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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Mishra, Ajit Kumar Verma, Vikash Manivannan, R. Kumar, N. Krishna Srividya, Mudunuru Sharma, Sanskriti IES College of Technology Department of Electrical & Electronics Engineering M.P Bhopal462044 India GGITS Dept of Artificial Intelligence & Machine Learning M.P Jabalpur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Computer Science & Engineering TN Chennai India Guru Nanak Institutions Technical Campus Ibrahimpatam Dept.of CSE Telangana Hyderabad India MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Business System M.P Jabalpur India

ISBN: (纸本)9798331539948

Modern artificial intelligence uses geographical data mining and regional analysis to analyze large datasets. Deep learning is a new geographic AI method. Example: Geographic Deep Mining Network (GDMN). The Spatial Feature Extractor (SFE), Temporal Geospatial Attention Mechanism (TGAM), and Geospatial Data Fusion Module (GDFM) comprise this technique. We examined six popular approaches and found GDMN performed best. It worked successfully for several reasons. GDMN always outperforms its competitors in memory, F1 score, IoU, training time, and inference time. This is always true. These findings suggest that GDMN might boost geospatial AI's data mining and location analysis abilities. After all the effort, we have developed a solid basis for location intelligence, making it useful in various subject areas. Because it's versatile, mapping specialists may use GDMN for better environmental monitoring or city development. Deep learning helps GDMN make better space choices and uncover insights in large, challenging data sets that no one knew about. GDMN's cutting-edge approaches make space and time research and prediction simpler. This wide perspective offers a more full and all-encompassing picture of today's challenges than previous techniques. This makes it impossible to overstate how significant this work is to location intelligence. Its unmatched research and insights usher in a new era. © 2024 IEEE.

关键词： Adversarial machine learning

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Spatial Reasoning Development in the K12 Environment

Spatial Reasoning Development in the K12 Environment

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Frontiers in Education (FIE) Conference

作者： Sandra B. Nite Niyazi Erdogan Trenton Gray Seonhu Lee High Performance Research Computing Texas A&M University College Station TX USA Educational Achievement College of Engineering Texas A&M University College Station TX USA Department of Computer Science and Engineering Texas A&M University College Station TX USA Allen Academy Bryan TX USA

Spatial awareness, spatial, reasoning, spatial visualization, and spatial abilities are various terms all related to various ideas or aspects of spatial ability. From infancy throughout our life spans spatial awareness is embedded in our lives in ways we do not realize. Spatial reasoning is one of the foundational key concepts in mathematics and has been shown to be highly correlated with success in mathematics overall. Higher levels of spatial reasoning are critical in many STEM fields. However, it is an area in which students struggle for competency. Studies have shown that boys generally are better than girls at spatial relationships, beginning at an early age. It is not clear how much is part of the difference in the ways the brain works and how much is part of the environment and experiences of children at play. However, research shows that spatial abilities can definitely be intentionally developed and improved. Thus, children of all ages could benefit from more intentional experiential learning in areas of spatial ability. Assessing various aspects of spatial reasoning is complex, but there are research-based types of activities that support the development of spatial abilities. Our theoretical framework is based on David Kolb's experiential learning theory. In this framework, a holistic approach is envisioned, recognizing the combination of cognition, environmental factors, and emotions in learning experiences. In a week-long summer camp, activities were focused on various aspects of spatial ability. These included flying drones, paper engineering through popups, and a spatial drawing activity. Each of the activities will be discussed in the context of the various aspect of spatial ability development and the importance of those aspects in STEM fields. Students completed a survey about how important they believed spatial skills were in science, mathematics, technology, and engineering. They reflected on the camp activities and their perception of the use of

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Deep Reinforcement Learning for Joint Service Placement and Request Scheduling in Mobile Edge computing Networks

Deep Reinforcement Learning for Joint Service Placement and ...

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IEEE Symposium on computers and Communications (ISCC)

作者： Yuxuan Deng Xiuhua Li Chuan Sun Jinlong Hao Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS.

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Deep Multi-kernel Clustering Network

Deep Multi-kernel Clustering Network

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IEEE International Conference on Data Mining (ICDM)

作者： Lina Ren Ruizhang Huang Shengwei Ma Yongbin Qin Yanping Chen Chuan Lin State Key Laboratory of Public Big Data Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang China Department of Information Engineering Guizhou Light Industry Technical College Guiyang China

In this paper, a deep multi-kernel clustering network, named DMKCN, is proposed to learn a high-quality and structurally separable kernel representation for the clustering task. Specifically, a multi-kernel learner is proposed to choose a suitable kernel function by learning a suitable combination of kernel functions automatically. A kernel-aid encoder module, consisting of a series of multi-kernel learners, is proposed to learn the structurally separable kernel representation. Besides, a dual self-supervised mechanism, consisting of a kernel self-supervised strategy and a representation self-supervised strategy, is designed to uniformly optimize the kernel representation learning and structural partition. The kernel self-supervised strategy is developed to supervise the multi-kernel learners with the consideration of an objective of clustering task, the representation self-supervised strategy is developed to guide the optimization of kernel representation learning by reconstructing the raw data. Extensive experiments on six real-world datasets demonstrate the outstanding performance of our proposed DMKCN.

