Logic Algorithm Research Articles

IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks

The advent of the Internet of Things (IoT) has revolutionised our interaction with the environment, facilitating seamless connections among sensors, actuators, and humans. Efficient task scheduling stands as a cornerstone in maximising resource utilisation and ensuring timely task execution in IoT systems. The implementation of efficient task scheduling methodologies can yield substantial enhancements in productivity and cost-effectiveness for IoT infrastructures. To that end, this paper presents the IoT-mist bat-inspired algorithm (IMBA), designed specifically to optimise resource allocation in IoT environments. IMBA’s efficacy lies in its ability to elevate user service quality through enhancements in task completion rates, load distribution, network utilisation, processing time, and power efficiency. Through comparative analysis, IMBA demonstrates superiority over traditional methods, such as fuzzy logic and round-robin algorithms, across all performance metrics.

Sad Girls on TikTok: Musical and Multimodal Participatory Practices as Affective Negotiations of Ordinary Feelings and Knowledges in Online Music Cultures

ABSTRACT This article examines musical participatory practices on TikTok as mediations of ordinary feelings and knowledges. It conducts a multimodal analysis of user-generated videos that recirculated the song “Complex” by Scottish singer-songwriter Katie Gregson-MacLeod, which became widely successful on the app in 2022. Through its features and sociotechnical affordances, TikTok fosters feminine-coded articulations of intimate affects and negotiations of ordinary feelings and knowledges that have been marginalized in popular music cultures. Its intersecting algorithmic logics and human practices also create new rules about what affective sensibilities are foregrounded and how they are circulated through multimodal and memetic participatory practices.

MTS-PRO2SAT: Hybrid Mutation Tabu Search Algorithm in Optimizing Probabilistic 2 Satisfiability in Discrete Hopfield Neural Network

The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there is a lack of research on the impact of introducing metaheuristic algorithms on the cost function under different proportions of positive literals. In order to fill in this gap and improve the efficiency of the metaheuristic algorithm in systematic logic, we proposed a metaheuristic algorithm based on mutation tabu search and embedded it in probabilistic satisfiability logic in discrete Hopfield neural networks. Based on the traditional tabu search algorithm, the mutation operators of the genetic algorithm were combined to improve its global search ability during the learning phase and ensure that the cost function of the systematic logic converged to zero at different proportions of positive literals. Additionally, further optimization was carried out in the retrieval phase to enhance the diversity of solutions. Compared with nine other metaheuristic algorithms and exhaustive search algorithms, the proposed algorithm was superior to other algorithms in terms of time complexity and global convergence, and showed higher efficiency in the search solutions at the binary search space, consolidated the efficiency of systematic logic in the learning phase, and significantly improved the diversity of the global solution in the retrieval phase of systematic logic.

TIM‐Net: A multi‐label classification network for TCM tongue images fusing global‐local features

AbstractCombining the extracted tongue features with other medical indicators can effectively judge the diseases of patients. The previous work usually only analyzes a certain feature of the tongue body and is unable to extract multiple features simultaneously. In this study, a multi‐label classification network named TIM‐Net is proposed, which integrates global and local features to achieve multi‐label intelligent diagnosis of Chinese medicine tongue images. First, a feature extraction network based on ResNet is proposed to capture the features of tongue images more sufficiently. Then, a multi‐label classification algorithm fusing global and local features is proposed, and targeted screening operations are carried out on the class‐related feature maps based on global confidence. In addition, a logical masking algorithm is proposed to ensure that the local features can only correct the feature labels they represent, and do not interfere with other feature labels. The classification accuracy is further improved by using local feature confidence and correcting the global classification results. Finally, the experimental results indicate that the classification accuracy of the tongue images is gradually improved through optimizing the feature extraction network and fusing local features, and it exceeds other state‐of‐the‐art multi‐label classification networks.

Automated Recognition and Tracking of Stationary and Moving Cars in Images and Videos: YOLOv5 and SSD Analysis

The detection and tracking of cars are a significant and useful aspect of traffic surveillance systems, which is essential for the efficient management of traffic and the security of drivers and passengers. The primary objectives of this investigation are vehicle detection and tracking. The automatic detection of cars in both still photos and video recordings are the main topic of this investigation. One of the many uses for Deep Learning, which combines fuzzy logic, neural networks, and evolutionary algorithms, is the detection and tracking of moving objects. In this work, the key object detection techniques YOLOv5 and SSD were used to analyse vehicle recognition and tracking with deep learning. The Single Shot Multi-Box Detector model architecture is then utilized as the main foundation for the detection of vehicles. The vehicle recognition model is then trained using the YOLOv5 and SSD algorithms, each of which contributes to the illustration of the detection effect. Comparing the detection rates obtained by both models on a range of cars is necessary to locate it. The objective of this work is to create an automated system for locating and tracking both moving and stationary cars in still images and moving movies. According to the research, using this method has improved the success rate of recognizing automobiles to 97.65%.

