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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： V Revathi B. Gunapriya Balasubramanian Prabhu Kavin E. Gangadevi Balamurugan Balusamy Shilpa Gite New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Computer Science Loyola College Chennai India Shiv Nadar University Delhi India Symbiosis Institute of Technology Pune. Symbiosis International (Deemed University) Pune India

Hate speech is any speech intended to incite hatred in others. Hate speech is escalating quickly and unpredictably in this digital age where everyone is connected via social media. Due to a lack of understanding of the distinction among hate speech numerous people do not realise they are engaging in hate speech when they criticise somewhat on social media. Victims experience a sense of social alienation as a result, and those who propagate it frequently end up in legal trouble. To combat people’s ignorance, it is crucial to detect any instances of hate speech in the sentences. A machine learning technique is frequently used to help classify each sentence in order to detect such sentences. Additionally, it is a difficult duty because to the enormous volume of rising hate speech on social media from diverse sources. This work offers a Lion optimisation Algorithm (LOA) with Processing Based Hate Speech Detection (HSD) and Classification model in response to this objective. The main goal of the proposed LOA-based HSD model is to recognise and categorise instances of hate speech on social media platforms. The described model uses data preprocessing at numerous phases, such as tokenization, vectorization, etc., to achieve this. In addition, the organisation of social media text into three categories–neutral, offensive, and hate language–is done using a convolutional neural network (ID-CNN) model. The proposed model is experimentally validated, and the outcomes are assessed from several angles. The results of the experiment showed that the LOA-ID-CNN model achieved favourably compared to current state-of-the-art techniques

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Classification of Jacobi solutions of double dispersion equation in uniform and inhomogeneous Murnaghan's rod

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Partial Differential Equations in Applied Mathematics 2024年 9卷

作者： Nisar, Kottakkaran Sooppy Silambarasan, Rathinavel Department of Mathematics College of Science and Humanities in Alkharj Prince Sattam Bin Abdulaziz University 11942 Saudi Arabia Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamilnadu Vellore 632014 India

The double dispersion equation comprising the Lame coefficient, nonlinear coefficient, and Poisson ratio components is described as the uniform and inhomogeneous Murnaghan's rod by A. M. Samsonov in Samsonov (2001). In this work, we apply the F expansion method to the double dispersion equation in the uniform and inhomogeneous Murnaghan's rod, extract the Jacobi elliptic function solution, and classify it into six families of unique solutions. The necessary condition and the degeneration of the Jacobi solutions based upon the elliptic function modulus are given for each solution. The six classifications are formed based on the solutions of the algebraic equations. © 2024 The Author(s)

关键词： Double dispersion wave F expansion method Inhomogeneous rod Jacobi elliptic function Uniform rod

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Lossless compression with state space models using bits back coding

arXiv

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arXiv 2021年

作者： Townsend, James Murray, Iain Department of Computer Science University College London School of Informatics University of Edinburgh

We generalize the 'bits back with ANS' method to time-series models with a latent Markov structure. This family of models includes hidden Markov models (HMMs), linear Gaussian state space models (LGSSMs) and many more. We provide experimental evidence that our method is effective for small scale models, and discuss its applicability to larger scale settings such as video compression. © 2021, CC0.

关键词： State space methods

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Exact solution of the longitudinally-graded rectangular plate under mixed instantaneous shock loading

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Waves in Random and Complex Media 2023年

作者： Amari, Abdelfattah Salman, Hayder Mahmood Department of Chemical Engineering College of Engineering King Khalid University Abha Saudi Arabia Research Laboratory of Processes Energetics Environment and Electrical Systems National School of Engineers of Gabes Gabes University Gabes Tunisia Department of Computer Science Al-Turath University College Baghdad Iraq

This study follows a novel path toward analysis of the thermoelastic reaction of the longitudinally-graded graphene-nanoplates enriched composite (LG-GNPEC) rectangular-shaped plates exposed to the mixed-form instantaneous shock. Three-dimensional form of the linear thermoelasticity theory is picked as the base for establishing the governing equations. Lord-Shulman theorem and energy balance relations are employed to determine the time-dependency of the system’s behavior. Due to simply-supported boundary conditions, solving the governing equations through an analytical solution on the bases of Navier’s approach is performed. Accuracy of the applied methods is verified by comparing the obtained results with those presented in the published studies. Results of this research can be directly considered in the analysis of the thermoelastic behavior of the advanced composite materials exposed to the mixed-form thermomechanical shocks. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Graphene

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An Ontology for the Social Determinants of Health Domain

