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Fusion of Artificial Intelligence and 5G in Defining Future UAV Technologies - A Review

Fusion of Artificial Intelligence and 5G in Defining Future ...

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Dash, Bibhu Ansari, Meraj F. Swayamsiddha, Swati University of the Cumberlands School of Computer and Info. Sciences WilliamsburgKY United States School of Electronics Engineering Kiit University Bhubaneswar India

ISBN: (纸本)9781665474924

In the modern world, drones or unmanned aerial vehicles (UAVs) are utilized for many different things and have lately attracted much scientific attention to the changing geo-political state. In addition to the military and businesses, scientists and researchers have used drones to study space and the ocean and find endangered species. Improved remote control and better communications are essential for any drone operation, which got a boost with innovation in 5G networks. A successful and fully operational modern 5G wireless network requires artificial intelligence (AI) procedures. Current 4G networks' reactive architecture with all-IP (Internet Protocol) internet access leads to inferior spectrum efficiency. Due to developments in AI and related subfields, such as machine learning and deep learning, fifth-generation (5G) wireless networks may now be proactive and predictive. The improved AI and sensor technology features are critical to achieving the 5G target and innovation with UAV techs. The study examines the possibilities of AI-based solutions in relation to UAV and 5G smart devices. The goal is to evaluate the various obstacles and unresolved problems facing UAV advancements and how the UAV revolution is changing the globe. © 2023 IEEE.

关键词： Drones

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Use of AI & Embedded Technology in Human Identity Chips for IoMT 4

Use of AI & Embedded Technology in Human Identity Chips for ...

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4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Dash, Bibhu Sharma, Pawankumar Swayamsiddha, Swati University of the Cumberlands School of Computer and Info. Sciences WilliamsburgKY United States KIIT University School of Electronics Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350323771

Technological innovations continue to change the way we do things. Such innovations are meant to make life easier and increase decision-making accuracy. Improper identification of patients has led to increased cases of wrong drug administration and treatment. Radiofrequency identification (RFID) has been around long enough and has proven effective in monitoring and tracking. Implanting RFID chips in patients increases operational efficiency, increases safety, aids in saving costs, and supports the journey of the Internet of Medical Things (IoMT). Artificial intelligence (AI) technology has proved to be more effective than humans in terms of the time taken to complete specificjobs and accuracy levels. RFID is the main technology used for human identity chips;its integration with AI technology will increase diagnostic accuracy and provides medical recommendations for the patient. This report discusses the implications of AI and embedded systems in human identity chips through a detailed review. The major finding in the report is that the increased efficiency of AI in human identity chips is the main driver of AI and RFID applications in modern healthcare. On the other hand, data and security concerns, human safety, and the high cost of adoption are the major barriers to AI and RFID adoption in healthcare and other industries. © 2023 IEEE.

关键词： Embedded systems

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Exploring Genetic Programming Models in computer-Aided Diagnosis of Skin Cancer Images 13

Exploring Genetic Programming Models in Computer-Aided Diagn...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ain, Qurrat Ui Al-Sahaf, Harith Xue, Bing Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand College of Engineering and Info. Technology AlShaab University Dep. of Cybersecurity Engineering Baghdad Iraq

ISBN: (纸本)9798350308365

Extracting important info.mation from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying info.mation can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant info.mation to discriminate between skin cancer classes. This paper provides an in-depth analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of utilizing feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyzes the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learned during the evolutionary process. © 2024 IEEE.

关键词： Genetic programming

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Integrating Structural info.mation: Comparing Classification-Based and Segmentation-Based Predictors in Bioinfo.matics (case study: protein-peptide region residue-level interaction) using structural info.mation 29

Integrating Structural Information: Comparing Classification...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Shafiee, Shima Fathi, Abdolhossein Safari, Faezeh Department of Computer Engineering and Information Technology Razi University Kermanshah Iran School of Electrical Eng. and Computer Science Faculty of Eng. Architecture and Info. Tech University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798331523114

Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method. © 2025 IEEE.

