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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Gorantla, Snehitha Veluchamy, S. Amrita School of Computing Department of Computer Science and Engineering - Artificial Intelligence Amrita Vishwa Vidyapeetham Chennai601103 India Amrita School of Computing Department of Computer Science and Engineering - Cyber Security Amrita Vishwa Vidyapeetham Chennai601103 India

ISBN: (纸本)9798350317022

A reinforcement learning agent for optimal green-house management through Proximal Policy Optimisation is developed and evaluated in this work. With variable elements like lighting, irrigation, humidity, and temperature, a virtual greenhouse is produced. By interacting with the surroundings, the agent modifies the controls and gets input on things like crop health and growth rate. For policy and value estimation, the neural network design consists of distinct actor and critic networks as well as shared feature layers. Compared to fixed or random policies, the trained agent shows the ability to regulate conditions for increased crop yield and sustainability. As the number of trials increased, the RL agent's average return per trial increased steadily. This suggests that the RL agent has become more adept at controlling the greenhouse environment in subsequent experiments. This demonstrates how well the RL agent can manage crop health and resource use in the greenhouse climate control system. © 2023 IEEE.

关键词： Crop Yield Greenhouse Neural Network PPO algorithm Reinforcement Learning

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Terahertz metasurface biosensor for high-sensitivity salinity detection and data encoding with machine learning optimization based on random forest regression

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Optical and Quantum Electronics 2024年第11期56卷 1-39页

作者： Wekalao, Jacob Mandela, Ngaira Department of Optics and Optical Engineering University of Science and Technology of China Hefei230026 China School of Digital Forensics and Cyber Security National Forensic Sciences University Gujarat Gandhinagar382007 India

This research presents a terahertz-based biosensor for high-precision salinity detection, employing a synergistic integration of graphene, gold, and silver in a metasurface configuration. The sensor exhibits exceptional performance characteristics within the 0.6–2 THz frequency range, demonstrating a maximum sensitivity of 571 GHzRIU−1, quality factor of up to 6.107, and a figure of merit of 2.189 RIU−1. Electromagnetic field distribution analysis demonstrates enhanced light-matter interactions, with peak performance observed at 1.6 THz. The investigation further explored the sensor's potential for multilevel data encoding, illustrating its versatility beyond conventional detection methods. Simulation results indicate the efficiency of the proposed sensor which surpasses some of the previous existing designs in terms of sensitivity among other performance parameters. A machine learning approach using random forest regression was employed to predict sensor responses, achieving coefficient of determination (R2) values ranging from 0.88 to 1.00 across multiple parametric studies. This research presents a promising platform for high-precision salinity sensing with potential applications in environmental monitoring, desalination processes, and aquaculture industries. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Aquaculture

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Vision transformer for contactless fingerprint classification

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Multimedia Tools and Applications 2024年 1-21页

作者： Kaplesh, Pooja Gupta, Aastha Bansal, Divya Sofat, Sanjeev Mittal, Ajay Cyber Security Research Centre Punjab Engineering College Chandigarh160012 India STME SVKM’s NMIMS University Chandigarh160014 India Computer Science and Engineering Punjab Engineering College Chandigarh160012 India UIET Panjab University Chandigarh160014 India

Contactless fingerprint recognition has gained attention due to its data security and hygiene as compared to the contact-based counterpart. In the previous years, deep learning models, mainly Convolutional Neural Networks (CNNs), have been extensively applied in this field and have given promising results. Recently, their successor, Vision Transformers (ViTs) have outperformed CNNs in various computer vision tasks due to their reliance on self-attention mechanism that focusses on capturing global and context-aware relationships between the image patches without inherent spatial bias. Due to the underlying advantageous and innovative feature extraction process, we propose to adapt ViT model for contactless fingerprint classification. To the best of our knowledge, this study would be first of its kind that applies ViT for contactless fingerprint classification. The performance of the model when evaluated on publicly available datasets, namely ISPFDv1, ISPFDv2, and UNFIT, results in average accuracies of 96%, 92%, and 91%, respectively. The comparative analysis with state-of-the-art CNN models using equal error rate (EER) metric shows that the proposed model outperforms them in classifying contactless fingerprints with a significant margin. Additionally, the Local Interpretable Model-agnostic Explanations (LIME) technique is integrated with the proposed model to enhance interpretability and explainability of the predictions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Personalized Educational Video Evaluation Combining Student's Cognitive and Teaching Style

Personalized Educational Video Evaluation Combining Student'...

