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18th European Conference on Games Based Learning, ECGBL 2024

作者： Filippidis, Adam P. Hilas, Constantinos S. Politis, Anastasios Rantos, Konstantinos Mouratidis, Haralambos Department of Computer Informatics and Telecommunications Engineering International Hellenic University Serres Campus Serres Greece Department of Computer Science Democritus University of Thrace Kavala Campus Greece Department of Computer and Systems Sciences Stockholm University Sweden

ISBN: (纸本)9781917204194

Cybersecurity is one of the most critical sectors of information systems and is considered a strategic priority for many Organizations. Information security investments are a crucial aspect of cybersecurity. However, it often happens that the economic decisions, which aim to improve information security, have poor efficiency. One reason is the lack of background on economics of the Chief Information Security Officers who are often not familiar with the evaluation of the non-technical aspects of security, e.g. economics. The other reason is the cultural gap between the different persons related to business management and administration. In this paper we present an interactive - simulation software designed to familiarize the aforementioned persons with information security investments. The software is actually a game that aims to boost learning for all the stakeholders who are involved in the investment process. Α better understanding of the processes, the technical, economic and human factors, along with the cultural convergence of the stakeholders will help boost the efficiency of investments. Design science is used as the research method. The creative method, a brainstorming technique, is combined with the five steps of Design science, i.e. explicate the problem, define the requirements, design and develop the artefact, demonstrate the artefact, and evaluate the artefact, to develop a process framework that will be able to cope with this problem. The artefact is an interactive-simulation platform in which a person could take the decision maker role and (a) deal with events that happen and (b) investigate different aspects of information security investment complexity. The evaluation of the artefact is made with a case study in the Industry, where real world data are used to feed the simulation and real-world decision makers play the game in order to better understand the different aspects of the problem and help us trim and enhance the functioning of the platform.

关键词： Efficiency

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A U-Net-Based CNN Model for Detection and Segmentation of Brain Tumor

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computers, Materials & Continua 2023年第1期74卷 1333-1349页

作者： Rehana Ghulam Sammar Fatima Tariq Ali Nazir Ahmad Zafar Abdullah A.Asiri Hassan A.Alshamrani Samar M.Alqhtani Khlood M.Mehdar Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Radiological Sciences Department College of applied medical sciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia

Human brain consists of millions of cells to control the overall structure of the human *** these cells start behaving abnormally,then brain tumors *** and initial stage brain tumor detection has always been an issue in the field of medicines for medical *** handle this issue,various deep learning techniques for brain tumor detection and segmentation techniques have been developed,which worked on different datasets to obtain fruitful results,but the problem still exists for the initial stage of detection of brain tumors to save human *** this purpose,we proposed a novel U-Net-based Convolutional Neural Network(CNN)technique to detect and segmentizes the brain tumor for Magnetic Resonance Imaging(MRI).Moreover,a 2-dimensional publicly available Multimodal Brain Tumor Image Segmentation(BRATS2020)dataset with 1840 MRI images of brain tumors has been used having an image size of 240×240 *** initial dataset preprocessing the proposed model is trained by dividing the dataset into three parts i.e.,testing,training,and validation *** model attained an accuracy value of 0.98%on the BRATS2020 dataset,which is the highest one as compared to the already existing techniques.

关键词： U-net brain tumor magnetic resonance images convolutional neural network segmentation

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Goods Shipment Application Through Bids 10

Goods Shipment Application Through Bids

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Mahalakshmi, A. Saranya, E. Gomathi, B. Elizabeth, J. Ruby Abinaya, M. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350384369

The practice of getting competitive quotes from carriers to convey goods is known as shipment bidding. Currently, open bidding is handled manually, and prospective bidders are frequently disqualified from taking part in the process. The system that is being proposed is designed to record every bidding procedure and make it cumulative in terms of monetary compensation. Real-time shipping tracking, feedback mechanisms, and bid evaluation tools are just a few of the features that the system offers. Shippers choose the best transportation company after weighing bids according to criteria including cost, level of service, and experience. In order to provide safe and effective financial operations, the proposed system also supports the payment and invoicing processes. Additionally, this application has analytical tools to produce reports and insights that help carriers cut costs. © 2024 IEEE.

关键词： Competition

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Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network

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Multimedia Tools and Applications 2024年 1-22页

作者： Alrowais, Fadwa Jamjoom, Arwa A. Karamti, Hanen Umer, Muhammad Alsubai, Shtwai Abate, Andrea F. Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Cerebral stroke indicates a neurological impairment caused by a localized injury to the central nervous system resulting from a diminished blood supply to the brain. Today, stroke stands as a global menace linked to the premature mortality of millions of people globally. Consequently, it is crucial to simulate how different risk factors impact the incidence of strokes and artificial intelligence is emerging as a suitable tool for achieving this goal. This study aims to construct dependable learning prediction models for stroke illness. The proposed approach can handle the inherent difficulty posed by a significant imbalance in classes, where the number of stroke patients is notably smaller compared to the healthy class. The study also provides a model based on an adaptive neuro-fuzzy inference system logic and convolutional neural networks (CNN) for accurate stroke prediction. An adaptive neuro-fuzzy inference system logic approach is adopted as it incorporates the capabilities of artificial intelligence and fuzzy inference, thereby having the potential to yield superior outcomes. The efficacy of the suggested method is extensively analyzed involving machine and deep learning classifiers and considering metrics relevant to encompass both the capacity for generalization and the accuracy in predicting. The proposed fuzzy-CNN model outperforms with the most considerable accuracy of 98.97% when using the original dataset, resampled dataset, and data imputation. K-fold cross-validation also shows superior results with an average accuracy of 99.6% for five folds. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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GAProtoNet: A Multi-head Graph Attention-based Prototypical Network for Interpretable Text Classification 31

