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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Vanitha, V. Kishor Kumar, P. Sreenivasulu, Y. Rawat, Ruchira Ramesh, S. Kumar, Jogendra Tamil Nadu Chennai603104 India Ravindra College of Engineering for Women Department of Electronics and Communications Engineering Andhra Pradesh Kurnool518452 India Sreenidhi Institute of Science and Technology Department of Electronics and Communications Engineering Telangana Hyderabad501301 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Institute of Artificial Intelligence and Machine Learning Saveetha University Department of Computational Intelligence Tamil Nadu Chennai602105 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350340600

In this study, a linear array of S-shaped Microstrip patch antennas is created and examined. IE3D V15.2 software was used to design, study, and simulate the antenna. The ground plane is 40x70 mm, and the patch is 30x60 mm, we'll say. In only one band, the bandwidth has increased. Gain at 2.43271 GHz is 3.68152 dBi, whereas bandwidth at 1.8744 GHz is 63.05 percent. Substratum thickness of 1.6 mm, dielectric constant of 4.2, and loss tangent of 0.0013 are taken into account in this computation. This antenna is mostly used in wireless systems. © 2023 IEEE.

关键词： Wireless networks

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Energy-Proficient Cluster Enrichment in Wireless Sensor Networks via Categorized Fuzzy Clustering and Multi-Hop Routing Optimization

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SN Computer Science 2025年第1期6卷 1-12页

作者： Shakya, Harish Kumar Chandanan, Amit Kumar Subbalakshmi, Chatti Khambra, Geetanjli Ansari, Mohd Shahnawaz Kushwaha, Ajay Roy, Vandana Artificial Intelligence and Machine Learning Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya (A Central University) Chhattisgarh Bilaspur India Department of Computer Science and Engineerings Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of Computer Science and Application The Bhopal School of Social Sciences (BSSS) College M.P. Bhopal India Department of Computer Science and Engineering Eklavya University Damoh MP Damoh India Computer Science and Engineering Rungta College of Engineering and Technology Bhilai India Department of Electronics Communication Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

The WSN's lifetime can be extended by practicing energy saving. By using the appropriate routing mechanism when executing clustering, it can be done successfully. For improved energy conservation, clustering, and routing work together. Clustering lowers duplicate data transmission, while superior routing enables energy-saving travel along the quickest route to the destination. Nodes can be grouped via clustering, and each group will have CH. CH provides data to the BS either directly or via the intermediary nodes. By reducing the workload of resource-limited nodes, clustering chooses CH from resource-rich nodes. The entire network needs to undergo re-clustering in the traditional WSN clustering approach. The suggested protocol creates an energy-efficient cluster head selection approach applying FIS in hierarchical WSNs, which in turn increases sensing network lifetime, power consumption rate, and network communication performance. The proposed FBUCA and Modified minimum spanning tree with fuzzy logic can efficiently route data packets. The load-balancing among clusters and decreased energy usage demonstrated by the proposed model in MATLAB R2016 simulations are key to increasing throughput and extending the network's lifetime. The proposed approach takes into account six of the aforementioned use scenarios. The experimental results confirmed the adaptability and sturdiness of the proposed method, showing that it works well with a large number of WSNs. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CH FBUCA Fuzzy logic WSN

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Optimizing Digital Marketing Strategies Through Search Engine Optimization

Optimizing Digital Marketing Strategies Through Search Engin...

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Contemporary Computing and Communications (InC4), IEEE International Conference on

作者： Reshmi Tatikonda Jainath Ponnala Ramya Thatikonda Dileep Kumar Yendluri M. Kempanna Bhuvanesh Ananthan Department of Computer Information University of the Central Missouri Warrensburg USA Department of Electronics and Communication Engineering Jawaharlal Technological University Hyderbad India Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

This article examines SEO and its significance in digital marketing. The goal is to comprehend the main elements that impact achieving high rankings in search engines and the consequences of the presence of websites. Additionally, it strives to study its influence on website visibility, organic traffic, and rank in search results. This document comprehensively examines search engine optimization techniques, including optimizing website content and external factors, optimizing textual materials, and enhancing technical factors. Moreover, it examines the connection between SEO and UX (user experience). It emphasizes the significance of maximizing user happiness and participation. The document further explores the possible future of search engine optimization and online advertising. It contains new trends, expected challenges, and advice for future investigations and real-world applications. In general, the main objective of this paper is to present valuable viewpoints regarding the realm of SEO and its importance in the digital promotion arena.

