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8th International Conference on Electromechanical Control Technology and Transportation, ICECTT 2023

作者： Xie, Xin Wang, Chen Zhang, Dongcheng Zheng, Liyuan Gao, Pengxiang Institute of Computer Applications Chinese Academy of Engineering Physics Mianyang China

ISBN: (纸本)9781510668331

Containers are currently the core technology of cloud computing, which has attracted wide attention from industry and academia. However, dynamically allocating resources to use containers reasonably is a huge challenge. This article proposes a container load prediction algorithm based on convolutional neural networks and attention (CAA-Net) to provide reference for container resource scheduling. CAA-Net obtains global information by global convolution and local information by local convolution, and fuses the two kinds of information to predict the load. Finally, this article verifies the accuracy of the algorithm through experiments. © 2023 SPIE.

关键词： Containers

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Application of Generative Graph Models in Biological Network Regeneration: A Selective Review and Qualitative Analysis

Application of Generative Graph Models in Biological Network...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Teji, Binon Roy, Swarup Guzzi, Pietro Hiram Bhandari, Dinabandhu Lab Department of Computer Applications Gangtok India University of Catanzaro School of Computer Science and Biomedical Engineering Italy Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350386226

Biological networks are essential for understanding the complex cellular mechanisms of living organisms. Simulating biological networks allows researchers to model and understand complex cellular processes without the need for extensive and costly experiments. The ability to generate synthetic graphs that closely resemble these real-world, complex biological processes is a vital research area. Graph generation in the context of biological networks is particularly significant because it can lead to insights into cellular functions, disease mechanisms, and therapeutic targets. Recent advances in deep learning, particularly in graph generative models, have opened new avenues for applications in the biological domain. These advancements have the potential to revolutionize our understanding of biological systems. However, despite the development of numerous effective graph generation models, there has been limited work assessing the qualitative aspects of the generated graphs within the context of biological networks. Addressing this gap is crucial for ensuring that synthetic graphs are not only structurally accurate but also biologically *** various graph generation models are available, their application to biological network recreation has been limited. In this paper, we focus on graph generation, specifically edge and node-independent models for biological networks. We assess four candidate models across four gene expression networks. Our systematic assessment examines the models' qualitative aspects, including graph structural properties, generation diversity, and computational efficiency. Our findings highlight the strengths and limitations of current models, offering insights to guide the development of more robust graph generation techniques that accurately replicate biological network characteristics. © 2024 IEEE.

关键词： Biotic

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Vocal Feature Extraction Based Hybrid ML Prototype for Parkinson's Disease Prediction 14

Vocal Feature Extraction Based Hybrid ML Prototype for Parki...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Saju, Binju Asha, V. Prasad, Arpana Mahesh, Deva Prem, Bestty Chandan, H.P. New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350335095

Parkinson's disease is a degenerative disorder that affects the nervous system and can lead to a range of motor and non-motor symptoms. Early detection of Parkinson's disease is important to provide effective treatment and management of symptoms. In recent years, interest in using machine learning based algorithms for predicting Parkinson's disease. The study uses XGBoost model to detect Parkinson disease from the open data set available from CerrahpaYa medicine faculty, Istanbul University. Feature scaling was done using Max-Abs-Scaler, Features were identified using DCNN and selected using RNCN. Finally regularization tuning using ridge regression was done to XGBoost model which has improved the model performance. The proposed model got an accuracy of 98.4%, precision 100% and Recall score 97. © 2023 IEEE.

关键词： Open Data

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Improved AO based Classification Framework for Eye Diseases 2

Improved AO based Classification Framework for Eye Diseases

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Saju, Binju Asha, V. Mathew, Jincy C Sangma, Sengcheng Ghosh, Shouvik Sivaguru, K. Adi Shankara Institute of Engineering & Technology Department of Computer Science - AI Engineering Kerala Kalady India New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350327533

A sensory organ that reacts to visible light, the human eye is a component of the sensory nervous system. A new approach is needed since, although physical detection of the eye condition is still a possibility, it is challenging and time-consuming. The study recommends a hybrid paradigm to classify various eye illnesses. Prior to further processing, pictures are first reduced in size, made grayscale, and noise-free using a combination of the Weiner and Median filters. Histogram equalization based on a Gaussian Mixture is also used to enhance pictures. Picture features are recovered with the aid of a thick convolutional neural network. An improved Aquila optimized ResNet101 model is used for classification. The effectiveness of the models is contrasted with that of related studies in the literature. A F1 score of 98.18%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 98.91%, and precision of 98% were attained by the suggested model. © 2024 IEEE.

