Powerful Algorithm Research Articles

Machine Learning and Big Data Analytics for Precision Cardiac RiskStratification and Heart Diseases

The data explosion hasushered in a new era where insights are mined from vast data pools known as big data. Strategies for harnessing this data have emerged as critical decision-making tools across fields, employing various data analysis methods. Data mining techniques play an essential role in extracting meaningful patterns and insights. Thispaper focuses on the intersection of data mining and healthcare, particularly the critical concern of heart disease prediction.It presents a novel system that estimates heart attack risk, combining data mining with machine learning. Employing classification, the system stratifies data intotwo classes: heart disease presence or absence. Two powerful algorithms,decision tree classification and Naïve Bayes classification, enhance accuracy in predicting heart disease risk, achieving up to 91% and 87% accuracy, respectively. This review paper comprehensively analyzes the system's architecture, methodologies, and outcomes in healthcare, emphasizing data mining and machine learning's potential in medicine. Subsequent sections delve into methodology, results, and implications,providing a holistic view of this innovativeapproach.

Anthropogenic noise in terrestrial Antarctica: a short review of background information, challenges and opportunities

Anthropogenic noise is an inevitable by-product of human activities. However, the potential effects of human noise on terrestrial Antarctica’s ecosystems have been understudied. Documented impacts encompass stress, alterations in behavioural patterns, auditory masking, and, in severe instances, mortality. This Perspective note aims to call attention to human-generated noise as potential sources of impact on Antarctic wildlife and to highlight the potential of soundscape analysis as a flexible, cost-effective tool for environmental monitoring across Antarctica, complementing other non-invasive approaches. Acoustic monitoring in terrestrial environments has been extensively used in different parts of the world to assess biodiversity, monitor populations’ status and trends, and identify and monitor sources of anthropogenic disturbance. Technological advances in passive acoustic monitoring allow for the gathering of detailed information with little need of human attention, and powerful processing tools and algorithms enable researchers to analyse large collections of audio data. Cold climates limit battery-operated instruments, but solar panels in Antarctic summer trials revealed over 100 days of unattended operation, which is promising for the incorporation of acoustic monitoring in Antarctica’s environmental management toolbox.

The explainable potential of coupling hybridized metaheuristics, XGBoost, and SHAP in revealing toluene behavior in the atmosphere

Toluene is a neurotoxic aromatic hydrocarbon and one of the major representatives of volatile organic compounds, known for its abundance, adverse health effects, and role in the formation of other atmospheric pollutants like ozone. This research introduces the enhanced version of the reptile search metaheuristics algorithm which has been utilized to tune the extreme gradient boosting hyperparameters, to investigate toluene atmospheric behavior patterns and interactions with other polluting species within defined environmental conditions. The study is based on a two-year database encompassing concentrations of inorganic gaseous contaminants every hour (NO, NO2, NOx, and O3), particulate matter fractions (PM1, PM2.5, and PM10), m,p-xylene, toluene, benzene, total non-methane hydrocarbons, and meteorological data. The experimental outcomes were validated against the results of extreme gradient boosting models optimized by seven other recent powerful metaheuristics algorithms. The best-performing model has been interpreted by employing Shapley additive explanations method. In the study, we have focused on the relationship between toluene and benzene, as its most important predictor, and provided a detailed description of environmental conditions which directed their interactions.

Design And Implementation of Network Security Using Neural Network

The number of internet users is increasing day by day. To keep internet surfing safe from vulnerable exploits we need to design new powerful algorithm. Hence this paper is mainly concerns with implementation of Neural Network using symmetric key cryptography to ensure confidentiality, authentication, integrity and message non-repudiation. In order to achieve the above objectives first an encryption algorithm is developed and implemented. The program takes a plain text as an input from the user to generate an intermediate cipher text and sends it to the source node of the neural network. At this point the intermediate cypher text is passed through some successive hidden layers of neural network’s concept to produce final cipher text. Now to recover the plain text from the final cipher text a decryption algorithm is developed and implemented.

