Search Results (83)

Computer-aided diagnosis (CAD) systems, which combine medical image processing with artificial intelligence (AI) to support experts in diagnosing various diseases, emerged from the need to solve some of the problems associated with medical diagnosis, such as long timelines and operator-related variability. The most explored medical application is cancer detection, for which several CAD systems have been proposed. Among them, deep neural network (DNN)-based systems for skin cancer diagnosis have demonstrated comparable or superior performance to that of experienced dermatologists. However, the lack of transparency in the decision-making process of such approaches makes them “black boxes” and, therefore, not directly incorporable into clinical practice. Trying to explain and interpret the reasons for DNNs’ decisions can be performed by the emerging explainable AI (XAI) techniques. XAI has been successfully applied to DNNs for skin lesion image classification but never when additional information is incorporated during network training. This field is still unexplored; thus, in this paper, we aim to provide a method to explain, qualitatively and quantitatively, a convolutional neural network model with feature injection for melanoma diagnosis. The gradient-weighted class activation mapping and layer-wise relevance propagation methods were used to generate heat maps, highlighting the image regions and pixels that contributed most to the final prediction. In contrast, the Shapley additive explanations method was used to perform a feature importance analysis on the additional handcrafted information. To successfully integrate DNNs into the clinical and diagnostic workflow, ensuring their maximum reliability and transparency in whatever variant they are used is necessary. Full article

Increasing urbanization and rising consumption rates are putting pressure on urban systems to efficiently manage Municipal Solid Waste (MSW). Waste collection, in particular, is one of the most challenging aspects of MSW management. Therefore, developing computer-aided tools to support decision-makers is crucial. In this paper, a Scatter Search algorithm is proposed to address the waste collection problem. The literature is relatively scarce in applying this algorithm, which has proven to be efficient in other routing problems, to real waste management problems. Results from real-world instances of an Argentine city demonstrate that the algorithm is competitive, obtaining, in the case of small instances, the same outcomes as those of an exact solver enhanced by valid inequalities, although requiring more computational time (as expected), and significantly improving the results of the latter for the case of larger instances, now requiring much less computational time. Thus, Scatter Search proves to be a competitive algorithm for addressing waste collection problems. Full article

Breast cancer, which has the highest mortality and morbidity rates among diseases affecting women, poses a significant threat to their lives and health. Early diagnosis is crucial for effective treatment. Recent advancements in artificial intelligence have enabled innovative techniques for early breast cancer detection. Convolutional neural networks (CNNs) and support vector machines (SVMs) have been used in computer-aided diagnosis (CAD) systems to identify breast tumors from mammograms. However, existing methods often face challenges in accuracy and reliability across diverse diagnostic scenarios. This paper proposes a three parallel channel artificial intelligence-based system. First, SVM distinguishes between different tumor types using local binary pattern (LBP) features. Second, a pre-trained CNN extracts features, and SVM identifies potential tumors. Third, a newly developed CNN is trained and used to classify mammogram images. Finally, a decision fusion that combines results from the three channels to enhance system performance is implemented using different rules. The proposed decision fusion-based system outperforms state-of-the-art alternatives with an overall accuracy of 99.1% using the product rule. Full article

Recent developments in Artificial Intelligence have increasingly focused on explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided diagnosis systems and its usage for knowledge discovery are gaining interest in the medical imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI are focused on interpreting the predictions made using deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This highlights the importance of proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process on learning and identifying representative prototypical parts from input images, and they are gaining increasing interest and results in MI applications. However, the medical field has unique characteristics that could benefit from more advanced implementations of these types of architectures. This narrative review summarizes existing PP networks, their application in MI analysis, and current challenges. Full article

With profound effects on patient care, the role of artificial intelligence (AI) in radiomics has become a disruptive force in contemporary medicine. Radiomics, the quantitative feature extraction and analysis from medical images, offers useful imaging biomarkers that can reveal important information about the nature of diseases, how well patients respond to treatment and patient outcomes. The use of AI techniques in radiomics, such as machine learning and deep learning, has made it possible to create sophisticated computer-aided diagnostic systems, predictive models, and decision support tools. The many uses of AI in radiomics are examined in this review, encompassing its involvement of quantitative feature extraction from medical images, the machine learning, deep learning and computer-aided diagnostic (CAD) systems approaches in radiomics, and the effect of radiomics and AI on improving workflow automation and efficiency, optimize clinical trials and patient stratification. This review also covers the predictive modeling improvement by machine learning in radiomics, the multimodal integration and enhanced deep learning architectures, and the regulatory and clinical adoption considerations for radiomics-based CAD. Particular emphasis is given to the enormous potential for enhancing diagnosis precision, treatment personalization, and overall patient outcomes. Full article

