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4th International Conference on Advancements in computing, ICAC 2022

作者： Attanayaka, Buddhima Nawinna, Dasuni Manathunga, Kalpani Abeygunawardhana, Pradeep K.W. Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Systems & Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398090

Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors. © 2022 IEEE.

关键词： Requirements engineering

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A measured inclusion of an RTI tool for the South Pacific 24

A measured inclusion of an RTI tool for the South Pacific

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7th International Conference on software Engineering and information Management, ICSIM 2024

作者： Sharma, Bibhya Reddy, Pritika Singh, Vineet School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Fiji Department of Computing Science and Information Systems Fiji National University Fiji

ISBN: (纸本)9798400709197

Good Governance of any country is reflected through transparency and accountability of its activities and the availability of information and open data in a timely manner to its citizens. The Right to information (RTI) Act guides the government and its citizens to achieve the objectives availability, and effective communication between the government and society, hence leading to good governance. While there are about 135 countries globally who have adopted the RTI Act, the Act is yet to be fully enacted in Fiji and the advocacy and awareness about RTI is in the infancy stage. This study entails details of an RTI awareness initiative in Fiji through the design and implementation of an e-course. The RTI e-course was developed using Articulate Storyline 360 and integrated into a learning management platform - Moodle. The course was then piloted to the university students in Fiji, Tonga and Vanuatu. The effectiveness of the course was measured in terms of course completion and collected feedback using an online questionnaire. The results from the Cronbach Alpha test showed that the RTI e-course was valid and reliable. Furthermore, the participants were satisfied with the design, structure and content of the e-course. The RTI e-course is the first-ever course on RTI as such this can be used by South Pacific countries to create awareness and educate their populace on their right to information. The awareness created will assist the people of the South Pacific to be more aware of the activities carried out by the government and eventually improve civic engagement and participation of the South Pacific populace to economic development of their society. © 2024 ACM.

关键词： Transparency

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SNSVM:SqueezeNet-Guided SVM for Breast Cancer Diagnosis

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Computers, Materials & Continua 2023年第8期76卷 2201-2216页

作者： Jiaji Wang Muhammad Attique Khan Shuihua Wang Yudong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Breast cancer is a major public health concern that affects women *** is a leading cause of cancer-related deaths among women,and early detection is crucial for successful ***,breast cancer can often go undetected until it has reached advanced stages,making it more difficult to ***,there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early *** proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography *** extracted features are then utilized to train a support vector machine(SVM)for mammography image *** SqueezeNet-guided SVMmodel,known as SNSVM,achieved promising results,with an accuracy of 94.10% and a sensitivity of 94.30%.A 10-fold cross-validation was performed to ensure the robustness of the results,and the mean and standard deviation of various performance indicators were calculated across multiple *** model also outperforms state-of-the-art models in all performance indicators,indicating its superior *** demonstrates the effectiveness of the proposed approach for breast cancer diagnosis using mammography *** superior performance of the proposed model across all indicators makes it a promising tool for early breast cancer *** may have significant implications for reducing breast cancer mortality rates.

关键词： SqueezeNet support vector machine breast cancer diagnosis

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Optimized Generative Adversarial Networks for Adversarial Sample Generation

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Computers, Materials & Continua 2022年第8期72卷 3877-3897页

作者： Daniyal M.Alghazzawi Syed Hamid Hasan Surbhi Bhatia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversitySaudi Arabia

Detecting the anomalous entity in real-time network traffic is a popular area of research in recent *** few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this *** are using Deep Convolutional Generative Adversarial Networks(DCGAN)to trick the malware classifier to believe it is a normal *** this work,a new dataset is created to fool the Artificial Intelligence(AI)based malware detectors,and it consists of different types of attacks such as Denial of Service(DoS),scan 11,scan 44,botnet,spam,User Datagram Portal(UDP)scan,and ssh *** discriminator used in the DCGAN discriminates two different attack classes(anomaly and synthetic)and one normal *** model collapse,instability,and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm(AO-MBHS).This algorithm helps the generator to create realistic malware samples to be undetected by the *** performance of the proposed methodology is evaluated using different performance metrics such as training time,detection rate,F-Score,loss function,Accuracy,False alarm rate,*** superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection *** support vector machines(SVM)is used as the malicious traffic detection application and its True positive rate(TPR)goes from 80%to 0%after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples.

关键词： Aquila optimizer convolutional generative adversarial networks mine blast harmony search algorithm network traffic dataset adversarial artificial intelligence techniques

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An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

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Computers, Materials & Continua 2022年第7期72卷 1173-1193页

作者： Daniyal M.Alghazzawi Osama Bassam J.Rabie Surbhi Bhatia Syed Hamid Hasan Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversitySaudi Arabia

The Deep Neural Networks(DNN)training process is widely affected by backdoor *** backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning *** state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same *** overcome this problem,in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies,and in order to achieve this objective,we are developing an improved Convolutional Neural Network(ICNN)model optimized using a Gradient-based Optimization(GBO)(ICNN-GBO)*** the ICNN-GBO model,we are injecting the triggers via a steganography and regularization *** are generating triggers using a single-pixel,irregular shape,and different *** performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate,stealthiness,pollution index,anomaly index,entropy index,and *** the CNN-GBO model is trained with the poisoned dataset,it will map the malicious code to the target *** proposed scheme’s effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 andMSCELEB 1M *** results demonstrate that the proposed methodology offers significant defense against the conventional backdoor attack detection frameworks such as STRIP and Neutral cleanse.

