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Privacy BERT-LstM: a novel NLP algorithm for sensitive information detection in textual documents

Neural Computing and Applications 2024年第25期36卷 15439-15454页

作者： Muralitharan, Janani Arumugam, Chandrasekar Department of Information Technology St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India

In this modern digital era, the increasing volume of textual data and the widespread adoption of natural language processing (NLP) techniques have presented a critical challenge in safeguarding sensitive privacy information. As a result, there is a pressing demand to design robust and accurate NLP-based techniques to perform efficient sensitive information detection in textual data. This research paper focuses on the detection and classification of sensitive privacy information in textual documents using NLP by proposing a novel algorithm named Privacy BERT-LstM. The proposed Privacy BERT-LstM algorithm employs BERT for obtaining contextual embeddings and LstM for sequential information processing, facilitating efficient sensitive information detection in textual documents. The BERT with its bidirectional characteristics captures the nuances and meaning of the textual documents, while the LstM derives the long-range dependencies in the textual data. Moreover, the proposed Privacy BERT-LstM algorithm with its attention mechanism highlights the important regions of the textual documents, contributing to efficient sensitive information detection. The comprehensive performance evaluation is conducted by employing the sMs spam Collection dataset in terms of standard performance metrics and comparing it with different state-of-the-art techniques, namely, CAssED, PRIVAFRAME, CNN-LstM, Conv-FFD, GCsA, TsIIP, and, C-PIIM. The experimental outcomes clearly illustrate that the Privacy BERT-LstM algorithm demonstrates superior performance in identifying various types of sensitive information by achieving an accuracy of 92.50%, F1-score of 85.02%, and Precision of 89.36%. The proposed algorithm outperforms existing baseline models, providing valuable advancements in sensitive information detection using NLP. Therefore, this research contributes to the advancement of privacy protection in NLP applications and opens avenues for future investigations in the domain of sensitive info

关键词： Classification (of information)

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Detection and Prevention of Tomato Leaf Disease using Convolutional Neural Network and Inception Net 3

Detection and Prevention of Tomato Leaf Disease using Convol...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and signal Processing, INCOs 2024

作者： Ajitha, E. Nivedha, V. Parvathi, B. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350361186

A state-of-the-art technique for analyzing images that is still relatively new but yields consistent results is Deep Learning(DL). Many DL techniques are used for leaf disease categorization. Regardless of the cuisine, tomatoes are a staple in every kitchen and are one of the most popular vegetables. It ranks third in terms of widespread production, behind sweet potatoes and potatoes. The number and quality of tomato crops are, however, impacted by several illnesses. An approach to crop disease diagnosis by deep learning is used. For disease categorization and detection, a Convolutional Neural Network is employed. Two pooling and two Convolutional layers are employed inside the model. The suggested model beat pre-trained InceptionV3, and VGGI6, according to the experiment findings. 98% training accuracy and 88.17% testing accuracy were attained by the CNN model. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT-based Food Quality Monitoring system Using Machine Learning 2

IoT-based Food Quality Monitoring System Using Machine Learn...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vasanthi, R. Ashika, K. Archana, D. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365269

One of the main considerations in preventing food waste is food safety and cleanliness. Foods should not be allowed to deteriorate or rot due to environmental elements including humidity, temperature, and darkness. The quality of the food should also be regularly checked. Deploying quality monitoring systems is beneficial due to the following reasons:. These food quality monitoring systems keep an eye on the external elements influencing food quality. Factors such as vacuum storage, refrigeration, and others later kept the items from spoiling. More specifically, the sensors in this system - which measure temperature, pH, humidity, gas, and DHT - provide the vital data required to assess the condition of the product that is being packed or kept. Leveraging Internet of Things (IoT) technology involves transmitting data to a computer system. subsequently, this system displays a user interface that tracks the product's evolution over time. The goal of the project is to enhance food quality assessment by employing machine learning algorithms. © 2024 IEEE.

关键词： Internet of things

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Real-Time sentiment Analysis Pipeline for Yelp Customer Feedback using ChatGPT’s LLM and Distributed systems 6

Real-Time Sentiment Analysis Pipeline for Yelp Customer Feed...

