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At present, the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity. The bacterial foraging optimization (BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization ability. But the BFO-based algorithm is easy to fall into local optimum. Therefore, this paper proposes the quantum bacterial foraging optimization (QBFO)-binary orthogonal matching pursuit (BOMP) channel estimation algorithm to the problem of local optimization. Firstly, the binary matrix is constructed according to whether atoms are selected or not. And the support set of the sparse signal is recovered according to the BOMP-based algorithm. Then, the QBFO-based algorithm is used to obtain the estimated channel matrix. The optimization function of the least squares method is taken as the fitness function. Based on the communication between the quantum bacteria and the fitness function value, chemotaxis, reproduction and dispersion operations are carried out to update the bacteria position. Simulation results show that compared with other algorithms, the estimation mechanism based on QBFO-BOMP algorithm can effectively improve the channel estimation performance of millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems. Meanwhile, the analysis of the time ratio shows that the quantization of the bacteria does not significantly increase the complexity.

The Internet of Things (IoT) is a subclass of the Industry 4.0 standard. The functionality of IoT depends on the Wireless Sensor Networks (WSNs) design. The IoT-empowered WSNs received the researcher's attention for the Smart Farming (SF) applications. SF nowadays is required to enhance farm productivity while minimizing the cost and resources. The agriculture sensors devices disposed over the farm collect the on-field farm data and transfer it wirelessly to the base station for decision-making and agriculture monitoring. As the nodes are resource restrained, the process of periodic farm data gathering and multi-hop delivery needs to be effective in terms of Quality of Service (QoS) and energy-efficiency of information transmission by reflecting the long-distance transmission difficulties of SF applications. To enhance the network lifetime substantially of densely deployed WSN for periodically monitoring of farm conditions, we propose a novel Nature-Inspired algorithm-based Cross-layer Clustering (NICC) protocol. We design NICC to find a reasonably better solution for clustering and routing in SF applications. NICC explores the idea of a nature-inspired optimization algorithm called Bacterial Foraging Optimization (BFO) with optimal fitness function, which models the trade-off among the energy efficiency and optimal data transmission. We design a BFO algorithm to select the optimal sensor node for clustering and routing problems based on cross-layer parameters-based fitness value computation. The cross-layer parameter includes the sensor parameters from layers like network layer, physical layer, and Medium Access Control (MAC). The numerical results show the superiority of the NICC protocol for various WSN-assisted SF scenarios against state-of-art clustering techniques.

This paper presents an adaptive bacterial foraging optimization (ABFO) algorithm for an active noise control system. The conventional active noise control (ANC) systems often use the gradient-based filtered-X least mean square algorithms to adapt the coefficients of the adaptive controller. Hence, there is a possibility to converge to local minima. In addition, this class of algorithms needs prior identification of the secondary path. The ABFO algorithm helps the ANC system to prevent falling into local minima. The proposed ANC system is also simpler since it does not need any prior information of the secondary path. Moreover, the adaptive strategy of the algorithm results in improved search performance compared with the basic bacterial foraging optimization algorithm, as well as other conventional algorithms. Experimental studies are performed for nonlinear primary path along with linear and nonlinear secondary path. The results show the effectiveness of the proposed ABFO-based ANC system for different kinds of input noise.

Feature selection is an important component of the machine learning domain, which selects the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a given number of features, there are 2 n possible feature subsets, making it challenging to select the optimal set of features from a dataset via conventional feature selection approaches. We opted to investigate glaucoma infection since the number of individuals with this disease is rising quickly around the world. The goal of this study is to use the feature set (features derived from fundus images of benchmark datasets) to classify images into two classes (infected and normal) and to select the fewest features (feature selection) to achieve the best performance on various efficiency measuring metrics. In light of this, the paper implements and recommends a metaheuristics-based technique for feature selection based on emperor penguin optimization, bacterial foraging optimization, and proposes their hybrid algorithm. From the retinal fundus benchmark images, a total of 36 features were extracted. The proposed technique for selecting features minimizes the number of features while improving classification accuracy. Six machine learning classifiers classify on the basis of a smaller subset of features provided by these three optimization techniques. In addition to the execution time, eight statistically based performance metrics are calculated. The hybrid optimization technique combined with random forest achieves the highest accuracy, up to 0.95410. Because the proposed medical decision support system is effective and ensures trustworthy decision-making for glaucoma screening, it might be utilized by medical practitioners as a second opinion tool, as well as assist overworked expert ophthalmologists and prevent individuals from losing their eyesight.

The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm Optimization Algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers.

