Cost-effective Algorithm Research Articles

An optimized ensemble search approach for classification of higher-level gait disorder using brain magnetic resonance images

Higher-Level Gait Disorder (HLGD) is a type of gait disorder estimated to affect up to 6% of the older population. By definition, its symptoms originate from the higher-level nervous system, yet its association with brain morphology remains unclear. This study hypothesizes that there are patterns in brain morphology linked to HLGD. For the first time in the literature, this work investigates whether deep learning, in the form of convolutional neural networks, can capture patterns in magnetic resonance images to identify individuals affected by HLGD. To handle this new classification task, we propose setting up an ensemble of models. This leverages the benefits of combining classifiers instead of determining which network is the most suitable, developing a new architecture, or customizing an existing one. We introduce a computationally cost-effective search algorithm to find the optimal ensemble by leveraging a cost function of both traditional performance scores and the diversity among the models. Using a unique dataset from a large population-based cohort (VESPR), the ensemble identified by our algorithm demonstrated superior performance compared to single networks, other ensemble fusion techniques, and the best linear radiological measure. This emphasizes the importance of implementing diversity into the cost function. Furthermore, the results indicate significant morphological differences in brain structure between HLGD-affected individuals and controls, motivating research about which areas the networks base their classifications on, to get a better understanding of the pathophysiology of HLGD.

Open Access Bacillus cereus Cocktail Secondary Growth Model for the Food Industry.

A cost-effective algorithm is presented, using a virtual dataset of growth rates from a cocktail of Bacillus cereus strains, for developing an open access, extended-range secondary growth model. Extended-range growth models can span the range of processing conditions typically used in food manufacturing and are therefore more relevant for industry. The open access extended-range secondary growth model for a cocktail of B. cereus strains was created using publicly available data, and the methodology can be adapted for modelling growth of other pathogens. An extended-range model can help manage B. cereus hazards in novel food categories with non-traditional formulations as estimations of B. cereus risks in these foods become more precise. This open access model, however, needs to be validated using data from B. cereus strain cocktails isolated from production facilities. Once validated, these independent factor models are valuable tools, in a pathogen decision support platform, which are tuned to local production environments. Such a platform can address the needs of current and future food product portfolios, effectively mitigating risks associated with B. cereus and other relevant pathogens.

Cost-effectiveness of follow-up algorithms for chronic thromboembolic pulmonary hypertension in pulmonary embolism survivors.

Achieving an early diagnosis of chronic thromboembolic pulmonary hypertension (CTEPH) in pulmonary embolism (PE) survivors results in better quality of life and survival. Importantly, dedicated follow-up strategies to achieve an earlier CTEPH diagnosis involve costs that were not explicitly incorporated in the models assessing their cost-effectiveness. We performed an economic evaluation of 11 distinct PE follow-up algorithms to determine which should be preferred. 11 different PE follow-up algorithms and one hypothetical scenario without a dedicated CTEPH follow-up algorithm were included in a Markov model. Diagnostic accuracy of consecutive tests was estimated from patient-level data of the InShape II study (n=424). The lifelong costs per CTEPH patient were compared and related to quality-adjusted life-years (QALYs) for each scenario. Compared to not performing dedicated follow-up, the integrated follow-up algorithms are associated with an estimated increase of 0.89-1.2 QALYs against an incremental cost-effectiveness ratio (ICER) of EUR 25 700-46 300 per QALY per CTEPH patient. When comparing different algorithms with each other, the maximum differences were 0.27 QALYs and EUR 27 600. The most cost-effective algorithm was the InShape IV algorithm, with an ICER of EUR 26 700 per QALY compared to the next best algorithm. Subjecting all PE survivors to any of the currently established dedicated follow-up algorithms to detect CTEPH is cost-effective and preferred above not performing a dedicated follow-up, evaluated against the Dutch acceptability threshold of EUR 50 000 per QALY. The model can be used to identify the locally preferred algorithm from an economical point-of-view within local logistical possibilities.

