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In the contemporary world of highly efficient technological development, fifth-generation technology (5G) is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second (Gbps). As far as the current implementations are concerned, they are at the level of slightly below 1 Gbps, but this allowed a great leap forward from fourth generation technology (4G), as well as enabling significantly reduced latency, making 5G an absolute necessity for applications such as gaming, virtual conferencing, and other interactive electronic processes. Prospects of this change are not limited to connectivity alone; it urges operators to refine their business strategies and offers users better and improved digital solutions. An essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G lines. Integrating Binary Greylag Goose Optimization (bGGO) to achieve a significant reduction in the feature set while maintaining or improving model performance, leading to more efficient and effective 5G network management, and Greylag Goose Optimization (GGO) increases the efficiency of the machine learning models. Thus, the model performs and yields more accurate results. This work proposes a new method to schedule the resources in the next generation, 5G, based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors (KNN), Gradient Boosting, and Extra Trees algorithms. The ensemble model shows better prediction performance with the coefficient of determination R squared value equal to. 99348. The proposed framework is supported by several Statistical analyses, such as the Wilcoxon signed-rank test. Some of the benefits of this study are the introduction of new efficient optimization algorithms, the selection of features and more reliable ensemble models which improve the efficiency of 5G technology. [ABSTRACT FROM AUTHOR]

The networks of wireless sensors provide the ground for a range of applications, including environmental monitoring and industrial operations. Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization. Network infrastructure planning should be focused on increasing performance, and it should be affected by the detailed data about node distribution. This work recommends the creation of each sensor's specs and radius of influence based on a particular geographical location, which will contribute to better network planning and design. By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization, our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation. Through implementing adaptive protocols according to varying environments and sensor constraints, our study aspires to improve overall network operation. We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization, Particle Swarm Optimization, Genetic Algorithms, and Whale Optimization about performance on real-world problems. Being the most efficient in the optimization process, Biruni displays the lowest error rate at 0.00032. The two other statistical techniques, like ANOVA, are also useful in discovering the factors influencing the nature of sensor data and network-specific problems. Due to the multi-faceted support the comprehensive approach promotes, there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made. Through delivering better performance and reliability for various in-situ applications, this research leads to a fusion of time series forecasters and a customized optimizer algorithm. [ABSTRACT FROM AUTHOR]

Direct marketing strategies in the banking sector have undergone evolution with the integration of predictive analytics and machine learning techniques. The focus of this study is on the utilization of these technologies to foresee bank term deposit subscriptions. The methodology encompasses data exploration, visualization, and the implementation of machine learning models. Datasets from Kaggle are employed, relationships within the data are explored through crosstabulations and heat maps, and feature engineering and preprocessing techniques are applied. The study individually implements models such as SGD Classifier, k-nearest neighbor Classifier, and Random Forest Classifier. The results indicate that the best performance among the evaluated models was exhibited by the Random Forest Classifier, achieving an accuracy of 87.5%, a negative predictive value (NPV) of 92.9972%, and a positive predictive value (PPV) of 87.8307%. These findings provide valuable insights for banks seeking to optimize their marketing strategies within the dynamic landscape of the financial industry. [ABSTRACT FROM AUTHOR]

This study delves into optimizing sustainable inventory management practices through the integration of advanced data analytics methodologies. In response to the complex dynamics of modern supply chains, where inventory control significantly impacts sustainability goals, this research aims to address the intricate interplay between decentralized decision-making, government policies, and strategic choices within supply chain networks. Employing models such as Game Theory and Gated Recurrent Unit (GRU), alongside statistical analyses, our investigation explores the transformative potential of informed decision-making frameworks. Through a comprehensive evaluation of inventory data, including statistical analyses, visual representations, and model evaluations, we illuminate the nuanced relationships and dependencies prevalent within inventory control strategies. Our findings underscore the significance of data-driven decision-making in optimizing inventory practices, mitigating risks, and fostering sustainability within supply chains. The insights gleaned from this study advocate for the continued application of advanced data analytics to pave the way for resilient, environmentally conscious, and economically viable supply chain practices. [ABSTRACT FROM AUTHOR]