关键词： Visualization Privacy machine learning algorithms machine learning Search engines Market research Prediction algorithms

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The Development of Blockchain technology with the Internet of Things: Transforming the Way We Manage Assets, Communicate Securely Online, and the Supply Chain

The Development of Blockchain Technology with the Internet o...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： P. G. Kuppusamy Monisha M K. A. Arokiaraj D. Parameswari Badi Alekhya Vijaya Vardan Reddy S P Department of Biomedical Engineering J N N Institute of Engineering Chennai Tamil Nadu India Department of Information Technology University of Technology and Applied Sciences Ibri Oman Department of Master of Business Administration St. Joseph's College of Engineering Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Jerusalem College of Engineering Pallikaranai Chennai Tamil Nadu Department of Artificial Intelligence and Machine Learning R.M.D Engineering College Chennai Tamil Nadu India Department EC-ACT R.M.K. Engineering College Chennai Tamil Nadu

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

In conjunction with the Internet of Things (IoT), the rapid development of blockchain technology is bringing about a revolution in the administration of assets, the provision of secure online communication, and the procedures involved in supply chain management. This study investigates the potential synergy between blockchain technology and the Internet of Things (IoT), focusing on how the combination of these two technologies can improve transparency, security, and efficiency across a variety of industries. Since blockchain technology offers a distributed and unchangeable record that guarantees the authenticity and provenance of data, it is ideally suited for the management of assets in a manner that is open and transparent. In the meantime, Internet of Things devices produce enormous volumes of data that can be safely shared and recorded on blockchain, which ultimately makes real-time monitoring and traceability much easier to accomplish. Among the applications that are investigated in this study are smart contracts, which are able to automate transactions; secure identity verification systems; and enhanced supply chain management, which increases visibility and responsibility. Furthermore, the paper analyzes the difficulties that are associated with putting this technology into practice, including scalability, energy consumption, and interoperability on a variety of different platforms. In the end, this paper highlights the revolutionary impact of combining blockchain technology with the Internet of Things (IoT). It presents a roadmap for future innovations and applications that have the potential to redefine how we manage assets, ensure secure communications, and streamline supply chain processes.

关键词： Industries Technological innovation Supply chain management Supply chains Smart contracts Real-time systems Blockchains Internet of Things Security Standards

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SINN Based Federated learning Model for Intrusion Detection with Blockchain technology in Digital Forensic

SINN Based Federated Learning Model for Intrusion Detection ...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Priyanka Pramod Pawar Deepak Kumar R Krupa Piyush Kumar Pareek H M Manoj K S Deepika Information Technology University of the cumberlands Cumberlands United States Mechanical Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Today, smart devices permeate every part of our lives, ushering in a new technological era known as Internet of Things (IoT). There ration of cybersecurity risks due to the proliferation of smart devices in IoT contexts and the massive volumes of sensitive data they store. In order to determine the time and place of these attacks and to collect evidence that can lead to the identification of the perpetrators, digital forensics is essential. Research into digital forensics in an IoT setting is fraught with difficulties owing to factors such as the dispersed nature of data storage, the need to be able to track and verify the provenance of evidence, the difficulty in obtaining data from many sources, and the lack of visibility into the evidence collection procedure. This is why the research suggested merging two potential technologies to offer a complete answer. The research employed federated learning to train representations locally using data kept by IoT devices, with the help of a dataset that was created to mimic attacks on the IoT ecosystem. The study utilises the BoT-IoT dataset and employs a classification method called Shepard Interpolation Neural Network (SINN) with the Shuffled Frog-Leaping algorithm (SFLA). After that, in order to make the blockchain lightweight, the study aggregated data using blockchain by gathering parameters from the IoT gateway

关键词： Interpolation Privacy Federated learning Scalability Digital forensics Ecosystems Blockchains Internet of Things Resource management Smart devices

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Gen AI Driven FAQ Chatbot Using Advanced RAG Architecture for Querying Annual Reports