An Optimal Framework Based on the GentleBoost Algorithm and Bayesian Optimization for the Prediction of Breast Cancer Patients' Survivability

Breast cancer is a primary cause of cancer-associated mortality among women globally, and early detection and personalized treatment are critical for improving patient outcomes. In this study, we propose an optimal framework for predicting breast cancer patient survivability using the GentleBoost algorithm and Bayesian optimization. The proposed framework combines the strengths of the GentleBoost algorithm, which is a powerful machine-learning algorithm for classification, and Bayesian optimization, which is a powerful optimization technique for hyperparameter tuning. We evaluated the proposed framework using the publicly available breast cancer dataset provided by The Surveillance, Epidemiology, and End Results (SEER) program and compared its performance with several popular single algorithms, including support vector machine (SVM), artificial neural network (ANN), and k-nearest neighbors (KNN). The experimental results demonstrate that the proposed framework outperforms these methods in terms of accuracy (mean= 95.16%, best = 95.35, worst = 95.1%, and SD = 0.008). The values of precision, recall, and f1-score of the best experiment were 92.3 %, 98.2 %, and 95.2 %, respectively, with hyperparameters of (number of learners = 246, learning rate = 0.0011, and maximum number of splits = 1240). The proposed framework has the potential to improve breast cancer patient survival predictions and personalized treatment plans, leading to the improved patient outcomes and reduced healthcare costs.

Scheduling of Continuous Annealing With a Multi-Objective Differential Evolution Algorithm Based on Deep Reinforcement Learning

This paper studies a multi-objective scheduling problem in a continuous annealing operation of the steel industry, which is to simultaneously determine the production batch sizes for coils and their schedules so as to minimize the cost of setup, earliness, and tardiness. A large batch can reduce the setup costs but lead to a cost increase in earliness and tardiness. Thus, the conflict objectives of minimizing setup cost, earliness, and tardiness can be formulated separately. In this paper, we formulate a multi-objective optimization model for the problem and develop an adaptive multi-objective differential evolutionary based on deep reinforcement learning (AMODE-DRL) for effectively obtaining the Pareto solutions. In the proposed AMODE-RDL, DRL is integrated into the MODE algorithm as a controller, which can adaptively select mutation operators and parameters according to different search domains. Computational results on randomly generated instances and the practical problem instances show that DRL can effectively guide MODE to select mutation operators and parameters. The proposed algorithm can obtain better solutions compared to other powerful multi-objective evolutionary and adaptive MODE algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Setup usually leads to a decrease in production capacity and an increase in production costs. Similarly, earliness and tardiness costs are also crucial. These costs correspond to production, customer demand, and inventory, which are common objectives in production systems. However, the three objectives are generally conflicting, and it is very hard for practitioners to make appropriate decisions with manual experience. The multi-objective optimization methods can provide different scheduling for practitioners who may choose the suitable one according to current working conditions. Accordingly, the proposed model and algorithm can extend to other fields with similar characteristic problems.

Impact of Component Correlation on Probabilistic Seismic Responses of Steel Frames by Modifying Non-Positive Definite Correlation Matrices

ABSTRACT This paper studies the effects of spatial correlation between the structural components on probabilistic seismic performance of steel moment resisting frames, using extended incremental dynamic analysis (IDA) method to consider the effects of aleatoric uncertainties caused by ground motion records. In this probabilistic assessment, a combination of Latin hypercube sampling (LHS) and Cholesky decomposition (CD) methods are utilized to take the epistemic uncertainties of the numerical model into account. In case the target correlation matrix is not positive definite, two powerful optimization algorithms are employed to find the closest positive definite correlation matrix.

Research on optimal power flow of AC/DC hybrid system for civil more electric aircraft

Modern civil more electric aircraft often use AC/DC systems instead of AC systems to reduce the weight of the power system. Compared with the AC/DC system of the land power grid, the aircraft power system needs to pursue economic operation mode under more complex security constraints. This paper establishes the optimal power flow model of the AC/DC hybrid system of more electric aircraft, which takes minimizing the active power loss of the line as the optimization objective, including the steady-state operation constraints and security constraints of the AC/DC system. The interior point method and prediction correction interior point method are used to solve the model. Then, based on the simplified steady-state model of the B787 power system, the model and algorithm are verified by experiments. The experimental results show that by optimizing the power flow distribution of the power system, the power utilization efficiency of the system has been significantly improved, and its stability and reliability can also be better guaranteed.

