Changes in weather patterns have an impact on a wide range of industries. Weather prediction that is intelligent and sophisticated is critical for reducing the influence of weather patterns. Agriculture is a big indus...
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In today's quickly developing world, air pollution is a major worry. As pollution rises in the earth's atmosphere every day, so do its rates. The problem is typically caused by toxic fumes released into the ai...
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This paper describes a method for constructing a learned model for estimating disease names using semantic representation learning for medical terms and an interpretable disease-name estimation method based on the mod...
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Vehicular Ad hoc Network (VANET) is a foundation stone for connected vehicles. As vehicles' safety depends heavily on the exchanged data's accuracy, VANET has a low tolerance for false data. The process of int...
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ISBN:
(纸本)9781450396707
Vehicular Ad hoc Network (VANET) is a foundation stone for connected vehicles. As vehicles' safety depends heavily on the exchanged data's accuracy, VANET has a low tolerance for false data. The process of intentionally exchanging inaccurate data is called misbehaving. machinelearning (ML)-based solutions were heavily invested in detecting misbehavior messages. However, they also have some limitations with respect to how much they can detect. To overcome such limitations, we introduce situation awareness (SA) as a powerful concept that can break the limits of the used ML models, leading to more accurate and reliable solutions. Situation awareness uses environmental elements and events to gain a holistic view of the system at any given time. In this paper, we propose using SA to predict the trust of the surrounding cars and consequently reevaluate the outcome of the used ML model. Based on the collected data and SA information, we may reject a message classified as benign by the ML model or vice versa. We used VeReMi dataset to evaluate the proposed approach called SAMM (Situation Awareness with machinelearning for Misbehavior Detection in VANET) on different ML models with a wide range of features. The results show that the proposed approach improves the system's accuracy for various misbehavior attacks by enhancing the recall rate up to 24% and 50% in some cases.
In this paper, we apply Deep Reinforcement learning (DRL) methods to automate the trading of single stock. The A2C, PPO, DDPG, TD3 and SAC deep reinforcement learning models are built and studied comparatively. Shangh...
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With the advancement of machinelearning for classifying and categorizing larger sets of data, there is a high need for greater computational power. Quantum computing in machinelearning advances to solve this in a le...
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Intelligent traffic management solutions that leverage machinelearning have gained a lot of interest in recent years. These techniques, however, cannot be deployed in real-world settings at a desirable pace due to te...
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ISBN:
(纸本)9781450395298
Intelligent traffic management solutions that leverage machinelearning have gained a lot of interest in recent years. These techniques, however, cannot be deployed in real-world settings at a desirable pace due to technological barriers. Thus, easily customizable, realistic simulation environments are needed to train and verify the effectiveness of machinelearning algorithms for traffic control. We propose an easily extendable traffic simulation system named e-SMARTS to allow researchers to experiment with novel data-driven traffic management algorithms in a setup that mimics real-world traffic conditions. We demonstrate the flexibility of e-SMARTS using widely researched traffic management solutions for Autonomous Intersection Management (AIM). In the demonstration, we present several pluggable algorithms for AIM and show that these computationally efficient algorithms can achieve effective and safe results.
Cervical cancer, which is the fourth leading cause of mortality among women, displays no symptoms in its early stages. Cervical cancer is currently diagnosed using only a few approaches using machinelearning techniqu...
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ISBN:
(纸本)9781665411202
Cervical cancer, which is the fourth leading cause of mortality among women, displays no symptoms in its early stages. Cervical cancer is currently diagnosed using only a few approaches using machinelearning techniques. Certain approaches such as PAP Test, HPV Test, Colposcopy and Biopsy require medical staff intervention and cancer is not detected until a certain stage is reached. These procedures are also too costly in developing countries. Detection of Cervical Cancer using machinelearning and Deep learning techniques come into play to solve this issue. A few to name are: CervDetect[1], a hybridized model using a combination of Random Forest and Shallow Neural Networks, ResNet50 – A Convolutional Neural Network’s pre-trained model works effectively on classification of cervical cancer cells using images. This research paper experiments and analyses two Support Vector machine (SVM) techniques as well as K-Nearest Neighbor (KNN), Random Forest(RF), Logistic Regression and Gaussian Naïve Bayes (GNB) algorithms for cervical cancer diagnosis. The dataset used is Cervical cancer (Risk Factors) data Set from UCI Repository[2] . There are 32 risk factors and four target variables in cervical cancer dataset: Citology, Hinselmann, Schiller and Biopsy. The two SVM-based techniques namely SVM Linear and SVM Radial, KNN, RF, Logistic Regression and GNB have diagnosed and categorized all four targets respectively. Following that, a comparison between these six methods is done and inferences are drawn on which algorithm performs better on each of the targets.
Software cost estimation still presents a real challenge to software development firms and practitioners. One of the crucial activities in project management is software cost estimation. The accuracy of the estimated ...
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Ambiguity is a pervasive issue in natural-language requirements. A common source of ambiguity in requirements is when a pronoun is anaphoric. In requirements engineering, anaphoric ambiguity occurs when a pronoun can ...
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ISBN:
(纸本)9781450392211
Ambiguity is a pervasive issue in natural-language requirements. A common source of ambiguity in requirements is when a pronoun is anaphoric. In requirements engineering, anaphoric ambiguity occurs when a pronoun can plausibly refer to different entities and thus be interpreted differently by different readers. In this paper, we develop an accurate and practical automated approach for handling anaphoric ambiguity in requirements, addressing both ambiguity detection and anaphora interpretation. In view of the multiple competing natural language processing (NLP) and machinelearning (ML) technologies that one can utilize, we simultaneously pursue six alternative solutions, empirically assessing each using a collection of approximate to 1,350 industrial requirements. The alternative solution strategies that we consider are natural choices induced by the existing technologies;these choices frequently arise in other automation tasks involving natural-language requirements. A side-by-side empirical examination of these choices helps develop insights about the usefulness of different state-of-the-art NLP and ML technologies for addressing requirements engineering problems. For the ambiguity detection task, we observe that supervised ML outperforms both a large-scale language model, SpanBERT (a variant of BERT), as well as a solution assembled from off-the-shelf NLP coreference resolvers. In contrast, for anaphora interpretation, SpanBERT yields the most accurate solution. In our evaluation, (1) the best solution for anaphoric ambiguity detection has an average precision of approximate to 60% and a recall of 100%, and (2) the best solution for anaphora interpretation (resolution) has an average success rate of approximate to 98%.
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