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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sinha, Pallav Chandra Sinha, Raj Jayoti Vidyapeeth Women's University Department of Computer Science Rajasthan Jaipur India School of Computer Applications Lovely Professional University Punjab Jalandhar India

ISBN: (纸本)9798331518097

Cyberstalking has become an increasingly prevalent and concerning issue in today's digital landscape. The widespread use of online platforms and social media has made individuals more susceptible to predatory behavior. This study delves into the potential of utilizing cloud computing and artificial intelligence (AI) to improve the identification, prevention, and reduction of cyberstalking. It investigates how AI-driven models and cloud infrastructure can work together to provide scalable, real-time solutions to combat this problem. The research also delves into the ethical considerations, technological frameworks, and legal ramifications of integrating AI into the battle against cyberstalking, with the goal of presenting a comprehensive strategy for future implementations. © 2024 IEEE.

关键词： Cloud platforms

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Deep Learning Empowered Natural Language Understanding Voicebot's for farmers: Kissan Samvaad Initiative 8

Deep Learning Empowered Natural Language Understanding Voice...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Jayalakshmi, V. Deepa, R. Ganesan, R. Sriram, N. Surendran, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Computer Science and Engineering chennai India Kg College of Arts and Science Department of Computer Applications Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The 'Kissan Samvaad' work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers. © 2024 IEEE.

关键词： agriculture chatbot kissan samvaad natural language understanding (nlu) RASA RNN

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A study on Electronic Health Records (EHR) in India: Security challenges, initiatives and blockchain integration 4th

A study on Electronic Health Records (EHR) in India: Securit...

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

作者： Bhargava, Misha Mahi, Samarjit Rao, Vidya Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

ISBN: (纸本)9781032911571

With its vast population and extensive healthcare requirements, India is moving toward digitalizing its health records. Safeguarding Electronic Health Records (EHRs) is crucial given the increase in security breaches and attacks, with India’s healthcare sector at the forefront. This study provides an overview of EHR security in India by exploring the EHR adoption rate in India and the barriers to its use. It focuses on the significant security challenges they bring, and the initiatives currently being implemented in India, such as the Ayushman Bharat Digital Mission (ABDM) and the recently released National Accreditation Board for Hospitals (NABH) Digital Health Standards. Integrating Blockchain in current EHR systems to resolve the observed challenges is then explored. The findings of this study show the urgency of integrating emerging technologies such as blockchain which have the potential for patient-controlled data sharing and data immutability possibly within the framework of the ABDM. This study aims to serve as a valuable resource for healthcare professionals, researchers and other regions seeking to understand and improve EHR security. © 2025 the Author(s).

关键词： Electronic health record

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Security issues on Forensics applications by Dynamic Malware injection - A Review 8

Security issues on Forensics Applications by Dynamic Malware...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Yuvarani, E. Gomathi, P.M. Sns College of Technology Department of Master of Computer Applications Tamil Nadu Coimbatore India Pkr Arts College for Women Department of Computer Science Tamil Nadu Erode India

ISBN: (纸本)9798350367904

Data security and privacy have become paramount concerns in today's digital age. Malware attacks pose significant threats to individuals and organizations alike. This research paper investigates the application of machine learning techniques to detect and prevent malware attacks. The study explores two primary approaches to malware detection: static analysis and dynamic analysis. Static analysis examines the code without executing it, while dynamic analysis involves monitoring the behavior of the code during execution. By combining these techniques, a more comprehensive and effective approach to malware detection can be achieved. This research study reviews existing research on malware detection and discusses the potential of machine learning algorithms to improve detection accuracy. Future research directions, such as incorporating advanced deep learning techniques and leveraging behavioral analysis, are explored to further enhance the resilience of systems against malware attacks. © 2024 IEEE.

