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Advancement in Technology (ICONAT), International Conference for

作者： Manthan S. Manavadaria Srikanth Chintakindi Rajasree Rajamohanan S Krishna Prasad P. Janardhan Saikumar Neerav Nishant EC Department CSPIT CHARUSAT Changa Petlad Gujarat India Associate Project Manager Cloud Software Group Department of Computer Science (Artificial intelligence and Data Science) Karpagam Academy of Higher Education Coimbatore India Department of Mechanical Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte Udupi India Department of ECE Audisankara College of Engineering and Technology Gudur India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

One of the main killers on a worldwide basis is cancer. In terms of global persistence, lung cancer stands out among the most frequent cancers. For lung cancer, the standard medical approach includes chemotherapy, surgical removal, and radiation therapy. When compared to other methods, this one isn’t extremely targeted and may even damage surrounding healthy cells. There has been recent recognition of nanotechnology as a possible tool for the treatment and management of lung cancer. Preprocessing, feature selection, segmentation, and model training must be executed in this precise sequence to ensure accuracy. As part of the preprocessing, the proposed approach employ the kernel correlation approach. Splitting the input image into smaller pieces with the goal of reducing the overall representation of the image is called image segmentation. To find features with high predictive power and remove redundant ones, feature selection was done in the training cohort. The model was trained using a Convolutional-KELM. As compared to CNN and ELM, the suggested approach performs better. Following implementation of the technique, the accuracy increased by 94.25%.

关键词： Training Image segmentation Accuracy Lungs Lung cancer Feature extraction Convolutional neural networks Kernel Medical diagnostic imaging Tumors

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Federated Learning for 6G Edge Intelligence: Concepts, Challenges and Solutions 10th

Federated Learning for 6G Edge Intelligence: Concepts, Chal...

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10th International Conference on Artificial Intelligence and Mobile Services, AIMS 2021 held as Part of the Services Conference Federation, SCF 2021

作者： Wang, Han Hu, Jianhe Xing, Chunxiao Zhang, Liang-Jie Research Institute of Information Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Computer Science and Technology Institute of Internet Industry Tsinghua University Beijing100084 China Department of Economics College of Letters and Science University of California Davis DavisCA95616 United States National Engineering Research Center for Supporting Software of Enterprise Internet Services Shenzhen518057 China Kingdee Research Kingdee International Software Group Company Limited Shenzhen518057 China

ISBN: (纸本)9783030960322

Along with continuous evolution, the future 6G network will become a converged "Cloud-Edge-Terminal" ecosystem which can carry various crucial AI applications on edge computing units, formulating an ubiquitous "Edge Intelligence" paradigm to enable differentiated service innovations and empower intelligent transformation of vertical industries. However, due to issues of data security, user privacy, wireless network transmission capability and etc., it is not feasible for conventional machine learning methods to build AI models by directly collecting massive distributed edge data together, and hence resulting a large number of "isolated data islands" in the edge units. In order to break the data sharing barrier and drive cross-edge data cooperation, this paper studies a federated learning based AI model training method by which sensitive raw data can be maintained and protected in its original edge units. Based on the general scheme, some challenging problems are discussed to implement this new paradigm in practical scenarios, and the corresponding promising solutions and key techniques are proposed to inspire further researches. © 2022, Springer Nature Switzerland AG.

关键词： Digital storage

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Technology acceptance model (TAM) for mobile durian plantation apps(Pro Durian);the analysis for Malaysian farmers

Technology acceptance model (TAM) for mobile durian plantati...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Noraziahtulhidayu Kamarudin Aiman Yusoff Abd Samad Hasan Basari Noor Zuraidin bin Mohd Safar Mohd Hatta Hussin Department of Multimedia Multimedia and Signal Processing Focus Group Faculty of Computer Science and Information Universiti Tun Hussein Onn Malaysia Department of Multimedia Faculty of Computer Science and Information Universiti Tun Hussein Onn Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Department of Information Security and Web Technology Faculty of Computer Science and Information Universiti Tun Hussein Onn Malaysia Johor Darul Takzim Pertubuhan Peladang Senggarang

During the application development process, it is important for a developer to analyze user requirements. This is crucial to the success of interactive systems and is an essential component of the design of information systems. User requirement analysis aids developers in understanding and analyzing users' needs and technology requirements. Throughout this case study, it is able to forecast the understanding, and technology acceptance, and at the same time improve the quality of work, reducing training and support costing and perennially improve customer or user satisfaction. The method of research selected is questionnaires that focused on collecting the data directly from Malaysian farmers through Google Forms as an online medium. The method was chosen because it is easy to implement and inexpensive due to the current situation of Covid-19. The questionnaire is designed with two sections where; a) includes the participant's demography to analyze information of the intended audience while b)focused on the knowledge of participants about recognition images. Results demonstrate the farmer's opinion and knowledge of the technology of image recognition in the agriculture sector. The quantitative research was undertaken using an online platform due to the Covid-19 pandemic. From the analysis, it is justified that the Technology Acceptance Model (TAM) together with the mobile farming image recognition app is useful in collecting farmers' feedback and positive results have an outcome on the challenges faced by the durian farmers.

