Ateles Research Articles

Not just females: the socio-ecology of social interactions between spider monkey males.

Male-male relationships are mostly characterized by competition. However, males also cooperate with one another if socio-ecological conditions are suitable. Due to their male philopatry, the need for cooperation in home range defence and high degree of fission-fusion dynamics, spider monkeys provide an opportunity to investigate how male-male interactions are associated with socio-ecological factors, such as the presence of potentially receptive females, the degree of food availability and the likelihood of home range defence. We tested predictions about changes in social interactions between wild spider monkey males in relation to these factors. First, males did not change their interaction patterns when potentially receptive females were in the subgroup compared to when they were absent. Second, males tended to be less tolerant of one another when feeding, but spent more time grooming, in contact and proximity with one another when food availability was lower than when it was higher. Third, males exchanged fewer embraces, spent less time grooming, in proximity and in contact with one another, and spent more time vigilant at the home range boundary area than at other locations. Our findings contribute to the understanding of social flexibility and the importance of considering males in socio-ecological models of any group-living species.

Modified spider monkey optimization algorithm based feature selection and probabilistic neural network classifier in face recognition

AbstractThis paper proposes a novel and robust predictive method using modified spider monkey optimization (MSMO) and probabilistic neural network (PNN) for face recognition. The limitation of the traditional spider monkey optimization (SMO) approach to obtaining an optimal solution for classification problems is overcome by enhancing the performance of SMO by modifying the perturbation rate with a non‐linear function, thereby improving the convergence of SMO. The framework comprises image preprocessing, feature extraction using dual tree complex wavelet transform (DT‐CWT), feature selection using the modified spider monkey optimization algorithm (MSMO), and classification using PNN. The proposed method is tested on the Yale and AR Face datasets. Experimental outcomes reveal that the proposed framework attain an accuracy of 99.4% with appreciable sensitivity, specificity, and G‐mean. To examine the efficacy of MSMO, parametric studies are conducted, which showed that MSMO converges faster with high fitness when compared to similar evolutionary algorithms like Genetic Algorithm (GA), Grey Wolf Optimization Algorithm (GWO), Particle Swarm Algorithm (PSO), and Cuckoo Search (CS) in selecting the optimal feature set. The MSMO‐PNN method outperforms similar state‐of‐the‐art methods, which reveals that the method proposed is competitive. The proposed model is robust to Gaussian and salt–pepper noise, obtaining the highest accuracy of 97.89% for varied noise density and variance.

Age-sex differences in the social behavior of zoo-housed Colombian black spider monkeys (Ateles fusciceps rufiventris)

Age-sex differences in the social behavior of zoo-housed Colombian black spider monkeys (Ateles fusciceps rufiventris)

My path to primatology: some stories from the field.

In this paper, I summarize the major facets of my 50-year career as a primatologist. I briefly describe the aspects of my upbringing and early education that led me to the study of primate behavior, first in captive settings and, later, in the wild. My research on the Arashiyama West Japanese macaques and my interactions with Japanese primatologists was a formative stage in my career, and I present the background of this international project and how it led to my growing focus on female life history studies. After a couple of failed attempts to establish a long-term study of primates in their native habitats, I began the Santa Rosa Primate Project in Costa Rica in 1983, which focuses mainly on white-faced capuchins, and to some extent on howlers and spider monkeys. The Santa Rosa project has expanded over the past four decades and continues to this day, with the participation of a large team of colleagues, local field assistants and students. I present some of the major findings of our Santa Rosa monkey research in the areas of female reproduction, sexual conflict and conservation of primates in a regenerating tropical dry forest. I also briefly describe how and why I came to develop a sideline of research on gender and science.

Sentiment analysis on twitter data based on spider monkey optimization and deep learning for future prediction of the brands

SummaryIn this manuscript, a deep neural network is proposed by integrating improved adaptive‐network‐based fuzzy inference system (IANFIS) for branding online products to overcome these issues. Here, the sentiment analysis (SA) and prediction on future branding of products that are extracted from the twitter data is carried out. After the review process classifying the products as positive, negative, and neutral assessments completely concentrated in three folds, prediction of a future brand that is carried out by IANFIS for weighting the products finally classify them. This scheme helps the respective retailers/retail brands with their digital marketing team to understand their brand perception as opposed to others. The performance of the proposed method is compared with the existing methods, such as sentiment analysis on twitter data based on particle swarm optimization and genetic algorithm, sentiment analysis on twitter data based on particle swarm optimization and convolutional neural network, sentiment analysis on twitter data based on whale optimization algorithm and support vector machine, and sentiment analysis on twitter data based on convolutional neural network and long short term memory. The simulation results show that the proposed method outperforms the state of art methods.

