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1st International Conference on Artificial Intelligence of Things, ICAIoT 2023

作者： Samad, Abdul Dhanda, Namrata Verma, Rajat Department of Computer Science and Engineering Amity University Uttar Pradesh Lucknow Campus Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (纸本)9783031487804

The news which is specially created to misguide as well as mislead the readers is termed fake news. Fake news can cause potential harm to both the individual and society. The problem of fake news has increased far more quickly in recent years. Social networks have significantly changed the scope and effect of their overall influence. Daily, a huge amount of information is released in print and online media, but it can be difficult to determine if the information is accurate or not. A significant amount of study has been done in this area in recent years with positive outcomes. In this modern era, machine learning is the answer to every issue. It can provide answers that people cannot readily think of and is applied in practically all disciplines, domains, and research. Many different algorithms in machine learning and artificial intelligence can help us in identifying as well as eliminating fake or false news. In this article, supervised learning is used. To determine the false news, the authors have used four machine learning algorithms in this study: Decision Tree (DT) classification, Logistic Regression (LR), Random forest (RF) and Passive Aggressive classifier (PAC). After comparing the output of each method, the one with the best accuracy is selected. The PAC outperforms the other classifiers and produces the best result. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision trees

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A study on Retinopathy of Prematurity(ROP) Screening Using Deep Learning Approaches and a Blood Vessel Segmentation Framework for ROP Affected Eyes 2

A study on Retinopathy of Prematurity(ROP) Screening Using D...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Madhu, K. John, Shinu Mathew Joseph, Asha Abraham, Bejoy Apj Abdul Kalam Technological University Computer Science and Engineering Thiruvananthapuram India St.Thomas College of Engineering and Technology Mattannur Department of Computer Science and Engineering Mattannur India Amal Jyothi College of Engineering Department of Information Technology Kottayam India College of Engineering Muttathara Department of Computer Science and Engineering Thiruvananthapuram India

ISBN: (纸本)9798350328202

The retina contains millions of photoreceptive cells called cones, rods, and blood vessels that nourish these cells. Retinopathy of prematurity is an eye disease affecting the retina of a prematurely born infant and is one of the leading causes of childhood blindness at a global level. Pediatric fundus images captured using retina imaging devices such as Retcams have been used by doctors for analyzing the retina and detecting various abnormalities present in the retina. Multiple stages in a single ROP image, underdeveloped retina vessels, immaturity of the macula and fovea,image quality issues like illuminations and artifacts present in an image make ROP disease detection, classification, and segmentation a difficult task for both an experienced practitioner and a computer-aided diagnostic system(CAD). Nowadays automated image analysis using artificial intelligence techniques is gaining popularity among practitioners and researchers. The objective of this survey is to do a systematic review of published works that use deep learning approaches for disease detection, classification, and segmentation in the context of Retinopathy of prematurity (ROP). This work presents a performance analysis and comparison of state-of-the-art deep learning architectures us ed for the above tasks. We also provide potential avenues for future research in ROP detection, classification, and segmentation using deep learning. After the study we proposed a segmentation framework called ROP-seg using a publicly available dataset for blood vessel segmentation that uses the combined power of Unet and Efficientnet architectures. The proposed system shows promising results in blood vessel segmentation of ROP affected eyes. © 2024 IEEE.

关键词： Blood vessels

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Segmenting the Brain Tumor using Deep Learning Techniques 3

Segmenting the Brain Tumor using Deep Learning Techniques

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pranitha, Ch. Lakshmi Sumathi, R. Harshitha, V. Kavya, N. Soni, A. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331509675

This study presents a U-Net architecture with EfficientNet-B7 as the feature extraction backbone is used to propose a robust brain tumor segmentation method. Using cutting-edge deep learning techniques, the suggested model precisely segments MRI images with an emphasis on identifying and defining tumor anomalies. A thorough preprocessing pipeline that handles missing values, augments data with albumentations, and normalizes data guarantees the consistency and resilience of the incoming data. The segmentation procedure uses a hybrid loss function that combines Soft Dice Loss and Binary Cross-Entropy (BCE), improving the model's ability to strike a compromise between segmentation consistency and pixel-level precision. To ensure efficiency and generalizability, the model training is optimized using the Adam optimizer in conjunction with a learning rate scheduler. Additionally, an early halting mechanism is incorporated to minimize overfitting. Using a standardized dataset, performance evaluation is carried out with 70%, 15%, and 15% splits for training, validation, and testing, respectively. To evaluate the quality of segmentation, important metrics are employed, such as the Dice Coefficient and the Intersection over Union (IoU). The accuracy of the model in identifying tumor boundaries is illustrated by visualizations of the anticipated segmentation results. The potential of combining U-Net with cutting-edge feature extractors such as EfficientNet-B7 to produce dependable, effective, and clinically significant segmentation results in medical imaging is demonstrated by this approach. © 2025 IEEE.

