Execution Time Research Articles

A novel hybrid model for lung and colon cancer detection using pre-trained deep learning and KELM

Cancer poses a significant threat to life due to its aggressive nature, high potential for metastasis, and heterogeneity. Globally, both the men and women are mostly affected by lung and colon (LC) cancer. Early and accurate detection is essential to enhance the well-being of patients facing LC cancer. The traditional techniques face challenges such as low accuracy and high execution time. This work combines the pre-trained deep learning (DL) models like ResNet-50, InceptionV3, and DenseNet with Kernel Extreme Learning Machine (KELM) for the accurate and fast diagnosis of LC cancer using histopathology images. The pre-trained DL models provide a strong foundation for feature extraction, capturing intricate patterns indicative of cancerous tissues. Feature fusion combines the complementary strengths of multiple pre-trained models, enhancing the method's ability to capture diverse features and improve classification accuracy. KELM efficiently handles the high-dimensional feature space generated by the DL models, ensuring fast and accurate classification. Mutation Boosted Dwarf Mongoose Optimization Algorithm (MB-DMOA) further optimizes the model parameters, mitigating the risk of getting stuck in local optima and facilitating better convergence towards the global optimum. By leveraging the advantages of each technique and integrating them into a cohesive framework, the proposed approach achieves superior performance in diagnosing LC cancer, ultimately improving patient outcomes. The proposed study attains accuracy (98.9%), F1-score (97.6%), specificity (96.5%), precision (96.7%) and sensitivity (95.8%). The results illuminate the efficacy of this approach within therapeutic environments.

A Comparative Analysis of the Machine Learning Methods for Predicting Diabetes

Diabetes can lead to various health problems and complications, such as cardiovascular disease, kidney damage (nephropathy), eye issues, neuropathy, and foot ailments. Therefore, early diagnosis of diabetes can be immensely beneficial in preventing the development of these conditions. Utilizing machine learning is one method to detect diabetes in individuals at an early stage. In this study, we compare the performance of nine machine-learning classification models in predicting diabetes. These models include XGBoost, gradient boosting, AdaBoost, logistic regression, decision tree, KNN, perceptron, random forest, and naïve bayes. We utilize several evaluation metrics, focusing on the f1-score, area under the curve (AUC), and computational runtime. Our comparison reveals that complex tree-based models exhibit the highest f1-score and AUC, albeit with longer execution times.

Cloud Quanta: Pioneering the Future of Quantum Computing in the Cloud

Quantum computing has emerged as a revolutionary approach to solving complex problems that demand powerful computational capabilities. With various cloud quantum computing services available, users are presented with different architectures and performances. In this study, we conduct research on select cloud quantum computing services to evaluate their performances and compare them. Using two different methods, visual programming and Qiskit, we test these services and analyze the impact of factors such as the number of qubits per backend and shots per run on execution time. Our findings provide users with insights to decide which cloud quantum computing services offer better performance or faster execution times based on their specific requirements.

Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning

Accurate bird species recognition is crucial for ecological conservation, wildlife monitoring, and biological research, yet it poses significant challenges due to the high variability within species and the subtle similarities between different species. This paper introduces an automatic bird species recognition method from images that leverages feature enhancement and contrast learning to address these challenges. Our method incorporates a multi-scale feature fusion module to comprehensively capture information from bird images across diverse scales and perspectives. Additionally, an attention feature enhancement module is integrated to address noise and occlusion within images, thus enhancing the model’s robustness. Furthermore, employing a siamese network architecture allows effective learning of common features within instances of the same class and distinctions between different bird species. Evaluated on the CUB200-2011 dataset, our proposed method achieves state-of-the-art performance, surpassing existing methods with an accuracy of 91.3% and F1 score of 90.6%. Moreover, our approach showcases a notable advantage in scenarios with limited training data. When utilizing only 5% of the training data, our model still achieves a recognition accuracy of 65.2%, which is significantly higher than existing methods under similar data constraints. Notably, our model exhibits faster execution times compared to existing methods, rendering it suitable for real-time applications.

