The Experts below are selected from a list of 60 Experts worldwide ranked by ideXlab platform

Nalinadevi Kadiresan - One of the best experts on this subject based on the ideXlab platform.

  • ICACCI - Multiclass Text Classification and Analytics for Improving Customer Support Response through different Classifiers
    2018 International Conference on Advances in Computing Communications and Informatics (ICACCI), 2018
    Co-Authors: Piyush Singh Parmar, P K Biju, Mani Shankar, Nalinadevi Kadiresan
    Abstract:

    In any industry, Customer Relationship Management (CRM) is a very important aspect of the business. In a complex business environment, providing an efficient customer support service is always a challenge. Customer reports the issues/defects in the system to the vendor by sending emails or by creating a ticket in CRM tools like Salesforce.com. The content of such reports includes detailed technical problems or complex workflow issues due to system failures. In the industrial automation systems, a Commissioning Engineer or a field operating Engineer generally reports such issues. Understanding and responding to the customer issues/defects and providing quick customer support is not an easy task. These CRM tools are not sufficiently astute to classify the defects into predefined classes. Text classification techniques are used to automatically identify and categorize the defects from the text messages. In this paper, five different machine learning classifiers (i.e. SVM, MNB, Decision tree, Random forest and K-nearest neighbors) are applied to perform multiclass text classification. The text messages are classified into predefined twelve technical system defects. The comparative analysis of five different classifiers on Customer Support dataset shows that the Support Vector Machine (SVM) has a better accuracy score in identifying the defects.

  • Multiclass Text Classification and Analytics for Improving Customer Support Response through different Classifiers
    2018 International Conference on Advances in Computing Communications and Informatics (ICACCI), 2018
    Co-Authors: Piyush Singh Parmar, P K Biju, Mani Shankar, Nalinadevi Kadiresan
    Abstract:

    In any industry, Customer Relationship Management (CRM) is a very important aspect of the business. In a complex business environment, providing an efficient customer support service is always a challenge. Customer reports the issues/defects in the system to the vendor by sending emails or by creating a ticket in CRM tools like Salesforce.com. The content of such reports includes detailed technical problems or complex workflow issues due to system failures. In the industrial automation systems, a Commissioning Engineer or a field operating Engineer generally reports such issues. Understanding and responding to the customer issues/defects and providing quick customer support is not an easy task. These CRM tools are not sufficiently astute to classify the defects into predefined classes. Text classification techniques are used to automatically identify and categorize the defects from the text messages. In this paper, five different machine learning classifiers (i.e. SVM, MNB, Decision tree, Random forest and K-nearest neighbors) are applied to perform multiclass text classification. The text messages are classified into predefined twelve technical system defects. The comparative analysis of five different classifiers on Customer Support dataset shows that the Support Vector Machine (SVM) has a better accuracy score in identifying the defects.

Mats Björkman - One of the best experts on this subject based on the ideXlab platform.

  • IECON - A solution for industrial device Commissioning along with the initial trust establishment
    IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013
    Co-Authors: Johan Åkerberg, Mikael Gidlund, Mats Björkman
    Abstract:

    Industrial device Commissioning along with the initial distribution of keying material is an important step for the security of industrial plants. An efficient key management system is required in cryptography for both symmetric key or public/private key encryption. Most of the key management system use either pre-installed shared keys or install keys using out-of-band channels. In addition to that, the sensor devices both wired and wireless need to be verified whether it is connected to the correct physical entity since these devices are linked with the physical world. Therefore in industrial plants there is a requirement to automate the trust bootstrapping process, where the devices from upper level in communication network will be aware that the communication device from below level is trusted. In this work, we present a workflow that uses the existing trust mechanism on employees to enable the initial bootstrap of trust in the devices, and also optionally support the Commissioning Engineer to download the required configuration data in the device as well. Thus, this approach presents a unique solution to the initial trust distribution problem reusing the existing features and facilities in industrial plants.

