17,450 resources related to Machine Intelligence
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.
IECON is focusing on industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.
Computational Intelligence and Intelligent Technologies are very important tools in building intelligent systems with various degree of autonomous behaviour. These groups of tools support such features as ability to learn and adaptability of the intelligent systems in various types ofenvironments and situations. The current and future Information Society is expecting to be implemented with the framework of the Ambient Intelligence (AmI) approach into technologies and everyday life.
The IEEE ICCI*CC series is a flagship conference of its field. It not only synergizes theories of modern information science, computer science, communication theories, AI, cybernetics, computational intelligence, cognitive science, intelligence science, neuropsychology, brain science, systems science, software science, knowledge science, cognitive robots, cognitive linguistics, and life science, but also promotes novel applications in cognitive computers, cognitive communications, computational intelligence, cognitive robots, cognitive systems, and the AI, IT, and software industries.
Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.
Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...
The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications.
IEEE Access, 2018
Today, the US healthcare industry alone can save $300 B per year by using machine intelligence to analyze a rich set of existing medical data; results from these analyses can lead to breakthroughs such as more accurate medical diagnoses, discovery of new cures for diseases, and cost savings in the patient admission process at healthcare organizations. Because healthcare applications intrinsically ...
2016 8th International Conference on Knowledge and Smart Technology (KST), 2016
We always make efforts to predict our future from the past and the present, since the prediction can make great changes in our life, especially in the fields of science and technology. Many organizations in the globe have surveys and announces emerging or disruptive technologies every year. Of course, they have developed their own processes to achieve the goal, but ...
2018 3rd International Conference for Convergence in Technology (I2CT), 2018
A Lot of large scale biological projects and experiments like Human Genome Project have been carried in recent past which have resulted in generation of mammoth biological data mostly DNA and Protein sequences. It is extremely difficult to manage such a huge amount of data by using traditional methods hence lot of biological databases related to protein families and sequences ...
2012 Third International Conference on Intelligent Control and Information Processing, 2012
This paper presents a Virtual Reality (VR) interactive platform for learning and control for machine intelligence based on Adaptive Dynamic Programming (ADP). Recent research results have provided strong evidences that ADP could be a key technique for brain-like intelligent systems design, and VR is a powerful human-computer interface which can provide a three-dimensional (3D) active virtual environment. Converge these two ...
Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289), 1999
We present a practical and systematic strategy for measuring machine (robot) intelligence. A lot of research related to intelligent control has been carried out, but the subjects of definition and measurement of machine intelligence are not clearly formulated yet. We propose a human-oriented definition of machine intelligence and an intelligence task graph (ITG) as a modeling and analysis tool. By ...
Tech Super Stars Panelist - Karen Bartleson: 2016 Technology Time Machine
Thermodynamic Intelligence, A Heretical Theory - Natesh Ganesh - ICRC 2018
Deep Learning & Machine Learning Inference - Ashish Sirasao - LPIRC 2019
Tech Super Stars Panelist - Joe Herkert: 2016 Technology Time Machine
Q&A with Dillon Graham: IEEE Rebooting Computing Podcast, Episode 18
IROS TV 2019- Industry 4.0: The Business of Robotics- Industrial CEO Summit Forum
Part 1 of 2: June 2020 INGR roadmap workshop readout
Robotics History: Narratives and Networks Oral Histories: Ruedigger Dillman
Visual Wake Words Challenge - Aakanksha Chowdhery - LPIRC 2019
Local Activity, Memristor, and 137 - Leon Chua: 2016 International Conference on Rebooting Computing
Fun and Games with Artificial Intelligence: David B. Fogel
Q&A with Karen Bartleson: IEEE Technology Time Machine Podcast, Episode 3
Panel: Machine Learning in the Valley - ICRC San Mateo, 2019
Panelist Yuval Elovici - ETAP Forum Tel Aviv 2016
Adaptive Learning and Optimization for MI: From the Foundations to Complex Systems - Haibo He - WCCI 2016
