A new random forest-based model for solving the Multiple Instance Learning problem under small tabular data, called the Soft Tree Ensemble Multiple Instance Learning, is proposed. A new type of soft decision trees is considered, which is similar to t...
This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle...
The standard pipeline in pedestrian detection is sliding a pedestrian model on an image feature pyramid to detect pedestrians of different scales. In this pipeline, feature pyramid construction is time consuming and becomes the bottleneck for fast de...
Soft sensors work as predictive frameworks encapsulating a set of easy-to-collect input data and a machine learning method (ML) to predict highly related variables that are difficult to measure. The machine learning method could provide a prediction...
Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduc...
This study shows the significant features predicting graduates’ job levels, particularly high-level positions. Moreover, it shows that data science methodologies can accurately predict graduate outcomes. The dataset used to analyze graduate out...
Surface soil moisture (SSM) plays a critical role in climate change, hydrological processes, and agricultural production. Decision trees and deep learning are widely applied to SSM retrieval. The former excels in interpretability while the latter out...
The damage done in earthquake disasters is correlated to the types of housing structures that are present. In the last two decades of urbanization in Dhaka, rapid growth without proper planning has been a major concern. This study evaluates the perfo...
In this paper, we apply the notion of soft rough neutrosophic sets to graph theory. We develop certain new concepts, including soft rough neutrosophic graphs, soft rough neutrosophic influence graphs, soft rough neutrosophic influence cycles and soft...
Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenario...
A precise rainfall-runoff prediction is crucial for hydrology and the management of water resources. Rainfall-runoff prediction is a nonlinear method influenced by simulation model inputs. Previously employed methods have some limitations in predicti...
Fuzzy sets, rough sets and soft sets are different tools for modeling problems involving uncertainty. Graph theory is another powerful tool for representing the information by means of diagrams, matrices or relations. A possible amalgamation of three...
Speech emotion recognition (SER) plays an important role in human-computer interaction (HCI) technology and has a wide range of application scenarios in medical medicine, psychotherapy, and other applications. In recent years, with the development of...
Unattended scheduled appointments (“patient no-shows” henceforth) adversely affect healthcare providers and patients’ health, disrupting the continuity of care, operational efficiency, and allocation of medical resources. Therefore,...
Soft and flexible capacitive tactile sensors are vital in prosthetics, wearable health monitoring, and soft robotics applications. However, achieving accurate real-time force detection and spatial localization remains a significant challenge, especia...
Cooperative spectrum sensing (CSS) has the ability to accurately identify the activities of the primary users (PUs). As the secondary users’ (SUs) sensing performance is disturbed in the fading and shadowing environment, therefore the CSS is a...
27 October 2020
Evaluating the susceptibility of regional landslides is one of the core steps in spatial landslide prediction. Starting from multiresolution image segmentation and object-oriented classification theory, this paper uses the four parameters of entropy,...
In the dynamic arena of healthcare research, where the complexities of data often rival the intricacies of biological systems, the ability to model and analyze such multifaceted datasets is crucial. This comprehensive review delves into the evolution...
Lean and flexible manufacturing is a matter of necessity for the automotive industries today. Rising consumer expectations, higher raw material and processing costs, and dynamic market conditions are driving the auto sector to become smarter and agil...
Efficient energy management relies on uncovering meaningful consumption patterns from large-scale electricity load demand profiles. With the widespread adoption of sensor technologies such as smart meters and IoT-based monitoring systems, granular an...
Necrotizing fasciitis (NF) is a severe and life-threatening soft tissue infection that requires timely and accurate diagnosis to improve patient outcomes. The early diagnosis of NF remains challenging due to its similarity to other subcutaneous soft...
Soft robotics is an emerging technology that provides robots with the ability to adapt to the environment and safely interact with it. Here, the ability of these robots to identify the surface of interaction is critical for grasping and locomotion ta...
Assessing the biomechanical properties of soft tissues can be useful because they are related to their pathophysiological state. This study explores the application of artificial intelligence (AI) to an Impact-Based Analysis Method (IBAM) to predict...
This paper presents the development of an intelligent soft-sensor system to add haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed, one for finger joint angles and the other for fingerti...
In this paper, a method is introduced to control the dark knowledge values also known as soft targets, with the purpose of improving the training by knowledge distillation for multi-class classification tasks. Knowledge distillation effectively trans...
Body condition score (BCS) in sheep (Ovis aries) is a widely used subjective measure of the degree of soft tissue coverage. Body condition score and liveweight are statistically related in ewes; therefore, it was hypothesized that BCS could be accura...
