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Evolutional Codes: Novel Efficient Graph Data Representation for Mobile Edge Computing

IEEE Transactions on Network science and engineering 2024年第1期11卷 1387-1397页

作者： Sun, Elaine Y.-N. Wu, Hsiao-Chun Huang, Scott C.-H. Kuan, Yen-Cheng Yung, Chi National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Chungli32003 Taiwan International College of Semiconductor Technology Hsinchu30010 Taiwan

To transmit big graph data emerging from prevalent social networks and biological networks over mobile devices has become challenging nowadays. Since graphs are usually represented by the associated adjacency matrices or graph data structures, dynamic node (vertex) re-ordering to reflect the nodes' real relations (for example, spatial or structural relations) is desirable in practice but impossible for these two existing graph-representation methods. Lately, we proposed a novel evolutional coding technique, which can restructure graph data dynamically, flexibly, and efficiently for transmission among mobile devices. Memory efficiency, in terms of number of memory storage units, is a crucial performance measure for mobile edge computing. To select the optimal graph-representation strategy for arbitrary graph data, we define a new graph-taxonomy metric dependent on the edge density (edges per node) in this work. According to our newly defined graph-taxonomy metric, one can choose the optimal graph-representation solution among the three aforementioned methods in terms of required memory-storage space for any application. Pertinent theorems and proofs for the underlying comparative studies on graph representation are presented in this article. © 2013 IEEE.

关键词： Digital storage

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Integration of MCC to detect disease in plants with DL Health Industry 4

Integration of MCC to detect disease in plants with DL Healt...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bewerwal, Anubhav Habelalmateen, Mohammed I. Alsailawi, H.A. Venkatesan, D. Dhabli, Dharmesh Jabbar, Qasim Abdul Bader, Nabaa M. Alwan, Ali Saad Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Bhimtal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Kerbala College of Medicine Department of Chemistry and Biochemistry Kerbala56001 Iraq St.Martin's Engineering College Dept.of Ai&ml Secundrabad India Vishwakarma Institute of Information Technology Pune India Alkunooze University College Medical Instrument Department Basra Iraq National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Kut University College Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

Mobile gadgets are progressively assuming a vital role in individuals' everyday routines as they streamline several practical activities. Mobile cloud computing overcomes the constraints of memory, CPU, and battery life, and enhances its functionalities by integrating mobile and cloud computing. Big data analytics solutions facilitate the extraction of value from data by considering four key factors: volume, diversity, velocity, and validity. The study explores the use of mobile cloud computing and big data analytics in the context of networked healthcare. The focus is on the justification and development of interconnected healthcare applications and systems, highlighting the rise of cloud computing in this domain. The paper presents a cloudlet-based mobile cloud computing architecture designed specifically for big data applications in the healthcare sector. In the proposed framework the study assess two types of models that is cloud-based model and Wi-fi Cloudlet-based model, the working mechanism model describe the differentiation in processing. Furthermore, this study evaluates the results obtained and compares both mechanism based on latency in processing. © 2024 IEEE.

关键词： Electronic health record

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ALIGNSUM: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference

ALIGNSUM: Data Pyramid Hierarchical Fine-tuning for Aligning...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Han, Yang Wang, Yiming Wang, Rui Chen, Lu Yu, Kai X-LANCE Lab Department of Computer Science and Engineering SJTU China MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9798891761681

Text summarization tasks commonly employ Pre-trained Language Models (PLMs) to fit diverse standard *** these PLMs excel in automatic evaluations, they frequently underperform in human evaluations, indicating a deviation between their generated summaries and human summarization *** discrepancy is likely due to the low quality of fine-tuning datasets and the limited availability of high-quality human-annotated data that reflect true human *** address this challenge, we introduce a novel human summarization preference alignment framework *** framework consists of three parts: Firstly, we construct a Data Pymarid with extractive, abstractive, and human-annotated summary ***, we conduct the Gaussian Resampling to remove summaries with extreme ***, we implement the two-stage hierarchical fine-tuning with Data Pymarid after Gaussian *** apply ALIGNSUM to PLMs on the human-annotated CNN/DailyMail and BBC XSum *** show that with ALIGNSUM, PLMs like BART-Large surpass 175B GPT-3 in both automatic and human *** demonstrates that ALIGNSUM significantly enhances the alignment of language models with human summarization preferences. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Multi-level Otsu thresholding and Morphological operations for Fundus Image Segmentation for Diabetic Retinopathy identification

