Logic Machine Research Articles

Fuzzy Logic and Machine Learning Algorithms for Detection and Classification of Falls and Activities of Daily Living

This article analyses the movements of young and elderly people using data collected from an accelerometer and a gyroscope. This study proposes Type I fuzzy logic (FL) and several machine learning (ML) algorithms for the detection and classification of daily life movements and falls. The results obtained demonstrate that a fuzzy logic system can efficiently integrate data from an accelerometer and a gyroscope to classify falls and movements in daily life with 97.4% accuracy. When ML classifiers are used, the performance across several algorithms is also very high.

Prediction of eco sustainability component using fuzzy Z numbers based ratio analysis and interval type 3 fuzzy logic system

Amid rapid global urbanization, understanding eco-sustainability challenges in megacities like Delhi is crucial both locally and globally. This study introduces a novel multi-criteria group decision-making approach, the Z-Fuzzy Step-Wise Weight Assessment Ratio Analysis (SWARA), to address these complex challenges. This method prioritizes key eco-sustainability components like air quality index (AQI), water quality index (WQI), Temperature, and Precipitation, based on their impact, aiming to enhance resource allocation and environmental quality. Criteria weights are determined using fuzzy analytical hierarchical processing with Z-numbers, which manage decision-makers' uncertainty through a reliability function, illustrating their confidence levels and supporting the adaptability and effectiveness of eco-sustainability measures. A case study is conducted on seven sub-cities in Delhi examining their unique environmental impacts and urban sustainability challenges. Moreover, the most effective component of eco-sustainability say AQI is also dependent on some factors like PM10, PM2.5, SO2, O3, NO2, and CO. Considering these factors as inputs, an interval type-3 fuzzy logic system (IT3FLS) has been constructed to predict AQI, supporting timely interventions and improvements in eco-sustainability management. The IT3FLS model demonstrated exceptional precision across all Delhi sub-cities, with low root means square error (<0.05), and high R2 values (>0.94), affirming its effectiveness. To validate the Z-Fuzzy SWARA rankings, we compared them with existing Z-Fuzzy methods, consistently ranking AQI first. The IT3FLS's AQI predictions were also compared with those from fuzzy logic and advanced machine learning algorithms, demonstrating superior performance. Finally, the study underscores the managerial implications, emphasizing their role in guiding decision-making and strategy implementation.

A Novel Integration of Data-Driven Rule Generation and Computational Argumentation for Enhanced Explainable AI

Explainable Artificial Intelligence (XAI) is a research area that clarifies AI decision-making processes to build user trust and promote responsible AI. Hence, a key scientific challenge in XAI is the development of methods that generate transparent and interpretable explanations while maintaining scalability and effectiveness in complex scenarios. Rule-based methods in XAI generate rules that can potentially explain AI inferences, yet they can also become convoluted in large scenarios, hindering their readability and scalability. Moreover, they often lack contrastive explanations, leaving users uncertain why specific predictions are preferred. To address this scientific problem, we explore the integration of computational argumentation—a sub-field of AI that models reasoning processes through defeasibility—into rule-based XAI systems. Computational argumentation enables arguments modelled from rules to be retracted based on new evidence. This makes it a promising approach to enhancing rule-based methods for creating more explainable AI systems. Nonetheless, research on their integration remains limited despite the appealing properties of rule-based systems and computational argumentation. Therefore, this study also addresses the applied challenge of implementing such an integration within practical AI tools. The study employs the Logic Learning Machine (LLM), a specific rule-extraction technique, and presents a modular design that integrates input rules into a structured argumentation framework using state-of-the-art computational argumentation methods. Experiments conducted on binary classification problems using various datasets from the UCI Machine Learning Repository demonstrate the effectiveness of this integration. The LLM technique excelled in producing a manageable number of if-then rules with a small number of premises while maintaining high inferential capacity for all datasets. In turn, argument-based models achieved comparable results to those derived directly from if-then rules, leveraging a concise set of rules and excelling in explainability. In summary, this paper introduces a novel approach for efficiently and automatically generating arguments and their interactions from data, addressing both scientific and applied challenges in advancing the application and deployment of argumentation systems in XAI.

