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1st International Conference on data engineering and Machine Intelligence, ICDEMI 2023

作者： Mohan, Ramya Kadry, Seifedine Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates

ISBN: (纸本)9789819776153

In addition to traditional approaches, several computerized techniques have been developed to enhance the results. The automation in the medical domain considerably reduced the burden and improved disease diagnosis, treatment planning, and implementation. With the advent of automation, the medical domain has greatly reduced the burden of diagnosing diseases, developing treatment plans, and implementing them. This research aims to develop image-processing assisted detection of Diabetic Foot Ulcer (DF) from digital photographs. Digital photographs are used to assist in diagnosing Diabetic Foot Ulcer (DF). As part of the developed tool, (i) images are collected and resized, (ii) Otsu/Kapur and Hummingbird-optimizer thresholding is supported, (iii) level-set segmentation is used to extract the DF region, and (iv) the developed scheme is compared with the binary mask and its performance is verified. A total of 300 images are examined in this work, and MATLAB software is used to conduct the experiments. Comparing Otsu + LS with Kapur + LS, the overall result achieved with Kapur’s enhanced image is better than that of Otsu + LS. Authentic clinical DF images can be used to verify the proposed scheme in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： MATLAB

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Lightweight Weight Update for Convolutional Neural Networks 1

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12th International Congress on Big data, Bigdata 2023

作者： Wang, Feipeng Ben, Kerong Zhang, Xian Yang, Meini College of Electronic Engineering Naval University of Engineering Wuhan China School of Computer and Big Data Science Jiujiang University Jiujiang China

ISBN: (数字)9783031447259

ISBN: (纸本)9783031447242

Convolutional neural networks are usually composed of convolutional layers and pooling layers. Pooling operations effectively control the weight update of convolutional neural networks. The existing pooling operations result in a large number of weight parameter updates of convolutional neural networks, causing large memory usage. In this paper, a pooling operation called ApproxM is proposed to address this problem. The proposed pooling operation is a simple and similar to median pooling. It takes the mean of multiple values near the median as the pooling result, and CNNs only update the weights of these values during the back propagation. Finally, extensive experiments on benchmark datasets demonstrate that the proposed pooling operation achieves top-1 results of 92.65% and 68.24% and top-5 results of 99.84% and 91.31% model test accuracy on Cifar-10 and Cifar-100 based on ResNet-20, respectively, and the corresponding number of weight updates in an 8 × 8 pool is 4, which is better than other pooling techniques in the experiments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image recognition

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An Analysis on Cognitive Radio Adhoc Networks: Categories, Problems, and Solutions 1

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14th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022

作者： Vinya, Viyyapu Lokeshwari Sahithi, G. Gopal, S. Venu Rao, G. Venkateswara Shaikh, Mehwish Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Shamshabad India Department of Computer Science and Engineering CVR College of Engineering Telangana Ibrahimpatnam India Department of Artificial Intelligenceand Data Science Vardhaman College of Engineering Telangana Shamshabad India Department of Computer Science and Engineering GIT GITAM-Deemed to be University A.P. Visakhapatnam India Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

The exploitation of finite spectrum resources is being addressed by the new technology known as Cognitive Radio (CR). It has emerged as a potential remedy for the spectrum shortage problem in the following generation of cellular networks by utilizing opportunities in the time, frequency, and spatial domains. Many group-oriented applications that are created for CRAHN deployment have grown significantly in relevance in recent years. Multicasting is a more effective method for enabling applications that rely on group communication. In contrast to traditional wired multicast routing protocols, multicast routing for CRNs must manage a range of challenges, because of the characteristics of CRNs, such as low bandwidth, mobility, and low power. In this paper, we discussed about various types, issues and solutions for CRAHN routing, Multicast Routing, Channel Assignment and Load balancing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multicasting

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QUALYST: data Quality Assessment System for Thailand Open Government data 20

QUALYST: Data Quality Assessment System for Thailand Open Go...

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20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Samakit, Tanapat Anutariya, Chutiporn Buranarach, Marut School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9798350300505

Open Government data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all;thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format. © 2023 IEEE.