Multi-level Otsu thresholding and Morphological operations f...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Anju Mishra Mrinal Pandey Laxman Singh Dept. of Computer Science and Technology Manav Rachna University Faridabad India Dept. of Computer Science and Engineering (AI-ML) KIET Group of Institutions Ghaziabad INDIA

Diabetic Retinopathy (DR) disease is found in eyes of diabetic patients, and become the primary cause of vision loss. Regular screening of retinal images can save vision and avoid the sightlessness situation. Although DR is not reversible disease, but if DR identified in early stages and then treatment can lower down the possibility of vision loss. Ophthalmologists can assess the disease from Optical Coherence Tomography (OCT) or Fundus Images. Assessing DR from fundus images is tedious task or may be inaccurate. computer assisted diagnosis of DR can provide sustainable assistance to ophthalmologists. Image segmentation has been accomplished by the most authoritative thresholding technique. Retinal blood vessel segmentation is the tedious task using fundus images. This paper includes two main parts, in which the author applied the Multiple preprocessing techniques, then in second part Multi-level Otsu thresholding and Morphological operations for Fundus Image Segmentation has performed and the resultant images are shared in each stage.

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Predictive Modeling for Depression Diagnosis Using Machine Learning and DSM-5 Criteria

Predictive Modeling for Depression Diagnosis Using Machine L...

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International Conference on computer and Information Technology (ICCIT)

作者： Murchona Acharjee Showmik Debnath Sujoy Chandra Das Rana M Luthfur Rahman Pir Md. Saidur Rahman Kohinoor AI & ML in Healthcare Decipline InteX Research Lab Sylhet Bangladesh Computer Science and Engineering Leading University Sylhet Bangladesh Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Depression is a pervasive mental health disorder that significantly impacts individuals’ quality of life and global productivity. Early and accurate diagnosis is essential for effective treatment, yet the process is often hindered by stigma and limited access to mental health professionals. This study presents a machine-learning-based predictive model for diagnosing depression using a dataset labeled according to DSM-5 criteria. The dataset, comprising 363 instances and 23 attributes across diverse demographics, was collected through an online survey and labeled with guidance from a professional psychiatrist. After preprocessing, we explored nine machine learning algorithms, focusing on balancing accuracy, precision, recall, and F1 score to identify the most effective approach. AdaBoost emerged as the top-performing algorithm, achieving an accuracy of 99.45% with an AUC score of 0.9. All results were validated through 10-fold cross-validation to ensure robustness and mitigate overfitting risks. To develop a personalized healthcare solution, we deployed the best model with fine-tuning in a web-based depression detection system. We anticipate that this system will enhance access to mental health facilities and raise awareness. Our findings suggest promising potential and propose future work to scale the dataset, integrate additional features such as behavioral and physiological data, and validate the model on external datasets while considering the ethical implications of real-world deployment.

关键词： Surveys Accuracy Machine learning algorithms Computational modeling Mental health Machine learning Predictive models Depression Data models Physiology

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Empirical Method for Rice Grain Variety Classification

Empirical Method for Rice Grain Variety Classification

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Communication & Computing (WCONF), World Conference on

作者： R Ravi Kumar Bhagya Sri G K Sridhar Nagurla Mahender M. Babu Reddy Sallauddin Mohmmad School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of CSE(AI&ML) CVR College of Engineering Hyderabad Telangana India Vaageswari College of Engineering Karimnager Telangana India Computer Science Krishna University Machilipatnam AP India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Rice is grown extensively worldwide and displays a wide genetic diversity., resulting in a variety of traits and characteristics across different types. In order to achieve realistic Rice Grain Classification., this research compared the use of Vgg19., MobileNet., MobileNet v2., CNN and DensNet algorithms. The data set used in this study collected from various data repositories such as kaggle.,GitHub.,VNMN and etc. The dataset comprises a total of 10.,000 high-resolution images of rice grains., with each image labeled according to its corresponding rice variety. The dataset included five distinct rice varieties: Akura., 1001., Konaram., MTU-64., and RNR. Each rice variety is represented by 2.,000 images in the dataset. Then CNN., Vgg19., MobileN et., MobileN et v2 and DensN et are trained and assessed using the enhanced dataset. Our research results revealed successful result with DenseN et emerging as the top performer., achieving an impressive accuracy of 96.87%. Following closely behind., MobileNet V2 demonstrated notable proficiency with a classification accuracy of 94.60%., while MobileNet and VGG 19 attained accuracies of 94 % and 90%., respectively.