Towards explainable artificial intelligence through expert-augmented supervised feature selection

This paper presents a comprehensive framework for expert-augmented supervised feature selection, addressing pre-processing, in-processing, and post-processing aspects of Explainable Artificial Intelligence (XAI). As part of pre-processing XAI, we introduce the Probabilistic Solution Generator through the Information Fusion (PSGIF) algorithm, leveraging ensemble techniques to enhance the exploration and exploitation capabilities of a Genetic Algorithm (GA). Balancing explainability and prediction accuracy, we formulate two multi-objective optimization models that empower expert(s) to specify a maximum acceptable sacrifice percentage. This approach enhances explainability by reducing the number of selected features and prioritizing those considered more relevant from the domain expert's perspective. This contribution aligns with in-processing XAI, incorporating expert opinions into the feature selection process as a multi-objective problem. Traditional feature selection techniques lack the capability to efficiently search the solution space considering our explainability-focused objective function. To overcome this, we leverage the Genetic Algorithm (GA), a powerful metaheuristic algorithm, optimizing its parameters through Bayesian optimization. For post-processing XAI, we present the Posterior Ensemble Algorithm (PEA), estimating the predictive power of features. PEA enables a nuanced comparison between objective and subjective importance, identifying features as underrated, overrated, or appropriately rated. We evaluate the performance of our proposed GAs on 16 publicly available datasets, focusing on prediction accuracy in a single objective setting. Moreover, we test our multi-objective model on a classification dataset to show the applicability and effectiveness of our framework. Overall, this paper provides a holistic and nuanced approach to explainable feature selection, offering decision-makers a comprehensive understanding of feature importance.

228 Expert Hand Motion During Microvascular Anastomosis Simulation: Big Data Analysis Using Machine Learning Hand Detection

INTRODUCTION: Microanastomosis is a complex surgical technique that demands delicate and coordinated hand movement. Advances in machine learning technology have enabled new methods to study human motion. Convolutional neural networks are algorithms specialized in visual tasks that enable discrete object detection and tracking including the hands of a surgeon. METHODS: A machine learning hand detector was developed using a pre-trained convolutional neural network capable of tracking 21 hand landmarks without sensors attached. En-to-side microanastomosis was simulated using 2 mm polyvinyl-alcohol vessels and 10/0 micro-suture and recorded with a video camera capturing hand movement in the surgical field. Participants (n = 4) were expert cerebrovascular surgeons with significant operative experience in cerebral bypass surgery. Calculation of motion of 14 hand joint angles was performed using a vectorization method from landmark detections. Time series analysis was performed to calculate the absolute angular motion for each joint of the hand. RESULTS: Four minutes of simulation for each surgeon yielded a total of 784.294 angular detections (14 hand joints total). For the dominant hand, the mean (SD) absolute angular motion was expert 1: 178.2 rad (98.3); expert 2: 261.8 rad (164.3); expert 3: 449.9 rad (307); expert 4: 380.1 rad (198.1).For the non-dominant hand, the mean (SD) absolute angular motion was expert 1: 132.9 rad (64.6), expert 2: 149.2 rad (72.3); expert 3: 389.2 rad (243); expert 4: 162.6 rad (87.2). CONCLUSIONS: A machine learning hand detector system can calculate hand joint motion during microvascular anastomosis. The absolute angular motion is a quantitative performance metric providing information about hand stability during microanastomosis simulation. Such studies using powerful artificial intelligence detection algorithms may have educational potential in the assessment of complex and delicate surgical skills and supervision of surgical training.