关键词： Deep learning

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A Hybrid Strategy Integrating Ant Colony Optimization and Locust-Inspired Algorithm (HACO-LA) to Boost Efficiency and Performance in cloud Resource Management 3

A Hybrid Strategy Integrating Ant Colony Optimization and Lo...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Saraswathy, S. Malathi, J. Subiksha, N. LakshmiNarayanan, S. Kumar, K. Sathish Livinesh, L. Sri Sai Ram Engineering College Department of Computer Science and Business Systems Tamilnadu Chennai India Sri Sai Ram Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309409

Efficient cloud resource management is vital, as it ensures the accurate selection and allocation of resources to diverse workloads or applications. This entails real-time balancing of workload performance, compliance, and cost to achieve optimization. Since there are many cloud users involved in scheduling tasks, or cloudlets, the scheduling process becomes complex. It involves selecting appropriate data centers, servers (hosts), and virtual machines (VMs). There are several bioinspired algorithms that are effective and popular for solving the NP-complete problem of cloudlet scheduling. By using these algorithms, cloudlets can be efficiently allocated to achieve faster execution times, better resource utilization, and a shorter waiting time. This study presents a hybrid technique that combines the strengths of both ant colony optimization and locust-inspired algorithm (HACO-LA), which have been shown to outperform other bio-inspired algorithms in cloud computing environments. Implementing this technique will lead to a decrease in the average response time, while also increasing the utilization of VMs and servers. The method was evaluated using the Cloud Sim toolbox with authentic data. It was compared to the preexisting Artificial Bee Colony Optimization (ABC) algorithm and Particle Swarm Optimization (PSO) algorithm. The findings demonstrated that HACO-LA surpassed both methods, leading to notable enhancements in server utilization, increased reliability, and decreased average response time. © 2024 IEEE.

关键词： Ant colony optimization

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Review of Efficient Load Balancing Technique to Improve QoS Parameters Fog Computing 2nd

Review of Efficient Load Balancing Technique to Improve QoS ...

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2nd International Conference on Innovations in Data Analytics, ICIDA 2023

作者： Sharma, Arti Mahapatra, Rajendra Prasad Sharma, Vineet Kumar Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad201204 India Department of Computer Science and Engineering KIET Group of Institutions Ghaziabad201206 India

ISBN: (纸本)9789819734658

Fog networking is an aspect of the IoT (Internet of Things) idea, which sees most of the products used by humans on a daily basis connected to one another. Smart phones, smart health monitoring equipment, as well as other similar technologies are examples. The goal of fog computing (FC) is to lessen the burden on CC. Fog computing advances processing of data, communication, storing and analysis nearer to edge nodes and applications. To alleviate some of the drawbacks of cloud computing, a significant number of apps and services are shifting to fog computing. The main problem in fog computing is how to improve service quality. It is clear to observe that the SNNC and EBS have improved QoS to a great extent. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fog computing

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Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

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Journal of Bionic engineering 2023年第3期20卷 1198-1262页

作者： Yan Han Weibin Chen Ali Asghar Heidari Huiling Chen Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran

Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the *** quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the ***,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray *** we all know,image segmentation is a critical stage in image processing and *** achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named *** utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image *** image segmentation scheme is called *** ran two sets of experiments to test the performance of RDMVO and ***,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark ***,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as *** test image dataset includes Berkeley images and COVID-19 Chest X-ray *** experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

关键词： COVID-19 Multilevel threshold image segmentation Kapur’s entropy Multi-verse optimizer Meta-heuristic algorithm Bionic algorithm

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Silver nano-particles' size prediction using deep learning model with green method

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Materials Today: Proceedings 2022年 69卷 1193-1199页

作者： Mathumathi, M. Vetriselvi, T. Matheswaran, P. Nandhini, R. Selvi, A. Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India

When it comes to casting technologies and other production processes such as welding, DL models minimise the need for costly experimental inquiry. One of the most important criteria in nanotechnology is the measurement of particle size. They've received a lot of interest for their chemical, physical, and clinical uses because of their outstanding qualities. By adopting a green chemical reduction process, the nano-silver crystals are generated in the biopolymer interface without any agglomeration. Nano-metal production relies on precise control of particle size, and this approach provides that. For the prediction size of Ag-NPs via the green approach, a hybrid ideal of Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and model is presented in this research. Temperature, starch stabiliser, and AgNO3 concentration are among the experimental variables fed into the CNN-LSTM model. The size of a nano particle is the output of the CNN-LSTM model. Making three subsets of the experimental data for training, validation, and testing was one of the initial steps in creating an adapting model. The silver nanoparticles were identified using X-ray diffraction and transmission electron microscopy. Ag-NPs' crystallographic airplanes were found to be face-centered cubic, according to XRD analysis (fcc).When comparing with other existing classifiers, DNN achieved high performance on precision, i.e. 89.87%, and this is due to the presence of simple structure. The CNN and auto encoder achieved nearly 82% and 80% of accuracy, where LSTM achieved 77.25% of accuracy and proposed CNN-LSTM achieved 83.50% of recall. © 2022

