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8th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2024

作者： Agustriawan, David Darmawan, Jeremie Theddy Permana, Angga Aditya Overbeck, Marlinda Vasty Yuliani, Sy. Sitorus, Budi Berlinton School of Engineering and Informatics Multimedia Nusantara University Department of Informatics Banten Tangerang Indonesia School of Life Sciences Indonesia International Institute for Life Sciences Department of Bioinformatics East Jakarta Indonesia School of Engineering and Informatics Multimedia Nusantara University Department of Information System Banten Tangerang Indonesia

ISBN: (纸本)9798350368970

Acute myeloid leukemia (AML) is a rapidly occurring disorder that is characterized by the clonal expansion of myeloid progenitors in the bone marrow and the proliferation of immature myeloid cells. Absolute monocyte count (AMC) and absolute lymphocyte count (ALC) have been used in the diagnostic and prognostic predictor of AML. Interestingly, dysregulation of DNA methylation levels is also observed in AML. Data on AML patients were downloaded from the GDC TCGA Acute Myeloid Leukemia (LAML) on *** website. Data exploration andfiltering based on selected genes and racial population groups was performed. DNMT3A, IDH1, IDH2, TET2, FLT3-ITD, NPM1, and WTl are selected as the potential methylation regulation genes in AML with low number of mutation cases. Univariate analysis was done using correlation test while the multivariate analysis was done using a forward step variable selection linear regression model. This study found that the univariate analysis showed a specific methylated gene locations have a role in AML. Moreover, it is also discovered that the highest correlated gene and CpG site for white and non-white populations are different. This study uncovered that monocyte is found to be inversely proportional while the lymphocyte is directly proportional to the methylation level found in certain genes. There may be therapeutic implication from these findings such as the use of demethylating agents. © 2024 IEEE.

关键词： Genes

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Robust Rabin Cryptosystem Biometric Privacy Preserving Authentication Technique for IoT Enabled Devices Over Cloud

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SN computer Science 2024年第5期5卷 590页

作者： Naidu, Devishree Ray, Niranjan K. School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar 751024 India Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur 440013 India

With the advent of growing technologies and digitalization, smart factories and healthcare systems in the cloud are more focused towards task-driven event processing. Also, most of the IoT-enabled devices are the sources to provide important data for these tasks. IoT offers many benefits but security is a major concern as devices are more prone to attack vulnerabilities over the cloud, leading to system failures. To tackle this, we introduces a new method named as Rabin Cryptosystem Biometric Privacy Preserving User Authentication System (RCBP2U-AS), which aims to secure IoT enabled devices over the cloud. The method works with secret generation for devices communicating with gateways, ensuring security on unsecured networks. This includes a unique encryption model for on-demand secret generation and session key management. The method achieves user authentication and device authentication using the public key concept. Moreover, the decryption process involves unique sequence generation using private key with a Blum–Blum–Shub pseudo-random bit generation algorithm. The system demonstration includes biometric implementation with three factor authentication. This significantly form the basis for secure authentication during data exchange from low powered device to cloud servers via gateway. The paper includes formal and informal security analysis over replay, password guessing, and other types of attacks using the AVISPA simulation Tool [7]. The performance evaluation done under various test case environments. Different complexity analysis function evaluations are done. The experimental result shows that the proposed RCBP2U-AS has less computation overhead and can detect numbers of attacks for IoT enabled devices over cloud. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Authentication AVISPA Biometrics Cloud computing Cloud server Internet of things Rabin cryptosystem Security

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BEDiff: denoising diffusion probabilistic models for building extraction

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Optoelectronics Letters 2025年第5期21卷 298-305页

作者： LEI Yanjing WANG Yuan CHAN Sixian HU Jie ZHOU Xiaolong ZHANG Hongkai College of Computer Science and Technology Zhejiang University of Technology Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information China Three Gorges University Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University College of Electrical and Information Engineering Quzhou University School of Electronic and Information Engineering Anhui Jianzhu University

Accurately identifying building distribution from remote sensing images with complex background information is challenging. The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds. Building on this concept, we propose a novel framework, building extraction diffusion model(BEDiff), which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion. Our approach begins with the design of booster guidance, a mechanism that extracts structural and semantic features from remote sensing images to serve as priors, thereby providing targeted guidance for the diffusion process. Additionally, we introduce a cross-feature fusion module(CFM) that bridges the semantic gap between different types of features, facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively. Our proposed BEDiff marks the first application of diffusion models to the task of building extraction. Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff, affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.

