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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Bharati, P. Vijaya Vignan's Institute of Engineering for Women Department of Computer Science and Engineering India

ISBN: (纸本)9798331518981

Handwritten Character Recognition (HCR) is a critical area of research within pattern recognition and artificial intelligence, with applications spanning from document digitization, optical character recognition (OCR), and handwriting analysis. Despite significant technological advancements, accurate recognition of handwritten characters remains challenging due to variations in writing styles, sizes, and inherent *** paper presents a novel approach to HCR, utilizing a combination of Deblur Generative Adversarial Networks (GAN) and Convolutional Neural Networks (CNN) to effectively address these challenges. Unlike traditional deblurring methods, which often introduce artifacts or blur the edges of characters, Deblur GAN is specifically trained to enhance the clarity of handwritten images while preserving essential character features. This allows the subsequent CNN to extract more accurate and discriminative features, leading to improved character *** extensive experimentation on benchmark datasets such as MNIST, an approach that significantly outperforms the existing methods is demonstrated in terms of recognition accuracy, particularly in scenarios involving blurred or indistinct handwriting. These findings highlight the potential of integrating deblurring techniques with deep learning models to address persistent challenges in handwritten character recognition, paving the way for enhanced automation and efficiency in text analysis tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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Crop Yield Prediction using Machine Learning 15

Crop Yield Prediction using Machine Learning

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

作者： Tony, Akash Lalkumar, Dona Saint, Luke Mohammed Hazim, A.S. Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur India

ISBN: (纸本)9798331300579

As the world faces growing challenges in ensuring food security, predicting crop yields accurately be- comes increasingly crucial. Traditionally, this prediction re- lied on methods limited by their inability to handle complex interactions between various factors like climate, soil, and farming practices. This is where Machine Learning (ML) emerges as a game-changer. By harnessing the power of data-driven insights, ML offers highly accurate predictions surpassing traditional methods. This paper specifically dives into the potential and challenges of utilizing ML for crop yield prediction, highlighting its strengths and areas for further development. While traditional approaches often fall short in capturing the intricate relationships impacting yield, the proposed method leverages satellite imagery to extract valuable features and deliver precise predictions on granular levels. Ultimately, this paves the way for farmers to make informed decisions, optimize resource allocation, maximize production, and contribute significantly to global food security. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI 4

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

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

作者： Swarna Sudha, M. Manjula, S. Bharathi, Indira Krishnasamy, Valarmathi Vijayalakshmi, K. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India School of Computer Science and Engineering Vellore Institute of Technology Chennai India PSR Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798331523923

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions. © 2025 IEEE.

关键词： Contrastive Learning

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A Review of the Repercussions of Mobile Phones and the Internet on Education

A Review of the Repercussions of Mobile Phones and the Inter...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Komarasamy, Dinesh Madhuradharani, Madhuradharani Krishnakumar, B. Parthasarathy, Karthikeyan Chinnusamy, Koushalya. K Kongu Engineering College Department of Computer Science and Engineering TamilNadu Perundurai India

ISBN: (纸本)9798350309140

Mobile phones and the Internet are the most popular modern technologies for life organization, especially in the field of education. Rapid advancements in mobile technology have made it possible for Internet-based mobile learning and its impact on education has become a studied concern. Numerous studies have been developed to discover the impacts of mobile, social media and Internet technology on education. The discoveries aided the studies in determining whether these technologies emphatically or contrarily influence the life of students regarding their academic performance. Motivated by this novel, this review paper synthesizes existing articles regarding three perspectives: benefits of technology in education;how mobile phones and the Internet have favored learning and teaching modes;the repercussions of mobile phones and the internet on education. The majority of works accentuate how these technologies unearth various dimensions of learning for students and highlight the obstacles in education. In contrast, the researchers also analyzed the awful academic situations among students created by mobile technology. But the inquiries are limited to the repercussions and impacts of these innovations and do not include any definite suggestions to mitigate the effects. This paper provides various ways for responsible and effective use of these technologies among students. © 2024 IEEE.

关键词： Education Internet Mobile phone and Social media Technology

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Ensuring Security and Efficiency in Digital Payments using Blockchain with NFC

Ensuring Security and Efficiency in Digital Payments using B...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Mandal, Shovon Shill, Pintu Chandra Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350359688

Bangladesh has recently adopted the worldwide trend of digitalization, namely in the area of financial activities. In Bangladesh, the centralized nature of digital payment systems poses notable obstacles, such as lengthy transaction procedures, vulnerability to fraudulent activities, and the potential for financial theft, despite their increasing popularity. In light of these concerns, our study presents an innovative remedy that utilizes the potential of blockchain technology, in conjunction with Near Field Communication (NFC) technology, to transform the field of electronic transactions. Our adoption of the proof-of-stake technique sets us apart from traditional hand-cash systems as well as the current centralized digital payment systems, providing enhanced efficiency and security. This connection facilitates fast, safe, and user-friendly transactions across several platforms, such as online and mobile applications, thereby greatly improving the digital wallet experience. Our decentralized system guarantees the integrity and immutability of every transaction while significantly decreasing transaction and maintenance expenses. This technique functions independently without any external intervention, fostering trust among users and strengthening the system against fraudulent activity. Our system is specifically designed for the high-volume transaction environment of Bangladesh's digital economy. It has the capability to efficiently handle large transaction volumes while ensuring full security. This article elucidates the revolutionary capacity of our blockchain-powered system to promote a secure, streamlined, and inclusive digital payment ecosystem in Bangladesh, representing a crucial stride towards a technologically progressive financial landscape. © 2024 IEEE.

