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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Srividya, K. Reddy, Gangannagari Varunteja Bakki, Vishwaja Adilakshmi, T. Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India

ISBN: (纸本)9783031770777

This study presents a new Technology-driven approach for early detection of bone cancer using preliminary image processing technologies and neural networks (CNN) used for diagnosing cancer from pathological images. The main aim is to provide a powerful and reliable tool for cancer diagnosis done in clinics. Survival rates of cancer decrease with an increase in age, urging the need for early diagnosis and precaution. The project aims to use machine learning and image preprocessing to provide solutions for the detection of cancer, necessary for timely intervention. Using carefully selected datasets and advanced image preprocessing techniques, the project aims to provide doctors with reliable tools to accurately diagnose bone cancer. Analysis of existing literature demonstrates the effectiveness of CNN and deep learning algorithms in the analysis of medical images as the basis of the proposed method. The proposed methodology includes image enhancement, data acquisition, CNN development, training, validation, transformation, and model evaluation. The CNN model is evaluated on accuracy, precision, recall, and f1-scores of bone cancer diagnosis images. It provides insight into model development by addressing issues such as classification error, class inequality, and biases. While comparisons with existing literature demonstrate the model's uses, practical considerations are also discussed. The findings suggest a significant potential to improve bone cancer diagnosis and stimulate further research on improving CNN model architecture and developing real-world deployment strategies. The training accuracy of the CNN model came out to be 94.64%. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Diseases

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EzRenter -All In One Rental Platform with Blockchain-Powered Smart Contracts 5

EzRenter -All In One Rental Platform with Blockchain-Powered...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Rose, Able Maria Babu, K. Ouseph, Rosmy Ullas, Sallu Menon, Roshni R. Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur India Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur India

ISBN: (纸本)9798350361155

This conference paper aims to create an innovative rental platform, acts as a bridge between users and providers. This platform transforms the rental landscape, offering a secure environment for seamless transactions through smart contract. It connects individuals and businesses with trustworthy owners, streamlining the rental process and improving user experiences. Unlike other platforms, this application spans diverse categories like tools, vehicles, electronics, clothing, real estate and more. Users enjoy a convenient and time-saving experience, eliminating the need to navigate multiple platforms. The vision is to transcend existing rental service limitations, providing an accessible and enjoyable rental experience for everyone. © 2024 IEEE.

关键词： Smart contract

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Road Lane Detection using Region-based Convolutional Neural Network (RCNN) 1

Road Lane Detection using Region-based Convolutional Neural ...

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

作者： Thondralnayagi, A. Nandhakumar, T. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Road lane markings are one of the main factors that cause traffic accidents and jeopardize the safety of both drivers and pedestrians. Both computer vision systems find it challenging to recognize road lanes. The irregular maintenance, insufficient drainage, and traffic patterns. It negatively affects driving comfort, road safety, and vehicle condition, and it could also lead to a lot of accidents. Recently, several machine learning algorithms have been used, although their accuracy and efficiency have not improved significantly. We suggest applying Region-Based Convolutional Neural Networks (RCNN) for path identification to tackle this issue. RCNNs are another kind of neural network that is capable of extracting significant information from picture and time series data. This makes it highly helpful for tasks requiring images, such as shape identification, object classification, and recognition. Image from a dataset with noise removed using a Wiener filter. The next step is to extract features from images using Linear Discriminant Analysis (LDA). Determine which lane line in the RCNN is efficient and provides accuracy, precision, recall, and F1-score as 95.6%, 94.05%, 94.58%, and 94.23%, respectively. © 2025 IEEE.

关键词： Convolutional neural networks

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Optimized Path Selection and Packet Transfer based on Block Chain using Wireless Sensor Network 1

Optimized Path Selection and Packet Transfer based on Block ...

