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arXiv 2023年

作者： Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China The University of Adelaide Australia DAMO Academy Alibaba Group China

The universal model emerges as a promising trend for medical image segmentation, paving up the way to build medical imaging large model (MILM). One popular strategy to build universal models is to encode each task as a one-hot vector and generate dynamic convolutional layers at the end of the decoder to extract the interested target. Although successful, it ignores the correlations among tasks and meanwhile is too late to make the model ‘aware’ of the ongoing task. To address both issues, we propose a prompt-driven Universal Segmentation model (UniSeg) for multi-task medical image segmentation using diverse modalities and domains. We first devise a learnable universal prompt to describe the correlations among all tasks and then convert this prompt and image features into a task-specific prompt, which is fed to the decoder as a part of its input. Thus, we make the model ‘aware’ of the ongoing task early and boost the task-specific training of the whole decoder. Our results indicate that the proposed UniSeg outperforms other universal models and single-task models on 11 upstream tasks. Moreover, UniSeg also beats other pre-trained models on two downstream datasets, providing the community with a high-quality pre-trained model for 3D medical image segmentation. Code and model are available at https://***/yeerwen/UniSeg. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Optimizing Retail Operations with Big Data-Driven Insights: From Inventory Management to Personalized Marketing

Optimizing Retail Operations with Big Data-Driven Insights: ...

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Automation and Computation (AUTOCOM), International Conference on

作者： D David Winster Praveenraj Rahul R Kurup Muntather Almusawi Manoj Varma Lakhamraju B. Arunkumar Prakhar Mittal School of Business and Management CHRIST University Bangalore India Department Of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of HR Technology Teckpros LLC (10500 Little Patuxent Columbia MD Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Department of IT AtriCure MAson Ohio

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

In this paper, we look into the role of big data analytics in the strategic transition of retail businesses particularly on inventory management, supply chain and marketing. Using such technologies as big data and machine learning, retailers can find new patterns within such information that can lead to improved efficiency, and satisfaction of consumers. The study also shows noteworthy performance gain in areas of stock out and overstock, inventory Turns and delivery correctness. Even more, the approach to the customer targeting, that stemmed from the principles of the customer segmentation and recommendation systems, led to the growth of the conversion, customer loyalty, and customer lifetime value. The research evidence suggests that information-based management strategies contribute to organizational performance and sustained competitive advantage of firms operating in the retail sector. Issues like data integration, privacy and infrastructural, components are also addressed and hence making it easy form the basis of any future learning and trying out on the real life challenges. The current research focuses on the significance of big data for designing growth and innovation strategies in the changing retail environment.

关键词： Technological innovation Data privacy Costs Supply chains Machine learning Big Data Inventory management Recommender systems Optimization Business

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Quantifying the Benefits of Verifiable Security Schemes in Communications Software Design

Quantifying the Benefits of Verifiable Security Schemes in C...

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International Conference on Computing and Networking technology (ICCNT)

作者： Lakshya Swarup Richa Tiwari Dattatraya Babanrao Nalawade N. Thangarasu Rajat Saini A Keerthika Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka Vishwakarma Institute of Technology Pune Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The increasing use of communications software programs for replacing sensitive data requires cozy designs that prevent malicious actors from viewing or tampering with data. Verifiable security schemes offer an additional layer of assurance for communications software program designs; quantifying the benefits of such schemes is crucial for assessing their practicality. This work proposes a prevalent framework for quantifying the blessings of verifiable security schemes in communications software layout. The framework evaluates three factors: facts leakage, overhead price, and reliability. The usage of information theoretical metrics inclusive of Shannon entropy and mutual records quantifies facts leakage. Overhead value is evaluated at the protocol and machine tiers, taking account of metadata and associated computational prices. Reliability is classified by way of looking at device disruption and denial of carrier attack risks and is, for this reason, calculated on the grounds of gadget availability. A simulation-based evaluation is supplied to demonstrate the effectiveness of the proposed framework. The proposed framework offers a complete assessment of verifiable protection schemes, permitting selection makers to greater as it should be compare extraordinary device designs and pick out the most suitable one for their utility. This will, in turn, contribute to improving the safety and robustness of the verbal exchange software program layout.

