [email protected] u.cn

Link:

February 2017

http://www.philipp e-fournierviger.com/dsprpaper5.pdf

Jerry Chun-Wei Lin, Email: [email protected] g Rage Uday Kiran, Email: [email protected] is.u-tokyo.ac.jp Yun Sing Koh, [email protected] nd.ac.nz Rincy Thomas. Rinc.thomas@re diffmail.com

3.

Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification

Dominant Emotions and Complementary Emotions, Emotion Recognition, Support Vector Extraction, Databases, Kernel, C-support Vector Classification.

Christer Loob ;

DOI:

Pejman Rasti ;

10.1109/FG.201 7.106

Iiris Lüsi ; Julio C.S. Jacques ; Xavier Baró

Link:

29 June 2017

; Sergio Escalera ; Tomasz Sapinski ;

https://ieeexplore.i eee.org/abstract/d ocument/7961829

Dorota Kaminska ; Gholamreza Anbarjafari 4.

Brain Intelligence: Go beyond Artificial Intelligence

Robot technology (RT), Big Data, Brain Intelligence (BI), Artificial Life.

Huimin Lu, Yujie Li, Min Chen, Hyoungseop Kim, Seiichi Serikawa.

DOI: https://doi.org/10 .1007/s11036017-0932-8

21 September 2017

Link: https://link.springe r.com/article/10.10 07/s11036-0170932-8

5.

Improving Person re-identification by attribute and identity learning.

Re-identification (reID), Attribute-Person Recognition (APR), Attribute Recognition,

Yutian Lin, Liang Zheng, Zhedong Zhenga. Yu Wua, Zhilan Hua,

DOI: https://doi.org/10.1 016/j.patcog.2019. 06.006

November 2019

Link: https://www.scien cedirect.com/scien ce/article/abs/pii/S 003132031930237 7

G. AI AS MULTIDISCIPLINARY DIMENSION

1.

Multidisciplinary Optimization of a Radial Compressor for Micro gas,

Genetic Algorithms, Neural Nets, ANN, Mechanical

T. Verstraete ,

DOI:

Z. Alsalihi ,

https://doi.org/10.111 5/1.3144162

March 24, 2010

Turbine Applications

Engineering Computing,

R. A. Van den Braembussche.

Link:

Michael Paul, Roxana Girju.

Link:

Computational Learning, (DHI) Digital Holographic Imaging, (DAISY) Digital Automated Identification System, Pattern Analysis, Statistical Modelling, (DiCANN) Dinoflagellate Categorisation by ANN.

Norman Macleod,

Link:

Human- robot interaction,

Hooman Aghaebrahimi Samani ,

Finite Element Analysis, Optimization,

https://asmedigital collection.asme.org /turbomachinery/a rticle-

Databases. 2.

A Two-Dimensional Topic-Aspect Model for Discovering Multi-Faceted Topics

Topic-Aspect Model (TAM), Bayesian modelling; unsupervised learning;

03 July 2010.

https://www.aaai.o rg/ocs/index.php/A AAI/AAAI10/paper/ viewPaper/1730

natural language processing NLP. 3.

4.

Time to Automate Foundation.

A Multidisciplinary Artificial Intelligence Model of an Affective Robot

Affective Artificial Intelligence and Affective Computing.

Phil Culverhouse, Mark Benfied.

Elham Saadatian

https://www.resea rchgate.net/profile /Phil_Culverhouse/ publication/461918 08_

DOI: https://doi.org/10.5 772/45662

September 2010.

January 1, 2012

Link: https://journals.sag epub.com/doi/full/ 10.5772/45662

5.

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges,

AI Cognitive Computing,

Yogesh Dwivedi,

Expert systems,

Laurie Hughes,

Machine learning,

DOI: https://doi.org/10.1 016/j.ijinfomgt.201 9.08.002

27 August 2019

opportunities, and Research agenda. agenda for research, 11. practice and policy.

Elvira Ismagilova, Gert Aarts, Tom Crick,

Link: https://www.scien cedirect.com/scien ce/article/pii/S026 840121930917X

Aled Eirug, Paul Jones, Arpan Kumar Kar, Many more to be listed.

H. NEURO-SCIENCES AND AI COMBINATION

1.

NeuroscienceInspired Artificial Intelligence

Artificial intelligence, Brain

Dharshan Kumaran,

Cognition, Neural Network

Christopher Summerfield,

Learning.

2.

A Standard Model • of the Mind: Toward a • Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics

Artificial Intelligence,• Cognitive Science, Neuroscience, Robotics.

Demis Hassabis,

DOI: https://doi.org/10.1 016/j.neuron.2017. 06.011

Link:

Matthew Botvinick.

https://www.scien cedirect.com/scien ce/article/pii/S089 6627317305093

John E. Laird,

DOI: https://doi.or

•

Christian Lebiere,

•

Paul S. Rosenbloom.

19 July 2017

g/10.1609/aimag.v 38i4.2744

Link:

https://www.aaai.o rg/ojs/index.php/ai magazine/article/vi ew/2744

12 August 2017

-3.

Towards artificial 12. Brain Signals, intelligence in 13. Ensemble algorithm mental health by with Multiple improving Parcellations for Schizophrenia Schizophrenia Prediction with prediction’, Multiple brain Feature Parcellation extraction, MATLAB ensemble-learning (The MathWorks). 14.

Russell Greiner, DOI Rimjhim Agrawal,

https://doi.org/10 .1038/s41537018-0070-8

18 January 2019

Venkataram Shivakumar, Janardhanan C. Narayanaswam y,

Link: https://www.natur e.com/articles/s41 537-018-0070-8

Matthew R. G. Brown. 4.

Using neuroscience to develop artificial intelligence

Brain-like circuits, 1. Shimon Ullman “Deep Network”, Computer vision, Email: Speech Recognition shimon.ullman and production, and @weizmann.ac. playing complex il games.

DOI:

15 Feb 2019

10.1126/science.aa u6595

Link: https://science.scie ncemag.org/conten t/363/6428/692.su mmary

5.

Artificial Neural Multi-agent Diagnostics and processing, Prognostics: SelfAutonomous or semiSoothing in autonomous system, Cognitive Systems15. Artificial cognitive neural framework (ACNF), software agents, Selfsoothing Emotional learning.

James A. Crow der, John Carbone, Shelli Friess.

Link:

21 May 2019

https://link.springe r.com/chapter/10.1 007/978-3-03017081-3_8

I. NATURAL LANGUAGE PROCESSING(NLP)

1.

Jumping NLP Curves: A Review of Natural Language Processing Research

Syntactics, Semantics, and Pragmatics, Computational Techniques,

Erik Cambria , Bebo White.

DOI: 10.1109/M 10 April 2014 CI.2014.2307227

Link:

https://ieeexplore.i eee.org/abstract/d ocument/6786458

Knowledge based Systems.

2.

Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective

Computational Linguistics, Data Mining and Machine Learning, Data science, Natural Language and Speech.

Nikolaos Aletra s, Dimitrios Tsara patsanis, Daniel Preoţiuc -Pietro, Vasileios Lamp os.

3.

Allen NLP: A Deep Semantic Natural Language Processing Platform

Computation and Language, Deep Learning, Natural Language Understanding.