Text Summarization and Document summarization using NLP

Background: Automatic text summarization has become increasingly crucial in navigating the ever- growing ocean of textual information. This research delves into exploring the potential of Natural Language Processing (NLP) techniques for creating efficient and informative summaries. We implemented and evaluated models based on Long Short-Term Memory (LSTM) networks, Sequence-to-Sequence (Seq2Seq) architectures, and transformer-based approaches. By leveraging these powerful algorithms, we aimed to generate concise summaries that capture the essence of the original text. The evaluation highlighted the strengths and limitations of each approach, showcasing the potential of NLP for text summarization while acknowledging the remaining challenges. This research not only contributes to the ongoing discussion on textsummarization techniques but also opens doorsfor further exploration, including integrating domain-specific knowledge, personalizing summaries based on user preferences, and applying these techniques to real-world information overload situations. Ultimately, this work underscores the promise of NLP-driven text summarization in facilitating efficient information access, comprehension, and utilization across various domains. Keywords: Natural Language Processing(NLP), Automatic Text Summarization, Real-world applications.

Prediction of Stunting in Toddlers Using Bagging and Random Forest Algorithms

Stunting is a condition of failure to thrive in toddlers. This is caused by lack of nutrition over a long period of time, exposure to repeated infections, and lack of stimulation. This malnutrition condition is influenced by the mother's health during pregnancy, the health status of adolescents, as well as the economy and culture and the environment, such as sanitation and access to health services. To find out predictions of stunting, currently we still use a common method, namely Secondary Data Analysis, namely by conducting surveys and research to collect data regarding stunting. This data includes risk factors related to stunting, such as maternal nutritional status, child nutritional intake, access to health services, sanitation, and other socioeconomic factors. This secondary data analysis can provide an overview of the prevalence of stunting and the contributing factors. To overcome this, the right solution is needed, one solution that can be used is data mining techniques, where data mining can be used to carry out analysis and predictions for the future, and provide useful information for business or health needs. Based on this analysis, this research will use the Bagging method and Random Forest Algorithm to obtain the accuracy level of stunting predictions in toddlers. Bagging or Bootstrap Aggregation is an ensemble method that can improve classification by randomly combining classifications on the training dataset which can reduce variation and avoid overfitting. Random Forest is a powerful algorithm in machine learning that combines decisions from many independent decision trees to improve prediction performance and model stability. By combining the Bagging method and the Random Forest algorithm, it is hoped that it will be able to provide better stunting prediction results in toddlers. This research uses a dataset with a total of 10,001 data records, 7 attributes and 1 attribute class. Based on the test results using the Bagging method and the Random Forest algorithm in this research, the results obtained were class precision yes 91.72%, class recall yes 98.84%, class precision no 93.55%, class recall no 65.28%, and accuracy of 91.98%.

Revolutionizing Pancreatic Cancer Diagnosis and Prognosis through Machine Learning Technology

Abstract: Pancreatic cancer presents significant challenges in terms of early detection and treatment, resulting in poor patient outcomes. This research article explores the application of random forest, a powerful machine learning algorithm, to enhance predictive modeling in pancreatic cancer research. Leveraging a dataset comprising clinical and molecular features, we trained and evaluated random forest models to predict key outcomes such as tumor progression, treatment response, and overall survival. Our findings demonstrate the effectiveness of random forest in accurately stratifying patients based on their prognosis and treatment outcomes. Furthermore, we identified key biomarkers and clinical variables contributing to predictive accuracy, providing valuable insights into the underlying biological mechanisms of pancreatic cancer progression. The integration of random forest into pancreatic cancer research holds promise for improving patient stratification, guiding treatment decisions, and ultimately, advancing personalized medicine approaches in the management of this challenging disease

Fake Review Detection using BERT Transfer Learning Algorithm

Abstract: In this research, our main focus is on tackling the issue of fake online reviews, which has become increasingly prevalent due to the vast amount of content generated by users on various online platforms. These reviews have a big impact on what people decide to buy, emphasizing how important it is for them to be real and trustworthy. However, the rise in fake reviews has cast a shadow of doubt over the credibility of online platforms, making it imperative to find effective ways to discern the real from the fake. To address this challenge, we propose a strong model that utilizes the capabilities of advanced deep learning algorithms—BERT (Bidirectional Encoder Representations from Transformers) and Bidirectional Long Short-Term Memory Networks (BiLSTMs)—to accurately identify and filter out fake reviews. BERT excels in understanding the nuances of language and BiLSTMs effectively capture the order of words and phrases. Our plan involves training and validating this model using datasets sourced from reputable platform like Kaggle. By amalgamating these powerful algorithms and datasets, we aim to significantly enhance the accuracy and credibility of our fake review detection system. Ultimately, our goal is to restore consumer trust and empower individuals to make more informed choices in the expansive realm of online consumerism.

