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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Shivaprasad, Tejas Moulya, A.S. Guruprasad, N. Global Academy of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9789380544519

As the network security landscape changes with time and market, organizations seek different and innovative approaches to strengthen their security defenses. This paper gives a theoretical explanation, highlighting the combination of honeypots and network monitoring tools as a dynamic strategy for enhancing security within networking environments. By using honeypots along with network monitoring tools, we bring out a multilayered defense strategy aimed at identifying and examining potential attack patterns. Our research dives into the theory of honeypots, their role in diverting malicious attacks, and their relationship with network monitoring tools. This combined framework helps organizations to detect, analyze, and ultimately reduce security threats. Through theoretical inputs and suggestions, this paper presents a framework for organizations seeking to enhance their cybersecurity defenses by exploring the complications of attacks through advanced network monitoring, along with honeypot security mechanisms. © 2024 Bharati vidyapeeth, New Delhi.

关键词： Honeypots Intrusion Detection Systems network monitoring tools network traffic Network Traffic Analyzers vulnerabilities

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Machine learning design of 400 MPa grade biodegradable Zn-Mn based alloys with appropriate corrosion rates

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International Journal of Minerals,Metallurgy and Materials 2024年第12期31卷 2727-2736页

作者： Wangzhang Chen Wei Gou Yageng Li Xiangmin Li Meng Li Jianxin Hou Xiaotong Zhang Zhangzhi Shi Luning Wang Beijing Advanced Innovation Center for Materials Genome Engineering State Key Laboratory for Advanced Metals and MaterialsSchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijing 100083China Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China School of Computer and Communication Engineering Shunde Innovation SchoolUniversity of Science and Technology BeijingShunde 528399China

The commonly used trial-and-error method of biodegradable Zn alloys is costly and *** this study,based on the self-built database of biodegradable Zn alloys,two machine learning models are established by the first time to predict the ultimate tensile strength(UTS)and immersion corrosion rate(CR)of biodegradable Zn alloys.A real-time visualization interface has been established to design Zn-Mn based alloys;a representative alloy is *** tensile mechanical properties and immersion corrosion rate tests,its UTS reaches 420 MPa,and the prediction error is only 0.95%.CR is 73μm/a and the prediction error is 5.5%,which elevates 50 MPa grade of UTS and owns appropriate corrosion ***,influences of the selected features on UTS and CR are discussed in *** combined application of UTS and CR model provides a new strategy for synergistically regulating comprehens-ive properties of biodegradable Zn alloys.

关键词： Zn alloys machine learning alloy design performance prediction

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"affectAlchemy": An Affective Dataset Based on Plutchik's Psychological Model for Text-Based Emotion Recognition and its Analysis Using ML Techniques 8

"affectAlchemy": An Affective Dataset Based on Plutchik's Ps...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Kapase, Ajay Uke, Nilesh Savant, Jatin Desai, Mrunali Ghatage, Shravani Rahangdale, Atul Vishwakarma Institute of Information Technology Computer Science & Engineering Pune India Trinity Academy of Engineering Computer Science & Engineering Pune India KITs College of Engineering Computer Science & Engineering Kolhapur India

ISBN: (纸本)9798350391770

A technique to identify people's attitudes, and sentiments towards specified targets such as things, services, and subjects, is called sentiment analysis. As a dedicated subset of NLP, it deals with predicting specific reactions broadly characterizing sentiments as positive, negative, or neutral. However, the accuracy and depth vary since there are a variety of human emotions that exist rather than just positive, negative, and neutral. In the past few decades, the term affective computing has become well-liked which involves the use of AI for identifying, interpreting, and responding to human emotions. A part of it called text-based affective systems are artificial emotional intelligent systems that often categorize the text into core wide range of emotions like fear, sadness, anger, and happiness. These affective systems use various psychological models for emotion recognition. Our study incorporates the development of a dataset based on Robert Plutchik's psychological model of emotions, which sets forth eight primary emotions which are combined to form more complex emotions, resulting in a total of 24 secondary emotions resulting total of 32 variety of emotions. The work introduces the "AffectAlchemy"dataset which is validated using multiclass classification machine learning techniques. This dataset can contribute to the field of affective computing for the development of models that can do enhanced emotion recognition, and improved emotional context understanding to develop emotion-aware applications and facilitating research in affective systems. © 2024 IEEE.

关键词： Intelligent systems

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Quantum Neural Network for Time Series Forecasting: Harnessing Quantum Computing's Potential in Predictive Modeling 2

Quantum Neural Network for Time Series Forecasting: Harnessi...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Balakrishnan, D. Mariappan, Umasree Raghavendra, Pagadala Geetha Manikanta Reddy, Pallela Karthikeya Dinesh, Rayavarapu Lakshmi Narasimha Jabiulla, Shaik Bugganapalli Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu 626126 India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar626005 India

ISBN: (纸本)9798350308846

Time series forecasting is a critical problem with applications in various fields, such as finance, energy, and environmental science. While classical machine learning methods have shown significant progress in this domain, the advent of quantum machine learning introduces a new perspective to address the complexities of time series prediction. In this research, we propose a novel Quantum Neural Network (QNN) approach for time series forecasting, leveraging the unique capabilities of quantum computing to capture intricate temporal dependencies and exploit quantum parallelism. By encoding time series data into quantum states and designing quantum circuits for processing, the QNN aims to achieve improved predictive accuracy compared to classical neural networks. Through extensive experiments on diverse time series datasets, we demonstrate the potential of our QNN framework and discuss the future prospects of quantum-powered time series prediction in real-world applications. © 2023 IEEE.

