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Real-Time Text Extraction and Video Interpreter System 8

Real-Time Text Extraction and Video Interpreter System

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Grandhi, Aneesh Sai Umair, Syed Yaswanth, Bolla Sai Naga Ravi, Aditya Padmashree, T. Sagar, B.M. Department of Information Science and Engineering Rv College of Engineering India Rv College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331505462

Video interpretation systems are widely used for assisting people with visual impairments. The main goal of a video interpreter system is to help people with visual impairments. By leveraging technologies such as Text-to-Speech, text extraction and Optical Character Recognition, the description of the surrounding environment can be converted to audio and textual data present on images and documents can be extracted and read out. The Real-Time Text Extraction and Video Interpreter System extracts text from images, such as posters and documents, and provides real time descriptions of video feeds through a camera. The generated text is read as speech to aid the visually impaired person. Such a model can be used to navigate outdoor environments, guiding the users about their surroundings. © 2024 IEEE.

关键词： Optical character recognition

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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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Fake News Detection Using ML Algorithms 5th

Fake News Detection Using ML Algorithms

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Pokkuluri, Kiran Sree Subha, Atluri Dhatri Sai Varma, Ch. Phaneendra Gurujukota, Ramesh Babu Usha Devi N, S.S.S.N. Raja Rao, P.B.V. Pamarthi, Nagaraju Shalem Raju, P.J.R. Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering University College of Engineering JNTU Kakinada Kakinada India

ISBN: (纸本)9783031770807

In response to the growing threat of fake news in our digital era, this paper aims to propose a machine learning system to identify the fake news. Its primary aim is to accurately discern between authentic news articles and deceptive misinformation. The urgency for such a solution arises from the significant consequences of fake news, including the erosion of trust, societal polarization, and potential safety risks. This paper employs machine learning algorithms to analyses textual and contextual elements within news articles. This entails data collection, pre-processing, and model training, followed by a comprehensive evaluation of the model’s performance against established benchmarks. In conclusion, this project is dedicated to fortifying the ongoing struggle against fake news, safeguarding the reliability of information sources, and nurturing a society that thrives on accurate and verified information. The proposed ML system is compared with the baseline methods and results are plotted using various parameters precision, sensitivity, specificity, accuracy and f1score. The proposed model exhibits an accuracy of 96.65% which is promising when compared with existing literature. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Adversarial machine learning

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Maximizing Learning Outcomes while Minimizing: Costs: A Cost-Effective Approach to M-Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第11期18卷 4-14页

作者： Suresh Kumar, B. Hemawathi, M.S. Dhanasekaran, P. Kaliappan, Vishnu Kumar Sivaganesan, D. Department of Electronics and Communication Engineering The Kavery Engineering College Tamil Nadu Salem India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India

In the realm of education, the pursuit of effective learning outcomes often faces the challenge of limited resources. This paper explores the intersection of maximizing learning outcomes and minimizing costs through a cost-effective approach to mobile learning (m-learning). Recognizing the ubiquitous presence of mobile devices and their potential to revolutionize learning, this study explores strategies that leverage the capabilities of mobile technology while being mindful of budgetary constraints. By leveraging the flexibility and accessibil- ity offered by m-learning platforms, educators can design engaging and interactive learning experiences that cater to diverse learners needs. The paper investigates various cost-effective methodologies, including utilizing open-source software, repurposing existing resources, and adopting collaborative learning environments. Furthermore, it examines the role of scalable technologies and adaptive learning algorithms in optimizing resource allocation and per- sonalizing learning experiences. Through case studies and empirical analysis, this research illustrates how institutions and educators can achieve significant cost savings without com- promising the quality of educational delivery. Ultimately, the findings highlight the transfor- mative potential of a cost-effective approach to m-learning in enhancing learning outcomes and widening access to education. © 2024 by the authors of this article.

关键词： Contrastive Learning

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Hate Speech Detection Using Recurrent Neural Networks (RNN) 5th

Hate Speech Detection Using Recurrent Neural Networks (RNN)

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Pokkuluri, Kiran Sree Sivakoti, Ramadevi Raja Rao, P.B.V. Murthy, P.T.S. Pamarthi, Nagaraju Varma, Ch. Phaneendra Gurujukota, Ramesh Babu Usha Devi N, S.S.S.N. Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering University College of Engineering JNTU Kakinada Kakinada India

ISBN: (纸本)9783031770807

Hate speech detection is a vital task in the context of content moderation, aiming to identify and mitigate harmful language in online platforms. This abstract presents a methodology utilizing Recurrent Neural Networks (RNNs) for hate speech detection. We have processed a heterogeneous dataset containing occurrences of hate speech and non-hate speech & preprocessed the text data, and represent words as vectors using pre-trained word embedding’s. Our chosen RNN architecture, adept at capturing sequential dependencies, processes the contextual information within the text. During training, the model optimizes using a binary cross-entropy loss function and undergoes validation for hyper parameter tuning. The effectiveness of the RNN-based hate speech detection model is evaluated using accuracy, F1 score, precision and recall. This methodology provides a robust framework for combating hate speech in digital spaces, contributing to a safer and more inclusive online. We have compared our work with various base line methods with several parameters and achieved an accuracy of 96.89%. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Recurrent neural networks

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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks

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Discover Internet of Things 2025年第1期5卷 1-22页

