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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Sharma, Pavan Yadav, Rakesh Kumar Rawat, Sur Singh Iftm University Department of Computer Science and Engineering Moradabad244102 India Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow226013 India Jss Academy of Technical Education Department of Computer Science and Engineering Noida201301 India

ISBN: (纸本)9789380544472

Plant diseases can sometimes be so bad that there is no grain harvest at all. Consequently, there is a great need for automatic plant disease identification and diagnosis in the realm of agricultural informatics. Many approaches have been put forth to solve this problem, but deep learning is quickly winning popularity due to its outstanding results. In this study, we assess the transfer learning capabilities of deep convolutional neural networks for the diagnosis of plant leaf diseases. We also consider how well the trained model will perform when applied to the specific task identified by our own data. One of the deep learning methods, the convolutional neural network algorithm (CNN), has been successfully applied to solve computer vision issues such as image classification, object segmentation, picture analysis, etc. In our study, we apply the Densenet201, Xception, InceptionResnetv2, NasNetMobile and VGG-16, hybrid with Support vector machine (SVM) along with a transfer learning approach to recognize diseases in images of different leaves. Instead of starting the training from scratch and arbitrarily initializing the weights, we use the pre-trained networks on the large labelled dataset, ImageNet, to build the weights. The proposed method's validity is supported by experimental findings, and it effectively detects plant diseases. The suggested method significantly outperforms other cutting-edge approaches in terms of performance like (F-scores of 0.93) for VGGG16+SVM, (F-score: 0.97) for DenseNet201+SVM, (F-score: 0.87) for Xception+SVM, (F-score: 0.97) for InceptionResnetv2+SVM, and (F-score: 0.93) NasNetMobile+SVM hybrid model on the dataset of plant images. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Deep neural networks

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Evaluating ASCON Lightweight Encryption Algorithm for Image Encryption

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SN computer science 2024年第8期5卷 1-16页

作者： Alghamdi, Yousef Munir, Arslan Department of Computer Science Kansas State University Manhattan 66506 KS United States Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton 33431 FL United States

Researchers over the past few decades have proposed specialized algorithms for image encryption, in order to solve the slow encryption speed and high computational requirements of traditional encryption algorithms. The National Institute of Standards and Technology (NIST) selected the Ascon algorithm as the new standard for lightweight cryptography applications. In this paper, the Ascon algorithm is tested and evaluated for image encryption. Experimental results show that the Ascon is fast, reliable, and secure. Experimental results further reveal that the Ascon either outperforms most of the contemporary image encryption algorithms in terms of image encryption metrics for quality, efficiency, and security or performs similar to other algorithms. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ascon Image encryption NIST Permutation Substitutions

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Saline Infusion Monitoring and Control System Using IoT 2

Saline Infusion Monitoring and Control System Using IoT

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2nd IEEE International Conference on Distributed Computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Ranjani, J. Hariharan, Shanmugasundaram Jeyashree, Anupama Aishwarya, E. Sri Sairam Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350347456

The use of technology in healthcare is becoming increasingly common with the growth of population and advancements in technology. The healthcare industry is integrating engineering principles to create automated systems instead of manual labor. So, we have proposed an IoT-based saline infusion monitoring system which can greatly improve patient safety by continuously monitoring the saline level using a level sensor and alerting healthcare providers if it falls below 10%. The system includes sensors to monitor the patient's blood pressure and heart rate, and a connection module for remote monitoring to send the alert notifications. The system stops the flow of saline if the level is low, ensuring that the patient's vein is not damaged. This system can significantly improve patient care and minimize the need for monitoring and management. © 2023 IEEE.

