Cost-effective Algorithm Research Articles

Correction to: A novel cost-effective sparsity-aware algorithm with Kalman-based gain for the identification of long acoustic impulse responses

Correction to: A novel cost-effective sparsity-aware algorithm with Kalman-based gain for the identification of long acoustic impulse responses

Cost-effective moving target defense against DDoS attacks using trilateral game and multi-objective Markov decision processes

Moving Target Defense (MTD) has emerged as a game changer to reverse the asymmetric situation between attackers and defenders, and as one of the most effective countermeasures to mitigate DDoS attacks, shuffling-based MTD has gained ever-growing attention in cyber security. Despite the increased security, frequent shuffles would significantly bring heavy burden to the system. Moreover, most existing work has not adequately considered the impact of MTD techniques on the defender, and especially ignored that on legitimate users. Due to the lack of cost-effective shuffling methods, it is difficult to reach the optimal balance between the performance and overhead associated with the MTD deployment. Building on our preliminary work in this field, we propose a novel cost-effective shuffling method, which involves common users as a trilateral game for strategy generation and resists DDoS attacks with several MTD mechanisms. The novel game model extends our previous work to further describe the interaction among the attacker, the defender and users in detail, and we exploit Multi-Objective Markov Decision Processes to find the optimal MTD strategy by solving the trade-off problem between the effectiveness and cost of shuffling. By designing a trilateral game cost-effective shuffling algorithm, we capture the best MTD strategy and reach a balance between them in a given shuffling scenario. Simulation and experiments on an experimental software-defined network (SDN) indicate that our approach can effectively mitigate DDoS attacks with an acceptable overload, and exhibit better performance than other related and state of the art approaches.

KRAS mutations testing in non-small cell lung cancer: the role of Liquid biopsy in the basal setting.

In advanced stage non-small cell lung cancer (NSCLC) patients, Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) testing may soon acquire a predictive significance to select patients for AMG510 treatment. Since tissue samples are not always available, liquid biopsy may represent a viable option for KRAS testing. Here, we review the last three years clinical practice performed on 194 plasma based liquid biopsies by next generation sequencing (NGS) SiRe® panel. In particular, 36 (18.6%) KRAS mutated cases were identified, with an overall median allelic frequency of 5.0% (ranging between 0.2% and 46.8%). No concomitant mutations were observed in the other NSCLC clinical relevant genes included in the SiRe® panel, such as epidermal growth factor receptor (EGFR) and v-Raf murine sarcoma viral oncogene homolog B (BRAF). Exon 2 p.G12C was the most common detected mutation (13/36, 36.1%). In conclusion, our data update and confirm that SiRe® NGS panel represents a robust analytical tool to assess KRAS mutational status on circulating tumor DNA. Further investigation is required to design more cost-effective diagnostic algorithms to harmonize clinical relevant biomarker testing on tissue and blood in advanced stage NSCLC clinical practice.

Benign And Pathological Electrocardiographic Changes In Basketball Athletes Of BrasíLia - Brazil

PURPOSE: Basketball has evolved a lot and the level of competition has increased considerably. Athlete's Heart Syndrome comprises a set of clinical, electrocardiographic (ECG) and echocardiographic (ECHO) changes, such as sinus bradycardia, myocardial hypertrophy or enlargement of the cardiac cavities. Thus, it is important to distinguish potentially fatal pathological changes from normal physiological adaptations. The aim of this study was to describe the clinical, ECG and ECHO characteristics of Brazilian basketball players and evaluate the presence of benign and pathological changes. METHODS: Fifteen male basketball athletes (23 ± 3.74 years) competing in the National Brazilian Basketball Gold League were evaluated during the last week of preparatory training for the competition. The athletes performed clinical evaluation (cardiovascular risk factors, medication use, sleep quality, and application of the Physical Activity Readiness Questionnaire - PAR-Q), anthropometric (body mass index - BMI, kg.m-2 muscle mass - MM, Kg; fat percentage - FP,%), resting ECG, and transthoracic doppler ECHO (left ventricular ejection fraction - LVEF,%; left ventricular diastolic diameter - LVDD, cm; left ventricular mass index - LVMI, g.m-2). ECG changes were classified as benign or malignant according to the Seattle Criteria. RESULTS: The athletes did not report cardiovascular risk factors, medication use and presented negative PAR-Q. Most athletes (90%) complained of poor sleep quality. The anthropometric variables were BMI 24.13 ± 2.05 Kg.m-2, MM 47.6 ± 5.01 Kg; FP 8.89 ± 4.79%. All individuals presented sinus rhythm (100%). The main benign ECG alteration was early repolarization alteration (10 - 66.7%). The malignant ECG change was LV hypertrophy with negative T-wave (3 - 100%). On ECHO we found LVEF 65.76 ± 2.35%, LVDD 53.15 ± 3.57 cm and LVMI 81.46 ± 11.72 g.m-2, within the normal range for age and body surface. CONCLUSIONS: The presence of ECG criteria for LV overload was not associated with the presence of hypertrophy or ventricular remodeling on echocardiography. Evidence supports the use of ECG in screening, coupled with a cost-effective interpretation algorithm to assist abnormal or borderline changes to identify possible cardiovascular causes and prevent sudden death in athletes.

