61,304 resources related to Machine Learning
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All fields of satellite, airborne and ground remote sensing.
The Annual IEEE PES General Meeting will bring together over 2900 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutorials and more
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.
2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
Machine learning, as the driving force of this wave of AI, provides powerful solutions to many real-world technical and scientific challenges. The 29th MLSP workshop, an annual event organized by the IEEE Signal Processing Society MLSP Technical Committee, will present the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, as well as special and regular single-track sessions.
GeMiC 2019 is a scientific conference aiming to bring together academic and industrial researchers from Germany and from abroad, to discuss research progress and prospects in the area of microwave and millimeter-wave theory and techniques.
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 Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
Methods, algorithms, and human-machine interfaces for physical and logical design, including: planning, synthesis, partitioning, modeling, simulation, layout, verification, testing, and documentation of integrated-circuit and systems designs of all complexities. Practical applications of aids resulting in producible analog, digital, optical, or microwave integrated circuits are emphasized.
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018
This paper examines industry-based doctoral dissertation research in a professional computing doctoral program for full time working professionals through the lens of different machine learning algorithms to understand topics explored by full time working industry professionals. This research paper examines machine learning algorithms and the IBM Watson Discovery machine learning tool to categorize dissertation research topics defended at Pace University. ...
2017 IEEE International Conference on Intelligence and Security Informatics (ISI), 2017
To assure cyber security of an enterprise, typically SIEM (Security Information and Event Management) system is in place to normalize security events from different preventive technologies and flag alerts. Analysts in the security operation center (SOC) investigate the alerts to decide if it is truly malicious or not. However, generally the number of alerts is overwhelming with majority of them ...
2011 10th International Conference on Machine Learning and Applications and Workshops, 2011
Recent developments in information systems as well as computerization of business processes by organizations have led to a faster, easier and more accurate data analysis. Data mining and machine learning techniques have been used increasingly in the analysis of data in various fields ranging from medicine to finance, education and energy applications. Machine learning techniques make it possible to deduct ...
2007 International Conference on Machine Learning and Cybernetics, 2007
IASS is the integrated anti-spam system, which adopts machine learning to filter spam in a intelligent, flexible, precise, and self-adaptive way. The methods of linear classification based on optimal separating hyperplane and K-means clustering are used in action recognition layer. The method of improved naive Bayes is used in content analysis layer. The application of machine learning helps improve the ...
2005 International Conference on Machine Learning and Cybernetics, 2005
This paper firstly analyses the actual underwriting methods of Chinese life insurance companies, and points out the merits and shortcomings of these methods. Then the incomplete database of insurance company is mined by the data mining's association rule algorithm. Thirdly the support vector machine (SVM) is applied to the underwriting process to classify the applicants. Finally the directions for improving ...
Linear Regression: Intro to Machine Learning Workshop - IEEE Region 4 Presentation
IEEE Day Future Milestone: Machine Learning in the future
Signal Processing and Machine Learning
ICASSP 2011 Trends in Machine Learning for Signal Processing
Deep Learning & Machine Learning Inference - Ashish Sirasao - LPIRC 2019
Federated Learning for Networking - Anwar Walid - IEEE Sarnoff Symposium, 2019
Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing
Large Scale Data Mining Using Genetics-Based Machine Learning 1
Cat and Mouse, Email Phishing and Machine Learning - Cybersecurity in a Hyperconnected World
Large Scale Data Mining Using Genetics-Based Machine Learning 3
Large Scale Data Mining Using Genetics-Based Machine Learning 2
Vladimir Vapnik accepts the IEEE John Von Neumann Medal - Honors Ceremony 2017
Keynote on Machine Learning: Andrea Goldsmith - B5GS 2019
Hardware-Software Co-Design for an Analog-Digital Accelerator for Machine Learning - Dejan Milojicic - ICRC 2018
Big Data and Machine Learning in Cancer Genomics
Shannon to Machine Learning: ML & DL for 5G - Erik Stauffer - B5GS 2019
Klaus-Robert Muller: Machine learning & AI for the Sciences — Towards Understanding
Fog Computing Test Bed: Cutting Costs and Latency in Data Transmission - Fog World Congress
Deep Graph Learning: Techniques and Applications - Haifeng Chen - IEEE Sarnoff Symposium, 2019
This paper examines industry-based doctoral dissertation research in a professional computing doctoral program for full time working professionals through the lens of different machine learning algorithms to understand topics explored by full time working industry professionals. This research paper examines machine learning algorithms and the IBM Watson Discovery machine learning tool to categorize dissertation research topics defended at Pace University. The research provides insights into differences in machine learning algorithm categorization using natural language processing.
To assure cyber security of an enterprise, typically SIEM (Security Information and Event Management) system is in place to normalize security events from different preventive technologies and flag alerts. Analysts in the security operation center (SOC) investigate the alerts to decide if it is truly malicious or not. However, generally the number of alerts is overwhelming with majority of them being false positive and exceeding the SOC's capacity to handle all alerts. Because of this, potential malicious attacks and compromised hosts may be missed. Machine learning is a viable approach to reduce the false positive rate and improve the productivity of SOC analysts. In this paper, we develop a user-centric machine learning framework for the cyber security operation center in real enterprise environment. We discuss the typical data sources in SOC, their work flow, and how to leverage and process these data sets to build an effective machine learning system. The paper is targeted towards two groups of readers. The first group is data scientists or machine learning researchers who do not have cyber security domain knowledge but want to build machine learning systems for security operations center. The second group of audiences are those cyber security practitioners who have deep knowledge and expertise in cyber security, but do not have machine learning experiences and wish to build one by themselves. Throughout the paper, we use the system we built in the Symantec SOC production environment as an example to demonstrate the complete steps from data collection, label creation, feature engineering, machine learning algorithm selection, model performance evaluations, to risk score generation.
