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2022 Iraqi International Conference on Communication and Information Technologies, IICCIT 2022

作者： Mohammed, Rasha Anwar Alheeti, Khattab M Ali College of Computer Sciences Also Information Technology University of Anbar Department of Computer Science Iraq University of Anbar College of Computer Sciences Also Information Technology Department of Computer Networking Systems Ramadi Iraq

ISBN: (纸本)9781665472203

The rapid growth and development of the Internet of Medical Things (IOMT) has significantly altered how diseases are managed, improved ways for diagnosing and treating diseases, and decreased the cost and errors associated with healthcare and as a result, network security has always been a crucial problem especially when it comes to maintaining the confidentiality of patients' medical data that include highly sensitive information which make it vulnerable to constant attacks by hackers. An Attack of this kind constitute one of the gravest risks to healthcare systems globally, especially those systems with a vulnerable infrastructure. This makes it easy to target these systems which not only put patients' privacy at risk but also their lives at risk which only serves to stress the importance of the establishment of a strong attack detection system. This study offered a deep learning strategy for identifying data as assault or normal. Data preparation and classification are the two parts of the proposed system. The selected data set has been pre-processed in the first phase then a Convolutional Neural Network (CNN) is employed in the second phase. The Convolutional Neural Network (CNN) model was applied to the dataset in the second phase. The findings imply that the Convolutional Neural Network can identify assaults quicker than previous approaches. The trial results indicated that the data set constructed using the CNN model as the basic classifier had the best classification accuracy of 99.28 with an error rate of 0.0846. © 2022 IEEE.

关键词： Convolution

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A Smart Irrigation System for Coconut Farming using IoT 3

A Smart Irrigation System for Coconut Farming using IoT

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Russia, S. Mohanapriya, B. Gowrishankar, V. Parimala Devi, M. Pradeep, K.K. Brindha, P. Velalar College of Engineering and Technology Department of Computer Science and Engineering India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Coconut farming is an essential agricultural practice that contributes significantly to the global economy by providing valuable products such as coconut water, oil, and meat. However, the management of coconut plantations faces numerous challenges, including labor shortages, pest control, and the demand for precise irrigation and fertilization. This paper proposes an integrated smart farming system that leverages advanced sensor technologies and automation to address these challenges. The system employs precision irrigation and fertigation methods, facilitated by a centralized motor controller and solenoid valves, to deliver water and nutrients efficiently to the base of each coconut tree. A pH sensor monitors soil acidity or alkalinity, ensuring optimal nutrient availability and promoting sustainable farming practices by reducing chemical inputs. Additional features include a tree motion sensor and a tilt sensor for monitoring tree stability under adverse weather conditions. Soil moisture sensors, temperature, humidity, and light intensity sensors provide real-time data to enhance decision-making. Farmers can remotely monitor and control the system via a Node-RED dashboard, receiving alerts through platforms like Telegram. By integrating these technologies, the proposed system aims to optimize resource utilization, enhance crop yield, and reduce the environmental impact of coconut farming. © 2025 IEEE.

关键词： Soil moisture

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LLM-Driven Cognitive Diagnosis with SOLO Taxonomy: A Model-Agnostic Framework

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Frontiers of Digital Education 2025年第2期2卷 20-20页

作者： Zhiang Dong Jingyuan Chen Fei Wu College of Computer Science and Technology Zhejiang University Hangzhou China

With the development of the Internet and intelligent education systems, the significance of cognitive diagnosis has become increasingly acknowledged. Cognitive diagnosis models (CDMs) aim to characterize learners’ cognitive states based on their responses to a series of exercises. However, conventional CDMs often struggle with less frequently observed learners and items, primarily due to limited prior knowledge. Recent advancements in large language models (LLMs) offer a promising avenue for infusing rich domain information into CDMs. However, integrating LLMs directly into CDMs poses significant challenges. While LLMs excel in semantic comprehension, they are less adept at capturing the fine-grained and interactive behaviours central to cognitive diagnosis. Moreover, the inherent difference between LLMs’ semantic representations and CDMs’ behavioural feature spaces hinders their seamless integration. To address these issues, this research proposes a model-agnostic framework to enhance the knowledge of CDMs through LLMs extensive knowledge. It enhances various CDM architectures by leveraging LLM-derived domain knowledge and the structure of observed learning outcomes taxonomy. It operates in two stages: first, LLM diagnosis, which simultaneously assesses learners via educational techniques to establish a richer and a more comprehensive knowledge representation; second, cognitive level alignment, which reconciles the LLM’s semantic space with the CDM’s behavioural domain through contrastive learning and mask-reconstruction learning. Empirical evaluations on multiple real-world datasets demonstrate that the proposed framework significantly improves diagnostic accuracy and underscoring the value of integrating LLM-driven semantic knowledge into traditional cognitive diagnosis paradigms.

