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7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022

作者： Aishwarya, R. Prashanth Kumar, K.N. Department of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (纸本)9789811996375

People are widely using Internet reviews like an information source before making each and every kind of purchases. Genuine evaluations usually have a big affect on subsequent brand purchases. With our approach, we go out of our way to look at posted evaluations from customers to better grasp distinct behavioural features. On the alternative side, using different natural language processing (NLP) techniques and sentiment analysis (SA), it is crucial in a number of ways to identify fraudulent reviews and remove them from the database. A hybrid CNN-LSTM deep learning (DL) model that employs SA model that can predict whether a review is positively or negatively trained on the assessment data set. The true reviewer is identified based on the crowd sourcing concept’s overall score. The e-commerce industry and various sites also depend on item ratings. Consumers can have confidence in the company’s products prior completing the purchase. In order of big e-commerce businesses including Myntra, Nykaa, Flipkart, etc., for tackling the problem of false feedback and eliminate fraudsters and phoney evaluators, maintaining users’ faith with digital shopping sites, a solution should be found. The "Amazon Yelp data set", which is a useful set of data for training on a relatively modest range and may be utilized to acquire maximum flexibility and accuracy, is employed to train the algorithms in this study. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Improved SqueezeNet Architecture for Brain Age Prediction using MRI 2

Improved SqueezeNet Architecture for Brain Age Prediction us...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

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

ISBN: (纸本)9798331541217

Predicting brain age from MRI scans is crucial for understanding neurological development and aging-related disorders. Robust preprocessing methods and flexible model architectures are required because variations in image quality, resolution, and acquisition methodologies can impact deep learning performance. The improved Squeeze Net architecture shown in this work can accurately predict brain age from MRI scans. The approach combines a bilateral filtering procedure for preprocessing to enhance picture quality, and then an Improved Squeeze Net (Imp-Squeeze Net) model for precise evaluation of brain age. The model includes convolutional layers, fire modules, and global average pooling, ending with a dense layer that employs SoftMax activation. Notably, a novel Focal Huber Loss function is introduced to handle the complex data distributions and enhance predictive performance. Comparing the performance of the proposed model to CNN (0.155), GRU (0.151), Bi-LSTM (0.130), RNN (0.157), Bi-GRU (0.160), and LSTM (0.158), a significant reduction in prediction error is observed. When compared to conventional techniques, experimental results show higher accuracy and robustness, highlighting the method's promise in brain research and clinical diagnostics. © 2024 IEEE.

关键词： Image quality

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A privacy-preserved blockchain for vehicles in smart societies 23

A privacy-preserved blockchain for vehicles in smart societi...

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6th International Conference on Advances in Robotics, AIR 2023

作者： Dewangan, Narendra Kumar Chandrakar, Preeti Computer Science & Engineering National Institute of Technology Raipur60104578 India

ISBN: (纸本)9781450399807

Data collection from the mobile vehicle is the most vulnerable task. In an open network of vehicles, device identities must be protected. Our work in this paper describes a proof-of-authentication-based blockchain to collect and store data from vehicular devices with privacy preservation in the blockchain. We are using Ed25519 for the identity generation for devices and vehicles, so we can protect the real identity of the vehicles by generating hash-based wallets. Providing privacy for the vehicle's real identity is required due to the immutable property of the blockchain. Since blockchain stores each transaction, if any personal information or private information is committed to the ledger, then the data provider can be in a vulnerable situation. To provide protection from this kind of situation, the data privacy of the vehicle and owner is required. We are dividing the areas of the smart city into different data collection nodes named CRA (Certification and Registration Authority). This CRA acts as the node for the proposed blockchain. The novelty of our work is that device and vehicle identities must be preserved while collecting data from mobile vehicles in the smart city scenario. The CRA is selected only to be a miner in the proposed system because the vehicles do not have large storage and computation power. The blockchain is implemented using a custom Python-based blockchain. Our proposed work involves collecting data from the devices embedded in the vehicles and storing it in the python-based, lightweight, consensus-enabled blockchain. Our proposed work improves the secure connection between devices and CRA. The proposed system is fast, secure, and privacy-preserving. © 2023 ACM.

