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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Rajbharath, R. Vijayamalaiya, S. Aabid, M. Salilan, Amal Manakula Vinayagar Institute of Technology Department of Computer Science Puducherry605107 India Manakula Vinayagar Institute of Technology Final Year Department of Computer Science and Engineering Puducherry605107 India

ISBN: (纸本)9798350315127

The analysis and modeling of a dataset related to sensory evaluations of coffee quality scores is presented in this report. The report explores various features related to aroma, flavor, aftertaste, acidity, body, balance, uniformity, cleanliness, sweetness, and overall quality of coffee. The report starts with data preprocessing, including handling missing values and encoding categorical variables. The report then drops irrelevant columns and extracts useful information from date columns. For the modeling phase, the report splits the data into training and testing sets and applies feature scaling using the MinMaxScaler to normalize the numeric features. The report then trains a Linear Regression model and evaluates its performance using mean squared error (MAE) and R- squared score. The model achieved a low MAE of 0.2567 and a high R-squared score of 0. 9476, indicating a good fit to the data. Furthermore, the report compares the performance of various regression models using cross-validation. The models included Linear Regression, Ridge, Lasso, ElasticNet, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, Support Vector Regressor (SVR), and MLP Regressor. finally, the report recommends the best coffee combination based on the analysis. The recommended coffee combination is from Coffee Quality Union, with the variety of Castillo, the country of origin of Colombia, the farm name of Finca El Paraiso, and the processing method of Double Anaerobic Washed. © 2023 IEEE.

关键词： Decision trees

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Face Mask and Physical Distance Detection using Deep Learning in Alarm Systems

Face Mask and Physical Distance Detection using Deep Learnin...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Divyavani, V. Vamshi, K. Srihith Reddy, S. Sowmya, A. Department of Computer Science and Engineering India Institute of Aeronautical Engineering BTech Iv Year Computer Science and Engineering India

ISBN: (纸本)9798350333602

This COVID-19 Pandemic is having devastating effects on societies and economies around the world. It is a communicable disease that spreads from another person who sneezes, coughs, and is in contact with another person. In this present COVID-19 situation, all must wear masks and maintain physical Distance. This is the most effective way to battle the virus. Our project uses a deep learning approach using the image classification method called CNN and YOLO. This proposed model gives better accuracy and is much more efficient than the existing model. The proposed model is implemented by using Python and OpenCV with TensorFlow. The construction of Face masks and Physical distance detection requires data collection, data splitting, data pre-processing, testing, and implementation. This proposed model gives an accuracy of 99% when a mask and physical distance is detected and it gives 95% accuracy when not following the practice. This model recognizes whether a person is wearing a mask and checks whether people are maintaining a physical distance of 2 meters. If they are not wearing a mask or not maintaining physical distance, then an alarm gives a signal. This model ensures safety and decreases the chances of virus spread. It can be implemented at Hospitals, Educational Institutions, Malls, Airports, etc. © 2023 IEEE.

关键词： COVID-19

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Modeling and Predictive Analytics of Breast Cancer Using Ensemble Learning Techniques:An Explainable Artificial Intelligence Approach

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computers, Materials & Continua 2024年第12期81卷 4033-4048页

作者： Avi Deb Raha Fatema Jannat Dihan Mrityunjoy Gain Saydul Akbar Murad Apurba Adhikary Md.Bipul Hossain Md.Mehedi Hassan Taher Al-Shehari Nasser A.Alsadhan Mohammed Kadrie Anupam Kumar Bairagi Computer Science and Engineering Discipline Khulna UniversityKhulna9208Bangladesh Information and Communication Engineering Noakhali Science and Technology UniversityNoakhali3814Bangladesh School of Computing Sciences and Computer Engineering University of Southern MississippiHattiesburgMS 39401USA Computer Skills Department of Self-Development SkillCommon First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Computer Science Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia

Breast cancer stands as one of the world’s most perilous and formidable diseases,having recently surpassed lung cancer as the most prevalent cancer *** disease arises when cells in the breast undergo unregulated proliferation,resulting in the formation of a tumor that has the capacity to invade surrounding *** is not confined to a specific gender;both men and women can be diagnosed with breast cancer,although it is more frequently observed in *** detection is pivotal in mitigating its mortality *** key to curbing its mortality lies in early ***,it is crucial to explain the black-box machine learning algorithms in this field to gain the trust of medical professionals and *** this study,we experimented with various machine learning models to predict breast cancer using the Wisconsin Breast Cancer Dataset(WBCD)*** applied Random Forest,XGBoost,Support Vector Machine(SVM),Multi-Layer Perceptron(MLP),and Gradient Boost classifiers,with the Random Forest model outperforming the others.A comparison analysis between the two methods was done after performing hyperparameter tuning on each *** analysis showed that the random forest performs better and yields the highest result with 99.46%*** performance evaluation,two Explainable Artificial Intelligence(XAI)methods,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-Agnostic Explanations(LIME),have been utilized to explain the random forest machine learning model.

关键词： Breast cancer prediction machine learning models explainable artificial intelligence random forest hyperparameter tuning

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SkyStrokes: Bridging Artistic Expression and Communication Barriers Through Aerial Digital Canvas 6

SkyStrokes: Bridging Artistic Expression and Communication B...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Bommala, Harikrishna Suvekha, Raman Reddy, Orra Venkata Srikanth Kumar, Neerati Shiva Rokati, Sai Kiran KG Reddy College of Engineering and Technology Department of Computer Science and Engineering Telanagana Moinabad India KG Reddy College of Engineering and Technology 4th Year Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350306613

This endeavors to address pressing societal concerns by introducing innovative solutions. It confronts the challenges of communication barriers encountered by the hearing-impaired, the excessive reliance on smartphones, and the environmental impact of paper waste. Through the implementation of 'on-Air writing,' an application is proposed that tracks object movements, enabling users to draw on a computer screen by simply waving their hands. By merging technology and social responsibility, this initiative strives to empower the deaf and mute community while advocating for eco-conscious practices. In doing so, it not only revolutionizes communication but also encourages a more inclusive, tech-savvy, and environmentally aware world. © 2024 IEEE.