Comparative and comprehensive study of linear antenna arrays’ synthesis

<span>In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), antlion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm.</span>

Hybrid machine learning approach based intrusion detection in cloud: A metaheuristic assisted model

Cloud computing provides various cost-effective on-demand services to the user and so it is rising like a real trend in the IT service model. However, due to its open and distributed architecture, it is highly vulnerable to attackers. The security and privacy of cloud users has become a major hurdle. The most prevalent approach for detecting attacks on the cloud is the Intrusion Detection System (IDS). Scalability and autonomous self-adaptation weren’t features of contemporary IDS deployed in traditional Internet or Intranet contexts. Furthermore, they lack determinism, making them inappropriate for cloud-based settings. This necessitates the development of new cloud-based IDS capable of fulfilling the firm’s security standards. Therefore, in this research work, we have introduced a new IDS model for the cloud environment. Our research work is made up of four major phases: “data pre-processing, optimal clustering, feature selection, and attack detection phase”. Initially, the collected raw data are pre-processed to enhance the quality of the data. Then, these pre-processed data are segmented with the newly introduced K-means clustering model, where we’ve optimally selected the centroids by introducing a new hybrid optimization model referred as Spider Monkey Updated with Sealion Optimization (SMSLO), which is the conceptual hybridization of standard SeaLion Optimization (SLnO) and Spider Monkey Optimization (SMO), respectively. At the end of segmentation, two clusters (attack data and non-attack data) will be formed. The data available in both clusters seems to be huge in dimensions, so we’ve lessened the dimensions of the data in the clusters by applying the “Principal Component Analysis (PCA)” algorithm. Subsequently, these dimensionality-reduced features pass into the attack detection phase. The attack detection phase is modeled with the optimized Deep Belief Network (DBN), which portrays the type of attack (Dos, Botnet, DDoS as well) that intruded into the network. Since the DBN makes the final detections; it is ought to be less prone to errors. We have lessened the detection errors such as the Mean Square Error (MSE) of DBN by fine-tuning its weight using a new hybrid optimization model (SMSLO). Finally, the result acquired from the proposed work (DBN+SMSLO) is validated.

Spider Monkey Particle Swarm Optimization (SMPSO) With Coverage Criteria for Optimal Test Case Generation in Object-Oriented Systems

System modeling in software engineering describes the procedure of creating a demonstration of real system in a conceptual way to know the system behavior. The software testing promotes reusing the model for the function of testing and this accelerates test case generation development. The test case generation verifies the reliability of system through enhanced test coverage. Test cases are the set of variables or conditions that defines the quality of product and level of correctness. This research aims to develop a method named Spider Monkey Particle Swarm Optimization (SMPSO) algorithm to generate test case using UML diagram. Accordingly, the proposed algorithm effectively generates the optimal test case by UML diagram through the construction of control graph. The proposed method achieved the coverage as 76, and generates the number of test cases as 82413.

Intelligent Resource Allocation in Industrial IoT using Reinforcement Learning with Hybrid Meta-Heuristic Algorithm

The exponential growth of networking technology has resulted in a massively expanded computer ecosystem. With implications in the Industrial Internet of Things (IIoT) and virtual reality, mobile networks operate and provide a multi-aspect strategy for multiple resource allocation paradigms and service-oriented possibilities in the computing sectors. The Mobile Edge Computing (MEC) model combines a virtual source with edge communication among execution. Thus, this study is to develop and implement a revolutionary resource allocation technique in the IIoT by combining optimal Reinforcement Learning (RL) with a hybrid meta-heuristic algorithm. The three basic levels in the proposed paradigm are "Data input layer," "Data management layer," and "Data analytics layer". The data management layer is responsible for the data collected from edge devices and external devices. The proposed model's goal at the data management layer is to provide an intelligent work allocation mechanism. The Harris Hawks-Spider Monkey Optimization (HH-SMO) method combines Harris Hawks Optimization (HHO) and Spider Monkey Optimization (SMO) to find the best job allocation. From the statistical analysis, the mean of HH-SMO-RL is 15.88%, 14.91%, 14.79%, and 5.99% superior to SMO-RL, HHO-RL, JA-RL, and DHOA-RL respectively, which has shown the resource allocation in IIoT using optimized RL respectively.

