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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Jyothi, B. N. Swarna Thaiyalnayaki, S. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518592

The large-scale networking is changing with more flexibility and manageability given by the Internet of Things (IoT) and Software-Defined Networking (SDN). However, they also present unique security challenges, especially when hackers occasionally exploit SDN infrastructure through IoT devices. IoT-SDN systems raise many security questions given their complexity and interconnectedness. This work proposes a deep learning-based novel, more notably Deep Recurrent Residual Learning (DRRL), sto improve network security in IoT-SDN systems. Resilient learning allows DRRL to effectively detect anomalies and targets by recording and altering temporal information. Using real-time analytics, this method continuously examines network traffic, detects odd activity, and responds fast to threats. Residual DRRL reduces false positives relative to standard approaches and enhances attack detection accuracy in simulated IoT-SDN systems, therefore reaching a 98.5% threat detection accuracy with a 2.3% false positive rate. The results show rapid implementation of security mechanisms, thereby reducing threat exposure. © 2024 IEEE.

关键词： Anomaly Detection DRRL IoT Network Security SDN

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Microstructure, Mechanical Properties and Corrosion Resistance of the Mo_(0.5)V_(0.5)NbTiZr_(x) High-Entropy Alloys with Low Thermal Neutron Sections

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Acta Metallurgica Sinica(English Letters) 2024年第10期37卷 1643-1656页

作者： Chao Xiang En-Hou Han Zhiming Zhang Huameng Fu Haifeng Zhang Jianqiu Wang Guodong Hu Institute of Corrosion Science and Technology Guangzhou510530China CAS Key Laboratory of Nuclear Materials and Safety Assessment Institute of Metal ResearchChinese Academy of Sciences(CAS)Shenyang110016China Shi-Changxu Innovation Center for Advanced Materials Institute of Metal ResearchChinese Academy of SciencesShenyang110016China Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang110016China School of Materials Science and Engineering University of Science and Technology of ChinaShenyang110016China

High-entropy alloys exhibit significant potential for application in the nuclear industry owing to their exceptional resistance to irradiation,excellent mechanical properties,and corrosion *** this work,the Mo_(0.5)V_(0.5)NbTiZr_(x)(x=0-2.0)high-entropy alloys containing alloying elements with low thermal neutron absorption cross section were designed and *** crystal structure,microstructure,mechanical properties and corrosion resistance of the studied alloys were *** the alloys possess a body-centered cubic crystal structure,which is consistent with the CALPHAD(acronym of CALculation of PHAse Diagram)modeling *** addition of Zr does not alter the crystal structure of the Mo_(0.5)V_(0.5)NbTiZr_(x) alloys;however,it leads to an increase in the lattice constant as Zr content *** addition of Zr initially enhances the yield strength,but subsequently leads to a decline as the Zr content increases ***,the corrosion resistance of the Mo_(0.5)V_(0.5)NbTiZr_(x) alloys in superheated steam at 400℃ and 10.3 MPa decreases with the increase of Zr *** effect of Zr content on the phase formation,mechanical properties and corrosion resistance of the Mo_(0.5)V_(0.5)NbTiZr_(x) high-entropy alloys are *** study has successfully developed a novel Mo_(0.5)V_(0.5)NbTiZr_(0.25) high-entropy alloy,which demonstrates exceptional properties including high yield strength,excellent ductility,and superior anti-corrosion *** findings of this research have significant implications for the design of high-entropy alloys in nuclear applications.

关键词： High-entropy alloys Phase formation Mechanical properties Corrosion resistance

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Tracking Waste Disposal and Generation of Fine to control and promote proper waste segregation

Tracking Waste Disposal and Generation of Fine to control an...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Ashok, K.N. Kavyashree, K.S. Keerthana, S. Nagashree, C.R. Navya, L. S J C Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

The metropolitan cities in India alone produce about 10 million tons of waste. That is, if left untreated, the waste materials will cause several types of pollution which are water, soil and air that destroy our ecosystem completely. People can avoid this madness by being waste conscious at an individual level. Lack of awareness and weak imposition of rules by authorities on separate household garbage pick-up are some reasons why many policies have failed to accomplish this. Thus, we aim to monitor and fine them whenever they don't obey the rules in segregating their household wastes. This project mainly deals with using web camera images to determine the percentage of non-segregating biodegradable from non-biodegradable waste during its collection by the municipal bodies for determining the fines based on improper disposal methods of waste products. One way of analysing the proportion of nonsegregated garbage is through Convolution Neural Networks (CNN), a deep learning algorithm. This groundbreaking solution advances responsible trash disposal methods that enhance a cleaner living environment for all people and future generations alike © 2024 IEEE.

关键词： Convolutional neural networks

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An alternative method for Image Encryption by using Neural Networks 15

An alternative method for Image Encryption by using Neural N...

