Adversarial attacks in the Artificial Intelligence have received extensive coverage in the latest years. Such attacks have affected deep learning models and neural networks due to the weaknesses that are part of the A...
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Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that wor...
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This paper addresses the accurate and early prediction of the onset and progression of diabetes, an important aspect in the management of this chronic metabolic disease given a variety of information like the history ...
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Mobile devices and social networks provide communication opportunities among the young generation,which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking c...
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Mobile devices and social networks provide communication opportunities among the young generation,which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among *** paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics *** describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying *** use forensics techniques,Machine Learning(ML),and Deep Learning(DL)algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the *** on a real-time dataset reveal better results for the detection of cyberbullying *** Random Forest in ML approach produces 87%of accuracy without SMOTE technique,whereas the value of F1Score produces a good result with SMOTE *** LSTM has 92%of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.
We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supe...
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We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.
The human eye, often considered the most crucial sense organ for perceiving and understanding the world, plays a fundamental role in our ability to see and interpret our surroundings. However, vision can be significan...
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Aspect’s extraction is a critical task in aspect-based sentiment analysis,including explicit and implicit aspects *** extensive research has identified explicit aspects,little effort has been put forward on implicit ...
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Aspect’s extraction is a critical task in aspect-based sentiment analysis,including explicit and implicit aspects *** extensive research has identified explicit aspects,little effort has been put forward on implicit aspects extraction due to the complexity of the ***,existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’dependency ***,in this paper,a multi-level knowledge engineering approach for identifying implicit movie aspects is *** proposed method first identifies explicit aspects using a variant of BiLSTM and CRF(Bidirectional Long Short Memory-Conditional Random Field),which serve as a memory to process dependent sentences to infer implicit *** can identify implicit aspects from four types of sentences,including independent and three types of dependent *** study is evaluated on a largemovie reviews dataset with 50k *** experimental results showed that the explicit aspect identification method achieved 89%F1-score and implicit aspect extraction methods achieved 76%*** addition,the proposed approach also performs better than the state-of-the-art techniques(NMFIAD andML-KB+)on the product review dataset,where it achieved 93%precision,92%recall,and 93%F1-score.
The fusion of computer vision and natural language processing (NLP) has given rise to the interdisciplinary field of automatic image captioning, which aims to generate descriptive text for images without human interve...
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The automated recognition and identification of license plates is an essential element of intelligent transportation systems that enable effective traffic management, security measures, and the development of efficien...
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Internet of things(IoT)has become more popular due to the development and potential of smart technology *** concerns against IoT infrastructure,applications,and devices have grown along with the need for IoT *** syste...
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Internet of things(IoT)has become more popular due to the development and potential of smart technology *** concerns against IoT infrastructure,applications,and devices have grown along with the need for IoT *** system security protocols are difficult due to the diverse capabilities of IoT devices and the dynamic,ever-changing environment,and simply applying basic security requirements is ***,this proposed work designs a malware detection and prevention approach for secure data transmission among IoT *** malware detection approach is designed with the aid of a deep learning *** initial process is identifying attack nodes from normal nodes through a trust value using contextual *** discovering attack nodes,these are considered for predicting different kinds of attacks present in the network,while some preprocessing and feature extraction strategies are applied for effective *** Deep LSTM classifier is applied for this malware detection *** completed malware detection,prevention is performed with the help of the Improved Elliptic Curve Cryptography(IECC)algorithm.A hybrid MA-BW optimization is adopted for selecting the optimal key during *** 3.8 software is used to test the performance of the proposed approach,and several existing techniques are considered to evaluate its *** proposed approach obtained 95%of accuracy,5%of error value and 92%of *** addition,the improved ECC algorithm is also compared with some existing algorithm which takes 6.02 s of execution *** to the other methods,the proposed approach provides better security to IoT gadgets during data transmission.
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