Artificial Intelligeence in Construction Industry

Abstract: The construction industry is currently experiencing an increasing skill shortage due to which, many businesses are pressurized. Artificial Intelligence is bringing transformation in construction industry through automation and in finance by implementing business models. Artificial Intelligence is providing novel solutions to the problems faced in construction industry. Artificial Intelligence when implemented in Construction Industry is expected to alter Business Models in logistics, customer relationship management, and support. Artificial Intelligence helps in providing amicable realistic solutions to situations, training, reducing injuries, and costly construction industry. Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Implementation of artificial intelligence in the construction industry and analysis of existing technologies. Here, the research is done on the applications which are done on Artificial intelligence. The study used fuzzy logic methods and machine learning to mark visual data and analyze it for potential threats and to reduce all possible risks. This technology allows users to explore different construction plans in real time and understand the impact of key building decisions on the various factors especially cost and duration of a project. The study highlights the potential of artificial intelligence and machine learning technology in the construction industry. The implementation of the proposed approaches will improve the efficiency of construction, its safety and quality. The results of the study will be useful in the standardization of construction processes and the processing of data on construction projects.

A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy

AimsThis study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with suboptimal blood glucose control despite metformin monotherapy. MethodsEmploying the Logic Learning Machine (LLM), an advanced artificial intelligence system, we scrutinized electronic health records of 1.5 million patients treated in 271 diabetes clinics affiliated with the Italian Association of Medical Diabetologists from 2005 to 2019. Inclusion criteria comprised patients on metformin monotherapy with two consecutive mean HbA1c levels exceeding 7.0%. The cohort was divided into “inertia-NO” (20,067 patients with prompt intensification) and “inertia-YES” (13,029 patients without timely intensification). ResultsThe LLM model demonstrated robust discriminatory ability among the two groups (ROC-AUC = 0.81, accuracy = 0.71, precision = 0.80, recall = 0.71, F1 score = 0.75). The main novelty of our results is indeed the identification of two main distinct subtypes of therapeutic inertia. The first exhibited a gradual but steady HbA1c increase, while the second featured a moderate, non-uniform rise with substantial fluctuations. ConclusionsOur analysis sheds light on the significant impact of HbA1c levels over time on therapeutic inertia in patients with T2D, emphasizing the importance of early intervention in the presence of specific HbA1c patterns.

Implementation of the Fuzzy Logic Mamdani Method in the KUB Chicken Egg Incubator

Poultry farming plays an important role in rural Indonesia's economy, with increasing demand for poultry meat and protein-rich eggs. One of the main challenges for farmers is the limited production of chicken seeds and suboptimal egg incubation methods. Modern egg incubators offer a solution with higher ease and efficiency compared to traditional methods. However, existing machines on the market have weaknesses, such as less accurate temperature and humidity control, and less optimal power source switching. The use of fuzzy logic methods in egg incubators has proven to be more efficient than manual methods, with a hatching success rate of 100% for 10 eggs. Fuzzy logic-based egg incubators start hatching earlier and more on days 18 and 19, while manual methods begin on days 19 and 20. The automatic egg-turning process in fuzzy logic machines saves labor and reduces the risk of error. This research highlights the importance of using accurate sensors and optimal temperature and humidity control systems to improve the success rate of chicken egg hatching. Index Terms—poultry farming; egg hatching; temperature; humidity; fuzzy logic

Elevating metaverse virtual reality experiences through network‐integrated neuro‐fuzzy emotion recognition and adaptive content generation algorithms

