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Decision Making: Applications in Management and engineering 2025年第1期8卷 401-418页

作者： Al-Safarini, Maram Y. Baashirah, Rania A. Computer Science Department Zarqa University Jordan Software Engineering Department University of Business & Technology Saudi Arabia

Artificial Intelligence (AI) is transforming numerous domains, including bioinformatics and information extraction systems, by advancing data processing capabilities, enhancing precision, and facilitating automation. The primary aim of this research is to examine the function of AI within the realms of bioinformatics and information extraction through a combination of quantitative and qualitative approaches. The quantitative component involved a sample of 152 participants, recognised as experts in bioinformatics and data science. The survey findings underscore the prevalent integration of AI in bioinformatics, particularly through the utilisation of reinforcement learning, neural networks, and natural language processing (NLP). Furthermore, AI substantially improves the analysis of biological data;however, it encounters challenges such as limited model interpretability and the lack of data standardisation. Notwithstanding these obstacles, AI-driven innovations in disease prognosis, personalised healthcare, and pharmaceutical development are anticipated to shape the future trajectory of bioinformatics. The qualitative findings, derived through thematic analysis encompassing core themes and sub-themes, reveal that AI significantly contributes to accelerating and refining information extraction processes. Technologies such as machine learning, NLP, and neural networks are instrumental in enhancing data processing efficiency. Nonetheless, issues such as inadequate data quality, elevated computational expenses, and the intricacy of AI models remain persistent. Looking ahead, AI is projected to integrate more effectively with big data infrastructures, enable real-time information extraction, and deliver increasingly tailored solutions. The study concludes with several policy recommendations to guide future implementation strategies. © 2025 Regional Association for Security and crisis management. All rights reserved.

关键词： Data integration

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Earlier Selection of Routes for Data Transfer In Both Wired and Wireless Networks 1

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5th International Conference on Smart Technologies in Data science and Communication, SMART-DSC 2022

作者： NagaMallik Raj, S. Neeraja, S. Rao, N. Thirupathi Bhattacharyya, Debnath Andhra Pradesh Visakhapatnam India Department of Computer Science & Software Engineering Lendi Institute of Engineering and Technology Jonnada Andhra Pradesh Vizianagaram India Department of Computer Science & Engineering Koneru Lakshmaiah Education Vaddeswa- ram Andhra Pradesh Guntur522502 India

ISBN: (数字)9789811968808

ISBN: (纸本)9789811968792

In both wired and wireless communication networks, the transfer of huge data was the major issue. To solve this issue, we are in the process of working with both admission control and control transfer mechanisms such that to transfer huge amount of data in the current networks without disturbing the regular flow of data in the networks. A scheduling algorithm was also in the process to achieve this goal of transfer of substance amount of data from source to desti nation securely. In the current system, for every transfer of bulk amounts, a request needs to be sent to the central processing control for the request of blocking of some bandwidth by giving the starting and ending time for the transfer of data. As the time slot is given, then we can transfer any amount of data within that slot of time. The reassignment of bandwidth and the multiple routing configurations are present and utilized in the system. As per the results, the working of the system is excellent in the performance, and good encouraging results were obtained from the system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Advance reservations Bandwidth allocation Bulk transfers Multiple routing Routing Time slot Wired networks Wireless networks

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Enabling Collaborative and Green Generative AI Inference in Edge Networks

Enabling Collaborative and Green Generative AI Inference in ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Tian, Meng Liu, Zhicheng Qiu, Chao Wang, Xiaofei Niyato, Dusit Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin China Nanyang Technological University College of Computing and Data Science Singapore Shenzhen University College of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350351255

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm's ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content. © 2024 IEEE.

关键词： Generative adversarial networks

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MushroomDetect: An Investigation into Local Mushroom Classification in Bangladesh Using Transfer Learning 15

MushroomDetect: An Investigation into Local Mushroom Classif...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Adhikari, Bornita Hossain, Md. Mikail Akter, Sumaia Ahamed, Md. Sazzadur Daffodil International University Department. of Computer Science and Engineering Dhaka Bangladesh Bangladesh Agricultural University Department. of Agricultural Chemistry Mmensingh Bangladesh Daffodil International University Department. of software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

