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An Hybrid Combination of Ontology Along with RDF technology for ICU Systems through CBM 4

An Hybrid Combination of Ontology Along with RDF Technology ...

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4th ieee international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Nikshya, J.Ebens Sanmugavalli, P. Jayachitra, S. Kaur, Manjeet Jalal, Sarah Salah Hussain, Ahmed Scad College of Engineering and Technology Department of Ece Tamilnadu Tirunelveli India M.Kumarasamy College of Engineering Department of Ece Karur India Psna College of Engineering and Technology Department of Ece Dindigul India Govt.P.G.College Department of Computer Science and Applications Sector 1 Panchkula Haryana Panchkula India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Modern data generation poses distinct issues due to its enormous volume, quick speed, and wide variation, necessitating the use of specialised tools and techniques for analysis and inference. these issues are never more evident than in the healthcare industry, where computerised patient records and biological research create data at an exponential rate. Furthermore, it is now essential to analyse data from all areas that have an influence on patient health, including medical and oral diseases, given the increased emphasis on holistic treatment. there are interdependencies among many medical and dental disorders, necessitating coordinated care. However, technological obstacles including different formats for data collection and storage, restricted patient information exchange, and insufficient decision support tools have made collaborative patient care extremely difficult. In response to these challenges, this work explores the development and application of ontology and resource description framework (RFD) rules to construct an evidence-based, reusable, cross-domain knowledge base. In a sample application of this knowledge base, the effectiveness of the technique for reasoning and decision assistance in a range of patient information domains is assessed using Protégé. © 2024 ieee.

关键词： Electronic health record

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An asynchronous architecture based on matrix operation 4

An asynchronous architecture based on matrix operation

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4th international conference on computer Engineering and Application, ICCEA 2023

作者： Diao, Wenqi Zhang, Hongrui Sun, Ruoyun He, Anping School of Information Science & Engineering Lanzhou University Lanzhou China

ISBN: (纸本)9798350347548

Matrix operations are widely available in various engineering and scientific computing applications;however, the calculation efficiency depends on the input data density. these operations cannot complete the calculation using only one inner/outer product method. Meanwhile, most of the current computational accelerator architectures use synchronous clock circuits. As the technology approaches its limit and the scale of the circuit increases, the disadvantage of clock circuits becomes more and more obvious. Clock circuits fail to meet the very low power requirements of specific fields. To address this problem, this paper proposes an asynchronous computing architecture that supports matrix computing micro-instructions. It selects the appropriate calculation method by sensing the data density. For SPGEMM, a two-way condensation technique is adopted to solve the problem of data volume. A 'one-to-one' merge strategy to reduce the uncertainty of the merge process is further proposed. For SRAM, an asynchronous management mechanism is developed to ensure the uniformity of the overall architecture. Finally, the area of this architecture is 17.3 mm2, and the power demand is only 0.00468 W in the 110-nm process. this research evaluates the device on the SuiteSparse set and random matrix set, it also achieves 62.3×, and 11.25× energy savings over OuterSPACE and SpArch, respectively. © 2023 ieee.

关键词： Energy conservation

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the Design of AI Algorithms for DG Setting 4

The Design of AI Algorithms for DG Setting

引用

4th ieee international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Ganeshkumar, N. Das, Purushottam Hussein, Laith Dhabliya, Dharmesh Al Hassan Alhaidry, Wesam Abed Jweeg, Muhsin Al-Ansari, Ahmed Saadalwan, Ali Gitam School of Technology Department of Computer Science and Engineering Karnataka India Department of Cse Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Vishwakarma Institute of Information Technology Information Technology Department Pune India National University of Science and Technology College of Technical Engineering DhiQar Iraq Al-Farahidi University Baghdad Iraq Al-kunooze University College Dental Department Basra Iraq Kut University College Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

Artificial intelligence (AI) is the bedrock of management systems and will continue to support human society both now and in the future. these systems' decision-making processes must be transparent, especially in sensitive areas such as autonomous driving and healthcare, to build trust. Although methods exist for achieving algorithmic transparency, there are no well-established design techniques to systematically address descriptive issues when reviewing AI. In response, we introduce the Explainability in Design (EID) approach, which operationalizes the definition of AI into six fundamental concepts. EID was refined through iterative empirical studies, including applied learning and interviews with practitioners. An empirical study with AI system designers tested the utility of EID, demonstrating its effectiveness in reducing barriers to integrating explanatory functionality in AI systems and enhancing decision-making processes. © 2024 ieee.

关键词： Autonomous Vehicles Design Methods Empirical Research Explanatory Capabilities Healthcare

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Deep Neural Networks based Detection and Analysis of Fake Tweets 4

Deep Neural Networks based Detection and Analysis of Fake Tw...