Link: https://peerj.com/ articles/cs93/?utm_source=m andiner&utm_medi um=link&utm_cam paign=mandiner_2 01912

Matt Gardner, Joel Grus,Mark Neumann, Oyvind Tafjord,

October 24, 2016

31 May 2018 Link: https://arxiv.org/a bs/1803.07640

Pradeep Dasigi, Nelson Liu, Matthew Peters, Michael Schmitz, Luke Zettlemoyer 4.

Artificial intelligence and communication: A Human–Machine Communication research agenda.

Human–machine communication (HMC), Virtual agents, Social bots, Media studies, ontological classification,

Andrea L Guzman, Seth C Lewis.

Natural Language Processing, Sentiment

Data scraping, Social media analysis,

July 4, 2019

https://doi.org/10.1 177/14614448198 58691

Link:

social configurations, Human Computer Interaction (HCI).

5.

DOI:

https://journals.sag epub.com/doi/abs/ 10.1177/14614448 19858691

Adil Rajput

DOI:

2020

Analysis, and Clinical Analytics

https://doi.org/10.1 016/B978-0-12819043-2.00003-4

Sentiment Analysis, Healthcare analytics, Clinical Analytics, Machine Learning

Link:

Corpus.

https://www.scien cedirect.com/scien ce/article/pii/B978 012819043200003 4

J. SPEECH RECOGNITION AND AI.

1.

2.

3.

Neural Networks used for Speech Recognition.

Speech recognition, Neural networks, Feed-forward Neural Networks (FFNN), Radial Basis Functions Neural Networks.

Wouter Gevaert, Georgi Tsenov, Valeri Mladenov,

Link:

Automated Speech Recognition in ATC Environment

ATM (Air Traffic Management), ATC (Air Traffic Control (ATC), Voice, Speech, Automated, Workload, Event, HMI (humanmachine interface).

José Manuel Cordero, Manuel Dorado, José Miguel de Pablo.

Link:

Automatic speech recognition for under-resourced languages: A survey

Automatic speech

Laurent Besacier, Etienne Barnard, Alexey Karpov, Tanja Schultz.

DOI:

recognition (ASR), Under-resourced languages, Speech and language resources acquisition, Statistical language modelling, Automatic pronunciation generation, Lexical modelling.

2010

http://www.doiser bia.nb.rs/img/doi/1 4509903/2010/145099031001001G.pdf

31 May 2019

https://pdfs.seman ticscholar.org/dcea /e274aa3f864a99d 62ce5778ac48294b 4d74f.pdf

https://doi.org/10.1 016/j.specom.201 3.07.008

Link: https://www.scien cedirect.com/scien ce/article/abs/pii/S 016763931300098 8

January 2014

4.

5.

Did you hear that? Adversarial Examples Against Automatic Speech Recognition

Computation and Language, Cryptography and Security (C.S), Automatic Speech Recognition, Human listener's perception.

Moustafa Link: Alzantot, https://arxiv.org/a Bharathan bs/1801.00554 Balaji, Mani Srivastava

2 Jan 2018

A Virtual Assistant Chatbot for College Advising System

SIML (Synthetic • Intelligence Markup Language), Microsoft • SAPI, NLP (Natural language processing),• STT, TTS.

Osama Badawy ,

22 January 2020

•

Khaled Eskaf, Mohamed Saad Zaghloul,

Link: https://link.springe r.com/chapter/10.1 007/978-3-03037051-0_70

Hadeel Mohamed.

•

K. AI IN EMOTIONS RECOGNITION.

1.

2.

Speech, Emotion 16. Deep neural networks19. Kun Han, 20. Dong Yu, Recognition Using (DNNs), Extreme learning machine 21. Ivan Tashev. Deep Neural (ELM), Network and 17. Emotion Recognition, Extreme Learning (SVM) Support Machine Vector Machine. 18. Evaluating deep Neural Networks, Haytham M. learning Speech Emotion Fayek architectures for Margaret Lech Recognition (SER), Speech Emotion Lawrence Affective computing, Recognition. Cavedon Deep learning.

Link: https://www.iscaspeech.org/archive /archive_papers/in terspeech_2014/i1 4_0223.pdf

DOI:

September 2014.

August 2017

https://doi.org/10.1 016/j.neunet.2017. 02.013

Link: https://www.scien cedirect.com/scien ce/article/abs/pii/S 089360801730059 X 3.

A Review: Speech Emotion Recognition

Emotional Speech database, Elicited features, HMM

G.N. Peerzade, R.R. Deshmukh,

Link: https://www.resea rchgate.net/profile

23 Feb 2018

4.

5.

Analysis of 2D Feature Spaces for Deep LearningBased Speech Recognition.

A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition

(Hidden Markov S.D. Model), GMM Waghmare. (Gaussian Mixtures Model), SVM (Support Vector Machine), ANN(Artificial Neural Network), KNN(Artificial Neural Network), Application.

/Ratnadeep_Desh mukh/publication/ 325774548_A_Revi ew_Speech_Emoti on_Recognition/lin ks/5b3b3dd9a6fdcc 8506eaa4b2/AReview-SpeechEmotionRecognition.pdf.

Convolutional neural network (CNN), audio signal feature maps, namely spectrograms, linear and Mel-scale spectrograms, and Chroma grams, Lithuanian wordrecognition task.

Korvel, Gražina; Treigys, Povilas; Tamulevicus, Gintautas; Bernataviciene, Jolita; Kostek, Bozena

DOI:

Artificial intelligence,

Yingying Jiang, Wei Li, M. Shamim Hossain, Min Chen. Abdulhameed Alelaiwi, Muneer AlHammadi.

DOI:

Multimodal information fusion, Data-driven emotion recognition.

https://doi.org/10. 17743/jaes.2018.0 066

December 20, 2018

Link: http://www.aes.or g/elib/browse.cfm?eli b=19880

January 2020

https://doi.org/10.1 016/j.inffus.2019.0 6.019

Link: https://www.scien cedirect.com/scien ce/article/pii/S156 6253519301381

L. PSYCHOLOGY AND AI

1.

Turning Gaming EEG Peripherals into Trainable Brain Computer Interfaces.

EEG, • Machine learning, Device control • BCI (Brain Computer • Interface), K-nearest Neighbours, SVM

Manisha Senad eera, Frederic Maire, Andry Rakotonirainy.

Link: https://link.springe r.com/chapter/10.1 007/978-3-31926350-2_44

22 November 2015

2.

3.

4.

5.

Brain E-Racing: An Exploratory Study on Virtual Brain-Controlled Drones.

• • • • • • •

Smart Brain Wave Sensor for Paralyzed- A Real Time Implementation.

Buildout of Methodology for Meticulous Diagnosis of KComplex in EEG for Aiding the Detection of Alzheimer’s by Artificial Intelligence

(Support Vector Machine) Brain-computer interfaces (BCI), User experience, Video games, E-sports, Virtual Reality, Accessibility.

• • •

Dante Tezza, Sarah Garcia, Tamjid Hossain, Marvin Andijar.

Link: https://link.springe r.com/chapter/10.1 007/978-3-03021565-1_10

Human Brain waves Interface (HBI), Arduino, Neuroscience, Brain scan, EEG (electroencephalogram ), MCH (Memory Controller Hub).

Mahadevaswam DOI: y, 10.26634/jpr.6.2.1 U B; 6618 Siraj, Ahmed.

(EEG) Electroencephalogram, (MCI) Mild Cognitive Impairment, Dementia, AI,

Rushikesh Pandya,

K-Complex, Neurology.