关键词： Software design Protocols Layout Reliability theory Software Robustness Software reliability Security Protection Standards

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MobiEdge: Mobile Service Provisioning for Edge Clouds with Time-varying Service Demands 27

MobiEdge: Mobile Service Provisioning for Edge Clouds with T...

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Wu, Tao Fan, Xiaochen Qu, Yuben Yang, Panlong School of Electronic Engineering National University of Defense Technology China Centre for Assessment and Demonstration Research Academy of Military Science China Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China School of Computer Science and Technology University of Science and Technology of China China

ISBN: (纸本)9781665408783

With the proliferation of mobile and Internet of Things (IoT) devices, there has been an unprecedented growth of data consumption and computation requests at the network edge. To support latency-sensitive and resource-intensive mobile services, cellular base stations can be integrated with Mobile Edge Computing (MEC) technologies for service provisioning. MEC prompts flexible and configurable provisions of applications or services to make more efficient responses to mobile users' demands. Nevertheless, the time-varying nature of service demands inevitably becomes a vital challenge for existing service provisioning solutions. In this work, we propose MobiEdge, a multi-frame service provisioning scheme across two distinct timescales for MEC networks under various constraints of computation, communication and storage resources. We show that the large timescale indicated by 'frame' is more suitable for adjusting edge server activation and service placement, while the small timescale indicated by 'time slot' is more feasible to schedule users' requests. By leveraging the submodular techniques, we formulate a joint optimization problem and further propose an approximation algorithm with theoretical analysis and proofs. Synthetic and trace-driven evaluation results validate that MobiEdge can benefit both service providers and mobile users in MEC with high profits (e.g., 94% of the optimal) and a relatively low complexity. © 2021 IEEE.

关键词： Mobile edge computing

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Leveraging Machine Learning in Real-Time Automation for Predictive Maintenance in Industry 4.0

Leveraging Machine Learning in Real-Time Automation for Pred...

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International Conference on Computing and Networking technology (ICCNT)

作者： Dharmesh Dhabliya D. Anandhasilambarasan Amanveer Singh Vinod Waiker Pratik Paonipagar Shaji Thomas Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Datta Meghe Institute of Management Studies Nagpur India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The transformation of modern industries by innovative technologies drives the need for Industry 4.0, where a connected machinery system designed to create smart factories will be established. Due to this shift, demand for realtime automation and predictive maintenance is increasing to maintain operations efficiently without any breakdown. Predictive maintenance through sensors’ real-time data analysis and historical records for correct failure detection has shown promising results with machine learning. this grip answers the query, including machine learning in realtime automation (Industry 4.0) and predictive maintenance to understand various relevant terminologies. Sensor data is constantly monitored, and intelligent algorithms based on machine learning can even predict where machines could fail before one has to report downtimes. In addition to extending the machinery’s life, this decreases downtime and maintenance costs. As we know, machine learning is used in real-time automation, giving us dynamic control of production processes, making it more profitable and less wasteful. Machine learning can deliver value across the supply chain and enhance factory operations by improving past data served to a machine or production line. The introduction of machine learning into Industry 4.0 allows companies to take a step toward preventative maintenance for their operations, which reduces costs and increases productivity countless times over. One of the benefits of machine learning for Industry 4.0 is real-time readiness, making it a great tool to help us make our factories truly smart and optimize them continuously with proper IT maintenance from specialists such as Noble1 Technologies.

关键词： Productivity Costs Machine learning algorithms Machine learning Transforms Real-time systems Production facilities Fourth Industrial Revolution Maintenance Predictive maintenance

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Combining Deep Features with the Invariant Local Binary Pattern features for Skin Cancer Classification

Combining Deep Features with the Invariant Local Binary Patt...

