Cloud services have become an essential infrastructure for enterprises and individuals. Access to these cloud services is typically governed by Identity and Access Management systems, where user authentication often r...
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Artificial intelligence (AI) has expanded its influence across various sectors including education, healthcare, and agriculture. In the agricultural setting, the multiagent system (MAS) is recognized as a powerful too...
Artificial intelligence (AI) has expanded its influence across various sectors including education, healthcare, and agriculture. In the agricultural setting, the multiagent system (MAS) is recognized as a powerful tool for optimizing resource allocation, enhancing decision-making processes, and improving overall farm productivity. Accurate prediction of rice yield levels is paramount importance in the agricultural sector. It allows farmers, policymakers, and stakeholders to make informed decisions regarding crop management. Individual machine-learning algorithms (MLA) have been used to predict rice yield levels, but they may not fully exploit the available information. Therefore, the novel system implemented based on stacking techniques to solve complex problems. The objective of this paper is to present an efficient system that leverages MAS based on a novel proposed stacking technique (Extra Trees Classifier (ETC), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA) and Gaussian Naive Bayes (GNB)) for improving the rice yield level prediction within the agricultural environment. Each algorithm brings its unique approach to handling complex relationships, and modeling class separability. The findings from this study provided valuable insights for decision-making in interconnected sectors and facilitating optimal business planning. The dataset incorporated climate variables such as monthly maximum and minimum temperatures and rainfall. The final result of the dataset consists of 1266 rows and 18 features. The results showed that the novel proposed stacking technique achieved the highest prediction accuracy 87% and the best individual Decision tree classifier obtained 77.5%. The novel proposed stacking technique increases the accuracy by 9.5% compared to the best individual MLA.
In Neural Networks, there are various methods of feature fusion. Different strategies can significantly affect the effectiveness of feature representation, consequently influencing the model’s ability to extract repr...
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Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel at...
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Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.
This work aims to construct exact solutions for the space-time fractional(2+1)-dimensional dispersive longwave(DLW)equation and approximate long water wave equation(ALW)utilizing the twovariable(G′/G,1/G)-expansion m...
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This work aims to construct exact solutions for the space-time fractional(2+1)-dimensional dispersive longwave(DLW)equation and approximate long water wave equation(ALW)utilizing the twovariable(G′/G,1/G)-expansion method and the modified Riemann-Liouville fractional *** recommended equations play a significant role to describe the travel of the shallow water *** fractional complex transform is used to convert fractional differential equations into ordinary differential *** wave solutions have been successfully achieved using the proposed approach and the symbolic computer Maple *** Maple package program was used to set up and validate all of the computations in this *** choosing particular values of the embedded parameters,we pro-duce multiple periodic solutions,periodic wave solutions,single soliton solutions,kink wave solutions,and more forms of soliton *** achieved solutions might be useful to comprehend nonlinear *** is worth noting that the implemented method for solving nonlinear fractional partial dif-ferential equations(NLFPDEs)is efficient,and simple to find further and new-fangled solutions in the arena of mathematical physics and coastal engineering.
Most of the studies on narrow-band near-infrared detection reported so far are related to the 1.3μm and 1.55μm spectral windows. There is insufficient research work done on radiation detection in the narrow band aro...
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作者:
Ning DongMengyue ChenQing YeDan ZhangHongxing DaiKey Laboratory of Beijing on Regional Air Pollution Control
Department of Environmental ScienceSchool of Environmental and Chemical EngineeringFaculty of Environment and LifeBeijing University of TechnologyBeijing 100124China Beijing Key Laboratory for Green Catalysis and Separation
Key Laboratory of Beijing on Regional Air Pollution ControlKey Laboratory of Advanced Functional MaterialsEducation Ministry of Chinaand Laboratory of Catalysis Chemistry and NanoscienceDepartment of Environmental Chemical EngineeringSchool of Environmental and Chemical EngineeringFaculty of Environment and LifeBeijing University of TechnologyBeijing 100124China
The octahedral molecular sieve(OMS-2)-supported Fe( xFe/OMS-2: x = 1, 3, 5, and 10) catalysts were prepared using the pre-incorporation method. Physicochemical properties of the as-synthesized materials were character...
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The octahedral molecular sieve(OMS-2)-supported Fe( xFe/OMS-2: x = 1, 3, 5, and 10) catalysts were prepared using the pre-incorporation method. Physicochemical properties of the as-synthesized materials were characterized by means of various techniques, and their catalytic activities for CO, ethyl acetate, and toluene oxidation were evaluated. Among all of the samples, performed the best, with the reaction temperature required to achieve 90% conversion( T 90%) being 160 ℃ for CO oxidation, 210 ℃ for ethyl acetate oxidation, and 285 ℃ for toluene oxidation. Such a good catalytic performance of 5Fe/OMS-2 was associated with its high(Mn^(3+) + Mn^(2+)) content and adsorbed oxygen species concentration, and good lowtemperature reducibility and lattice oxygen mobility as well as strong interaction between Fe and OMS-2. In addition, catalytic mechanisms of the oxidation of three pollutants over the 5Fe/OMS-2 catalyst were also studied. It was found that CO, ethyl acetate or toluene was first adsorbed, then the related intermediates were formed, and finally the formed intermediates were completely converted into CO_(2) and H_(2)O.
When it comes to maximizing the effectiveness of a business and promoting professional growth, employee performance prediction is an extremely important factor. This research article investigates the use of machine le...
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Existing studies for rumor detection rely heavily on a large number of labeled data to operate in a fully-supervised manner. However, manual data annotation in realistic cases is very expensive and time-consuming. In ...
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Pulmonary Illness has become very common due to different emerging viruses, bacteria, pollution, and lifestyle. If these diseases are not diagnosed in a patient, they may have a severe impact and fatal conditions in a...
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