Ensemble of Boosting Algorithms for Parkinson Disease Diagnosis
Abstract
Parkinson’s disease (PD) is a common dynamic neurodegenerative disorder due to the lack of the brain’s chemical dopamine, impairing motor and nonmotor symptoms. The PD patients undergo vocal cord dysfunctions, producing speech impairment, an early and essential PD indicator. The researchers are contributing to building generic data-driven decision-making systems due to the non-availability of the medical test(s) for the early PD diagnosis. This article has provided an automatic decision-making framework for PD detection by proposing a weighted ensemble of machine learning (ML) boosting classifiers: random forest (RF), AdaBoost (AdB), and XGBoost (XGB). The introduced framework has incorporated outlier rejection (OR) and attribute selection (AS) as the recommended preprocessing. The experimental results reveal that the one-class support vector machine-based OR followed by information gain-based AS performs the best preprocessing in the aimed task. Additionally, one of the proposed ensemble models has outputted an average area under the ROC curve (AUC) of 0.972, outperforming the individual RF, AdB, and XGB classifiers with the margins of \(0.5\,\%\), \(3.7\,\%\), and \(1.4\,\%\), respectively, while the advised preprocessing is incorporated. Since the suggested system provides better PD diagnosis results, it can be a practical decision-making tool for clinicians in PD diagnosis.KeywordsParkinson diseaseOutlier rejectionAttribute selectionMachine learning modelsEnsemble classifiers
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Minimally invasive surgery (MIS) offers several advantages to patients including minimum blood loss and quick recovery time. However, lack of tactile or haptic feedback and poor visualization of the surgical site often result in some unintentional tissue damage. Visualization aspects further limits the collection of imaged frame contextual details, therefore the utility of computational methods such as tracking of tissue and tools, scene segmentation, and depth estimation are of paramount interest. Here, we discuss an online preprocessing framework that overcomes routinely encountered visualization challenges associated with the MIS. We resolve three pivotal surgical scene reconstruction tasks in a single step; namely, (i) denoise, (ii) deblur, and (iii) color correction. Our proposed method provides a latent clean and sharp image in the standard RGB color space from its noisy, blurred, and raw inputs in a single preprocessing step (end-to-end in one step). The proposed approach is compared against current state-of-the-art methods that perform each of the image restoration tasks separately. Results from knee arthroscopy show that our method outperforms existing solutions in tackling high-level vision tasks at a significantly reduced computation time.
Agriculture plays a significant role in the growth of the national economy. It relay on weather and other environmental aspects. Some of the factors on which agriculture is dependent are Soil, climate, flooding, fertilizers, temperature, precipitation, crops, insecticides and herb. The crop yield is dependent on these factors and hence difficult to predict. To know the status of crop production, in this work we perform descriptive study on agricultural data using various machine learning techniques. Crop yield estimates include estimating crop yields from available historical data such as precipitation data, soil data, and historic crop yields. This prediction will help farmers to predict crop yield before farming. Here we are utilizing three datasets like as clay dataset, precipitation dataset, and production dataset of Karnataka state, then we structure an assembled data sets and on this dataset we employ three different algorithms to get the genuine assessed yield and the precision of three different methods. K-Nearest Neighbor(KNN), Support Vector Machine(SVM), and Decision tree algorithms are applied on the training dataset and are tested with the test dataset, and the implementation of these algorithms is done using python programming and spyder tool. The performance comparison of algorithms is shown using mean absolute error, cross validation and accuracy and it is found that Decision tree is giving accuracy of 99% with very less mean square error(MSE). The proposed model can exhibit the precise expense of assessed crop yield and it is mark like as LOW, MID, and HIGH.
Context
IoT is a rapidly emerging paradigm that now encompasses almost every aspect of our modern life. As such, ensuring the security of IoT devices is crucial. IoT devices can differ from traditional computing (e.g., low power, storage, computing), thereby the design and implementation of proper security measures can be challenging in IoT devices. We observed that IoT developers discuss their security-related challenges in developer forums like Stack Overflow (SO). However, we find that IoT security discussions can also be buried inside non-security discussions in SO.
