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Cognition2Vocation: meta-learning via ConvNets and continuous transformers

Neural computing and Applications 2024年第21期36卷 12935-12950页

作者： Kamran, Sara Hosseini, Saeid Esmailzadeh, Sayna Kangavari, Mohammad Reza Hua, Wen School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Computing and Information Technology Sohar University Sohar Oman Department of Computing The Hong Kong Polytechnic University Hong Kong

Estimating the suitability of individuals for a vocation via leveraging the knowledge within cognitive factors comes with numerous applications: employment resourcing, occupation counseling, and workload management. Accordingly, the enterprises aim to hire the most suitable person from a massive array of similar applicants, maximizing performance and minimizing the gap between strategic indicators and predefined targets. While cognitive factors signify the best-suited person from similarly skilled workers, inferring pertinent latent cues from noisy and growing social network contents is time-intensive. To tackle the challenges involved, we propose a framework that, on the one hand, extends influential features based on the correlations between cognitive cues and, on the other hand, leverages a novel continuous transformer to mitigate the overlapping and approximation issues in discrete modeling. Rather than relying on discrete patterns that may evolve frequently, we use continuous elements that include not only numerous aggregating components but also sense minor irregular fluctuations. In a hybrid manner, we fuse multiple base models to transfer a higher representation to the meta-learning unit, agglomerating outputs from gradient boosters and the ConvNets. The experimental results show that our proposed framework can outperform trending vocation estimation methods by 1.36% in F1-Score and approximately 1% in accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Knowledge management

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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge computing

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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Automated BPMN Diagram Generation 6

Automated BPMN Diagram Generation

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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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Enhancing Timeliness and Accuracy in Blood Transportation through Drone technology in Saudi Arabia's Government Clinic 22

Enhancing Timeliness and Accuracy in Blood Transportation th...

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22nd International Learning and technology Conference, L and T 2025

作者： Alseraihi, Meaad Aljadani, Amjaad Alsahafi, Samar Alzein, Maryam Elhag, Salma King Abdulaziz University Faculty of Computing & Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

Timely blood sample transportation is crucial for healthcare, particularly in emergencies, in Saudi Arabia's government clinics, the current process involves daily noon collections by SPL logistics representatives, causing delays in testing and limited availability for urgent cases. Through consultations with a healthcare professional, we identified Key inefficiencies include restricted collection times, absence of onsite laboratories, and lengthy transportation, often exceeding an hour. This study applied business process re-engineering (BPR) to address these issues by integrating drone technology, using the Business Process Model and Notation (BPMN) framework, the current process (AS-IS) was analyzed, and a future model (TO-BE) incorporating drones was developed, simulations using Bizagi revealed a significant reduction in average process time from 3 hours and 20 minutes to 1 hour and 19 minutes, the findings highlight the potential of drones to revolutionize healthcare logistics, reducing delays and improving patient outcomes, future research proposes a hybrid approach, integrating drones with traditional logistics systems for enhanced efficiency and scalability. © 2025 IEEE.

关键词： Reengineering

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"Cropmaster" - Real-Time Coordination of Multirobot Systems for Autonomous Crop Harvesting: Design and Implementation 5

"Cropmaster" - Real-Time Coordination of Multirobot Systems ...

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5th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Pramod, Isuranga Arachchi, Avishka Mallawa Rashen, Chemuthu Chinthaka, Gayan Pandithage, Dinithi Gamage, Narmada Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331533694

The CropMaster is an autonomous rover system designed to enhance Scotch Bonnet production by improving disease management, crop sorting, autonomous navigation, and real-time environmental monitoring. Equipped with sensors to measure sunlight, humidity, pH, NPK content, and soil moisture, the rover securely transmits analyzed data to a web-based dashboard. LIDAR technology enables efficient autonomous navigation, allowing the rover to move around fields and avoid obstacles. The MQTT protocol facilitates communication between multiple rovers, preventing duplicate measurements and ensuring data is sent to the dashboard for comprehensive data collection across large areas. TensorFlow's machine learning models allow the rover to accurately assess crop health and detect early-stage diseases, followed by automated pesticide and fertilizer application through a spraying system. To maintain reliability, the rover's operations, including data transfer and task execution, are continuously monitored for Quality of Service (QoS). All collected data is stored in the cloud for long-term access. Built with a lightweight aluminum and plastic chassis and robotic arms, the rover is designed for adaptability and operational efficiency, aiming to improve crop management and increase yields across extensive agricultural fields. © 2025 IEEE.

关键词： Robotic arms

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Harnessing Deep Learning for Enhancing Brain Tumor Classification in a Sustainable Healthcare Environment 22

Harnessing Deep Learning for Enhancing Brain Tumor Classific...