Application of TDABC in assessing the profitability of examinations and the level of idleness of computed tomography in the gynecological oncology sector

Gynecological malignancies represent one of the main mortality factors in women, with imaging exams being important tools for diagnosis, staging and surgical planning. Such exams are carried out at Diagnostic Imaging Centers (CDI) which, due to the use of sophisticated technology and qualified labor, are expensive and impact health systems. Given this context, this research aims to demonstrate how TDABC can be used to evaluate the profitability of exams and the levels of idleness that exist in the context of the computed tomography sector of the gynecological oncology unit. For this purpose, a methodology classifiable as qualitative and descriptive was used and the case study format was adopted, with data collection through documentary research, informal interviews to learn about the process and estimates of the execution time of the activities. The results showed that the tests carried out are deficient, as the cost of providing these services is higher than the prices paid by the Unified Health System (SUS), which resulted in a total loss of R$ -79,347.04 in the month. The levels of idleness of the activities involved were also determined, both in terms of inactive minutes and in monetary values. Therefore, it was concluded that the total idleness of the tomography sector surveyed reached R$ 102,932.81 and is equivalent to 41.62% on average for the activities covered (varying between 3.66% in the case of equipment and 81.59% in relation to nurses’ activity). Regarding the contributions made, it is worth noting that the information made available by TDABC can support actions aimed at reducing time between activities and optimizing the performance of the employees involved, in addition to demonstrating TDABC's adherence within the scope of entities that perform diagnostic imaging services. From the results obtained, it is concluded that it is essential to optimize processes to reduce exam time and increase productivity, as well as the need to readjust the values in the SUS table to avoid compromising investment of resources in other sectors of the hospital.

Determination of change of direction deficit thresholds across a spectrum of angles in basketball players

ABSTRACT Change of direction deficit (CODD) offers valuable insights into a player’s balance between linear and multidirectional speed. However, there are still no established reference values for CODD. The objectives of this study were to determine CODD thresholds for various change of direction angles in basketball players according to gender and analyse the relationships between CODD and execution time in speed tests. One hundred and thirty basketball players (46% female; age: 23.7 ± 5.29 years; height: 189.1 ± 11.1 cm; body mass: 84.3 ± 15.7 kg) undertook 10-m linear and change of direction speed test at 45º, 90º and 180º. A k-means cluster analysis was conducted to standardise CODD thresholds and a one-way analysis of variance to identify the differences between clusters. The results revealed angulation-specific CODD thresholds, ranging from 3% to 8%, 17% to 25% and 43% to 51% for 45º, 90º and 180º cutting angles, respectively for the pooled sample. Furthermore, differences inter-clusters (p < 0.05) were observed for execution time at all cutting angles for both genders. Therefore, strength and conditioning coaches are encouraged to assess CODD as a highly valid variable for evaluating change of direction performance and to use current CODD thresholds to tailor training programmes according to each athlete’s needs.

Multi‐armed bandit based online model selection for concept‐drift adaptation

AbstractEnsemble methods are among the most effective concept‐drift adaptation techniques due to their high learning performance and flexibility. However, they are computationally expensive and pose a challenge in applications involving high‐speed data streams. In this paper, we present a computationally efficient heterogeneous classifier ensemble entitled OMS‐MAB which uses online model selection for concept‐drift adaptation by posing it as a non‐stationary multi‐armed bandit (MAB) problem. We use a MAB to select a single adaptive learner within the ensemble for learning and prediction while systematically exploring promising alternatives. Each ensemble member is made drift resistant using explicit drift detection and is represented as an arm of the MAB. An exploration factor controls the trade‐off between predictive performance and computational resource requirements, eliminating the need to continuously train and evaluate all the ensemble members. A rigorous evaluation on 20 benchmark datasets and 9 algorithms indicates that the accuracy of OMS‐MAB is statistically at par with state‐of‐the‐art (SOTA) ensembles. Moreover, it offers a significant reduction in execution time and model size in comparison to several SOTA ensemble methods, making it a promising ensemble for resource constrained stream‐mining problems.

Extraction of laser stripe centerlines from translucent optical components using a multi-scale attention deep neural network

The precise extraction of laser stripe centerlines is critical for line-laser 3D scanning systems. However, conventional methods relying on threshold segmentation and morphological operations face significant challenges when confronted with pervasive optical phenomena, including specular reflection, scattering, and bleeding, which are commonly observed in translucent optical components. These methods typically require complex preprocessing procedures and often yield poor precision in centerline extraction. In this paper, we introduce a novel learning-based approach, complemented by a meticulously curated dataset, explicitly designed to address these challenges. Our proposed method leverages a multi-scale attention U-Net-like architecture, initially tasked with the segmentation of laser stripes from the complex background environment. Subsequently, it employs the Steger algorithm for the precise extraction of laser stripe centerlines. The experimental results, obtained by comprehensively evaluating real-world captured images, clearly demonstrate the effectiveness of our deep neural network combined with the Steger algorithm. This combined approach exhibits exceptional accuracy even when challenged by the interferences from specular reflection, scattering, and bleeding artifacts. Specifically, our method achieves a mean intersection over union (mIoU) of 84.71% for the laser stripe detection task, accompanied by a mean square error (MSE) of 10.371 pixels. Also, the average execution time for the centerline extraction task is notably efficient at 0.125 s.

