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Optimizing Fog computing Performance: A Machine learning-Based Task Scheduling Approach Using Fuzzy Logic and Clustering 1

Optimizing Fog Computing Performance: A Machine Learning-Bas...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Oli, Sanjay Joshi, Maulik S Singh, Aniket Rayal, Ashish St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India Gujarat Power Engineering and Research Institute GTU Department of Humanity and Science Gujarat India Veer Madho Singh Bhandari Uttarakhand Technical University Department of Computer Science and Engineering Pithoragarh Campus India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India

ISBN: (纸本)9798331518578

The ability to deliver results promptly has become crucial for analytical frameworks due to the swift increase in data volumes. Complex algorithms are needed to schedule data processing activities on distributed compute clusters efficiently. Because it is impractical to create and fine-tune a scheduling policy for every task, current systems rely on basic, generic heuristics and neglect workload peculiarities. Effective task scheduling is essential for optimising the performance of fog computing systems. Fog nodes are dynamic, heterogeneous, and resource-constrained, and traditional task scheduling techniques, which were largely created for centralised cloud settings, frequently cannot accommodate these characteristics. We provide a cutting-edge machine learning (ML) based solution that adjusts job assignment to the evolving needs of the Fog environment in order to get beyond these restrictions. Fuzzy logic, a reliable unsupervised learning method, is used with the K-Means clustering algorithm in our method to effectively group. We have used Python to model and assess our suggested method. The simulation findings validate the effectiveness of our scheduling method, demonstrating notable improvements in response time, reduced network usage, and execution time reduction in comparison to other ML and non-ML based scheduling techniques available in the Python framework. © 2025 IEEE.

关键词： Fog computing

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Enhancing the Performance of Photonic Crystal AND Gates with Machine learning Optimization 3

Enhancing the Performance of Photonic Crystal AND Gates with...

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3rd international conference on Distributed computing and High Performance computing, DCHPC 2024

作者： Parandin, Fariborz Mohammadi, Alireza Islamic Azad University Kermanshah Branch Department of Electrical Engineering Kermanshah Iran Islamic Azad University Kermanshah Branch Department of Computer Engineering Kermanshah Iran

ISBN: (纸本)9798350381580

Recent advances in photonic crystals have opened up new possibilities for developing high-speed, low-power optical devices. One promising application is using photonic crystals to realize AND gates, fundamental building blocks in digital logic circuitry. This paper presents a machine learning-based approach to optimizing the performance of AND gates. We utilize the Extra Trees Regressor model to train on a dataset of simulation results and predict the output power for different input and parameter configurations. Our results show that the model can accurately predict the output power with an RMSE (Root Mean Square Error) of 0.18. We then use the model to identify optimal parameter settings for the radius of the rods and the lattice constant. We find that the optimal parameters are R= 0.05 um and x=0.12 um. The paper further scrutinizes the optimization process for optical gate outputs, leveraging predictions from the Extra Trees Regressor for conditional calculations. It meticulously examines the impact of parameters such as rod radius and lattice constant on gate functionality, emphasizing their role in achieving desired output states. Simulation results elucidate the efficacy of optimized parameters in realizing the behavior of an AND gate within the photonic crystal framework. © 2024 IEEE.

关键词： Lattice constants

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Exploring Advanced Techniques for Enhancing Data Privacy and Security in Digital Systems 1

Exploring Advanced Techniques for Enhancing Data Privacy and...

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1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Kumari, Rimjhim Sriramulu, Srinivasan Galgotias University Department of Computer Science and Engineering Greater Noida India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

As digital interactions proliferate, the imperative to fortify data privacy and security becomes paramount. This paper explores advanced techniques - encryption algorithms, biometric authentication, machine learning for anomaly detection, and blockchain technology - as integral components of a comprehensive strategy for safeguarding digital systems. The research reveals that a holistic approach, synthesizing these techniques, offers robust defense against evolving cyber threats. Findings underscore the significance of user-centric security measures, continuous adaptation to emerging threats, and the ethical considerations inherent in technological advancements. The contribution of this research lies in providing practical insights for practitioners and researchers, shaping a nuanced understanding of the dynamic landscape of data privacy and security. Through actionable recommendations, the paper empowers stakeholders to navigate the intricate challenges and opportunities presented by the digital age, ensuring the resilience and trustworthiness of digital systems. The proposed paper talked about how encryption algorithms, biometric authentication, machine learning for anomaly detection and blockchain work. The paper equips stakeholders with tangible recommendations to skillfully navigate the complex landscape of the digital era, promoting the durability and dependability of digital systems. © 2024 IEEE.