关键词： Blockchain

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Robust Smart Face Recognition System Based on Integration of Local Binary Pattern (LBP), CNN and MTCNN for Attendance Registration

Robust Smart Face Recognition System Based on Integration of...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Sriman, B. Wise, D.C. Joy Winnie Silviya, S.H. Annie Kumar, D. Saravaanaa Sujan, K. Venkat Sai Bruhathi, S. Rajalakshmi Institute of Technology Computer Science and Engineering TamilNadu Chennai India

ISBN: (纸本)9798350334357

Face recognition is one of the most effective image-processing applications and is essential in the technological era. The identification of the facial image is a current problem for authentication purposes, particularly in the case of student attendance. The design of this system aims to digitally replace the outdated method of collecting attendance with handwritten records. The methods now used to take attendance are complicated and time-consuming. Hence, this method is suggested as a solution to all of these issues. The suggested method uses the integrated benefits of Local Binary Pattern(LBP), CNN, and MTCNN. Attendance reports will be created and maintained in excel format following face recognition. The created system is less expensive to install and requires less work. © 2023 IEEE.

关键词： Local binary pattern

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Analysis of Challenges in Decentralized Storage Framework for Sharing Medical Data 14

Analysis of Challenges in Decentralized Storage Framework fo...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Deshmukh, Sushama A. Kasar, Smita L. Chichani, Yogesh Maharashtra Institute of Technology Computer Science & Engineering MS Aurangabad India

ISBN: (纸本)9798350335095

Clinical trials can make use of healthcare data to find novel treatments, promote health equity, and ultimately enhance patient outcomes. It is undeniable that current and reliable clinical data are crucial for medical advancements for the reason that of the tamper-proof nature, source provenance, and, decentralized ledger blockchain knowledge has in recent times emerged as a crucial constituent for numerous applications, together with healthcare as well as IoT. Health systems may now scale up and keep consistency on a decentralized platform because of the Internet of Things (IoT) as well as Block Chain networks. As a result, numerous researchers have created IoT e-Health systems that are enabled with blockchain and investigated the benefits of blockchain technique in a variety of e-Healthcare domains. In this research, a deeper analysis of the decentralized storage framework for accessing medical details in smart applications is performed to address the demand for privacy, encryption, and security in IoT applications the analysis is made on the basis of methodology, comparative criteria, and metrics. The comparative study of various development platforms for decentralized applications is analyzed using the research papers to provide a technically efficient solution. A focused and flexible idea can be generated by the researchers through the analysis on the different datasets, and techniques available, assessing their benefits in real-time applications. The research will interpret the various methods available in the decentralized framework along with their advantages, disadvantages, and research gaps, which paves a way for the researchers to gain knowledge about the methods available for sharing medical data in smart healthcare applications. © 2023 IEEE.

关键词： Blockchain

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Enhancing Accuracy in Image Recognition for Deep Faked Images using Convolutional Networks Compared with Decision Tree Algorithm 4

Enhancing Accuracy in Image Recognition for Deep Faked Image...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Chanakya, A.S. Surendran, R. Krishnan, Sumathy Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science Engineering Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331540364

This study's main goal is to improve deep fake image identification accuracy by using convolutional neural networks (CNN) and contrasting the outcomes with decision tree algorithms. Examining methods like feature extraction, transfer learning, and model evaluation can help deep fake detection systems become more reliable and effective. A dataset from Kaggle. For training and assessment, a combination of real and deep faked images is used. The dataset contains samples produced using different circumstances and methods for deep fake production. In this work, two different groups were implemented, each using a different algorithmic strategy for image identification. Group 1 utilized CNNs, or convolutional neural networks. Group 2 used the Decision Tree Algorithm, a hierarchical tree-like topology. With 20 samples in each group, there were 40 samples in total. In order to provide sufficient statistical power for further analyses, the sample size was determined. Using ***, an a priori power analysis was performed with the statistical power (G-power) set at 80%. Behavioural science conventions were followed while choosing the significance level (alpha, a) and type II error rate (beta, ß). The values selected were 0.2 and 0.05, respectively. A 95% confidence interval was chosen to assess the precision of the estimated effectiveness of the two algorithmic approaches. The accuracy of the Decision Tree was 79.2%, while the CNNs outperformed it with a 97.36% accuracy. A two-tailed significance test result showing a statistically significant p-value of 0.001 (p © 2024 IEEE.

关键词： Convolutional neural networks

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Movie Recommendation Systems Using Improved Non-Negative Matrix Factorization

Movie Recommendation Systems Using Improved Non-Negative Mat...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Sherly Alphonse, A. Verma, Harshit School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350325188