Objective
In this paper, we aim to understand the challenges IoT developers face while applying security practices and techniques to IoT devices. We have two goals: (1) Develop a model that can automatically find security-related IoT discussions in SO, and (2) Study the model output (i.e., the security discussions) to learn about IoT developer security-related challenges.
Methods
First, we download all 53K posts from StackOverflow (SO) that contain discussions about various IoT devices, tools, and techniques. Second, we manually labeled 5,919 sentences from 53K posts as 1 or 0 (i.e., whether they contain a security aspect or not). Third, we then use this benchmark to investigate a suite of deep learning transformer models. The best performing model is called SecBot. Fourth, we apply SecBot on the entire 53K posts and find around 30K sentences labeled as security. Fifth, we apply topic modeling to the 30K security-related sentences labeled by SecBot. Then we label and categorize the topics. Sixth, we analyze the evolution of the topics in SO.
Results
We found that (1) SecBot is based on the retraining of the deep learning model RoBERTa. SecBot offers the best F1-Score of .935, (2) there are six error categories in misclassified samples by SecBot. SecBot was mostly wrong when the keywords/contexts were ambiguous (e.g., ‘gateway’ can be a security gateway or a simple gateway), (3) there are 9 security topics grouped into three categories: Software, Hardware, and Network, and (4) the highest number of topics belongs to software security, followed by network security and hardware security.
Conclusion
IoT researchers and vendors can use SecBot to collect and analyze security-related discussions from developer discussions in SO. The analysis of nine security-related topics can guide major IoT stakeholders like IoT Security Enthusiasts, Developers, Vendors, Educators, and Researchers in the rapidly emerging IoT ecosystems.
With the rapid improvement of human standards, the development of urban centers has significantly contributed to water contamination and environmental pollution hence compromising the safety of drinking water for public health. The ecological safety and human health have continuously lowered due to hazardous pollution factors like chemicals and pathogens. Different algorithms and blackbox Machine Learning models have been used to develop water quality/safety criteria. This work presents an Explainable Artificial Intelligence method, SHAP (SHapley Additive exPlanations) to transparently and explainably assess the most important metrics that these models use in determining water quality. The simulated results will provide theoretical support to policy makers on how to maintain water quality or safety within urban areas and improve pollution control, water and ecological management
The recent development of wireless communications has prompted many diversified applications in both industrial and medical sectors. Zigbee is a short-range wireless communication standard that is based on IEEE 802.15.4 and is vastly used in both indoor and outdoor applications. Its performance depends on networking parameters, such as baud rates, transmission power, data encryption, hopping, deployment environment, and transmission distances. For optimized network deployment, an extensive performance analysis is necessary. This would facilitate a clear understanding of the trade-offs of the network performance metrics, such as the packet delivery ratio (PDR), power consumption, network life, link quality, latency, and throughput. This work presents an extensive performance analysis of both the encrypted and unencrypted Zigbee with the stated metrics in a real-world testbed, deployed in both indoor and outdoor scenarios. The major contributions of this work include (i) evaluating the most optimized transmission power level of Zigbee, considering packet delivery ratio and network lifetime; (ii) formulating an algorithm to find the network lifetime from the measured current consumption of packet transmission; and (iii) identifying and quantizing the trade-offs of the multi-hop communication and data encryption with latency, transmission range, and throughput.
This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable Artificial Intelligence (XAI) models for data imputations and numerical or categorical hydroclimatic predictions from nonlinearly combined multidimensional predictors. The AI models considered in this paper involve Extreme Gradient Boosting, Light Gradient Boosting, Categorical Boosting, Extremely Randomized Trees, and Random Forest. These AI models can transform into XAI models when they are coupled with the explanatory methods such as the Shapley additive explanations and local interpretable model-agnostic explanations. The review highlights that the IAI models are capable of unveiling the rationale behind the predictions while XAI models are capable of discovering new knowledge and justifying AI-based results, which are critical for enhanced accountability of AI-driven predictions. The review also elaborates the importance of domain knowledge and interventional IAI modeling, potential advantages and disadvantages of hybrid IAI and non-IAI predictive modeling, unequivocal importance of balanced data in categorical decisions, and the choice and performance of IAI versus physics-based modeling. The review concludes with a proposed XAI framework to enhance the interpretability and explainability of AI models for hydroclimatic applications.
