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Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification

Intelligent Automation & Soft Computing 2023年第2期35卷 2353-2366页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Hyperspectral(HS)image classiﬁcation is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,*** recognition of features from the HS images is important for effective classiﬁcation ***,the recent advancements of deep learning(DL)models make it possible in several application *** addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of *** this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classiﬁcation(RDADL-HIC)*** proposed RDADL-HIC technique aims to effectively determine the HSI *** addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad ***,RDA with gated recurrent unit(GRU)approach is used for the identiﬁcation and classiﬁcation of *** design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models *** experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different *** comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.

关键词： Hyperspectral images image classiﬁcation deep learning adagrad optimizer nasnetlarge model red deer algorithm

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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Multisensor Information Fusion for Condition Based Environment Monitoring

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Intelligent Automation & Soft Computing 2023年第4期36卷 1013-1025页

作者： A.Reyana P.Vijayalakshmi Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641050India Department of Electronics and Communication Engineering Hindusthan College of Engineering and TechnologyCoimbatore641050India

Destructive wildﬁres are becoming an annual event,similar to climate change,resulting in catastrophes that wreak havoc on both humans and the *** result,however,is disastrous,causing irreversible damage to the *** location of the incident and the hotspot can sometimes have an impact on earlyﬁre detection *** the advancement of intelligent sen-sor-based control technologies,the multi-sensor data fusion technique integrates data from multiple sensor *** primary objective to avoid wildﬁre is to identify the exact location of wildﬁre occurrence,allowingﬁre units to respond as soon as *** to predict the occurrence ofﬁre in forests,a fast and effective intelligent control system is *** proposed algorithm with decision tree classiﬁcation determines whetherﬁre detection parameters are in the acceptable range and further utilizes a fuzzy-based optimization to optimize the complex *** experimental results of the proposed model have a detection rate of ***,providing real-time monitoring of certain environ-mental variables for continuous situational awareness and instant responsiveness.

关键词： Decision tree communication wildﬁre data fusion wireless sensor networks

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Skin Cancer Detection Using Deep Learning 10

Skin Cancer Detection Using Deep Learning

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Mahalakshmi, A. Padmavathy, C. Priya, N. Priyanka, G. Babu, Lidiya Archana, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353778

Skin cancer is a significant global health concern that requires early detection and accurate diagnosis for effective treatment. Traditionally, dermatologists with specialized training have been responsible for diagnosing skin cancer. However, the emergence of deep learning models, particularly Convolutional Neural Networks (CNNs), offers a promising approach for utilizing dermatoscopic images in the early identification and categorization of skin cancer. The HAM10000 dataset, comprising a vast library of high-quality dermatoscopic images displaying a variety of skin lesions, significantly contributes to advancing skin cancer diagnosis. This research leverages the HAM10000 dataset to develop and evaluate a CNN model tailored for accurate skin cancer classification. The suggested CNN model is an advanced deep learning architecture adept at image classification tasks, particularly in recognizing various forms of skin cancer. It consists of multiple layers of dense neural networks, pooling, and convolution designed to extract detailed characteristics from skin lesion images. To ensure comprehensive representation of various skin lesions and maximize performance, the training dataset is extensively oversampled. This oversampling technique enhances the model's ability to generalize across different lesion types, thereby improving classification accuracy. Furthermore, the Adam optimizer refines the model's learning process by effectively adjusting its parameters during training, leading to increased accuracy. By training the model for more than one hundred epochs with a batch size of 323, it learns intricate patterns and distinguishing features within skin lesion photos, which enhances its ability to classify skin cancer accurately. These advancements in deep learning-based skin cancer categorization represent a significant step towards leveraging artificial intelligence to improve early diagnosis and detection. Such innovations have the potential to support medical profe

关键词： Convolutional neural networks

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A Hybrid Deep Learning Approach for Automatic Melanoma Diagnosis: Integrating Convolutional Neural Networks with Vision Transformers 4

A Hybrid Deep Learning Approach for Automatic Melanoma Diagn...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Priyanka, G. Mahalakshmi, A. Priya, N. Sivaranjani, N. Archana, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Hindusthan college of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

The most lethal type of skin lesion is melanoma. The likelihood of survival for melanoma is significantly increased by early detection. Nevertheless, a number of characteristics, such as diminished contrast between the skin and abrasion and visual similarities between non-melanoma and melanoma, make it challenging to identify melanoma with accuracy. The novel melanoma recognition method described in this study consists of three primary phases: preprocessing, segmentation and classification. To maximize image quality and get the data ready for segmentation, the preprocessing step included denoising techniques, grayscale conversion, Gaussian thresholding, and Contrast Limited Adaptive Histogram Equalization (CLAHE). Three widely used edge detection algorithms-Canny, Sobel, and Prewitt-were applied to the segmentation process in order to identify lesion boundaries and extract features unique to melanoma. Performance criteria such as Root Mean Square Error (RMSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) were used to assess the accuracy of the segmentation. Based on the comparison analysis, Sobel was found to be the best algorithm for correctly identifying melanoma lesions. The outcomes showed how well the preprocessing methods worked to enhance image quality and how well the segmentation algorithms identified melanoma lesions. This research's subsequent phase will concentrate on classifying images using deep learning models. The segmented images are used to train deep learning model namely Convolutional Neural Networks with Vision Transformer. Superior performance is obtained in terms of accuracy, precision, recall, and F1 score when denoising, segmentation, and classification techniques are combined. This highlights the potential of deep learning and image processing fusion in improving skin disease recognition and performance of classification systems. © 2024 IEEE.

