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Multi-Dimensional QoS Evaluation and Optimization of Mobile Edge Computing for IoT:A Survey

Chinese Journal of Electronics 2024年第4期33卷 859-874页

作者： Jiwei HUANG Fangzheng LIU Jianbing ZHANG Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Department of Computer Science and Technology China University of Petroleum

With the evolvement of the Internet of things(IoT), mobile edge computing(MEC) has emerged as a promising computing paradigm to support IoT data analysis and processing. In MEC for IoT, the differentiated requirements on quality of service(QoS) have been growing rapidly, making QoS a multi-dimensional concept including several attributes, such as performance, dependability, energy efficiency, and economic factors. To guarantee the QoS of IoT applications, theories and techniques of multi-dimensional QoS evaluation and optimization have become important theoretical foundations and supporting technologies for the research and application of MEC for IoT,which have attracted significant attention from both academia and industry. This paper aims to survey the existing studies on multi-dimensional QoS evaluation and optimization of MEC for IoT, and provide insights and guidance for future research in this field. This paper summarizes the multi-dimensional and multi-attribute QoS metrics in Io T scenarios, and then several QoS evaluation methods are presented. For QoS optimization, the main research problems in this field are summarized, and optimization models as well as their corresponding solutions are elaborated. We take notice of the booming of edge intelligence in artificial intelligence-empowered Io T scenarios, and illustrate the new research topics and the state-of-the-art approaches related to QoS evaluation and optimization. We discuss the challenges and future research directions.

关键词： Internet of things Mobile edge computing Quality of service Quality of service evaluation Quality of service optimization

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An EfficientNet-Based Method for Interpretable Early Detection of Alzheimer 10

An EfficientNet-Based Method for Interpretable Early Detecti...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Mirsharifi, Fakhrossadat Keyvanpour, Mohammad Reza Alzahra University Data Mining Laboratory Faculty of Engineering Department of Computer Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798331532543

Alzheimer's disease is a degenerative impairment that unhurriedly impairs remembrance, cognition, and behavior, ultimately leading to a loss of independence. This study focuses on detecting Alzheimer's across four categories, NonDementia, VeryMildDementia, MildDementia, and ModerateDementia that represent different levels of cognitive decline. Accurate early prediction is essential, as it enables timely intervention, which may help slow or even halt symptom progression, thereby improving patients' quality of life. Applying machine learning and deep learning in Alzheimer's detection presents a cost-effective solution, reducing the reliance on manual analysis and providing a scalable, efficient diagnostic tool. However, In prediction models, we still face a lack of interpretability, as healthcare providers must understand and trust the results. Previous research has utilized algorithms like Support Vector Machines, Random Forest, k-Nearest Neighbors for Alzheimer's classification;nonetheless, these models often lack transparency. This research makes use of the Alzheimer's Disease Neuroimaging Initiative or ADNI dataset, applying EfficientNet for feature extraction owing to its superior precision and efficiency in computation. A Dense layers is used as the classifier, chosen for its seamless integration within the neural network framework and compatibility with interpretability methods. GradCAM++ is applied to enhance interpretability, highlighting specific brain regions that influence each decision. Through our design and applied methods, this research achieves a high classification accuracy of 97.84%, reflecting an improvement of nearly 2% over previous approaches. This approach shows promise in supporting healthcare professionals with early diagnosis and the planning of targeted interventions. © 2024 IEEE.

关键词： Support vector machines

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An Effective Feature Selection for Type II Diabetes Prediction 10

An Effective Feature Selection for Type II Diabetes Predicti...

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10th International Conference on Web Research, ICWR 2024

作者： Teimoory, Ghazaleh Kakavand Reza Keyvanpour, Mohammad Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350394986

Diabetes is a condition related to metabolism that arises due to various reasons. Each year, a significant portion of patients experience life-threatening complications leading to fatality. This illness is classified into three distinct forms: Type 1, Type 2, and Gestational diabetes. It is of utmost importance to predict type 2 diabetes, which arises from cellular insulin utilization deficiency or secretion disorder, as it enables the prevention of complications or delays in the onset of the disease. Predicting the occurrence of illnesses using machine learning and artificial intelligence can substantially mitigate its costs. However, due to interpretability challenges in proposed models, physicians and patients are hesitant to adopt them. Previous studies have utilized various algorithms including Naïve Bayes, SVM, KNN, and decision tree algorithms for patients' classification. In this paper, conducted on the Pima dataset, we employed a preprocessing method utilizing the most significant features selected by the Random Forest algorithm. Additionally, for model testing, we utilized the SVM algorithm, known for its high discriminative power in binary classification tasks and its relatively good interpretability. The results indicate that the proposed model achieved an accuracy of 80.09%, outperforming other models by a 2.26% improvement. Furthermore, there were notable improvements in precision and specificity metrics with the proposed model. By utilizing these methods, web-based applications can be employed to motivate physicians and patients for diabetes prediction. © 2024 IEEE.