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Empirical Validation of software Integration Practices in Global software Development

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SN computer science 2020年第3期1卷 1-23页

作者： Ilyas, Muhammad Khan, Siffat Ullah Rashid, Nasir Software Engineering Research Group (SERG_UOM) Department of Computer Science & IT University of Malakand Lower Dir District Chakdara Pakistan

software complexity accrues, as modern societies are highly dependent on large-scale, software-intensive systems that increasingly operate within an environment of continuous availability. Global software development (GSD) vendors strive to reduce this complexity by decomposing the target software product into various components that are developed in-house, outsourced or purchased as commercial off-the-shelf (COTS) components. Subsequently, these components are integrated into a final working product. However, there is a lack of specific practices to be followed by GSD vendors in the software integration phase. In our previous study, we identified a list of nine software integration critical success factors (CSFs) for GSD vendors. In order to assist GSD vendors in adoption of the identified CSFs, we conducted another Systematic Literature Review (SLR) for identification of practices and validated the results using questionnaire survey. We identified 116 software integration practices/solutions that will help GSD vendors in trimming down the complexity of their software integration process. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Empirical study Global software development Practices software integration Solutions Systematic literature review

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Robust Image Ordinal Regression with Controllable Image Generation

arXiv

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arXiv 2023年

作者： Cheng, Yi Ying, Haochao Hu, Renjun Wang, Jinhong Zheng, WenHao Zhang, Xiao Chen, Danny Wu, Jian School of Software Technology Zhejiang Univerisity China School of Public Health Zhejiang University China Alibaba Group China College of Computer Science and Technology Zhejiang University China School of Computer Science and Technology Shandong University China Department of Computer Science and Engineering University of Notre Dame United States Second Affiliated Hospital School of Medicine Zhejiang University China

Image ordinal regression has been mainly studied along the line of exploiting the order of categories. However, the issues of class imbalance and category overlap that are very common in ordinal regression were largely overlooked. As a result, the performance on minority categories is often unsatisfactory. In this paper, we propose a novel framework called CIG based on controllable image generation to directly tackle these two issues. Our main idea is to generate extra training samples with specific labels near category boundaries, and the sample generation is biased toward the less-represented categories. To achieve controllable image generation, we seek to separate structural and categorical information of images based on structural similarity, categorical similarity, and reconstruction constraints. We evaluate the effectiveness of our new CIG approach in three different image ordinal regression scenarios. The results demonstrate that CIG can be flexibly integrated with off-the-shelf image encoders or ordinal regression models to achieve improvement, and further, the improvement is more significant for minority categories. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Empowering software Startups with Agile Methods and Practices: A Design science Approach

SSRN

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SSRN 2022年

作者： Gandomani, Taghi Javdani Nafchi, Mina Ziaei Parizi, Reza M. Department of Computer Science Shahrekord University Shahrekord Iran CHB Science and Technology Park Shahrekord Iran Data Science Research Group Department of Computer Science Shahrekord University Shahrekord Iran Department of Software Engineering and Game Development Kennesaw State University MariettaGA30060 United States

In recent years, the number of software startups is increasing rapidly and employing a proper software development methodology in these startups is a concern. Recently, these startups have been using agile software development methodologies and practices. However, due to the special conditions of these startups and the focus on producing the Minimum Viable Product, it seems that the use of these methodologies and practices is not done correctly. Accordingly, these startups face more severe challenges, making their success more difficult. The literature review shows that few studies have been done regarding the correct methods of using agile methods and practices in software startups. This research has used Design science Research Methodology (DSRM) to fill this research gap and develop agile adaptation guidelines in software startups. The developed guidelines were validated with the participation of 23 experts from 7 software startup teams through survey questionnaires and open discussion. Eventually, final guidelines containing 13 recommendations were developed in three categories: selection of agile methods and practices, preparation for adaptation, and application and adaptation of agile methods and practices. The evaluation results show the simplicity of understanding these guidelines, their usefulness, and their support for the expected agility of the software development process. © 2022, The Authors. All rights reserved.