关键词： Blood pressure

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Test-Driven Development (TDD) in Small Software Development Teams: Advantages and Challenges 5

Test-Driven Development (TDD) in Small Software Development ...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Ramzan, Hafiz Arslan Ramzan, Sadia Kalsum, Tehmina Institute of Computer and Software Engineering Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan National University of Modern Languages Department of Computer Science Multan Pakistan University of Engineering and Technology Department of Software Engineering Taxila Pakistan

ISBN: (纸本)9798350394764

In the context of small software development teams, this research article gives a thorough investigation of the adoption of test-driven development (TDD) approaches. It aims to highlight the benefits that TDD offers, such as improved code quality through modularization and proactive defect spotting which results in effective debugging and development processes. It also discusses the complex issues that arise when TDD is implemented in smaller teams, such as the learning curve and resource constraints. This study significantly advances the understanding of how TDD can be used to optimize software development techniques in organizations or software houses having small development teams. It also explores the possible advantages and challenges. © 2024 IEEE.

关键词： Modular construction

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Hybridized CNN-Densenet Model for Dermatological Classification of Histopathological Skin Images 2

Hybridized CNN-Densenet Model for Dermatological Classificat...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： De, Anubhav Mishra, Nilamadhab Chang, Hsien-Tsung School of Computing Science & Engineering Vit Bhopal University Madhya Pradesh India Chang Gung University Department of Computer Science and Information Engineering Taoyuan333 Taiwan

ISBN: (纸本)9798350370188

This research paper presents a deep neural network model for the classification and prediction of skin diseases from dermatoscopic images. The model integrates a traditional Convolutional Neural Network with a Pre-trained Densenet model, achieving state-of-the-art performance on the HAM10000 dataset. The methodology involved rigorous data preprocessing, including data cleaning, exploratory data analysis, data splitting, normalization, and label encoding. Techniques such as Model Hybridization, Batch Normalization, Max Pooling, and Data Fitting were employed to optimize the model architecture and data fitting. Initial iterations of the CNN model achieved an accuracy of 76.22% on Test Data and 75.69% on Validation Data. To improve, the model was hybridized with DenseNet architecture and hyperparameter tuning was employed. The model demonstrated an impressive accuracy of approximately 89% on the HAM10000 dataset. This model offers customization potential for more nuanced clinical uses and presents a significant advancement in automating skin disease diagnosis. © 2024 IEEE.

关键词： Neural network models

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Breast Cancer Diagnosis Using Elephant Herding Optimization and Sparse Autoencoder Through Gene Expression Analysis 15th

Breast Cancer Diagnosis Using Elephant Herding Optimization ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Kavya, B. Meghana, G. Rajaguru, Harikumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar University Bangalore India Department of Computer Science and Engineering PES University RR Campus Bangalore India

ISBN: (纸本)9783031810794

Gene expression analysis is an efficient modality to detect breast cancer. But analysis of the gene expression data is a complicated task due to the involvement of large number of numerical data for each subject. This research work focuses on the development of computerized diagnosis systems for analyzing the gene expression data and classifying the subjects as either normal or breast adenocarcinoma. Sparse autoencoder is used for dimensionality reduction and Elephant Herding Optimization is used for transforming the features. Four different supervised classifiers namely support vector machine, random forest classifier, decision trees, and naive bayes classifier are examined for classification task. Among them, random forest provides the superior balanced accuracy of 86% while it was further enhanced to 92% with the help of sparse autoencoders and elephant herding optimization algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transcription

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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Identification of earthquake induced structural damage based on synchroextracting transform

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Earthquake engineering and engineering Vibration 2024年第2期23卷 475-487页

作者： Roshan Kumar Gaurav Kumar Wei Zhao Arvind R Yadav Gang Yu Jayendra Kumar Evans Amponsah Department of Electronic and Information Technology Miami College of Henan UniversityHenan 475004China Alliance University Alliance College of Engineering and DesignBanglore 562106India Department of Electronics and Comm Parul UniversityVadodra 391760India School of Electrical Engineering University of JinanJinan 250022China Department of Electronics and Comm NIT JamshedpurJamshedpur 831014India Department of Civil engineering Kwame Nkrumah University of Science and TechnologyKumasi 10959Ghana

Several popular time-frequency techniques,including the Wigner-Ville distribution,smoothed pseudo-Wigner-Ville distribution,wavelet transform,synchrosqueezing transform,Hilbert-Huang transform,and Gabor-Wigner transform,are investigated to determine how well they can identify damage to *** this work,a synchroextracting transform(SET)based on the short-time Fourier transform is proposed for estimating post-earthquake structural *** performance of SET for artificially generated signals and actual earthquake signals is examined with existing *** other tested techniques,SET improves frequency resolution to a great extent by lowering the influence of smearing along the time-frequency ***,interpretation and readability with the proposed method are improved,and small changes in the time-varying frequency characteristics of the damaged buildings are easily detected through the SET method.