Combined optimization of spot positions and weights for better FLASH proton therapy.

In Intensity Modulated Proton Therapy (IMPT), the weights of individual pencil-beams or spots are optimized to fulfil dosimetric constraints. Theses spots are usually located on a regular lattice and their positions are fixed during optimization. In many cases, the range of spot weights may however be limited, leading sometimes to sub-optimal plan quality. An emblematic use case is the delivery of a plan at ultra-high dose rate (FLASH-RT), for which the spot weights are typically constrained to high values. To improve further the quality of IMPT FLASH plans, we propose here a novel algorithm to optimize both the spot weights and positions directly based on the objectives defined by the treatment planner. For all cases considered, optimizing the spot positions lead to an enhanced dosimetric score, while maintaining a high dose rate. Overall, this approach resulted in a substantial plan quality improvement compared to optimizing only the spot weights, and in a similar execution time.

"Extending Amdahl's and Gustafson Baris's Laws by Adding Communication Overheads"

The extension of Amdahl's law and Gustafson-Barsis' law presented in this article provides insight into the relationship between communication costs and network topology in parallel computing. By considering communication latency in parallel computation execution time, these extensions can help researchers and developers optimize the interconnected network architecture of processing nodes and improve the performance of parallel computing systems. Today, parallel computer systems consisting of hundreds and thousands of processing nodes based on multiprocessor chip technology, high-speed optical transmission such as supercomputers are being researched, developed, and applied in many fields many areas. Although chip technology has progressed to the 3 nm process, the network architecture connecting processing nodes continues to be a problem that greatly affects the communication delay in the parallel computation time of the applications. In this paper, extensions of Amdahl's law and Gustafson-Barsis' law are presented with the addition of communication costs depending on the topology of the topology. These extensions provide insight into the relationship between communication costs and network topology in parallel computing. By considering communication latency in parallel computation execution time, these extensions can help researchers and developers optimize the interconnected network architecture of processing nodes and improve the performance of parallel computing systems.

High Utility Itemset Extraction using PSO with Online Control Parameter Calibration

This study investigates the use of evolutionary computation for mining high-value patterns from benchmark datasets. The approach employs a fitness function to assess the usefulness of each pattern. However, the effectiveness of evolutionary algorithms heavily relies on the chosen strategy parameters during execution. Conventional methods set these parameters arbitrarily, often leading to suboptimal solutions. To address this limitation, the research proposes a method for dynamically adjusting strategy parameters using temporal difference approaches, a machine learning technique called Reinforcement Learning (RL). Specifically, the proposed IPSO RLON algorithm utilizes SARSA learning to intelligently adapt the Crossover Rate and Mutation Rate within the Practical Swarm Optimization Algorithm. This allows IPSO RLON to effectively mine high-utility itemsets from the data.The key benefit of IPSO RLON lies in its adaptive control parameters. This enables it to discover optimal high-utility itemsets when applied to various benchmark datasets. To assess its performance, IPSO RLON is compared to existing approaches like HUPEUMU-GRAM, HUIM-BPSO, IGA RLOFF, and IPSO RLOFF using metrics like execution time, convergence speed, and the percentage of high-utility itemsets mined. From the evaluation it is observed that the proposed IPSO RLON perfroms better than the other methodology.

Estimation of distribution algorithms with solution subset selection for the next release problem

Abstract The Next Release Problem (NRP) is a combinatorial optimization problem that aims to find a subset of software requirements to be delivered in the next software release, which maximize the satisfaction of a list of clients and minimize the effort required by developers to implement them. Previous studies have applied various metaheuristics, mostly genetic algorithms. Estimation of Distribution Algorithms (EDA), based on probabilistic modelling, have been proved to obtain good results in problems where genetic algorithms struggle. In this paper we propose to adapt three EDAs to tackle the multi-objective NRP in a fast and effective way. Results show that EDAs can be applicable to solve the NRP with rather good quality of solutions. Furthermore, we prove that their execution time can be significantly reduced using a per-iteration solution subset selection method while maintaining the overall quality of the solutions obtained, and they perform the best when limiting the search time as in an interactive tool that requires fast responsiveness. The experimental framework, code and datasets have been made public in a code repository.

