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2023 International Conference on Intelligent computing and Control, IC and C 2023

作者： Ang, Qi Zhi Yau, Peter Chunyu Sum, Chin Sean Cao, Qi Wong, Dennis University of Glasgow School of Computing Science United Kingdom Singapore Institute of Technology Singapore Macao Polytechnic University Faculty of Applied Sciences China

ISBN: (纸本)9781665457095

This paper provides an overview, presents the work-in-progress study of how software architecture can be modernized using the latest technologies. We will discuss the reasons why software modernization is required, how to choose an optimal solution from the architecture perspective, related migration strategy and the related points to note. The paper will also discuss some of the common issues faced while reviewing the architecture. These issues would provide some insights to some frequently faced problems in development and deployment. Finally, a discussion on how containerization technology and load balancer could help in resolving some of the common issues faced. In this paper, we have evaluated our proposed approach in a realistic case study involving an actual organization, which was anonymized for identity protection A certain organization would be used to provide the corresponding review mentioned. © 2023 IEEE.

关键词： Legacy systems

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Alzheimer's Disease Diagnosis and Categorization using Deep Learning 1

Alzheimer's Disease Diagnosis and Categorization using Deep ...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Sangeetha, M. Gunasekaran, Hemalatha Azalini, T. Nakshathra, J. Uma Mageshwari, P. Manjula Devi, R. Dr.N.G.P.Institute of Technology Department of Information Technology Tamilnadu Coimbatore India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350372748

Alzheimer's disease (AD) is a significant global health concern, accounting for the sixth highest cause of mortality worldwide. Early diagnosis is critical to improving patient care and treatment outcomes. This study aims to classify Alzheimer's disease (AD) using Magnetic Resonance Imaging (MRI) brain images, allowing for prompt intervention and optimal disease treatment. The dataset consists of 6400 MRI pictures classified into four categories: very mildly demented, mildly demented, moderately demented, and non-demented, with an 80-10-10 split for training, validation, and testing. Initially, the utilization of VGG16 architecture results with an accuracy of 87%. To improve accuracy and reduce training time, the GoogleNet (Inception V3) Convolutional Neural Network (CNN) architecture was used. This method resulted with an accuracy of 97.62%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Digital Forensic Tools: Comparison of Autopsy TSK and Forensic Explorer

Digital Forensic Tools: Comparison of Autopsy TSK and Forens...

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Information technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Mitchell Farnan James Pratt Andrew David Masoud Shakiba School of Computing Electrical and Applied Technology Unitec Institute of Technology Auckland New Zealand

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research aims to compare the effectiveness of two prominent digital forensics tools, Autopsy TSK and Forensic Explorer, in analysing a well-known forensic image, specifically the Lone Wolf image from Digital Corpora. The aim is to assess their capabilities in detecting and handling forensic artefacts and their responses to selected anti-forensic techniques. Autopsy TSK and Forensic Explorer exhibited robust performance in identifying and analysing forensic artefacts within the Lone Wolf image. They successfully retrieved most of the available digital evidence. Data was concealed using encryption techniques, both tools failed to detect the hidden information, highlighting a limitation in their ability to uncover encrypted steganographic content. In cases of altered Exif and metadata, both tools could retrieve the original information from the hex data of the files. However, it is noteworthy that neither tool provided an alert or warning to guide the investigator regarding potential data manipulation. Based on the findings of this research, it is advisable for digital forensic investigators to exercise caution and employ additional techniques or tools when handling cases which may involve encrypted steganography.

关键词： Steganography Communication systems Digital forensics Autopsy Metadata Encryption Information technology

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Washroom Occupancy Tracking and Hygiene Monitoring System Using IoT in Universities 17th

Washroom Occupancy Tracking and Hygiene Monitoring System Us...

