Prospects of probiotics in beekeeping: a review for sustainable approach to boost honeybee health.

Honeybees are vital for global crop pollination, making indispensable contributions to agricultural productivity. However, these vital insects are currently facing escalating colony losses on a global scale, primarily attributed to parasitic and pathogenic attacks. The prevalent response to combat these infections may involve the use of antibiotics. Nevertheless, the application of antibiotics raises concerns regarding potential adverse effects such as antibiotic resistance and imbalances in the gut microbiota of bees. In response to these challenges, this study reviews the utilization of a probiotic-supplemented pollen substitute diet to promote honeybee gut health, enhance immunity, and overall well-being. We systematically explore various probiotic strains and their impacts on critical parameters, including survival rate, colony strength, honey and royal jelly production, and the immune response of bees. By doing so, we emphasize the significance of maintaining a balanced gut microbial community in honeybees. The review also scrutinizes the factors influencing the gut microbial communities of bees, elucidates the consequences of dysbiosis, and evaluates the potential of probiotics to mitigate these challenges. Additionally, it delineates different delivery mechanisms for probiotic supplementation and elucidates their positive effects on diverse health parameters of honeybees. Given the alarming decline in honeybee populations and the consequential threat to global food security, this study provides valuable insights into sustainable practices aimed at supporting honeybee populations and enhancing agricultural productivity.

Beehive Smart Detector Device for the Detection of Critical Conditions That Utilize Edge Device Computations and Deep Learning Inferences.

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3-5% better accuracy results.

Machine learning-based decision support system for orthognathic diagnosis and treatment planning.

BACKGROUND: Dento-maxillofacial deformities are common problems. Orthodontic-orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning-based decision support system for treatment of dento-maxillofacial malformations. METHODS: Patients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes. RESULTS: The binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human-computer interaction. There was no statistically significant difference between the actual and AI- groups. CONCLUSIONS: Machine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers.

Ensembled CNN with artificial bee colony optimization method for esophageal cancer stage classification using SVM classifier.

BACKGROUND: Esophageal cancer (EC) is aggressive cancer with a high fatality rate and a rapid rise of the incidence globally. However, early diagnosis of EC remains a challenging task for clinicians. OBJECTIVE: To help address and overcome this challenge, this study aims to develop and test a new computer-aided diagnosis (CAD) network that combines several machine learning models and optimization methods to detect EC and classify cancer stages. METHODS: The study develops a new deep learning network for the classification of the various stages of EC and the premalignant stage, Barrett's Esophagus from endoscopic images. The proposed model uses a multi-convolution neural network (CNN) model combined with Xception, Mobilenetv2, GoogLeNet, and Darknet53 for feature extraction. The extracted features are blended and are then applied on to wrapper based Artificial Bee Colony (ABC) optimization technique to grade the most accurate and relevant attributes. A multi-class support vector machine (SVM) classifies the selected feature set into the various stages. A study dataset involving 523 Barrett's Esophagus images, 217 ESCC images and 288 EAC images is used to train the proposed network and test its classification performance. RESULTS: The proposed network combining Xception, mobilenetv2, GoogLeNet, and Darknet53 outperforms all the existing methods with an overall classification accuracy of 97.76% using a 3-fold cross-validation method. CONCLUSION: This study demonstrates that a new deep learning network that combines a multi-CNN model with ABC and a multi-SVM is more efficient than those with individual pre-trained networks for the EC analysis and stage classification.

Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction.

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.

A Novel Prairie Dog-Based Meta-Heuristic Optimization Algorithm for Improved Control, Better Transient Response, and Power Quality Enhancement of Hybrid Microgrids.

The growing demand for electricity driven by population growth and industrialization is met by integrating hybrid renewable energy sources (HRESs) into the grid. HRES integration improves reliability, reduces losses, and addresses power quality issues for safe and effective microgrid (MG) operation, requiring efficient controllers. In this regard, this article proposes a prairie dog optimization (PDO) algorithm for the photovoltaic (PV)-, fuel cell (FC)-, and battery-based HRESs designed in MATLAB/Simulink architecture. The proposed PDO method optimally tunes the proportional integral (PI) controller gain parameters to achieve effective compensation of load demand and mitigation of PQ problems. The MG system has been applied to various intentional PQ issues such as swell, unbalanced load, oscillatory transient, and notch conditions to study the response of the proposed PDO controller. For evaluating the efficacy of the proposed PDO algorithm, the simulation results obtained are compared with those of earlier popular methodologies utilized in the current literature such as bee colony optimization (BCO), thermal exchange optimization, and PI techniques. A detailed analysis of the results found emphasizes the efficiency, robustness, and potential of the suggested PDO controller in significantly improving the overall system operation by minimizing the THD, improving the control of active and reactive power, enhancing the power factor, lowering the voltage deviation, and keeping the terminal voltage, DC-link voltage, grid voltage, and grid current almost constant in the event of PQ fault occurrence. As a result, the proposed PDO method paves the way for real-time employment in the MG system.

Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm.

In electrical impedance tomography (EIT) detection of industrial two-phase flows, the Gauss-Newton algorithm is often used for imaging. In complex cases with multiple bubbles, this method has poor imaging accuracy. To address this issue, a new algorithm called the artificial bee colony-optimized radial basis function neural network (ABC-RBFNN) is applied to industrial two-phase flow EIT for the first time. This algorithm aims to enhance the accuracy of image reconstruction in electrical impedance tomography (EIT) technology. The EIDORS-v3.10 software platform is utilized to generate electrode data for a 16-electrode EIT system with varying numbers of bubbles. This generated data is then employed as training data to effectively train the ABC-RBFNN model. The reconstructed electrical impedance image produced from this process is evaluated using the image correlation coefficient (ICC) and root mean square error (RMSE) criteria. Tests conducted on both noisy and noiseless test set data demonstrate that the ABC-RBFNN algorithm achieves a higher ICC value and a lower RMSE value compared to the Gauss-Newton algorithm and the radial basis function neural network (RBFNN) algorithm. These results validate that the ABC-RBFNN algorithm exhibits superior noise immunity. Tests conducted on bubble models of various sizes and quantities, as well as circular bubble models, demonstrate the ABC-RBFNN algorithm's capability to accurately determine the size and shape of bubbles. This outcome confirms the algorithm's generalization ability. Moreover, when experimental data collected from a 16-electrode EIT experimental device is employed as test data, the ABC-RBFNN algorithm consistently and accurately identifies the size and position of the target. This achievement establishes a solid foundation for the practical application of the algorithm.

Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot.

This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthropomorphic arms was performing a task with upper body motion. The objective of this study was to develop an algorithm for joint reconfiguration of the receptionist robot called Namo so that the robot can still perform a set of emblematic gestures if an actuator fails or is damaged. We proposed a gesture similarity measurement to be used as an objective function and used bio-inspired artificial intelligence methods, including a genetic algorithm, a bacteria foraging optimization algorithm, and an artificial bee colony, to determine good solutions for joint reconfiguration. When an actuator fails, the failed joint will be locked at the average angle calculated from all emblematic gestures. We used grid search to determine suitable parameter sets for each method before making a comparison of their performance. The results showed that bio-inspired artificial intelligence methods could successfully suggest reconfigured gestures after joint motor failure within 1 s. After 100 repetitions, BFOA and ABC returned the best-reconfigured gestures; there was no statistical difference. However, ABC yielded more reliable reconfigured gestures; there was significantly less interquartile range among the results than BFOA. The joint reconfiguration method was demonstrated for all possible joint failure conditions. The results showed that the proposed method could determine good reconfigured gestures under given time constraints; hence, it could be used for joint failure recovery in real applications.

Corn Harvester Bearing Fault Diagnosis Based on ABC-VMD and Optimized EfficientNet.

The extraction of the optimal mode of the bearing signal in the drive system of a corn harvester is a challenging task. In addition, the accuracy and robustness of the fault diagnosis model are low. Therefore, this paper proposes a fault diagnosis method that uses the optimal mode component as the input feature. The vibration signal is first decomposed by variational mode decomposition (VMD) based on the optimal parameters searched by the artificial bee colony (ABC). Moreover, the key components are screened using an evaluation function that is a fusion of the arrangement entropy, the signal-to-noise ratio, and the power spectral density weighting. The Stockwell transform is then used to convert the filtered modal components into time-frequency images. Finally, the MBConv quantity and activation function of the EfficientNet network are optimized, and the time-frequency pictures are imported into the optimized network model for fault diagnosis. The comparative experiments show that the proposed method accurately extracts the optimal modal component and has a fault classification accuracy greater than 98%.

Training of artificial neural networks with the multi-population based artifical bee colony algorithm.