关键词： Machine learning Neural network Prediction model Predictive features Quantum computing Time series

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Energy Harvesting based Electromyography Analysis for Muscle Activity 3

Energy Harvesting based Electromyography Analysis for Muscle...

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3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Sidharthan, Pooja Swapna Kumar, S. Vidya Academy of Science & Technology Dept. of Electronics & Communication Engineering Kerala Thrissur India

ISBN: (纸本)9798350346169

Energy is the fundamental part of life and there is great demand of using renewable energy sources. Energy can be harvested from human body movements using piezoelectric transducers. The energy wasted during walking and exercising can be converted into useful energy is considered in this paper. The proposed system consists of the piezoelectric transducers on which the muscle force is applied. The AC signal from piezoelectric transducers is converted to DC using bridge rectifier. Then the DC signal is boosted by a DC-DC boost converter and the energy is stored in a supercapacitor. Due to the fast discharging rate of supercapacitor, it is thereby discharged to a rechargeable battery. This can power up the Arduino and thereby the electromyography sensor which analyses the muscle activity. A mathematical model can be used determine the work-done by lifting some known weights and comparing with electromyographic value. Series-parallel combination of piezoelectric transducers provides more voltage and current. Amplitude variations while lifting different known weights are analyzed using electromyography sensors. Deviation of conceptual work-done and measured value is analyzed. © 2023 IEEE.

关键词： Piezoelectricity

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Breast Cancer Recognition Using Integrated Lasso Based Artificial Intelligence Approach 3

Breast Cancer Recognition Using Integrated Lasso Based Artif...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Balakrishnan, D. Mariappan, Umasree Sreedevi, G. Alagusundar, Nanditha Abhishekh, P. Sowjyasree, V. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu 626126 India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar626001 India

ISBN: (纸本)9798350302288

Breast cancer is the most found disease in the women's which spreads throughout the body if it doesn't concentrated well. There are more technologies has been introduced earlier for the detection of breast cancer. However there is no efficient technology to find the breast cancer in the severe stage and reaches distant metastasis. In the existing work, it is done by using the various machine learning algorithms, which will predict the disease occurrence by learning the various genes associated with them by Analysing the gene structure. However those techniques are complex in nature and cannot predict the diseases in the accurate way due to its varying characteristics. These issues are resolved in the proposed methodology by introducing the method namely Lasso based Artificial Intelligence Approach (LAIA) for breast cancer Recognition which is in the distant metastasis stage. In the proposed research work, input datasets were pre-processed first which is then given as input to the LAIA method which can detect the disease more accurately. The analysis were done in the matlab simulation environment and then the comparison has been made against the various existing techniques such as adaboost and XGBoost algorithms. The metrics considered for the evaluation are accuracy, F1 Score, Positive Predictive Value, Sensitivity and AUC values. The comparison analysis proved that the proposed methodology attains better performance improvement than the existing methodologies. © 2023 IEEE.

关键词： Diseases

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An effective medical image fusion method utilizing moth-flame optimization and coupled neural P systems

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Neural Computing and Applications 2025年第16期37卷 10037-10077页

作者： Dinh, Phu-Hung Le, Thi-Hong-Ha Giang, Nguyen Long Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Hong Duc University 565 Quang Trung Dong Ve Thanh Hoa Viet Nam Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Graduate University of Science and Technology Vietnam Academy of Science and Technology Hanoi Viet Nam

Medical image fusion is crucial for various applications, yet existing fusion models often encounter issues such as low quality, information loss, and insufficient contrast. This paper presents a novel approach to medical image fusion that directly addresses these challenges by enhancing brightness and contrast, improving overall image quality, and preserving essential structural features. Our method begins by decomposing input images into a base component and two detail components using a three-component decomposition (TCD) method with a truncated Huber filter (THF). The detail components are subsequently fused using an integrated approach combining the structure tensor saliency detection operator (STSDO), distance-weighted regional energy (DWRE), fast guided filter (FGF), and coupled neural P system (CNPS). The base components are then fused through an adaptive method guided by the moth-flame optimization (MFO) algorithm. To evaluate our approach, we used a dataset of 120 pairs of medical images and assessed performance with metrics such as QMLI, QCI, QAG, QAB/F, QP, QW, QE, and QCB. Experimental results demonstrate that the proposed model significantly enhances brightness and contrast and more effectively preserves information compared to recent methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image fusion

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An Intelligent System to Predict Diabetic Retinal Diseases Based on Diabetic Attributes

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SN computer science 2024年第6期5卷 755页

作者： Eswari, M. Shanmuga Balamurali, S. Ramkumar, K. Department of Computer Applications Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Rajalakshmi Institute of Technology

