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A Sustainable Privacy-Preserving Aggregation Authentication Protocol for Smart Grid 1

2nd International Conference on Smart Grid Energy Systems and Control, SGESC 2023

作者： Chaudhary, Dharminder Soni, Tanmay Singh, Soumyendra Durgarao, M.S.P. Gupta, Surisetty Mahesh Chandra Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9789819976300

ISBN: (纸本)9789819976294

Smart grid (SG) is an advanced framework for controlling computers, automation, new technologies, and smart equipment that is more efficient and dependable. Smart grid possesses advanced self-healing capabilities and enables electricity customers to become active participants. Both security and privacy are two very important attributes of SG communication. The proposed model is efficient for communication between smart grids which uses a privacy-preserving aggregation mechanism. The model structure uses super increasing sequences to organize data and then encrypts it using the homomorphic Paillier approach. Data aggregation is done directly on cipher at local gateways without following any decryption for data transfers from users to smart grid operation center. The aggregate outcome of the source data may be accessed at the operation center that saves time. Finally, our findings demonstrate that the proposed method supports batch verification that makes it cost-effective in terms of computing and communication as compared to existing architectures. The proposed design is fast symmetric encryption, and it is suitable for massive user groups, and it can be adopted to support the flexible and rapid growth of residential scales in smart grids. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Smart power grids

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Premature Birth Prediction Using Machine Learning Algorithms - A Comparative Analysis 7

Premature Birth Prediction Using Machine Learning Algorithms...

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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Padmavathi, A. Maurya, Laxmi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350350067

Premature birth (PB) presents a significant global health challenge, often leading to enduring developmental issues and neonatal mortality. Approximately 4-16% of new-born worldwide is born prematurely. Leveraging machine learning, a predictive model has been developed to anticipate preterm birth risk. This model evaluates maternal characteristics and other pertinent factors to provide timely alerts to healthcare providers. Through the analysis of extensive datasets, the system offers valuable insights for forecasting and classifying preterm birth risks. First and foremost, this study aims to enhance prenatal care and outcomes for high-risk pregnancies by harnessing artificial intelligence to refine the accuracy and promptness of preterm birth prediction. Employing machine learning algorithms, our study achieves a remarkable 99% accuracy rate throughout all the techniques. © 2024 IEEE.

关键词： Contrastive Learning

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MBTI Based Personality Prediction using Social Media Data 15

MBTI Based Personality Prediction using Social Media Data

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Prasanna Kumar, R. Bharathi Mohan, G. Rithani, M. Pallavi, Gundala Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Existing methods for assessing an individual's personality are very cumbersome due to those tests being very monotonous and erroneous due to a self-report bias. Whereas data-driven, automatic personality deduction raises privacy concern due to them needing external personal information. In our model, a person's personality can be classified based on the 16 categories of Myers-Briggs personality types. With the availability of a huge amount of data on human behavior and personality traits, a machine learning model can be trained, and it can predict the personality trait of a person. A MBTI dataset is used to analyze and train the model. Upon analyzing the dataset, we found out that the classes are not balanced. To overcome this problem, we used binary classification. Next, we used the NLTK library to pre-process the data. We selected four machine learning models and trained our data in those models. Furthermore, we compared the results of four different machine learning models and chose the most accurate model. This research can be used to personalize advertisements, coaching for students and companies can use this research to attract specific types of audience. © 2024 IEEE.

关键词： Logistic regression Myers-Briggs personality types Naïve Bayes NLTK Personality prediction Random Forest SVM

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Deepfake Image Forgery Detection using Local Feature Descriptors 15

Deepfake Image Forgery Detection using Local Feature Descrip...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dienash, S. Swapna, T.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Deepfake Image Forgery,generates fake images after manipulating the original source images. This increases the risk of losing trust in images or videos. This paper proposes an efficient methodology to detect Deepfake images from real images. The methodology involves extracting Local Feature De scriptors(Local Binary Pattern (LBP),Frequency Decoded Local Binary Pattern (FDLBP),Binary Gabor Pattern (BGP)). The fusion of these features are fed to the DenseNet to classify the image as fake or real. The model uses Convolutional Neural Network (CNN) architecture that utilizes all three Local Feature Descriptors that extract features of the images. The model has an accuracy of 93.65% which is performing better than Deep learning models. © 2024 IEEE.

关键词： Binary Gabor Pattern (BGP) DenseNet Frequency Decoded Local Binary Pattern (FDLBP) Local Binary Pattern (LBP) Local Feature Descriptors (LFD)

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Applications of Clustering in Cognitive Radio Networks 15

Applications of Clustering in Cognitive Radio Networks

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Joykutty, Anu Maria Baranidharan, B. Rajagiri School of Engineering & Technology Department of Computer Science & Engineering Kochi India SRM Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331300579

With the use of cognitive radio (CR) technology, wireless devices can utilize licensed spectrum when it is unused by the licensed users. CR was developed as a response to the wireless spectrum shortage issue caused by the static spectrum allocation. Using CR technology, the unlicensed users can opportunistically access the spectrum during the absence of usage by the licensed users. Clustering is a widely used technique in Cognitive Radio Networks (CRNs) to efficiently manage network resources, optimize spectrum utilization, and enhance overall network performance. This study focuses on the applications of clustering techniques in the area of CRNs like spectrum management, QoS Support, topology management, energy efficiency, routing etc. © Grenze Scientific Society, 2024.