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How are Websites Used During Development and What are the Implications for the Coding Process?

SSRN

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SSRN 2022年

作者： Alghamdi, Omar Clinch, Sarah Skeva, Rigina Jay, Caroline Department of Computer Science The University of Manchester M13 9PL United Kingdom College of Computing and Informatics Saudi Electronic University Riyadh6867 Saudi Arabia

Websites are frequently used to support the development process. This paper investigates how websites are used when writing code and programmers’ perceptions of the potential impact of this on their behaviour and the quality of the resulting software. We interviewed 18 programmers (13 students enrolled in undergraduate computer science courses, and 5 experienced professionals), and analysed the data thematically. The findings were used to develop a survey, which was distributed to 276 programmers (251 students, 25 experienced professionals). The results indicate that use of websites, especially Stack Overflow, is viewed as an essential part of programming by both students completing coursework and professionals developing code in *** also found that developers have experience of encountering a diverse set of problematic code snippets online, that copying code from websites without checking its quality or understanding how it worked is common, and that using online resources in this way had a potentially counter-productive effect on learning. Based on these findings, we make a number of recommendations, including better consideration of online code reuse in taught programmes, co-development and code-reuse practices in professional settings, and software licensing training for professional developers. © 2022, The Authors. All rights reserved.

关键词： Websites

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Cloud-Powered Patient Interaction: Humanoid Robots in Hospital Reception using NLP

Cloud-Powered Patient Interaction: Humanoid Robots in Hospit...

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International Conference on Communications and Signal Processing

作者： N. Naveenkumar S. Priyadarshini R. Swathy Nilamadhab Mishra P. Thirumaraiselvan S. Renukadevi Department of Computer Science and Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of English Vinayaka Mission’s Kirupananda Variyar Arts & Science College Vinayaka Mission’s Research Foundation (Deemed To Be University) Salem Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India Department of Mathematics Bharathi Women’s College (Autonomous) Chennai Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

This research presents a novel method for improving patient engagement in hospital reception areas by using cloud computing and natural language processing (NLP) approaches to power humanoid robots. In order to improve the patient experience overall, the greeting procedure is being streamlined and personalized via the deployment of humanoid robots. To facilitate an effective connection between the hospital’s computer systems and the humanoid robots, cloud computing enables the processing and analysis of data in real-time. The ability to comprehend and react to patient questions, provide pertinent information, and aid with navigation inside the hospital grounds are all made possible by the robots’ natural language processing skills. Robots may access and update patient data using cloud resources, proving accurate and up-to-date information transmission. And since the system is cloud-based, it can learn and adapt on the go, so the robots may improve their communication abilities in response to human input. Results in decreasing wait times, improving information accuracy, and increasing overall patient happiness have been encouraging since the deployment of this cloud-powered patient contact system. To improve healthcare administration and patient care by integrating advanced technology into healthcare environments. Adding new features and improving the system in response to input from users (both patients and doctors) are potential directions for future growth.

关键词： Cloud computing Hospitals Navigation Humanoid robots Information processing Signal processing Natural language processing

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Future of Cybersecurity

Future of Cybersecurity

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Recent Trends in Information Technology (CSCTIT), college of Science International Conference of

作者： Yasamin Alagrash Senior lecturer in the computer science department college of science Mustansiriyah University Baghdad Iraq Department of computer science Ph.D. in computer science and informatics college of engineering Oakland university MI USA Computer science department MSC in data security university of technology Baghdad Iraq

Cybersecurity is critical to the digital world's ongoing progress. Whether you are a company owner or a customer, you must be prepared to adapt to new technology, policies, and methods that will aid in the creation of a safer and more secure online environment. As a result, the frequency of exposed network surfaces compels us to invest in more advanced protection measures.

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Early Melanoma Detection and Classification Using CNN and Confusion Matrix Analysis

Early Melanoma Detection and Classification Using CNN and Co...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Dinesh Kumar K K. Jaya Deepthi S. Saravanakumar Balamurugan S Govindharaj I Pulikandla Akshay Reddeppa Department of Computer Science and Engineering Vel Tech University Chennai India Department of Artificial Intelligence and Machine Learning Mohan Babu University Tirupati India Department of Information Technology Paavai Engineering College Namakkal India School of Computing and IT JAIN (Deemed-to-be University) Bengaluru India Department of Artificial Intelligence and Machine Learning Vel Tech University Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

This article brings up a new method for detecting and classifying skin cancers using Convolutional Neural Networks (CNN) and Confusion Matrix analysis. The main work of this work evolves around detection of Melanoma and Non Melanoma cells. The large dataset on skin cancers is used to teach the CNN model to accurately classify different types of skin lesions into different malignant and non-cancerous groups. The integration of Confusion Matrix, in this case, allows for accurate identification of the classification errors made by the model as well as possible areas for improvement hence making it possible to comprehensively evaluate how well the model performs. Therefore, early recognition is indispensable to successful treatment as skin cancer is a typical yet fatal condition. Results shows that the suggested proposed model achieves 95.5% accuracy and performance in comparison with other methods found in literature. Additionally, Modified DenseNet201 model has a sensitivity of 95.96% as well as a specificity of 98.03%.

