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Research on helping employment difficulties of students by department of computer science and software engineering,Jincheng college of Sichuan University

Research on helping employment difficulties of students by d...

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2014 International Conference on Information engineering and Education Science

作者： Ling Hou Department of Computer Science and Software Engineering Jincheng College of Sichuan University

In developing school,every university should pay full attention to employment-oriented development of each *** outstanding students or stylistic active students,schools should set up the appropriate platform,so that they play a meaningful role,and be demonstrated,especially the employment difficulties faced by the students,they need to be more concerned *** should take effective measures to them,and help needy students to solve the problems pertinently,and then make them back to normal *** article took Sichuan University,Jin Cheng College of computer Science and software engineering for example to research helping employment difficulties faced by *** practical work,we explored a classification for students from poor management,and use the bedroom and class structures difficult for students to demonstrate the platform,practical and effective measures to counselor part-time teacher two fold emotional care models.

关键词： help employment difficulties category management class activities part-time teacher

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Events and findings of the Purdue University Calumet computer Information Technology & Graphics (CIT & G) department teaching partnership with the College of software engineering of Southeast University in Nanjing China 09

Events and findings of the Purdue University Calumet Compute...

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10th ACM Special Interest Group for Information Technology Education, SIGITE 2009

作者： Winer, Charles Wu, Hanqian Department of Computer Information Technology and Graphics Purdue University Calumet Hammond IN United States College of Software Engineering Southeast University Nanjing China

ISBN: (纸本)9781605587653

In this poster session we are reporting on the results of two, three-week summer graduate teaching experiences that took place in Nanjing, China over a two-year period. A faculty exchange program was entered into between Southeast University of Nanjing China and Purdue University Calumet of Hammond, Indiana, USA. One of the goals of the exchange program was to expose Chinese students to the instructional methods employed by United States Universities. By understanding the cultural differences and utilizing various teaching methodologies employed by American teachers, the faculty and students involved in these three-week classroom intensive training courses were able to adapt and successfully complete the graduate level material that was presented.

关键词： Teaching

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Enhanced facial action unit detection with adaptable patch sizes on representative landmarks

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Neural Computing and Applications 2025年第5期37卷 3777-3791页

作者： Cakir, Duygu Yilmaz, Gorkem Arica, Nafiz Department of Software Engineering Bahcesehir University Istanbul Turkey Department of Computer Engineering Bahcesehir University Istanbul Turkey Department of Computer Engineering Piri Reis University Istanbul Turkey

The human face displays expressions through the contraction of various facial muscles. The Facial Action Coding System (FACS) is a widely accepted taxonomy that describes all visible changes in the face in terms of action units (AUs). In this study, AUs are examined by finding the most active landmarks of the face and then examining the most representative patch sizes of each landmark for the AU detection task. Sparse learning is employed to learn the most active landmarks for each AU, and then the active landmark patches are fed to ViT and Perceiver mechanisms independently. Experiments indicate that using active landmark patches with their most representative size improves the results when compared to using all the landmarks, especially when it is used on more challenging datasets as a support for the attention mechanism of the classifier. The results demonstrate that the proposed method improves the performance of the employed models and are further supported by experiments conducted across different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Face recognition

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A Scalable Unsupervised and Back Propagation Free Learning With SACSOM: A Novel Approach to SOM-Based Architectures

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 955-967页

作者： Hirani, Gaurav R. Wang, Kevin I-Kai Abdulla, Waleed H. The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand

The field of computer vision is predominantly driven by supervised models, which, despite their efficacy, are computationally expensive and often intractable for many applications. Recently, research has expedited alternative avenues such as self-organizing maps (SOM)-based architectures, which offer significant advantages such as tractability, the absence of back-propagation, and feed-forward unsupervised learning. However, these SOM-based approaches frequently suffer from lower accuracy and limited generalization capabilities. To address these shortcomings, we propose a novel model called split and concur SOM (SACSOM). SACSOM overcomes the limitations of closely related SOM-based algorithms by utilizing multiple parallel branches, each equipped with its own SOM modules that process data independently with varying patch sizes. Furthermore, by creating groups of classes and using respective training samples to train independent subbranches in each branch, our approach accommodates datasets with a large number of classes. SACSOM employs a simple yet effective labeling technique requiring minimal labeled samples. The outputs from each branch, filtered by a threshold, contribute to the final prediction. Experimental validation on MNIST-digit, Fashion-MNIST, CIFAR-10, and CIFAR-100 demonstrates that SACSOM achieves competitive accuracy with significantly reduced computation time. Furthermore, it exhibits superior performance and generalization capabilities, even in high-noise scenarios. The weights of the single-layered SACSOM provide meaningful insights into the patch-based learning pattern, enhancing its tractability and making it ideal from the perspective of explainable AI. This study addresses the limitations of current clustering techniques, such as K-means and traditional SOMs, by proposing a lightweight, manageable, and fast architecture that does not require a GPU, making it suitable for low-powered devices. © 2024 IEEE.

关键词： Unsupervised learning

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Reconfiguring Role-Based Access Control via Role Clustering

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IEEE Access 2025年 13卷 72984-72993页

作者： Durdag, Osman Coskuncay, Ahmet Ataturk University Department of Software Engineering Erzurum25240 Turkey Ataturk University Department of Computer Engineering Erzurum25240 Turkey

Role-based access control (RBAC) systems have become a widely used and accepted method by many organizations today due to their efficiency and ease of management. However, to truly benefit from RBAC systems, the roles in the relevant system must be well defined. Bottom-up, top-down, and hybrid approaches exist to accurately define the roles to be included in access control systems. Even if RBAC systems are designed carefully, the role privilege assignments tend to wear out and become chaotic after some time. This study demonstrates a role mining approach to reconfiguring such existing client specific RBAC systems by identifying clusters of similar roles using the assigned privileges. In this study, masked RBAC data of 10 clients of a software company are used. The approach creates role clusters via Agglomerative Hierarchical Clustering, an unsupervised clustering algorithm that works in a bottom-up technique. The experiment results show that it is better using the resulting candidate clusters as a reference in reconfiguring the RBAC systems, instead of using them to replace the existing roles. Additionally, it has been observed that in some client tables with a limited number of unique roles, the pairwise F1 score was remarkably high, indicating a strong correspondence between the candidate and expected roles. © 2013 IEEE.

关键词： Access control Data mining Companies software Complexity theory Hands Unified modeling language Usability System analysis and design Semantics

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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MI-BMPI motor imagery brain–mobile phone dataset and performance evaluation of voting ensembles utilizing QPDM

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Neural Computing and Applications 2025年第6期37卷 4679-4696页

作者： Yilmaz, Cagatay Murat Yilmaz, Bahar Hatipoglu Kose, Cemal Department of Software Engineering Karadeniz Technical University Trabzon61080 Turkey Department of Computer Engineering Karadeniz Technical University Trabzon61080 Turkey

EEG-based interfaces are an active research area with great potential. We, therefore, focused on classifying motor imaging (MI) tasks from various problem areas. Because of that, we applied MI patterns to voting ensembles differently and constructed voters. They employ quasi-probabilistic distribution models based on sub-classifiers of different frequency bands and time segments. Much previous work focused on just a few MI tasks for BCIs. To that end, we constructed a new mobile EEG dataset, abbreviated as MI-BMPI, containing two significant gestures for mobile phone interfaces. The research experiments used a consumer market EEG system, the mobile wireless Emotiv EPOC Flex neuroheadset. Experiments were carried out on the BCI Competition IV Dataset 2a and MI-BMPI. On the BCI and BMPI datasets, promising results were obtained in classifying various MI tasks. In conclusion, new solutions were introduced for tougher EEG-based interfaces, which have the potential to classify MI tasks and develop EEG-based interfaces. In addition to the average performance improvements, more stable results were achieved for subject and task variations. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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A novel multimodal EEG-image fusion approach for emotion recognition: introducing a multimodal KMED dataset

