This paper presents a system for cattle detection and counting in aerial videos, integrating the YOLO11n object detection model with three tracking algorithms: Euclidean Distance, ByteTrack, and DeepSORT. The aim is to evaluate and compare these approaches in terms of tracking accuracy and computational efficiency in real UAV-based monitoring scenarios. The YOLO11n model achieved high detection performance, with 86.5% precision, 97% recall, an F1-score of 93%, and a [email protected] of 95.5%. Tracking performance was assessed using the MOTA, HOTA, and IDF1 metrics across four videos. The Euclidean Distance-based approach, despite its simplicity, demonstrated competitive results in all metrics, with HOTA consistently above 0.89 and IDF1 ranging from 0.857 to 0.923. Furthermore, runtime analysis showed that Euclidean Distance achieved lower or comparable execution times per frame when compared to ByteTrack and DeepSORT, while maintaining reliable animal counts. These findings indicate that lightweight tracking algorithms are not only viable but, in some scenarios, preferable for real-time, onboard cattle monitoring using UAVs.

Procurement fraud brings severe economic and social damage around the World. Losses in revenue reach the magnitude of trillions of dollars. Control bodies around the World expend great efforts in an attempt to reduce such waste. The audit work involves the analysis of procurement by specialists. The high number of processes analyzed and the time required for fraud analysis come in an inefficient and low fraud detection rate. Several computational models have been proposed in recent years to automate the detection and prediction of procurement fraud. However, most of these models depend on human intervention to extract red flags that the machine should consider in detecting fraud. We propose the use of BERT with NLP techniques for the automatic extraction of red flags used in detecting fraud in procurement. Experimental results show that pre-trained contextualized language models are competitive with other methods.

Leveraged by IoT and Industry 4.0 solutions, Wireless Sensor Networks (WSNs) have been proposed as an important alternative for large-scale monitoring applications. Such technology provides sensor nodes with the intelligent and autonomous ability to monitor large areas, create self-organizing structures, detect events and process massive data. In this context, data-driven schemes are increasingly needed. For this, some data clustering techniques (DCTs) are used to tackle common problems in WSNs; however, the vast majority of techniques do not consider the data monitored by the sensors to perform topological changes and provide better network structures. This work addresses an architecture for this type of application and evaluates the impact of different DCTs on network performance and the creation of priority node groups.

The baseband processing centralization enabled by the Cloud Radio Access Network generates stringent latency and high bandwidth requirements. Therefore, some studies proposed using hybrid architectures and baseband functional splitting to ease such requirements. In the literature, the ideal functional splitting in hybrid RAN architectures has been tackled using integer linear programming. Such approaches guarantee optimality, but they have low scalability, making them infeasible for real deployments. On the other hand, Meta Heuristics can provide practical solutions to large combinatorial problems with a good level of accuracy (not rarely achieving optimality). This paper proposes new modeling of the functional splitting problem in Cloud Fog RANs using the meta-heuristic optimization named Soccer Game Optimization. We compared our solution to an integer linear programming formulation evaluating the correctness, energy efficiency, and network coverage. Results show that meta-heuristic achieve statistically optimality equal to the ILP in coverage and energy-efficiency.

Summary Internet of things environment is adopted widely in different industries and business organizations with varying capacity. It provides a favorable environment to outsource the crowdsourced data in the cloud to minimize the cost of computation, which is called crowdsourcing. Crowdsourcing is a technique where individuals or organizations obtain goods and services. A professional or industry outsource the crowdsourced data in the cloud, where confidentiality and authenticity of data become essential. Signcryption is the cryptographic technique that serves both the authenticity and the privacy of transmitted messages. This technique ensures secure authentic data transmission and storage. Therefore, this paper proposes an identity‐based signcryption scheme. In the proposed PSSCC framework, the user does pairing free computation during signcryption, which makes efficient calculation on user‐side. Moreover, PSSCC framework is proved secure under modified bilinear Diffie‐Hellman inversion and modified bilinear strong Diffie‐Hellman problems. The performance analysis of PSSCC with related schemes indicates that the proposed system supports efficient communication along with less computation cost.

Tuberculosis is an infectious disease transmitted by Mycobacterium tuberculosis, being the leading cause of death from infection. The sputum bacilloscopy method is the technique for detecting the bacillus that is currently most used, not only in the search for infectious cases but also as a thermometer to check the effectiveness of the treatment. In this context, computational techniques have been developed to help the specialist for a better diagnosis. In this work, we promote a methodology for automated detection of the bacillus using RetinaNet. A set of the 928 images was used for evaluating this method. The results were promising, achieving an accuracy of 67.1%, recall of 86. 56%, and an F-score of 75.61%. Finally, we believe that our method is capable of acting in the diagnosis of tuberculosis.

This article proposes a new low-cost LoRaWAN Power Switch system for intelligent agricultural applications with the aim of reducing labor costs and improving water consumption in irrigation. As an example of testing this controller, the irrigation scenario of the Center for Agricultural Sciences (CCA) of the Federal University of Piauí (UFPI) was used. The proposed methodology was divided into two stages, namely, the real implementation of the IoT devices with the Radioenge LoRaWAN and LoRa ESP32 modules by Radioenge Company and Espressif Systems respectively, as well as the study and analysis and comparison of the network parameters of the LoRaWAN architecture for both devices in different points identified as possible Smart Farming scenarios. The performance evaluation was carried out based on measurements of a real Non-Line of View (NLOS) scenario. In this assessment, factors such as distance between nodes and different scattering factors (SF) were considered. In addition, the received signal strength indicator (RSSI), the signal-to-noise ratio (SNR) and the packet loss rate were analyzed. The results of this study show a new low-cost long-range device, as well as the feasibility of using the technologies employed for Smart Farm applications up to 1.1km range from gateway device for this scenario.

The environmental issues, the continuous growth in electricity demand, and the increased penetration of renewable energy resources motivated the transformation of conventional power grids into modernized Smart Grids. With this Demand Response applications are implemented on Home Energy Management Systems, aiming to shape the load consumption profile of consumers, in order to reduce utility operational costs and the consumer energy bill price without affecting their convenience. For Demand Response algorithms to be fully exploited in real microgrid environments, households must be equipped with an infrastructure capable of monitoring and controlling residential loads and distributed energy resources, such as renewable energy resources and energy storage systems. Such infrastructure should be able to monitor and detect events that occur on a daily basis in real situations and that may hinder the benefits of the Demand Response algorithm, and send this information to the Home Energy Management Systems in order to keep the DR algorithm updated. In this context, this paper proposes an Internet of Things infrastructure based on an edge-fog-cloud computing architecture in order to monitor and control residential loads. The proposed infrastructure was implemented in a real testbed scenario and the results show that the proposed solution is able to assist the Demand Response algorithm within a microgrid.

Abstract The act of lending is based on trust in the borrower to honour the obligation of paying back the lender. Greater spreads on credit operations may help predict the expected recovery of the credit, based on the sufficiency and liquidity of the guarantee. This study aims to understand how predictive models can provide different estimations of expected recovery based on the same data sets. It classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. It finds that a simple logistic regression model can easily be extended to a multiple logistic regression model by integrating more than one prediction variable, which indicates increasing difficulty in obtaining multiple observations with an increasing number of independent variables. It compares the efficiency of the logistic regression with that of a linear regression in predicting whether recovery is due in a credit operation, and, thus, identifies the best model for this purpose.

