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

作者： Tamilarasi, R. Kumar, P. N. Santhosh Ghantasala, G. S. Pradeep Rao, D. Nageswara Bathla, Priyanka Gupta, Gaurav Sonia Alliance University Department of Computer Science and Engineering Bengaluru India Hyderabad India Alliance University Department of Computer Science and Engineering Bangalore India Chandigarh University Punjab India Shoolini University Solan India Shoolini University Yogananda School of AI Computer and Data Sciences Solan India

ISBN: (纸本)9789380544519

Cloud computing usage in electronic government (e-government) is developing across nations and presents a range of difficulties. Infrastructure problems, such as unreliable internet connections and insufficient devices, make it difficult to integrate cloud solutions efficiently. Concerns about data security and privacy arise when private information about citizens is stored in the cloud and could be hacked or accessed without permission. Economic limits and resource constraints could prevent cloud infrastructure installation, thereby reducing e-government service scalability and efficiency. Compliance with various regulations adds complexity to the legal and governmental aspects of managing cloud data. A scientific investigation reveals that while cloud computing-driven e-government services offer numerous advantages, factors such as IT infrastructure, internet accessibility, and confidence in emerging technologies could pose barriers to their adoption. Also, recommend trust-focused cloud computing solutions for e-government services (CCSE-GS). Finally, this study focuses on the number of obstacles that developing countries must overcome to fully realize the potential of cloud computing for e-government services. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Challenges about e-government Cloud Computing E-Government models IT infrastructure

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UAV-Aided data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

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computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh Ali Wagdy Mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.

关键词： NB-IoT UAV GSK stop points optimization

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A Diversity Inventory Monitoring System of Riparian Vegetation 4

A Diversity Inventory Monitoring System of Riparian Vegetati...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Putra, Dian Pratama Nugraha, Nanda Satya Suparyanto, Teddy Hidayat, Alam Ahmad Sudigyo, Digdo Pardamean, Bens STIPER Agricultural Institute Faculty of Agricultural Technology Yogyakarta55282 Indonesia STIPER Agricultural Institute Yogyakarta Faculty of Agricultural Technology Faculty of Forestry Yogyakarta55282 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

To strengthen conservation efforts for preserving biodiversity in a conservation area, forest inventory is important to understand the natural succession process in the area and to establish a monitoring strategy. Further, tree inventory aims to monitor the output yielded in the area. More specifically, the tree inventory in the watershed area plays a key role to achieve Sustainable Development Goals (SDG), especially in riparian zones which are also vital parts of green zones in forests. However, the traditional inventory approach is time-consuming and laborious therefore the development of an expert system to assist in inventory monitoring is required. In this study, we develop a monitoring system via a mobile application to collect, analyze and visualize tree inventory data. The application includes algorithms required to compute tree biodiversity, distribution, and richness for the given input of the data of all tree species in a conservation area. For the model validation stage, we compare the traditional inventory approach with our proposed application-based approach to compute diversity inventory in two riparian locations: Klaten Conservation Park and Wonosobo Conservation Park. After the three-day data collection in the areas, we obtain that the accuracy of reading data of our proposed system can achieve more than 90% in comparison with the manual approach. This demonstrates that the system can assist forestry workers to perform more efficient tree inventories in different locations. © 2022 IEEE.

关键词： Biodiversity

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Unleashing the Power of Big data: A Comprehensive Analysis and Future Directions 6

Unleashing the Power of Big Data: A Comprehensive Analysis a...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sharma, Annu Kumar, Gaurav Farooq, Mansoor Gupta, Manish Kumar Raj, Saranya Rai, Ashok Kumar RajaRajeshwari College of Engineering Department of Computer Science & Application Karnataka Bengaluru India Indian Institute of Science Education and Research Bhopal Department of Computer Science & Engineering Madhya Pradesh Bhopal India University of Kashmir Department of Management Studies Jammu & Kashmir India Madan Mohan Malaviya University of Technology Department of Information Technology & Computer Application UP Gorakhpur India Dronacharya Group of Institutions Department of Data Science & Engineering Uttar Pradesh Greater Noida India Madan Mohan Malaviya University of Technology Department of Computer Science & Engineering UP Gorakhpur India

ISBN: (纸本)9798350304480

Big data (BD) has emerged as a transformative force, offering unprecedented opportunities for organizations to extract valuable insights and drive informed decision-making. This research paper presents a comprehensive analysis of the current state of BD, explores its potential applications across industries, and outlines future directions for maximizing its benefits. This paper examines the key components of BD, including data collection, storage, processing, and analytics. It investigates the technologies and methodologies that enable efficient handling of large volumes of structured and unstructured data. Additionally, it explores the impact of cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) on the growth and utilization of BD. Overall, this research paper provides a comprehensive analysis of BD, its current applications, challenges, and future directions. It serves as a valuable resource for researchers, practitioners, and policymakers seeking to unlock the full power of BD and harness its benefits across diverse domains. © 2023 IEEE.

