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Predicting Chronic Kidney Disease Progression Using Classification and Ensemble Learning 8th

Predicting Chronic Kidney Disease Progression Using Classifi...

引用

8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Valli Mayil, V. Ratha Jeyalakshmi, T. Gupta, Rishu Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Department of MCA Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India

ISBN: (纸本)9789819732418

A serious medical problem that impacts millions of people globally is chronic kidney disease, or CKIDD. Accurate CKIDD progression prediction is essential for disease management and early intervention. Machine learning (ML) has shown great potential in predicting the course of CKIDD by employing a variety of medical, laboratory, and demographic factors. In our research, we suggested an ensemble approach for the increased level of accuracy. We contrast the outcome with well-known machine learning models such as neural networks, Dec-Trees, random forest, and support vector machines. On a sizable dataset of CKIDD patients, where good progression outcomes are identified, our model is trained. Our model is trained on a big dataset of CKIDD patients and proposed to find the result that the patient is affected or not. The chronic kidney disease dataset from Kaggle is utilized to test the model. Standard metrics including accuracy, precision, recall, F1-score, and area under receiver operating characteristic curve (AUC-ROC) are considered for proving the capability of the proposed work. In comparison to the conventional method, our model's experiment results demonstrate 100% accuracy in pattern prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Detection of Animal Hunters in Forest Using Regional Convolutional Neural Network Algorithm

Detection of Animal Hunters in Forest Using Regional Convolu...

引用

2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Sakthivanitha, M. Lakshmi, R.Bagavathi Chitra, A. Priscila, S.Silvia Department of Information Technology Chennai India Drbccc Hindu College Department of Computer Applications Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Chennai India

ISBN: (纸本)9798350317060

Detection of objects moving in a video is regarded as a vital problem in image processing applications i.e. the detection during the movement of camera is considered significant in video processing. In this paper, the movable objects for instance humans moving in forest environment is detected using a deep learning algorithm called regional convolutional neural network (RCNN). In this classification model, the classifier segregate the moving human in a dynamic environment. The region of motion is initially detected via background compensation method. The RCNN is utilised then to locate accurate the person moving in that region of motion. The RCNN model is a lightweight classifier that is designed specifically for the smaller objects. The results of both motion and object detection is fused together to obtain the moving objects. If a moving information is missed in the current frame, it is then recalled in the subsequent frame as per the spatial or temporal data. The simulation is conducted on 30 videos, where 24 is used for training the classifier and remaining 6 is taken for testing purposes. The result of simulation shows that the proposed RCNN model obtains improved accuracy, specificity, sensitivity and f-measure. © 2023 IEEE.

关键词： Object detection

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Anomaly Detection in Medical Images Using Deep Reinforcement Learning 2

Anomaly Detection in Medical Images Using Deep Reinforcement...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rastogi, Divya Sharma, Aparna Yadav, R.K. Varshney, Neeraj Saraswat, Laxmi Computer Science and Application Sharda University UP Greater Noida India Electronics & Communication Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Abes Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350371055

Anomaly detection in clinical pix is vital to identify and diagnose diseases. Deep reinforcement gaining knowledge is a practical gadget-mastering approach implemented for more than a few medical obligations. This technique applies a synthetic intelligence version to research by revealing an iterative procedure to discover a set of top-of-the-line parameters for ultimate selection-making. The cause of this examination is to use deep reinforcement mastering for anomaly detection in medical pix. This approach is used to identify strange areas in clinical pix, including tumors, mental diseases, and other abnormalities. Deep reinforcement mastering is mixed with Convolutional Neural Networks (CNNs) to extract capabilities from the medical pictures and then classify them consistent with their deviation from normal. The detection accuracy is evaluated using a novel assessment metric, referred to as the precision-recollect (PR) curve, which is designed to degree the version's accuracy. The results show that deep reinforcement learning can gain high-performance anomaly detection accuracy compared with conventional methods. © 2024 IEEE.