关键词： Forecasting

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Detecting Fake News using Advanced Language Models: BERT and RoBERTa 10

Detecting Fake News using Advanced Language Models: BERT and...

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10th International Conference on Web Research, ICWR 2024

作者： Angizeh, Leila Behboudi Keyvanpour, Mohammad Reza Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350394986

The fake news story refers to information falsified or made up to manipulate or deceive the public. It is a serious issue that undermines the credibility of information sources and influences public opinion, discourse, and important decisions and outcomes. The content and source of news articles and stories must be considered when identifying and classifying fake news. Detecting fake news is an important and active research area with many potential applications and implications for society. Many challenges are involved in determining whether a news article is authentic. Several deep learning models that employ natural language processing (NLP) have shown excellent results in detecting fake news. To assess the truthfulness of news articles, our methodology is based on state-of-the-art language models based on transformers. Bidirectional Encoder Representation from Transformers (BERT) and Robustly Optimized BERT Technique (RoBERTa) is one of the most advanced models. Our findings reveal that the BERT model achieved an accuracy of 64%, while the RoBERTa model slightly outperformed it with an accuracy of 66%. These results are particularly significant when compared to similar research in this domain, which reported a maximum accuracy of 62% for both models on Liar dataset. © 2024 IEEE.

关键词： Fake detection

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Efficient Prediction of Cardiovascular Disease via Extra Tree Feature Selection 14

Efficient Prediction of Cardiovascular Disease via Extra Tre...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Abroodi, Mina Keyvanpour, Mohammad Reza Teimoory, Ghazaleh Kakavand Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

ISBN: (纸本)9798331511272

All abnormalities related to the heart can be classified as heart diseases. These diseases are also known as cardiovascular diseases. Predicting heart disease is crucial due to its status as a leading global cause of mortality. According to predictions, if this disease can be diagnosed earlier, treatment cost can be reduced, prompt initiation of care for patients and millions of lives can be saved. Utilizing machine learning and artificial intelligence for this purpose is paramount. Previous research has explored various methodologies such as KNN and SVM. However, the preprocessing phase significantly influences model performance, especially in e-health domains. This work proposes a groundbreaking method that uses the Extra Tree Classification method for feature selection, optimizing the selection of key attributes from the Cleveland Heart Disease dataset. Our method is chosen for the low computational cost and interpretability of feature selection through randomized scoring. Additionally, we employ logistic regression, renowned for its effectiveness and efficiency in classification tasks and to ensure the robustness of the model, we used the k-fold cross validation method. Our findings demonstrate a notable accuracy of 87.09% when predicting heart disease, with a precision of 87.45% and an F-measure of 86.02%, surpassing previous benchmarks. Considering the high interpretability of models like logistic regression, this level of accuracy is particularly valuable in medical contexts where transparency and explainability are critical, making it a competitive alternative to more complex, less interpretable methods. These findings highlight the effectiveness of combining Extra Tree-based feature selection with logistic regression in improving the predictive accuracy of heart disease diagnostics. Furthermore, we address missing values and their impact on model performance, further strengthening the reliability of our approach. ©2024 IEEE.

关键词： Electronic health record

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STANet: Spatio-Temporal Attention-Enhanced WaveNet for Crime Hotspot Prediction 15

STANet: Spatio-Temporal Attention-Enhanced WaveNet for Crime...

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15th International Conference on Information and Knowledge Technology, IKT 2024

作者： Roshankar, Rojan Keyvanpour, Mohammad Reza Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

ISBN: (纸本)9798331522254

An accurate prediction of crime hotspots is critical for optimizing law enforcement strategies and urban planning. In this paper, we introduce STANet, a Spatio-Temporal Attention-Enhanced WaveNet model developed to predict crime hotspots using spatial and temporal crime data. K-Means clustering and advanced data preprocessing techniques are combined in STANet to analyze five years of crime incidents reported in Chicago. In the model, spatial-temporal dependencies are incorporated through WaveNet architecture and enhanced through attention mechanisms in order to capture complex crime patterns more effectively. As a result of our experiments, we are able to demonstrate that STANet outperforms traditional models, such as XGBoost, DNN, and decision trees, with The model achieved an accuracy of 86%, along with precision and recall values that demonstrate a balanced performance in predicting crime hotspots. As a result of this mechanism, the model can identify and focus on the most relevant time steps dynamically, improving its accuracy in predicting the future. STANet can be used to predict hotspots for crime, offering actionable insights for resource allocation and crime prevention. To enhance the predictive capability of the model, further exploration will involve expanding the dataset and incorporating additional features. © 2024 IEEE.

关键词： Resource allocation

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Improving Drug-Target Interaction Prediction Using Enhanced Feature Selection 15

Improving Drug-Target Interaction Prediction Using Enhanced ...