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4th international conference on Signal Processing and Communication, ICSPC 2023

作者： Mareeswari, G. Dinesh, Erana Veerappa Ramco Institute of Technology Department of Computer Science and Engineering Virudhunagar India

ISBN: (纸本)9798350300772

Social networking is a wedge for interchanging thoughts, individual perspectives and views but without adversely affecting the sentimental, religious, or maybe private thoughts of the group. Furthermore, the spread of fake news flashes continues to be a pattern on social networking. this paper discusses a hybrid method used towards the control over this kind of ill intentions by developing a method that analysis and also detects posts, toxic comments, or fake news inside any multimedia or text format. this particular paper will help to identify the precision of fake news utilizing Deep Neural Networks. News articles extracted from Twitter is represented as embedding vectors. the hybridized Convolutional Neural Network and LSTM (Long-Short Term Memory) framework is built to learn the contextual dependencies between the words present in news articles. We compare many methods for detecting fake news. the Natural Language Interference (NLI) designs may also be qualified. the information compilation, interpretation, and then tests procedure are clarified completely along with existing different study analyses in the identity of linguistic variants to come down with truthful and false information. Next, we check as well as train a pair of learning breakthroughs to produce exact fake information detectors. © 2023 ieee.

关键词： Deep neural networks

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Quantitative Study on Impact of Node Selfishness on Performance of MANETs 4

Quantitative Study on Impact of Node Selfishness on Performa...

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4th ieee international conference on Smart Internet of things, SmartIoT 2020

作者： Shan, Axida Fan, Xiumei Zhang, Xinghui Xi'an University of Technology Department of Automation and Information Engineering Xi'an China School of Information Science and Technology Baotou Teachers College China College of Electronic and Information Engineering Ankang University Ankang China

ISBN: (纸本)9781728165141

Mobile ad hoc networks (MANETs) are kinds of wireless networks that are various features like dynamic mobility, constraint resources, fully decentralized etc. the communication among the nodes highly depends on the cooperation between nodes. Data forwarding is the main task during the communication process. Because of constraint of limited resources, it is rational that some nodes behave selfishly and refuse to burden forwarding tasks for others. In this paper, we quantitatively investigate the impacts of node selfishness in MANETs from various aspects, including mobility, presence, and combination of selfish nodes. the network performance is assessed in terms of average packet delivery ratio and average end-To-end delay. We carried out extensive simulations on OMNeT++ platform and analyzed the results. Our results quantitatively showed that node selfishness has significant impacts on the communication performance in MANETs. As a result, our experimental results imply that the impact of node selfishness should be evaluated in an integrated way of mobility, presence and combination. © 2020 ieee.

关键词： Mobile ad hoc networks

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TARG: A Reference-Free, Lossless, Customized General-Purpose Encoder for Genome Sequence in Raw, FASTA, or Multi-FASTA Formats 4

TARG: A Reference-Free, Lossless, Customized General-Purpose...

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4th international conference on computer, Communication, Control and information technology, C3IT 2024

作者： Roy, Subhankar Ghosh, Partha Mukhopadhyay, Anirban Academy of Technology Dept. of Computer Science and Engg. Hooghly India University of Kalyani Dept. of Computer Science and Engg. Nadia India

ISBN: (纸本)9798350389128

the ability to efficiently encode and store vast amounts of genome sequence data is paramount for various analytical and computational tasks. General-purpose compressors fail to efficiently compress genome sequences, primarily due to the highly repetitive nature of genomic data, the complexity and variability of genomes across organisms, the large size of genome sequences, the limitations of standard compression algorithms, and the absence of optimization for genomic data patterns. this article addresses this challenge through the development of a reference-free, lossless, customized general-purpose encoder for genome sequences in raw, FASTA (FAST-ALL), or multi-FASTA formats. the proposed encoder offers a versatile solution that can handle diverse genome datasets without relying on preexisting reference sequences, ensuring compatibility with emerging genome sequencing technologies. We tested and evaluated the encoder thoroughly to show that it works well and is reliable. We looked at compression ratios, compression and decompression speeds, memory, and how well it recreated the original genome sequences. this encoder not only addresses the pressing need for efficient data storage and transmission in genomics but also paves the way for further advancements in genome analysis and interpretation. Unlike other algorithms, the proposed method, TARG (Two-bit coding encoded using ASCII with Run length encoding followed by General-purpose encoding), does not depend on time-consuming parameter settings. Based on experiments, the average compression ratio of TARG on 10 benchmark genome datasets is 4.33, while the current state-of-the-art general-purpose algorithms gzip, bzip2, zstd, and bsc have respective averages of 3.26, 3.51, 4.21, and 4.28. © 2024 ieee.

关键词： Genome

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Artificial Intelligence Model Implementation in Web-Based Application for Pineapple Object Counting 4

Artificial Intelligence Model Implementation in Web-Based Ap...