关键词： DCNN Diabetic retinopathy Gaussian Mixture IAO ResNet

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Extensive Review on Predicting Heart Disease Using Machine Learning and Deep Learning Techniques

Extensive Review on Predicting Heart Disease Using Machine L...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Hemalatha, S. Kavitha, T. Chitra, K. Babitha, B. Kaya, N. Keshika, R. Kongu Engineering College Perundurai Department of Computer Applications Erode India

ISBN: (纸本)9798350348217

A variety of illness that affect your heart are referred to as heart disease. The accurate diagnosis of heart disease can reduce the risk of serious health problems, whereas an inaccurate diagnosis can be fatal. This research compares the findings and analyses of the cardiovascular illness using various machine learning and deep learning methods. © 2023 IEEE.

关键词： Diagnosis

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AI Based Infringement Discernment System for Network Security 1

AI Based Infringement Discernment System for Network Securit...

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1st International Conference on Computational Intelligence for Information, Security and Communication applications, CIISCA 2023

作者： Prasad, Arpana Asha, V. Madhwaraj, K.G. Mathew, Jincy Shet, Namrata Kumar B, Naveen New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350339727

One of the emerging systems in the globe that mimics human conduct is an Intrusion detection system based on artificial intelligence. Security is one of the most common issues due to the technology's rapid growth and growing importance in our linked society. Intrusion detection systems (IDS) are among the best methods for spotting various types of attacks among the many available options. We attempted to analyze the network intrusion detection system methods utilizing machine learning in this research (ML). We have highlighted the most current Network based intrusion detection system strategies employing ML techniques and approaches. The typical attack that they can detect, and their difficulties based on our analysis of recent research. This study's goal is to detect the attack on the basis of specific pattern inthe network traffic. The computers have a lot of data saved. AI- based infringement discernment system's primary usage is to encounter the malware or hacking. It is crucial to protect and preserve confidentiality inside the company while working with sensitive material. These infringement systems were now being created today to address challenges in the real world;they are particularly helpful in prevent network misuse and find web problems. This Infringement Discernment System uses a variety of methods, including machine learning, decision trees, fuzzy semantics, etc. © 2023 IEEE.

关键词： Fuzzy logic

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Performance Analysis of Olympic Games using Data Analytics 2

Performance Analysis of Olympic Games using Data Analytics

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Asha, V. Sreeja, S.P. Saju, Binju Nisarga, C.S. Purushotham, Gowda Prasad, Arpana New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350346640

This paper is used to analyze data from the Olympic Games in order to identify patterns and trends. The data set includes information on athletes, events, and results from the Games. The analysis will focus on identifying trends in athlete performance, event popularity, and results over time. The study will begin by cleaning and preparing the data for analysis. Next, descriptive statistics will be used to summarize the data. Finally, inferential statistics will be used to identify patterns and trends in the data. It is expected that the analysis will reveal patterns and trends in the data that will be helpful in understanding the Olympic Games and predicting future results. © 2023 IEEE.

