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Review and analysis for state-of-the-art NLP models

International Journal of Systems, Control and Communications 2023年第1期15卷 48-78页

作者： Dikshit, Subrit Dixit, Rahul Shukla, Abhiram Department of Computer Science and Engineering Indian Institute of Information Technology Pune India Department of Civil Engineering Indian Institute of Technology Kanpur India

Natural language processing (NLP) is an area of research and study that makes it possible for computers to comprehend human language by utilising software engineering concepts from computer science and artificial intelligence. This work presents seven classifications and 21 state-of-the-art models with a survey of the operating principles. We also provide a comparative study of all models based on metrics such as accuracy, F1 score, exact match, squad scores, glue and superglue dataset scores. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Artificial intelligence

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Explicable knowledge graph (X-KG): generating knowledge graphs for explainable artificial intelligence and querying them by translating natural language queries to SPARQL

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International Journal of Information technology (Singapore) 2024年第3期16卷 1605-1615页

作者： Shaikh, Numair Chauhan, Tavishee Patil, Jayesh Sonawane, Sheetal Department of Computer Engineering SCTR’s Pune Institute of Computer Technology Maharashtra Dhankawdi Pune 411043 India

Knowledge graphs represent a potent instrument for the classification and exhibition of data, as they encompass a systematic approach for the containment and retrieval of multifarious datasets. In finance, the utilization of knowledge graphs for the organization of company-oriented data constitutes an invaluable source of insights, thus enabling informed decision-making. In a parallel, knowledge graph systems centered on COVID-19 within the healthcare sphere may assist medical professionals in the making of resolute choices. These applications highlight knowledge graphs’ ability to revolutionize decision-making procedures by providing a comprehensive comprehension of the given subject. To tackle this, we propose a solution that begets and implements knowledge graphs in two separate domains: finance and healthcare. To ensure the creation of explicable AI systems and improve the accessibility of information within these knowledge graphs, we introduce the conversion of natural language queries into SPARQL queries. By fine-tuning our model, we illustrate the system’s superior performance. Furthermore, we appraise the adequacy of the constructed knowledge graphs and contrast them with widely employed alternatives. Our work accentuates the adaptability of the proposed solution, as it can operate seamlessly with diverse datasets requiring minimal modifications. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Explainable artificial intelligence Knowledge graphs Natural language processing Q& A systems Query translation

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A Hybrid IoT and Machine Learning Approach for Crop Recommendation Using a Voting Ensemble Model 2

A Hybrid IoT and Machine Learning Approach for Crop Recommen...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Suresh, A. Geetha Vani, B. Lavanya, M. Kumar, R.G. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering A.P India

ISBN: (纸本)9798350317558

Agriculture is a key component of a nation's economy. In the traditional method, farmers rely on experience to determine the ideal climate and environment for a given crop. technology is advancing, and while the system depends on it to some extent, improvements are still required. This study presents an Internet of Things (IoT) based crop recommendation system that uses Machine learning (ML) to use the value that IoT devices collect from the soil. Initially, we utilized baseline ML classifier models like Support vector machine (SVM), K-Nearest Neighbor(KNN), Random forest (RF), and Random forest (NB).Then we employed an ensemble model to detect the suitable crops. We employed several IoT sensors in our research. The DHT11 sensor collects real-time environmental data, such as temperature and humidity. During crop recording, vital soil parameters like potassium and phosphorus - which are essential for crop growth - are measured using an NPK sensor, a pH sensor, and a rain sensor. These sensors can be seamlessly integrated by the IoT device, offering an effective way to collect crucial agricultural data. The Voting Base Ensemble performs exceptionally well in terms of precision, recall, and F1 score measures. The voting Base Ensemble algorithm improves metrics such as F-score, recall, and precision by up to 98%. A thorough evaluation of soil conditions is made possible by the integration of various sensors, and the application of an ensemble model enables accurate crop recommendations based on environmental factors. © 2024 IEEE.