Recent developments in information systems as well as computerization of business processes by organizations have led to a faster, easier and more accurate data analysis. Data mining and machine learning techniques have been used increasingly in the analysis of data in various fields ranging from medicine to finance, education and energy applications. Machine learning techniques make it possible to deduct meaningful further information from those data processed by data mining. Such meaningful and significant information helps organizations to establish their future policies on a sounder basis, and to gain major advantages in terms of time and cost. This study applies classification algorithms used in data mining and machine learning techniques on those data obtained from individuals during the vocational guidance process, and tries to determine the most appropriate algorithm.
IASS is the integrated anti-spam system, which adopts machine learning to filter spam in a intelligent, flexible, precise, and self-adaptive way. The methods of linear classification based on optimal separating hyperplane and K-means clustering are used in action recognition layer. The method of improved naive Bayes is used in content analysis layer. The application of machine learning helps improve the performance of IASS.
This paper firstly analyses the actual underwriting methods of Chinese life insurance companies, and points out the merits and shortcomings of these methods. Then the incomplete database of insurance company is mined by the data mining's association rule algorithm. Thirdly the support vector machine (SVM) is applied to the underwriting process to classify the applicants. Finally the directions for improving this algorithm are pointed out. The algorithm proposed in this paper has promising future in underwriting process.
Personalized Healthcare (PH) is a new patientoriented healthcare approach which expects to improve the traditional healthcare system. The focus of this new advancement is the patient data collected from patient Electronic health records (EHR), Internet of Things (IoT) sensor devices, wearables and mobile devices, web-based information and social media. PH applies Artificial Intelligence (AI) techniques to the collected dataset to improve disease progression technique, disease prediction, patient selfmanagement and clinical intervention. Machine learning techniques are widely used in this regard to develop analytic models. These models are integrated into different healthcare service applications and clinical decision support systems. These models mainly analyse the collected data from sensor devices and other sources to identify behavioral patterns and clinical conditions of the patient. For example, these models analyse the collected data to identify the patient's improvements, habits and anomaly in daily routine, changes in sleeping and mobility, eating, drinking and digestive pattern. Based on those patterns the healthcare applications and the clinical decision support systems recommend lifestyle advice, special treatment and care plans for the patient. The doctors and caregivers can also be engaged in the care plan process to validate lifestyle advice. However, there are many uncertainties and a grey area when it comes to applying machine learning in this context. Clinical, behaviour and lifestyle data in nature are very sensitive. There could be different types of biased involved in the process of data collection and interpretation. The training data model could have an older version of the dataset. All these could lead to an incorrect decision from the system without the user's knowledge. In this paper, some of the standards of the ML models reported in the recent research trends, identify the reliability issues and propose improvements.
A novel real-time acoustic feedback (RTAF) based on machine learning to reduce the duration and to improve the progress in the rehabilitation is presented. Wearable technology (WT) has emerged as a viable means to provide low-cost digital healthcare and therapy course outside the medical environment like hospitals and clinics. In this paper we show that the RTAF together with WTs can offer an excellent solution to be used in rehabilitation. The method of RTAF based on machine learning as well as a study for proving its effectiveness are presented below. The results show a faster recovery time using RTAF. The proposed RTAF shows a great potential to be used and deployed to support digital healthcare, therapy and rehabilitation.
The importance of vocational and technical training is growing day by day in parallel to the developing technology. It is inevitable to utilise opportunities presented by information and communication technologies in order to determine vocational fields in vocational and technical training in the most efficient manner. In this respect, it is possible to create a more efficient tool compared to the current methods by utilising machine learning which is an artificial intelligence model in energy applications that predicts events in the future depending on the past experiences. In the current study, a software is developed that ensures that the system learns about the successful and unsuccessful choices made in the past by applying “Naive Bayes” algorithm, which is a machine learning algorithm, to the data collected concerning the individuals who turned out to be successful or unsuccessful in the vocational technical training process in energy applications. In the software developed, it is aimed that the system recommends the most suitable vocational field for the individual by according to the data collected from the individual who is in the occupation selection process in field energy applications.
In this paper, we present a framework which enables medical decision making in the presence of partial information. At its core is ontology-based automated reasoning, machine learning techniques are integrated to enhance existing patient datasets in order to address the issue of missing data. Our approach supports interoperability between different health information systems. This is clarified in a sample implementation that combines three separate datasets (patient data, drug-drug interactions and drug prescription rules) to demonstrate the effectiveness of our algorithms in producing effective medical decisions. In short, we demonstrate the potential for machine learning to support a task where there is a critical need from medical professionals by coping with missing or noisy patient data and enabling the use of multiple medical datasets.
Microservices architecture facilitates building distributed scalable software products, usually deployed in a cloud environment. Monitoring microservices deployed in a Kubernetes orchestrated distributed advanced analytics machine learning engines is at the heart of many cloud resource management solutions. In addition, measuring resource utilization at more granular level such as per query or sub-query basis in an MPP Machine Learning Engine (MLE) is key to resource planning and is also the focus of our work. In this paper we propose two mechanisms to measure resource utilization in Teradata Machine Learning Engine (MLE). First mechanism is the Cluster Resource Monitoring (CRM). CRM is a high-level resource measuring mechanism for IT administrators and analytics users to visualize, plot, generates alerts and perform live and historical- analytics on overall cluster usage statistics. Second mechanism is the Query Resource Monitoring (QRM). QRM enables IT administrators and MLE users to measure compute resource utilization per individual query and its sub-queries. When query takes long time, QRM provides insights. This is useful to identify expensive phases within a query that tax certain resources more and skew the work distribution. We show the results of proposed mechanisms and highlight use-cases.
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