A new cost effective algorithm for online identification of line outage contingency using current phasor of PMU

Abstract In this study, a new algorithm has been developed for online identification of line outage contingencies. The proposed algorithm is based on two novel parameters i.e., generator bus current deviation index (GBCDI) and line current distribution factor (LCDF). The proposed GBCDI is used to select the principal bus (PB), and therefore a line is selected named as principal line (PL), which is connected with the PB. The proposed LCDF for different contingency cases can be easily calculated and available to the power system operators. Now, the current through PL is monitored, and the PMU provided observed current through the PL is compared with calculated line current using LCDF for different contingency cases via the proposed objective function. Furthermore, PMU error is also incorporated in the objective function to make the developed model realistic. Moreover, for the proposed scheme, the PMUs are required to be placed at generator buses only, and this ensures cost-competitiveness over several existing methods. Additionally, parallel line outage modeling is also demonstrated which reveals the ability of the proposed scheme to identify the outage of parallel line. The proposed technique is applied on IEEE 6 bus, 14 bus, 57 bus, 118 bus systems, and the test results are compared with existing literature. Test results show the superiority of the proposed approach over several existing schemes in terms of identification accuracy and cost-effectiveness.

DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing

Cloud infrastructures are suitable environments for processing large scientific workflows. Nowadays, new challenges are emerging in the field of optimizing workflows such that it can meet user’s service quality requirements. The key to workflow optimization is the scheduling of workflow tasks, which is a famous NP-hard problem. Although several methods have been proposed based on the genetic algorithm for task scheduling in clouds, our proposed method is more efficient than other proposed methods due to the use of new genetic operators as well as modified genetic operators and the use of load balancing routine. Moreover, a solution obtained from a heuristic used as one of the initial population chromosomes and an efficient routine also used for generating the rest of the primary population chromosomes. An adaptive fitness function is used that takes into account both cost and makespan. The algorithm introduced in this paper utilizes a load balancing routine to maximize resources’ efficiency at execution time. The performance of the proposed algorithm is evaluated by comparing the results with state of the art algorithms of this field, and the results indicate that the proposed algorithm has remarkable superiority in comparison to other algorithms and performs task scheduling with the least makespan and cost.

A novel cost-effective sparsity-aware algorithm with Kalman-based gain for the identification of long acoustic impulse responses

In this paper, a new robust sparsity (or sparseness)-aware adaptive filtering algorithm is proposed for the purpose of system identification and acoustic echo cancelation. It is named the improved proportionate fast normalized least mean square (IPFNLMS) algorithm. This latter has been derived by an effective integration of the update control matrix of the improved proportionate NLMS (IPNLMS) algorithm to the Kalman-based adaptation gain of the fast-NLMS (FNLMS) algorithm. Simulations were carried out both in synthetic and real long acoustic impulse responses at different sparseness levels with stationary and non-stationary inputs, followed by a verification with real experiment data. Results have shown interesting improvements for the proposed algorithm with respect to its ancestors in terms of convergence speed, steady-state performance, tracking capability and robustness against system sparsity variation.

