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The Evaluation of Classroom Teaching Quality of College Business English Translation Based on AI and Central Tendency Adaptive Enhancement

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Journal of Combinatorial Mathematics and Combinatorial Computing 2024年 119卷 53-62页

作者： Xiao, Ting Li, Qiong School of Foreign Languages Xiangtan Institute of Technology Xiangtan411100 China Faculty of Data Science City University of Macao 999078 China Department of Science and Technology Hunan Industry Polytechnic Changsha410208 China

Student-centeredness is a teaching theory proposed by British and American scholars in linguistics, psycholinguistics, applied linguistics, and second language acquisition theory."The student-centered approach is different from the traditional teacher-centered approach, but it is implemented in a teacher-led environment. In this study, word2vec, paragraph2vec, pos2vec and LDA (latent dirichlet allocation) are combined to form a semantic representation vector for college business English translation. The key point of the college business English translation reform is to update the concept and theoretical understanding, so as to improve the teachers’ business English teaching theory and teaching practice, and to do a good job of college business English translation reform. Finally, it is shown that the proposed intelligent evaluation framework is more accurate than the traditional method in terms of automatic grading and rubric generation for college business English translation. © 2024 the Author(s), licensee Combinatorial Press.

关键词： Students

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Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis

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Water Resources Management 2025年 1-17页

作者： Hawamdeh, Alaa Tarawneh, Anwar Sharrab, Yousef Al-Fraihat, Dimah School of Science University of Jordan Amman Jordan Faculty of Engineering Al-Zaytoonah University of Jordan Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Isra University Amman11622 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan

Natural disasters can have devastating effects on the environment and natural resources, making flood inundation mapping and hydraulic modeling essential for forecasting critical characteristics like flood depth and water surface elevation. This study investigated key flood-influencing factors including rainstorm intensity, precipitation depth, soil type, geologic setting, and topographic features, while conducting hydraulic modeling of storm flows for 50- and 100-Year return periods, which estimated water depths in Wadi Al Wala reaching 15 m during 50-Year storms and 25 m during 100-Year storms. To enhance prediction capabilities, a Deep Neural Network (DNN) model was developed using 1980–2018 meteorological data, featuring three hidden layers (10, 20, and 30 neurons) with ReLU activation, trained on 80% of data and validated on 20% using Python’s Keras library. Using relative humidity, rainfall, wind speed, and wet bulb temperature as inputs, the model achieved 92% prediction accuracy based on MSE metrics. The integration of HEC-HMS hydrological modeling, HEC-RAS hydraulic modeling, and DNN analysis produced effective flood hazard predictions that support strategic infrastructure planning and improved disaster management in vulnerable regions. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Deep neural networks

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DAC: Discriminative Associative Classification

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SN Computer science 2023年第4期4卷 401页

作者： Seyfi, Majid Xu, Yue Nayak, Richi Data Science Discipline Science and Engineering Faculty Queensland University of Technology Brisbane QLD Australia

In this paper, discriminative associative classification is proposed as a new classification technique based on class discriminative association rules (CDARs). These rules are defined based on discriminative itemsets. The discriminative itemset is frequent in one data class and has much higher frequencies compared with the same itemset in other data classes. The CDAR is a class associative rule (CAR) in one data class that has higher support compared with the same rule in other data classes. Compared to associative classification, there are additional challenges as the Apriori property of the subset is not applicable. The proposed algorithm is designed particularly based on well-defined distinguishing characteristics of the rules, to improve the accuracy and efficiency of the classification in data classes. A novel compact prefix-tree structure is defined for holding the rules in data classes. The empirical analysis shows the effectiveness and efficiency of the proposed method on small and large real datasets. © 2023, The Author(s).

关键词： Class discriminative association rules data mining Discriminative associative classification Discriminative itemsets

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Mining Discriminative Itemsets Over data Streams Using Efficient Sliding Window

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SN Computer science 2023年第5期4卷 489页

作者： Seyfi, Majid Nayak, Richi Xu, Yue Data Science Discipline Science and Engineering Faculty Queensland University of Technology Brisbane QLD Australia

