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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Agarwal, Soumil Byramdharmika, M. Dhathri, Sree Thanikaiselvan, V. Subashanthini, S. Amirtharajan, Rengarajan Vellore Institute of Technology School of Electronics Engineering Vellore India Engineering and Information Systems Vellore Institute of Technology School of Computer Science Vellore India Sastra Deemed University School of Electrical & Electronics Engineering Thanjavur India

ISBN: (纸本)9798350353068

In today's digital world, safeguarding data at all times is important, especially with the widespread use of images across various processes. Image encryption is vital in concealing sensitive information through cryptographic techniques, rendering data indecipherable to unauthorized parties. However, existing encryption systems often fall short in terms of both efficiency and security. This research explores chaos theory for image encryption methodologies to address these limitations. A comprehensive study of image encryption fundamentals was conducted, including chaos theory, various chaotic maps, DNA encoding, and encryption system evaluation metrics. Implementation of different chaotic maps such as the logistic map, tent map, and combined logistic-tent map, along with 3D chaotic systems like the Lorenz and Rossler systems, was performed for encryption purposes. Additionally, the 4D extension of the Rossler hyperchaotic system was introduced for enhanced encryption capabilities. Encryption methods were evaluated using metrics such as UACI, NPCR, correlation coefficients, image entropy and histogram analysis, with results compared and tabulated. This research advances image encryption techniques by leveraging chaos theory to achieve heightened security and efficiency in safeguarding image data. © 2024 IEEE.

关键词： Chaotic systems

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Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

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computers, Materials & Continua 2022年第12期73卷 4695-4711页

作者： Mohammed Altaf Ahmed Sara A Althubiti Dronamraju Nageswara Rao ELaxmi Lydia Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science and Engineering Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Cyberattacks are developing gradually sophisticated,requiring effective intrusion detection systems(IDSs)for monitoring computer resources and creating reports on anomalous or suspicious *** the popularity of Internet of Things(IoT)technology,the security of IoT networks is developing a vital *** of the huge number and varied kinds of IoT devices,it can be challenging task for protecting the IoT framework utilizing a typical *** typical IDSs have their restrictions once executed to IoT networks because of resource constraints and ***,this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with Deep Learning(BAIDS-DFPDL)model in IoT *** presented BAIDS-DFPDLmodelmainly focuses on the identification and classification of intrusions in the IoT *** accomplish this,the presented BAIDS-DFPDL model follows blockchain(BC)technology for effective and secure data transmission among the ***,the presented BAIDSDFPDLmodel designs Differential Flower Pollination based feature selection(DFPFS)technique to elect ***,sailfish optimization(SFO)with Restricted Boltzmann Machine(RBM)model is applied for effectual recognition of *** simulation results on benchmark dataset exhibit the enhanced performance of the BAIDS-DFPDL model over other models on the recognition of intrusions.

关键词： Internet of things feature selection intrusion detection blockchain security deep learning

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Application of Random Forest Regressions on Stellar Parameters of A-type Stars and Feature Extraction

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Research in Astronomy and Astrophysics 2022年第2期22卷 189-194页

作者： Shu-Xin Chen Wei-Min Sun Ying He Qiqihar University Qiqihar 161006China Key Lab of In-fiber Integrated Optics Ministry Education of ChinaHarbin Engineering UniversityHarbin 150009China Department of Computer Science and Technology Tianjin Ren'ai CollegeTianjin 301636China

Measuring the stellar parameters of A-type stars is more difficult than FGK stars because of the sparse features in their spectra and the degeneracy between effective temperature(T_(eff))and gravity(log g).Modeling the relationship between fundamental stellar parameters and features through machine learning is possible because we can employ the advantage of big data rather than sparse known *** soon as the model is successfully trained,it can be an efficient approach for predicting Teffand log g for A-type stars especially when there is large uncertainty in the continuum caused by flux calibration or *** this paper,A-type stars are selected from LAMOST DR7 with a signal-to-noise ratio greater than 50 and the Teffranging within 7000 to 10,000 *** perform the Random Forest(RF)algorithm,one of the most widely used machine learning algorithms to establish the regression relationship between the flux of all wavelengths and their corresponding stellar parameters(T_(eff))and(log g)*** trained RF model not only can regress the stellar parameters but also can obtain the rank of the wavelength based on their sensibility to *** to the rankings,we define line indices by merging adjacent *** objectively defined line indices in this work are amendments to Lick indices including some weak *** use the Support Vector Regression algorithm based on our new defined line indices to measure the temperature and gravity and use some common stars from Simbad to evaluate our *** addition,the Gaia Hertzsprung-Russell diagram is used for checking the accuracy of Teffand log g.