关键词： Agriculture Crop Recommendation Ensemble IoT Machine Learning

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IoV-fog-cloud framework for road anomalies detection using SVM-nAVDD approach

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10899-10915页

作者： Kumar, Navin Sood, Sandeep Saini, Munish Department of Computer Engineering and Technology Guru Nanak Dev University Punjab Amritsar India Department of Computer Applications National Institute of Technology Haryana Kurukshetra India

The detection of road anomalies in highway management is a crucial yet manpower-demanding task. Although there have been many previous types of research based on visual and acceleration data collected from vehicles. However, to distinctively differentiate a pothole and speedbump from acceleration remains a challenge. The visual methods require high computation supported by expensive GPUs. This paper proposes a novel contribution to depth detection using angular velocity for pothole detection. The solution is accorded by three-layer architecture inclusive of fog-based cloud infrastructure where each layer performs a respective function based on computing capability. The data acquisition is accomplished using an IoV setup equipped with M8N GPS, and MPU-6050 mounted on an Arduino MEGA 2560. The results are compared with the state-of-art algorithms and methodologies. The proposed SVM-nAVDD achieved the precision of 0.9092–0.9123 for the tested datasets and depth/height detection with 92% accuracy. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Data acquisition

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Security in Bluetooth and Wearable Devices: A Survey 4

Security in Bluetooth and Wearable Devices: A Survey

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

作者： Kumar, Hardik Bhaidasna, Hetal Patel, Kajol Parul Institute of Engineering and Technology Computer Engineering Vadodara India Parul Institute of Engineering and Technology-Diploma Computer Engineering Vadodara India

ISBN: (纸本)9798331523923

The rise of Bluetooth tech & wearable gadgets has greatly changed our personal lives. These devices offer amazing convenience & help us connect in many areas like healthcare, fitness, and daily activities. But, with this fast growth, we need to think about important & privacy issues too. Bluetooth, which many people use to communicate wirelessly, faces various security risks. There are threats like eavesdropping, man-in-the-middle attacks, and device spoofing. All these vulnerabilities could lead to unauthorized access, data leaks, or even messing up how devices work. Wearable devices are especially enticing for cyber attackers since they collect & share sensitive personal information, including health metrics and location info. Protecting these gadgets is important because if something goes wrong with the data, it doesn't just affect personal privacy;it can also impact safety & well-being. This review paper takes a close look at the security challenges that come with Bluetooth and wearable gadgets. It dives into main security threats and vulnerabilities, discussing how attackers might exploit these weaknesses to gain access or disrupt device operations. Plus, it highlights the consequences of security breaches in wearables. This shows why we need strong security measures to keep our data safe and private. Understanding these security challenges is super important for finding ways to reduce risks. The goal of this paper is to emphasize how vital it is to set up strong security frameworks & good practices. Doing this can help keep our private information safe & make sure users feel secure in a world that's increasingly connected. © 2025 IEEE.

关键词： Differential privacy

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Integration of cloud-based scheme with industrial wireless sensor network for data publishing in privacy of point source

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International Journal of Cloud Computing 2024年第2期13卷 124-138页

作者： Ravindhar, N.V. Sasikumar, S. Bharathiraja, N. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Wireless sensor networks (WSNs) are normally conveyed in arbitrary regions with no security. The source area uncovers significant data about targets. In this paper, a plan dependent on the cloud utilising data publishing in privacy of point source is proposed to resolve the issue of source area security. Then, at that point, a cloud-moulded phony area of interest is made to add counterfeit parcels into the WSN to confound the enemy and give a far-reaching protection area. Every important parcel is steered through a way that is very hard for the area of interest finding enemy to discover straightforwardly. Recreation results represent that the plan can forestall antagonistic catch and keep a significant degree of security insurance simultaneously. The energy utilisation in this plan applies restricted impact on the organisation lifetime contrasted and a cloud-based plan and an all-course irregular steering calculation conspire. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Wireless sensor networks

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Pattern Analysis and Regressive Linear Measure for Botnet Detection

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computer Systems Science & engineering 2022年第10期43卷 119-139页

作者： B.Padmavathi B.Muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Department of Computer Science and Engineering Easwari Engineering CollegeChennaiIndia Department of Computer Science and Engineering United Institute of TechnologyCoimbatoreIndia

