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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Classification of Ultrasound Breast Cancer Image Using Tuning Up the Hyper-Parameter of Convolutional Neural Network

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SSRN 2023年第7期17卷 264-269页

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management & Research Gwalior India Department of Computer Engineering Madhav Institute of Technology and Science Gwalior India SOS in Computer Science & Applications Jiwaji University Gwalior India

Breast cancer in women is a significant public health concern worldwide, with many cases going undiagnosed until the advanced stages. Early detection is crucial for proper treatment and improved outcomes. There are some pre-trained models used by authors for the detection of breast tumor, but these models require extensive computation power due to their many layers and parameters. To address this issue, it is required to proposed convolutional neural network (CNN) model with fewer training parameters for classification of ultrasound images dataset to determine that a particular image is either benign or malignant. In this paper, CNN model is proposed with changes in some hyperparameters like the number of filters, filter size, batch normalization, learning rate, epoch, and batch size, to achieve better accuracy with less computational power. The proposed model was compared to other pre-trained models, including ResNet50, Efficient Net, and VGG16, using two databases (database A for training and validation data, and database B for testing data). Our proposed classifier outperformed than pre-trained classifiers in terms of accuracy. © 2023, The Authors. All rights reserved.

关键词： Neural network models

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Electrocardiogram Classification Using Convolutional Neural Network 5th

Electrocardiogram Classification Using Convolutional Neural ...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Kachhadiya, Kashyap Hasmani, Abdullah Gorasiya, Drashti Patel, Bansari Patel, Krishna Vadhavana, Vaishali Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Changa India

ISBN: (纸本)9789819773701

Circulation of Blood relies significantly precise electrical initiation. Arrhythmias can arise from any disturbance in the excitatory wave’s regular pattern in the heart. Electrocardiograms (ECG) are a popular diagnostic tool due to their speed, affordability, and non-invasive nature. This article presents a method for classifying ECGs that makes use of Convolutional Neural Network (CNNs) and the Continuous Wavelet Transform. Using Continuous Wavelet Transform (CWT), ECG signals are broken down into different time–frequency parts, while CNN extracts feature from the resulting 2D-scalogram. To further enhance the model considering the RR interval for arrhythmia diagnosis, 4 RR interval parameters are taken out and connected to CNN parameters to provide an ECG that is completely integrated classifier layer. This proposed model examines the impact of CNN and CWT on ECG classification alongside RR intervals. After training Accuracy of 97.27% was achieved. Based on the proposed method further models can designed for efficient clinical applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Robust asymmetric Cherenkov radiation in tilted anisotropic medium

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science China(Physics,Mechanics & Astronomy) 2024年第1期67卷 46-51页

作者： Shuwen Xue Yadong Xu Linkang Han Zhi Ning Chen Qiaoliang Bao Shan Zhu Huanyang Chen Institute of Electromagnetics and Acoustics and Department of Physics College of Physical Science and TechnologyJiujiang Research InstituteXiamen UniversityXiamen 361005China School of Physical Science and Technology Institute of Theoretical and Applied PhysicsSoochow UniversitySuzhou 215006China Department of Electrical and Computer Engineering National University of SingaporeSingapore 8117583Singapore Institute of Energy Materials Science(IEMS) University of Shanghai for Science and TechnologyShanghai 200093China

Cherenkov radiation(CR)is available for a wide variety of terahertz(THz)radiation sources,but its efficiency is deeply affected by intrinsic *** find that if the tilted angle(α)of anisotropic material and radiation angle(θ)meet the condition ofθ+α=π/2,the intensity of radiation fields for the charged particle bunch(CPB)moving from left to right cannot be influenced by intrinsic losses,which means long-distance radiation can be ***,we observe an asymmetric CR when the CPB moves from the opposite *** addition,we select natural van der Waals(vd W)materialα-MoO3as an example,further confirming that the radiation field can reach the far field and the asymmetric CR radiation can also be *** wonderful properties with long-distance radiation will extend the application of CR to a certain extent for future design and fabrication.

