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Potential Impacts of Online-Based Learning 2.0 and Certification on Employability 1

3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Saini, Dilip Kumar Jang Bahadur Singh, Sumeshwar Negi, Neerja Bhandari, Sonam Kumar, Raj Joshi, Kapil Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India School of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of School of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun Dehradun India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Rapid advancement in technology innovation has seen an expansion in online-based learning programs as a major aspect of numerous organizations’ course offerings around the globe. Online learning in colleges has gotten more interactive, adaptable, collaborative, and participative. It has gained a fundamental part to play later on as a classroom teaching tool and a self-study platform for skill improvement. The reason for this examination is to consider the likely effects of online-based learning and accreditation regarding employability. The term employability implies a set of abilities, understanding, and individual qualities that make a graduate bound to pick up work and prevail in their chosen occupation. This paper has especially investigated the issues of online certification real availability, their impacts on employability, and their overall points of interest and weaknesses. Recommendations for further work and refinements of the pedagogical implementation that builds on these findings are presented in the final stages of this research paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： E-learning

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Adaptive iterated local search algorithm for dynamic patient admission scheduling problems

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Soft Computing 2025年第7期29卷 1-11页

作者： Turky, Ayad Sabar, Nasser R. Song, Andy Hussain, Abir Liatsis, Panos Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computing Technologies RMIT University Melbourne Australia Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the patient admission scheduling (PAS) problem, a complex aspect of healthcare resource management. PAS aims to allocate patients to hospital beds within a planning horizon, subject to a variety of healthcare constraints. The goal is to maximize management efficiency and patient comfort to improve medical treatment. In this work, we consider a practical variant of PAS known as dynamic PAS (DPAS). DPAS considers several factors and constraints, such as the daily registration of new patients, urgent patients, uncertainties in stay lengths, operating theatre resources, and the surgery scheduling process. An effective and efficient adaptive iterated local search (AILS) algorithm is proposed to solve DPAS. To enable the search to explore the search space efficiently, the proposed AILS adaptively integrates a number of components. Two adaptive perturbation strategies are devised to locate unexplored areas in the search space. To exploit the newly discovered areas effectively, we propose an adaptive local search mechanism as an intensification strategy to find a high-quality solution. The proposed AILS algorithm is compared to benchmark problems used by existing algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Specifically, out of 30 tested instances, AILS obtains 17 of the best-known results using less computational time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Resource allocation

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Enhanced Classification of Intel Images Using Refined EfficientNet and MobileNetV2 Frameworks 4

Enhanced Classification of Intel Images Using Refined Effici...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Jain, Raghav Sharma, Rishabh Tiwari, Devesh Joshi, Kireet Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttrakhand Dehradun248002 India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering U.P Greater Noida India

ISBN: (纸本)9798350349900

The proposed study describes the functioning of two modern deep learning models, namely EfficientNet and MobileNetV2, within the scope of image classification from a wide range of datasets. This study is designed to categorize images into six groups, namely: buildings, mountains, forests, sea/ocean, glaciers, and streets. The study utilizes a dataset from the Kaggle repository to assess the effectiveness of these models. The research methodology will include data preprocessing, modeling, and model evaluation. This paper will find out the balance between accuracy and efficiency which is provided by each model. EfficientNet is compared with MobileNetV2 because of its scalable structure and compromise between model depth, width, and resolution versus the latter which is known for its low weight and efficiency, especially in constrained circumstances. The research utilizes a framework of accuracy, precision, recall, F1-score metrics, and model efficiency analyses for comprehensive result evaluation and scrutiny. The analysis of this study indicates the unique advantages of separate models, for instance, EfficientNet is more accurate in most cases and MobileNetV2 is a good solution when you have to save computer resources. Comparing models highlights the importance of the selection that is made based on the specific application instead of performance metrics or computational demands. © 2024 IEEE.

关键词： Deep Learning EfficientNet Intel MobileNetV2

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Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 333-353页

作者： Rao, Bosen Zhang, Jiale Wu, Di Zhu, Chengcheng Sun, Xiaobing Chen, Bing Yangzhou University School of Information Engineering Yangzhou225127 China University of Southern Queensland School of Mathematics Physics and Computing ToowoombaQLD4350 Australia Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

The emergence of new machine learning methods has led to their widespread application across various domains, significantly advancing the field of artificial intelligence. However, the process of training and inferring machine learning models relies on vast amounts of data, which often include sensitive private information. Consequently, the privacy and security of machine learning have encountered significant challenges. Several studies have demonstrated the vulnerability of machine learning to privacy inference attacks, but they often focus on specific scenarios, leaving a gap in understanding the broader picture. We provide a comprehensive review of privacy attacks in machine learning, focusing on two scenarios: centralized learning and federated learning. This article begins by presenting the architectures of both centralized learning and federated learning, along with their respective application scenarios. It then conducts a comprehensive review and categorization of related inference attacks, providing a detailed analysis of the different stages involved in these attacks. Moreover, the article thoroughly describes and compares the existing defense methods. Finally, the article concludes by highlighting open questions and potential future research directions, aiming to contribute to the ongoing competition between privacy attackers and defenders. © 2020 IEEE.

