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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Christy, S. Jenita Nesa Kumari, G. Rosline Bharath Institute of Higher Education & Research Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331518592

An important development in medical diagnostics is the use of artificial intelligence (AI) helps to investigate the creation and application of the system intended to detect skin cancer using pictures of skin lesions. The method uses Convolutional Neural Networks (CNN), a type of machine learning technique, to identify lesions as benign or malignant based on its analysis of dermatoscopic pictures. Expert dermatologists gather and annotate a sizable dataset of photos of skin lesions as the first step in the procedure. Preprocessing techniques such as segmentation, normalisation, and augmentation are used to these pictures in order to increase the analysis's focus and quality. The colour, texture, and form characteristics of the lesions are analysed using feature extraction techniques, which are essential for precise categorization. CNN gains the ability to identify trends and characteristics linked to various forms of skin cancer. After training, a different set of pictures is used to verify the model and evaluate its performance in terms of ROC-AUC (Receiver Operating Characteristic - Area Under Curve), accuracy, sensitivity, and specificity. After that, the AI system is incorporated into an intuitive user interface, giving dermatologists and medical professionals a trustworthy instrument for screening for skin cancer. The system's accessibility is further increased via a mobile application version, which makes initial screenings possible in underserved and rural places. Dense Net 201 performs best in training and test accuracy as well as training loss, despite being the deepest and having the most parameters. With fewer layers and parameters than Dense Net 201 and Inception V3, VGG16 performs admirably as well, particularly in training accuracy and loss. Finally, the Dense Net 201 performs the best in terms of training and test accuracy as well as training loss. Dense Net 201 is the best effective model if training time and computer resources are not limited. © 202

关键词： Convolutional neural networks

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Optimal Trained Ensemble of Classifiers for Brain Age Prediction Using MRI

Optimal Trained Ensemble of Classifiers for Brain Age Predic...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Vishnupriya, G.S. Rajakumari, Brintha S. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350382693

Prediction of brain age is necessary for a better understanding of the development of the human brain and ageing. Recently MRI data have been used for the estimation of brain age. However, variance in the development of brain age between the different as well as similar subjects gives variations in the developments at different ages. Therefore, advanced models are needed to predict brain age more accurately. Hence, this paper intends to propose an optimally trained ensemble classifier for brain age prediction by using MRI data. Initially, the MRI image is preprocessed by using the Bilateral Filtering process to eliminate the unwanted noise. Subsequently, the preprocessed images are segmented by the DBSCAN model and followed by feature extraction. Features such as LGBPHS, statistical features and pixel features are extracted from the segmented image. Finally, the prediction process is done via an ensemble classification model, which is a combination of RNN, Deepmaxout and GRU. Moreover, the weights of all the classifiers are optimally tuned by a proposed Self Improved-Bald Eagle Search Optimization (SI-BESO) to enhance its performance. The performance of the suggested ensemble model is evaluated over the conventional algorithms as well as classifiers in terms of performance measures like MAPE, MSE, MSLE, and MAE. © 2024 IEEE.

关键词： Image segmentation

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Wide and fault diameter in Kneser graphs for enhanced fault tolerance in parallel computing

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International Journal of Networking and Virtual Organisations 2024年第3期31卷 169-190页

作者： Sundara Rajan, R. Kirithiga Nandini, G. Lin, Yuqing Reji, Remi Mariam Department of Mathematics Hindustan Institute of Technology and Science Chennai603 103 India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai603 103 India School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia Department of Mathematics Christian College Kerala Chengannur689 122 India

A system’s fault tolerance is its capacity to function even if one or more of its components fail. Implementing a fault-tolerant network becomes an important criterion for reliable computing. Reliability measures play a significant part in recognising the role of faulty and non-faulty processors in a parallel computing system. Parallel computing is used primarily for saving time, solving big problems, and doing multiple tasks at once at the same time. Various reliability measures have been introduced to evaluate a network’s fault-tolerance capability. We have measured the wide diameter and fault diameter of the Kneser graphs in this study. Also, we have verified the fault diameter obtained using an experimental study. Further, we have described some applications of wide diameter and fault diameter in parallel and distributed computing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Fault tolerant computer systems

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Leveraging Internet of Things (IoT) Sensors and Deep Learning Techniques for Precision Agriculture 1

Leveraging Internet of Things (IoT) Sensors and Deep Learnin...

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1st International Conference on Innovative engineering sciences and Technological research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep Learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep Learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep Learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

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Design of 5G Multi-Frequency Antenna Based on Multi-Objective Sequential Domain Patching

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Progress In Electromagnetics research C 2024年 145卷 53-61页

作者： Zhu, Wenjian Chen, Jiayi Hu, Panshi Song, Zhi Xue, Yanbing School of Automation and Electrical Engineering Dalian Jiaotong University China School of Computer and Communication Engineering Dalian Jiaotong University Applied Basic Research Project of Liaoning Science and Technology Department Dalian116028 China

