Fuzzy Logic Approach Research Articles

Implementation of Fuzzy Logic in Temperature and Fermentation Time Control Systemon Alcohol Content of Cassava Tape Product

The fermentation process of cassava tape involves complex biochemical reactions and requires careful attention to various factors. Temperature and fermentation time are crucial parameters that significantly affect the final quality of cassava tape. Along with the development of artificial intelligence (AI), the fermentation process can be controlled and monitored more precisely, thereby increasing the efficiency and consistency of the final product. This study aims to determine the Mamdani fuzzy logic approach in the temperature control system and fermentation duration in the cassava tape production process to optimally regulate the alcohol content of cassava tape. The research method is a literature study, testing Mamdani fuzzy logic using Matlab software, and analyzing input variables manually. The results of the study showed that the optimal temperature for cassava tape fermentation was between 25°C - 30°C and the optimal time for cassava tape fermentation was with a "long" duration. From the defuzzification results, the final results showed an alcohol content of 15.9% at a temperature of 29°C and a fermentation time of 80 hours so that the alcohol content of cassava tape was in accordance with the specifications.

Design of Temperature and Humidity Control System for Cocoa Bean Dryer Using Fuzzy Mamdani Method

This research aims to develop a fuzzy logic control system for optimizing temperature and humidity during cocoa bean fermentation. The study employs a literature review method and data analysis using MATLAB. A Mamdani-type fuzzy inference system is designed to control the temperature and humidity of a cocoa bean dryer. Input variables include temperature (0-65°C) and humidity (0-100%), while output variables are heater and fan settings. The system's performance is evaluated using a case study, demonstrating its ability to adjust heater and fan operations based on environmental conditions. Results show that for an input of 65°C temperature and 50% humidity, the system recommends turning off the heater (membership value 79.8) and running the fan at high speed (membership value 84.7). This fuzzy logic approach offers a promising solution for maintaining optimal conditions during cocoa bean fermentation, potentially improving the quality and consistency of the final product. Keywords: cocoa fermentation, fuzzy logic control, temperature regulation

A fuzzy logic approach to improve the sensitivity of the rapid entire body assessment method

Conventional ergonomic observation methods, such as rapid entire body assessment (REBA), are limited in their sensitivity and reliability, particularly in detecting changes in input variables. This study integrates fuzzy logic with the REBA method, utilizing trapezoidal membership functions to fuzzify the input variables. The center of gravity method was employed for defuzzification, and if–then rules were formulated to enhance the REBA method. The findings revealed that the fuzzy REBA method is more sensitive to changes in the risk of work-related musculoskeletal disorders (WMSDs) than conventional REBA. Furthermore, the intraclass correlation coefficient (ICC) of the risk scores obtained with the fuzzy REBA method ranged from 0.76 to 0.89, indicating reliable outcomes. The proposed fuzzy REBA method offers a more reliable approach for on-site ergonomic assessments, thereby reducing observational errors and potentially reducing the incidence of WMSDs in the workplace.

Multi-Objective Welding Optimization for AA5052 Using Taguchi-Fuzzy Approach

<div>This study investigates the influence of tungsten inert gas (TIG) welding parameters on the dilution and hardness of AA5052 aluminum alloy. Employing Taguchi’s L27 orthogonal array, the research systematically explores the effects of current, voltage, and welding speed. Analysis of the experimental data utilizes signal-to-noise ratio, analysis of variance (ANOVA), and regression techniques. The study compares a traditional regression model with a fuzzy logic approach for result validation, finding that the latter exhibits marginally better predictive accuracy. Optimal welding parameters are identified as 150 A current, 20 V voltage, and 45 mm/s welding speed, yielding a maximum dilution of 52.81% and hardness of 145.3 HV 0.5. Current emerges as the most significant factor influencing both dilution and hardness. Microstructural examination, hardness profiling, and tensile testing of specimens welded under optimized conditions reveal a characteristic hardness distribution across the weld zones and ductile fracture behavior.</div>