Exploiting the Capabilities of Classifiers to Examine a Website Defacement Data Set

Website defacement is the illegal electronic act of changing a website. In this paper, the capabilities of robust machine learning classifiers are exploited to select the best input feature set for evaluation of a website’s defacement risk. A defacement mining data set was obtained from Zone-H, a private organization, and a sample consisting of 93,644 data points was pre-processed and used for modelling purposes. Using multi-dimensional features as input, enormous modelling computations were carried out to determine the optimal outputs, in terms of performance. Reason and hackmode presented the highest contributions for the evaluation of website defacement, and were thus chosen as outputs. Various machine learning models were examined, and decision tree (DT), k-nearest neighbours (k-NN), and random forest (RF) were found to be the most powerful algorithms for prediction of the target model. The input variables 'domain', 'system', 'web_server', 'redefacement', 'type', 'def_grade', and 'reason/hackmode' were tested and used to shape the final model. Using the cross-validation (CV) technique, the key performance factors of the models were calculated and reported. After calculating the average scores for the hyperparameter metrics (i.e., max-depth, min-sample-leaf, weight, max-features, and CV), both targets were evaluated, and the learning algorithms were ranked as RF &gt; DT &gt; k-NN. The reason and hackmode variables were thoroughly analysed, and the average score accuracies for the reason and hackmode targets were 0.85 and 0.585, respectively. The results comprise a significant development, in terms of modelling and optimizing website defacement risk. This study successfully addresses key cybersecurity concerns, particularly website defacement.

A novel decision support system for proactive risk management in healthcare based on fuzzy inference, neural network and support vector machine

BackgroundThe nature of activities practiced in healthcare organizations makes risk management the most crucial issue for decision-makers, especially in developing countries. New technologies provide effective solutions to support engineers in managing risks. PurposeThis study aims to develop a Decision Support System (DSS) adapted to the healthcare constraints of developing countries that enables the provision of decisions about risk tolerance classes and prioritizations of risk treatment. MethodsFailure Modes and Effects Analysis (FMEA) is a popular method for risk assessment and quality improvement. Fuzzy logic theory is combined with this method to provide a robust tool for risk evaluation. The fuzzy FMEA provides fuzzy Risk Priority Number (RPN) values. The artificial neural network is a powerful algorithm used in this study to classify identified risk tolerances. The risk treatment process is taken into consideration in this study by improving FMEA. A new factor is added to evaluate the feasibility of correcting the intolerable risks, named the control factor, to prioritize these risks and start with the easiest. The new factor is combined with the fuzzy RPN to obtain intolerable risk prioritization. This prioritization is classified using the support vector machine. FindingsResults prove that our DSS is effective according to these reasons: (1) The fuzzy-FMEA surmounts classical FMEA drawbacks. (2) The accuracy of the risk tolerance classification is higher than 98%. (3) The second fuzzy inference system developed (the control factor for intolerable risks with the fuzzy RPN) is useful because of the imprecise situation. (4) The accuracy of the fuzzy-priority results is 74% (mean of testing and training data). ConclusionsDespite the advantages, our DSS also has limitations: There is a need to generalize this support to other healthcare departments rather than one case study (the sterilization unit) in order to confirm its applicability and efficiency in developing countries.

A Topology Control Algorithm Taking into Account Energy and Quality of Transmission for Software-Defined Wireless Sensor Network

The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for softwaredefined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the Max Power algorithm in terms of average node degree and energy expansion ratio.