关键词： asymmetric Cherenkov radiation intrinsic losses long-distance radiation

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Multi-task learning andjoint refinement between camera localization and object detection

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Computational Visual Media 2024年第5期10卷 993-1011页

作者： Junyi Wang Yue Qi State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518052China Qingdao Research Institute of Beihang University Qingdao 266104China School of Computer Science and Technology Shandong UniversityQingdaoChina

Visual localization and object detection both play important roles in various *** many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely ***,few researchers consider these two tasks simultaneously,because of a lack of datasets and the little attention paid to such *** this paper,we explore multi-task network design and joint refinement of detection and *** address the dataset problem,we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic *** dataset provides localization and detection information,and is publicly available at https://***/drive/folders/1U28zk0N4_I0db zkqyIAK1A15k9oUKOjI?usp=sharing for benchmarking localization and object detection *** this dataset,we have designed a multi-task network,JLDNet,based on YOLO v3,that outputs a target point cloud and object bounding *** dynamic environments,the detection branch also promotes the perception of *** includes image feature learning,point feature learning,feature fusion,detection construction,and point cloud ***,object-level bundle adjustment is used to further improve localization and detection *** test JLDNet and compare it to other methods,we have conducted experiments on 7 static scenes,our constructed dataset,and the dynamic TUM RGB-D and Bonn *** results show state-of-the-art accuracy for both tasks,and the benefit of jointly working on both tasks is demonstrated.

关键词： visual localization object detection joint optimization multi-task learning

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A Novel Method for Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments 2

A Novel Method for Dynamic Scheduling for Stochastic Edge-Cl...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Manikonda, Jhansi Rani Srija, S. Vineela, M. Kotamraju, Yasaswini Sreyas Institute of Engineering and Technology Computer Science and Engineering Hyderabad India Auroras Institute of Technical Research Computer Science and Engineering Hyderabad India Vignan Institute of Technology and Science Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397635

IoT -based applications have helped to popularize the Fog computing paradigm, which enables the seamless integration of mobile edge and cloud computing resources. The effective scheduling of application operations is difficult under these circumstances because of the restricted resource capabilities, IoT versatility qualities, asset heterogeneity, network ordered progression, and stochastic conduct. Thus, current heuristics and Reinforcement Learning-based approaches are inadequate for this issue. This kind of system only works well in centralized settings since it is incapable of dealing with variations in workload over time. When it comes to adapting quickly to dynamic situations, Offbeat Advantage-Actor-Critic (A3C) learning stands apart for its capacity to utilize the Residual Recurrent Neural Network (R2N2) to refresh model boundaries. This outcomes in an A3C-based scheduler for stochastic Edge Cloud frameworks, which permits a few specialists to advance simultaneously in a decentralized manner. We utilize the R2N2 engineering to store a wide number of host and assignment boundaries, as well as fleeting patterns, to settle on the most ideal booking choices. In order to meet the demands of the application, the proposed model may be adapted by changing different hyper-parameters. We may justify our decision to use hyper-parameters by doing a sensitivity analysis. Studies on true information show a lessening in energy utilization, reaction time, Service- Level-Agreement, and functional expenses of 14.4 %, 7.74 %, 31.9%, and 4.64 percent when contrasted with present status of-the-craftsmanship calculations. An Actor-Critical Advantage Actor-Critical Edge Computing Cloud Computing Deep Reinforcement Learning Task Scheduling Recurrent Neural Network. © 2023 IEEE.