AbstractInteractions between individuals and digital material have completely changed with the advent of the Metaverse. Due to this, there is an immediate need to construct cutting‐edge technology that can recognize the emotions of users and continuously provide material that is relevant to their psychological states, improving their overall experience. An inventive method that combines natural language processing adaptive content generation algorithms and neuro‐fuzzy‐based support vector machines natural language processing (SVM‐NLP) is proposed by researchers to meet this demand. With this merging, the Metaverse will be able to offer highly tailored and engaging experiences. Initially, a neuro‐fuzzy algorithm was developed to identify people's emotional moods from their physiological reactions and other biometric information. Fuzzy Logic and Support Vector Machine work together to manage the inherent ambiguity and unpredictability, which results in a more exact and accurate categorization of emotions. A key component of the ACGA is NLP technology, which uses real‐time emotional data to dynamically modify and personalize characters, stories, and interactive features in the Metaverse. The novelty of the proposed approach lies in the innovative integration of neuro‐fuzzy‐based SVM‐NLP algorithms to accurately recognize and adapt to users' emotional states, enhancing the Metaverse experience across various applications. The proposed method is implemented using Python software. This adaptive approach significantly enhances users' immersion, emotional involvement, and overall satisfaction within the augmented reality environment by tailoring information to their responses. The findings show that the SVM‐NLP emotion identification algorithm based on neuro‐fuzzy, has a high degree of accuracy in recognizing emotional states, which holds promise for creating a Metaverse that is more emotionally compelling and immersive. Stronger human–computer interactions and a wider range of applications, including virtual therapy, educational resources, entertainment, and social media networking, might be made possible by integrating SVM‐NLP. These sophisticated systems are around 92% accurate in interpreting the emotions.

Portfolio optimization and risk management through Hierarchical Risk Parity and Logic Learning Machine: a case study applied to the Turkish stock market

This study explores an innovative approach to portfolio optimization, bridging traditional Modern Portfolio Theory (MPT) with advanced Machine Learning techniques. We start by recognizing the significance of Markowitz's model in MPT and quickly proceed to focus on the Hierarchical Risk Parity (HRP) method. HRP overcomes some of the limitations of Markowitz's model, particularly in managing complex asset correlations, by offering a more refined risk management strategy that ensures balanced risk distribution across the portfolio. The paper then introduces an innovative Machine Learning approach that employs the Logic Learning Machine (LLM) method to enhance the explainability of the Hierarchical Risk Parity strategy. Such integration is considered the core research part of the study, given that its application makes the output of the model more accessible and transparent. A case study based on the Turkish stock market has been provided as an example. The combination of traditional financial theories with modern Machine Learning tools marks a significant advancement in investment management and portfolio optimization, emphasizing the importance of clarity and ease of understanding in complex financial portfolio models.

Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic Control

Background: International guidelines for diabetes care emphasize the urgency of promptly achieving and sustaining adequate glycemic control to reduce the occurrence of micro/macrovascular complications in patients with type 2 diabetes mellitus (T2DM). However, data from the Italian Association of Medical Diabetologists (AMD) Annals reveal that only 47% of T2DM patients reach appropriate glycemic targets, with approximately 30% relying on insulin therapy, either solely or in combination. This artificial intelligence analysis seeks to assess the potential impact of timely insulin initiation in all eligible patients via a “what-if” scenario simulation, leveraging real-world data. Methods: This retrospective cohort study utilized the AMD Annals database, comprising 1,186,247 T2DM patients from 2005 to 2019. Employing the Logic Learning Machine (LLM), we simulated timely insulin use for all eligible patients, estimating its effect on glycemic control after 12 months within a cohort of 85,239 patients. Of these, 20,015 were employed for the machine learning phase and 65,224 for simulation. Results: Within the simulated scenario, the introduction of appropriate insulin therapy led to a noteworthy projected 17% increase in patients meeting the metabolic target after 12 months from therapy initiation within the cohort of 65,224 individuals. The LLM’s projection envisages 32,851 potential patients achieving the target (hemoglobin glycated &lt; 7.5%) after 12 months, compared to 21,453 patients observed in real-world cases. The receiver operating characteristic (ROC) curve analysis for this model demonstrated modest performance, with an area under the curve (AUC) value of 70.4%. Conclusions: This study reaffirms the significance of combatting therapeutic inertia in managing T2DM patients. Early insulinization, when clinically appropriate, markedly enhances patients’ metabolic goals at the 12-month follow-up.