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

作者： Nithya, R. Venkatasalam, K. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

When designing wireless sensor networks, security and energy efficiency are regarded as crucial considerations. Since nodes in wireless networks run on power sources, it is crucial to create an energy-efficient technique to reduce energy usage and increase the lifespan of the network. This research introduces BC-RSA, an energy-effective and reliable clustering-based data packet transmission technique that combines blockchain-enabled safe data transfer with a clustering technique based on the reptile search algorithm (RSA) in wireless sensor networks. The clustering process is carried out by the suggested BC-RSA technique employing the RSA technology after node initialization. Cluster head identification and cluster building are done via the clustering approach. Blockchain-driven protected data transmission between cluster members and heads occurs using the proposed approach. The BC-RSA technique's experimental validation is evaluated from a number of viewpoints, and the outcomes are contrasted with those of other existing approaches. The results acquired confirmed that the BC-RSA technique has demonstrated higher performance in terms of latency, productivity, efficacy, network lifespan, and distribution ratio. © 2025 IEEE.

关键词： Sensor nodes

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Web service discovery with incorporation of web services clustering

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International Journal of computers and Applications 2023年第1期45卷 51-62页

作者： Jalal, Sunita Yadav, Dharmendra Kumar Negi, Chetan Singh Department of Computer Engineering College of Technology GBPUAT Pantnagar India Department of Computer Science and Engineering MNNIT Allahabad Prayagraj India

With the rapid growth and adoption of web-based technologies, more and more service providers offer various services on the web. This can facilitate the organizations to develop business applications using web services composition over the Internet according to their business requirements. The primary requirement for developing composite web service is to discover the most relevant web service for realizing each function of the required composite web service. Finding the most relevant web service is still a challenging task. In this paper, we propose web service discovery approach that incorporates web services clustering. Clustering technique combines latent Dirichlet allocation(LDA) and k-Medoids and reduces search space for web service discovery. Web service discovery uses a match-making algorithm that utilizes WordNet database and Hungarian method to compute the semantic similarity score for a web service based on the requested query. Experimental results show that the proposed approach performs better than LDA-based approach. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Web services

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Energy Efficient and SecurityAware Task off Loading for Multi-Tier Edge Cloud Computing Systems 1

Energy Efficient and SecurityAware Task off Loading for Mult...

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

作者： Divya, S. Saranya, S. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

The massive progression of the multi-tier edge cloud computing systems integration has created the possibility of data orchestration and resource control in edge and cloud layers. However, there are many shortcomings, including inappropriate methods of scheduling tasks and security breaches that prevent scalability. To solve these problems, we designed an energy-efficient and security-aware task offloading framework for multi-tier edge cloud computing. Firstly, we estimate the energy ratio of tasks using a Fuzzy Logic Energy Management (FL-EM) algorithm and distribute resources according to different layers. Subsequently, the task-offloading parallel squirrel (TO-PS) method is used to effectively extend the task with better energy consumption and computational efficiency. Finally, it is decided to protect the work data using the Provable Energy-Aware Cryptographic Security (PE-ACS) algorithm. The proposed algorithm achieves a 96.7% success rate in protecting high-level data from cybercrime. Additionally, it enhances security and task performance while reducing energy consumption in multi-tier edge cloud computing systems. © 2025 IEEE.

关键词： Cloud security

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Early Stage Pepper Leaf Disease Detection using Deep Learning based Capsule Network with Convolutional Neural Network 1

Early Stage Pepper Leaf Disease Detection using Deep Learnin...

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

作者： Jeevitha, S. Venkadesh, R. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Agriculture plays a vital part in the economy of some countries, providing benefits such as food, national income, and employment. This results in substantial growth in agricultural produce, financial losses, and a decline in quality and quantity. Farmers have traditionally detected diseases through eye tests, but this method has drawbacks. However, previous research suggests that binary classification needs improvement because some types are more challenging to determine. To address these issues, various pepper diseases are classified based on DL techniques to address these issues. First, image pre-processing uses a Median Filter (MF) to reduce noise and increase brightness. Furthermore, the Active Contour Segmentation (ACS) method detects affected and unaffected areas in the image. Moreover, the Texture Color Intensity Feature Selection (TCIFS) method is employed to select the optimal features of pepper disease. Finally, the proposed Capsule Network with Convolutional Neural Network (CapsNet-CNN) technique detects pepper leaf disease. The proposed method attains an accuracy of 98.32%, recall of 97.43%, and precision of 98.04% compared to other methods. © 2025 IEEE.