A Classy Multifacet Clustering and Fused Optimization Based Classification Methodologies for SCADA Security

Detecting intrusions from the supervisory control and data acquisition (SCADA) systems is one of the most essential and challenging processes in recent times. Most of the conventional works aim to develop an efficient intrusion detection system (IDS) framework for increasing the security of SCADA against networking attacks. Nonetheless, it faces the problems of complexity in classification, requiring more time for training and testing, as well as increased misprediction results and error outputs. Hence, this research work intends to develop a novel IDS framework by implementing a combination of methodologies, such as clustering, optimization, and classification. The most popular and extensively utilized SCADA attacking datasets are taken for this system’s proposed IDS framework implementation and validation. The main contribution of this work is to accurately detect the intrusions from the given SCADA datasets with minimized computational operations and increased accuracy of classification. Additionally the proposed work aims to develop a simple and efficient classification technique for improving the security of SCADA systems. Initially, the dataset preprocessing and clustering processes were performed using the multifacet data clustering model (MDCM) in order to simplify the classification process. Then, the hybrid gradient descent spider monkey optimization (GDSMO) mechanism is implemented for selecting the optimal parameters from the clustered datasets, based on the global best solution. The main purpose of using the optimization methodology is to train the classifier with the optimized features to increase accuracy and reduce processing time. Moreover, the deep sequential long short term memory (DS-LSTM) is employed to identify the intrusions from the clustered datasets with efficient data model training. Finally, the proposed optimization-based classification methodology’s performance and results are validated and compared using various evaluation metrics.

Do spider monkeys use artificial canopy bridges to cross linear infrastructure?

Abstract Deforestation impacts canopy connectivity when landscapes are fragmented due to roads and other types of linear infrastructure. Natural canopy bridges become vital to arboreal animals, especially for animals that are reluctant to use the ground. When canopy regrowth cannot occur, artificial canopy bridges have been implemented to mitigate the consequences of linear infrastructure. The aim of our study was to evaluate the evidence for the use of artificial canopy bridges by spider monkeys (Ateles spp.) to cross linear infrastructure that interrupts canopy connectivity. We report details of five cases in which the absence of evidence for spider monkeys using artificial canopy bridges to cross linear infrastructure was based on systematic monitoring. We examined the factors that may constrain spider monkeys to use artificial faunal overpasses and made recommendations for effective artificial faunal overpasses for spider monkeys.

Mammal use of canopy bridges along the Nuevo Xcan-Playa del Carmen highway, Quintana Roo, Mexico

Abstract There are few highways in Mexico that have built canopy bridges as a mitigation strategy for maintaining connectivity of arboreal fauna. Main target species have been primates, both, howler (Alouatta pigra, A. palliata) and spider monkeys (Ateles geofforyi), as well as several other arboreal priority species such as the kinkajou (Potos flavus), the northern tamandua (Tamandua mexicana) and the Mexican hairy porcupine (Sphiggurus mexicanus). The Nuevo Xcan-Playa del Carmen highway built 22 canopy bridges along its 54 km length. All bridges were surveyed using camera traps installed at both ends and after an 8,418 trap/night effort, 10 records of four mammal species were recorded using the canopy bridges: the kinkajou, opossum (Didelphis virginiana) and squirrels (Sciurus deppei and S. yucatanensis). More monitoring is required to properly assess the effectiveness of these mitigation measures, as the need for cost/benefit feedback is necessary to enhance further mitigation in this or other projects. Also, long term monitoring is required for properly assessing the use patterns of species. The current study was shortly after the infrastructure became operational, so it covers the adaptation period for several species but its insufficient to properly assess the current use.

