Patent Application
20230153733
The present application generally relates to information technology and, more particularly, to climate-related technologies. More specifically, logistics account for an increasingly significant amount of impact on the transportation and supply chain sectors. Additionally, within the context of such sectors, greenhouse gas (GHG) emissions (e.g., scope 1 and scope 3 emissions) are dependent on multiple parameters. However, conventional emissions data management approaches typically account only for distance and weight in emission estimations, leading to inaccuracies and resource wastage.
In one embodiment of the present invention, techniques for generating GHG emissions estimations associated with logistics contexts using machine learning techniques are provided. An exemplary computer-implemented method can include obtaining multiple items of input data related to multiple aspects of at least one logistics context, and deriving one or more contextual features from the multiple items of input data by processing at least a portion of the multiple items of input data using one or more data profiling techniques. The method also includes training at least one machine learning model related to energy consumption based at least in part on the one or more contextual features, and generating at least one energy consumption estimate attributed to at least a portion of at least one logistics implementation by processing data pertaining to the at least one logistics implementation using the at least one trained machine learning model. Further, the method additionally includes generating at least one greenhouse gas emissions estimate attributed to the at least a portion of the at least one logistics implementation based at least in part on the at least one energy consumption estimate, and performing one or more automated actions based at least in part on the at least one generated greenhouse gas emissions estimate.
Another embodiment of the invention or elements thereof can be implemented in the form of a computer program product tangibly embodying computer readable instructions which, when implemented, cause a computer to carry out a plurality of method steps, as described herein. Furthermore, another embodiment of the invention or elements thereof can be implemented in the form of a system including a memory and at least one processor that is coupled to the memory and configured to perform noted method steps. Yet further, another embodiment of the invention or elements thereof can be implemented in the form of means for carrying out the method steps described herein, or elements thereof; the means can include hardware module(s) or a combination of hardware and software modules, wherein the software modules are stored in a tangible computer-readable storage medium (or multiple such media).
These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.
As described herein, an embodiment of the present invention includes enhancing and/or modifying GHG emissions estimations associated with a logistics context based at least in part on multiple external parameters and one or more machine learning techniques. In such an embodiment, the external parameters can include spatial-temporal variations in ambient conditions (e.g., underlying weather data, temperature data, thermodynamics data, material properties information, topographic information, mileage (i.e., distance traveled per unit of fuel consumption for a given vehicle), etc.). One or more embodiments include processing and/or incorporating multiple such external factors, and deriving therefrom one or more contextual features, wherein the contextual feature(s) can include one or more driver profiles, one or more route profiles, and/or one or more vehicle profiles. Further, such an embodiment can include identifying one or more cohorts from and/or among historical data using the one or more contextual features.
As further detailed herein, at least one embodiment also includes training at least one machine learning model to learn relationships between physics-based simulations and/or models using cohort analytics to generate an estimate of energy (e.g., fuel) consumption, and using this energy consumption estimate to generate a modified and/or enhanced GHG emissions estimate associated with a given supply chain-related transport context and/or portions thereof. In one or more embodiments, the energy consumption estimate (e.g., element 114 in
As used herein, scope 1 emissions include direct GHG emissions that occur from sources that are controlled and/or owned by an organization (e.g., emissions associated with fuel combustion in boilers, furnaces, vehicles, etc.), and scope 3 emissions are the result of activities from assets not owned and/or controlled by a reporting organization, but that the organization indirectly impacts in its value chain. Scope 3 emissions include emissions sources not within an organization's scope 1 and scope 2 boundary. The scope 3 emissions for one organization can be, for example, the scope 1 and 2 emissions of another organization. Scope 3 emissions, also referred to as value chain emissions, often represent a majority of an organization's total GHG emissions.