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15th International Conference on Information and Knowledge Technology, IKT 2024

作者： Taheri, Maryam Keyvanpour, Mohammad Reza Mousavi, Mohadeseh Sadat Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

ISBN: (纸本)9798331522254

A deep understanding of drug-target interaction (DTI) mechanisms is essential for the design of effective and safe drugs. While traditional wet lab experiments play a critical role, they are often associated with significant time and financial investments. Therefore, computational methods based on interaction prediction can significantly reduce the search space for the mentioned experiments. This paper outlines an innovative multistage approach for the prediction of DTI. Feature vectors, comprising various descriptors and the drug-encoded FP2 fingerprint, are extracted from protein sequences and drug structures in the first step. One major obstacle in this field is the data imbalance caused by the limited number of known interactions. The FFS-RF-DT algorithm, a powerful combination of forward feature selection, random forest and decision tree classification, is subsequently used to optimize predictive performance. This effectively eliminates irrelevant features, resulting in a more focused and efficient model. At the final stage, the XGBoost classifier processes the balanced dataset containing the optimal features to predict DTIs, yielding new drug-target interactions. The proposed model demonstrated better performance, achieving an accuracy of 96.96%, representing a 5.5% improvement over other models. In addition to its superior classification performance, the proposed model exhibits a much lower time complexity than comparable models. This makes it a promising candidate for DTI applications. © 2024 IEEE.

关键词： Feature Selection

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Sentiment Analysis on Drug Reviews Using Ensemble Learning Method 20

Sentiment Analysis on Drug Reviews Using Ensemble Learning M...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Eskandarisani, Sahar Keyvanpour, Mohammad Reza Faculty of Engineering Data Mining Laboratory Department of Computer Engineering Alzahra University Tehran Iran Faculty of Engineering Department of Computer Engineering Alzahra University Tehran Iran

ISBN: (纸本)9798350383942

Sentiment analysis, a branch of natural language processing, has gained considerable attention recently. This analytical approach is now widely used across multiple sectors, such as healthcare, finance, and customer service. Sentiment analysis in the healthcare domain can contribute to advancements in this field and help doctors to gain a deeper understanding of patient sentiments, enabling them to make more informed prescriptions. In our research, we concentrated on sentiment analysis of drug reviews. We applied ensemble learning methods, including the stacking learning method, using three base models and one meta-model. We also employed three feature extraction methods: TF-IDF, Bag of Words (BOW), and Continuous Bag of Words (CBOW). Among these methods, the stacking learning model with TF-IDF achieved the highest accuracy, reaching 89.8%. © 2024 IEEE.

关键词： Learning systems

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Co-clustering method for cold start issue in collaborative filtering movie recommender system

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Multimedia Tools and Applications 2024年 1-25页

作者： AbbasiRad, Ensieh Keyvanpour, Mohammad Reza Tohidi, Nasim Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Computer Engineering Faculty K.N.Toosi University of Technology Tehran Iran Computer Science & amp Engineering University of Michigan Ann ArborMI United States

Recommender systems play an essential role in decision-making in the information age by reducing information overload via retrieving the most relevant information in various applications. They also present great opportunities and challenges for business, government, education, and other fields. The cold start problem is a significant issue in these systems. If recommender systems fail to provide satisfactory personalized recommendations for new users, the user’s trust can easily be lost. Hence, in this paper, using co-clustering and utilizing user demographic information and the behavioral history of users, a solution to this critical issue for recommending movies is introduced. In the proposed method, in addition to dealing with the problem of relative cold start, the problem of absolute cold start is also addressed. The proposed method was evaluated via two RMSE and MAE criteria, which accordingly has achieved 0.85 and 0.49 on the Movielens dataset and 1.05 and 0.6 on the EachMovie dataset, respectively, according to the number of comments that Cold Start users have registered. Moreover, it achieved 0.9 and 0.55 on the Movielens dataset and 1.42 and 0.89 on the EachMovie dataset, respectively, according to the number of registered comments for the cold start items. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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HFANet: Hierarchical Feature-Enhanced Aggregation Network for Camouflaged Object Detection

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Zhang, Xinchao Zhu, Hengliang Mao, Guojun Department of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China

Camouflaged object detection (COD) aims to identify target objects in complex scenes with extremely high similarity to their surroundings, and has significant applications in military, medical, and other fields. This paper proposes a hierarchical feature-enhanced aggregation network (HFANet) for COD, aiming to address the situations that the target object is highly similar to the background. First, we adopt the pyramid vision Transformer model as the backbone for feature extraction. On top of it, the object-region amplification module and deep interaction guidance module are stacked to enhance the perception of camouflaged objects in complex scenes. Second, an enhanced receptive field module is designed to improve edge perception of camouflaged objects. At last, a multi-scale interactive fusion module is designed by cross-scale connection through adjacent layers, effectively improving the accuracy of COD. The proposed method is evaluated on three challenging datasets: CAMO, CHAMELEON, and COD10K. Evaluation results demonstrate superior performance compared to the state-of-the-art methods. © Shanghai Jiao Tong University 2024.

关键词： Object detection

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