In this paper, we present an efficient novel method for mining discriminative itemsets over data streams using the sliding window model. Discriminative itemsets are the itemsets that are frequent in the target data stream, and their frequency in the target stream is much higher in comparison to their frequency in the rest of the streams. The problem of mining discriminative itemsets has more challenges than mining frequent itemsets, especially in the sliding window model, as during the window frame sliding, the algorithms have to deal with the combinatorial explosion of itemsets in more than one data stream, for the transactions coming in and going out of the sliding window. We propose a single scan algorithm using two novel in-memory data structures for mining discriminative itemsets in a combination of offline and online sliding windows. Offline processing is used for controlling the generation of many unpromising itemsets. Online processing is used for getting more up-to-date and accurate online answers between two offline slidings. The discovered discriminative itemsets are accurately updated in the offline sliding window periodically, and the mining process is continued in the online sliding between two periodic offline slidings. The extensive empirical analysis shows that the proposed algorithm provides efficient time and space complexities with full accuracy. The algorithm can handle large, fast-speed, and complex data streams. © 2023, The Author(s).

关键词： data stream mining Discriminative itemsets Prefix tree Sliding window model

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AI-Enhanced Imaging Techniques for Understanding Alzheimer's Progression 2

AI-Enhanced Imaging Techniques for Understanding Alzheimer's...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Chaudhari, Aarti Saratkar, Saniya Thute, Trupti Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Alzheimer's disease (AD) is a fatal neuronal disorder which occurs mainly in the elderly and causes a gradual dementia with or without memory loss, functional disability and death. AD is the most prevalent form of dementia, and the condition is positively affiliated with amyloid-beta plaques, tau protein tangles, neuronal ischemic losses, and brain atrophy. Even after studying for a long, the cause(s) of AD has not been well established, though it is well understood that age, genetics (specifically APOE ?4 gene), and lifestyle are some of the leading causes. At the moment there is no cure and management involves the alleviation of symptoms and the prevention of disease progression. The key for better patient management is the early-stage triage as this would increase the chance of getting proper care, fulfilling criteria for clinical trials, and receive better therapeutic management. For a long time now, an early diagnosis has been emphasized due to the improved prognosis, but little has been done in that area instead a lot of efforts are directed at treatment. MRI, PET, and fMRI consisting of imaging approaches that enables the identification of brain changes and progression of the disease. Healthcare & medicine;Artificial Intelligence (AI) is changing the way Alzheimer's is diagnosed and treated. In fact, classifiers such as Convolutional Neural Networks, Recurrent Neural Networks, and Transformer models can investigate imaging data and distinguish the features of early stage, progression, and survive rate, so as to assist with medical treatment planning. The general strength of AI to analyses and manage large amounts of information provides new chances for enhancing clinical practice and investigation in Alzheimer's disease. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Analysis of Factors influencing Early Detection of Breast Cancer 3

Analysis of Factors influencing Early Detection of Breast Ca...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Malik, Hassal Sonia Sial, Umaira Ahmad Bukhari, Faisal Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (纸本)9798331504861

Carcinoma, particularly breast cancer, poses a formidable challenge, especially in Pakistan where it stands as the most prevalent cancer among women. This research investigates the complex landscape of breast cancer in Pakistan, utilizing a dataset from local hospitals. Through comprehensive statistical analyses and advanced machine learning techniques, including XGBoost and RandomForest, the study uncovers significant associations between socio-economic and psychological factors and the timing of breast cancer diagnosis among Pakistani women. Factors such as embarrassment, fear of treatment, and lack of awareness emerge as crucial determinants. The findings provide valuable insights for tailored interventions and contribute to the global fight against breast cancer. ©2024 IEEE.

关键词： Lung cancer

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Recommendation Model for Learning Material Using the Felder Silverman Learning Style Approach

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第4期4卷 811-820页

作者： Hasibuan, M.S. Abdul Aziz, R.Z. Dewi, Deshinta Arrova Kurniawan, Tri Basuki Syafira, Nasywa Aliyah Faculty Computer Science Institute Informatics and Business Darmajaya Bandar Lampung35136 Indonesia Faculty of Data Science and Information Technology INTI International University Nilai Malaysia Faculty of Technology and Information Science University Kebangsaan Malaysia Malaysia Faculty of Education Yogyakarta State University Yogyakarta Indonesia

