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6th International Conference on Intelligent Medicine and Image Processing, IMIP 2024

作者： Watanabe, Kaoru Noborio, Hiroshi Koeda, Masanao Department of Computer Science Osaka Electro-Communication University Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Japan

ISBN: (纸本)9798400710032

In neurosurgery to remove brain tumors, DICOM data, a medical imaging standard, is generated preoperatively using CT and MRI. This data is used for surgical planning. However, brain deformation problems, known as brain shift, occur during surgery, resulting in discrepancies from the preoperative surgical plan. Deviations from the surgical plan due to brain shift are a life-threatening problem as they reduce the success rate of surgery. Brain shifts have not yet been elucidated, and DICOM data acquired from MRIs and CTs for surgical planning remain stored and archived in hospital information centers. Here, it is planned to extract the physical parameters of the brain model from a large amount of brain deformation data using pre- and post-operative brain DICOM pairs, which are stored in large quantities in hospitals and discarded after a particular time. This study investigates the optimization of parameters in the brain shift detection algorithm. © 2024 ACM.

关键词： Digital Imaging and communications in Medicine (DICOM)

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The Impact of CyRIS in Ascertaining Accurate Implementation of Projects 14

The Impact of CyRIS in Ascertaining Accurate Implementation ...

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14th International Conference on Computing communication and Networking Technologies, ICCCNT 2023

作者： Sharifi, Ahma Zia Walid, Md. Abul Ala Zia, Ghezal Ahmad Jan Salehin, Md. Nazmus Safi, Ihsan Ul Haq Al Islam, Iqbal Faculty of Computer Science Nangarhar Univeristy Department of Networking and Communication Afghanistan Department of Computer Science and Engineering Khulna9203 Bangladesh Berlin Germany Department of Computer Science and Engineering Natore Bangladesh Nangarhar University Department of Networking and Communication Faculty of Computer Science Afghanistan Department of Computer Science & Engineering Savar Dhaka Bangladesh

ISBN: (纸本)9798350335095

This systematic review seeks to evaluate the impact of CyRIS in ascertaining accurate implementation of projects with a primary focus on the merits of its functionalities. Core to the study is the view that CyRIS-driven project implementation reduces the risks of inaccuracy to an acceptable level. Although CyRIS is an extensively researched area in cyber security, a comprehensive review of subcategories, specifically, the accurate implementation of projects using the tool remains understudied. The proposed systematic review of literature provides insights and future directions from selected databases, including scienceDirect, Springer Journals, SAGE Journals, and Wiley Online Library. Moreover, the limitations and strengths of each article collected are assessed using the PRISMA model to identify risks of bias. The research, which consists of journal articles, conferences, and symposium papers, is analyzed using "Cyber-Range Instantiation systems and accurate implementation of projects"as the key phrase and "Cyber-Risk Models", "Cyber Range Instantiation", and "Cyber Security Training", and "Accurate Implementation of Projects"as the keywords. The findings of the study will bridge the awareness gap in using CyRIS-led Cyber-Risk Models to execute accurate training scenarios, improve weaknesses of implementation, simulate real-time attack scenarios, and highlight existing gaps as well as prospects for future research. © 2023 IEEE.

关键词： Cybersecurity

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Transformer Internal and Inrush Current Fault Detection Using Machine Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 153-168页

作者： R.Vidhya P.Vanaja Ranjan N.R.Shanker Department of Electronics and Communication Engineering Loyola-ICAM College of Engineering and TechnologyChennai600034India Embedded System Technologies Department of Electrical and Electronics EngineeringCollege of EngineeringGuindyChennai 600025India Department of Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringChennai 600055India

Preventive maintenance in the transformer is performed through a dif-ferential relay protection system,and it protects the transformer from internal and external ***,the Current Transformer(CT)in the differential protec-tion system mal-operates during inrush *** saturates due to magnetizing inrush currents and causes false tripping of the differential ***,iden-tiﬁcation of tripping in protection relay either due to inrush current or internal faults needs to be *** the above problem,continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the trip-ping in relay due to inrush or internal *** transformer’s internal fault leads to high breathing process in the transformer breather,never for inrush *** inrush currents,CT temperature is *** monitoring of breather and CT of the transformer through thermal imaging and radiometric pix-els detect the causes of CT saturation and differentiates *** wavelet threshold image analytics(HWT-IA)based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95%for identiﬁcation of the false tripping of differential protection system of transformer.

