Perovskite solar cells have shown great potential in the field of underwater solar cells due to their excellent optoelectronic properties;however,their underwater performance and stability still hinder their practical...
详细信息
Perovskite solar cells have shown great potential in the field of underwater solar cells due to their excellent optoelectronic properties;however,their underwater performance and stability still hinder their practical *** this research,a 1H,1H,2H,2H-heptadecafluorodecyl acrylate(HFDA)anti-reflection coating(ARC)was introduced as a high-transparent material for encapsulating perovskite solar modules(PSMs).Optical characterization results revealed that HFDA can effectively reduce reflection of light below 800 nm,aiding in the absorption of light within this wavelength range by underwater solar ***,a remarkable efficiency of 14.65%was achieved even at a water depth of 50 ***,the concentration of Pb^(2+)for HFDA-encapsulated film is significantly reduced from 186 to 16.5 ppb after being immersed in water for 347 ***,the encapsulated PSMs still remained above 80%of their initial efficiency after continuous underwater illumination for 400 ***,being exposed to air,the encapsulated PSMs maintained 94%of their original efficiency after 1000 h light *** highly transparent ARC shows great potentials in enhancing the stability of perovskite devices,applicable not only to underwater cells but also extendable to land-based photovoltaic devices.
Lung cancer is considered one of the most dangerous cancers, with a 5-year survival rate, ranking the disease among the top three deadliest cancers globally. Effectively combating lung cancer requires early detection ...
详细信息
Lung cancer is considered one of the most dangerous cancers, with a 5-year survival rate, ranking the disease among the top three deadliest cancers globally. Effectively combating lung cancer requires early detection for timely targeted interventions. However, ensuring early detection poses a major challenge, giving rise to innovative approaches. The emergence of artificial intelligence offers revolutionary solutions for predicting cancer. While marking a significant healthcare shift, the imperative to enhance artificial intelligence models remains a focus, particularly in precision medicine. This study introduces a hybrid deep learning model, incorporating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), designed for lung cancer detection from patients' medical notes. Comparative analysis with the MIMIC IV dataset reveals the model's superiority, achieving an MCC of 96.2% with an Accuracy of 98.1%, and outperforming LSTM and BioBERT with an MCC of 93.5 %, an accuracy of 97.0% and MCC of 95.5 with an accuracy of 98.0% respectively. Another comprehensive comparison was conducted with state-of-the-art results using the Yelp Review Polarity dataset. Remarkably, our model significantly outperforms the compared models, showcasing its superior performance and potential impact in the field. This research signifies a significant stride toward precise and early lung cancer detection, emphasizing the ongoing necessity for Artificial Intelligence model refinement in precision medicine. Authors
The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security...
详细信息
The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.
Breast cancer stands as one of the world’s most perilous and formidable diseases,having recently surpassed lung cancer as the most prevalent cancer *** disease arises when cells in the breast undergo unregulated prol...
详细信息
Breast cancer stands as one of the world’s most perilous and formidable diseases,having recently surpassed lung cancer as the most prevalent cancer *** disease arises when cells in the breast undergo unregulated proliferation,resulting in the formation of a tumor that has the capacity to invade surrounding *** is not confined to a specific gender;both men and women can be diagnosed with breast cancer,although it is more frequently observed in *** detection is pivotal in mitigating its mortality *** key to curbing its mortality lies in early ***,it is crucial to explain the black-box machine learning algorithms in this field to gain the trust of medical professionals and *** this study,we experimented with various machine learning models to predict breast cancer using the Wisconsin Breast Cancer Dataset(WBCD)*** applied Random Forest,XGBoost,Support Vector Machine(SVM),Multi-Layer Perceptron(MLP),and Gradient Boost classifiers,with the Random Forest model outperforming the others.A comparison analysis between the two methods was done after performing hyperparameter tuning on each *** analysis showed that the random forest performs better and yields the highest result with 99.46%*** performance evaluation,two Explainable Artificial Intelligence(XAI)methods,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-Agnostic Explanations(LIME),have been utilized to explain the random forest machine learning model.
