scholarly journals Intelligent networks assisted by cognitive computing and machine learning

IEEE Network ◽  
2019 ◽  
Vol 33 (3) ◽  
pp. 6-8 ◽  
Author(s):  
Min Chen ◽  
Honggang Wang ◽  
Sanjeev Mehrotra ◽  
Victor C. M. Leung ◽  
Iztok Humar
Keyword(s):  
Machine Learning ◽  
Cognitive Computing ◽  
Intelligent Networks

scholarly journals Towards Neuromorphic Learning Machines Using Emerging Memory Devices with Brain-Like Energy Efficiency

2018 ◽  
Vol 8 (4) ◽  
pp. 34 ◽  
Author(s):  
Vishal Saxena ◽  
Xinyu Wu ◽  
Ira Srivastava ◽  
Kehan Zhu
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
Memory Devices ◽  
Cognitive Computing ◽  
Mixed Signal ◽  
Von Neumann ◽  
Emerging Memory

The ongoing revolution in Deep Learning is redefining the nature of computing that is driven by the increasing amount of pattern classification and cognitive tasks. Specialized digital hardware for deep learning still holds its predominance due to the flexibility offered by the software implementation and maturity of algorithms. However, it is being increasingly desired that cognitive computing occurs at the edge, i.e., on hand-held devices that are energy constrained, which is energy prohibitive when employing digital von Neumann architectures. Recent explorations in digital neuromorphic hardware have shown promise, but offer low neurosynaptic density needed for scaling to applications such as intelligent cognitive assistants (ICA). Large-scale integration of nanoscale emerging memory devices with Complementary Metal Oxide Semiconductor (CMOS) mixed-signal integrated circuits can herald a new generation of Neuromorphic computers that will transcend the von Neumann bottleneck for cognitive computing tasks. Such hybrid Neuromorphic System-on-a-chip (NeuSoC) architectures promise machine learning capability at chip-scale form factor, and several orders of magnitude improvement in energy efficiency. Practical demonstration of such architectures has been limited as performance of emerging memory devices falls short of the expected behavior from the idealized memristor-based analog synapses, or weights, and novel machine learning algorithms are needed to take advantage of the device behavior. In this article, we review the challenges involved and present a pathway to realize large-scale mixed-signal NeuSoCs, from device arrays and circuits to spike-based deep learning algorithms with ‘brain-like’ energy-efficiency.

Semantic Manipulations and Formal Ontology for Machine Learning based on Concept Algebra

2012 ◽  
pp. 194-220
Author(s):  
Yingxu Wang ◽  
Yousheng Tian ◽  
Kendal Hu
Keyword(s):  
Machine Learning ◽  
Operational Semantics ◽  
Mathematical Structure ◽  
Expressive Power ◽  
Formal Ontology ◽  
Cognitive Computing ◽  
Human Beings ◽  
Knowledge Modeling ◽  
Wide Range ◽  
Cognitive Robots

Towards the formalization of ontological methodologies for dynamic machine learning and semantic analyses, a new form of denotational mathematics known as concept algebra is introduced. Concept Algebra (CA) is a denotational mathematical structure for formal knowledge representation and manipulation in machine learning and cognitive computing. CA provides a rigorous knowledge modeling and processing tool, which extends the informal, static, and application-specific ontological technologies to a formal, dynamic, and general mathematical means. An operational semantics for the calculus of CA is formally elaborated using a set of computational processes in real-time process algebra (RTPA). A case study is presented on how machines, cognitive robots, and software agents may mimic the key ability of human beings to autonomously manipulate knowledge in generic learning using CA. This work demonstrates the expressive power and a wide range of applications of CA for both humans and machines in cognitive computing, semantic computing, machine learning, and computational intelligence.

Cognitive Intelligence

2016 ◽  
Vol 10 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Yingxu Wang ◽  
Bernard Widrow ◽  
Lotfi A. Zadeh ◽  
Newton Howard ◽  
Sally Wood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computational Intelligence ◽  
Cognitive Systems ◽  
Cognitive Computing ◽  
Cognitive Intelligence ◽  
Basic Studies ◽  
Deep Reasoning ◽  
Intelligence Theories ◽  
The Brain

The theme of IEEE ICCI*CC'16 on Cognitive Informatics (CI) and Cognitive Computing (CC) was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel (Part I) in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

scholarly journals Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Saad Awadh Alanazi ◽  
M. M. Kamruzzaman ◽  
Madallah Alruwaili ◽  
Nasser Alshammari ◽  
Salman Ali Alqahtani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care Services ◽  
Fog Computing ◽  
Well Being ◽  
Reproductive Rate ◽  
Cognitive Computing ◽  
Infected People ◽  
Global Challenge ◽  
Smart Health

COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing characteristics, and the lack of a vaccine or effective medicines. A model for measuring and preventing the continued spread of COVID-19 is urgently required to provide smart health care services. This requires using advanced intelligent computing such as artificial intelligence, machine learning, deep learning, cognitive computing, cloud computing, fog computing, and edge computing. This paper proposes a model for predicting COVID-19 using the SIR and machine learning for smart health care and the well-being of the citizens of KSA. Knowing the number of susceptible, infected, and recovered cases each day is critical for mathematical modeling to be able to identify the behavioral effects of the pandemic. It forecasts the situation for the upcoming 700 days. The proposed system predicts whether COVID-19 will spread in the population or die out in the long run. Mathematical analysis and simulation results are presented here as a means to forecast the progress of the outbreak and its possible end for three types of scenarios: “no actions,” “lockdown,” and “new medicines.” The effect of interventions like lockdown and new medicines is compared with the “no actions” scenario. The lockdown case delays the peak point by decreasing the infection and affects the area equality rule of the infected curves. On the other side, new medicines have a significant impact on infected curve by decreasing the number of infected people about time. Available forecast data on COVID-19 using simulations predict that the highest level of cases might occur between 15 and 30 November 2020. Simulation data suggest that the virus might be fully under control only after June 2021. The reproductive rate shows that measures such as government lockdowns and isolation of individuals are not enough to stop the pandemic. This study recommends that authorities should, as soon as possible, apply a strict long-term containment strategy to reduce the epidemic size successfully.

AI, Machine Learning! What next? Cognitive Computing

2018 ◽  
Author(s):  
Jailendra Kumar
Keyword(s):  
Machine Learning ◽  
New Technology ◽  
Indian Institute ◽  
Cognitive Computing ◽  
Student Community ◽  
Track Record ◽  
Research Outcomes ◽  
Potential Applications ◽  
Advanced Computing ◽  
Technology Area

Continuing with its track record of showcasing the latest technological trends, Advanced Computing and Communications Society (ACCS) dedicated its 24th edition of International Conference on Advanced Computing and Communications (ADCOM 2018) to assimilate the advances in Cognitive Computing and Applications. ADCOM 2018 was organized at the Indian Institute of Information Technology, Bangalore (IIIT-B) from 21st September to 23rd September of 2018. The three-day conference saw delegates from industry, academia, government R&D and student community coming together on this common platform to share their knowledge and learn from each other about the research outcomes and potential applications of this new technology area.

Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies

2017 ◽  
Vol 11 (4) ◽  
pp. 80-92
Author(s):  
Junjie Bai ◽  
Kan Luo ◽  
Jun Peng ◽  
Jinliang Shi ◽  
Ying Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Affective Computing ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Cognitive Computing ◽  
Bayes Classifier ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Neuro Fuzzy

Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.

Semantic Manipulations and Formal Ontology for Machine Learning based on Concept Algebra

2011 ◽  
Vol 5 (3) ◽  
pp. 1-29 ◽  
Author(s):  
Yingxu Wang ◽  
Yousheng Tian ◽  
Kendal Hu
Keyword(s):  
Machine Learning ◽  
Operational Semantics ◽  
Mathematical Structure ◽  
Expressive Power ◽  
Formal Ontology ◽  
Cognitive Computing ◽  
Human Beings ◽  
Knowledge Modeling ◽  
Wide Range ◽  
Cognitive Robots

Towards the formalization of ontological methodologies for dynamic machine learning and semantic analyses, a new form of denotational mathematics known as concept algebra is introduced. Concept Algebra (CA) is a denotational mathematical structure for formal knowledge representation and manipulation in machine learning and cognitive computing. CA provides a rigorous knowledge modeling and processing tool, which extends the informal, static, and application-specific ontological technologies to a formal, dynamic, and general mathematical means. An operational semantics for the calculus of CA is formally elaborated using a set of computational processes in real-time process algebra (RTPA). A case study is presented on how machines, cognitive robots, and software agents may mimic the key ability of human beings to autonomously manipulate knowledge in generic learning using CA. This work demonstrates the expressive power and a wide range of applications of CA for both humans and machines in cognitive computing, semantic computing, machine learning, and computational intelligence.

Towards Machine-Learning-Based 5G and Beyond Intelligent Networks: The MARSAL Project Vision

2021 ◽  
Author(s):  
John S. Vardakas ◽  
Kostas Ramantas ◽  
Eftychia Datsika ◽  
Miquel Payaro ◽  
Sofie Pollin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intelligent Networks
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