关键词： Transparency

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Leveraging Bayesian Networks for Enhanced Transparency in Financial data Provenance Tracking

Leveraging Bayesian Networks for Enhanced Transparency in Fi...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Nithya, K. Selvan, Mary Gnanavel, V.K. Solainayagi, P. Venkatesh, K. Department of Electronics and Communication Engineering Chennai India R.M.K College of Engineering and Technology Department of Artificial Intelligence and Data Science Puduvoyal India Asan Memorial College of Engineering and Technology Department of Computer Science and Engineering Korea Republic of Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Kalasalingam Academy of Research and Education Department of CSE India

ISBN: (纸本)9798331542344

This work describes a probabilistic model based on Bayesian Networks for improving the record-keeping process of financial data and, thus, increasing the traceability of such information. To enhance fraud control and data provenance, the proposed system captures the probabilistic relationships between financial variables to enhance the identification of anomalous behaviours. The validation of the system was performed on the large data set of financial transactions and the system claimed an accuracy of 91 percent. 5%, precision of 89.2%, and recognition of 87. A special ranking of 8% in fraud detection was achieved, which is higher than the Decision Tree model with 85.3%and Random Forest model with 88. 7%. The performance of the Bayesian Network in terms of tracing was evaluated with the results for a single transaction being 28 ms, and for a sequence of transactions being 134 ms which is slightly slower than the traditional method but the advantage of utilizing a BN is its good level of interpretability. The system also obtained a True Positive Rate (TPR) of 89 percent. 8%) False Positive Rate 5%. 1% more than its rivals which proves its efficiency in the detection of frauds. Worst case performance analysis revealed the fact that though the framework is highly efficient for small to medium amounts of data the time required for processing data increases with the increase in the size of the data. There is also an aspect of interpretability which The Bayesian Network received a Score of 9. by financial experts only, it received a 5/10 score mainly on issues regarding the organization's decision making which is transparent. These outcomes show that Bayesian Networks are a stable and understandable solution for elements of financial data traceability, especially in highly responsible financial systems as applied in ISE. Further research will be directed towards achieving higher scalability for large scale use. © 2024 IEEE.

关键词： Decentralized finance

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A Conceptual Evaluation of the data as Partner (DAP) Framework 9

A Conceptual Evaluation of the Data as Partner (DAP) Framewo...

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9th IEEE International Smart Cities Conference, ISC2 2023

作者： Hassan, Sonia Sobnath, Drishty Snell, Esther Isiaq, Olufemi Solent University Department of Science and Engineering Southampton United Kingdom Creative Computing Institute Universiy of Arts Department of Computer and Data Science and Ai United Kingdom

ISBN: (纸本)9798350397758

This paper explores the innovative data As Partner (DAP) philosophy as a transformative approach to organisational data strategy, drawing upon established concepts of information value, technology acceptance, and inclusivity for people with disabilities (PwD) in smart cities. The study conducted a comprehensive systematic review, after meticulous screening, 20 eligible papers were selected, forming the foundation for deriving the key components of the DAP framework. Subsequently, a qualitative focus group study engaged 18 participants from diverse sectors to evaluate the practical applicability of the DAP conceptual framework. Thematic analysis of the focus group sessions revealed five key themes: practical value, pragmatic performance, ethical perspective, clarity of the framework, and enhancement tools. These findings offer valuable insights for refining the DAP framework and optimising its integration into real-world scenarios, in empowering individuals in smart cities, especially those with disabilities. Future research should explore larger and more diverse samples to generalise findings and assess the framework's long-term impact through longitudinal evaluations. By adopting user-centred methodologies, the DAP framework can be further enhanced as a strategic tool, empowering data to actively participate in organisational decision-making processes. © 2023 IEEE.