Modernization of drying chambers based on fuzzy regulation

One of the most important stages of woodworking is drying, thanks to which the physical and mechanical properties of wood are improved and its shelf life is increased. Drying of wood is a process that significantly affects the external environment. The fact is that drying most often takes place in convection drying chambers, which consume a lot of energy and are not efficient enough due to constant air exchange with the atmosphere. At the same time, about 50 % of the heat energy received is lost, and carbon dioxide released from the chamber negatively affects the environment. The paper proposes the modernization of a typical drying chamber to increase its efficiency by replacing the classical PID control with fuzzy logic algorithms in order to reduce the duration of transient processes.

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques.

Enhancing heart disease detection and classification using fuzzy logic and metaheuristic algorithms in a blockchain assisted healthcare environment

Heart disease (HD) is a leading cause of mortality worldwide, emphasizing the need for accurate and efficient detection and classification methods. Recently, Blockchain (BC) provides seamless and secure sharing of heart disease data amongst healthcare providers, specialists, and researchers. This allows collaborative efforts, data exchange, and integration of diverse datasets, leading to a more comprehensive analysis and accurate detection of heart diseases. BC provides a decentralized and tamper-proof platform for storing sensitive patient data related to heart disease. This ensures the integrity and security of the data, reducing the risk of unauthorized access or data manipulation. Therefore, this study presents a new blockchain-assisted heart disease detection and classification model with feature selection with optimal fuzzy logic (BHDDC-FSOFL) technique. The presented BHDDC-FSOFL technique uses BC technology to store healthcare data securely. In addition, the disease detection module encompasses the design of biogeography teaching and learning-based optimization (BTLBO) algorithm for feature selection (FS) procedure. Moreover, an adaptive neuro-fuzzy inference system (ANFIS) classifier can be exploited for HD detection and classification. Furthermore, the ebola search optimization (ESO) algorithm is used for the parameter tuning of the ANFIS classifier. The integration of ANFIS classifier enables the modeling of uncertainty and imprecision in HD data, while metaheuristic algorithms aid in optimizing the classification process. Additionally, the utilization of BC technology ensures secure and transparent storage and sharing of healthcare data. To demonstrate the enhanced HD classification results of the BHDDC-FSOFL technique, a detailed experimental analysis was made on the HD dataset. The extensive result analysis pointed out the improved performance of the BHDDC-FSOFL technique compared to recent approaches in terms of different measures. Therefore, the proposed model offers a reliable and privacy-enhancing solution for healthcare providers and patients in a BC-assisted healthcare environment.

State-level multidimensional agricultural drought susceptibility and risk assessment for agriculturally prominent areas

ABSTRACT Given the growing climate variability, quantifying droughts has gained significant importance, particularly in agriculturally concentrated areas such as Iowa. This study presents a novel approach for evaluating the risk of agricultural drought, which combines geospatial methods with a fuzzy logic algorithm. The approach integrates a diverse array of meteorological, physical, and social factors, yielding a more comprehensive and nuanced understanding of the impacts of drought. This study has covered the agricultural sector within the Corn Belt region of Iowa and formulated maps illustrating the vulnerability and risk of drought for the timeframe spanning from 2015 to 2021. The risk maps illustrate significant progress in drought risk analysis, fully representing the spatial and temporal dimensions of vulnerability to drought. The uniqueness of this study is ascribed to its methodological framework, which integrates a thorough assessment of prior research to inform the assignment of weights to parameters in the fuzzy logic-based index. The findings demonstrate a notable increase in the proportion of Iowa’s land area classified as at a'very high’ drought risk, rising from 0.66% in 2015 to 5.39% in 2018. This upward trend suggests an escalating susceptibility to drought conditions. Mid-Iowa and the western portion of the state exhibited increased ‘high’ and ‘extremely high’ drought threats during the period. The accuracy of our drought susceptibility maps was validated using a Kappa coefficient of 75%. The drought risk indicator has the potential to be utilized in the context of drought mitigation and program monitoring. Moreover, this methodology can be modified for implementation in diverse geographical areas across the globe.