Rajvi Shah,

AI-enabled Artificial intelligence, emotion-aware robot: The fusion of Emotion-aware, smart clothing, edge Personal Robot, clouds and robotics. Smart clothing.

08 June 2019

August 2019

Link: https://search.proq uest.com/openvie w/a453cb2f3017dc 1a547c82826e7787 ee/1?pqorigsite=gscholar&c bl=2042732

Shrey Nadiadwala,

Manan Shah.

DOI: https://doi.org/10 .1007/s41133019-0021-6

05 October 2019

Link: https://link.springe r.com/article/10.10 07/s41133-0190021-6

Jun Yanga, Rui Wanga, Xin Guan, Mohammad Mehedi Hassan.

DOI: https://doi.org/10.1 016/j.future.2019.0 9.029

Link:

January 2020

https://www.scien cedirect.com/scien ce/article/pii/S016 7739X19305643

Mel-frequency cepstrum coefficients (MFCCs), modulation spectral (MS).

M. COGNITIVE CYBER SYSTEMS

1.

2.

Cyber-physicalsocial system in intelligent transportation.

Cognitive Internet of Vehicles.

Cyber-physical system (CPS), Cyber-physical-social system (CPSS), Intelligent transportation system (ITS), Computational modelling , Security, Analytical models.

Gang Xiong; Fengha Zhu; Xiwei Liu; Xisong Dong; Wuling Huang.

CIoV

Min Chen Yuanwen Tian, Giancarlo Fortino Jing Zhang.

(Cognitive Internet of Vehicles) Internet of Vehicles;

Industrial AI, Industry 4.0, Big data,

10.1109/JAS.201 5.7152667

https://ieeexplore.i eee.org/abstract/d ocument/7152667

DOI: https://doi.org/10.1 016/j.comcom.201 8.02.006

https://www.scien cedirect.com/scien ce/article/pii/S014 0366417311015

Jay Lee, Hossein Davari, Jaskaran Singh.

DOI: https://doi.org/10.1 016/j.mfglet.2018. 09.002

Smart -manufacturing Cyber physical systems.

May 2018

Link:

Vehicular network, Autonomous Driving.

Industrial Artificial Intelligence for industry 4.0-based manufacturing systems.

10 July 2015

Link:

Cognitive computing;

3.

DOI:

Link: https://www.scien cedirect.com/scien ce/article/pii/S221 3846318301081

October 2018

4.

5.

Blockchain and IoT22. DNN (Deep Neural Based Cognitive network), CNN Edge Framework (Convolutional Neural for Sharing network), NBT, AI, Economy Services Blockchain, Smart in a Smart City cities, Cognitive Systems, Security, Cloud Computing, geo-tagged multimedia. IoT-based cognitive edge Framework. 23.

Md. Abdur Rahman ; Md. Mamunur Rashid ; M. Shamim Hossain.

Brain-Inspired Systems: A Transdisciplinary Exploration on Cognitive Cybernetics, Humanity, and Systems Science Toward Autonomous Artificial Intelligence.

Yingxu Wang, Sam Kwong, Henry Leung, Jianhua Lu , Michael.

Brain-inspired cognitive systems (BCSs), Cognitive systems, Program processors, Brain modelling, Computational modelling, Brain models, Intelligent systems.

DOI: : 10.1109/ACCE SS.2019.289606 5

30 January 2019

Link: https://ieeexplore.i eee.org/abstract/d ocument/8629866/ keywords#keyword s

DOI: 10.1109/MSMC. 2018.2889502

15 January 2020

Link: https://ieeexplore.i eee.org/abstract/d ocument/8960591

N. QUANTUM COMPUTING AND AI COMBINATION

1.

Can artificial intelligence benefit from quantum computing?

Artificial Intelligence (AI), Reversible Computing,

Vicente MoretBonillo

DOI: https://doi.org/10 .1007/s13748014-0059-0

Quantum Computing. Link: https://link.springe r.com/article/10.10 07/s13748-0140059-0

13 September 2014

2.

An introduction to quantum machine Learning.

Quantum machine learning, Quantum computing, Artificial Intelligence, Machine learning.

Maria Schuld, Ilya Sinayskiy, Francesco Petruccione.

DOI:

15 Oct 2014

https://doi.org/10. 1080/00107514.20 14.964942

Link: https://www.tandf online.com/doi/abs /10.1080/0010751 4.2014.964942

3.

A NASA perspective on quantum computing: Opportunities and challenges.

Quantum computing, Quantum annealing, Combinatorial optimization, Planning and scheduling,

Rupak Biswas, Zhang Jiang, Kostya Kechezhi, Sergey Knysh, Salvatore Mandrà.

Machine learning Boltzmann sampling.

5.

When will useful quantum computers be constructed? Not in the foreseeable future, this physicist argues. Here's why: The case against: Quantum computing

Qubit, Computers, Quantum mechanics, Transistors, Logic gates, Quantum computing

An echo state network architecture based on quantum logic gate and its optimization

Quantum neural network (QNN), echo state networks (ESNs),

May 2017

https://doi.org/10.1 016/j.parco.2016.1 1.002

Link: https://www.scien cedirect.com/scien ce/article/pii/S016 7819116301326

Fault diagnosis,

4.

DOI:

Mikhail Dyakonov

DOI: 10.1109/MSPE C.2019.8651931

25 February 2019

Link: https://ieeexplore.i eee.org/abstract/d ocument/8651931

quantum echo state network (QESN),

Liu Junxiu,

DOI:

Sun Tiening,

https://doi.org/10.1 016/j.neucom.201 9.09.002

LuoYuling.

Link:

2 January 2020

https://www.scien cedirect.com/scien ce/article/pii/S092 5231219312627

particle swarm optimization (PSO), Time series, Financial applications.

O. ANN (ARTIFICIAL NEURAK NETWORK) IN AI.

1.

Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring

Continuous glucose monitoring (CGM),

C. PérezGandía,

artificial neural A. Facchinetti, network model G. Sparacino, (NNM), autoregressive model (ARM). C. Cobelli, E.J. Gómez, M. Rigla, A. de Leiva, and

DOI:

18 Jan 2010

https://doi.org/10. 1089/dia.2009.007 6

Link: http://liebertpub.c om/doi/full/10.108 9/dia.2009.0076

M.E. Hernando

2.

China’s primary energy demands in 2020: Predictions from an MPSO– RBF estimation model

Mix-encoding Particle Swarm Optimization and Radial Basis Function (MPSO– RBF),

Shiwei Yu,

DOI:

Yi-Ming Wei,

https://doi.org/10.1 016/j.enconman.2 012.03.016

Ke Wang.

China’s energy demand

September 2012

Link: https://www.scien cedirect.com/scien ce/article/pii/S019 6890412001628

Forecasting Radial basis function (RBF) neural network, Energy intensity.

3.

Application of Artificial Neural Network for Identification of

Rotor Vibrations, AI, Non-linear Support, Critical

Ivan Pavlenko,

Link: https://link.springe r.com/chapter/10.1

16 June 2018

Bearing Stiffness Characteristics in Rotor Dynamics Analysis

Frequency, Mode shape,

Vitalii Simonov skiy,

Artificial neural network “Virtual Gene Developer”.

Vitalii Ivanov,

007/978-3-31993587-4_34

Jozef Zajac, Jan Pitel.

4.