Technical Perspective: From Binary Join to Free Join

Technical Perspective: From Binary Join to Free Join

Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder.

Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder's adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub ( https://github.com/tjcadd2020/xMarkerFinder ), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server ( https://www.biosino.org/xmarkerfinder ).

Anticholinergic burden and the cognitive functions in elderly and senile patients with arterial hypertension

The aim of the study was to evaluate the effect of anticholinergic load (AHN) on cognitive functions (CF) in elderly and senile multimorbid patients with arterial hypertension (AH).Materials and methods. 330 patients aged 60 years and older with essential AH were included in the study (median age 79 [72; 84] years, 158 (51 %) of women). All the patients were underwent the assesment of CF using the Montreal Cognitive Assessment Scale (MoCA), Mini-Mental Status Scale (MMS), Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog), Trial Making Test (TMT), Digit Symbol Substitution Test (DSST), Verbal Association Test (literal (letter) and categorical (animal) associations), Boston Naming Test (BNT), Word-Color Interference Test, Stroop colorword conflict test. The anticholinergic load was determined using the anticholinergic load scale (Anticholinergic Cognitive Burden Scale, ACB).Results. Compared with patients who do not take anticholinergic drugs, multimorbid elderly and senile hypertensive patients with 2 or more points on the ACB scale had significantly lower final scores on the MoCA test (23 [21; 24.3] versus 24 [22; 25] points, respectively, p=0.042) and on MMSE (26 [24; 29] vs. 27.5 [25; 29] points, respectively, p=0.015), they spent statistically more time completing part B of the TMT test (217.5 [187.3; 246.3] vs. 204 [166.8; 247.3] seconds, respectively, p=0.038). The difference between the execution time of part B and part A of the TMT test in patients with 2 or more points on the ACB scale was statistically significantly greater than in patients with 0 points on this scale (141 [103.8; 168.5] versus 124 [83.8; 162] sec, respectively, p=0.034). Age, gender, education, and the structure of concomitant diseases did not differ between the groups.Conclusion. The results obtained indicate the adverse effect of аnticholinergic вurden on CF of multimorbid elderly and senile hypertensive patients and dictate the need to optimize pharmacotherapy in this category of patients.

Source code obfuscation with genetic algorithms using LLVM code optimizations

Abstract With the advent of the cloud computing model allowing a shared access to massive computing facilities, a surging demand emerges for the protection of the intellectual property tied to the programs executed on these uncontrolled systems. If novel paradigm as confidential computing aims at protecting the data manipulated during the execution, obfuscating techniques (in particular at the source code level) remain a popular solution to conceal the purpose of a program or its logic without altering its functionality, thus preventing reverse-engineering on the program even with the help of computing resources. The many advantages of code obfuscation, together with its low cost, makes it a popular technique. This paper proposes a novel methodology for source code obfuscation that can be used together with other traditional obfuscation techniques, making the code more robust against reverse engineering attacks. Three program complexity metrics are used to define three different single-objective combinatorial optimization versions of the problem, which are solved and analysed. Additionally, three multi-objective problems are defined, those considering each of the selected metrics together with the program execution time, in order to avoid strong obfuscations penalizing the performance. The goal of the defined problems is to find sequences of LLVM optimizations that lead to highly obfuscated versions of the original code. These transformations are applied to the back-end pseudo-assembly code (i.e., LLVM Intermediate Representation), thus avoiding any further optimizations by the compiler. Classical genetic algorithms (GAs) are used to solve the studied problems, namely a basic cellular GA for the single-objective problems and the popular NSGA-II for the multi-objective ones. The promising results show the potential of the proposed technique.

Machine Learning Algorithms That Emulate Controllers Based on Particle Swarm Optimization—An Application to a Photobioreactor for Algal Growth

Particle Swarm Optimization (PSO) algorithms within control structures are a realistic approach; their task is often to predict the optimal control values working with a process model (PM). Owing to numerous numerical integrations of the PM, there is a big computational effort that leads to a large controller execution time. The main motivation of this work is to decrease the computational effort and, consequently, the controller execution time. This paper proposes to replace the PSO predictor with a machine learning model that has “learned” the quasi-optimal behavior of the couple (PSO and PM); the training data are obtained through closed-loop simulations over the control horizon. The new controller should preserve the process’s quasi-optimal control. In identical conditions, the process evolutions must also be quasi-optimal. The multiple linear regression and the regression neural networks were considered the predicting models. This paper first proposes algorithms for collecting and aggregating data sets for the learning process. Algorithms for constructing the machine learning models and implementing the controllers and closed-loop simulations are also proposed. The simulations prove that the two machine learning predictors have learned the PSO predictor’s behavior, such that the process evolves almost identically. The resulting controllers’ execution time have decreased hundreds of times while keeping their optimality; the performance index has even slightly increased.

