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The Future of Online Shopping: Blockchain- Powered E-Commerce Platforms

The Future of Online Shopping: Blockchain- Powered E-Commerc...

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Sri, Nemani Divya Ayyasamy, Ramesh Kumar Chinnasamy, P. Vellanki, Sreelakshmi Vujwala, Maddula MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Faculty of Information and Communication Technology Kampar Malaysia School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798331540685

A cutting-edge online marketplace that uses blockchain technology to transform how we purchase and sell goods and services is known as a blockchain- powered e-commerce platform. This platform uses a decentralized network of computers, also known as nodes, to maintain a transparent and impenetrable record of all transactions instead of depending on a central authority like a bank or a company. A blockchain-based e-commerce platform is a secure, transparent, and trusted online marketplace. It does away with the need for intermediaries, improves security, and provides a world market where users can confidently conduct peer-to-peer transactions. This novel strategy could change how online commerce and shopping are performed. © 2024 IEEE.

关键词： Marketplaces

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Privacy preserving large language models: ChatGPT case study based vision and framework

IET Blockchain

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IET Blockchain 2024年第S1期4卷 706-724页

作者： Ullah, Imdad Hassan, Najm Gill, Sukhpal Singh Suleiman, Basem Ahanger, Tariq Ahamed Shah, Zawar Qadir, Junaid Kanhere, Salil S. School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW Australia Higher Colleges of Technology Abu Dhabi United Arab Emirates School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Management Information Systems Department College of Business Administration Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Technology Sydney International School of Technology and Commerce SydneyNSW Australia Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Sydney Australia

The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy-preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre-processing and preserving private context and the private training process for large-scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy;specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and

关键词： Blockchain

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Factors Influencing Participation in Data Crowdsourcing: A Systematic Literature Review 8

Factors Influencing Participation in Data Crowdsourcing: A S...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Panjaitan, Re. Miracle Setiawan, Budi Darma Brawijaya University Faculty of Computer Science Department of Informatics Engineering Malang Indonesia

ISBN: (纸本)9798350342604

Crowdsourcing is becoming a trending concept used by many organizations, without being limited to a particular field. As of 2018, crowdsourcing was used by 85% of the largest worldwide companies. This trend of crowdsourcing would be increasing as it offers numerous advantages to companies in achieving their goals, such as the capability of obtaining data and information from the crowd. The crowd-provided data and information contain the essence of human ingenuity, which enables companies to do problem-solving with a human-centered approach. The more contributors participate, the more likely the crowdsourcing program will succeed. Several studies give different lists of factors that influence the participation rate of the crowd. However, there's a lack of discussion about the factors and it leads to a situation where organizations need to examine many factors when developing a crowdsourcing platform to get great crowd engagement. Therefore, this study conducted a Systematic Literature Review (SLR) to perform screening and analysis of preceding research systematically. The factors in each of these literatures are generated by considering the case of the study or the region of the people related to the research. This diversity helps identify the factors influencing contributor participation in data crowdsourcing from research all over the world. A study reflecting on these research findings would help answer research questions thoroughly. This study found that there are 19 factors that positively influence contributor participation in data crowdsourcing out of 26 factors found in the 16 selected documents. © 2023 IEEE.

关键词： Crowdsourcing

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Modified MobileNetV2 with Convolutional Block Attention Module for Facial Expression Recognition

Modified MobileNetV2 with Convolutional Block Attention Modu...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Nafi'an, Muh. Hafidh Ilmi Bachtiar, Fitra Abdurrachman Setiawan, Budi Darma Brawijaya University Computer Science Faculty Informatics Engineering Department Malang Indonesia

ISBN: (纸本)9798350382914

Facial emotion recognition is one of the artificial intelligence implementations used to recognize emotions based on data learned by computers. Unlike humans, who can recognize a person's emotions directly, computers need several trainings procedures conducted by humans to be able to recognize a person's emotions. Previous research has proposed various methods with deep learning and traditional machine learning approaches to classify emotions based on faces. Some studies obtained relatively high accuracy, but on evaluation by cross-validation, the results were much lower than the accuracy obtained. Therefore, this study proposes an approach using a modified MobileNetV2 deep learning architecture in the residual layer by adding a Convolutional Block Attention Module (CBAM) to improve accuracy and data generalization. This experiment uses the Karolinska Directional Emotional Faces (KDEF) public benchmark dataset. Preprocessing is done with image augmentation to enrich the training data by resizing, center cropping, random horizontal flipping, random affine, and random rotation. The proposed method is evaluated by 5-fold cross-validation and compared with MobileNetV2 without modification and state-of-the-art methods. Experimental results show that the proposed model outperform all comparative methods by achieving a 5 -fold cross-validation accuracy of 94.387%. © 2023 IEEE.

