Abstract: Present disclosure generally relates to data analytics in wireless networks, more particularly relates to systems and methods for optimizing supply demand in telecommunication network. System may prepare data for optimization using raw telecom data. Further, the system may build quadratic optimization objective function by reading index table (cell—grid information). System may build quadratic program inequality constraints, and prepare right hand side of constraints for all mentioned constraints maintaining the index. Thereafter, the system may execute optimizer and find the optimal solution ensuring hyper-parameter tuning, and calculate focal point of each cell using cell-grid allocation vector. The system may read the optimal solution from optimization process, and estimate electronic tilt values (i.e., Remote Electrical Tilt (RET)) ensuring the business guidelines. Thereafter, the system may use line of sight method to get inclination value (optimal tilt value) of cell from the focal point on the ground.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: The disclosure features a method which includes inputting or receiving information on one or more features of a plurality of residential properties and prices of the residential properties including a marketed price, a listing price, and a closing price, providing the information to a Machine Learning Algorithm to determine the relationship between the one or more features and the prices of the residential properties to create a Machine Learned Model, inputting or receiving information on one or more features of a new residential property into the Machine Learned Model, and predicting a base price of the new residential property from the Machine Learned Model based on the one or more features of the new residential property. The disclosure also features one or more non-transitory, computer-readable storage media storing instructions capable of performing the method and a computer or computer system capable of performing the method.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: A method including receiving a first set of one or more street level images of houses into a Machine Learned Model trained with a second set of street level images with one or more exterior features of the houses labeled, identifying the one or more exterior features in the first set of street level images by way of the Machine Learned Model, and quantifying and outputting the counts and/or two-dimensional areas for each of the identified exterior features in the first set of one or more street level images is described. Non-transitory, computer-readable storage media having instructions for executing the method steps by one or more processors as well as computer or computer systems capable of performing the method steps are also described.

Abstract: The disclosure features a method which includes inputting or receiving information on one or more features of a plurality of residential properties and prices of the residential properties including a marketed price, a listing price, and a closing price, providing the information to a Machine Learning Algorithm to determine the relationship between the one or more features and the prices of the residential properties to create a Machine Learned Model, inputting or receiving information on one or more features of a new residential property into the Machine Learned Model, and predicting a base price of the new residential property from the Machine Learned Model based on the one or more features of the new residential property. The disclosure also features one or more non-transitory, computer-readable storage media storing instructions capable of performing the method and a computer or computer system capable of performing the method.

Abstract: Present disclosure generally relates to data analytics in wireless networks, more particularly relates to systems and methods for optimizing supply demand in telecommunication network. System may prepare data for optimization using raw telecom data. Further, the system may build quadratic optimization objective function by reading index table (cell—grid information). System may build quadratic program inequality constraints, and prepare right hand side of constraints for all mentioned constraints maintaining the index. Thereafter, the system may execute optimizer and find the optimal solution ensuring hyper-parameter tuning, and calculate focal point of each cell using cell-grid allocation vector. The system may read the optimal solution from optimization process, and estimate electronic tilt values (i.e., Remote Electrical Tilt (RET)) ensuring the business guidelines. Thereafter, the system may use line of sight method to get inclination value (optimal tilt value) of cell from the focal point on the ground.

Abstract: Present disclosure generally relate to wireless networks, more particularly relates to systems and methods for determining spatial clusters in a network to enable connected community of telecommunication cellular towers. The system may prepare cell data using one or more circle data, city data, cell Identity (ID) data, latitude data, longitude data, azimuth data, and height data. System may compute geohash based on creating geohash neighbours and geohash bounding box data and compute sectors of the telecommunication towers. Further, the system may compute sector affinity of the telecommunication towers and perform clustering of the telecommunication towers.

Abstract: A process and apparatus for reducing the sulfur content of naphtha. The process includes introducing at least a portion of a naphtha feed stream to a selective hydrodesulfurization zone under selective hydrodesulfurization conditions in the presence of a selective hydrodesulfurization catalyst to form a low sulfur stream which contains mercaptan and thiophene compounds. At least a portion of the low sulfur stream is separated into at least two streams, a mercaptan rich stream containing mercaptan and thiophene compounds and an overhead stream containing hydrogen sulfide and liquid petroleum gas. The mercaptan rich stream is treated in an adsorbent zone to remove at least a portion of the mercaptan and thiophene compounds to form a mercaptan lean stream.

Abstract: Processes for the production of transportation fuel from a renewable feedstock. A catalyst is used which is more selective to hydrodeoxygenate the fatty acid side chains compared to decarboxylation and decarbonylation reactions. A gaseous mixture of carbon monoxide and hydrogen can be supplied to the conversion zone. Water may also be introduced into the conversion zone to increase the amount of hydrogen.

Abstract: Processes for pyrolyzing biomass. A catalyst is used to both pyrolyze and deoxygenate the biomass within the pyrolysis zone. A source of carbon monoxide is also passed to the pyrolysis reactor. The source of carbon monoxide may comprise a stream of gas that includes carbon monoxide, or a material capable of generating or being converted in carbon monoxide within the pyrolysis zone. The carbon monoxide may be used as a reactant for a water gas shift reaction or as a reducing agent to remove oxygen from oxygenated hydrocarbons. The catalyst preferably comprises iron (III) oxide.