关键词： Accuracy Sensitivity Neural networks Melanoma Medical services Skin Mobile applications Convolutional neural networks Lesions Reliability

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A conceptual framework for determining metaverse adoption in higher institutions of gulf area: An empirical study using hybrid SEM-ANN approach

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computers and Education: Artificial Intelligence 2022年 3卷

作者： Akour, Iman A. Al-Maroof, Rana Saeed Alfaisal, Raghad Salloum, Said A. Information Systems Department College of Computing & Informatics University of Sharjah United Arab Emirates English Language & Linguistics Department Al Buraimi University College Al Buraimi Oman Faculty of Art Computing and Creative Industries Universiti Pendidikan Sultan Idris Malaysia School of Science Engineering and Environment University of Salford United Kingdom

The metaverse is a kind of imagined world with immersive digital spaces that increase, allowing a more interactive environment in educational settings. The metaverse is an expansion of the synchronous communication that embraces an effective number of users to share different experiences. The study aims to investigate the students' perceptions towards metaverse system for educational purposes in the Gulf area. The conceptual model comprises the adoption properties, namely trialability, observability, compatibility, and complexity, users' satisfaction, personal innovativeness, and Technology Acceptance Model (TAM) constructs. The novelty of the paper lies in its conceptual model that correlates both personal-based characteristics and technology-based features. In addition, the novel approach of hybrid analysis will be used in the current study to perform deep-learning-based analysis of structural equation modelling (SEM) and artificial neural network (ANN). Moreover, the importance-performance map analysis (IPMA) is used in the current study to evaluate the involved factors for their importance and performance. The study identified Perceived Usefulness (PU) to be an essential predictor of the factor of Users’ Intention to Use the Metaverse System (MS). The fact was discovered during ANN and IPMA analysis. Furthermore, this study is practically significant, as it helped the concerned authorities in educational sector in understanding the significance of each factor and allowed them to make efforts and plans according to the order of significance of factors. Another important implication of the study is methodological in nature. It validates that deep ANN architecture can offer deep insight into non-linear relationships shared by various factors of a theoretical model. © 2022 The Authors

关键词： Neural networks

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Estimation of Clinical Parameters for Glaucoma Detection Using Image Processing and Deep Learning Techniques from the Biomedical Human Eye Images

Estimation of Clinical Parameters for Glaucoma Detection Usi...

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Emerging Innovations and Advanced computing (INNOCOMP), International Conference on

作者： Guru Prasad M S Chandrappa S Gulshan Dhasmana Naveen Kumar H N Shilpa Mathpati Ninad Gangodkar Dept. of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India Dept. of Computer Science and Engneering Jain (Deemed-to-be University) Bengaluru India Department of ECE Vidyavardhaka College of Engineering Mysuru India School of Computing and Information Technology REVA University Bengaluru India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

The technique for distinguishing the essential glaucoma is being exhibited in this segment utilizing a changed neural system with the back spread calculation ideas. The morphological administrators ideas are being utilized for the handling of the cup and disc, in the sense, the cup and the disc zones are discovered from which the proportion is registered, from where the illness can be recognition seeing the proportion. Utilizing an updated highlight extraction process, the highlights of the disc portion and the cup can be discovered, which could be utilized in the idea of cup to plate apportion (cup to disc ratio) recognition, regardless of whether the given test image is glaucomatic or not. Classification is also done using artificial neural networks (ANN) along with the training process. Simulations have been done in the Matlab environment by developing. m codes. Once the developed program is run, the simulation results are observed. From the simulation results, it can be observed that using the computed values of the cup to disc ratio, the severity of the disease can be computed (whether it is normal or moderate or severe glaucoma). Databases have been taken from the hospitals and on-line standard websites. The quantitative results are tabulated in a table. Finally, it was found that after taking a survey of 50 images, 16 were affected with normal, 16 were affected with moderate and the rest were affected with severe case of glaucoma. The Matlab results of the simulation done using coding demonstrated the profoundness with respect to the strategy proposed or developed in this exploration write-up using the concept of neural networks.

关键词： Glaucoma Surveys Training Technological innovation Simulation Artificial neural networks Standards

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