Ateles Research Articles

Terrestrial behavior of a solitary female spider monkey (<i>Ateles chamek</i>) on a beach in Manu National Park, Peru

While predominantly arboreal, spider monkeys of various species sometimes have been observed descending to the ground. Individuals of Ateles chamek have been reported to access some kind of food resources (rotten wood or salt licks) on the ground. We describe the terrestrial behavior of an adult female Ateles chamek (Peruvian spider monkey) on a sandy bank of the Pinquén River, Manu, Peru. This behavior lasted several minutes, with sequences of walking on four legs, pausing, and standing upright on the hind legs. The probability that she was a pet is very low due to the location being remote from population centers. The possibility of the individual looking for water to drink directly from the river is not discarded but due to the low water level and short distance between the two banks of the river, it is believed that this solitary adult female (migrant sex in the species) had intentions to cross the river, and that our presence ultimately influenced her decision not to cross.

Cloud computing: an efficient load balancing and scheduling of task method using a hybrid optimization algorithm

<p>The cloud computing trend on the internet is vital as it allows data and applications to be managed over the internet instead of requiring personal devices. The job of users is scheduled in the resources of the cloud in order to improve performance. Schedu ling tasks is an non - deterministic polynomial (NP) - hard problem, as it may have multiple solutions. Various researchers have proposed different load balancing and job scheduling algorithms to optimize the scheduling process in cloud environments, each with disadvantages. Therefore, this research proposes a novel hybrid load balancing and scheduling of tasks by the whale optimization algo rithm (WOA) and seagull optimization algorithm (SOA) in the cloud. This hybrid proposed whale - seagull optimization algorithm (WSOA) optimizes task scheduling in the cloud b y reducing processing time, response, and execution time, maximizing central processing unit (CPU) utilization, memory utilization, throughput, reliability, and balancing the load. The algorithm is simulated using the CloudSim toolkit package. As compared with existing approaches, simulation results showed better performance in terms of response time, processing time, execution time, CPU utilization, memory utilization, throughput, and reliability and is analyzed by comparing with the harries hawks optimiza tion (HHO), hybrid dragonfly and firefly algorithm (ADA), spider monkey algorithm (SMA) and bird swarm optimization (BSO).</p>

Multi-objective hybrid optimization for micro strip patch antenna design

Due to their low price, light weights, as well as simple installation, Micro strip Patch Antennas (MPAs) have been made to perform in a double and multi-band applications. The MP receiver is created with an Electromagnetic Band Gap (EBG) structure in order to decrease the micro strip patch cross-polarized radiation but also achieve the crucial radiation criteria. The polymeric liquid crystals substratum is employed to decrease raw material costs, and also the applicable shape framework are employed to enhance receiver execution. We have established a new optimization based method which has two operating stages. In the begining stage, we have designed a Micro strip patch antenna with certain parameters. Afterwards, these design parameters length, width, height, substrate thickness under area such as get optimized by the newly introduced Battle Royale Customized Spider Monkey Optimization (BRCSMO) algorithm in order to get an antenna with higher performance. We have evaluated the proposed method with regard to measures like receiver profit, productivity, bandwidth, decline loss as well as Total Active Reflection coefficient (TARC) and the outcomes showed that this proposed technique can offer superior outcomes than other approaches.

A Multi-Phase SMO-Based MAC Protocol for IoT-Enabled WBAN Systems

This paper proposes an optimised Medium Access Control (MAC) protocol to minimise the number of channel access collisions and to improve the bandwidth efficiency of IoT-based Wireless Body Area Networks (WBANs). The frame structure of the proposed MAC protocol consists of three different phases, namely Contention Phase (CP), Transmission Phase (TP), and Inactive Phase (IP). During CP, the proposed protocol prioritises the life-sensitive data. Based on allocated priority, the communicating sensor nodes (SNs) compete for channel access. During TP, the successful SNs transmit their data. We use TDMA mechanism for data transmission and Spider Monkey Optimisation (SMO) technique to improve the bandwidth efficiency of the system by selecting the best possible TDMA time slot according to the data packet size of the SNs. During IP, the SNs go into sleep mode to save energy. We simulate the scenario on MATLAB simulator. The simulation results show that the proposed SMO-MAC improves the throughput by 22.23%, 14.00%, and 17.62%, residual energy by 60.76%, 81.17%, and 124.34%, communication reliability by 70.32%, 52.60%, and 52.60%, and data packet transmission delay by 80.74%, 70.10%, and 77.40% as compared to DRT-MAC, PSO-MAC, and GA-MAC, respectively.