关键词： large language models cognitive diagnosis models intelligent education system SOLO taxonomy knowledge representation

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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Radio Environment Map Based Routing Protocol for UAV Networks 29

Radio Environment Map Based Routing Protocol for UAV Network...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zheng, Siyue Zhu, Xiaojun Qin, Zhenrui Dong, Chao Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Northwest Missouri State University School of Computer Science and Information Systems United States Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering China

ISBN: (纸本)9798350330717

Unmanned Aerial Vehicles (UAVs), now often deployed on battlefields, use wireless networks to communicate with each other. Such wireless communication, however, may become unreliable under the jamming from opponents, and existing UAV routing protocols perform poorly when dealing with the jamming. In this paper, we propose to use the spectrum jamming prediction data from the radio environment map (REM) to update the network topology. The key is to model the network as a weighted graph where the weights of the edges between two neighboring nodes are functions of the location of the jammer. We then update the routing paths accordingly in real-time. The protocol can be incorporated with existing table-driven protocols. We implement the protocol in a commercial network simulator. Simulations show that our routing protocol improves the packet delivery ratio and throughput of existing table-driven routing protocols. © 2023 IEEE.

关键词： Radio Environment Maps routing protocol spectrum jamming UAV ad hoc network

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Analysing the Accuracy of Detecting Phishing Websites using Ensemble Methods in Machine Learning 3

Analysing the Accuracy of Detecting Phishing Websites using ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Menaka, S. Harshika, Jonnalagadda Philip, Sarah John, Rashi Bharathiraja, N. Murugesan, S. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Chitkara University Institute of Engineering and Technology Department of Computer Science and Engineering Chennai India Tagore Engineering College Department of Information Technology Rathinamangalam Chennai India

ISBN: (纸本)9781665462167

Phishing attacks are now one of the prevalent dangers that firms, service providers and internet users must deal with. Rather than targeting software vulnerabilities, it targets human vulnerabilities. It is the act of enticing users to attain their personal data using fake emails and websites. Like how e-commerce sectors are growing, phishing attacks are also developing. Preventing phishing attempts is a critical aspect of protecting online transactions. Since hacktivists, spy agencies and cybercriminals now have a rich field in which they can operate sophisticated phishing attacks, prompt detection of phishing attempts is more critical than ever. To properly respond to various phishing attacks, it is required to gain a thorough understanding of these attacks, and suitable response techniques must be used. The challenges faced in this research is finding the appropriate datasets and Feature extraction prompted the study of several modules, in addition to understanding every module and attaining the desired outcome from it. Machine learning techniques are used to accurately identify phishing attacks before cause harm to a user. Being able to handle the changing nature of phishing attempts and offering an accurate method of classification, it is one of the most practical ways to approach the situation. © 2023 IEEE.

关键词： Websites

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An Innovative Method for Mobile Network Payment Security System Based on hybrid LSTM-Ensemble Learning-Based Model 3

An Innovative Method for Mobile Network Payment Security Sys...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Sujatha, N. Pramod, S. Aravindh, S. Babu, Balagopala Ramesh Devi, S. Rukmani Ananthi, M. B. M. S. College of Engineering Department of Mathematics Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Yelahanka Department of Mathematics Karnataka Bangalore India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India SRKR Engineering College Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Tamilnadu Chennai India KGiSL Institute of Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India

ISBN: (纸本)9798350354171

It is common practice in many industries to use cash since it accounts for more than 85% of payments in almost all developing countries. A cell phone is become an everyday item. Mobile phones have practically become people's constant companions due to the plethora of other uses they offer beyond just communicating. Since they are both cheap and multipurpose, soon everyone will be utilizing them. In an ideal world, everyone would use their cell phone for everything. Verification, preprocessing, and training the model are the three parts that make up the method. In order to confirm the payment, the customer's biometric template is checked during the verification procedure. The three stages of fingerprint preparation include matching fingerprint templates, extracting fingerprint minutiae, and fingerprint preprocessing. The training of the model was carried out using a Hybrid LSTM-Ensemble Learning. The average accuracy of the suggested method is 92.76%, which is higher than LSTM and Ensemble Learning. © 2024 IEEE.

