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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： R. Ganesh B. Srinivasa Rao Sreemayi Goriparthi Priyank Udaybhai Trivedi Neerav Nishant P Naresh Kumar Reddy Computer Science and Engineering (AI&ML) Institute of Aeronautical Engineering Hyderabad Telangana India Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering (AI&ML) Guru Nanak Institute of Technology Ibrahimpatnam Telanagana India Institute of Infrastructure Technology Research and Management Maninagar Ahmedabad Gujarat India Department of Computer Science and Engineering School of Engineering Babu Banarasi Das University Lucknow Uttar Pradesh India Department of Freshman Mohan Babu University Tirupathi India

Every big city across the world has a similar parking problem: it's expensive and hard to find. There is an immediate need for creative parking solutions to fulfill the anticipated parking demand. Using encrypted wireless networks and sensor data, this article suggests an advanced new parking system called Smart Parking. Smart Parking provides a safe and smart parking environment for its customers. The allocated parking spot is secure and discrete. The parking guidance system works well and is easy to use. Every step of the parking procedure will be offered continuously. Smart parking is an advanced parking management system from the perspective of the administration. The suggested method involves a series of steps, including preprocessing, DHE for contrast enhancement, CHT for feature extraction, and Adaptive CNN Ensemble for model training. The proposed method has better results than the other two models (CNN and SVM).

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Generalized Learning of Coefficients in Spectral Graph Convolutional Networks 15

Generalized Learning of Coefficients in Spectral Graph Convo...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Coskun, Mustafa Grama, Ananth Koyuturk, Mehmet Ankara University Department of Ai and Data Engineering Ankara Turkey Purdue University Department of Computer Science IN United States Case Western University Department of Data and Computer Science OH United States

ISBN: (纸本)9798331508821

Spectral Graph Convolutional Networks (GCNs) have gained popularity in graph machine learning applications due, in part, to their flexibility in specification of network propagation rules. These propagation rules are often constructed as polynomial filters whose coefficients are learned using label information during training. In contrast to learned polynomial filters, explicit filter functions are useful in capturing relationships between network topology and distribution of labels across the network. A number of algorithms incorporating either approach have been proposed;however the relationship between filter functions and polynomial approximations is not fully resolved. This is largely due to the ill-conditioned nature of the linear systems that must be solved to derive polynomial approximations of filter functions. To address this challenge, we propose a novel Arnoldi orthonormalization-based algorithm, along with a unifying approach, called G-Arnoldi-GCN that can efficiently and effectively approximate a given filter function with a polynomial. We evaluate G-Arnoldi-GCN in the context of multi-class node classification across ten datasets with diverse topological characteristics. Our experiments show that G-Arnoldi-GCN consistently outperforms state-of-the-art methods when suitable filter functions are employed. Overall, G-Arnoldi-GCN opens important new directions in graph machine learning by enabling the explicit design and application of diverse filter functions. Code link: https://***/mustafaCoskunAgu/GArnoldi-GCN © 2024 IEEE.

关键词： Convolutional neural networks

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Eyecheck Diabscan: A Productive Approach for Few-Shot Excellence in Retinopathy Diagnosis

Eyecheck Diabscan: A Productive Approach for Few-Shot Excell...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Nuthanakanti Bhaskar E. Krishnaveni Sinde Jayavanth Rao Kumbala Pradeep Reddy G Vinoda Reddy Avala Raji Reddy Department of Computer Science and Engineering CMR Technical Campus Hyderabad India Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering (DS) CMR Engineering College Hyderabad India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad India Department of Computer Science and Engineering (AI&ML) CMR Technical Campus Hyderabad India Department of Mechanical Engineering CMR Technical Campus Hyderabad India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

The design and implementation of a few-shot learning-based diabetic retinopathy diagnostic system is the focus of this research project. According to the NEI (National Eye Institute), The United States report 2021, diabetic retinal degeneration is one of the most common causes of vision loss in people aged 20 to 74. Early recognition and treatment are therefore deemed essential. The National Eye Institute is a resource-based informational center about diseases and eye health. The primary objective of this endeavor is to improve the few-shot learning technique's capacity for early-stage diabetic eye disease detection. This suggested model may operate using a restricted amount of data, i.e., less data processing by enhancing data optimizing, and localize the necessary discriminative regions. Additionally, it enables the model to recognize and follow unique, novel patterns among the inputs. This approach is going to operate along with an assessment of proposals pertaining to patients' geographic and financial obstacles. Diabetic Retinopathy Identification is applied in the medical and healthcare sectors. This enables us to manage progression risk, forecast therapy results, and lessen adverse consequences and acute implications.