关键词： methods:data analysis surveys stars:early-type stars:abundances

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Fault Diagnosis Based on the Updating Strategy of Interval-Valued Belief Structures

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Chinese Journal of Electronics 2025年第4期23卷 753-760页

作者： Xiaobin Xu Ping Liu Yanbo Sun Chenglin Wen Institute of Systems Science and Control Engineering School of Automation Hangzhou Dianzi University Hangzhou China Department of Control Science and Engineering School of Astronautics Harbin Institute of Technology Harbin China

This paper presents the dynamic method for fault diagnosis based on the updating of Interval-valued belief structures (IBSs). The classical Jeffrey's updating rule and the linear updating rule are extended to the framework of IBSs. Both of them are recursively used to generate global diagnosis evidence with the form of Interval basic belief assignment (IBBA) by updating the previous evidence with the incoming evidence. The diagnosis decision can be made by global diagnosis evidence. In the process of evidence updating, the similarity factors of evidence are used to determine switching between the extended Jeffrey's and linear updating rules, and to calculate the linear combination weights. The diagnosis examples of machine rotor show that the proposed method can provide more reliable and accurate results than the diagnosis methods based on Dempster-Shafer evidence theory.

关键词： Fault diagnosis Accuracy Evidence theory Rotors Switches Reliability theory Real-time systems

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Progressive Expression of Bean Leaf Lesions: A Comprehensive Analysis Using Spatio-Temporal Disease Classification Solutions Based on CNN and LSTM Networks 5

Progressive Expression of Bean Leaf Lesions: A Comprehensive...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Upadhyay, Deepak Manwal, Manika Gupta, Ajay Kumar Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttrakhand Dehradun India Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

The appearance of Bean leaf lesions can lead to major damage and losses in crop production, requiring innovative approaches for early detection. This study proposes a new hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model for bean leaf lesion detection, especially in the classification of severity levels. It is based on a highly maintained database with 7000 images representing lesions at different stages. The proposed model presents a high overall accuracy of 98.52% which highlights its ability to distinguish lesions on each of the four severity levels - mild, moderate, severe, and critical. The architecture of the model comprises CNN to extract features from leaf images based on spatial awareness and LSTM for temporal lesion detection that captures how the pattern changes over time. The evaluation of performance parameters such as precision, recall, and F1-score helps augment the analysis conducted on accuracy since they provide insights into what works well within the model for further development. The addition of a confusion matrix depicts the forecast performance of the model and more specifically what severity classes present where the accuracy is good or needs attention. Not only does this study add to the state-of-the-art in disease detection of crops but also contributes towards a wider discussion on utilizing artificial intelligence applied for precision agriculture, reasonable decision making, and reliable crop management. © 2024 IEEE.

关键词： agricultural disease detection Bean leaf lesions CNN confusion matrix Convolutional Neural Network crop health deep learning image analysis Long Short-Term Memory LSTM machine learning model evaluation overall accuracy performance parameters precision agriculture severity classification sustainable farming

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A Deep Learning Approach for Classification of Vitiligo and Scar Images 10

A Deep Learning Approach for Classification of Vitiligo and ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajesh, E. Madanagopal, C. John, Reovwin Thanikaiselvan, V. Tamizharasi, T. Amirtharajan, Rengarajan Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sastra Deemed University School of Electrical & Electronics Engineering Thanjavur613 401 India

ISBN: (纸本)9798350353068

Skin diseases such as vitiligo present significant challenges in accurate diagnosis due to their similarities with other conditions, often requiring time-consuming assessments by medical professionals. Leveraging advancements in deep learning and medical image processing, this research proposes a novel approach to efficiently and accurately classify these conditions. The proposed model comprises multiple convolutional layers for extracting intricate features from skin images, followed by max-pooling layers to downsample spatial dimensions while retaining essential information. Additionally, fully connected layers enable complex feature learning and classification. This architecture, with six convolutional layers progressively increasing in depth and spatial reduction through max-pooling, culminates in three fully connected layers for precise classification. This comprehensive design ensures effective classification between vitiligo and scar images. By utilising this architecture, our model achieves a high training accuracy of 93.53% and precision of 96.50%, demonstrating its effectiveness in discriminating between vitiligo and scar images. This approach holds promise for enhancing diagnostic accuracy and efficiency in clinical settings, contributing to improved patient outcomes and healthcare delivery. © 2024 IEEE.

关键词： Principal component analysis

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Interaction graph learning of line cascading failure in power networks and its statistical properties

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Energy Informatics 2023年第1期6卷 17页

作者： Ghasemi, Abdorasoul de Meer, Hermann Kantz, Holger Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran Faculty of Computer Science and Mathematics Passau University Passau Germany Max Planck Institute for the Physics of Complex Systems Dresden Germany