Summary Corporate insolvency has significant adverse effects on an economy. With the number of multinationals increasing rapidly, corporate bankruptcy can severely disrupt the global financial environment. However, multinationals do not fail instantaneously; objective strategies combined with a rigorous analysis of both qualitative and quantifiable data can go a long way in identifying an organization's financial risks. Recent advancements in information and communication technologies have made data collection and storage an easy task. The challenge becomes mining the appropriate data about a company's financial risks and implementing it in forecasting a company's insolvency probabilities. In recent years, machine learning has been incorporated into big data analytics owing to its massive success in learning complex models. Machine learning algorithms such as Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks, Gaussian Processes, and Adaptive Learning have been used in the analysis of Big Data to predict the financial risks of companies. In this paper, credit scoring is explored with regards to data processed using the collateral as an independent variable. The obtained results indicate that RF algorithm is promising for use in credit risk management. This research shows the advantages of the RF approach over the SVM algorithm are its speed and operational simplicity, and SVM has the benefit of higher classification accuracy than RF. The paper compares the SVM and RF algorithms to forecast the recovered value in a credit task. The execution of the projected intelligent systems uses tests and algorithms for authentication of the projected model.

Coronavirus disease (COVID-19) has already infected more than 20 million people worldwide and is responsible for more than 744,000 deaths. A major problem faced in the diagnosis of COVID-19 is the inefficiency and scarcity of medical tests. The use of computed tomography (CT) has shown promise in the evaluation of patients with suspected COVID-19 infection. The analysis of the CT examination is complex and requires the effort of a specialist, which can lead to diagnostic errors. The use of CAD systems can minimize the problems generated by the analysis of CTs by specialists. This article presents a methodology for diagnosing COVID-19 using a trainable resource extractor using CNN and multiple classifiers. First, the quality of the images was improved using histogram equalization and CLAHE. Then, a basic CNN is used to extract resources from 708 CTs, 312 with COVID-19, and 396 Non-COVID-19. After the extracted data, we used multiple classifiers for classification in COVID-19 and Non-COVID-19. The results show an accuracy of 97.88%, recall of 97.77%, the precision of 97.94%, F-score of 0.978, AUC of 0.977, and kappa index of 0.957. The results obtained show that the proposed methodology can be used as a CAD system to aid in the diagnosis of COVID-19.

The demand response (DR) aims to balance the purveyance and demand of electricity to maximize the reliability and efficiency of the energy supply process in the electrical power system (EPS). However, one of the main impediments to the insertion of DR in the residential context is the need of programming the use of various electrical appliances and the scheduling of renewable resources and storage system in the same time interval, that requires a range of specific knowledge and time availability of the consumer to handle the various home appliances. This article presents a preference-based multi-objective optimization model based on real-time electricity price to solve the problem of optimal residential load management. The proposal's purpose is to minimize both the electricity consumption associated cost and the inconvenience caused to consumers. The proposed model was formalized as a nonlinear programming problem subject to a set of constraints associated with the consumption of electrical energy and operational aspects related to the residential appliance categories. The proposed multi-objective model was solved computationally by the Constrained Many-Objective Non-Dominated Sorted Genetic Algorithm (NSGA-III) to determine the new scheduling of residential appliances, renewable energy resources, and energy storage system utilization for the entire time horizon, considering consumer preferences. The results show that the multi-objective DR model proposed using the NSGA-III technique can minimize the total cost associated with energy consumption as well as reduce the inconvenience of consumers, besides helping consumers to take advantage of DR's benefits without requiring manual intervention.

Segmentation is a crucial step to obtain success for classifying medical images. However, it is a highly complex task due to the abnormal shapes and the presence of other artifacts. In this study, a melanoma segmentation approach based on deep learning is proposed. In conjunction with post-processing techniques, the proposed modified U-net network has proven to be highly effective in lesions segmentation. The experiments were performed in two public datasets (PH2 and DermIS) and reached an average Dice coefficient of 0.933 in the PH2 dataset and Dice = 0.872 in the DermIS dataset. Considering the high-performance methodologies available in the literature, the proposed solution is very promising, surpassing other methods with very promising results.

Lung cancer is the leading cause of cancer-related death worldwide. Early diagnosis of pulmonary nodules on chest CT scans provides a chance to design an effective treatment. The focus of this study is the classification problem of benign and malignant pulmonary nodules in CT images. Thus, it is proposed to apply texture maps directly to the 3D nodules as a previous of the feature extraction process. For this, the local binary patterns (LBP), with branches, such as using neighbors with borders (LBP-6), average dimensions (LBP-M), and a 3×3×3 neighborhood (LBP-3×), to highlight the nodule texture. Convolutional Neural Networks, such as DenseNet, ResNet, and LeNet, were used as attribute extractors using the 3D texture maps computed. Then, those deep features are used as input to train a Random Forest classifier. In the experiments, it is used LIDC-IDRI image database. The LIDC-IDRI database was used with two segmentation process, one made by radiologists, present in the base itself (B1), and one performed automatically by a third party (B2). In B1, the best result was the original nodules' attributes extracted with the DenseNet architecture reaching an accuracy of 0.8371, a specificity of 0.9130, sensitivity of 0.7328, and Kappa of 0.6591. In B2, the best result was a combination of attributes of the original nodule combined with the extracted LBP-6 with LeNet architecture that reached an accuracy of 0.9037, a specificity of 0.8453, sensitivity 0.9266, and Kappa of 0.7641. In conclusion, it is possible to improve the classification accuracy by including a texture map computation as part of the process.

Metastatic tumor is one that spreads from its place of origin to other parts of the body. A tumor formed by metastatic cancer cells is called a metastatic tumor or metastasis. The early identification of these tumors is essential to increase the chances of success in treating the disease. However, for this identification it is necessary to analyze extensive tissues of the affected organs, which is a tiring and error-prone task. In this paper, it is present three deep learning strategies for automatic identification of metastasis in histopathological images. For the development and evaluation of these strategies it was used the PCam database, which is composed of 327,680 color images extracted from histopathological exams of sections of lymph nodes. The obtained results using the fine tuning technique are promising, showing that deep learning models can be used for metastasis identification.

COVID-19 is an infectious disease caused by a type of coronavirus recently discovered, called SARS-CoV-2. It has infected more than 20 million people worldwide and it is responsible for more than 737,000 deaths. This work presents a study that explores linear regression mechanisms combined with a sliding and cumulative time window approach to provide inputs to assist in decision making for public policies, within the scope of the COVID-19 pandemic evolution, whether they are hardening or easing the isolation. Data from five states of Brazil were collected and applied a Ridge regression to predict the curve behavior of cases and deaths of COVID-19. As a result, an Explained Variance Status (EVS) up to 0.998 and 0.999 is presented, considering cases and deaths, respectively. It was concluded that sliding time window bring more information about the infection than cumulative, since public policy changes in a few time-lapse.