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17th International Conference on Knowledge Management in Organisations, KMO 2023

作者： Kasinathan, Vinothini Mustapha, Aida Rai, Kallychurn Dooshyant Ching, Tham Hoong School of Computing and Technology Asia Pacific University of Technology and Innovation Kuala Lumpur57000 Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Panchor84600 Malaysia

ISBN: (纸本)9783031340444

The accelerated technological growth majorly influencing our everyday lives, Internet of Things (IoT) has been a trending topic and current interest in the contemporary world. IoT has benefited in revolutionising lifestyles, improving quality of life. Therefore, with the help of IoT, the study proposes a prototype for washroom occupancy tracking and hygiene monitoring system called the Washroomium. The prototype is evaluated with targeted users within the Asia Pacific University campus and received favourable feedback. It is hoped that this prototype would be able to provide a pleasant washroom experience for the stakeholders of university by bringing them an at-desk indication on the occupancy and hygiene status of washroom cubicles, alleviating queues and disappointment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge management

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Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

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Computers, Materials & Continua 2024年第6期79卷 3585-3629页

作者： Imtiaz Ahmad Tariq Hussain Babar Shah Altaf Hussain Iqtidar Ali Farman Ali Institute of Computer Sciences and Information Technology The University of AgriculturePeshawarPakistan School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhouChina College of Technological Innovation Zayed UniversityDubaiUnited Arab Emirates School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Department of Applied AI School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063South Korea

Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and *** of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering *** have several challenges,including throughput,energy usage,and network lifetime *** strategies have been applied to get over these *** may,therefore,be thought of as the best way to solve such ***,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy *** paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)*** the help of APSO in the implementation of the WSN,the main methodology of this article has taken *** simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 *** sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to *** number of dead nodes likewise dropped for the given combination,falling between 71 and *** network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based *** to underwater,WSN can make use of the proposed *** overall results have been evaluated and

关键词： Wireless sensor network cluster head selection low energy adaptive clustering hierarchy accelerated particle swarm optimization

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LIE SYMMETRY ANALYSIS, PARAMETRIC REDUCTION AND CONSERVATION LAWS OF (3+1) DIMENSIONAL NONLINEAR DISPERSIVE SOLITON EQUATION

arXiv

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arXiv 2025年

作者： Singhal, Anshika Joshi, Urvashi Arora, Rajan Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee India

The core focus of this research work is to obtain invariant solutions and conservation laws of the (3+1)-dimensional Zakharov–Kuznetsov (ZK) equation which describes the phenomenon of wave stability and solitons propagation. ZK equation is significant in fluid dynamics, nonlinear optics, and acoustics. ZK equation plays an important role in understanding the behavior of nonlinear, dispersive waves especially in the presence of the magnetic field. Lie symmetry analysis has been applied to the (3+1)-dimensional ZK equation to derive invariant solutions. These solutions reveal how waves retain their shape as they travel, how they interact in space, and the impact of magnetic fields on wave propagation. Using the Lie symmetry, the (3+1) dimensional Zakharov-Kuznetsov equation is reduced to various ordinary differential equations. It also concludes with the conservation laws and non-linear self-adjointness property. Additionally, we align our findings with real-life problems, namely plasma physics, electromagnetic wave propagation, and water wave dynamics, providing the applicability of our theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Electromagnetic wave propagation in plasma

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Bridging the Gap in Comprehensive Network Security and Data Management Through IoT Innovation

Bridging the Gap in Comprehensive Network Security and Data ...

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Electrics and Computer (INTCEC), Interdisciplinary Conference on

作者： Suthesh Ravichandran Jatinder Singh Andrew David Masoud Shakiba School of Computing Electrical and Applied Technology Unitec Institute of Technology Auckland New Zealand

ISBN: (数字)9798350349450

ISBN: (纸本)9798350349467

The increase of connected devices on the Internet of Things has raised significant concerns related to data security, privacy, and network integrity. In response to these challenges, our paper brings a holistic project aimed at developing a solution. Our innovative solution CyberGuard Pi integrates multiple key functionalities to addresses the concerns. The CyberGuard Pi device incorporates Pi-hole for efficient ad blocking, providing users with a positive online experience while minimizing security risks associated with malicious advertisements. To bolster user privacy, a Virtual Private Network is implemented, safeguarding data transmission and protecting against potential threats. Furthermore, the CyberGuard Pi features robust network monitoring and scanning capabilities, offering real-time insights into network activities within the local LAN area. An Intrusion Detection System is integrated to identify and mitigate potential threats, ensuring the integrity of the IoT ecosystem. To facilitate seamless and secure data storage, our CyberGuard pi comes with a Network-Attached Storage cloud system enabling users to store and retrieve data with ease. This component enhances data security concerns associated with traditional storage methods. Our CyberGuard Pi represents a comprehensive approach to address network security challenges posed by the growing number of connected devices by combining ad blocking, VPN, network monitoring, intrusion detection, and cloud-based storage functionalities.