Gen AI Driven FAQ Chatbot Using Advanced RAG Architecture fo...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： Mehul K V. R. Kanagavalli Saradha K R Gowtham P N Sachin M P Surya U Godhandaraman R Girish S Naveen R Department of Information Technology Sri Sairam Engineering College Chennai India Department of Humanities and Science Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning) Sri Sairam Engineering College Chennai India Department of Mechanical Engineering Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

In the rapidly evolving business landscape, stakeholders require timely and accurate access to financial and operational information from annual reports. However, the extensive and complex nature of these reports makes it difficult to efficiently extract key analytical trends, financial performance measures, and comparative insights. The traditional manual approach to analyse these documents is time-consuming, error prone, and inefficient. Stakeholders-including creditors, customers, suppliers, employees, government agencies, and industry analysts-rely on this data to make informed decisions. The absence of an automated mechanism to handle frequent queries regarding financial performance, operational metrics, and sustainability targets impedes large-scale informed decision making. This paper presents a Gen AI-driven solution that leverages Natural Language Processing (NLP), LangChain Framework and Retrieval Augmented Generation (RAG) Architecture to efficiently extract and analyze financial annual reports and its data. The approach involves utilizing PyPDF2 for data extraction, segmenting content using RecursiveCharacterTextSplitter, generating embeddings via FastEmbedEmbeddings module, and storing information in Qdrant for rapid similarity search. The response generation is powered by DeepSeek LLM, deployed locally via Ollama, ensuring privacy and computational efficiency. A Gradio-based UI is implemented for an intuitive interface. This system enhances financial data accessibility, fostering transparency, compliance, and data-driven decision-making.

关键词： Text analysis Regulators Retrieval augmented generation Decision making Computer architecture User interfaces Strategic planning Market research Stakeholders Sustainable development

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Diabetic Retinopathy detection using Region based convolutional neural network (RCNN) and classification based on Retinal Fundus Images

Diabetic Retinopathy detection using Region based convolutio...

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Research Methodologies in Knowledge Management, artificial intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Presilla R Rajeshwari S B Jagadish S Kallimani Sanjeetha R Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering (Cyber Security) Sambhram Institute of Technology Bangalore India

ISBN: (数字)9798331598488

ISBN: (纸本)9798331598495

Diabetes mellitus is a chronic disease in which the body either fails to produce sufficient insulin or cannot efficiently utilize it due to a hormonal imbalance. Individuals with diabetes are prone to visual impairments, such as diabetic retinopathy (DR) and glaucoma, as well as other complications, including neuropathy, nephropathy, and cardiomyopathy. Among these, DR is one of the most prevalent consequences of diabetes. Detecting morphological alterations in the retina is challenging, necessitating an effective automated detection system. Advances in deep learning models and image processing techniques have facilitated the early prediction of DR. Numerous research studies on DR diagnosis systems have been published in recent years, highlighting the growing significance of deep learning (DL) models in medical image classification. With this objective in mind, this study introduces the Region-based Convolutional Neural Network (RCNN) model to identify DR in retinal fundus images. The RCNN approach incorporates noise removal using Gabor filtering (GF) and a segmentation strategy that enhances region growth. Furthermore, the seed location and thresholds for the region-expanding segmentation method are improved using the Glowworm Swarm Optimization (GSO) system. In adding, SqueezeNet with a class attention learning layer is used for feature vector extraction. The RCNN-GSO methodology is validated using the benchmark MESSIDOR dataset, demonstrating superior performance over recent approaches, with a maximum accuracy of 98.22%. The simulation results confirm that the RCNN-GSO technique outperforms state-of-the-art approaches for DR diagnosis.

关键词： Deep learning Image segmentation Diabetic retinopathy Accuracy Visual impairment Glowworm swarm optimization Predictive models Retina Feature extraction Convolutional neural networks

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An Effective Expectation of Diabetes Mellitus via Improved Support Vector machine through Cloud Security

An Effective Expectation of Diabetes Mellitus via Improved S...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Dayanand Lal N Hema K Spoorthi T Ramesh Chegoni Madhura G K Department of Computer Science & Engineering GITAM School of Technology GITAM University India Department of IT CMR Technical Campus Kandlakoya India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