Intelligent Temperature-Controlled Poultry Feed Dispensing System with Fuzzy Logic Algorithm

This study introduces a novel fuzzy logic algorithm tailored to the thermoneutral zone of poultry, offering a precise and adaptive approach to feed dispensation. This involved the utilization of an LCD module to present essential information such as the selected age, real-time ambient temperature, current time, and the dispensed feed quantity. Data gathered during the process were stored in a memory device. The design of the fuzzy logic algorithm centered on the thermoneutral zone of the chicken serves as the determinant for feed dispensed by the system. It's crucial to note that while the system lacked artificial intelligence (AI), its logical analysis operated based on the fuzzy logic algorithm. Rigorous testing ensued, encompassing the comparison of feed dispensation between automated and manual systems and the assessment of feed waste and broiler weight. Significant feed waste reduction in the first week demonstrated the efficacy of the fuzzy-based method, with consistently low p-values of 0.00069, 0.015195, and 0.034 across subsequent weeks confirming the consistent outperformance in broiler weight compared to the traditional feeding technique. The findings contribute to the advancement of temperature-based poultry feed systems, addressing key challenges in optimizing feed quantity. The study successfully met its objectives, demonstrating the system's capability to dispense feeds effectively across varying ambient temperatures. Notably, the study revealed a consistent alignment of system outputs with those obtained from a digital thermometer and digital weighing scale, confirming the accuracy and reliability of the temperature-based feed dispensing system.

A Fuzzy Logic Approach to Enhance GPS Accuracy for Blood Cooler Box Tracking

An innovative technological development, the position tracking system uses latitude and longitude coordinates to determine the GPS's location. The covid-19 pandemic has caused the demand for cool box delivery for convalescent blood plasma to increase significantly. The obstacle in the field is the hospital's position and patient rooms far from the reach convalescent donors, so they require a cool box for delivery. The problem is tracking the cool box accurately and precisely so that it can be monitored by the hospital properly. For this reason, it is necessary to increase the accuracy of the GPS position in the cool box by using fuzzy logic algorithms The Ublox NEO-6M is one GPS module that can be utilized for navigation. This module uses UART connection using the NMEA 0183 protocol and has an accuracy level of around 2.5 meters to 10 meters. In this research, validation of the accuracy of the GPS coordinate position was carried out on a Blood Cool Box device which was designed using the fuzzy logic method. The Sugeno method fuzzy logic algorithm is used to validate the accuracy of GPS coordinate positions based on latitude and longitude obtained from the GPS sensor module. The test results show a Mean Absolute Percent Error (MAPE) value of 21.66% which can be concluded that the Sugeno fuzzy logic method algorithm has forecasting model capabilities that are suitable for use as a validation method for testing GPS position coordinates.

An Optimized Stimulation Control System for Upper Limb Exoskeleton Robot-Assisted Rehabilitation Using a Fuzzy Logic-Based Pain Detection Approach.

The utilization of robotic systems in upper limb rehabilitation has shown promising results in aiding individuals with motor impairments. This research introduces an innovative approach to enhance the efficiency and adaptability of upper limb exoskeleton robot-assisted rehabilitation through the development of an optimized stimulation control system (OSCS). The proposed OSCS integrates a fuzzy logic-based pain detection approach designed to accurately assess and respond to the patient's pain threshold during rehabilitation sessions. By employing fuzzy logic algorithms, the system dynamically adjusts the stimulation levels and control parameters of the exoskeleton, ensuring personalized and optimized rehabilitation protocols. This research conducts comprehensive evaluations, including simulation studies and clinical trials, to validate the OSCS's efficacy in improving rehabilitation outcomes while prioritizing patient comfort and safety. The findings demonstrate the potential of the OSCS to revolutionize upper limb exoskeleton-assisted rehabilitation by offering a customizable and adaptive framework tailored to individual patient needs, thereby advancing the field of robotic-assisted rehabilitation.

