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6th International Conference on Advancements in Computing, ICAC 2024

作者： Ajmal, Farhath Wijekoon, Poorna Dhanamina, Haritha Ravishan, Yasiru Nawinna, Dasuni Attanayaka, Buddhima Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

The automation of business process modelling has become crucial for organizations seeking to improve their operational efficiency. This research presents a novel methodology that leverages fine-tuned GPT models to automate the conversion of textual business process descriptions into Business Process Model and Notation (BPMN) diagrams. The study builds on previous work, addressing gaps related to the handling of unstructured text and the absence of anaphora resolution in our solution we use fine-tuned GPT models to accurately extract and structure business process activities from text input from end users, which are then converted into BPMN diagrams using a custom generation framework. The evaluation results demonstrate the system's accuracy with an average precision of 80%, recall of 76%, and F1 score of 78%. These findings outline the potential of using advanced language models to streamline business process modelling, offering significant improvements in accuracy and efficiency. The research concludes by suggesting enhancements for future iterations, including fine-tuning with advanced models and expanding support for complex BPMN elements, aiming to further optimize workflow for organizations. © 2024 IEEE.

关键词： Efficiency

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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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Unveiling Big Data Insights: A Neutrosophic Classification Approach for Enhanced Prediction with Machine Learning

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Neutrosophic Sets and systems 2024年 72卷 154-172页

作者： Elsherif, Ahmed Z.M. Salama, A.A. Khaled, O.M. Herajy, Mostafa Elsedimy, E.I. Khalid, Huda E. Essa, Ahmed K. Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt Department of Information Technology Management Faculty of Management Technology and Information Systems Port Said University Port Said Egypt University of Telafer The Administration Assistant for the President of the Telafer University Telafer Iraq

The ever-growing volume and complexity of Big Data pose challenges for traditional classification tasks. This paper explores the potential of Neutrosophic Sets (NS), a powerful framework for handling uncertainty, in building robust classification models for Big Data prediction using Machine Learning (ML) techniques. We provide a detailed background on NS and discuss its advantages over Fuzzy Sets. We then propose a methodology that integrates NS with relevant ML algorithms for classification. We evaluate the performance of our Neutrosophic-based model on a Big Data source. The results are analyzed to assess the effectiveness of the Neutrosophic approach for Big Data prediction. This research contributes to the advancement of uncertainty management in Big Data classification and paves the way for further exploration of Neutrosophic-based ML models for various prediction tasks. Results show that the Neutrosophic Neural Networks (NNs) model achieved commendable performance across various metrics, with an accuracy of 79.08%, precision of 74.58%, recall of 77.64%, and an F1-score of 75.63%. These metrics indicate that the Neutrosophic NNs model effectively balances the trade-offs between precision and recall, providing a robust classification performance in the context of the evaluated dataset. © 2024, University of New Mexico. All rights reserved.

关键词： Prediction models

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LLM-Driven Cross-Platform Code Generation for Polyhedral Optimized NPDP Codes 15th

LLM-Driven Cross-Platform Code Generation for Polyhedral Op...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Palkowski, Marek Faculty of Computer Science and Information Systems West Pomeranian University of Technology in Szczecin Zolnierska 49 Szczecin71210 Poland

ISBN: (纸本)9783031856990

This paper explores using large language models (LLM) for automatic and source-to-source programming across various cross-platform languages and libraries, including generating CUDA code for high performance computing (HPC). Our proposed solution aims to demonstrate the capabilities of ChatGPT in generating valid CUDA code based on existing OpenMP code obtained from a polyhedral compilers Traco and Dapt. Specifically, we intend to showcase the model’s proficiency in producing equivalent and valid GPU code. Furthermore, we extend the application of our solution by prompting the AI model to generate similar code for another input benchmark, emphasizing its adaptability and versatility across different scenarios. This approach seeks to highlight the model’s capacity to provide automated and efficient solutions for diverse programming challenges. We chose non-trivial kernels from the non-serial polyadic dynamic programming (NPDP) benchmark with non-uniform loops. The focus is on ensuring code validity and understanding limitations in obtaining valid CUDA code. Additionally, we assess the efficiency and scalability of NVIDIA A100 GPU codes generated by artificial intelligence (AI) models, comparing them with optimized Intel Xeon Gold CPU codes from polyhedral optimizers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dynamic programming

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Predicting Drug-Drug Interactions with Machine-Learning and ATC-SMILES Combined Representation 6

Predicting Drug-Drug Interactions with Machine-Learning and ...

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6th International Conference on Intelligent Computing in Data Sciences, ICDS 2024

作者： Radwan, Yasmin Atef Gouda, Karam Abdelghany Abdelbaky, Ibrahim Zaghloul Arafa, Mona Mohamed Higher Institute of Computer Science & Information Technology EI-Shorouk Academy Information Systems Department Egypt Benha University Faculty of Computers and Artificial Intelligence Information Systems Department Egypt

ISBN: (纸本)9798350351200

Polypharmacy is a potential strategy for managing such intricate disorders, encompassing conditions like cancer, diabetes, and age-related issues in older individuals. Nonetheless, when a medication is combined with one or more drugs that either enhance, diminish, or counteract its intended effects, it can lead to undesired adverse reactions. In severe cases, these interactions can cause serious morbidity and increased mortality rates globally. In this study, we collected a Drug-Drug interaction dataset from the DrugBank database. Various chemical features were then extracted from the Simplified Molecular-Input Line-Entry System of interacting drug pairs. Our emphasis was on representing the Molecular Access System fingerprints of these drug pairs. Molecular Access System fingerprints signify the presence or absence of specific substructures in the molecule and were generated using the RDKit Open-Source Cheminformatics Software. Furthermore, we incorporated Anatomical Therapeutic Chemical classifications into our analysis. Finally, we employed various machine learning algorithms, using K-Nearest Neighbor, Random Forest, Logistic Regression, Support Vector Machine, and XGBoost for learning the extracted features and predict large-scale Drug-Drug interactions among various drug pairs. Among these models, the XGBoost model exhibited superior performance across most measurement metrics. © 2024 IEEE.

