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

作者： Gupta, Priyanka Revathi, V. Hameed, Ali Abduhussien Gupta, Manish Yadav, Suman Avdhesh Rao, Bolleddu Devananda Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq Lovely Professional University Phagwara India Iilm University Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

Energy-efficient design and operation are essential for next-generation sensor networks to persist and require less maintenance. This paper describes a novel energy-efficient sensor network. It uses superior data routing and aggregation. Three novel algorithms-EARA, DFAA, and Adaptive Data Sampling-make this strategy practicable. EARA sends data with little energy, DFAA collects all data to eliminate duplicates, and ADSA adjusts sample rates based on relevance and variability. Each piece of software sends, manages, and changes sample rates with minimal energy. This innovative energy-saving strategy is tested against static-duty cycling, non-adaptive data aggregation, fixed-rate data sampling, high-energy data transmission, centralized data routing, and unoptimized sensor node location. The recommended solution outperforms network lifetime, energy usage, data accuracy, latency, scalability, and dependability. Energy savings, ease of use, affordability, adaptability, long-lastingness, and network lifespan are usually better with the proposed response. Scatter plots illustrate how much better the proposed method saves energy and works properly. The EARA, DFAA, and ADSA algorithm-based techniques offer great potential for energy-efficient sensor networks. This strategy stabilizes and prolongs sensor network configurations by optimizing data pathways, aggregating data, and adaptively sampling data. The results show that it works, making it a novel strategy for the future generation of sensor networks that demand higher performance and less energy. © 2024 IEEE.

关键词： Adaptive Data Sampling Data Aggregation Data Management Energy Consumption Energy Efficiency Energy-Aware Routing Network Lifetime Performance Evaluation Sensor Networks Sustainability

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A Detailed Structured Monitoting System for ICU Patient Mortality rate by using AI Algorithm 5

A Detailed Structured Monitoting System for ICU Patient Mort...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Joshi, Shivani Kumar, Rajiv Rai, Vipin Dwivedi, Avinash Tripathi, Vikash Chandra G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Nvidia Ai and Learning & Research Center Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model's training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously © 2024 IEEE.

关键词： accuracy Artificial Neural Networks (ANN) exploratory data analysis high-risk patients ICU patient admission medical technology mortality outcomes periodic updates prediction model

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Mediapipe-Powered SVM Model for Real-Time Kuchipudi Mudras Recognition 2

Mediapipe-Powered SVM Model for Real-Time Kuchipudi Mudras R...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Kishore Kumar, A.V. Emmadisetty, Raagaveda Chandralekha, M. Saleem, Kashif Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Chennai India College of Applied Studies and Community Service King Saud University Department of Computer Sciences and Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798350371314

In the fields of computer vision and gesture recognition, Recognizing Kuchipudi mudras in the real time, a traditional Indian dance form, is one of the challenging tasks. In this research work, an innovative solution was proposed for this problem. Support Vector Machine model along with The Google's Mediapipe framework are used for the real-time recognition of Kuchipudi mudras. The goal is to build a system that is able to differentiate the complex hand mudras in Kuchipudi performances with good accuracy and efficiency. The methodology includes image capture and preprocessing, extraction of hand landmarks by Mediapipe, as well as training an SVM model on a labeled data set. In addition to the above, real-time testing of the model is performed, and the webcam makes it possible to identify and display predicted mudras almost instantly. The SVM model correctly predicts various Kuchipudi's mudras. The presented research opens up the expressly possibility of real-time detection of movements in traditional dances and, in the future, can be used as a software module for teaching and grading dancers. I have used the Support Vector Machine technique;it resulted in surprisingly accurate detection of unique hand movements in Kuchipudi. © 2024 IEEE.

关键词： Grading

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Brain Tumor Detection Using ADARN Optimizer 2

Brain Tumor Detection Using ADARN Optimizer

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar, Rajiv Dwivedi, Avinash Joshi, Shivani Tripathi, Vikas Chandra Nvidia Ai Learning & Research Center G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

The use of MRI scans to identify and categorise brain tumors is the main focus of this study. The goal is to develop a precise and reliable method for early identification and accurate categorisation of brain tumours, including pituitary neoplasms, meningiomas, and gliomas. Deep learning methods were utilized to analyze MRI image databases, resulting in a 96% identification accuracy for brain tumors and a 98% categorization accuracy for the three types. This research demonstrates the potential of deep learning techniques for accurate brain tumor identification and categorization. The early identification and precise categorization of brain tumors can assist medical professionals in making informed decisions about the best treatment options for patients, leading to better outcomes and survival rates. The article provides an extensive review of current methods for identifying and classifying brain tumors using MRI data. It emphasizes the critical importance of this field of study in improving patient outcomes and reducing unnecessary treatments. © 2024 IEEE.