Development in Quantum computing paves the path to Quantum key distribution (QKD) by using the principles of quantum physics. QKD enables two remote parties to produce and share secure keys while removing all computin...
详细信息
This research requires to improve the accuracy of early diabetic forecasting in a human body or patient by applying diverse machine learning approaches. Approaching to creation of machine learning models by using pati...
详细信息
Crowd management becomes a global concern due to increased population in urban *** management of pedestrians leads to improved use of public *** of pedestrian’s is a major factor of crowd management in public *** are...
详细信息
Crowd management becomes a global concern due to increased population in urban *** management of pedestrians leads to improved use of public *** of pedestrian’s is a major factor of crowd management in public *** are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the *** this paper,we have proposed a new method for pedestrian’s behavior *** filter has been used to detect pedestrian’s usingmovement based ***,we have performed occlusion detection and removal using region shrinking method to isolate occluded *** verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each *** Wolf Optimizer(GWO)has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient(XG)Boost *** has been evaluated using pedestrian’s data from avenue and UBI-Fight datasets,where both have different *** mean achieved accuracies are 91.3%and 85.14%over the Avenue and UBI-Fight datasets,*** results are more accurate as compared to other existing methods.
Due to the exponential increase in data volume, the widespread use of intelligent information systems has created significant obstacles and issues. High dimensionality and the existence of noisy and extraneous data ar...
详细信息
Due to the exponential increase in data volume, the widespread use of intelligent information systems has created significant obstacles and issues. High dimensionality and the existence of noisy and extraneous data are a few of the difficulties. These difficulties incur high computing costs and have a considerable effect on the accuracy and efficiency of machine learning (ML) methods. A key idea used to increase classification accuracy and lower computational costs is feature selection (FS). Finding the ideal collection of features that can accurately determine class labels by removing unnecessary data is the fundamental goal of FS. However, finding an effective FS strategy is a difficult task that has given rise to a number of algorithms built using biological systems based soft computing approaches. In order to solve the difficulties faced during the FS process;this work provides a novel hybrid optimization approach that combines statistical and soft-computing intelligence. On the first dataset of diabetes disease, the suggested approach was initially tested. The approach was later tested on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset after yielding encouraging results on diabetes dataset. While finding the solution, typically, data cleaning happens at the pre-processing stage. Later on, in a series of trials, different FS methods were used separately and in hybridized fashion, such as fine-tuned statistical methods like lasso (L1 regularization) and chi-square, as well as binary Harmony search algorithm (HSA) which is based on soft computing algorithmic approach. The most efficient strategy was chosen based on the performance metric data. These FS methods pick informative features, which are then used as input for a variety of traditional ML classifiers. The chosen technique is shown along with the determined influential features and associated metric values. The success of the classifiers is then evaluated using performance metrics like accuracy, preci
In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary *** tactical...
详细信息
In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary *** tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and *** in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and *** present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile *** on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each *** analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.
Breast cancer is one of the deadly diseases prevailing in *** detection and diagnosis might prevent the death *** diagnosis of breast cancer remains a significant challenge,and early diagnosis is essential to avoid th...
详细信息
Breast cancer is one of the deadly diseases prevailing in *** detection and diagnosis might prevent the death *** diagnosis of breast cancer remains a significant challenge,and early diagnosis is essential to avoid the most severe manifestations of the *** existing systems have computational complexity and classification accuracy problems over various breast cancer *** order to overcome the above-mentioned issues,this work introduces an efficient classification and segmentation ***,there is a requirement for developing a fully automatic methodology for screening the cancer *** paper develops a fully automated method for breast cancer detection and segmenta-tion utilizing Adaptive Neuro Fuzzy Inference System(ANFIS)classification *** proposed technique comprises preprocessing,feature extraction,classifications,and segmentation ***,the wavelet-based enhancement method has been employed as the preprocessing *** texture and statistical features have been extracted from the enhanced ***,the ANFIS classification algorithm is used to classify the mammogram image into normal,benign,and malignant ***,morphological processing is performed on malignant mam-mogram images to segment cancer *** analysis and comparisons are made with conventional *** experimental result proves that the pro-posed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.
暂无评论