Abstract: Training a machine to learn and to identify time slots to select in a user's electronic calendar, in one aspect, may include receiving an invitee list and meeting meta data associated with a meeting via an electronic calendar application. Candidate timeslots for the meeting may be determined and a database associated with the electronic calendar application analyzed to obtain user factors associated with the candidate timeslots and invitees in the invitee list. Based on the user factors, a predictive model may be generated that is associated with an invitee in the invitee list by a machine learning algorithm. The predictive model may be executed to determine a time slot to select for the invitee.

Abstract: Training a machine to learn and to identify time slots to select in a user's electronic calendar, in one aspect, may include receiving an invitee list and meeting meta data associated with a meeting via an electronic calendar application. Candidate timeslots for the meeting may be determined and a database associated with the electronic calendar application analyzed to obtain user factors associated with the candidate timeslots and invitees in the invitee list. Based on the user factors, a predictive model may be generated that is associated with an invitee in the invitee list by a machine learning algorithm. The predictive model may be executed to determine a time slot to select for the invitee.

Abstract: In an approach to modelling population movement scenarios, one or more computer processors receive at least one credential of a user. The one or more computer processors determine, based, at least in part, on the received at least one credential, at least one context of a first population movement scenario, wherein the at least one context provides scenario-specific information for the first population movement scenario. The one or more computer processors receive a plurality of parameters associated with the first population movement scenario. The one or more computer processors model, based, at least in part, on the determined at least one context and the received plurality of parameters, the first population movement scenario. The one or more computer processors generate at least one visualization of the first population movement scenario model.

Abstract: A system configured to manage battery energy of a mobile device includes a primary mobile device and at least one peer device. The primary mobile device includes a power unit, a main communication module to electrically communicate with a peer device, and a peer assisted module in electrical communication with the power unit and the main communication module. The peer assisted module determines an energy level of the energy unit, and determines a task to be executed by the peer device. The peer device receives the task transmitted from the primary mobile device. The peer device further includes a peer process management module that executes at least one computation that completes the task, and communicates a completed task to the primary mobile device.

Abstract: A system configured to manage battery energy of a mobile device includes a primary mobile device and at least one peer device. The primary mobile device includes a power unit, a main communication module to electrically communicate with a peer device, and a peer assisted module in electrical communication with the power unit and the main communication module. The peer assisted module determines an energy level of the energy unit, and determines a task to be executed by the peer device. The peer device receives the task transmitted from the primary mobile device. The peer device further includes a peer process management module that executes at least one computation that completes the task, and communicates a completed task to the primary mobile device.

Abstract: An optimal vehicle battery recommendation system includes at least one sensor to detect a manipulation of the vehicle. A driver behavior module determines a driving behavior of a driver of the vehicle. A vehicle consumption module determines battery information of an on-board battery currently connected to the vehicle, and determines energy consumption of the vehicle. A battery capacity advisor module is in electrical communication with the driver behavior module and the vehicle consumption module. The battery capacity module determines a replacement battery option that changes at least one battery characteristic of the on-board battery based on the driver behavior model and the vehicle consumption model.

Abstract: An optimal vehicle battery recommendation system includes at least one sensor to detect a manipulation of the vehicle. A driver behavior module determines a driving behavior of a driver of the vehicle. A vehicle consumption module determines battery information of an on-board battery currently connected to the vehicle, and determines energy consumption of the vehicle. A battery capacity advisor module is in electrical communication with the driver behavior module and the vehicle consumption module. The battery capacity module determines a replacement battery option that changes at least one battery characteristic of the on-board battery based on the driver behavior model and the vehicle consumption model.

Abstract: A system configured to manage battery energy of a mobile device includes a primary mobile device and at least one peer device. The primary mobile device includes a power unit, a main communication module to electrically communicate with a peer device, and a peer assisted module in electrical communication with the power unit and the main communication module. The peer assisted module determines an energy level of the energy unit, and determines a task to be executed by the peer device. The peer device receives the task transmitted from the primary mobile device. The peer device further includes a peer process management module that executes at least one computation that completes the task, and communicates a completed task to the primary mobile device.

Abstract: A system configured to manage battery energy of a mobile device includes a primary mobile device and at least one peer device. The primary mobile device includes a power unit, a main communication module to electrically communicate with a peer device, and a peer assisted module in electrical communication with the power unit and the main communication module. The peer assisted module determines an energy level of the energy unit, and determines a task to be executed by the peer device. The peer device receives the task transmitted from the primary mobile device. The peer device further includes a peer process management module that executes at least one computation that completes the task, and communicates a completed task to the primary mobile device.

Abstract: An optimal vehicle battery recommendation system includes at least one sensor to detect a manipulation of the vehicle. A driver behavior module determines a driving behavior of a driver of the vehicle. A vehicle consumption module determines battery information of an on-board battery currently connected to the vehicle, and determines energy consumption of the vehicle. A battery capacity advisor module is in electrical communication with the driver behavior module and the vehicle consumption module. The battery capacity module determines a replacement battery option that changes at least one battery characteristic of the on-board battery based on the driver behavior model and the vehicle consumption model.

Abstract: An optimal vehicle battery recommendation system includes at least one sensor to detect a manipulation of the vehicle. A driver behavior module determines a driving behavior of a driver of the vehicle. A vehicle consumption module determines battery information of an on-board battery currently connected to the vehicle, and determines energy consumption of the vehicle. A battery capacity advisor module is in electrical communication with the driver behavior module and the vehicle consumption module. The battery capacity module determines a replacement battery option that changes at least one battery characteristic of the on-board battery based on the driver behavior model and the vehicle consumption model.

Abstract: Dynamic provisioning of resources is employed to replicate capabilities and/or services in a distributed computing infrastructure to overcome potential disruptions in the capabilities and/or services. Predictive tools for weather forecasts, risk profile analysis based on geographical location of data/service centers, and historical data are employed to improve service resiliency. Further, for each local computing service that is considered for replication, the cost of disruption is compared with the total cost of replication to ensure that a computing infrastructure service provider is selected in a cost-efficient manner.

Abstract: Dynamic provisioning of resources is employed to replicate capabilities and/or services in a distributed computing infrastructure to overcome potential disruptions in the capabilities and/or services. Predictive tools for weather forecasts, risk profile analysis based on geographical location of data/service centers, and historical data are employed to improve service resiliency. Further, for each local computing service that is considered for replication, the cost of disruption is compared with the total cost of replication to ensure that a computing infrastructure service provider is selected in a cost-efficient manner.