Study on Sentiment Classification Strategies Based on the Fuzzy Logic with Crow Search Algorithm

In recent times, sentiment analysis research has gained wide popularity. That situation is caused by the nature of online applications that allow users to express their opinions on events, services, or products through social media applications such as Twitter, Facebook, and Amazon. This paper proposes a novel sentiment classification method according to the Fuzzy rule-based system (FRBS) with crow search algorithm (CSA). FRBS is used to classify the polarity of sentences or documents, and the CSA is employed to optimize the best output from the fuzzy logic algorithm. The FRBS is applied to extract the sentiment and classify its polarity into negative, neutral, and positive. Sometimes, the outputs of the FRBS must be enhanced, especially since many variables are present and the rules between them overlap. For such cases, the CSA is used to solve this limitation faced by FRBS to optimize the outputs of FRBS and achieve the best result. We compared the performance of our proposed model with different machine learning algorithms, such as SVM, maximum entropy, boosting, and SWESA. We tested our model on three famous data sets collected from Amazon, Yelp, and IMDB. Experimental results demonstrated the effectiveness of the proposed model and achieved competitive performance in terms of accuracy, recall, precision, and the F–score.

Design and control of 2-DoF joystick using MR-fluid rotary actuator

The purpose of this paper is to design a control algorithm for a 2-DoF rotary joystick model. Firstly, the structure of the joystick, which composes of two magneto-rheological fluid actuators (shorten MRFA) with optimal configuration coupled perpendicularly by the gimbal mechanism to generate the friction torque for each independent rotary movement, is introduced. The control strategy of the designed joystick is then suggested. Really, because of two independent rotary movements, it is necessary to design two corresponding controllers. Due to hysteresis and nonlinear dynamic characteristics of the MRFA, controllers based an accurate dynamic model are difficult to realize. Hence, to release this issue, the proposed controller (named self-turning fuzzy controllers-STFC) will be built through the fuzzy logic algorithm in which the parameters of controllers are learned and trained online by Levenberg-Marquardt training algorithm. Finally, an experimental apparatus will be constructed to assess the effectiveness of the force feedback controls. Herein, three experimental cases are performed to compare the control performance of open-loop and close-loop control method, where the former is done through relationship between the force at the knob and the current supplied to coil while the latter is realized based on the proposed controller and PID controller. The experimental results provide strongly the ability of the proposed controller, meaning that the STFC is robust and tracks well the desirable force with high accuracy compared with both the PID controller and the open-loop control method.

A Review of the Commercially Available ECG Detection and Transmission Systems—The Fuzzy Logic Approach in the Prevention of Sudden Cardiac Arrest

Sudden cardiac death (SCD) constitutes a major clinical and public health problem, whose death burden is comparable to the current worldwide pandemic. This comprehensive review encompasses the following topics: available rescue systems, wearable electrocardiograms (ECG), detection and transmission technology, and a newly developed fuzzy logic algorithm (FA) for heart rhythm classification which is state-of-the art in the field of SCD prevention. Project “PROTECTOR”, the Polish Rapid Transtelephonic ECG to Obtain Resuscitation for development of a rapid rescue system for patients at risk of sudden cardiac arrest (SCA), is presented. If a lethal arrhythmia is detected on the basis of FA, the system produces an alarm signal audible for bystanders and transmits the alarm message along with location to the emergency medical center. Phone guided resuscitation can be started immediately because an automated external defibrillator (AED) localization map is available. An automatic, very fast diagnosis is a unique feature of the PROTECTOR prototype. The rapid detection of SCA is based on a processor characterized by 100% sensitivity and 97.8% specificity (as measured in the pilot studies). An integrated circuit which implements FA has already been designed and a diagnosis is made within few seconds, which is extremely important in ischemic brain damage prophylaxis. This circuit could be implemented in smart implants (Sis).