关键词： Emotion Recognition

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Wideband Matched Filtering Antennas with Absorptive Resonators

Wideband Matched Filtering Antennas with Absorptive Resonato...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Cui, Jiaqian Zhu, Lei University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9781733509657

This paper presents a design method of filtering antennas with wide matching bands to realize the integration of multiple functions such as absorbing, filtering, and radiating, and protect the system as well. In our design, an absorptive stub is employed to absorb the out-of-band signals. As such, a cascaded topology with lossy stubs, lossless stubs, and the resonant antenna is proposed to acquire the absorptive filtering antenna. The characteristic of the absorptive stub, and the response of the equivalent circuit are specifically analyzed here. Finally, an absorptive filtering slot antenna is implemented to prove that the method can be used to achieve a matching bandwidth far beyond that of a conventional resonant antenna. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Topology

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Enhancing Medical Image Diagnosis with Convolutional Neural Networks 2

Enhancing Medical Image Diagnosis with Convolutional Neural ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Mahadevan, K. Pushparani, S. SashiRekha, K. Sivagami, V.M. Usharani, R. Department of Computer Science and Engineering CARE College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Chennai India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350383867

This paper is a comparative analysis of medical image diagnosis algorithms with Convolutional Neural Networks (CNNs) and other methods;such as Support Vector Machine (SVM), Random Forest, and k-nearest Neighbors (k-NN) The study performs evaluations on datasets with multimodalities like medical images and diagnostic and various imaging modalities. Results shows that CNNs have the upper hand over traditional, static algorithms across several metrics that assesses the performance of the system such as accuracy, precision, recall, and F1. In particular, CNN precision was 90.5% while the recall was 93.7%, and the accuracy was 92.1% as well as F1-score 92.0%. In addition, the high area under the Receiver Operating Characteristic curve (AUC-ROC) of over 0.95 for the CNN also signed for its excellent discriminatory power. These outcomes only exemplify CNN usefulness in the diagnostic area because it is an exact and accurate method for analyzing medical images. Although the nets CNNs are deployed in clinical cases may help address concerns regarding interpretability, compliance with regulations, and ethical opinions, such issues still exist and require further exploration. Overall, this research takes the first steps in directing algorithmic approach towards medical diagnosis and show the feasibility in deployment of CNNs as inseparable pieces of equipment in modern healthcare. © 2024 IEEE.

关键词： Random forests

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Analysis of Sensing Matrix Design for Under-Sampling MRI 9

Analysis of Sensing Matrix Design for Under-Sampling MRI

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9th International Conference on Communication and Information Processing, ICCIP 2023

作者： Andryani, Nur afny catur Oey, Endra Laurent, Velix Arvino Nasya, Alexandra Husin, Husna Sarirah Computer Science Department School of Computer Science Bina Nusantara University Indonesia Computer Engineering Department Faculty of Engineering Bina Nusantara University Indonesia Taylor's University Malaysia

