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Trust Based Cooperative Secure Routing Protocols for Internet of Things (IoT) 2

Trust Based Cooperative Secure Routing Protocols for Interne...

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Mekala, Sagar Ramji, Bhukya CVR College of Engineering Dept. of Computer Science and Engineering Hyderabad India Kakatiya Institute of Technology and Science Dept. of Computer Science and Engineering Hanmakonda India

ISBN: (纸本)9798350366570

The Internet of Things has rapidly emerged and continues to create services, software, sensors-embedded devices, and protocols. IoT allows physical objects to communicate, exchange information, and make decisions while performing critical jobs. The Internet of Things is enhanced by wireless sensor networks, which serve as a continuous layer. To gain adoption and spread, Internet of things-based sensor networks must overcome serious general and specialized risks and technological hurdles. The main drawbacks of wireless sensor networks are insufficient resources. Internet of Things (IOT)-based Wireless Sensor Networks have issues in energy consumption, longevity, throughput, routing, and security. The goal of this effort was to enhance network longevity, throughput, packet latency/loss, and better encounters with hostile nodes. Consider wireless energy harvesting in the proposed three-layer cluster-based wireless sensor network routing protocol to enhance network lifespan. The proposed approach is a Trust based Cooperative Secure Routing Protocol (TCSRP) technique with security system to obtain malicious activities of objects and to slant them into blacklist. Finally, experiments were done to test the methodology. It outperforms most traditional routing protocols like AODV, DSR, and DSDV. © 2024 IEEE.

关键词： Network layers

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COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

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Enhancing Transaction Security through Iris Recognition 4

Enhancing Transaction Security through Iris Recognition

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Arepalli, Gopi Suresh Bhavana, Paluvai Krishna, Yakkanti Venkata Sai Surendrababu, Chittibomma Lakireddy Bali Reddy College of Engineering Dept. of computer Science and Engineering Mylavaram India Dept. of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350385793

UPI frauds have led to increased use of biometrics for personal identification to ensure security and accuracy. Biometric UPI based on iris recognition technology improves customer service by providing a safe and paperless environment. This system replaces the existing UPI pin and affords protection to the UPI payment from cyber-attacks. During enrollment, genuine user's iris samples and UPI ID are retained in the database, and transactions are completed by capturing and matching iris patterns by using G-6 iris recognizer and biometric Framework. This innovative technology has revolutionized the way to make payments and has significantly reduced the risk of fraudulent activities. With biometric UPI, customers can enjoy a seamless experience when making transactions. The use of biometrics also ensures that there is no chance of identity theft or unauthorized access to sensitive information. © 2024 IEEE.

关键词： Authentication Protocol

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Face Recognition using MTCNN, Inception - Resnet with Ensemble Approach 3

Face Recognition using MTCNN, Inception - Resnet with Ensemb...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mahanthesha, U. Tejaswini, R.M. Nagarathna, C.R. Ravikumar, Y. Tejal Bnm Institute of Technology Dept.of Ai and Ml Bangalore India University of Manchester Dept of Advanced Computer Science United Kingdom

ISBN: (纸本)9798331515911

One of the critical task included in the computer vision in face recognition. Security system, surveillance system, computer-human interactions are some of the applications where face recognition has been widely used. This paper proposes an novel face recognition model that inculcates deep learning and feature extraction techniques. Our model, named "Inception-Resnet"is built to efficiently and accurately recognize faces from images. Inception-Resnet employs a deep convolutional neural network and it also employs a deep CNN architecture that includes multiple layers of convolutional and pooling operations for robust feature extraction. The network is trained using a large-scale dataset consisting of labelled face images, that allows it to learn differential facial features. Additionally, Inception-resnet utilizes a loss function during training phase to encourage the network to learn compact and separable face embeddings in the feature space. Face verification is conducted using an ensemble approach which combines multiple classifiers, including Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and logistic regression, for the classification of face embeddings. © 2025 IEEE.

