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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sakthivel, S. Agalya, M. Sudha, R.V. Lathika, V. Selvi, P. Suriyapriya, N. Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Vivekananda College of Technology for Women Tiruchengode Department of Computer Science and Engineering Tamilnadu Namakkal India Vivekananda College of Technology for Women Tiruchengode Department of Information Technology Tamilnadu Namakkal India Computer Science and Engineering Jayam College of Engineering and Technology Tamilnadu Dharmapuri India Vivekananda College of Technology for Women Tiruchengode Department of Information Technology Namakkal India

ISBN: (纸本)9798350397253

There has been a meteoric rise in the use of wireless sensor networks (WSNs) in recent years. WSN's compact size and low cost are enticing many different industries to adopt them for use in a wide range of applications. There are several applications of IoT technology, including environmental monitoring, building security, and precision agriculture. Given that most WSNs are deployed in an unsupervised and potentially hostile environment, security risks are significant. To ensure the privacy of data during transmission from sensors to the base station, a number of methods have been proposed. The work in question is concerned with a crucial part of data and network security: detecting attacks. With the goal of preventing malicious attacks and maintaining the security of wireless sensor networks, anomaly detection has emerged as a significant obstacle. After the input is given, It consist of three phases such as preprocessing, Feature selection and training the model using SVM-RFE-MRMR. The SVMRFE-MRMR methodology is used for classification in the suggested method. While SVM-RFE takes feature-decision correlation into consideration, it does so by neglecting feature-feature relationships, MRMR is able to provide features with little redundancy and maximum relevance. Because of this SVM-RFE-MRMR is used to generate a set of features that is both minimal in redundancy and maximal in relevance. The proposed model produces an accuracy of about 98.6% when compared other models such as SVM and CNN. © 2023 IEEE.

关键词： Support vector machines

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Comparison of Dynamic DES with Existing Symmetric Key Algorithms 9

Comparison of Dynamic DES with Existing Symmetric Key Algori...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Sunny, Leya Elizabeth Paul, Varghese Soe Cochin University of Science and Technology Division of Information Technology Kochi India Rajagiri School of Engineering and Technology Department of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350314090

The concept of security is fundamental to networking. The basic objective of security is to defend against various types of assaults on these networks that hold sensitive data. By altering criteria such as key size, altering the number of iterations and ultimately using confusion box as the S-box, the security of cryptographic algorithms can be increased. Data exists in different forms such as text, images, audio and video. In DES, S-boxes are used to increase the security of the data. All of these 8 S-boxes have 64 elements. Unique numbers in the 0-15 range can be found in each row. These S- boxes are generated dynamically by our suggested system depending on the key and no storage of these elements are required. The comparison of Dynamic DES with other symmetric algorithms like DES, AES and Blowfish yields better results for Dynamic DES in terms of Balance, Bit difference, Time and Memory needed for Execution. © 2023 IEEE.

关键词： Cryptography

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A Semi-supervised Protein Complex Identification Algorithm Based on Sparseness Constraint

A Semi-supervised Protein Complex Identification Algorithm B...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

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Generating Synthetic Text using Generative Adversarial Networks

Generating Synthetic Text using Generative Adversarial Netwo...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sahal, Nisha Krishnamoorthy, Ramkumar Singh, Navjot College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Generative adverse Networks (GANs) are a class of deep learning architectures that generate new facts primarily based on education records. Currently, GANs have been increasingly used to generate artificial text. This technique entails schooling a generator community to generate artificial text samples from a given dictionary of tokens. Moreover, a discriminator network is educated to differentiate synthetic text from actual text samples within the education data. By using education, the networks in an adversarial style, where generating more realistic textual content, the networks converge closer to a Nash equilibrium wherein the output of the generator is sort of indistinguishable from actual samples. as compared to different text era techniques, GAN s are capable of produce more coherent and sensible textual content. Additionally, the discriminator community can be used to construct higher-structured language fashions from education facts that could then be used to enhance the exceptional of the generator's outputs. © 2024 IEEE.

关键词： construct discriminator Generative increasingly Networks

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YOLO-LRHG: Long Range Hand Gesture detection using YOLO with attention mechanism 7

YOLO-LRHG: Long Range Hand Gesture detection using YOLO with...