关键词： Long short-term memory

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Regeneration Sites Selection and Regenerator Assignment Strategies for Translucent Elastic Optical Networks: Survey and Analysis

Regeneration Sites Selection and Regenerator Assignment Stra...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Bala, Shibsankar Chatterjee, Monish Sarkar, Pradyut Asansol Engineering College Department of Computer Science & Engineering West Bengal Asansol India Maulana Abul Kalam Azad University of Technology Department of Computer Science & Engineering West Bengal India

ISBN: (纸本)9798350390759

Elastic Optical Networks (EONs) form the backbone of the present day Internet. In Translucent EONs (T-EONs), optical signals are propagated beyond their transparent reach and so in order to maintain the desired Quality of Transmission (QoT) level, the signals have to undergo 3R (re-shaping, re-timing and re-amplification) regeneration at some nodes designated as regeneration sites, also called regenerators. So identification of regenerators is an important problem and is known as Regeneration Site Selection (RSS) problem. Once the regenerators are identified, an important problem is the assignment of optical signals to these regenerators to meet various objectives of the network operator such as minimizing total used spectrum, total number of regenerations etc. and is known as the Regenerator Assignment (RA) problem. In this paper we extend two RSS algorithms originally designed for traditional translucent Wavelength Division Multiplexed (WDM) optical networks to make them applicable for Orthogonal Frequency Division Multiplexing (OFDM) based T-EONs. We further study and analyses two RA algorithms. In addition, we present a survey of the various RSS and RA strategies for T-EONs available in the literature and thereby identify the future scope of research. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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TSInc: Tie strength based incremental community detection using information cascades

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International Journal of Information Technology (Singapore) 2024年第5期16卷 3243-3253页

作者： Das, Soumita Biswas, Anupam Department of Computer Science and Engineering National Institute of Technology Assam Silchar 788010 India

Community detection in dynamic networks is of great importance in social network analysis. For instance, online social networks such as Facebook, WhatsApp and LinkedIn are rapidly evolving with time. However, the majority of previous research have focused on community detection in static networks. It is computationally expensive to repeatedly use a static algorithm on snapshots of dynamic networks. In this paper, we propose a community detection algorithm namely, Tie Strength based Incremental Community detection (TSInc), aiming to find communities and track the dynamic events in each snapshot. TSInc determines communities using tie strength based information cascades. Following the initial snapshot, we construct a new subgraph based on the dynamic events in the subsequent snapshots and quickly assign communities. These communities in combination with the unchanged communities are merged to obtain the inherent community structure. These procedure is repeated for the remaining snapshots as well. Experimental results on several real-world dynamic networks and state-of-the-art approaches shows that TSInc algorithm outperforms the baseline algorithms in terms of effectiveness of the detected communities. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cascades Community Dynamic networks Information Diffusion Tie Strength Tie strength

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Prediction of material property using optimized augmented graph-attention layer in GNN

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Materials Today: Proceedings 2022年 69卷 1419-1424页

作者： Sathana, V. Mathumathi, M. Makanyadevi, K. Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

Research in materials science continues to focus on the development of ML models capable of accurately predicting material properties (MPP). Graph neural networks (GNNs) have also proven to be quite actual in extracting physicochemical properties from MPP, despite the fact that many other techniques have been offered in the past. Despite this, existing GNN models fail to distinguish between the contributions of various atoms adequately. To forecast the properties of inorganic materials, we have developed an upgraded graph neural network model dubbed IGAT-GNN. OAGAT and a global attention layer, where a meta-heuristic method is used to achieve optimal weight in OAGAT, are the distinguishing features of IGAT-GNN. Each of the two methods, OAGAT layers and global attention layers, is used to better understand the local relationships among nearby atoms and their overall influence to the material's stuff. This results in much improved prediction performance for various qualities that have been examined. As we have demonstrated through numerous experiments, the IGAT-GNN outperforms other currently available GNN models while also revealing new information about the relationship between atoms and their respective material properties. The proposed model alewives the accuracy of 99.95%, which is the high quality accuracy than other algorithm. © 2022

关键词： Machine learning

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