关键词： Contrastive Learning

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Artificial Intelligence in Cybersecurity: Enhancing Automated Defense Mechanisms to Combat Sophisticated Cyber Threats and Guarantee Digital Resilience 5

Artificial Intelligence in Cybersecurity: Enhancing Automate...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Prakash, V.S. Murugesan, P. Poongothai, P. Shivakumar, B.N. Vijayakumar, P. Shriidhar, P.J. Department of Computer Science Bengaluru India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Cmr Institute of Technology Department of Mathematics Bengaluru India Kongu Engineering College Department of Computer Applications Perundurai Erode India Department of Cse Salem India

ISBN: (纸本)9798350376685

The rapid evolution of cyber threats has necessitated a paradigm shift in cybersecurity approaches, with a growing emphasis on leveraging artificial intelligence (AI) to bolster automated defense mechanisms. This research delves into the integration of AI technologies within cybersecurity frameworks, exploring their potential to enhance digital resilience against sophisticated cyber threats. By examining the current landscape of cyber attacks and the limitations of traditional defense strategies, the study underscores the need for intelligent, adaptive systems capable of learning from vast datasets and responding in real-time to emerging threats. The research focuses on the development and evaluation of AI-driven defense mechanisms, including machine learning algorithms for anomaly detection, predictive analytics for threat forecasting, and natural language processing for cyber threat intelligence. Through a series of controlled experiments and case studies, the effectiveness of these AI-enhanced systems is assessed in terms of their accuracy, speed, and adaptability to new attack vectors. Findings indicate that AI-augmented cybersecurity solutions can significantly improve the detection and mitigation of advanced persistent threats, zero-day exploits, and other complex cyber attacks. Moreover, the research highlights the importance of ethical considerations and the responsible use of AI in cybersecurity to prevent the misuse of powerful AI tools and ensure privacy and fairness. The study concludes with recommendations for the strategic implementation of AI in cybersecurity, emphasizing the need for a holistic approach that combines human expertise with AI capabilities to achieve robust digital resilience. By fostering a collaborative ecosystem between cybersecurity professionals, AI developers, and policymakers, this research aims to contribute to the ongoing effort to safeguard digital infrastructure against the ever-evolving landscape of cyber threats. © 2024 IEE

关键词： Adversarial machine learning

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A Novel Optimizer in Deep Neural Network for Diabetic Retinopathy Classification

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computer systems science & Engineering 2022年第12期43卷 1099-1110页

作者： Pranamita Nanda N.Duraipandian Department of Computer Science and Engineering Velammal Institute of TechnologyChennai601204TamilnaduIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennai602105TamilnaduIndia

In severe cases, diabetic retinopathy can lead to blindness. For decades,automatic classification of diabetic retinopathy images has been a challenge. Medical image processing has benefited from advances in deep learning systems. Toenhance the accuracy of image classification driven by Convolutional Neural Network (CNN), balanced dataset is generated by data augmentation method followed by an optimized algorithm. Deep neural networks (DNN) are frequentlyoptimized using gradient (GD) based techniques. Vanishing gradient is the maindrawback of GD algorithms. In this paper, we suggest an innovative algorithm, tosolve the above problem, Hypergradient Descent learning rate based Quasi hyperbolic (HDQH) gradient descent to optimize the weights and biases. The algorithms only use first order gradients, which reduces computation time andstorage space requirements. The algorithms do not require more tuning of thelearning rates as the learning rate tunes itself by means of gradients. We presentempirical evaluation of our algorithm on two public retinal image datasets such asMessidor and DDR by using Resnet18 and Inception V3 architectures. The findings of the experiment show that the efficiency and accuracy of our algorithm outperforms the other cutting-edge algorithms. HDQHAdam shows the highestaccuracy of 97.5 on Resnet18 and 95.7 on Inception V3 models respectively.

关键词： CNN diabetic retinopathy data augmentation gradient descent deep learning optimization

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Lighting the Smart Way: A Technological Odyssey in Urban Street Illumination 10

Lighting the Smart Way: A Technological Odyssey in Urban Str...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Vinu, M.S. Priya, P Kanaga Thilakaveni, P. Hermina, J. M. Ashforn Prashanthini, K. Misba, M. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Psg Polytechnic College Department of Computer Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Ramakrishna Engineering College Department of Robotics and Automation Coimbatore India Bethlahem Institute of Engineering Department of Computer Science and Engineering Nagercoil Karungal India

ISBN: (纸本)9798350384369

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure © 2024 IEEE.

关键词： Air quality

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