Designing an EV charging network: a fuzzy logic and genetic algorithm approach

The complex problem of designing an EV charging network considering both the transportation and electric distribution networks is investigated in this paper. The two conflicting objectives, maximizing charging service coverage and minimizing power system operating costs and associated network constraints are included. The network constraints include the choice of site location suitable for both the electrical and transportation networks and in the later traffic flow conditions. The approach uses a fuzzy logic and genetic algorithm to find a non-dominated solution representing a trade-off between the conflicting objectives. A case study based on the IEEE 33-bus distribution network and 25-node transportation networks is used to assess the performance of the approach. The results demonstrate that the fuzzy logic method can generate a reasonable, non-dominated solution that provides valuable insights into the trade-off between conflicting objectives. The approach is also computationally efficient and scalable, making it suitable for solving various-scale EV charging network design problems.

A fuzzy logic approach for tie strength assessment in relationship management: Design and performance comparison of two implemented fuzzy-based models

In an increasingly digitalised and interconnected world, assessing the strength of interpersonal ties in social networks is crucial for fields such as business, marketing and sociology. Traditional methods for evaluating Tie Strength (TS), which often classify relationships as either ”strong” or ”weak”, fail to capture the uncertainty and ambiguity of human interactions. This study proposes a Fuzzy-based System for Assessment of Tie Strength (FSATS). We develop and evaluate two models: FSATSM1, which utilises three input parameters Interaction Time (IT), Level of Intimacy (LoI) and Emotional Intensity (EI); and FSATSM2, where we introduce Reciprocity (Rc) as an additional parameter. Through simulations, we compare the performance of both models for the assessment of TS. The simulation results show that for FSATSM1, when IT is 0.9 and EI is 0.7 for all values of LoI, the TS values are more than 0.5. While, for FSATSM2, when IT is 0.9, for EI 0.1 (Rc more than 0.8), EI 0.5 (Rc more than 0.5) and EI 0.9 (Rc more than 0.2), all values of TS are more than 0.5, indicating a strong relationship. The results suggest that FSATSM2 provides a more accurate reflection of real-world relationships, which can be applied in contexts such as Social Customer Relationship Management (SCRM), enabling businesses to enhance customer engagement strategies.

Research Progress of Dangerous Driving Behavior Recognition Methods Based on Deep Learning

In response to the rising frequency of traffic accidents and growing concerns regarding driving safety, the identification and analysis of dangerous driving behaviors have emerged as critical components in enhancing road safety. In this paper, the research progress in the recognition methods of dangerous driving behavior based on deep learning is analyzed. Firstly, the data collection methods are categorized into four types, evaluating their respective advantages, disadvantages, and applicability. While questionnaire surveys provide limited information, they are straightforward to conduct. The vehicle operation data acquisition method, being a non-contact detection, does not interfere with the driver’s activities but is susceptible to environmental factors and individual driving habits, potentially leading to inaccuracies. The recognition method based on dangerous driving behavior can be monitored in real time, though its effectiveness is constrained by lighting conditions. The precision of physiological detection depends on the quality of the equipment. Then, the collected big data are utilized to extract the features related to dangerous driving behavior. The paper mainly classifies the deep learning models employed for dangerous driving behavior recognition into three categories: Deep Belief Network (DBN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). DBN exhibits high flexibility but suffers from relatively slow processing speeds. CNN demonstrates excellent performance in image recognition, yet it may lead to information loss. RNN possesses the capability to process sequential data effectively; however, training these networks is challenging. Finally, this paper concludes with a comprehensive analysis of the application of deep learning-based dangerous driving behavior recognition methods, along with an in-depth exploration of their future development trends. As computer technology continues to advance, deep learning is progressively replacing fuzzy logic and traditional machine learning approaches as the primary tool for identifying dangerous driving behaviors.