Diagnosis Algorithm of Cardiac Sarcoidosis: Where Are We?

This commentary discusses the recently published article, "Speckle-tracking echocardiography as an effective screening tool for cardiac involvement among patients with systemic sarcoidosis in an Indian cohort: A prospective observational study". Owing to improved imaging modalities and increased awareness of cardiac sarcoidosis (CS), the prevalence of CS has risen. Given that CS is a relatively rare condition with high mortality, it is essential for the initial test to demonstrate high sensitivity. However, several guidelines offer differing opinions on the diagnosis algorithm for CS. This commentary highlighted the importance of speckle-tracking echocardiography as an effective screening tool. Due to its high negative predictive value (NPV), it would be useful to rule out cardiac involvement. Nevertheless, larger prospective multicenter studies are warranted to develop a more cost-effective diagnosis algorithm for CS.

Maximizing influence via link prediction in evolving networks

Influence Maximization (IM), targeting the optimal selection of k seed nodes to maximize potential information dissemination in prospectively social networks, garners pivotal interest in diverse realms like viral marketing and political discourse dissemination. Despite receiving substantial scholarly attention, prevailing research predominantly addresses the IM problem within the confines of existing networks, thereby neglecting the dynamic evolutionary character of social networks. An inevitable requisite arises to explore the IM problem in social networks of future contexts, which is imperative for certain application scenarios. In this light, we introduce a novel problem, Influence Maximization in Future Networks (IMFN), aimed at resolving the IM problem within an anticipated future network framework. We establish that the IMFN problem is NP-hard and advocate a prospective solution framework, employing judiciously selected link prediction methods to forecast the future network, and subsequently applying a greedy algorithm to select the k most influential nodes. Moreover, we present SCOL (Sketch-based Cost-effective lazy forward selection algorithm Optimized with Labeling technique), a well-designed algorithm to accelerate the query of our IMFN problem. Extensive experimental results, rooted in five real-world datasets, are provided, affirming the efficacy and efficiency of the proffered solution and algorithms.

Horizon picking algorithm using global optimization and coherence measurement in poststacked seismic data

Accurate horizon recognition within poststack seismic sections is important in seismic interpretation. Horizon picking techniques influence diverse seismic structural analyses and inversion methodologies. Despite encountering challenges such as computational demands and time constraints, the past few years have witnessed the development of numerous 2D and 3D methods. With the progress of modern computing, more robust and efficient techniques, harnessing artificial intelligence, have come to the fore. In this context, we develop an innovative cost-effective algorithm grounded in a global optimization framework. This algorithm combines the very fast simulated annealing method with a set of stability parameters and coherence measurements between neighboring seismic traces, which is used as the objective function. The search is executed on continuous and sequential clusters of seismic traces, with a focus on maximizing coherence amidst the presented events. We evaluate the algorithm’s efficacy by applying it to two distinct input data sets — enveloped and nonenveloped seismic traces. The initial application is to the time-migrated Marmousi data set, a synthetic marine data set originated from a complex geologic sedimentary basin situated in Angola, Africa. Subsequently, we apply the algorithm to depth-migrated land data extracted from a past survey conducted in the Tacutu Basin in northern Brazil. Outcomes arising from the application to 2D synthetic and field data sets underscore the method’s viability as a compelling alternative for seismic horizon picking within the time or depth domain.

GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning

Privacy must be preserved when working with client data in machine learning. Federated learning (FL) provides a way to preserve user data privacy by aggregating locally trained models without sharing the user data. Still, the privacy of user identity is not preserved. Secure aggregation is a popular technique in FL for aggregating gradients without disclosing individual data. However, it is costly and inaccurate. Therefore, we propose a novel, scalable, cost-effective group signature-based secure aggregation algorithm in FL, called GSFedSec, where secure aggregation helps conceal the user’s update while the group signature helps conceal their identity. Our algorithm preserves the data and their source. Our simulation results show that the proposed algorithm does not suffer from a loss of accuracy, handles increases in network size competently, offers computational and communication efficiency, and is secure against various security attacks. We have compared the results of efficiency and security against existing algorithms in FL. Also, the security of the algorithm is verified using Burrows–Abadi–Needham (BAN) logic and simulated via the Automated Validation of Internet Security Protocols and Applications (AVISPA) protocol.