As also depicted in
Outputs from contextual features derivation component 104, including contextual features such as vehicle profile 105, driver profile 107, and/or route profile 109, are provided to and/or processed by cohort analytics component 108, which identifies one or more cohorts using the contextual features. In one or more embodiments, each identified cohort represents a similar set of logistic characteristics. In such an embodiment, the cohorts are cohorts of logistics having similar features, derived using contextual features. For example, logistics with similar route profiles based on the route, topography, weather, etc., can be grouped into the same cohorts. In another example, logistics using similar fleet and driver profiles can be clustered as a cohort.
Also, cohort analytics component 108 normalizes the contextual features attributed to each cohort. Additionally, outputs from physics-based processing component 106, including data from thermodynamics simulation 111, mileage estimation 113, and/or topographic analysis 115, are provided to and/or processed by machine learning model 112, which can include, for example, at least one machine learning-based time series model (e.g., regression analysis, random forest regression, recurrent neural network (RNN)). Further, in at least one embodiment, logistic profile information associated with each of the identified cohorts from cohort analytics component 108 is processed by and/or enables the machine learning model 112 to learn one or more relationships between physics-based simulation models and/or analyses, such as provided by physics-based processing component 106.
Additionally, in one or more embodiments ground truth information 110 pertaining to relevant energy values is also provided as input to the machine learning model 112, and based at least in part on processing such input, in addition to inputs from component 106 and component 108, machine learning model 112 generates at least one energy consumption estimate 114. By way merely of illustration consider a cold supply chain example such as in connection with the transportation of perishable goods such as milk, frozen foods, etc. In such a scenario, it is important to maintain lower temperatures through a refrigeration system on the given vehicle. The energy required to maintain the temperature is proportional to the difference in transportation temperature and the ambient temperature. Therefore, energy consumption (including fuel consumption) will be lower for transportation during night because of lower ambient temperatures. As such, because daytime transportation and nighttime transportation will have different energy/fuel consumption, they should be accounted for in generating an accurate GHG emission estimate (as per one or more embodiments). In such an embodiment, the machine learning model will take the ambient temperature as one of the features, and based at least in part on the ambient temperature value, the model will determine the fuel consumption estimate (e.g., higher fuel consumption for higher ambient temperatures and lower fuel consumption for lower ambient temperatures).
Referring again to
Additionally,
Further,
Additionally, in connection with each type of profiling, one or more embodiments include encoding and scaling for categorical and numerical features, respectively.
By way of illustration,
Additionally,
Further,
Accordingly, one or more embodiments include using a range of features to perform simulation(s) of one or more sub-systems related to GHG emissions to identify the impact of such features on overall GHG emissions. For example, such an embodiment can include processing weather data, terrain topography, mileage analysis, etc. to perform simulation of relevant sub-systems related to GHG emissions to identify the impact of these types of data on the overall GHG emissions. Contextual features can be derived from such data and used to profile drivers, routes and vehicles associated with given supply chain transportation implementations, wherein such profiles can then be used to identify one or more data cohorts. One or more embodiments can then include using the identified cohorts to train at least one machine learning model to learn inter-relationships between physics-based simulations to provide GHG emission estimates (e.g., enhanced GHG emission estimates as compared to conventionally-generated estimates).
At least one embodiment can include combining results from physics-based simulations, augmented by cohort analytics to provide an estimate of GHG emissions pertaining to one or more supply chain transportation instances. Such an embodiment can include enhancing and/or increasing the level of accuracy of GHG emissions estimates (e.g., as compared to conventional estimate approaches) using machine learning-based estimates of relevant energy (e.g., fuel) consumption, as detailed above and herein.
By way merely of illustration, consider an example embodiment involving cold-weather supply chains, wherein temperature control is needed to transport perishable goods. Energy consumption, and thereby, emissions is dependent on the ambient conditions. On the 1st order, energy consumption can be proportional to the difference in transportation temperature and the ambient temperature, as follows:
wherein R represents the thermal resistance, which is a material property which denotes how insulating a material is. Highly insulating materials have higher R values than low insulating materials.
