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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Mandal, Shovon Shill, Pintu Chandra Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350359688

Bangladesh has recently adopted the worldwide trend of digitalization, namely in the area of financial activities. In Bangladesh, the centralized nature of digital payment systems poses notable obstacles, such as lengthy transaction procedures, vulnerability to fraudulent activities, and the potential for financial theft, despite their increasing popularity. In light of these concerns, our study presents an innovative remedy that utilizes the potential of blockchain technology, in conjunction with Near Field Communication (NFC) technology, to transform the field of electronic transactions. Our adoption of the proof-of-stake technique sets us apart from traditional hand-cash systems as well as the current centralized digital payment systems, providing enhanced efficiency and security. This connection facilitates fast, safe, and user-friendly transactions across several platforms, such as online and mobile applications, thereby greatly improving the digital wallet experience. Our decentralized system guarantees the integrity and immutability of every transaction while significantly decreasing transaction and maintenance expenses. This technique functions independently without any external intervention, fostering trust among users and strengthening the system against fraudulent activity. Our system is specifically designed for the high-volume transaction environment of Bangladesh's digital economy. It has the capability to efficiently handle large transaction volumes while ensuring full security. This article elucidates the revolutionary capacity of our blockchain-powered system to promote a secure, streamlined, and inclusive digital payment ecosystem in Bangladesh, representing a crucial stride towards a technologically progressive financial landscape. © 2024 IEEE.

关键词： Decentralized finance

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Breast Cancer Image Classification: Leveraging Deep Learning and Large Language Models for Semantic Integration 2

Breast Cancer Image Classification: Leveraging Deep Learning...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Harini, K.K. Nandhini, R. Rajeswari, A.M. Deepalakshmi, R. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350383652

Breast cancer, a significant global health problem that is mainly affecting women, demonstrates the importance of early detection to enhance survival rates. Medical image classification is a significant field that utilizes deep learning methods such as Convolutional Neural Networks (CNNs), Region-Based CNNs (R-CNNs), and Deep Neural Networks (DNNs). Despite this, challenges such as over fitting persist, particularly with limited data or complex models. Although these models learn hierarchical features, interpreting them in intricate image structures is still challenging and complicates decision rationale. To address this, we propose a comprehensive approach for breast cancer image classification. The initial process involves image segmentation using CNN to identify regions of interest, followed by precise object detection with OpenCV. data quality and diversity are ensured by preprocessing and augmentation with ImagedataGenerator, while informative features are extracted using the InceptionV3model. Our analysis of textual descriptions and embeddings is done by integrating Sentence Transformers and CLIP from the Large Language Model (LLM) framework. Achieving robust decision-making is ensured by setting a threshold of 0.7 for image classification based on predicted malignancy probability. Finally, improving the computations of similarity scores can improve prediction accuracy for reliable diagnostic interpretations. An accuracy of 0.92 in breast cancer diagnosis is guaranteed by integrating LLM and CNN for classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparison of Distributed Key and Access Management for Wireless Ad Hoc Network 18

Comparison of Distributed Key and Access Management for Wire...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Nandhini Devi, S. Sathiya Lakshmi, E. Shiny, A. Nithya, M. Sri Sairam Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9789380544519

A remote organization comprises of the many organization geographies that are broadly circulated, associated by moving hubs alluded to as portable hubs. Transfer innovation is utilized to upgrade parcel conveyance and work on the exhibition of remote organizations, tantamount to overall similarity for microwave access and LTE-A. Verification is crucial in an extremely remote geography because of thousands of clients speaking with each other with no recognizable proof. A public-key cryptanalytic rule that works with the Distributed key exchange algorithm is used for these authentications. the strategy for the Disseminated key trade calculation is that 2 clients solidly trade keys that they in the long run use to encode a message. The goal of our research is to reduce the amount of work in the relay network and increase performance in a way that is comparable to the quality factor. These parts. contemplate making a dependable way to decrease idleness and increment data rates, lastly, information move is one of the critical difficulties in remote systems administration, then, at that point, we will generally consider a jump by-bounce plot. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Comparison data Transmission Delay Performance Wireless Ad-Hoc Networks

