Performance Of Optimization Model Research Articles

A Locally Optimized Data-Driven Tool to Predict Sepsis-Associated Vasopressor Use in the ICU.

To train a model to predict vasopressor use in ICU patients with sepsis and optimize external performance across hospital systems using domain adaptation, a transfer learning approach. Observational cohort study. Two academic medical centers from January 2014 to June 2017. Data were analyzed from 14,512 patients (9,423 at the development site and 5,089 at the validation site) who were admitted to an ICU and met Center for Medicare and Medicaid Services definition of severe sepsis either before or during the ICU stay. Patients were excluded if they never developed sepsis, if the ICU length of stay was less than 8 hours or more than 20 days or if they developed shock up to the first 4 hours of ICU admission. Forty retrospectively collected features from the electronic medical records of adult ICU patients at the development site (four hospitals) were used as inputs for a neural network Weibull-Cox survival model to derive a prediction tool for future need of vasopressors. Domain adaptation updated parameters to optimize model performance in the validation site (two hospitals), a different healthcare system over 2,000 miles away. The cohorts at both sites were randomly split into training and testing sets (80% and 20%, respectively). When applied to the test set in the development site, the model predicted vasopressor use 4-24 hours in advance with an area under the receiver operator characteristic curve, specificity, and positive predictive value ranging from 0.80 to 0.81, 56.2% to 61.8%, and 5.6% to 12.1%, respectively. Domain adaptation improved performance of the model to predict vasopressor use within 4 hours at the validation site (area under the receiver operator characteristic curve 0.81 [CI, 0.80-0.81] from 0.77 [CI, 0.76-0.77], p < 0.01; specificity 59.7% [CI, 58.9-62.5%] from 49.9% [CI, 49.5-50.7%], p < 0.01; positive predictive value 8.9% [CI, 8.5-9.4%] from 7.3 [7.1-7.4%], p < 0.01). Domain adaptation improved performance of a model predicting sepsis-associated vasopressor use during external validation.

Comparative Analysis of Two Machine Learning Algorithms in Predicting Site-Level Net Ecosystem Exchange in Major Biomes

The net ecosystem CO2 exchange (NEE) is a critical parameter for quantifying terrestrial ecosystems and their contributions to the ongoing climate change. The accumulation of ecological data is calling for more advanced quantitative approaches for assisting NEE prediction. In this study, we applied two widely used machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to build models for simulating NEE in major biomes based on the FLUXNET dataset. Both models accurately predicted NEE in all biomes, while XGBoost had higher computational efficiency (6~62 times faster than RF). Among environmental variables, net solar radiation, soil water content, and soil temperature are the most important variables, while precipitation and wind speed are less important variables in simulating temporal variations of site-level NEE as shown by both models. Both models perform consistently well for extreme climate conditions. Extreme heat and dryness led to much worse model performance in grassland (extreme heat: R2 = 0.66~0.71, normal: R2 = 0.78~0.81; extreme dryness: R2 = 0.14~0.30, normal: R2 = 0.54~0.55), but the impact on forest is less (extreme heat: R2 = 0.50~0.78, normal: R2 = 0.59~0.87; extreme dryness: R2 = 0.86~0.90, normal: R2 = 0.81~0.85). Extreme wet condition did not change model performance in forest ecosystems (with R2 changing −0.03~0.03 compared with normal) but led to substantial reduction in model performance in cropland (with R2 decreasing 0.20~0.27 compared with normal). Extreme cold condition did not lead to much changes in model performance in forest and woody savannas (with R2 decreasing 0.01~0.08 and 0.09 compared with normal, respectively). Our study showed that both models need training samples at daily timesteps of &gt;2.5 years to reach a good model performance and &gt;5.4 years of daily samples to reach an optimal model performance. In summary, both RF and XGBoost are applicable machine learning algorithms for predicting ecosystem NEE, and XGBoost algorithm is more feasible than RF in terms of accuracy and efficiency.

