Significance testing aims to determine whether a proposition about the population distribution is the truth or not given observations. However, traditional significance testing often needs to derive the distribution o...
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Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the blac...
Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA.
Today various water-pump operating systems are available in the market, which allows the user/farmer to operate his water-pump from a distant location using mobile app or on a phone call to the system which is integra...
Today various water-pump operating systems are available in the market, which allows the user/farmer to operate his water-pump from a distant location using mobile app or on a phone call to the system which is integrated with the water-pump. While studying the existing system we came across a problem that, existing systems does not provide any solution for optimizing the usage of water and power. Optimizing the use of power and water in farming has become a real-time problem these days. To overcome this problem, we have proposed an IOT based system "Advanced Irrigation System". This system provides automation for operating the water pump in the farm by automatically turning ON and OFF the water-pump depending on the type of the crop and its moisture requirements. Also, proposed system provides farmer interface via a mobile application in his regional language, where he can check the moisture, humidity and temperature of his farm. Along with this proposed system has a Google Assistant integrated, using which famer can operate the water-pump remotely over a voice command.
Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder....
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ISBN:
(数字)9798331540012
ISBN:
(纸本)9798331540029
Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-datadataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection.
The performance of most causal effect estimators relies on accurate predictions of high-dimensional non-linear functions of the observed data. The remarkable flexibility of modern Machine Learning (ML) methods is perf...
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Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and dee...
Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& amp; P 500 dataset reveal that it outperforms traditional stock prediction approaches.
An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three altern...
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Advanced Multimedia Technology had provided a new lease of life to current videos and made life amazing in the field of entertainment. On the other hand, special lightening effects, rapid object-camera motions and sce...
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In the rapidly evolving field of natural language processing (NLP), enhancing model performance in understanding and generating human language has become increasingly critical. As the need for better language models i...
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ISBN:
(数字)9798350376425
ISBN:
(纸本)9798350376432
In the rapidly evolving field of natural language processing (NLP), enhancing model performance in understanding and generating human language has become increasingly critical. As the need for better language models increases, enhancing the current systems’ shortcomings becomes crucial. There are difficulties in existing models precisely due to the fundamental problem of striking a balance between the level of detail achievable and the amount of computational resources that a model requires for accurate estimation in practical settings. Current models, present several problems including but not limited to overfitting, average performance on out-of-sample data and less applicability for subtle linguistic features. Such obstacles limit their capability to execute in real conditions, where high degree of accuracy and precision is paramount. To overcome these challenges, this study introduces a novel framework that employs the BERT model which was developed to capture bidirectional context and achieve greater depth of the semantic relations between words. The proposed approach is designed to improve the model’s effectiveness to a considerable extent through the use of mechanisms such as attention as well as transformer architecture included in BERT. The proposed BERT model demonstrates exceptional results, achieving an accuracy of 99.10% and a precision of 98.30%. These metrics reflect the model's superior ability to understand and generate accurate language representations, surpassing existing models in both effectiveness and efficiency. This advancement underscores the potential of BERT to address critical challenges in NLP, offering a promising solution for applications requiring high precision and robust language comprehension.
Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3DMR lies in performing sequential and ...
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