Background
: Lack of depth perception from medical imaging systems is one of the long-standing technological limitations of minimally invasive surgeries. The ability to visualize anatomical structures in 3D can improve conventional arthroscopic surgeries, as a full 3D semantic representation of the surgical site can directly improve surgeons’ ability. It also brings the possibility of intraoperative image registration with pre-operative clinical records for the development of semi-autonomous, and fully autonomous platforms.
Methods
: Depth estimation and segmentation processes of feature- and texture-less tissue structures is an extremely challenging task. The lack of accurate ground-truth depth data, non-even lighting conditions with the occurrences of intra-frame over- and under-exposed regions, and occlusions limit the application of stereo vision and monocular supervised learning techniques inside the joint space. On the other hand, unsupervised/self-supervised monocular depth estimation techniques suffer from depth discontinuity, poor depth estimation in texture-less regions, no association with depth scale, and poor depth gradients are common. The provision of fully segmented 3D maps solves the grand visualization challenge of knee arthroscopy, and our method is widely applicable to other forms of minimally invasive surgeries and 3D reconstruction of medical images in general. We apply a novel technique that provides the ability to combine both supervised and self-supervised loss terms, and in doing so eliminates the drawback of each technique. It enables the estimation of edge-preserving depth maps from a single untextured arthroscopic frame. The proposed image acquisition technique projects artificial textures on the surface to improve the quality of disparity maps from stereo images. Moreover, integration of attention-ware multi-scale feature extraction technique along with scene global contextual constraints and multiscale depth fusion, the model able to predict reliable and accurate tissue depth of the surgical sites that complies with scene geometry.
Results
: A total of 4128 stereo frames from a knee phantom were used to train a network, and during the pre-trained stage, the network learns disparity maps from the stereo images. The fine-tuned training phase uses 12,695 knee arthroscopic stereo frames from cadaver experiments along with their corresponding coarse disparity maps obtained from the stereo matching technique. In a supervised fashion, the network learns the left image to the disparity map transformation process, whereas the self-supervised loss term refines the coarse depth map by minimizing reprojection, gradients, and structural dissimilarity loss. Together, our method produces high-quality 3D maps with minimum re-projection loss that are 0.0004132 (structural similarity index), 0.00036120156 (L1 error distance) and 6.591908e-05 (L1 gradient error distance).
Conclusion
: Machine learning techniques for monocular depth prediction is studied to infer accurate depth maps from a single-color arthroscopic video frame. Moreover, the study integrates segmentation model hence, 3D segmented maps were inferred that provides extended perception ability and tissue awareness.
Algal bloom is a significant issue when managing water quality in freshwater; specifically, predicting the concentration of algae is essential to maintaining the safety of the drinking water supply system. The chlorophyll-a (Chl-a) concentration is a commonly used indicator to obtain an estimation of algal concentration. In this study, an XGBoost ensemble machine learning (ML) model was developed from eighteen input variables to predict Chl-a concentration. The composition and pretreatment of input variables to the model are important factors for improving model performance. Explainable artificial intelligence (XAI) is an emerging area of ML modeling that provides a reasonable interpretation of model performance. The effect of input variable selection on model performance was estimated, where the priority of input variable selection was determined using three indices: Shapley value (SHAP), feature importance (FI), and variance inflation factor (VIF). SHAP analysis is an XAI algorithm designed to compute the relative importance of input variables with consistency, providing an interpretable analysis for model prediction. The XGB models simulated with independent variables selected using three indices were evaluated with root mean square error (RMSE), RMSE-observation standard deviation ratio, and Nash-Sutcliffe efficiency. This study shows that the model exhibited the most stable performance when the priority of input variables was determined by SHAP. This implies that on-site monitoring can be designed to collect the selected input variables from the SHAP analysis to reduce the cost of overall water quality analysis. The independent variables were further analyzed using SHAP summary plot, force plot, target plot, and partial dependency plot to provide understandable interpretation on the performance of the XGB model. While XAI is still in the early stages of development, this study successfully demonstrated a good example of XAI application to improve the interpretation of machine learning model performance in predicting water quality.