关键词： Data Analytics

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Machine Learning based prototype for Customer Segmentation using RFM 2

Machine Learning based prototype for Customer Segmentation u...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Asha, V. Saju, Binju Dhirendra, Singh Navnit Kaswan, Yuvraj Prajwal, G.C. Sreeja, S.P. Master of Computer Applications New Horizon College of Engineering Bangalore India

ISBN: (纸本)9798350397635

One way to hike consumer satisfies the services of the company provides is through an use of the customer relationship management (CRM) system. It can be difficult to determine the proper info what customer requires from data in your CRM system. Businesses can use data mining processes to segment and retrieve important customer information. Basis of consumer's RFM (Recency, Frequency, and Monetary) score, we can classify the customer segmentation. The RFM model has been utilised as the foundation for client segmentation in a number of research. However, the approaches suggested in earlier research are extremely particular to particular businesses, the score of RFM range employed as likewise more arbitrary. Additionally, is organizations grow, problems arise with RFM scoring. Measurements of RFM scores require periodic corrections, and current techniques make these corrections difficult. Determine a correct RFM score range, this study provided a unique technique that used a combination of K-Means and the Davies-Bouldin Index (DBI), circumventing the shortcomings of previous methods. As the amount of data rises, the suggested technique makes it easier to calculate RMF ratings. This is based on research conducted in the telecom industry. The K-Means method used in this study also produced the correct RFM score range which is depended on the ideal K values of the K-Means algorithm. The proposed solution only depends on each customer's RFM value from the corresponding data, so it can be used in different industries. © 2023 IEEE.

关键词： K-means clustering

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Credit Card Fraud Detection Using Machine Learning 3

Credit Card Fraud Detection Using Machine Learning

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

作者： Tressa, Neethu Asha, V. Govindaraj, M. Padanoor, Sangamesh Tabassum, Rahila Dharmesh, Desai Vatsal Saju, Binju Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350302288

A credit card is a credit payment facility provided by banks or similar financial organizations that allow the card holders to borrow money to pay for products and services from merchants accepting credit cards. Since everything is now done electronically, there is a risk of card misuse and loss of funds for account holders. It is essential that the credit card companies should be able to recognize fraudulent credit card transactions so that the customers are not charged for products which they did not purchase. These types of problems can be solved using data science and machine learning techniques. This can be done by modeling the dataset using machine learning algorithms combined with credit card fraud detection. The essential key to machine learning algorithms can be the historical credit card transactions with data of those found to be fraudulent. The first step is to evaluate and pre-process the data before feeding the machine learning algorithm to the credit card dataset to determine algorithm parameters and calculate performance metrics. The pattern can be then used to detect whether a new transaction is legitimate or not. The goal is to determine the occurrence of fraudulent transaction. © 2023 IEEE.

关键词： Crime

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GD-STFA: Gradient Descent Sea Turtle Foraging Algorithm enabled Deep Q Network for Diabetic Retinopathy Detection

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Multimedia Tools and applications 2024年第18期83卷 53817-53836页

作者： Sabeena, A.S. Jeyakumar, M.K. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumaracoil - 629180 Tamilnadu Thuckalay India Department of Computer Applications Noorul Islam Centre for Higher Education Kumaracoil - 629180 Tamilnadu Thuckalay India

Diabetic Retinopathy (DR) refers to a retinal disease that happens due to high glucose levels in the blood, and it leads to blindness globally. In order to delay or avoid vision loss and degradation, early detection, and treatment are necessary. Hence, the conception of an automated technique for precise DR recognition is necessary. Thus, a Deep Q network (DQN) with a Gradient Descent-Sea Turtle Foraging Algorithm (GD-STFA) is proposed that inherits Gradient Descent (GD) in Sea Turtle Foraging Algorithm (STFA), to detect DR from images. This method involves various phases, like pre-processing, segmentation, as well as DR detection. Here, the DR image quality is enhanced in the pre-processing phase using a median filter. After that, the segmentation is done by utilizing Swin-UNet and it is trained using the proposed GD-STFA algorithm. Finally, the DQN trained with GD-STFA finds if a person has DR or normal from his retinal images. The experimentation is performed in detail and validated on the Indian Diabetic Retinopathy Image Dataset benchmark dataset. Ultimately, the experimental outcomes demonstrate that the model is better than traditional techniques regarding optimal accuracy of 0.98, sensitivity of 0.984 and specificity of 0.971. Also, regarding the segmentation accuracy, the proposed model is around 0.906 accurate with a Dice coefficient of 0.963. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2023.

关键词： Median filters

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