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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Singh, Jagendra Shaik, Nazeer Sahu, Dinesh Prasad Tiwari, Mohit Alam, Mohammad Shabbir Upreti, Kamal School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Ghaziabad India

ISBN: (纸本)9789819788606

This article investigates machine learning techniques’ effectiveness at using computed tomography (CT) images to forecast breast cancer, hoping to expedite early identification and plan treatment. Drawing on many different machine learning models, such as CNN, SVM, VGG16, RNN and RF, we did extensive work to measure their performance distinguishing between malignant and benign breast tissue regions. The dataset includes 2,430 CT pictures, with 70% for training and 30% for testing. It has been carefully selected and prepared in order to guarantee robustness and consistency. The precision, and in-sensitivity measure the accuracy, sensitivity, specificity is used as analytic indicators to measure the models’ ability to predict regions of breast cancer accurately. Our findings show that the proposed CNN model achieved an accuracy of 98.75%, superior performance. Other machine learning models are also highlighted in this study, demonstrating how breast cancer can be predicted using various methods. This research will determine the forms and technologies suitable for breast cancer forecasting. Medical imaging and clinical decision-making can move forward because of this research, offering a glimpse into how integrated machine-learning systems can bring greater precision to diagnosis and prognosis. By careful experimentation and analysis, we hope to prepare people for early intervention and personalized treatment methods. This will make for improved patient outcomes in fighting breast cancer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Adaptive DDoS Detection with DNN and FUZZY

Adaptive DDoS Detection with DNN and FUZZY

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Maheswari, K. Elankavi, R. Lahari, Koneti Navitha, Jootoori Manoj, Peta Maheshwar Reddy, Bala Obanna Gari Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350361537

Elastic, on-demand, and completely managed computer system resources and services are some of the highly alluring aspects of cloud computing. Several challenges include not being able to access data entering or leaving cloud services, data theft and misuse, lack of control over access to sensitive data, and advanced threats including distributed denial of service attacks (DDoS) and virtual machine escape. DDoS assaults are among of the most popular. This title demonstrates an innovative architecture that combines the strengths of neural networks and fuzzy neural networks with a well-posed stacking sparse AutoEncoder (AE) for feature learning. The result is an incredibly powerful hybrid that can distinguish between DDoS attack and regular network traffic. This AEF proposal's enhancements result in a smaller network that prevents overfitting by employing a fuzzy expert approach, reduced reconstruction error, and prevented exploding and vanishing gradients. Using performance criteria like detection precision, accuracy, recall, and F1-Score, a comparison between the suggested method and ten cutting-edge techniques will be carried out. To validate the results, experiments will be run on regular NSL-KDD datasets. © 2024 IEEE.

关键词： Denial-of-service attack

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Localization of Victims in Disaster Area Using RSSI with Machine Learning Techniques

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SN computer Science 2025年第4期6卷 1-14页

作者： Langpoklakpam, Bidyarani Murry, Lithungo K. Department of Computer Science and Engineering National Institute of Technology Nagaland Chumukedima Nagaland Dimapur 797103 India

Aftermath of disaster, efficient and accurate localization of victims and resources is crucial for effective rescue operations. Traditional localization algorithms face challenges posed by environmental noise, multipath reflection, and various uncertainties, all of which can compromise range accuracy and therefore, affect the localization precision. This paper addresses the localization problem by leveraging machine learning techniques for a disaster area network. We first generate a set of Received Signal Strength Indicator (RSSI) values collected by Anchor Nodes (ANs) for signals received from Target Nodes (TNs). Using the RSSI data collected from victims, we train machine learning models to accurately estimate the locations of individuals with maximum coverage. In this paper, we investigate the influence of placing the anchor nodes in a grid pattern as well as randomly distributing these nodes throughout the deployment area. We examine the effect on various network parameters with varying number of anchor nodes and target nodes. Through simulation, we investigate the performance of machine learning algorithms including Support Vector Regression (SVR), Random Forest (RF), k-Nearest Neighbor (k-NN), and Gradient Boosting (GB) with optimization-based techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) using Received Signal Strength Indicator (RSSI) in terms of Root Mean Square Error (RMSE), Mean Localization Error (MLE) and Node Localization Efficiency (NLE). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Disaster area network Localization Machine learning algorithms Received signal strength indicator (RSSI) Regression models

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Machine Learning-Based Selection of Type 2 Diabetes Specific Genes and Microarray Datasets 1

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Congress on Smart Computing Technologies, CSCT 2023

作者： Saxena, Aditya Department of Computer Engineering and Applications Institute of Engineering & Technology GLA University Mathura India

ISBN: (数字)9789819750818

ISBN: (纸本)9789819750801

Type 2 diabetes is a chronic complex disease whose mechanism is still elusive. Gene expression profiling using microarray is routinely used to identify etiologically important genes between diabetics and normal individuals, but this method suffers with some of the limitations such as background noise, low sensitivity, and high dimensionality.. To rectify them, statistical meta-analysis is extensively used by combining multiple datasets. In this study, we highlighted the application of some machine learning algorithms: SVM, LDA, and LASSO for selection of relevant genes and expression datasets after meta-analysis of microarray datasets and also provided biological significance of identified genes in light of published GWAS studies. Association of these selected genes with GWAS could likely point toward the probable link between genome and transcriptome. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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computers, Materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan Doaa Sami Khafaga El-Sayed M.El-kenawy Marwa M.Eid Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning

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A Reliable Client Detection System during Load Balancing for Multi-tenant Cloud Environment

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SN computer Science 2023年第1期4卷 86页

作者： Singh, Ashutosh Kumar Chhabra, Sakshi Gupta, Rishabh Saxena, Deepika Department of Computer Applications National Institute of Technology Haryana Kurukshetra India Department of Computer Applications Panipat Institute of Engineering and Technology Haryana Samalkha India

Cloud computing is a broad-scale distributed computing system and is a widely accepted archetype that has many luxurious features, including traffic scalability, resource allocation, accessibility, inter-communication cost, and many more. Security is considered one of the primary concerns in the cloud. This paper chooses to formulate this problem by improving the Virtual Machine (VM) allocation policy. The authors have enhanced this policy from a different perspective by maintaining the difficulty in co-tenancy between attackers and targets. A secure resource allocation mechanism has been proposed to prevent multi-tenancy attacks, where attackers and the target are co-tenants on the same server. The multi-objective approach is implemented for a secure load balancing model called reliable client detection system (RCDS). This model inquires the safe or unsafe states all along VM distribution which accomplishes and estimates the reliability of the clients as per the historical performances. When cloud data centers have received the demands to deploy the upcoming jobs, the introduced model helps to find a secure physical machine for balancing the load with avoiding threats. It is evident from the results that RCDS can effectively diminish the risks and security score when increased from 100 to 1000 numbers of cloudlets under the safe states. Performance evaluation demonstrates that RCDS achieves high throughput, avoids traffic overflow, and reduces traffic up to 33.37% in the network. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Allocation policy Cloud computing Multi-tenancy Resource utilization Security

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Enhanced Framework for Authentic and Anonymous Data Sharing Over Cloud Storage Service

Enhanced Framework for Authentic and Anonymous Data Sharing ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Naik, Archana Anjali, M. Ushashree, P. Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

This study introduces the concept of anonymous data exchange in banking applications using cloud computing. By intertwining innovative methodologies, the proposed framework bolsters security while introducing novel dimensions to the authentication landscape. Key to the system's architecture is the integration of RSA and SHA algorithms within the framework. In the contemporary digital landscape, the banking sector confronts escalating challenges in establishing robust authentication and security measures to safeguard sensitive customer data and transactions. Color-coded passwords offer a pioneering approach, enhancing traditional authentication methods with added security. Furthermore, the system advocates for integrate multi-factor authentication techniques, thereby reinforcing the authentication process. This integration ensures the confidentiality, integrity, and authenticity of sensitive data transmitted between clients. The performance of this integrated algorithm is compared with other encryption algorithms, and it is observed that the proposed mechanism encryption time is 10% faster. The encrypted message was tested on an online simulator to attempt deciphering and could not be decrypted, confirming its security. © 2024 IEEE.

关键词： Authentication Protocol

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Innovative Skin Melanoma Diagnosis using Deep Learning and African Gorilla Troops Optimizer 1

Innovative Skin Melanoma Diagnosis using Deep Learning and A...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Parthasarathi, P. Nivedha, S. Balamurugan, M. Yuvaraj, D. Siamala Devi, S. Sangeethaa, S.N. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India Sona College of Technology Dpartment of Computer Science and Engineering Salem India Kongu Engineering College Department of Computer Science and Design Perundurai India Sri Eshwar College of Engineering Kuniamuthur Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350352931

Skin melanoma, a dangerous kind of skin cancer, necessitates accurate identification and diagnosis to improve patient outcomes. The use of deep learning algorithms into medical imaging promises significant advancements in skin disease analysis, particularly melanoma diagnosis. This study looks at the usage of a novel optimisation technique, the African Gorilla Troops Optimizer (AGTO), to enhance deep learning models in the segmentation and analysis of skin images. The AGTO, which is based on African gorilla groups' social and hunting activities, is an effective approach of improving convolutional neural networks (CNNs) for accurate and reliable skin image segmentation. The proposed approach uses AGTO to distinguish melanoma lesions from benign skin disorders, which increases their identification. Extensive research on benchmark skin image datasets has confirmed the AGTO-enhanced deep learning model's durability and performance, emphasising its clinical use in dermatology and the proposed model shows 98 %accuracy. This work not only underlines the need of advanced optimisation techniques in medical image processing, but it also paves the way for more effective diagnostic tools for skin problems. © 2024 IEEE.

关键词： Image segmentation

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Analysis of optical networks in presence of nodes noise and crosstalk

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Journal of Optical Communications 2024年第3期45卷 547-554页

作者： Shukla, Rahul Deo Pratap, Ajay Suryavanshi, Raghuraj Singh Amity Institute of Information Technology Amity University Lucknow Campus Uttar Pradesh Lucknow India Department of Computer Science & Engineering Pranveer Singh Institute of Technology Kanpur India

Optical packet switching has gained lot of momentum in last decade due to the advantages of optical fiber over copper cables. Optical switching is beneficial in optical networks which form connections of links and switching nodes. In these high speed networks minimum delay and high throughput are two important parameters which are considered. To minimize network delay shortest path algorithm is used for route selections. In previous studies while choosing shortest path distance among various nodes is considered. In this work we have shown that it is necessary to consider both distance and number of hops while choosing path from source to destination to minimize power per bit used for the transmission. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Optical switches

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