Predicting BlastInduced Air Overpressure: A Robust Artificial Intelligence System Based on Artificial Neural Networks and Random Forest

ANN, Random Forest, Xuan-Nam Empirical, Hybrid Bui , model, Air Hoang Nguyen. Overpressure, Openpit mine.

DOI: https://doi.org/10 .1007/s11053018-9424-1

07 November 2018

Link: https://link.springe r.com/article/10.10 07/s11053-0189424-1

5.

Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification

ANN, Breast Cancer, Classification, Artificial Neural Network, Machine Learning Database, Naïve Bayes, Classification is an important data mining.

Mucahid DOI: Mustafa Saritas.

30 June 2019

https://doi.org/10. 18201//ijisae.2019 252786

Link: https://ijisae.org/IJI SAE/article/view/9 34

P. AUGMENTED AND VIRTUAL REALITY WITH AI.

1.

Augmented Reality in built environment: Classification and implications for future research.

Augmented Reality,

Xiangyu Wang,

DOI:

Built environment

Mi Jeong Kim,

Review,

Peter E.D.Love,

https://doi.org/10.1 016/j.autcon.2012. 11.021

Shih-Chung Kang.

July 2013

Architecture/engineeri ng/construction, Classification.

2.

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

Big data, Visualization, Mixed Reality, Virtual Reality, Human interaction, Virtual Reality, Augmented Reality.

Link: https://www.scien cedirect.com/scien ce/article/abs/pii/S 092658051200216 6

Aleksandr Ometov Yevgeni, Ekaterina Olshannikova, Thomas Olssan.

DOI: https://doi.org/10 .1186/s40537015-0031-2

01 October 2015

Link: https://link.springe r.com/article/10.11 86/s40537-0150031-2

3.

An augmented reality application for improving shopping experience in large retail stores

Virtual Reality, Augmented Reality and Commerce, Smart Shopping, Deep Learning, HumanComputer Interaction, 3D visualization.

4.

5.

Edmanuel Cruz, Sergio OrtsEscolano, Francisco GomezDonoso.

Link: https://link.springe r.com/article/10.10 07/s10055-0180338-3

A Systematic Review of the Convergence of Augmented Reality, Intelligent Virtual Agents, and the Internet of Things

Augmented reality • (AR), intelligent • virtual agents (IVAs),• and the Internet of • things (IoT), • Convergence • Research, Mixed Reality.

Gerd Bruder, Brandon Belna, Stefanie Mutter, Damla Turgut, Greg Welch, Nahal Norouzi.

Link:

Reverend robot: automation and clergy

Augmented Reality, Artificial Intelligence, and the Re‐

Mohammad Yaqub Chaudhary,

DOI:

https://link.springe r.com/chapter/10.1 007/978-3-03004110-6_1

https://doi.org/10. 1111/zygo.12515

24 February 2018

13 February 2019

14 May 2019

Enchantment of the World, Artificial Intelligence and Online Spirituality.

William Young. Link: https://onlinelibrar y.wiley.com/doi/ful l/10.1111/zygo.125 15

Q. FUZZY LOGIC AND AI.

1.

2.

A Multilevel Inverter for Photovoltaic Systems with Fuzzy Logic Control.

Reduction in pingpong effect in heterogeneous networks using fuzzy logic.

Fuzzy logic, Control systems, Pulse width modulation inverters, Photovoltaic systems, Optimal control, Pulse width modulation, Signal processing algorithms, Phase modulation, Power generation, Array signal processing.

Carlo Cecati ;

DOI:

Fabrizio Ciancetta ;

10.1109/TIE.201 0.2044119

Ping-pong Effect, Heterogenous Networks, Vertical Handovers, Fuzzy System.

Razali Ngah ,

DOI:

Siti Z. Mohd Hashim ,

https://doi.org/10 .1007/s00500018-3246-2

01 March 2010

Pierluigi Siano. Link: https://ieeexplore.i eee.org/abstract/d ocument/5422767

Bushra Naeem.

25 May 2018

Link: https://link.springe r.com/article/10.10 07/s00500-0183246-2

3.

Odorous emission reduction from a waste landfill with an optimal protection system based on fuzzy logic

Fuzzy optimal protection system (FOPS), CALPUFF (coupled with the air pollutant transport software) and Fuzzy Logic, Decision Support System (DSS),

Immacolata Bortone, Simeone Chianese, Alessandro Erto,

Link: https://link.springe r.com/article/10.10 07/s11356-0182514-0

02 July 2018

Early Warning System,

Giovanni Francesco.

Solid Waste odour Management, 4.

Towards the use of fuzzy logic systems in rotary wing unmanned aerial vehicle: a review

Evolving Fuzzy, Learning Machine, Quadcopter, Rotary Wing, Unmanned Aerial vehicle (UAV).

Sreenatha G. Anavatti, Mahardhika Pratama ,

DOI: https://doi.org/10 .1007/s10462018-9653-z

17 August 2018

Matthew A. Garratt, Link: Md Meftahul Ferdaus.

5.

Research on sports video image based on fuzzy algorithms

Visual Communication, Image Representation, Image segmentation, Fuzzy Clustering, Clustering Algorithm, Motion Video, (SA) Simulated annealing algorithm, (DBSCAN) Density-Based Spatial Clustering of Applications with Noise,

Wu-Yeh Chang

https://link.springe r.com/article/10.10 07/s10462-0189653-z

DOI:

May 2019

https://doi.org/10.1 016/j.jvcir.2019.02. 033

Link: https://www.scien cedirect.com/scien ce/article/pii/S104 7320319300847

(BIRCH) Balanced Iterative Reducing and Clustering using Hierarchies.

R. INTERNET OF THINGS (IOT) AND ARTIFICIAL INTELIGENCE.

1.

2.

3.

Smart home and smart city solutions enabled by 5G, IoT, AAI and CoT services

An Emerging Era in the Management of Parkinson's Disease: Wearable Technologies and the Internet of Things

Interaction and Humans in Internet of Things

Smart homes, Cities and towns, Artificial intelligence, Internet of Things, Security, Wireless communication, 5G Service, Distributed artificial Intelligence, Big data.

K E Skouby ;

DOI:

P Lynggaard.

10.1109/IC3I.201 4.7019822

Parkinson's disease, Biomedical monitoring,

Cristian F. Pasluosta ,

Internet of things(IoT), Wearable sensors, Body sensor networks, Neurophysiology.

IoT, Automation, Novel Interaction, Smart Homes.

26 January 2015

Link: https://ieeexplore.i eee.org/abstract/d ocument/7019822/ keywords#keyword s

Heiko Gassner , Juergen Winkler , Jochen Klucken .

Markku Turune n,

DOI: 10.1109/JBHI.20 15.2461555

Link: https://ieeexplore.i eee.org/abstract/d ocument/7169494

DOI:

Daniel Sonntag

https://doi.org/10. 1007/978-3-31922723-8_80

KlausPeter Engelbrec ht

Link:

Thomas Olsson Dirk SchnelleWalka

28 July 2015

30 August 2015

https://link.springe r.com/chapter/10.1 007/978-3-31922723-8_80

Andrés Lucero.

3.

Artificial Intelligence-Based Semantic Internet of

Internet of Things(IoT); smart city(SC); smart

Kun Guo, Yeeming Lu,

DOI:

26 April 2018

Things in a UserCentric Smart City

home; artificial intelligence(AI); Semantic modelling.