When it comes to achieving food security, one of the main obstacles is ensuring that the diet includes high-quality foods. The development of scientific technologies has made manual local identification completely impractical because it takes so long and is so imprecise. To address this problem Using a machine vision-based approach to swiftly identify the most popular and farmed mushrooms from photos raises the accuracy level to our desired level. Pictures of our chosen species were carefully clicked to compile the dataset. We carefully selected new circumstances for seven varieties of selected mushrooms, including 'Ear mushroom,' 'Golden Oyster,' 'Milky Mushroom,' 'Oyster mushroom(Po2),' 'Oyster mushroom(Po10),' 'Pink Oyster,' and 'Reishi Mushroom,' to generate a bespoke dataset. We collected the 500 basic photographs and divided them into 7 groups. To get a position where training a dataset will be simple, we sought to increase the amount of raw dataset with augmentation. InceptionV3, InceptionResNetV2, and DenseNet201 were the transfer learning models employed in the initial stage. After putting DenseNet201 into practice, we received the intended outcome. All of these models provide us with a respectable level of accuracy, but after training DenseNet201, we were able to reach an accuracy of 96.72%. Accidental mushroom identification may also be possible in this study. © 2024 IEEE.

关键词： Shellfish

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SConE: Simplified Cone Embeddings with Symbolic Operators for Complex Logical Queries 61

SConE: Simplified Cone Embeddings with Symbolic Operators fo...

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Findings of the Association for Computational Linguistics, ACL 2023

作者： Nguyen, Chau Duc Minh French, Tim Liu, Wei Stewart, Michael ARC Centre for Transforming Maintenance through Data Science Department of Computer Science and Software Engineering School of Physics Mathematics and Computing The University of Western Australia

ISBN: (纸本)9781959429623

Geometric representation of query embeddings (using points, particles, rectangles and cones) can effectively achieve the task of answering complex logical queries expressed in first-order logic (FOL) form over knowledge graphs, allowing intuitive encodings. However, current geometric-based methods depend on the neural approach to model FOL operators (conjunction, disjunction and negation), which are not easily explainable with considerable computation cost. We overcome this challenge by introducing a symbolic modeling approach for the FOL operators, emphasizing the direct calculation of the intersection between geometric shapes, particularly sector-cones in the embedding space, to model the conjunction operator. This approach reduces the computation cost as a non-neural approach is involved in the core logic operators. Moreover, we propose to accelerate the learning in the relation projection operator using the neural approach to emphasize the essential role of this operator in all query structures. Although empirical evidence for explainability is challenging, our approach demonstrates a significant improvement in answering complex logical queries (both non-negative and negative FOL forms) over previous geometric-based models. © 2023 Association for Computational Linguistics.

关键词： computer circuits

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Sensors-Based Ambient Assistant Living via E-Monitoring Technology

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computers, Materials & Continua 2022年第12期73卷 4935-4952页

作者： Sadaf Hafeez Yazeed Yasin Ghadi Mohammed Alarfaj Tamara al Shloul Ahmad Jalal Shaharyar Kama Dong-Seong Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Electrical Engineering King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumiKorea

Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted *** from ambient assisted living,the task of monitoring human activities plays an important role in different fields including virtual reality,surveillance security,and human interaction with *** systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human *** this paper,we propose multiple methods such as random occupancy pattern,spatio temporal cloud,waypoint trajectory,Hilbert transform,Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human *** features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization ***,the Masi entropy classifier is applied for action recognition and *** have been performed on three challenging datasets,namely,UTDMHAD,50 Salad,and *** experimental evaluation,the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1%with UTD-MHAD dataset,90.6%with 50 Salad dataset,and 89.5%with CMU-MMAC *** experimental results validated the proposed system.

关键词： Classification algorithm human action recognition motion sensors machine learning Masi entropy

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Automated computer Vision-based Detection of Solar Panel Defects Using a Thermal Camera Mobile Application 10

Automated Computer Vision-based Detection of Solar Panel Def...

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10th International Conference on Future Internet of Things and Cloud, FiCloud 2023

作者： Hajjdiab, Hassan Mohamed, Gasm ELbary Alzaabi, Salim Alhalabi, Marah Gad, Abdalla Yaghi, Maha Alkhedher, Mohammad Ghazal, Mohammed Concordia University Department of Computer Science and Software Engineering Montreal Canada Abu Dhabi University College of Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350316353

