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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Rangasamy, Gokul Primya, T. Kumar, Gurupriya Sivakumar, Harinath Kanagaraj, G. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350328202

The threat posed by phishing websites is significant in the digital era due to the widespread use of online activities, which has prompted the need for a comprehensive remedy. The project involves building a website that acts as a database for information about phishing URLs, along with retrieving domain details for in-depth examination. Users will be able to contribute and retrieve phishing data, encouraging teamwork and alertness. In addition, a specialized Chrome extension will provide real-time detection of phishing websites with the support of machine learning techniques and pattern matching. In response to the growing demand for a secure online environment, this effort works as a cohesive ecosystem to protect online users by gathering important data and actively alerting them, when possible, dangers are detected. © 2024 IEEE.

关键词： Websites

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Two Stage Video Classification Approach Using Convolution Neural Network 2

Two Stage Video Classification Approach Using Convolution Ne...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Veena, H.N. Rajani Gowda, Naveen Chandra Ramani, D Roja SJB Institute of Technology Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India Newhorizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

This research study focuses on video categorization, which is a crucial area of computer vision with uses in entertainment, education, and surveillance. Convolutional Neural Networks (CNNs) are used in a two-stage approach in the suggested methodology. The video is first broken down into frames, which are subsequently supplied into a CNN model. Another CNN model that predicts the video class uses the output from this model as input. A publicly accessible dataset is used to assess the efficacy of this approach, demonstrating its competitive accuracy performance in comparison to the most advanced techniques. The outcomes indicate that the technique can effectively classify videos and has the potential to be used in a variety of real-world scenarios. © 2024 IEEE.

关键词： 3D CNN Autoencoder Convolution Neural Networks (CNN) Long-Term Recurrent Convolutional Network (LRCN) Recurrent Neural Network(RNN)

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3D Point Cloud Classification Algorithm based on Improved PointNet++ 4

3D Point Cloud Classification Algorithm based on Improved Po...

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4th International Conference on Machine Learning and Intelligent Systems engineering, MLISE 2024

作者： Sun, Zhijin Hu, Jiayu Jiang, Saibiao Li, Ziwen Deng, Kaitao Zhang, Ziyang School of Electronic Information Engineering Zhuhai College of Science and Technology Zhuhai China School of Computer Science Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9798350375077

3D point cloud classification is one of the important tasks before autonomous driving technology. However, the traditional PointNet++ algorithm has some limitations, such as sensitivity to noise and outliers, and high computational complexity. To solve these problems, this paper proposes a 3D point cloud classification algorithm based on improved PointNet++. This algorithm adds a layer of statistical filtering method to preprocess the input point cloud data before inputting the point cloud into the PointNet++ network, which effectively improves the quality of the point cloud. At the same time, the CONV1D convolution is changed to a depth-separable convolution, which reduces the computational complexity. Experimental results show that compared with the traditional PointNet++ algorithm, our improved model can achieve better training accuracy and training efficiency under limited training resources. © 2024 IEEE.

关键词： Convolution

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A Solution Supporting Secure Transmission of Big Data

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Tsinghua science and technology 2023年第5期28卷 838-848页

作者： Haonan Ling Yan Gao Huibin Wang Ming Chen Institute of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China China Electronic Systems Engineering Corporation Beijing 100840China College of Computer and Information Engineering Chuzhou UniversityChuzhou 239000

Traditional Internet protocol networks cannot provide the service of selecting a secure path to transmit various types of data with specific security ***,to solve the“secure path transmission”problem,this paper first proposes a search-transmit model for secure network path transmission,i.e.,to find a multi-attribute optimized path that meets the specific security requirements in the search phase,and then transmit the packets along the optimized path in the transmission ***,we propose a solution to the search-transmit *** idea of the solution is to use the particle swarm optimization algorithm to search for a secure path that meets the multi-attribute requirements and then set the source route on the router to control the packet ***,a prototype system based on network function virtualization is developed to evaluate the feasibility and performance of the proposed *** results show that the proposed solution outperforms existing algorithms in terms of performance.

关键词： network secure path search-transmit model multi-attribute path optimization segment routing network function virtualization

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An Enhanced Deep Learning Model for Tomato Leaf Disease Prediction 8

An Enhanced Deep Learning Model for Tomato Leaf Disease Pred...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Dhivyaa, C.R. Nithya, K. Vignesh, T. Sudhakar, R. Kumar, K. Sathis Janani, T. Kongu Engineering College Computer Science and Engineering Erode India Kongu Engineering College Computer Technology-PG Erode India Nandha College of Technology Computer Science and Engineering Erode India Bannari Amman Institute of Technology Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Information Technology Sathyamangalam India