Drone area coverage primarily involves using unmanned aerial vehicles (UAVs) for extensive monitoring, surveying, communication, and other tasks over specific regions. The significance and value of this technology are multifaceted. Firstly, UAVs can rapidly and efficiently reach remote or inaccessible areas to perform tasks such as terrain mapping, disaster monitoring, or search and rescue, significantly enhancing response speed and execution efficiency. Secondly, drone area coverage in agricultural monitoring, forestry conservation, and urban planning offers high-precision data support, aiding scientists and decision-makers in making more accurate judgments and decisions. Additionally, drones can serve as temporary communication base stations in areas with poor communication, ensuring the transfer of crucial information. Drone area coverage technology is vital in improving work efficiency, reducing costs, and strengthening decision support. This paper aims to solve the optimization problem of multi-UAV area coverage flight path planning to enhance system efficiency and task execution capability. For multi-center optimization problems, a region decomposition method based on the Voronoi graph is designed, transforming the multi-UAV area coverage issue into the single-UAV area coverage problem, greatly simplifying the complexity and computational process. For the single-UAV area coverage problem and its corresponding area, this paper contrives a convolutional neural network with the channel and spatial attention mechanism (CSAM) to enhance feature fusion capability, enabling the model to focus on core features for solving single-UAV path selection and ultimately generating the optimal path. Simulation results demonstrate that the proposed method achieves excellent performance. Full article

The use of refuse-derived fuel (RDF) in cement kilns offers a multifaceted approach to sustainability, addressing environmental, economic, and social aspects. By converting waste into a valuable energy source, RDF reduces landfill use, conserves natural resources, lowers greenhouse gas emissions, and promotes a circular economy. This sustainable practice not only supports the cement industry in meeting regulatory requirements but also advances global efforts toward more sustainable waste management and energy production systems. This research promotes the integration of RDF as fuel in cement kilns to reduce the use of fossil fuels by improving the control of the combustion. Addressing the variable composition of RDF requires continuous monitoring to ensure operational stability and product quality, traditionally managed by operators through visual inspections. This study introduces a real-time, computer vision- and deep learning-based monitoring system to aid in decision-making, utilizing existing kiln imaging devices for a non-intrusive, cost-effective solution applicable across various facilities. The system generates two detailed datasets from the kiln environment, undergoing extensive preprocessing to enhance image quality. The YOLOv8 algorithm was chosen for its real-time accuracy, with the final model demonstrating strong performance and domain adaptation. In an industrial setting, the system identifies critical elements like flame and clinker with high precision, achieving 25 frames per second (FPS) and a mean average precision (${mAP}^{50}$) of 98.8%. The study also develops strategies to improve the adaptability of the model to changing operational conditions. This advancement marks a significant step towards more energy-efficient and quality-focused cement production practices. By leveraging technological innovations, this research contributes to the move of the industry towards sustainability and operational efficiency. Full article

Solar radiation impacts diverse aspects of city life, such as harvesting energy with PV panels, passive heating of buildings in winter, cooling the loads of air-conditioning systems in summer, and the urban microclimate. Urban digital twins and 3D city models can support solar studies in the process of urban planning and provide valuable insights for data-driven decision support. This study examines the calculation of solar incident radiation at the city scale in Sofia using remote sensing data for the large shading context in a mountainous region and 3D building data. It aims to explore the methods of geometry optimisation, limitations, and performance issues of a 3D computer-aided design (CAD) tool dedicated to small-scale solar analysis and employed at the city scale. Two cases were considered at the city and district scales, respectively. The total face count of meshes for the simulations constituted approximately 2,000,000 faces. A total of 64,379 roofs for the whole city and 4796 buildings for one district were selected. All calculations were performed in one batch and visualised in a 3D web platform. The use of a 3D CAD environment establishes a seamless process of updating 3D models and simulations, while preprocessing in Geographic Information System (GIS) ensures working with large-scale datasets. The proposed method showed a moderate computation time for both cases and could be extended to include reflected radiation and dense photogrammetric meshes in the future. Full article