Hui Gao, Ruohan Cao.

https://doi.org/10. 3390/s18051341

Link: https://www.mdpi. com/14248220/18/5/1341 4.

5.

Internet of Robotic Things: Driving Intelligent Robotics of Future - Concept, Architecture, Applications and Technologies

Agent-based Internet of Things: State-of-the-art and research challenges

Robot sensing systems, Robot kinematics, Internet of Things, Cloud computing, Computer architecture, Intelligent robots. Internet of Robotic Things(IoRT).

Ranbir Singh Batth, Anand Nayyar , Amandeep Nagpal.

Software agents

Claudio Savaglio, Maria Ganzha, Marcin Paprzycki, Costin Bădică,

Internet of Things IoT, cyber-physicality, Agent-Based Computing (ABC), IoT ecosystem.

DOI: 10.1109/ICCS.20 18.00033

14 January 2019

Link: https://ieeexplore.i eee.org/abstract/d ocument/8611051/ keywords#keyword s

Mirjana Ivanović, Giancarlo Fortinoa.

DOI:

January 2020

https://doi.org/10.1 016/j.future.2019.0 9.016

Link: https://www.scien cedirect.com/scien ce/article/pii/S016 7739X19312282

S. ROBOTICS AND ARTIFICIAL INTELLIGENCE

1.

The Robot as a Consumer: A Research Agenda.

Robots, Artificial intelligence (AI), consumer behaviour, disruptive technology.

Link: Stanislav Hristov Ivanov, Craig Webster

https://papers.ssrn .com/sol3/papers.c fm?abstract_id=29 60824

1 May 2017

2.

3.

4.

5.

Adoption of Robots, Artificial Intelligence and Service Automation by Travel, Tourism and Hospitality Companies – A Cost-Benefit Analysis

Robots, Artificial Intelligence, Service Automation, SelfService Technology, Tourism, Cost-Benefit Analysis.

Technology, innovation, employment and power: Does robotics and artificial intelligence really mean social transformation?

Artificial Intelligence, Employment, Robotics, social transformation, sociology, Technological innovation.

Ross Boyd, Robert J. Holton.

Link:

Ethical governance is essential to building trust in robotics and artificial intelligence systems

Intelligent autonomous systems (IAS), Governing artificial intelligence: ethical, legal, and technical opportunities and challenges, Ethics, standards, regulation, responsible research and innovation, and Public engagement.

Alan F. T. Winfield,

DOI:

Robotics, Artificial Intelligence, and the Evolving Nature of Work

Future of work, Robots, AI, Economics.

Link: Stanislav Hristov Ivanov, Craig Webster

Marina Jirotka.

26 July 2017

https://papers.ssrn .com/sol3/papers.c fm?abstract_id=30 07577

https://journals.sag epub.com/doi/abs/ 10.1177/14407833 17726591

https://doi.org/10. 1098/rsta.2018.008 5

August 29, 2017

15 October 2018

Link: https://royalsociet ypublishing.org/doi /full/10.1098/rsta. 2018.0085

Link: Stanislav Hristov Ivanov, Craig Webster

T. GAMING, SPORTS AND AI

05 October

https://link.springe 2019 r.com/chapter/10.1 007/978-3-03008277-2_8

1.

2.

3.

Guest editorial: Special issue on sports analytics

Data mining (and machine learning).

Ulf Brefeld,

Intelligent sports commentary recommendation system for individual cricket players

Artificial intelligence (AI); Information retrieval; Sports broadcasting; colour commentary; human computer interaction (HCI); automation.

V. Subramaniyasw amy and R. Logesh, V. Indragandhi.

Link:

Incept B: A CNN Based Classification Approach for Recognizing Traditional Bengali Games

Sports Activity Recognition,

Mohammad Shakirul Islama, Ferdouse Ahmed Foysal, Nafis Neehala.

DOI:

Albrecht Zimmerman.

Object Detection, Deep Learning, Classification,

Link:

25 July 2017

https://link.springe r.com/article/10.10 07/s10618-0170530-1

01 Jan 2018

https://pdfs.seman ticscholar.org/ae55 /ba64f1e44e7b1b8 b04188b51374026 d94959.pdf

2018

https://doi.org/10.1 016/j.procs.2018.1 0.436

Link:

TensorFlow,

https://www.scien cedirect.com/scien ce/article/pii/S187 7050918321343

Inception-v3, Transfer Learning, Computer Vision, Convolutional neural network (CNN). 4.

Computational Intelligence in Sports: A Systematic

Data mining techniques, knowledge extraction, Identification Information, data archaeology, Innovative Patterns.

Luiz A. L. Rodrigues, Anderson P. Avila-Santos, Danilo S. Sanches, Jacques D. Brancher.

Link:

Machine learning (ML), Predictive accuracy , Classification models, Artificial Neural Network (ANN),

Rory Bunker, Fadi Thabtah.

DOI:

Literature Review

5.

A machine learning framework for sport result prediction

https://www.hinda wi.com/journals/ah ci/2018/3426178/

https://doi.org/10.1 016/j.aci.2017.09. 005

30 October 2018

January 2019

Event forecasting, Data mining,

Link:

Sport result prediction.

https://www.scien cedirect.com/scien ce/article/pii/S221 0832717301485#!

U. AI SEARCH HEURISTIC TECHNIQUES AND ALGORITHMS

1.

A hybrid method based on cuckoo search Algorithm for global optimization problems.

Cuckoo search algorithm, Hill climbing, optimization problems, slow convergence, Exploration and Exploitation.

2.

3.

The use of new intelligent techniques in designing retaining walls

Role of AI and BioInspired Computing in Decision Making

Safety factor (SF), Ant colony optimization (ACO), Stone masonry retaining wall, Artificial Neural Network (ANN), Optimization Algorithm.

Decision support systems (DSS), artificial intelligence (AI), Intelligent Support Systems for Decision Making (ISSDM), Optimization, Classifier, Automata,

Mohammad Shehab, Ahamad Tajudin Khader, Makhlouf Laouchedi

Link: http://ejournal.uum.edu.m y/index.php/jict/ar ticle/view/8261/12 04

Mohammadreza Link: Koopialipoor, Bhatawdekar Ramesh Murlidhar,

12 June 2018

https://link.springe r.com/article/10.10 07/s00366-01800700-1

09 January 2019

Ahmadreza Hedayat, Danial Jahed Armaghan

Surekha Paneer selvam.

DOI: https://doi.org/10. 1007/978-3-03032530-5_8

Link: https://link.springe r.com/chapter/10.1

29 December 2019

007/978-3-03032530-5_8

Fuzzy Logic, Decision Making. 4.

5.

A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum behaved particle swarm algorithm

Deep learning assisted heuristic tree search for the container premarshalling problem

Firefly algorithm

Serkan Dereli,

DOI:

(FA), Particle swarm optimization (PSO), Artificial bee colony (ABC),

Raşit Köker .

https://doi.org/10 .1007/s10462019-09683-x

Quantum Particle Swarm Optimization (QPSO), Inverse Kinematics, 7-DOF Robotic Manipulator.

Container premarshalling problem (CPMP), Deep Learning Heuristic Tree Search (DLTS),

30 January 2019

Link: https://link.springe r.com/article/10.10 07/s10462-01909683-x

André Hottung,

DOI:

Shunji Tanaka,

https://doi.org/10.1 016/j.cor.2019.104 781

Kevin Tierney.

Tree search, Deep learning.

