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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Claudiu Trăistaru Florin Pop Costin Bădică Daniel Ciochiu Mircea Bădoi Gabriel-Cătălin Nedianu University Politehnica of Bucharest University of Craiova Craiova Romania National Institute of Research and Development in Informatics (ICI) Academy of Romanian Scientists University Politehnica of Bucharest Bucharest Romania Computer and Information Technology Department University of Craiova Craiova Romania Software Development Department NetDania SRL Craiova Romania

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

Network engineers are essential to the management of computer systems, as they configure services and devices on the network to guarantee effective data routing and improve computer networks which make easier to perform everyday tasks like sending and receiving emails as well as more sophisticated ones like cloud computing and online gaming. In addition, modern networks have to serve a rising number of IoT devices without compromising performance and handle more sophisticated cybersecurity threats. Network engineers must always be learning and adapting to new protocols and technologies if they are to successfully tackle these difficulties. Moreover, the introduction of Large Language Models (LLMs) that are available as open-source software has revolutionized technical innovation by enabling the automation of network setups and augmenting the capabilities of network administration. These developments represent an important step forward in the field of network engineering, with the goal of maximizing efficiency and guaranteeing strong network security and functioning.

关键词： Performance evaluation Technological innovation Protocols Large language models Network security Routing Internet of Things

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Angular Profile Estimation on Phased Array Weather Radar by Maximizing Marginal Likelihood

Angular Profile Estimation on Phased Array Weather Radar by ...

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IEEE International Conference on Radar

作者： Eiichi Yoshikawa V. Chandrasekar Koji Nishimura Daichi Kitahara Yuuki Wada Tomoo Ushio Electrical and Computer Engineering Colorado State University Fort Collins CO USA Research Institute for Sustainable Humanosphere Kyoto University Uji Kyoto Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Kanagawa Japan Division of Electrical Electronic and Infocommunications Engineering Osaka University Suita Osaka Japan

ISBN: (数字)9798331539566

ISBN: (纸本)9798331539573

This paper proposes Maximum mARginal Likelihood Estimator (MARLE) for retrieving angular profile of volume targets such as precipitation on phased array weather radar. MARLE aims to achieve super resolution and unbiased estimation simultaneously, which is difficult for existing adaptive beamformers or sparse reconstruction methods. MARLE is based on maximization of a marginal likelihood which mathematically tends to produce sparse solutions to accomplish super resolution. Furthermore, the marginal likelihood is derived based on Bayes' theorem with probabilistic properties of radar observation are considered, where is no technical assumption to produce biases. We tested MARLE with simulated radar received signals and compared it with existing methods. In the simulation, angular profiles of weather measured by CSU-CHILL radar were used as reference. In addition, the other simulation supposing three targets existing closer than mainlobe width of a phased array antenna was implemented. In these simulations, MARLE successfully worked as a super resolution and unbiased estimator.

关键词： Phased arrays Meteorological radar Superresolution Estimation Reconstruction algorithms Radar antennas Probabilistic logic Bayes methods Iterative methods Tuning

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Deep Learning Approaches for Solving Challenges in computer Vision Applications and Exploring Evolving Patterns

Deep Learning Approaches for Solving Challenges in Computer ...

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International Conference on Contemporary Computing and informatics (IC3I)

作者： Sujay Gejji B. R. Supreeth Pathan Firoze Khan S. Venkat K Pradeepa S. Kaliappan Department of Electronics & Communication Engineering S. G. Balekundri Institute of Technology Belagavi Karnataka India Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Bangalore Department of Computer Science & Technology Madanapalle institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and communication Engineering S. A. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science KPR College of Arts Science and Research Coimbatore Tamil Nadu India Division of Research and Development Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Deep learning has brought substantial advancements to computer vision in recent years by providing answers to challenging visual interpretation problems. It investigates several DL methodologies, such as self-supervised learning strategies, hybrid CNN-RNN models, enhanced CNNs, generative adversarial networks (GANs), and vision transformers (ViTs). Novel CNN architectures are designed, GANs are used for image synthesis, hybrid models are used to integrate spatial and temporal information, self-attention is used in ViTs, and self-supervised learning is used for feature extraction. The results of the study show significant improvements: the accuracy of Enhanced CNNs was $81.6 \%$, the accuracy of GANs was $14.8 \%$, the accuracy of Hybrid CNN-RNN models was $86.2 \%$, the accuracy of ViTs was $79.2 \%$, and the accuracy of transfer for self-supervised learning techniques was $76.8 \%$. These findings demonstrate the improved effectiveness and performance of the suggested techniques, highlighting their potential to resolve challenging problems in feature extraction, video analysis, object recognition, picture enhancement, and image classification. It concludes that advances in computer vision will be facilitated by the ongoing development of DL methods. These advancements will lead to more precise and adaptable applications in a range of fields, including autonomous driving, medical imaging, surveillance, and augmented reality.