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Hybrid Approach for Privacy Enhancement in data Mining Using Arbitrariness and Perturbation

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computer Systems science & engineering 2023年第3期44卷 2293-2307页

作者： B.Murugeshwari S.Rajalakshmi K.Sudharson Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India Department of Information Technology Velammal Institute of TechnologyChennai601204India

Imagine numerous clients,each with personal data;individual inputs are severely corrupt,and a server only concerns the collective,statistically essential facets of this *** several data mining methods,privacy has become highly *** a result,various privacy-preserving data analysis technologies have ***,we use the randomization process to reconstruct composite data attributes ***,we use privacy measures to estimate how much deception is required to guarantee *** are several viable privacy protections;however,determining which one is the best is still a work in *** paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data ***-more,this paper investigates the use of arbitrary nature with perturbations in privacy *** to the research,arbitrary objects(most notably random matrices)have"predicted"frequency *** shows how to recover crucial information from a sample damaged by a random number using an arbi-trary lattice spectral selection *** system's conceptual frame-work posits,and extensive practicalfindings indicate that sparse data distortions preserve relatively modest privacy protection in various *** a result,the research framework is efficient and effective in maintaining data privacy and security.

关键词： data mining data privacy arbitrariness data security perturbation

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VEye-AI Vision Assistant 10

VEye-AI Vision Assistant

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Joy, Alen Nazeer, Naja Abdul Gregory, Neha Maria Davis, Jasmy Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350384369

This paper aims to deal with the difficulties faced by blind while interacting with their surroundings. The proposed system introduces VEye - An AI Vision Assistant, a wearable device with an integrated camera and an ear-piece, using AI algorithms to convert real-time visual input from camera to audio description. It includes other features such as obstacle detection, text recognition, face recognition for authorized individuals, object identification, GPS integration and an emergency button. This device assists the visually challenged people in their day- to-day activities and develop a feeling of independence in them. © 2024 IEEE.

关键词： Obstacle detectors

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A Comparative Analysis of Quantum and Classical Algorithms for Heart Disease Prediction

A Comparative Analysis of Quantum and Classical Algorithms f...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathya, S. Ramasamy, Madhumathi Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375442

Quantum Computing (QC) works based on the principle of quantum mechanics, which is different from traditional computers. Heart disease remains the leading cause of mortality worldwide and the development of advanced predictive models for early diagnosis is necessary. In this paper, the Support Vector Machine (SVM), Quantum Support Vector Machine (QSVM), and Variational Quantum Classifier (VQC) are used to predict heart disease. Among these algorithms, QSVM and VQC are used for heart disease prediction using QC principles. The SVM and QSVM achieves the accuracy of 80% and 69% and VQC achieves the highest accuracy of 86% with ZFeatureMap and RealAmplitude in 100 iterations. The FeatureMap and Ansatz are used for turning data into quantum states. The VQC employs a parameterized quantum circuit to minimize the classification error and optimization is done using gradient-free COBYLA. In addition to ZFeatureMap, other FeatureMaps like ZZFeatureMap and PauliFeatureMap are also performed in VQC. © 2024 IEEE.

关键词： Quantum computers

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Efficient Chaos Based Homomorphic Permutation and SPM-BOW Algorithm for Secure Image Storage and Retrieval in Cloud 4

Efficient Chaos Based Homomorphic Permutation and SPM-BOW Al...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Iyswarya, R. Anitha, R. Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Storing digital images in the cloud, managed by third-party services, offers numerous advantages over traditional on-premises storage. It empowers scalability, accessibility from anywhere with internet, reliability through redundancy, cost-effectiveness with pay-as-you-go models. Given the rising reliance on cloud storage and mounting concerns regarding data privacy, a pioneering technique to securely retrieve images within cloud environments. The proposed approach focuses on safeguarding sensitive image content while ensuring compliance with privacy regulations. By leveraging advanced encryption techniques, this approach safeguards image data prior to its transmission to cloud servers, effectively reducing the likelihood of unauthorized access and potential data breaches. The technique involves extracting features directly from encrypted files, ensuring streamlined image retrieval while upholding user privacy and maintaining the integrity of the encrypted *** applying discrete cosine transform (DCT) coefficients and sophisticated encryption techniques, image content is obscured to ensure confidentiality. Local features extracted from encrypted big-blocks serve as the foundation for image representation. Leveraging these features, a feature vector is constructed using the Spatial Pyramid Matching and Bag of Words (SPM - BOW) model, facilitating efficient image retrieval while maintaining privacy. The proposed framework significantly advances cloud storage solutions by achieving 91.8% accuracy in image retrieval, surpassing the performance of other existing retrieval algorithms in terms of security and efficiency. © 2024 IEEE.