Autonomic Feature Selection using Computational Intelligence

This paper introduces an autonomic method to optimize Feature Selection (FS) in autonomic systems while also presenting a taxonomy of FS techniques. Feature selection is a dimension reduction technique that has been proven to lead to improved performance by avoiding overfitting and to address complexity, thus providing faster and cost-effective algorithms. To be successful, the current FS methods are heavily reliant on two key elements: (1) a well defined and static learning objective, and (2) a relevant dataset. Current FS approaches involve mostly a manual process and do not take into account the constant change in the state of the underlying system. However, the method to automate the FS process presented in this paper, the Autonomic Feature Selection (AFS), uses representation and transfer learning concepts borrowed from the deep learning area. Representations of various types of monitoring data from a system are learned, and the resulting knowledge is transferred and re-used. The AFS is targeted to self-aware software systems, where the learning objective and (or) the availability of the relevant data are subject to change. The results suggest that although building and transferring learned representations is computationally expensive, its benefits outweigh the cost, by offering the potential to build flexible and re-usable knowledge structures.

Towards the Development of Rapid and Low-Cost Pathogen Detection Systems Using Microfluidic Technology and Optical Image Processing

Waterborne pathogens affect all waters globally and proceed to be an ongoing concern. Previous methods for detection of pathogens consist of a high test time and a high sample consumption, but they are very expensive and require specialist operators. This study aims to develop a monitoring system capable of identifying waterborne pathogens with particular characteristics using a microfluidic device, optical imaging and a classification algorithm to provide low-cost and portable solutions. This paper investigates the detection of small size microbeads (1–5 µm) from a measured water sample by using a cost-effective microscopic camera and computational algorithms. Results provide areas of opportunities to decrease sample consumption, reduce testing time and minimize the use of expensive equipment.

Performance of Naïve Bayes, C4.5 and KNN using Breast Cancer, Iris and Hypothyroid Datasets

Data mining usually specifies the discovery of specific pattern or analysis of data from a large dataset. Classification is one of an efficient data mining technique, in which class the data are classified are already predefined using the existing datasets. The classification of medical records in terms of its symptoms using computerized method and storing the predicted information in the digital format is of great importance in the diagnosis of various diseases in the medical field. In this paper, finding the algorithm with highest accuracy range is concentrated so that a cost-effective algorithm can be found. Here the data mining classification algorithms are compared with their accuracy of finding exact data according to the diagnosis report and their execution rate to identify how fast the records are classified. The classification technique based algorithms used in this study are the Naive Bayes Classifier, the C4.5 tree classifier and the K-Nearest Neighbor (KNN) to predict which algorithm is the best suited for classifying any kind of medical dataset. Here the datasets such as Breast Cancer, Iris and Hypothyroid are used to predict which of the three algorithms is suitable for classifying the datasets with highest accuracy of finding the records of patients with the particular health problems. The experimental results represented in the form of table and graph shows the performance and the importance of Naïve Bayes, C4.5 and K-Nearest Neighbor algorithms. From the performance outcome of the three algorithms the C4.5 algorithm is a lot better than the Naïve Bayes and the K-Nearest Neighbor algorithm.

Dimensioning and Layout Planning of 5G-Based Vehicular Edge Computing Networks Towards Intelligent Transportation

Fast-response communication is crucial for Vehicular Ad Hoc Network (VANET). In practice, the conventional VANETs, suffering from the high mobility of the vehicles and the ever-growing data to percept and process, cannot meet the demand of fast response currently. In this paper, we study the Dimensioning and Layout Planning (DLP) problem under 5G-based Vehicular Edge Computing Network (VECN) architecture which integrates the 5G Micro Base Station (gNB) and Edge Computing (EC) to reduce the response time. The DLP problem aims to minimize the total placement cost under the constraint of the full coverage. This paper formulates the DLP problem as an integer linear program (ILP) and then proposes a Greedy Algorithm (GA) and a Cost-Effective Heuristic Algorithm (CEHA) toimprove the computation efficiency. The case studies have verified the feasibility and scalability of the DLP formulation and showed that the proposed CEHA is fairly effective and efficient to solve the DLP problem.

