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Hybrid Electroencephalography-Induced Robot Navigation for Rehabilitative Platform 20

Hybrid Electroencephalography-Induced Robot Navigation for R...

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20th IEEE India Council International Conference, INDICON 2023

作者： Ghosh, Ahona Ghosh, Lidia Dey, Sharmistha Sarkar, Indranil Saha, Sriparna Maulana Abul Kalam Azad University of Technology Department of Computer Science & Engineering West Bengal India Rcc Institute of Information Technology Department of Computer Application Kolkata India University Institute of Computing Chandigarh University Punjab Gharuan India Guru Nanak Institute of Technology Department of Computer Application India

ISBN: (纸本)9798350305593

Decoding upper limb movement from electroencephalography (EEG) signals is an emerging field of research that has the potential to enhance the quality of life for individuals with motor impairments. This paper provides an informative overview of EEG-based upper limb movement decoding and addresses the limitations found in existing studies by proposing a novel rehabilitative robotics framework. The framework utilizes three types of EEG signals: motor imagery, error-related potential, and P300 signals, to navigate an OpenManipulatorX robotic arm. The novelty of this research lies in the utilization of a phase-sensitive Common Spatial Pattern approach for feature extraction, as well as the successful recognition of commands for robot control using Linear Discriminant Analysis. The proposed method achieves an impressive average success rate of 95% for target reaching, making it suitable for the design of prosthetics in real-time rehabilitative platforms. © 2023 IEEE.

关键词： Electroencephalography Linear Discriminant Analysis OpenManipulatorX Phase-sensitive Common Spatial Pattern Rehabilitation

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Developing a Smart and Adaptive Load-Balancing System in Cloud Computing with QoS Optimization

Developing a Smart and Adaptive Load-Balancing System in Clo...

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International Conference on Information Science and Communications Technologies (ICISCT)

作者： Anupam Yadav Ashish Sharma Department of Computer Engineering and Application GLA University Mathura India

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

Algorithm for resource provisioning and cloud assignment for providing content in cloud based content delivery network is developed where dynamic use of large and small scale fluctuations in user demand rates are considered along with minimum lease period constraint. Virtual machines are simulated computers within a physical computer that can run their operating systems and applications. Intelligent Resource Allocation (IRA) is a QoS-aware resource allocation technique that uses advanced technologies like machine learning and artificial intelligence to optimize resource distribution in real-time. Profit-driven scheduling algorithms for cloud public service (PS) scheduling focus on optimizing resource allocation to maximize profit while prioritizing tasks related to public services. First-Come-First- Serve is the scheduling technique in which incoming tasks are served according to the first come first served procedure and the first arriving task is served first. Using entropy-based adaptive neuro-fuzzy inferences system (E-ANFIS) that combines fuzzy logic and neural networks, many factors can be analyse in real- basic for optimizing resource allocation and utilization. The conclusions established that the proposed technique, E-ANFIS, exhibits superior performance to the others, achieving an energy efficiency of 89%. Future works of this study can be direct more research efforts towards enhancing the application of the framework to multi-cloud and edge computing to integrate and optimise resources within different computing paradigms.

关键词： computers Fuzzy logic Cloud computing Adaptive systems Heuristic algorithms Quality of service Organizations Virtual machines Resource management Time factors

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Leveraging Data from Vast Unexplored Seas: Positive Unlabeled Learning for Refining Prediction Area in Good Fishing Ground Prediction 27th

Leveraging Data from Vast Unexplored Seas: Positive Unlabel...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Konii, Haruki Nakano, Teppei Miyazawa, Yasumasa Ogawa, Tetsuji Department of Communications and Computer Engineering Waseda University Tokyo Japan Intelligent Framework Laboratory Tokyo Japan Application Laboratory Japan Agency for Marine-Earth Science and Technology Yokohama Japan

ISBN: (纸本)9783031781919

This paper proposes a Positive Unlabeled (PU) learning approach to narrow down the prediction area in the context of good fishing ground prediction. PU learning, a type of semi-supervised learning, is particularly suitable for scenarios characterized by limited positive examples and abundant unlabeled data, as often encountered in fishing ground prediction tasks, where the explored sea areas identified as productive fishing spots are treated as positive instances and the vast unexplored sea areas as unlabeled data. Conventional methods often struggle to accurately model the characteristics of good fishing grounds, resulting in overly broad prediction areas or overly restrictive constraints. To tackle this challenge, we present a PU learning-based method designed to identify negative examples from the unlabeled data and consequently refine the area predicted as positive. Specifically, we train a prediction model for fishing duration, which can be considered a surrogate indicator of good fishing grounds. Subsequently, we apply this model to predict the fishing duration for unexplored areas;those areas exhibiting short fishing durations are deemed reliable negative examples. By incorporating both past positive examples and selected negative examples into a binary classification framework for predicting good fishing grounds, we aim to fine-tune the prediction area. To the best of our knowledge, this study represents the first application of PU learning in the domain of good fishing ground prediction. Experimental comparisons conducted using data from bullet tuna trolling validate the efficacy of the proposed methodology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Correlation Between 3D Magnetic Resonance Imaging and Clinical Outcomes in Traumatic Brain Injury 15

Correlation Between 3D Magnetic Resonance Imaging and Clinic...