关键词： Differential privacy

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Parkinson's Disease Diagnosis: Integrating Decision Trees and Random Forest 18

Parkinson's Disease Diagnosis: Integrating Decision Trees an...

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18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Abhinandhan, S. Sreedevi, A.G. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. Computer Science and Engineering Chennai India

ISBN: (纸本)9789380544519

This research work attempts to create a powerful machine-learning model that can identify the prefrontal cortex of individuals with Parkinson's Disease by analyzing their voice recordings. The disorder of Parkinson's, a progressive neurodegenerative condition that is characterized by the presence of tremors, muscular rigidity, and coordination issues, is early detection and requires no invasiveness. Leveraging the advanced computational techniques, this research pioneered a two-phase approach that incorporated Decision Tres and random Forest to have more specific diagnoses. The amalgamation of these methods not only addresses the diagnostic issues, but also facilitates the formulation of individualized treatment strategies. Ultimately, these strategies have a preeminent impact on the aspect of liveliness of patients with Parkinson's disease. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Decision Trees Diagnosis healthcare Machine learning Parkinson's disease personalized treatment Quality of life Random Forest

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Voice Recognition and Expression Research in Oral English teaching of College English 3

Voice Recognition and Expression Research in Oral English Te...

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3rd international conference on Intelligent computing, Communication and Convergence, ICI3C 2023

作者： Qu, Ying Wuhan Polytechnic Wuhan China

ISBN: (纸本)9798350343922

At present, most of college oral English teaching focuses on the study of words and grammar. There are only some oral English learning software, whose function is relatively simple, and it is difficult for learners to find mistakes and correct incorrect pronunciation entirely by themselves. Through the application of speech recognition technology, it can make the pronunciation error correction function, greatly improve the efficiency of oral teaching, and would get great value. In language pronunciation learning, effective feedback is a great help to learners. This paper used speech recognition technology based on Hidden Markov Model (HMM) to score and correct students' spoken English, and solved a series of problems in the application of speech recognition technology to spoken English learning, especially in speech error correction. The experimental results of this paper showed that after the experiment, the number of students in the control group who are very interested in oral English was 21 less than that in the experimental group. Speech recognition can provide learners with timely, accurate and objective evaluation and feedback guidance, help learners find out the differences between their own pronunciation and the standard pronunciation, correct pronunciation errors, and thus improve the efficiency of oral English learning. © 2023 IEEE.

关键词： College English Hidden Markov Model Oral English teaching Oral Grade Speech Recognition

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PSO-optimized U-Net for Automatic Retinal Optic Disk Segmentation 14

PSO-optimized U-Net for Automatic Retinal Optic Disk Segment...

引用

14th IEEE international conference on Control System, computing and engineering, ICCSCE 2024

作者： Huong, Audrey Ngu, Xavier Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Batu Pahat Johor Malaysia

ISBN: (纸本)9798350364507

Retinal optic disk (OD) fundus imaging is a valuable tool for glaucoma diagnostics, while deep learning methods are often used to assist ophthalmologists in their clinical practice. Nonetheless, the performance of deep learning-assisted OD evaluation can be severely affected by the experiment variations and the use of small samples in network training. This study proposes using a particle swarm optimization (PSO)-optimized U-Net model to automatically segment OD in fundus photographs. The PSO method is used to fine-tune the training hyperparameters, i.e., learner, number of epochs, mini-batch size, and initial learning rate, to achieve good generalization performance even with a small training dataset. The findings showed a considerably good performance of the trained model with average overlap measures of 0.93-0.96 and mean performance evaluation metrics ranging between 0.89 and 0.99, comparable with the results of the state-of-the-art methods. This study concluded that the segmentation model trained using the proposed optimization framework could potentially be used for early glaucoma detection and improve healthcare delivery. © 2024 IEEE.

关键词： Ophthalmology

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The Impact of Mobile Applications on the Development of Academic learning 10

The Impact of Mobile Applications on the Development of Acad...

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Kavitha, N. Anitha, T. Chandrika, M. Leena Venugopal, N. Vinupriya, R. Priya, K.M. Sri Eshwar College of Engineering Department of Science and Humanities Coimbatore India Sri Krishna College of Engineering and Technology Department of Science and Humanities Coimbatore India Sri Ramakrishna Engineering College Department of Science and Humanities Coimbatore India Dr. N. G. P. Institute of Technology Department of Science and Humanities Coimbatore India