ISBN: (纸本)9798400708909

MRI currently is the most powerful medical diagnostic tomography system. It provides high resolution with high detail medical image. Its magnetization sensing is also free from radiation impact hence safe for the patient. However, the data acquisition process takes quite a long time with high-rate sampling. The sensing patient should stay still in the MRI sensing device for 20 to 40 minutes. It can affect claustrophobia for some patients. The high-rate sampling will also linearly affect the cost, which is considerably high. Hence, the research on enabling under-sampling MRI imaging has been raised. The main objective of under-sampling MRI Imaging is reducing the scanning time by under-sampling the data acquisition while at the same time the resulting qualified reconstructed image can be maintained. Compressive Sensing (CS) framework is one of the approaches that can be utilized on solving projection with less measurement. It can recover signals which have sparsity property. The signal is sparse if the signal being sensed has low information rate. Thus, the framework of Compressive Sensing matches under-sampling MRI Imaging. The sparsity requirement is closely related to the design of sensing matrix and dictionary on the Compressive Sensing framework. This paper presents analysis of two proposed sensing matrix designs for Under-sampling MRI Imaging: square sensing matrix and sorting-based sensing matrix designs. The proposed sensing matrix design is intended to appropriately sample the data measurement on certain sampling rate in frequency domain to maintain the quality of the reconstructed MRI image closely to the ground truth (full sampling MRI Imaging). The simulations show that both proposed sensing matrix designs are able to conduct under-sampling MRI Imaging for at least at 40% sampling rate with average of PSNR 27.72 and 33.07 respectively subject to the ground truth. © 2023 Copyright held by the owner/author(s)

关键词： Medical imaging

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Paddy Leaf Disease Identification on Infrared Images based on CNN with Auto Encoders 2

Paddy Leaf Disease Identification on Infrared Images based o...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Kandukuri, Prabhakar Sowrab, B. Sameer Ramesh, G. Singamaneni, Kranthi Kumar Koneru Lakshmaiah Education Foundation Computer Science and Engineering Guntur India Gitam Deemed to Be University Computer Science and Engineering Visakhapatnam India Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India Jain Deemed-to-be University Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350325799

The main food that people in India consume on a daily basis is paddy. According to data, the stress caused by rice illnesses, which reduce yields by 70%, was felt by the paddy farmers. If not controlled within a certain time frame, these plant diseases, which are typically brought on by pests, insects, and pathogens, have a significant negative impact on productivity. This research proposed auto encoders rooting convolutional neural network model to control these paddy diseases, and it has been implemented with various per-trained models like Vgg16, Resnet50, and inceptionV6. This model accurately determines whether a paddy plant is healthy or has one of four different types of paddy diseases. These models were developed, tested using auto encoders and a dataset of thermal pictures that was made available to the public, and they achieved the maximum model accuracy of 90.6%. A 84.8% was attained in the prior iteration without the use of auto encoders. With the use of this model, people can quickly detect paddy illness and assist farmers in raising productivity and yield. © 2023 IEEE.

关键词： Neural network models

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Evaluating Artificial Neural Networks and Emerging Machine Learning Methods for Engine Performance and Emissions Prediction: A Review 4

Evaluating Artificial Neural Networks and Emerging Machine L...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Galat, Umesh Rewatkar, Rajendra Kothare, Chandrakant Datta Meghe Institute of Research and Higher Education Aids Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Shri Shankarprasad Agnihotri Collge of Engineering Department of Mechanical Engineering Maharashatra Wardha India

ISBN: (纸本)9798331523923

These due to the shift towards the cleaner energy generation and newer and sophisticated engine technologies the prediction of the performance and emission of engine is of vital importance for the designer for the purpose of ANR as well as the emission control standards. This review also assesses the performance of ANNs and the new machine learning techniques in this area. ANNs especially the single layer and multi-layer networks have shown good levels of predictions of engine performance and emission for ethanol-gasoline, bioethanol and other fuel blends. For example, the models based on Levenberg-Marquardt optimization and the Bayesian regularization have revealed essential correlation coefficients and minimized mean squared errors. Based on comparative analyses, the further improvement of the predictive ability and speed of ANNs is stressed, including applications of ANNs integrated with other approaches like Response Surface Methodology (RSM), and other developing approaches like Extreme Learning Machines (ELM) and Support Vector Regression (SVR). However, challenges are concluded to remains including data quality, model complexities and computational resources. This review reveals that, for tackling these issues and enhancing the progress in developing efficient and sustainable engine solutions, detailed databases and refined mathematical modeling approaches are required. The directions for future research are to obtain better quality data, enhance computational time, and determine more fuel blends and engine settings for the model. © 2025 IEEE.

关键词： Adversarial machine learning

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Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

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computer Systems science & engineering 2022年第9期42卷 1273-1287页

作者： S.Prakash P.Vishnu Raja A.Baseera D.Mansoor Hussain V.R.Balaji K.Venkatachalam Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of ECE Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce *** relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

关键词： Chronic disease classification iterative weighted map reduce machine learning methods ensemble nonlinear support vector machines random forests

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