An Intelligent Vice Cluster Head Election Protocol in WSN

Wireless sensor networks (WSNs) has a practical ability to link a set of sensors to build a wireless network that can be accessed remotely; this technology has become increasingly popular in recent years. Wi-Fi-enabled sensor networks (WSNs) are used to gather information from the environment in which the network operates. Many obstacles prevent wireless sensor networks from being used in a wide range of fields. This includes maintaining network stability and extending network life. In a wireless network, sensors are the most essential component. Sensors are powered by a battery that has a finite amount of power. The battery is prone to power loss, and the sensor is therefore rendered inoperative as a result. In addition, the growing number of sensor nodes off-site affects the network's stability. The transmission and reception of information between the sensors and the base consumes the most energy in the sensor. An Intelligent Vice Cluster Head Selection Protocol is proposed in this study (IVC LEACH). In order to achieve the best performance with the least amount of energy consumption, the proposed hierarchical protocol relies on a fuzzy logic algorithm using four parameters to calculate the value of each node in the network and divides them into three hierarchical levels based on their value. This improves network efficiency and reliability while extending network life by 50 percent more than the original Low Energy Adaptive Clustering Hierarchy protocol. Keywords: Wireless Sensor Networks, Sensors, Communication Protocol, Fuzzy logic, Leach protocol.

Smart Air Filter Based on Activated Charcoal and Aloe Vera Using Fuzzy Logic Algorithm

Smoking is one of the main risk factors of several chronic diseases such as lung cancer, upper respiratory tract cancer, heart disease, stroke, bronchitis, emphysema and others, even smoking can cause death. Cigarette smoke inhaled by passive smoking is the main cause of lung cancer in people who are not smokers. The risk of lung cancer increases by 20-30% in non-smokers but always surrounded by cigarette smoke, compared to non-smokers who are not exposed to smoke. This study aims to develop a smart device to filter cigarette smoke so that the air released from this device is free from the dangers of cigarette smoke. The materials used in this device as a cigarette smoke filter are activated charcoal and aloe vera, the pores of the charcoal open and can become adsorbents. This device uses fuzzy logic as a control method that can provide decisions that resemble human decisions. Arduino is used to adjust the fan rotation in absorb cigarette smoke and the LCD to display the level of cigarette smoke.

Power Conversion Performance of a Single-Phase PV Inverter with Fuzzy Logic Algorithm

The paper presents a low-power conversion system focusing on implementing new solar inverter control techniques implemented with Fuzzy Logic. The power generated by a solar panel requires robust approaches and efficient methods to be used at its maximum. Therefore, a promising strategy is a Fuzzy Logic based on the Maximum Power Point Tracking (MPPT) algorithm. To gather efficient power conversion, our proposed model uses a control loop composed of Fuzzy Proportional Integrative (PI) regulators, Clarke and Park transform, followed by a synchronization grid mechanism Second-order generalized integrator (SOGI) based phase-locked loops (PLLs). The proposed technique examines photovoltaic system (PV) performance with respect to its non-linearities and eventual shaded conditions that can occur in the PV array. The shading effect is tested by varying the irradiance, which determines the variation of the output current and implicitly of the output power. The simulation results show that the inverter control system is very efficient, generating stable and nearly sinusoidal current and voltage characteristics. Thus, the inverter converts over 99 % of the power generated by PV arrays.

Multi-criterial analysis for modelling Matengo/Ngolo pits agro-ecological zones using fuzzy logic in Southern Tanzania

It has known that grain production is declining globally, leading to food insecurity becoming increasingly apparent in tropical countries, particularly in Sub-Saharan Africa. Countries in Sub-Saharan Africa must concentrate on indigenous agricultural methods to mitigate the impact of climate change on grain production while preserving ecological balances and achieving sustainable goals. Matengo/Ngolo pits, practised on steep slopes in the Matengo highlands, southern Tanzania, are indigenous knowledge invented by local communities over the past 300 years. Despite its effectiveness in increasing agricultural productivity, soil moisture retention, and other environmental advantages, Matengo/Ngolo agricultural technique has resulted in severe land cover changes that substantially influence other producing sectors. Understanding the agro-ecological zones is essential for enhancing policy development for the expansion and restrictive of Matengo/Ngolo pits practice that intercepting by decreasing its influence on the shrinkage of other ecological services, achieving sustainable agricultural practice in the Matengo highlands. Therefore, this study employed the multi-criteria parameters under the fuzzy logic algorithm in ArcGIS 10.8 for modelling the Matengo/Ngolo pits agro-ecological zone to realize sustainable land management in Matengo highlands.