关键词： computer vision Visualization Accuracy Computational modeling Surveillance Self-supervised learning Transformers Generative adversarial networks Object recognition Image classification

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Image Captioning with Reinforcement Learning

Image Captioning with Reinforcement Learning

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Anand Verma Saurabh Agarwal K.V. Arya Ivan Petrlik Roberto Esparza Ciro Rodriguez Department of Information Technology ABV-IIITM Gwalior Madhya Pradesh India Department of Computer Science and Engineering Multimedia and Information Security Research Group ABV-Indian Institute of Information Technology and Management Gwalior India Faculty of Industrial and Systems Engineering National University Federico Villarreal Lima Peru Faculty of Systems Engineering and Informatics National University Mayor de San Marcos

Image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO.

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A Systematic Literature Review on Faculty Learning in Service-Learning

A Systematic Literature Review on Faculty Learning in Servic...

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IEEE Education engineering (EDUCON)

作者： Surendra Reddy Bandi Gopalkrishna Joshi Ashok Shettar Rohit Kandakatla Department of Computer Science & Engineering Hyderabad Institute of Technology and Management Hyderabad India Centre for Engineering Education Research KLE Technological University Hubballi India KLE Technological University Hubballi India Department of Electronics and Communication KG Reddy College of Engineering and Technology Hyderabad India

Service-learning is a pedagogical approach which makes learning more meaningful. researchers have reported this so far taking the cases of student learning experiences and impact studies. There are also studies which analyzed the role of community and academic institutions in the success of Service-learning. Faculty being an important stakeholder, less research took place on the role of faculty. When it is targeted to undergraduate students, Service-learning courses require to be designed and delivered in a distinctive style. Undergraduate students think and learn independently compared to children under 18 years of age. There are learning theories which are applicable for faculty and students. Faculty learning is equally important to make learning more effective. Faculty becoming co-learners while teaching increases the confidence of students in the learning process. As per the literature available, faculty learning as a specific area is not given focus in the process as compared to the other areas of service-learning like models of integrating service-learning into curriculum, benefits of service-learning, complementing professional skills and improved ethics etc. Hence this study is an attempt to explore the concept of faculty learning in service-learning and report the significance of faculty learning in implementing service-learning courses to undergraduate engineering students.

关键词： Ethics Systematics Bibliographies Stakeholders engineering students

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Early detection of gynecological malignancies using ensemble deep learning models: ResNet50 and inception V3

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informatics in Medicine Unlocked 2025年 53卷

作者： Kwatra, Chetna Vaid Kaur, Harpreet Mangla, Monika Singh, Arun Tambe, Swapnali N. Potharaju, Saiprasad Department of Computer Science & Engineering Lovely Professional University Punjab Phagwara India Department of Information Technology SVKM's Dwarkadas J Sanghvi College of Engineering Mumbai India Department of Information Technology K. K.Wagh Institute of Engineering Education & Research MH Nashik India Department of CSE Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

Background and objective: Improving patient outcomes and lowering death rates depend on the early identification of gynecological cancers. This work intends to improve the accuracy and dependability of early gynecological tumor diagnosis by means of a hybrid deep learning model combining ResNet50 and Inception v3 architectures. Methods: The proposed ensemble model combines multi-scale feature extraction of Inception v3 with the deep residual learning capability of ResNet50. A significant number of gynecological images were employed for training, testing, and assessment of the proposed model. By entailing accuracy, sensitivity, specificity, and F1 score, among other parameters the performance of the model was assessed. Results: The first experiment depicted displays that the ensemble model performed better than single models with a training accuracy of 99.80 %, a validation accuracy of 99.80 %, and a test accuracy of 99.80 %. Comparing the two studies done in the current research, the model has shown to have a high sensitivity of 99 %, specificity of 99 %, and F1 score of 0.99, making it better in the identification of gynecological cancers and significantly reducing low true negatives and low true positives. Conclusions: Ensembling of ResNet50 with Inception v3 for early diagnosis of gynecological cancers is promising and reproducible. Thus, according to the presented results, this method can contribute to the diagnoses of diseases by doctors quickly and accurately and, therefore, improve the treatment outcomes and the patient's health © 2025 The Authors

关键词： Deep learning Gynecological malignancies Inception V3 Medical image analysis ResNet50

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Protected Health Information Recognition of Unstructured Code-Mixed Electronic Health Records in Taiwan 18

Protected Health Information Recognition of Unstructured Cod...

