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Building information modeling (BIM) can greatly improve the management and planning of historic building conservation projects. However, implementing BIM in the heritage has many challenges, including issues with modeling irregular features, surveying data occlusions, and a lack of predefined libraries of parametric objects. Indeed, surface features can be manually distinguished and segmented depending on the level of human involvement during data scanning and BIM processing. This requires a significant amount of time and resources, as well as the risk of making too subjective decisions. To address these bottlenecks and improve BIM digitization of building geometry, a novel deep learning based scan-to-HBIM workflow is used during the recording of the historic building in historic Jeddah, Saudi Arabia, a UNESCO World Heritage site. The proposed workflow enables access to laser scanner and unmanned aerial vehicle imagery data to create a complete integrated survey using high-resolution imagery acquired independently at the best position and time for proper radiometric information to depict the surface features. By employing deep learning with orthophotos, the method significantly improves the interpretation of spatial weathering forms and façade degradation. Additionally, an HBIM library for Saudi Hijazi architectural elements is created, and the vector data derived from deep learning-based segmentation are accurately mapped onto the HBIM geometry with relevant statistical parameters. The findings give stakeholders an effective tool for identifying the types, nature, and spatial extent of façade degradation to investigate and monitor the structure. [ABSTRACT FROM AUTHOR]

Classifying imbalanced data presents a significant challenge, and many studies have proposed methodologies to address this issue. Among them, Convolutional Neural Networks have demonstrated superior performance for imbalanced image classification. This paper initially employs various data pre-processing methods such as over-sampling, under-sampling, and SMOTE to enhance the original dataset. Subsequently, an Ensemble CNN learning model is used to train and predict the data. In order to comprehensively evaluate models trained on imbalanced data, we used metrics such as Accuracy, Recall, Precision, F1-score, and G-mean. On the CIFAR-10 and Fashion-MNIST datasets, different samples from each category were extracted as imbalanced data for experimental research. Compared to the AdaBoost-DenseNet model, our proposed methodology increases the test accuracy on the CIFAR-10 dataset by 9%. Similarly, the F1-score and G-mean improved by 0.096 and 0.069, respectively. Compared to traditional methodologies, our proposed method significantly improves accuracy, recall, precision, and other performance indicators. [ABSTRACT FROM AUTHOR]

The cultivation of grapes encounters various challenges, such as the presence of pests and diseases, which have the potential to considerably diminish agricultural productivity. Plant diseases pose a significant impediment, resulting in diminished agricultural productivity and economic setbacks, thereby affecting the quality of crop yields. Hence, the precise and timely identification of plant diseases holds significant importance. This study employs a Convolutional neural network (CNN) with and without data augmentation, in addition to a DCNN Classifier model based on VGG16, to classify grape leaf diseases. A publicly available dataset is utilized for the purpose of investigating diseases affecting grape leaves. The DCNN Classifier Model successfully utilizes the strengths of the VGG16 model and modifies it by incorporating supplementary layers to enhance its performance and ability to generalize. Systematic evaluation of metrics, such as accuracy and F1-score, is performed. With training and test accuracy rates of 99.18 and 99.06%, respectively, the DCNN Classifier model does a better job than the CNN models used in this investigation. The findings demonstrate that the DCNN Classifier model, utilizing the VGG16 architecture and incorporating three supplementary CNN layers, exhibits superior performance. Also, the fact that the DCNN Classifier model works well as a decision support system for farmers is shown by the fact that it can quickly and accurately identify grape diseases, making it easier to take steps to stop them. The results of this study provide support for the reliability of the DCNN classifier model and its potential utility in the field of agriculture. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop an automated pipeline capable of designing custom total knee replacement implants from CT scans. The developed pipeline firstly utilised a series of machine learning methods including classification, object detection, and image segmentation models, to extract geometrical information from inputted DICOM files. Statistical shape models then used the information to create femur and tibia 3D surface model predictions which were ultimately used by computer aided design scripts to generate customised implant designs. The developed pipeline was trained and tested using CT scan images, along with segmented 3D models, obtained for 98 Korean Asian subjects. The performance of the pipeline was tested computationally by virtually fitting outputted implant designs with 'ground truth' 3D models for each test subject's bones. This demonstrated the pipeline was capable of repeatably producing highly accurate designs, and its performance was not impacted by subject sex, height, age, or knee side. In conclusion, a robust, accurate and automatic, CT-based total knee replacement customisation pipeline was shown to be feasible and could afford significant time and cost advantages over conventional methods. The pipeline framework could also be adapted to enable customisation of other medical implants. [ABSTRACT FROM AUTHOR]

In today's era of advanced technologies, the concerns related to global security have led to video surveillance gadgets. Human gait recognition as a biometric is considered an evolving technology for intelligent surveillance monitoring. This research study exploits vulnerabilities associated with a convolutional neural network (CNN)-based gait recognition system under various walking conditions involving clothing, carrying items, and speed. In the first stage, we design a CNN model capable of identifying individuals based on their gait characteristics. Subsequently, in the next stage, we design a five-pixel adversarial attack in which we perturb the gait features of individuals computed using the fast gradient sign method. The resulting perturbation is added to only five random pixels to create naturalistic adversarial samples similar to the original samples. Further, the main aim of this study is to determine and analyze the performance of the CNN-based gait recognition system under an adversarial attack. The research concludes that gait recognition systems based on CNN models are highly susceptible to adversarial attacks, motivating researchers to design defense mechanisms to mitigate the effect of these attacks. [ABSTRACT FROM AUTHOR]

In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned Aerial Vehicle (UAV) in both civil and military sectors. Among various UAVs, the ability of rotary wing UAVs (RUAVs) in vertical take-off and landing, to hover and perform quick maneuvering attract researchers to develop models fully autonomous control framework. The majority of first principle techniques in modeling and controlling RUAV face challenges in incorporating and handling various uncertainties. Recently various fuzzy and neuro-fuzzy based intelligent systems are utilized to enhance the RUAV's modeling and control performance. However, the majority of these fuzzy systems are based on batch learning methods, have static structure, and cannot adapt to rapidly changing environments. The implication of Evolving Intelligent System based model-free data-driven techniques can be a smart option since they can adapt their structure and parameters to cope with sudden changes in the behavior of RUAVs real-time flight. They work in a single pass learning fashion which is suitable for online real-time deployment. In this paper, state of the art of various fuzzy systems from the basic fuzzy system to evolving fuzzy system, their application in a RUAV namely quadcopter with existing limitations, and possible opportunities are analyzed. Besides, a variety of first principle techniques to control the quadcopter, their impediments, and conceivable solution with recently employed evolving fuzzy controllers are reviewed. [ABSTRACT FROM AUTHOR]