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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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Secure Watermarking Scheme for Color DICOM Images in Telemedicine Applications

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Computers, Materials & Continua 2022年第2期70卷 2525-2542页

作者： Kamred Udham Singh Hatem Salem Abu-Hamatta Abhishek Kumar Achintya Singhal Mamoon Rashid A.K.Bashir Department of Computer Science and Information Engineering National Cheng Kung UniversityTainanTaiwan Department of Applied Sciences Aqaba University CollegeAl Balqa Applied UniversityAqabaJordan School of Computer Science&IT JAIN(to be Deemed University)BangaloreIndia Department of Computer Science Banaras Hindu UniversityVaranasiIndia Department of Computer Engineering Faculty of Science and TechnologyVishwakarma UniversityPune411048India Department of Computing and Mathematics ManchesterMetropolitan UniversityUK

Teleradiology plays a vital role in the medical field,which permits transmitting medical and imaging data over a communication *** ensures data reliability and provides convenient communication for clinical interpretation and diagnostic *** transmission of this medical data over a network raises the problems of legal,ethical issues,privacy,and copyright *** copyright protection of medical images is a significant issue in the medical *** schemes are used to address these issues.A gray-level or binary image is used as a watermark frequently in color image watermarking *** this paper,the authors propose a novel non-blind medical image watermarking scheme based on 2-D LiftingWavelet Transform(LWT),Multiresolution Singular Value Decomposition(MSVD),and LU factorization to improve the robustness and authenticity of medical *** this scheme,multiple color watermarks are embedded into the colored DICOM(Digital Imaging and Communications inMedicine)images obtained from Color Doppler images(DICOM format),and the average results achieved by our proposed scheme is 46.84 db for Peak Signal-to-Noise Ratio(PSNR),37.46 db for Signal-to-Noise Ratio(SNR),0.99 for Quality of Image and 0.998 for Normalized Correlation for various image processing *** results make our watermarking technique an ideal candidate for medical image watermarking.

关键词： Digital imaging medicine multiresolution medical image watermarking

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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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QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-based Service in Cloud

QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-ba...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： J Jesy Janet Kumari Thangam S A. Saleem Raja Dept of Computer Science and Engineering Amrita School of Computing Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Sultanate of Oman

Due to the growing number of cloud users working on various cloud apps on specific infrastructure, resource allocation in cloud computing is implicitly difficult. Most resource allocation strategies currently in use focused on delivering efficiency determined by the workload of applications across many domains, such as business and scientific. Cloud service brokers compete strongly with one another to provide quality of service improvements based on the need, demand and the rapid growth of the services that are offered. Such conflict makes it difficult and complex to provide simple service selection and composition services in the cloud, this needs to be addressed to lighten the load on local resources. Because cloud-based services are too sophisticated and scalable, it is preferable to use an optimization strategy to choose the services that will meet the needs of the clients. To do this, a hybrid algorithm Modified Ant Colony System Cloud Services Composition (MACSCSC) is proposed to employ Ant Colony Optimization (ACO) incorporated with Genetic Algorithm (GA) to smoothly increase cloud-based services. When allocating cloud resources these meta-heuristic algorithms can achieve significantly better performance, lower costs and instances, better utilization of resources, and increased energy efficiency. The empirical findings on different real datasets have been demonstrated to show the improvements of the suggested method to overcome the drawbacks such as allocation of resources and high power consumption.

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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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Work in Progress: The Effectiveness of Knowledge Management System on Companies Performance

Work in Progress: The Effectiveness of Knowledge Management ...

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computing and Information technology (ICCIT-1441), 2020 International Conference on

作者： Fatma Hamed Al Akhzami Malak Hamed Al Hasani Muna Saif Al Rahbi Information Technology Department College of Computing and Information Sciences University of Technology and Applied Science Muscat Sultanate of Oman

In the current challenging environment with the highly speed development that the companies’ world faced, finding and keeping up with the auxiliary tools and systems has become a requirement and a necessity to deal with the complexity and uncertainty changes. One of the efficient ways to reach the previous goal is to implement knowledge management system (KMS). KMS is a systematic way that stores and retrieves knowledge to improve the process of understanding and collaboration [1]. The major purpose of this research is to examine the effectiveness of the knowledge management system on performance and a case study of Petroleum Development Oman (PDO) company is selected. This research will adopt mixed methodology consisting of quantitative and qualitative approaches through conducting questionnaire and interviews primarily. In addition to evaluate literature review as additional source. After that, the collected data will be analyzed with the appropriate data analysis technique.