关键词： computer Vision Gesture recognition Hand tracking Human-computer Interaction Machine Learning Social Issues

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Improved-Equalized Cluster Head Election Routing Protocol for Wireless Sensor Networks

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computer Systems science & engineering 2023年第1期44卷 845-858页

作者： Muhammad Shahzeb Ali Ali Alqahtani Ansar Munir Shah Adel Rajab Mahmood Ul Hassan Asadullah Shaikh Khairan Rajab Basit Shahzad Department of Computer Science Institute of Southern PunjabMultan5932Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Computer Skills Deanship of Preparatory YearNajran UniversityNajran61441Saudi Arabia Department of Engineering National University of Modern LanguagesIslamabad44000Pakistan

Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of *** routing protocols are available,but the issue is still *** is one of the most important techniques in the existing routing *** the clustering-based model,the important thing is the selection of the cluster *** this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each ***,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small *** proposed scheme performs hierarchal routing and direct routing with some energy *** simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its ***,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results.

关键词： Bubble sort algorithm gateway energy thresholds wireless sensor networks

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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IoT cryptography with privacy-preserving ElGamal public key cryptosystem using oppositional artificial flora optimization algorithm

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Multimedia Tools and Applications 2025年第11期84卷 9287-9308页

作者： Paramasivam, Devi Sivaji, Sathyalakshmi Dayanandan, Venkata Subramanian Alqahtani, Abdullah Saleh Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India Department of CSE Hindustan Institute of Technology and Science Kelambakam Tamil Nadu Chennai603103 India Department of CSE Velammal Institute of Technology Chennai India Department of Self-Development Skills - Common First Year Deanship King Saud University Riyadh12373 Saudi Arabia

Privacy and security have received significant interest among the research communities Due to the increased exploitation of the Internet of Things (IoT). Since IoT devices gather most of the public data like identity, location, contact numbers, and energy utilization, security, and privacy become crucial. The lack of well-defined IoT-enabled security and privacy schemes will prevent the applicability of the IoT technologies from the client side. Therefore, this paper presents an ElGamal public key cryptosystem (EGPKC) for generating optimal keys utilizing an Oppositional Artificial Flora Optimization (EGPKC-OAFA) technique intended for preserving privacy in IoT. Additionally, the EGPKC approach with an optimum generation of authentication keys is applied on the medium access control (MAC) header for secure authentication. Besides, the optimal key selecting process for EGPKC occurs by employing the OAFA approach which inherits the oppositional-based learning (OBL) concept and classical artificial flora approach. To evaluate the effective analysis of the EGPKC-OAFA algorithm, huge simulations such as Known plaintext attack (KPA), chosen-plaintext attack (CPA), key sensitivity, and correlation coefficient are employed and the result is determined by different feature intervals. Accordingly, KPA and CPA reached the minimum value of 0.0987 and -0.0499 respectively. As a result, the proposed EGPKC-OAFA algorithm obtained superior performance compared to other methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

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Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

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computer Systems science & engineering 2023年第1期44卷 205-219页

作者： J.Jean Justus U.Sakthi K.Priyadarshini B.Thiyaneswaran Masoud Alajmi Marwa Obayya Manar Ahmed Hamza Department of Computer Science&Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Sona College of TechnologySalem636005India Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer *** MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so *** existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task *** the same time,the task scheduling process is yet to be explored in the multi-core *** paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy *** GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks *** performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark *** experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy *** overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.

关键词： Task scheduling energy efficiency multi-core systems fitness function makespan

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Optimal Wavelet Neural Network-Based Intrusion Detection in Internet of Things Environment

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computers, Materials & Continua 2023年第5期75卷 4467-4483页

作者： Heba G.Mohamed Fadwa Alrowais Mohammed Abdullah Al-Hagery Mesfer Al Duhayyim Anwer Mustafa Hilal Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

As the Internet of Things(IoT)endures to develop,a huge count of data has been *** IoT platform is rather sensitive to security challenges as individual data can be leaked,or sensor data could be used to cause *** typical intrusion detection system(IDS)studies can be frequently designed for working well on databases,it can be unknown if they intend to work well in altering network *** learning(ML)techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable *** article introduces a new Bird Swarm Algorithm with Wavelet Neural Network for Intrusion Detection(BSAWNN-ID)in the IoT *** main intention of the BSAWNN-ID algorithm lies in detecting and classifying intrusions in the IoT *** BSAWNN-ID technique primarily designs a feature subset selection using the coyote optimization algorithm(FSS-COA)to attain ***,to detect intrusions,the WNN model is *** last,theWNNparameters are optimally modified by the use of *** experiment is performed to depict the better performance of the BSAWNNID *** resultant values indicated the better performance of the BSAWNN-ID technique over other models,with an accuracy of 99.64%on the UNSW-NB15 dataset.

关键词： Internet of things wavelet neural network security intrusion detection machine learning

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Cofee Quality Prediction Using Machine Learning

Cofee Quality Prediction Using Machine Learning

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Rajbharath R. Vijayamalaiya S. Aabid MK. Amal Salilan Department of Computer Science Manakula Vinayagar Institute of Technology Puducherry India Final Year Department of Computer Science and Engineering Manakula Vinayagar Institute of Technology Puducherry India

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