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Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers 22

Towards Feature Selection for Ranking and Classification Exp...

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45th international ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Dacrema, Maurizio Ferrari Moroni, Fabio Nembrini, Riccardo Ferro, Nicola Faggioli, Guglielmo Cremonesi, Paolo Politecn Milan Milan Italy ContentWise Milan Italy Univ Padua Padua Italy

ISBN: (纸本)9781450387323

Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced. However, feature selection can be itself a computationally expensive process. While for decades confined to theoretical algorithmic papers, quantum computing is now becoming a viable tool to tackle realistic problems, in particular special-purpose solvers based on the Quantum Annealing paradigm. This paper aims to explore the feasibility of using currently available quantum computing architectures to solve some quadratic feature selection algorithms for both ranking and classification. The experimental analysis includes 15 state-of-the-art datasets. The effectiveness obtained with quantum computing hardware is comparable to that of classical solvers, indicating that quantum computers are now reliable enough to tackle interesting problems. In terms of scalability, current generation quantum computers are able to provide a limited speedup over certain classical algorithms and hybrid quantum-classical strategies show lower computational cost for problems of more than a thousand features.

关键词： Feature Selection Quantum computing Quantum Annealing machine learning

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An ensemble multi-objective particle swarm optimization approach for exchange rates forecasting problem 2020

An ensemble multi-objective particle swarm optimization appr...

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4th international conference on machine learning and soft computing (icmlsc)

作者： Thi Thu Huong Dinh Van Truong Vu Lam Thu Bui Sai Gon Univ Ho Chi Minh Vietnam Le Quy Don Tech Univ Hanoi Vietnam

ISBN: (纸本)9781450376310

In this paper, the authors propose an ensemble multi-objective particle swarm optimisation approach (named EMPSO) for forecasting the currency exchange rate chain. The proposed algorithm consists of two main phases. The first phase uses a multi-objective particle swarm optimisation algorithm to find a set of the best optimal particles (named leaders). The second phase then uses these leaders to jointly calculate the final results by using the soft voting ensemble method. The two objective functions used here are predictive error and particle diversity. The empirical data used in this study are six different sets of currency exchange rates. Through comparison results with other evolutionary algorithms and other multi-objective PSO algorithms, the proposed algorithm shows that it can achieve better as well as more stability results on experimental data sets.

关键词： Time series forecasting PSO multi-objective PSO ensemble learning.

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An Explainable Intrusion Detection System 23

An Explainable Intrusion Detection System

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Yun Wang, Pan Wang, ZiXuan Cao, Mengting School of Modern Posts Nanjing University of Posts & Telecommunications Nanjing China

ISBN: (纸本)9781665494571

Malicious traffic detection has become a challenge in modern communications. As the research of intrusion detection systems becomes more and more in-depth, from machine learning to deep learning, the accuracy of intrusion detection continues to improve, the complexity of the model is getting higher and higher, and the interpretability of the model is lower. The model is like a black box, it is difficult for people to know the reason behind the decision. In fact, the interpretability of the model has made some achievements in the fields of biology, computer vision, and natural language processing. Therefore, in this article, for an intrusion detection system based on deep neural networks, we propose an interpretable artificial intelligence framework to enhance the transparency of machine learning decision-making, that is, which important features the model uses to determine attack or non attack. We use the SHAP method to explain, and combine local and global explanations to realize IDS interpretation in all directions. The partial explanation gives us the reason why the model makes a decision on a particular instance, which gives us a certain reference value. The global interpretation gives the important characteristics of the model as a whole, which helps to improve the transparency of IDS. We use the CICIDS2017 dataset to verify the feasibility of the intrusion detection system's two-classification experiment. Interpretability work can help network security personnel better understand the decision-making of intrusion detection system. © 2021 IEEE.

关键词： Deep learning Smart cities Computational modeling Biological system modeling Decision making Neural networks Intrusion detection

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Detect Android malware by using deep learning: Experiment and Evaluation 21

Detect Android malware by using deep learning: Experiment an...

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5th international conference on machine learning and soft computing, icmlsc 2021

作者： Hoang Khoa, Nghi Tan Cam, Nguyen Pham, Van-Hau Nguyen, Anh Gia-Tuan Information Security Laboratory University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh City Viet Nam Hoa Sen University Ho Chi Minh City Viet Nam University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh City Viet Nam

ISBN: (纸本)9781450387613

With the emergence of deep learning, recent years have witnessed a booming of artificial intelligence (AI) applications and services in many fields of modern society, ranging from face recognition to video surveillance to many recommendation systems. In addition, there is a tremendous increase in cyberattacks or data leakage which draws much attention from both the professional and the public due to the popularity of mobile devices like Android. Driving by this trend, many security approaches push AI or deep learning to Android malware detection to enhance the accuracy and capability of scanning or detecting systems. However, research on this field is still in the early stage since there is the diversity of deep learning models and a shortage of standardized Android datasets for evaluating the efficiency of each approach. To this end, we conduct a comparative study of famous deep learning models on one dataset to give an overview of their powers in providing the method of dissecting Android malware. © 2021 ACM.

