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Optimized Artificial Neural Network Techniques to Improve Cybersecurity of Higher Education Institution

Computers, Materials & Continua 2022年第8期72卷 3385-3399页

作者： Abdullah Saad AL-Malaise AL-Ghamdi Mahmoud Ragab Maha Farouk S.Sabir Ahmed Elhassanein Ashraf A.Gouda Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Mathematics Department College of ScienceUniversity of BishaBishaSaudi Arabia Mathematics Department Faculty of ScienceDamanhour UniversityDamanhourEgypt

Education acts as an important part of economic growth and improvement in human *** educational sectors have transformed a lot in recent days,and information and Communication technology(ICT)is an effective part of the education *** every action in university and college,right from the process fromcounselling to admissions and fee deposits has been *** records,quiz,evaluation,mark,and grade submissions involved the utilization of the ***,security is essential to accomplish cybersecurity in higher security institutions(HEIs).In this view,this study develops an Automated Outlier Detection for CyberSecurity in Higher Education Institutions(AOD-CSHEI)*** AOD-CSHEI technique intends to determine the presence of intrusions or attacks in the *** AOD-CSHEI technique initially performs data pre-processing in two stages namely data conversion and class *** addition,the Adaptive Synthetic(ADASYN)technique is exploited for the removal of outliers in the ***,the sparrow search algorithm(SSA)with deep neural network(DNN)model is used for the classification of data into the existence or absence of intrusions in the HEIs ***,the SSA is utilized to effectually adjust the hyper parameters of the DNN *** order to showcase the enhanced performance of the AOD-CSHEI technique,a set of simulations take place on three benchmark datasets and the results reported the enhanced efficiency of the AOD-CSHEI technique over its compared methods with higher accuracy of 0.9997.

关键词： Higher security institutions intrusion detection system artificial intelligence deep neural network hyperparameter tuning deep learning

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Fuzzy Logic Expert System for Analyzing Student Performance

Fuzzy Logic Expert System for Analyzing Student Performance

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P Aruna Lokhesh R J Shajith Rooban G G Shakthivel K Tarun S R Department of Computing Coimbatore Institute of Technology Coimbatore India MSc Software Systems Coimbatore Institute of Technology Coimbatore India

Assessing the effectiveness of academics at various tiers has grown progressively difficult when employing traditional approaches that predominantly depend on numerical ratings to gauge their teaching and research accomplishments. With the indexing of academic performance in international databases using impact indices at various scales, the demand for advanced computer models has arisen. In this study, we suggest employing expert systems that utilize fuzzy logic to tackle the evaluation of educators, even when confronted with imprecise data and uncertainties. As a novel contribution, this research develops a fuzzy logic model and implements an algorithm using Python and Anaconda, which allows for fuzzy logic rule inference and comprehensive analysis. The implementation of the system is done using Python and leveraging Anaconda's data science environment, providing a seamless and efficient development process. The results of this pilot study involve testing and validating the proposed model through a user-friendly graphical interface, providing comprehensive insights based on minimum criteria along with additional explanations. This approach opens up new possibilities for more accurate assessment of academic performance, particularly when dealing with ambiguous data and uncertain conditions.

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Performance Evaluation of Clustering Algorithms for Enhancing Test Case Prioritization in Regression Testing

Performance Evaluation of Clustering Algorithms for Enhancin...

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Advanced Research in computing (ICARC), International Conference on

作者： S. Prabath Puviskar W.V.S.K. Wasalthilaka B.T.G.S Kumara Department of Computing & Information Systems Sabaragamuwa University of Sri Lanka Sri Lanka Department of Software Engineering Sabaragamuwa University of Sri Lanka Sri Lanka

Regression testing plays a crucial role in maintain software quality as applications evolving and getting more complex. Selection of a regression testing technique significantly influences overall software quality. In initial stages, this involves picking relevant test cases and remove unnecessary redundancies during minimization phase. Finally, prioritization of test cases phases most important ones is executed first which continue process iteratively or whole testing carried out which effect time and cost intensively. Test Case Prioritization method effective way to address the issue. In this study, we proposed approach to enhance Test Case Prioritization by integrating fault based methods and time of execution analysis. The study also evaluates the efficiency in proposed methodology through, employing APFD metric. The study aims to improve the precision and effectiveness of test case prioritization methods, advancing overall quality and reliability in dynamic software development environments. Notably, AHC outperforms in comparisons, showcasing its efficacy in improving outcomes.

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Artificial Intelligence Models in Power Generation for Energy Consumption Prediction

Artificial Intelligence Models in Power Generation for Energ...

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International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

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

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

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.

