In this paper, we introduce new discourse quality metrics and an evaluation method, and furthermore, we provide a pilot implementation and evaluate it in a specific use case – that of public speaking. Voice analysis ...
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Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater *** machine learning(ML)approaches have been enhanced to improve streamflow *** techniques have been viewed as a vi...
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Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater *** machine learning(ML)approaches have been enhanced to improve streamflow *** techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone *** researchers have also emphasised using hybrid models to improve forecast ***,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance *** study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML ***’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML *** study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,***,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.
Lung cancer is a dangerous disease that can be fatal, and a correct diagnosis is essential for figuring out the best way to treat it. The optimum treatment for people with lung cancer requires the classification of th...
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Lung cancer is a dangerous disease that can be fatal, and a correct diagnosis is essential for figuring out the best way to treat it. The optimum treatment for people with lung cancer requires the classification of the disease into its histological types, such as adenocarcinoma (ADC), small cell lung cancer (SCLC), and squamous cell carcinoma (SCC). Each histological subtype has its features and may react differently to different types of medicine. So, knowing the exact subtype helps guide treatment choices and improve the patient's outcome. Lung cancer subtypes are necessary for personalized treatment. It helps doctors choose tumor-specific treatments such as surgery, radiation, chemotherapy, targeted drugs, and immunotherapies. Precise categorization improves prognosis, avoids needless medicines, and lets patients participate in clinical studies targeting their cancer subtype. Precision medicine improves lung cancer outcomes with accurate categorization. The current algorithms in this domain have shown deficiencies in performance criteria such as specificity, F-score, sensitivity, and precision in recognition. These limitations may stem from challenges such as the complexity and heterogeneity of histopathological images, variations in staining techniques, and the presence of confounding factors. Deep learning methods have made it easier to look at histopathology slides of cancer and see what's going on. Several studies have shown that convolutional neural networks (CNN) are essential for classifying histopathological pictures of different kinds of cancer, like brain, skin, breast, lung, and colon cancer. This study divides lung cancer images into three groups: normal, adenocarcinoma, and squamous cell carcinoma. We have been training deep learning algorithms to identify lung cancer in histopathology slides better, and utilizing deep learning strategies and cutting-edge algorithms such as VGG-19, ResNet-50 v2, EfficientNetB1, and others indicates a comprehensive ap
A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this *** of all,the initial values of chaotic system are encrypted by RSA algorithm,and...
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A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this *** of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public *** make the chaotic sequence more random,a mathematical model is constructed to improve the random ***,the plain image is compressed and encrypted to obtain the secret ***,the secret image is inserted with numbers zero to extend its size same to the plain *** applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier ***,a meaningful carrier image embedded with secret plain image can be obtained by inverse ***,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic ***,information entropy of the plain image is employed to produce the initial conditions of chaotic *** a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the *** simulations show that the normalized correlation(NC)values between the host image and the cipher image are *** is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.
Deep neural networks (DNNs) possess potent feature learning capability, enabling them to comprehend natural language, which strongly support developing dialogue systems. However, dialogue systems usually perform incor...
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In the digital era, secure communication is vital, especially in the automotive industry with Vehicle-to-Everything (V2X) protocols. As vehicles become more connected, they face security threats, raising concerns abou...
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Breast Carcinoma, generally known as breast cancer, primarily affects women, though men can develop it as well. Because of the existence of breast tissue and exposure to female hormones, notably oestrogen, women are a...
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In this paper, we present an efficient convolutional neural network (CNN)-based model to estimate both elevation and azimuth arrival angles of multiple sources with high resolution (small source angular separation). T...
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In this paper, we introduce EMD-Based Hyperbolic Diffusion Distance (EMD-HDD), a new method for constructing a meaningful distance metric for hierarchical data with latent hierarchical structure. Our method relies on ...
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Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart *** power is one of the most significant resources in *** enhancing a power factor,the clustering tech...
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Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart *** power is one of the most significant resources in *** enhancing a power factor,the clustering techniques are *** the forward of data in WSN,more power is *** the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and *** the existing system,it does not deal with end-to-end delay and deliv-ery of *** overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is *** Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness *** Swarm Optimization(CSO)helps to solve the complex opti-mization ***,it consists of chickens,hens,and *** divides the chicken into *** Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the *** proposed GA-CSO with LBCM improves the life-span of the ***,it minimizes the energy consumption and also bal-ances the load over the *** proposed method outperforms by using the following metrics such as energy efficiency,ratio of packet delivery,throughput of the network,lifetime of the sensor ***,the evaluation result shows the energy efficiency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.
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