关键词： Analytics artificial intelligence Big data Internet of Things Processing

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Incremental Learning with Pseudo Labels for Enhanced Glomeruli Segmentation Using SAM

Incremental Learning with Pseudo Labels for Enhanced Glomeru...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Liu, Xinkai Peng, Boyuan Zhu, Xin Iwasaki, Tsuyoshi Sato, Atsuya Kazama, Junichiro James Huang, Jie Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan Department of Nephrology and Hypertension Fukushima Medical University Fukushima Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan

ISBN: (纸本)9798350350548

The advent of the Segment Anything Model (SAM) marks a significant milestone in deep learning semantic segmentation tasks. In this study, an incremental learning model based on pseudo labels is proposed. The model generates bounding box prompts for SAM, enhancing the accuracy of glomeruli segmentation in renal pathology images. The model uses DenseNet-121 along with contrastive learning to enable self-supervised pre-training using mouse glomeruli images. Transfer learning is then conducted using labeled human renal pathology images to develop a binary classification model for glomeruli. Unlabeled images of human renal pathology are subsequently introduced for incremental learning. Class activation maps (CAMs) generated by the incremental learning model are post-processed to extract bounding rectangles for connected regions. These rectangles are then used as prompts for SAM segmentation. The proposed approach resulted in an average Dice similarity coefficient (DSC) of 0.825 in the glomeruli segmentation task. This result demonstrates the effectiveness of the incremental learning model and post-processing algorithms in improving SAM’s segmentation accuracy for renal pathology. © 2024 IEEE.

关键词： Contrastive Learning

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Multiclass Classification and Defect Detection of Steel Tube Using Modified YOLO 30th

Multiclass Classification and Defect Detection of Steel Tu...

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30th International Conference on Neural Information Processing, ICONIP 2023

作者： G, Deepti Raj B, Prabadevi Gadekallu, Thippa Reddy Bhatia Khan, Surbhi Saraee, Mohammed School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon College of Information Science and Engineering Jiaxing University Jiaxing314001 China Department of Data Science School of Science Engineering and Environement University of Salford Manchester United Kingdom School of science engineering and environment University of Salford Salford United Kingdom

ISBN: (纸本)9789819981830

Steel tubes are widely used in hazardous high pressure environments such as petroleum, chemicals, natural gas and shale gas. Defects in steel tubes have serious negative consequences. Using deep learning object recognition to identify and detect defects can greatly improve inspection efficiency and drive industrial automation. In this work, we use a well-known YOLOv7(You Only Look Once version7) deep learning model and propose to improve it to achieve accurate defects detection of steel tube images. First, the classification of the dataset is checked using a sequential model and AlexNet. A Coordinate Attention (CA) mechanism is then integrated into the YOLOv7 backbone network to improve the expressive power of the feature graph. Additionally, the SIoU (SCYLLA-Intersection over Union) loss function is used to speed up convergence due to class imbalance in the dataset. Experimental results show that the evaluation index of the optimized and modified YOLOv7 algorithm outperforms other models. This study demonstrates the effectiveness of using this method in improving the model’s detection performance and providing a more effective solution to steel tube defects. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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AlloyGAN: Domain-Promptable Generative Adversarial Network for Generating Aluminum Alloy Microstructures 22

AlloyGAN: Domain-Promptable Generative Adversarial Network f...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Yin, Biao Fan, Yangyang Josselyn, Nicholas Rundensteiner, Elke Chief Material Scientist DeepAlloy PortlandOR United States Worcester Polytechnic Institute Department of Data Science WorcesterMA United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA United States