关键词： Median filters

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Intelligent Anti-Theft System with Automated Spray Control using ESP32 WROOM 14

Intelligent Anti-Theft System with Automated Spray Control u...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Shanmugam, Geetha Balusamy, Deepa Elamparithi, S. Vetriselvan, R. Praamoth, Mourin S. Department of Computer Science and Engineering Nandha Engineering College Erode India

ISBN: (纸本)9798331531935

This project demonstrates a low-cost anti-theft solution built around the ESP32-WROOM microcontroller, with integrated chemical spray for security. The system is designed to detect unauthorized entry and respond with spraying disinfectant chemicals such as pepper spray, thus ensuring security as well as being effective against attackers. The ESP32-WROOM microcontroller is known for its Wi-Fi and Bluetooth capabilities, which plays a key role in providing real-time monitoring and control. Users can also access the system via mobile or web applications. In case of any abnormal situation, the user will be notified immediately. This hospitality provides a living environment for its users. Therefore, they can move with peace of mind and even safeguard their valuable things from anywhere. This system also has a modular design that saves energy for easy installation and is suitable for residential, small business and commercial environments. © 2025 IEEE.

关键词： Disinfectants

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Enhancing Dyslexia Detection: A Robust Machine Learning Approach 3

Enhancing Dyslexia Detection: A Robust Machine Learning Appr...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Manimaran, V. Shanthi, N. Aravindhraj, N. Rakesh, K. Revanth, K. Saran, S. Department of Computer Science and Engineering Kongu Engineering College Erode India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Dyslexia, recognized as a common learning disability, adversely affects reading, spelling, and word decoding capabilities, frequently resulting in considerable academic and social obstacles if not identified at an early stage. Conventional diagnostic approaches, encompassing neuroimaging and standardized assessments, prove to be effective yet are characterized by high costs and limited scalability. In response to this challenge, the present study introduces a hybrid classification model that integrates Random Forest and XGBoost methodologies to improve dyslexia detection through binary classification. The dataset employed comprises both numerical and categorical variables, such as "Gender" and "Native Language," which were processed via one-hot encoding to facilitate the transformation of categorical information. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to mitigate class imbalance, while Principal Component Analysis (PCA) was implemented to diminish dimensionality, thereby enhancing model performance. Preliminary assessments of individual classifiers indicated that Random Forest surpassed both Decision Tree and Neural Network (MLPClassifier), achieving an accuracy of 95.31%, an F1-score of 0.95, and precision metrics of 0.94 for class 1 and 0.97 for class 0. Expanding upon these findings, a hybrid model that merges Random Forest and XGBoost through the application of a VotingClassifier was constructed. This hybrid framework attained an elevated accuracy of 96.23%, accompanied by consistently robust precision, recall, and F1-scores of 0.96. The results underscore the efficacy of the hybrid methodology in offering a scalable and dependable solution for the early detection of dyslexia within both clinical and educational contexts. © 2025 IEEE.

关键词： Adversarial machine learning

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Heart Disease Classification using Adaptive Recurrent Neural Network Method 1

Heart Disease Classification using Adaptive Recurrent Neural...

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

作者： Divya, M. Karthikeyan, A. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Cardiovascular diseases are still among the leading causes of mortality, which require fast and reliable classification methods for diagnosing and preventing the disease. This work thus presents an elaborate framework involving an ARNN for heart disease categorization. First, the technique of Variational Autoencoders (VAE) is used to detect and remove irrelevant or undesirable data records, purging the dataset. Subsequent to this, the Information Gain Feature Selection (IGFS) technique is employed to select the most informative features that lower dimensionality and increase the model's accuracy. Finally, the ARNN is used for classification using the fact that it is a dynamic network that can accommodate temporal characteristics of the data. The experimental results also show that the accuracy, precision, and recall of the approach are higher and higher than those of the conventional method. The proposed framework demonstrates tremendous promise for real-world implementation in medical diagnostics and is a sound and scalable approach to heart disease prediction. Even more striking, these results stress the meaning of applying modern techniques to machine learning in healthcare-sector development. The ARNN achieve 95.6% accuracy for predict the cardiovascular disease. © 2025 IEEE.

关键词： Spatio-temporal data

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