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4th international conference on information Management and technology, ICIMTech 2019

作者： Rahutomo, Reza Perbangsa, Anzaludin Samsinga Lie, Yulius Cenggoro, Tjeng Wawan Pardamean, Bens Bionformatics Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information Systems Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Indonesia's horticulture sector will gain the capacity to address traditional agricultural problems by adopting Agricultural 4.0 technology. As a pillar of Industrial Revolution 4.0, Artificial Intelligence (AI) can provide the benefits of time efficiency and minimize human error compared to traditional methods. the research used the crowd-sourcing method as a well-established approach in data gathering collaboration. Python and Flask also used in the research for web-based application development, Keras-RetinaNet for AI training models, and Web Server Gateway Interface (WSGI) for AI Model Deployment. this research resulted in a web-based AI application that can be used to count pineapple objects on a very wide area by using hundreds of aerial photography as training dataset. the accuracy of pineapple object counting with AI model utilization will optimized the use of resources such as water, fertilizer, insecticides, and packaging materials. © 2019 ieee.

关键词： Antennas

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Attention Module Based on Feature Normalization 4

Attention Module Based on Feature Normalization

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4th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

作者： Du, Qiliang Wang, Yimin Tian, Lianfang School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350343113

Attention mechanism is an essential component in convolutional neural networks. Although attention modules like SE and CBAM have achieved tremendous success in fields such as image classification and object detection, most existing attention modules can only perform feature fusion in either channel or spatial dimensions. this greatly limits the flexibility of attention mechanisms to adapt in both channel and spatial dimensions, leading to underutilization of feature information. To address these issues, we propose a Feature Normalization-based Attention Module (FNAM) in this paper. this module generates a feature normalization weight map from the input feature map. During the normalization process, the scale and shift parameters for each channel are derived from the mean and standard deviation of the feature map. Consequently, this approach effectively utilizes feature information from all dimensions. To demonstrate the feasibility and effectiveness of this attention module, we conducted ablation experiments. In the image classification task, the FNAM module shows a significant improvement in the performance of classification networks on the CIFAR dataset compared to other mainstream attention modules. In the object detection task on the VOC dataset, the FNAM module outperforms most other attention modules. Particularly, concerning small and medium-sized objects in the dataset, its detection accuracy reaches the highest, with improvements of 1.6% and 1.5% respectively. © 2023 ieee.

关键词： computer vision

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A Diversity Inventory Monitoring System of Riparian Vegetation 4

A Diversity Inventory Monitoring System of Riparian Vegetati...

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4th international conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Putra, Dian Pratama Nugraha, Nanda Satya Suparyanto, Teddy Hidayat, Alam Ahmad Sudigyo, Digdo Pardamean, Bens STIPER Agricultural Institute Faculty of Agricultural Technology Yogyakarta55282 Indonesia STIPER Agricultural Institute Yogyakarta Faculty of Agricultural Technology Faculty of Forestry Yogyakarta55282 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

To strengthen conservation efforts for preserving biodiversity in a conservation area, forest inventory is important to understand the natural succession process in the area and to establish a monitoring strategy. Further, tree inventory aims to monitor the output yielded in the area. More specifically, the tree inventory in the watershed area plays a key role to achieve Sustainable Development Goals (SDG), especially in riparian zones which are also vital parts of green zones in forests. However, the traditional inventory approach is time-consuming and laborious therefore the development of an expert system to assist in inventory monitoring is required. In this study, we develop a monitoring system via a mobile application to collect, analyze and visualize tree inventory data. the application includes algorithms required to compute tree biodiversity, distribution, and richness for the given input of the data of all tree species in a conservation area. For the model validation stage, we compare the traditional inventory approach with our proposed application-based approach to compute diversity inventory in two riparian locations: Klaten Conservation Park and Wonosobo Conservation Park. After the three-day data collection in the areas, we obtain that the accuracy of reading data of our proposed system can achieve more than 90% in comparison with the manual approach. this demonstrates that the system can assist forestry workers to perform more efficient tree inventories in different locations. © 2022 ieee.

关键词： Biodiversity

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Beyond Search: A Network-Based Exploration of Buyer Behavior in the OLX Car Market 4

Beyond Search: A Network-Based Exploration of Buyer Behavior...

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4th international conference on Multimedia Processing, Communication and information technology, MPCIT 2024

作者： Ashvath, Y. Moin Ramya, G.R. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375466

the information on used cars offers a thorough analysis of the second-hand car industry. this dataset may be utilized to construct predictive models that can estimate the price of a used car based on its characteristics, including the make, model, year, mileage, fuel type, and condition. this dataset enables comprehensive data analysis to extract insights into market trends, pricing dynamics, and buyer-seller behavior. the main goals are to examine price patterns depending on different characteristics, assess the influence of fuel type and geographical factors on pricing and demand, investigate the correlation between mileage and resale value, and determine the most popular automobile models. Furthermore, the dataset enables the development of predictive models that can estimate both selling prices and the amount of time it takes to sell a product. Methodologies encompass the processes of data cleaning and exploratory data analysis (EDA). the results will assist in identifying communities or clusters within the dataset, uncovering groups of cars with similar attributes or market characteristics. the results aim to offer comprehensive market insights, improve comprehension of buyer behavior, and create predictive tools to assist sellers, buyers, and platform operators in decision-making. Ultimately, this will optimize strategies and operations in the used car market. © 2024 ieee.

关键词： Sales

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