关键词： Cognitive radio

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A Machine Learning Approach for Credit Card Fraud Detection in Massive Datasets Using SMOTE and Random Sampling 6

A Machine Learning Approach for Credit Card Fraud Detection ...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sreenivas Prasad, B. Akash Babu, N. Reddy, Harthikeswar Singh, Rimjhim Padam Kanchan, Sneha Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering & Computer science Malaysia

ISBN: (纸本)9798350381689

The surge in digital transactions has paved the way for an alarming rise in credit card fraud, compelling the need for robust detection systems. The swift progress of technology has transformed customer payment habits, driving them toward a cashless society, with the rise of digital payments making it easier for fraudsters to commit fraud. The impact of credit card fraud on revenue can be significant. No matter the scale of the businesses, credit card fraud will have a significant effect on the businesses. This paper addresses the escalating threat by proposing a fraud detection model utilizing the 'IEEE CIS Credit Fraud Detection' dataset from Kaggle, constructing a machine learning model aimed at enhancing the accuracy of fraudulent transaction identification, presenting an integrated approach to credit card fraud detection that combines user separation and innovative techniques for improved accuracy and real-world impact, ultimately strengthening the global financial ecosystem. © 2024 IEEE.

关键词： Electronic money

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Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis

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Tsinghua science and Technology 2023年第5期28卷 952-964页

作者： Shan Cui Tao Zhu Xiao Zhang Liming Chen Lingfeng Mao Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Computer School University of South ChinaHengyang 421001China School of Computing Ulster UniversityBelfastBT151APUK

Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily *** uses sensor devices to collect user behavior data,obtain human activity information and identify *** Logic Networks(MLN)are widely used in HAR as an effective combination of knowledge and *** can solve the problems of complexity and uncertainty,and has good knowledge expression ***,MLN structure learning is relatively weak and requires a lot of computing and storage ***,the MLN structure is derived from sensor data in the current *** that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules,MLN structure can be *** this end,we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN *** a rulebase can reduce the time required for MLN structure *** apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning *** addition,we evaluate the parameters of the rulebase building scheme to check its stability.

关键词： Markov Logic Network(MLN) structure learning rulebase construction probabilistic latent semantics

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Semantic Analysis Using Deep Learning for Predicting Stock Trends 2

Semantic Analysis Using Deep Learning for Predicting Stock T...

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

作者： Galphade, Manisha Nikam, V.B. Yedurkar, Dhanalekshmi Singh, Prabhishek Stephan, Thompson Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Improving energy efficiency in scalable WSNs through IoT-driven approach

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International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Singh, Omkar Kushwaha, Ajay Shriram Sharma, Col. Rahul Kumar, Lalit National Institute of Fashion Technology Bihar Patna India Department of Computer Science & Application Sharda School of Computing Sciences & Engineering Sharda University UP Greater Noida India School of Computer Science & Engineering ILM University Greater Noida India

A novel QoS-Driven Optimization for Energy-Efficient Clustering Routing (QDOEECR) protocol tailored for IoT-based Wireless Sensor Networks (WSNs), addressing the critical challenge of energy efficiency in sensor nodes with limited battery life. The protocol’s innovative approach lies in its advanced techniques for optimizing the selection of sub-sink nodes and identifying the optimal path to the sink node, ensuring improved adherence to Quality of Service (QoS) standards. Performance evaluations, conducted through MATLAB simulations, demonstrate the QDOEECR protocol's superior performance, achieving significant advancements over existing routing protocols. These include 30–35% reduction in end-to-end delay, 25–30% improvement in throughput, 35–40% decrease in energy consumption, 28–32% reduction in packet loss, and 32–38% extension in network lifetime. These results establish QDOEECR as a groundbreaking solution that not only enhances energy efficiency but also elevates network performance metrics beyond the capabilities of current state-of-the-art protocols like CBCCP, HCSM, and ZEAL. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： CBCCP HCSM IoT QDOEECR WSN ZEAL

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Segmenting the Brain Tumor using Deep Learning Techniques 3

Segmenting the Brain Tumor using Deep Learning Techniques

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pranitha, Ch. Lakshmi Sumathi, R. Harshitha, V. Kavya, N. Soni, A. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331509675

This study presents a U-Net architecture with EfficientNet-B7 as the feature extraction backbone is used to propose a robust brain tumor segmentation method. Using cutting-edge deep learning techniques, the suggested model precisely segments MRI images with an emphasis on identifying and defining tumor anomalies. A thorough preprocessing pipeline that handles missing values, augments data with albumentations, and normalizes data guarantees the consistency and resilience of the incoming data. The segmentation procedure uses a hybrid loss function that combines Soft Dice Loss and Binary Cross-Entropy (BCE), improving the model's ability to strike a compromise between segmentation consistency and pixel-level precision. To ensure efficiency and generalizability, the model training is optimized using the Adam optimizer in conjunction with a learning rate scheduler. Additionally, an early halting mechanism is incorporated to minimize overfitting. Using a standardized dataset, performance evaluation is carried out with 70%, 15%, and 15% splits for training, validation, and testing, respectively. To evaluate the quality of segmentation, important metrics are employed, such as the Dice Coefficient and the Intersection over Union (IoU). The accuracy of the model in identifying tumor boundaries is illustrated by visualizations of the anticipated segmentation results. The potential of combining U-Net with cutting-edge feature extractors such as EfficientNet-B7 to produce dependable, effective, and clinically significant segmentation results in medical imaging is demonstrated by this approach. © 2025 IEEE.

关键词： Deep learning

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