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Jordanian Cybersecurity Conference (IJCC), International

作者： Haidar Jabbar Samir Al-Janabi Francis Syms Faculty of Applied Science and Technology Humber Polytechnic Toronto Canada Department of Computing and Software McMaster University Hamilton Canada

ISBN: (数字)9798331518462

ISBN: (纸本)9798331518479

Amid the rising complexity and frequency of cyber threats, integrating emerging technologies such as artificial intelligence (AI) within IT project management frameworks is crucial for fostering cyber resilience. This paper proposes an AI-enhanced Cyber-Resilient IT Project Management Framework that integrates predictive analytics and machine learning across the cybersecurity process. The proposed framework emphasizes governance and risk management through proactive risk assessment, real-time threat detection, and automated incident response, enhancing resilience against evolving threats. The framework's adaptability and effectiveness are illustrated through its potential applications in diverse domains such as forensics, healthcare, and other sectors requiring robust data protection and cybersecurity strategies.

关键词： Forensics Project management Medical services Machine learning Threat assessment Real-time systems Risk management Computer crime Predictive analytics Resilience

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Innovative Approaches in Predictive Analysis and Personalized Online Shopping Recommendations with AI Powered-Chat

Innovative Approaches in Predictive Analysis and Personalize...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Bazeer Ahamed Fajlul Kareem Mohamed Yasin Noor Mohamed University of Technology and Applied Sciences College of Computing and Information Sciences-Al Musannah Sultanate of Oman Information Technology Sultan Qaboos University Muscat Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Customers are more exposed than ever to the necessary quantity of information about goods and services nowadays. As a result, there is a great deal of variability in consumer demand, making it difficult for retailers to provide the appropriate goods and services in accordance with client preferences. Recommender systems can use consumer evaluations, comments, and shared experiences regarding a product as an efficient way to learn about the preferences of the customer. Users' buying lists, viewer lists, and product purchase counts are regarded as the primary core attributes for doing the analysis of the items watched and purchased in order to recommend products to users. This uses a hybrid recommendation methodology that blends data analytics, collaborative filtering, and machine learning. It is possible to create a framework for forecasting consumer behavior. Users are increasingly using social media in the current situation to get prompt customer assistance; however, the majority of these complaints were unanswered or not addressed at all. A new conversational system has been created to automatically generate responses to user inquiries on social media in order to address this problem.

关键词： Technological innovation Ethics Data analysis Consumer behavior Social networking (online) Machine learning Electronic commerce Predictive analytics Forecasting Recommender systems

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Using Sentiment Analysis to Explore the Accommodation Experience in the Sharing Economy through Topic Modeling 4

Using Sentiment Analysis to Explore the Accommodation Experi...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Bandara, H.M.A.P. Charles, J. Lekamge, L.S. Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences Sri Lanka

ISBN: (纸本)9798350398090

The rapid proliferation of internet-based technology has made the sharing economy the next e-commerce business model. Recently, sharing economy lodging platforms have gained a significant market share in the tourism and lodging industry. Tourism and hospitality industries are now being significantly disrupted by Airbnb, an online lodging platform. For businesses and customers who utilize these accommodation platforms, online reviews serve as quality indicators, affecting their decisions to make a transaction. Sentiment analysis and text mining can be used to analyze these online reviews to identify various factors embedded in them that can influence how guests perceive lodging in the sharing economy. Peer-to-peer accommodation platforms can benefit from analyzing these aspects since they can utilize the results to streamline their operations and give customers better services. Current research on this domain has only identified a limited number of important factors, such as trust, quality, security, price, cleanliness, and indoor environmental quality. However, there can be many other factors that can affect the accommodation experience. These factors would require further attention. Therefore, in this study a dataset pertaining to the Airbnb platform was considered which contained a total of 401 964 review comments. Word cloud, frequency distribution, and topic modeling were used as data analysis techniques to identify various factors affecting accommodation experience. Results indicate that factors including location, safety, host-guest interaction, amenities, proximity to restaurants and transit options, and apartment uniqueness can be primarily taken into account to give superior services to their clients. © 2022 IEEE.