This paper introduces a 5G multi-frequency antenna design method based on multi-objective sequential domain patching. By etching helical metamaterials on radiation patches and loading asymmetric electric-inductive-capacitive metamaterials on the transmission line side, the antenna structure is designed and optimized using electromagnetic simulation software, HFSS, and MATLAB. The resulting multi-frequency antenna operates across multiple frequency bands: n1, n41, 3.5G, and 4.9G. To ensure antenna performance and radiation efficiency, the antenna multi-frequency bands are precisely controlled. The experimental results show that the error between each operating frequency band and target frequency bands of the antenna is within 7.2%. Compared with conventional antenna design methods, the use of metamaterials and intelligent algorithms to optimize the structural parameters and loading positions of metamaterials can improve antenna performance while shortening the design cycle. Overall, this research offers novel insights into the design of 5G high-performance antennas. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Metamaterials

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PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm

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International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 132-147页

作者： Tandon, Righa Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan173 234 India Department of Data Science School of Computing Mohan Babu University AP Tirupati517 102 India

Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using a matrix array symmetric key (MASK), digital signature algorithm (DSA), and intelligent water drop (IWD). The proposed security scheme also ensures the handling of many security attacks like key-guessing, non-repudiation, replay, and modification. In the proposed scheme, to preserve the identity of the vehicle, we have provided the different pseudonyms for each vehicle in the network that ensure secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of timed delay during vehicle-to-vehicle communication. In this algorithm, we have used Dijkstra’s algorithm for finding the optimal shortest path during vehicular communication. The results obtained show that the proposed scheme is effective and efficient, as it reduces the time delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Encryption algorithms

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Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

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Multimedia Tools and Applications 2024年第30期83卷 74757-74783页

作者： Prasad, Vadamodula Ganeshan, R. Rajeswari, R. Department of Computer Science & amp Engineering Lendi Institute of Engineering & ampTechnology Jonnada Village Andhra Pradesh Vizianagaram District 535005 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

Autism Spectrum Disorder (ASD) is a neurodevelopment-based disability caused by variations in the brain. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. This paper aims at ASD classification using MRI Images by Artificial Gannet Optimization enabled Deep Convolutional Neural Network (AGO_DCNN). Here, an MRI image is assumed that be subjected to pre-processing of the image and feature extraction phases as an input. The bilateral filter is employed to remove the noises from the input MRI image and in the pre-processing of an image stage Region of Interest (ROI) extraction is conducted. The pivotal region extraction phase receives a filtered image after which AGO is used to extract the pivotal region. AGO is a newly designed approach by merging two optimizations namely Artificial Ecosystem-based Optimization (AEO) and Gannet Optimization Algorithm (GOA). At the same time, features are extracted from the input image during the feature extraction stage. In a feature extraction stage, some features like texture and statistical features are extracted. At last, the classification of ASD is conducted using DCNN, wherein by AGO the classifier is tuned. Furthermore, AGO_DCNN attained better outcomes in terms of maximal True Positive Rate (TPR), True Negative Rate (TNR), and accuracy values of about 93.7%, 90.8%, 96.8% and False Positive Rate (FPR) and minimal False Negative Rate (FNR) and values of 78.7% and 74.7%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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A Systematic Review of Challenges in Information Retrieval from Online Social Networking Platforms 3rd

A Systematic Review of Challenges in Information Retrieval f...

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Chowdhary, Mohit Chahal, Poonam Department of Computer Science and Engineering Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (纸本)9789819725076

With evolution of massive advancements in technology the World Wide Web (WWW) has become the significant source of short and crisp textual messages. The emergence of short textual messages in internet are called as micro-text are due to existence of various blogging and social networking sites like Facebook, twitter, Instagram etc. These micro texts are highly important as they contain an opinion on a wide range of events pertaining to social issues, individual, products, organisations etc. The quality of text available online is highly variable as it includes the semantic information context and temporal words. Due to this there exist many inconsistencies in the text available online and further poses numerous challenges in sentiment analysis. The online text is the kind of unstructured data. In this paper various challenges and issues for sentiment analysis for online text are discussed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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An Efficient Deep Learning Approach for Automatic License Plate Detection with Novel Feature Extraction 2

An Efficient Deep Learning Approach for Automatic License Pl...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kothai, G. Povammal, E. Amutha, S. Deepa, V. KPR Institute of Engineering and Technology Coimbatore641407 India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur60203 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil626126 India

In the domain of traffic management, road toll collection, and parking lot systems, vehicle number plate detection and identification play a pivotal role. Unlike conventional methods that treat license plate detection and character recognition as separate tasks, the system simultaneously addresses both challenges within a single neural network. Our Proposed methodology capitalizes on the efficiency and accuracy of the one-stage object detection algorithm known as YOLO (You Only Look Once) to locate license plates under diverse and challenging conditions. To augment the quality of input images with low resolution or poor clarity, we employ super-resolution generative adversarial networks (SRGANs). The image enhancement process substantially improves the visual quality of captured license plate images, facilitating more precise character recognition. Quantitative assessment of propounded system reveals compelling results. The license plate detection component achieves an outstanding average accuracy rate of 98.5%, surpassing previous methods by 15.2%. This comprehensive approach not only reduces the dependency on manual labour but also elevates processing precision. It seamlessly integrates into existing transportation infrastructure, resulting in heightened operational efficiency, reduced traffic congestion, and enhanced security measures. The rapid evolution of neural networks and deep learning techniques has streamlined the deployment of such applications, revolutionizing the field of traffic monitoring and management with unprecedented ease and precision. One-stage object detector, widely referred to as YOLO, is used to find licence plates in difficult circumstances. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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