关键词： Dermatology

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Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

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computer Systems science & engineering 2023年第4期45卷 939-953页

作者： Sindhuja Manickavasagam Poonkuzhali Sugumaran Department of Information Technology Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia

The combination of machine learning(ML)approaches in healthcare is a massive advantage designed at curing illness of millions of *** efforts are used by researchers for detecting and providing primary phase insights as to cancer *** cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the *** disease is the unrestrained progress of irregular cells which begin off in one or both *** previous detection of cancer is not simpler procedure however if it can be detected,it can be curable,also finding the survival rate is a major challenging *** study develops an Ant lion Optimization(ALO)with Deep Belief Network(DBN)for Lung Cancer Detection and Classification with survival rate *** proposed model aims to identify and classify the presence of lung ***,the proposed model undergoes min-max data normalization approach to preprocess the input ***,the ALO algorithm gets executed to choose an optimal subset of *** addition,the DBN model receives the chosen features and performs lung cancer ***,the optimizer is utilized for hyperparameter optimization of the DBN *** order to report the enhanced performance of the proposed model,a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.

关键词： Lung cancer feature selection ant lion optimization classification disease diagnosis metaheuristics

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A Comprehensive Survey on Low-rate DDoS Attacks Detection Based on Deep Learning 5

A Comprehensive Survey on Low-rate DDoS Attacks Detection Ba...

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5th International Conference on computers, Information Processing and Advanced Education, CIPAE 2024

作者： Liu, Zengguang Yin, Xiaochun Liu, Deyong Information Engineering College Shandong Vocational College of Science and Technology Weifang China Computer Science and Engineering College Weifang University of Science & Technology Shouguang China

ISBN: (纸本)9798331527662

Low-rate Distributed Denial-of-Service attacks, abbreviated as LDDoS, are experiencing an explosive and continuous growth in recent years. Meanwhile, people worked hard for making great contributions to prevent LDDoS attacks by deep learning methods. However, the existing surveys don’t give detailed summaries on all aspects of neural network for LDDoS attacks. This paper investigates the trend and the challenges of LDDoS attacks. And then, it classifies the neural network methods into DL-based cluster, RL-based cluster, FL-based cluster and GNN-based cluster. And it presents the comprehensive survey along with state-of-the-art detection solutions for each of them. At last, the future research directions about developing an LDDoS detection system with broad-spectrum capabilities, cost-effective mechanisms and robustness against adversarial attacks are given. © 2024 IEEE.

关键词： Federated learning

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Ensemble-Based Network Attack Prediction in WSN 15

Ensemble-Based Network Attack Prediction in WSN

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Tejaswini, B. Suruthi, V. Safia Naveed, S. Kcg College of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

computer network security and integrity are severely impacted by network attacks. The ability to predict and prevent these attacks is crucial for maintaining a secure network environment. Supervised ML (Machine Learning) techniques have emerged as effective tools for network attack prediction because of their ability to analyze large amounts of network data and identify patterns indicative of malicious activity. We present a comprehensive analysis of supervised machine-learning techniques for the prediction of network attacks. We gather and preprocess the data, extracting pertinent features and converting them into a format suitable for machine learning algorithms. We assess the performance of these algorithms. We investigate the interpretability of the trained models to gain insights into the underlying patterns and characteristics of network attacks. This allows network administrators to understand the nature of attacks and develop appropriate defense strategies. Additionally, we discuss the challenges and limitations associated with the application of supervised ML techniques in the domain of network attack prediction, such as the need for real-time analysis and the emergence of sophisticated evasion techniques. © 2024 IEEE.

关键词： Network attacks prediction Patterns recognition Performance evaluation Real-time Analysis Supervised machine learning algorithms

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Physiological Data-Based Stress Detection: From Wrist Sensors to Cloud Computing and User Feedback Integration

Physiological Data-Based Stress Detection: From Wrist Sensor...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Karpagam, G.R. Harsha Vardhan, V.M. Kabilan, K.K. Pranav, P. Ramesh, Prednya Suvan Sathyendira, B. PSG College of Technology Computer Science & Engineering Coimbatore India

ISBN: (纸本)9798350378177

Stress Detection employing physiological data desires to grasp and apprehend physical and physiological signals. Skin conductance, three axes acceleration and temperature data are gathered by the system using various sensors. The data from the deployed wearable sensors is the basis of data collection and is stored in the AWS cloud ecosystem. The stress levels are classified as stress, non-stress and amusement by random forest and logistic regression machine learning models. Random forest model, based on the data collected from the wearable sensors, classifies stress levels, achieving an astonishing accuracy of 99%. © 2024 IEEE.

关键词： Physiological models

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SHADOW: A framework for Systematic Heuristic Analysis and Detection of Observations on the Web

SHADOW: A framework for Systematic Heuristic Analysis and De...

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2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： Rengarajan, Aaditya Senthilkumar, Lohith Padmanabh, Neelesh Ramalingam, Akhil Computer Science and Engineering PSG College of Technology Coimbatore India

ISBN: (纸本)9798350353488

The cyberspace contains vast amounts of information that are crucial for cybersecurity professionals to gather threat intelligence, prevent cyberattacks, and secure organizational networks. Unlike earlier and less targeted attacks, modern cyber-attacks are more organized and sophisticated, often targeting specific groups, which leaves many users unaware of the vulnerable resources within the cyberspace. The increasing freedom on information access in the deep and dark web has led many organizations to identify their data loose on these spaces. Therefore, creating methods to crawl and extract valuable information from the deep web is a critical concern. Some deep web content can be accessed through the surface web by submitting query forms to retrieve the needed information, but it is not as simple in all cases. This paper proposes a system of framework to identify these leaks and notify relevant parties on the same in-time. © 2024 IEEE.

关键词： Cyber attacks

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