FPGA-enhanced IoT methods for disease pre-screening and prediction: An energy optimization approach

The research paper has introduced a system based on the Internet of Things (IoT) that has the potential to contribute to the creation of a Consumer Electronics (CE) product. In the event that any health-related measure deviates from the normal range, this system, which is based on IoT technology, will send a notification to the user. The data collected by this system is sent to an analysis system based on a Field Programmable Gate Array (FPGA) through a mobile application, and then transferred to the cloud for storage. To display this raw data, a wearable IoT device can be utilized, and the system will process the data accordingly. The major innovation of this proposed project lies in the development of a fuzzy classifier that can accurately predict the pathological condition of an illness. By implementing a fuzzy classifier using FPGA, the execution time is greatly reduced i.e. 57.7μs compared to other algorithms. Ans also the accuracy of the proposed model is 98.8 %, which higher then other machine learning model such as 97 %, 96.5 %, 93.2 % respectively for SVM, KNN and DT models.previous models like KNN, DT, SVM, and NB (Naive Bayes).

Self-Adaptive Incremental PCA-Based DBSCAN of Acoustic Features for Anomalous Sound Detection

In modern industry, maintaining continuous machine operations is important for improving production efficiency and reducing costs. Therefore, the smart technology of acoustic monitoring to detect anomalous machine conditions earlier before breakdowns works as part of predictive maintenance and is applied not only in industry fault detection but also in safety monitoring and surveillance systems. This paper proposes a self-adaptive unsupervised machine learning algorithm with dimension-reduction technology to detect anomalous sounds after extracting acoustic machine features. Technically, the automatic EPS calculation algorithm-based genetic algorithm optimizes the automatic clustering algorithm’s configuration for incremental principal component analysis and density-based spatial clustering algorithms with noise. IPCA is enhanced by the sequential Karhunen–Loeve (SKL) algorithm, and the condensation algorithm works as the second layer of the algorithm to reduce the number of effective components. This architecture could select an optimized set of parameters based on different test environments and keeps performance quality with fewer computational requirements. In the experiments, 228 sets of normal sounds and 100 sets of anomaly sounds are used. The sound files are collected from the same machine type (stepper motors) at a real plant site. We compare the proposed algorithm with K-means++, one-class SVM, agglomerative clustering, DCGAN and DCNN-Autoencoder, and this new algorithm performs best, with an AUC of 0.84 and the shortest execution time. The algorithm is generic and can be applied to detect anomalies in machines to provide early warning to people to avoid serious accidents or disasters.

Crop recommendation system and pest classification using weighted support vector machine on climate data

Introduction: The primary cause of the significant decline in crop productivity is farmers' poor crop selection. A number of pests, including weeds, insects, plant diseases, and the poisonous nature of the most current remedies, offer challenges to the current approach. Therefore, for the most effective and precise classification and recommendations, these factors should be considered together.Methods: Levy flight Grey Wolf Optimization (LGWO) and the WSVM (Weight-Support Vector Machine) method are recommended in this research for the intention of upgrading the efficiency of the system as well as resolving the above-mentioned issues. A CRS (Crop Recommendation System) utilizing the LGWO-WSVM algorithm is to be developed in order to increase crop productivity. This study's primary stages include crop suggestion, FS (Feature selection), and pre-processing. The KNN (K-Nearest Neighbour) technique is utilized for the pre-processing of the climatic dataset in order to accommodate incorrect values and missing variables. Results: The best fitness values are utilized to identify more pertinent weather features. These chosen qualities are then applied to the categorization phase. In order to create a system which integrates the predictions of the LGWO-WSVM model to recommend an appropriate crop depends on the kinds of the particular soil and features having greater accuracy.Conclusion: In order to get the best recommendation outcomes, it is also utilized to categorize the pest traits. The test outcomes indicate that the recommended LGWO-WSVM strategy overtakes the current methods by accuracy, precision, recall, and execution time.