关键词： Accuracy Anomalous Evaluated Evaluation Measure

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Securing the Cyber Supply Chain: A Risk-based Approach to Threat Assessment and Mitigation 4

Securing the Cyber Supply Chain: A Risk-based Approach to Th...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Phani Prathyusha, J.R.V.S.L.P. Esther Jyothi, V. Jhansi, Vadlamudi Sampreeth Chowdary, N. Madhuri, A. Sindhura, S. Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Applications Vijayawada India P.V.P. Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India Nri Institute of Technology Department of Computer Science and Engineering Agiripalli India

ISBN: (纸本)9798350300093

The system of Cyber Supply Chain (CSC) is characterized by its complexity, consisting of several subsystems, each responsible for a distinct set of responsibilities. Securing the supply chain presents a challenge due to the presence of vulnerabilities and threats throughout the system that has the potential to be taken advantage of at any time, considering that any component of the system is susceptible to such attacks. As a result, supply chain security is difficult to achieve. This has the potential to create a significant interruption to the overall continuity of the company. Therefore, it is of the utmost importance to identify the hazards and make educated guesses about their likely outcomes so that organizations can take the appropriate precautions to ensure the safety of their supply chains. By leveraging a range of factors, such as the expertise and incentives of threat actors, Tactics, Techniques, and Procedures (TT and P), as well as Indicators of Compromise (IoC), the analysis of Cyber Threat Intelligence (CTI) offers valuable information on both identified ansignd unidentified cybersecurity threats. In order to increase the safety of the cyber supply chain, the purpose of this article is to investigate and speculate on potential dangers. The CTI and Machine Learning (ML) approaches have been employed by us in order to study and forecast the risks based on the CTI attributes. This makes it possible to detect the inherent CSC vulnerabilities, which enables suitable control. To enhance the overall security of computer systems, it is imperative to implement specific actions, including the collection of CTI data and the adoption of various machine learning techniques. These techniques encompass Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Cat Boost, and Gradient Boost, which are employed in analyzing the Microsoft Malware Prediction dataset to create predictive analytics. This is done in order to illustrate t

关键词： Supply chains

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Security Challenges in Multi-Cloud Environments: Solutions and Best Practices 7

Security Challenges in Multi-Cloud Environments: Solutions a...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Madanan, Mukesh Patel, Pratik Agrawal, Prateek Mudholkar, Pankaj Mudholkar, Megha Jaganraja, V. Dhofar University Department of Computer Science Oman National Forensic Sciences University India India Marwadi University Faculty of Computer Applications Gujarat Rajkot360003 India Marwadi University Department of Computer Engineering Gujarat Rajkot360003 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai600062 India

ISBN: (纸本)9798350350067

The aim of this research paper is to investigate how machine learning can be incorporated to improve security around multi-clouds, with specific reference to the challenges. Based on the datasets obtained from Kaggle, this study uses both unsupervised learning, more specifically the Isolation Forest technique and supervised learning, such as regression-based forecast for identifying possible security threats. The evaluation shows that through the use of ML techniques risk that originates from multi-cloud systems may be well managed while, at the same time, the utilization of cloud resources could be enhanced to produce the best results. The results make it possible to conclude that machine learning still holds a huge potential in the sphere of cloud security and provides a range of recommendations and recommendations for organizations that have to manage their multi-cloud environment. Possible future studies are devoted to the investigation of deep learning models and the incorporation of blockchain technology for more security and effectiveness of the system. © 2024 IEEE.

关键词： Cloud security

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A Neural Network Approach to Signature Verification with Mathematical Moments 22

A Neural Network Approach to Signature Verification with Mat...

引用

22nd OITS International Conference on Information Technology, OCIT 2024

作者： Suryadevara, Thireesha Rafi, Mudassir Sah, Dipak Kumar Kumar, Rahul Department of Computer Science & Engineering SRM University AP Amaravati 522240 India Department of Computer Engineering and Applications GLA University Mathura 2814006 India

ISBN: (纸本)9798331510404

The present work develops a method for identity authentication and verification of static signatures stored in the database employing artificial neural network. The present method uses mathematical moments for feature extraction such as mean, variance, skewness and kurtosis. First of all, the method suggests to scan the signature images, then after a sequence of preprocessing steps the resulting images are subjected to feature extraction, however, at present already existing standard databases have been used. Subsequently, the system is trained from the genuine signature of individuals, and then an ANN is used to classify the signature images. The suggested method’s effectiveness has been demonstrated by comparison with the four current methodologies and experimental findings. © 2024 IEEE.

关键词： Moments Neural Network Offline Signature Verification

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Contribution Unveiling Cutting-Edge Machine Learning Techniques for Image Segmentation

Contribution Unveiling Cutting-Edge Machine Learning Techniq...