Nowadays, artificial intelligence has gained recognition in every aspect of life. Artificial neural networks, one of the most efficient artificial intelligence techniques, is remarkably successful in computers' acquisition of the learning and interpretation capabilities of humans and attainment of meaningful results. Whether artificial intelligence networks can yield meaningful results is directly related to how the network is trained. The traditional algorithms, which are used to train artificial intelligence networks, do not always yield successful results in complicated problems and real-life problems. Metaheuristic algorithms are efficient techniques developed in order to figure out time-consuming and challenging problems fast and as optimally as possible. This study makes use of the artificial bee colony algorithm, which has been widely used recently in solving many kinds of problems so as to train artificial neural networks efficiently. Within this study, different strategies are used on subpopulations, so that the algorithm can search without getting tangled with the local optima. And also same and different parameter settings are considered for each population to reflect different search behaviours. The proposed approach was analysed through applied results of different data sets. The results yielded that the used strategies can be promising alternatives to the current search algorithms.

Fuzzy Clustering Algorithm Based on Improved Global Best-Guided Artificial Bee Colony with New Search Probability Model for Image Segmentation.

Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been extensively investigated and successfully implemented in image segmentation. FCM is useful in various aspects, such as the segmentation of grayscale images. However, FCM has some limitations in terms of its selection of the initial cluster center. It can be easily trapped into local optima and is sensitive to noise, which is considered the most challenging issue in the FCM clustering algorithm. This paper proposes an approach to solve FCM problems in two phases. Firstly, to improve the balance between the exploration and exploitation of improved global best-guided artificial bee colony algorithm (IABC). This is achieved using a new search probability model called PIABC that improves the exploration process by choosing the best source of food which directly affects the exploitation process in IABC. Secondly, the fuzzy clustering algorithm based on PIABC, abbreviated as PIABC-FCM, uses the balancing of PIABC to avoid getting stuck into local optima while searching for the best solution having a set of cluster center locations of FCM. The proposed method was evaluated using grayscale images. The performance of the proposed approach shows promising outcomes when compared with other related works.

A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm.

The rapid development of Cloud Computing (CC) has led to the release of many services in the cloud environment. Service composition awareness of Quality of Service (QoS) is a significant challenge in CC. A single service in the cloud environment cannot respond to the complex requests and diverse requirements of the real world. In some cases, one service cannot fulfill the user's needs, so it is necessary to combine different services to meet these requirements. Many available services provide an enormous QoS and selecting or composing those combined services is called an Np-hard optimization problem. One of the significant challenges in CC is integrating existing services to meet the intricate necessities of different types of users. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. This article presents the Artificial Bee Colony and Genetic Algorithm (ABCGA) as a metaheuristic algorithm to achieve the desired goals. If the fitness function of the services selected by the Genetic Algorithm (GA) is suitable, a set of services is further introduced for the Artificial Bee Colony (ABC) algorithm to choose the appropriate service from, according to each user's needs. The proposed solution is evaluated through experiments using Cloud SIM simulation, and the numerical results prove the efficiency of the proposed method with respect to reliability, availability, and cost.

Problem Characteristics and Dynamic Search Balance-Based Artificial Bee Colony for the Optimization of Two-Tiered WSN Lifetime with Relay Nodes Deployment.

Lifetime optimization is one of the key issues among the many challenges of wireless sensor networks. The introduction of a small number of high-performance relay nodes can effectively improve the quality of the network services. However, how to deploy these nodes reasonably to fully enhance the network lifetime becomes a very difficult problem. In this study, a modified and enhanced Artificial Bee Colony is proposed to maximize the lifetime of a two-tiered wireless sensor network by optimal deployment of relay nodes. First, the dimension of the problem is introduced into the candidate search equation and the local search is adjusted according to the fitness of the problem and number of iterations, which helps to balance the exploration and exploitation ability of the algorithm. Second, in order to prevent the algorithm from falling into local convergence, a dynamic search balance strategy is proposed instead of the scout bee phase in the original Artificial Bee Colony. Then, a feasible solution formation method is proposed to ensure that the relay nodes can form the upper-layer backbone of the network. Finally, we employ this algorithm on a test dataset obtained from the literature. The simulation results show that the proposed algorithm for two-tiered wireless sensor network lifetime optimization can obtain higher and stable average network lifetime and more reasonable relay node deployment compared to other classical and state-of-the-art algorithms, verifying the competitive performance of the proposed algorithm.

Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks.