关键词： Brain Tumor Classification Convolutional Neural Network identification Image Augmentation MRI Scans

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Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning 2

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Kavitha, S.J. Cibi, A. Sutherlin, Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Pallavaram Chennai India Virtusa Consulting Services Private Limited Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798331542573

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector. © 2024 IEEE.

关键词： Zero-shot learning

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Exploring the Role of Internet of Things in Wireless Sensor Networks for Industry 4.0 Applications 1

Exploring the Role of Internet of Things in Wireless Sensor ...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Palarimath, Suresh Maran, Pyingkodi N.r, Wilfred Blessing Sujatha, T. Renuga, S. R.s, Chithra College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Ponjesly College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350365016

Recent developments in computer networking have made it possible to access information from a faraway location via either wireless or wired networks. The recent advancements in wireless infrastructure are directly responsible for the advent of wireless sensor networks, often known as WSNs. These networks make it easier to keep track of environmental happenings and activities, as well as document them and manage them. In WSNs, data relaying is accomplished through the use of a variety of routing strategies. Industry 4.0, also known as the fourth industrial revolution, is characterized by the incorporation of highly developed physical automation systems. These systems are made up of several pieces of machinery and gadgets that are linked together by sensors and are operated by software. The goal of the fourth industrial revolution is to improve the effectiveness and dependability of business processes. The Internet of Things (IoT) can be leveraged in manufacturing, which gives a means of linking engines, power grids, and sensors to the cloud in an industrial context. This is accomplished through the use of Industrial IoT. The purpose of this paper is to gain an understanding of how the Internet of Things functions in wireless sensor networks and its possible applications in a variety of settings. © 2024 IEEE.

关键词： Wireless sensor networks

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Use of Polygram Substitution in Spotting the Swindler in Secret Sharing 11th

Use of Polygram Substitution in Spotting the Swindler in Sec...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Yadav, Poonam Satheesh Kumar, S. Muthukumaran, V. Sandeep Kumar, Mathivanan Radhika, E.K. Department of Computer Science School of Applied Sciences Reva University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Applications Sindhi College Bangalore560024 India

ISBN: (纸本)9789819717231

In the polygram substitution, one set of characters is replaced by another set based on algorithm. The model is proposed for calculating and estimating secure online secret shares, where dynamic modification in any of the shares will be identified if number of the shares are more than specified threshold value. The security is based on the intractability of factoring as it uses Lagrange polynomial function. This is further enhanced by a graphical masking and unmasking technique;it includes two phases: encryption of the individual secrets and sharing of the data. The generation of the shares is done by masking using Polygram substitution, and the shares are regenerated and unmasking of the qualified shares is done to find original secret. Masking is done on original secret, then shares are generated. Each encrypted share contains information which is different than original secret. Masking by Polygram substitution gives additional protection of secret data on the Internet. Moreover, we achieve efficient recovery of the original secret data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Polynomials

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Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

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computer Systems science & engineering 2022年第9期42卷 1273-1287页

作者： S.Prakash P.Vishnu Raja A.Baseera D.Mansoor Hussain V.R.Balaji K.Venkatachalam Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of ECE Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce *** relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

关键词： Chronic disease classification iterative weighted map reduce machine learning methods ensemble nonlinear support vector machines random forests

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Enhancing the Robustness of Model-Predictive Control using GMMs as Outlier Detection 12

Enhancing the Robustness of Model-Predictive Control using G...

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12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Féret, Leander J. Gepperth, Alexander Lambeck, Steven Jumo GmbH & Co. Kg Think Tank Fulda Germany Fulda University of Applied Sciences Applied Computer Science Department Fulda Germany Fulda University of Applied Sciences Electrical Engineering Department Fulda Germany

ISBN: (纸本)9798350350982

This article proposes a flexible control strategy to improve the reliability of data-driven model-predictive control (MPC). Prediction models are usually trained on logged data, which may be incomplete, leading to model failure within the optimization algorithm of the MPC. To counter this, we propose a Gaussian Mixture Model (GMM) for detecting outliers during live operation. These outliers are found in the observed system states as well as the calculated control signals regarding the logged training data of the ai-model. If an outlier is detected, we assume that the data-driven prediction model will be inaccurate, and an appropriate fallback strategy is used instead, which includes but is not limited to an alternative controller or a complete system-shutdown. In addition, outliers are stored for subsequent improvement of the prediction model. This outlier-protected control (OPC) architecture is evaluated in terms of control quality using the integrated squared error (ISE) on three nonlinear dynamical systems. The outcomes indicate a significant enhancement in control quality in comparison to an unprotected but otherwise equivalent architecture. These findings suggest that a very simple and efficient outlier detection can greatly benefit data-driven MPC in general whenever feasible fallback strategies exist. © 2024 IEEE.

关键词： Prediction models

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