关键词： Data privacy

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IndoCL: Benchmarking Indonesian Language Development Assessment

IndoCL: Benchmarking Indonesian Language Development Assessm...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Lin, Nankai Wu, Hongyan Zheng, Weixiong Liao, Xingming Jiang, Shengyi Yang, Aimin Xiao, Lixian School of Computer Science and Technology Guangdong University of Technology China College of Computer National University of Defense Technology China School of Information Technology and Engineering Guangzhou College of Commerce China School of Computer Science and Intelligence Education Lingnan Normal University China Faculty of Asian Languages and Cultures Guangdong University of Foreign Studies China

ISBN: (纸本)9798891761681

Recently, the field of language acquisition (LA) has significantly benefited from natural language processing technologies. A crucial task in LA involves tracking the evolution of language learners' competence, namely language development assessment (LDA). However, the majority of LDA research focuses on high-resource languages, with limited attention directed toward low-resource languages. Moreover, existing methodologies primarily depend on linguistic rules and language characteristics, with a limited exploration of exploiting pre-trained language models (PLMs) for LDA. In this paper, we construct the IndoCL corpus (Indonesian Corpus of L2 Learners), which comprises compositions written by undergraduate students majoring in Indonesian language. Moreover, we propose a model for LDA tasks, which automatically extracts language-independent features, relieving laborious computation and reliance on specific language. The proposed model uses sequential information attention and similarity representation learning to capture the differences and common information from the first-written and second-written essays, respectively. It has demonstrated remarkable performance on both our self-constructed corpus and publicly available corpora. Our work could serve as a novel benchmark for Indonesian LDA tasks. We also explore the feasibility of using existing large-scale language models (LLMs) for LDA tasks. The results show significant potential for improving LLM performance in LDA tasks. © 2024 Association for Computational Linguistics.

关键词： Students

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A Review of Colorectal Cancer Histopathology Image Segmentation Using Deep Learning Methods 6

A Review of Colorectal Cancer Histopathology Image Segmentat...

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6th International Conference on Control and Robotics, ICCR 2024

作者： Liu, Tengxiao Yang, Huiya Huang, Jiayi Zhou, Yihan School of Biomedical Engineering Anhui Medical University Hefei230032 China School of Medicine Shanghai Jiao Tong University Shanghai200240 China Tongji University Department of Computer Science and Technology Shanghai201804 China University of Nottingham Ningbo China Faculty of Science and Engineering Ningbo315110 China

ISBN: (纸本)9798331518158

Colorectal cancer (CRC) is the third most common cancer in humans with a rising incidence and a high mortality rate. Image segmentation of histopathological slices based on deep learning models can effectively accelerate the diagnostic process. In this paper, an overview of the datasets of the introduced models is provided and the performance of deep learning models is compared on common datasets. We classify the CRC pathological image segmentation models proposed in recent years into categories based on several basic models: Convolutional Neural Network (CNN), U-Net, Transformer and others. We list effective quantitative evaluation metrics in image segmentation and apply them to these models for comprehensive comparative analysis. Besides, improvement of different models on each module of the traditional model with respective advantages and disadvantages are sorted out. Finally, limitations and future directions are proposed from three aspects of computing power requirements, datasets and the model itself to provide inspiration for researchers who want to enter and go deeper into this field in the future. © 2024 IEEE.

关键词： Convolutional neural networks

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Algorithm for Identifying College Students with Financial Difficulties

Algorithm for Identifying College Students with Financial Di...

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2023 International Conference on computer Network Security and Software engineering, CNSSE 2023

作者： Yin, Zong-Ping Yin, Chun-Xiao Hefei Technology College Anhui China School of Big Data and Computer Science Guizhou Normal University China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

Evaluating the consumption of students' canteens at school is helpful to accurately identify students with financial difficulties from families. To obtain the canteen consumption data, the daily average consumption of all students and students with difficulties should be calculated as the reference standard, and then the daily difficulty index of each student should be calculated. Accumulate a student's daily difficulty index to obtain the difficulty index, and then convert it into the difficulty degree through feature scaling. According to the scope of the difficulty degree, students are evaluated as poor, medium, and rich. By comparing the consumption evaluation results with the manual evaluation results, we can evaluate the funding compliance of colleges and classes. © 2023 SPIE.