关键词： Sentiment analysis

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Exsolution of CoFe(Ru)nanoparticles in Ru-doped(La_(0.8)Sr_(0.2))_(0.9)Co_(0.1)Fe_(0.8)Ru_(0.1)O_(3−δ)for efficient oxygen evolution reaction

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Nano Research 2022年第8期15卷 6977-6986页

作者： Yi Liang Yu Cui Yang Chao Ning Han Jaka Sunarso Ping Liang Xin He Chi Zhang Shaomin Liu School of Applied Physics and Materials Wuyi UniversityJiangmen 529020China Department of Materials Engineering KU LeuvenLeuven 3001Belgium Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of TechnologyJalan Simpang TigaKuching 93350SarawakMalaysia College of Chemical Engineering Beijing University of Chemical TechnologyBeijing 100029China

The rational modification of perovskite oxides(ABO3−δ)is essential to improve the efficiency and stability of oxygen *** engineering represents a facile approach to modify perovskites for enhanced *** compositional design and in situ exsolution,a Ru-doped(La_(0.8)Sr_(0.2))_(0.9)Co_(0.1)Fe_(0.8)Ru_(0.1)O_(3−δ)(LSCFR)perovskite anchored with CoFe(Ru)alloy particles on the surface was fabricated for oxygen evolution reaction(OER)in this *** results and calculations indicate that Ru-doping promotes the exsolution of CoFe(Ru)from the perovskite *** exsolution in the reduced atmosphere for 3 h,the catalyst(LSCFR-3)exhibited superior OER performance with an overpotential of 347 mV and a Tafel slope of 54.65 mV·dec^(−1),and showed good stability in contrast to the pristine *** exsolution of CoFe(Ru)particles,Ru doping,and the increase of surface oxygen vacancies are responsible for the enhancement of OER *** findings obtained in this study highlight the possibility of controlling exsolution and composition of nanoparticles by element doping and prove that in situ exsolution is an effective strategy for designing OER catalysts.

关键词： perovskite oxides in situ exsolution oxygen evolution reactions CoFe nanoparticles B-site cation doping

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Progress of Nursing Informatics for Mental Health Initiatives 2

Progress of Nursing Informatics for Mental Health Initiative...

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2nd International Conference on Interdisciplinary Cyber Physical Systems, ICPS 2022

作者： Alenezi, Atallah Alghamdi, Abdulrahman A Shaqra University Applied Medical Science-Department of Nursing Shaqra Saudi Arabia Shaqra University College of Computing and Information Technology Shaqra Saudi Arabia

ISBN: (数字)9781665470223

ISBN: (纸本)9781665470223

While there is significant evidence linking information technology (IT) to enhanced safety of patients, quality of care, availability, and effectiveness, nursing staff must clearly show computer, information systems, and digital skills capabilities in order to utilise IT for practise, training, as well as research. The nurse practitioner has developed IT skills and knowledge for all stages of nurses, both novice and veteran. In addition, latest approved standards eloquent role-specific anticipations for advanced training. Regrettably, there is major worry that several nurses may lack these skills and that professional nurses are unprepared to instruct them. Often these students and faculty have newbie levels of skill, according to IT competency assessments that have primarily concentrated on nursing curriculum. Various researches, primarily in nursing education, have found that only focused interventions result in significant improvements in IT capabilities. This study contains medical and mental health nursing staff with a refresh on the literary works on IT capabilities, recent technological progress to establish and inventions, and their repercussions for the area of expertise. The mental health care that are now available are inextricably connected to the innovation that has been available to us for years of research and practise. Web, smartphone, sensor, as well as informatics technical advances can do more than just improve existing support systems. New technologies may further assist us better comprehend the information about mental disorder and challenge our underlying assumptions about the configuration, limitations, and modes of psychiatric treatment. © 2022 IEEE.

关键词： Students

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Designing of a Voice-Based Programming IDE for Source Code Generation: A Machine Learning Approach

Designing of a Voice-Based Programming IDE for Source Code G...