关键词： Adaptation models Energy consumption Analytical models Linear regression Artificial neural networks Predictive models Data models Artificial intelligence Power generation Streams

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Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress

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Computers in Biology and Medicine 2025年 185卷 109534-109534页

作者： Hameed, Ibtehaaj Khan, Danish M. Ahmed, Syed Muneeb Aftab, Syed Sabeeh Fazal, Hammad Department of Telecommunications Engineering NED University of Engineering and Technology Karachi Pakistan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Selangor47500 Malaysia

This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currently, the most used non-invasive method for measuring brain activity is the EEG, due to its high temporal resolution, user-friendliness, and safety. A Brain Computer Interface (BCI) framework can be made using these signals which can provide a new communication channel to people that are suffering from motor disabilities or other neurological disorders. However, implementing EEG-based BCI systems in real-world scenarios for motor imagery recognition presents challenges, primarily due to the inherent variability among individuals and low signal-to-noise ratio (SNR) of EEG signals. To assist researchers in navigating this complex problem, a comprehensive review article is presented, summarizing the key findings from relevant studies since 2017. This review primarily focuses on the datasets, preprocessing methods, feature extraction techniques, and deep learning models employed by various researchers. This review aims to contribute valuable insights and serve as a resource for researchers, practitioners, and enthusiasts interested in the combination of neuroscience and deep learning, ultimately hoping to contribute to advancements that bridge the gap between the human mind and machine interfaces. © 2024 Elsevier Ltd

关键词： Electroencephalography

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Organization studies Based Appraisal of Institutional Propositions in the Nigeria Data Protection Regulation

arXiv

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arXiv 2023年

作者： Sabo, Sumayya Babangida Utulu, Samuel Avemaria C. Department of Information Systems School of Information Technology and Computing American University of Nigeria Adamawa State Yola Nigeria

The central notion in the organization studies is that the formation, sustenance and demise of organizations are directly connected to the socio-cultural contexts where they are situated. So, the extent organizations are able to handle the challenges to personal data management can be linked to the socio-cultural contexts where they are situated. This justifies the reasons why regulations such as the Nigeria Data Protection Regulation (NDPR) are produced to initiate institutional propositions that shape socio-cultural contexts of organizations into forms that promote efficient personal data management. This study, a part of a larger study, appraises the NDPR in order to assess how the institutional propositions in the regulation can help shape the socio-cultural contexts of organizations in Nigeria, and then, promote personal data management practices. The study uses the narrative literature method to select the publications that were used to reach its objectives. The study addresses the dearth of scientific research that describes how institutional propositions in the NDPR can impact on the socio-cultural contexts of organizations in Nigeria. It also ameliorates gaps in scientific knowledge about how institutional propositions in the NDPR can be legitimized and help restructure socio-cultural contexts of organizations in Nigeria, and in effect, promote efficient personal data management practices. © 2023, CC BY.

关键词： information management

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Discussion Manipulation, Language and Domain Dependent Models: An Overview

Discussion Manipulation, Language and Domain Dependent Model...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Filip Chalás Igor Stupavský Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava

The amount of antisocial online behavior (AOB) in the political field has been on the rise in recent years. In the research, we are dealing with the reason for confusing AOB with other forms of antisocial behavior. We specifically dealt with NLP in the political field in the Slovak language, with a specific number of prefixes and suffixes. In this paper, we focus on the area of detection of manipulation of political discussions. The data source consisted of previously collected data in the period from April 2018 with an update until November 19, 2022 realized within this research from the site ***, which we supplemented with new claims. We trained several classification models on preprocessed data and evaluated them. For multi-class classification, the best results were achieved using logistic regression and a support vector machine trained on the resampled dataset—both achieving an accuracy of 0.56 and a macro F1 score of 0.39. In the case of binary classification, the best results were achieved by logistic regression—accuracy 0.7 and macro F1 score 0.56. These models could help detect manipulation in online political discussions.

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ASDF: A Differential Testing Framework for Automatic Speech Recognition systems

arXiv

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arXiv 2023年

作者： Xian Yuen, Daniel Hao Chen Pang, Andrew Yong Yang, Zhou Chong, Chun Yong Lim, Mei Kuan Lo, David School of Information Technology Monash University Malaysia School of Computing and Information Systems Singapore Management University Singapore