关键词： Cyber attacks

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Security model to identify block withholding attack in blockchain

Security model to identify block withholding attack in block...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Keshta, Ismail Reegu, Faheem Ahmad Ahmad, Adeel Saxena, Archana Chandan, Radha Raman Mahalakshmi, V. Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Computer Applications Invertis University UP Bareilly India Department of Computer Science and Engineering Shambhunath Institute of Engineering and Technology College Jhalwa Prayagraj India Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9781032669663

Block interception attack, also known as block withholding attack, is an attack method in the blockchain. The attacker penetrates the target mining pool for passive mining to destroy the target mining pool. This paper briefly introduces the mining mechanism and the working principle of block interception attack, summarizes several models of block interception attack, studies the attack methods and benefits of the existing block interception attack models, and analyses their attack effects. A block interception attack model is constructed to increase the rate of return while increasing the rate of return. The completed model is verified through simulated mining experiments. The typical and made models are compared and analyzed based on the rate of return. Finally, the applicable environments of different attack models are given based on the experimental results. © 2024 The Author(s).

关键词： Blockchain

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UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution Remote Sensing Images

UV-Mamba: A DCN-Enhanced State Space Model for Urban Village...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Lulin Chen, Ben Zou, Xuechao Xing, Junliang Tao, Pin School of Computer Technology and Applications Qinghai University Xining China School of Computer Science and Technology Beijing Jiaotong University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798350368741

Due to the diverse geographical environments, intricate landscapes, and high-density settlements, the automatic identification of urban village boundaries using remote sensing images remains a highly challenging task. This paper proposes a novel and efficient neural network model called UV-Mamba for accurate boundary detection in high-resolution remote sensing images. UV-Mamba mitigates the memory loss problem in lengthy sequence modeling, which arises in state space models (SSM) with increasing image size, by incorporating deformable convolutions (DCN). Its architecture utilizes an encoder-decoder framework and includes an encoder with four deformable state space augmentation (DSSA) blocks for efficient multi-level semantic extraction and a decoder to integrate the extracted semantic information. We conducted experiments on two large datasets showing that UV-Mamba achieves state-of-the-art performance. Specifically, our model achieves 73.3% and 78.1% IoU on the Beijing and Xi'an datasets, respectively, representing improvements of 1.2% and 3.4% IoU over the previous best model while also being 6× faster in inference speed and 40× smaller in parameter count. Source code and pre-trained models are available at https://***/Devin-Egber/UV-Mamba. © 2025 IEEE.

关键词： High-Resolution Remote Sensing Images Semantic Segmentation State Space Model (SSM) Urban Village

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Network Media Platform to Build a Sustainable Development of Ecological Environment Using Blockchain Technology 3

Network Media Platform to Build a Sustainable Development of...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Sundar, G. Patchaiammal, P. Swetha, V.P. Rengarajan, Manikandan Satheesh, Nimmati Sasikala, P. Sindhi College Department of Computer Applications Chennai600077 India Panimalar Engineering College Department of MBA Chennai India RandD Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala Avadi Chennai6000627 India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India Department of Computer Science Bangalore India

ISBN: (纸本)9798350396157

Social technology has grown rapidly and effectively as a result of new information technology. The importance of the big data approach, which was among the most prominent current occurrences, has improved as a result of the amount of information generated from social media. For the installation of renewable energies, the Energy Network seems to have become a central feature. Because there are so many different sustainable energy sources and how they interact with the grid, the system is extremely complex and presents a number of intrinsic challenges, such as the coordination and monitoring of distributed sustainable energy generation, which can be extremely difficult to handle. This study presents a blockchain-based approach for managing digital information in a network as a solution to these problems. Blockchain is a distributed ledger that implements agreement, inevitability, and transparency for online data using encryption schemes, hash chains, and consensus mechanisms. The suggested approach can employ blockchain's features to achieve efficient network content creation management, copyright management, system administration, and user instructional techniques. Our suggested method can consistently provide the network media platform with crucial assistance, enabling it to pursue long-term, ecologically sustainable expansion. © 2024 IEEE.

关键词： Smart contract

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IAOMDR: Improved Aquila Optimized Hybrid deep learning Model for Classification of Diabetic Retinopathy 2

IAOMDR: Improved Aquila Optimized Hybrid deep learning Model...