关键词： Convolutional neural networks

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Multilingual Sentiment Analysis for YouTube Educational Videos Using NLP And Machine Learning Approaches 3

Multilingual Sentiment Analysis for YouTube Educational Vide...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Martina Jose Mary, M. Shyamala Devi, R. Yogeshkannah, K. Prem, S. Pooranachandiran, G. Viyash, V. IFET College of Engineering Dept of Computer Science Engineering India

ISBN: (纸本)9798350391565

Sentiment analysis has emerged as a critical method for measuring the usefulness of YouTube educational videos in the ever-expanding ecosystem of online educational content. The goal of this project is to create a multilingual sentiment evaluation framework that analyzes and discerns the emotional tenor and attitudes depicted in these movies using machine learning and natural language processing (NLP) techniques. This work intends to provide educators, content providers, and learners with essential insights into the emotional impact of educational materials by constructing a sophisticated model capable of interpreting sentiment across many languages. By using this innovative strategy, we seek to raise the standard of online instruction and create an environment that promotes engaging and successful learning across language and cultural barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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Brain Tumour Detection using MRI Images 1

Brain Tumour Detection using MRI Images

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jayashree Mallick, Ahmed Faraz Das, Ashutosh Singh, Abhiraj Shivaprakash, T. Preethi Salian, K. Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering India Vijaya Vittala Institute of Technology Dept of Computer Science and Engineering Bangalore India Nmamit Dept of Information Science and Engineering Nitte Udupi India

ISBN: (纸本)9798331529246

An essential diagnostic technique for brain tumors, Magnetic Resonance Imaging (MRI) enables early detection and improved patient outcomes. However, manual interpretation of MRI scans can be time-consuming and subject to human error. Deep learning has revolutionized medical image analysis by providing automated and accurate classifications, significantly enhancing diagnostic processes. This study leverages the pre-trained Convolutional Neural Network (CNN) model Xception to classify MRI brain data, demonstrating its effectiveness in identifying brain tumors. The model was trained and validated using a diverse dataset, achieving a high test accuracy of 98.78% and training accuracy of 99.96%. These findings highlight the potential of the Xception model in supporting clinical decision-making and reducing diagnostic workloads. The study contributes to the field by showcasing Xception's implementation, dataset preparation, model design, training, and evaluation. Furthermore, it emphasizes the importance of using deep learning techniques for reliable and efficient brain tumor classification, setting the groundwork for future advancements in automated diagnostic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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IoT: Communication protocols and security threats

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 1-13页

作者： Gerodimos, Apostolos Maglaras, Leandros Ferrag, Mohamed Amine Ayres, Nick Kantzavelou, Ioanna School of Computer Science and Informatics University of Thessaly Lamia Greece School of Computing at Edinburgh Napier University Edinburgh United Kingdom Technology Innovation Institute Abu Dhabi United Arab Emirates School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Engineering Dept.of Informatics and Computer Engineering University of West Attica Athens Greece

In this study, we review the fundamentals of IoT architecture and we thoroughly present the communication protocols that have been invented especially for IoT technology. Moreover, we analyze security threats, and general implementation problems, presenting several sectors that can benefit the most from IoT development. Discussion over the findings of this review reveals open issues and challenges and specifies the next steps required to expand and support IoT systems in a secure framework. © 2023 The Authors

关键词： Network architecture

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Text Transmission Through Li-Fi in WSN Using Smart Based Secure Morse Code Algorithm

Text Transmission Through Li-Fi in WSN Using Smart Based Sec...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Vasudevan, I. Malathi, P. Siddik, S. Therasa, P.R. Nasurudeen, M.Mohamed Charaan, R.M.Dilip Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Dept.of Cse Chennai India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Pavaai Engineering College Dept.of Eee Namakkal India R.M.K Engineering College Dept.of Cse Kavaraipettai India V.S.B Engineering College Dept.of Cse Karur India

ISBN: (纸本)9798331509828

Cyber security is one the most challenging task in today's world. Most of the existing Li-Fi technology in wireless sensor network is to detect the data within the short range of distance less accuracy. In this research, we proposed a Smart based Secure Morse Code (SSMC) technique. Here there are two part, first is encoding and next is decoding. This proposed encoding technique takes user input in text based form and convert these text to standard morse code pattern and decoding does in the opposite direction. The transmitter parts encode the user input and send the pattern through laser module. The receiver part receives the pattern sends to microcontroller and then decodes and displays the data. In our experiment results shows that the proposed technique can transmit data over long distance with help of morse code encoding decoding algorithm. This technique takes 5ms for the whole transmission and process the transmission accurately. © 2025 IEEE.