关键词： Wavelet threshold inrush transformer breather current transformer thermal image

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A Perspective Analysis of Optical Biosensors in Machine Learning Applications 18

A Perspective Analysis of Optical Biosensors in Machine Lear...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kumar, B. Naveen Panduranga Rao, M.V. Sharan, Preeta Varun, K.R. Kallam, Suresh Kumaresan, M. Faculty of Engineering and Technology Department of Computer Science and Engineering Bengaluru India The Oxford College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9789380544519

Optical biosensors, a novel invention at the intersection of biotechnology and optics, are revolutionizing the field of biomedical diagnostics. Optical biosensors act by identifying variations in light's wavelength, phase, polarization, or intensity caused by interactions between a sensing element and the target analyte. Optical biosensors outperform other sensor types in sensing applications due to their quick, accurate, and extremely sensitive results. When evaluating optical sensors, total power, core power, efficient area, and indices are the most crucial variables to consider. Recent years have seen notable advancements in machine learning (ML), a branch of artificial intelligence (AI). This review investigates the complementary nature of optical biosensors and machine learning methods. Here, the potential utility of ML for optical biosensors is explored in detail. The advantages and drawbacks of the most popular machine learning methods are listed based on an analysis of the optical biosensing data. It highlights the possible effects of machine learning on improving the sensitivity, selectivity, and adaptability of optical biosensing devices, and it summarizes current advancements in the field. Finally, the study considers the challenges and limitations that associated with current optical biosensing techniques. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning optical biosensor photonic crystal surface plasmon resonance surface-enhanced Raman scattering

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Advances in Medical Image Processing for Liver Tumour Recognition: A Comprehensive Survey 2

Advances in Medical Image Processing for Liver Tumour Recogn...

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2nd International Conference on computer, communication and Control, IC4 2024

作者： Deepa, B. Marimuthu, C.N. Geetha, S. Maheswari, S. Anna University Faculty of Information and Communication Engineering Chennai600025 India Nandha Engineering College Department of Electronics and Communication Engineering Erode638052 India Nandha Engineering College Department of Computer Science and Engineering Erode638052 India

ISBN: (纸本)9798350387933

The last several decades have seen a lot of activity in the medical image processing domain especially when it comes to the segmentation of liver and liver tumours. The survival rate of liver cancer is rather low when compared to some other types of cancer. Nevertheless, early detection greatly improves the prognosis of liver cancer patients. As a consequence, multiple researchers have developed automated deep-learning (DL) systems for forecasting the development of cancer cells using various medical imaging modalities. A summary of some previously published studies on liver cancer diagnosis is currently lacking in review publications. But new mechanisms and architecture in the prognosis of liver cancer were beyond the possibility of these investigations. This review emphases on the topologies of DL for liver cancer diagnosis. The survey that follows examines the datasets used, describes existing DL-based designs, assesses the benefits and drawbacks of earlier studies, and discusses image processing techniques. Furthermore, a thorough examination of several imaging modalities, performance metrics and results, challenges and future research goals are provided. The comparison study of different methods reveals that HFCNN has the highest accuracy, at 97.22%. © 2024 IEEE.

关键词： Medical imaging

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A Comprehensive Analysis of Identifying Patients with Chronic Kidney Disease 2

A Comprehensive Analysis of Identifying Patients with Chroni...

