Hybridizing  Ant Colony Optimization Algorithm for Optimizing Edge-Detector Techniques

Almufti, Saman M.

doi:10.25007/ajnu.v11n2a1320

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…This is crucial because features with larger numerical ranges could disproportionately influence the model's performance. Techniques like MinMaxScaler, StandardScaler, and RobustScaler are employed to rescale data to a specific range or to adjust it based on statistical properties such as mean and standard deviation [26], [27].…”

Section: Preprocessing: Normalizationmentioning

confidence: 99%

Comprehensive Classification of Fetal Health Using Cardiotocogram Data Based on Machine Learning

Alkurdi,

Abdulazeez

2024

ijcs

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In the realm of obstetrics, the evaluation of fetal health remains a paramount yet challenging endeavor. Traditional approaches, such as electronic fetal monitoring (EFM), despite their widespread adoption, continue to grapple with uncertainties regarding their impact on neonatal outcomes and the reduction of emergency cesarean deliveries. This ambiguity is compounded by a prevailing confusion within the obstetric community about interpreting fetal heart rate patterns, often leading to inconsistent and subjective assessments. Addressing these complexities, our study presents an innovative machine learning-based techniques for the comprehensive classification of fetal health using cardiotocogram (CTG) data, offering a more objective and nuanced alternative to conventional methods. The core of our proposed solution is a novel model employing a sophisticated ensemble of machine learning classifiers, including Multi-Support Vector Machine (Multi-SVM), Decision Tree, Random Forest with Hyperparameter Tuning, XGBoost, and Neural Networks. This model is unique in its application, processing datasets in four different forms: raw datasets, datasets processed with MinMaxScaler, datasets subjected to feature selection using SelectKBest, and a combination of MinMaxScaler processing and SelectKBest feature selection. Such meticulous preprocessing, encompassing normalization and feature selection, is pivotal in ensuring equitable contribution from each feature, thereby optimizing the model's learning process and predictive accuracy. The effectiveness of our model is rigorously evaluated using a dataset comprising 2126 individual records from CTG exams, classified by specialist obstetricians into three types: Normal, Suspect, and Pathological. These records are exhaustively analyzed using various metrics, including Accuracy, Precision, Recall, F1-Score, ROC AUC, and Confusion Matrix. Among the classifiers, XGBoost emerged as the most proficient, consistently outperforming others across multiple metrics. This indicates its superior ability to accurately identify and categorize the different states of fetal health. Our findings thus underscore the significant promise of machine learning in revolutionizing fetal health monitoring, offering a more reliable, objective, and comprehensive method for assessing fetal well-being, with profound implications for prenatal care and clinical decision-making.

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Section: Preprocessing: Normalizationmentioning

confidence: 99%

Comprehensive Classification of Fetal Health Using Cardiotocogram Data Based on Machine Learning

Alkurdi,

Abdulazeez

2024

ijcs

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“…In the domain of augmented and virtual reality (AR/VR), gesture detection facilitates immersive experiences, allowing users to interact with virtual environments in a natural and intuitive manner [22]. This has profound implications in fields ranging from gaming and entertainment to education and training simulations [23].…”

Section: Cmentioning

confidence: 99%

“…These classifiers, underpinned by algorithms that learn from and make predictions on data, are fundamental to a wide array of applications, from natural language processing to image recognition and beyond [32], [33]. The efficacy of a machine learning classifier lies in its ability to discern and learn from complex and often non-linear relationships within data, enabling it to adapt and improve over time with exposure to more information [34], [35].…”

Section: Machine Learning Classifiermentioning

confidence: 99%

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Almufti,

Abdulazeez

2024

ijcs

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Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.

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“…Ant system methods belong to the swarm-based metaheuristic algorithm the first algorithm that used ant behavior was proposed in 1992 by Marco Dorigo et al (Almufti, 2022). After its appearance, many modifications have been proposed for it and it has been adept to solve various problems in different fields.…”

Section: Pgjsrt Conclusionmentioning

confidence: 99%

Ant Based System: Overview, Modifications and Applications from 1992 to 2022

Almufti

Maribojoc²,

Pahuriray³

2022

PGSRT

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The complexity in real-world problems motivated researchers to innovate efficient problem-solving techniques. Generally natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization and Non-deterministic polynomial hard (NP-Hard) problems because of their ability to adjust to variety of conditions. This paper shows an overview for swarm based algorithm that based on ant behavior. The first algorithm that inspired ant behavior in search for food source was developed in 1992 and was tested in solving TSP problem Ant Colony Optimization (ACO) is a metaheuristic inspired by some ant species' pheromone trail laying and following behavior. Artificial ants in ACO are stochastic solution construction processes that use (artificial) pheromone information that is modified depending on the ants' search experience and possibly accessible heuristic information to generate candidate solutions for the problem instance under consideration. Many notable research achievements have been gained since the proposal of the Ant System, the first ACO algorithm. These contributions concentrated on the creation of high-performance algorithmic variants, the creation of a generic algorithmic framework for ACO algorithms, the successful application of ACO algorithms to a wide range of computationally difficult problems, and the theoretical understanding of ACO algorithm properties. Since the appearance of ACO many modifications for improving the performance of the algorithm and has been applied to various Applications in several fields. At the end of this paper, the improvements are listed

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Hybridizing Ant Colony Optimization Algorithm for Optimizing Edge-Detector Techniques

Cited by 5 publications

References 6 publications

Comprehensive Classification of Fetal Health Using Cardiotocogram Data Based on Machine Learning

Comprehensive Classification of Fetal Health Using Cardiotocogram Data Based on Machine Learning

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Ant Based System: Overview, Modifications and Applications from 1992 to 2022

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