The physical logic of protein machines

Proteins are intricate molecular machines whose complexity arises from the heterogeneity of the amino acid building blocks and their dynamic network of many-body interactions. These nanomachines gain function when put in the context of a whole organism through interaction with other inhabitants of the biological realm. And this functionality shapes their evolutionary histories through intertwined paths of selection and adaptation. Recent advances in machine learning have solved the decades-old problem of how protein sequence determines their structure. However, the ultimate question regarding the basic logic of protein machines remains open: how does the collective physics of proteins lead to their functionality? and how does a sequence encode the full range of dynamics and chemical interactions that facilitate function? Here, we explore these questions within a physical approach that treats proteins as mechano-chemical machines, which are adapted to function via concerted evolution of structure, motion, and chemical interactions.

Proactive and Reactive Maintenance Strategies for Self-Healing Digital Twin Islanded Microgrids Using Fuzzy Logic Controllers and Machine Learning Techniques

Proactive and Reactive Maintenance Strategies for Self-Healing Digital Twin Islanded Microgrids Using Fuzzy Logic Controllers and Machine Learning Techniques

Text processing using LLM for automatic creation of agricultural crops knowledge bases

The complexity of subject areas in which intelligent information systems operate is steadily increasing. Tasks assigned to smart agriculture systems are increasingly focused on automating and robotizing areas of human activity. Solving such tasks requires adaptive and flexible methods capable of accommodating dynamic changes in the environment in real-time. The mivar approach to creating intelligent decision-making systems enables working with adaptive discrete structures and provides methods for managerial decision-making based on adaptive active logical inference from the mivar rule knowledge base. The mivar logical inference machine forms the core of expert systems based on the mivar approach. As a result of the development of the mivar approach across various subject areas, different versions of mivar logical inference machines with their algorithms for rule traversal in the knowledge base have been created. Recent advancements in artificial intelligence and machine learning have opened new opportunities for enhancing the mivar approach. The integration of large language models for automating text processing in mivar systems significantly enhances the accuracy and efficiency of decision-making processes based on expert systems for sustainable agriculture. This paper demonstrates the feasibility of using automated text processing, intended for human training, through large language models, and its subsequent application in action planning tasks within technical systems. The proposed methodology is aimed at creating extensive knowledge bases based on textual information for real-time monitoring and decision-making in smart agriculture systems.

A Rule-Based Approach with Fuzzy Logic for Adaptive Network Optimization

This study introduces an adaptive network optimization framework grounded in fuzzy logic and rule-based machine learning for cognitive radio systems. The primary objective is the concurrent minimization of interference, congestion, and bit error rate, coupled with enhancements in throughput and signal-to-noise ratio. A comprehensive set of rules, guided by linguistic variables for qualitative network aspects, is established alongside membership functions for quantitative analysis.The incorporation of machine learning into our approach enables adaptability to diverse network conditions, resulting in overall improved efficiency. The positive impact of machine learning is particularly evident in the reduction of congestion during specific timeframes. Specifically, on Saturday at hour 6, congestion decreases from the conventional 3.055 to 2.86. This notable improvement underscores the efficacy of machine learning in expediting the sensing mechanism of secondary users, facilitating the rapid identification of unused channels from primary users. The findings contribute to the advancement of cognitive radio systems, providing a robust and adaptable solution to address the intricate dynamics of modern wireless networks.