The most common illness among individuals and the general population in the medical field is diabetes. This is coupled with a careful diabetic retinal that has no signal. The historical record offers unrecoverable insight. To avoid a hallucinatory dispute at the time, early detection of retinal illness is necessary. Positively, the results of diabetic patients have been able to foretell the threat of diabetic-related eyeailments such as Diabetic Retinopathy, Glaucoma, and Diabetic Macular Edema.A diabetic retinal disease prediction exemplary have been created by via system learning rules, such as narrow neural network, bilayered neural network, wide neural network and trilayer neural network, in close proximity to the onset of the disease. The trilayer neural network with various activation functions accuracy based results servedas the foundation for the final model that evaluates real time data to predict the likelihood of *** learner representation accuracy of 0.95and the evaluation phase accuracy of 0.94 are *** ideal diabetic features-based diabetic retinal system outcome occupies a crucial place in the medical decision support system to make prompt assessments in the field of E-healthcare system to connect ophthalmology field to eradicate insulin based eye problems at the time of regular assessment. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Boosting computer-aided Design Diabetic Mellitus Ensemble Machine Learner Retinal eye Disease

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Detection of communicable and non-communicable diseases using hyperparameter optimization with Bi-LSTM model in pathology images

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International Journal of Intelligent Computing and Cybernetics 2023年第4期16卷 649-664页

作者： Shiva Sumanth Reddy C.Nandini Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangaloreIndia Dayananda Sagar Academy of Technology and Management BangaloreIndia

Purpose:The present research work is carried out for determining haemoprotozoan diseases in cattle and breast cancer diseases in humans at early *** combination of LeNet and bidirectional long short-term memory(Bi-LSTM)model is used for the classification of heamoprotazoan samples into three classes such as theileriosis,babesiosis and ***,BreaKHis dataset image samples are classified into two major classes as malignant and *** hyperparameter optimization is used for selecting the prominent *** main objective of this approach is to overcome the manual identification and classification of samples into different haemoprotozoan diseases in *** traditional laboratory approach of identification is time-consuming and requires human *** proposed methodology will help to identify and classify the heamoprotozoan disease in early stage without much of human ***/methodology/approach:LeNet-based Bi-LSTM model is used for the classification of pathology images into babesiosis,anaplasmosis,theileriosis and breast images classified into malignant or *** optimization-based super pixel clustering algorithm is used for segmentation once the normalization of histopathology images is *** edge information in the normalized images is considered for identifying the irregular shape regions of images,which are structurally ***,it is compared with another segmentationapproach circularHough Transform(CHT).The CHT is used toseparatethe *** Canny edge detection and gaussian filter is used for extracting the edges before sending to ***:The existing methods such as artificial neural network(ANN),convolution neural network(CNN),recurrent neural network(RNN),LSTM and Bi-LSTM model have been compared with the proposed hyperparameter optimization approach with LeNET and *** results obtained by the proposed hyperparameter optimization-Bi-LSTM model showed the accuracy of

关键词： Breast cancer detection Convolution neural network Deep neural network Haemoprotozoan disease detection Hyper-parameter optimization LeNet Bi-LSTM

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Drowsiness Detection for Drivers using Hybrid T-Distributed Stochastic Neighbor Embedding with Convolutional Neural Network 2

Drowsiness Detection for Drivers using Hybrid T-Distributed ...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Balakrishnan, D. Mariappan, Umasree Nikhitha, Balabhadra Akshitha, Bokkisam Visali, Bedamsetty Snehalatha, Bandikattu Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar626005 India

ISBN: (纸本)9798350308846

Drowsiness Detection (DD) is the procedure of identifying signs of drowsiness in individuals, especially in critical situations like driving, heavy machinery operation, or aircraft piloting. Hybrid Bi-directional Long Short Term Memory with Convolutional Neural Network (HBLSTM-CNN) was proposed for driver's DD. At first, an eye dataset was gathered and subsequently subjected to pre-processing using a shape predictor. The pre-processed images are initially analyzed using a BiLSTM to identify the most prominent characteristics, which are subsequently subjected to additional processing in a CNN to serve the purpose of DD. Nonetheless, as the data's dimensionality grows, BiLSTM demands more computational resources, including memory and processing power. So in this paper, Hybrid t-distributed Stochastic Neighbor Embedding with CNN (Ht-SNE-CNN) is introduced for driver's DD. In Ht-SNE-CNN, t-SNE is utilized for reducing dimensionality because of its inherent simplicity, obviating the requirement to train a sophisticated model. It directly computes a lower-dimensional depiction of the data while emphasizing the preservation of local similarities. This inherent simplicity frequently leads to faster and more direct utilization, particularly when handling high-dimensional data. The reduced data are given as input to CNN for driver's DD. The experimental findings demonstrate that the suggested HtSNE-CNN outperforms HBLSTM-CNN with regard to exactness, precision, and recall for detecting driver drowsiness. © 2023 IEEE.

关键词： bi-directional long short term memory convolutional neural network dimensionality reduction Drowsiness detection t-distributed stochastic neighbor embedding

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