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

作者： Srikantha, H. Joseph, Ginu Anie Yadav, Surendra School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Arka Jain University Department of Nursing Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

This article identifies a correlation between the 3D Magnetic Resonance Imaging (MR) of patients with worrying brain harm (TBI) and the patient's medical results. The research team compares 3-D MR imaging outcomes with the patient's observe-up go-to facts, including neurologic assessments, Glasgow Coma Scale rating, and other parameters. The effects confirmed that patients who had a better degree of injury discovered from three-D MR scans had worse scientific effects in comparison to sufferers who had milder harm. This correlation became great for acute and subacute TBI patients. The authors endorse that 3-D MR scans can be used to predict the clinical results of TBI patients and can have the potential to inform treatment selections. © 2024 IEEE.

关键词： Article Correlation Glasgow Identifies Magnetic Resonance

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Code-Mixed English-Indian Languages: Hate Speech Dataset Analysis

Code-Mixed English-Indian Languages: Hate Speech Dataset Ana...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Manoj Kumar Saini Rahul Pradhan Dilip Kumar Sharma Dept. of Computer Engineering and Application GLA University Mathura India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

In the era of Social Media Networks (SMN) and Online Forum (OF) such as Facebook, Instagram, Blogging Sites, Gaming Platforms etc., users tend to comment significantly in English and Indian Code Mixed (CM) languages. Code-mixing is a common manifestation in linguistically diverse social groups such as India. Many people do not know English, so thousands of people on these platforms write comments in code-mixed languages such as Hindi-English (Hinglish). Researchers have retrieved comments on the CM language from SMN and OF. Many people respond in the form of Hate Speech (HS) and Offensive Language (OL) to insult or hurt others, a person, or specific groups. SMN and OF encounter issues with pinpointing and eradicating HS and disagreeable information in diverse CM Indian languages. OL is straightforwardly or inadvertently used in hate speech posts to demean someone and that can cause psychological distress to persons. So, a specific and effective Code Mixed dataset and classification are required to detect HS and OL. Therefore, in this study, we carried out an in-depth analysis on the retrieval methodology and classification of existing data sets for HS and OL. In this paper, we study and analyze 6 (SIX) datasets that were widely adopted in the literature.

关键词： Technological innovation Codes Social networking (online) Social groups Hate speech Semantics Psychology Syntactics Complexity theory Web sites

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Secure Management of Digital Academic Certificates Using Blockchain Technology 3rd

Secure Management of Digital Academic Certificates Using Blo...

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3rd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021

作者： Biswas, Anindya Kumar Dasgupta, Mou Ray, Sangram Department of Computer Application National Institute of Technology Raipur Raipur492010 India Department of Computer Science and Engineering National Institute of Technology Sikkim Ravangla Sikkim737139 India

ISBN: (纸本)9789811958670

The education sector is striving/needed to bring innovation in its different activities and the blockchain technology (BT), which is proposed for Bitcoin cryptocurrency, has promising property in securely decentralizing many applications including the ledger for banking systems. In this paper, we employ blockchain in academics for secure storage and easy retrieval/online verification of the hard-earned certificates to be used in diverse areas namely jobs, higher educations, forgery-detection, etc. Note that the academic certificates are indispensable for everyone’s lives, and it is the time to keep such important property in non-destructible manner but with easy accessibility to all concerned. The basic idea is that an educational institution while publishing the results of examinations, in addition to the distribution of certificates/marksheets to the students must incorporate the same in blockchain as smart certificates and make them available in peer-to-peer (P2P) network. This avoids the maintenance of registers for results kept by colleges, universities, education sectors, etc.;however, it supports for proofs of degrees claimed by students and online quick verification for any issue including fake certificate submission. An institution based on the departments, disciplines and various programs run creates multiple blockchains corresponding to the registers maintained, though the first block called the genesis block contained institute’s identity, would be the common for all. For conceptualization, a prototype design for management of blockchain-based smart certificates with various uses is presented. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Development of a Generic Mesh Converter for Numerical Simulations 21st

Development of a Generic Mesh Converter for Numerical Simula...