GAProtoNet: A Multi-head Graph Attention-based Prototypical ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wen, Ximing Tan, Wenjuan Weber, Rosina O. College of Computing and Informatics Drexel University Philadelphia United States Department of Computer Science Tsinghua University Beijing China

ISBN: (纸本)9798891761964

Pretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on text classification tasks with their powerful word embeddings, but their black-box nature, which leads to a lack of interpretability, has been a major concern. In this work, we introduce GAProtoNet, a novel white-box Multi-head Graph Attention-based Prototypical Network designed to explain the decisions of text classification models built with LM encoders. In our approach, the input vector and prototypes are regarded as nodes within a graph, and we utilize multi-head graph attention to selectively construct edges between the input node and prototype nodes to learn an interpretable prototypical representation. During inference, the model makes decisions based on a linear combination of activated prototypes weighted by the attention score assigned for each prototype, allowing its choices to be transparently explained by the attention weights and the prototypes. Experiments on multiple public datasets show our approach achieves superior results without sacrificing the accuracy of the original black-box LMs. We also compare with four alternative prototypical network variations and our approach achieves the best accuracy and F1 among all. Our case study and visualization of prototype clusters also demonstrate the efficiency in explaining the decisions of black-box models built with LMs. Our codes are available at https://***/ximingwen/GAProtoNet. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Predictive Health Analysis Using ML for Proactive Risk Detection

Predictive Health Analysis Using ML for Proactive Risk Detec...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Karthik, R. Dhanush, B. Jivitesh Kanna, S. Hari Rama Chandran, A. K.S. Rangasamy College of Technology Department of Computer Science and Business Systems Namakkal India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

The advancements in modern computing technologies have significantly contributed to the development of advanced healthcare monitoring systems., enabling the early detection of critical conditions., such as falls. This review highlights key challenges and applications within healthcare monitoring and prediction models that consider relevant health parameters. In particular., this study discusses about the Random Forest algorithm., which is known for its capacity to manage complex datasets and generate precise patient health forecasts. By optimizing computational efficiency while enhancing predictive accuracy., this study demonstrates the efficacy of machine learning in healthcare. © 2025 IEEE.

关键词： Electronic health record

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Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 95-117页

作者： Pradeep Kumar Roy Sunil Saumya Jyoti Prakash Singh Snehasish Banerjee Adnan Gutub Department of Computer Science and Engineering Indian Institute of Information TechnologySuratIndia Indian Institute of Information Technology DharwadIndia Department of Computer Science and Engineering National Institute of TechnologyPatnaIndia The York Management School University of YorkYorkUK Computer Engineering Department College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic *** have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs *** clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature *** goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)*** final review included 133 *** research themes include question quality,answer quality,and expert *** terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack *** scope of most articles was confined to just one platform with few cross-platform *** with ML outnumber those with ***,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

关键词： answer quality community question answering deep learning expert user machine learning question quality

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A Hybrid Strategy Integrating Ant Colony Optimization and Locust-Inspired Algorithm (HACO-LA) to Boost Efficiency and Performance in cloud Resource Management 3

A Hybrid Strategy Integrating Ant Colony Optimization and Lo...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Saraswathy, S. Malathi, J. Subiksha, N. LakshmiNarayanan, S. Kumar, K. Sathish Livinesh, L. Sri Sai Ram Engineering College Department of Computer Science and Business Systems Tamilnadu Chennai India Sri Sai Ram Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309409

Efficient cloud resource management is vital, as it ensures the accurate selection and allocation of resources to diverse workloads or applications. This entails real-time balancing of workload performance, compliance, and cost to achieve optimization. Since there are many cloud users involved in scheduling tasks, or cloudlets, the scheduling process becomes complex. It involves selecting appropriate data centers, servers (hosts), and virtual machines (VMs). There are several bioinspired algorithms that are effective and popular for solving the NP-complete problem of cloudlet scheduling. By using these algorithms, cloudlets can be efficiently allocated to achieve faster execution times, better resource utilization, and a shorter waiting time. This study presents a hybrid technique that combines the strengths of both ant colony optimization and locust-inspired algorithm (HACO-LA), which have been shown to outperform other bio-inspired algorithms in cloud computing environments. Implementing this technique will lead to a decrease in the average response time, while also increasing the utilization of VMs and servers. The method was evaluated using the Cloud Sim toolbox with authentic data. It was compared to the preexisting Artificial Bee Colony Optimization (ABC) algorithm and Particle Swarm Optimization (PSO) algorithm. The findings demonstrated that HACO-LA surpassed both methods, leading to notable enhancements in server utilization, increased reliability, and decreased average response time. © 2024 IEEE.

关键词： Ant colony optimization

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Exact Solution of the Full RMSA Problem in Elastic Optical Networks

IEEE Networking Letters

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IEEE Networking Letters 2024年第1期6卷 55-59页

作者： David, Fabio Rezende, Jose F. De Barbosa, Valmir C. Federal University of Rio de Janeiro Informatics and Computer Science Department Rio de Janeiro21941-914 Brazil Federal University of Rio de Janeiro Systems Engineering and Computer Science Program Centro de Tecnologia Rio de Janeiro21941-914 Brazil

Exact solutions of the Routing, Modulation, and Spectrum Allocation (RMSA) problem in Elastic Optical Networks (EONs), so that the number of admitted demands is maximized while those of regenerators and frequency slots used are minimized, require a complex ILP formulation taking into account frequency-slot continuity and contiguity. We introduce the first such formulation, ending a hiatus of some years since the last ILP formulation for a much simpler RMSA variation was introduced. By exploiting a number of problem and solver specificities, we use the NSFNET topology to illustrate the practicality and importance of obtaining exact solutions. © 2019 IEEE.

关键词： Topology

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