Cost-Effective V2X Task Offloading in MEC-Assisted Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) will become an essential part of every city in the near future. They should support various vehicle-to-everything (V2X) applications that improve road safety or even enable autonomous driving. Recently, the European Telecommunications Standards Institute (ETSI) introduced a multi-access (mobile) edge computing concept as a promising solution to satisfy the V2X delay and computational requirements. Based on this concept, the tasks generated by V2X applications can be offloaded to servers at the edge of the radio access network (RAN). There is a need for a task offloading algorithm that minimizes the ITS operator expenses connected with the servers deployment and maintenance while satisfying the requirements of the V2X applications. Most of the existing papers in the literature do not pay much attention to queuing delays at servers. In this paper, the queuing delays are analyzed by considering a general-type task computational time distribution. A non-linear optimization problem is formulated to minimize the ITS operator expenses subject to delays and computational resources constraints. The flexibility is also improved by considering that a delay constraint is satisfied with a given probability. To solve this problem, a method for linearization of the problem is proposed, and consequently, an algorithm based on Integer Linear Programming (ILP) is designed. A heuristic algorithm called Cost-effective Heuristic Algorithm for Task offloading (CHAT) is also introduced that provides close to optimal results and has much lower computational complexity than the ILP algorithm. The efficiency of the CHAT algorithm is studied in several scenarios in terms of the computational time, delays, and the total server energy consumption as the cost function. The results show that the CHAT algorithm satisfies the requirements of the V2X applications in all the considered scenarios and reduces the ITS operator expenses over twice compared with other algorithms proposed in the literature.

A Cost-Effective Algorithm for Selecting Optimal Bandwidth to Clear Malicious Codes

Malicious code has posed a severe threat to modern society. Delivering antivirus program to networks is an important task of a cybersecurity company. As the bandwidth resource in a company is limited and precious, cybersecurity companies have to make a tradeoff between the impact(i.e. the economic loss) of malicious codes and the bandwidth assigned to transmit the antivirus programs. This paper addresses the malicious code and bandwidth tradeoff(MCBT) problem. By developing a novel malicious code and antivirus program interacting model, the total loss, which is the sum of the bandwidth usage fee and the economic loss, is quantified. On this basis, the MCBT problem is modelled as a constrained optimization problem that we refer to as the MCBT model, where the independent variable stands for bandwidth, and the objective function stands for the total loss. Some optimal bandwidth is determined by solving the MCBT model. Based on this, we propose a heuristic algorithm named DOWNHILL, which outperforms random strategies. Finally, the influence of some factors on the optimal bandwidth and the corresponding optimal total loss is uncovered through numerical simulations. To our knowledge, this is the first time the MCBT problem is treated in this way.

CORDIC-Astrocyte: Tripartite Glutamate-IP3-Ca 2+ Interaction Dynamics on FPGA.

Real-time, large-scale simulation of biological systems is challenging due to different types of nonlinear functions describing biochemical reactions in the cells. The promise of the high speed, cost effectiveness, and power efficiency in addition to parallel processing has made application-specific hardware an attractive simulation platform. This paper proposes high-speed and low-cost digital hardware to emulate a biological-plausible astrocyte and glutamate-release mechanism. The nonlinear terms of these models were calculated using a high-precision and cost-effective algorithm. Subsequently, the modified models were simulated to study and validate their functions. We developed several hardware versions by setting different constraints to investigate trade-offs and find the best possible design. FPGA implementation results confirmed the ability of the design to emulate biological cell behaviours in detail with high accuracy. As for performance, the proposed design turned out to be faster and more efficient than previously published works that targeted digital hardware for biological-plausible astrocytes.

The Validation of the BCR/ABL1-like Predictor across Laboratories Shows Reproducibility of Results

The Validation of the BCR/ABL1-like Predictor across Laboratories Shows Reproducibility of Results