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective *** bots’patterns or features over the network have to be analyzed in both linear and non-linear *** linear and non-linear features are composed of high-level and low-level *** collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier ***,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor ***,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets *** simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so ***,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's *** F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

关键词： BotNet threat intrusion features linearity and non-linearity redundancy regressive linear measure classification redundancy eliminationbased learning model

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Automatic lung cancer detection using hybrid particle snake swarm optimization with optimized mask RCNN

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Multimedia Tools and Applications 2024年第31期83卷 76807-76831页

作者： Sudha, R. Maheswari, K. M. Uma Department of Computer Science and Engineering School of computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India Department of Computing Technologies School of Computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India

As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that is crucial to a person's survival. The first diagnosis of the malignant nodules is typically made using chest radiography (X-rays) and computed tomography (CT) scans;however, the potential presence of benign nodules results in incorrect conclusions. The early phases of both benign and malignant nodules exhibit striking similarities. In this paper, a novel deep learning-based model is proposed for the precise diagnosis of malignant nodules. The proposed approach consists of two stages namely, pre-processing and lung nodule detection. Initially, the Lung CT scan images are collected from the dataset. Then, to remove the noise present in the input image, we apply an adaptive median filter. Then, to enhance the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. After pre-processing, the image is given to the optimized mask RCNN classifier to detect the malignant and benign nodules. To enhance the performance of the Mask RCNN classifier, the hyper-parameters are optimally selected using hybrid particle snake swarm optimization (PS2OA). The proposed PS2OA is a hybridization of particle swarm optimization (PSO) and snake swarm optimization (SSO). The performance of the proposed approach is analyzed based on different metrics and effectiveness compared with state-of-the-art works. The proposed approach attained the maximum accuracy of 97.67%. This work aimed at assisting radiologists to detect and diagnose small-size pulmonary nodules more accurately. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

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computer Systems Science & engineering 2023年第3期44卷 2293-2307页

作者： B.Murugeshwari S.Rajalakshmi K.Sudharson Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India Department of Information Technology Velammal Institute of TechnologyChennai601204India

Imagine numerous clients,each with personal data;individual inputs are severely corrupt,and a server only concerns the collective,statistically essential facets of this *** several data mining methods,privacy has become highly *** a result,various privacy-preserving data analysis technologies have ***,we use the randomization process to reconstruct composite data attributes ***,we use privacy measures to estimate how much deception is required to guarantee *** are several viable privacy protections;however,determining which one is the best is still a work in *** paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data ***-more,this paper investigates the use of arbitrary nature with perturbations in privacy *** to the research,arbitrary objects(most notably random matrices)have"predicted"frequency *** shows how to recover crucial information from a sample damaged by a random number using an arbi-trary lattice spectral selection *** system's conceptual frame-work posits,and extensive practicalfindings indicate that sparse data distortions preserve relatively modest privacy protection in various *** a result,the research framework is efficient and effective in maintaining data privacy and security.

关键词： Data mining data privacy arbitrariness data security perturbation

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Soundtrack Success: Unveiling Song Popularity Patterns Using Machine Learning Implementation

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SN computer Science 2024年第3期5卷 1-11页

作者： Arora, Shruti Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Music streaming services are getting increasingly popular as a result of the prevalent need for web and smart gadgets. Melophiles are drawn toward a range of musical genres and create a unique digital footprint. The emotional reactions of the music listeners may cause physiological changes as well as neurological benefits. However, any musician should avoid being influenced by an algorithmic recommendation presented in the research. The most prominent aspect for any musician is to stay true to their own musical style and to be novel, honest, and sincere. The main objective of this research work is to provide precise music predictions to the artists before they launch their music in the market. This work also aims to improve the accuracy of the prediction models using some feature-engineering-based techniques to manipulate the Spotify dataset and predict whether the song will be a hit or not. The proposed work leverages state-of-the-art machine learning models, including Linear Regression, Lasso Regression, Ridge Regression, Elastic Net, Random Forest, GBM, and neural networks to explore the predictive factors influencing the inclusion of a particular music track in the featured Spotify Hit-50 list. Furthermore, it offers descriptive statistical analysis of the features that can help in popularity prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Hit song science audio features Music information retrieval Music streaming data Regression

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