关键词： Encoding (symbols)

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Bio-inspired Heuristic Optimization-based Cascaded Network for Diabetic Retinopathy Screening 3

Bio-inspired Heuristic Optimization-based Cascaded Network f...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Shuvo, Eshat Ahmad Rahman, Wahidur Hossain, Md Shakhawat Islam, Md. Tarequl Iqbal, Md. Sadiq Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Center Independent University Dept. of Computer Science & Engineering & RIoT Bangladesh Khwaja Yunus Ali University Dept. of Computer Science & Engineering Sirajganj Bangladesh

ISBN: (纸本)9798350388282

Diabetic retinopathy (DR) is a common yet fatal complication of diabetic patients in which high levels of blood sugar damage the blood vessels in the retina, the light-sensitive eye tissue crucial for human vision. Early detection and timely intervention are essential to managing DR and preventing severe vision loss. Traditionally, the examination is performed by ophthalmologists manually examining the retinal fundus images to check for signs of DR. This approach is helpful but subjective, time-consuming and tedious. Artificial intelligence (AI)-guided computer vision has recently become very compelling and practical for image analysis and diagnosis. Existing AI-based methods achieved sufficient accuracy at the cost of high computing resources and large datasets. This paper proposes a cascaded network incorporating deep learning and the traditional machine learning approaches with a bio-inspired heuristic optimization algorithm for DR detection from fundus images. The proposed method achieved sufficient accuracy (97.1%) when trained using limited data and low computing machines. The AI models for the cascaded networks were selected through an exhaustive search in which five popular CNN models were used for extracting features;the Bacterial foraging optimization (BFO) was used to determine optimal features, and seven traditional machine models were used to detect the DR. The ResNet50-BFO-SVC cascaded network was found to be most suitable in this study. The proposed cascaded network brings efficiency, accuracy, scalability, and robustness to DR screening. © 2024 IEEE.

关键词： Large datasets

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Automatic Enlarged Lymph Node Detection by Volume Estimation from 3D Abdominal CT Images Based on Speed Up Robust Features and Maximum Intensity Projection

Automatic Enlarged Lymph Node Detection by Volume Estimation...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Al-Shamasneh, Ala'a R. Prince Sultan University Dept.of Computer Science Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350347050

Lymph nodes parts of immune system, play important role in clinical practice, detection is challenging due to low contrast surrounding regions, irregular shapes, variable sizes, and sparsely distributed location. detected the lymph node manually, can be time-consuming, error-prone, and depend on the observer experience. I proposed a fully automatic method for 3D abdominal lymph node detection on CT scans. Detecting lymph node important cause, the main route for metastasis and deciding resection area, so it's important to identify location and structure of lymph node to make suitable surgical procedure. the proposed method consists of candidate lymph node detection procedure, and false positive reduction steps. First Maximum intensity projection (MIP) applied to produce algorithm with low computational time and to eliminate vessels, combination done between the speeded up robust features (SURF) and the blob detection with simplified features for extracting the predicted nodes, adding new calculation parameter to the system which is the volume of the LN, to add extra accuracy for the proposed method, which allow the system from verifying the candidate LN after apply projection between the three axis of the same CT bulk. the method is evaluated for pelvic LN detection on 49 volumes containing 142 LNs, yielding an 85.9%, detection rate with 0.734 false positive per volume, with an average running time of 12-35 seconds per volume. © 2023 IEEE.

关键词： computerized tomography

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