关键词： A

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Personalized Assistance Through Artificial Intelligence: Smart Voice Appliances 8th

Personalized Assistance Through Artificial Intelligence: Sma...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Jain, Archana Rathore, Yogesh Singh Maurya, Santosh Kumar Malik, Suraj Bansal, Nidhi Pandey, Mahima Shankar Saraswat, Bal Krishna Computer Science and Engineering Department School of Engineering and Technology IIMT University Uttar Pradesh Meerut India Computer Science and Application Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Computer Science and Engineering Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Galgotia College of Engineering and Technology Uttar Pradesh Greater Noida India Nagpur Campus Pune India

ISBN: (纸本)9789819766772

In today’s world of growing technology, we can do things we never thought we could do before, but to achieve these ideas, there is a need for a platform that can do all our work easily and comfortably. So, we humans have developed applications such as the Human Voice Assistant with the ability to interact with the environment in one of the most visible forms of human interaction namely HUMANVOICE. The most popular Android mobile app "Google Assistant" and "Google Voice Search" were developed by Google. Various applications such as Microsoft Cortana and Amazon Alexa are also used as voice assistants. The Apple iPhone’s voice assistant named "SIRI" helps the users to communicate with the services and applications of iOS by voice and respond to the user’s voice commands. We have built an application where the voice assistant will be available on a specific desktop or laptop. It can change the overall course of interaction between the user and the system. The system designed here is such that all the services provided by the system are accessible through the user’s voice commands. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： engineering education

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A Survey on 360°Images and Videos in Mixed Reality:Algorithms and Applications

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Journal of computer Science & Technology 2023年第3期38卷 473-491页

作者：张方略赵军红张赟 Stefanie Zollmann School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand College of Media Engineering Communication University of ZhejiangHangzhou 310018China Department of Computer Science University of OtagoDunedin 9054New Zealand

Mixed reality technologies provide real-time and immersive experiences,which bring tremendous opportunities in entertainment,education,and enriched experiences that are not directly accessible owing to safety or *** research in this field has been in the spotlight in the last few years as the metaverse went *** recently emerging omnidirectional video streams,i.e.,360°videos,provide an affordable way to capture and present dynamic real-world *** the last decade,fueled by the rapid development of artificial intelligence and computational photography technologies,the research interests in mixed reality systems using 360°videos with richer and more realistic experiences are dramatically increased to unlock the true potential of the *** this survey,we cover recent research aimed at addressing the above issues in the 360°image and video processing technologies and applications for mixed *** survey summarizes the contributions of the recent research and describes potential future research directions about 360°media in the field of mixed reality.

关键词： 360°image mixed reality 360°image processing virtual reality scene reconstruction virtual reality content manipulation

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Deep-EERA:DRL-Based Energy-Efficient Resource Allocation in UAV-Empowered Beyond 5G Networks

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Tsinghua Science and Technology 2025年第1期30卷 418-432页

作者： Shabeer Ahmad Jinling Zhang Ali Nauman Adil Khan Khizar Abbas Babar Hayat School of Electronic Engineering Beijing University of Posts and TelecommunicationBeijing 1000876China Department of Information and Communication Engineering Yeungnam UniversityGyeongsan 38541Republic of Korea School of Information Engineering Xi’an Eurasia UniversityXi’an 710065China Department of Computer Science Hanyang UniversitySeoul 04763Republic of Korea

The rise of innovative applications,like online gaming,smart healthcare,and Internet of Things(IoT)services,has increased demand for high data rates and seamless connectivity,posing challenges for Beyond 5G(B5G)*** is a need for cost-effective solutions to enhance spectral efficiency in densely populated areas,ensuring higher data rates and uninterrupted connectivity while minimizing *** Aerial Vehicles(UAVs)as Aerial Base Stations(ABSs)offer a promising and cost-effective solution to boost network capacity,especially during emergencies and high-data-rate ***,integrating UAVs into the B5G networks presents new challenges,including resource scarcity,energy efficiency,resource allocation,optimal power transmission control,and maximizing overall *** paper presents a UAV-assisted B5G communication system where UAVs act as ABSs,and introduces the Deep Reinforcement Learning(DRL)based Energy Efficient Resource Allocation(Deep-EERA)*** efficient DRL-based Deep Deterministic Policy Gradient(DDPG)mechanism is introduced for optimal resource allocation with the twin goals of energy efficiency and average throughput *** proposed Deep-EERA method learns optimal policies to conserve energy and enhance throughput within the dynamic and complex UAV-empowered B5G *** extensive simulations,we validate the performance of the proposed approach,demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.