A Modified Spider Monkey Optimization Algorithm Based on Good-Point Set and Enhancing Position Update

A Modified Spider Monkey Optimization Algorithm Based on Good-Point Set and Enhancing Position Update

Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task.

To design and develop an approach named HC+SMA-SSA scheme for classifying motor imagery task. The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal. Subsequently, "Modified Stockwell Transform (ST) and common spatial pattern (CSP) based features are extracted". Then, optimal channel selection is made by a novel hybrid optimization model named as Spider Monkey Assisted SSA (SMA-SSA). Here, "Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BI-GRU)" models are used for final classification, whose outcomes are averaged at the end. At last, the improvement of SMA-SSA based model is proven over different metrics. A superior sensitivity of 0.939 is noted for HC+SMA-SSA that was higher over HC with no optimization and proposed with traditional ST. The proposed method achieved effective classification performance in terms of performance measures.

Automatic Video Traffic Surveillance System with Number Plate Character Recognition Using Hybrid Optimization-Based YOLOv3 and Improved CNN

Recently, the increased count of surveillance cameras has manipulated the demand criteria for a higher effective video coding process. Moreover, the ultra-modern video coding standards have appreciably enhanced the efficiency of video coding, which has been developed for gathering common videos over surveillance videos. Various vehicle recognition techniques have provided a challenging and promising role in computer vision applications and intelligent transport systems. In this case, most of the conventional techniques have recognized the vehicles along with bounding box depiction and thus failed to provide the proper locations of the vehicles. Moreover, the position details have been vigorous in terms of various real-time applications trajectory of vehicle’s motion on the road as well as movement estimation. Numerous advancements have been offered throughout the years in the traffic surveillance area through the random propagation of intelligent traffic video surveillance techniques. The ultimate goal of this model is to design and enhance intelligent traffic video surveillance techniques by utilizing the developed deep learning techniques. This model has the ability to handle video traffic surveillance by measuring the speed of vehicles and recognizing their number plates. The initial process is considered the data collection, in which the traffic video data is gathered. Furthermore, the vehicle detection is performed by the Optimized YOLOv3 deep learning classifier, in which the parameter optimization is performed by using the newly recommended Modified Coyote Spider Monkey Optimization (MCSMO), which is the combination of Coyote Optimization Algorithm (COA) and Spider Monkey Optimization (SMO). Furthermore, the speed of the vehicles has been measured from each frame. For high-speed vehicles, the same Optimized YOLOv3 is used for detecting the number plates. Once the number plates are detected, plate character recognition is performed by the Improved Convolutional Neural Network (ICNN). Thus, the information about the vehicles, which are violating the traffic rules, can be conveyed to the vehicle owners and Regional Transport Office (RTO) to take further action to avoid accidents. From the experimental validation, the accuracy and precision rate of the designed method achieves 97.53% and 96.83%. Experimental results show that the proposed method achieves enhanced performance when compared to conventional models, thus ensuring the security of the transport system.

A hybrid wrapper spider monkey optimization-simulated annealing model for optimal feature selection

In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration and exploitation, a hybrid model with a popular meta-heuristic algorithm named spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset from dataset by removing the noise and outliers prior to feeding the data to the wrapper SMO. To enhance the quality of the solution, simulated annealing is deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%.

An improved spider monkey optimization algorithm for multi-objective planning and scheduling problems of PCB assembly line

In recent years, the demand for printed circuit boards (PCBs) with various electronic components has tremendously increased due to high customization in smart electronic devices. To meet the market competition, efficient and realistic decisions with optimum planning and scheduling are required to enhance the productivity of PCB industries. Processing time of surface mounting technology (SMT) machines defines the pace of PCB production because of time consuming and critical process. In addition, it is observed that various planning and scheduling problems exist in PCB assembly line which are required to emphasize to increase the productivity. Therefore, in this study critical planning and scheduling problems i.e., component allocation problem (CAP) and component placement sequence problem (CPSP) are considered to optimize. Moreover, the periodic maintenance (PM) is also considered to maintain the efficiency of machines and make the problem more realistic. A mathematical model is formulated to minimize the maximum completion time, total energy consumption and the total maintenance time simultaneously. To solve this multi-objective NP-hard problem, an improved spider monkey optimization (ISMO) algorithm is developed. The efficiency and effectiveness of ISMO algorithm is improved by embedding few additional features. a) Two heuristics to ensure better initial solutions; b) evolutionary state judgment based on change in Pareto entropy to ensure the tradeoff between exploration and exploitation; c) an archive-based Q-Learning strategy to ensure the parametric adaptive tuning. Finally, computational experiments and comprehensive performance comparisons with other well-known multi-objective algorithms are performed with several size problem instances. The statistical results and analysis demonstrate that the proposed ISMO algorithm outperforms the other competitors.