关键词： Smart city

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Adaptive Operator Selection for Meta-Heuristics: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Pei, Jiyuan Mei, Yi Liu, Jialin Zhang, Mengjie Yao, Xin Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington New Zealand Lingnan University School of Data Science Hong Kong Hong Kong

Appropriate selection of search operators plays a critical role in meta-heuristic algorithm design. Adaptive selection of suitable operators to the characteristics of different optimization stages is an important task that owns promising potential to improve the performance of a meta-heuristic algorithm. A variety of adaptive operator selection methods have been proposed in last decades, from the machine learning and optimization communities. However, the existing studies have not been systematically reviewed so far. To fill the gap, this paper provides a comprehensive survey of adaptive operator selection for meta-heuristics. According to the information required for selection, adaptive operator selection methods are classified into two categories: (i) stateless methods and (ii) state-based methods. Each category is further summarized into several key components. The strategies of each component belonging to the two categories are reviewed respectively. The motivation, strengths and weaknesses of the proposed strategies are also discussed. Furthermore, studied meta-heuristics and optimization problems in the literature are summarized. The effects from the difference of meta-heuristics and problems to the specific design of methods are discussed, together with the guidance of selecting the suitable method in different application scenarios. At the end, emerging challenges that could guide further research are discussed. © 2020 IEEE.

关键词： Adaptive algorithms

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Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

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2024 International Conference on Advances in data engineering and Intelligent Computing Systems, ADICS 2024

作者： Vasanth Kumar, V. Tamizhselvan, C. Manoharan, L. Vishnu Sekhar, R. Vinoth Kumar, V. Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Chennai India School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India School Of Bioengineering Srm Institute Of Science And Technology Integrated Bioprocessing Laboratory Department Of Biotechnology Kattankulathur India

ISBN: (纸本)9798350364828

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric. © 2024 IEEE.

关键词： Decision trees

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American Sign Language Recognition System for Numerical and Alphabets

American Sign Language Recognition System for Numerical and ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Sathyanarayanan, D. Reddy, T. Srinivasa Sathish, A. Geetha, P. Arunkumar, J.R. Deepak, S. Prem Kumar Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering Potti Sriramulu Chalivudi Mallikarjuna Rao College of Engineering and Technology vijayawada India Department of Computer Science and Engineering Roever Engineering College Perambalur621220 India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Samayapuram Trichy India Department of Computer Science and Engineering Modern Institute of Technology and research centre Alwar Rajasthan India

ISBN: (纸本)9798350305708

Sign language is a helpful visual technique that can be utilized by individuals who have difficulty communicating verbally. This language is used frequently by people as well. However, the vast majority of individuals don't understand sign language very well, and as a result, they frequently struggle when attempting to communicate with someone who is fluent in it. The community of people who have trouble speaking or hearing will benefit from an initiative called the Sign Language Recognition System. It is extremely challenging for deaf mutes to communicate, which is why we require a linguistic framework to understand what it is that they are attempting to express. In light of this, we present in this research an American Sign Language (ASL) handwriting translator that is built on convolutional neural networks. Instructing computers to recognize specific characters is one way to accomplish this goal. The Support Vector Machine (SVM) and the Convolutional Neural Network are two types of artificial neural networks that are being utilized in our project we have suggest the identification of American Sign Language. In addition, the Decision Tree and Voting Classifier are applied to the dataset in order to do an analysis. We have demonstrated the design and execution of an American Sign Language (ASL) translator that also incorporates fingerspelling as a direct consequence of this. © 2023 IEEE.

关键词： Convolution Neural Network Decision Tree Sign Language Support Vector Machine Voting Classifier

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The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation 29

The Evolution of Zero Trust Architecture (ZTA) from Concept ...

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29th International Conference on Information Technology, IT 2025

作者： Nasiruzzaman, Md Ali, Maaruf Salam, Iftekhar Miraz, Mahdi H. Xiamen University Malaysia School of Computing and Data Science Malaysia Universiteti Metropolitan Tirana Faculty of Computer Science and IT Albania The Doctoral College United Kingdom Xiamen University Malaysia Mahdi H. Miraz Is with the School of Computing and Data Science Malaysia Wrexham University School of Computing Faculty of Arts Science and Technology United Kingdom University of South Wales Faculty of Computing Engineering and Science United Kingdom

ISBN: (纸本)9798331517649

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always-verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA's benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats. © 2025 IEEE.

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