Staring at the Sun with the Keck Planet Finder: An Autonomous Solar Calibrator for High Signal-to-noise Sun-as-a-star Spectra

Extreme precision radial velocity (EPRV) measurements contend with internal noise (instrumental systematics) and external noise (intrinsic stellar variability) on the road to 10 cm s−1 “exo-Earth” sensitivity. Both of these noise sources are well-probed using “Sun-as-a-star” RVs and cross-instrument comparisons. We built the Solar Calibrator (SoCal), an autonomous system that feeds stable, disk-integrated sunlight to the recently commissioned Keck Planet Finder (KPF) at the W. M. Keck Observatory. With SoCal, KPF acquires signal-to-noise ratio (S/N) ∼ 1200, R = 98,000 optical (445–870 nm) spectra of the Sun in 5 s exposures at unprecedented cadence for an EPRV facility using KPF’s fast readout mode (<16 s between exposures). Daily autonomous operation is achieved by defining an operations loop using state machine logic. Data affected by clouds are automatically flagged using a reliable quality control metric derived from simultaneous irradiance measurements. Comparing solar data across the growing global network of EPRV spectrographs with solar feeds will allow EPRV teams to disentangle internal and external noise sources and benchmark spectrograph performance. To facilitate this, all SoCal data products are immediately available to the public on the Keck Observatory Archive. We compared SoCal RVs to contemporaneous RVs from NEID, the only other immediately public EPRV solar data set. We find agreement at the 30–40 cm s−1 level on timescales of several hours, which is comparable to the combined photon-limited precision. Data from SoCal were also used to assess a detector problem and wavelength calibration inaccuracies associated with KPF during early operations. Long-term SoCal operations will collect upwards of 1000 solar spectra per six-hour day using KPF’s fast readout mode, enabling stellar activity studies at high S/N on our nearest solar-type star.

A manifesto for Intelligent Experiences

Let's look beyond the impressive capabilities of current AI tools, to envision 'intelligent experiences' that can surprise and delight us, are culturally enriching, and are inclusive, fair and environmentally sound. Intelligent experiences are encounters with creative works infused with new and emerging technologies. They encompass sensory, emotional and intellectual engagement with various interfaces and artifacts powered by machine learning models. These experiences are where humans meaningfully engage with machine learning's predictive capabilities. They manifest the outputs of machine logic in forms that humans can understand and interact with. Crucially, intelligent experiences foster serendipitous, unexpected interactions rather than trying to replicate interactions learned from the past. This goes beyond efficiency, or mimicking the works of human artists. Intelligent experiences are celebrations of diverse interpretations and interactions, and mediators of constructive discussions rather than heated exchange. They provoke us to search for meaning and come to new interpretations of cultural works and ultimately of ourselves. The arts, like any other field, have a responsibility to use technologies responsibly, so we call for intelligent experiences to be not only delightful and enriching, but also socially and environmentally just. They need to express a multiplicity of data rights and ownership, and acknowledge and reward effortful human creative practice. For this to work, we need to ask questions of AI itself, and consider how the technology needs to evolve to make a lasting impact on human creativity. This vision for intelligent experiences will require AI to become more subjective, playful, able to deal with ambiguity, open to failure, improvised, negotiated, and being open to critique from those who engage with it. We believe intelligent experiences can help to radically change how we think about AI design, moving beyond the current paradigm of learning patterns from large amounts of data, to embrace human traits such as bias, disagreement, and uncertainty as a signal with creative potential rather than noise that needs to be removed.

Designing accurate lightweight intrusion detection systems for IoT networks using fine-tuned linear SVM and feature selectors