关键词： Recurrent neural networks

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Poetry in Pixels: Prompt Tuning for Poem Image Generation via Diffusion Models 31

Poetry in Pixels: Prompt Tuning for Poem Image Generation vi...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Jamil, Sofia Reddy, Bollampalli Areen Kumar, Raghvendra Saha, Sriparna Joseph, K.J. Goswami, Koustava Department of Computer Science & Engineering Indian Institute of Technology Patna India Adobe Research India

ISBN: (纸本)9798891761964

The task of text-to-image generation has encountered significant challenges when applied to literary works, especially poetry. Poems are a distinct form of literature, with meanings that frequently transcend beyond the literal words. To address this shortcoming, we propose a PoemToPixel framework designed to generate images that visually represent the inherent meanings of poems. Our approach incorporates the concept of prompt tuning in our image generation framework to ensure that the resulting images closely align with the poetic content. In addition, we propose the PoeKey algorithm, which extracts three key elements in the form of emotions, visual elements, and themes from poems to form instructions which are subsequently provided to a diffusion model for generating corresponding images. Furthermore, to expand the diversity of the poetry dataset across different genres and ages, we introduce MiniPo, a novel multimodal dataset comprising 1001 children's poems and images. Leveraging this dataset alongside PoemSum, we conducted both quantitative and qualitative evaluations of image generation using our PoemToPixel framework. This paper demonstrates the effectiveness of our approach and offers a fresh perspective on generating images from literary sources. The code and dataset used in this work are publicly available. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Secured Data Storage Using OAuth and Encryption of Data Using AES with Patterns and Hidden Layer

Secured Data Storage Using OAuth and Encryption of Data Usin...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Vilasini, V. Hariharan, K. Chezhian, Nethra Kaviyarasu, D. PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350378177

In today's digital era, the use of data is becoming more day by day. Data is serving as a main source for all the applications we rely on. As the volume of data rapidly increases, the need for security also increases in the same phase. This brings the need for securing the data in a more effective manner. The need to protect sensitive data is underscored by the increasing intricacy of cyberattacks and the expansion of virtual commerce. It is imperative to rely on several legal and regulatory requirements for the need for data security. The failure to do so may result in severe penalties. In order to maintain trust in digital interactions, data security during transmission and storage is becoming more important as the world gets smaller. If the data is not secured in an effective manner, the growth of the companies and several other technologies may collapse and result in higher threats to the world. Both firms and governments are required to preserve intellectual property and information related to national security. The availability, confidentiality, and integrity of data are critical for sustaining innovation, trust, and business continuity. This project focusses on the two-level authentication of the system where the users are allowed to store the sensitive data. The first level authentication is provided using OAuth where it is a single sign on mechanism which is advantageous to the users to access it easily. The second level authentication is provided using the encryption technique where the Advanced Encryption Standard is used along with the creative usage of user-generated patterns as a hidden layer in the encryption process. Here a symmetric key is used for the encryption to make it more secure. The two-level authentication brings high level security for the stored data. © 2024 IEEE.

关键词： Data integrity

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Intelligent Systems for Data Driven Agriculture: Enhancing Farmer Productivity Through Automation and Artificial Intelligence

Intelligent Systems for Data Driven Agriculture: Enhancing F...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Lokesh, S. Madhavan, Arvind Ramanathan, R.M. Prakash Anand, Krteen PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350378177

Numerous obstacles endanger the livelihoods of smallholder farmers and global food security. Crop output losses due to plant diseases alone are estimated to be between 15% to 25% every year. However, farmers' capacity to recognize and treat illnesses in a timely manner is hampered by things like a lack of agronomic expertise, limited access to actionable insights, and information gaps. The work proposes a comprehensive end-to-end web application that uses machine learning models to help farmers with configurable expert advice, smart farm management, and automatic disease detection. Thus, enabling farmers to identify the infection using their smart phones and obtain crop - fertilizer recommendations for the crops to be treated in the farmer's area based on historical data analysis of yield-influencing elements such as geography, weather, and soil. The proposed system will employ Generative Artificial Intelligence, such as ChatGPT's natural language processing and generation capabilities, to engage in conversational interactions with farmers. The work achieves close to 95% accuracy after utilizing 30 epochs in the area of disease detection and 97% accurate models are achieved for fertilizer outputs. These features along with it's high accuracy can assist farmers in making informed decisions, utilising the data obtained to reduce crop output losses. © 2024 IEEE.

关键词： Smart agriculture

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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