关键词： Convolutional neural network gradient-based optimization steganography backdoor attack and regularization attack

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Explainable Transformer-based Intrusion Detection in Internet of Medical Things (IoMT) Networks 23

Explainable Transformer-based Intrusion Detection in Interne...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Kalakoti, Rajesh Nomm, Sven Bahsi, Hayretdin School of Information Technology TalTech Tallinn Estonia School of Informatics Computing and Cyber Systems Northern Arizona University United States

ISBN: (纸本)9798350374889

Internet of Medical Things (IoMT) systems have brought transformative benefits to patient monitoring and remote diagnosis in healthcare. However, these systems are prone to various cyber attacks that have a high impact on security and privacy. Detecting such attacks is crucial for implementing timely and effective countermeasures. Machine learning methods have been applied for intrusion detection tasks in various networks, but explaining the reasons for detection decisions remains an obstacle for security analysts. In this paper, we demonstrate that Transformer architecture, the core of the recent revolutionary large language models, constitutes a promising solution for intrusion detection in IoMT networks. We utilized a comprehensive dataset, CICIoMT2024, recently released specifically for these networks. We created a binary classification model for discriminating attacks from benign traffic and a multi-class model for the identification of specific attack types. We applied Explainable AI (XAi) methods such as LIME and SHAP to generate posthoc explanations for the model decisions. We evaluated and compared the quality of explanations based on three metrics: faithfulness, sensitivity, and complexity. Our findings demonstrate that the applied XAI methods enhance transparency in the predictions of Transformer-based intrusion detection models for IoMT networks, proving that both transparency and high performance can be achieved simultaneously. © 2024 IEEE.

关键词： Network intrusion

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Artificial Intelligence Models in Power Generation for Energy Consumption Prediction 4

Artificial Intelligence Models in Power Generation for Energ...

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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Tan, Lit Yang Chen Kwan, Charis Shwu Ajibade, Samuel-Soma M. Ramly, Athirah Mohd School of Engineering and Technology Sunway University Department of Computing and Information Systems Selangor Malaysia School of Architecture Computing and Engineering University of East London Department of Computing London United Kingdom

ISBN: (纸本)9798350353266

The incorporation of artificial intelligence (AI) into power-related applications signifies a new and unexplored domain in machine learning for predicting power generation. This novel method utilizes prediction models, often used in different fields, to predict energy-related patterns, providing a unique and specialized viewpoint. The synergy of academicians, AI experts, and industry professionals in the energy sector has resulted in the creation of customized AI models to optimize operational efficiency. By customizing various AI models to suit the distinct attributes of energy scenarios and datasets, these models are positioned to transform energy management methods. This study examines the utilization of AI models to enhance energy efficiency in power generation in Malaysia. The project seeks to predict future power consumption in various sectors, analyze growth rates, and identify sectors with investment potential by developing a Linear Regression model. In addition, a thorough power plan is developed using the estimated energy usage. A comparative analysis is performed to determine the most appropriate model for this particular scenario, which will improve decision-making in the energy sector. The results of this study present promising opportunities for further investigation. By broadening the study's focus to encompass a broader array of AI models and their assessment of performance, it is possible to gain useful insights for predicting power generation. Furthermore, the integration of real-time data streams and the inclusion of feedback loops in the AI models could improve their ability to adapt and increase their accuracy as time progresses. © 2024 IEEE.

关键词： Prediction models

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Recursive Weighted 2-Means Split Algorithm For Under-segmentation Reduction 28

Recursive Weighted 2-Means Split Algorithm For Under-segment...

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28th International Conference on information Society and University Studies, IVUS 2023

作者： Magylaitė, Kornelija Arlauskas, Lukas Ryselis, Karolis Software Engineering Department Kaunas University of Technology Lithuania Information Systems Department Kaunas University of Technology Lithuania

Human body segmentation is utilized in various applications as an intermediate step. This problem is best solved using supervised machine learning solutions, however, they require annotated data for training. Unfortunately, annotating data for segmentation is an extremely labor-intensive task. It could be improved by using semi-automatic segmentation algorithms, however, their accuracy tends to be low for complex scenes. The errors made by such algorithms can be manually corrected by a human. This process could be more efficient when automatic correction tools are added to the data processing pipeline. This research aims to improve the final semi-automatic segmentation accuracy by improving an existing random forest classifier for correcting point cloud segmentation based on metrics of recursive 2-Means split. We replace the K-Means clustering with a weighted K-Means clustering and optimize the weights. Experiments have revealed that the segmentation accuracy of 62.4% is improved to 66.1% with a weight ratio of 0.4. Since higher accuracy leads to less manual labor, this is a sought-after improvement that reduces the time to prepare datasets for human body segmentation. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： K-means clustering

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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Computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

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Machine Intelligence Research 2024年第6期21卷 1077-1091页

作者： Yu Xie Yanfeng Liang Chao Wen A.K.Qin Maoguo Gong School of Computer and Information Technology Shanxi UniversityTaiyuan030000China Key Laboratory of Collaborative Intelligence Systems of Ministry of Education Xidian UniversityXi’an710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyMelbourne3000Australia

Graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic *** import-antly,real-world graph data are often scattered in different *** by these observations,this article presents federated collaborative graph neural networks for few-shot graph classification,termed *** its owned graph examples,each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learn-ing model that can generalize to novel categories not seen while *** each branch,initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggrega-tion layers for few-shot graph classification,which can reduce over-fitting while learning with scarce labeled graph ***,multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effect-ively tackle the graph data island *** experimental results on few-shot graph classification benchmarks demonstrate the ef-fectiveness and superiority of our proposed framework.

关键词： Graph neural networks federated learning collaborative learning few-shot learning graph classification

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