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22nd International Learning and technology Conference, L and T 2025

作者： Bajaber, Adnan Salem Ragab, Mahmoud King Abdulaziz University Faculty of Computing and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

Brain tumor affect human health owing to their location. Early detection plays a pivotal role in improving treatment efficacy and patient outcomes. Computer-Aided detection systems, leveraging medical imaging techniques, have emerged as valuable tools for assisting clinicians in diagnosing cancer. Deep learning, particularly convolutional neural networks, has shown remarkable promise in medical image analysis, offering enhanced accuracy and efficiency. This study employed transfer learning and deep neural networks for the classification of brain tumors into its different classes. The pre-trained CNN models, used in the classification were EfficientNet, ResNet, DenseNet, and Xception and a series of image pre-processing and augmentation were carried out before feeding the images to the classification models to enhance classification performance. Four ensemble models were built after getting the results from the classification models, ICAVG for image classification average predictions, ICMAX for image classification maximum predictions, FCAVG for extracted features classification average predictions, and FCMAX for extracted features classification maximum predictions. The results from these four ensemble models were again combined into CAVG for the average results of both ICAVG and FCAVG, and CMAX for the maximum results from both ICMAX and FCMAX. The proposed model was tested on a brain tumor dataset of 3064 samples and the investigational validation of the proposed technique portrayed a superior accuracy value of 99.02% over existing models. Based on high predictive results this research work endeavors to contribute to the advancement of cancer diseases detection, facilitating early diagnosis and improving patient outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

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A Hybrid Meta-Classifier of Fuzzy Clustering and Logistic Regression for Diabetes Prediction

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Computers, Materials & Continua 2022年第6期71卷 6089-6105页

作者： Altyeb Altaher Taha Sharaf Jameel Malebary Department of Information Technology Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddahSaudi Arabia

Diabetes is a chronic health condition that impairs the body’s ability to convert food to energy,recognized by persistently high levels of blood *** diabetes can cause many complications,including retinopathy,nephropathy,neuropathy,and other vascular *** learning methods can be very useful for disease identification,prediction,and *** paper proposes a new ensemble learning approach for type 2 diabetes prediction based on a hybrid meta-classifier of fuzzy clustering and logistic *** proposed approach consists of two ***,a baselearner comprising six machine learning algorithms is utilized for predicting ***,a hybrid meta-learner that combines fuzzy clustering and logistic regression is employed to appropriately integrate predictions from the base-learners and provide an accurate prediction of *** hybrid metalearner employs the Fuzzy C-means Clustering(FCM)algorithm to generate highly significant clusters of predictions from *** predictions of base-learners and their fuzzy clusters are then employed as inputs to the Logistic Regression(LR)algorithm,which generates the final diabetes prediction *** were conducted using two publicly available datasets,the Pima Indians Diabetes Database(PIDD)and the Schorling Diabetes Dataset(SDD)to demonstrate the efficacy of the proposed method for predicting *** compared with other models,the proposed approach outperformed them and obtained the highest prediction accuracies of 99.00%and 95.20%using the PIDD and SDD datasets,respectively.

关键词： Ensemble learning fuzzy clustering diabetes prediction machine learning

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Enhancing Android Malware Detection in Internet of Vehicles using Self-Attention Transformer Model

Enhancing Android Malware Detection in Internet of Vehicles ...

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2024 International Symposium on Securing Next Generation Systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Alterazi, Hassan A. Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The current trend for vehicles to be significantly correlated with vehicles, unspecified devices, and organization upsurges the latent for exterior attacks on vehicle's cyber-security. The main network security function is intrusion detection with open connectivity, like connected cars and self-driving. Particularly, when a vehicle is associated with an exterior device over a device in the vehicle or when it connects with an exterior structure, cybersecurity is mandatory to defend the network of software inside the vehicle. Present technique with this concern comprises intrusion detection and a vehicle gateway system. Conversely, it is challenging to block mischievous code based on behaviors of application. This study presents an Enhancing Android Malware Detection using Self-Attention Transformer Model (EAMD-SATM) model in Internet of Vehicles. The projected EAMD-SATM model categorizes and recognizes the Android malware efficiently and accurately. To attain this, the EAMD- SATM approach endures a min-max approach utilizing data pre-processing at the initial stage. Furthermore, the EAMD-SATM method employs self-attention-based transformer (SA-T) technique for the detection of Android malware. To improve the SA-T technique solution, the EAMD-SATM technique applies the improved mother optimization (IMO) technique for the parameter tuning process. The simulation validation of the EAMD-SATM algorithm can be established on a benchmark Android malware dataset. The experimental outcomes highlighted the important performance of the EAMD-SATM approach in the Android malware recognition method. © 2024 Copyright for this paper by its authors.

关键词： Android malware

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A review of tracking concept drift detection in machine learning 4th

A review of tracking concept drift detection in machine lear...

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4th Annual International Conference on Data Science, Machine Learning and Blockchain technology, AICDMB 2023

作者： Abdu, Nail Adeeb Ali Basulaim, Khaled Omer Information Technology Department Faculty of Engineering & Computing University of Science and Technology Aden Yemen Information Technology Engineering Department Faculty of Engineering University of Aden

ISBN: (纸本)9781032426853

The availability of time series streaming data has increased dramatically in recent years. Since the last decade, there has been a growing interest in learning from realtime data. While extracting significant information from data streams, online learning faces changes in data distribution. Concept drift refers to hidden data contexts that learning systems are unaware of it. Using previous training instances from the data stream, the classifier categorizes new instances. Hence in this regard, it is obvious to have considerable methods to discover the similarity or diversity status of the present streaming data that compared to the buffered data. This issue is defined and explored in contemporary literature as concept drift. This paper was initiated to identify the constraints of the contemporary methods devised to handle the concept drift in data streams in relation to supervised learning, we also review and categories the concept drift detectors with their key points. Eventually, the article presented observations can provide decision boundary detection at improved levels and discussing open research challenges and possible new research direction. © 2024 The Author(s).

关键词： Machine learning

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

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6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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