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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Behera, Sagarika Kolipakula, Nikhita Swetha, P. Yogish, Yogish Prathuri, Jhansi Rani Cmr Institute of Technology Dept. of Computer Sc. and Engineering Bengaluru India

ISBN: (纸本)9798350334395

Text classification is a fundamental activity in natural language processing (NLP), that includes classifying text resources into predetermined groups. With the growing concerns over data privacy and confidentiality, there is a need to perform text classification on encrypted data to protect sensitive information. In recent years, homomorphic encryption has emerged as a powerful technique that makes computations possible on encrypted data without compromising its confidentiality. This paper provides an overview of text classification using machine learning algorithms on encrypted data. In this research work, we have taken a data set of movie reviews. The data set is cleaned by removing stop words and performing stemming, lemmatization, and tokenization. We utilized a semantic model with inverse document frequency weighting and term frequency weighting to represent the data (TF-IDF). For the classification of the data set, we have applied six machine learning algorithms Logistic Regression, Naïve Bayes, KNN, Support Vector Machine, Random Forest, and Decision Tree. The plain text is encrypted using a somewhat homomorphic encryption technique called the RSA method. The ciphertext is subjected to the machine learning algorithms mentioned above. We have compared the results for accuracy. The Random Forest algorithm was found to give 75.56% accuracy compared to other algorithms on encrypted data. In this work, we have calculated the time taken by each algorithm for execution on plain text and ciphertext. It was observed that the Random Forest method took more time and the KNN method took less time comparatively. © 2023 IEEE.

关键词： Logistic regression

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A Comprehensive Study on Traffic Sign Detection in ITS

A Comprehensive Study on Traffic Sign Detection in ITS

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Purwar, Surabhi Chaudhry, Rashmi Netaji Subhas University of Technology Dept of Computer Science & Engineering New Delhi India

ISBN: (纸本)9798350323887

Intelligent transportation system (ITS) is comprised of several component like Commuter information, Traffic management, vehicle info, public safety etc. To ensure the smooth flow of traffic, the recognition and apprehension of traffic signs are fundamental building blocks of the system. In real time scenario this system Driver Personal Assistant (DAP) can assist driver to travel in stress-free environment and which is one of the reason researchers are engrossed for expansion of such type algorithms in this direction. Due to various symbols of traffic sign images in various countries brought up by shifting perspectives, motion blur, lighting, and other parameters, accurate detection is a big challenging. Several techniques comprise of Artificial Intelligence, Machine learning and Deep Learning has been established in real time span for the enhancement of object detection and recognition of traffic sign. The content is potential for researchers' association and has developed meaningful interest in several studies on optics -based TSDR (detection and Recognition of Traffic Sign). Three major component detection, Recognition, and classification combined in any algorithm make the system useful for deployment in real time. Variety of techniques had been communicated over a period. All-inclusive brief summary for traffic sign detection is presented in this paper. In addition to this exhaustive summary of algorithms, on detection, recognition and classification are investigated and grouped in tabular representation. An algorithm based on CNN has been implemented and shows better accuracy and less percentage loss in detection of GTSRB traffic sign data set. © 2023 IEEE.

关键词： Object detection

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An Overview on Breast Cancer Detection & Classification Methods Using Machine Learning Techniques 3

An Overview on Breast Cancer Detection & Classification Meth...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Philip, Rohan Mathew Raj, Rishabh K Shaji, Ashwin Althaf, Fathima Sudarsan, Dhanya Muthoot Institute of Technology and Science Dept. of Computer Science & Engineering Kerala India

ISBN: (纸本)9798350322842

Breast cancer is a prevalent and deadly disease that primarily affects women. Breast cancer has claimed the lives of many people all over the world. Early breast cancer detection, on the other hand, can significantly improve a woman's chances of survival. There are various approaches and procedures for detecting cancer in breast tissue. Breast cancer may be detected using Image Processing, Machine Learning, and Deep Learning methodologies and techniques. People who use trustworthy techniques and make better selections will be able to discover breast cancer in its early stages and save a woman's life. This research aims to contemplate and make inferences from diverse studies on the subject of classification using Machine Learning approaches. The researchers and physicians who devote their time and expertise to enhancing breast cancer screening methods will definitely profit from this research study. © 2023 IEEE.