  • A solution for industrial device Commissioning along with the initial trust establishment
    IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, 2013
    Co-Authors: Johan Åkerberg, Mikael Gidlund, Mats Björkman
    Abstract:

    Industrial device Commissioning along with the initial distribution of keying material is an important step for the security of industrial plants. An efficient key management system is required in cryptography for both symmetric key or public/private key encryption. Most of the key management system use either pre-installed shared keys or install keys using out-of-band channels. In addition to that, the sensor devices both wired and wireless need to be verified whether it is connected to the correct physical entity since these devices are linked with the physical world. Therefore in industrial plants there is a requirement to automate the trust bootstrapping process, where the devices from upper level in communication network will be aware that the communication device from below level is trusted. In this work, we present a workflow that uses the existing trust mechanism on employees to enable the initial bootstrap of trust in the devices, and also optionally support the Commissioning Engineer to download the required configuration data in the device as well. Thus, this approach presents a unique solution to the initial trust distribution problem reusing the existing features and facilities in industrial plants.

Piyush Singh Parmar - One of the best experts on this subject based on the ideXlab platform.

  • ICACCI - Multiclass Text Classification and Analytics for Improving Customer Support Response through different Classifiers
    2018 International Conference on Advances in Computing Communications and Informatics (ICACCI), 2018
    Co-Authors: Piyush Singh Parmar, P K Biju, Mani Shankar, Nalinadevi Kadiresan
    Abstract:

    In any industry, Customer Relationship Management (CRM) is a very important aspect of the business. In a complex business environment, providing an efficient customer support service is always a challenge. Customer reports the issues/defects in the system to the vendor by sending emails or by creating a ticket in CRM tools like Salesforce.com. The content of such reports includes detailed technical problems or complex workflow issues due to system failures. In the industrial automation systems, a Commissioning Engineer or a field operating Engineer generally reports such issues. Understanding and responding to the customer issues/defects and providing quick customer support is not an easy task. These CRM tools are not sufficiently astute to classify the defects into predefined classes. Text classification techniques are used to automatically identify and categorize the defects from the text messages. In this paper, five different machine learning classifiers (i.e. SVM, MNB, Decision tree, Random forest and K-nearest neighbors) are applied to perform multiclass text classification. The text messages are classified into predefined twelve technical system defects. The comparative analysis of five different classifiers on Customer Support dataset shows that the Support Vector Machine (SVM) has a better accuracy score in identifying the defects.

  • Multiclass Text Classification and Analytics for Improving Customer Support Response through different Classifiers
    2018 International Conference on Advances in Computing Communications and Informatics (ICACCI), 2018
    Co-Authors: Piyush Singh Parmar, P K Biju, Mani Shankar, Nalinadevi Kadiresan
    Abstract:

    In any industry, Customer Relationship Management (CRM) is a very important aspect of the business. In a complex business environment, providing an efficient customer support service is always a challenge. Customer reports the issues/defects in the system to the vendor by sending emails or by creating a ticket in CRM tools like Salesforce.com. The content of such reports includes detailed technical problems or complex workflow issues due to system failures. In the industrial automation systems, a Commissioning Engineer or a field operating Engineer generally reports such issues. Understanding and responding to the customer issues/defects and providing quick customer support is not an easy task. These CRM tools are not sufficiently astute to classify the defects into predefined classes. Text classification techniques are used to automatically identify and categorize the defects from the text messages. In this paper, five different machine learning classifiers (i.e. SVM, MNB, Decision tree, Random forest and K-nearest neighbors) are applied to perform multiclass text classification. The text messages are classified into predefined twelve technical system defects. The comparative analysis of five different classifiers on Customer Support dataset shows that the Support Vector Machine (SVM) has a better accuracy score in identifying the defects.

S. Handley - One of the best experts on this subject based on the ideXlab platform.

  • The control and Commissioning of high precision mechanical systems using advanced digital motion control algorithms
    IEE Colloquium on Configurable Servo Control Systems, 1994
    Co-Authors: S. Handley, A. Higginson
    Abstract:

    The study presented in this paper is centred around a VME based multiprocessor, multi-axis control system, which has wide application in high precision production machines. A mechatronics approach adopted throughout the project, which brought together many technologies, has resulted in a very dramatic improvement in system performance. The key to this improvement was finding a control strategy to reshape the overall system dynamics resulting in a much higher bandwidth and improved dynamic response. This programme of research has demonstrated that state control techniques offer a viable approach to designing distributed intelligent controls for fast acting precision servo systems. Two self Commissioning strategies were implemented, the results are discussed and compared to the 'tuning parameters' set by an experienced Commissioning Engineer.