Intrusion detection systems (IDSs) play a crucial role in ensuring the security and integrity of Internet of Things (IoT) networks by blocking unwanted packets and facilitating secure traffic flow. However, traditional IDSs based on data mining, fuzzy logic, heuristics, rough sets, or conventional machine learning (ML) techniques often lack accuracy and are not energy efficient, primarily due to inappropriate feature selection or the use of all features in datasets. To address these challenges, this study proposes a lightweight, accurate, and high-performance IDSs for IoT networks using fine-tuned Linear Support Vector Machines (LSVMs) and feature selection methods. Four feature selectors, including Importance Coefficient-, Forward- and Backward-Sequential-, and Correlation Coefficient-based approaches, were applied to identify the most important and efficient features from three datasets: KDD Cup-1999, BotIoT-2018, and N-BaIoT-2021. The fine-tuned LSVMs algorithm was then trained on subsets of the selected and full features of the datasets to detect various IoT botnet attacks. Evaluation results show that the IDS models trained with subsets of relevant features outperform those trained with the full feature sets of the datasets in terms of training and test performance and accuracy. The study concludes that it is possible to develop lightweight IDSs by training them with a reduced number of features (6) instead of using the full features (40, 15, 115) in KDD Cup-1999, BotIoT-2018, and N-BaIoT-2021, respectively. The findings highlight a potential for significantly improving the efficiency and accuracy of IDSs on IoT networks using the fine-tuned feature selectors and LSVMs.

Verb-driven machine reading comprehension with dual-graph neural network

Logical reasoning of context is vital for reading comprehension, which requires to explore the logical relationship through sentence structure. However, previous methods of logical symbols and graph-based models do not make full explore the relationships among entities. In this paper, we present a verb-driven dual-graph network (VDGN) that utilizes core verbs of sentences to model the inter-sentence relationship by the ability of verbs to express linguistic context and the shortest dependency path to model the relationship between entities of intra-sentence. We construct a context graph and a query graph respectively through the above method. In order to predict the answer correctly, our framework fuses information from the context graph and the query graph applying a bi-directional attention mechanism on graph data. We evaluate our approach on two public logical reasoning machine reading comprehension(MRC) datasets: ReClor and LogiQA. Experiments on representative benchmark datasets demonstrate the effectiveness of our approach.

Segmentation of polarimetric radar imagery using statistical texture

Abstract. Weather radars are increasingly being used to study the interaction between wildfires and the atmosphere, owing to the enhanced spatio-temporal resolution of radar data compared to conventional measurements, such as satellite imagery and in situ sensing. An important requirement for the continued proliferation of radar data for this application is the automatic identification of fire-generated particle returns (pyrometeors) from a scene containing a diverse range of echo sources, including clear air, ground and sea clutter, and precipitation. The classification of such particles is a challenging problem for common image segmentation approaches (e.g. fuzzy logic or unsupervised machine learning) due to the strong overlap in radar variable distributions between each echo type. Here, we propose the following two-step method to address these challenges: (1) the introduction of secondary, texture-based fields, calculated using statistical properties of gray-level co-occurrence matrices (GLCMs), and (2) a Gaussian mixture model (GMM), used to classify echo sources by combining radar variables with texture-based fields from (1). Importantly, we retain all information from the original measurements by performing calculations in the radar's native spherical coordinate system and introduce a range-varying-window methodology for our GLCM calculations to avoid range-dependent biases. We show that our method can accurately classify pyrometeors' plumes, clear air, sea clutter, and precipitation using radar data from recent wildfire events in Australia and find that the contrast of the radar correlation coefficient is the most skilful variable for the classification. The technique we propose enables the automated detection of pyrometeors' plumes from operational weather radar networks, which may be used by fire agencies for emergency management purposes or by scientists for case study analyses or historical-event identification.