The biggest obstacle that students have when participating in a virtual learning environment (e-learning) is discovering a platform that has functionalities that can be customized to fit their needs. This is usually accomplished in several ways using educational resources such as learning materials and virtual classroom design elements. Our research has tried to meet this demand by suggesting an extra element in the virtual classroom design, i.e., classifying the students’ learning styles through machine-learning techniques based on information gathered from questionnaires. This feature allows teachers or instructors to modify their lesson plans to better suit the learning preferences of their students. Additionally, this feature aids in the creation of a learning path that serves as a guide for students as they choose their course materials. In this study, we have selected the Felder-Silverman Learning Style Model (FSLSM) in the questionnaire design, which focuses on identifying the students' learning styles. After that, we employ several machine learning algorithms to create a prediction model for the students’ learning styles. The algorithms include Decision Tree, Support Vector Machines, K-Nearest Neighbors, Naïve Bayes, Linear Discriminant Analysis, Random Forest, and Logistic Regression. The best prediction model from this exercise contributes to the recommendation model that was created using a collaborative filtering algorithm. We have carried out a pre-test and post-test method to evaluate our suggestions. There were 138 learners who were following a learning path and participated in this study. The findings of the pretest and post-test indicated a notable increase in students' motivation to study. This is confirmed by the fact that learners' satisfaction with online learning climbed to 87% when the learning style was considered, from 60% when it wasn't. © 2023, Ital Publication. All rights reserved.

关键词： Students

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Efficient Multi-Object Tracking Techniques using DAC Feature for Video Processing Applications 21

Efficient Multi-Object Tracking Techniques using DAC Feature...

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21st International Conference on ICT and Knowledge Engineering, ICT and KE 2023

作者： Boonchan, Wannadit Jongsawat, Nipat Tungkasthan, Anucha Data and Information Science Faculty of Science and Technology RMUTT Pathum Thani Thailand

ISBN: (纸本)9798350308716

Object Tracking (OT) is still a challenging area of research, especially Multi-Object Tracking Accuracy (MOTA) in complex scenes. In the past, most popular methods used the global bounding box feature to represent an object because these methods calculate very quickly. However, it is inefficient of accuracy when applied to multi-object tracking processes where objects in a scene have similar box characteristics, such as cars and people. Therefore, this paper presents a technique for increasing the efficiency of tracking multiple objects in video files by adding global low-level visual content to the global bounding box feature. The experimental results show that the proposed method can increase tracking accuracy and maintain reasonable processing performance. © 2023 IEEE.

关键词： Video signal processing

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Uncertainty propagation in the internet of things

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Discover Internet of Things 2024年第1期4卷 1-20页

作者： Pal, Shantanu Khalifa, Sara Miller, Dimity Dedeoglu, Volkan Dorri, Ali Ramachandran, Gowri Moghadam, Peyman Kusy, Brano Jurdak, Raja The School of Information Technology Faculty of Science Engineering and Built Environment Deakin University MelbourneVIC 3125 Australia The School of Information Systems Faculty of Science Queensland University of Technology BrisbaneQLD4000 Australia The School of Electrical Engineering and Robotics Faculty of Engineering Queensland University of Technology BrisbaneQLD4000 Australia The Distributed Sensing Systems Group Data61 CSIRO PullenvaleQLD 4069 Australia The School of Computer Science Faculty of Science Queensland University of Technology BrisbaneQLD 4000 Australia The CSIRO Robotics Data61 CSIRO PullenvaleQLD 4069 Australia

The Internet of Things (IoT) detects context through sensors capturing data from dynamic physical environments, in order to inform automation decisions within cyber physical systems (CPS). Diverse types of uncertainty in the IoT pipeline can propagate within and across nodes, involving complex interactions with security, privacy, and trust that remain largely unexplored. This paper conducts an in-depth analysis of the types of uncertainties in IoT and how they propagate within IoT nodes and networks. We consider adversarial uncertainty in the context of distributed IoT networks to capture perturbations due to malicious actors in the network that can influence IoT security, privacy and trust. We examine the propagation of adversarial uncertainty and well-known uncertainty types, namely aleatoric and epistemic uncertainty, within the five distinct stages of sensing, communication, storage, processing and decision-making in an IoT pipeline, across network layer boundaries, and across nodes within an IoT network. Using this mapping, we analyse the interactions between the uncertainty types and their propagation, and security, privacy, and trust in IoT. Based on this analysis, we discuss guidelines and considerations for mitigating uncertainty in IoT through a smart grid use case study. © The Author(s) 2024.

关键词： Differential privacy

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An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

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Computers, Materials & Continua 2024年第4期79卷 243-262页

作者： Yasmine M.Ibrahim Reem Essameldin Saad M.Darwish Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt Faculty of Computers and Information Technology Egyptian E-Learning University(EELU)Giza12611Egypt Faculty of Computers and Data Science Alexandria UniversityAlexandria21554Egypt

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm(WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs)for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLPis dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.

关键词： Hate speech detection whale optimization neutrosophic sets social media forensics

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