January 2020

Link: https://www.scien cedirect.com/scien ce/article/pii/S030 5054819302230

V. AI WITH THE COMBINATION OF SUPERVISED AND UNSUPERVISED LEARNING

1.

Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification

Classification; R. Sathya, Annamma Clustering; Learning; Abraham. MLP (Multilayer Perceptron); SOM (Self-Organizing Maps); Supervised learning; Unsupervised learning; Error backpropagation learning algorithm,

Link: https://www.resea rchgate.net/publica tion/273246843_C omparison_of_Sup ervised_and_Unsu pervised_Learning_ Algorithms_for_Pat tern_Classification.

DOI:

February 2013

KSOM (Kohonen SOM) of unsupervised learning mode. 2.

Semi-Supervised and Unsupervised Extreme Learning Machines

Pattern classification,

Gao Huang ;

Link:

Pattern clustering,

Shiji Song ;

Regression analysis,

Jatinder N. D. Gupta ;

https://ieeexplore.i eee.org/abstract/d ocument/6766243

Semi-supervised extreme Learning machines (ELM), manifold regularization, Automated Clustering. 3.

4.

D2D power control based on supervised and unsupervised learning

Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy

10.14569/IJARAI .2013.020206

Cheng Wu

Big Data, Decision Trees, Unsupervised Learning, PC algorithms, ML algorithms, Distributed QLearning, CART Decision Tree, D2D Communication, Energy Efficiency, Reinforcement Learning.

Zhiqiang Fan ,

Link:

Xinyu Gu ,

https://ieeexplore.i eee.org/abstract/d ocument/8322607/ keywords#keyword s

Artificial intelligence,

Christopher Tack.

Machine learning (Supervised and Unsupervised for musculoskeletal medicine),

12 March 2014

Shiwen Nie , Ming Chen.

26 March 2018

DOI: 10.1109/C ompComm.2017. 8322607

Link:

February 2019

https://www.scien cedirect.com/scien ce/article/abs/pii/S 246878121830159 0

Low back pain, DOI:

Physiotherapy.

https://doi.org/10.1 016/j.msksp.2018. 11.012

5.

Toward an artificial intelligence

Learning, ANN (Artificial Neural

Tailin Wu,

Link:

19 September 2019

physicist for unsupervised learning

Network), Simulated Annealing (Monte Carlo Methods), Time Series Analysis (Theoretical Techniques), NonLinear Dynamics, Networks.

Max Tegmark

https://journals.ap s.org/pre/abstract/ 10.1103/PhysRevE. 100.033311

W. AI WITH THE COMBINATION OF ECG AND BRAINWAVES.

1.

An EEG-based method for detecting drowsy driving state

Fatigue, Electroencephalograph y (ECG), Indexes, Electrodes, Driver circuits, Independent component analysis (ICA), Brain modeling, Fast Fourier transforms, Spectrum analysis.

Ming-ai Li ; Cheng Zhang ; Jin-Fu Yang.

Link: https://ieeexplore.i eee.org/abstract/d ocument/5569757/ keywords#keyword s

09 September 2010

DOI: 10.1109/FSKD.2 010.5569757

2.

A Generic Framework for EEG-Based Biometric Authentication

Electroencephalograph y (ECG), Feature extraction, Authentication, Wavelet transforms, Time-frequency analysis, Noise, Biometrics (Access Control), Brain Waves.

Aditya Sundararajan ; Alexander Pons ; Arif I. Sarwat

Link:

01 June 2015

https://ieeexplore.i eee.org/abstract/d ocument/7113462/ keywords#keyword s

DOI: 10.1109/ITNG.20 15.27

3.

EEG slow waves in traumatic brain injury: Convergent findings in mouse and man

Traumatic brain injury (TBI), EEG data, Slow waves, Coherence,

Mo H.Modarres, Nicholas N.Kuzmab, Tracy Kretzmer, Allan I. Packe,

Link: https://www.scien cedirect.com/scien ce/article/pii/S245 1994416300025#!

January 2017

Translational.

4.

CreativeBioMan: A Brain- and BodyWearable, Computing-Based, Creative Gaming System

Miranda M. Lim.

DOI:

Artificial intelligence,

Min Chen,

Link:

Games,

Yingying Jiang ;

https://ieeexplore.i eee.org/abstract/d ocument/8961340/ keywords#keyword s

Brain models, Emotion recognition, Creativity,

Yong Cao ; Albert Y. Zomaya

Computational modelling.

https://doi.org/10.1 016/j.nbscr.2016.0 6.001

16 January 2020

DOI: 10.1109/MSMC. 2019.2929312

5.

EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

Kohonen neural D. Jude network (KNN), Brain Hemanth. signals, human emotions, classification accuracy, Email: Neuro fuzzy inference system, SVM,

judehemanth@ karunya.edu

EEG (Electrocorticography ).

Link: https://www.resea rchgate.net/publica tion/338875452_E EG_signal_based_ Modified_Kohonen _Neural_Networks _for_Classification_ of_Human_Mental _Emotions

31 January 2020

DOI: 10.33969/AIS.20 20.21001

X. AI AND MLP (MULTILAYER PERCEPTRON).

1.

How effective is the Grey Wolf optimizer in training multi-layer perceptrons

Grey Wolf Optimizer (GWO), Multi-Layer Perceptron (MLP), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolution Strategy

Seyedali Mirjalili

Link: https://link.springe r.com/article/10.10 07/s10489-0140645-7

DOI:

17 January 2015

https://doi.org/10 .1007/s10489014-0645-7

(ES), Population-based Incremental Learning (PBIL). Evolutionary Algorithm. 2.

3.

4.

Advanced classification of Alzheimer's disease and healthy subjects based on EEG markers

Multilayer Perceptron Method to Estimate RealWorld Fuel Consumption Rate of Light Duty Vehicles

Prediction of Li-Ion Battery State of Charge Using Multilayer Perceptron and Long Short-Term Memory Models

Electroencephalograph ic (EEG) Marker, Feature dimensionality reduction, support vector machine(SVM), Recursive feature elimination(RFE), Error back propagation(EBP), Multilayer perceptron(MLP), Feature selection, Correlation method

Vitoantonio Bevilacqua; Angelo Antonio Salatino ; Carlo Di Leo ; Giacomo.

TOPIC: AI-Driven Big Data Processing: Theory, Methodology, and Applications.

Yawen Li ; Guangcan Tang ; Jiameng Du ; Nan Zhou ; Yue Zhao ; Tian Wu .

Artificial intelligence, Big data, Multilayer perceptron(MLP), Fuel consumption rate, Light-duty vehicles, Intelligent systems, Traffic engineering computing.

Lithium Ion, Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), State of Charge (SOC), Loss Function Optimizer and Battery Management System.

Link: https://ieeexplore.i eee.org/abstract/d ocument/7280463/ keywords#keyword s

01 October 2015

DOI: 10.1109/IJCNN.2 015.7280463

Link:

01 May 2019

https://ieeexplore.i eee.org/abstract/d ocument/8703740/ keywords#keyword s

DOI: 10.1109/ACCES S.2019.2914378

Asadullah Khalid ; Aditya Sundararajan ; Ipsita Acharya ; Arif I. Sarwat.

Link: https://ieeexplore.i eee.org/abstract/d ocument/8790533/ keywords#keyword s

DOI:

08 August 2019

10.1109/ITEC.20 19.8790533 5.

