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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pramanik, Pijush Kanti Dutta Biswas, Tarun Galgotias University School of Computer Applications and Technology Greater Noida India Indian Institute of Information Technology Department of Computer Science & Engineering Ranchi India

ISBN: (纸本)9798350370249

Mobile crowd computing (MCC) has emerged as a promising paradigm to leverage the underutilised computational resources of smart mobile devices (SMDs). However, the energy constraints of these devices pose a significant challenge in efficiently executing computational tasks. This study analyses the energy efficiency of various scheduling algorithms for MCC environments. The study compares the performance of seven scheduling algorithms: PPIA, RAS, PSO, GA, MaxMin, MinMin, and MCT. These algorithms were evaluated in terms of their energy efficiency when executing different sets of tasks with varied instruction lengths. The experiment used a simulated MCC environment with real data from five SMDs. The results demonstrate that the PSO algorithm consistently outperformed the other scheduling schemes regarding energy efficiency across all task sets. The GA algorithm also showed promising results. The MinMin algorithm exhibited the lowest energy efficiency. The findings of this study provide valuable insights for designing and implementing energy-efficient task scheduling strategies in MCC systems. Analysing the different algorithms under varying task loads can help MCC service providers make informed decisions when selecting appropriate scheduling techniques to optimise the energy utilisation of SMDs. © 2024 IEEE.

关键词： Crowd computing Energy-efficiency Mobile cloud Particle swarm optimisation Task scheduling

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Knowledge-enriched joint-learning model for implicit emotion cause extraction

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CAAI Transactions on Intelligence technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning

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Secure medical data storage in DPOS-hyper ledger fabric block chain using PM-ECC and L2-DWT

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International Journal of computers and applications 2023年第7-8期45卷 516-522页

作者： Shinzeer, C.K. Bhagat, Avinash Kushwaha, Ajay Shriram School of Computer Applications Lovely Professional University Punjab India Sharda School of Engineering & Technology Department of CSA Sharda University UP Greater Noida India

Governments and individuals have taken extraordinary measures to protect the health of the people during the COVID pandemic. Stored medical data remains the main target for hackers, and hence it needs to be stored securely. To achieve this objective, this paper proposes a novel model using Delegated Proof of Stake-Hyper ledger Fabric Block Chain (DPOS-HFBC). Primarily, by employing LL Subbandeigen Value decomposition employed Discrete Wavelet Transform (L2-DWT), the patient’s Lung Computed Tomography (CT) image data are collected and embedded. For embedding, the patient’s name and ID are taken. In embedding, a Pseudorandom number generator using the Mersenne twister algorithm employed in Elliptic Curve Cryptography (PM-ECC) is applied for key encryption. It covered the image that was embedded with the original and then stored in DPOS-HFBC. Likewise, for authorization, every patient’s biometric ID was hashed and stored in DPOS-HFBC. Data requesters request data in the Interplanetary File System (IPFS) of DPOS-HFBC, and the attributes from the request are extracted and sent to the authority for verification. After verifying, the authority shares their biometric ID with the requester and this gets hashed and then verified in DPOS-HFBC. To show the model’s supremacy, the proposed method was evaluated and compared with existing methods. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Public key cryptography

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Enhancing Insulin Dosage Prediction for Diabetes Management Using Multiple-linear Regression with PCA 6

Enhancing Insulin Dosage Prediction for Diabetes Management ...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Wambura, Yusuph Maswi Moharana, Tapaswini Dash, Satya Ranjan Abdullah, Azian Azamimi KIIT University Bhubaneswar School of Computer Applications India KIIT University Bhubaneswar School of Computer Engineering India Faculty of Electronic Engineering and Technology Malaysia

ISBN: (纸本)9798331516796

The optimal dosage of insulin for diabetic patients using MLR and PCA introduces an advanced method for predicting optimal insulin doses for individuals with diabetes by leveraging machine learning techniques. Our approach integrates data preprocessing, feature extraction using Principal Component Analysis (PCA), and the development of multiple linear regression models. We evaluate our models using metrics like Mean Absolute Error (MAE) to demonstrate the limitations of traditional insulin dosage calculation methods. By utilizing historical patient data and real-time monitoring, our approach provides a more personalized and adaptable insulin therapy regimen. Our results indicate that combining PCA with multiple linear regression significantly improves the accuracy of insulin dosage recommendations, offering a promising solution for diabetes management. Additionally, conventional multi-linear regression models often overlook demographic and clinical similarities among patients, relying on generalized data that diminishes predictive accuracy. To address these challenges, By leveraging demographic and clinical similarities, our MLR model predicts optimal insulin dosages for diabetic patients, aiming to maintain effective glucose control and minimize hypoglycemia risk. Moreover, the model forecasts future blood glucose levels, offering proactive alerts to healthcare providers when glucose levels are anticipated to exceed the safe range of 80 to 180mg dL. Experimental findings demonstrate that our MLR algorithm significantly supports healthcare professionals in prescribing precise insulin doses, ensuring sustained blood glucose levels within the desired range and thereby improving the overall management of diabetes. © 2024 IEEE.