关键词： Accuracy Computational modeling Food security Crops computer architecture Quality control Genetics Agriculture Classification algorithms

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Advancements in Brain Disease Diagnosis for Comparing Traditional Methods and Deep Learning Approaches in Medical Imaging 1

Advancements in Brain Disease Diagnosis for Comparing Tradit...

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1st International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2024

作者： Kumari, Sunita Singh, Laxman Pasha, Moghal Irfan Kumaraswamy, M. Choudhary, Amar Agarwal, Monika Department Computer Science and Engineering ABES Institute of Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE-AI&ML CVR College of Engineering Hyderabad India Department of CST Sasi Institute of Technology and Engineering Tadepalligudem India Alliance University Bengaluru India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (纸本)9798350391268

This work explores improvements in the diagnosis of brain disease through traditional vs deep learning base techniques with respect to Medical Imaging. The dataset containing 3,000 images of brain tumors was preprocessed to resize (224x224), normalize and augmentation techniques like rotation, flipping were further implemented. The dataset are splitted into70% training and 30%. testing across four deep learning models, Convolutional Neural Networks (CNN), VGG16. The models were trained to classify brain tumor images into more or less complex categories. After training VGG19 gave top accuracy of 98.6 per cent, followed byVVG16 at second place with an accuracy rate of 96.75%, while the ResNet50 and CNN were rated third and fourth in terms of their accuracies;as neither performed well with just92.3%and89.3%. VGG19 showed the best precision (98.8%), recall(98.5%) and F1-score(98.65) among metrics of all models utilised in this research(which ever they might be). VGG19 had low false positives and negatives which are important for accurate diagnosis as can be seen in the confusion matrices. The type of research exemplifies how deep learning can improve the accuracy and certainty within medical imaging diagnostics, particularly in difficult tasks such as brain tumor classification. This study highlights the strong possibilities deep learning could lead to transformative improvements in clinical neuroscience by employing advanced neural network architectures and meticulous preprocessing strategies. © 2024 IEEE.

关键词： Neuroimaging

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Interactive Hyperspectral Imaging Data Visualization: A Catalyst for Sustainable Horticultural Practices with Artificial Intelligence

Interactive Hyperspectral Imaging Data Visualization: A Cata...

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Disruptive Technologies (ICDT), International Conference on

作者： Minal Bafna Hina Singh Kaushal Kumar Amit Vinay Kumar Singh Jagendra Singh Department of Physics Agrawal P G College Jaipur India Department of CSE Ajay Kumar Garg Engineering College Ghaziabad India Department of Computer Science (AI & ML) Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science IMS Engineering College Ghaziabad India School of Computer Science & Engineering Lovely Professional University Jalandhar India School of Computer Science Engineering and Technology Bennett University Greater Noida India

This work investigates the revolutionary potential of ai-powered interactive visualisation for sustainable horticulture practises. The amount of spectrum data provided by hyperspectral photography on the Earth's surface enables the observation and analysis of various phenomena that are vital to environmentally sustainable land use, agriculture, and research. However, there are numerous obstacles in handling and analysing the massive amounts of complex data provided by hyperspectral sensors. To address these issues, our study culminated in the development of an aidriven interactive visualisation system that enables users to rapidly extract relevant information from hyperspectral data. The framework delivers great dependability in the interpretation of complicated hyperspectral data, with outstanding recall, accuracy, precision, and F1 score values. Furthermore, based on our statistics, user engagement scores show how well the framework engages users. The framework's utility and applicability are proved by the effects it has on decision-making, user happiness, and visual clarity. Our research shows that combining ai and hyperspectral imaging can improve horticulture practises and encourage resource- and environmentally-friendly ways, which has substantial practical implications. By using this strategy, growers and environmental scientists can avoid resource waste, make datadriven decisions, and ameliorate the negative environmental effects of horticulture practises.