Eighth-Order Numerov-Type Methods Using Varying Step Length

This work explores a well-established eighth-algebraic-order numerical method belonging to the explicit Numerov-type family. To enhance its efficiency, we integrated a cost-effective algorithm for adjusting the step size. After each step, the algorithm either maintains the current step length, halves it, or doubles it. Any off-step points required by this technique are calculated using a local interpolation function. Numerical tests involving diverse problems demonstrate the significant efficiency improvements achieved through this approach. The method is particularly effective for solving differential equations with oscillatory behavior, showcasing its ability to maintain high accuracy with fewer function evaluations. This advancement is crucial for applications requiring precise solutions over long intervals, such as in physics and engineering. Additionally, the paper provides a comprehensive MATLAB-R2018a implementation, facilitating ease of use and further research in the field. By addressing both computational efficiency and accuracy, this study contributes a valuable tool for the numerical analysis community.

To Exit or Not to Exit: Cost-Effective Early-Exit Architecture Based on Markov Decision Process

Recently, studies on early-exit mechanisms have emerged to reduce the computational cost during the inference process of deep learning models. However, most existing early-exit architectures simply determine early exiting based only on a target confidence level in the prediction, without any consideration of the computational cost. Such an early-exit criterion fails to balance accuracy and cost, making it difficult to use in various environments. To address this problem, we propose a novel, cost-effective early-exit architecture in which an early-exit criterion is designed based on the Markov decision process (MDP). Since the early-exit decisions within an early-exit model are sequential, we model them as an MDP problem to maximize accuracy as much as possible while minimizing the computational cost. Then, we develop a cost-effective early-exit algorithm using reinforcement learning that solves the MDP problem. For each input sample, the algorithm dynamically makes early-exit decisions considering the relative importance of accuracy and computational cost in a given environment, thereby balancing the trade-off between accuracy and cost regardless of the environment. Consequently, it can be used in various environments, even in a resource-constrained environment. Through extensive experiments, we demonstrate that our proposed architecture can effectively balance the trade-off in different environments, while the existing architectures fail to do so since they focus only on reducing their cost while preventing the degradation of accuracy.

Multi-Armed Bandit Algorithms: Innovations and Applications in Dynamic Environments

This paper delves into the fundamental concept of the Multi-Armed Bandit (MAB) problem, structuring its analysis around two primary phases. The initial phase, exploration, is dedicated to investigating the potential rewards of each arm. Subsequently, the exploitation phase utilizes insights from exploration to maximize returns. The discussion then progresses to elucidate the core methodologies and workflows of three principal MAB algorithms: Upper Confidence Bound (UCB), Thompson Sampling, and Epsilon-Greedy. These algorithms are meticulously analyzed for their unique approaches and efficiencies in handling the MAB problem. Expanding the scope further, the paper spotlights three practical applications of MAB algorithms. The first application involves Dynamic Resource Allocation in Multi-Unmanned Aerial Vehicle (UAV) Air-Ground Networks, leveraging the K-armed Bandit framework. This is followed by an exploration of Product Pricing Algorithms grounded in MAB principles, offering innovative solutions for dynamic pricing strategies. Lastly, the paper examines a cost-effective MAB algorithm tailored for dense wireless networks, addressing the complexities and demands of modern network infrastructures. This comprehensive study not only highlights the versatility of MAB algorithms but also underscores their growing importance in diverse real-world applications.