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2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Saju, Binju Barwal, Sahil Asha, V. Kumar, Shubham Tressa, Neethu Nikhil, T. Adi Shankara Institute of Engineering & Technology Department of Computer Science-AI Engineering Kalady India New Horizon College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350328202

One of the scariest illnesses that causes irreversible blindness is Diabetic Retinopathy (DR). As a result, early exposure to Diabetic Retinopathy can help to preserve vision. The study proposes a hybrid model to classify Diabetic Retinopathy images. Initially, pre-processing of the photographs includes resizing them. Grey scale translation and noise removing using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is as opposed to other studies in literature. The proposed model has acquired F1 score of 98.98%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 99.1%, and precision of 99%. © 2024 IEEE.

关键词： Median filters

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Assessing the impact of Covid-19 on the Italian comic book industry: a panel vector autoregressive analysis of financial ratios

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Quality and Quantity 2025年 1-29页

作者： Magrini, Alessandro Rimediotti, Luca Department of Statistics Computer Science Applications “G. Parenti” University of Florence Florence 50134 Italy Independent Researcher Prato 59100 Italy

The comic book market is a niche that has significantly benefited from the more time spent at home by people during the outbreak of Covid-19. This is particularly true in Italy, where sales of comics have met an unprecedented expansion during the two decades before the pandemic. It is therefore important for policy makers to understand the impact of Covid-19 on the economic performance of the Italian comic book industry. To this purpose, yearly data on 10 financial ratios are collected for a panel of 13 Italian comic publishers in the period 2011-2022, and an interrupted time series (ITS) design based on panel vector autoregressive (PVAR) models is applied. In order to maximize the statistical power of the design given the small number of both time points (T=9) and cross-sectional units (n=13), we correct least squares estimates based on the half panel jackknife and perform model reduction through system sequential elimination. The results highlight a worsened performance for small publishers used to base most of their business on comic fairs (Round Robin, Magic Press Edizioni), while companies able to renew their business by launching new products and re-editions (the historic Astorina and Sergio Bonelli Editore, but also the young Tunué) experienced a significant growth compared to the pre-pandemic period. Publishers that dedicated most of their activity to the publication of manga (Bao Publishing, Coconino, Edizioni BD, Edizioni Star Comics, and also Panini with the label Planet Manga) received even more benefits from the pandemic. © The Author(s) 2025.

关键词： Comics Firm performance Half panel jackknife Interrupted time series Panel VAR models

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Hyperspectral Image Based Rainfall with Humidity Prediction by Feature Extraction and Classification Using Deep Learning Techniques 5

Hyperspectral Image Based Rainfall with Humidity Prediction ...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Hariharan, S. Prakasam, S. Sri Chandrasekarendra Saraswathi Viswa Mahavidhyalaya University Department of Computer Science and Applications Tamilnadu Kanchipuram631561 India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

Climate studies require knowledge of the temperature as well as humidity profiles in atmospheric boundary layer. One benefit of ground-based infrared hyperspectral spectrometer is its ability to measure atmospheric radiances with a moderate vertical resolution and a high temporal resolution. This paper makes use of ground-based infrared hyperspectral measurements to retrieve temperature as well as humidity profiles. Existing inversion methods still have issues with high computational complexity or subpar performance, despite some advancements based on statistical learning or physical models. This research proposes a novel technique for hyperspectral image-based rainfall and air humidity prediction utilizing Deep Learning (DL) model. Input is collected as hyperspectral image, which are processed for noise removal as well as normalization. Then, the image features are extracted using Convolutional Gaussian Vector Multilayer Neural Networks (CGVMNN). This extracted feature has been classified using Transfer Graph Temporal Logistic Neural Network (TGTLNN). the classified output shows region with rainfall and air humidity. the experimental analysis has been carried out for various hyperspectral image dataset in terms of accuracy, average precision, recall, F-1 score, ROC. The proposed technique accuracy 96%, Fl Score of 95%, Average Precision of 94%, Recall of 92%, ROC of 93%. © 2024 IEEE.

关键词： Multilayer neural networks

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