关键词： Convolutional neural networks

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Zero Cost Online Food Delivery System with Machine Learning Prediction 7

Zero Cost Online Food Delivery System with Machine Learning ...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Reddy, Yaparla Adithya Kumar Swaroop, Chebrolu Satya Terence, Sebastian Reddy, Kummetha Sai Manoj Santhosh, Korrapati Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

The growth of Food Delivery Application (FDA), helps customers to get variety of food items in their door steps. This helps restaurant to serve their customers with different food items, and to increase their business. FDA also provides jobs to millions of food delivery persons. But the major drawback of FDA is high commission charges for food orders. This article presents zero cost online food delivery system with ML prediction models. Zero cost online food delivery system provides user friendly interface to restaurant, home makers, customers and delivery persons. In this system, the restaurant or home makers can showcase their food items. The customers would be able to place the order. Delivery person can show interest towards delivery of the order. The customers have an option to select the delivery person for delivery and pay the delivery charges. All the payments are done through UPI payment. Advantage of the system is, it will not collect commission from home makers (or) restaurant owners and customers for the orders and also Machine Learning models is used to suggest food items based on different factors. Random Forest, Decision Tree Regression, K-Nearest Neighbor, and Extra Tree Classifier models are used to suggest food items to the customer. © 2023 IEEE.

关键词： Decision trees

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A Review on Dam Water Level Alerting System Using IoT

A Review on Dam Water Level Alerting System Using IoT

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2023 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2023

作者： Dudhe, Priyanka Magarde, Gaurav Raj, Madan Karemore, Aditya Wankhede, Jayesh Jhulelal Institiue of Technology Dept. of Computer Science Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350398748

The goal we outline in this article is the application system with Internet of Things (lOT) integration to secure the public's safety regarding the early warning of flood events caused by a rise in water levels in the dams and reservoirs. To accomplish the goal, a Software-as-a-Service is upheld, incorporating information about the area and data from periodic water level monitoring using wireless technologies and Global System for Mobile Communication (GSM). As a result, the public is automatically informed of the earlier stages of the water level rise. © 2023 IEEE.

关键词： Water levels

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Intrusion Detection System using Ensemble Learning Analytics 8

Intrusion Detection System using Ensemble Learning Analytics

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Dasari, Anil Kumar Biswas, Saroj Kr. Sanyal, Saptarsi Purkayastha, Biswajit National Institute of Technology Silchar Dept. of Computer Science & Engineering Silchar India

ISBN: (纸本)9798350334012

An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect attacks using conventional techniques like a virus detection system, misuse detection software i.e. the database of attack signatures that it uses to compare packets. Despite the researchers' significant efforts, IDS still struggles to identify new intrusions, to improve detection accuracy, and to reduce false alarm rates. To overcome the problems mentioned above this paper proposes an unique model named Intrusion Detection System using Machine Learning Analytics (IDSMLA), which uses SMOTE oversampling technique to deal with class imbalance problem, it also uses Minimum Redundancy Maximum Relevance (mRMR) to perform feature selection as feature selection reduces time complexity by eliminating irrelevant features and hence increasing the accuracy of the model and finally to perform classification task, the proposed model IDSMLA uses Extra Trees(ET) bagging ensemble technique. The performance of the proposed model IDSMLA is measured using accuracy and F1-score using 10-folds cross validation. Experimental results have demonstrated that the proposed model IDSMLA greatly outperforms different single-classifier based models, different ensemble models as well as different models present in literature. © 2023 IEEE.

关键词： Intrusion detection

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Rice Category Identification through Deep Transfer Learning Features and Machine Learning Classifiers: An Intelligent Approach

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第7期51卷 765-784页

作者： Terlapu, Panduranga Vital Prasan, U.D. Kumar, T. Ravi Bendalam, Vijaya Pappu, Sasibhushana Rao Rao, M. Jayanthi Mohitha, Maddula Ratna Jayaram, D. Dept. of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Sasibhushana Rao Pappu is an Assistant Professor of Dept. of Department of Computer Science and Engineering GITAM Andhra Pradesh Visakhapatnam India Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Department of Information Technology Chaitanya Bharathi Institute of Technology Gandipet Telangana Hyderabad500075 India