Plant-parasitic nematodes (PPNs) reduce the high profitability of many crops and degrade their quantitative and qualitative yields globally. Traditional nematicides and other nematode control methods are being used against PPNs. However, stakeholders are searching for more sustainable and effective alternatives with limited side effects on the environment and mankind to face increased food demand, unfavorable climate change, and using unhealthy nematicides. This review focuses on upgrading the pre-procedures of PPN control as well as novel measures for their effective and durable management strategies on economically important crops. Sound and effective sampling, extraction, identification, and counting methods of PPNs and their related microorganisms, in addition to perfecting designation of nematode–host susceptibility/resistance, form the bases for these strategies. Therefore, their related frontiers should be expanded to synthesize innovative integrated solutions for these strategies. The latter involve supplanting unsafe nematicides with a new generation of safe and reliable chemical nematicidal and bionematicidal alternatives. For better efficacy, nematicidal materials and techniques should be further developed via computer-aided nematicide design. Bioinformatics devices can reinforce the potential of safe and effective biocontrol agents (BCAs) and their active components. They can delineate the interactions of bionematicides with their targeted PPN species and tackle complex diseases. Also, the functional plan of nematicides based on a blueprint of the intended goals should be further explored. Such goals can currently engage succinate dehydrogenase, acetylcholinesterase, and chitin deacetylase. Nonetheless, other biochemical compounds as novel targets for nematicides should be earnestly sought. Commonly used nematicides should be further tested for synergistic or additive function and be optimized via novel sequential, dual-purpose, and co-application of agricultural inputs, especially in integrated pest management schemes. Future directions and research priorities should address this novelty. Meanwhile, emerging bioactivated nematicides that offer reliability and nematode selectivity should be advanced for their favorable large-scale synthesis. Recent technological means should intervene to prevail over nematicide-related limitations. Nanoencapsulation can challenge production costs, effectiveness, and manufacturing defects of some nematicides. Recent progress in studying molecular plant–nematode interaction mechanisms can be further exploited for novel PPN control given related topics such as interfering RNA techniques, RNA-Seq in BCA development, and targeted genome editing. A few recent materials/techniques for control of PPNs in durable agroecosystems via decision support tools and decision support systems are addressed. The capability and effectiveness of nematicide operation harmony should be optimized via employing proper cooperative mechanisms among all partners. Full article

Icons are integral to the signature systems within architectural spaces, serving pivotal roles through human–environment interactions. However, previous icon designs often exhibited a considerable randomness and neglected the needs of visually impaired individuals. To address these issues and to overcome the limitations of the computer-aided design methods, such as most of them focusing only on text design which are not compatible with icons, this study presents an intelligent assistance method named “Universal Colour” for icon colour design based on universal design principles. Such a system enables the rapid generation of icon colour schemes and supports visual and quantitative filtering and comparison during the decision-making process for colour scheme optimization. To assess its usability, fifty-two participants conducted icon colour design experiments using this system, resulting in 87% of the design schemes meeting the universality requirements. The results have demonstrated that Universal Colour has the potential to significantly enhance efficiency and cognitive aspects within the decision-making process for users, regardless of their proficiency in icon design, thereby facilitating the generation of universal icon colour schemes in architectural design. Full article

Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions. Full article

Peer-to-peer lending, a novel element of Internet finance that links lenders and borrowers via online platforms, has generated large profits for investors. However, borrowers’ missed payments have negatively impacted the industry’s sustainable growth. It is imperative to create a system that can correctly predict loan defaults to lessen the damage brought on by defaulters. The goal of this study is to fill the gap in the literature by exploring the feasibility of developing prediction models for P2P loan defaults without relying heavily on personal data while also focusing on identifying key variables influencing borrowers’ repayment capacity through systematic feature selection and exploratory data analysis. Given this, this study aims to create a computational model that aids lenders in determining the approval or rejection of a loan application, relying on the financial data provided by applicants. The selected dataset, sourced from an open database, contains 8578 transaction records and includes 14 attributes related to financial information, with no personal data included. A loan dataset is first subjected to an in-depth exploratory data analysis to find behaviors connected to loan defaults. Subsequently, diverse and noteworthy machine learning classification algorithms, including Random Forest, Support Vector Machine, Decision Tree, Logistic Regression, Naïve Bayes, and XGBoost, were employed to build models capable of discerning borrowers who repay their loans from those who do not. Our findings indicate that borrowers who fail to comply with their lenders’ credit policies, pay elevated interest rates, and possess low FICO ratings are at a higher likelihood of defaulting. Furthermore, elevated risk is observed among clients who obtain loans for small businesses. All classification models, including XGBoost and Random Forest, successfully developed and performed satisfactorily and achieved an accuracy of over 80%. When the decision threshold is set to 0.4, the best performance for predicting loan defaulters is achieved using logistic regression, which accurately identifies 83% of the defaulted loans, with a recall of 83%, precision of 21% and f1 score of 33%. Full article