关键词： Deep Reinforcement Learning(DRL) Unmanned Aerial Vehicles(UAVs) resource allocation energy efficiency 5G and beyond network

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A Smart CIoT with Secure Healthcare Framework Using Optimized Deep Recuperator Neural Network Long Short-Term Memory

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IEEE Internet of Things Journal 2024年第6期11卷 10551-10562页

作者： Narmatha, C. Manimurugan, S. Karthikeyan, P. University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Oakland University Department of Electrical and Computer Engineering RochesterMI112345 United States National Chung Cheng University Department of Computer Science and Information Engineering Chiayi62102 Taiwan RV University School of Computer Science and Engineering Bengaluru560059 India

The healthcare system currently relies on the facility to store and process large amounts of health data, supported by efficient management. The Internet of Things (IoT) has driven the growth of Adroit Healthcare, which has vast data processing capabilities and extensive data collection. The consumer IoT (CIoT), also known as the IoT in the context of individual use cases, is dominated by personal healthcare applications. The remarkable expansion of the CIoT can be attributed to the extensive embrace of wearable technology, facilitating everyday monitoring of vital health indicators like blood pressure, heart rate, and respiration rate. However, the CIoT raises concerns about data security, confidentiality, and customer trust. This study proposes an integrating blockchain and deep learning (DL) approach to promote responsible use of CIoT. The study develops an Intelligent IoT (IIoT) and healthcare diagnostic model, named BT-PWO-DRNN-LSTM, using blockchain technology and Prairie Wolf optimization-based deep recuperator neural network (DRNN) and long short-term memory (LSTM). The BT-PWO-DRNN-LSTM approach includes three key processes: 1) encrypted transactions;2) cryptographic hash feature;and 3) medical diagnosis. The PWO technique is used for secure communication of medical images, the neighborhood catalogue disposition (NCD) technique is used in the cryptographic hash feature operation (CHFO), and the DL approach is employed as a classification algorithm for diagnosing disorders. The use of PWO methodology for secure healthcare communication and optimal parameter fine-tuning highlights the originality of the study. The BT-PWO-DRNN-LSTM prototype showed positive results with high sensitivity, specificity, and accuracy during the diagnostic process. © 2014 IEEE.

关键词： Blockchain

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Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

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computers, Materials & Continua 2023年第5期75卷 4293-4315页

作者： Muhammad Hammad Waseem Malik Sajjad Ahmed Nadeem Ishtiaq Rasool Khan Seong-O-Shim Wajid Aziz Usman Habib Department of Computer Sciences and Information Technology University of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Computer Science and AI College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of AI&DS School of ComputingNational University of Computer and Emerging SciencesIslamabad44000Pakistan

Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target ***,improving predictive accuracy is a crucial step for informed *** the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or *** ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification *** network weights and the activation functions are the two crucial elements in the learning process of an *** weights affect the prediction ability and the convergence efficiency of the *** traditional settings,ANNs assign random weights to the *** research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random *** proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer *** system computes the confusion matrix-based metrics for traditional and proposed *** proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other *** results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research ***,the proposed framework is of use to predict and classify cancer patients ***,this will facilitate the effective management of cancer patients.

关键词： ANN decision support systems gene-expression data hybrid classification machine learning predictive analytics

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A deep convolutional neural network for COVID-19 detection using chest X-rays

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Research on Biomedical engineering 2022年第1期38卷 139-148页

作者： Bassi, Pedro R. A. S. Attux, Romis Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Campinas Brazil

Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia, and normal. Methods: We fine-tuned neural networks pretrained on ImageNet and applied a twice transfer learning approach, using NIH ChestX-ray14 dataset as an intermediate step. We also suggested a novelty called output neuron keeping, which changes the twice transfer learning technique. In order to clarify the modus operandi of the models, we used Layer-wise Relevance Propagation (LRP) to generate heatmaps. Results: We were able to reach test accuracy of 100% on our test dataset. Twice transfer learning and output neuron keeping showed promising results improving performances, mainly in the beginning of the training process. Although LRP revealed that words on the X-rays can influence the networks’ predictions, we discovered this had only a very small effect on accuracy. Conclusion: Although clinical studies and larger datasets are still needed to further ensure good generalization, the state-of-the-art performances we achieved show that, with the help of artificial intelligence, chest X-rays can become a cheap and accurate auxiliary method for COVID-19 diagnosis. Heatmaps generated by LRP improve the interpretability of the deep neural networks and indicate an analytical path for future research on diagnosis. Twice transfer learning with output neuron keeping improved DNN performance. © 2021, Sociedade Brasileira de Engenharia Biomedica.

关键词： COVID-19

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Medical Plant Detection using Transfer learning Coupled with K-Nearest Neighbour Algorithm 3

Medical Plant Detection using Transfer learning Coupled with...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Srambickal, Sai Subramanian Srivastava, Aman Somasundaram, S.K. School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Information Security Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Medical Plants are being used for more than a thousand year, with evidences dating before the Mauryain Era (around 322 BCE), they are widely used in the Ayurvedic school of medicine for therapeutic as well as medicinal purposes, it requires an expert to identify the species as well as the medical property associated with a given plant. This paper aims to use deep learning in order to identify a given plant using the leaves of the plant by using a pretrained Convolutional Neural Network (CNN) model namely ResNet50 and MobileNet to extract the latent vector which will then be passed to K-Nearest Neighbour (KNN) algorithm in order to identify the plant class, the best accuracy of the proposed models comes out to be 97.65%. © 2024 IEEE.

关键词： Neural network models

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