Predicting anticancer hyperfoods with graph convolutional networks

BackgroundRecent efforts in the field of nutritional science have allowed the discovery of disease-beating molecules within foods based on the commonality of bioactive food molecules to FDA-approved drugs. The pioneering work in this field used an unsupervised network propagation algorithm to learn the systemic-wide effect on the human interactome of 1962 FDA-approved drugs and a supervised algorithm to predict anticancer therapeutics using the learned representations. Then, a set of bioactive molecules within foods was fed into the model, which predicted molecules with cancer-beating potential.The employed methodology consisted of disjoint unsupervised feature generation and classification tasks, which can result in sub-optimal learned drug representations with respect to the classification task. Additionally, due to the disjoint nature of the tasks, the employed approach proved cumbersome to optimize, requiring testing of thousands of hyperparameter combinations and significant computational resources.To overcome the technical limitations highlighted above, we represent each drug as a graph (human interactome) with its targets as binary node features on the graph and formulate the problem as a graph classification task. To solve this task, inspired by the success of graph neural networks in graph classification problems, we use an end-to-end graph neural network model operating directly on the graphs, which learns drug representations to optimize model performance in the prediction of anticancer therapeutics.ResultsThe proposed model outperforms the baseline approach in the anticancer therapeutic prediction task, achieving an F1 score of 67.99%±2.52% and an AUPR of 73.91%±3.49%. It is also shown that the model is able to capture knowledge of biological pathways to predict anticancer molecules based on the molecules’ effects on cancer-related pathways.ConclusionsWe introduce an end-to-end graph convolutional model to predict cancer-beating molecules within food. The introduced model outperforms the existing baseline approach, and shows interpretability, paving the way to the future of a personalized nutritional science approach allowing the development of nutrition strategies for cancer prevention and/or therapeutics.

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.

Imaging-based patient inclusion model for clinical trial performance optimization.

105 Background: In the era of precision medicine, development of new cancer therapies relies strongly on effective selection of target patient population. We hypothesize that computational analysis of imaging data can be used for development of a quantitative population enrichment strategy in clinical trials and thus we aim to establish an appropriate framework for this analysis. Methods: This hypothesis was tested among soft-tissue sarcoma (STS) patients accrued into a randomized Phase III clinical trial (SARC021) that evaluated the efficacy of evofosfamide (Evo), a hypoxia activated prodrug, in combination with doxorubicin (Dox). Notably, SARC021 failed to meet its survival objective (PMC7771354). We tested whether an inclusion/exclusion model based on radiomic analysis and relevant clinical covariates could have been employed to result in a significant treatment benefit of the Evo+Dox combination compared to the standard Dox monotherapy. A total of 163 radiomics features were extracted from lung metastases of 303 patients from the SARC021 trial, divided into demographically matched training and test sets. Stability analysis identified the most reproducible features. Univariable and multivariable models were utilized to discriminate OS in the two treatment groups. Results: A bespoke enrichment framework was established for individualized patient selection, based on model-derived risk score threshold. A radiomic feature, Short Run Emphasis, was identified as the most informative. When combined with tumor histology and smoking history information, an enriched subset (42%) of patients had longer OS in Evo+Dox vs. Dox groups [p = 0.01, Hazard Ratio (HR) = 0.57 (0.36-0.90)], overperforming a clinical-only approach. Application of the same model and threshold value in an independent test set confirmed the significant survival difference (p = 0.002, HR = 0.29 (0.13-0.63), 38% patients included). The breakdown of Dox+Evo treatment benefit depending on proportion of patients included based on the model is shown in the Table. Notably, this process also identified patients most likely to benefit from doxorubicin alone. Conclusions: The study presents a first of its kind radiomic approach for patient enrichment in clinical trials based on a quantitative score. In particular, we have shown that had the novel model been used for selective patient inclusion into the SARC021 trial, it would have met its primary survival objective for patients with metastatic STS.[Table: see text]

Thermal performance optimization of envelope in the energy-saving renovation of existing residential buildings

Based on the life cycle cost analysis, a thermal performance optimization model of building envelope was developed for the energy-saving renovation of existing residential buildings. The calculation formulas of the economic insulation layer thickness of the roof, external wall and floor over the basement were obtained simultaneously, and the optimal relationship of thermal performance among building envelope units was proposed; that is, the equivalent U-value of each opaque building envelope units should be equal. Moreover, the calculation methods of ultimate and optimum energy saving ratio were put forward. For a case study existing residential building in Beijing, compared to the energy-saving renovation scenarios determined by the limit value method (LVM), the saving ratio of insulation material of those determined by the optimization model ranges from 0.53% to 73.59%; and the saving ratio of insulation investment ranges from 0.21% to 64.93% when the building envelope energy saving ratio been taken the same value and changing from 0.65 to 0.85. The limit U-value of envelopes prescribed by Design Standard for Energy Efficiency of Residential Buildings in Beijing (DB11/891–2012) cannot meet the optimal relationship. Therefore, energy-saving renovation scenario determined by LVM cannot obtain the best economic benefit. The ultimate and optimum energy saving ratio achieved are inversely proportional to the U-value of windows. However, improving the performance of windows cannot necessarily achieve the best economic benefit. It is necessary to consider the technical advancement and economic rationality to choose the windows.

