Wireless sensor network (WSN) sensor nodes should have adequate energy. Reduced energy usage is essential to maximize the endurance of WSNs. Combining WSN with a more significant energy source, a cluster head (CH), is another effective strategy for extending WSN durability. A CH is dependent on the communication inside and between clusters. A CH’s energy level extends the cluster’s life for the complete WSN. Determining the energy required in WSNs while developing clustering algorithms is challenging. For maintaining energy efficiency in WSNs, this research offers K-medoids with sunflower-based clustering and a cross-layer-based optimal routing approach. An efficient fitness function generated from diverse objectives is used to choose the CH. After CH selection, sunflower optimization (SFO) indicates the best data transmission line to the sink node. The proposed protocol, SFO-CORP, increased the network lifetime by 19.6%, 13.63%, 11.13%, and 4.163% compared to the LEACH, EECRP, FEEC-IIR, and CL-IoT protocols, respectively. The experimental results showed that it performed better for packet delivery ratio, energy consumption, end-to-end delay, network lifetime, and computation efficiency.

In a variety of medical applications, electrocardiogram (ECG) records are essential for the identification of cardiovascular disorders. Cardiovascular issues such cardiac arrhythmias and coronary heart disease (CHD) are among the most prevalent and can result in cardiac arrest or sudden cardiac death. This study employs ECG signal preprocessing and feature extraction to identify cardiac arrhythmias and evaluate CHD risk. This research stresses the use of a Support Vector Machine (SVM) classifier for cardiac arrhythmia identification after ECG signal preprocessing. The preprocessed ECG signal is then subjected to arrhythmic beat classification to find anomalies. Extracted R-peaks from the ECG signal are divided into normal and arrhythmic risk subjects using the SVM classification-based approach for abnormality identification. When compared to other similar classifiers, the K-Nearest Neighbor (KNN) classifier offers the highest classification accuracy of 97.5%.

In this study, a multilayered botnet detection system for network-based botnet detection based on ANFIS is suggested. Two layers make up the suggested system, one of which is utilized for feature extraction and the other for botnet detection. Using mean and variance, the feature extraction layer pulls features from network traffic data. The botnet detection layer employs ANFIS to identify botnet activity based on the collected features. Using the CICIDS2017 dataset, the suggested system's performance is assessed. The experimental findings demonstrate that the suggested algorithm works better than other current botnet detection techniques regarding accuracy, precision, and recall. The proposed system obtained 99.83% accuracy, 99.75% precision, and 99.91 % recall. The proposed multilayered ANFIS-based botnet detection system offers an excellent way to identify botnet activity in a network. It can be utilized in various network security applications, such as threat intelligence, malware detection, and intrusion detection.

Aim/Purpose: Traditional hyperparameter tuning methods’ inefficiency and low accuracy in diabetes prediction. Application and evaluation of Bayesian optimization aim to increase the accuracy of diabetes prediction models. Background: Bayesian optimization (BO) has emerged as a powerful technique for hyperparameter tuning in machine learning models with its methodical approach for optimizing complex functions with expensive evaluations. Selecting the optimal hyperparameters for models can significantly increase prediction accuracy in the healthcare sector, particularly for diabetes prediction, hence improving patient outcomes and resource management. Methodology: Among the well-known diabetes databases used in the study is the Pima Indian Diabetes Database. Among the machine learning models developed and whose hyperparameters are modified via Bayesian optimization are Support Vector Machines (SVM), Random Forest, and Gradient Boosting Machines (GBM). The optimization process is compared with traditional tuning methods to assess improvements in model performance. Contribution: Problems include the increasing prevalence of diabetes worldwide and the role vital function prediction models play in early diagnosis and therapy. Especially when dealing with enormous datasets common in healthcare, grid search, and random search are sometimes computationally taxing and inefficient. However, Bayesian optimization promises a more practical and economical approach by selecting hyperparameters iteratively based on earlier evaluations. Findings: Bayesian optimization not only yields faster calculation times but also outperforms traditional methods. More precisely, models adapted by Bayesian optimization show greater sensitivity and specificity, which are crucial for accurate and prompt diabetes diagnosis. Recommendation for Researchers: By highlighting the specific elements and network characteristics that are behind the anomalies it discovers, on a real-time or large scale dataset. Future Research: This work can be improvised using several recent artificial intelligence algorithms with the integration of IoT on real-time datasets.

The growing need for continuous, real-time monitoring of vital physiological parameters like heart rate and blood pressure is driven by the demand for enhanced healthcare, particularly in remote or critical scenarios. Traditional healthcare systems often struggle with delays, inaccuracies, and high costs, making timely medical interventions more challenging. This study aims to design and implement a portable health monitoring system that use widely available technology and advanced wireless communication to address these issues effectively. The system integrates various sensors to track key physiological metrics and applies biomedical signal acquisition along with pre-processing and feature extraction techniques to reduce noise and improve signal quality. Wireless transmission enables real-time data transfer, supporting remote monitoring and analysis. To enhance communication efficiency, the system incorporates Massive Multiple-Input Multiple-Output (MIMO) technology within 5G Internet of Things (IoT) networks, ensuring higher data transfer speeds, reduced latency, and reliable connectivity. The findings demonstrate notable improvements in signal clarity and data transmission, reflected in a significant boost in Signal-to-Noise Ratio (SNR) and a reduction in Bit Error Rate (BER), underscoring the system’s effectiveness. By integrating 5G Massive MIMO technology, the performance of Wireless Body Area Networks (WBANs) is substantially improved, leading to more efficient patient monitoring.

Document categorization is a crucial task in organizing large collections of text. Traditional clustering methods like K-means often struggle with uncertainties in data. This paper presents a novel approach that combines Fuzzy K-Means (FKM) clustering with Bacterial Foraging Optimization (BFO) to enhance document clustering performance. The proposed method, FKM-BFO, benefits from fuzzy clustering’s ability to assign documents to multiple clusters, reflecting the inherent overlap in topics, while using the BFO algorithm to optimize the clustering process. FKM allows documents to belong to multiple clusters with varying degrees of membership, making it more suitable for real-world text data. However, FKM is sensitive to initial centroid placements and may get stuck in local optima. To address this, BFO, inspired by the foraging behaviour of bacteria, is used to optimize the initial centroids and guide the FKM algorithm to a global optimum. This combination improves clustering accuracy by better determining the cluster center and membership values. We evaluate the FKM-BFO approach using benchmark datasets like 20 Newsgroups and Reuters-21578. The results show that FKM-BFO outperforms traditional clustering methods, such as K-means and Fuzzy C-Means, in terms of accuracy and robustness, especially in handling noisy and high-dimensional data. This hybrid approach offers an effective solution for document categorization, providing higher accuracy and stability. Future work could explore its scalability and applicability to larger, real-time document clustering tasks.