Suicide is a significant public health issue that devastates individuals and society. Early warning systems are crucial in preventing suicide. The purpose of this research is to create a deep learning model to identif...
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Software trustworthiness is an essential criterion for evaluating software quality. In componentbased software, different components play different roles and different users give different grades of trustworthiness af...
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Software trustworthiness is an essential criterion for evaluating software quality. In componentbased software, different components play different roles and different users give different grades of trustworthiness after using the software. The two elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.
In recent years,it has been evident that internet is the most effective means of transmitting information in the form of documents,photographs,or videos around the *** purpose of an image compression method is to enco...
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In recent years,it has been evident that internet is the most effective means of transmitting information in the form of documents,photographs,or videos around the *** purpose of an image compression method is to encode a picture with fewer bits while retaining the decompressed image’s visual *** transmission,this massive data necessitates a lot of channel *** order to overcome this problem,an effective visual compression approach is required to resize this large amount of *** work is based on lossy image compression and is offered for static color *** quantization procedure determines the compressed data quality *** images are converted from RGB to International Commission on Illumination CIE La^(∗)b^(∗);and YCbCr color spaces before being *** the transform domain,the color planes are encoded using the proposed quantization *** improve the efficiency and quality of the compressed image,the standard quantization matrix is updated with the respective image *** used seven discrete orthogonal transforms,including five variations of the Complex Hadamard Transform,Discrete Fourier Transform and Discrete Cosine Transform,as well as thresholding,quantization,de-quantization and inverse discrete orthogonal transforms with CIE La^(∗)b^(∗);and YCbCr to RGB *** to signal noise ratio,signal to noise ratio,picture similarity index and compression ratio are all used to assess the quality of compressed *** the relevant transforms,the image size and bits per pixel are also *** the(n,n)block of transform,adaptive scanning is used to acquire the best feasible compression *** of these characteristics,multimedia systems and services have a wide range of possible applications.
The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can qu...
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The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.
The combination of machine learning(ML)approaches in healthcare is a massive advantage designed at curing illness of millions of *** efforts are used by researchers for detecting and providing primary phase insights a...
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The combination of machine learning(ML)approaches in healthcare is a massive advantage designed at curing illness of millions of *** efforts are used by researchers for detecting and providing primary phase insights as to cancer *** cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the *** disease is the unrestrained progress of irregular cells which begin off in one or both *** previous detection of cancer is not simpler procedure however if it can be detected,it can be curable,also finding the survival rate is a major challenging *** study develops an Ant lion Optimization(ALO)with Deep Belief Network(DBN)for Lung Cancer Detection and Classification with survival rate *** proposed model aims to identify and classify the presence of lung ***,the proposed model undergoes min-max data normalization approach to preprocess the input ***,the ALO algorithm gets executed to choose an optimal subset of *** addition,the DBN model receives the chosen features and performs lung cancer ***,the optimizer is utilized for hyperparameter optimization of the DBN *** order to report the enhanced performance of the proposed model,a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.
Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoret...
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Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route *** proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.
Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission *** is depicted as significant research challenges that pose during the design of network protocol...
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Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission *** is depicted as significant research challenges that pose during the design of network protocol in wireless *** on the extensive review,it is noted that the substantial link percentage is symmetric,i.e.,many links are *** is identified that the synchronous acknowledgement reliability is higher than the asynchronous ***,the process of establishing bidirectional link quality through asynchronous beacons underrates the link reliability of asym-metric *** paves the way to exploit an investigation on asymmetric links to enhance network functions through link ***,a novel Learning-based Dynamic Tree routing(LDTR)model is proposed to improve network performance and *** the evaluation of delay measures,asymmetric link,interference,probability of transmission failure is *** proportion of energy consumed is used for monitoring energy conditions based on the total energy *** learning model is a productive way for resolving the routing issues over the network model during *** asymmetric path is chosen to achieve exploitation and exploration *** learning-based Dynamic Tree routing model is utilized to resolve the multi-objective routing ***,the simulation is done with MATLAB 2020a simulation environment and path with energy-efficiency and lesser E2E delay is evaluated and compared with existing approaches like the Dyna-Q-network model(DQN),asymmetric MAC model(AMAC),and cooperative asymmetric MAC model(CAMAC)*** simulation outcomes demonstrate that the anticipated LDTR model attains superior network performance compared to *** average energy consump-tion is 250 J,packet energy consumption is 6.5 J,PRR is 50 bits/sec,95%PDR,average delay percentage is 20%.
Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts researchers for finding the accurate tertiary structure of the protein...
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Unmanned Combat Aerial Vehicles (UCAVs) are becoming a critical part of the military to automate complex missions with minimum risk and increased efficiency. Path planning is a necessary routine for UCAVs to guide the...
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Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compressio...
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Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.
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