Currently, there exist effective strategies for providing practical recommendations crucial in many industries, this encompasses a range of online platforms, such as electronic commerce, social networking sites, and various web-based services, as a result of the big data explosion. There are numerous customized movie recommendation systems available today that use publicly accessible movie datasets (like MovieLens) and provide enhanced performance measures (such the Root-Mean-Square Error (RMSE)). The ability to scale and the practicality of usage feedback and verification are determined through real-world implementation and assessment, they are two important difficulties that movie recommendation systems currently struggle with. This research suggests a novel approach using non-negative matrix factorization that resolves these issues through similarity and matrix factorization. Finding two matrices, both consisting of non-negative values, and their resulting product when multiplied together closely resembles the original matrix is the goal of non-negative matrix factorization. Additionally, it places the latent elements under non-negative constraints. The suggested approach offers novel update procedures to discover the latent variables for rating prediction. In contrast to the majority of collaborative filtering techniques, the suggested technique can forecast all the unknowable ratings. Its computational complexity is quite low, and it is simple to implement. Empirical research using the MovieLens shows that the suggested approach is more forgiving of the sparsity and scalability issues and produces good results. © 2023 IEEE.

关键词： collaborative filtering matrix factorization Movie ratings recommendation

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Crop Yield Prediction Using Multimodal Meta-Transformer and Temporal Graph Neural Networks

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 545-553页

作者： Sarkar, Somrita Dey, Anamika Pradhan, Ritam Sarkar, Upendra Mohan Chatterjee, Chandranath Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Industrial and Systems Engineering Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Agricultural and Food Engineering Kharagpur721302 India Indian Institute of Technology Patna Department of Computer Science and Engineering Patna801106 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Crop yield prediction is a crucial task in agricultural science, involving the classification of potential yield into various levels. This is vital for both farmers and policymakers. The features considered for this task are diverse, including weather, soil, and historical yield data. Recently, plant images captured in different modalities, such as red-green-blue, infrared, and multispectral bands, have also been utilized. Most of these data are inherently temporal. Integrating such multimodal and temporal data is advantageous for yield classification. In this work, a deep learning framework based on meta-transformers and temporal graph neural networks has been proposed to achieve this goal. Meta-Transformers allow the modeling of multimodal interactions, while temporayel graph neural networks enable the utilization of time sequences. Experimental results on the publicly available EPFL multimodal dataset demonstrate that the proposed framework achieves a high classification accuracy of nearly 97%, surpassing other state-of-the-art models, such as long short-term memory networks, 1-D convolutional neural networks, and Transformers. In addition, the proposed model excels in accuracy metrics, with a precision of approximately 98%, an F1-Score of 91%, and a recall of 94% in crop yield prediction. © 2024 IEEE.

关键词： Distribution transformers

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Comparative Analysis of Execution of CNN-Based Sanguine Data Transmission with LSB-SS and PVD-SS

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computer Systems science & engineering 2023年第8期46卷 1707-1721页

作者： Alaknanda S.Patil G.Sundari Arun Kumar Sivaraman Department of Electronics and Communication Sathyabama Institute of Science and TechnologyChennaiTamil Nadu600119India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

The intact data transmission to the authentic user is becoming crucial at every moment in the current ***;is a technique for concealing the hidden message in any cover media such as image,video;and audio to increase the protection of *** resilience and imperceptibility are improved by choosing an appropriate embedding *** paper gives a novel system to immerse the secret information in different videos with different *** audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user’s face image.A hidden message is first included in the audio from the multimedia file;using LSB *** Stego-video is created in the second stage by merging the authorized user’s face into the frame of the video;by using PVD ***-audio is linked again with the stego-video in the third *** incorporated perspective techniques(LSB-SS and PVD-SS algorithms)with more significant data immersing capacity,good robustness and imperceptibility are proposed in this research *** spread spectrum approach is used to increase the complexity of secret data *** different video files are tested with different voice files with the results such as PSNR,SSIM,RMSE and MSE as 52.3,0.9963,0.0024 and 0.0000059,respectively.

关键词： Audio steganography data hiding information security pixel value differencing

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Real-time XSS Vulnerability Detection 3

Real-time XSS Vulnerability Detection

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Roy, Ritocheta Mandal, Siddharth Kavya, Saguturu Dedeepya, Seetha Sai Singh, Alpana Reddy, Elluri Lokesh Patil, Rujula Nitin Ramamoorthi, Jaya Subalakshmi Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350338607

In today's world, web application attacks have become a major concern as they can lead to unauthorized access to important information and even disruption of online services. In the context of web application attacks, one example is XSS (Cross-site Scripting) attack. These attacks are dangerous because they can steal important information like passwords and credit card details or take users to harmful websites without their knowledge. Therefore, in this paper, a plugin has been designed to classify the websites as vulnerable and alert users on not giving data to the respective page to prevent any data loss or potential cyber-attack. This method produces a true negativity of 74.49%, a true positivity of 8.16%, false negativity of 2.04%, and false positivity of 15.31%. © 2023 IEEE.

关键词： Websites

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