Prediction of forex trend movement using linear regression line, twostage of multi-layer perceptron and dynamic time warping algorithms.

Linear Regression Line, Two-stage of Multi-Layer Perceptron (MLP), Dynamic Time Warping algorithms.

Leslie Tiong Ching Ow

Link: http://ejournal.uum.edu.m y/index.php/jict/ar ticle/view/8210

22 January 2020

Y. ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS WITH AI.

1.

2.

Capturing the essence of word-ofmouth for social commerce: Assessing the quality of online ecommerce reviews by a semisupervised approach.

Online review,

Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry

Bitcoin, cryptocurrenc y, Digital money, Electronic money, emerging technologies, financial services, online payments, peer-to-peer network, technology adoption

Review quality,

Xiaolin Zheng, Shuai Zhu, Zhangxi Lin.

Review mining, Semi-supervised learning,

Link: https://www.scien cedirect.com/scien ce/article/pii/S016 7923613001735

Social network,

DOI:

Online Review Quality Mining (ORQM).

https://doi.org/10. 1016/j.dss.2013.0 6.002 Michal Polasik, Anna Iwona Piotrowska,To masz Piotr Wisniewski, Radoslaw Kotkowski , Geoffrey Lightfoot.

Link:

December 2013.

31 Aug 2015

https://www.tandf online.com/doi/abs /10.1080/1086441 5.2016.1061413

DOI: https://doi.org/10. 1080/10864415.2 016.1061413

3.

Two Decades of Recommender Systems at Amazon.com

Collaboration, Internet and web services, Recommender systems, Real-time

Brent Smith ;

Link:

Greg Linden.

https://ieeexplore.i eee.org/abstract/d ocument/7927889/

15 May 2017

keywords#keyword s

systems, Social network services, Artificial intelligence, Business

DOI: 10.1109/MIC.201 7.72 4.

A Fuzzy Decision Support Model with Sentiment Analysis for Items Comparison in eCommerce: The Case Study of http://PConline.com

Computational linguistics, Decision support systems, Electronic commerce, Fuzzy set theory, Internet, Probability, Sentiment analysis, Cybernetics, Data Models.

Pu Ji ; Hong-Yu Zhang, Jian-Qiang Wang

Link: https://ieeexplore.i eee.org/abstract/d ocument/8515098/ keywords#keyword s

30 October 2018

DOI: 10.1109/TSMC.2 018.2875163

5.

A 2020 perspective on “Client risk informedness in brokered cloud services: An experimental pricing study”

Cloud computing, Digital intermediation,

Di Shang, Robert J. Kauffman

IT services,

Link: https://www.scien cedirect.com/scien ce/article/pii/S156 7422320300259

4 February 2020

Market design, Artificial Intelligence. DOI: https://doi.org/10. 1016/j.elerap.202 0.100948

Z. FUTURE OF ARTIFICIAL INTELLIGENCE. 1.

Artificial intelligence: The future is Super Intelligent

Topic: Life 3.0: Being Human in the Age of Artificial Intelligence

Staurt Russell.

Link: https://www.natur e.com/articles/548 520a#article-info

DOI:

31 August 2017

https://doi.org/10 .1038/548520a

2.

Brain Intelligence: Go beyond Artificial Intelligence

Information communication technology (ICT), Robot technology (RT), Artificial Intelligence (AI), Brain Intelligence (BI), Artificial Life.

Huimin Lu, Yujie Li, Min Chen Hyoungseop Kim, Seiichi Serikawa

Link: https://link.springe r.com/article/10.10 07/s11036-0170932-8

21 September 2017

DOI: https://doi.org/10 .1007/s11036017-0932-8 3.

Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace

Career Planning, Disruptive Technology, Employees, Artificial Intelligence, STARA (Smart Technology, Artificial Intelligence, Robotics, and Algorithms.)

David Brougham, Jarrod Haar.

Link:

March 2018

https://www.camb ridge.org/core/jour nals/journal-ofmanagement-andorganization/article /smart-technologyartificialintelligencerobotics-andalgorithms-staraemployeesperceptions-of-ourfutureworkplace/41DB31 2743EA253848ED8 46B2882F5DE

DOI: •

4.

High-performance medicine: the convergence of human and artificial in-telligence

Health care, Machine Learning, Deep Learning, Cloud Computing, Artificial Intelligence.

Eric J. Topol.

https://doi.org/10.1 017/jmo.2016.55

Link: https://www.natur e.com/articles/s41

07 January 2019

591-018-03007#article-info

DOI: https://doi.org/10 .1038/s41 591-0180300-7

5.

What the Near Future of Artificial Intelligence Could Be

Artificial Intelligence,• Big Data, Complexity, Foresight analysis, Historical Data, Synthetic Data.

Luciano Floridi

Link: https://link.springe r.com/chapter/10.1 007/978-3-03029145-7_9

29 January 2020

4. TOP 5 RESEARCH PAPERS IN AI (ARTIFICIAL INTELLIGENCE): The just-released Google Scholar ranking of most highly cited publications reveal the tremendous rise in interest surrounding artificial intelligence (AI) research. Of the five most highly-cited papers in Nature – which itself is ranked by Google Scholar as the most influential journal – three are related to AI, and one has raked in more than 16,000 citations. The 2019 Google Scholar Metrics ranking, which is freely accessible online, tracks papers published between 2014 and 2018, and includes citations from all articles that were indexed in Google Scholar as of July 2019. Below is a selection of its most highly cited articles published by the world's most influential journals. 1. "Deep Residual Learning for Image Recognition" (2016) AUTHORS: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun;

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 25,256 citations ABSTRACT: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. Link:

http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep _Residual_Learning_CVPR_2016_paper.html 2. "Deep learning" (2015) Nature 16,750 citations Authored by 2018 Turing Award winners, Yann LeCun, Yoshua Bengio and Geoffrey Hinton – known collectively as the 'Godfathers of AI' – the paper is a seminal review of the potential of the AI technologies. ABSTRACT: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug

discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. Link: https://www.nature.com/articles/nature14539

3. "Going Deeper with Convolutions" (2015) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 14,424 citations This paper by Google AI researchers describes their new objectdetection system, GoogLeNet, built using a deep neural network system codenamed Inception. It received top marks in the 2014 ImageNet L*arge Scale Visual Recognition Challenge – an international computer vision competition. In 2018, Google’s parent company, Alphabet, was the sixth most prolific corporate entity in high-quality research output in the Nature Index. ABSTRACT: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for

ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection. Link: https://ieeexplore.ieee.org/document/72985944. "Fully Convolutional Networks for Semantic Segmentation" (2015) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 10,153 citations A team from the University of California, Berkeley was responsible for this highly influential AI paper, which describes a state-of-the-art approach to building AI systems that can identify objects in images. ABSTRACT: Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-ofthe-art in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet , the VGG net , and GoogLeNet ) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image. Link: https://ieeexplore.ieee.org/document/7298965 5. “ Large-scale Video Classification with Convolutional Neural Networks” (2014)

Proceedings of the IEEE/CVF Conference on Computer Vision 865 citations AUTHORS: Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei. ABSTARCT: Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on largescale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multiresolution, foveated architecture as a promising way of speeding up the training. Our best spatio-temporal networks display significant performance improvements compared to strong feature-based baselines (55.3% to 63.9%), but only a surprisingly modest improvement compared to single-frame models (59.3% to 60.9%). We further study the generalization performance of our best model by retraining the top layers on the UCF101 Action Recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63.3% up from 43.9%). Link:

https://www.cvfoundation.org/openaccess/content_cvpr_2014/papers/ Karpathy_Large-scale_Video_Classification_2014_CVPR_paper.pdf

