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International Research Conference on Smart computing and systems Engineering (SCSE)

作者： M. A. F. Saroth P. M. A. K. Wijerathne B. T. G. S. Kumara Department of Computing and Information Systems Faculty of Computing Sabaragamuwa University of Sri Lanka Department of Software Engineering Faculty of Computing Sabaragamuwa University of Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

A successful software development process depends on software requirements. Those requirements are often classified into two categories: functional requirements (FR) and non-functional requirements (NFR). In software development, NFRs play a crucial role, but they are often forgotten by the developers. Therefore, it causes adverse consequences. As a result, an NFR classification method is required to assist software developers in classifying NFR from requirement documents. The majority of research in this area focuses on categorizing requirements as FR or NFR. However, NFR can be divided into more than two categories. As a result, a multi-label categorization strategy was required. This study presented an approach for categorizing NFR based on product, process, and external factors. To categorize the NFR, the authors developed four machine learning-based algorithms entitled Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K Nearest Neighbor Classifier (KNN) classifier, as well as one deep learning method named Long Short-Term Memory (LSTM) model. Ultimately, the authors examine the models and conclude that the LSTM model which gained an accuracy of 99.69% is appropriate for this type of classification.

关键词： Support vector machines Deep learning Logistic regression Machine learning algorithms Computational modeling software Classification algorithms

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An embedded vertical‐federated feature selection algorithm based on particle swarm optimisation

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CAAI Transactions on Intelligence technology 2023年第3期8卷 734-754页

作者： Yong Zhang Ying Hu Xiaozhi Gao Dunwei Gong Yinan Guo Kaizhou Gao Wanqiu Zhang School of Information and Control Engineering China University of Mining and TechnologyXuzhouChina The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina School of Computing University of Eastern FinlandKuopioFinland The Macao Institute of Systems Engineering Macao University of Science and TechnologyTaipaChina

In real life,a large amount of data describing the same learning task may be stored in different institutions(called participants),and these data cannot be shared among par-ticipants due to privacy *** case that different attributes/features of the same instance are stored in different institutions is called vertically distributed *** pur-pose of vertical‐federated feature selection(FS)is to reduce the feature dimension of vertical distributed data jointly without sharing local original data so that the feature subset obtained has the same or better performance as the original feature *** solve this problem,in the paper,an embedded vertical‐federated FS algorithm based on particle swarm optimisation(PSO‐EVFFS)is proposed by incorporating evolutionary FS into the SecureBoost framework for the first *** optimising both hyper‐parameters of the XGBoost model and feature subsets,PSO‐EVFFS can obtain a feature subset,which makes the XGBoost model more *** the same time,since different participants only share insensitive parameters such as model loss function,PSO‐EVFFS can effec-tively ensure the privacy of participants'***,an ensemble ranking strategy of feature importance based on the XGBoost tree model is developed to effectively remove irrelevant features on each ***,the proposed algorithm is applied to 10 test datasets and compared with three typical vertical‐federated learning frameworks and two variants of the proposed algorithm with different initialisation ***-mental results show that the proposed algorithm can significantly improve the classifi-cation performance of selected feature subsets while fully protecting the data privacy of all participants.

关键词： Evolutionary optimization feature selection privacy protection vertical‐federated learning

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Application of the Refactoring to the Understandability and the Cognitive Complexity of a software 7

Application of the Refactoring to the Understandability and ...

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7th IEEE International conference for Convergence in technology, I2CT 2022

作者： Wijendra, Dinuka R. Hewagamage, K.P. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka University of Colombo School of Computing Department of Information Systems Colombo Sri Lanka

ISBN: (纸本)9781665421683

Cognitive complexity of a software determines the methodology of comprehending the internal logic of a given software by an individual, quantitatively. The procedure of handling a software by different users is different, which results the cognitive complexity as a subjective measurement. The quantification of the cognitive complexity is still not standardized due to the varied number of factors affected for the cognitive complexity determination and its nature of the subjectivity. This paper evaluates the relationship between the cognitive complexity and understandability as one of the qualitative factors to determine the cognitive complexity and the usage of refactoring techniques to reduce the cognitive complexity without refraining its calculation process with respective to the internal logic of the software as in other standard software complexity metrics perform. © 2022 IEEE.

关键词： Computer circuits

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Explainable Federated Learning for Botnet Detection in IoT Networks

Explainable Federated Learning for Botnet Detection in IoT N...