Advancements in data analytics and the proliferation of the Internet of Things (IoT) have opened new frontiers in disease surveillance and early outbreak detection. In this paper, we present a comprehensive framework that integrates IoT-driven predictive data analytics with a secure blockchain network to revolutionize the early warning of disease outbreaks. Our system model comprises edge devices equipped with sensors for data collection and processing, coupled with a blockchain network ensuring data integrity and transparency. Within this framework, we focus on the pivotal role of a Support Vector Machine (SVM) for disease outbreak prediction, showcasing its exceptional accuracy and performance. Through extensive experimentation and comparative analysis, we demonstrate that the SVM, when embedded in our IoT ecosystem, excels in predicting disease outbreaks, outperforming other machine learning models. This approach not only enhances the timeliness and precision of outbreak detection but also facilitates informed decision-making and resource allocation. Furthermore, our system model's integration with blockchain technology ensures the secure storage and validation of prediction results, bolstering the trustworthiness of collected data. This research represents a significant leap forward in proactive disease management and public health, offering a blueprint for future endeavors in epidemiology and healthcare. It underscores the transformative potential of IoT-driven predictive analytics in safeguarding global health and well-being. [ABSTRACT FROM AUTHOR]

In this research, we employed a deep convolutional neural network, often known as a Deep CNN, to propose a novel approach to the detection of illnesses in the leaves of plants. In order to train the Deep CNN model, a dataset that is already accessible is employed. This dataset contains photographs of the leaves of 39 distinct plant species. Six different methods of data augmentation were utilized, including image inversion, gamma correction, noise injection, principal component analysis (PCA), color enhancement, rotation, and scaling. We came to the conclusion that adding more data to a model can improve its accuracy. The proposed model was trained using many epochs, batch sizes, and dropout percentages over the course of its development. When utilizing validation data, the suggested model performs better than methods of transfer learning that are commonly utilized. Extensive simulations demonstrate that the proposed model is capable of an astounding 83.12% accuracy in data classification. The proposed research is more accurate than the many machine learning technologies that are currently in use. In addition to that, we put the suggested model through our consistency and reliability testing. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have excelled in artificial intelligence, particularly in image-related tasks such as classification and object recognition. However, manually designing CNN architectures demands significant domain expertise and involves time-consuming trial-and-error processes, along with substantial computational resources. To overcome this challenge, an automated network design method known as Modified Teaching-Learning-Based Optimization with Refined Knowledge Sharing (MTLBORKS-CNN) is introduced. It autonomously searches for optimal CNN architectures, achieving high classification performance on specific datasets without human intervention. MTLBORKS-CNN incorporates four key features. It employs an effective encoding scheme for various network hyperparameters, facilitating the search for innovative and valid network architectures. During the modified teacher phase, it leverages a social learning concept to calculate unique exemplars that effectively guide learners while preserving diversity. In the modified learner phase, self-learning and adaptive peer learning are incorporated to enhance knowledge acquisition of learners during CNN architecture optimization. Finally, MTLBORKS-CNN employs a dual-criterion selection scheme, considering both fitness and diversity, to determine the survival of learners in subsequent generations. MTLBORKS-CNN is rigorously evaluated across nine image datasets and compared with state-of-the-art methods. The results consistently demonstrate MTLBORKS-CNN's superiority in terms of classification accuracy and network complexity, suggesting its potential for infrastructural development of smart devices. [ABSTRACT FROM AUTHOR]

