This work introduces novel architecture components and training procedures to create augmented neural networks with the ability to process data bidirectionally via an end-to-end approximate inverse. We develop pseudoi...
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Handwritten documents generated in our day-to-day office work, class room and other sectors of society carry vital information. Automatic processing of these documents is a pipeline of many challenging steps. The very...
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Video fingerprinting has come out to be one of the crucial technologies for multimedia content recognition, copyright protection and management. This review puts flashlight on the recent advancements on various video ...
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The Internet of Things (IoT) has revolutionized data transfer in industries through the MQTT protocol. MQTT is a lightweight messaging protocol designed for efficient communication among IoT devices. It operates on a ...
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The Network Intrusion Detection System (NIDS), a cornerstone in network security, relies heavily on public datasets. These datasets, on the other hand, suffer from imbalanced classes, with more samples of regular netw...
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Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict softwa...
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Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.
Although a software developer is capable of creating excellent software without the necessity for competitive coding. Having their hands in it may increase the project's efficiency and scalability. For every devel...
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Personalized recommender systems are becoming more popular to reduce the issue of information overload. It is also observed that the recommendations provided by multi-criteria recommender system (MCRS) are more accura...
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Cardiovascular conditions (CVDs) remain a major global health concern, challenging early threat assessment and forestalment. In this study, we employ four distinct machine learning algorithms - K Nearest Neighbors (KN...
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Video stabilization is an important aspect of digital video processing, which addresses the challenge of reducing unwanted motion-induced artifacts in video recordings This research paper presents video provides a det...
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