With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a \"genetic\" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of \"survival of the fittest\" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

With an increased chronic disease and an ageing population, remote health monitoring is a substantial method to enhance the care of patients and decrease healthcare expenses. The Internet of Things (IoT) presents a promising solution for remote health monitoring by collecting and analyzing vital data like body temperature, ECG, and heart rate, giving real-time insights to medical professionals. However, maintaining effectual monitoring in environments with bandwidth or energy constraints presents crucial threats. While machine analysis and human insight performance must be content, conveying extra data to gratify both would be evaded for efficient resource application. Therefore, this article proposes an Enhanced Security Mechanism for Human-Centered Systems using Deep Learning with Jellyfish Search Optimizer (ESHCS-DLJSO) approach for IoT healthcare applications. The projected ESHCS-DLJSO approach allows IoT devices in the healthcare field to securely convey medical data and early recognition of health problems in the human-machine interface. To achieve this, the ESHCS-DLJSO approach utilizes a min-max normalization technique to transform the input data into a more suitable format. The bacterial foraging optimization algorithm (BFOA) method is used for feature extraction. Moreover, a convolutional neural network with long short-term memory (CNN-LSTM-Attention) technique is used for disease detection and classification. Finally, the ESHCS-DLJSO technique employs the jellyfish search optimizer (JSO) technique for hyperparameter tuning. The simulation of the ESHCS-DLJSO technique is examined on an IoT healthcare security dataset. The performance validation of the ESHCS-DLJSO technique portrayed a superior accuracy value of 99.43% over existing approaches.

With the emergence of Wireless Sensor Networks (WSNs), a large number of academics have worked over the last several decades to increase energy efficiency and clustering. Several clustering algorithm techniques, including optimization-based, fuzzy logic-based, and threshold-based, were created to minimize energy consumption and improve network performance. Optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), and their variants are presented. But the challenge of selecting the efficient Cluster Head (CH) and cluster formation around it with minimal overhead and energy consumption remains the same. We propose a novel energy-efficient and lightweight clustering technique for WSNs based on the Modified Bacterial Foraging Optimization Algorithm (MBFA). In this study, the goal of developing the MBFA is to reduce energy consumption, communication overhead, and enhance network performance. The MBFA-based CH selection procedure is based on a unique fitness function. The fitness function computes essential characteristics such as remaining energy, node degree, and distance from sensor node to Base Station (BS). Using the fitness value, the MBFA identifies the sensor node as CH. To justify efficiency, the suggested clustering protocol is simulated and tested against state-of-the-art protocols.

A cognitive radio network (CRN) is a dynamic and intelligent radio technology that optimizes spectral efficiency to enhance user experience in any terrestrial communication network. The cooperative spectrum sensing methods tend to significantly improve the sensing performance of cognitive radio to enable interference-free communication in a multi-user environment. Further, the cognition ability of cognitive radio, such as dynamic decision making and self-adaptation, is enhanced by applying Artificial Intelligent (AI) algorithms for learning and optimization. In this research, an AI-based cooperative prediction-based spectrum sensing (CPSS) model is considered to collect the prediction results of channel state information from parallel cognitive users. An evolutionary swarm-based learning model called SpecBFO (Spectrum-based Bacterial Foraging Optimization) algorithm is proposed to enable rapid spectrum decision making. The performance of the suggested SpecBFO model is evaluated to study the convergence probability and time complexity by analyzing the cost function. The experimental results confirms that the running time of the proposed work is optimal and achieves more accuracy compared to Genetic Algorithm (GA) by 80%, Particle Swarm Optimization (PSO) by 86.37%, and Bacterial Foraging Optimization (BFO) by 91.43% under minimum iteration at an SNR of − 15 dB.

This study focuses on improving the detection of breast cancer at an early stage. The common approach for diagnosing breast cancer is mammography, but it is quite tedious as it is subject to subjective analysis. To address these challenges, the research will explore how the mammogram analysis employs deep learning-based techniques to enhance the screening process. Various computer vision models, including Visual Geometry Group (VGG) 19, Inception V3, and custom 20 Convolutional Neural Network (CNN) architecture, are investigated using the Digital Database for Screening Mammography (DDSM) mammogram dataset. The research community widely uses the DDSM for mammographic image analysis. In the domain of CNNs, the models have demonstrated considerable promise due to their efficacy in various tasks, such as image recognition and classification. It is also seen that the CNN model performance is enhanced through hyperparameter optimization. However, manually tuning hyperparameters is laborious and time-consuming. To overcome this challenge, automatic hyperparameter optimization of CNNs uses population-based metaheuristic approaches. This automation mitigates the time required for finding optimal hyperparameters and boosts the CNN model’s efficacy. The proposed approach involves using the Bacterial Foraging Optimization (BFO) algorithm to optimize CNN to enhance breast cancer detection. BFO optimizes hyperparameters such as filter size, number of filters, and hidden layers in the CNN model. The experiments show that the proposed BFO-CNN method outperforms other state-of-the-art methods in terms of accuracy, showing improvements of 7.62% for VGG 19, 9.16% for InceptionV3, and 1.78% for the custom CNN-20 layer model. In conclusion, this work aims to leverage deep learning techniques and automatic hyperparameter optimization to enhance breast cancer detection through mammogram analysis. The BFO-CNN model has much potential to improve breast cancer diagnosis accuracy compared to conventional CNN architecture.