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Fuzzy knowledge based intelligent decision support system for ground based air defence

Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2317-2340页

作者： Ahmad, Adnan Amjad, Rawan Basharat, Amna Farhan, Asma Ahmad Abbas, Ali Ezad Department of Computer Science School of Computing National University of Computer and Emerging Sciences Islamabad Pakistan Department of Data Science School of Computing National University of Computer and Emerging Sciences Lahore Pakistan Islamabad Pakistan

This research proposes an Intelligent Decision Support System for Ground-Based Air Defense (GBAD) environments, which consist of Defended Assets (DA) on the ground that require protection from enemy aerial threats. A Fire Control Officer is responsible for assessing threats and assigning the most appropriate weapon to neutralize them. However, the decision-making process can be prone to errors, risking resource wastage and endangering DA protection. To address this problem, this research proposes a hybrid approach that combines a knowledge-driven fuzzy inference system with machine learning models to optimize resource allocation while incorporating expert knowledge in the decision-making process. Since sensory data obtained from multiple radars may be incomplete or incorrect, a fuzzy knowledge graph-based system is used for data fusion and providing it to the connected modules. Feature selection is optimized by including the most important parameters, such as the vitality of defended assets and threat score, in the threat evaluation. The results from these subsystems are visualized using a Geographical Information System, allowing for real-time mapping of the GBAD environment and displaying the results in a user-friendly web interface. The proposed system has undergone rigorous testing and evaluation, resulting in an efficient and accurate weapon assignment model with a low RMSE value of 0.037. Overall, this Intelligent Decision Support System provides an effective solution for optimizing decision-making processes in GBAD environments and can significantly improve DA protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graphic methods

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Machine Learning for Threat Detection

Machine Learning for Threat Detection

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Sutanto, Dustin Fransisca, Febri Liem, Steven Chowanda, Andry Muliono, Yohan Izdihar, Zahra Nabila School of Computer Science Cyber Security Program Computer Science Department Bina Nusantara Jakarta Indonesia School of Computer Science Computer Science Program Computer Science Department Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9798350364910

These days almost all people in the world use the Internet as the internet is constantly evolving. Cyber attack scale are increased thanks to cybercriminals that have become sophisticated in employing threats. Since the 1980s, Intrusion Detection Systems (IDS) have been developed to defend computer networks against malicious activity and unauthorized access. Companies choose to implement IDS the part of their Cyber Security strategy to protect their data and network from potential threats. in this case, IDS has played a crucial role in detecting abnormal traffic and protecting digital assets. furthermore, a high-accuracy IDS is needed in concern of having false positive feedback from the IDS machine itself that can cause various problems. to accompany the need for high accuracy and trainable IDS machines, we experimented with a hybrid system using machine learning that is combined with traditional IDS machines resulting in higher accuracy and lower error rate. By adjusting the parameter the accuracy improved and could achieve an accuracy of 99.66% of classifying the traffic. © 2024 IEEE.

关键词： Network intrusion

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Security Challenges and Issues in Cloud Computing

Security Challenges and Issues in Cloud Computing

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Zakiyyah, Rika Permana, Daniel Valenecio, Devan Chowanda, Andry Muliono, Yohan Izdihar, Zahra Nabila Cyber Security Program Computer Science Department School of Computer Science Bina Nusantara Jakarta Indonesia Computer Science Program Computer Science Department School of Computer Science Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9798350364910

Managing data has changed significantly because of cloud computing, which offers scalabe, flexible and reasonably priced solutions to enterprises and to people as well such as Amazon, Google, and Microsoft expanding their infrastructure to meet the demand from the customer. With such advantages there will also be disadvantages such as security problems. This paper will discuss about security problems such as encryption, access control, multi-factor authentication, regular audits, secure configurations, incident response planning. This paper will also talk about security measures to deal with threats such as malicious insiders, data abuse, unsecured interfaces and APIs, shared technology complications, data loss or leakage, hijacking, and enigmatic risk profiles. This study examines and evaluates a range of cloud computing security concerns and issues through a systematic analysis of the literature. The goal of the research is to raise public awareness of the need for cloud computing security and to offer possible remedies. Identify and classify network security threats and vulnerabilities in cloud computing by doing an extensive examination of the body of research and empirical investigations is one of the answer to the questions from the paper. In the end, this take a look at unearths vulnerabilities and their practical implications through very well reading network safety in cloud computing. © 2024 IEEE.