Gain scheduling of backstepping sliding mode controller using optimized type-1 Sugeno fuzzy logic approach for quadcopter

PurposeThis paper aims to propose an optimized type-1 Sugeno fuzzy logic backstepping sliding mode controller (T1-SFL-BSMC) to control the altitude and rotational behaviour of a quadcopter. The proposed controller improves the performance of the system by overcoming the limitations of conventional trial-and-error techniques for BSMC gain design.Design/methodology/approachIn this study, the type-1 Sugeno fuzzy logic controller (T1-SFLC) technique is proposed which dynamically modifies the BSMC gains. In addition, the parameters of T1-SFLC are further tuned by five different optimization techniques, namely, genetic algorithm, particle swarm optimization (PSO), pattern search (PS), simulated annealing (SA) and adaptive neural network (ANN).FindingsThe effectiveness of these optimization techniques in tuning the T1-SFLCs for the gain scheduled BSMC is evaluated in terms of time response metrics; rising time, settling time and root mean square error (RMSE) for altitude and rotational behaviour. The results demonstrated that PSO-optimized T1-SFL-BSMC provides better responses in terms of time response metrices with reduced rise and settling times and minimized RMSE when compared to other optimized T1-SFL-BSMC.Originality/valueThis paper proposes a novel approach for controlling quadcopters by combining fuzzy logic with a BSMC. It is then improved by using many optimization strategies. A more dependable and effective approach to controller design is provided by the suggested method, which is essential for unmanned aerial vehicle applications.

Parametric Optimization of Surface Roughness of Ni-W-P Electroless Coating Using Central Composite Design Coupled with Fuzzy Logic Approach

Parametric Optimization of Surface Roughness of Ni-W-P Electroless Coating Using Central Composite Design Coupled with Fuzzy Logic Approach

A fuzzy logic approach within the DPSIR framework to address the inherent uncertainty and complexity of water security assessments

A fuzzy logic approach within the DPSIR framework to address the inherent uncertainty and complexity of water security assessments

Alzheimer's disease classification by supervised and intelligent techniques

Background Significant advancements in neuroimaging have emerged over the past decade, notably through positron emission tomography (PET) and magnetic resonance imaging (MRI) for diagnosing Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI). Combining imaging modalities with machine learning (ML) techniques enhances diagnostic accuracy. Objective To develop predictive models using pre-treatment brain imaging data to distinguish between normal controls (NC), MCI, and AD stages, improving diagnostic precision. Methods We utilized the Alzheimer's Disease Neuroimaging Initiative database, processing 3D MRI, PET Florbetaben, and PET Flortaucipir images. Techniques included convolutional neural networks (CNN), fuzzy logic, and multi-layer perceptron (MLP). Feature extraction involved amyloid-β volume, tau protein levels, and empty space volumes. Results The fuzzy logic approach achieved a classification accuracy of 99.1%, outperforming CNN (90.67%) and MLP (94%). Integration of multimodal data significantly enhanced performance compared to single-modality approaches. Conclusions Our study demonstrates that integrating advanced ML techniques with multimodal neuroimaging can effectively classify AD stages. These findings address critical gaps in early detection and provide a foundation for future clinical applications.

Development of an Optimized Neural Network Model using Hyperparameters Optimization for Electrical Load Prediction

