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Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

computer Systems science & engineering 2023年第4期45卷 483-495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

Owing to massive technological developments in Internet of Things(IoT)and cloud environment,cloud computing(CC)offers a highly flexible heterogeneous resource pool over the network,and clients could exploit various resources on *** IoT-enabled models are restricted to resources and require crisp response,minimum latency,and maximum bandwidth,which are outside the *** was handled as a resource-rich solution to aforementioned *** high delay reduces the performance of the IoT enabled cloud platform,efficient utilization of task scheduling(TS)reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user ***,this article concentration on the design of an oppositional red fox optimization based task scheduling scheme(ORFOTSS)for IoT enabled cloud *** presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud *** achieves the makespan by performing optimum TS procedures with various aspects of incoming *** designing of ORFO-TSS method includes the idea of oppositional based learning(OBL)as to traditional RFO approach in enhancing their efficiency.A wide-ranging experimental analysis was applied on the CloudSim *** experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.

关键词： Metaheuristics task scheduling cloud computing internet of things makespan red fox optimizer

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Thyroid detection using deep hybrid learning: The review 4th

Thyroid detection using deep hybrid learning: The review

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Poonam Kaur, Harpreet Department of Computer Science and Engineering Chandigarh Engineering College Landran India

ISBN: (纸本)9781032911571

Your thyroid is a little gland that is situated in your neck. It produces hormones that aid in the regulation of several physiological functions. Thyroid disorders come in a variety of forms, such as goiter (enlarged thyroid), hypothyroidism (underactive thyroid), and thyroid cancer. These conditions can have various symptoms and require different treatments. There are two categories of thyroid diseases: primary and secondary. When you have primary thyroid illness, your thyroid gland is the disease’s original source. When you have secondary thyroid illness, your pituitary gland is the disease’s original source. Various diagnostic methods are employed to assess thyroid health. Blood tests measure hormone levels, imaging techniques like ultrasound visualize gland structure, and fine needle aspiration biopsy analyzes tissue samples. It has been observed that machine learning approaches are applied to the prediction of TD accuracy. Many methods have been employed in the past to abstract knowledge utilizing wellestablished deep hybrid learning algorithms for thyroid detection.. Some technique is used for the early prediction of thyroid detection before. The main idea behind this research is to early detection of thyroid. The already existing techniques are efficient for the detection however there is still need to find the new path to obtain the desired accuracy. © 2025 the Author(s).

关键词： Endocrinology

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Deep Learning Classification Techniques on Detecting Diabetic Retinopathy dataset 7

Deep Learning Classification Techniques on Detecting Diabeti...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Revathi, B. Usharani, C. Kezial Elizabeth, S.K. Nagaraj, P. Nithya, D. Ramco Institute of Technology Department of Artificial Intelligence And Data Science Virudhunagar India Mangayarkarasi College of Engineering Department of Computer Science And Engineering Madurai India Kalasalingam Academy of Research And Education Department of Computer Science And Engineering Virudhunagar Krishnankoil India Theni Kammavar Sangam College of Technology Department of Computer Science And Engineering Theni India

ISBN: (纸本)9798350359299

Deep learning algorithms can summarize images to understand how to carry out necessary tasks. The purpose of this study is to compare several deep learning methods. Both experience-based and explanation-based learning are possible in deep learning. The most widely utilized algorithms, such as Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), Generative Adversarial Networks (GAN), Radial Basis Function Networks (RBFN), and Deep Belief Networks (DBN), and the Diabetic Retinopathy dataset is utilized in this study to evaluate the effectiveness of the algorithms. A comparative study of the classifiers reveals that CNN performs more accurately than the other approaches. © 2024 IEEE.

