Abstract: Examples relate to an apparatus, a device, a method, a computer program (or computer-readable medium) and computer system for determining presence of a noisy neighbor virtual machine. Some aspects of the present disclosure relate to an apparatus for a computer system, the apparatus comprising interface circuitry, machine-readable instructions, and processor circuitry to execute the machine-readable instructions to obtain performance information of one or more hardware performance measurement components of the computer system, determine, based on the performance information, a deviation of a utilization of the computer system from an expected utilization of the computer system, and determine presence of a first virtual machine having a workload that impacts a performance of one or more second virtual machines based on the deviation.

Abstract: Images of an insect are subjected to at least a first convolutional neural network to develop feature maps based on anatomical pixels at corresponding image locations in the respective feature maps. The anatomical pixels correspond to a body part of the insect. A computer calculates an outer product of the first feature map and the second feature map to form an integrated feature map. Extracting fully connected layers from respective sets of integrated feature maps and applying the fully connected layers to a classification network for identifying the genus and the species of the insect.

Abstract: Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.

Abstract: Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.

Abstract: A method of identifying a living creature includes training a convolutional neural network model using pretrained convolutional neural networks to generate proposals about the regions where there might be an anatomical object within a digital image. Introducing a residual connection to get the input from the previous layer to the next layer helps in solving gradient vanishing problem. The next step is to design an object detector network that does three tasks: classifying the boxes with respective anatomies, tightening the boxes, and generating a mask (i.e., pixel-wise segmentation) of each anatomical component. In constructing the architecture of the object detector network, the network uses per-pixel sigmoid, and binary cross-entropy loss function (to identify the k anatomical components) and rigorously train them.

Abstract: Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.

Abstract: Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.

Abstract: Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.