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6th International Conference on Mobile Computing and sustainable Informatics, ICMCsI 2025

作者： Revathy, s. shahid, J. Mohammad salih, s. Computer Science and Engineering St.Joseph’s Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

This work proposes a real-time sentiment analysis pipeline on customer feedback using Yelp and addresses the high-volume dynamic user-generated contents processing problem. The proposal integrates state-of-the-art machine learning models and distributed systems, making use of Large Language Models within a Docker container called ChatGPT for the real-time sentiments analysis. The system ingests live Yelp data streams through TCP/IP, processes them in a scalable Apache spark cluster, and ensures that data flows seamlessly through Apache Kafka. Enriched feedback is indexed into Elasticsearch, providing powerful analytics and visualizations through tools like Kibana, Tableau, and Power BI. The low-latency design of the pipeline gives enterprises actionable insights for real-time decision-making, enabling enhanced customer sentiment analysis and business intelligence. scalable and efficient, this architecture fits all large volume Enterprise Feedback Management (EFM) needs when looking to achieve insights fast from customer sentiment. © 2025 IEEE.

关键词： sales

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Energy-Efficient Clustering in Wireless sensor Networks: A Multi-Objective Approach Using PsO and Fuzzy Logic 4

Energy-Efficient Clustering in Wireless Sensor Networks: A M...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIs 2024

作者： sabaresan, V. Thanigai selvan, M. Rajkumar, s. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

Energy efficiency is one of the biggest challenges in WsNs;typically, these networks are plagued with several problems such as uneven distribution of energy amongst nodes, hotspots, and clustering with big disparities. This work tries to some extent solve some of these problems through a prototype: Efficient Wireless sensor Network with Multi-Objective Clustering, or EEMOC, which is a new framework that exploits the power of Particle swarm Optimization to realize optimal cluster head selection. In addition, it uses the FIs in the dynamic selection of a cluster radius to maximize its effectiveness in networking. EEMOC exploits the fuzzy logic benefits in governing uncertainty in data and optimizes the sizing of clusters, thereby controlling hotspots and optimizing the energy distribution scheme. simulation results show EEMOC to be far superior to traditional clustering algorithms and may significantly extend the network lifetime with the utmost saving in energy under various operating conditions. The technique which integrates PsO with FIs provides a robust platform for improving WsN performance. © 2024 IEEE.

关键词： Particle swarm optimization (PsO)

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Enhancing Medical Visual Question and Answering system 3

Enhancing Medical Visual Question and Answering System

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3rd International Conference on Automation, Computing and Renewable systems, ICACRs 2024

作者： Keerthana, s.M. santhosh, s. sethuanand, K. Department of Computer Science and Engineering St.Joseph’s Institute of Technology Chennai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532420

This project tackles fundamental weaknesses of the current versions of the Medical VQA system: namely, low accuracy in the interpretation of complex medical images and ambiguity in query handling. Its developed version, built with CNNs and RNNs, tends to be highly dependent on heavy labeled datasets with difficulties in data processing of high dimensionality, so it has very limited applicability in clinical settings. Our proposed system, integrating ManualNet, GoogLeNet, and MobileNet, would extract more specific features to be utilized with the Transformer-based models including BERT and GPT for the advanced question processing. The application of attention mechanisms along with data augmentation increases the alignment between vision and text and even better in dealing with the problem of data scarcity. This solution should enhance accuracy, adaptability, and efficiency in Medical VQA systems which ensure more reliable diagnostic support and therefore better care for patients. Thus, this project exhibits an extremely robust AI-driven approach toward solving the current challenges and progression of the practical application of Medical VQA in the healthcare field. © 2024 IEEE.

关键词： Question answering

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Multilevel Augmentation for Identifying Thin Vessels in Diabetic Retinopathy Using UNET Model

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Intelligent Automation & soft Computing 2023年第2期35卷 2273-2288页

作者： A.Deepak Kumar T.sasipraba Faculty of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India

Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different methods and *** Learning(ML)techniques are used for analyz-ing the images andﬁnding out the location of the *** restriction of the ML is a dataset size,which is used for model *** problem has been overcome by using an augmentation method by generating larger datasets with multidimensional *** models are using only one augmentation tech-nique,which produces limited features of dataset and also lacks in the association of those data during DR detection,so multilevel augmentation is proposed for *** proposed method performs in two phases namely integrated aug-mentation model and dataset correlation(***).It eliminates overﬁt-ting problem by considering relevant *** method is used for solving the Diabetic Retinopathy problem with a thin vessel identiﬁcation using the UNET *** based image segmentation achieves 98.3%accuracy when com-pared to RV-GAN and different UNET models with high detection rate.