关键词： Nearest neighbor search

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Adaptive Anomaly Detection in Dynamic Graph Networks

Adaptive Anomaly Detection in Dynamic Graph Networks

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2024 International Visualization, Informatics and technology Conference, IVIT 2024

作者： Mir, Aabid Ahmad Zuhairi, Megat F. Musa, Shahrulniza Namoun, Abdallah Universiti Kuala Lumpur Malaysian Institute of Information Technology Malaysia Islamic University of Madinah Faculty of Computer and Information Systems Saudi Arabia

ISBN: (纸本)9798350354447

Over the past few years, the detection of anomalies in dynamic graph networks has attracted substantial attention worldwide because of its applications in various fields such as cybersecurity, financial fraud detection, and social network analysis. Traditional methods may not be as adaptive to the dynamic nature of such networks, as it requires more efficient and scalable solutions. To mitigate the issue, a Variational Graph Convolutional Network (V-GCN) is proposed. It is an approach that combines variational autoencoders (VAEs) with graph convolutional networks (GCNs) for anomaly detection in dynamic graph data to enhance anomaly detection. The methodology involves developing a V-GCN architecture that combines the capabilities of VAEs and GCNs, applying an adaptive learning strategy to update model parameters in real time, and carrying out experiments using standard datasets. The experimental results indicate that the anomaly detection performance considerably enhanced using the proposed model. It outperforms the baseline methods, where the V-GCN attains an AUC-ROC of 95%, an average precision of 92%, an F1 score of 91%, and an anomaly score of 94%. © 2024 IEEE.

关键词： Graph neural networks

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Assessing Quality of Service Performance in SDN Controllers: POX vs. OpenDaylight 6

Assessing Quality of Service Performance in SDN Controllers:...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Lokuliyana, Shashika Wijesiri, Pipuni Jayakody, Anuradha Abeygunawardhana, Pradeep K.W. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

This research offers a comparative analysis of Qual-ity of Service (QoS) within Software Defined Networks (SDN) by assessing the POX and OpenDaylight controllers across various network topologies. The results show that OpenDaylight has a 32.59% higher average throughput than POX, demonstrating its superior data handling capabilities. Conversely, POX exhibits a 54.53% lower average delay and an 85.05% higher average jitter compared to OpenDaylight, highlighting its effectiveness in maintaining lower latency and variability in packet transmission times. These contrasting performances provide essential insights for network engineers in selecting the optimal SDN controller based on specific network demands, significantly influencing the overall network efficiency and stability. © 2024 IEEE.

关键词： Quality of service

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Polymorphic Security Enhancements for Corporate Environments Utilizing Behavior Analytics

Polymorphic Security Enhancements for Corporate Environments...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Wijesinghe, W.M.A.J. Abeysinghe, Y.P.P. Rukshana, M.A.F. Adhikari, A.M.P.S. Abeywardhana, Kavinga Yapa Siriwardhana, Deemantha Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350367560

Employers should recognize that employees are the most vulnerable aspect of business environments since these cyber hazards are growing because of user neglect, lack of fundamental security discipline, and a fast-changing threat context. The theoretical approach proposed in the study bridges the difference by carefully following and handling the staff's internet presence. Overall, the study describes a comprehensive solution to govern the security of employees' internet presence by utilizing Artificial Neural Networks (ANN) for advanced intrusion detection, Natural Language Processing (NLP) for implementation and enforcement of browser-based security policies, visual and text data recognition to identify phishing attempts, and lastly analyzing and profiling users based on their online presence via a mathematical model. As a result, this study provides a comprehensive solution for mitigating risks related to human factors in corporate environments. © 2024 IEEE.

关键词： Phishing

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Enhancing Agricultural Efficiency with IoT-Enabled Precision Irrigation Management Using LoRaWAN technology 16

Enhancing Agricultural Efficiency with IoT-Enabled Precision...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Jayasinghe, J.A.S.K. Thennakoon, T.M.H.L. Ruchira, U.G.C. Wimalasena, N.G.C.L. Jayakody, J.A.D.C.A. Pandithage, Dinithi Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331505264

The Internet of Things-empowered precision irrigation management system with LoRaWAN technology is presented given the growing food requirements across the world and pressing calls for judicious use of water in agriculture. Flow Pilot, specifically designed for chili farms, optimizes water application while enhancing crop health using precise and automated irrigation. The Flow Pilot features advanced sensors such as soil moisture, temperature, humidity, and rain sensors that collect real-time environmental data transmitted over a LoRaWAN network for analysis. Flow Pilot uses the Machine learning algorithm by analyzing sensor data to make localized irrigation recommendations based on crop needs, environmental factors, and weather forecasting. LoRaWAN gateways will support this for long-ranged communications over power-consuming agricultural fields. Besides this, it can use different network architectures. There is also an automated water tank management module that regulates the supply of water and pH levels to have optimal conditions for crop growth. A visually simple dashboard interface enables farmers to inspect and operate irrigation processes remotely, hence rendering the whole process far more efficient. Finally, over the extensive period of field tests executed by the project, Flow Pilot was able to save water, improve crop yields, and help support sustainable agriculture. For example, this testing supports the potential of IoT with LoRaWAN technologies, such as the Flow Pilot implementation, for a game change in conventional irrigation practices into a modern, scalable, and reliable solution. © 2024 IEEE.

关键词： Soil moisture

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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