Although IoT technology is advanced, wireless systems are prone to faults and attacks. The replaying information about routing in the case of multi-hop routing has led to the problem of identity deception among nodes. The devastating attacks against the routing protocols as well as harsh network conditions make the situation even worse. Although most of the research in the literature aim at making the IoT system more trustworthy and ensuring faultlessness, it is still a challenging task. Motivated by this, the present proposal introduces a trust-aware routing mechanism (TARM), which uses an edge node with mobility feature that can collect data from faultless nodes. The edge node works based on a trust evaluation method, which segregates the faulty and anomalous nodes from normal nodes. In TARM, a modified gray wolf optimization (GWO) is used for forming the clusters out of the deployed sensor nodes. Once the clusters are formed, each cluster's trust values are calculated, and the edge node starts collecting data only from trustworthy nodes via the respective cluster heads. The artificial bee colony optimization algorithm executes the optimal routing path from the trustworthy nodes to the mobile edge node. The simulations show that the proposed method exhibits around a 58% hike in trustworthiness, ensuring the high security offered by the proposed trust evaluation scheme when validated with other similar approaches. It also shows a detection rate of 96.7% in detecting untrustworthy nodes. Additionally, the accuracy of the proposed method reaches 91.96%, which is recorded to be the highest among the similar latest schemes. The performance of the proposed approach has proved that it has overcome many weaknesses of previous similar techniques with low cost and mitigated complexity.

Detection of Optic Disc Localization from Retinal Fundus Image Using Optimized Color Space.

Optic disc localization offers an important clue in detecting other retinal components such as the macula, fovea, and retinal vessels. With the correct detection of this area, sudden vision loss caused by diseases such as age-related macular degeneration and diabetic retinopathy can be prevented. Therefore, there is an increase in computer-aided diagnosis systems in this field. In this paper, an automated method for detecting optic disc localization is proposed. In the proposed method, the fundus images are moved from RGB color space to a new color space by using an artificial bee colony algorithm. In the new color space, the localization of the optical disc is clearer than in the RGB color space. In this method, a matrix called the feature matrix is created. This matrix is obtained from the color pixel values of the image patches containing the optical disc and the image patches not containing the optical disc. Then, the conversion matrix is created. The initial values of this matrix are randomly determined. These two matrices are processed in the artificial bee colony algorithm. Ultimately, the conversion matrix becomes optimal and is applied over the original fundus images. Thus, the images are moved to the new color space. Thresholding is applied to these images, and the optic disc localization is obtained. The success rate of the proposed method has been tested on three general datasets. The accuracy success rate for the DRIVE, DRIONS, and MESSIDOR datasets, respectively, is 100%, 96.37%, and 94.42% for the proposed method.

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM.

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200-3202 cm-1) was compared. Finally, five spectral preproccessing algorithms, Savitzky-Golay 1-Der (SGD), Savitzky-Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm's accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.

COVID-19 detection based on pre-trained deep networks and LSTM model using X-ray images enhanced contrast with artificial bee colony algorithm.

Coronavirus (COVID-19) is an infectious disease that has spread across the world within a short period of time and is causing rapid casualties. The main symptoms of this virus are shortness of breath, fever, cough, and a sore throat. The virus is detected through samples, such as throat swabs and sputum, taken from people who meet the possible case definition and the results are usually obtained within a few hours or a day. The development of test kits to detect the COVID-19 virus is still an open research topic, and automated and faster diagnostic tools are needed. Recent studies have shown that biomedical images can be used for COVID-19 testing. This study proposes the hybrid use of pre-trained deep networks and the long short-term memory (LSTM) for the classification of COVID-19 from contrast-enhanced chest X-rays. In the proposed system, a transformation function is applied to X-ray images first. Then, the artificial bee colony (ABC) algorithm is used to optimize the parameters obtained from the transformation function. The pre-trained deep network models and LSTM are preferred to extract features from the contrast-enhanced chest X-rays. At the final stage, COVID-19, normal (healthy), and pneumonia chest X-ray are classified using softmax. To evaluate the performance of the proposed method, the "COVID-19 radiography" dataset, which is widely used in the literature, is preferred. From the proposed model, 98.97% accuracy, 98.80% precision, and 98.70% sensitivity rates are obtained. Experimental results reveal that the proposed model provides efficient results compared to other methods. Thanks to the application of ABC-based image enhancement, increased classification of 2.5% has been achieved against other state-of-the-art models.

Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks.