Lung cancer is one of the leading death causes by cancer worldwide. Early diagnosis increases the patient's cure chances. This diagnosis is made by computed tomography, an imaging exam that provides accurate information about the nodule. However, it depends on many external factors, from equipment quality to the fatigue of expert who analyzes. Image processing techniques might be great allies in early nodule detection, once it has no human limitations. This study presents an evaluation of two deep learning approaches, 3D U-Net and 3D V-Net, with different configurations of architectures, parameters, and data augmentation distribution applied to pulmonary nodules segmentation. The best results obtained mean an IoU of 0.74 and 0.99 for 3D U-Net and 3D V-Net, respectively. The second network obtained the best results because it is a much more robust network than the 3D U-Net, since it is a network developed for volumetric data processing.

Diabetes is one of the fastest-growing chronic diseases in the world. Diabetic retinopathy, a complication of Diabetes that affects vision, and if not treated promptly, can lead to total blindness of the patient. This abnormality has no cure, but if discovered in its early stages, there is a high chance that the patient will not reach total blindness. Detection of retinal background exudates is essential for the early diagnosis of diabetic retinopathy. In this paper, we present a deep learning model with a Convolutional Neural Network to diagnose exudates' presence or absence. The best results are about 99.52% sensitivity, 100% specificity, and about 99.76% accuracy for 1,608 images. Thus, the authors believe the proposed method can integrate a clinical system.

Coronavirus disease (COVID-19) has infected more than 3.6 million people worldwide and it is responsible for more than 250,000 deaths. A major problem faced in the diagnosis of COVID-19 is the inefficiency and scarcity of medical tests. The use of computed tomography (CT) has shown promise for the evaluation of patients with suspected COVID-19 infection. CT exam analysis is complex and requires specialist effort, which can lead to diagnostic errors. The use of CAD systems can minimize the problems generated by the analysis of CTs by specialists. This paper presents a methodology for diagnosing COVID-19 using convolutional neural network (CNN) for feature extraction in CT exams and its classification using XGBoost. The methodology consists of using a CNN to extract features from 708 CTs, 312 with COVID-19, and 396 Non-COVID-19. After the extracted data, we used XGBoost for classification. The results show an accuracy of 95.07, recall of 95.09, precision of 94.99, F-score of 95, AUC of 95, and a kappa index of 90. The results obtained show that the proposed methodology can be used as a diagnostic aid system by specialists.

Lung cancer is the most prevalent cancer in the world and early detection and diagnosis enable more treatment options and a far greater chance of survival. In this work, we propose an algorithm based on 3D Convolutional Neural Network (CNN) to classify pulmonary nodules as benign or malignant in computed tomography images. Three architecture of 3D CNNs are proposed, containing different input sizes and numbers of convolutional layers. In addition, we investigated data augmentation techniques and modifications in the network training cost function to address the problem of imbalanced data. The best result was achieved for input size of 32×32×32 pixels, 2 blocks of convolutional layers and 2 pooling layers. Also, the modification of cost function achieved promising results, with accuracy of 0.9188, kappa of 0.8019, sensitivity of 0.8481, specificity of 0.9479 and AUC of 0.8980 in the test set during malignant nodule detection.

Glaucoma is an eye disease responsible for the second most common cause of blindness in the world. The need to detect this disease in its early stages is notorious, considering that late treatment can cause loss of vision. In this context, computational methods are being developed to assist specialists in the task of analyzing ocular images to provide greater precision to the diagnosis. In this paper, we present a methodology for automatic classification of glaucoma using Capsule Network (CapsNet), a recent model of deep learning that analyzes the hierarchical spatial relationships between characteristics to represent images, so that it requires fewer training samples than traditional CNNs to achieve efficient classification. Before the execution of CapsNet, we applied a pre-processing step to the images, in order to highlight the characteristics. Our results were promising, with 90.90% accuracy, 86.88% recall, 94.64% precision, 90.59% f1-score, 0.904 AUC and 0.801 kappa index. The main contribution of our method is the fact that we have achieved promising results without the need to apply data to increase and segment the region of the optical disc. Thus, our study showed the potential of the capsules in identifying the relationships between the characteristics, even in the face of a reduced set of training.

A transmission power system carries the energy produced by generators to load points over long distances and to ensure the power demand is satisfactorily reached, the electric utilities use devices running applications for monitoring, control and protection, in order to maintain system operation in a normal state. These applications mainly require continuous data communication and low latency, which makes crucial to define the appropriate communication technology. In this context, this paper proposes a study which simulates a scenario based on the IEEE 14-bus transmission test system, on NS-3 (Network Simulator 3) tool, using the wireless Long Term Evolution (LTE) and WiMAX technologies to move data across data links on the system. The simulations generate metrics like delay, throughput and packet loss, to compare the technologies and express the degree of compatibility of each one with the applications tested, which can guide the utilities in choosing the communication technology to be deployed in its system.

Routing, Modulation Level and Spectrum Assignment (RMLSA) are some of the main problems studied in elastic optical networks. This work focuses on the study of guard band selection, with one or more free slots between the circuits, which are used in the solutions of the RMLSA problem in order to reduce the interference between adjacent circuits in the spectrum optical. In this context, a new approach, called ADVANCE, which uses a convolutional neural network to adaptive guard band selection is proposed. A proposal performance is compared to other adaptive proposals: AGBA, GBUN and UTOPIAN. The proposal achieves a reduction in the bandwidth blocking probability of at least 86.56% relative to AGBA, 84.60% relative to GBUN and 73.26% relative to UTOPIAN.

The increased demand for electricity has caused several problems for traditional electrical power systems, such as voltage fluctuations and interruptions in supply. These events, power quality disturbances, cause several losses for both the concessionaire and its consumers, either by damaging appliances or interrupting their operation. Among these power quality disturbances, the voltage sag stands out for being the most frequent event, causing several losses. Therefore, it is extremely important to locate the source of these disturbances in the electrical distribution system, in order to mitigate the problem. In general, methods for locating disturbances use few electrical meters and an analysis of the characteristics of voltage and current signals, which results in the estimation of a large region as a result. This paper proposes a approach to find not a region, but the bus in the power distribution system in which the voltage sag disorder originated by using a model of deep learning.

This paper presents a structural recognition system using machine learning algorithms (Multilayer Perceptron, Support Vector Machine and Random Forest) and the environment information to analyzes the feasibility of the use of machine learning methods for the construction of topological maps. The proposed method combines the recognized information from a given scene with a topological graph to create a map. This map can be used to plan high-level tasks of robotic navigation. The topological nodes are used to store semantic information, such as the robot's poses, sensor data and scene characteristics. The machine learning algorithms classification of the structural information as either rooms, corridors or doors obtained a satisfactory performance. The structural recognition provided by classification presents accuracy greater than 97% and topological maps built efficiently of classification.

This paper presents an efficient methodology based on Graph Theory for imposing the radial topology constraint in unfeasible solutions that appear in Evolutionary Algorithms during the evolution of the optimal solution in Distribution Network Reconfiguration. To reach a better computational time, this paper also presents an effective Backward-Forward method to solve power flow in radial DS (RDS). The algorithm developed was tested in distribution systems With the well-known switch reduction problem to achieve a lower number of active power losses.

In a Health Plan, beneficiaries can cancel their contracts at any given time. For that reason, Health Insurance/Plan Providers (HIP) need to avoid optional contract cancellations to keep their financial operations stable. This work's main purpose is to develop an approach to predict the optional contract cancellation in a Private HIP and help them to prevent those cancelations.