ISBN: (纸本)9798350384369

The educational mobile application market, particularly for second or foreign language learning, has swiftly expanded owing to the incorporation of mobile technology into educational practices and the evolution of smartphones. However, there is a scarcity of research about the impact of mobile applications on the process of learning the language. Mobile learning incorporates the utilization of technologies like smartphones and iPads to facilitate the acquisition of a new language by pupils. Acquiring English as a second language has become significantly more convenient due to the abundance of applications currently accessible. This research entails categorizing mobile applications based on the educational stages of elementary, secondary, and higher education students. The analyzed literature predominantly focuses on exploring the utilization, assessment, and appraisal of mobile applications within language instruction settings. The research also encompassed an examination of mobile app design, technique, theory, and pedagogical features. It is crucial to have in mind that these programs are specifically created to enhance students' language proficiency. Therefore, the emphasis should be on acquiring these skills through the use of mobile technology. Apps have demonstrated more efficacy in facilitating the development of listening skills compared to other language skills. © 2024 IEEE.

关键词： Smartphones

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Comparative Evaluation of Mango Fruit Diseases Classification Using Machine learning

Comparative Evaluation of Mango Fruit Diseases Classificatio...

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2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

作者： Kumar, Sandeep Gupta, Bhupesh Chhabra, Mohit Garg, Lucy Department of Computer Science & Engineering Mullana India Panipat Institute of Engineering Technology Department of Computer Science & Engineering Panipat India

ISBN: (纸本)9798350376470

A fruit valued for its great flavor, scent, and nutritional content;the mango (Mangifera indica L.) is one of the most significant tropical fruits in the world economically. However, several illnesses that compromise fruit quality and output significantly hinder the commercial cultivation and trading of mangoes. This widespread illness has significant financial consequences and represents a danger to mango farming worldwide. A key component of effectively managing the illness and protecting crops is the timely and accurate assessment of the disease's severity. The message highlights how critical it is to detect illnesses as soon as possible and with precision to enable effective crop management. The main illnesses that afflict mango fruits are covered in detail in this document, along with information on their genesis, symptoms, epidemiology, and management techniques. The primary disorders covered are anthracnose, mango malformation, powdery mildew, mango bacterial black spot, and mango abrupt decline (mango decline-associated virus). Every disease is thoroughly investigated, covering its causes, methods of spread, symptoms, and effects on fruit output and quality. Machine learning (ML) approaches have become more effective tools for agricultural system disease detection and categorization in recent years. To shed light on the efficacy and relevance of machine learning (ML) algorithms for categorizing mango fruit illnesses, this research study compares and contrasts them. © 2024 IEEE.

关键词： Fruits

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Proceedings - 2023 IEEE international conference on Quantum computing and engineering, QCE 2023

Proceedings - 2023 IEEE International Conference on Quantum ...

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4th IEEE international conference on Quantum computing and engineering, QCE 2023

ISBN: (纸本)9798350343236

The proceedings contain 302 papers. The topics discussed include: an empirical comparison of optimizers for quantum machine learning with SPSA-based gradients;towards redefining the reproducibility in quantum computing: a data analysis approach on NISQ devices;crosstalk-based parameterized quantum circuit approximation;a study on concentric feature embeddings in variational multiclass classification for small-scale quantum computers;QubiC 2.0: a flexible advanced full stack quantum bit control system;harnessing the power of long-range entanglement for Clifford circuit synthesis;investigating students’ strengths and difficulties in quantum computing;design of quantum machine learning course for a computer science program;quantum computing, math, and physics (QCaMP): introducing quantum computing in high schools;and leveraging dual enrollment programs to expand secondary education in quantum computation.

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Measuring Subjective Image Quality using Machine learning Techniques 15

Measuring Subjective Image Quality using Machine Learning Te...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Garg, Anvesha Anandhasilambarasan, D. Pushparajesh, V. Ahluwalia, Gunveen Mali, Manisha P. Yuvaraj, S. Quantum University Research Center Quantum University India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore641021 India Karnataka Bangalore India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India

ISBN: (纸本)9798350370249

This technical abstract discusses the use of gadget mastering strategies to degree the subjective first-class of digital pictures. Traditionally, rating subjective picture satisfaction has required manual intervention by means of human observers. However, gadget studying may be used to measure photographs in a speedy and goal manner. Such structures may be educated to assign rankings based totally on person possibilities, image content, digicam parameters, or any combination of those. The utility of gadget getting-to-know strategies to purchasers and photo satisfaction scores can be useful to both producers and consumers. For manufacturers, gadget getting-to-know structures offer the ability to quickly stumble on and greatly lessen exceptional control problems. For purchasers, device learning systems can supply extra accurate, varied, and in-intensity rankings of products;this may cause greater knowledgeable purchasing choices and higher outcomes. ©2024 IEEE.

关键词： Contrastive learning

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