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

作者： Vollala, Satyanarayana International Institute of Information Technology Department of Computer Science and Engineering Naya Raipur India

ISBN: (纸本)9798350370249

Information security has emerged as a requirement and a hot topic in the modern digital world. The world today is surrounded by tonnes of gigabytes of data generated from digital platforms continually streaming through the Internet. In this perspective, computer scientists are strongly pushed to protect information against unscrupulous persons who evolve quickly with technology. The goal of cryptography is to encrypt and decrypt information by using complex mathematical functions and logics. Encryption not only for text data, but image, audio, and video kind of data as well. This research focuses on encryption and decryption of images. Different kinds of image encryption algorithms have been developed upto this point. In each algorithm, pixel permutation and pixel substitution steps are performed by using mathematical functions such as Logistic map, Cubic-logistic map, and Arnold map. Various Image cryptanalysis works has been going on these conventional image encryption algorithms. In this work, we introduce a new method of encrypting and decrypting images without using any conventional steps like substitution and permutation of image pixels. This proposed method uses a deep learning technique, i.e., convolutional autoencoders. This method targets both compression and encryption so that images can travel with much speed and security over the network. © 2024 IEEE.

关键词： Autoencoders Convolutional Neural Networks Cryptography Neural Cryptography

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Suicidal Thought Detection using Max Voting Ensemble Technique 2

Suicidal Thought Detection using Max Voting Ensemble Techniq...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Goel, Roma Digalwar, Mayuri Department of Computer Science Engineering Indian Institute of Information Technology Nagpur441108 India

Online social networking (SN) data presents a data stream that is rich in context and temporal information. It holds promise for predicting suicidal thoughts and behaviors. The fusion of SN data with machine learning algorithms offers a potential path forward. This research proposes a Max Voting Ensemble classifier model applied to a Reddit dataset for the identification of suicidal ideation. The preprocessing involves data cleansing, tokenization, and lemmatization. Additionally, TF-IDF and Word2Vec word embedding techniques are applied. Diverse machine learning algorithms, including Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Multinomial Naive Bayes (MNB), AdaBoost, and XGBoost, are implemented. The results of selected Machine Learning Classifiers (MLCs) are amalgamated using a Max Voting Ensemble classifier. The research findings clearly indicate that the Max Voting Ensemble classifier yields improved precision of 91.39% coupled with a substantial accuracy of 87.5%. The application of Ensembling Techniques (ET) to SN data holds the potential to address the complexities and modeling challenges inherent in predicting acute Suicidal Ideation within these dynamic time scales. © 2024 Elsevier B.V.. All rights reserved.

关键词： Logistic regression

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Cutting-edge AI for Plant Health: Advanced Residual Neural Networks in Leaf Diseases Segmentation and Classification 3

Cutting-edge AI for Plant Health: Advanced Residual Neural N...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Priyatharsini, R. C. Dyana Kumari, G. Rosline Nesa Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518592

Agricultural Farming is the backbone of India. Farmers face many challenges in agricultural farming to get high yield of production. In agriculture, plant diseases lower mass productivity. Farmers always experience many challenges in satisfying their requirements as a result of major losses in agriculture. The main objective of this study was the plants grown in the Kanyakumari region in order to evaluate the pH of the soil. This paper analyses various diseases that impact on the categories of plants like creeper, climber and shrubs based on Monocot and Dicot family. The existing system focused on the various classification algorithms with various parameters but with the drawback of less accuracy and high computational time. Despite this, a variety of techniques have to be used in order to detect plant illnesses early on and improve productivity in agriculture. More algorithms using machine learning are at present readily available and all of them have the potential to be improved to provide better accuracy levels. For the purpose of segmenting and classifying the affected leaves, the hybrid model is introduced that combines Deep Convolutional Neural Networks (DCNNs) with ResNets. A comparison with existing models has been conducted and the proposed method offers a higher accuracy rate of 99.72%. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Improving Healthcare with Machine Learning and Deep Learning 4

Improving Healthcare with Machine Learning and Deep Learning

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kothai, G. George Princess, T. Subramani, Nandhagopal Balakrishnan, D. Ravi, P. Amutha, S. Coimbatore India Kpr institute of Engineering and Technology Coimbatore India Coimbatore India KGiSL institute of Technology Coimbatore India Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering Krishnankoil India