An Ontology for the Social Determinants of Health Domain

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Navya Martin Kollapally Yan Chen Julia Xu James Geller Department of computer science New Jersey Institute of Technology Newark NJ USA Department of computer information systems Borough of Manhattan Community College New York USA JX consulting Texas USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Social Determinants of Health (SDOH) are societal factors, such as where a person was born, grew up, works, lives, etc., along with socio-economic and community factors that affect an individual’s health. SDOH are correlated with many clinical outcomes, hence it is desirable to record SDOH data in Electronic Health Records (EHRs). Besides storing images, text, etc., EHRs rely on coded terms available in standard ontologies and terminologies to record observations and analyses. There is a substantial amount of research on understanding the clinical impact of SDOH, ranging from screening tools to practice-based interventions. However, there is no comprehensive collection of terms for recording SDOH observations in EHRs. Our research goal is to develop an ontology that covers the terms describing SDOH. We present a prototype ontology called Social Determinant of Health Ontology (SOHO) that covers relevant concepts and IS-A relationships describing impacts and associations of social determinants. We describe the evaluation techniques that we applied to SOHO, including human experts’ review and algorithmic evaluation.

关键词： Terminology Prototypes Ontologies Distance measurement Recording Electronic medical records Bioinformatics

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Development Effort Estimation in Free/Open Source Software from Activity in Version Control systems

arXiv

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arXiv 2022年

作者： Robles, Gregorio Capiluppi, Andrea Gonzalez-Barahona, Jesus M. Lundell, Björn Gamalielsson, Jonas Department of Telematic and Computational Systems Engineering Universidad Rey Juan Carlos Spain Department of Computer Science University of Groningen Netherlands School of Informatics University of Skövde Sweden

Effort estimation models are a fundamental tool in software management, and used as a forecast for resources, constraints and costs associated to software development. For Free/Open Source Software (FOSS) projects, effort estimation is especially complex: professional developers work alongside occasional, volunteer developers, so the overall effort (in person-months) becomes non-trivial to determine. The objective of this work it to develop a simple effort estimation model for FOSS projects, based on the historic data of developers' effort. The model is fed with direct developer feedback to ensure its accuracy. After extracting the personal development profiles of several thousands of developers from 6 large FOSS projects, we asked them to fill in a questionnaire to determine if they should be considered as full-time developers in the project that they work in. Their feedback was used to fine-tune the value of an effort threshold, above which developers can be considered as full-time. With the help of the over 1,000 questionnaires received, we were able to determine, for every project in our sample, the threshold of commits that separates full-time from non-full-time *** finally offer guidelines and a tool to apply our model to FOSS projects that use a version control system. © 2022, CC BY-SA.

关键词： Feedback

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IoT-Based Smart Home Energy Management System (SHEMS) Using Networking and Automation

IoT-Based Smart Home Energy Management System (SHEMS) Using ...

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Data science and Business systems (ICDSBS), International Conference on

作者： Arokia Martin N Kirubakaran N Senthil Kumar G Selvaganesan C Jegadeeshwari P Vijayaraja V Department of Computer Science and Business Systems Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Dr. MGR Educational and Research Institute (Deemed to be University) Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science RMK College of Engineering and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

Physical energy meter reading concept is outdated and it is not efficient, leading to lots of wastage of manpower and errors, btaining accurate real-time readings is challenging and complex to achieve consistently. Current implementations of IoT technology allow real-time monitoring and energy management capabilities with the integration of IoT ecosystems, Digital Communication Technology networks, and Machine Learning algorithms to further analyze data as well as enhance user convenience. However, despite their promising potential, these systems face ongoing challenges, including adaptability, scalability, interoperability and enhanced cyber security resilience, particularly in large-scale deployments. This review examines present SHEMS and their capabilities, from strengths such as real-time tracking of energy usage, optimization and consumption of energy at offpeak hours by the application of predictive analytics using Machine Learning. This also elucidates areas for improvement, such as an improved integration of renewable energy technologies, simplification of the complexities surrounding large-scale deployment and sustainability over the long term. Addressing these deficits, this paper further promotes advancements in the design of SHEMS that are more secure, scalable and inter-operable, increasing communication across IoT ecosystems and by using smart grid technology, which could improve effective management of energy and a potential wider uptake. These suggestions are aimed at fine-tuning existing technologies to be easily integrated into modern, automated energy systems for better energy management.

关键词： Renewable energy sources Machine learning algorithms Automation Smart homes Machine learning Predictive models Real-time systems Smart grids Predictive analytics Optimization

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Integrating Deep Neural Networks and Digital HRM for Systematic Human-Job Matching and Digital Transformation Strategy

Integrating Deep Neural Networks and Digital HRM for Systema...