关键词： Decentralized finance

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An Android App for Ayurvedic Herb Identification and Classification Using Machine Vision 1

An Android App for Ayurvedic Herb Identification and Classif...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Vinod, Devi Simon, Santhosh Radhakrishnan, Anuja Jacob, Pramod Mathew Dilhaque Ahemmed, C.P. Adwaith, M. Mathew, Nithin Providence College of Engineering Department of Computer Science and Engineering Kerala Chengannur India

ISBN: (纸本)9798350307795

Kerala, a southwestern state in India, is renowned for its rich tradition of Ayurveda, an ancient system of holistic healing. However, it was a major concern regarding difficultly in identification of medical herbs. It is not possible for a naive user to confirm whether the identified leaf is correct or not. So, we propose an Android app-based solution for this problem. The app scans the herb leaf image and can promptly provide information about the herb's name, medicinal properties, and display the success rate of identification. The proposed system employs a comprehensive dataset of Ayurvedic plant leaves, enabling it to accurately classify and identify plant species. By harnessing the power of deep learning through computer Vision technique, the model extracts intricate features from leaf images, while machine learning algorithms refine the classification process which identifies the scanned leaf. The outcome is a robust, user-friendly mobile application which can be used anywhere in world to scan and identifies the leaves. © 2024 IEEE.

关键词： computer vision

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Forecasting Electricity Demand in Greece: A Functional Data Approach in High Dimensional Hourly Time Series

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SN computer science 2024年第5期5卷 1-16页

作者： Varelas, George Tzimas, Giannis Alefragis, Panayiotis Data and Media Laboratory Department of Electrical and Computer Engineering University of Peloponnese Patras Greece

This paper presents a novel approach to forecasting electricity demand in Greece by employing functional data analysis to analyze power consumption. Unlike traditional methods that predict a single future point, this approach forecasts a curve representing daily power consumption. This functional object enables electricity providers to better tailor their pricing and supply strategies in the upcoming day's megawatt hours (MWh) market. To achieve this, the ARIMA algorithm supplemented by functional principal components is utilized for one-step-ahead forecasting. The analysis also incorporates a functional regression using a functional linear model for functional responses, identifying patterns that link one day's consumption to the preceding day. The results demonstrate that the FDA-based method enhances the accuracy of short-term demand forecasts, outperforming classical time series algorithms and neural networks. By providing a more dynamic and comprehensive model of electricity demand, this approach offers significant implications for energy policy and management practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electricity demand forecasting FDA FPCA High dimensional time series

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Extraction of road using semi-supervised deep learning method 4th

Extraction of road using semi-supervised deep learning metho...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Mishra, Jalaj Khan, Mohd Jawed Negi, Himanshi Alam, Jitendra Alam, Junaid Singh, Pankaj Pratap Department of Computer Science & Engineering Greater Noida Institute of Technology Greater Noida India Department of Computer Science & Engineering Central Institute of Technology Assam Kokrajhar India

ISBN: (纸本)9781032911571

Detection of road networks using high-resolution aerial or remote sensing imagery constitutes a significant focus within modern research efforts. Currently, deep learning models demonstrate efficiency to a certain degree, they are constrained by a notable reliance on annotated datasets, a practice divergent from optimal methodologies due to the scarcity of extensive, high-quality labeled data. The precision of road prediction is additionally influenced by the latent information embedded within pixels exhibiting diminished confidence values. These pixels play a crucial role in discriminating between road and non-road regions within the images, yet their utilization by deep learning models is suboptimal. To handle these two different issues, opted U-Net as our base model with a sole generator paired with twin dis-criminators and presented this combined setup of U-Net on the Generative Adversarial Network (GAN). At the base of the U-Net architecture, a bottleneck module is incorporated to augment the functionalities of the proposed model. This proposed model attains 98% accuracy on DeepGlobe Road Dataset. Comparative and quantitative assessment demonstrates the enhanced efficiency of our model compared to similar methodologies. © 2025 the Author(s).

关键词： Generative adversarial networks

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Spectral-Spatial Feature Extraction for Change Detection in Bi-temporal Hyperspectral Images 9

Spectral-Spatial Feature Extraction for Change Detection in ...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Afjal, Masud Ibn Mondal, Md. Nazrul Islam Al Mamun, Md. Rajshahi University of Engineering And Technology Department of Computer Science And Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350394474

Change detection (CD) is an important task, which is evaluating changes between bi-temporal images. Hyperspectral images (HSIs), which are very spectrally informative, are excellent at identifying different change classes and catching minute variations. Although hyperspectral binary CD has received most of the attention recently, specific information about fine change classes has not been adequately provided. Current approaches, which depend on band selection and image difference, struggle to deal with the enormous dimensionality of data, lose out on nonlinear interactions between bands, and preserve spectral characteristics, all of which restrict their overall performance. This paper proposes a unique hybrid spectral-spatial feature extraction method for binary and multiple CD in bi-temporal HSIs. Our method reduces the feature space while effectively extracting meaningful spectral-spatial information. This is accomplished by incorporating a feature extraction module based on principle component analysis (PCA) into an optimized hybrid 3D-2D convolutional neural network (CNN). In multiclass and binary CD, the generated subspace outperforms baseline and advanced methods. Comprehensive experiments were conducted on two publicly available HSI-CD datasets: China wetland agriculture land (CH) and USA irrigated farmland (USA). These experiments thoroughly verify the effectiveness and efficiency of the proposed methodology, demonstrating its superiority with optimal multiclass CD performances of 97.81% and 97.73%, and binary CD performances of 97.84% and 97.79%, for the CH and USA datasets, respectively, with 20% training samples. © 2024 IEEE.

关键词： Feature extraction

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