关键词： Diabetic retinopathy Accuracy Shape Shape measurement Retina Pattern recognition Ophthalmology Proposals Few shot learning Recommender systems

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IoT-Based Power Metering Automation for Improving data Integrity and Reliability in Power Marketing Meters

IoT-Based Power Metering Automation for Improving data Integ...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Samreen, Sumaiya Chanti, Yerrolla Mounika, P. Mandava, Manjusha Kruthika, C.G. Malla Reddy Engineering College for Women It Department Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

The goal of this paper is to provide an internet of things (IoT)-based approach for automated electricity metering. When measuring power using standard methods, inaccuracies and erroneous readings are possible, putting the data's integrity and authenticity at risk. The suggested method employs sensors linked to the internet of things (IoT) and machine learning algorithms to detect abnormalities and inconsistencies in real-time energy use statistics. This was evident during the testing phase. The proposed method offers several benefits over the present standard of power metering systems in use. The testing findings demonstrated that the proposed system is more reliable and effective in terms of 0.957 success rate, 0.943 accuracy, 0.967 recall, 0.956 F1 score and 0.982 AUC. © 2023 IEEE.

关键词： Anomaly detection Cloud-based data storage and analysis platform Internet of Things (IoT) Machine learning algorithm Power metering Real-time data analysis User interface

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A bio-inspired metaheuristic approach for cloud task scheduling using lateral hyena based particle swarm optimization

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Multimedia Tools and Applications 2025年第18期84卷 20023-20046页

作者： Malik, Meena Nandan, Durgesh Prabha, Chander Uddin, Mueen Acharya, Biswaranjan Hu, Yu-Chen Chandigarh University Mohali India School of CS & AI SR University Warangal506371 India Chitkara University Institute of Engineering and Technology Chitakara University Rajpura India College of Computing and IT University of Doha for Science & Technology Doha Qatar Department of Computer Engineering AI & BDA Marwadi University Gujarat Rajkot India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

Being a rapidly emerging field, cloud computing encourages the rapid growth of all the essential internet services. One major challenge in a cloud computing environment is balancing the load among various machines to optimize the appropriate workload, thereby providing efficient task scheduling. Various studies have been proposed and implemented for task scheduling to provide a fair load balance among all the available virtual machines on the cloud. This research work targets the optimization of virtual machines by Lateral Hyena -Particle Swarm Optimization (LH-PSO) algorithm. Our main motivation is to push parallel task scheduling proficiently, producing low response time and rapid results for all the assigned tasks. This work attempts to optimize virtual machines by proposing a Lateral Hyena-based solution and parallel task scheduling. The proposed solution provides a flexible system where virtual machines will be load-balanced by serving equal tasks. The proposed solution attempts to optimize a fitness function based on the velocity and position value of particles mapped to the characteristics of incoming requests. The practice aims to find optimized virtual machines solving tasks with an optimal solution. The performance is analyzed using the Cloudsim simulator to assess all parameters such as load distribution, average response time, memory utilization, processor utilization, and average turnaround time. The analytical conclusions represent an enhancement over the existing schemes compared to the above-mentioned parameters. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Virtual machine

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Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention

Scalable Efficient Training of Large Language Models with Lo...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lv, Xingtai Ding, Ning Zhang, Kaiyan Hua, Ermo Cui, Ganqu Zhou, Bowen Department of Electronic Engineering Tsinghua University China Shanghai AI Laboratory China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9798891761643

Improving the effectiveness and efficiency of large language models (LLMs) simultaneously is a critical yet challenging research goal. In this paper, we find that low-rank pre-training, normally considered as efficient methods that will compromise performance, can be scalably effective when reduced parameters are precisely targeted. Specifically, applying the low-dimensional module only to the attention layer - resolves this issue and enhances both effectiveness and efficiency. We refer to this structure as Low-dimensional Projected Attention (LPA) and provide an explanatory analysis. Through extensive experimentation at parameter scales of 130M, 370M, and scaling up to 3B, we have validated the effectiveness and scalability of LPA. Our results show that LPA model can save up to 12.4% in time while achieving an approximate 5% improvement in test perplexity (ppl) and on downstream tasks compared with the vanilla Transformer. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction 1

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Sriramkumar, R. Dayananda Sagar University AI & ML Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bagalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids. © 2025 IEEE.