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21st Asia Simulation Conference, AsiaSim 2022

作者： Liu, Minjuan Deng, Guyu Li, Jifeng Li, Haifeng Xiao, Yonghao Institute of Computer Application China Academy of Engineering Physics Sichuan Mianyang621900 China

ISBN: (纸本)9789811991974

Mesh models from various numerical simulation programs have great diversity in the data formats and structures, leading to difficulties in sharing and reusing of mesh data for collaborative simulations between commonly used commercial programs and in-house codes. To break the barrier against the free exchange of mesh models, in this work a unified mesh model with full coverage of simulation-related data is deliberately designed. Based on such mesh model and the open source mesh data management framework MOAB (Mesh-Oriented data-base), a generic mesh model converter is developed, enabling the high-efficient conversions of mesh models between various simulation programs. The converter could be easily extended for conversion between new pair of mesh models by integration of the corresponding IO interfaces. The correctness and scalability of the converter is validated through a series of examples. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information management

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Security Risk Assessment Method for Measurement Control Systems Based on Attack Tree Model and Common Vulnerability Scoring System 23

Security Risk Assessment Method for Measurement Control Syst...

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Proceedings of the 2023 International Conference on Artificial Intelligence, Systems and Network Security

作者： Shiyong Li Shasha Wu Xinglin Guo Institute of Computer Application China Academy of Engineering Physics China

ISBN: (纸本)9798400716966

Oriented to the demands of Measurement control system(MCS) security risk assessment, in order to further improve the accuracy and rationality of the assessment results. Aiming at the problems of traditional Industrial control system (ICS) security risk assessment in which subjective intention has a large impact on the results, the impact of security events on assets is calculated without considering the decision-makers security demands on the system, and the MCS has new security characteristics. In this paper, a security risk assessment of MCS for equipment based on attack tree and Common Vulnerability Scoring System v3.1(CVSS3.1) is proposed. First, an attack tree model of MCS is constructed by analyzing the potential attack paths of MCS, and new security attributes are assigned to the leaf nodes according to the security characteristics of MCS, and the subjective-objective weighting design method is introduced to solve the occurrence probability of security events after the security attributes are quantified; Then, based on CVSS3.1 scoring and considering that the decision-makers have different security demands for assets in different scenarios and tasks, a method for calculating the security risk value of MCS assets is proposed. Finally, the method was verified by examples. The results show that the method proposed in this paper reduces the influence of subjective factors on the assessment results, and calculates the occurrence probability of asset security events accurately and effectively. At the same time, this method considers the security demands of the decision-makers for the system, scientifically and reasonably reflects the impact of the attack on the security state of the system, and can provide theoretical support for the formulation of system security protection strategies.

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A Software Defect Prediction Model Enhanced by Self-Attention Mechanism and Particle Swarm Optimization

A Software Defect Prediction Model Enhanced by Self-Attentio...

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Software engineering and computer Science (CSECS), International Conference on

作者： Jingjing Li Yizheng Tao Bin Jin Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Software defect prediction is a key aspect of software quality assurance that involves the use of historical data and a variety of analytical techniques to estimate which parts of the code are most likely to contain errors. Not only can software defects lead to system failure, but they can also have serious economic and social consequences. Most of the current methods rely on a single type of feature and fail to explore the multiple information in the code fully. Meanwhile, manually tuning hyperparameters is both time-consuming and difficult to achieve optimal results. Based on the encoder architecture, this research proposes an enhanced software defect prediction model, which combines the self-attention mechanism and particle swarm optimization (PSO-BiEncoders-SelfAttn). This model achieves efficient feature extraction, dynamic adjustment of feature fusion weights, and automated optimization of hyperparameters. This study trained and tested the model on five different publicly available datasets and evaluated it using metrics including precision, recall, and F-measure. The proposed PSO-BiEncoders-SelfAttn model improves the state-of-art, achieving an average F-measure improvement of 7.92% according to the experimental result.

关键词： Measurement Codes Computational modeling Software quality Predictive models Feature extraction Particle swarm optimization Tuning Optimization Software engineering

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Improving Cancer Prediction in Recommender Systems through Gradient Boosting 15

Improving Cancer Prediction in Recommender Systems through G...

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

作者： Sengupta, Pompi Das Kharbas, Vikas Kumar Adarsha, H. Arka Jain University Department of Management Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

The analysis of various styles of most cancers is frequently reliant on accurate prediction models. Recommender systems are powerful equipment that allows the prediction of cancer development and recurrence based totally on information accumulated from affected person information. But, standard feed-forward neural networks (FFNNs) utilized in current cancer prediction fashions are limited via their potential to capture subtle nonlinear relationships among affected person variables. Gradient boosting is a device gaining knowledge of set of rules that makes use of an ensemble of weak selection bushes to produce an effective predictive version. This have a look at examines the capability for boosting algorithms to enhance the predictive ability of the FFNN fashions currently used in recommender structures for predicting most cancers progression and recurrence. The outcomes of our test reveal that boosting algorithms are able to boom model accuracy over FFNNs whilst additionally lowering training time. Moreover, nonlinear relationships between affected person variables are captured extra successfully by means of boosting than with FFNNs. those findings keep crucial implications for the development of powerful predictive models for cancer progression and recurrence. © 2024 IEEE.

关键词： Cancer Implications Networks Neural Predictive Produce Recurrence Relationships

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