The convergence of machine learning and medical data presents an exciting frontier in the realm of healthcare, with the potential to revolutionize the early detection of diseases. In this study, we introduce innovativ...
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(纸本)9789819739363
The convergence of machine learning and medical data presents an exciting frontier in the realm of healthcare, with the potential to revolutionize the early detection of diseases. In this study, we introduce innovative machine learning models designed for the early prediction of three critical ailments: diabetes, heart disease, and liver disorders. To enhance the performance of these models, we rigorously fine-tuned their hyperparameters, a critical aspect of the model development process. Our approach involved the utilization of various classification algorithms, such as logistic regression (LR), extra tree (ET), support vector machine (SVM), Naïve Bayes (NB), decision tree (DT), and random forest (RF). Furthermore, we employed ensemble learning techniques like bagging and boosting, using the aforementioned traditional algorithms as base estimators. All these algorithms underwent extensive hyperparameter tuning to optimize their predictive capabilities. To assess the performance of these models, we conducted a thorough tenfold cross-validation, enabling us to make a comprehensive comparative analysis and identify the most effective models for each dataset. Notably, our efforts bore fruit with exceptional results. For instance, we achieved an impressive accuracy rate of 99.22% in predicting diabetes using the traditional SVM classifier. In the case of the Statlog heart dataset, we reached an accuracy of 85.67% by utilizing the random forest classifier within a bagging ensemble. In predicting liver disorders, we achieved a 73.75% accuracy by employing both boosting random forest and boosting extra tree classifiers. Additionally, we elucidated the reasons behind the variation in results, providing valuable insights. These experimental findings underscore the superiority of our proposed models over existing methods in terms of predictive accuracy. Consequently, our research represents a significant step forward in the early diagnosis and prevention of diseases within t
Current revelations in medical imaging have seen a slew of computer-aided diagnostic(CAD)tools for radiologists *** tumor classification is essential for radiologists to fully support and better interpret magnetic res...
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Current revelations in medical imaging have seen a slew of computer-aided diagnostic(CAD)tools for radiologists *** tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging(MRI).In this work,we reported on new observations based on binary brain tumor categorization using HYBRID ***,the collected image is pre-processed and augmented using the following steps such as rotation,cropping,zooming,CLAHE(Contrast Limited Adaptive Histogram Equalization),and Random Rotation with panoramic stitching(RRPS).Then,a method called particle swarm optimization(PSO)is used to segment tumor regions in an MR *** that,a hybrid CNN-LSTM classifier is applied to classify an image as a tumor or *** this proposed hybrid model,the CNN classifier is used for generating the feature map and the LSTM classifier is used for the classification *** effectiveness of the proposed approach is analyzed based on the different metrics and outcomes compared to different methods.
Destructive wildfires are becoming an annual event,similar to climate change,resulting in catastrophes that wreak havoc on both humans and the *** result,however,is disastrous,causing irreversible damage to the *** loc...
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Destructive wildfires are becoming an annual event,similar to climate change,resulting in catastrophes that wreak havoc on both humans and the *** result,however,is disastrous,causing irreversible damage to the *** location of the incident and the hotspot can sometimes have an impact on earlyfire detection *** the advancement of intelligent sen-sor-based control technologies,the multi-sensor data fusion technique integrates data from multiple sensor *** primary objective to avoid wildfire is to identify the exact location of wildfire occurrence,allowingfire units to respond as soon as *** to predict the occurrence offire in forests,a fast and effective intelligent control system is *** proposed algorithm with decision tree classification determines whetherfire detection parameters are in the acceptable range and further utilizes a fuzzy-based optimization to optimize the complex *** experimental results of the proposed model have a detection rate of ***,providing real-time monitoring of certain environ-mental variables for continuous situational awareness and instant responsiveness.
Using color fundus imaging to identify diabetic retinopathy (DR) is a tough process that requires skilled doctors to comprehend the existence and significance of certain small characteristics. This effort is further c...
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Unmanned aerial vehicles offer services such as military reconnaissance in potentially adversarial controlled *** addition,they have been deployed in civilian critical infrastructure *** this environment,real-time and...
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Unmanned aerial vehicles offer services such as military reconnaissance in potentially adversarial controlled *** addition,they have been deployed in civilian critical infrastructure *** this environment,real-time and massive data is exchanged between the aerial vehicles and the ground control *** on the mission of these aerial vehicles,some of the collected and transmitted data is sensitive and ***,many security protocols have been presented to offer privacy and security ***,majority of these schemes fail to consider attack vectors such as side-channeling,de-synchronization and known secret session temporary information *** last attack can be launched upon adversarial physical capture of these *** addition,some of these protocols deploy computationally intensive asymmetric cryptographic primitives that result in high *** this paper,an authentication protocol based on lightweight quadratic residues and hash functions is *** formal security analysis is executed using the widely deployed random oracle *** addition,informal security analysis is carried out to show its robustness under the Dolev–Yao(DY)and Canetti–Krawczyk(CK)threat *** terms of operational efficiency,it is shown to have relatively lower execution time,communication costs,and incurs the least storage costs among other related ***,the proposed protocol provides a 25%improvement in supported security and privacy features and a 6.52%reduction in storage *** overall,the proposed methodology offers strong security and privacy protection at lower execution time,storage and communication overheads.
Managing Electrical Energy has become crucial nowadays where everything we use works on electrical energy. Utilizing energy in an efficient and effective way can save a lot of energy. Reducing unnecessary usage of ele...
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Aquaculture is being used to a greater extent for conservation goals, such as species recovery, habitat restoration, and mitigating the effects of wild capture on harvested species that are vulnerable. Deep Learning (...
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Network Intrusion Detection System (NIDS) is most momentous safety techniques in mobile networks. Intrusion detection (ID) is significant approach aimed at identifying attacks then applying it on security procedures t...
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A complex endocrine disorder that affects fertile women, polycystic ovary syndrome, also known as PCOS, is characterized by an extensive spectrum of symptoms. This work extends previous efforts in PCOS detection and t...
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Cybercriminals design false webpages in an attempt to trick people into disclosing private information, such as passwords and credit card numbers. Strong cybersecurity measures are desperately needed, as phishing atta...
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