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7th IEEE International Conference on Electronic information and Communication technology, ICEICT 2024

作者： Yang, Yu-Yu Yang, Hsu-Han Yang, Jung-Kuei Dong Hwa University Department of Computer Science and Information Engineering Hualien Taiwan Dongguan University Of Technology Guang Dong-TaiWan College of Industrial Science & Technology China

ISBN: (纸本)9798350384437

The interpretation of hand gestures is crucial for effective non-verbal communication, particularly in HumanRobot Interaction (HRI). Despite the importance, existing research has mainly concentrated on short-range gesture recognition, inadequate for safe and effective interactions with mobile robots such as UAVs over longer distances. This study addresses this gap by exploring long-range hand gesture recognition using RGB images captured by webcams. We introduce the LRHG-10m dataset, which extends the original LRHG dataset with images captured at 8 to 10 meters in various indoor and outdoor environments. We evaluated seven attention mechanisms and the Ghost module for their performance in detecting small objects under low-parameter conditions. Our proposed YOLO-LRHG model integrates these mechanisms with an anchor-free approach, achieving an impressive mAP@0.5 of 98.5% with 3M parameters and 97.5% with 1.75M parameters, balancing computational efficiency, detection distance, and precision. © 2024 IEEE.

关键词： Deep learning

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Discovering Patterns with Neural Networks in Agricultural Crop Monitoring

Discovering Patterns with Neural Networks in Agricultural Cr...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Akhilendra Pratap Kaushik, Neeraj Pawar, Rahul Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

neural networks have emerged as practical tools for discovering styles in agricultural crop monitoring. The motive of this take a look at was to research whether or not neural network strategies will be implemented to seize and perceive patterns in various parameters related to crop monitoring. Data was accrued from four corn fields inside the Midwest US from October 2007 to October 2009. Linear and non-linear neural networks were used to analyze the information, to identify enormous styles associated with crop manufacturing. Effects showed that the neural networks have been able to as they should be perceived styles inside the records, with the non-linear network generating satisfactory results. The results confirmed that the most important parameters related to crop production were the width of the kernel, duration of the cob, and precise leaf location. These findings advocate that neural networks may be a promising technique for understanding complicated crop monitoring relationships. © 2024 IEEE.

关键词： emerged enormous implemented important parameters

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Advancing Plant Disease Prediction Accuracy with ViT Based Deep Learning Model

Advancing Plant Disease Prediction Accuracy with ViT Based D...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Sam, Felicita Kavitha, B.R. School of Computer Science Engineering and Information Systems Vellore Institute of Technology TamilNadu Vellore India

ISBN: (纸本)9798331517892

Agriculture is the key source for many people's livelihoods and an important contributor to a country's economy. There is a huge demand to streamline the process in the field of agriculture by integrating Computing into the traditional way of farming. Computing plays a key role in handling this sector with many newer revolutions that would automate the various processes involved in overall farming. Particularly, machine and deep learning based models have shown remarkable results in smart farming. Rice crop is considered in this work which is the major crop in India. It is necessary to meet the demands of the growing population in terms of sufficient production of this crop. One of the key issues in this crop is the plant diseases that impact the overall crop yield production considerably. Among the various diseases, the most commonly occurring disease is White stem Borer. Diagnosing the disease at an early stage could treat the problem at the earliest to ensure the appropriate amount of yield production. In this paper, a Vision transformer (ViT) based deep learning model is proposed to predict the white stem borer disease on the PlantDoc dataset and the results obtained are compared with the other promising CNN based pre trained models like VGG19, InceptionV3, Resnet50. The experimental output shows that Vision transformer based model outperforms the other proven models with an accuracy of 96%. © 2024 IEEE.

关键词： Plant diseases

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Conversational Recommendation System Based on Utterance Act and Emotion

Conversational Recommendation System Based on Utterance Act ...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： An, Jiahao Dang, Huibo Department of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China Department of Transportation Information Shaanxi College of Communications Technology Xi'an710018 China

Conversational recommendation system (CRS) aims to acquire user preferences of conversation and then make recommendations to users. Existing CRS enhance the characterization of items by introducing external information to improve the recommendation effect, but they ignore the most essential utterance semantic attributes in the dialogue, and do not fully con-sider the different emotion or act feedback of users. Based on this, this paper fully explores the utterance semantics, makes the construction of user characteristics fuller by extracting the act and emotion of the utterance, and predicts the act of the conversation process, and then enhances the response through the emotional vocabulary, to improve the interaction experience between the user and the system. A large number of experiments on public datasets show that the model proposed in this paper outperforms the most advanced methods. © 2024 J. An & H. Dang.

关键词： Conversational recommendation Personal recommendation Semantic fusion Utterance act

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Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks

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International Journal of Intelligent Networks 2024年第1期5卷 255-266页

作者： Ahmad, Rizwana Anwar, Dil Nashin Bany Salameh, Haythem Elgala, Hany Ayyash, Moussa Almajali, Sufyan El-Khazali, Reyad Electronic and Electrical Engineering Department University of Cambridge United Kingdom Electrical and Computer Engineering Department University at Albany-State University of New York United States Artificial Intelligence Research Center Al Ain University Al Ain United Arab Emirates Telecommunications Engineering Department Yarmouk University Irbid Jordan Department of Computing Information and Technology Chicago State University ChicagoIL United States Department of Computer Science Princess Sumaya University for Technology Amman Jordan Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates

Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy. © 2024 The Authors

关键词： Data transfer

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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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