Editorial

Dear Readers,&amp;nbsp; It gives me great pleasure to announce the ninth regular issue of 2023. I would like to thank all the authors for their sound research and our editorial board and guest reviewers for the extremely valuable reviews and suggestions for improvement. These contributions, together with the generous support of the consortium members, enable us to run our journal successfully and maintain its quality.&amp;nbsp; Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends.&amp;nbsp; As we want to secure the financial support also for the years to come, we are looking for institutions and libraries to financially support our diamond open access journal as consortium members, who will then benefit from the research community, international visibility, and the opportunity to manage special issues and focused topics within the journal. Please think about the possibility of such financial participation by your institution, we would be very grateful for any kind of support.&amp;nbsp; In this regular issue, I am very pleased to introduce five accepted papers involving 22 authors from 10 different countries: Austria, Brazil, Estonia, France, Lebanon, Malaysia, Spain, The Netherlands, United Kingdom, and Vietnam.&amp;nbsp; In a collaborative research effort, Jan Bergstra from The Netherlands and John V. Tucker from the United Kingdom introduce the Naive Fracterm Calculus, which is a new perspective on elementary arithmetic and can be described as naive when compared to a variety of algebraic and logical, axiomatic formalisations of elementary arithmetic. Ricardo Caceffo, Jacques Wainer, Guilherme Gama, Islene Garcia, and Rodolfo Azevedo from Brazil conducted a study based on perceptual learning modules, more specifically a variation of perceptual learning based on multiple-choice questionnaires to be used in an introductory programming course, and report on related issues. In a collaborative research work between colleagues from Malaysia, Lebanon and France, Mohammad Kchouri, Norharyati Harum, Hussein Hazimeh, and Ali Obeid suggest a new technique to detect falls by combining Fuzzy Logic and Support Vector Machine, achieving an overall accuracy of about 99.87% in detecting the fall function. In an international research collaboration between Estonia, Austria, Spain, Luis P. Prieto, Gerti Pishtari, Yannis Dimitriadis, Mar&amp;iacute;a Jes&amp;uacute;s Rodr&amp;iacute;guez-Triana, Tobias Ley, and Paula Odriozola-Gonz&amp;aacute;lez propose and explore single-case learning analytics, which defines a process in which doctoral students, researchers, and computational elements collaborate to extract insights into a single learner&amp;rsquo;s experience and learning process. Last but not least, also in a joint research work by researchers&amp;nbsp;from Vietnam and the United Kingdom, Nguyen Van Hieu, Ngo Le Huy Hien, Luu Van Huy, Nguyen Huy Tuong, and Pham Thi Kim Thoa present their approach PlantKViT for forest plants classification, which is based on a combination model of Vision Transformer and KNN and achieves a 93% accuracy.&amp;nbsp; Enjoy Reading!&amp;nbsp; Cordially,&amp;nbsp; Christian G&amp;uuml;tl, Managing Editor&amp;nbsp; Graz University of Technology, Graz, Austria

Smart Fall Detection by Enhanced SVM with Fuzzy Logic Membership Function

Falling is a critical issue for disabled people, and it leads to potentially serious injuries and death. Smart fall detection is a technology that depends on sensors and auxiliary devices that seek to improve the quality of life and enhance the lifestyle of disabled people. So far, the most widely used fall prediction methods collect data from inertial measurement unit (IMU) sensors. In addition, they use thresholds to identify falls based on artificial experiences or machine learning (ML) algorithms. Nonetheless, these approaches still require extensive classification and calibration. In this paper, we suggest a new technique to detect falls by combining Fuzzy Logic (FL) and Support Vector Machine (SVM). The FL model is built by using a fuzzy membership function along with the input dataset to obtain the intermediate output. Because combining these two algorithms is not an easy task, we leverage SVM with a kernel comprised of a fuzzy membership function and thus build a new model known as FSVM. Besides, the hyperplane of the SVM is used as the separating plane to replace the traditional threshold method for detecting falling Activities of Daily Living (ADLs) on a comprehensive dataset containing simulated falling ADLs, non-falling ADLs, and scripted ADLs, including falling ADLs and unscripted ADLs performed by volunteers with our designed device. The results show that no false-positive rate had been triggered, and 100% specificity was achieved for ADL. An overall accuracy of about 99.87% in detecting the fall function was obtained. Furthermore, the overall sensitivity of 100% with no false negative rate obtained was achieved by implementing the proposed method. The attained results validate that our introduced method can effectively learn from features extracted from a multiphase fall model.&amp;nbsp;