Machine Learning Algorithms Predict Prolonged Opioid Use in Opioid-Naïve Primary Hip Arthroscopy Patients.

Introduction:Excessive opioid use after orthopaedic surgery procedures remains a concern because it may result in increased morbidity and imposes a financial burden on the healthcare system. The purpose of this study was to develop machine learning algorithms to predict prolonged opioid use after hip arthroscopy in opioid-naïve patients.Methods:A registry of consecutive hip arthroscopy patients treated by a single fellowship-trained surgeon at one large academic and three community hospitals between January 2012 and January 2017 was queried. All patients were opioid-naïve and therefore had no history of opioid use before surgery. The primary outcome was prolonged postoperative opioid use, defined as patients who requested one or more opioid prescription refills postoperatively. Recursive feature elimination was used to identify the combination of variables that optimized model performance from an initial pool of 17 preoperative features. Five machine learning algorithms (stochastic gradient boosting, random forest, support vector machine, neural network, and elastic-net penalized logistic regression) were trained using 10-fold cross-validation five times and applied to an independent testing set of patients. These algorithms were assessed by calibration, discrimination, Brier score, and decision curve analysis.Results:A total of 775 patients were included, with 141 (18.2%) requesting and using one or more opioid refills after primary hip arthroscopy. The stochastic gradient boosting model achieved the best performance (c-statistic: 0.75, calibration intercept: −0.02, calibration slope: 0.88, and Brier score: 0.13). The five most important variables in predicting prolonged opioid use were the preoperative modified ones: Harris hip score, age, BMI, preoperative pain level, and worker's compensation status. The final algorithm was incorporated into an open-access web application available here: https://orthoapps.shinyapps.io/HPRG_OpioidUse/.Conclusions:Machine learning algorithms demonstrated good performance for predicting prolonged opioid use after hip arthroscopy in opioid-naïve patients. External validation of this algorithm is necessary to confirm the predictive ability and performance before use in clinical settings.

Deep Learning for Visual Analytics of the Spread of COVID-19 Infection in Crowded Urban Environments

The novel SARS-CoV-2 coronavirus caused a global pandemic in 2020 with millions of diagnosed cases and a staggering number of deaths. As a preventive measure, many governments issued social distancing and shelter-in-place mandates to limit human contact and slow the rate of infection. The large extent and duration of the crisis is poised to transform the health sector and alter current practices in retail, business, manufacturing, and construction. While medical researchers are working on antidote and vaccine solutions, contact tracing and self-isolation are deemed effective methods to control community spread. This paper presents a visual analysis approach that uses convolutional neural networks (CNNs) to generate quantifiable metrics of contact tracing. In particular, the YOLO-v3 architecture was trained on an annotated video dataset containing pedestrians. Network pruning and non-maximum suppression were applied to optimize model performance, resulting in 69.41% average precision. The fully trained model was then tested on sample crosswalk video data from Xiamen, China, recorded during the COVID-19 pandemic, followed by projecting detected pedestrians onto an orthogonal map for contact tracing by tracking movement trajectories and simulating the spread of droplets among the healthy population. Results demonstrate that the proposed technique is capable of tracing and documenting infection sources, times, and locations.

An analytical model for performance prediction and optimization of thermoelectric generators with varied leg cross-sections

With the improvement on materials performance gradually reaching its bottleneck, more and more works focus on optimizing the device structures to seek further performance enhancement of thermoelectric (TE) devices. This paper proposed an analytical model to calculate the performance of thermoelectric generators (TEGs) with varied leg cross-sections considering temperature-dependent material properties. The explicit expressions of output power and efficiency are derived and the model is verified by finite element method (FEM). Furthermore, the optimization of leg geometry for higher device performance is performed based on the presented model, the results show that the key to maximum output power is to minimize the leg resistance; and increasing the leg volume may simultaneously raise the output power and efficiency. In addition, we apply the presented model to optimize the leg shape of a commercial TEG module and find that the optimized geometry can simultaneously achieve an enhancement up to 43.1% for output power and 9.67% for efficiency. The presented model thus may be useful in designing high-performance TEGs and shed considerable light on the principles of TEG design.