Rice category identification by image analysis is essential to ensure the quality and safety of rice production. In this study, we propose an intellectual approach to improve rice category identification using deep transfer learning features and machine learning classifiers. Specifically, we extracted features from three pre-trained models (Inception V3, VGG-19 and VGG-16) using transfer learning techniques. These were used as inputs to train MultiLayerPerceptron (MLP) and support vector machine (SVM) classifiers. The results of our experiments show that the proposed strategic results achieve high accuracy in identifying rice categories. The SVM (polynomial kernel) achieves the second-highest accuracy among all models and features, with an accuracy of 0.9948 using the VGG-19 and 0.9912 using Inception V3. The MLP classifier with (30 30) hidden layers achieve the first high accuracy, with an accuracy of 0.9972 (99.72%) using VGG-19 features. The results also show that the choice of deep transfer learning model and machine learning (ML) classifier can significantly affect the accuracy of rice category identification. Among the three pretrained models, VGG-19 features consistently perform the best, followed by Inception V3 and VGG-16. The choice of MLP hidden layer size also affects the accuracy, with 30 HL neurons achieving the best performance. Our proposed approach using deep TL features and ML classifiers shows promising results in improving rice category identification. Our study provides valuable insights into optimizing ML models for agricultural image analysis. © (2024) International Association of Engineers. All rights reserved.

关键词： Support vector machines

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A Solid Waste Management System Using Smart Bins in a Decentralized Manner in Ethereum Blockchain Network for Incentivization 2

A Solid Waste Management System Using Smart Bins in a Decent...

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2nd International Conference on Computational Systems and Communication, ICCSC 2023

作者： Amitha, A.K. Pamba, Raja Varma LBS Institute of Technology for Women Dept.of Computer Science and Engineering Thiruvananthapuram India

ISBN: (纸本)9781665493932

Waste generation in the world is raising in a huge way that the requirement to devise new solutions is crucial. The Solid Waste Management, SWM finds it difficult to handle manually these waste generated day by day, as a result of urbanization. With the advent of the technologies, it is possible to find some really good solutions for the waste segregation, waste collection, tracking, and documenting on the entire process. In this paper, we propose an automated solid waste management system by bringing the IoT and block chain technologies together. Here the data related to the bin is collected and is passed to the blockchain smart contracts that can automate incentives. The system enables waste segregation, which will ease the further procedures in SWM. Smart contract is deployed in the public blockchain Ethereum network platform and the automated reward based on the weight and type of the waste through an API is proposed in this paper. © 2023 IEEE.

关键词： Ethereum

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An Artificial Intelligence Approach in 5G New Radio Beam Enhancement 3

An Artificial Intelligence Approach in 5G New Radio Beam Enh...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Upadhyay, Deepak Mittal, Saksham Jain, Ayushi Sharma, Ravi Bagla, Piyush Tripathi, Neha Tiwari, Pallavi Graphic Era Hill University Dept. of Computer Science & Engineering Uttarakhand Dehradun India National Institute of Technology Computer Science & Engineering Dept. Punjab Jalandhar India Dept. of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350300710

The paper presents a novel application of artificial neural networks in the context of 5G new radio beamforming. The research leverages the intricate mechanism of ANNs to demonstrate the efficacy of this approach in optimizing the beamforming process and improving the overall performance of the 5G new radio system. The utilization of ANNs allows for real-time adaptation and decision-making, thereby mitigating the limitations of conventional beamforming techniques. The results of the study are quite promising, indicating a substantial enhancement in the accuracy and efficiency of beamforming in 5G new radio. The proposed method is expected to have a significant impact on the development of advanced wireless communication systems. © 2023 IEEE.

关键词： Beamforming

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TL-CL: Task And Language Incremental Continual Learning

TL-CL: Task And Language Incremental Continual Learning

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Satapara, Shrey Srijith, P.K. Dept. of Artificial Intelligence Indian Institute of Technology Hyderabad Hyderabad India Dept. of Computer Science and Engineering Indian Institute of Technology Hyderabad Hyderabad India

ISBN: (纸本)9798891761643

This paper introduces and investigates the problem of Task and Language Incremental Continual Learning (TLCL), wherein a multilingual model is systematically updated to accommodate new tasks in previously learned languages or new languages for established tasks. This significant yet previously unexplored area holds substantial practical relevance as it mirrors the dynamic requirements of real-world applications. We benchmark a representative set of continual learning (CL) algorithms for TLCL. Furthermore, we propose Task and Language-Specific Adapters (TLSA), an adapter-based parameter-efficient fine-tuning strategy. TLSA facilitates cross-lingual and cross-task transfer and outperforms other parameter-efficient finetuning techniques. Crucially, TLSA reduces parameter growth stemming from saving adapters to linear complexity from polynomial complexity as it was with parameter isolation-based adapter tuning. We conducted experiments on several NLP tasks arising across several languages. We observed that TLSA outperforms all other parameter-efficient approaches without requiring access to historical data for replay. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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