We consider line failure cascading in power networks where an initial random failure of a few lines leads to consecutive other line overloads and failures before the system settles in a steady state. Such cascades are rooted in non-obvious, long-range, and higher-order couplings among the lines’ flows induced by physical constraints on the network. Failure interaction graph encodes which and to what extent other lines in a networked system are affected after each line failure and can help to predict the final state after an initial disturbance. We perform data analytics on the final lines’ steady states of cascade trajectories to infer a specific line’s state given the states of others. We use a generative model to reconstruct possible steady states, and a predictive model aims to predict the probability of each line’s failures after the initial failure as a regression problem. The generative model uses regularized pseudolikelihood estimator to infer interaction weights by solving the inverse Ising problem and deploys Glauber dynamics to generate steady states. The discriminative model uses boosted trees to efficiently learn over training and predict over test data the state of each line as a target finding an appropriate subset of other lines’ states as explanatory variables. We analyze the degree distribution of the corresponding interaction graphs to study the number of other components affected by each line failure (out-degree) or the number of lines that affect the state of a given line (in-degree). Both models show that the in-degree follows a power-law distribution. Finally, we discuss the possible application of the interaction graph for early link removal to mitigate the failure-cascading consequences. © 2023, The Author(s).

关键词： Forecasting

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Efficient MMSE Equalization for Direct-Sequence Spread-Spectrum Underwater Communications

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IEEE Internet of Things Journal 2025年第11期12卷 16325-16335页

作者： Liu, Mengzhuo Liu, Jun Peng, Zheng Cui, Jun-Hong Beihang University School of Electronic and Information Engineering Beijing100191 China Jilin University College of Computer Science and Technology Changchun130012 China UESTC Shenzhen Institute for Advanced Study Shenzhen518110 China Smart Ocean Technology Co. Ltd Shenzhen518057 China

To facilitate the exploration and exploitation of underwater resources, autonomous systems and Underwater Acoustic Networks (UANs) are deployed for tasks unsuitable for direct human intervention and exchange information between devices. To keep the reliability of information exchanged, Direct Sequence Spread Spectrum (DSSS) communication is commonly adopted for this scenario. To simplify the channel equalization process in DSSS communication, a Minimum Mean Square Error (MMSE) equalizer is often utilized. However, the characteristics of underwater acoustic channels and acoustic modem, including long delay spreads and limited computational resources, lead to high computational complexity for MMSE equalization, thereby reducing decoding efficiency. To address this challenge, we propose a refined MMSE equalizer, termed the Efficient MMSE Equalizer (EME). Unlike conventional MMSE methods, the EME approach involves initially despreading the received chip sequence, followed by equalizing on the noisy symbols. By reducing the size of the correlation matrix in the core computational step of MMSE equalization, our method significantly improves computational efficiency. We assess the computational complexity of the proposed EME approach in comparison to conventional MMSE equalization and validate its performance through simulations and experimental studies. The results demonstrate that the EME achieves a bit error rate (BER) performance comparable to that of conventional MMSE equalization under high signal-to-noise ratio (SNR) conditions, while significantly enhancing computational efficiency. © 2014 IEEE.

关键词： Equalizers

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An Intelligent Framework for Grassy Shoot Disease Severity Detection and Classification in Sugarcane Crop 2

An Intelligent Framework for Grassy Shoot Disease Severity D...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Banerjee, Deepak Kukreja, Vinay Hariharan, Shanmugasundaram Jain, Vishal Dutta, Soumi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering UP Greater Noida India Institute of Engineering & Management West Bengal Kolkata India

ISBN: (纸本)9781665456302

The Grassy Shoot Disease is a severe problem in sugarcane crops, affecting their productivity and causing significant economic losses. The research aims to introduce a model that utilizes both CNN and SVM techniques to make precise predictions about the severity levels of Grassy Shoot Disease in sugarcane cultivation. The methodology involves data preprocessing, CNN-based feature extraction, SVM-based classification, and model evaluation. The data preprocessing phase involved data cleaning, normalization, and augmentation, followed by the extraction of features using a three-layer CNN model. Following feature extraction, the extracted features were fed into an SVM-based classifier with regularisation to avoid overfitting. The classifier's overall accuracy was 81.53%, and its precision, recall, F1-score, and support values ranged from 65.71% to 85.37% depending on the severity level. These results show that the suggested method is a solid method for accurately estimating the degrees of Grassy Shoot Disease severity in sugarcane crops. © 2023 IEEE.

关键词： Feature extraction

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CNN-SVM Model for Accurate Detection of Bacterial Diseases in Cucumber Leaves 3

CNN-SVM Model for Accurate Detection of Bacterial Diseases i...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

ISBN: (纸本)9798350300710

This research paper presents a deep learning approach for detecting and classifying plant diseases in citrus crops. The proposed model uses a convolutional neural network (CNN) architecture with three convolutional layers, three pooling layers, and two fully connected layers, followed by support vector machine (SVM) classifiers. The model was trained and tested using a dataset of citrus crop images containing nine different classes of diseases. The performance of the model was evaluated based on precision, recall, F1-score, support, accuracy, and average metrics. The overall accuracy of the model was found to be 86.03%, with a weighted average F1 score of 86.10%. The model achieved the highest precision score of 86.96% for the Citrus nematode class and the lowest precision score of 84.00% for the Dothiorella blight class. © 2023 IEEE.

关键词： Convolutional neural networks

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