Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

Agarwal, Nidhi; Gokilavani, M.; Nagarajan, S.; Saranya, S.; Alsolai, Hadeel; Abdelaziz, Amira Sayed

doi:10.32604/cmc.2023.030074

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…This approach exhibits the ability to accurately determine the coordinates of nodes within a network. Experimental results consistently confirm the effectiveness of the IAOAB-NLS model, irrespective of fluctuations in network parameters [25]. Additionally, an optimization-based localization learning algorithm (OLLA) is proposed by [26], which demonstrates good performance in indoor and outdoor scenarios.…”

Section: Introductionsupporting

confidence: 57%

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Lee,

Cheng,

et al. 2024

Electronics

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In positioning systems in wireless sensor networks, the accuracy of localization is often affected by signal distortion or attenuation caused by environmental factors, especially in indoor environments. Although using a combination of K-Nearest Neighbor (KNN) algorithm and fingerprinting matching can reduce positioning errors due to poor signal quality, the improvement in accuracy by increasing the number of reference points and K values is not significant. This paper proposes a Differential Evolution-based KNN (DE-KNN) method to overcome the performance limitations of the KNN algorithm and enhance indoor area positioning accuracy in WSNs. The DE-KNN method aims to improve the accuracy and stability of indoor positioning in wireless sensor networks. According to the simulation results, in a simple indoor environment with four reference points, when the sensors are deployed in both fixed and random arrangements, the positioning accuracy was improved by 29.09% and 30.20%, respectively, compared to using the KNN algorithm alone. In a complex indoor environment with four reference points, the positioning accuracy was increased by 32.24% and 33.72%, respectively. When the number of reference points increased to five, in a simple environment, the accuracy improvement for both fixed and random deployment was 20.70% and 26.01%, respectively. In a complex environment, the accuracy improvement was 23.88% and 27.99% for fixed and random deployment, respectively.

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Section: Introductionsupporting

confidence: 57%

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Lee,

Cheng,

et al. 2024

Electronics

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“…The practical application of the SVM model to real-world voice data transcends theoretical constructs, materializing as a tangible demonstration of predictive potency. Enshrined within the code snippets, the adeptness of the SVM model in navigating both the training and test datasets offers empirical validation of its practical efficacy [24,26]. Moreover, the symphony orchestrated by melding biomedical insights, machine learning capabilities, and the accessibility of datasets resonates with a harmonious rhythm.…”

Section: Related Work and Comparisonmentioning

confidence: 99%

“…In essence and at its core, this research represents a true and remarkable convergence of disciplines, serving as a synthesis and fusion of technology and healthcare paradigms, all to address a pressing and exigent unmet need in the realm of PD. By plumbing and delving into the profound depths of voice data intricacies and harnessing the sheer and raw power and prowess embodied within the SVM model [22][23][24], it aspires to usher in and herald a novel era of early PD prediction, thereby facilitating and enabling optimized interventions and ultimately leading to vastly improved patient outcomes. The significance of this endeavour extends far beyond the narrow confines of the research arena, sending ripples of hope and promise cascading across clinics and lives, offering a beacon of hope where once uncertainty and trepidation prevailed.…”

Section: Background and Significancementioning

confidence: 99%

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Dahiya,

Deepakshi

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Parkinson's Disease (PD), a progressively debilitating neurological disorder impacting a substantial global population, stands as a significant challenge in modern healthcare. The gradual onset of motor and non-motor symptoms underscores the criticality of early detection for optimal treatment outcomes. In response to this urgency, novel avenues for early diagnosis are being explored, where the amalgamation of biomedical voice analysis and advanced machine learning techniques holds immense promise. Individuals afflicted by PD experience a nuanced deterioration of bodily functions, necessitating interventions that are most effective when initiated at an early stage. The potential of biomedical voice measurements to encode subtle health indicators presents an enticing opportunity. The human voice, an intricate interplay of frequencies and patterns, might offer insights into the underlying health condition. OBJECTIVES: This research embarks on a comprehensive journey to delve into the intricate connections between voice attributes and the presence of PD, with the aim of expediting its detection and treatment. METHODS: At the heart of this exploration is the Support Vector Machine (SVM) model, a versatile machine learning tool [1-2]. Functioning as a virtual detective, the SVM model learns from historical data to decipher the intricate patterns that differentiate healthy individuals from those with PD [3-4]. RESULTS: Through the power of pattern recognition, the SVM becomes a predictive instrument, a potential catalyst in unravelling the latent manifestations of PD using the unique patterns harbored within the human voice. Embedded within this research are the practical demonstrations showcased through code snippets [5-7]. By synergizing the intricate voice measurements with the SVM model, we envision the emergence of a diagnostic paradigm where early PD detection becomes both accessible and efficient. This study not only epitomizes the synergy of voice and machine interactions but also attests to the transformative potential of technology within the domain of healthcare. . CONCLUSION: Ultimately, this research strives to harness the intricate layers of voice data, as exemplified through the provided model code [8-11], to contribute to the evolution of an advanced tool for PD prediction. By amalgamating the principles of machine learning and biomedical analysis, we aspire to expedite early PD diagnosis, thereby catalyzing more efficacious treatment strategies. In traversing this multidimensional exploration, we aspire to pave the path toward a future where technology plays an instrumental role in enhancing healthcare outcomes for individuals navigating the challenges of PD, ultimately advancing the pursuit of early diagnosis and intervention.

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“…An important aspect of EDA involved the analysis of outliers in the dataset. It was observed that there were no significant outliers that could disproportionately influence the classifiers' training or testing processes [2,9,10,[18][19][20]27]. Additionally, the distribution of "cp" (chest pain type) values revealed that the majority of patients had a value of 0, suggesting that this attribute may be a prominent feature for classification…”

Section: Data Split and Outlier Analysismentioning

confidence: 99%

Predictive Modelling for Heart Disease Diagnosis: A Comparative Study of Classifiers

Agarwal,

Deepakshi,

Harikiran

et al. 2024

EAI Endorsed Trans Perv Health Tech

Self Cite

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INTRODUCTION: Cardiovascular diseases, including heart disease, remain a significant cause of morbidity and mortality worldwide. Timely and accurate diagnosis of heart disease is crucial for effective intervention and patient care. With the emergence of machine learning techniques, there is a growing interest in leveraging these methods to enhance diagnostic accuracy and predict disease outcomes. OBJECTIVES: This study evaluates the performance of three machine learning classifiers—Naive Bayes, Logistic Regression, and k-Nearest Neighbors in predicting heart disease based on patient attributes. METHODS: In this study, we explore the application of three prominent machine learning classifiers—Naive Bayes, Logistic Regression, and k-Nearest Neighbors (kNN)—to predict the presence of heart disease based on a set of patient attributes. RESULTS: Using a dataset of 303 patient records with 14 attributes, including age, sex, and cholesterol levels, the data is pre-processed, scaled, and split into training and test sets. Each classifier is trained on the training set and evaluated on the test set. Results reveal that Naive Bayes and k-Nearest Neighbors classifiers outperform Logistic Regression in terms of accuracy, precision, recall, and area under the ROC curve (AUC). CONCLUSION: This study underscores the promising role of machine learning in medical diagnosis, showcasing the potential of Naive Bayes and k-Nearest Neighbors classifiers in improving heart disease prediction accuracy. Future work could explore advanced classifiers and feature selection techniques to enhance predictive accuracy and generalize findings to larger datasets.

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Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

Cited by 8 publications

References 20 publications

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

Predictive Modelling for Heart Disease Diagnosis: A Comparative Study of Classifiers

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