Examine Heuristic Data Lake Management Using AWS: A Big Data Handling Approach

In this era of technology, the most valued asset can be ‘Data’. With the increasing number of data, the value of it keeps increasing. Data storage and data manipulate for to achieve some particular goals or business requirements increasing in number and storing it has become a complex and tedious task. With the use of some advanced technologies like hadoop, it simplified the data storing process, but due to rapid development and excessive use of AI and ML, tons of data is collected. The quintessence is to ascertain an extra cost effective storage alternative. This paper provides with an effective solution to store data over the cloud with numerous benefits over traditional data storage methods by developing a data lake using AWS a Cost Effective Data Lake Management algorithm (CEDLMA). Furthermore, the functionalities of data lake include managing and storing sorted as well as unsorted data, gathering various analytics from the data lake as per business requirements. Proposed work is evaluated with AWS’s IAM and S3 services.

Evaluation of the Effectiveness of Feature Selection Methods Combined with Regression Algorithms to Predict Particulate Matter (PM10) in Gandhinagar, Gujarat, India

Feature selection is one of the important data pre-processing techniques that are used to increase the performance of machine learning models, to build faster and more cost-effective algorithms, and to make it easier to interpret the predictions made by the models. The main objective of this research work is to investigate the influence features to predict particulate matter (PM10). This research uses 24-hour average pollutant concentration data of 36 air quality monitoring stations provided by Gandhinagar Smart City Development Limited (GSCDL), Gandhinagar, Gujarat. Important features were identified using five feature selection techniques (correlation, forward selection, backward elimination, Exhaustive Feature Selection (EFS), and feature importance derived using Random Forest Regressor). With selected features six regression algorithms (Multiple Linear Regression, Random Forest, Decision Tree, K-nearest Neighbour, XGBoost, and Support Vector Regressor) were trained to predict PM10. Further, the models were compared based on the Root Mean Square Error (RMSE) and Coefficient of determination (R2) parameters to identify the model with good performance. This proposed model can be utilized as an early warning system, providing air quality information to local authorities to develop air-quality improvement initiatives.

Towards a Simplified and Cost-Effective Diagnostic Algorithm for the Surveillance of Intraductal Papillary Mucinous Neoplasms (IPMNs): Can We Save Contrast for Later?

The increased detection of pancreatic cysts in recent years has triggered extensive diagnostic investigations to clarify their potential risk of malignancy, resulting in a large number of patients undergoing numerous imaging follow-up studies for many years. Therefore, there is a growing need for optimization of the current surveillance protocol to reduce both healthcare costs and waiting lists, while still maintaining appropriate sensibility and specificity. Imaging is an essential tool for evaluating patients with intraductal papillary mucinous neoplasms (IPMNs) since it can assess several predictors for malignancy and thus guide further management recommendations. Although contrast-enhanced magnetic resonance imaging (MRI) with magnetic resonance cholangiopancreatography (MRCP) has been widely recommended by most international guidelines, recent results support the use of unenhanced abbreviated-MRI (A-MRI) protocols as a surveillance tool in patients with IPMN. In fact, A-MRI has shown high diagnostic performance in malignant detection, with high sensitivity and specificity as well as excellent interobserver agreement. The aim of this paper is, therefore, to discuss the current available evidence on whether the implementation of an abbreviated-MRI (A-MRI) protocol for cystic pancreatic lesion surveillance could improve healthcare economics and reduce waiting lists in clinical practice without significantly reducing diagnostic accuracy.

Development of Cost-Effective Fatty Liver Disease Prediction Models in a Chinese Population: Statistical and Machine Learning Approaches.