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PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning

arXiv

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arXiv 2024年

作者： Ying, Chengyang Hao, Zhongkai Zhou, Xinning Xu, Xuezhou Su, Hang Zhang, Xingxing Zhu, Jun Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University China Guangzhou China

Designing generalizable agents capable of adapting to diverse embodiments has achieved significant attention in Reinforcement Learning (RL), which is critical for deploying RL agents in various real-world applications. Previous Cross-Embodiment RL approaches have focused on transferring knowledge across embodiments within specific tasks. These methods often result in knowledge tightly coupled with those tasks and fail to adequately capture the distinct characteristics of different embodiments. To address this limitation, we introduce the notion of Cross-Embodiment Unsupervised RL (CEURL), which leverages unsupervised learning to enable agents to acquire embodiment-aware and task-agnostic knowledge through online interactions within reward-free environments. We formulate CEURL as a novel Controlled Embodiment Markov Decision Process (CE-MDP) and systematically analyze CEURL’s pre-training objectives under CE-MDP. Based on these analyses, we develop a novel algorithm Pre-trained Embodiment-Aware Control (PEAC) for handling CEURL, incorporating an intrinsic reward function specifically designed for cross-embodiment pre-training. PEAC not only provides an intuitive optimization strategy for cross-embodiment pre-training but also can integrate flexibly with existing unsupervised RL methods, facilitating cross-embodiment exploration and skill discovery. Extensive experiments in both simulated (e.g., DMC and Robosuite) and real-world environments (e.g., legged locomotion) demonstrate that PEAC significantly improves adaptation performance and cross-embodiment generalization, demonstrating its effectiveness in overcoming the unique challenges of CEURL. The project page and code are in https://***/ceurl. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Multimedia Classification via Tensor Linear Discriminant Analysis

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IEEE Transactions on Broadcasting 2024年第4期70卷 1139-1152页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Yan, Kun Chih-Hao Huang, Scott Wu, Yiyan San Jose State University Department Of Applied Data Science San JoseCA95192 United States Louisiana State University School Of Electrical Engineering And Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center For Ai Applications Taoyuan32003 Taiwan Guilin University Of Electronic Technology Guangxi Key Laboratory Of Wireless Wideband Communication And Signal Processing Department Of Information And Telecommunication Guilin541004 China National Tsing Hua University Department Of Electrical Engineering Hsinchu300 Taiwan Western University Department Of Electrical And Computer Engineering LondonONN6A 3K7 Canada

Linear discriminant analysis (LDA) is a well-known feature-extraction technique for data analytic and pattern classification. As the dimensionality of multimedia data has increased in this big era, it is often to characterize data by tensors. Over the past two decades, researchers have thus explored to extend LDA to the general tensor space, especially in two common ways: LDA of tensors using tensor decomposition methods (by conversion of tensors to matrices) and LDA of tensors built upon the T-product. However, both of the aforementioned approaches have restrictions thereby. A critical problem about how to carry out LDA of arbitrary scatter tensors based on the Einstein product still remains unsolved by the existing methods. Therefore, we propose a novel tensor LDA (a.k.a. TLDA) approach, which can carry out the LDA of arbitrary-dimensional scatter-tensors without any need of tensor decomposition. Besides, for reducing the computation time, we also design a parallel paradigm to execute our proposed TLDA in this work. Numerical experiments conducted over real multimedia data demonstrate the efficacy of our proposed new TLDA in terms of classification accuracy. Moreover, the comparison of the classification accuracies, computational-complexities, and memory-complexities of our proposed novel TLDA scheme and other existing tensor-based LDA methods is made. By leveraging TLDA for high-dimensional feature extraction, segmentation, and user-item interaction data processing, future multimedia recommendation systems can facilitate more accurate, engaging, and satisfactory user experience over the Internet. © 1963-12012 IEEE.

关键词： Tensors

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