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2022 International Research Conference on Smart computing and Systems Engineering, SCSE 2022

作者： Nizzad, A.R.M. Thelijjagoda, Samantha British College of Applied Studies School of Computing Sri Lanka Sri Lanka Institute of Information Technology Department of Information Systems Engineering Sri Lanka

ISBN: (数字)9781665473750

ISBN: (纸本)9781665473750

Humans are precise in recognizing natural languages and responding contextually unlike machines. However, speech recognition or Automatic speech recognition often refers to converting human speech or voice to textual information with the help of artificial intelligence algorithms. With the advancement of Artificial Intelligence technologies and extensive research being conducted in AI, speech recognition has received much attention and has emerged as a subset of Natural Language Processing where the advancement and accuracy in speech recognition will open many ways to provide a high standard of human-computer interaction. In this study, using the pre-trained transformer model with a transfer learning approach, the English to Python dataset was used to train the transformer model to produce syntactically correct source code in python. Additionally, the Word2Vec model was used to generate voice-to-text as input for the model. For the purpose of demonstration, a custom Python IDE is developed to generate source code from voice input. The results and findings suggest that in the transformer model, with the use of transfer learning, any dataset can be trained to produce syntactically correct source code. The model's training loss and validation loss were below 5 and 2.1, respectively. Future research can focus on generating valid source code from any human spoken language without restricting it to English only. © 2022 IEEE.

关键词： Python

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Breast Cancer Diagnosing Empowered with Transfer Learning 8

Breast Cancer Diagnosing Empowered with Transfer Learning

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Ashfaq, Ayesha Yi, Wenhui Si, Jinhai Nasir, Muhammad Umar School of Electronics Science and Information Technology Xi'an Jiaotong University Xian China School of Computing and Applied Sciences Riphah International University Lahore Pakistan

ISBN: (纸本)9781665450515

Breast cancer is the most common type of cancer worldwide in the last five years. Breast cancer is the second most common cause of death in women, after skin cancer. Breast cancer detection at an early stage has the potential to reduce morbidity and mortality. There are many technologies and diagnostic tests available to diagnose breast cancer. Artificial neural networks can automatically extract multiple features and make prediction about breast cancer. Multiple labeled images are required to train neural networks that are an unconventional method for some types of data images, such as breast magnetic resonance imaging (MRI). The most effective solution is to fine-tuning the neural network. In this paper, the proposed transfer learning model uses AlexNet in a convolutional neural network to extract range features from breast cancer MRI images in order to train the model. The proposed model is more efficient and significant because it achieves higher accuracy during the training and testing processes, with 99.65% and 90.57% of training and testing, respectively. As a result, this study demonstrates that fine-tuning in the neural network can detect breast cancer using magnetic resonance imaging and that training a neural network classifier by feature extraction using the proposed model is faster and more efficient. © 2022 IEEE.

关键词： Diseases

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Implementation and Use of Clinical Decision Support System in Malawi National ART EMR System

Implementation and Use of Clinical Decision Support System i...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Pachawo Bisani Goodall Nyirenda Department of Computing University of Malawi Zomba Malawi Department of Computing and Information Technology Malawi University of Business and Applied Sciences Blantyre Malawi

The introduction of Malawi national antiretroviral therapy (NART) electronic medical record (EMR) system has improved the management and reporting of ART patient data. Designed to aid in decision-making by providers for individual patients in their care, the system has been integrated with a Clinical Decision Support System (CDSS). NART EMR system was introduced in 2006, and by 2021, it had been extended to over 206 healthcare facilities in the country. Despite several evaluations conducted on the NART EMR, there has been limited attention given to evaluating the implementation and usage of the CDSS within the NART EMR system. Hence, the study sought to establish how CDSS in the NART EMR system has been implemented and used by healthcare providers in Malawi to improve decision making on an individual patient on antiretroviral therapy (ART) at any point in time. The study employed a qualitative methodology, gathering data through observations, interviews, and an in-depth analysis of the system. The study establishes that smooth integration of CDSS in EMR system into regular and normal clinical workflow contributes to the success of CDSS use. Despite the successes of CDSS in EMR system, issues of system quality and information quality must be addressed during design and implementation of the CDSS to enhance efficiency and effectiveness of the system. The study suggests that those responsible for implementation should prioritize improving the underlying logic of the clinical decision support intervention in order to address existing concerns and improve the accuracy of the provided information. Additionally, the study recommends active consultation with the system's real users during the implementation process.