YOLOv7t-CEBC Network for Underwater Litter Detection

The issue of marine litter has been an important concern for marine environmental protection for a long time, especially underwater litter. It is not only challenging to clean up, but its prolonged presence underwater can cause damage to marine ecosystems and biodiversity. This has led to underwater robots equipped with powerful visual detection algorithms becoming the mainstream alternative to human labor for cleaning up underwater litter. This study proposes an enhanced underwater litter detection algorithm, YOLOv7t-CEBC, based on YOLOv7-tiny, to assist underwater robots in target identification. The research introduces some modules tailored for marine litter detection within the model framework, addressing inter-class similarity and intra-class variability inherent in underwater waste while balancing detection precision and speed. Experimental results demonstrate that, on the Deep Plastic public dataset, YOLOv7t-CEBC achieves a detection accuracy (mAP) of 81.8%, markedly surpassing common object detection algorithms. Moreover, the detection frame rate reaches 118 FPS, meeting the operational requirements of underwater robots. The findings affirm that the enhanced YOLOv7t-CEBC network serves as a reliable tool for underwater debris detection, contributing to the maintenance of marine health.

Using Artificial Intelligence Algorithms to Detect Hate Speech in Social Media Posts

Detecting hate speech on social media is of great importance to prevent negative impacts on people and communities and to remove such content. However, detecting hate speech is a complex and challenging process due to linguistic and cultural diversity. Therefore, it is important to develop powerful and effective machine learning algorithms. Since detecting such content using traditional methods can be time-consuming and costly, it is stated that artificial intelligence-based machine learning algorithms have great potential in this regard. The aim of this study is to evaluate the performance of artificial intelligence-based machine learning algorithms used in detecting posts containing hate speech on social media. The study focuses on the problem of detecting and managing hate speech on social media platforms. In this study, we will compare the performances of different algorithms and determine the most suitable methods. Additionally, the effects of the dataset and feature extraction methods on algorithm performance will be analyzed. Algorithms are often based on natural language processing techniques and try to detect hate speech by learning features in texts. The performance of these algorithms can vary depending on factors such as language, culture, the attributes they use, and the training dataset, so a comprehensive analysis is required. In the research, the performance of the algorithms used in detecting hate speech was compared with the dataset and feature extraction methods. In this process, the algorithms' linguistic and cross-cultural effectiveness, feature selection and representation, false positive and false negative rates, and overall accuracy will be analyzed.

Surrogate optimisation strategies for intraocular lens formula constant optimisation.

To investigate surrogate optimisation (SO) as a modern, purely data-driven, nonlinear adaptive iterative strategy for lens formula constant optimisation in intraocular lens power calculation. A SO algorithm was implemented for optimising the root mean squared formula prediction error (rmsPE, defined as predicted refraction minus achieved refraction) for the SRKT, Hoffer Q, Holladay, Haigis and Castrop formulae in a dataset of N = 888 cataractous eyes with implantation of the Hoya Vivinex hydrophobic acrylic aspheric lens. A Gaussian Process estimator was used as the model, and the SO was initialised with equidistant datapoints within box constraints, and the number of iterations restricted to either 200 (SRKT, Hoffer Q, Holladay) or 700 (Haigis, Castrop). The performance of the algorithm was compared to the classical gradient-based Levenberg-Marquardt algorithm. The SO algorithm showed stable convergence after fewer than 50/150 iterations (SRKT, HofferQ, Holladay, Haigis, Castrop). The rmsPE was reduced systematically to 0.4407/0.4288/0.4265/0.3711/0.3449 dioptres. The final constants were A = 119.2709, pACD = 5.7359, SF = 1.9688, -a0 = 0.5914/a1 = 0.3570/a2 = 0.1970, C = 0.3171/H = 0.2053/R = 0.0947 for the SRKT, Hoffer Q, Holladay, Haigis and Castrop formula and matched the respective constants optimised in previous studies. The SO proves to be a powerful adaptive nonlinear iteration algorithm for formula constant optimisation, even in formulae with one or more constants. It acts independently of a gradient and is in general able to search within a (box) constrained parameter space for the best solution, even where there are multiple local minima of the target function.