ISBN: (纸本)9798350396638

The rising need for tomato cultivation has raised concerns about detecting and diagnosing diseases affecting tomato plants. Tomato leaf disease prediction faces several challenges, including the variability of symptoms, limited availability of high-quality data, lack of standardization in disease identification and classification, and difficulty in accounting for environmental factors such as temperature and humidity. These challenges can make it challenging to develop accurate and reliable predictive models, particularly in developing countries. Detecting these diseases in the earlier stage is essential to prevent losses in yield and ensure food security. In current years, deep learning approaches have been increasingly utilized to tackle this issue. In this work, an improved deep learning model is introduced for predicting leaf diseases of tomato plants. The proposed model is based on a visual geometric group VGG-16 architecture, which has been proven effective in image classification tasks. The paper presents a Bayesian optimization method that involves fine-tuning a pre-trained VGG-16 model with a collection of images. To further increase the accuracy of the proposed work, data augmentation techniques are incorporated, such as random rotations, flips, and brightness adjustments, which increase the diversity of the training data. The diseases of tomato leaf prediction model are tested on both a publicly accessible dataset and custom dataset of tomato plant's leaf images. The experimental results demonstrate that the VGG-16 model with Bayesian optimization approach performs well than other methods. © 2023 IEEE.

关键词： Forecasting

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'Precision Agriculture Farming' Enhancing Farming Efficiency through technology Integration 1

'Precision Agriculture Farming' Enhancing Farming Efficiency...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Prashant Kumar, Pratyaksh Singh, Praveen Kumar Rana, Arun Kumar Galgotias College of Engineering And Technology Dept. Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350356816

Precision agriculture, or precision farming, or smart farming, refers to a contemporary approach that combines technology and data analytics with agricultural practices. This research paper examines the concept of precision agriculture, its principles, advantages, disadvantages, and potential to transform farming practices. Through the use of various technologies including GPS, sensors, drones, and data analytics, precision agriculture seeks to maximize resource efficiency, enhance crop yields, reduce the environmental footprint, and boost overall profitability for the farm. The paper also addresses case studies and real-life implementations of precision agriculture and illustrates its effectiveness in various sectors of agriculture. It further identifies the challenges and constraints of adopting precision agriculture and proposes possible solutions to overcome these challenges. This paper generally offers insight into the revolutionary potential of precision agriculture in reshaping the future of farming. © 2024 IEEE.

关键词： Smart agriculture

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Towards Safer Digital Spaces: Automated Detection of Cyberbullying Through Multi-Modal Learning 5

Towards Safer Digital Spaces: Automated Detection of Cyberbu...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Karthikeyan, M. Saravana Francis Raj, J. Relin Krishnan, R. Santhana Kumari, D. Abitha Murali, S. Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RMK Engineering College Department of Computer Science and Engineering Thiruvallur India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

Cyberbullying and online harassment present significant challenges to digital safety, demanding robust detection systems capable of identifying abusive content across multiple formats. This work proposes a multi-modal framework that leverages CLIP and RoBERTa embeddings to detect and categorize cyberbullying and harassment in both text and image-based posts. By utilizing two curated datasets, HarassDetect-TextImage and CyberGuard-MultiModal, the model is trained on a diverse range of offensive and non-offensive content. Text data is preprocessed through advanced techniques, while CLIP and RoBERTa generate embeddings capturing the context of both textual and visual inputs. For sequential analysis of threaded posts, a Transformer layer further enhances the model's contextual awareness, enabling it to recognize harassment patterns that unfold over multiple messages. A Support Vector Machine (SVM) classifier then categorizes content into Non-Cyberbullying, Minor, Moderate, and Severe classes, each associated with confidence levels guiding automated actions. High-confidence classifications trigger immediate responses, such as content restriction or user warnings, while moderate-confidence cases are flagged for manual review. This adaptive framework not only improves accuracy but also allows for a continuous feedback loop, updating the model to address evolving patterns of cyberbullying. Evaluation results demonstrate the efficacy of this approach in creating safer online environments through automated and contextually aware cyberbullying detection. © 2024 IEEE.

关键词： Support vector machines

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Towards a lightweight security framework using blockchain and machine learning

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Blockchain(Research and Applications) 2024年第1期5卷 107-118页

作者： Shereen Ismail Muhammad Nouman Diana W.Dawoud Hassan Reza School of Electrical Engineering and Computer Science University of North DakotaND 58202USA Department of Computer Science COMSATS University IslamabadIslamabad 44000Pakistan College of Engineering and Information Technology University of DubaiDubaiUnited Arab Emirates