Solar energy production has risen significantly globally in recent years. Solar Panels and Photovoltaic PV modules are the key components of solar energy production. Therefore, ensuring their health and efficiency and finding technologies to facilitate this became more important. However, the PV inspection costs money, time, and human resources. Thus, many technologies and techniques have been introduced to facilitate PV cell inspection, such as using a drone with a thermal camera. Moreover, as technology evolves every day, so do mobile devices. Nowadays, smartphones have brought many conveniences to our lives. Smartphones have become smarter with applications, and now they can be used as health monitoring devices, projectors, and more. One of smartphones' most revolutionary features and technology is that they can come with a thermal camera or be compatible with thermal cameras. Thermal camera application varies and is diverse, from detecting heat on an object to inspecting photovoltaic PV cells. This paper proposes a novel system consisting of a thermal camera mobile app to detect the defects in PV modules and estimate the defect percentage. The result of this work has shown a potential research and further development area in the field of renewable energy and PV cell inspection. © 2023 IEEE.

关键词： Solar panels

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Designing a Model for Predicting Asthma in Adolescent Using Map Reduce and Federated Learning

Designing a Model for Predicting Asthma in Adolescent Using ...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ekpo, Raphael Henshaw Chukwudi Osamor, Victor Azeta, Ambrose A. Akindeji, Kayode Orelaja, Adeyinka Elizabeth Covenant University Department of Computer and Information Sciences Km 10 Idiroko Road Ogun State Ota Nigeria Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Durban University of Technology Faculty of Engineering and the Built Environment Department of Electrical Power Engineering South Africa Department of Computer Science and Quantitative Methods ClarksvilleTN United States

ISBN: (纸本)9798350358155

This study aims at designing Map Reduce and federated learning-based asthma prediction model for adolescent to provide answers to the problems associated with the existing asthma prediction model for adolescent. This study leveraged on two diverse datasets: the Nigerian Hospital Asthma dataset and the National Survey of Children Health dataset for benchmarking purposes. Symmetrical uncertainty and normalization interaction were employed for feature selection. The model was trained using Federated Artificial Intelligence (AI) Technology Enabler (FATE) and complemented with XGBoost model and one central server for federated algorithm averaging. The study was implemented with python programming language on Google Collaboratory environment. The results of the analysis showed considerable high accuracy of 0.98, precision (0.98%), recall (0.98%) and F1-socre (0.99%) for the asthmatic class and precision of 0.98%, recall of 0.98%, as well as F1-score of 0.99% for the non-asthmatic class. The implemented model was benchmark with the existing asthma prediction models in the literature. Simulated attack was also performed using implemented model with and without Map Reduce. This study concludes that the researchers' model has the potentials to outperform some of the existing machine learning models on asthma prediction in adolescent, thereby facilitating collaborative sharing of the model with other hospitals, protect the patient information from leakage, meeting the criteria of healthcare data protection regulation as well as beneficial for enhancing the security of the dataset. © 2024 IEEE.

关键词： Federated learning

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Comparative Performance Evaluation of Random Forest, Extreme Gradient Boosting and Linear Regression Algorithms Using Nigeria’s Gross Domestic Products 6th

Comparative Performance Evaluation of Random Forest, Extreme...

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6th EAI International Conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2023

作者： Adewale, M.D. Ebem, D.U. Awodele, O. Azeta, A. Aggrey, E.M. Okechalu, E.A. Olayanju, K.A. Owolabi, A.F. Oju, J. Ubadike, O.C. Otu, G.A. Muhammed, U.I. Oluyide, O.P. African Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Abuja Nigeria Department of Computer Science University of Nigeria Nsukka Nigeria Department of Computer Science Babcock University Ogun Ilishan-Remo Nigeria Department of Software Engineering Namibia University of Science and Technology Windhoek Namibia

ISBN: (纸本)9783031518485

Statistical methods like linear regression analysis are frequently used to create predictive analytic models. However, these methods have limitations that may affect the accuracy of the models. Using a typical dataset, this study seeks to accomplish two main goals. First, we fitted three predictive models, including linear regression analysis and two ensemble machine learning algorithms: Random Forest Regressor and Extreme Gradient Boosting Regressor. Secondly, we compared the performance of the models using a 5-fold cross-validation technique. The Random Forest Regressor outperformed the other models, with a Mean Absolute Error (MAE) of 10.138, Mean Square Error (MSE) of 139.729, Mean Absolute Percentage Error (MAPE) of 0.071, Root Mean Square Error (RMSE) of 11.821, and Normalised Mean Square Error (NMSE) of 13.782. These results suggest that the Random Forest Regressor is optimal for developing predictive models with similar datasets. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Machine learning

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