Serological evidence for potential yellow fever virus infection in non-human primates, southeastern Mexico

BackgroundArthropod-borne flaviviruses like dengue virus (DENV) and yellow fever virus (YFV) are major human pathogens. In Latin America, YFV is maintained in sylvatic cycles involving non-human primates (NHP) and forest-dwelling mosquitos. YFV supposedly does not circulate north of Panama.MethodsWe conducted a serologic study for flaviviruses and other emerging viruses in NHP from southeastern Mexico. A total of thirty sera of black-handed spider monkeys (Ateles geoffroyi, n = 25), black howler monkeys (Alouatta pigra, n = 3), and mantled howler monkeys (Al. palliata, n = 2) sampled in 2012 and 2018 were screened by an indirect immunofluorescence assay (IFA) to detected IgG antibodies against DENV, YFV, Zika virus (ZIKV), West Nile virus (WNV), Rift Valley fever virus, Crimean-Congo hemorrhagic fever virus, Middle East respiratory syndrome coronavirus, and Zaire Ebola virus, and confirmed by plaque reduction neutralization tests (PRNT90) representing all mosquito-borne flavivirus serocomplexes circulating in the Americas.ResultsA total of 16 sera (53.3%; 95% CI, 34.3–71.7) showed IFA reactivity to at least one tested flavivirus with end-point titers ranging from 1:100 to 1:1000. No serum reacted with other viruses. Monotypic and high mean PRNT90 endpoint YFV titers of 1:246 were found in 3 black-handed spider monkey sera (10.0%; 95% CI, 2.1–26.5) sampled in 2018 in Tabasco, compared to all other flaviviruses tested. Monotypic endpoint PRNT90 titers of 1:28 for Ilheus virus and 1:22 for WNV in serum of black howler monkeys sampled in 2018 in Tabasco suggested additional flavivirus exposure.ConclusionsOur findings may suggest unnoticed YFV circulation. Intensification of YFV surveillance in NHP and vectors is warranted in Mexico and potentially other areas considered free of yellow fever.

Estimating the impact of the illegal trade of primates in Mexico: a potential threat to wildlife.

The primates of Mexico, Ateles geoffroyi, Alouatta palliata, and Alouatta pigra, are seriously threatened by habitat loss, fragmentation, and illegal hunting and trade. Very little is known about the extent of illegal trade and its impacts on declining primate populations. Our study proposes a potential method based on estimating the number of individuals that die in the trade before being detected and those that probably cannot be detected. This facilitates estimating the number of animals extracted and allows an assessment of how trafficking impacts their populations. We derive estimates from seizure data of primates in Mexico between 2010 and 2019. To do this, we created wildlife detection rates and mortality rates from the existing literature (scientific articles, journalistic articles, and notes) to estimate the number of primates that die during capture, transport, and sale and the number of trafficked primates that were not detected by Mexican authorities. We estimate that 946 primates were removed from the wild for the pet trade each year (spider monkey Ateles geoffroyi= 854; black howler monkeys Alouatta pigra= 38, mantled howler monkey Alouatta palliata= 54). The annual reduction in population size caused by trafficking was greatest for Ateles geoffroyi (2.2%), followed by Alouatta pigra (1.3%), and Alouatta palliata (0.4%). Our estimates show the percentage of impacts that trafficking has on Mexican primate populations. Nevertheless, trade has the potential to impact declining populations and still must be addressed.

Vigilance has mainly a social function in a wild group of spider monkeys (Ateles geoffroyi).