Cyber-attacks pose a significant challenge to the security of Internet of Things(IoT)sensor networks,necessitating the development of robust countermeasures tailored to their unique characteristics and *** prevention and detection techniques have been proposed to mitigate these *** this paper,we propose an integrated security framework using blockchain and Machine Learning(ML)to protect IoT sensor *** framework consists of two modules:a blockchain prevention module and an ML detection *** blockchain prevention module has two lightweight mechanisms:identity management and trust *** management employs a lightweight Smart Contract(SC)to manage node registration and authentication,ensuring that unauthorized entities are prohibited from engaging in any tasks,while trust management uses a lightweight SC that is responsible for maintaining trust and credibility between sensor nodes throughout the network’s lifetime and tracking historical node *** and transaction validation are achieved through a Verifiable Byzantine Fault Tolerance(VBFT)mechanism to ensure network reliability and *** ML detection module utilizes the Light Gradient Boosting Machine(LightGBM)algorithm to classify malicious nodes and notify the blockchain network if it must make decisions to mitigate their *** investigate the performance of several off-the-shelf ML algorithms,including Logistic Regression,Complement Naive Bayes,Nearest Centroid,and Stacking,using the WSN-DS *** is selected following a detailed comparative analysis conducted using accuracy,precision,recall,F1-score,processing time,training time,prediction time,computational complexity,and Matthews Correlation Coefficient(MCC)evaluation metrics.

关键词： Blockchain Machine learning IoT Security Integration Smart contracts

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Provisioning Intelligent Water Wave Optimization Approach for Underwater Acoustic Wireless Sensor Networks

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computer Systems science & engineering 2022年第11期43卷 625-641页

作者： M.Manikandan A.Rajiv Kannan Department of Computer Science and Engineering Vidhyaa Vikas College of Engineering and TechnologyTiruchengodeNamakkalTamilnaduIndia Department of Computer Science and Engineering KSR College of Engineering(Autonomous)TiruchengodeNamakkalTamilnaduIndia

In the Acoustics channel,it is incredibly challenging to offer data transfer for time-sourced applications in an energy-efficient manner due to higher error rate and propagation ***,conventional re-transmission over any failure generally initiates significantly larger end-to-end delay,and therefore it is not probable for time-based ***,standard techniques without any re-transmission consume enormous *** investigation proposes a novel multi-hop energy-aware transmission-based intelligent water wave optimization *** ensures reduced end-to-end while attaining potential amongst overall energy efficiency end-to-end packet *** merges a naturally inspired meta-heuristic approach with multi-hop routing for data packets to reach the *** appropriate design of this Meta heuristic-based energy-aware scheme consumes lesser energy than the conventional one-hop transmission strategy without ***,there is no hop-by-hop re-transmission *** proposed model shows only lesser delay than conventional methods with *** work facilitates extensive work to carry out the proposed model performance with the MATLAB simulation *** results illustrate that the model is exceptionally energyefficient with lesser packet *** 500 nodes,the packet delivery ratio of proposed model is 100%,average delay is reduced by 2%,total energy consumption is 8 J,average packet redundancy is 1.856,and idle energy is *** proposed model outperforms existing approaches like OSF,AOR,and DMR respectively.

关键词： Acoustic applications energy efficiency network communications underwater sensor networks meta-heuristic approach intelligent water wave optimization

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A Robust Protocol Design to Perform Data Communication over Wireless Sensor Networks with Enhanced Privacy Preserving Principles 4

A Robust Protocol Design to Perform Data Communication over ...

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

作者： Sathyavani, Bandela Kumar Yadav, Rakesh John Shiny, J. Revathi, R. Bulakh, Ashish Kumar Jain, Shilpa School of Engineering Sr University Department of Eee Telangana Warangal India Amity School of Engineering and Technology Amity University Bihar Patna India Velammal College of Engineering and Technology Madurai India Psgr Krishnammal College for Women Department of Computer Science-Information Technology Coimbatore India Cdgi Department of Computer Science and Engineering Indore India Ies College of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331523923

the growth of wireless sensor networks has transformed all automation and industrial systems. Nonetheless, guaranteeing safe and privacy-preserving connectivity in sensor networks continues to be a difficult task. Current authentication and routing techniques frequently jeopardize privacy, entail significant design intricacy, also vulnerable to malware assaults. To mitigate these restrictions, we present an innovative secure protocol, the Asymmetric Cryptography-based Modified Cuckoo Search Optimization Protocol (MCuSO) for wireless sensor networks. Our proposed technique impacts asymmetric credentials with belief supervision for the privacy preservation of WSN data. This method attains an equilibrium in the middle of security and efficiency. Through comprehensive simulations utilizing the MATLAB platform, we illustrate that MCuSO attains an average network throughput of 94% and a network residual energy of 89%, in comparison to leading other algorithms. Moreover, the protocol efficiently reduces malware attacks. These findings underscore MCuSO as a viable outcome of resource limited WSN devices, providing an advantageous equilibrium of information security, data integrity and data efficacy. © 2025 IEEE.

关键词： Authentication Protocol

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