Electrical load prediction has become essential to the efficient operation, control and management of modern electric power systems. Various machine learning prediction models have been developed for electrical load prediction, this includes Support Vector Regression, Fuzzy Logic and Neural Network (NN) modelling approaches. However, incorrect selection of model hyperparameters, which are parameters that affect the output of the prediction models, could result in low prediction accuracy of machine learning models. Hence, the development of an optimized NN model for electrical load prediction was presented in this study. Historical daily data of temperature, rainfall, relative humidity and windspeed for Osogbo, Nigeria was obtained from the National Aeronautics and Space Administration website; while electrical load data for the same location was collected from the Transmission Company of Nigeria. The data captured a period of five years (2017 to 2021). The NN models were developed with MATLAB R2022a software, and two hyperparameters, hidden layers and neuron counts, were optimized using the Bayesian optimization technique to enhance the quality of the models. The models were evaluated using mean absolute error (MAE), and root mean square error (RMSE). The MAE and RMSE for the non-optimized NN model were 6.5247 and 8.2725 respectively. Meanwhile, for the optimized NN model, the MAE and RMSE were 5.6571 and 7.4289 respectively. The obtained results show that the optimized NN performed better than the non-optimized NN models. Therefore, for more accurate load prediction, the method developed of this research is suggested for use by utility providers.

Evaluation of the Recycling Conditions Through Injection Molding Using Fuzzy Logic Approach

Recycling plastic products is still essential and crucial in every country around the globe due to its positive benefits on the environment and the economy. There are several mixing procedures for recycling, and it is crucial to understand how these mixtures affect the quality of products by standards and specifications. Consequently, it is helpful to apply analysis and prediction techniques to find out scientifically. Artificial intelligence "AI" techniques are widely used in many manufacturing engineering fields such as recycling operations. This is because of the many advantages that artificial intelligence techniques offer, including the ability to reduce human errors, save time, provide digital support, and make objective decisions. This study intends to employ the fuzzy logic method as one of the "AI" techniques for predicting a significant property that customers frequently need based on their quality levels and standards. This study employed the injection molding process to forecast the values of a mechanical characteristic, specifically tensile strength, under specific operating conditions based on data from the authors' earlier work. This investigation was conducted using two distinct mixing plans. The first mixed all the raw materials, while the second mixed 50% of the raw materials with 50% of the recycled materials. The fuzzy logic results were acquired, and the mean absolute percentage error for the two plans was calculated. Additionally, the outcomes of the current study, which employed the fuzzy logic approach, were contrasted with those of the earlier study, which utilized the response surface methodology approach. Furthermore, the results showed that the response surface technique approach is more accurate than the fuzzy logic since it has the lowest mean absolute percentage error.

Fuzzy Logic Approach for Evaluating Electromobility Alternatives in Last-Mile Delivery: Belgrade as a Case Study

This paper proposes a methodology based on the fuzzy approach, which provides decision-making support to the organizer of last-mile delivery (LMD) in selecting sustainable delivery models for a specific territory. Solving this task is essential to ensure that the delivery process is efficient and aligned with all three dimensions of sustainable development. The goal is to select the most suitable electromobility alternative for delivery implementation based on the characteristics of the requirements and the current circumstances. The proposed methodology involves the creation of a mechanism consisting of a series of fuzzy logic systems that will model expert opinions and produce a preference value as the output, defining the suitability of applying a particular LMD model. A specific methodological contribution is the creation of harmonized membership functions for fuzzy variables as a result of comparing symmetric and asymmetric membership functions aimed at achieving the most valid results. The results guide the delivery organizer in making the best decision when choosing from the analyzed models. The applicability and adequacy of the methodology are demonstrated through the results and analysis of a case study focused on the evaluation of electromobility alternatives in last-mile delivery in a part of the city of Belgrade. The obtained preference values, which range from 0 to 1 for all tested variants, are as follows within the interval: [0.481, 0.776] for e-motorcycles, [0.376, 0.564] for e-cargo bikes, and [0.5, 0.624] for e-scooters. The specific values of these indicators aim to support decision-makers in selecting a delivery model for a defined task based on the given constraints.