Depression is a mental disorder characterized by persistent sadness, loss of interest, and a set of behavioral changes. The high prevalence of depression imposes a significant burden on the world population, demanding methods capable of monitoring and treating this mental disorder. Currently, a large number of mobile applications have been designed to provide support to depressive people. This paper aims to identify, analyze and characterize the current state of mobile applications focused on depression. To do so, we conducted a systematic review of applications for depression assistance. The two most popular mobile app stores (Google Play Store and Apple App Store) have been explored to find the most relevant apps. After applying the inclusion and exclusion criteria and performing the quality assessment of the results, 216 applications were selected for the data extraction phase, where we summarized their benefits and limitations and identified gaps and trends. The results of this review evidenced that there is a growth in the diversity of apps' purposes such as chatbot, online therapy, educational tools, mood tracker, testing, and self-help.

Nowadays, access to healthcare services in rural or remote regions remains a major issue in both developing and developed countries. The advent of mobile health (m-Health) services is becoming a major improvement for patients. The main objective of this paper is the development and Quality of Experience (QoE) evaluation of a mHealth solution for healthcare professionals in remote areas. The system architecture is based on a Service Oriented Architecture (SOA). Android OS was chosen for developing the application, mainly, due to its open source APIs and the vast diversity of covered mobile devices. The system was evaluated and demonstrated in a real pilot in cooperation with a healthcare institution involving 42 patients and 4 healthcare professionals. A total of 294 patients evaluated the solution. Hardware issues, such as network disconnection and energy issues were reported in 4% of all the cases. This system reduced significantly care costs lessen the need for physical contact between patient and physician.

The correct location of the source of voltage sags is not a trivial task due to the short duration of these events and their rapid propagation in the distribution feeder. This paper proposes a method based on an ensemble method of the type Adaboost, with neural networks as base classifiers to determine the area where the voltage sag source is located. A voltage sag at a bus affects all other feeders, i.e. this disturbance is propagated in the whole system. The data management from smart meters installed in distribution feeders and decision support tools can become a viable alternative. In this sense, the smart meters could extract feature of the voltage sag and send it to the utility. At the utility, the AdaBoost performs location of the region by measuring the input's features similarity to samples from the training set. For this purpose, it was necessary to analyze the relevance of each feature extracted from smart meters' voltage signals to establish the structure which best represents the propagation of the disturbance in the system. The AdaBoost with Neural Network was tested in different scenarios of the 13-bus IEEE test feeder and was able to estimate the region with a good accuracy.

The voltage sag disturbance stands out as the most evident waveform change that is detected in electric networks, since the presence of these events in the network causes damages to the consumers. The first step in diagnosing the problem is to identify the location in the distribution system that is connected to the source causing the sinking disorder. This work presents a methodology based on clustering algorithm combined with decision rule to point out the region (cluster) that aggregates the place of origin. Clustering algorithm is responsible for analyzing the voltage signal data from different measurement nodes and separating these data into clusters. Then the Partial Decision Trees (PART) algorithm is responsible for defining the decision rule set that will confront the characteristics of each cluster and define which group aggregates the disturbance source location. For the clustering task, the k-means and fuzzy c-means clustering algorithms are evaluated and compared. The methodology was evaluated using the IEEE 34-bus test feeder system and the results show a hit rate higher than 90%.

The Electrical Power Quality (PQ) studies are commonly related to disturbances that alter the sinusoidal voltage features and/or current wave shapes. The classification approaches of electrical power quality disturbances found in the literature mainly consist of three steps: 1) signal analysis and feature extraction, 2) feature selection and 3) disturbances classification. However, there are some problems inherent in disturbances classification. The manual extraction of features is an imprecise and complex process, which can influence the resuits and, therefore, does not deal well with noisy signals. This paper proposes an approach based on Deep Learning using the raw data, without pre-processing, manual extraction or manual feature selection of the PQ disturbances signals for the classification of fifteen electrical power quality disturbances. A deep network is used, which consists of a hybrid architecture, composed by convolutional layers, a pooling layer, an LSTM layer, and batch normalization to extract features automatically. We adopted a 1-D convolution to adapt the input. The extracted features are used as input to fully connected layers, the last one being a SoftMax layer. The results are compared with state of the art methods based on the three steps, showing that the proposed approach had satisfactory performance even with noisy data.

The problem of voltage magnitude control, based on dispatching reactive power, consists in defining the adjustments that must be applied to synchronous generators to bring the voltages of the load buses whose magnitudes are violated within a suitable value range. The utilities usually determine the sequence of adjustments and sent to the selected control device to correct voltage magnitude violations, which makes the use of data communication technologies a necessity to enable this operation. In this context, this work proposes an application of a communication infrastructure for the control of voltage magnitude in transmission power systems. The adjustments in the voltage magnitude of control devices and the sequence of their execution are determined in the Control Manager utilizing two linear sensitivity-based methodologies and, then, transmitted via a WiMAX (Worldwide Interoperability for Microwave Access) network to Control Nodes, which in turn are connected to synchronous generators or condensers. NS-3 (Network Simulator 3) is used to simulate the 118-bus IEEE transmission test system with voltage magnitude violations. The results display an operating scenario that presents voltage violation and the applied communication infrastructure ensures that the sequence of adjustments are forwarded to the control points in order to bring the system state to the appropriate voltage magnitude ranges. The communication infrastructure WiMAX simulated in the NS-3 ensures that the sequence of adjustments are forwarded to the control points in appropriate delay and demonstrates the applicability of WiMAX technology for the control voltage magnitude. At last, to validate the proposed approach, in this paper we present the voltage magnitude in generators and load buses, as well as the availability, the delay and the volume of data transmitted by the WiMAX network as the adjustments in control variables, are executed.

The proposal of this work consists in an approach based on the utilization of two Wi-Fi receivers, using RSS information received by the transmission nodes (Access Point) for the SLAM problem. The solution presented is based on extended Kalman filter using RSS coming from different transmitters in the environment. This approach was chosen because the Cellbot platform has two Wi-Fi receivers, therefore combining the two range-only sensors improves the precision of the system. The system considers that a robot navigates in an unknown environment where it receives different Wi-Fi signals from a known source. Through the odometry and Wi-Fi signals, the robot localizes itself and the position of each Wi-Fi transmitter. The results show the viability of the application, and an increase of 38% in precision of the robot pose by using proposed approach.

The most common way of performing regression testing is by executing all test cases associated with a software system. However, this approach is not scalable since time and cost to execute the test cases increase together with the system’s size. A way to address this consists of prioritizing the existing test cases, aiming to maximize a test suite’s fault detection rate. To address the limitations of existing approaches, in this paper we propose a new approach to maximize the rate of fault detection of test suites. Our proposal has three steps: i) infer code components’ criticality values using a fuzzy inference system; ii) calculate test cases’ criticality; iii) prioritize the test cases using ant colony optimization. The test cases are prioritized considering criticality, execution time and history of faults, and the resulting test suites are evaluated according to their fault detection rate. The evaluation was performed in eight programs, and the results show that the fault detection rate of the solutions was higher than in the non-ordered test suites and ones obtained using a greedy approach, reaching the optimal value when possible to verify. A sanity check was performed, comparing the obtained results to the results of a random search. The approach performed better at significant levels of statistic and practical difference, evidencing its true applicability to the prioritization of test cases.

