#WISE MEMORY OPTIMIZER 3.5.2 SOFTWARE#
#WISE MEMORY OPTIMIZER 3.5.2 PROFESSIONAL#

For devices on an enumerable type of bus, such as a PCI bus, theĪCPI system must identify which device on the enumerable bus is Such as _CID, _CLS, _HRV, _SUB) for each device to enable OSPM to do

ISA bus), OSPM enumerates the devices’ identifier(s) and the ACPI systemįirmware must supply an _HID object (plus one or more optional objects Object that specifies a device’s unique persistent ID, or a control method that generates it.įor any device that is on a non-enumerable type of bus (for example, an Object that contains a Unicode identifier for a device.

Object that evaluates to the slot-unique ID number for a slot. Object that evaluates to a device’s Plug and Play subsystem ID. Object that provides physical location description information. Object that provides a human readable description of a device in multiple languages. Object that evaluates to an integer hardware revision number. Object that evaluates to a device’s Plug and Play hardware ID.

#WISE MEMORY OPTIMIZER 3.5.2 SOFTWARE#

Object that associates a logical software name (for example, COM1) with a device. Object that evaluates to a package of coded device-class information. Object that evaluates to a device’s Plug and Play-compatible ID list. Object that evaluates to a device’s address on its parent bus. Objects are listed in the table below: Table 6.1 Device Identification Objects ¶ Device identification objects associate each platform device with a PlugĪnd Play device ID for each device. The supporting source codes of this work can be found at. The overall results are satisfactory, thereby confirming the usefulness of AAPSO in dealing with varied real-life problems. Apart from Pneumonia detection, AAPSO is further evaluated on some standard UCI datasets, gene expression datasets for cancer prediction and a COVID-19 prediction dataset. Extensive experimentation and thorough analysis on a publicly available Pneumonia dataset establish the superiority of the proposed method over several other frameworks used for Pneumonia detection. The proposed method successfully eliminates non-informative features obtained from the ResNet50 model, thereby improving the Pneumonia detection ability of the overall framework. We name our feature selection method as adaptive and altruistic PSO (AAPSO). Then, we propose a feature selection technique based on particle swarm optimization (PSO), which is modified using a memory-based adaptation parameter, and enriched by incorporating an altruistic behavior into the agents. We first extract deep features from the pre-trained ResNet50, fine-tuned on a target Pneumonia dataset. In this paper, we propose a CAD system for Pneumonia detection from Chest X-rays, using the concepts of deep learning and a meta-heuristic algorithm.

#WISE MEMORY OPTIMIZER 3.5.2 PROFESSIONAL#

Computer-aided based diagnosis (CAD) systems can be used in such countries due to their lower operating costs than professional medical experts. Although it can be detected and treated with very less sophisticated instruments and medication, Pneumonia detection still remains a major concern in developing countries. Pneumonia is one of the major reasons for child mortality especially in income-deprived regions of the world.

Wise memory optimizer 3.5.2