Evaluating buyer performance through supplier satisfaction: a balanced scorecard and fuzzy logic approach

PurposeThis study develops and validates a comprehensive framework integrating the balanced scorecard (BSC) and fuzzy logic to evaluate supplier satisfaction in the Indian power sector.Design/methodology/approachA mixed-methods approach, including the literature review, expert consultations and a survey of 300 suppliers, was employed. The BSC categorized supplier satisfaction across financial, purchasing, coordination and corporate perspectives. Fuzzy set theory and fuzzy logic converted qualitative data into quantitative measures.FindingsKey areas for improvement across financial, purchasing, coordination and corporate perspectives were identified. The developed Supplier Satisfaction Index offers a nuanced tool for benchmarking and enhancing supplier relationships.Research limitations/implicationsWhile the study provides a comprehensive framework for evaluating supplier satisfaction, it is limited to the context of the Indian power sector. Future research could extend the application of this framework to different industries and geographical contexts to assess its generalizability.Practical implicationsThe findings offer actionable insights for practitioners to improve supplier satisfaction through targeted strategies in financial interactions, purchasing clarity, coordination efficiency and corporate practices. Implementing these strategies can enhance supplier relationships and overall supply chain performance.Originality/valueThis research extends the BSC’s applicability to external supplier relationships. It demonstrates robustness when integrated with fuzzy logic and offers a multidimensional approach to supplier satisfaction evaluation, addressing the inherent ambiguities and subjectivities in traditional assessments.

Reproductive performance of Channa striata in wetland ecosystems: a fuzzy logic approach to water quality and eco-climatic factors for long-term sustainable management and aquaculture advancement.

The striped snakehead, Channa striata, is commercially and nutritionally important due to its medicinal properties, such as wound healing and antimicrobial abilities. This study investigated the reproductive biology of C. striata in relation to hydro-climatic changes using a fuzzy logic approach for long-term management in the wetland ecosystem (Gajner beel), Bangladesh. A total of 1200 C. striata individuals were collected monthly during January to December 2019 using various fishing methods. Measurements of total length (TL), body weight (BW), and gonad weight (GW) were taken with a precision of 0.01cm and 0.01g, respectively. The GSI (gonadosomatic index in %), MGSI (modified gonadosomatic index in %), and DI (Dobriyal index) were used to determine size at sexual maturity (Lm) and the spawning season. Results revealed a wide range of TL, ranging from 12.7 to 46cm. The Lm was estimated between 21.7 and 25.33cm based on various model as TL vs. GSI, MGSI, and DI; TL-fork length (FL) regressions; and maximum length (Lmax) for this species. The spawning period was observed from April to July, peaking in May, indicated by higher GSI, MGSI, and DI values. Eco-climatic factors showed a significant relationship with GSI, except for total dissolved solids (TDS). Rising temperatures and declining rainfall could delay spawning of C. striata. A fuzzy logic approach was employed to predict future GSI changes over extended periods (e.g., 50 or 60years), enabling anticipation of shifts in the timing and duration of the spawning season. Finally, the findings of our research might be effective to carry out specific conservation efforts of C. striata in response to climate variability in sub-tropical water.

Drill Cutting Samples Assessment for the Palynofacies Analysis and Paleoenvironmental Reconstruction: Codó Formation, São Luís-Grajaú Basin (Brazil)

The Codó Formation has 170.000 km² of extension and represents part of the Cretaceous Supersequence of the São Luís-Grajaú Basin, located in the northeastern part of Brazil. This formation is composed of shales, siltstones, limestones, sandstones, and some anhydrite levels. The marine influence in the Codó Formation is still being discussed in terms its geographic extension and time interval. Previous palynological studies on the 2-ANP-5-MA well and other sections have shown the presence of dinoflagellate cyst (Subtilisphaera genus) and fresh-water algae (Scenedesmus, Pediastrum and Botryococcus). This research aims to classify the sedimentary organic matter in drill cuttings of the 2-ANP-5-MA well. The drill cutting samples are from 2469–2559 m depth, and 25 slides were made with 3 m intervals between them. We applied the usual palynological method of preparation to identify the sedimentary organic matter (SOM) groups. Through the distribution analysis of the main groups and subgroups of the SOM, and application of the APP-diagram, four palynofacies were defined, as well as the redox conditions, the principal kerogen type generated and the depositional paleoenvironment. In addition, a statistical treatment with multivariate techniques (PCA and K-means clustering) and Fuzzy logic was used, aiming a better application of the drill cutting samples to paleoenvironmental reconstructions. In this stratigraphic section, the Codó Formation record is interpreted as a closed lagoonal system to a fluvial system passing through a marine incursion. The statistical and Fuzzy logic approaches appear as automated tools for grouping drill cutting samples, with a proposition of the PARE (Paleoenvironmental Reconstruction Ratio) that shows a good relationship with SOM data and previous works done in this stratigraphic section.