The increasing prevalence of nonalcoholic fatty liver disease (NAFLD) in China presents a significant public health concern. Traditional ultrasound, commonly used for fatty liver screening, often lacks the ability to accurately quantify steatosis, leading to insufficient follow-up for patients with moderate-to-severe steatosis. Transient elastography (TE) provides a more quantitative diagnosis of steatosis and fibrosis, closely aligning with biopsy results. Moreover, machine learning (ML) technology holds promise for developing more precise diagnostic models for NAFLD using a variety of laboratory indicators. This study aims to develop a novel ML-based diagnostic model leveraging TE results for staging hepatic steatosis. The objective was to streamline the model's input features, creating a cost-effective and user-friendly tool to distinguish patients with NAFLD requiring follow-up. This innovative approach merges TE and ML to enhance diagnostic accuracy and efficiency in NAFLD assessment. The study involved a comprehensive analysis of health examination records from Suzhou Municipal Hospital, spanning from March to May 2023. Patient data and questionnaire responses were meticulously inputted into Microsoft Excel 2019, followed by thorough data cleaning and model development using Python 3.7, with libraries scikit-learn and numpy to ensure data accuracy. A cohort comprising 978 residents with complete medical records and TE results was included for analysis. Various classification models, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were constructed and evaluated based on the area under the receiver operating characteristic curve (AUROC). Among the 916 patients included in the study, 273 were diagnosed with moderate-to-severe NAFLD. The concordance rate between traditional ultrasound and TE for detecting moderate-to-severe NAFLD was 84.6% (231/273). The AUROC values for the RF, LightGBM, XGBoost, SVM, KNN, and LR models were 0.91, 0.86, 0.83, 0.88, 0.77, and 0.81, respectively. These models achieved accuracy rates of 84%, 81%, 78%, 81%, 76%, and 77%, respectively. Notably, the RF model exhibited the best performance. A simplified RF model was developed with an AUROC of 0.88, featuring 62% sensitivity and 90% specificity. This simplified model used 6 key features: waist circumference, BMI, fasting plasma glucose, uric acid, total bilirubin, and high-sensitivity C-reactive protein. This approach offers a cost-effective and user-friendly tool while streamlining feature acquisition for training purposes. The study introduces a groundbreaking, cost-effective ML algorithm that leverages health examination data for identifying moderate-to-severe NAFLD. This model has the potential to significantly impact public health by enabling targeted investigations and interventions for NAFLD. By integrating TE and ML technologies, the study showcases innovative approaches to advancing NAFLD diagnostics.

IgA deficiency in coeliac disease; verification of a cost-effective screening algorithm in a medical diagnostic laboratory

IgA deficiency in coeliac disease; verification of a cost-effective screening algorithm in a medical diagnostic laboratory

Incidence, Prognostic Factors, and Survival of Gastric Cancer in Iran: A review of evidence

Gastric cancer (GC) is the fifth most prevalent cancer and the fourth in terms of cancer-related death worldwide. The GC is the most commonly diagnosed and the most important cause of cancer-related death in Iranians. Due to the significant burden of stomach cancer in Iran, we aimed to review all articles that have been conducted on the Incidence, Prognostic Factors, and survival of Gastric Cancer in Iran and compare them with the global situation. We surveyed Google Scholar, Pub Med, Scopus, Web of Science (ISI), and Science Direct databases using the keywords of Gastric Cancer, Stomach Cancer, Gastric Neoplasm, Stomach Neoplasm, and Iran and obtained English language articles. After a two-step screening of articles, 51 out of 202 remained eligible for our study (related to Incidence, Prognostic Factors, and survival of stomach Cancer in Iran). We found that although Iran is one of the high-burden countries for GC, the incidence rate and mortality rate are very different in different provinces (up to a six-fold and thirty-fold difference in mortality rate and incidence rate have been reported respectively in reviewed articles between Northern and Southern Iran). Considering that the initial stage is one of the most important and probably the most important prognostic factors, through upper endoscopy screening, the disease can be diagnosed in earlier stages and the survival of patients can be improved. We recommend the Ministry of Health identify high-risk people according to their combination of risk factors (especially geographical region) and make a national cost-effective screening algorithm for Iran.