DIABETIC MACULAR EDEMA CLASSIFICATION WITH OCT IMAGES USING GENERATIVE ADVERSARIAL NETWORK AND ACTIVE CONTOUR MODEL

The major reason for blindness is diabetic macular edema (DME) and hence detection of DME at early stage using optical coherence tomography (OCT) is commonly employed for diagnosing retinal diseases. An accurate disease identification and classification poses a challenging task due to the difficulty in differentiating the abnormal and healthy regions. To overcome these issues and to accurately classify the DME, an effective DME classification approach named antlion spider monkey optimization-based generative adversarial network (ALSMO-based GAN) is proposed in this research for segmenting the retinal layers and to classify the DME more accurately. With the generator and the discriminator components of GAN, the DME is effectively classified so that the devised ALSMO algorithm can be used to train the process of GAN. The inspiration of the foraging and the hunting behavior enable the optimization to increase the rate of convergence and to achieve global optimal solution by reducing the local optima. With the segmented retinal layer, the classification process is progressed through the extraction of relevant features from the retinal layers. The performance of the developed method is verified using measures like accuracy, sensitivity, and specificity which attained values of 92.5%, 98%, and 92.3%, respectively.

Implementation of Enhanced Spider Monkey Optimization for D2D Communication through IoT

Implementation of Enhanced Spider Monkey Optimization for D2D Communication through IoT

Delay aware optimal resource allocation in MU MIMO-OFDM using enhanced spider monkey optimization

In multiple users MIMO- OFDM system allocates the available resources to the optimal users is a difficult task. Hence the scheduling and resource allocation become the major problem in the wireless network mainly in case of multiple input and multiple output method that has to be made efficient. There is various method introduced to give an optimal solution to the problem yet it has many drawbacks. So we propose this paper to provide an efficient solution for resource allocation in terms of delay and also added some more features such as high throughout, energy efficient and fairness. To make optimal resource allocation we introduce optimization algorithm named spider monkey with an enhancement which provides the efficient solution. In this optimization process includes the scheduling and resource allocation, the SNR values, channel state information (CSI) from the base station. To make more efficient finally we perform enhanced spider - monkey algorithm hence the resource allocation is performed based on QoS requirements. Thus the simulation results in our paper show high efficiency when compared with other schedulers and techniques.

Hybrid metaheuristic technique for optimal container resource allocation in cloud

Resource allocation in cloud is becoming more challenging and complex due to the intensifying requirements of cloud services. Efficient management of virtual resources in the cloud is of enormous importance, as it has a large impact on both the operational cost and scalability of the cloud’s surroundings. However, one of the most difficult aspects of cloud computing is optimal resource allocation. The resource allocation is done using the objective of reducing the costs connected with it. Hence, an attempt is made to optimize the resource allocation of containers using a new Combined Spider and Honey Bee Optimization (CS-HBO). In the proposed model, the optimal allocation relies on certain constraints like “Total Network Distance (TND), System Failure, Balanced Cluster Use, and threshold distance”. At last, the supremacy of the presented approach is examined over prevailing techniques regarding various metrics such as cost and convergence analysis. Accordingly, for cost analysis, the proposed method attains the least cost, which is 65.57%, 4.15%, 1.04%, 6.87% and 6.76% improved than existing Marriage in Honey Bee optimization (MHBO), Spider Monkey Optimization (SMO), Whale Random update assisted Lion Algorithm (WR-LA), Genetic Algorithms (GA), and Accelerated particle swarm optimization (APSO) models at the 100th iteration.

A discrete cuckoo search algorithm for traveling salesman problem and its application in cutting path optimization

The Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem with various industrial applications. This paper proposes a discrete cuckoo search algorithm based on random walk and cluster analysis to solve the traveling salesman problem (TSP). Although the original cuckoo search algorithm is not suitable to solve the TSP, this algorithm is modified to solve the TSP. Lévy flights and random preference walk in the original cuckoo search algorithm are replaced with novel tools, including the local adjustment operator and discrete random walk. The former is utilized to preserve superior solutions found by the algorithm, while the latter is employed to maintain the diversity of the population. For large-scale TSP problems, the k-means algorithm is first utilized to divide cities into k categories for optimization, while random algorithms are adopted to combine them. A simple 2-opt operator is utilized as the local optimization operator to accelerate the algorithm's convergence rate. Multiple groups of standard TSPLIB datasets are chosen to compare the proposed method with state-of-the-art optimization algorithms that also use the 2-opt/k-opt optimization operator. According to the experimental results, the stability and accuracy of the proposed algorithm are superior to the state-of-the-art optimization algorithms. Regarding the solution accuracy, the algorithm improves 0.39%, 0.29%, 0.84%, 0.4%, 0.07% and 2.22% in the optimal solution over discrete cuckoo search (DCS), discrete bat algorithm (DBA), discrete sine–cosine algorithm (DSCA), random-key cuckoo search (RKCS) and discrete spider monkey optimization (DSMO) on the standard test cases, and 0.58%, 0.19%, 0.85%, 0.43%, 0.36% and 5.83% in the average optimal solution. Regarding stability, the variance of the optimal solutions of RKCS, DSMO, and novel discrete differential evolution (NDEE) for 30 independent runs is 38.5, 52.6, and 59.4 times higher than that of the algorithm in this paper. Finally, the proposed algorithm optimizes air knife migration during glass cutting. Accordingly, the path for air knife migration is reduced by 8556 mm.