关键词： Image classification

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Utilizing Spatio-temporal Satellite Images and machine learning to Examine the Impacts of Changing Land Use and Land Cover on Sohar City 24

Utilizing Spatio-temporal Satellite Images and Machine Learn...

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24th international Arab conference on Information Technology, ACIT 2023

作者： Shafi, Muhammad Sohar University Faculty of Computing and Information Technology Sohar Oman

ISBN: (纸本)9798350384307

The growing urbanization of cities and towns in the Gulf Cooperation Council (GCC) states as a result of socioeconomic change has primarily resulted in a great deal of strain on the few natural resources and the loss of productive areas. In fact, there hasn't been much focus on the spatial patterns of urbanization and how they affect the environment and agricultural resources, especially in Oman. It may be possible to better understand the connection between spatial growth patterns and its effects on agricultural production by predicting urban growth in Sohar City. This study aims to analyze spatiotemporal dynamics of land use/land cover (LULC) for the last six years (2017-2022) and its impacts especially on the reduction of agricultural land. The spatiotemporal data was obtained from Sentinel-2 with a cell size of 10 meters which was then cropped using the vector boundaries of Sohar city. Part of the data was labelled manually which was used to train supervised machine learning technique for classifying the data into different land covers such as water bodies, trees, crops, etc. Accuracy assessment was carried out by com-paring the automatic classification results with ground truth and an accuracy of 93.4% was obtained. It has been found that built up area is increasing rapidly while hugely affecting the arable land. It's also interesting to see that during the COVID19 pandemic, this urban growth was stopped as evident from the satellite data. The results of this study could address the dangers and declines of urban sustainability for Sohar city, as well as provide geographical guidelines for observing future changes in LULC dynamics. The national strategy for future urban development in Oman also benefits from identifying regions of bare soil and vegetation that are amenable to urbanization. © 2023 IEEE.

关键词： Geographic Information System Land Use Land Cover Oman Sohar Spatiotemporal Urban Sprawl

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BKIDset - A New Intrusion Detection Dataset To Mitigate The Class Imbalance Problem 15

BKIDset - A New Intrusion Detection Dataset To Mitigate The ...

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15th international conference on Advanced computing and Applications (ACOMP)

作者： Nguyen The Anh Le Huy Hoang Vu Dinh Minh Tran Hoang Hai Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

ISBN: (纸本)9781665406390

Applying machine learning techniques to Intrusion Detection System (IDS) is one efficient method to detect existing and new types of network attacks with minimal human supervision and great accuracy. The classification performance of such systems depend heavily on the dataset they are trained on. Most currently deployed datasets suffer from a high imbalance between normal network traffic and anomaly network traffic, which limits the system's ability to detect minority classes with high accuracy. In this paper, we propose a new model for generating and collecting anomaly network traffic, which is used in conjunction with CICIDS2017 [1] dataset to create a new dataset namely BKIDset, which mitigates the class imbalance problem encountered in CICIDS2017 dataset and introduces additional types of denial-of-service attacks. The paper also proposes different machine learning models trained on BKIDset, that achieve a high accuracy score of 99%.

关键词： machine learning algorithms Intrusion detection Telecommunication traffic machine learning Computer architecture Network architecture Denial-of-service attack

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Advancing intrusion detection with ML and deep learning: a comparative approach

Advancing intrusion detection with ML and deep learning: a c...

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international conference on Energy, Power, Environment, Control and computing (ICEPECC 2025)

作者： Mashaal Shahid Syed Yasser Arafat Fahad Tariq Department of CSIT Mirpur University of Science and Technology Mirpur Azad Jammu and Kashmir (AJK) Pakistan Department of Training Fauji Foundation Head office Rawalpindi Pakistan