Tree-based methods and deep neural networks (DNNs) have drawn much attention in the classification of images. Interpretable canonical deep tabular data learning architecture (TabNet) that combines the concept of tree-based techniques and DNNs can be...
Soft robots demonstrate significant advantages in applications within complex environments due to their unique material properties and structural designs. However, they also face challenges in fault diagnosis, such as nonlinearity, time variability,...
Several studies have addressed the benefits of mentoring from the mentor’s perspective, especially those related to soft skills. However, to the best of our knowledge, there are no studies that either relate the mentoring in STEM areas with fem...
This paper presents a data-driven framework for optimizing energy-efficient locomotion in a bio-inspired soft inchworm robot. Leveraging a feedforward neural network, the proposed approach accurately models the nonlinear relationships between actuati...
Sour waters are one of the main aqueous byproducts generated during petroleum refining and require processing in sour water treatment units (SWTUs) to remove contaminants such as H2S and NH3 in compliance with environmental legislations. Therefore, m...
Soft robotics is a specialized field of robotics that focuses on the design, manufacture, and control of robots made of soft materials, as opposed to those made of rigid links. One of the primary challenges for the future use of continuous or hyper-r...
In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship m...
The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing inter...
Accurately predicting the state of surface water quality is crucial for ensuring the sustainable use of water resources and environmental protection. This often requires a focus on the range of factors affecting water quality, such as physical and ch...
Landslides are natural and often quasi-normal threats that destroy natural resources and may lead to a persistent loss of human life. Therefore, the preparation of landslide susceptibility maps is necessary in order to mitigate harmful effects. The k...
Vehicular Ad-hoc network (VANET) is an imminent technology having both exciting prospects and substantial challenges, especially in terms of security. Due to its distributed network and frequently changing topology, it is extremely prone to security...
Objective: The objective of this study is to develop a convolutional neural network (CNN) for the automatic detection of soft and hard tissue landmarks and the classification of lip thickness on lateral cephalometric radiographs. Methods: A dataset o...
Background: The aim of the present work was to analyze a 10-year retrospective series of surgically treated medication-related osteonecrosis of the jaws (MRONJ) cases, reporting the clinical outcome and success rate for each adopted procedure in orde...
Knowledge distillation transfers information from a high-capacity teacher to a smaller student, but its behavior across regression, classification, and heterogeneous tabular model families remains insufficiently understood. This paper presents a comp...
Heat stress is one of the main welfare and productivity problems faced by dairy cattle in Mediterranean climates. The main objective of this work is to predict heat stress in livestock from shade-seeking behavior captured by computer vision, combined...
Intrusion detectors for Internet of Things (IoT)-enabled substations must run on gateways with limited memory, yet the strongest published detectors are heavy tree ensembles. This study compresses a five-member soft-voting ensemble into compact multi...
Truck cranes, which are crucial construction equipment, need to maintain good operational performance to ensure safe use. However, the complex and ever-changing working conditions they face often make it challenging to test their performance effectiv...
The prediction of solar energetic particle (SEP) events or solar radiation storms is one of the most important problems in the space weather field. These events may have adverse effects on technology infrastructures and humans in space; they may also...
1 November 2022
Soft-computing and statistical learning models have gained substantial momentum in predicting type 2 diabetes mellitus (T2DM) disease. This paper reviews recent soft-computing and statistical learning models in T2DM using a meta-analysis approach. We...
Relapse and metastasis occur in 30–40% of breast cancer patients, even after targeted treatments like trastuzumab for HER2-positive breast cancer. Accurate individual prognosis is essential for determining appropriate adjuvant treatment and ear...
Soybean frogeye leaf spot (FLS), a serious soybean disease, causes severe yield losses in the largest production regions of China. However, both conventional field monitoring and machine learning algorithms remain challenged in achieving rapid and ac...
This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely “AB–ADTree”, for groundwater spring potential mapping (GSPM) at the Chilgazi water...
In the last decade, ground-level ozone exposure has led to a significant increase in environmental and health risks. Thus, it is essential to measure and monitor atmospheric ozone concentration levels. Specifically, recent improvements in machine lea...
Achieving rapid and accurate obstacle avoidance in complex and dynamic environments remains a significant challenge for robots. To enhance the adaptability and flexibility of humanoid arms for obstacle avoidance, a brain-inspired multi-pathway motion...
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