Analysis on the Driving and Braking Control Logic Algorithm for Mobility Energy Efficiency in Electric Vehicle

Analysis on the Driving and Braking Control Logic Algorithm for Mobility Energy Efficiency in Electric Vehicle

A novel method for effective intrusion detection based on convolutional speaking neural networks

These days, most people can't imagine their life without their networks. People rely more and more on networks as new technologies like 5G and the Internet of Things proliferate, and networks are becoming larger and more complicated by the day. Consequently, the enormous networked society is under increasing danger from cyber attacks, which are also growing in number, sophistication, and variety. Users' private data can end up in the wrong hands. Data communicated via a network might be compromised in some way. It is also possible to target the computer infrastructure that is linked to the network. Thus, NIDS is crucial in ensuring that contemporary society's network communication environment is secure and dependable. This work proposes a detection model that is both lightweight and effective. This model is able to keep its high detection accuracy by combining the benefits of SNN and CNN with logical algorithm design. On top of that, it can bring attention to clear strengths in relation to computing efficiency and power usage. The suggested model was tested against a number of state-of-the-art models using an extensive and integrated set of benchmarks. With a 23 % improvement in detection accuracy (AD), a 19 % improvement in delay, and a 28 % improvement in consumption energy, the suggested technique outperformed the Spiking DFR + MLP and Spike-DHS methods in the simulations.

English Sentence Semantic Feature Extraction Method Based on Fuzzy Logic Algorithm

Semantic features play a pivotal role in natural language processing, providing a deeper understanding of the meaning and context within textual data. In the realm of machine learning and artificial intelligence, semantic feature extraction involves translating linguistic elements into numerical representations, often utilizing advanced techniques like word embeddings and deep learning models. The integration of semantic features enhances the precision and context-awareness of language models, enabling applications such as sentiment analysis, document categorization, and information retrieval to operate with greater accuracy and relevance. The paper introduces a novel approach, Hierarchical Mandhami Optimized Semantic Feature Extraction (HMOSFE), designed to enhance semantic feature extraction from English sentences. The proposed HMOSFE model comprises fusion of hierarchical clustering and fuzzy-based feature extraction, HMOSFE aims to capture intricate semantic relationships within sentences, providing nuanced insights into the underlying meaning of textual content. The model employs pre-trained word embeddings for term representation, calculates a similarity matrix using cosine similarity, and utilizes hierarchical clustering for document grouping. Fuzzy logic contributes to assigning weights to features, enabling a more refined understanding of semantic significance. The paper presents comprehensive results, including semantic similarity estimations, clustering distances, and fuzzy memberships, demonstrating the effectiveness of HMOSFE across diverse documents.

Scoring model based on cardiac CT and clinical factors to predict early good mitral valverepair in rheumatic mitral disease.

We aimed to evaluate the mitral valve calcification and mitral structure detected by cardiac computed tomography (cardiac CT) and establish a scoring model based on cardiac CT and clinical factors to predict early good mitral valve repair (EGMR) and guide surgical strategy in rheumatic mitral disease (RMD). This is a retrospective bi-center cohort study. Based on cardiac CT, mitral valve calcification and mitral structure in RMD were quantified and evaluated. The primary outcome was EGMR. A logical regression algorithm was applied to the scoring model. A total of 579 patients were enrolled in our study from January 1, 2019, to August 31, 2022. Of these, 443 had baseline cardiac CT scans of adequate quality. The calcification quality score, calcification and thinnest part of the anterior leaflet clean zone, and papillary muscle symmetry were the independent CT factors of EGMR. Coronary artery disease and pulmonary artery pressure were the independent clinical factors of EGMR. Based on the above six factors, a scoring model was established. Sensitivity = 95% and specificity = 95% were presented with a cutoff value of 0.85 and 0.30 respectively. The area under the receiver operating characteristic of external validation set was 0.84 (95% confidence interval [CI] 0.73-0.93). Mitral valve repair is recommended when the scoring model value > 0.85 and mitral valve replacement is prior when the scoring model value < 0.30. This model could assist in guiding surgical strategies for RMD. The model established in this study can serve as a reference indicator for surgical repair in rheumatic mitral valve disease. • Cardiac CT can reflect the mitral structure in detail, especially for valve calcification. • A model based on cardiac CT and clinical factors for predicting early good mitral valverepair was established. • The developed model can help cardiac surgeons formulate appropriate surgical strategies.