Nerve pain, eye difficulties, skin issues, renal problems, heart disease, depression, and a host of other illnesses are all linked to diabetes if left untreated. In an effort to lengthen the human lifespan, scientists all around the world have applied a variety of methods for early disease prediction. Diseases of the eyes, heart, and kidneys are prioritised because they can cause serious consequences and even death if left untreated. After conducting an in-depth analysis of the strategies used by several researchers, the following models were constructed and tested using the datasets most frequently favoured by leading scientists throughout the world. Diabetic retinopathy (DR) is an eye condition, and this research seeks to construct a hybrid learning model for early prediction of DR. The proposed approach combines elements of both machine learning and Deep learning. Score Level Fusion is the last arbiter of the model's precision. Images from the first dataset of its sort reflecting the Indian population, the Indian Diabetic Retinopathy Imaging Dataset, were used to make the prediction. Predicting the DR and recommending the necessary treatment to patients is possible based on severity levels, which ultimately leads to the elimination of blindness.

关键词： Support vector machines Deep learning Heart Retinopathy Blindness Predictive models Retina

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An Analysis of Fault Diagnosis Approaches in Memristor Crossbar Array

An Analysis of Fault Diagnosis Approaches in Memristor Cross...

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IEEE International Symposium on Smart Electronic Systems (iSES)

作者： Dev Narayan Yadav Phrangboklang Lyngton Thangkhiew Indranil Sengupta Center for Artificial Intelligence and Machine Learning Siksha ‘O’ Anusandhan Bhubaneswar India Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

The use of memristors has been explored for various applications due to its unique computing capability, in particular in-memory computing (IMC). IMC offers both storage and computation using the same hardware, which overcomes to a large extent the CPU-memory bottleneck that exists in current-day computing architectures. Memristors are often fabricated in a crossbar, which offers various benefits over other memory technologies, including in-memory computing, low power, and high density. However, the still immature nano-scale fabrication technology can lead to a number of faulty cells in the crossbar (typically, 30–40% faulty cells). For some applications like neuromorphic computing, a limited number of faults can be tolerated. However, for some other applications like Boolean function realization, a single faulty cell may lead to incorrect results. We therefore need a fault diagnosis approach to use the memristor devices efficiently. In this paper, we have analyzed various fault diagnosis approaches available in the literature. We observe that most of the existing approaches employ March-based test, which is used for conventional RAM. Nevertheless, memristor storage and operation techniques differ from conventional memory, necessitating a different approach and fault models to be used.

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PFCM Based Segmentation and TFA Based DCNN Model for Skin Cancer Classification Using Dermoscopic Images

PFCM Based Segmentation and TFA Based DCNN Model for Skin Ca...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Manoj H M Priyanka Pramod Pawar Krupa R Piyush Kumar Pareek Deepak Kumar Lalitha Bandeppa Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India Information Technology University of the Cumberlands Cumberlands United States Mechanical Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Worldwide, skin lesions have emerged as a serious health concern; however, the survival rate can be improved by identifying skin lesions sooner with dermoscopy pictures. It will be a painstaking process to classify the skin lesion from those dermoscopy photographs. Using deep learning models improves the accuracy of skin lesion categorization. There has been a recent influx of convolutional neural networks (CNN) into this field; CNNs have well-established methods for feature extraction, which improves classification. This project aims to create AI-based solutions, namely deep learning (DL) procedures, to differentiate among benign and malignant skin lesions in dermoscopy pictures. Seven types of skin lesions were used in the Skin Cancer MNIST: HAM10000 dataset. Visual illness diagnosis using deep learning models is becoming increasingly common. For the purpose of skin cancer classification, this study suggests using a deep convolutional neural network (DCNN). In order to enhance the accuracy of the proposed DCNN classifier, the Tangent Flight Algorithm (TFA) is recommended as the optimum approach for optimising the weights, and possibilistic fuzzy clustering (PFCM) is employed for segmentation. Results from this study demonstrate that, compared to convolutional approaches, the suggested technology provides superior accuracy. The proposed TFA based DCNN model outperformed state-of-the-art prediction methods with training accuracy of 0.962, testing accuracy of 0.947, sensitivity of 0.824, and F1-score of 0.822.

关键词： Deep learning Training Image segmentation Accuracy Sensitivity Skin Lesions Convolutional neural networks Skin cancer Testing

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