Implementation of fuzzy logic approach for thalassemia screening in children

Thalassemia is one of the most dangerous blood disorders that can lead to severe complications. It is an inherited disease, usually detected after a child is two to four years old. Identification of thalassemia is a complex task, involving many variables. Doctors generally diagnose thalassemia by using a complete blood count (CBC) and high-performance liquid chromatography (HPLC) test results. However, HPLC tests are expensive and time consuming, hence the need for other methods to identify thalassemia. There are many studies on the application of artificial intelligence for medical applications. In this study, we developed a new fuzzy-based approach to identify thalassemia based on a patient’s blood laboratory results. First, we analyzed the CBC data for blood disorder prediction. Secondly, we adopt the test results of peripheral blood smear (PBS) to identify whether the person has thalassemia. We conducted several experiments using 30 (thirty) hospital patient data and the results were compared with the results provided by experts. The experimental results show that the system can determine blood disorders with 93% accuracy and 100% precision in thalassemia prediction. This system is very effective to help doctors in diagnosing thalassemia patients.

Implementation and Comparison of Coffee Bean Drying Temperature SettingsBased on Fuzzy Logic

This research examines the implementation and measurement of temperature in drying coffee beansusing a fuzzy logic approach. Two main methods, namely the Mamdani Method and the SugenoMethod, were applied and evaluated in this context. Data on temperature, humidity, and water contentof coffee beans are collected during the drying process for use in the implementation of both methods.Implementation is carried out using MATLAB software, with detailed steps for each method. TheMamdani method involves fuzzification processes, inference using fuzzy rules, and defuzzification toobtain concrete values for temperature settings. Meanwhile, the Sugeno Method also involvesfuzzification of input data, but uses a linear fuzzy model for inference, so it does not require adefuzzification stage. The results and discussion of this study highlight the performance differencesbetween the two methods. Evaluation is carried out based on temperature prediction accuracy andenergy efficiency. The Mamdani Method shows good accuracy in predicting temperature, while theSugeno Method highlights efficiency and efficiency in the temperature regulation process. Therefore,this study provides valuable insight into selecting a suitable method for temperature regulation ofcoffee bean dryers

AI for IOMT Security: a Survey of Intrusion Detection Systems, Architectures, Attacks and Challenges

The Internet of Medical Things (IoMT) has transformed healthcare by allowing real-time patient monitoring, remote diagnoses, and effective data interchange. The increasing reliance on interconnected medical equipment has increased cybersecurity risks for healthcare organizations. This survey offers an extensive examination of Intrusion Detection Systems (IDSs) targeted for IoMT contexts. This surveyemphasizes the proposed methods that used to build IDS,classifying them into machine learning (ML), deep learning (DL), fuzzy logic (FL), and hybrid approaches for safeguarding healthcare networks. This paper investigates the IoMT architecture, identifies security concerns across multiple tiers, and analyzes potential vulnerabilities including denial-of-service attacks, ransomware, and man-in-the-middle attacks. The research highlights the significance of IDSs in alleviating cyber threats and protecting sensitive medical information through a comparison of cutting-edge methodologies. We outline significant issues that persist and emphasize domains requiring additional research to enhance the security and resilience of IoMT systems.