Abstract: Methods and systems for cancer disease estimation are disclosed. The methods and systems include: receiving an input for a patient for selecting at least one of: a cancer disease deep learning model, a cancer disease stochastic model, or a cancer disease probability distribution function-based model; and perform cancer disease estimation processes based on the cancer disease deep learning model, the cancer disease stochastic model, or the cancer disease probability distribution function-based model. Other aspects, embodiments, and features are also claimed and described.

Abstract: In accordance with some embodiments, systems, methods, and media for monitoring a production process of a product are provided. In some embodiments, a system comprises a remote server, a communications connection between the remote server and a facility database, at least one processor coupled to the communication system, and a memory device having stored thereon a set of computer readable instructions. The set of computer readable instructions cause the at least one processor to: receive a first set of data related to the production process, calculate a first monitoring index indicator for the production process based on the first set of data, receive a second set of data related to the production process after one or more performance variables of the production process are modified, calculate a second monitoring index indicator for the production process based on the second set of data, and output a result.

Abstract: In accordance with some embodiments, systems, methods, and media for predicting the conversion time of Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) in a patient are provided. In some embodiments, a system include a memory and a processor coupled to the memory. The processor is configured to: receive a plurality of risk factor indications and a plurality of interaction indications of a patient. Each interaction indication is an indication of interaction between two risk factor indications of the plurality of risk factor indications. The processor is further configured to obtain a trained machine learning model; apply the plurality of risk factor indications and the plurality of interaction indications to the trained machine learning model; and output a result based on the trained machine learning model.

Abstract: A data-driven model to predict the returns of the production of corn in the U.S. is described. In one example, the model can account for 25 elements or factors presumed by the U.S. department of agriculture (USDA) to be contributing to the returns from corn production in the US. The model is designed on the basis of a number of parameters, including the selection of a significant set of the 25 factors, the extent or percentage of contribution of each factor, the extent of contribution to unknown factors, the identification of which of the significant factors are interacting, and others. In one example, 7 out of the 25 factors were found to be statistically significant, and 6 interaction terms were identified. The proposed model accurately predicts the returns from corn production in the U.S. with 98.22% accuracy.

Abstract: Aspects of software vulnerability prediction are described. In some examples vulnerability data is obtained from a vulnerability database for the software. The total cumulative vulnerability of the software is estimated using the vulnerability data. The total cumulative vulnerability is based at least in part on a time based nonlinear differential equation model. The time based nonlinear differential equation model generates a complete vulnerability life cycle. A graph is generated to display a cyclic increasing behavior of the complete vulnerability life cycle of the software.

Abstract: Procedures to identify the probabilities for different states in a vulnerability life cycle are described. The probabilities are used to develop a number of statistical models to evaluate the risk factor of a particular vulnerability at time “t”. A transition probability matrix of all states of a particular vulnerability as a function of time is also described. A Markov chain process can be iterated to reach a steady state of the transition probability matrix, with the initial probabilities reaching the absorbing states, including exploited and patched states. A risk factor is also introduced for use as an index of the risk of a vulnerability being exploited. Finally, statistical models that can calculate the risk factor more conveniently without going through the Markovian process are described.

Abstract: A statistical model for predicting an expected path length (“EPL”) of the steps of an attacker is described. The model is based on utilizing vulnerability information along with an attack graph. Using the model, it is possible to identify the interaction among vulnerabilities and individual variables or risk factors that drive the EPL. Gaining a better understanding of the relationship between the vulnerabilities and their interactions can provide security administrators with a better view and understanding of their security status. In addition, a number of different attributable variables and their contribution in estimating the EPL can be ranked. Thus, it is possible to utilize the ranking process to take precautions and actions to minimize the EPL.

Abstract: According to the embodiments, a statistical model is developed to estimate the probability of being in a certain stage of a particular vulnerability in its life cycle. The methodology with the application of Markov chain theory gives the basis for calculating estimates for probabilities for different stages of a life cycle of the vulnerability considered. Using the developed method, it is possible to evaluate the risk level of a particular vulnerability at a certain time. These developments allow an advantage in taking measures to avoid exploitations and introduce patches for the vulnerability before an attacker takes the advantage of that particular vulnerability.

Abstract: Aspects of software vulnerability prediction are described. In some examples vulnerability data is obtained from a vulnerability database for the software. The total cumulative vulnerability of the software is estimated using the vulnerability data. The total cumulative vulnerability is based at least in part on a time based nonlinear differential equation model. The time based nonlinear differential equation model generates a complete vulnerability life cycle. A graph is generated to display a cyclic increasing behavior of the complete vulnerability life cycle of the software.