Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)*** left untreated,these diseas...
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Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)*** left untreated,these diseases can have severe consequences and spread,especially among *** detection is crucial to prevent their spread and improve a patient’s chances of ***,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and *** study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the *** method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and *** proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,*** illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.
Diagnosing autism spectrum disorder (ASD) conventionally demands significant time and resources. Language deficits are key markers of ASD, particularly in constructing narratives. This study leverages computational mo...
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In this modern era, the use of vehicles is increasing tremendously due to the increase in the population. The escalating issue of traffic congestion has emerged as a critical challenge in the real world. We currently ...
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High precision and reliable wind speed forecasting have become a challenge for *** events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb daily *** accurate pr...
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High precision and reliable wind speed forecasting have become a challenge for *** events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb daily *** accurate prediction of wind speed and overcoming its uncertainty of change,several prediction approaches have been presented over the last few *** wind speed series have higher volatility and nonlinearity,it is urgent to present cutting-edge artificial intelligence(AI)*** this aspect,this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning(IWSP-CSODL)*** presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter *** the presented IWSP-CSODL model,the prediction process is performed via a convolutional neural network(CNN)based long short-term memory with autoencoder(CBLSTMAE)*** optimally modify the hyperparameters related to the CBLSTMAE model,the chicken swarm optimization(CSO)algorithm is utilized and thereby reduces the mean square error(MSE).The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct *** comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.
In order to increase the effectiveness and precision of cyber threat detection in computer networks, deep learning techniques are being applied in the construction of intelligent intrusion detection systems (IDS). The...
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This research employs Convolutional Neural Networks (CNNs) to distinguish between ripe and spoiled fruits using deep learning. The CNN model achieves an impressive accuracy rate of 98.79% in accurately categorizing th...
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It is imperative to take great measures of nutrition facts to stay away from different illnesses. The Standard admission of various nutrition facts are basic for keeping the correct equilibrium of nutrition content in...
To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints,we propose a Loop Subgraph-Level Greedy Mapping(LSLGM)algorithm using parallelism and processing element *** the...
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To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints,we propose a Loop Subgraph-Level Greedy Mapping(LSLGM)algorithm using parallelism and processing element *** the constraint of a reconfigurable array,the LSLGM algorithm schedules node from a ready queue to the current reconfigurable cell array *** mapping a node,its successor’s indegree value will be dynamically *** its successor’s indegree is zero,it will be directly scheduled to the ready queue;otherwise,the predecessor must be dynamically *** the predecessor cannot be mapped,it will be scheduled to a blocking *** dynamically adjust the ready node scheduling order,the scheduling function is constructed by exploiting factors,such as node number,node level,and node *** with the loop subgraph-level mapping algorithm,experimental results show that the total cycles of the LSLGM algorithm decreases by an average of 33.0%(PEA44)and 33.9%(PEA_(7×7)).Compared with the epimorphism map algorithm,the total cycles of the LSLGM algorithm decrease by an average of 38.1%(PEA_(4×4))and 39.0%(PEA_(7×7)).The feasibility of LSLGM is verified.
The size of the global blockchain market in food supply chains and agricultural activities was expected to reach USD 133 million in 2020. The growing need among stakeholders to track and trace the finished goods and r...
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In the new era of automation, Robotic Process Automation (RPA) has emerged as a powerful suite of tools for automating mundane, repetitive, rule-based, and structured tasks using software bots without disrupting exist...
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