Global navigation satellite systems (GNSSs) are an integral part of global positioning. However, because GNSS performance is impacted by signal obscuration and the presence of multipath in urban and deep urban environments, it is not accurate, reliable, and widely available enough to be a standalone system in all environments. This creates two problems: (1) the GNSS user does not know when or where GNSS performance may be degraded and (2) the GNSS user has limited ability to mitigate these issues. No mitigation strategy exists to improve the availability of GNSSs themselves. Inertial measurement units (IMUs) and sensor fusion provide other costly methods to improve positioning performance, but most systems still rely on GNSSs for absolute position. Spirent’s GNSS Foresight service aims to solve both issues. As a cloud-based solution, GNSS Foresight provides satellite and signal information, and this can be employed to support the decision-making strategy and calculations in the GNSS receiver to improve its positioning solution performance, integrity, and reliability. In this paper, GNSS Foresight is introduced, and a performance evaluation of GNSS Foresight in dense urban areas is presented. Using the data collected from two urban areas in North America, we evaluated GNSS Foresight and compared the performance of GNSS positioning solutions with and without Foresight-aided data. The comparison results show the observed improvements in GNSS receiver operation. Foresight can also be used to develop measurement engine performance enhancements in the acquisition of new satellites and the tracking/re-acquisition of current satellites using line-of-sight (LOS) satellite information. In the positional computation process, Foresight enables receivers to prioritize LOS signals over degraded non-line-of-sight (NLOS) signals, hence significantly reducing positioning errors and outperforming conventional GNSS positioning, particularly in difficult urban environments. Full article

Early detection is crucial for the survival and recovery of lung cancer patients. Computer-aided diagnosis system can assist in the early diagnosis of lung cancer by providing decision support. While deep learning methods are increasingly being applied to tasks such as CAD (Computer-aided diagnosis system), these models lack interpretability. In this paper, we propose a convolutional neural network model that combines semantic characteristics (SCCNN) to predict whether a given pulmonary nodule is malignant. The model synthesizes the advantages of multi-view, multi-task and attention modules in order to fully simulate the actual diagnostic process of radiologists. The 3D (three dimensional) multi-view samples of lung nodules are extracted by spatial sampling method. Meanwhile, semantic characteristics commonly used in radiology reports are used as an auxiliary task and serve to explain how the model interprets. The introduction of the attention module in the feature fusion stage improves the classification of lung nodules as benign or malignant. Our experimental results using the LIDC-IDRI (Lung Image Database Consortium and Image Database Resource Initiative) show that this study achieves 95.45% accuracy and 97.26% ROC (Receiver Operating Characteristic) curve area. The results show that the method we proposed not only realize the classification of benign and malignant compared to standard 3D CNN approaches but can also be used to intuitively explain how the model makes predictions, which can assist clinical diagnosis. Full article

Parkinson’s disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other neurological diseases and minor changes in brain structure, make the diagnosis of this disease a challenge and cause inaccuracy of about 25% in the diagnostics. The research community utilizes different machine learning techniques for diagnosis using handcrafted features. This paper proposes a computer-aided diagnostic system using a convolutional neural network (CNN) to diagnose PD. CNN is one of the most suitable models to extract and learn the essential features of a problem. The dataset is obtained from Parkinson’s Progression Markers Initiative (PPMI), which provides different datasets (benchmarks), such as T2-weighted MRI for PD and other healthy controls (HC). The mid slices are collected from each MRI. Further, these slices are registered for alignment. Since the PD can be found in substantia nigra (i.e., the midbrain), the midbrain region of the registered T2-weighted MRI slice is selected using the freehand region of interest technique with a 33 × 33 sized window. Several experiments have been carried out to ensure the validity of the CNN. The standard measures, such as accuracy, sensitivity, specificity, and area under the curve, are used to evaluate the proposed system. The evaluation results show that CNN provides better accuracy than machine learning techniques, such as naive Bayes, decision tree, support vector machine, and artificial neural network. Full article