Recent years have witnessed wider adoption of Automated Speech Recognition (ASR) techniques in various domains. Consequently, evaluating and enhancing the quality of ASR systems is of great importance. This paper proposes ASDF, an Automated Speech Recognition Differential Testing Framework for to test ASR systems. ASDF extends an existing ASR testing tool, the CrossASR++, which synthesizes test cases from a text corpus. However, CrossASR++ fails to make use of the text corpus efficiently and provides limited information on how the failed test cases can improve ASR systems. To address these limitations, our tool incorporates two novel features: (1) a text transformation module to boost the number of generated test cases and uncover more errors in ASR systems and (2) a phonetic analysis module to identify on which phonemes the ASR system tend to produce errors. ASDF generates more high-quality test cases by applying various text transformation methods (e.g., change tense) to the text in a failed test cases. By doing so, ASDF can utilise a small text corpus to generate a large number of audio test cases, something which CrossASR++ is not capable of. In addition, ASDF implements more metrics to evaluate the performance of ASR systems from multiple perspectives. ASDF performs phonetic analysis on the identified failed test cases to identify the phonemes that ASR systems tend to transcribe incorrectly, providing useful information for developers to improve ASR systems. The demonstration video of our tool is made online at https://***/watch?v=DzVwfc3h9As. The implementation is available at https://***/danielyuenhx/asdf-differential-testing. Copyright © 2023, The Authors. All rights reserved.

关键词： Speech recognition

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Phytochemical screening, antioxidant properties, and photocytotoxicity of Clinacanthus nutans leaf extracts

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Vegetos 2024年第4期37卷 1652-1661页

作者： Chen, Chee-Shien Loke, Chui-Fung Poh, Tze-Ven Tan, Siow-Ping Department of Physical Science Faculty of Applied Sciences Tunku Abdul Rahman University of Management and Technology Kuala Lumpur 53300 Malaysia Research & Development and Commercialization Tunku Abdul Rahman University Management and Technology Kuala Lumpur 53300 Malaysia Department of Computer Science and Embedded Systems Faculty of Computing and Information Technology Tunku Abdul Rahman University Management and Technology Kuala Lumpur 53300 Malaysia

Clinacanthus nutans (Burm. f.) Lindau (C. nutans) is renowned in many tropical countries for its wide range of traditional uses and medicinal properties. This study qualitatively screened ethanol, hexane, and ethyl acetate (EA) leaf extracts of C. nutans for phytochemicals and evaluated their antioxidant activities using 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging and ferric reducing antioxidant power (FRAP) assays. Furthermore, the photocytotoxicity of the extracts against the A431 human skin squamous carcinoma cells was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Qualitative phytochemicals screening revealed the presence of terpenes, alkaloids, phenols, quinones, and chlorophyll, whereas flavonoids and anthraquinones were absent in all crude extracts. In the DPPH assay, the EA crude extract exhibited the lowest EC₅₀ value of 31 ± 2 μg/mL, whereas the same extract demonstrated the highest FRAP value of 7.42 ± 1.87 mmol Fe2+/g DW. After photodynamic therapy (PDT) activation, EA crude extract demonstrated the strongest photocytotoxic activity against A431 cells with an IC₅₀ value of 8 ± 1 μg/mL, followed by the hexane crude extract (IC₅₀ = 16 ± 2 μg/mL) and the ethanol crude extract (IC₅₀ = 20 ± 1 μg/mL). However, all three crude extracts showed negligible activity against the A431 cells in the absence of PDT treatment, even at concentrations as high as 200 μg/mL after 24 h of dark incubation. These findings suggest that C. nutans could serve as a potential source of natural antioxidants and photosensitizers for photodynamic therapy in cancer treatment. © 2023, The Author(s) under exclusive licence to Society for Plant Research.

关键词： Antioxidant activity Clinacanthus nutans PDT treatment Photocytotoxicity Phytochemical screening

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Help and Haggle: Social Commerce Through Randomized, All-or-Nothing Discounts 23

Help and Haggle: Social Commerce Through Randomized, All-or-...

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24th ACM Conference on Economics and Computation, EC 2023

作者： Yang, Luyi Jin, Chen Shao, Zhen Haas School of Business The University of California BerkeleyCA United States Department of Information Systems and Analytics School of Computing National University of Singapore Singapore Singapore The University of Science and Technology of China HeFei Anhui China

ISBN: (纸本)9798400701047

This paper studies a novel social commerce practice known as "help-and-haggle,"whereby an online consumer can ask friends to help her "haggle"over the price of a product. Each time a friend agrees to help, the price is cut by a random amount, and if the consumer cuts the product price down to zero within a time limit, she will get the product for free;otherwise, the product reverts to the original price. Help-and-haggle enables the firm to promote its product and boost its social reach as consumers effectively refer their friends to the firm. We model the consumer's dynamic referral behavior in help-and-haggle and solve for the optimal price-cut distribution for the firm that trades off social reach, promotion expense, and product sales. Our results are as follows. © 2023 Owner/Author(s).

关键词： Dynamic programming

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