Deep Graph Learning: Techniques and Applications - Haifeng Chen - IEEE Sarnoff Symposium, 2019
5G & the Role of AI - Keynote Jennifer Yates - IEEE Sarnoff Symposium, 2019
AI at the Edge - VS Joshi: Fog World Congress 2018
Part 1: Derek Footer and Miku Jah - Agricultural Food Systems Panel - TTM 2018
Today, the US healthcare industry alone can save $300 B per year by using machine intelligence to analyze a rich set of existing medical data; results from these analyses can lead to breakthroughs such as more accurate medical diagnoses, discovery of new cures for diseases, and cost savings in the patient admission process at healthcare organizations. Because healthcare applications intrinsically imply a vast amount of data, the execution of any algorithm on medical data is computationally intensive. Significant advancements made in computational power in the past decade have provided the opportunity for many researchers to successfully implement various machine intelligence-based healthcare applications, which didn't run efficiently on earlier computational platforms. In this paper, we provide a survey of machine intelligence algorithms within the context of healthcare applications; our survey includes a comprehensive list of the most commonly used computational models and algorithms. We view the application of these algorithms in multiple steps, namely, data acquisition, feature extraction, and aggregation, modeling, algorithm training, and algorithm execution and provide details-as well as representative case studies-for each step. We provide a set of metrics that are used to evaluate modeling and algorithmic performance, which facilitate the comparison of the presented models and algorithms. Medical cyber-physical systems are presented as an emerging application case study of machine intelligence in healthcare. We conclude our paper by providing a list of opportunities and challenges for incorporating machine intelligence in healthcare applications and provide an extensive list of tools and databases to help other researchers.
We always make efforts to predict our future from the past and the present, since the prediction can make great changes in our life, especially in the fields of science and technology. Many organizations in the globe have surveys and announces emerging or disruptive technologies every year. Of course, they have developed their own processes to achieve the goal, but the insights of experts from related domains are usually absolute. In the era of Bigdata, due to the enormous amount of information, domain experts are struggling with timeliness and completeness in developing insights for the future. In KISTI, we introduced a methodology in which human experts are collaborating with machine intelligence to overcome the information flood. Data-intensive analysis methodology is applied to implement the machine intelligence to predict emerging technologies. The intelligent service platform, named InSciTe, includes data gathering, text mining, identity resolution, reasoning, complex event processing, and prescriptive analytics modules. InSciTe generates candidates of emerging technologies with the evidences why they are selected as candidates, and then domain experts make the final decision. In this talk, I will introduce our intelligent service platform based on the data-intensive analysis. Besides, I will show several case studies in the domains of ICT, internet security, and healthcare as joint works with NIPA, KISA, and KRIBB respectively. For the cases with KRIBB, human experts collaborated with machine intelligence interactively to derive the results. We named this approach as Chi(Computer Human Interacting)-Delphi method for technology foresight. As Web goes to connect machine intelligences in the era of Internet of Things, the collaboration between human intelligence and machine intelligence will be eventually the next great wave for predicting the future.
A Lot of large scale biological projects and experiments like Human Genome Project have been carried in recent past which have resulted in generation of mammoth biological data mostly DNA and Protein sequences. It is extremely difficult to manage such a huge amount of data by using traditional methods hence lot of biological databases related to protein families and sequences have come into existence. Many databases contain information about proteins and their families. It is important and necessary to know the family of protein (unknown) hence we need to classify proteins. Using laboratory way for doing this is an arduous task, hence in this research we will use the advanced Machine Intelligence computing techniques like Artificial Neural Networks, Naïve Bayes Classifier, Decision Trees etc.