A survey on active learning and human-in-the-loop deep learning for medical image analysis.

Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy. However, the unique challenges posed by medical image analysis suggest that retaining a human end-user in any deep learning enabled system will be beneficial. In this review we investigate the role that humans might play in the development and deployment of deep learning enabled diagnostic applications and focus on techniques that will retain a significant input from a human end user. Human-in-the-Loop computing is an area that we see as increasingly important in future research due to the safety-critical nature of working in the medical domain. We evaluate four key areas that we consider vital for deep learning in the clinical practice: (1) Active Learning to choose the best data to annotate for optimal model performance; (2) Interaction with model outputs - using iterative feedback to steer models to optima for a given prediction and offering meaningful ways to interpret and respond to predictions; (3) Practical considerations - developing full scale applications and the key considerations that need to be made before deployment; (4) Future Prospective and Unanswered Questions - knowledge gaps and related research fields that will benefit human-in-the-loop computing as they evolve. We offer our opinions on the most promising directions of research and how various aspects of each area might be unified towards common goals.

Machine Learning Algorithms Predict Functional Improvement After Hip Arthroscopy for Femoroacetabular Impingement Syndrome in Athletes.

Despite previous reports of improvements for athletes following hip arthroscopy for femoroacetabular impingement syndrome (FAIS), many do not achieve clinically relevant outcomes. The purpose of this study was to develop machine learning algorithms capable of providing patient-specific predictions of which athletes will derive clinically relevant improvement in sports-specific function after undergoing hip arthroscopy for FAIS. A registry was queried for patients who had participated in a formal sports program or athletic activities before undergoing primary hip arthroscopy between January 2012 and February 2018. The primary outcome was achieving the minimal clinically important difference (MCID) in the Hip Outcome Score-Sports Subscale (HOS-SS) at a minimum of 2 years postoperatively. Recursive feature selection was used to identify the combination of variables, from an initial pool of 26 features, that optimized model performance. Six machine learning algorithms (stochastic gradient boosting, random forest, adaptive gradient boosting, neural network, support vector machine, and elastic-net penalized logistic regression [ENPLR]) were trained using 10-fold cross-validation 3 times and applied to an independent testing set of patients. Models were evaluated using discrimination, decision-curve analysis, calibration, and the Brier score. A total of 1,118 athletes were included, and 76.9% of them achieved the MCID for the HOS-SS. A combination of 6 variables optimized algorithm performance, and specific cutoffs were found to decrease the likelihood of achieving the MCID: preoperative HOS-SS score of ≥58.3, Tönnis grade of 1, alpha angle of ≥67.1°, body mass index (BMI) of >26.6 kg/m2, Tönnis angle of >9.7°, and age of >40 years. The ENPLR model demonstrated the best performance (c-statistic: 0.77, calibration intercept: 0.07, calibration slope: 1.22, and Brier score: 0.14). This model was transformed into an online application as an educational tool to demonstrate machine learning capabilities. The ENPLR machine learning algorithm demonstrated the best performance for predicting clinically relevant sports-specific improvement in athletes who underwent hip arthroscopy for FAIS. In our population, older athletes with more degenerative changes, high preoperative HOS-SS scores, abnormal acetabular inclination, and an alpha angle of ≥67.1° achieved the MCID less frequently. Following external validation, the online application of this model may allow enhanced shared decision-making.

Thermal boundary resistance at graphene-pentacene interface explored by a data-intensive approach

As the machinery of artificial intelligence matures in recent years, there has been a surge in applying machine learning (ML) techniques for material property predictions. Artificial neural network (ANN) is a branch of ML and has gained increasing popularity due to its capabilities of modeling complex correlations among large datasets. The interfacial thermal transport plays a significant role in the thermal management of graphene-pentacene based organic electronics. In this work, the thermal boundary resistance (TBR) between graphene and pentacene is comprehensively investigated by classical molecular dynamics simulations combined with the ML technique. The TBR values along the a, b and c directions of pentacene at 300 K are 5.19 ± 0.18 × 10−8 m2 K W−1, 3.66 ± 0.36 × 10−8 m2 K W−1 and 5.03 ± 0.14 × 10−8 m2 K W−1, respectively. Different architectures of ANN models are trained to predict the TBR between graphene and pentacene. Two important hyperparameters, i.e. network layer and the number of neurons are explored to achieve the best prediction results. It is reported that the two-layer ANN with 40 neurons each layer provides the optimal model performance with a normalized mean square error loss of 7.04 × 10−4. Our results provide reasonable guidelines for the thermal design and development of graphene-pentacene electronic devices.