Vigilance is a widespread behavior that allows individuals to socially acquire information and/or effectively detect potential risks posed by predators and conspecifics. In this study, we aimed to investigate how social factors (i.e., subgroup size, number of males and immatures in the subgroup, presence of fission and fusion events, subgroup activity), individual characteristics (i.e., sex, presence of dependent offspring) and possible vulnerability to predation (i.e., being in smaller subgroups or lower in the canopy) explain variation in vigilance behavior in a wild group of spider monkeys (Ateles geoffroyi). We collected vigilance data during focal samples of all adults, subadults and juveniles of the group (N = 38), and ran generalized linear mixed models with a Bayesian approach. We found that the proportion of time both sexes spent in vigilance increased with subgroup size and during fusion events. Individuals also spent more time in vigilance when the subgroup was resting or socializing compared to other activities. Moreover, the proportion of time spent in vigilance increased in subgroups with more immatures. An alternative model with similar fit suggested that the proportion of time spent in vigilance increased in females when subgroups included more adult and subadult males. Overall, these results suggest that our study group mainly directed vigilance toward conspecifics (i.e., social vigilance), probably as a result of the relatively low predation pressure experienced, and contribute to the understanding of the multiple social factors affecting vigilance in group-living primates.

Spider Monkey Optimization Based Optimal Sizing of Battery Energy Storage for Micro-Grid

Spider Monkey Optimization Based Optimal Sizing of Battery Energy Storage for Micro-Grid

Performance analysis of the global maximum power point tracking based on spider monkey optimization for PV system

In recent years, numerous in-depth studies have been conducted in the field of maximum power point tracking, and numerous innovative methodologies have also been suggested. According to the ongoing research in this field, the technique that would combine the advantages of all existing procedures would have the highest efficiency. In this paper, the study begins with an extensive description of the various MPPT approaches that are required for SPV systems. The benefits and drawbacks of these technique has been discussed. A global maximum power point tracking (GMPPT) technique that amalgamates the advantages of existing techniques has been presented. The proposed system utilizes the swarm intelligence-driven technique known as Spider Monkey Optimization (SMO). The details of how the novel SMO-GMPPT operates with SPV system is also discussed. Its working and the outcomes of the descriptive Simulink model has been presented. The simulation results have also been validated with the hardware results. The results show that SMO-GMPPT, has reduced steady state error (%), transient state error (%), and settling time as 0.061, 1.229 and 3ms respectively. Also dynamic state error in various conditions is reduced. The findings demonstrate the ability of SMO-GMPPT and results in a quick and reliable approach for GMPPT, to monitor the genuine global peak under partial shading events.

Discrete spider monkey optimization algorithm for latency‐sensitive VNF deployment and resource allocation

SummaryThe next‐generation networks are expected to support diverse latency‐sensitive services, that is, auto‐driving, telemedicine, and so on. Deploying these services in current hardware‐based networks faces significant challenges. The emergence of network function virtualization (NFV) and software defined network (SDN) makes network deployment flexible and agile. In order to meet the strict latency constraints, joint VNF deployment and resource allocation problems are needed. In this paper, we consider the resource‐delay interpendency and formulate the joint VNF deployment and resource allocation problems as linear programming (LP) problems. Discrete spider monkey optimization (DSMO) algorithm is proposed to solve this problem, which imitates spider monkeys' fission and fusion social behavior during foraging. The two‐phase decision update mechanism greatly increases the diversity of the spider monkey decision population and promotes the algorithm to perform a global search of the solution space. Evaluation results show that the proposed solutions achieve superior performance in terms of latency, CPU utilization and service acceptance rate.

An Optimized Reinforcement Learning-Based Routing Protocol Using Spider Monkey Optimization Algorithm

In Wireless Sensor Networks, numerous machine-learning algorithms are used in the packet routing strategy. The recommended routing approach is implemented using adaptive reinforcement learning and a spider monkey optimization algorithm. Data packets are sent over the network via optimum route establishment—the above attempts to save energy by changing the Q-value at each instance. Technology enables adaptive mechanisms to demonstrate intelligent behavior in complex and dynamic situations such as Wireless Sensor Networks (WSN). Recently, new routing methods for WSNs have been devised in response to such a notion. Due to user interference and overall network performance, the numerous routing pathways cause frequent channel-switching occurrences. The spider monkey optimization method obtains routing metrics such as bandwidth, end-to-end latency, throughput, overhead, and packet delivery ratio. Compared to other machine learning algorithms, such as RLBR, the performance of WSN is improved and obtained better performance results.