关键词： Multiple linear regression

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Providing a Robust Blockchain-Based Framework for the Secure Transmission of EHRs across the HioT 5

Providing a Robust Blockchain-Based Framework for the Secure...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Joy, Destin N. Yadav, Chandra Shekhar Jhunjhunu India Noida Institute of Engineering and Technology Greater Noida School of Computer Applications Noida India

ISBN: (纸本)9798350389609

"Electronic-Health Records (EHRs)"requires a safe and effective method of exchange, and the present research aims to provide that requirement. "Health Internet-of-Things (HIoTs)"encompasses the integration of various interconnected gadgets related to healthcare, including fitness bands, heart rate tracks, diabetes meters, as well as additional sensors integrated into healthcare devices. Medical practitioners could remotely monitor the condition of their patients' parameters with the use of HIoT. Potential users include seniors, individuals with chronic diseases, and others who need round-the-clock monitoring. There could be significant costs, complicated encryption procedures, and low transaction rates with the present "Elliptic-Curve Cryptography (ECC)"framework. One major issue is that there is now no reliable or affordable means to secure the transfer of EHR data through connected devices. Lower throughput, excessive transaction and signature costs, and ineffective calculation and transmission distinguish the current approaches. The safe sharing of EHR information and its broad adoption are impeded by these obstacles. The present research proposes a new framework called "Block-Chain Lightweight-Signcryption (BC-LS)"which combines LS methods with the latest BC technology to address the existing obstacles. There are two phases involved in the process that make up the suggested BC-LS framework. The initial phase involves the creation of "Wireless Body-Area Networks (WBANs)"for patients through "Neighborhood Sensor-Nodes (NSNs)". A "Cluster-Head (CH)"defined by the nearby NSNs can communicate EHR metadata through the "Gateway SNs (GSNs)"of the WBAN. Following a subsequent phase, the GSN combines encryption of data and signature using an LS approach for approved participants. Regarding secure access with keys and data sharing across public channels, the framework employs the "Inter-Planetary File-System (IPFS)". Using the metrics of "Security Ratio,""Encryption Time,"

关键词： Blockchain

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A glance at in-context learning

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Frontiers of computer Science 2024年第5期18卷 231-232页

作者： Yongliang WU Xu YANG School of Computer Science&Engineering Key Lab of New Generation Artificial Intelligence Technology&Its Interdisciplinary Applications(Ministry of Education)Southeast UniversityNanjing 211189China

The advent of large scale models such as CLIP[1],GPT-2[2],and GPT-3[3]has marked a significant shift in the field of Artificial Intelligence(AI).Unlike the early days of Al,characterized by the design of distinct models or the utilization of pre-trained models for fine-tuning in specific tasks,these modern models adopt a unified approach.

关键词： such has unified

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UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution Remote Sensing Images

UV-Mamba: A DCN-Enhanced State Space Model for Urban Village...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Lulin Chen, Ben Zou, Xuechao Xing, Junliang Tao, Pin School of Computer Technology and Applications Qinghai University Xining China School of Computer Science and Technology Beijing Jiaotong University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798350368741

Due to the diverse geographical environments, intricate landscapes, and high-density settlements, the automatic identification of urban village boundaries using remote sensing images remains a highly challenging task. This paper proposes a novel and efficient neural network model called UV-Mamba for accurate boundary detection in high-resolution remote sensing images. UV-Mamba mitigates the memory loss problem in lengthy sequence modeling, which arises in state space models (SSM) with increasing image size, by incorporating deformable convolutions (DCN). Its architecture utilizes an encoder-decoder framework and includes an encoder with four deformable state space augmentation (DSSA) blocks for efficient multi-level semantic extraction and a decoder to integrate the extracted semantic information. We conducted experiments on two large datasets showing that UV-Mamba achieves state-of-the-art performance. Specifically, our model achieves 73.3% and 78.1% IoU on the Beijing and Xi'an datasets, respectively, representing improvements of 1.2% and 3.4% IoU over the previous best model while also being 6× faster in inference speed and 40× smaller in parameter count. Source code and pre-trained models are available at https://***/Devin-Egber/UV-Mamba. © 2025 IEEE.