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Flight Delay Prediction Using Machine Learning 8

Flight Delay Prediction Using Machine Learning

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Reddy, Ravula Tarun Basa Pati, Peeta Deepa, K. Sangeetha, S. Tresa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India University of Technology and Applied Sciences Department of Engineering Al Musannah Oman

ISBN: (纸本)9798350334012

The prediction of flight delay can be considered as one of the most challenging problems to solve. Delay of an aircraft is not only a problem for an airline but also for the passengers. Flights can be delayed due to several reasons, the weather being the primary one. In this paper, our focus is to predict the delay of the flights due to bad weather. The dataset used consists of flight data from JFK airport from the sources Bureau of Transportation statistics and weather data from the National Centers for Environmental Information. The results of different machine learning algorithms like Linear Regression, SVR, Decision Tree Regressor, Random Forest Regressor, Ridge, and Lasso Regressor, for prediction of flight delay, are compared. XGboost regressor had the best performance in all the scenarios with least RMSE score of 0.81. © 2023 IEEE.

关键词： Decision trees

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A Deep Learning Based Approach for Janya Raga Classification in Carnatic Music 6

A Deep Learning Based Approach for Janya Raga Classification...

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6th SLAAI International Conference on Artificial Intelligence, SLAAI-ICAI-2022

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

ISBN: (纸本)9781665476072

Janya Ragas are Carnatic music ragas derived from the fundamental ragas called Melakarta Raga, by the permutation and combination of various ascending and descending swaras. Raga recognition is an important task, essential to research in Music Information Retrieval in Indian Classical Music. Even though the automatic raga recognition methods have been widely adopted in Carnatic music, most of the existing methods are restricted only to Melakarta Ragas. It is an important task to identify the Melakarta Raga of a Janya Raga, especially for improved music recommendation performance. In this study, we used deep learning-based approach to address this problem. Music files were collected for 65 Janya Ragas belonging to seven Melakarta Ragas. Classification-based supervised deep learning models: 1D CNN, LSTM and 1D CNN-LSTM were used in the study. The models were implemented to learn similarities from 1-20 mean values of Mel-Frequency Cepstral Coefficients (MFCCs) features of audio samples, by using TensorFlow and Keras APIs. The results revealed that the 1D CNN-LSTM model outperformed the other models with an accuracy of 82.0%. In the future, 1D CNN-LSTM based Siamese neural networks can be introduced to reduce the dependence on large amounts of labeled audio data. © 2022 IEEE.

关键词： Music

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Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

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Journal of Electronic Science and technology 2024年第2期22卷 48-68页

作者： Gracieth Cavalcanti Batista Johnny Oberg Osamu Saotome Haroldo F.de Campos Velho Elcio Hideiti Shiguemori Ingemar Soderquist Division of Electronic and Embedded Systems KTH Royal Institute of TechnologyStockholm 16440Sweden Electronic Engineering Division Aeronautics Institute of TechnologySao Josédos Campos SP 12228-900Brazil Laboratory of Applied Computing and Mathematics National Institute for Space ResearchSao Josédos Campos SP 12227-900Brazil Department of C4ISR Institute of Advanced StudiesSao Josédos Campos SP 12228-001Brazil Saab AB Linkoping 58188Sweden

Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,*** key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)***,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or *** alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced *** a high effort is required for image edge *** a support vector regression(SVR)model is proposed to reduce this computational load and processing *** dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable *** show that the implementation in hardware is 68 times faster than that in *** architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without *** is also the lowest power consumption in current machine learning hardware ***,the circuitry area is 41 times *** with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning *** system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.

关键词： Dynamic partial reconfiguration(DPR) Field programmable gate array(FPGA)implementation Image edge detection Support vector regression(SVR) Unmanned aerial vehicle(UAV) pose estimation

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