ISBN: (纸本)9798350345346

The global metal market, expected to exceed 18.5 trillion by 2030, faces costly inefficiencies from defects in alloy manufacturing. Although microstructure analysis has improved alloy performance, current numerical models struggle to accurately simulate solidification. In this research, we thus introduce AlloyGAN - the first domain-driven Conditional Generative Adversarial Network (cGAN) involving domain prior for generating alloy microstructures of previously not considered chemical and manufactural compositions. AlloyGAN improves cGAN process by involving prior factors from solidification reaction to generate scientifically valid images of alloy microstructure given basic alloy manufacturing compositions. It achieves a faster and equally accurate alternative to traditional material science methods for assessing alloy microstructures. We contribute (1) a novel Alloy-GAN design for rapid alloy optimization;(2) unique methods that inject prior knowledge of the chemical reaction into cGAN-based models;and (3) metrics from machine learning and chemistry for generation evaluation. Our approach highlights the promise of GAN-based models in the scientific discovery of materials. AlloyGAN has successfully transitioned into an AIGC startup with a core focus on model-generated metallography. We open its interactive demo at: https://***/ © 2023 IEEE.

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Packet warriors: An academic mobile action game for promoting OSI model concepts to learners

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2019年第6期13卷 41-51页

作者： Uiphanit, Thanakorn Bhattarakosol, Pattarasinee Suanpong, Kwanrat Iamsupasit, Sompoch Technopreneurship and Innovation Management Program Chulalongkorn University Bangkok10330 Thailand Technopreneurship and Innovation Management Program Suan Sunandha Rajabhat University Bangkok Thailand Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Commerce Chulalongkorn Business School Chulalongkorn University Bangkok10330 Thailand Faculty of Psychology Chulalongkorn University Bangkok10330 Thailand

This research aims to develop a game to teach the theoretical concepts of the OSI reference model to students who must learn data communication and networking as a compulsory or an elective subject . The survey was administered to most students at Suan Sunandha Rajabhat University by a questionnaire to assess a suitable game for students of this generation. The result showed that action games are the most popular game for them. Then, a script of the OSI reference model was drawn based on the theoretical contents and the software design and implementation were performed. After finishing this development process, 19 third-year undergraduate students majoring in information sciences at Suan Sunandha Rajabhat University tested this system. The evaluation process was performed using pretest and posttest data analyzed by a paired samples t-test at a 95% confidence level. The result of this study shows that the action game 'Packet Warrior' is an efficient tool to help students improve their learning capabilities via fun and good relationships with friends. © 2019, International Association of Online Engineering.

关键词： Students

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Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Mammogram Breast Cancer Classification Based on Deep-Convolu...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Amadea, Avissa Aurellia Sari, Christy Atika Rachmawanto, Eko Hari Sari, Wellia Shinta Noori, Mohammed S. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Based on WHO's data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient's diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection. © 2023 IEEE.

关键词： Diseases

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Comprehensive Analysis on Anomaly Detection in Cybersecurity dataset using Deep Learning Algorithms

Comprehensive Analysis on Anomaly Detection in Cybersecurity...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Nidhishree, G. Vidyalakshmi, K. Vidyavardhaka College of Engineering Department of Computer Science and Engineering (Artificial Intelligence & Machine Learning) Mysuru India Ssahe University Sri Siddhartha Institute of Technology Department of Computer Science and Engineering (Data Science) Tumakuru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cybersecurity has become very important due the rapid growth of digital technologies. It plays an important role in protecting computer systems and networks from potential attacks. Cybersecurity involves a set of technologies and algorithms that sometimes bring in threats to the security of computer networks, the end system, attacks on data, inappropriate access, changes and harm. Cyber-defense mechanisms involved at the host, application, network and data level require a constantly improved process to secure the system from any feasible attack, targeting data security and privacy. The network anomaly detection is challenging because of the complex network topologies and high dimensional data, leading to unbalanced performance, high false positive rates and difficulty of handling time-series data. Deep Learning (DL) methods appear as an essential element in network Intrusion Detection Systems (NIDS) to recognize malicious behaviors or anomalies. Anomaly detection and network security are extensively utilized for intrusion detection performed by utilizing Cyber security to reduce malicious behavior in the networks. DL based-based detection utilizes many algorithms, which are capable of capturing complex patterns, effective for high-dimensional data. In DL, the anomaly is solved by two ways: flow-based detection that analyzes the network flows and packet-level data to identify anomalies. © 2024 IEEE.

关键词： anomaly detection cyber security deep learning intrusion detection system privacy and security

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