ISBN: (纸本)9798331540364

Due to the evolving lifestyle choices and the state of the environment, people nowadays suffer from a wide range of ailments. Consequently, early illness prediction becomes a critical obligation. For doctors, however, making a meaningful prognosis based only on symptoms becomes too difficult. Predicting illness correctly is the hardest problem. ML is crucial in helping to anticipate illnesses and find treatments in order to solve this issue. Using disease data, MRIs, chest X-rays, and diabetes records, machine learning (ML) assists in diagnosing and treating various conditions. Given the patient's symptoms, we utilize techniques such as SVM, Decision Trees, Random Forest Classifier, Gaussian Bayes and Multinomial Bayes, and Gradient Boost to predict illnesses. Given an X-ray image, we employ CNN models such as VGG for image classification with transfer learning to predict if a person has tuberculosis, pneumonia, or is disease-free. Brain MRIs (Magnetic Resonance Imaging) can be used to predict brain tumors. With the aid of Support Vector Machines, we are able to forecast if the patient has a pituitary tumor, meningioma, glioma, or no tumor at all (SVM). Diabetes is predicted using a number of important variables, including age and body mass index (BMI). Random forest classifiers are what we use to determine if a person has diabetes or not. © 2024 IEEE.

关键词： Support vector machines

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Leveraging Artificial Neural Networks for Breast Cancer Detection and Prognosis

Leveraging Artificial Neural Networks for Breast Cancer Dete...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Goel, Aadya Mishra, Pallavi Bhatia, Rachna School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Advanced Sciences Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331512965

Breast cancer remains one of the leading causes of mortality among women worldwide, making early detection and accurate classification critical for improving patient out-comes. This study explores the application of Artificial Neural Networks (ANN) for breast cancer multiclass classification and prediction using the CBIS-DDSM: Breast Cancer Image Dataset. The model was trained to classify mammographic images into three categories: benign, malignant, and benign without callback, achieving an impressive accuracy of 99.6%. The choice of ANN was driven by its proven capability to model complex, nonlinear relationships within data, offering a significant improvement over traditional machine learning technique. Despite the challenges posed by the limited size and heterogeneity of the dataset, the ANN demonstrated robust performance, highlighting its potential for clinical applications. The high accuracy of the model underscores its value as a reliable tool for aiding radiologists in the early detection and prognosis of breast cancer, which could lead to more effective treatment planning and better patient outcomes. This study lays the foundation for integrating advanced AI technologies into clinical settings, offering promise for more precise, timely breast cancer diagnoses. © 2025 IEEE.

关键词： Lung cancer

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Smart Traffic Management System for Urban Mobility Enhancement Using RF Sensors, Cameras, and Machine Learning 4

Smart Traffic Management System for Urban Mobility Enhanceme...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Vivekanandan, G. Pathmanaban, J. James, Prabahar Godwin Barani Sree, S. Priyanka, V. Samvardhini, V.M. Sri Sai Ram Institute of Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331508845

This paper introduces a Smart Traffic Management System (STMS)employing RF sensors, cameras, and machine learning algorithms to monitor and optimize urban traffic. The system dynamically adjusts traffic signal timings, offers real-time route recommendations based on GPS data, and incorporates adaptive control mechanisms to reduce congestion and improve overall mobility. Simulation studies and real-world testing demonstrate the effectiveness of the STMS in enhancing traffic flow, minimizing waittimes, and contributing to sustainable urbandevelopment. © 2024 IEEE.

关键词： Traffic signals

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IOT Wearable Medical Device for Heart Disease Recognition Based ML and DL: A Classification Approach 15th

IOT Wearable Medical Device for Heart Disease Recognition Ba...

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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： Ajesh, F. Philip, Felix M. Jims, Anupama Alapatt, Bosco Paul Department of Computer Science and Engineering Sree Buddha College of Engineering Kerala Alappuzha India Bangalore India Department of Computer Science and Engineering CVV Institute of Science and Technology Kerala India Delhi-NCR India

ISBN: (纸本)9783031810794

In the past few years, heart disease has become the foremost worldwide contributor to mortality. This ailment, with a profound effect on the functioning of the heart, leads to issues such as infections in the coronary arteries and diminished blood vessel performance. These complications can culminate in severe unlikely events like heart attacks and strokes. In India alone, approximately one person succumbs to heart disease every minute. To curb the fatalities stemming from cardiac disorders, there is an urgent need for a swift and efficient detection strategy. IoT sensors are utilized in conjunction with Machine Learning (ML) and Deep Learning (DL) techniques to identify heart disease. In this research, we have successfully applied IoT devices and a sensor network to detect heart diseases. This study introduces a medical IoT device designed to gather heart data from patients both before and after the onset of heart disease. This continuously transmitted data is processed using RBF, MLP, and Bi-LSTM models for predicting heart disease. The deep learning approach utilizes past analyses to learn critical features related to heart disease, achieving efficiency in handling complex data. After conducting a series of experiments, we evaluate the system’s performance using metrics such as f-measure, sensitivity, specificity, loss function, and Receiver Operating Characteristic (ROC) curves. The HDRBi-LSTM method, in combination with IoT-based analysis, achieves an impressive accuracy rate of 99.5% with minimal time complexity (5 s), effectively reducing heart disease mortality by simplifying the diagnosis of this condition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anesthetics

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