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2023 IEEE International Conference on Systems, Man, and cybernetics, SMC 2023

作者： Weng, Jinta Dong, Haoyu Deng, Yifan Hu, Yue Wu, Hao Huang, Heyan School of Cyber Security University of Chinese Academy of Sciences China School of Computer Science and Cyber Engineering Guangzhou University China School of Computer Science and Technology Southeast Academy of Information Technology Beijing Institute of Technology China

ISBN: (纸本)9798350337020

AI-powered technologies, like ChatGPT and learning analytic technologies, have encouraged the sharing of online teaching resources and the transformation of teaching methods and learning pathways. However, the mixed resources and the result-oriented video evaluation repeatedly let students fall into an information trap and only appeal to students' attention to unsuitable resources. Inspired by human-computer interaction, a novel online video assessment LPSA(Linguistic- Presentative-scientific-Artistic) is proposed, integrated by cognitive style and teaching style, to realize more precise learning detection and teaching quality assessment. The LPSA evaluation consists of a four-level classification and eight secondary indexes quantified by machine learning algorithms. By automatically searching for an appropriate threshold of all secondary indexes, a real-time video assessment system is developed to certify its technical feasibility and pedagogical availability. The results show that the proposed AI-assisted assessment could realize practical pedagogical recommendations and real-time supervision. © 2023 IEEE.

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Analysis of Tomato Leaf Diseases Detection Models 8th

Analysis of Tomato Leaf Diseases Detection Models

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Satyarthi, Devshri Arya, K.V. Rathore, Santosh Singh Mishra, Richa Multimedia and Information Security Research Group Department of Computer Science and Engineering ABV-IIITM 474015 Gwalior India

ISBN: (纸本)9789819738588

Plant leaf disease distresses the growth of the plant. Therefore, early detection and classification of plant leaf disease is vital for healthy crops such as tomatoes. This early detection of tomato leaf disease in plants is also necessary to stop victims in the agriculture field, in tomato leaf disease plants such as bacterial, early blight, septoria, mosaic virus, and one healthy class. Recently, various deep learning-based architectures, such as AlexNet, ResNet50, and VGG16, have been widely applied for classifying and detecting leaf diseases. Primarily, image processing, convolution neural networks, and deep learning methods have been explored to develop a robust detection model to detect, identify, and classify tomato leaf diseases. However, the previous works’ comprehensive analysis of these models is missing. Therefore, this paper analyzes different deep-learning models for Tomato leaf disease detection. It includes data collection, pre-processing, feature extraction, classification, identification, and detection using AlexNet, ResNet50, and VGG16. The experimental analysis is performed on a plant village dataset for a Tomato plant consisting of one plant, four diseases, and one healthy class. The experimental results showed that ResNet yielded the best performance among the used deep learning models, producing an accuracy value of 98.15%, precision of 98.19%, recall of 97.25%, and F1-score of 97.72%. It is followed by the AlexNet model with an accuracy value of 97.8%, precision of 98.45%, recall of 95.82%, and F1-score of 96.14%. The VGG 16 had the lowest accuracy value of 75%, precision of 74.04%, recall of 68.06%, and F1-score of 70.93%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fruits

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Public Awareness of Phishing in Indonesia: System Literature Review

Public Awareness of Phishing in Indonesia: System Literature...