Validation of a national algorithm for diabetes screening

Abstract Background The continuing upward trend in diabetes diagnosis across the world has led governments to adopt new treatment programs. According to the International Federation for Diabetes (2017) Europe and in its Member States spent from 10% to 20% of healthcare costs, for diabetes. The purpose of this study is to assess the feasibility of an algorithm to identify both the regulatory needs of diabetic individuals and to screen for undiagnosed individuals in the community. Methods The population sample consisted of 2100 people. The survey was conducted in two phases over the period 2017-2018. In the first phase we identified the 188 already diagnosed people with diabetes who were asked to answer a questionnaire comparing their current standard of care to NICE indicators. In the second phase, the remaining 1912 non-diabetic patients were asked to answer the Findrisk questionnaire. Those who were classified as high risk and very high risk were selected and referred to a clinical laboratory where they were subjected to a glucose curve to detect any diagnosis. Results The diabetes prevalence at the onset of the study was 9% (188/2100 people). Fifty-two new incidents were diagnosed. Thirty-six new cases were diagnosed by randomized glucose values after triple check and symptomatic diabetes symptoms consistent with the World Health Organization’s recommendations on diabetes diagnosis. Sixteen new cases were diagnosed via OGTT. The prevalence of diabetes at the end of the survey reached 11.4% (240/2,100 people).. Conclusions It has been found that one person in every forty in the general population suffers from diabetes without been yet diagnosed. Deviations from international diabetes standards have been observed. A new diabetes strategy should be implemented in the future, allowing the catholic implementation of the Findrisk Diabetes Prevention tool every ten years. Key messages Despite existing guidelines and recommendations, very few diabetic patients fulfill NICE follow up criteria. FINDRISK and OGTT can be used as part of a cost-effective national diabetes screening algorithm.

Assessment of Surgeon Variation in Adherence to Evidence-Based Recommendations for Treatment of Trigger Finger

Stenosing tenosynovitis (trigger finger) affects approximately 2% of the population. Given the prevalence of trigger finger and rising health care costs, adherence to the cost-effective and evidence-based treatment algorithm will permit better outcomes and allocation of resources. To examine treatment patterns for trigger finger and to determine surgeon-level and patient-level factors that influence adherence to evidence-based treatment. This retrospective population-based cohort study examined deidentified claims for treatment of trigger finger from a national insurance provider using the Clinformatics Data Mart database. Patients were included if they were 18 years or older and treated from January 1, 2002, through December 31, 2016 (excluding a washout period from July 1, 2008, until June 30, 2010), with a new diagnosis of single-digit trigger finger. Data were analyzed from December 21, 2018, through April 28, 2019. Cost-effective and evidence-based research published in July 2009 for the treatment of trigger finger. After excluding the 1-year washout period on either side of July 1, 2009, adherence to the recommended treatment algorithm of 2 corticosteroid injections before surgical release of trigger finger was compared with practice before publication of research supporting this cost-effective and evidence-based approach. In this analysis of 83 667 patients with trigger finger, 52 698 (63.0%) were women, and 20 045 (24.0%) had type 1 or 2 diabetes. Mean (SD) age was 61 (13) years. From 2002 to 2016, an overall increasing trend in adherence to the cost-effective and evidence-based approach to treatment was noted, with no significant increase in adherence in the postpublication era (67.5% vs 73.3%; P = .27). Substantial variation in adherence was observed at the surgeon level (intraclass correlation, 33%). Plastic surgeons had no change in adherence over time compared with orthopedic surgeons (odds ratio [OR], 1.00; 95% CI, 0.98-1.02; P = .90), whereas general surgeons had increased adherence (OR, 1.04; 95% CI, 1.02-1.06; P < .001). Higher-volume surgeons were also more adherent to these evidence-based recommendations (OR, 1.59; 95% CI, 1.53-1.65; P < .001). This study found substantial surgeon-level variation in adherence to evidence-based treatment of trigger finger. Surgeon specialty and volume were associated with differences in adherence. Efforts to understand surgeon barriers to implementation, regardless of physician specialty, appear to be necessary, and better implementation strategies may permit increased uptake of evidence-based treatment of trigger finger.

The Changing Epidemiology of HIV-Associated Adult Meningitis, Uganda 2015-2017.