关键词： Discrete cosine transforms

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A Comprehensive review on An Ensemble-Based Machine Learning Approach for Phishing Website Detection 2

A Comprehensive review on An Ensemble-Based Machine Learning...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Justindhas, Y. Raghul, V. Pramadeish, Sm Prakash, S. Easwari Engineering College Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798350387933

Phishing Website Detection with ML is a literature review that surveys the use of machine learning in combatting phishing attacks. It provides insights into ML algorithms, feature engineering, datasets, and application domains. The review also addresses challenges and highlights the need for enhancing model robustness, interdisciplinary collaboration, and privacy-preserving solutions. As the digital threat landscape evolves, this review emphasizes the crucial role of ML in enhancing online security. © 2024 IEEE.

关键词： Websites

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Adapting Contextual Embedding to Identify Sentiment of E-commerce Consumer Reviews with Addressing Class Imbalance Issues

Adapting Contextual Embedding to Identify Sentiment of E-com...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Rakib Mollah, Md. Abdur Jahangir Kabir, Mir Md. Reza, Md. Sazid Kabir, Monika Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh Td School University of Technology Sydney Data Science and Innovation Sydney Australia Murdoch University Information Technology Perth Australia

ISBN: (纸本)9798350350289

Understanding consumer attitudes toward specific products is crucial for boosting sales in the e-commerce industry. To effectively target customers with popular products based on reviews, the classification of consumer feedback becomes imperative. However, classifying product reviews can be challenging, particularly when dealing with imbalanced data labels, which often result in suboptimal classification performance. This study builds upon previous efforts that utilized the Amazon Fine Food Reviews dataset for classification tasks. While these prior attempts showed promise, they were hindered by either poor embeddings or the prevalent class imbalance issue. In response, this research tries to solve these problems by using word embeddings with RoBERTa, a pre-trained transformer-based language model, to classify reviews. Additionally, the XGBoost classifier was implemented, along with embeddings from the language model. Losses were first calculated with equal weights for all class labels, and a re-weighted loss was subsequently adopted to balance the impact of each class on the loss function during training. The incorporation of RoBERTa and XGBoost, along with the class label re-weighting, contributed to improved capturing of intricate word relationships within reviews. As a result, this approach achieves significantly improved accuracy in both binary and multiclass classifications compared to earlier endeavors. Notably, it attained an impressive accuracy of 83.84% in multiclass classification and 93.29% in binary classification tasks, marking a substantial advancement in the field of consumer review analysis. © 2024 IEEE.

关键词： Embeddings

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StyleGAN and DCGAN for Face Generation: A Comparative Evaluation 8th

StyleGAN and DCGAN for Face Generation: A Comparative Evalua...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Archana, M. Joseph, Ashwin Antony Ajith, Alex V. Koshy, Alan N. Saritha, S. Department of Computer Science Engineering Rajagiri School of Engineering and Technology Kochi India

ISBN: (纸本)9789819765805

Advent of GAN networks has enabled several tasks such as text to face generation easier. It helps in synthesizing several instances of data from the actual data. It gives an idea on new possibilities for existing dataset. The current scenario in literature sees a parade of GAN architectures. The performance of each GAN architecture varies in a wide range of outputs. Hence, there always exists a confusion of the GAN architecture to be chosen for the purpose of the task in question. This paper attempts to make a comparative evaluation of two famous GAN architectures, namely, StyleGAN and DCGAN. The architectures are implemented and the images generated are compared on the basis of the quality of images. It is seen that StyleGAN responds more accurately to features like smile, age, and gender. From the experiments, it is seen that the FID score of StyleGAN has a lower value as compared to that of DCGAN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network architecture

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