作者： Selvam, Prabu Karthikeyan, P. Manochitra, S. Sujith, A.V.L.N. Ganesan, T. Ayyasamy, Rajaram Shuaib, Mohammed Alam, Shadab Rajendran, A. School of Computing SRM Institute of Science and Technology Tiruchirappalli621105 India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai625009 India Department of Computer Science Engineering St Joseph’s College of Engineering Tamil Nadu Chennai600119 India School of Engineering Malla Reddy University Telangana Hyderabad500043 India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamil Nadu Nagapattinam611002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of ECE Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusion patterns due to the heterogeneity and distributed IoT devices. There is a pressing need for scalable, real-time, and privacy-preserving solutions to secure IoT and IIoT systems. For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. By integrating federated learning, the proposed approach ensures privacy by enabling decentralized model training among IoT devices without exchanging sensitive data. The hybrid CRNN model improves detection accuracy for a variety of attack types in IoT networks by combining Recurrent Neural Networks for sequential pattern recognition and Convolutional Neural Networks for feature extraction from raw data. The proposed model classifies incoming network traffic into different attack classes. The experimental results conducted on the Edge-IIoT dataset reveal that the proposed model has attained the detection accuracy of 98.93% with balanced precision and recall for all the attack types and low-support classes such as SQL Injection and Man-in-the-Middle. This balance improves model’s ability to reduce both false negatives and false positives compared with conventional approaches. Together with real-time detection, our approach excels in a privacy-preserving manner, providing a practical, scalable solution for securing complex IoT networks, and addressing critical gaps in current IDS frameworks. © The Author(s) 2025.

关键词： Federated learning

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A computer-Aided Diagnosis System for the Detection of Parkinson’s Disease 8th

A Computer-Aided Diagnosis System for the Detection of Pa...

引用

8th International Symposium on Intelligent Informatics, ISI 2023

作者： Abhijith, K.P. Sarath, R. Santhosh, Partha Mohan, Jesna Abraham, Bejoy Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Kerala Trivandrum India Department of Computer Science and Engineering College of Engineering Muttathara Kerala Trivandrum India

ISBN: (纸本)9789819721467

Parkinson’s disease (PD) is a neurological condition that worsens over time and causes accidental or uncontrolled movements, stiffness, and problems with balance and coordination. Usually, symptoms are minor, to begin with, and worsen with time. As the condition develops, individuals may have trouble speaking and moving about. They could also have mental and psychological issues, such as fatigue, sadness, sleeplessness, and cognitive impairment. The development of useful, technologically supported methods for monitoring the development of PD symptoms in everyday life has the potential to change disease assessment and hasten diagnosis. The creation of simple, technology-based techniques for tracking PD symptoms over time in daily life has the potential to revolutionize disease evaluation and speed up diagnosis. The proposed work aims to detect Parkinson’s disease from a patient’s vocal features at a very early stage. This can be achieved by training machine learning models to detect the vocal characteristics distinct in patients with PD from the data of patients who are already diagnosed with Parkinson’s Disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Classification Model for IDS Using Auto Cryptographic Denoising Technique

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computer Systems science & engineering 2023年第4期45卷 671-685页

作者： N.Karthikeyan P.Sivaprakash S.Karthik Department of Computer Science&Engineering PPG Institute of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the *** are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting *** algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of *** this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam *** classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and *** neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 *** their power and limitations in the proposed methodology that could be used in future works in the IDS area.

关键词： Auto cryptographic denoising(ACD) classifier intrusion detection system(IDS) optimizer performance measures

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Soil Image Classification Using Transfer Learning Approach: MobileNetV2 with CNN

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SN computer science 2024年第1期5卷 199页

作者： Banoth, Ravi Kumar Murthy, B. V. Ramana Department of Computer Science and Engineering University College of Engineering Osmania University Telangana Hyderabad India Department of Computer Science and Engineering Stanley College of Engineering and Technology for Women Abids Telangana Hyderabad India

This paper presents a novel study on soil image classification, leveraging the synergistic potential of transfer learning and convolutional neural networks (CNNs). The proposed approach combines the strengths of the MobileNetV2 architecture with a customized CNN model for accurate and efficient soil type recognition. The pre-trained MobileNetV2 is used to capture generic features before fine-tuning it with a dedicated soil image dataset comprising four distinct classes: red, clay, black, and yellow soils. To enhance the model’s capacity for discerning intricate soil textures, a specially designed CNN architecture is incorporated. The model’s performance is rigorously evaluated on a dataset of 108 images, each sized at 256 × 256 pixels, achieving an exceptional accuracy rate of 100% on the test dataset. The promising results demonstrate the efficacy of the proposed methodology in soil image classification tasks, offering potential applications in precision agriculture, environmental monitoring, and land management. While these findings showcase remarkable accuracy, further investigations are recommended to assess the model’s generalization across diverse environmental conditions and an expanded range of soil image datasets. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Black soil Clay soil Convolutional neural networks (CNNs) MobileNetV2 Red soil Soil image classification Yellow soil

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Learner Phase of Partial Reinforcement Optimizer with Nelder-Mead Simplex for Parameter Extraction of Photovoltaic Models

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Journal of Bionic engineering 2024年第6期21卷 3041-3075页

作者： Jinpeng Huang Zhennao Cai Ali Asghar Heidari Lei Liu Huiling Chen Guoxi Liang Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehran***Iran College of Computer Science Sichuan UniversityChengdu610065SichuanChina Department of Artificial Intelligence Wenzhou PolytechnicWenzhou325035China

This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed *** LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of ***,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO *** comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been *** accuracy and stability of simulated data and real data in the parameter extraction of PV systems are *** to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent *** further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and *** summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.

关键词： Partial reinforcement optimizer Learner phase Nelder-Mead simplex algorithm Parameter extraction

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