Multi-objective Optimization for Cruising Phase in Civil Aircraft’s Trajectory Planning

Cruising phase is the important stage for aircraft performance optimization. This paper introduces a method of simultaneous optimization of multi-objective such as flight fuel cost, time cost, and shortest re-planning path under the constraints of hazardous environment, into trajectory planning for cruising phase. A dynamic optimization model of the route under hazardous weather and a performance optimization model for cruising phase of the vertical profile are established, and intelligent algorithms are used to solve them. Taking a certain route as an example, a dynamic real-time optimization route is generated by using the dynamic route optimization model. Combining with the performance data of a certain type of aircraft, the performance optimization model of the cruising phase is used to optimize its performance of cruising phase. In the end, this paper verifies the optimization results of the multi-objective optimization algorithm for route and performance.

Towards Mountain Fire Safety Using Fire Spread Predictive Analytics and Mountain Fire Containment in IoT Environment

Mountains are popular tourist destinations due to their climate, fresh atmosphere, breathtaking sceneries, and varied topography. However, they are at times exposed to accidents, such as fire caused due to natural hazards and human activities. Such unforeseen fire accidents have a social, economic, and environmental impact on mountain towns worldwide. Protecting mountains from such fire accidents is also very challenging in terms of the high cost of fire containment resources, tracking fire spread, and evacuating the people at risk. This paper aims to fill this gap and proposes a three-fold methodology for fire safety in the mountains. The first part of the methodology is an optimization model for effective fire containment resource utilization. The second part of the methodology is a novel ensemble model based on machine learning, the heuristic approach, and principal component regression for predictive analytics of fire spread data. The final part of the methodology consists of an Internet of Things-based task orchestration approach to notify fire safety information to safety authorities. The proposed three-fold fire safety approach provides in-time information to safety authorities for making on-time decisions to minimize the damage caused by mountain fire with minimum containment cost. The performance of optimization models is evaluated in terms of execution time and cost. The particle swarm optimization-based model performs better in terms of cost, whereas the bat algorithm performs better in terms of execution time. The prediction models’ performance is evaluated in terms of root mean square error, mean absolute error, and mean absolute percentage error. The proposed ensemble-based prediction model accuracy for fire spread and burned area prediction is higher than that of the state-of-the-art algorithms. It is evident from the results that the proposed fire safety mechanism is a step towards efficient mountain fire safety management.

CogNet: classification of gene expression data based on ranked active-subnetwork-oriented KEGG pathway enrichment analysis.

Most of the traditional gene selection approaches are borrowed from other fields such as statistics and computer science, However, they do not prioritize biologically relevant genes since the ultimate goal is to determine features that optimize model performance metrics not to build a biologically meaningful model. Therefore, there is an imminent need for new computational tools that integrate the biological knowledge about the data in the process of gene selection and machine learning. Integrative gene selection enables incorporation of biological domain knowledge from external biological resources. In this study, we propose a new computational approach named CogNet that is an integrative gene selection tool that exploits biological knowledge for grouping the genes for the computational modeling tasks of ranking and classification. In CogNet, the pathfindR serves as the biological grouping tool to allow the main algorithm to rank active-subnetwork-oriented KEGG pathway enrichment analysis results to build a biologically relevant model. CogNet provides a list of significant KEGG pathways that can classify the data with a very high accuracy. The list also provides the genes belonging to these pathways that are differentially expressed that are used as features in the classification problem. The list facilitates deep analysis and better interpretability of the role of KEGG pathways in classification of the data thus better establishing the biological relevance of these differentially expressed genes. Even though the main aim of our study is not to improve the accuracy of any existing tool, the performance of the CogNet outperforms a similar approach called maTE while obtaining similar performance compared to other similar tools including SVM-RCE. CogNet was tested on 13 gene expression datasets concerning a variety of diseases.

Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning

Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R2 of ≈0.6, a root-mean-square error (RMSE) of ≈356 kg dry matter (DM)/ha and a mean absolute error (MAE) of 262 kg DM/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farm’s in situ measurement, pasture management and grazing practices have on the model’s predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.

DECISION SUPPORT MODEL FOR SUPPLY CHAIN MANAGEMENT

The purpose of this paper is the creation of a model for supply chain performance optimization and the development of a prototype of the decision support system.The study covered an efficient and agile supply chain type. The Analytic Hierarchy Process was used (AHP) for the evaluation and ranking of supply chains.The research on this topic have dealt with the evaluation and ranking of suppliers within supply chains not considering the characteristics of different types of supply chains. The contribution of this work is in the development of a new model that enables the evaluation and ranking of supply chains considering the priorities of key performance indicators in different types of supply chains, providing management with the support in decision making through simulation and the finding of optimum solutions for the specific supply chain type, as well as the possibility of evaluation and ranking of different supply chain types on the basis of weighted overall performance of each supply chain.Developed and suggested models provide company management with monitoring and control of individual key performance indicators and total supply chain performance, and in this way, become the support to the management in strategic decision making.

Hybrid Fuzzy-Genetic Model for Fitness-Based Performance Optimization in Wireless Networks

In recent times, the application of autonomic soft computing techniques for design and optimization of wireless access networks is progressively becoming prevalent. These computational learning techniques are capable of handling uncertain and imprecise networking information while driving toward the optimal solution set in the problem search space. The approach proposed by this paper presents the application of the fuzzy logic inference combined with the evolutionary genetic algorithm to optimize the performance parameters in wireless networks. In particular, we consider optimal bit rate allocation and transmission power consumption through the joint design of fuzzy-genetic modeling framework. The sample training data generated through simulations of IEEE 802.11 wireless access network are analyzed for optimization by supplying it to the expert hybrid model comprising of the conjunctive design of both the computational intelligent techniques. Specifically, we contemplate the binary encoding scheme, single-point crossover, reversing mutation, and two fitness functions for executing the binary genetic operations of crossover and mutation. It is generally observed that the proposed hybrid model with polynomial fitness function yields better performance with scalable network datasets than the logarithmic fitness function in terms of higher objective value. Moreover, the results obtained through simulation experiments exhibit significant throughput gains and power efficiency for the deployed fitness functions with the evolving size of training dataset. Compared with the existing methods, our hybrid learning model demonstrates performance enhancement with higher expected fitness measure, improved throughput and power efficiency.

Right Ventricle Segmentation of Magnetic Resonance Image Using the Modified Convolutional Neural Network

In this paper, the segmentation model is developed using the convolutional neural network for automatic segmentation of a right ventricle MRI image. The proposed model is trained end-to-end using an RVSC dataset that contains the right ventricle magnetic resonance images. The proposed model gives state-of-art achievement for dice metric and also for the Jaccard index. The proposed model achieves an optimal model performance of dice metric performance with 0.91 (0.10) for the training dataset and 0.88 (0.12) for the validation dataset.

Rapid determination of active components in Ginkgo biloba leaves by near infrared spectroscopy combined with genetic algorithm joint extreme learning machine

Near-infrared spectroscopy(NIRS) combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM) production process. This paper focused on the ginkgo leaf macroporous resin purification process, which is the key technology of Yinshen Tongluo Capsules, in order to achieve the rapid determination of quercetin, kaempferol and isorhamnetin in effluent. The abnormal spectrum was eliminated by Mahalanobis distance algorithm, and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY). The key information bands were selected by synergy interval partial least squares(siPLS); based on that, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA) and Monte Carlo uninformative variable(MC-UVE) were used to select wavelengths to obtain less but more critical variable data. With selected key variables as input, the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM) algorithm. The performance of the model was compared with that of partial least squares regression(PLSR). The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables. The calibration set correlation coefficient R_c and the validation set correlation coefficient R_p of quercetin, kaempferol and isorhamnetin were all above 0.98. The root mean square error of calibration(RMSEC), the root mean square error of prediction(RMSEP) and the relative standard errors of prediction(RSEP) were 0.030 0, 0.029 2 and 8.88%, 0.041 4, 0.034 8 and 8.46%, 0.029 3, 0.027 1 and 10.10%, respectively. Compared with the PLSR me-thod, the performance of the GA-ELM model was greatly improved, which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.