关键词： Economics Data privacy Data security Intrusion detection Memory Network security Virtual private networks

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Development of Automated Techniques for Object-Oriented Image Analysis in Hyper Spectral Images

Development of Automated Techniques for Object-Oriented Imag...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Abrol, Monika Pandey, Rajendra P. Pawar, Rahul Sanskriti University Department of Applied Science Uttar Pradesh Mathura India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

the development of computerized strategies for item-orientated image evaluation in Hyper Spectral photos (HSI), an emerging field of applying machine-gaining knowledge of synthetic intelligence, has ended up an increasing number of crucial in a selection of domain names. This kind of analysis calls for a particular and correct illustration of the gadgets of interest from the hyperspectral photos. For this reason, characteristic extraction, classifiers, and clustering techniques have been proposed if you want to come across and classify them greenly. The maximum, not unusual feature extraction techniques used to extract statistics from HSI consist of radiometry, spectral band shapes, and spectral correlation. These function extraction strategies produce specific characteristic descriptors that can be utilized in aggregate with item classifiers and clustering solutions to detect and classify the objects gift in the HSI. Characteristic extraction strategies, together with Radiometric Normalized distinction flora Index (NDVI) and significant components analysis (PCA), have been observed to achieve success in numerous scenarios. Classifiers, linear and nonlinear SVM, neural networks, and choice bushes are the most famous strategies for reading HSI. Using a single this kind of strategy has been seen to offer the most straightforward restricted outcomes;however, using a combination of those strategies has been visible to enhance the classification performance. © 2024 IEEE.

关键词： characteristic classification development function strategies

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Predicting Student's Final Examination Grades in Statistics Courses Using a Regression Model Based on Internal Assessment Scores

Predicting Student's Final Examination Grades in Statistics ...

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computing and Data Analytics (ICCDA), International Conference on

作者： Jagath Prasad Sreedhar Ranjini S Nair Abraham Varghese College of Computing and Information Sciences University of Technology and Applied Sciences Muscat

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

Learning analytics is the process of collecting data on learners and their environment, analyzing it, and using those insights to improve learning experiences and outcomes. Analysis will help teachers identify students who might need extra help early on. Also, courses can be tailored to individual learning styles and the entire learning environment can be fine-tuned for better results. A multiple linear regression model is presented here in this work to predict students' Final Exam grades from internal assessment scores. In this case, the response variable is the student's score in the Final Exam, while the assessment components, such as Quiz, Midterm, and SCL, are predictor variables. The data were collected from a group of students who studied Probability Statistics course at the advanced diploma level program. A regression model was created using R software in this study, accompanied by graphical representations and tables to demonstrate the model.

关键词： Analytical models Data analysis Computational modeling Linear regression Predictive models Probability Data models Software

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Exploring the Potential of Recurrent Neural Networks for Medical Image Segmentation

Exploring the Potential of Recurrent Neural Networks for Med...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Jain, Aaditya Mandal, Sanjeev Kumar Abrol, Monika Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Applied Science Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

Recurrent Neural Networks (RNNs) are a modern-day state-of-the-art algorithm that is brand new modern getting used for clinical picture segmentation. RNNs are, in particular, nicely applicable for this undertaking due to the fact they can be skilled to bear in mind patterns over long sequences brand new information. This enables them to perceive structural patterns in an image and carry out sophisticated segmentation obligations together with tumor or organ boundary identification. similarly, RNNs have the ability to contain earlier know-how from different pics and medical data, as well as contextual know-how from external resources such as electronic fitness information. This paper critiques the contemporary in RNNs for medical picture segmentation, outlining the key methods and programs contemporary RNNs inside the field. We discuss both the successes and demanding situations of trendy RNN-based procedures and provide destiny studies directions for the improvement of modern-day extra correct and efficient segmentation equipment. © 2024 IEEE.

关键词： development directions outlining segmentation incorporate

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