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Silchar Subsection Conference (SILCON), IEEE

作者： S Muthuperumal Mithun D'Souza Abbas Thajeel Rhaif Alsahlanee U. Kavya Kamila Ibragimova S.Suma Christal Mary Department of Mathematics K.Ramakrishnan College of Engineering Trichy India Department of Computer Science St. Joseph's University Bangalore India Department of Communications Systems Engineering University of Thi-Qar Nasiriyah Thi-Qar Iraq Department of CSE Vignana Bharathi Institute of Technology Ghatkesar Hyderabad India Department of Computer Engineering Tashkent University of Information Technologies Uzbekistan Department of Information Technology Panimalar Engineering College Poonamalle Chennai India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

Human Resource Management (HRM) is a tactical method that businesses use to efficiently oversee employees, encompassing procedures like hiring, training, and performance reviews. HRM aims to optimize human capital, enhance employee satisfaction, and ensure legal compliance, which is vital for organizational performance. The integration of Deep Neural Networks (DNNs) into HRM offers unprecedented opportunities for systematic human-job matching and digital transformation. Digital technologies such as big data analytics, cloud computing, and 5G connectivity have shifted HRM towards data-driven practices. Combining DNNs with digital HRM creates a framework for more accurate and efficient human resource allocation. This research explores the synergy between DNNs and digital HRM to revolutionize human-job matching and develop innovative digital transformation strategies. It presents a CNN-GRU hybrid model, where CNNs extract features from textual job descriptions and resumes, and GRUs handle sequential data such as work experience. The CNN layers are used to extract features, while the GRU component to captures temporal dependencies. This integration enhances the accuracy of job recommendations and matches. The research includes methodologies using smart digital tools like user profiling, cloud computing, and data visualization. Despite potential challenges like computational complexity and data privacy concerns, the proposed approach aims to modernize HRM practices and improve agility. This paper provides insights, approaches, and case studies to drive organizational success through more structured, data-driven systems for human-job matching in the digital era.

关键词： Training Cloud computing Systematics Accuracy Reviews Digital transformation Resumes Feature extraction Convolutional neural networks Human resource management

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Augmented Reality Based Video Shooting Guidance for Novice Users

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Proceedings of the ACM on Human-computer Interaction 2022年第MHCI期6卷 1-20页

作者： Li, Jiefeng She, Yingying Liu, Fang Yu, Chun Wang, Xiaoli Xu, Yuxin School of Informatics Xiamen University 422 Siming South Road Xiamen China School of Film Xiamen University 422 Siming South Road Xiamen China School of Film Xiamen University 422 Siming South Road Xiamen China Department of Computer Science and Technology Tsinghua University 30 Shuangqing Rd Beijing China Faculty of Information University of Toronto 27 King’s College Cir TorontoON Canada

Using mobile phones to shoot video is considerably common in our daily life. However, novice users have difficulty in controlling the camera properly due to lack of professional knowledge and skill. In this paper, in order to assist novice users in learning and imitating professional camera movement from watching high quality sample videos, we propose ARCAM, an Augmented Reality (AR) based video shooting guidance method. Using AR, we visualized the concept of camera movement and embedded it into natural scene to provide real-time guidance. User can follow the guidance while shooting video by matching a calibration frame to the guidance, to achieve the desired camera movement. We conducted a user study comparing the effectiveness of ARCAM to a traditional static arrow guidance. Results showed that ARCAM was more effective in helping users understand the camera work in the sample videos and move the camera with more accuracy. Our work provides insights on designing mobile video shooting application and suggests that AR has great potential in assisting novice video shooters. © 2022 Association for Computing Machinery.

关键词： Augmented reality

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Role of RPA to Seize Opportunities in 5G Edge Computing in Telecommunications

Role of RPA to Seize Opportunities in 5G Edge Computing in T...

引用

Advances in Computing Research on science Engineering and Technology (ACROSET), International Conference on

作者： Dileep Kumar Yendluri Reshmi Tatikonda Ramya Tatikonda Jainath Ponnala M. Kempanna A. Bhuvanesh Department of Computer Science University of the Cumberlands Williamsburg USA Department of Computer Information Systems University of the Central Missouri Warrensburg USA Department of Information Technology University of the Cumberlands Williamsburg USA Department of Electronics and Communication Engineering Jawaharlal Technological University Hyderabad India Department of AI and ML Bangalore Institute of Technology Bangalore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

This paper explores the role of Robotic Process Automation (RPA) in leveraging opportunities presented by 5G edge computing in the telecommunications industry. Integrating RPA and 5G edge computing offers numerous benefits, including enhanced operational efficiency, real-time decision-making, improved service delivery, and cost savings. However, it also poses challenges such as complex integration, security concerns, and the need for effective change management. The paper comprehensively analyzes the potential benefits, challenges, and recommendations for implementing RPA in 5G edge computing. It explores use cases, future advancements, emerging technologies, and anticipated opportunities and challenges. The findings highlight the need for clear objectives, process optimization, collaboration between IT and business teams, and continuous monitoring to leverage RPA in 5G edge computing effectively.

关键词： Intelligent automation Industries 5G mobile communication Decision making Real-time systems Telecommunications Security Optimization Monitoring Edge computing

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