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Neural Computing and Applications 2025年第6期37卷 5187-5202页

作者： Hatipoglu Yilmaz, Bahar Kose, Cemal Yilmaz, Cagatay Murat Department of Computer Engineering Karadeniz Technical University Trabzon61080 Turkey Department of Software Engineering Karadeniz Technical University Trabzon61080 Turkey

Nowadays, bio-signal-based emotion recognition have become a popular research topic. However, there are some problems that must be solved before emotion-based systems can be realized. We therefore aimed to propose a feature-level fusion (FLF) method for multimodal emotion recognition (MER). In this method, first, EEG signals are transformed to signal images named angle amplitude graphs (AAG). Second, facial images are recorded simultaneously with EEG signals, and then peak frames are selected among all the recorded facial images. After that, these modalities are fused at the feature level. Finally, all feature extraction and classification experiments are evaluated on these final features. In this work, we also introduce a new multimodal benchmark dataset, KMED, which includes EEG signals and facial videos from 14 participants. Experiments were carried out on the newly introduced KMED and publicly available DEAP datasets. For the KMED dataset, we achieved the highest classification accuracy of 89.95% with k-Nearest Neighbor algorithm in the (3-disgusting and 4-relaxing) class pair. For the DEAP dataset, we got the highest accuracy of 92.44% with support vector machines in arousal compared to the results of previous works. These results demonstrate that the proposed feature-level fusion approach have considerable potential for MER systems. Additionally, the introduced KMED benchmark dataset will facilitate future studies of multimodal emotion recognition. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image fusion

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A graph neural network technique for the prediction of cerebral stroke using an unbalanced medical dataset

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Multimedia Tools and Applications 2025年 1-37页

作者： Sha, Mohemmed Department of Software Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Cerebral stroke is a major health problem, and if not recognized and treated immediately, it can result in considerable morbidity and fatality. Predicting the possibility of a stroke can help with intervention, resulting in improved patient outcomes. However, constructing reliable stroke prediction algorithms can be difficult, especially when working with unbalanced medical data. Timely detection of the various warning signs of a stroke can significantly reduce its severity. To deal with unbalanced data, this research proposed the hybridization of two techniques Noise Reduction and oversampling technique (Noise Reduction-Border Line- Synthetic Minority Oversampling Technique (NR-BL-SMOTE) which only oversamples or strengthens the borderline minority class. It aims at eliminating noise from minority groups while preventing the making of noisy samples and BL-SMOTE is used to create synthetic data to balance the dataset. After that, feature extraction is done to extract features from the cerebral stroke prediction dataset using fine-tuned VGG19 architecture to improve the performance of the proposed model. The fine-tuned model uses an already trained network and re-trains the part of that by the new data set. Finally, classification is done using a graph neural network (GNN) to classify the prediction of normal or cerebral stroke (ischemic and hemorrhagic). Further, to optimize the accuracy of the classification performance;Improved Golden Jackal Optimization (IGJO) is done by repeating their iterations on the GNN hidden layer until we get the final optimal value. The proposed model will be simulated using the PYTHON tool. Then in the simulation results, the proposed approach achieves more powerful classification accuracy than established technologies, demonstrating its efficiency in stroke prediction and classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Graph neural networks

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Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

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computer Modeling in engineering & Sciences 2025年第1期142卷 909-931页

作者： Adel Binbusayyis Mohemmed Sha Department of Software Engineering College of Computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid *** the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the *** also possesses a better impact on averting overloading and permitting effective energy *** though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized *** overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning *** accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data ***,the pre-processed data are taken for training and *** that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in *** PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed *** hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on *** results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

关键词： Smart Grid machine learning particle swarm optimization XGBoost dynamic inertia weight update

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