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Cyber Security and Resilience (CSR), IEEE International Conference on

作者： Rajesh Kalakoti Hayretdin Bahsi Sven Nõmm Department of Software Science School of Information Technology Taltech Tallinn Estonia Department of Software Science School of Information Technology TalTech Estonia School of Informatics Computing and Cyber Systems Northern Arizona University United States

ISBN: (数字)9798350375367

ISBN: (纸本)9798350375374

The widespread use of Internet of Things (IoT) de-vices has increased the vulnerability to botnet attacks, presenting significant challenges to network security. Federated learning (FL) is a promising approach for detecting IoT botnets while preserving data privacy. However, the black-box nature of FL models impedes their interpretability and transparency, which are crucial for trust and accountability in security applications. In this paper, we propose an approach to generate explanations for the server model induced for intrusion detection tasks in the FL setting without direct access to data of IoT device-based clients. This involves aggregating SHAP (SHapley Additive ex-Planations) value explanations from individual IoT device-based client models to approximate the server model's explanations. We evaluated this approach by comparing the aggregated client-based explanations with the server-based explanations obtained when the server has access to the data of participating IoT device clients. We employed a deep neural network (DNN) model trained in a horizontal federated learning (HFL) setting with the federated averaging (FedAvg) algorithm. DNN model achieved high detection rates of Accuracy, Precision, Recall & Fl-Score in Botnet Detection of multiclass classification on both IoT device-based client-side models and the server-side model. Additionally, we analyzed the importance of features contributing to IoT botnet detection using the generated SHAP explanations. The results demonstrate that the aggregation of client-based SHAP explanations closely approximates the server-based explanations, achieving comparable explainability without compromising data privacy.

关键词： Data privacy Federated learning Botnet Intrusion detection Artificial neural networks Network security Feature extraction

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An Efficient Technique to Prevent Data Misuse with Matrix Cipher Encryption Algorithms

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Computers, Materials & Continua 2023年第2期74卷 4059-4079页

作者： Muhammad Nadeem Ali Arshad Saman Riaz Syeda Wajiha Zahra Ashit Kumar Dutta Moteeb Al Moteri Sultan Almotairi Department of Computing Abasyn UniversityIslamabadPakistan Department of Computer Science National University of TechnologyIslamabadPakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Management Information Systems Business Administration CollegeKing Saud UniversityRiyadh11451Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Many symmetric and asymmetric encryption algorithms have been developed in cloud computing to transmit data in a secure *** cryptography is a data encryption mechanism that consists of different steps and prevents the attacker from misusing the *** paper has developed an efficient algorithm to protect the data from invaders and secure the data from *** this algorithm is applied to the cloud network,the attacker will not be able to access the *** encrypt the data,the values of the bytes have been obtained by converting the plain text to ASCII.A key has been generated using the Non-Deterministic Bit Generator(NRBG)mechanism,and the key is XNORed with plain text bits,and then Bit toggling has been *** that,an efficient matrix cipher encryption algorithm has been developed,and this algorithm has been applied to this *** capability of this algorithm is that with its help,a key has been obtained from the plain text,and only by using this key can the data be decrypted in the first steps.A plain text key will never be used for another plain *** data has been secured by implementing different mechanisms in both stages,and after that,a ciphertext has been *** the end of the article,the latest technique will be compared with different *** will be a discussion on how the present technique is better than all the other techniques;then,the conclusion will be drawn based on comparative analysis.

关键词： Symmetric cryptography ciphertext encryption decryption cloud security matrix cipher

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State of the Art Survey on Artificial Intelligence-Based Approach for Early Diagnosis of Tuberculosis

State of the Art Survey on Artificial Intelligence-Based App...

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Electrical and Computer Engineering Researches (ICECER), International Conference on

作者： Gibson Chengetanai Stewart Muchuchuti Freedmore Sidume Rabson Tamukate Kushata Ntwaetsile Patience R. Nthekeng School of Computing & Information Systems Botswana Accountancy College Gaborone Botswana School of Computing & Information Systems Botswana International University of Science and Technology Palapye Botswana Information Technology Dept Botswana Accountancy College Gaborone Botswana

ISBN: (数字)9798331539733

ISBN: (纸本)9798331539740

Tuberculosis (TB) has been affecting people and high deaths from it has been recorded globally. Techniques around use of artificial intelligence (AI) based approaches are gaining momentum in early diagnosis of TB. This research has managed to uncover current approaches that are being used in TB diagnosis. It was found out that the AI-based approaches are offering better accuracy especially if the data sets used to train the models are high. Combining various AI-based models was observed to offer better accuracy, i.e., the ensemble approach. The pitfall of the current AI-based models are that they lack interpretability since they are black box and thus cannot offer decision on how the have arrived at the decision, which is a critical area to consider in the medical field. This pitfall can thus be addressed by utilising explainable artificial intelligence, and this has been identified as a future area in this study.

关键词： Deep learning Surveys Accuracy Machine learning algorithms Electronic equipment Tuberculosis Explainable AI Flight recording Ensemble learning Medical diagnostic imaging

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Cognitive Complexity Reduction through Control Flow Graph Generation 7

Cognitive Complexity Reduction through Control Flow Graph Ge...