Elastic optical networks have been highlighted for attending to large volumes of data with different transmission rates. To meet high transmission rates, you need to troubleshoot RMLSA (Routing, Modulation Level, Spectrum Assignment) which is to select a route, choose a modulation level and a range of free spectrum. In the context, this paper we present a new RMLSA algorithm aware of the physical layer effects using the fuzzy system to assist in choosing the best route in elastic optical networks, called Fuzzy-RQoTO. The proposed algorithm selects the route of best quality for a given pair (o, d) considering metrics the relative fragmentation and number of occupied slots. The Fuzzy-RQoTO algorithm attempts to reduce the blockage caused by the degradation of the transmission quality of the optical circuits and concomitantly selects a best route for the establishment of the circuit. A performance evaluation study was performed on the NSFNet and EON topologies comparing the performance of the Fuzzy-RQoTO algorithm with three algorithms already proposed in the literature. The Fuzzy-RQoTO algorithm presented a minimum gain of 27.5% in terms of circuit blocking probability. In terms of bandwidth blocking probability, Fuzzy-RqoTO reported a minimum gain of 21.9%.

In this paper, we present a fuzzy system for structural recognition in which the environment information is used to generate a topological map. The proposed method combines the recognized information from a given scene with a topological graph to create a map. This map can be used to plan high-level tasks of robotic navigation. The topological nodes are used to store semantic information, such as the robot's poses, sensor data and scene characteristics. The fuzzy system categorizes the structural information as either rooms, corridors or doors.

When a large area coverage is the key application, localization and tracking techniques are facing challenges on current Internet of Things (IoT) scenario, mainly regarding areas with critical propagation environments or tracking mobile objects, such as in agriculture and cattle farming. In order to achieve a good communication solution, Low Power Wide Area Network (LPWAN) protocols offer different solutions profiles. As such, this paper presents the design and deployment of a solution based on Long Range (LoRa) modulation protocol to attend localization and tracking applications using only one base station and so, a GPS device to provide the coordinates to be transmitted. The proposal is evaluated, demonstrated, and validated in a real scenario and it is ready for use.

A Smart Meter (SM) is an electronic device that records and monitors power consumption at time intervals and can send this information to the monitoring and billing center of power companies. Thus, SMs are responsible for providing bi-directional communication between consumers and a Smart Grid central system. However, the development of a reliable SM solution is still a challenge as the majority of current works are limited to theoretical proposals. The deployment of SMs infrastructure in a real testbed is crucial not only for application of theoretical models in real environments but also to address many premises that may emerge in practical scenarios. Then, this paper proposes a low-cost Smart Meter able to provide bidirectional communication between homes and Electric Power Companies (EPC) using IEEE 802.15.4. To present and experimenting the performance of the produced SM, an Advanced Metering Infrastructure (AMI) that allows EPCs and users monitoring the energy consumption and billing in real-time through a mobile application was created. In addition to the SM applicability demonstration, a brief network performance assessment to verify the limitations of adopted communication interfaces was considered. Thus, based on real experimentation and demonstration, it was concluded the high applicability and potentiality of the proposed work in real large scale scenarios.

The purpose of face recognition is to identify a person based on their face images. There are still some challenges and problems to be overcome, although several improvements have been achieved recently. These difficulties are mainly due to environment conditions, such as lighting changes, occlusion, changes in racial expressions and head position. This work presents an approach to face recognition based on combination of the curved Gabor filter, entropy and Support Vector Machine (SVM). The curved Gabor filter is used to perform the feature vector extraction of an image. Then, the results from the Gabor response curve are segmented into non-overlapping blocks to reduce interference from local variations in the image. The entropy is used to maintain the most representative image data and to provide a reduction in the feature vector. As classifier we use the SVM. A set of experiments was performed to evaluate this approach based on the characteristics of scenarios encountered in a real environment, using the AR face database. The results obtained from the experiments exceed the state-of-the-art approaches available in the literature in 4 of the 5 tests and the two additional final tests.

In the context of sensor clouds, the provisioning process is essential since it is responsible for selecting physical sensors that will be allocated to compose virtual sensors. In literature, most works consider the allocation of all sensors within the region of interest. Such an approach, however, can cause serious problems such as wasted energy consumption. The objective of this paper is to present an approach to reducing energy consumption in provisioning of virtual sensors by similarity of heterogenous sensors. The approach minimizes the number of selected nodes and accordingly reduces the total energy consumption of sensor nodes that make up the cloud. Results from initial experiments show that the approach reduces energy consumption by 73.97%, providing a solution to be considered in sensor cloud scenarios.

This paper presents an approach using range measurement through homography calculation to build 2D visual occupancy grid and control the robot through monocular vision. This approach is designed for a Cellbot architecture. The robot is equipped with wall following behavior to explore the environment, which enables the robot to trail objects contours, residing in the fuzzy control the responsibility to provide commands for the correct execution of the robot movements while facing the adversities in the environment. In this approach the Cellbot camera works as a sensor capable of correlating the images elements to the real world, thus the system is capable of finding the distances of the obstacles and that information is used for the occupancy grid mapping and for fuzzy control input. Experimental results with V-REP simulator are presented to validate the proposal, and the results were favorable to the use in robotics and in acceptable computing time.

Fraud and abuse are two factors directly related to high health care costs, since they correspond to expenses that can be eliminated without prejudice to the quality of services provided. In Brazil, the health insurance companies implement a claim authorization process which assists in the detection of fraud and abuse. This process consists of a prior analysis of the services requested by providers, allowing them to detect patterns linked to fraud and abuse. This analysis is commonly performed manually, making the execution expensive and non-scalable. Health insurance companies have invested in the use of data mining and machine learning techniques to detect suspicious fraudulent patterns. However, the use of these techniques in claim authorization process is affected by the class imbalance problem, due to the fact that there are much more authorized service requests than unauthorized ones. This paper presents the investigation results of the effects of class imbalance in health insurance claims authorization domain. By means of an experiment, the performance loss of several classifiers was measured in different class distributions and also the performance recovery provided by treatment methods. The results show that the studied classification algorithms are affected differently by class imbalance. They also show that the recovery performance is lower the higher the class imbalance.

Pervasive mobile games utilise contextual data about players and their environment to explore new means of interaction and enhance the gaming experience. However, the inherent imperfection of contextual data acquisition poses a challenge for developers and designers of pervasive games. In these games, both sensor inaccuracies and uncertainties need to be identified and properly handled to prevent disrupting the gaming experience. This paper presents a model for the development of pervasive mobile games based on fuzzy systems. It supports the use of fuzzy set theory to represent contextual uncertainty and applies fuzzy logic to design game rules. A pervasive mobile game, called Radar, is presented to showcase the model's applicability; the game exploits contextual data from GPS and motion sensors to control the animation frequency of a simulated radar using a type-I Mamdani fuzzy inference system. Thus, the proposed model is capable of handling sensor inaccuracies while providing an intuitive approach to the design of pervasive games.