关键词： cross-term damage detection earthquake signal synchroextracting transform time-frequency

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Secure and Privacy-Preserving Human Interaction Recognition of Pervasive Healthcare Monitoring

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IEEE Transactions on Network science and engineering 2023年第5期10卷 2439-2454页

作者： Yang, Chao-Lung Tampubolon, Hendrik Setyoko, Aji Hua, Kai-Lung Tanveer, Mohammad Wei, Wei National Taiwan University of Science and Technology Department of Industrial Management Taipei106 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan Indian Institute of Technology Indore Department of Mathematics Indore453552 India Xi'An University of Technology School of Computer Science and Engineering Shaanxi 710048 China

A cloud-based Artificial Intelligence (AI) service has recently empowered the Internet of Medical Things (IoMT) in many applications on the remote Human Interaction Recognition of Pervasive Healthcare Monitoring (HIR-PHM). In this work, a framework of the skeleton-based HIR-PHM under secure Edge-Fog-Cloud computing (EFCC) was proposed to manage the computation and storage resources, latency, cyber-attack, and privacy-preserving simultaneously. At the Edge, IoMT with a camera, record the human interaction as videos and sends them to the Fog, which installed a human pose estimation model, PoseNet, to convert the videos into human skeleton data. At the Cloud, a skeleton-based Spatial-Temporal Graph Convolution Network with Pairwise Adjacency Matrix (STGCN-PAM) was employed to recognize the human interaction. A Hybrid of One Time Password (OTP), Hashed Messages Authentication Code (HMAC), and symmetric cipher were employed to secure the skeleton data. The UT-Interaction dataset was used to evaluate the proposed framework. Besides, the computation performance and latency under EFCC were compared with Edge-Fog and Edge-Cloud deployments. Experimental results confirmed two major contributions: 1) the proposed framework has promising performance compared with other methods on the dataset. 2) The skeleton-based HIR-PHM with secure EFCC shows the advantages of better computation performance in terms of frame per second. © 2013 IEEE.

关键词： Authentication

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Modernizing Home Protection: An IoT-Driven Approach with Smart Lock and Android Application 14

Modernizing Home Protection: An IoT-Driven Approach with Sma...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Rajput, Summaiya Talpur, Noureen Boudville, Rozan Abro, Ghulam Mustafa E Talpur, Beenish Zahid, Faizan Mehran University of Engineering and Technology Department of Telecommunications Engineering Jamshoro Pakistan Computer Information Science Department Seri Iskandar32610 Malaysia Electrical Engineering Studies College of Electrical Engineering Universiti Teknologi MARA Permatang Pauh Pulau Pinang13500 Malaysia University of Petroleum and Minerals Dhahran Interdisciplinary Research Center for Aviation and Space Exploration King Fahd Dammam31261 Saudi Arabia University of Catania Electronics and Computer Engineering Department of Electrical Italy

ISBN: (纸本)9798350364507

In the field of access control locks, conventional methods often require users to download applications with Bluetooth connectivity, and biometric measures such as fingerprints or face recognition that introduce challenges like access to only authorized parties, limited device accessibility and intricate registration processes. To address these challenges, the research paper presents an innovative IoT-based Smart Door Lock System, aiming to redefine access control. Diverging from traditional approaches, this system eliminates the need for application downloads and simplifies the complexities tied to biometrics. The core innovation lies in the simplicity of a user-friendly 4-digit PIN for primary access, prioritizing ease of use. To overcome real-time access constraints, the system introduces One-time Password (OTP) and One Touch Access (OTA) modes. These modes eliminate the intricacies linked with traditional methods, ensuring immediate and hassle-free access for both authorized and unauthorized users. © 2024 IEEE.

关键词： Face recognition

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