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IEEE Punecon

作者： Ritesh Maurya Drishti Arora T Gopalakrishnan Ankita R Deshpande Malay Kishore Dutta Mohan Karnati Amity Centre for Artificial Intelligence Amity University Noida India Dept. of Computer Science and Engineering Amity University Noida India Department of Information Technology Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Manipal India

Melanoma, a potentially fatal form of skin cancer, represents a global health concern, necessitating efficient and precise detection for improved patient outcomes. However, the subjective and daunting nature of manual examination and diagnosing skin lesions by dermatologists often leads to delayed identification or misdiagnosis. This research paper introduces a novel technique for automated melanoma classification to address this pressing problem. The proposed research methodology combines deep features with rotation-invariant handcrafted local-binary pattern (RI-LBP) features for skin lesion classification. RI-LBP extracts microstructures present at the local level whereas deep features capture the high-level intuitive features present in a skin lesion image. After combining RI-LBP with the deep features, the combined feature vector is used to train and test the XGBoost machine learning classifier for classification purposes. Moreover, the investigation undertakes a comparative analysis of numerous machine learning algorithms to evaluate their efficacy in the classification of melanoma using the suggested combined feature set. The XGBoost classifier has obtained an Area Under the Curve value of 0.91 in differentiating pernicious melanomas and benign skin lesion dermoscopic images. Thus, the proposed method is suitable for aiding dermatologists, enhancing the efficacy and precision of melanoma identification and treatment, and ultimately yielding superior patient care and outcomes.

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Analysis of Spam Messages Using Various Machine Learning Classifier

Analysis of Spam Messages Using Various Machine Learning Cla...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： P Nagaraj. R Gopal. B Sunethra R Sumathi. V Muneeswaran. K Vignesh. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar India Department of Information Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar India

ISBN: (纸本)9798350333855

Background:As people using social media increases the data generation also increases and the data generated may be safe or unsafe. If we see some applications like Twitter and mail. We get a lot of emails or twits that include all dangerous and useful things. Here to be safe from the threats and dangers we need a filter that separates useful messages from spam and helps us not to drown in a trap. And one of the approaches to do this is explained in this paper. In this paper, the algorithm followed is the Naïve Bayes classifier. This also provides the comparison between using Naïve Bayes, KNN, and Logistic Regression to solve the same problem that is spam filtering and term frequency-inverse document frequency (TFIDF).

关键词： Machine learning algorithms Social networking (online) Filtering Blogs Machine learning Filtering algorithms Electronic mail

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Analysis of Visual Communication Effect of Color in Print Advertising Design Based on Decision Tree Classification Algorithm

Analysis of Visual Communication Effect of Color in Print Ad...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： B. Nancharaiah A. Visva Sangeetha A Brahmareddy Ramaganesh M Nookala Venu R. Vijaya Kumar Reddy Department of Electronics and Communication Engineering Usha Rama College of Engineering and Technology Telaprolu Andra Pradesh India Department of English PSNA College of Engineering and Technology (Autonomous) Dindigul Tamilnadu India Department of Computer Science & Engineering Marri Laxman Reddy Institute of Technology and Management Autonomous Institute Hyderabad Telangana Department of Mechanical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Internet of Things (IoT) Madhav Institute of Technology & Science A Govt. Aided UGC Autonomous Institute Gwalior Madhya Pradesh India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andra Pradesh India

With the rapid development of modern computer network technology and image storage technology, the number of image data on the Internet has increased dramatically, and there is a phenomenon that image data is extremely rich, but useful image information and knowledge are poor. Therefore, there is an urgent need for a technology for mining useful information in image data, so image mining technology came into being. As an exciting and active branch of cognitive science and computer science, digital image processing technology has received great attention and made great progress in recent years. In particular, the rapid growth of visual information and the ever-increasing need for applications have caused the study of algorithms that can handle a variety of diverse domains and activities to become the primary focus of image processing and fields that are closely related to it. This study makes use of a classification approach known as decision trees, and the decision tree algorithm is the end product of a data classification scheme in which the tree structure plays a central role. The nodes of the tree that are not leaf nodes represent the test of the data characteristics; each branch of the tree represents an output of the test, and each leaf node indicates a classification. By specifying the path that starts at the root node and continues to each leaf node, one can obtain a variety of categorization criteria. According to the findings of the research presented in this article, the algorithm presented in this paper possesses outstanding effectiveness and is suited for widespread use.