The paper presents a new security aspect for a Mobile Ad-Hoc Network (MANET)-based IoT model using the concept of artificial intelligence. The Black Hole Attack (BHA) is considered one of the most affecting threats in the MANET in which the attacker node drops the entire data traffic and hence degrades the network performance. Therefore, it necessitates the designing of an algorithm that can protect the network from the BHA node. This article introduces Ad-hoc On-Demand Distance Vector (AODV), a new updated routing protocol that combines the advantages of the Artificial Bee Colony (ABC), Artificial Neural Network (ANN), and Support Vector Machine (SVM) techniques. The combination of the SVM with ANN is the novelty of the proposed model that helps to identify the attackers within the discovered route using the AODV routing mechanism. Here, the model is trained using ANN but the selection of training data is performed using the ABC fitness function followed by SVM. The role of ABC is to provide a better route for data transmission between the source and the destination node. The optimized route, suggested by ABC, is then passed to the SVM model along with the node's properties. Based on those properties ANN decides whether the node is a normal or an attacker node. The simulation analysis performed in MATLAB shows that the proposed work exhibits an improvement in terms of Packet Delivery Ratio (PDR), throughput, and delay. To validate the system efficiency, a comparative analysis is performed against the existing approaches such as Decision Tree and Random Forest that indicate that the utilization of the SVM with ANN is a beneficial step regarding the detection of BHA attackers in the MANET-based IoT networks.

Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications.

In unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs), a UAV is employed as a mobile sink to gather data from sensor nodes. Incorporating UAV helps prolong the network lifetime and avoid the energy-hole problem faced by sensor networks. In emergency applications, timely data collection from sensor nodes and transferal of the data to the base station (BS) is a prime requisite. The timely and safe path of UAV is one of the fundamental premises for effective UWSN operations. It is essential and challenging to identify a suitable path in an environment comprising various obstacles and to ensure that the path can efficiently reach the target point. This paper proposes a hybrid path planning (HPP) algorithm for efficient data collection by assuring the shortest collision-free path for UAV in emergency environments. In the proposed HPP scheme, the probabilistic roadmap (PRM) algorithm is used to design the shortest trajectory map and the optimized artificial bee colony (ABC) algorithm to improve different path constraints in a three-dimensional environment. Our simulation results show that the proposed HPP outperforms the PRM and conventional ABC schemes significantly in terms of flight time, energy consumption, convergence time, and flight path.

Applying hybrid genetic and artificial bee colony algorithms to simulate a bio-treatment of synthetic dye-polluted wastewater using a rhamnolipid biosurfactant.

The present work aims at optimization and advanced simulation of removal efficiency of dye material from a synthetic wastewater using a locally generated rhamnolipid (RL) biosurfactant. For this purpose, bio-treatment of dye polluted synthetic wastewater was experimentally, kinetically, and statistically investigated by the ion flotation process in the presence of the RL. The removal rate of methylene blue (MB) as the dye material was assessed by the ultraviolet (UV)-visible absorbance measurements. The impact of operating variables including RL concentration (as a dye collector, 5-50 ppm), methyl isobutyl carbinol (MIBC) dosage (as a frother, 10-70 ppm), solution pH (2-12) and aeration rate (1-5 l/min) were assessed through one-way analysis of variance (ANOVA) and Anderson-Darling as the normality analysis strategy. The process was simulated using two artificial neural network (ANN) optimization algorithms, i.e., genetic algorithm (GA) and artificial bee colony (ABC) as a novel approach. The statistical results indicated that the dye removal process was significantly influenced by all operating variables (pvalue<0.05) while their relative intensity followed the order of aeration rate > solution pH > RL concentration > MIBC dosage. Anderson-Darling approach disclosed that the all factors were perfectly followed the normal trend with A2 less than unity and p-value of greater than 0.05 at 95% confidence level. Main effect plots revealed that except MIBC dosage with nonlinear trend, the rest of factors had an ascending influence on the removal efficiency. The process was optimized by interpreting the interaction effect among various variables to reach the maximum dye bioflotation. The maximum removal of 97 ± 0.13% was achieved at pH 12, airflow rate of 5 l/min, MIBC and rhamnolipid concentrations of 30 and 40 ppm, respectively with a flotation kinetic rate of 0.015 sec-1. Finally, the intelligent simulation results showed that the process could be modelled using an artificial bee colony algorithm of 4-7-1 structure with 99% and 98.8% accuracies in the training and testing steps, respectively. Further, we found that the artificial bee colony algorithm was superior to the genetic algorithm in terms of complexity analysis.