Optimizing Machine Learning Models for Accessible Early Cognitive Impairment Prediction: A Novel Cost-Effective Model Selection Algorithm

Optimizing Machine Learning Models for Accessible Early Cognitive Impairment Prediction: A Novel Cost-Effective Model Selection Algorithm

Exploring Binary Classification Models for Parkinson’s Disease Detection

Parkinson’s disease (PD) is a degenerative condition characterised by a combination of motor symptoms, such as tremors and rigidity, and non-motor symptoms, including cognitive impairment and anxiety. Although there is currently no known cure, there are available treatments that can effectively mitigate symptoms and enhance life expectancy. Our research primarily centred around vocal disorders, given that an estimated 90% of individuals diagnosed with Parkinson’s disease experience the onset of such disorders in the early stages of the disease. In this study, a dataset is collected from UCI repository consisting of 252 cases was utilised, with 188 cases classified as Parkinson’s disease and 64 cases classified as non-Parkinson’s disease. The results of our study indicate that the K-nearest neighbours (KNN) algorithm demonstrated a peak accuracy of 98.52% and an average accuracy of 97.33%, surpassing the performance of previously established models. Additionally, we introduced the implementation of Parzen Windows Estimation (a KDE technique) and computed classifier accuracies based on mean test set scores after multiple rounds of data reshuffling and model training. Our results enhance credibility compared to previous works in this field. Given the high misdiagnosis rate (26%) and the expense of imaging tests, deploying a precise and cost-effective algorithm for early PD detection is critical.

A secure authenticated image encryption scheme based on elliptic curve cryptography

The picture archiving and communication system is a powerful mechanism to store and securely transmit images and reports. The development of a cost-effective encryption algorithm in terms of time and space complexity is needed. Our main goal is to protect the content and integrity of the image being transmitted. We implement an efficient authenticated encryption algorithm based on the Lehmer Random Number Generator, the Sophie Germain Prime Generator and Elliptic Curve Cryptography to transmit the digitally preserved images (including medical) which serves our purpose. Our method achieves randomness along with authentication with the help of random number generator and Elliptic Curve Cryptography which is unique compared to the existing schemes. The performance metrics of the proposed system stay on top of the state-of-the-art techniques that were recently derived. We obtained negative correlation coefficient values of −0.000242, −0.001349, −0.001313 for vertical, horizontal, and diagonal coefficients respectively. This makes our scheme better as it is difficult to find any relationship between the original and encrypted image. On average, our method uses only 10 seconds for encryption using a 521-bit curve.

Cost-effective identification of the field maturity of tobacco leaves based on deep semi-supervised active learning and smartphone photograph

As a key technology for tobacco leaf harvesting, automatic identification of the field maturity of tobacco leaves is an important issue in the tobacco industry. For years, researchers have been exploring machine learning methods to perform the identification task, with traditional supervised learning (SL) schemes being studied in depth. However, the actual use of an SL algorithm is always costly and cumbersome, as it requires a large amount of labor and time to collect and label enough tobacco leaf samples. Therefore, a cost-effective algorithm solution is desired by the industry. To this end, we investigate the efficacy and efficiency of semi-supervised learning (SSL) scheme for this task, attempting to significantly reduce the labeling cost and provide a flexible way to control the cost. Considering the advantages of smartphones in terms of availability and economy, we use smartphone photography as an image acquisition tool, thereby building a tobacco leaf maturity dataset containing more than 7000 tobacco leaf images of three field maturity categories (unripe, ripe and overripe) of three stem positions (bottom, middle and top). According to the fact that the maturity of the unripe, ripe and overripe leaves increases sequentially, we propose a maturity structure constraint (MSC) to make the identification model converge to a confident space and be generalizable. Furthermore, we propose a sample selection method based on self-supervised representation learning, which can effectively select high-quality samples for the identification network. Finally, we design an SSL algorithm framework called maturity structure constraint based semi-supervised learning (MSC-SSL) for the identification task. Experimental results show that our SSL framework is capable to significantly reduce the sample usage and improve the identification accuracy. It achieves the accuracies of 91.92% (bottom position/with 33.16% training samples), 87.06% (middle position/with 27.57% training samples) and 87.64% (top position/with 28.02% training samples) when auto stopped, as compared to the SSL benchmarks of 87.37% and 90.40%, 83.53% and 85.10%, 81.65% and 84.64%, respectively. Furthermore, our method can match or exceed the accuracy of the SL scheme using only about 25% of the labeled samples required by the SL scheme.