关键词： Cloud security

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Unsupervised spectral feature selection algorithms for high dimensional data

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Frontiers of computer science 2023年第5期17卷 27-40页

作者： Mingzhao WANG Henry HAN Zhao HUANG Juanying XIE School of Computer Science Shaanxi Normal UniversityXi’an 710119China Department of Computer Science School of Engineering&Computer ScienceBaylor UniversityWacoTX 76798USA

It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data,especially for those with very small number of *** selection especially the unsupervised ones are the right way to deal with this challenge and realize the ***,two unsupervised spectral feature selection algorithms are proposed in this *** group features using advanced Self-Tuning spectral clustering algorithm based on local standard deviation,so as to detect the global optimal feature clusters as far as *** two feature ranking techniques,including cosine-similarity-based feature ranking and entropy-based feature ranking,are proposed,so that the representative feature of each cluster can be detected to comprise the feature subset on which the explainable classification system will be *** effectiveness of the proposed algorithms is tested on high dimensional benchmark omics datasets and compared to peer methods,and the statistical test are conducted to determine whether or not the proposed spectral feature selection algorithms are significantly different from those of the peer *** extensive experiments demonstrate the proposed unsupervised spectral feature selection algorithms outperform the peer ones in comparison,especially the one based on cosine similarity feature ranking *** statistical test results show that the entropy feature ranking based spectral feature selection algorithm performs *** detected features demonstrate strong discriminative capabilities in downstream classifiers for omics data,such that the AI system built on them would be reliable and *** is especially significant in building transparent and trustworthy medical diagnostic systems from an interpretable AI perspective.

关键词： feature selection spectral clustering feature ranking techniques entropy cosine similarity

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Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

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computers, Materials & Continua 2024年第5期79卷 3333-3353页

作者： Aiman Muhammad Arshad Bilal Khan Sadique Ahmad Muhammad Asim Department of Computer Science City University of Science and Information TechnologyPeshawar25000Pakistan EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia School of Computer Science and Technology Guangdong University of TechnologyGuangzhou510006China

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personalinformation, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic,semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, andmarketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French,***, the research on RomanUrdu is not up to the ***, this study focuses on detecting the author’sage and gender based on Roman Urdu text messages. The dataset used in this study is Fire’18-MaponSMS. Thisstudy proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiplelinguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model iscontrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Na飗e Bays(NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable,RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 withRandom Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender predictioncalculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguisticfeatures for age and gender prediction.

关键词： Digital forensics author profiling for security AdaBoostM1 random forest ensemble learning

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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Evaluation of Educational Fund Management and Operational Cost Efficiency: Analyzing U.S. Public schools data

Evaluation of Educational Fund Management and Operational Co...

引用

2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Eleeas, Kevina Nugraha Muljana, Richie Ayuningtyas, Lintang Diah Gunawan, Alexander Agung Santoso Setiawan, Karli Eka School of Computer Science Bina Nusantara University Jakarta Data Science Program 11480 Indonesia School of Computer Science Bina Nusantara University Jakarta Computer Science Department 11480 Indonesia

ISBN: (纸本)9798350350524

This study investigates the challenges and opportunities of managing educational funds and controlling operational costs in two educational systems. It examines how factors such as school infrastructure, technology integration, teacher professional development, and parental involvement influence educational funding and quality. This study also explores the importance of equitable and efficient fund allocation, especially for remote schools that face resource constraints and development gaps. To analyze the financial trends of educational fund management, this study applies three machine learning models: decision trees, random forests, and support vector machines. The results show that the random forest model has the highest overall accuracy (90%) and the decision tree model has perfect accuracy in classifying institutions based on local income. This study identifies the most relevant features and demonstrates the effectiveness of the models in categorizing institutions and evaluating the fund management performance in the education sector. © 2024 IEEE.

关键词： Decision trees

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A privacy-preserving method for publishing data with multiple sensitive attributes

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 222-238页

作者： Tong Yi Minyong Shi Wenqian Shang Haibin Zhu School of Computer Science and Engineering Guangxi Normal UniversityGuilinChina School of Computer Science Communication University of ChinaBeijingChina Department of Computer Science and Mathematics Nipissing UniversityNorth BayOntarioCanada

The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy ***,sensitive information disclosure may also be caused by these personalised *** address the matter,this article develops a personalised data publishing method for multiple *** to the requirements of individuals,the new method partitions SAs values into two categories:private values and public values,and breaks the association between them for privacy *** the private values,this paper takes the process of anonymisation,while the public values are released without this *** algorithm is designed to achieve the privacy mode,where the selectivity is determined by the sensitive value frequency and undesirable *** experimental results show that the proposed method can provide more information utility when compared with previous *** theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an *** overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.

关键词： data privacy data publishing

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On learning the right attention point for feature enhancement

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science China(Information sciences) 2023年第1期66卷 131-143页

作者： Liqiang LIN Pengdi HUANG Chi-Wing FU Kai XU Hao ZHANG Hui HUANG College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computer Science National University of Defense Technology School of Computing Science Simon Fraser University

We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.

关键词： point convolution feature enhancement attention point deep neural network

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Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

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Journal of computer science & Technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

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