This paper presents a Virtual Reality (VR) interactive platform for learning and control for machine intelligence based on Adaptive Dynamic Programming (ADP). Recent research results have provided strong evidences that ADP could be a key technique for brain-like intelligent systems design, and VR is a powerful human-computer interface which can provide a three-dimensional (3D) active virtual environment. Converge these two subjects, we design an interactive system to facilitate and demonstrate the learning and control in VR environment with ADP. Our main goal in this paper is two-fold. First, we demonstrate that VR could be a useful platform to demonstrate and visualize machine intelligence research through the simulated 3D environment. Second, the integration of VR into machine intelligence research can provide a powerful platform to simulate, validate and facilitate the real-time interaction between the intelligent system and an unstructured environment. We discuss the detailed design strategy of the VR platform, and also demonstrated the interactive system performance based on the triple link inverted pendulum benchmark.
We present a practical and systematic strategy for measuring machine (robot) intelligence. A lot of research related to intelligent control has been carried out, but the subjects of definition and measurement of machine intelligence are not clearly formulated yet. We propose a human-oriented definition of machine intelligence and an intelligence task graph (ITG) as a modeling and analysis tool. By using an ITG, the machine contribution of human-machine cooperative systems is easily separated from the human contribution and directly described as numerical equations. Therefore we conclude that the ITG is very useful for estimating the machine intelligence quotient. This research will help engineers design intelligent robots which support human-friendly interfaces and perform environment controls with high performance.
Intimate to the functioning and behavior of intelligent systems is the manner in which information is represented internally. The conventional approach to intelligent system design assumes a particular bias in the manner by which this information is represented. Typically, this is characterized by an "abstract" or "objective" design methodology which holds that intelligence is not a function of the physical nature of the system. Such an approach suffers from several shortcomings, most notably problems relating to scaling and complexity. Recent physiological research, however, has demonstrated that physical bodily form is a fundamental building block in the organization of mammalian cortical structures. Consequently, this article explores such a biologically motivated "subjective" or "egocentric" approach to system design, and demonstrates its utility in a simple robot arm control problem.
Bringing computing systems to the stage of Machine Intelligence will require a massive scaling in processing, memory, and interconnectivity, and thus a major change in how electronic systems are designed. Long overlooked because of its unsuitability for the exacting demands of enterprise computing, 3D waferscale integration offers a promising scaling path, due in large part to the fault- tolerant nature of many cognitive algorithms. This work explores this scaling path in greater detail, invoking a simple model of brain connectivity to examine the potential for 3D waferscale integration to meet the demanding interconnectivity requirements of Machine Intelligence.
A human-interface analogy is used to determine objective measures of machine intelligence. This measure is calculated for four candidate control systems used in an assistive device study with patients that exhibit neuromotor disability.
The fuzzy logic is applied to resolve the monitoring problem of products quality and products quantity which are increasingly varying as market requirement. A series of fuzzy rules are employed and the fuzzy system may generate suggested supply change rate. At the same time, the operation of supplier is also dynamically changing and the evaluation and selection for supplier are the basis of supply chain cooperation. So whether it is scientific to select a supplier is crucial for sustaining and developing a company. Therefore, in this paper the neural network is introduced to dynamically assess suppliers and recommend to substituting for new ones when necessary, only supplementing fuzzy logic system with its advantages. The methodology is described for the deployment of this proposed hybrid approach to enhance the machine intelligence of a supply chain network with the description of a case study to exemplify its underlying principles.
This paper explores the concept of machine intelligence required for the smart sensor environment. The increasing complexity of the hardware requirements for smart sensor applications has led to the inclusion of field programmable gate arrays, FPGA, in the design of these systems. Two main advantages can be capitalized on with this approach, based on the characteristics of the FPGAs. Firstly, the design can be easily modified, reducing the design cycle time considerably. Also, as FPGAs are inherently parallel devices, both local processing and system partitioning are provided. The total system design in this project includes an application specific integrated circuit, ASIC, the FPGA, and a microcomputer. In this fashion the requirements for a smart sensor are realised.
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