GHG emissions will vary for different ambient conditions, and therefore, such varying conditions are accounted for in the calculation. For example, consider a first route that includes daytime transportation and an ambient temperature of 24° C., a transport temperature of 4° C., and emissions of K x (24-4)=K x 20, wherein K represents thermal conductance, which is a material property which denotes how conducting a material is. Highly conducting materials have higher K values than low conducting materials. Note also that thermal resistance is the inverse of thermal conductance (i.e., R=1/K or K=1/R). Additionally, consider a second route that includes nighttime transportation and an ambient temperature of 14° C., a transport temperature of 4° C., and emissions of K x (14-4)=K x 10.
By way of additional example, consider an embodiment wherein logistics-related transportation is carried out under foggy weather and/or heavy traffic. In such a use case, fuel consumption can be significantly increased, which will be accounted for in the calculation. For example, consider a first 100 kilometer route that includes foggy weather and limited visibility, with a maximum speed of 30 kilometers per hour (km/hr), an average speed of 21 km/hr, actual emissions of Kfx (fuel)=Kf x 12 (an example fuel consumption value), and reported emissions (using a conventional approach) of Kd x (distance)=Kd x 100. Additionally, consider a second 100 km route that includes clear weather and full visibility, with a maximum speed of 60 km/hr, an average speed of 45 km/hr, actual emissions of Kfx (fuel)=Kf x 8 (an example fuel consumption value), and reported emissions of Kd x (distance)=Kd x 100.
Accordingly, at least one embodiment can include implementing the following equation: F=∫sourcedestinationE(v)dx; {v=f(x)},
wherein F=fuel consumption, E=fuel economy or efficiency, v=speed, and x=position of the vehicle along the route.
One or more embodiments can also include encompassing transportation under varied topography. Transportation uphill typically requires more fuel (and generates more GHG emissions) than downhill transportation, and as such, topography of routes is included in calculation of GHG emissions estimations in such an embodiment. Consider a first example use case including a loaded vehicle travelling predominantly uphill and an empty vehicle travelling predominantly downhill, and a second example use case including a loaded vehicle travelling predominantly downhill and an empty vehicle travelling predominantly uphill. The emissions would be higher in the first use case, and one or more embodiments would include capturing such a distinction.
Accordingly, as detailed herein, at least one embodiment includes generating accurate GHG emissions estimates and/or enhanced GHG emissions estimates (as compared to conventionally-generated estimates) in connection with supply chain logistics. Such an embodiment includes capturing one or more spatio-temporal variations in ambient conditions and using underlying thermodynamics, material properties, topographic information, mileage, etc., to generate more accurate GHG emission calculations using machine learning techniques. Further, such an embodiment includes deriving contextual features from various forms of input data to profile drivers, routes and/or vehicles to identify one or more data cohorts, which can then be used to train at least one machine learning model to learn relationships between physics-based simulations to provide accurate GHG emissions estimates.
Step 404 includes deriving one or more contextual features from the multiple items of input data by processing at least a portion of the multiple items of input data using one or more data profiling techniques. In at least one embodiment, the one or more contextual features pertain to one or more ambient conditions associated with the multiple aspects of at least one logistics context. Additionally or alternatively, deriving one or more contextual features can include generating at least one driver profile, at least one route profile, and/or at least one vehicle profile. Further, one or more embodiments can also include identifying one or more data cohorts based at least in part on the one or more contextual features.
Step 406 includes training at least one machine learning model related to energy consumption based at least in part on the one or more contextual features. In at least one embodiment, training the at least one machine learning model includes training the at least one machine learning model, using the one or more contextual features, to learn relationships between multiple physics-based simulations related to greenhouse gas emissions estimates.
Step 408 includes generating at least one energy consumption estimate attributed to at least a portion of at least one logistics implementation by processing data pertaining to the at least one logistics implementation using the at least one trained machine learning model. In one or more embodiments, generating at least one energy consumption estimate includes generating at least one estimate pertaining to fuel consumed by at least one vehicle participating in the at least one logistics implementation.