Test case selection is to minimize the time and effort spent on software testing in real-time practice. During software testing, software firms need techniques to finish the testing in a stipulated time while uncompromising on quality. The motto is to select a subset of test cases rather than take up all available test cases to uncover most bugs. Our proposed model in the research study effort is termed SCARF-RT, which stands for Similarity coefficient (SC), Creating Acronyms, Regression test (RT), and Fuzzy set (FS) with Dataset (DS). Clustering of test cases using ranking and also based on similarity coefficients is to be implemented. This research considered eleven different features for clustering the test cases. Two techniques have been used. Firstly, each cluster will, to a certain extent, encompass a collection of distinct traits. Depending on the coverage of the feature, a cluster of test cases might be chosen. The ranking approach was used to create these groupings. The second methodology finds similarity among test cases based on eleven features. Then, the maxmin composition is used to find fuzzy equivalences upon which clusters are formed. Most similar test cases are clustered. Test cases of every cluster are selected as a test suite. The outcomes of this research show that the selected test cases based on the proposed approaches are better than existing methodologies in selecting test cases with less duration and at the same time not compromising on quality. Both fuzzy rank-based clustering and similarity coefficient-based clustering test case selection approaches have been developed and implemented. With the help of these methods, testers may quickly choose test cases based on the suggested characteristics and complete regression testing more quickly. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) affects a large proportion of elderly people. Symptoms include tremors, slow movement, rigid muscles, and trouble speaking. With the aging of the developed world's population, this number is expected to rise. The early detection of PD and avoiding its severe consequences require a precise and efficient system. Our goal is to create an accurate AI model that can identify PD using human voices. We developed a transformer-based method for detecting PD by retrieving dysphonia measures from a subject's voice recording. It is uncommon to use a neural network (NN)-based solution for tabular vocal characteristics, but it has several advantages over a tree-based approach, including compatibility with continuous learning and the network's potential to be linked with an image/voice encoder for a more accurate multi modal solution, shifting SOTA approach from tree-based to a neural network (NN) is crucial for advancing research in multimodal solutions. Our method outperforms the state of the art (SOTA), namely Gradient-Boosted Decision Trees (GBDTs), by at least 1% AUC, and the precision and recall scores are also improved. We additionally offered an XgBoost-based feature-selection method and a fully connected NN layer technique for including continuous dysphonia measures, in addition to the solution network. We also discussed numerous important discoveries relating to our suggested solution and deep learning (DL) and its application to dysphonia measures, such as how a transformer-based network is more resilient to increased depth compared to a simple MLP network. The performance of the proposed approach and conventional machine learning techniques such as MLP, SVM, and Random Forest (RF) have also been compared. A detailed performance comparison matrix has been added to this article, along with the proposed solution's space and time complexity. [ABSTRACT FROM AUTHOR]

The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study's overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection. [ABSTRACT FROM AUTHOR]

Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes can dramatically affect wind power system performance and predictability. Researchers and practitioners are creating more advanced wind power forecasting algorithms that combine more parameters and data sources. Advanced numerical weather prediction models, machine learning techniques, and real-time meteorological sensor and satellite data are used. This paper proposes a Recurrent Neural Network (RNN) forecasting model incorporating a Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm to predict wind power data patterns. The performance of this model is compared with several other popular models, including BER, Jaya Algorithm (JAYA), Fire Hawk Optimizer (FHO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO)-based models. The evaluation is done using various metrics such as relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), mean bias error (MBE), Pearson's correlation coefficient (r), coefficient of determination (R2), and determination agreement (WI). According to the evaluation metrics and analysis presented in the study, the proposed RNN-DFBER-based model outperforms the other models considered. This suggests that the RNN model, combined with the DFBER algorithm, predicts wind power data patterns more effectively than the alternative models. To support the findings, visualizations are provided to demonstrate the effectiveness of the RNN-DFBER model. Additionally, statistical analyses, such as the ANOVA test and the Wilcoxon Signed-Rank test, are conducted to assess the significance and reliability of the results. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods. [ABSTRACT FROM AUTHOR]

Introduction: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. Methodology: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. Results: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. Conclusions: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods. [ABSTRACT FROM AUTHOR]

Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials' exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending them to achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, filters, and antennas with a negative refractive index utilize metamaterials. This paper proposed an improved dipper throated-based ant colony optimization (DTACO) algorithm for forecasting the bandwidth of the metamaterial antenna. The first scenario in the tests covered the feature selection capabilities of the proposed binary DTACO algorithm for the dataset that was being evaluated, and the second scenario illustrated the algorithm's regression skills. Both scenarios are part of the studies. The state-of-the-art algorithms of DTO, ACO, particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization (WOA) were explored and compared to the DTACO algorithm. The basic multilayer perceptron (MLP) regressor model, the support vector regression (SVR) model, and the random forest (RF) regressor model were contrasted with the optimal ensemble DTACO-based model that was proposed. In order to assess the consistency of the DTACO-based model that was developed, the statistical research made use of Wilcoxon's rank-sum and ANOVA tests. [ABSTRACT FROM AUTHOR]

According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women's death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach is necessary for early cancer identification. This research proposes a novel framework integrating metaheuristic optimization with deep learning and feature selection for robustly classifying breast cancer from ultrasound images. The structure of the proposed methodology consists of five stages, namely, data augmentation to improve the learning of convolutional neural network (CNN) models, transfer learning using GoogleNet deep network for feature extraction, selection of the best set of features using a novel optimization algorithm based on a hybrid of dipper throated and particle swarm optimization algorithms, and classification of the selected features using CNN optimized using the proposed optimization algorithm. To prove the effectiveness of the proposed approach, a set of experiments were conducted on a breast cancer dataset, freely available on Kaggle, to evaluate the performance of the proposed feature selection method and the performance of the optimized CNN. In addition, statistical tests were established to study the stability and difference of the proposed approach compared to state-of-the-art approaches. The achieved results confirmed the superiority of the proposed approach with a classification accuracy of 98.1%, which is better than the other approaches considered in the conducted experiments. [ABSTRACT FROM AUTHOR]

Attempting to address optimization problems in various scientific disciplines is a fundamental and significant difficulty requiring optimization. This study presents the waterwheel plant technique (WWPA), a novel stochastic optimization technique motivated by natural systems. The proposed WWPA's basic concept is based on modeling the waterwheel plant's natural behavior while on a hunting expedition. To find prey, WWPA uses plants as search agents. We present WWPA's mathematical model for use in addressing optimization problems. Twenty-three objective functions of varying unimodal and multimodal types were used to assess WWPA's performance. The results of optimizing unimodal functions demonstrate WWPA's strong exploitation ability to get close to the optimal solution, while the results of optimizing multimodal functions show WWPA's strong exploration ability to zero in on the major optimal region of the search space. Three engineering design problems were also used to gauge WWPA's potential for improving practical programs. The effectiveness of WWPA in optimization was evaluated by comparing its results with those of seven widely used metaheuristic algorithms. When compared with eight competing algorithms, the simulation results and analyses demonstrate that WWPA outperformed them by finding a more proportionate balance between exploration and exploitation. [ABSTRACT FROM AUTHOR]

Recently, different techniques have been applied to detect, predict, and reduce traffic congestion to improve the quality of transportation system services. Deep learning (DL) is becoming increasingly valuable for solving critiques. DL applications in transportation have been collected in several recently published surveys over the last few years. The existing research has discussed the cloud environment, which does not provide timely traffic forecasts, which is the cause of frequent traffic accidents. Thus, a solid understanding of the difficulties in predicting congestion is required because the transportation system varies widely between non-congested and congested states. This research develops a bi-directional recurrent neural network (BRNN) using Gated Recurrent Units (GRUs) to extract and classify traffic into congested and non-congested. This research uses a bidirectional recurrent neural network to simulate and forecast traffic congestion in smart cities (BRNN). Urban regions worldwide struggle with traffic congestion, and conventional traffic control techniques have failed miserably. This research suggests a data-driven approach employing BRNN for traffic management in smart cities, which uses real-time data from sensors and linked devices to control traffic more efficiently. The primary measures include predicting traffic metrics such as speed, weather, current, and accident probability. Congestion prediction performance has also been improved by extracting more features such as traffic, road, and weather conditions. The proposed model achieved better measures than the existing state-of-the-art methods. This research also explores an overview and analysis of several early initiatives that have shown promising results; moreover, it explores two potential future research approaches to increase the accuracy and efficiency of large-scale motion prediction. [ABSTRACT FROM AUTHOR]