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2023 International Conference on Data science and Its Applications, ICoDSA 2023

作者： Chandra, Anthony William Chandiarta, Zefanya Setiyawan, Lutfi Gunawan, Alexander Agung Santoso Edbert, Ivan Sebastian Bina Nusantara University Cyber Security Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350305197

In an era where technology is advancing at an unprecedented rate, the internet has become an indispensable tool for everyday life, offering both convenience and efficiency. While the internet has simplified tasks ranging from communication to information retrieval, it also poses significant challenges in the realm of cyber security. Users are not just potential victims but can also become unwitting perpetrators of cybercrimes. This highlights the urgent need for cyber security awareness among the general populace. Focusing on Indonesia, a nation where cyber security education is still in its infancy, this study aims to assess the level of public awareness regarding the specific threat of phishing. Employing the Systematic Literature Review (SLR) methodology, this research analyzes a comprehensive range of literature encompassing subjects such as phishing, data leakage, broader cyber security threats, and the current state of public awareness about phishing. The objective is to provide a detailed overview of existing knowledge and identify areas where educational efforts should be concentrated. The results indicate a considerable gap in the understanding of phishing among Indonesians. Many studies reviewed did not offer a clear definition or explanation of phishing, creating an environment where individuals are more susceptible to these cyber-attacks. This research underscores the urgent need for targeted educational initiatives aimed at increasing public awareness and understanding of phishing and other cyber threats. © 2023 IEEE.

关键词： cybersecurity

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IoT-Based Plant Health Monitor Using NodeMCU and ESP8266 5

IoT-Based Plant Health Monitor Using NodeMCU and ESP8266

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

作者： Tandel, Rajat Ebenezer, V. Santhiya, P. Edwin, Bijolin Stewart Kirubakaran, S. Thanka, Roshni Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Sciences and Cyber Security Coimbatore India

ISBN: (纸本)9798331518097

Plant Monitor Using NodeMCU(ESP8266)is a technology that is advanced;it uses IoT-Internet of Things to measure and monitor different plant phenomena. This system, which is based on sensors that collect data on different factors such as soil type, temperature, humidity, and other vital metrics vital for plant growth, is also integrated. Bringing IoT into the picture, the system can remotely sense the metrics and having control over other factors which include watering, shading, and even security provision for the plants to make it thrive. Besides that, it will also send notifications to users, hence allowing them to engage in the monitoring process through mobile applications with support from their smartphones. The Smart Plant Monitoring System though creates an impact on the efficiency of plant care also impacts on water conservation, automation of plant care process and better agricultural practices. © 2024 IEEE.

关键词： Efficiency

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A novel medical image data protection scheme for smart healthcare system

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 821-836页

作者： Mujeeb Ur Rehman Arslan Shafique Muhammad Shahbaz Khan Maha Driss Wadii Boulila Yazeed Yasin Ghadi Suresh Babu Changalasetty Majed Alhaisoni Jawad Ahmad School of Computer Science and Informatics Cyber Technology InstituteDe Montfort UniversityLeicesterUK Department of Biomedical Engineering University of GlasgowGlasgowUK School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK Robotics and Internet‐of‐Things Laboratory Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Engineering College of Computer Science King Khalid UniversityAbhaSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

关键词： data analysis medical image processing security

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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Vehicular Intrusion Detection System Based on Hybrid Quantum Neural Networks

Vehicular Intrusion Detection System Based on Hybrid Quantum...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhou, Jianhua Liu, Yueheng Li, Sifan Cao, Yue Chen, Wenkai Ahmed, Naveed Wuhan University School of Cyber Science and Engineering China Wuhan Cyber Security Association China Technology Co. Ltd China Prince Sultan University College of Computer and Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350351255

Traditional Deep Learning (DL) method is increasingly used in vehicular Intrusion Detection Systems (IDSs). However, there are still some limitations. It combines various models, resulting in a massive model size and numerous parameters, requiring more computational resources. Furthermore, real-time performance is crucial in vehicular IDS. Large models typically require more time for detection, failing to meet the timeliness requirements. Quantum computing offers parallel computing capabilities, prompting our proposal of a Hybrid Quantum Neural Network (HQNN)-based IDS to improve the timeliness of intrusion detection. This IDS employs HQNN to enhance feature extraction and speed up traditional convolution. It retains most of the structure of classical Convolutional Neural Network (CNN), comprising attention layers, batch normalization layers, and quantum circuits. Through quantum superposition and entanglement, certain complex non-linear functions are compressed. Furthermore, residual connections enable seamless gradient flow during back propagation. This accelerates computation and expedites convergence. Comparative experiments on the Car-hacking dataset show improved detection time and convergence speed, with exceptionally high accuracy in practical applications. © 2024 IEEE.

关键词： Network intrusion

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