BackgroundCentral nervous system (CNS) infections remain a major public health problem in Sub-Saharan Africa, causing 15%–25% of AIDS-related deaths. With widespread availability of antiretroviral therapy (ART) and the introduction of improved diagnostics, the epidemiology of infectious meningitis is evolving.MethodsWe prospectively enrolled adults presenting with HIV-associated meningitis in Kampala and Mbarara, Uganda, from March 2015 to September 2017. Participants had a structured, stepwise diagnostic algorithm performed of blood cryptococcal antigen (CrAg), CSF CrAg, Xpert MTB/RIF for tuberculous (TB) meningitis (TBM), Biofire multiplex polymerase chain reaction, and traditional microscopy and cultures.ResultsWe screened 842 consecutive adults with HIV presenting with suspected meningitis: 57% men, median age 35 years, median CD4 26 cells/mcL, and 55% presented on ART. Overall, 60.5% (509/842) were diagnosed with first-episode cryptococcal meningitis and 7.4% (62/842) with second episode. Definite/probable TB meningitis was the primary diagnosis in 6.9% (58/842); 5.3% (n = 45) had microbiologically confirmed (definite) TB meningitis. An additional 7.8% (66/842) did not meet the diagnostic threshold for definite/probable TBM but received empiric TBM therapy. Bacterial and viral meningitis were diagnosed in 1.3% (11/842) and 0.7% (6/842), respectively. The adoption of a cost-effective stepwise diagnostic algorithm allowed 79% (661/842) to have a confirmed microbiological diagnosis at an average cost of $44 per person.ConclusionsDespite widespread ART availability, Cryptococcus remains the leading cause of HIV-associated meningitis. The second most common etiology was TB meningitis, treated in 14.7% overall. The increased proportion of microbiologically confirmed TBM cases reflects the impact of new improved molecular diagnostics.

Computer-aided polyp detection based on image enhancement and saliency-based selection

This paper presents a computer-aided polyp detection algorithm applicable to both colonoscopy and wireless capsule endoscopy (WCE). The proposed system has three integral parts: image enhancement, saliency map formation and Histogram of gradients (HOG) feature extraction for final classification. We propose a novel and efficient image enhancement algorithm, which enhances the saliency of clinically important features in endoscopic images. A saliency detection method is applied to the enhanced images to highlight the initial polyp candidates. In the classification stage, polyp candidates are selected after performing an image enhancement step and a saliency detection step. Exhaustive experiments have been performed on three publicly available databases: CVC ColonDB, CVC ClinicDB, and ETIS Larib to evaluate the performance of the proposed polyp detection algorithm. Comparison with the state-of-the-art methods shows that the proposed method outperforms the existing ones in terms of recall (=86.33%) and F2 score (=75.51%) for CVC ColonDB and in terms of recall (=74.04%) for the ETIS Larib dataset. With a significantly reduced number of search windows resulting from the saliency-based selection, the proposed scheme ensures a cost-effective and efficient polyp detection algorithm.

Features of the course of acute pancreatitis in patients with obesity.

The purpose of the study is to investigate the course of acute pancreatitis in obese patients, the development of local and systemic complications and mortality rates. We took and analyzed 482 histories of acute pancreatitis treated at Kyiv Regional Clinical Hospital from January 1, 2011 to February 2, 2019. The data were statistically processed in the Excel 2010 program using a descriptive method applying relative, absolute numbers, mean square deviations and their errors. A correlation between variables was studied using the Pearson's test (R2). The significance of the difference between the two independent groups was tested with Student's t-test. We included 482 patients in our study, i.e. 260 patients (54%) with obesity (the study group), and for comparison, 222 (46%) patients with normal body mass, constituting a control group. Obese patients had a higher mean age (55.4 ± 9.4 years, P = 0.01); also, they showed a statistically higher incidence of severe course of acute pancreatitis [85 (32.7%) vs. 16 (7.2%); P = 0.01*]. We noted an increase in the rate of acute pancreatitis with severe course in obese patients with mass gain (from 10.20% to 53.93%, P = 0.03*). Hospitalization time of obese patients was longer than in case of patients with normal body mass. In addition, we observed a two-fold longer hospitalization of obese patients at intensive care units (5.8 ± 0.8 vs. 2.7 ± 0.5 days, P = 0.01*). When investigating the mortality rate, we found out that the main cause of death was the progression of organ failure - 30 cases (6.3%), pulmonary embolism (TB) - 15 (3.1%) and DIC - 18 (3.7%). C onclusions: The presence of obesity in patients involves a high risk of severe acute pancreatitis. This risk increases with body mass increase. In addition, in obese patients the hospitalization and in-patient care takes longer, which increases the total cost of treatment and requires a cost-effective algorithm in the future. A high mortality rate in obese patients requires an improved treatment algorithm.