5. MAJOR USE CASES OF ARTIFICIAL INTELLIGENCE (AI): A. Artificial Intelligence in Sports – A Computer System That Defeats A World Champion – Deep Blue

Deep Blue was a chess-playing computer developed by IBM. Deep Blue won its first game against a world champion on 10 February 1996, when it defeated Garry Kasparov in game one of a six-game match. Today, the Artificial Intelligence available on the free chess games on your phones are exponentially faster and better than Deep Blue. B. Artificial Intelligence for Rescue Missions: We can start by developing systems which help first responders find victims of earthquakes, floods, and any other natural disasters. Normally, responders need to examine aerial footage to determine where people could be stranded. However, examining a vast number of photos and drone footage is very time and labour intensive. This is a time critical process and it might very well be the difference between life and death for the victims. An Artificial Intelligence system developed at Texas A&M University permits computer programmers to write basic algorithms that can examine extensive footage and find missing people in under two hours.

C. Artificial Intelligence for Wildlife Poaching Prevention:

Hunting of Wildlife species and poaching is a global problem as it leads to extinction. For example, the latest African census showed a 30% decline in elephant populations between 2007 and 2014. Wildlife conservation areas have been established to protect these species from poachers, and these areas are protected by park rangers. Uganda’s Queen Elizabeth National Park uses Predictive modeling to predict poaching threat levels. Such models can be used to generate efficient and feasible patrol routes for the park rangers. D. Artificial Intelligence for Smart Agriculture: Neural networks work well to provide smart agricultural solutions. Everything ranging from complete monitoring of the soil and crop yield to providing predictive analytic models to track and predict various factors and variables that could affect future yields. For example, the Berlin-based agricultural tech Startup PEAT has developed a deep learning algorithm-based application called Plantix which can identify defects and nutrient deficiencies in the soil.Their algorithms correlate particular foliage patterns with certain soil defects, plant pests and diseases. E. Artificial Intelligence in Healthcare – Better Surgeries and Prosthetics

Robots today are machine learning-enabled tools that provide doctors with extended precision and control. These machines enable shortening the patients’ hospital stay, positively affecting the surgical experience and reducing medical costs all at once. Similarly, mind-controlled robotic arms and brain chip implants have begun helping paralyzed patients regain mobility and sensations of touch. F. Artificial Intelligence Tracking Wildlife Populations It is amazing to see that applications like iNaturalist and eBirds collect data on the species encountered. This helps keep track of species populations, ecosystems and migration patterns.

Some other Applications of AI includes: ▪

▪

▪

▪

Voice assistants such as Amazon Alexa, Google Assistant, and Microsoft Cortana. Google’s search suggestions, Amazon’s product recommendations or Netflix’s movie suggestions. Modern cars, which use ANI to identify when anti-lock brakes kick in or how to best inject fuel into an engine. Email spam filters – yes, this is why you no longer get those ads for viagra and other magic pills…

▪

▪

▪

Commercial aircraft, which use ANI to manage 1000s of operations and adjustments per second when the plane is in autopilot Google self-driving cars Android phones use ANI to monitor battery and CPU power to allocate resources based on usage (the same is also true of energy grids that use similar technologies to load balance energy demands across the network).

6. PROS AND CONS OF AI: John McCarthy the Father of AI said that ‘Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves.’ Everything in excess is dangerous and so is the case with Artificial Intelligence.

7. FUTURISTIC SCOPE OF AI: “Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial.” Max Tegmark, President of the Future of Life Institute Artificial intelligence is impacting the future of virtually every industry and every human being. Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future. “Predicting the future isn’t magic, it’s artificial intelligence.” – Dave Waters •

With companies spending nearly $20 billion collective dollars on AI products and services annually, tech giants like Google, Apple, Microsoft and Amazon spending billions to create those products and services, universities making AI a more prominent part of their respective curricula (MIT alone is dropping $1 billion on a new college devoted solely to computing, with an AI focus), and the U.S. Department of Defence upping its AI game, big things are bound to happen. Some of those developments are well on their way to being fully realized; some are merely theoretical and might remain so. All

are disruptive, for better and potentially worse, and there’s no downturn in sight. A recent study by Pwc estimates AI will contribute £230 billion ($320 billion) to the Middle East economy by 2030. "In the future, if you don’t know coding, you don’t know programming, it’s only going to get more difficult.” Because for controlling AI you need to be the Smartest.

THE IDEA THAT CAN CHANGE THE WORLD OF AI: Humanity will abandon speech and communicate through a 'collective AI consciousness' using nothing but THOUGHTS by 2050.

•

This is according to Marko Krajnovic, the producer of the exhibit in Dubai

•

'Collective consciousness' exhibit was unveiled at the Museum of the Future

•

Krajnovic interviewed a series of AI experts to come up with the concept

•

He believe the collective consciousness will help people read each other’s thoughts, and be part of the very fabric of the human brain.

•

Called HIBA, which stands for Hybrid Intelligence Biometric Avatar, the consciousness will take on the personas of its users and exchange information between them.

•

HIBA will have the ability to connect the minds of the cleverest of us, combining those minds with everything it can find out practically and put it all together in hybrid intelligence.'

This is how Experts Aim to get AI implementation in the World. Marching from Today ANI to Tomorrow’s AGI and Future’s ASI making it the reality. It is an issue troubling some of the greatest minds in the world at the moment, from Bill Gates to Elon Musk. SpaceX and Tesla CEO Elon Musk described AI as our 'biggest existential threat' and likened its development as 'summoning the demon'. He believes super intelligent machines could use humans as pets. Professor Stephen Hawking said it is a 'near certainty' that a major technological disaster will threaten humanity in the next 1,000 to 10,000 years. So, give your Best but be prepared for the Worst is the Gist

of Futuristic AI. https://youtu.be/wTbrk0suwbg

8. CONCLUSION: AI: Leading to the Era of Technological Revolution and Evolution of Human Consciousness that will give birth to the World of Humanoid, Cyborg, Android, Robots where the controlling power will be there in the hands of One who would be more Intelligent and Emergent with technological advancements whether it could be an Artificial Human or the Existing Human who is possessing the Super Intelligence. Today, Tomorrow and Future will be the AI YUG, one who wants to survive should work on improving the Natural Intelligence capable of Controlling the AI Humans rather than being controlled by them….

9. PERSONAL REVIEW AND FEEDBACK (PRF): I really enjoyed doing this Assignment and got to know and explore many dimensions related to the field of AI (Artificial Intelligence). AI Study helped me to realise my Potential and Strength. In order to create any Innovation first we have to develop ourselves, we need to sharpen ourselves so that we are able to give proper Instructions to the machine… This Journey of AI was just the Self-Realization Path that made me realize the Importance of Human Brain, Human Intelligence and Human Existence. Actually, this proved that we can synthesize the Artificial Intelligence with

the Study and Development of Human Intelligence. This also made me realise that we are just standing at the starting step of the ladder towards the advancement of AI technologies and we have to dig in thoroughly in order to get prepare for the future Challenges that can cause threat to the Human Existence. So, overall this was just an Amazing task and Assignment that I have worked on.

10. REFERENCES:

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