IoT-Based Reinforcement Learning Using Probabilistic Model for Determining Extensive Exploration through Computational Intelligence for Next-Generation Techniques.

Computing intelligence is built on several learning and optimization techniques. Incorporating cutting-edge learning techniques to balance the interaction between exploitation and exploration is therefore an inspiring field, especially when it is combined with IoT. The reinforcement learning techniques created in recent years have largely focused on incorporating deep learning technology to improve the generalization skills of the algorithm while ignoring the issue of detecting and taking full advantage of the dilemma. To increase the effectiveness of exploration, a deep reinforcement algorithm based on computational intelligence is proposed in this study, using intelligent sensors and the Bayesian approach. In addition, the technique for computing the posterior distribution of parameters in Bayesian linear regression is expanded to nonlinear models such as artificial neural networks. The Bayesian Bootstrap Deep Q-Network (BBDQN) algorithm is created by combining the bootstrapped DQN with the recommended computing technique. Finally, tests in two scenarios demonstrate that, when faced with severe exploration problems, BBDQN outperforms DQN and bootstrapped DQN in terms of exploration efficiency.

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing.

One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.

Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition.

A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database and the University of Notre Dame Collection F database for 3D face and 3D ear datasets, respectively. Experimental results reveal that the proposed model achieves an accuracy of 99.25% using the proposed score-level fusion. Comparative analyses show that the proposed method performs better than other state-of-the-art biometric algorithms in terms of accuracy.

Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring.

Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic's beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article proposes an Intelligent Remote Patient Activity Tracking System system that can monitor patient activities and vitals during those activities based on the attached sensors. An Internet of Things- (IoT-) enabled health monitoring device is designed using machine learning models to track patient's activities such as running, sleeping, walking, and exercising, the vitals during those activities such as body temperature and heart rate, and the patient's breathing pattern during such activities. Machine learning models are used to identify different activities of the patient and analyze the patient's respiratory health during various activities. Currently, the machine learning models are used to detect cough and healthy breathing only. A web application is also designed to track the data uploaded by the proposed devices.

Medical Image Captioning Using Optimized Deep Learning Model.

Medical image captioning provides the visual information of medical images in the form of natural language. It requires an efficient approach to understand and evaluate the similarity between visual and textual elements and to generate a sequence of output words. A novel show, attend, and tell model (ATM) is implemented, which considers a visual attention approach using an encoder-decoder model. But the show, attend, and tell model is sensitive to its initial parameters. Therefore, a Strength Pareto Evolutionary Algorithm-II (SPEA-II) is utilized to optimize the initial parameters of the ATM. Finally, experiments are considered using the benchmark data sets and competitive medical image captioning techniques. Performance analysis shows that the SPEA-II-based ATM performs significantly better as compared to the existing models.

Reproductive performance of genetically engineered mice housed in different housing systems.

The genetically engineered mice require special husbandry care and are mainly housed in Individually Ventilated Cage (IVC) systems and Static Micro Isolator Cages (SMIC) to minimize the risk for spreading undesirable microorganisms. However, the static micro isolation cage housing like SMIC are being replaced with IVC systems in many facilities due to a number of benefits like a higher density housing in limited space, better protection from biohazards and allergens and decreased work load due to decreased frequency of cage changing required in this system. The purpose of this study was to examine the reproductive performance of genetically engineered mice housed in individually ventilated cages (IVC) and Static Micro Isolator Cages (SMIC). When the B6C3-Tg (APPswe, PSEN1dE9) 85Dbo/Mmjax transgenic mice were housed in these two housing systems, the number of litters per dam, number of pups born per dam and number of pups weaned per dam were found to be slightly higher in the IVC as compared to the SMIC but the difference was not significant (P<0.05). In case of Growth Associated Protein 43 (GAP-43) knockout mice, the number of litters born per dam and the number of pups born per dam were marginally higher in the IVC as compared to those housed in SMIC but the difference was not significant (P<0.05). Only the number of pups weaned per dam were found to be significantly higher as compared to those housed in the SMIC system at P<0.05.

Establishment of Elevated Serum Levels of IL-10, IL-8 and TNF-ß as Potential Peripheral Blood Biomarkers in Tubercular Lymphadenitis: A Prospective Observational Cohort Study.

BACKGROUND: Tubercular lymphadenitis (TL) is the most common form of extra-pulmonary tuberculosis (TB) consisting about 15-20% of all TB cases. The currently available diagnostic modalities for (TL), are invasive and involve a high index of suspicion, having limited accuracy. We hypothesized that TL would have a distinct cytokine signature that would distinguish it from pulmonary TB (PTB), peripheral tubercular lymphadenopathy (LNTB), healthy controls (HC), other lymphadenopathies (LAP) and cancerous LAP. To assess this twelve cytokines (Tumor Necrosis Factor (TNF)-α, Interferon (IFN) -γ, Interleukin (IL)-2, IL-12, IL-18, IL-1ß, IL-10, IL-6, IL-4, IL-1Receptor antagonist (IL-1Ra), IL-8 and TNF-ß, which have a role in pathogenesis of tuberculosis, were tested as potential peripheral blood biomarkers to aid the diagnosis of TL when routine investigations prove to be of limited value. METHODS AND FINDINGS: A prospective observational cohort study carried out during 2010-2013. This was a multi-center study with three participating hospitals in Delhi, India where through random sampling cohorts were established. The subjects were above 15 years of age, HIV-negative with no predisposing ailments to TB (n = 338). The discovery cohort (n = 218) had LNTB (n = 50), PTB (n = 84) and HC (n = 84). The independent validation cohort (n = 120) composed of patients with cancerous LAP (n = 35), other LAP (n = 20) as well as with independent PTB (n = 30), LNTB (n = 15) and HC (n = 20). Eight out of twelve cytokines achieved statistical relevance upon evaluation by pairwise and ROC analysis. Further, variable selection using random forest backward elimination revealed six serum biosignatures including IL-12, IL-4, IL-6, IL-10, IL-8 and TNF-ß as optimal for classifying the LNTB status of an individual. For the sake of clinical applicability we further selected a three analyte panel (IL-8, IL-10 and TNF-ß) which was subjected to multinomial modeling in the independent validation cohort which was randomised into training and test cohorts, achieving an overwhelming 95.9% overall classifying accuracy for correctly classifying LNTB cases with a minimal (7%) misclassification error rate in the test cohort. CONCLUSIONS: In our study, a three analyte serum biosignatures and probability equations were established which can guide the physician in their clinical decision making and step wise management of LNTB patients. This set of biomarkers has the potential to be a valuable adjunct to the diagnosis of TL in cases where AFB positivity and granulomatous findings elude the clinician.

Draft Genome Sequence of Urease-Producing Sporosarcina pasteurii with Potential Application in Biocement Production.

We describe here the draft genome sequence of Sporosarcina pasteurii, a urease-producing bacterium with potential applications in biocement production.