Internet of Things Enabled Smart Animal Farm Prototype

Livestock plays very important economic, social and cultural roles in the well being of rural communities across the world. Quality environmental conditions, automation and monitoring are the key necessities of running a good and profitable livestock farm. Air quality, temperature of the surroundings and humidity play a major role while deciding the fan speeds of the exhaust System used in all aspects of livestock farming. Another important part of livestock production is increasing incubation speeds of eggs by performing artificial incubation. It is a requirement to maintain the temperature at a constant value in this system. This paper describes two mutually exclusive Fuzzy Logic algorithm-based systems to automate the exhaust system and an artificial egg incubator. The other important part of a livestock farm is production of milk and milk products. It is required to monitor the health of cows by overseeing their activities at any point of time. This can be done by determining and monitoring the activities performed by the cow. This paper describes a simple Deep Learning Model to classify the activities of a cow broadly as standing, walking or grazing. The Exhaust and the Incubator system are controlled and monitored using Internet of Things (IOT) System using a native web application developed using the Flask framework.

Risk Assessment Algorithm for Power Transformer Fleets Based on Condition and Strategic Importance

In every electric power system, power transformers (PT) play a critical role. Under ideal circumstances, PT should receive the utmost care to maintain the highest operative condition during their lifetime. Through the years, different approaches have been developed to assess the condition and the inherent risk during the operation of PT. However, most proposed methodologies tend to analyze PT as individuals and not as a fleet. A fleet assessment helps the asset manager make sound decisions regarding the maintenance scheduling for groups of PT with similar conditions. This paper proposes a new methodology to assess the risk of PT fleets, considering the technical condition and the strategic importance of the units. First, the state of the units was evaluated using a health index (HI) with a fuzzy logic algorithm. Then, the strategic importance of each unit was assessed using a weighting technique to obtain the importance index (II). Finally, the analyzed units with similar HI and II were arranged into a set of clusters using the k-means clustering technique. A fleet of 19 PTs was used to validate the proposed method. The obtained results are also provided to demonstrate the viability and feasibility of the assessment model.

Enhanced IoT-Based Climate Control for Oyster Mushroom Cultivation Using Fuzzy Logic Approach and NodeMCU Microcontroller

Urban farming has the potential to utilise unused space in the community to alleviate food shortages and increase the community’s income through local food production. When Internet of Things (IoT) technology is integrated with urban farming, it can further improve its efficiencies and yield. The work in this paper improved our previous work of using an IoT-based climate control system to regulate the cultivation environment of oyster mushrooms automatically. Even though the climate control system could produce two batches of mushroom yields, there were several limitations, such as less efficient climate control due to threshold-based corrective action, water wastage, and system instability. This paper aims to address these stated limitations by implementing a fuzzy logic algorithm and redesigned the climate control system. Two crisp input variables from DHT22 sensors representing temperature and humidity were fed into the Node MCU microcontroller’s fuzzy logic coded in C language. The temperature and humidity conditions were divided into five fuzzy trapezoidal membership functions resulting in 25 fuzzy rules to control the duration of running the water pump and ventilation fan. An internal, lightweight web server were managed all HTTP client requests. The enhanced system also included a safety measurement to avoid overheating the microcontroller and causing water wastage. Upon analysis of the data captured in two months, the result showed a decrease of 40% in water utilisation and an increase of mushrooms yield up to 226%. The enhanced climate control system also facilitated maintaining and controlling the temperature and humidity conducive for optimal mushroom cultivation.