The process of choosing and narrowing down universities for graduate program admission plays a crucial role in the overall application procedure. This project aims to examine the uses of machine learning models to pre...
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The process of choosing and narrowing down universities for graduate program admission plays a crucial role in the overall application procedure. This project aims to examine the uses of machine learning models to predict the likelihood of a student being accepted into a master's program. Hence this will provide students with an early indication of their admission prospects. In the past, models were constructed using different algorithms such as random forest, multiple linear regression and k-nearest neighbor. Results have shown that logistic regression out performs these algorithms. The admission of students into educational institutions is a critical issue, and this study addresses the application of machine learning algorithms to predict the chance of admission of students into master's programs. These models include SVM, Gaussian Naive Bayes, and Logistic Regression, with experiments demonstrating that the Logistic Regression model surpasses the others. This will give students a better understanding of their admission prospects in advance.
Fake reviews are a significant challenge for online consumer and social media platforms, as they mislead consumers and disrupt fair market competition. Traditional centralized detection methods face numerous limitatio...
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ISBN:
(数字)9798331522216
ISBN:
(纸本)9798331522223
Fake reviews are a significant challenge for online consumer and social media platforms, as they mislead consumers and disrupt fair market competition. Traditional centralized detection methods face numerous limitations in handling massive data volumes, heterogeneous distributions, and privacy compliance. To address these issues, this paper proposes a data quality-aware federated learning framework for efficiently detecting fake reviews while preserving the privacy of data holders. Specifically, we introduce a data quality evaluation module during the federated learning training process. This module quantifies the quality of each client's data based on multiple metrics, including annotation accuracy, textual completeness, and user behavior. Higher-quality data clients are assigned greater weights during global model aggregation, thereby mitigating the interference of low-quality or noisy data. Experimental results demonstrate that compared to traditional federated averaging and other baseline methods, our approach significantly improves accuracy, precision, and recall. Additionally, it exhibits greater robustness and faster convergence under various non-independent and identically distributed (non-IID) and multi-noise scenarios. Furthermore, differential privacy ensures the security of participants' raw data and quality evaluation information, enhancing the feasibility of real-world deployment.
Recent events resulted in the consolidation of a degree program in Modeling & Simulation engineering with a degree in computerengineering, though with a major in Modeling & Simulation engineering. The resulti...
Recent events resulted in the consolidation of a degree program in Modeling & Simulation engineering with a degree in computerengineering, though with a major in Modeling & Simulation engineering. The resulting major strongly highlights the computational aspects of M&S. However, the needs of discrete event simulation in computerengineering have somewhat of a different focus. For instance, the management of simultaneous events is crucial in digital circuit simulation. This paper looks at refocusing a course on discrete event simulation software design to meet the needs of a computerengineering degree while maintaining applicability to the more general community. It discusses modifications in the treatment of models and then mapping those models to software.
An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect atta...
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An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect attacks using conventional techniques like a virus detection system, misuse detection software i.e. the database of attack signatures that it uses to compare packets. Despite the researchers' significant efforts, IDS still struggles to identify new intrusions, to improve detection accuracy, and to reduce false alarm rates. To overcome the problems mentioned above this paper proposes an unique model named Intrusion Detection System using Machine Learning Analytics (IDSMLA), which uses SMOTE oversampling technique to deal with class imbalance problem, it also uses Minimum Redundancy Maximum Relevance (mRMR) to perform feature selection as feature selection reduces time complexity by eliminating irrelevant features and hence increasing the accuracy of the model and finally to perform classification task, the proposed model IDSMLA uses Extra Trees(ET) bagging ensemble technique. The performance of the proposed model IDSMLA is measured using accuracy and F1-score using 10-folds cross validation. Experimental results have demonstrated that the proposed model IDSMLA greatly outperforms different single-classifier based models, different ensemble models as well as different models present in literature.
Field Programmable Gate Array (FPGA)-based embedded systems have become mainstream in the last decade, often in security-sensitive applications. However, even with an authenticated hardware platform, compromised softw...
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In path-based testing, various test coverage criteria can be used to generate test paths from a system model. The question of the realistic economic effectiveness of these individual criteria deserves further investig...
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In path-based testing, various test coverage criteria can be used to generate test paths from a system model. The question of the realistic economic effectiveness of these individual criteria deserves further investigation, as the answer strongly depends on the presence of defects in a system, topology of the model of a system under test, and other factors. This study presents an open benchmark testbed for measuring the effectiveness of test paths in detecting artificially introduced defects in a tested system. This framework offers a good level of scalability for various experiments in this field. To document its functionality and added value, an example use case comparing the effectiveness of test paths satisfying Edge, Edge-pair, Test dept. Level 3 and Prime Path coverage for the detection of 75 artificial defects is presented.
The primary component in this paper is to investigate the facial emotional states and EEG indicators, especially in pressure, for the duration of the interplay with games. The proposed paper identifies sure precise ex...
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The primary component in this paper is to investigate the facial emotional states and EEG indicators, especially in pressure, for the duration of the interplay with games. The proposed paper identifies sure precise expressions in game enthusiasts whose facial feelings are segmented frames were separated into special areas, then the mind indicators are classified based on their frequencies, ranges are also analysed, and the signal value is *** on facial features are extracted from the localized areas, used fuzzy c-means class, and directed onto an emotion space. Then the EEG sign price is evaluated with the brink fee. After that, the strain data can be ship thru by using a SMS alert by using GSM module and buzzer alerts the use of Arduino micro controller. The cease results are accurate and robust.
Locating a specific mobile application screen from existing repositories is restricted to basic keyword searches, such as Google Image Search, or necessitates a complete query screen image, as in the case of Swire. Ho...
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Investments in quantum technologies, which are based on the effects of quantum mechanics, have escalated in recent years, with big players such as IBM, Google, and Microsoft engaging in the race for quantum supremacy....
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ISBN:
(数字)9798331539498
ISBN:
(纸本)9798331539504
Investments in quantum technologies, which are based on the effects of quantum mechanics, have escalated in recent years, with big players such as IBM, Google, and Microsoft engaging in the race for quantum supremacy. Some potential benefits of this cutting-edge technology include developing new drugs and materials through accurate molecule simulation, establishing more secure and reliable communication channels, and finding faster solutions for complex optimization problems. As quantum technologies evolve, however, the demand for new software, interfaces, and end-to-end systems to properly program and explore the advantages of quantum mechanics becomes paramount. This requires engineers, especially software engineers, and computer scientists, to have multidisciplinary knowledge and skills. Unfortunately, introductory quantum computing and technologies courses are mostly available in physics, and most engineering and computer science courses do not have modules on quantum physics, leading to a talent gap in the current job market. Therefore, this paper proposes a short teaching plan for introducing quantum computing to students with a more technology-based background, such as computer science and engineering students. This plan uses active learning methodologies, such as Challege-Based Teaching, to better engage these students and contextualize the many applications of this new technology. The initial teaching plan consists of a 3-hour seminar introducing the main quantum mechanics concepts and the technology applications, a 3-hour introduction to the math fundamentals and quantum logic gates, and a 3-hour handson workshop using the IBM Qiskit platform. Two versions of this plan were executed in two different moments: (1) a one-day event open to undergraduate students from different institutions and backgrounds, and (2) a 3-day internal training course for computer science and information technology management undergraduate students from our higher education institu
Motivated by the tremendous progress we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as ...
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