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18th World Congress on Medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Lee, You-Qian Wang, Bo-Hong Su, Chu-Hsien Chen, Pei-Tsz Lin, Wu-Qing Wu, Chi-Shin Dai, Hong-Jie Intelligent System Lab College of Electrical Engineering and Computer Science Department of Electrical Engineering National Kaohsiung University Science and Technology Kaohsiung Taiwan Department of Psychiatry National Taiwan University Hospital Taipei Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine Kaohsiung Medical University Kaohsiung Taiwan

ISBN: (纸本)9781643682648

Electronic health records (EHRs) at medical institutions provide valuable sources for research in both clinical and biomedical domains. However, before such records can be used for research purposes, protected health information (PHI) mentioned in the unstructured text must be removed. In Taiwan's EHR systems the unstructured EHR texts are usually represented in the mixing of English and Chinese languages, which brings challenges for de-identification. This paper presented the first study, to the best of our knowledge, of the construction of a code-mixed EHR de-identification corpus and the evaluation of different mature entity recognition methods applied for the code-mixed PHI recognition task. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： Mixing

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Color-avoiding connected spanning subgraphs with minimum number of edges

arXiv

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arXiv 2023年

作者： Pintér, József Varga, Kitti Department of Stochastics Institute of Mathematics Budapest University of Technology and Economics Hungary HUN-REN-BME Stochastics Research Group Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Hungary HUN-REN-ELTE Egerváry Research Group Hungary MTA-ELTE Momentum Matroid Optimization Research Group Hungary

We call a (not necessarily properly) edge-colored graph edge-color-avoiding connected if after the removal of edges of any single color, the graph remains connected. For vertex-colored graphs, similar definitions of color-avoiding connectivity can be given. In this article, we investigate the problem of determining the maximum number of edges that can be removed from either an edge- or a vertex-colored, color-avoiding connected graph so that it remains color- avoiding connected. First, we prove that this problem is NP-hard, and then, we give a polynomial-time approximation algorithm for it. To analyze the approximation factor of this algorithm, we determine the minimum number of edges of color-avoiding connected graphs on a given number of vertices and with a given number of colors. Furthermore, we also consider a generalization of edge-color-avoiding connectivity to matroids. © 2023, CC BY.

关键词： Color

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Security Analysis for Threats to Patient Data in the Medical Internet of Things 11

Security Analysis for Threats to Patient Data in the Medical...

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11th International Conference on System Modeling and Advancement in research Trends, SMART 2022

作者： Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Shrivastava, Abhudaya Constantin, Neagu Bogdan Raboaca, Maria Simona Verma, Chaman School of Computer Science and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida201302 India The State University of New Jersey Department of Computer Science CamdenNJ United States Gheorghe Asachi Technical University of Iasi Neagu Bogdan Constantin Department of Power Engineering Iasi700050 Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

The current model of the healthcare system, which is being changed, is based on hospitals and specialists. The new model will be less centralised and will put more attention on each patient. The Medical Internet of Things (MIoT) makes it possible for machines to talk to each other and for humans to intervene in real time. These changes will soon make medical care much easier to get, cheaper, and more reliable. The promise of the Medical Internet of Things (MIoT) is that it will make patients happier with their healthcare overall by making it easier for them to get personalized e-health services no matter where they are or what time it is. The Medical Internet of Things (MIoT), also called the healthcare Internet of Things, is made up of medical devices and software that are all connected to each other. Many Internet of Things (IoT) applications are based on the management of personally identifiable information, such as a patient's name, address, medical history, and health conditions. This is one reason why IoT applications have a special place in the field of healthcare services. When it comes to the Internet of Things (IoT), protecting the privacy and safety of patients without compromising data security is one of the hardest things to do. The privacy and safety of the information that IoT devices collect is still a big concern, both when the information is being sent to the cloud for storage and when it is being sent there. This is because there is a lot of data and it is important and sensitive. In this paper, we look at the technical flaws and research challenges for future work in security and privacy, as well as the current challenges and needs related to the flow of data in the MIoT. To proposed approach for Security Analysis for Threats to Patient Data in the Medical Internet of Things © 2022 IEEE.

关键词： Internet of things

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Auxiliary informatics System: An Advancement Towards A Smart Home Environment

TechRxiv

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TechRxiv 2022年

作者： Gupta, Ishu Yadav, Preetesh K. Pareek, Sourav Shakeel, Saif Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

The main problem that we face today is not about getting services, but how efficiently we manage them. We are living in an era where we cannot neglect the fact that we are now depending on various electronic devices which we use daily in our life. These devices have become an inseparable part of our life, in the present scenario technology is evolving so rapidly that it’s a fact if we do not accept any upcoming trend then we lag behind in the eye of others. Managing all these devices and remembering to tune them is quite a hectic task, as we have to do other main tasks too. Using every device efficiently at the same time is not possible. Hence, we came up with a solution, with a system that will manage all these devices according to predefined rules or instructions. Current smart home systems work in a way, to establish a connection between nodes and a hub and are controlled by smartphones, computers, remote, etc. In this research paper, we have proposed a model that will not only establish a connection but will work smartly according to instructions and algorithms saved earlier. This Auxiliary informatics System (AuxIS) will serve the requirement of managing all the devices in a parallel fashion with very little human intervention and hence, will make a smarter home environment. © 2022, CC BY.

关键词： Internet of things

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