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In recent times, due to the growing global population and increased food demand, smart agriculture is becoming more vital. In this context, Internet of Things (IoT) technologies have emerged as a significant pathway to innovative agricultural techniques. Due to their low capacity, these IoT nodes have faced energy limits and complicated routing methods. As a result, in the sphere of IoT-based agriculture, transmitting data failure, energy consumption, network lifetime reduction, and delay occur. To overcome this problem, this study proposes a novel combination of optimized intelligent smart irrigation systems to improve the energy management performance of the system. Here, the optimal cluster head formation and selection is performed by Hierarchy Shuffled Shepherd Clustering (HSSC) method. Also, the finest energy regulation and routing path are provided by the proposed Emperor Penguin Jellyfish Optimizer (EPJO) method. The simulation of this work is performed on Network Simulator-2 (NS2) software. The simulation consequences from the proposed method are validated and compared with the conventional methods. Thus, the proposed approach results demonstrate that the developed model has much lesser energy consumption and improved network lifetime as compared to the traditional works. [Display omitted] [ABSTRACT FROM AUTHOR]

Knowledge representation and extraction are very important tasks in data mining. In this work, we proposed a variety of rule-based greedy algorithms that able to obtain knowledge contained in a given dataset as a series of inhibitory rules containing an expression “attribute ≠ value” on the right-hand side. The main goal of this paper is to determine based on rule characteristics, rule length and coverage, whether the proposed rule heuristics are statistically significantly different or not; if so, we aim to identify the best performing rule heuristics for minimization of rule length and maximization of rule coverage. Friedman test with Nemenyi post-hoc are used to compare the greedy algorithms statistically against each other for length and coverage. The experiments are carried out on real datasets from UCI Machine Learning Repository. For leading heuristics, the constructed rules are compared with optimal ones obtained based on dynamic programming approach. The results seem to be promising for the best heuristics: the average relative difference between length (coverage) of constructed and optimal rules is at most 2.27% (7%, respectively). Furthermore, the quality of classifiers based on sets of inhibitory rules constructed by the considered heuristics are compared against each other, and the results show that the three best heuristics from the point of view classification accuracy coincides with the three well-performed heuristics from the point of view of rule length minimization. [ABSTRACT FROM AUTHOR]

[Display omitted] • The suggested approach BC-AI is utilized to determine the prediction of breast cancer using computer vision. • A data-driven computer-aided diagnostic (CAD) method for identifying patients as malignant, non-cancerous, or neither. • Feature extraction using the GLCM and HOG-based approach. • Self-constructing ensemble learning fuzzy algorithm (S-ELFA) blends a fuzzy methodology with an advanced neural network for optimum breast cancer illness detection and diagnosis. Breast cancer is a leading cause of mortality among women. Early detection will increase the chances of successful treatment and minimize the death rate. Even though many studies have been conducted to detect breast cancer, medical experts still face difficulty distinguishing between malignant and benign tumors. Hence, a technique enabling medical practitioners to effectively identify breast cancer was developed in this study. A computer-aided diagnostic (CAD) tool is used for classifying and diagnosing patients. The input images are pre-processed at the initial stage and a algorithm based on histogram of oriented gradients (HOG) and gray level co-occurrences matrix (GLCM) is applied to extract key features from pre-processed images. Then, shuffle shepherded optimization (SSO) selects the best features from the extracted features. Finally, the proposed self-constructing ensemble learning fuzzy algorithm (S-ELFA) identifies benign and malignant tumors. ROC, sensitivity, specificity, precision, and accuracy metrics were used to evaluate the developed method. [ABSTRACT FROM AUTHOR]

Recently, heavy metal air pollution has received significant interest in computing the total concentration of every toxic metal. Chemical fractionation of possibly toxic substances in airborne particles becomes a vital element. Among the primary and secondary air pollutants, airborne particulate matter (APM) received considerable internet among research communities owing to the adversative impact on human health. Hence, size distribution details of airborne heavy metals are important in assessing the adverse health effects over the globe. Recently, deep learning models have gained significant interest over the mathematical and statistical prediction models. In this view, this paper presents a novel arithmetic optimization algorithm (AOA) with multi-head attention based bidirectional long short-term memory (MABLSTM) model for predicting the size fractionated airborne particle bound metals. The proposed AOA-MABLSTM technique focuses on the forecasting of the size-fractionated airborne particle bound matter. The presented model intends to examine the concentration of PM and distinct sized-fractionated APM. The proposed model establishes MABLSTM based accurate predictive approaches for atmospheric heavy 83 metals is used for determining temporal trend of heavy metal. The proposed model employs AOA based hyperparameter tuning process to optimally tune the hyperparameters included in the MABLSTM method. To demonstrate the improved outcomes of the AOA-MABLSTM approach, a comparison study is performed with recent models. The stimulation results emphasized the betterment of the presented model over the other methods. Aluminum metal had an RMSE of 73.200 for AOA-MABLSTM. On Cu metal, the AOA-MABLSTM approach had an RMSE of 6.747. On Zn metal, the AOA-MABLSTM system lowered the RMSE by 45.250. [Display omitted] • A novel arithmetic optimization algorithm (AOA) with multi-head attention based bidirectional long short-term memory (MABLSTM) method. • The presented model intends to examine the concentration of PM and distinct sized-fractionated APM. • The proposed model establishes MABLSTM based accurate predictive approaches for atmospheric heavy 83 metals. • The proposed model employs AOA based hyperparameter tuning process to tune the hyperparameter included in the MABLSTM model. • To demonstrate the improved outcomes of the AOA-MABLSTM approach, a comparison study is performed with recent models. [ABSTRACT FROM AUTHOR]

The growth and implementation of biofuels and bioenergy conversion technologies play an important part in the production of sustainable and renewable energy resources in the upcoming years. Recycling sources from waste could efficiently ease the risk of world source strain. The waste classification was a good resolution for separating the waste from the recycled objects. It is inefficient and expensive to rely solely on manual classification of garbage and recycling sources. Convolutional neural networks (CNNs) have lately been used to classify recyclable waste, and this is the primary way for recycling the waste. This study presents a recycling waste classification using emperor penguin optimizer with deep learning (RWC-EPODL) model for bioenergy production. RWC-EPODL model focuses on recycling waste materials recognition and classification. When it comes to detecting and classifying trash, the RWC-EPODL model uses two stages. At the initial stage, the RWC-EPODL model uses AX-RetinaNet model for the recognition of waste objects. In addition, Bayesian optimization (BO) algorithm is applied as hyperparameter optimizer of the AX-RetinaNet model. Following the EPO algorithm with a stacked auto-encoder (SAE) model, the EPO algorithm is used to fine-tune the parameters of the SAE technique for trash classification. The RWC-EPODL model's experimental validation is examined through a number of studies. The RWC-EPODL approach has a 98.96 percent success rate. The comparative result analysis reported the better performance of the RWC-EPODL model over recent approaches. [Display omitted] • Novel recycling waste classification using emperor penguin optimizer with deep learning (RWC-EPODL) for bioenergy production • The presented RWC-EPODL model majorly focuses on the recognition and classification of recycling waste materials. • The proposed RWC-EPODL model uses AX-RetinaNet model for the recognition of waste objects. • The proposed model employs the EPO algorithm with stacked auto-encoder (SAE) model for waste classification. • To demonstrate the improved outcomes of the RWC-EPODL model, a series of experiments has been conducted to test the model. [ABSTRACT FROM AUTHOR]