关键词： Smart power grids

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Ensemble Techniques for the Prediction of Heart Disease with Reduced Entropy 2

Ensemble Techniques for the Prediction of Heart Disease with...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Mahesh, T.R. Mohamed Musthafa, M. Rahman, A.M.J.Md. Zubair Viswanath, J. Vanitha, K. Vinoth Kumar, V. Department of Computer Science and Engineering Bangalore India Tamil Nadu Erode India Al- Ameen Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Madanapalle Institute of Technology & Science Department of Ai Andhra Pradesh India Department of Computer Science and Engineering Coimbatore India Vit University School of Information Technology and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350360523

Healthcare industry generates a vast amount of data, the majority of which is sophisticated and massive in size. This information, however, is not "extracted"in order to uncover hidden facts for effective decision-making. Diagnosing heart disease, a noncommunicable disease, is one of the more difficult problems in medicine because it entails the patient's past health history. An accurate and effective automated system can be quite beneficial in detecting cardiac problems. Modern data mining techniques may be able to solve this issue. In the healthcare industry, various information-extracting technologies such as association rule mining, classification, and clustering are used to forecast cardiac disease. In order to address this issue, the research investigates the use of machine learning methods for cardiac disease prediction, including Naive Bayes (NB), logarithmic regression (LR), Support Vector Machine (SVM), the Decision Tree (DT), Random Forests (RF), and the k-nearest-neighbor algorithm (KNN). The Random Forest ensemble technique outperforms with 99% accuracy. © 2024 IEEE.

关键词： Forecasting

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EAP Based Certificateless Authentication Technique to Access Cloud Services in Openstack 8th

EAP Based Certificateless Authentication Technique to Access...

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8th International Conference on Cognition and Recognition, ICCR 2021

作者： Raghavendra, K. Ramesh, B. Chandrika, J. Computer Science and Engineering AI-ML Department Dayananda Sagar University Kudlu Gate Hosur Main Road Karnataka Bangalore560068 India Department of Computer Science and Engineering Malnad College of Engineering Karnataka Hassan India

ISBN: (纸本)9783031224041

Nowadays cloud services have gained more interest. The main advantage of the cloud is, it reduces management costs and efficient usage of resources. The major role of efficient authentication means not only providing authentication but it should also reduce the authentication traffic and provide security. There are many authentication mechanisms most of them were only for a web application. Here in this paper, we focus on both web and non-web applications authentication in cloud environment to access SaaS service. For non-web application services of cloud, ABFAB has created an architecture. ABFAB also defines how to use existing EAP/AAA and GSS-EAP for both web and non-web-based applications. In this paper, we mainly focus on efficient AAA to access cloud services. To demonstrate the proposed system, we use the moonshot from Github, freeradius an opensource, and Openstack for cloud service with our proposed authentication method. We have done the performance analysis of our authentication method compared with the other authentication mechanism to access cloud services. This analysis shows a significant reduction in the computation time required for authentication and reduction in the authentication traffic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Authentication

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ABLE: Personalized Disability Support with Politeness and Empathy Integration

ABLE: Personalized Disability Support with Politeness and Em...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mishra, Kshitij Burja, Manisha Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In today's dynamic world, providing inclusive and personalized support for individuals with physical disabilities is imperative. With diverse needs and preferences, tailored assistance according to user personas is crucial. In this paper, we introduce ABLE (Adaptive, Bespoke, Listen and Empathetic), a Conversational Support System for Physical Disabilities. By tracking user personas, including gender, age, and personality traits based on the OCEAN model, ABLE ensures that support interactions are uniquely tailored to each user's characteristics and preferences. Moreover, integrating politeness and empathy levels in responses enhances user satisfaction and engagement, fostering a supportive and respectful environment. The development of ABLE involves compiling a comprehensive conversational dataset enriched with user profile annotations. Leveraging reinforcement learning techniques and diverse reward mechanisms, ABLE trains a model to generate responses aligned with individual user profiles while maintaining appropriate levels of politeness and empathy. Based on rigorous empirical analysis encompassing automatic and human evaluation metrics based on persona-consistency, politeness accuracy, empathy accuracy, perplexity, and conversation coherence, the efficacy of ABLE is assessed. Our findings underscore ABLE's success in delivering tailored support to individuals grappling with physical disabilities. To the best of our knowledge, this is the very first attempt towards building a user's persona-oriented physical disability support system. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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