UCAV Path Planning for Avoiding Obstacles using Cooperative Co-evolution Spider Monkey Optimization

The unmanned combat aerial vehicle (UCAV) path planning problem is a complex global optimization problem that aims to seek an optimal flight route that avoids the threats and constraints on the battlefield. However, as the number and degree of the path threats increases, the recent UCAV path planning algorithms all suffer from being trapped into local optima with a low convergence rate, which leads to the poor performance. Therefore, to resolve these problems, we propose a Cooperative co-Evolution (CE)-based Spider Monkey Optimization (SMO) algorithm, named CESMO to address UCAV path planning problem for avoiding obstacles. First, a cooperative co-evolution strategy is proposed to prevent the algorithms from falling into local optima easily, enhancing the coherence of the path. After that, a further division is applied to the algorithm for diversifying the search direction of each individual in the sub-groups. Finally, the spider monkey optimization-based search mechanism is designed to further strengthen the search efficiency and boost the convergence rate. Experimental results demonstrate that our proposed method is more competitive than other state-of-the-art evolutionary algorithms for UCAV path planning problem considering the quality and stability of the final paths.

Enhancement of Supply Chain Management through Educating Key Processes with the Hub and Spoke Model Based on Multi-Adaptive Spider Monkey Optimization Algorithm

Supply chain performance improvement has become a critical issue for gaining a competitive edge for companies. Over the last decade of the evolution of supply chain management (SCM), a steady stream of research and articles dealing with the theory and practice of SCM have been published, but the topic of performance measurement has not received adequate consideration. However, many critical drawbacks prevent the existing performance measurement systems from making a significant contribution to the development and improvement of SCM and thus, several researches is still needed in this area. So, we present a unique multi-adaptive spider monkey optimization algorithm (MASMOA) to enhance the ability of hub and spoke model to educate the key process throughout the SCM. Initially, for the pre-processing step, we use a normalizing technique to remove unwanted noises from the raw dataset, and then we use principal component analysis (PCA) to extract the unwanted features from the pre-processed data. The information was then encrypted and fed into the hub-and-spoke model for data protection purpose. The proposed method is then used to optimize the hub and spoke model's transport process over hubs. The optimized data is then decoded using a decryption technique to transform the encrypted information into actual format. The Rivest–Shamir–Adleman (RSA) algorithm was used for the encryption and decryption operations. Finally, performance measures such as encryption and decryption execution time, security rate, and scalability level are compared to comparable techniques. Using the MATLAB environment, the results are shown.

ARPS: An Autonomic Resource Provisioning and Scheduling Framework for Cloud Platforms

With Cloud computing becoming mainstream for the execution of various applications, the multi-objective scheduling algorithms for providing the most suitable services to users have gained much attention. As provisioning Cloud services that satisfy end-users quality of service (QoS) requirements is complex and challenging, scheduling algorithms for cloud computing tend to focus on optimizing the execution cost or the execution time within user-defined deadline constraints. This paper addresses the problem of efficiently allocating Cloud services among competing jobs to achieve multiple end-users QoS. We design and develop a framework called Autonomic Resource Provisioning and Scheduling (ARPS) framework. ARPS framework has the decision-making capability to schedule the jobs at the best resources within the deadline and optimizes both the execution time and the cost simultaneously. The ARPS framework is also integrated with the spider monkey optimization (SMO) algorithm based scheduling mechanism. Our proposed mechanism is intended to solve a multi-objective optimization problem, including minimizing processing time, cost, and energy consumption. We study the effectiveness of the proposed scheduling mechanism through extensive simulation analysis using Cloudsim To assess the relative performance of our method, we compare it against four existing mechanisms. Experimental results show that the proposed mechanism outperforms its counterparts.