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International Conference on Electrical and Electronics Engineering, ICEEE 2023

作者： Shaik, Nazeer Gupta, Ankur Bhati, Sunita Kumar, Jaideep Singh, Jagendra Budhiraja, Ishan Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology Andhra Pradesh Anantapur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Applications JECRC University Jaipur India Raj Kumar Goel Institute of Technology AKTU University Lucknow India

ISBN: (纸本)9789819986606

Segregation of images is a critical step in processing images, computer vision, and a variety of other disciplines. The technique involves decomposing an illustration into numerous components or components, every single one that consists of an ensemble of elements with identical features for example African descent, frequency, or consistency. The most important objective of appearance, the intention of fragmentation seems to reduce complexity or customize the mathematical representation of an illustration in a way that is more readily reasonable and more straightforward for assessment. It is widely employed to locate boundaries and features in photos, and this is favorable in an assortment of industries including clinical imaging, object detection, recognition, and autonomous vehicles. Several image segmentation techniques are available, and in incorporating thresholding, zone is an area-based differentiation and corner-based recognition. A threshold segment is a simple and commonly used technique that involves setting a threshold value and dividing the pixels into two classes based on their intensity values. Region-based segmentation involves grouping pixels based on their spatial proximity and similarity in characteristics, while edge-based segmentation involves detecting edges or boundaries in an image and using them to separate different regions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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Future Database Technologies for Big Data Analytics

Future Database Technologies for Big Data Analytics

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Rani, Geeta Sharma, Taruna Sharma, Avinash Chitkara University Institute of Engineering and Technology Dept of Computer Applications Punjab India Maharishi Markandeshwar Engineering College Department of Computer Science and Engineering Mullana India

ISBN: (纸本)9798350335835

Data is valuable asset for organizations but it cannot be used directly for business decision making. It needs to be processed in a form that is easily understandable and can to be used by decision makers. The database technology is continuously changing as the data is growing at a rapid date and business requirements change with time. With the advent of IOT, social media and other technology advancements, data become more complex in terms of volume, variety and veracity. Traditional technology can't be used for handling this data due to its complex and dynamic nature of Big Data. So more advanced and sophisticated tools are essential to handle complex nature of data. In this paper the various database tools are discussed and analysed by different vendors which are suitable for big data Analytics. © 2023 IEEE.

关键词： Big data

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4th International Conference on Soft Computing for Security applications, ICSCSA 2024

作者： Roselinkiruba, R. Jude Moses Anto Devakanth, J. Sharmila, L. Vasumathy, M. Misba, M. Saranya Jothi, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India Madanapalle Institute of Technology and Science Department of Computer Applications Andra Pradesh India Department of Information Technology Kingston Engineering College Vellore India

ISBN: (纸本)9798331515720

Steganography is the method that convert data into bits to make it suitable for hiding. While embedding data the factors such as stego image quality and distortion which cannot be observed by the human eyes. The aim of this study is to increase the stego image with good quality. Initially, the image pixels are separated into different bit planes then it is compared with the secret data bit planes using Fruitfly Optimization Improved Hybridized Algorithm Pixel Similarity (FOIS-PS). For that similar pixel Fitness Function (FF) is applied based on the three important criteria such as edge, entropy and correlation. Finally, the data is embedded in the LSB bits based on their Embedding Capacity (EC). Using this method, the size of the input is reduced. Then, the data is embedded based on difference of pixels within each group of similar pixels. Evaluation metrics shows the quality of the stego image is boosted and also reduces the processing time. © 2024 IEEE.

关键词： Image quality

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Secured Reversible Data Hiding Scheme with NMI Interpolation and Arnold Transformation

Secured Reversible Data Hiding Scheme with NMI Interpolatio...

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International Conference on Network Security and Blockchain Technology, ICNSBT 2023

作者： Jana, Manasi Jana, Biswapati Joardar, Shubhankar Jana, Sharmistha Lu, Tzu Chuen Department of Computer Applications Haldia Institute of Technology West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal West Midnapore India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India West Bengal Midnapore India Department of Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9789819944323

A novel and efficient 2-level secure reversible data hiding (RDH) method has been proposed based on image interpolation and Arnold transformation. An input image is scaled-down to original image and then scaled up to cover image using NMI interpolation method. The gray values of pixels are classified into a set of ranges. A difference value is calculated from adjacent pixels and it is used to find out the lower bound and upper bound of the range interval to which it belongs to. Before embedding, the secret data is scrambled using Arnold transformation implementing the 1st level security of proposed method. The scrambled secret data again updated to modified secret message to implement 2nd level security of proposed scheme. The input image is taken as reference during data embedding phase to raise the quality of the stego image. The experimental analysis shows that the proposed method has relatively higher imperceptibility and data embedding capacity than other schemes. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Interpolation

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