ISBN: (数字)9781837243150

In network security, Intrusion Detection Systems (IDS) is increasingly important due to rise in cyber-attacks. To ensure accurate detection and prevention, IDS leverages machine learning (ML) and Deep learning (DL) algorithms on benchmark datasets; CICIDS2017 and NSL-KDD. These datasets, with diverse attack types, are ideal for training and testing. Traditional IDS struggle to detect zero-day attacks due to lack of predefined signatures, leading to higher false positive rates and vulnerabilities. Our proposed methodology focuses on a DL-based NIDS. We pre-process and normalize data from CICIDS2017, NSL-KDD, and a custom home network dataset. Supervised ML models, including Random Forest, Decision Tree, and Gaussian Naive Bayes, were applied to CICIDS 2017, achieving high accuracy. For DL, BiLSTM and Transformer models were trained on NSL-KDD for 30–60 epochs, with accuracy rates of 87%–99%. We utilized dual approach of normalization technique using Log Transformation and enhanced the Log Transformation by adding weightage to the features and retrained the model LSTM-CNN on NSL-KDD. Additionally, an LSTM-CNN model was applied to the Home Network dataset, the accuracy result of H-N Version1 was 100%.

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Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques 23

Optimizing Tensor Computations: From Applications to Compila...

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2023 ACM/SIGMOD international conference on Management of Data, SIGMOD 2023

作者： Boehm, Matthias Interlandi, Matteo Jermaine, Chris TU Berlin Berlin Germany Mircosoft GSL Los AngelesCA United States Rice University HoustonTX United States

ISBN: (纸本)9781450395076

machine learning (ML) training and scoring fundamentally relies on linear algebra programs and more general tensor computations. Most ML systems utilize distributed parameter servers and similar distribution strategies for mini-batch stochastic gradient descent training. However, many more tasks in the data science and engineering lifecycle can benefit from efficient tensor computations. Examples include primitives for data cleaning, data and model debugging, data augmentation, query processing, numerical simulations, as well as a wide variety of training and scoring algorithms. In this survey tutorial, we first make a case for the importance of optimizing more general tensor computations, and then provide an in-depth survey of existing applications, optimizing compilation techniques, and underlying runtime strategies. Interestingly, there are close connections to data-intensive applications, query rewriting and optimization, as well as query processing and physical design. Our goal for the tutorial is to structure existing work, create common terminology, and identify open research challenges. © 2023 ACM.

关键词： machine learning

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Photonic Quantum Policy learning in OpenAI Gym 2

Photonic Quantum Policy Learning in OpenAI Gym

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IEEE international conference on Quantum computing and Engineering (QCE) / Quantum Week

作者： Nagy, Daniel Tabi, Zsolt Haga, Peter Kallus, Zsofia Zimboras, Zoltan Wigner Res Ctr Phys Budapest Hungary Ericsson Res Budapest Hungary Eotvos Lorand Univ Budapest Hungary MTA BME Lendulet QIT Res Grp Budapest Hungary

ISBN: (纸本)9781665416917

In recent years, near-term noisy intermediate scale quantum (NISQ) computing devices have become available. One of the most promising application areas to leverage such NISQ quantum computer prototypes is quantum machine learning. While quantum neural networks are widely studied for supervised learning, quantum reinforcement learning is still just an emerging field of this area. To solve a classical continuous control problem, we used continuous-variable quantum machine learning. We introduce proximal policy optimization for photonic variational quantum agents and also study the effect of the data re-uploading. We present performance assessment via empirical study using Strawberry Fields, a photonic simulator Fock backend and a hybrid training framework connected to an OpenAI Gym environment and TensorFlow. For the restricted CartPole problem, the two variations of the photonic policy learning achieve comparable performance levels and a faster convergence than the baseline classical neural network of same number of trainable parameters.

关键词： quantum computation machine learning reinforcement learning quantum circuits

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A machine-learning Approach to Classify Depressive Posts from Reddit Social Media

A Machine-Learning Approach to Classify Depressive Posts fro...

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Next-Generation computing, IoT and machine learning (NCIM), international conference on

作者： Adib Wahid Quader Md Shafayet Jamil Mohammad Irfan Uddin Muhammed J. A. Patwary Department of Computer Science & Engineering International Islamic University Chittagong Bangladesh

Depression leading to suicide has become a major concern in our society. It's high time to investigate the reason behind it. As numerous people are expressing their concerns regarding depression on Reddit, such post can be taken into consideration in order to find the reason behind it. There has been a lot of research work in this field but most of it is done using traditional machine learning approach with NLP techniques. Moreover, recently there's has not been any significant research work regarding depression. In this paper, we found the indicators that could indicate how relevant Reddit users feel about depression using machine learning, ensemble learning, and deep learning techniques. Moreover, we pinpointed the terms that appear more frequently in narratives of depression. To strengthen the research work, we took the dataset over the period from 2017 to 2021. Furthermore, we demonstrated the effectiveness of LSTM & BERT on recent dataset.

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