关键词： High-Resolution Remote Sensing Images Semantic Segmentation State Space Model (SSM) Urban Village

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Privacy-preserving multi-keyword hybrid search over encrypted data in cloud

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 261-274页

作者： Singh, Niharika Kumar, Jitendra Singh, Ashutosh Kumar Mohan, Anand School of Computer Sciences Bennett University Uttar Pradesh Noida India CISIR Department of Electrical and Electronics Engineering University Teknologi Petronas Perak Seri Iskandar Malaysia Department of Computer Applications National Institute of Technology Tiruchirappalli Tamilnadu Tiruchirappalli India Department of Computer Applications National Institute of Technology Kurukshetra Haryana Kurukshetra India Department of Electronics Engineering Indian Institute of Technology-BHU Uttar Pradesh Varanasi India

Data outsourcing to the cloud is gaining popularity day by day in cloud computing. It requires a dynamic architecture that avoids loopholes and stops intruders from entering the cloud environment. To retrieve the enciphered documents from the cloud, the latest technique that is enrolled effectively is multi-keyword fuzzy search. The proposed research work uses this technique along with the n-gram corpus algorithm that helps to achieve relevancy score and sub-linear search time. This algorithmic combination is able to predict the privacy ranking measure of the retrieved data. To validate the opposed approach, simulation analysis has been performed to preserve user-oriented privacy in a cloud environment. The ranking precision of the proposed method is compared with (Fu et al. in IEEE Trans Inf Forensics Secur 12(8):1874–1884. https://***/10.1109/TIFS.2017.2692728, 2017) which uses a stemming algorithm and a matching algorithm based on the inner product. The results express the efficiency of the proposed approach by showing a relative improvement in security precision up to 55%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Privacy-preserving techniques

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Osteoarthritis Prediction in Knee Joint Using Deep Learning Techniques 27

Osteoarthritis Prediction in Knee Joint Using Deep Learning ...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Singh, Shubham Kumar Chouhan, Kuldeep Agrawal, Arun Prakash Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350392319

Osteoarthritis is a disorder that transpires when the articular cartilage in the knee breaks down and changes to the underbelly of the cartilage. Normal joint fracture protection for the bones is provided by the slick substance known as articular cartilage. Early prediction can slow down the progression of the condition. Medical professionals' expertise is utilized to predict severity. In this research, we address the issue that arises when a model's validation accuracy is lower than its training accuracy. Specifically, we find that as dataset size increases, the validation accuracy of Osteoarthritis prediction falls. We used a Deep Convolutional Neural Network to develop an automatic osteoarthritis severity assessment system using the Kellgren and Lawrence (KL) grading system. With this method, X-ray images are used to predict the severity of osteoarthritis. This study's objective is to predict the severity stage of knee osteoarthritis in the form of prediction accuracy (in %) of osteoarthritis using a transfer learning and deep learning approach. As a result, EfficientNetB5 provides a prediction accuracy of over 97.45% as demonstrated in this work. © 2024 IEEE.

关键词： Convolutional neural networks

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Internet of Brain, Thought, Thinking, and Creation

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Chinese Journal of Electronics 2022年第6期31卷 1025-1042页

作者： ZHANG Zhimin YIN Rui NING Huansheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Engineering Research Center for Cyberspace Data Analysis and Applications

Thinking space came into being with the emergence of human civilization. With the emergence and development of cyberspace, the interaction between those two spaces began to take place. In the collision of thinking and technology, new changes have taken place in both thinking space and cyberspace. To this end, this paper divides the current integration and development of thinking space and cyberspace into three stages, namely Internet of brain(IoB), Internet of thought(IoTh), and Internet of thinking(IoTk). At each stage, the contents and technologies to achieve convergence and connection of spaces are discussed. Besides, the Internet of creation(IoC) is proposed to represent the future development of thinking space and cyberspace. Finally, a series of open issues are raised, and they will become thorny factors in the development of the Io C stage.

关键词： thinking space IoTk Internet of thinking IoTh Internet of thought cyberspace development Internet of Things Internet of brain cyber-physical systems IoB human civilization emergence

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