The hydrothermal coordination can be defined as a problem to determine the optimum usage of the hydroelectric and thermoelectric resources available during a period. In hydrothermal generation systems with a predominance of hydroelectric power plants, like in the Brazilian system, the problem consists in replacing the thermal generation by hydropower generation to minimize the system operational costs. Therefore, this paper presents a model based on Particle Swarm Optimization, and Network Flow applied to the problem of hydrothermal coordination. The goal is to determine an optimal operational strategy for the reservoirs of the hydroelectric power plants considering each hydroelectric power plant separately, its operational constraints and guaranteeing the applicability of the solutions, in order to minimize the operating cost of the system. The proposed approach is compared with four other optimization methods: a Genetic Algorithm (GA), a model based on Genetic Algorithms and the Takagi-Sugeno Fuzzy Inference System (GA+Fuzzy), a model based only on PSO and an optimization algorithm that employs Network Flow and Reduced Gradient (NF+RG). A hydroelectric system composed of three hydroelectric power plants and six different hydrological scenarios were used to test the algorithms. The primary objective was to illustrate the viability and applicability of the proposed algorithm, and, based on the obtained results, show the efficacy and energy gains that are possible.

Among common disturbances in Electric Power Quality, the most relevant and with greatest rate of occurrence are voltage sags, which cause substantial economical loss to concessionary companies and to customers. Constant monitoring is an essential part of identifying existing disturbances. However, the costs involved make monitoring the complete system infeasible, thus only a reduced number of monitors are available, which need to be installed in strategic positions in order to cover the greatest number of possible events. This work presents an approach for solving the problem of allocating monitors of electric power quality, considering various aspects of the problem, such as topological coverage, voltage sags that have happened but have not been monitored and total cost of equipment installed. The Monte Carlo Method was used for modeling the time series of faults in the distribution system and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was used in the construction of this model. The approach was submitted to IEEE 13, 34 and 37-bus distribution systems, which were simulated using DigSILENT Power Factory 15.1 software. The results allow the user to make decisions regarding the amount and the position of the monitors to be installed, in order to seek adequacy to the financial reality of the power supplies' and as to avoid unnecessary costs that would not result in improvements in monitoring performance.

In sensor clouds environments, the provisioning process is a crucial task since it is responsible for selecting physical sensors nodes that will be allocated to compose virtual sensors. In literature, most works consider the allocation of all sensors within the region of interest. However, this approach causes serious problems such as wasted energy consumption. Therefore, the objective of this paper is to present ACOSIM, an approach to minimize the overall sensor cloud energy consumption by selecting only a subset of sensor nodes to compose the virtual sensors. Results from initial experiments show that the approach reduces the sensor cloud energy consumption the by 73.97%, providing a solution to be considered in sensor cloud scenarios.

Glaucoma is an eye disease that causes irreversible vision loss. Retinography is done manually by the ophthalmologist and is the cheapest, least invasive and most effective way to diagnose glaucoma. The ratio between the diameter of the outer part of the Optic Disc (OD) and the cup (internal part) called CDR (cup-to-disc ratio) is an important indicator of glaucoma presence in patients. This paper proposes a semiautomatic approach that includes the segmentation of OD and cup regions. The proposed approach consists of four stages. The first stage consists of preprocessing the retinal image, in order to remove blood vessels and a possible influence in the segmentation stage. In the second stage we apply the Seeded Fuzzy C-means algorithm to segment the preprocessed image in order to indentify cup and OD. The third step involves the application of a post-processing so that non-segmented regions are filled. Finally, the last step calculates the value of the CDR associated with the retinal image. To verify the applicability of the proposed approach, we carried out tests in two public image databases: DRISHTI-GS and RIM-ONE r3. The results obtained illustrate the feasibility of applying the approach in order to effectively assist ophthalmologists in the segmentation of cup and OD, as well as the calculation of the CDR.

Voltage magnitudes in transmission power systems must be kept within the limits defined by regulatory agencies for safe operations. Thus, this paper proposes an approach to determine and apply a sequence of adjustments to control the voltage magnitude in such systems. The adjustments follow two methodologies based on a reduced Jacobian matrix (Jqv) of the system power balance equations. Computational simulations made in the IEEE (Institute of Electrical and Electronic Engineers) 57-bus system evaluated the proposed approach for operational scenarios with voltage magnitude violations. The results obtained provide the reactive power dispatch to correct voltage magnitude violations, voltage profiles and active power losses as the recommended adjustments are executed, showing that the control methodologies were able to manage the test system across different states without voltage magnitude violations.

The Demand Response (DR) program is used by public electric utilities to encourage consumers to change their consumption profiles in order to improve the reliability and efficiency of the electric power system (EPS). However, operational particularities due to different categories of residential appliances, consumer satisfaction and comfort are not usually taken into consideration when designing a DR model, since the main aim is to minimize electricity costs. This article presents an optimized DR model for residential consumers, which is based on the real-time price (RTP) of electricity to minimize electricity costs associated with consumption as well as considering the operational aspects of the residential appliances. The results of the inconvenience values show that the different categories of residential appliances that were taken into consideration in this work did not undermine the satisfaction/comfort of the consumer.

Software testing consists in the dynamic verification of the behavior of a program on a set of test cases. When a program is modified, it must be tested to verify if the changes did not imply undesirable effects on its functionality. The rerunning of all test cases can be impossible, due to cost, time and resource constraints. So, it is required the creation of a test cases subset before the test execution. This is a hard problem and the use of standard Software Engineering techniques could not be suitable. This work presents an approach for test case prioritization and selection, based in relevant inputs obtained from a software development environment. The approach uses Software Quality Function Deployment (SQFD) to deploy the features relevance among the system components, Mamdani fuzzy inference systems to infer the criticality of each class and Ant Colony Optimization to select test cases. An evaluation of the approach is presented, using data from simulations with different number of tests.

With the evolution of e-commerce and Online Social Networks, the web information has constantly increased, so the relevance to create methods for automatic knowledge extraction and data mining earned notoriety. Information as opinion evaluation is a point studied by Sentiment Analysis area, which is becoming important nowadays. Be aware of the best reviews is a factor that must be taken into account. Sousa et al. proposed an approach to estimate the degree of importance of reviews about product and services using Fuzzy System, reporting good results. This work proposes an experimental study between their approach using Fuzzy Systems and an execution using Artificial Neural Network to verify which is the most appropriate to solve the problem to estimate the importance of reviews.

This paper presents a strategy as attempt to solve the problem of choosing the best combination among the M combinations of shortest paths in optical translucent networks. Fixed routing algorithms demands a single route to each pair of nodes. The existence of multiple shortest paths to some pairs of nodes originates the problem of choose the shortest path which fits better the network requests. The algorithm proposed in this paper is an adaptation of Ant Colony Optimization (ACO) metaheuristic and attempt to define the set of routes that fits in an optimized way the network conditions, resulting in reduced number of blocked requests and better adjusted justice in route distribution. A performance evaluation is conducted in real topologies by simulations, and the proposed algorithm shows better performance between the compared algorithms.