引用

7th IEEE International conference for Convergence in technology, I2CT 2022

ISBN: (纸本)9781665421683

The cognitive complexity of a software determines the human comprehension effort to determine its underlying logic. The human comprehension effort preliminary depends on the person, who deals with the software, the source code and the problem to be addressed. The understandability of a given source code is varying with each user such that the cognitive complexity results with a subjective measurement. The graphical representation of the logical behavior of a source code implies the individual to comprehend the logic easily rather than referring to its original code base. This paper evaluates the possibility of using control flow graph representation to reduce the cognitive complexity of a given source code, thereby ensuring a less software complexity. © 2022 IEEE.

关键词： Graphic methods

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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2024 International Conference on Advancement in Renewable Energy and Intelligent systems, AREIS 2024

作者： Raji, V. Maheswari, S. Dharinya, S. Sree Chinniah, P. Kavin, K.S. Raja, Kiruba Thangam S.K.P. Engineering College Dept. of Information Technology Tiruvannamalai India Tamilnadu Erode India School of Computer Science Engineering and Information Systems Vit University Dept. of Software and Systems Engineering Vellore India ST. Joseph College of Engineering Dept. of Ece Tamilnadu Sriperumbudur India Government College of Engineering Dept. of Eee Tamil Nadu Tirunelveli India Vit University Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350387230

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial Intelligence Adoption in QSR Industry 22

Artificial Intelligence Adoption in QSR Industry

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6th International Conference on E-Education, E-Business and E-technology, ICEBT 2022

作者： Lee, Angela S.H Bengeri, Atul Kan, Chong Chin Department of Computing and Information Systems School of Engineering and Technology Sunway University Malaysia Linc Education Singapore Singapore

ISBN: (纸本)9781450397216

According to research conducted by Allied Market Research (Kale and Deshmukh, 2020), the Quick-service restaurant (QSR) industry has been enjoying significant growth over the last several years and the trend is expected to continue at least over the next five years. This is due to the agility of most QSR brands in adapting to consumer needs, quality food products and service innovation. Offering the convenience of brands reach for their consumers such as a restaurant within the reach of the customer, as well as providing online ordering and food delivery convenience. The convenience of the omnichannel digital services offered by typical QSR restaurant brands to their customer provides convenience for service delivery and at the same time provides an opportunity to collect, collate, check, consume and analyse the data to examine, explore and provide hindsight, insight and foresight for the QSR industry to be ahead of the competition. Not only are these digital customer channels a great way of providing a better customer experience, but they also help QSR operators in capturing massive data such as sales transactions, customer details, product performance, and daypart (breakfast, lunch and dinner) sales insights. With the ability to analyse and perform analytics on the data and from a data strategy perspective, it is paramount to have a robust big data platform, data warehouse and ability to consolidate data from all the IT systems. This is to enable the development of BI/dashboards to provide better business insights, such as real-time and daypart product sales. Data could also be used to develop new digital products for more positive customer engagements such as upselling and loyalty programmes or for implementing of "smart kitchen"that is able to connect various datasets for maximum kitchen efficiency and cost optimisation. Due to a lack of data science approach and machine learning capability, most QSR brands are missing this sales uplifting opportunity. To develo

关键词： Data Analytics

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Toward Effective Secure Code Reviews: An Empirical Study of Security-Related Coding Weaknesses

arXiv

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arXiv 2023年

作者： Charoenwet, Wachiraphan Thongtanunam, Patanamon Pham, Van-Thuan Treude, Christoph School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia School of Computing and Information Systems Singapore Management University Singapore

Identifying security issues early is encouraged to reduce the latent negative impacts on the software systems. Code review is a widely-used method that allows developers to manually inspect modified code, catching security issues during a software development cycle. However, existing code review studies often focus on known vulnerabilities, neglecting coding weaknesses, which can introduce real-world security issues that are more visible through code review. The practices of code reviews in identifying such coding weaknesses are not yet fully investigated. To better understand this, we conducted an empirical case study in two large open-source projects, OpenSSL and PHP. Based on 135,560 code review comments, we found that reviewers raised security concerns in 35 out of 40 coding weakness categories. Surprisingly, some coding weaknesses related to past vulnerabilities, such as memory errors and resource management, were discussed less often than the vulnerabilities. Developers attempted to address raised security concerns in many cases (39%-41%), but a substantial portion was merely acknowledged (30%-36%), and some went unfixed due to disagreements about solutions (18%-20%). This highlights that coding weaknesses can slip through code review even when identified. Our findings suggest that reviewers can identify various coding weaknesses leading to security issues during code reviews. However, these results also reveal shortcomings in current code review practices, indicating the need for more effective mechanisms or support for increasing awareness of security issue management in code reviews. © 2023, CC BY-NC-ND.

关键词： software design

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