关键词： Image segmentation diabetic retinopathy image augmentation semantic segmentation CNN

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mLBOA-DML: modified butterfly optimized deep metric learning for enhancing accuracy in intrusion detection system

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Journal of Reliable Intelligent Environments 2023年第3期9卷 333-347页

作者： Prabhakaran, Varun Kulandasamy, Ashokkumar Department of Computer Science and Engineering St.Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Intrusion detection is a prominent factor in the cybersecurity domain that prevents the network from malicious attacks. Cloud security is not satisfactory for securing the user’s information because it is based on standard protocols. Hence, cloud users cannot fully trust the security offered by cloud service providers. The state-of-the-art techniques create clusters for classes and manually label the unknown classes to detect novel attacks. This notion binds the network traffic associated with each attack together and drifts the similarity between the same attacks. These techniques are often prone to errors and degrade performance. To overcome this drawback, various researchers have developed different intrusion detection system which relies on specific attack patterns to distinguish between normal and abnormal behavior. This paper presents a modified Lagrange interpolated Butterfly optimization algorithm-based deep metric learning (mLBOA-DML) architecture for intrusion detection to detect both host-based and network attacks. DML architecture parameters are optimized utilizing mLBOA algorithm via its global optimization capability for increasing the accuracy of attack prediction. DML algorithm does both feature extraction and classification processes. When evaluated using the UNsW-NB15 and NsL-KDD datasets, the proposed model offers improved accuracy near 99% for both datasets. © 2023, The Author(s), under exclusive licence to springer Nature switzerland AG.

关键词： Intrusion detection

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Early Detection of Pancreatic Cancer using Machine Learning Algorithm 5

Early Detection of Pancreatic Cancer using Machine Learning ...

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5th International Conference on Mobile Computing and sustainable Informatics, ICMCsI 2024

作者： Bhoomika, B.K. Kanmani, K. smilarubavathy, G. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350395235

Pancreatic cancer is very challenging since it has poor results when it is diagnosed at the later stages, and proper testing tools are lacking to treat patients. Endoscopic harmonic ultrasound imaging is used to characterize pancreatic masses. For detecting the pancreatic cancer machine learning techniques are mostly used. The main aim of this work is to compare and explain how NN and logistic algorithms give better results when it works with ensemble ML algorithms for diagnosing pancreatic cancer. The datasets are collected through the Kaggle platform. When compared to related work from the literature, it is shown that the ANN approach with a logistic algorithm achieved 90.50% accuracy from another machine learning algorithm. © 2024 IEEE.

关键词： Diseases

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smart shoes for Fitness and Performance Analysis of sportsmen 5

Smart Shoes for Fitness and Performance Analysis of Sportsme...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： selvan, shirley Jayaraman, srimathi Varadharajan, srinivedhini Rajendran, Madhumitha Palanivel, Elaveni Kumaresan, Jaspin Electronics and Communication Engg St.Joseph's College of Engineering Chennai India Computer Science and Engineering St.Joseph's Institute of Technology Chennai India

ISBN: (纸本)9798350383522

This work proposes smart wearable technology used in today's world to benefit and improve our country's sports. In any sport, an athlete must be both physically and mentally strong. We have come up with a smart shoe-based application that monitors athlete's foot temperature, heart rate, spO2 levels, and posture levels. The voltage created by the force applied by the legs generates electrical energy, which is detected by piezoelectric sensors. Foot temperature is measured with the LM35 temperature sensor, posture level with the ADXL345 accelerometer, heart rate, and spO2 with the MAX30100 sensor. Heart rate and spO2 values are displayed on the OLED display. The sensor is connected to an ADC module, from which digital data is sent to the node MCU. Digital data transmitted by the MCU node is received via Bluetooth module on the player's and coach's smartphones (Android application) for self-management. This hardware-based application is designed to monitor fitness and to provide a summary to the sportsman and his coach. © 2024 IEEE.

关键词： Android Application Fitness monitoring Node MCU self-management sportsmen

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