关键词： Students

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An Improved Genetic Algorithm for College Course Scheduling 19th

An Improved Genetic Algorithm for College Course Scheduling

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Wang, Chenle Wang, Bin International Engineering College Xi’an University of Technology Xi’an710048 China School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China

ISBN: (纸本)9783031651250

The course scheduling is a NP-complete problem. At present, various intelligent optimization algorithms have provided many feasible solutions to the course scheduling problem in colleges and universities, with differentiated advantages and disadvantages. This work tries to design a general and efficient algorithm to solve the large-scale course scheduling. In particular, we analyze and model the problem of course scheduling in colleges and universities, and put forward the improved genetic algorithm to solve the problem of large course scheduling under various constraints. First, the teaching task number is stored in a two-dimensional time-class matrix, which represents the information of teachers, and a two-dimensional classroom matrix is established to store the classroom information of the class. Next, we apply the improved genetic algorithm to cross and mutate the time-class matrix to obtain new individuals, thereby adjusting the corresponding classroom matrix. Then, the excellent individual is selected from the parent and child generation, and iterated until the optimal individual is produced. Finally, experimental results show that the convergence speed of the proposed algorithm is faster and higher fitness values can be obtained. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Scheduling algorithms

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Path Planning for Mobile Robots Based on Improved A∗ Algorithm 3

Path Planning for Mobile Robots Based on Improved A∗ Algori...

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3rd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Wang, Weijing Xu, Xiangrong Miao, Haining Petrovic, Petar B. Rodic, Aleksandar Wang, Zhixiong Anhui University of Technology School of Computer Science and Technology Maanshan China Anhui University of Technology School of Mechanical Engineering Maanshan China University of Belgrade Faculty of Mechanical Engineering Belgrade Serbia Institute Mihajlo Pupin University of Belgrade Belgrade Serbia Osaka University School of Medicine Osaka Japan

ISBN: (纸本)9798350360813

Addressing the issues of excessive redundant nodes, non-smooth paths, and low search efficiency in the path planning process of the traditional A∗ algorithm, this paper proposes an improved Dynamic Expansion Neighborhood A∗ algorithm for optimal path planning. Firstly, building upon the traditional A∗ algorithm, the search neighborhood is dynamically expanded by combining the 8-neighbor and 24-neighbor search algorithms, dynamically adjusting the search scope to reduce redundant nodes. Secondly, through modification of the heuristic function, introducing weights, and optimizing the estimation function to enhance path search efficiency. Finally, the obtained path is subjected to smoothing processing to eliminate corner sharp points, improving path smoothness. Simulation results demonstrate that the improved A∗ algorithm outperforms the traditional algorithm in terms of length, node count, and smoothness of the planned path. © 2023 IEEE.

关键词： Motion planning

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Experimental Evaluation of Remote Sensing–Based Climate Change Prediction Using Enhanced Deep Learning Strategy

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 642-656页

作者： Madhavi, Macharapu Kolikipogu, Ramakrishna Prabakar, S. Banerjee, Sudipta Maguluri, Lakshmana Phaneendra Raj, G. Bhupal Balaram, Allam Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India School of Commerce and Management Studies Dayananda Sagar University Bangalore India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Maharashtra Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram Guntur India School of Agriculture SR University Warangal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Climate change is one of the most pressing global challenges of our time, with far-reaching impacts on ecosystems, economies, and human societies. Accurate prediction of climate change patterns is crucial for developing effective mitigation and adaptation strategies. Remote sensing data, with its ability to provide comprehensive and continuous observations of the Earth’s surface, plays a vital role in monitoring and predicting these changes. However, the complexity and high dimensionality of remote sensing data present significant challenges for traditional predictive models. In this study, we present an Enhanced Deep Learning Strategy for climate change prediction using remote sensing data, integrating a Cascaded Inception-LGBM model. The proposed model combines the feature extraction capabilities of the Inception module with the predictive power of the Light Gradient Boosting Machine (LGBM). The methodology was evaluated on various climate variables, including temperature, precipitation, and CO₂ levels, achieving an accuracy of 97.22%. Comparative analysis with state-of-the-art models demonstrated the superior performance of our approach, particularly in terms of RMSE, MAE, and R² metrics. Robustness tests further confirmed the model’s generalization capabilities under different data conditions. This study underscores the potential of advanced deep learning techniques in enhancing climate change prediction accuracy and offers insights into the key drivers of climate variability. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change prediction Deep learning Environmental monitoring Feature extraction Inception module Light Gradient Boosting Machine Model accuracy NDVI Remote sensing Spectral indices

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