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Classifying Arabian Gulf Tweets to Detect People's Trends: A case study 9

Classifying Arabian Gulf Tweets to Detect People's Trends: A...

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9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022

作者： Balhaf, Khaled Darwish, Omar Rawashdeh, Emad Awad, Mohammad Abu Darweesh, Dirar Tashtoush, Yahya Rawashdeh, Saif Department of Computer Science Jordan University of Science and Technology Irbid Jordan Information Security and Applied Computing Eastern Michigan University Ypsilanti United States

ISBN: (纸本)9798350320480

Recently, media and business companies are utilizing social media to reach a large set of users to maximize the amount of gained profit. Actually, these companies are looking for the best ways to satisfy their user's requirements. It is very difficult to understand these requirements because of the large set of users on social media like Twitter. For this reason, the goal of our research project is to build a classifier that can detect Arabian trends among Gulf area Twitter users. The new built classifier can assist these companies to deliver the convenient products and media contents like photos and videos according to users' trends. By using our own designed Java-based tool, we have collected a significant dataset of tweets. Also, two experiments of tweet classification have been implemented to compare the effects of balanced and imbalanced training data and to measure the effect of data size on the accuracy of classifiers. In both experiments, Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Naïve Bayes algorithms are used as classifiers. The first experiment uses small, imbalanced data sets and four classes of data, which are Sport, Politics, Islam and Culture. The Light and Root Stemmers were used with each classifier. The best outcome achieved in our research project by utilizing a Naïve Bayes algorithm with the Light Stemmer technique. It achieved an accuracy reaching 76.27%. In the second experiment, we used a balanced large data set with the same classifiers. In addition, we have added one more class to the new data set which is Economics. The experimental results showed that the best accuracy (81.17%) is obtained by using SVM with the Light Stemmer method. The Light Stemmer achieved the best outcomes for all classifiers since almost all of the tweets were written in dialects. © 2022 IEEE.

关键词： Classification (of information)

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Stochastic Modelling of Elasticity Tensors

arXiv

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arXiv 2024年

作者： Shivanand, Sharana Kumar Rosić, Bojana Matthies, Hermann G. Scientific Computing Center Karlsruhe Institute of Technology Germany Applied Mechanics and Data Analysis University of Twente Netherlands Institute of Scientific Computing Technische Universität Braunschweig Germany

We present a novel framework for the probabilistic modelling of random fourth order material tensor fields, with a focus on tensors that are physically symmetric and positive definite (SPD), of which the elasticity tensor is a prime example. Given the critical role that spatial symmetries and invariances play in determining material behaviour, it is essential to incorporate these aspects into the probabilistic description and modelling of material properties. In particular, we focus on spatial point symmetries or invariances under rotations, a classical subject in elasticity. Following this, we formulate a stochastic modelling framework using a Lie algebra representation via a memoryless transformation that respects the requirements of positive definiteness and invariance. With this, it is shown how to generate a random ensemble of elasticity tensors that allows an independent control of strength, eigenstrain, and orientation. The procedure also accommodates the requirement to prescribe specific spatial symmetries and invariances for each member of the whole ensemble, while ensuring that the mean or expected value of the ensemble conforms to a potentially ‘higher’ class of spatial invariance. Furthermore, it is important to highlight that the set of SPD tensors forms a differentiable manifold, which geometrically corresponds to an open cone within the ambient space of symmetric tensors. Thus, we explore the mathematical structure of the underlying sample space of such tensors, and introduce a new distance measure or metric, called the ‘elasticity metric’, between the tensors. © 2024, CC BY.

关键词： Tensors

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