  • TheSelf Commissioning of High PerformanceServo Drivesfor Precision Mechanical Systems.
    1991
    Co-Authors: A. Higginson, S. Handley
    Abstract:

    High Precision Servo Systems are now being employed in many manufacturing applications as demand grows for products to be machined to exmmely high tolerances and guaranteed quality. These systems, at present, suffer from poor dynamic performance and prove very difficult to commission on the shop floor. This paper discusses the complex problems that have to be overcome during the Commissioning process and reports the progress made to date od the self Commissioning of High Precision Mechanical Systems, to ensure that they are tuned correctly to give the best possible dynamic perfomce: The results of two self Commissioning strategies are discussed and compared to the 'tuning parameters' set by an experienced Commissioning Engineer. The results so far are very encouraging and clearly point the way forward in this very important area of technology.

  • The self Commissioning of high performance servo drives for precision mechanical systems
    Proceedings IECON '91: 1991 International Conference on Industrial Electronics Control and Instrumentation, 1991
    Co-Authors: A. Higginson, S. Handley
    Abstract:

    The authors discuss the complex problems that must be overcome during the Commissioning process of high-precision servo systems and report the progress made to date on the self-Commissioning of high-precision mechanical systems to ensure that they are tuned correctly to give the best possible dynamic performance. The results of two self-Commissioning strategies are discussed and compared to the tuning parameters set by an experienced Commissioning Engineer. The resonance method has proved to be unsatisfactory in some situations, since it requires many programmable parameters and a skilled Commissioning Engineer to assess a suitable scaling factor to achieve stability. It was found not suitable for the coordinate measuring machine, where its repeatability was limited. The position error method has proved to be satisfactory on the test rig and on the coordinate measuring machine.

Mani Shankar - One of the best experts on this subject based on the ideXlab platform.

  • ICACCI - Multiclass Text Classification and Analytics for Improving Customer Support Response through different Classifiers
    2018 International Conference on Advances in Computing Communications and Informatics (ICACCI), 2018
    Co-Authors: Piyush Singh Parmar, P K Biju, Mani Shankar, Nalinadevi Kadiresan
    Abstract:

    In any industry, Customer Relationship Management (CRM) is a very important aspect of the business. In a complex business environment, providing an efficient customer support service is always a challenge. Customer reports the issues/defects in the system to the vendor by sending emails or by creating a ticket in CRM tools like Salesforce.com. The content of such reports includes detailed technical problems or complex workflow issues due to system failures. In the industrial automation systems, a Commissioning Engineer or a field operating Engineer generally reports such issues. Understanding and responding to the customer issues/defects and providing quick customer support is not an easy task. These CRM tools are not sufficiently astute to classify the defects into predefined classes. Text classification techniques are used to automatically identify and categorize the defects from the text messages. In this paper, five different machine learning classifiers (i.e. SVM, MNB, Decision tree, Random forest and K-nearest neighbors) are applied to perform multiclass text classification. The text messages are classified into predefined twelve technical system defects. The comparative analysis of five different classifiers on Customer Support dataset shows that the Support Vector Machine (SVM) has a better accuracy score in identifying the defects.

  • Multiclass Text Classification and Analytics for Improving Customer Support Response through different Classifiers
    2018 International Conference on Advances in Computing Communications and Informatics (ICACCI), 2018
    Co-Authors: Piyush Singh Parmar, P K Biju, Mani Shankar, Nalinadevi Kadiresan
    Abstract:

    In any industry, Customer Relationship Management (CRM) is a very important aspect of the business. In a complex business environment, providing an efficient customer support service is always a challenge. Customer reports the issues/defects in the system to the vendor by sending emails or by creating a ticket in CRM tools like Salesforce.com. The content of such reports includes detailed technical problems or complex workflow issues due to system failures. In the industrial automation systems, a Commissioning Engineer or a field operating Engineer generally reports such issues. Understanding and responding to the customer issues/defects and providing quick customer support is not an easy task. These CRM tools are not sufficiently astute to classify the defects into predefined classes. Text classification techniques are used to automatically identify and categorize the defects from the text messages. In this paper, five different machine learning classifiers (i.e. SVM, MNB, Decision tree, Random forest and K-nearest neighbors) are applied to perform multiclass text classification. The text messages are classified into predefined twelve technical system defects. The comparative analysis of five different classifiers on Customer Support dataset shows that the Support Vector Machine (SVM) has a better accuracy score in identifying the defects.