Step 410 includes generating at least one greenhouse gas emissions estimate attributed to the at least a portion of the at least one logistics implementation based at least in part on the at least one energy consumption estimate. Step 412 includes performing one or more automated actions based at least in part on the at least one generated greenhouse gas emissions estimate. In at least one embodiment, performing one or more automated actions includes modifying at least one existing greenhouse gas emissions estimate using the at least one generated greenhouse gas emissions estimate. Additionally or alternatively, performing one or more automated actions can include automatically retraining the at least one machine learning model based at least in part on the at least one generated greenhouse gas emissions estimate and resulting data from the at least one logistics implementation.
Further, in one or more embodiments, software implementing the techniques depicted in
It is to be appreciated that “model,” as used herein, refers to an electronic digitally stored set of executable instructions and data values, associated with one another, which are capable of receiving and responding to a programmatic or other digital call, invocation, or request for resolution based upon specified input values, to yield one or more output values that can serve as the basis of computer-implemented recommendations, output data displays, machine control, etc. Persons of skill in the field find it convenient to express models using mathematical equations, but that form of expression does not confine the models disclosed herein to abstract concepts; instead, each model herein has a practical application in a computer in the form of stored executable instructions and data that implement the model using the computer.
The techniques depicted in
Additionally, the techniques depicted in
An embodiment of the invention or elements thereof can be implemented in the form of an apparatus including a memory and at least one processor that is coupled to the memory and configured to perform exemplary method steps.
Additionally, an embodiment of the present invention can make use of software running on a computer or workstation. With reference to
Accordingly, computer software including instructions or code for performing the methodologies of the invention, as described herein, may be stored in associated memory devices (for example, ROM, fixed or removable memory) and, when ready to be utilized, loaded in part or in whole (for example, into RAM) and implemented by a CPU. Such software could include, but is not limited to, firmware, resident software, microcode, and the like.
A data processing system suitable for storing and/or executing program code will include at least one processor 502 coupled directly or indirectly to memory elements 504 through a system bus 510. The memory elements can include local memory employed during actual implementation of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during implementation.
Input/output or I/O devices (including, but not limited to, keyboards 508, displays 506, pointing devices, and the like) can be coupled to the system either directly (such as via bus 510) or through intervening I/O controllers (omitted for clarity).
Network adapters such as network interface 514 may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems and Ethernet cards are just a few of the currently available types of network adapters.
As used herein, including the claims, a “server” includes a physical data processing system (for example, system 512 as shown in
The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
It should be noted that any of the methods described herein can include an additional step of providing a system comprising distinct software modules embodied on a computer readable storage medium; the modules can include, for example, any or all of the components detailed herein. The method steps can then be carried out using the distinct software modules and/or sub-modules of the system, as described above, executing on a hardware processor 502. Further, a computer program product can include a computer-readable storage medium with code adapted to be implemented to carry out at least one method step described herein, including the provision of the system with the distinct software modules.
In any case, it should be understood that the components illustrated herein may be implemented in various forms of hardware, software, or combinations thereof, for example, application specific integrated circuit(s) (ASICS), functional circuitry, an appropriately programmed digital computer with associated memory, and the like. Given the teachings of the invention provided herein, one of ordinary skill in the related art will be able to contemplate other implementations of the components of the invention.
Additionally, it is understood in advance that implementation of the teachings recited herein are not limited to a particular computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any type of computing environment now known or later developed.
For example, cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (for example, networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (for example, country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (for example, storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (for example, web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (for example, host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (for example, mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (for example, cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes.
Referring now to
Referring now to
Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.
Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75. In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources.
In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and GHG emission estimation 96, in accordance with the one or more embodiments of the present invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of another feature, step, operation, element, component, and/or group thereof.
At least one embodiment of the present invention may provide a beneficial effect such as, for example, enhancing GHG emissions estimations associated with logistics contexts using machine learning techniques.
The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