关键词： Classification (of information)

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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks

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Discover Internet of Things 2025年第1期5卷 1-22页

作者： Selvam, Prabu Karthikeyan, P. Manochitra, S. Sujith, A.V.L.N. Ganesan, T. Ayyasamy, Rajaram Shuaib, Mohammed Alam, Shadab Rajendran, A. School of Computing SRM Institute of Science and Technology Tiruchirappalli621105 India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai625009 India Department of Computer Science Engineering St Joseph’s College of Engineering Tamil Nadu Chennai600119 India School of Engineering Malla Reddy University Telangana Hyderabad500043 India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamil Nadu Nagapattinam611002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of ECE Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusion patterns due to the heterogeneity and distributed IoT devices. There is a pressing need for scalable, real-time, and privacy-preserving solutions to secure IoT and IIoT systems. For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. By integrating federated learning, the proposed approach ensures privacy by enabling decentralized model training among IoT devices without exchanging sensitive data. The hybrid CRNN model improves detection accuracy for a variety of attack types in IoT networks by combining Recurrent Neural Networks for sequential pattern recognition and Convolutional Neural Networks for feature extraction from raw data. The proposed model classifies incoming network traffic into different attack classes. The experimental results conducted on the Edge-IIoT dataset reveal that the proposed model has attained the detection accuracy of 98.93% with balanced precision and recall for all the attack types and low-support classes such as SQL Injection and Man-in-the-Middle. This balance improves model’s ability to reduce both false negatives and false positives compared with conventional approaches. Together with real-time detection, our approach excels in a privacy-preserving manner, providing a practical, scalable solution for securing complex IoT networks, and addressing critical gaps in current IDS frameworks. © The Author(s) 2025.

关键词： Federated learning

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IBAttack: Being Cautious about data Labels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1484-1493页

作者： Agarwal, Akshay Singh, Richa Vatsa, Mayank Ratha, Nalini IISER Bhopal Department of Data Science and Engineering Bhopal462066 India IIT Jodhpur Department of Computer Science and Engineering Rajasthan Jodhpur342037 India University at Buffalo Department of Computer Science and Electrical Engineering BuffaloNY14260 United States

Traditional backdoor attacks insert a trigger patch in the training images and associate the trigger with the targeted class label. Backdoor attacks are one of the rapidly evolving types of attack which can have a significant impact. On the other hand, adversarial perturbations have a significantly different attack mechanism from the traditional backdoor corruptions, where an imperceptible noise is learned to fool the deep learning models. In this research, we amalgamate these two concepts and propose a novel imperceptible backdoor attack, termed as the IBAttack, where the adversarial images are associated with the desired target classes. A significant advantage of the adversarial-based proposed backdoor attack is the imperceptibility as compared to the traditional trigger-based mechanism. The proposed adversarial dynamic attack, in contrast to existing attacks, is agnostic to classifiers and trigger patterns. The extensive evaluation using multiple databases and networks illustrates the effectiveness of the proposed attack. © 2020 IEEE.

关键词： computer vision

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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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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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Toxic Comments Classification using Machine Learning and Word Embedding Techniques 2

Toxic Comments Classification using Machine Learning and Wor...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Prabha, Sneh Yadav, Aditya Yadav, Aman Kumar, Akash Tyagi, Manu Department of Computer Science and Engineering Inderprastha Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Toxic comments are comments which are disrespectful, unreasonable and infuriating that make the reading uncomfortable. Sometimes these comments are inappropriate and not good for the public. These comments make daily content consumers uncomfortable and sometime even cause emotional as well as emotional harm. There are some people whose purpose for coming online is to create trouble for others and make their work and daily life difficult by making inappropriate comments. This kind of behavior can be described as anti-social and harmful to society. Many steps have already been taken to catch these troublemakers and punish them accordingly by blocking users who are found guilty. This cannot be done personally by checking one by one, to achieve this purpose we need to make a model. We have applied three Machine Learning Models logistic regression, Naive bayes, SVM and three Word Embedding Techniques, Word2Vec, FastText and Glove which can classify comments, and the content creator take appropriate action against users who makes these comments by reporting and even by deleting their account if needed. Out of all six techniques SVM has outperformed for classification of toxic and non-toxic comments and normal text classification in general. SVM gives the best results with 93.61% accuracy and F1 score of 93%. Proposed technique has improvised accuracy score as compared to existing techniques. © 2025 IEEE.

关键词： Embeddings

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AI-Based Bone Cancer Detection Using Image Processing and CNN 5th

AI-Based Bone Cancer Detection Using Image Processing and CN...

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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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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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