Energy-efficient device-to-device communication in internet of things using hybrid optimization technique

<span lang="EN-US">Device-to-device (D2D) communication has grown into notoriety as a critical component of the internet of things (IoT). One of the primary limitations of IoT devices is restricted battery source. D2D communication is the direct contact between the participating devices that improves the data rate and delivers the data quickly by consuming less battery. An energy-efficient communication method is required to enhance the communication lifetime of the network by reducing the node energy dissipation. The clustering-based D2D communication method is maximally acceptable to boom the durability of a network. The oscillating spider monkey optimization (OSMO) and oscillating particle swarm optimization (OPSO) algorithms are used in this study to improve the selection of cluster heads (CHs) and routing paths for D2D communication. The CHs and D2D communication paths are selected depending on the parameters such as energy consumption, distance, end-to-end delay, link quality and hop count. A simulation environment is designed to evaluate and test the performance of the OSMO-OPSO algorithm with existing D2D communication algorithms (such as the GAPSO-H algorithm, adaptive resource-aware split-learning (ARES), bio-inspired cluster-based routing scheme (Bi-CRS), and European society for medical oncology (ESMO) algorithm). The results proved that the proposed technique outperformed with respect to traditional routing strategies regarding latency, packet delivery, energy efficiency, and network lifetime.</span>

Automatic epileptic seizure detection using MSA-DCNN and LSTM techniques with EEG signals

To identify epilepsy, Electroencephalography (EEG) is an important and common tool used to study the electrical activity of the human brain. The machine learning-based classifier is utilized to detect the seizure by manually extracting the features from the EEG signals in previous works. Though, these effective benefits have been proved already with automatic feature extraction, they are unable to achieve the classification of multiple classes. Meanwhile, real-time epileptic seizure detection is unable to keep the capacity because the identifiable EGG is too long. Hence, this paper is decided to develop an enhanced deep learning architecture with EEG signal for performing automatic epileptic seizure detection. The EEG signal is collected from the standard datasets. The Fourier-Bessel Series Expansion-Based Empirical Wavelet Transform (FBSE-EWT) method is used to decompose the signal from the gathered data. In feature extraction, the decomposed signals are used and while extracting the signal features, the autoencoder is involved in that process. To reduce the computational overload, the relief-F feature ranking method is used for choosing the important signal features. The Long Short Term Memory (LSTM) and Multi-Scale Atrous-based Deep Convolutional Neural Networks (MSA-DCNN) are used that is named as Hybrid Deep Scheme (HDS) to detect epileptic seizures with the top-ranked features for epileptic seizure classification. The Black Widow Optimization (BWO) and the Spider Monkey Optimization (SMO) are combined to generate Adaptive Spider Monkey Black Widow Optimization (ASMBWO) that is employed to perform the parameter tuning into a classification technique. Using different measures, the experimental analysis is done between the conventional epileptic seizure detection and the proposed model to establish an enhanced performance of the proposed model.

Hybrid Variable-Length Spider Monkey Optimization with Good-Point Set Initialization for Data Clustering

Hybrid Variable-Length Spider Monkey Optimization with Good-Point Set Initialization for Data Clustering

Quantum spider prioritized routing and Cramer Shoup cryptosecured data transmission in wireless body area network

The wireless body area network (WBAN) offers healthcare applications for remote patient monitoring. In healthcare, prioritizing patient data has a high impact on the energy usage, delays, and congestion involved in routing. Energy usage, battery life, and absorption rates are current research obstacles in WBAN. The path loss ratio and residual energy were generally the focus of attention during data transfer. Despite this, compromised transaction security caused serious problems with the delivery of vital health data. The Quantum Spider Cramer Shoup Public Key Cryptosystem (QS-CSPKC), which is proposed in this study, is an optimized priority-aware (for many patients) and transaction security technique for vital sensed data in WBAN, selects the optimum energy efficient and reliable path to assure secure data transfer. The proposed QS-CSPKC method employs Dual Contention Access Periods (DCAPs) for managing regular data (i.e., normal sensed or low priority data) and life-threatening data (i.e., abnormal sensed data or high priority data). After prioritizing, the Quantum Spider Monkey Optimized Routing algorithm is used to allocate an optimal shortest route for normal low priority data (i.e., with 0 as the quantum value) and an energy-efficient emergency route for critical high priority data (i.e., with 1 as the quantum value). In addition, to prevent illegal access during transmission, security keys are generated using the Cramer Shoup public key cryptosystem. By using this model, only legitimate users have access to information for which they are authorized. The evaluation results were validated through simulations, showing that the proposed QS-CSPKC method performs efficient routing and secure data transmission better than conventional techniques in terms of throughput, packet loss, average power consumption, and energy use.