关键词： Bioinfo.matics

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TAG2TEXT: GUIDING VISION-LANGUAGE MODEL VIA IMAGE TAGGING 12

TAG2TEXT: GUIDING VISION-LANGUAGE MODEL VIA IMAGE TAGGING

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12th International Conference on Learning Representations, ICLR 2024

作者： Huang, Xinyu Zhang, Youcai Ma, Jinyu Tian, Weiwei Feng, Rui Zhang, Yuejie Li, Yaqian Guo, Yandong Zhang, Lei Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China OPPO Research Institute China China Academy for Engineering and Technology Fudan University China

This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features. In contrast to prior works which utilize object tags either manually labeled or automatically detected with an off-the-shelf detector with limited performance, our approach explicitly learns an image tagger using tags parsed from image-paired text and thus provides a strong semantic guidance to vision-language models. In this way, Tag2Text can utilize large-scale annotation-free image tags in accordance with image-text pairs, and provides more diverse tag categories beyond objects. As a result, Tag2Text demonstrates the ability of a foundational image tagging model, with superior zero-shot performance even comparable to fully supervised models. Moreover, by leveraging the tagging guidance, Tag2Text effectively enhances the performance of vision-language models on both generation-based and alignment-based tasks. Across a wide range of downstream benchmarks, Tag2Text achieves state-of-the-art results with similar model sizes and data scales, demonstrating the efficacy of the proposed tagging guidance. Codes, demo and pre-trained models are available at https://***/xinyu1205/recognize-anything. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Image annotation

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Exploration and Assessment of Critical Covariates of Breast Cancer Outcomes via Between-Group Test of Survival Rates at Sir Run Run Shaw Hospital 13

Exploration and Assessment of Critical Covariates of Breast ...

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13th International Conference on info.mation Technology in Medicine and Education, ITME 2023

作者： Zhao, Youbing Zhang, Lingli Wu, Jiajun Hu, Wenxian Dong, Feng Qin, Aihong Zeng, Hao Xie, Hao Ma, Tongqing Liu, Enjie Lin, Shengyou Jin, Zhefan School of Media Engineering Communication University of Zhejiang Hangzhou China School of Info. Science and Engineering Yanshan University Qinhuangdao China Sir Run Run Shaw Hospital Zhejiang University Division of Surgical Oncology Hangzhou China University of Strathclyde Dept. of Computer & Info. Sciences Glasgow United Kingdom School of Computer Science University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9798350319156

Breast cancer, being the most pervasive malignancy globally, poses a substantial threat to the well-being of the female population. Given the pronounced heterogeneity in survival times among affected individuals, a comprehensive examination of the factors governing treatment outcomes becomes imperative. Leveraging a sizable dataset comprising 2,757 breast cancer patients from Sir Run Run Shaw Hospital, the current study endeavors to assess the foremost covariates influencing the survival outcomes in breast cancer through survival analysis. The analytical framework commences with the application of Kendall analysis to effectuate dimensionality reduction on the dataset. Subsequently, an overarching survival analysis encompassing the pertinent factors is carried out, followed by a series of individual factor-specific survival assessments. Lastly, the results are visualised and discussed. The primary objective of this study is to evaluate the crucial covariates by employing a between-group test of survival rates. © 2023 IEEE.

关键词： Hospitals

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Standardization Review on Non-Terrestrial Networks 39

Standardization Review on Non-Terrestrial Networks

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39th International Conference on info.mation Networking, ICOIN 2025

作者： Kyung, Yeunwoong Lee, Jaewook Ko, Haneul Oh, Seungmin Lee, Jaehwan Kim, Youngjun Kongju National University Div. of Info. & Comm. Eng Cheonan Korea Republic of Pukyong National University Dep. of Info. and Comm. Engineering Busan Korea Republic of Kyung Hee University Dep. of Electronic Eng Yongin-si Korea Republic of Kongju National University Dep. of Computer Science and Eng Cheonan Korea Republic of Kyungnam University School of Computer Science and Eng Changwon Korea Republic of