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2nd International Conference on computer, communication and Control, IC4 2024

作者： Geetha, S. Marimuthu, C.N. Maheswari, S. Deepa, B. Anna University Faculty of Information and Communication Engineering Chennai600025 India Nandha Engineering College Department of Electronics and Communication Engineering Erode638052 India Nandha Engineering College Department of Computer Science and Engineering Erode638052 India

ISBN: (纸本)9798350387933

Chronic Kidney Disease (CKD) is a prevalent and progressive health condition affecting millions of individuals worldwide. Early identification and management of CKD are critical to mitigate its adverse effects on patients' health and reduce the burden on healthcare systems. This study presents a comprehensive analysis of various methodologies and approaches for identifying patients with Chronic Kidney Disease. The research encompasses a detailed review of traditional and emerging diagnostic techniques, by exploring the sensitivity, specificity, and accuracy of these methods, considering their applicability in diverse patient populations and healthcare settings. Furthermore, this analysis delves into the role of artificial intelligence (AI) and machine learning in enhancing CKD detection and risk prediction. The synthesis of this comprehensive analysis provides valuable insights into the current landscape of CKD identification and offers recommendations for optimizing diagnostic strategies. © 2024 IEEE.

关键词： Risk assessment

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Implementation of Secure and Verifiable Access Control Procedures using the NTRU Cryptosystem to Store Big Data in the Cloud Environment

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Recent Patents on engineering 2025年第2期19卷 57-70页

作者： Babu B, Ravindra Saxena, Swati Widjaja, Gunawan Vinodha D, Vedha Omarov, Batyrkhan Ramaiah, Gurumurthy B. Vats, Prashant Adama Ethiopia Department of Computer Science KCG College of Technology Chennai India Universitas Krisnadwipayana Jawa Barat 13077 Indonesia Department of Electronics and Communication Engineering JCT College Of Engineering and Technology Pichanur Coimbatore India International University of Tourism and Hospitality Turkistan Kazakhstan Technical and Vocational Training Institute Addis Ababa Ethiopia Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India

Background: Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores. Methods: However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility. Results: In this patent paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour. Conclusion: It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users. © 2025 Bentham science Publishers.

关键词： Cloud security

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Mutual character dialogue generation with semi-supervised multitask learners and awareness

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1357-1363页

作者： Ahrari Khalaf, Ayesheh Hassan Abdalla Hashim, Aisha Olowolayemo, Akeem Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia Department of Computer Science Faculty of Information and Communication Technology International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia

Consistent efforts have been ongoing to improve the friendliness and reliability of informal dialogue systems. However, most research focuses solely on mimicking human-like answers. Therefore, the interlocutors’ awareness features of the dialogue system are left unexplored. Meanwhile, cognitive science research reveals that awareness is a crucial indicator of an effective, high-quality informal conversation. This research aims to boost the quality of the conversational generation system by factoring in awareness of the interlocutors in the design and training of the dialogue system model. The Generative Pre-Trained Transformer-2 (GPT-2) model was implemented into the Persona Perception (P²) Bot to achieve the objectives of this study. This was to precisely develop model's understanding, P² Bot was implemented using a transmitter–receiver-based structure. The P² Bot leverages mutual persona awareness to improve the quality of customized dialogue generation. GPT-2 is a 1.5B parameter transformer model that produces state-of-the-art accuracy in a zero-shot setting on seven of the eight evaluated language modeling datasets. The observations of the proposed model on a sizable open-source dataset, PERSONA-CHAT, proved successful, with improvement above the state-of-the-art baselines in both automatic measures and human assessments. The model has achieved 82.2% accuracy on Hits@1 performance metrics in the original data and 68.8% on the revised data. On the human evaluation, the model scored an average of 2.66, pointing out that the responses provided were coherent and informative. A dialogue generation model with character and awareness which can communicate like an informative human expert was introduced. This study presents the submerging of GPT-2 model on a mutual persona perception dialogue generating model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cognitive science Conversational agent Dialogue generation Generative Pre-trained Transformer 2 (GPT-2) Natural language understanding (NLU)

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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