Design and Implementation of Fuzzy Logic for Obstacle Avoidance in Differential Drive Mobile Robot

Autonomous mobile robots based on wheel drive are widely used in various applications. The differential drive mobile robot (DDMR) is one type with wheel drive. DDMR uses one actuator to move each wheel on the mobile robot. Autonomous capabilities are needed to avoid obstacles around the DDMR. This paper presents implementing a fuzzy logic algorithm for obstacle avoidance at a low cost (DDMR). The fuzzy logic algorithm input is obtained from three ultrasonic sensors installed in front of the DDMR with an angle difference between the sensors of 45$^0$. Distance information from the ultrasonic sensors is used to regulate the speed of the right and left motors of the DDMR. Based on the test results, the Mamdani inference system using the fuzzy logic algorithm was successfully implemented as an obstacle avoidance algorithm. The speed values of the right and left DDMR wheels produce values according to the rules created in the Mamdani inference system. DDMR managed to pass through a tunnel-shaped environment and reach its goal without hitting any obstacles around it. The average speed produced by DDMR in reaching the goal is 4.91 cm/s.

An IoT-Based Road Bridge Health Monitoring and Warning System.

Recent earthquakes worldwide have led to significant loss of life and structural damage to infrastructure, especially road bridges. Existing bridge monitoring systems have limitations, including restricted detection capabilities, subjectivity, human error, labor-intensive inspections, limited access to remote areas, and high costs. Aging infrastructures pose a critical concern for organizations and government funding policies, showing signs of decay and impending structural failure. To address these challenges, this research proposes an IoT-based bridge health status monitoring and warning system that is wireless, low-cost, durable, and user-friendly. The proposed system builds upon engineering standards and guidelines to classify bridge health status into categories ranging from excellent to collapse condition. It incorporates deflection, vibration, temperature, humidity, and infrared sensors, combined with IoT and a fuzzy logic algorithm. The primary objective is to reduce bridge maintenance costs, extend lifespans, and enhance transportation safety through an early warning system via a mobile application. Additionally, a Google Maps interface has been developed to display bridge conditions along with real-time traffic video. To validate the proposed system, a 3-D prototype model was constructed and tested. Practical testing of the fuzzy logic algorithm aligned with the simulation outcomes, demonstrating expected accuracy in determining bridge health status.

An Intelligent DNA Nanoreactor for Easy-to-Read In Vivo Tumor Imaging and Precise Therapy.

Nanomaterial-based in vivo tumor imaging and therapy have attracted extensive attention; however, they suffer from the unintelligent "always ON" or single-parameter responsive signal output, substantial off-target effects, and high cost. Therefore, achieving in vivo easy-to-read tumor imaging and precise therapy in a multi-parameter responsive and intelligent manner remains challenging. Herein, an intelligent DNA nanoreactor (iDNR) was constructed following the "AND" Boolean logic algorithm to address these issues. iDNR-mediated in situ deposition of photothermal substance polydopamine (PDA) can only be satisfied in tumor tissues with abundant membrane protein biomarkers "AND" hydrogen peroxide (H2 O2 ). Therefore, intelligent temperature-based in vivo easy-to-read tumor imaging is realized without expensive instrumentation, and its diagnostic performance matches with that of flow cytometry, and photoacoustic imaging. Moreover, precise photothermal therapy (PTT) of tumors could be achieved via intelligent heating of tumor tissues. The precise PTT of primary tumors in combination with immune checkpoint blockade (ICB) therapy suppresses the growth of distant tumors and inhibits tumor recurrence. Therefore, highly programmable iDNR is a powerful tool for intelligent biomedical applications.

An Intelligent DNA Nanoreactor for Easy‐to‐Read In Vivo Tumor Imaging and Precise Therapy

AbstractNanomaterial‐based in vivo tumor imaging and therapy have attracted extensive attention; however, they suffer from the unintelligent “always ON” or single‐parameter responsive signal output, substantial off‐target effects, and high cost. Therefore, achieving in vivo easy‐to‐read tumor imaging and precise therapy in a multi‐parameter responsive and intelligent manner remains challenging. Herein, an intelligent DNA nanoreactor (iDNR) was constructed following the “AND” Boolean logic algorithm to address these issues. iDNR‐mediated in situ deposition of photothermal substance polydopamine (PDA) can only be satisfied in tumor tissues with abundant membrane protein biomarkers “AND” hydrogen peroxide (H2O2). Therefore, intelligent temperature‐based in vivo easy‐to‐read tumor imaging is realized without expensive instrumentation, and its diagnostic performance matches with that of flow cytometry, and photoacoustic imaging. Moreover, precise photothermal therapy (PTT) of tumors could be achieved via intelligent heating of tumor tissues. The precise PTT of primary tumors in combination with immune checkpoint blockade (ICB) therapy suppresses the growth of distant tumors and inhibits tumor recurrence. Therefore, highly programmable iDNR is a powerful tool for intelligent biomedical applications.