关键词： software design

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On the Privacy of Mental Health Apps: An Empirical Investigation and its Implications for Apps Development

arXiv

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arXiv 2022年

作者： Iwaya, Leonardo Horn Ali Babar, M. Rashid, Awais Wijayarathna, Chamila Centre for Research on Engineering Software Technologies The University of Adelaide AdelaideSA5005 Australia Australia Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Bristol Cyber Security Group Department of Computer Science University of Bristol United Kingdom

An increasing number of mental health services are offered through mobile systems, a paradigm called mHealth. Although there is an unprecedented growth in the adoption of mHealth systems, partly due to the COVID-19 pandemic, concerns about data privacy risks due to security breaches are also increasing. Whilst some studies have analyzed mHealth apps from different angles, including security, there is relatively little evidence for data privacy issues that may exist in mHealth apps used for mental health services, whose recipients can be particularly vulnerable. This paper reports an empirical study aimed at systematically identifying and understanding data privacy incorporated in mental health apps. We analyzed 27 top-ranked mental health apps from Google Play Store. Our methodology enabled us to perform an in-depth privacy analysis of the apps, covering static and dynamic analysis, data sharing behaviour, server-side tests, privacy impact assessment requests, and privacy policy evaluation. Furthermore, we mapped the findings to the LINDDUN threat taxonomy, describing how threats manifest on the studied apps. The findings reveal important data privacy issues such as unnecessary permissions, insecure cryptography implementations, and leaks of personal data and credentials in logs and web requests. There is also a high risk of user profiling as the apps' development do not provide foolproof mechanisms against linkability, detectability and identifiability. Data sharing among third parties and advertisers in the current apps' ecosystem aggravates this situation. Based on the empirical findings of this study, we provide recommendations to be considered by different stakeholders of mHealth apps in general and apps developers in particular. We conclude that while developers ought to be more knowledgeable in considering and addressing privacy issues, users and health professionals can also play a role by demanding privacy-friendly apps. Copyright © 2022, The Authors. All r

关键词： Data privacy

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on computer Aided Systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Acupuncture State Detection at Zusanli (ST-36) Based on Scalp EEG and Transformer

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4023-4034页

作者： Wenhao Rao Meiyan Xu Haochen Wang Weicheng Hua Jiayang Guo Yongheng Zhang Haibin Zhu Ziqiu Zhou Jiawei Xiong Jianbin Zhang Yijie Pan Peipei Gu Duo Chen School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Department of Computer Science and the Institute of Human Factors Engineering Minnan Normal University Zhangzhou China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China China Mobile Group Jiangsu Company Ltd Sihong Branch Suqian China Second Affiliated Hospital of Nanjing University of Chinese Medicine Nanjing University of Chinese Medicine Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo China College of Software Engineering Zhengzhou University of Light Industry Zhengzhou China

In clinical acupuncture practice, needle twirling (NT) and needle retention (NR) are strategically combined to achieve different therapeutic effects, highlighting the importance of distinguishing between different acupuncture states. Scalp EEG has been proven significantly relevant to brain activity and acupuncture stimulation. In this work, we designed an acupuncture paradigm to collect scalp EEG to study the differences in EEG changes during different acupuncture states. Since deep learning (DL) has been increasingly used in EEG analysis, we propose the Acupuncture Transformer Detector (ATD), a model based on Convolutional Neural Networks (CNN) and Transformer technology. ATD encapsulates the local and global features of EEG under the acupuncture states of Zusanli acupoint (ST-36) in an end-to-end classification framework. The experiment results from 28 healthy participants show that the proposed model can efficiently classify the EEG in different states, with an accuracy of $85.47\pm 0.73\%$ . In this study, time-frequency analysis revealed that power changes were mainly confined to the delta frequency band under different acupuncture states. Brain topography revealed that ST-36 was activated primarily on the left frontal and parieto-occipital areas. This method provides new ideas for automatic recognition of acupuncture status from the perspective of DL, offering new solutions for standardizing acupuncture procedures.

关键词： Acupuncture Electroencephalography Transformers Convolutional neural networks Needles Brain modeling Medical diagnostic imaging Training Temperature measurement Feature extraction

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Out-of-Distribution Detection by Principal Component Correspondence

Out-of-Distribution Detection by Principal Component Corresp...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoyuan Guan Zhiyong Gan Ling Deng Wei Shi Jiankang Chen Shenshen Bu Chunliang Zhao Jianfang Hu Yuren Zhou Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Network Business Group China Unicom Guangzhou China Department of Network Intelligence Pengcheng Laboratory Shenzhen China School of Data and Science Qingdao University of Science and Technology Qingdao China School of Software Engineering Sun Yat-Sen University Zhuhai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Out-of-distribution (OOD) detection is vital for the safe application of intelligent systems in real-world scenarios. This paper proposes an enhancement to OOD detection by leveraging the consistency in cognition between two models, both pretrained on in-distribution (ID) data. Specifically, for a given test sample, we first apply Principal Component Analysis (PCA)-based projection on the feature vectors from each model. These obtained feature vectors (with correlation between dimensions decoupled by PCA projection) are then aligned using a multiple linear mapping, which is fitted using the least squares method on the training data. We hypothesize that the regression error for OOD data will be larger than that for ID data, making it a useful metric for OOD detection. Our experimental results demonstrate the effectiveness of this method. When combined with existing robust baselines, our approach achieves state-of-the-art performance in OOD detection.

关键词： Training Measurement Analytical models Training data Vectors Data models Intelligent systems Facial features Principal component analysis Least mean squares methods

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