Intelligent agents consist in a promising computing technology for the development of complex distributed systems. Despite the available theoretical references for guiding the designer of these agents, there are few proposed testing techniques to validate these systems. It's known that this validation depends on all the selected test cases, which should provide information regarding the components in the structure of the agent that show unsatisfactory performance. This article presents the application of Artificial Immune Systems (AIS), through Clonal Selection Algorithm (CLONALG), for the problem of optimization of selection of test cases for testing computing systems that are based on intelligent agents. In order to validate the use of CLONALG, comparisons between the Genetic Algorithms (GA) and Ant Colony Optimization Algorithms (ACO) techniques were performed. In the experiments with the approach testing intelligent agents with different types of architecture in partially and completely observable environments, the approach selected a group of satisfactory test cases in terms of the generated information about the irregular performance of the agent. From this result, the approach enables the identification of problematic episodes, allowing the designer to make objective changes in the internal structure of the agent in such a way to improve its performance.

The indexed Web increases every day, making the development of automatic methods for knowledge extraction more relevant. The area of Sentiment Analysis or Opinion Mining aims to extract opinions from the user-generated content and to define the semantic orientation of each individual opinion. This work proposes an approach to estimate the degree of importance of comments generated by web users by using a Fuzzy system. The Fuzzy system has three inputs: author reputation, number of tuples (feature; quality word), and percentage of correctly spelled words and one output: importance degree of the comment. The importance degree has been used to select the best comments in a Corpus. The paper also describes an experiment which was used to compare the results of a sentiment orientation method before and after the selection of the best comments. It was conducted with 1620 reviews also about smartphones (982 positives and 594 negatives) and our approach improved the results of sentiment orientation method up to approximately 10% in f-measure in positive reviews and 20% in f-measure in negative reviews.

In mobile robots navigation, there are two main topics: reaching a goal and avoiding obstacles on the path. However, a relevant problem in autonomous navigation is dealing with a lot of uncertainty in the environment. The fuzzy logic is a popular method used by most researchers and it can deal with uncertainty, proximity or uncompleted information. In this work, we present an approach with a safe autonomous navigation using the interval Type-2 Fuzzy System in a two wheeled wall-following robot on static and unknown environments. The results were reached with dynamic simulations on three distinct scenes. These simulations have been done to analyze the error and time. The results reached a satisfactory performance navigation of robot using type-2 fuzzy system.

Computational Intelligence (CI) embraces techniques designed to address complex real-world problems in which traditional approaches are ineffective or infeasible. Some of these techniques are being used to solve several complex problems, such as the team allocation, building products portfolios in a software product line and test case selection/prioritization. However, despite the usefulness of these applications, the development of solutions based in CI techniques is not a trivial activity, since it involves the implementation/adaptation of algorithms to specific context and problems. This work presents Athena, a visual tool developed aiming at offering a simple approach to develop CI-based software systems. In order to do this, we proposed a drag-and-drop approach, which we called CI as a Service (CIaaS). Based on a preliminary study, we can state that Athena can help researchers to save time during the development of computational intelligence approaches.

The clustering problem has been considered as one of the most important problems among those existing in the research area of unsupervised learning (a Machine Learning subarea). Although the development and improvement of algorithms that deal with this problem has been focused by many researchers, the main goal remains undefined: the understanding of generated clusters. As important as identifying clusters is to understand its meaning. A good cluster definition means a relevant understanding and can help the specialist to study or interpret data. Facing the problem of comprehend clusters - in other words, create labels - this paper presents a methodology to automatic labeling clusters based on techniques involving supervised and unsupervised learning plus a discretization model. Considering the problem from its inception, the problem of understanding clusters is dealt similar to a real problem, being initialized from clustering data. For this, an unsupervised learning technique is applied and then a supervised learning algorithm will detect which are the relevant attributes in order to define a specific cluster. Additionally, some strategies are used to create a methodology that presents a label (based on attributes and their values) for each cluster provided. Finally, this methodology is applied in four distinct databases presenting good results with an average above 88.79% of elements correctly labeled.

In mobile robots navigation, there are two main topics: reaching a goal and avoiding obstacles on the path. However, a relevant problem in autonomous navigation is dealing with a lot of uncertainty in the environment. The fuzzy logic is a popular method used by most researchers and it can deal with uncertainty, proximity or uncompleted information. In this work, we present an approach with a safe autonomous navigation using the interval Type-2 Fuzzy System in a two wheeled wall-following robot on static and unknown environments. The results were reached with dynamic simulations on three distinct scenes. These simulations have been done to analyze the error, distance and time. The results reached a satisfactory performance navigation of robot using type-2 fuzzy system.

The Wireless Sensor Networks (WSNs) are composed of small sensor nodes capable of sensing (collecting), processing and transmitting data related to some phenomenon in the environment. Despite the numerous possibilities of use, the WSN has serious constraints, mainly related to the energy consumption during data transmission. These constraints require the implementation of enhancements in routing protocols that enable the sensor nodes to communicate efficiently and effectively with minimum power consumption. In our proposal, a fuzzy system is used to estimate the route quality based on the number of hops and the energy level of the nodes that compose a route. The estimated route quality is used by the routing protocol for selecting a specified path to send a message. An Ant Colony Optimization (ACO) algorithm is used to adjust, in an automatic way, the rule base of the fuzzy system in order to enhance the estimation of the route quality, hence increasing the energy efficiency of the network. The simulations, using the Directed Diffusion (DD) and Ad-Hoc On-Demand Multipath Distance Vector (AOMDV) multipath routing protocols, showed that the proposal is effective from the point of view of important metrics related to WSNs such as: number of messages delivered to the sink node, average cost of message, packet loss rate, and time of death of the first sensor node.

Software Product Line (SPL) development is a new approach to software engineering that aims at the development of a whole range of products. One of the problems which hinders the adoption of that approach is related with the management of the products of the line. Additionally, the scope of a software product line is determined by the bounds of the capabilities provided by the collection of products in the product line. This introduces new challenges related to the scope problem. One of the main three different forms of scoping is the Product Portfolio Scoping (PPS). PPS aims at defining the products that should be developed as well as their key features. While this has an impact on the actual reuse opportunities, it is usually driven from marketing aspects. Defining a product portfolio by considering costumers satisfaction and cost aspects is a NP-hard problem. This work presents a hybrid approach, which combines fuzzy inference systems and the multi-objective metaheuristics NSGAII to support product management by generating portfolios of products, based in segments of users and the development cost of the assets of the SPL. Fuzzy inference systems are used to generate development cost of an asset by using coupling, number of code lines and cyclomatic complexity and also to estimate the quality of the products generated by the optimization module of our approach. The NSGA-II metaheuristic is used to search for products minimizing the cost and maximizing the relevance of the candidate products. The results show that the proposed approach is effective in proposing the best products in terms of relevance and cost of the assets.