ISBN: (纸本)9798331506940

As the demand for global connectivity increases, non-terrestrial networks (NTNs), particularly satellite-based systems, have gained significant attention in communication network standardization. The 3rd Generation Partnership Project (3GPP) has been at the forefront of developing standards that incorporate satellite communications into the broader terrestrial network framework. This paper reviews the progression of NTN standardization, focusing on three key stages: (1) the use of satellites as relays or the next generation NodeBs (gNBs), (2) the integration of user plane functions (UPF) within satellites, and (3) the inclusion of control plane network functions (NFs) within satellites. These developments in standardizations reflect a shift from basic relay functions to fully integrated satellite networks. © 2025 IEEE.

关键词： Network function virtualization

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Deep Natural Language Processing in unstructured big data analysis and insights extraction-A quantitative study 2

Deep Natural Language Processing in unstructured big data an...

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2nd IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security, iSSSC 2022

作者： Dash, Bibhu Swayamsiddha, Swati Ali, Azad University of the Cumberlands School of Computer and Info. Sciences WilliamsburgKY United States Kiit University School of Electronics Engineering Bhubaneswar India

ISBN: (纸本)9781665490566

In today's digital age, businesses create tremendous data as part of their regular operations. On legacy or cloud platforms, this data is stored mainly in structured, semi-structured, and unstructured formats, and most of the data kept in the cloud are amorphous, containing sensitive info.mation. With the evolution of AI, organizations are using deep learning and natural language processing to extract the meaning of these big data through unstructured data analysis and insights (UDAI). This study aims to investigate the influence of these unstructured big data analyses and insights on the organization's decision-making system (DMS), financial sustainability, customer lifetime value (CLV), and organization's long-term growth prospects while encouraging a culture of self-service analytics. This study uses a validated survey instrument to collect the responses from Fortune-500 organizations to find the adaptability and influence of UDAI in current data-driven decision making and how it impacts organizational DMS, financial sustainability and CLV. © 2022 IEEE.

关键词： Big data

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Reverse ETL for Improved Scalability, Observability, and Performance of Modern Operational Analytics - A Comparative Review 20

Reverse ETL for Improved Scalability, Observability, and Per...

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20th OITS International Conference on info.mation Technology, OCIT 2022

作者： Dash, Bibhu Swayamsiddha, Swati School of Computer and Info. Sciences University of the Cumberlands WilliamsburgKY United States School of Electronics Engineering Kiit University Bhubaneswar India

ISBN: (纸本)9781665493482

We are on the verge of the Fourth Industrial revolution, in which data analytics and industry insights will play a critical role in data-driven, quicker, and better decision-making. A data warehouse (DW) is a store of strategic data accumulated over time using ETL techniques from various sources. Traditional DW operations primarily consist of obtaining data from many sources, converting the data into a suitable format, and then putting the data into a DW schema for further analysis. Reverse ETL fits into the current data stack and is distinct from regular ETL as firms pursue creative data strategies. This paper will discuss the significance and process of reverse ETL, why it is taking center stage in cloud environments to eliminate data silos created by SQL in data engineering, and why modern analytics prefers reverse ETL for scalability, performance, and better visual representations. © 2022 IEEE.

关键词： Data warehouses

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Concrete Structural Crack Damage Classification Using Nonlinear Dimension Reduction and Broad Learning System

Concrete Structural Crack Damage Classification Using Nonlin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Bingshu Lin, Jia Zhuang, Xiaodong Zhang, Guanghui Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi'an China Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen China School of Computer Science and Technology Shandong University Qingdao China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781665410205

Concrete structural crack damage classification is of importance for road safety. This paper proposes a new method based on broad neural network for crack damage classification in concrete structures. It includes three stages. Firstly, a pre-trained deep neural network is used to extract the features from crack images. Secondly, principal component analysis is used to project the retrieved features from high dimensions to low dimensions. Thirdly, broad learning system is employed to predict the classification using the low-dimensional features. Experimental results demonstrate that this method reduces the model's training time and improves classification accuracy. © 2024 IEEE.

关键词： Deep neural networks

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