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Balancing And Risks: Managing Machine Learning In Mental Health Applications

Balancing And Risks: Managing Machine Learning In Mental Hea...

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International Conference on Computing and Networking technology (ICCNT)

作者： Sheela Upendra Pushpendra Kumar Verma Prerana Gupta M. Kulanthaivel P. Karthigaikumar Vikas B. Maral Department of Symbiosis College of Nursing Symbiosis International (Deemed University) Pune India School of Computer Science and Applications IIMT University Meerut Department of Psychitary TMU Department of Microbiology Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore Tamil Nadu India Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The Evaluation Technique, which uses AI, has the potential to completely change the way mental health services are provided. An overview of artificially intelligent technology (AI) and its most recent developments in the medical industry is provided in this article. It also reviews current research on mental health, emphasizing how AI may help with diagnosis and therapy while taking into account its present drawbacks, unexplored territory, and ethical ramifications. New Developments: We have taken a look at psychological wellbeing analyses and evaluations that classify or split psychological problems like discomfort, extreme personality disorder, and various mental illnesses, such suicide ideas and attempts, into different categories. These evaluations make use of medical records, scales for measuring mood, neuroimaging data, cutting-edge monitoring systems, and information from social media sites. The promise of statistical machine learning to treat issues with mental health is shown by these study findings, but it is crucial to identify the methods that provide the greatest outcomes. Together, these investigations add to the expanding corpus of research that shows how AI may be used in psychiatric treatments. As AI methods develop, they hold the potential to help mental health professionals define psychotic symptoms accurately, recognize chronic illnesses at an earlier, asymptomatic stage when measures can be more effective, and customize treatment plans based on unique patient characteristics. However, care must be taken to avoid extrapolating too soon from preliminary results. To close the gap between clinical therapy and behavioral health research, further study is necessary.

关键词： Neuroimaging Social networking (online) Shape Reviews Mood Medical treatment Mental health Machine learning Monitoring Medical diagnostic imaging

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Selective ensemble learning for cross-muscle ALS disease identification with EMG signal

Selective ensemble learning for cross-muscle ALS disease ide...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xujian Wang Shenghua Teng Chenxu Hao Yan Liu Ji He Shuo Zhang Dongsheng Fan College of Electronic and Information Engineering Shandong University of Science and Technology China School of Computer Science and Technology University of Chinese Academy of Sciences China Department of Neurology Peking University Third Hospital China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Electromyography (EMG) provides a useful way to identify amyotrophic lateral sclerosis(ALS) disease. EMG signals sampled from different muscles show different sensitivities on ALS disease identification, in which to find out the most sensitive muscles to ALS is meaningful. In this paper, a selective ensemble learning method is proposed for cross-muscle ALS disease identification. First, omics features are extracted from time, frequency and wavelet domains of the original EMG signals and their adaptively decomposed components respectively. Second, the ensemble learning method with selective voting strategy is proposed for ALS identification in cross-individual and cross-muscle scenarios. Finally, the contributions of each sample to the individual identification are comprehensively analyzed using the ridge regression model. Two EMG datasets from different human race and different devices are used to evaluate the performance of the proposed method. Experimental results illustrate the effectiveness of the proposed method on cross-individual and cross-muscle ALS identification, i.e. the classification accuracy and sensitivity improve by 1% ~ 11% and 1%. ~ 18% respectively.

关键词： Time-frequency analysis Wavelet domain Sensitivity Muscles Feature extraction Electromyography Ensemble learning

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