The Wireless Sensor Networks (WSNs) are composed of small sensor nodes capable of sensing (collecting), processing and transmitting data related to some phenomenon in the environment. However, the sensor nodes have severe constraints, such as: low network bandwidth, short wireless communication range, and limited CPU processing capacity, memory storage and power supply. Therefore, maximizing the benefits of limited resources in WSNs have become one relevant and challenging issue. One of the most relevant problem is related with the energy consumption during data transmission, since, sensor nodes are battery-powered and recharging or replacing batteries, in most cases, is infeasible. Communication in WSN consumes more energy than sensing and processing performed by the network nodes. The strategy proposed in this paper, to reduce the energy consumption, consists in optimizing the operations of routing protocols. The WSN routing protocols must have self configuration features in order to find out which is the best route for communication, thus increasing delivery assurance and decreasing the energy consumption between nodes that comprise the network. This paper presents a proposal for estimating the quality of routes using fuzzy systems to assist the Directed Diffusion routing protocol. The fuzzy system is used to estimate the degree of the route quality, based on the number of hops and the energy level of the nodes that compose a route. An Ant Colony Optimization (ACO) algorithm is used to adjust, in an automatic way, the rule base of the fuzzy system in order to improve the classification strategy of routes, hence increasing the energy efficiency of the network. The simulations showed that the proposal is effective from the point of view of three metrics: packet loss rate, message delay to the sink node and time of death of the first sensor node.

The Wireless Sensor Networks (WSNs) are composed of small sensor nodes capable of sensing (collecting), processing and transmitting data related to some phenomenon in the environment. The sensor nodes have severe constraints, such as: limited power supply, low network bandwidth, short wireless communication range, and limited CPU processing and memory storage. Communication in WSN consumes more energy than sensing and processing performed by the network nodes. Therefore, as the sensor nodes are battery-powered and recharging or replacing batteries, in most cases, is infeasible, maximizing the benefits of limited resources in WSNs have become one relevant and challenging issue. The WSN routing protocols must have autoconfiguration features in order to find out which is the best route for communication, thus increasing delivery assurance and decreasing the energy consumption between nodes that comprise the network. This paper presents a proposal for estimating the quality of routes using fuzzy systems to assist the Directed Diffusion routing protocol. The fuzzy system is used to estimate the degree of the route quality, based on the number of hops and the lowest energy level among the nodes that form the route. An Ant Colony Optimization (ACO) algorithm is used to adjust in an automatic way the rule base of the fuzzy system in order to improve the classification strategy of routes, hence increasing the energy efficiency of the network. The simulations showed that the proposal is effective from the point of view of the packet loss rate, the necessary time to send a specific number of messages to the sink node and the lifetime of the first sensor node, which is defined as the period that the first sensor node die due to the battery depletion.

The success of the team allocation in a agile software development project is essential. The agile team allocation is a NP-hard problem, since it comprises the allocation of self-organizing and cross-functional teams. Many researchers have driven efforts to apply Computational Intelligence techniques to solve this problem. This work presents a hybrid approach based on NSGA-II multi-objective metaheuristic and Mamdani Fuzzy Inference Systems to solve the agile team allocation problem, together with an initial evaluation of its use in a real environment.

Reservoir Operation Rules (ROR) are functions that determine the operating volume of each reservoir in order to establish a coupled behavior between hydroelectric power plants. This paper proposes the implementation of ROR through a hybrid approach based on fuzzy systems and Particle Swarm Optimization (PSO). Fuzzy inference systems of the Mamdani type are used to estimate the operating volume of each hydroelectric plant, which is based on the value of the stored energy in the hydroelectric system. In order to represent the particular behavior of each reservoir for the operation of the system in an optimized configuration, a fuzzy system for each hydroelectric plant was designed. The PSO algorithm is used to adjust the membership functions that represent the consequent of the linguistic rules of the fuzzy system. A computational model for simulating the operation of hydroelectric systems is also used to implement the proposed ROR, based on developed Fuzzy-PSO (ROR-FPSO) systems, and to compare them to the Parallel Operation Rules (ROR-PO), to the operation rules based on Mathematical Functions (ROR-MF) and to the operational rules based on Takagi-Sugeno Fuzzy Systems (ROR-TSFS). The results illustrate the effectiveness of the ROR-FPSO, which maximizes the hydroelectric benefits associated to hydrothermal generation system, when compared to other ROR already found in literature.

The three-phase voltage and current waveforms from a Power System (PS) are not considered pure sinusoids due to the presence of, among others, the harmonic distortion. This work presents an approach based on the Particle Swarm Optimization (PSO) method for the harmonic component estimation in a PS. PSO is a technique of search/optimization that models the social behavior observed in many species of birds, schooling fish and even human social behavior. The technique uses a population of particles to search inside a multidimensional search space. The objective of the PSO is to adjust the speed and position of each particle, seeking for the best solution within the search space. The results demonstrate that the method can precisely identify the harmonic components in the distorted waveforms and it shows considerable advantages if compared to the most common algorithm for this purpose, the Discrete Fourier Transform.

Wireless sensor networks (WSNs) are composed of sensor nodes in order to detect and transmit features from the physical environment. Generally, the sensor nodes transmit informations to a special node, called sink. The use of an unique sink represents a bottleneck in a network, especially for applications in real time. In this sense, some researches have directed studies to the use of multiple sinks. The approach proposed by this paper presents the application of Genetic Fuzzy System (GFS) for the selection of routes in WNSs, in order to make the communication between multiple sensor nodes and multiple sink nodes. Fuzzy Inference System of Mamdani are used to determine the most appropriate sink node through consideration of some characteristics of the sensors network, such as energy and number of hops. Genetic Algorithms are employed to obtain the optimal adjustment of Mamdani's fuzzy inference system parameters. By applying GAs, we intend to achieve both a fuzzy database and a fuzzy rules base to maximize performance of the application of Mamdani's inference system in the selection of routes in Wireless Sensor Networks. The proposed route selection was applied by means of computer simulations to demonstrate the feasibility of the approach implemented. The results obtained through simulations demonstrated a sensor network with a longer lifetime, through the choice of the adequate sink used for sending packets through the network in order to find the best routes.

Wireless sensor networks (WSNs) are composed of sensor nodes in order to detect and transmit features from the physical environment. Generally, the sensor nodes transmit information to a special node called sink. Some recent researches have led to the selection of routes in sensor networks with multiple sink nodes. The approach proposed by this paper presents the application of Genetic Fuzzy System (GFSs) for the selection of routes in WSNs, in order to make the communication between multiple sensor nodes and sink nodes. The results obtained through simulations demonstrated a sensor network with a longer lifetime, through the choice of the adequate sink used for sending packets through the network in order to find the best routes.

The operation planning in hydrothermal systems with great hydraulic participation, as it is the case of Brazilian system, seeks to determine an operation policy to specify how hydroelectric plants should be operated, in order to use the hydroelectric resources economically and reliably. This paper presents an application of Takagi-Sugeno Fuzzy Inference Systems to obtain an operation policy (PBFIS Policy Based on Fuzzy Inference Systems) that follows the principles of the optimized operation of reservoirs for electric power generation. PBFIS is obtained through the application of an optimization algorithm for the operation of hydroelectric plants. From this optimization the relationships between the stored energy of the system and the volume of the reservoir of each plant are extracted. These relationships are represented in the consequent parameters of the fuzzy linguistic rules. Thus, PBFIS is used to estimate the operative volume of each hydroelectric plant, based on the value of the energy stored in the system. In order to verify the effectiveness of PBFIS, a computer simulation model of the operation of hydroelectric plants was used so as to compare it with the operation policy in parallel; with the operation policy based on functional approximations; and also with the result obtained through the application of the optimization of individualized plants' operation. With the proposed methodology, we try to demonstrate the viability of PBFIS' obtainment and application, and with the obtained results, we intend to illustrate the effectiveness and the gains which came from it.