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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Seethalakshmi, K. Bhagyalakshmi, A. Anitha, G. Sheela, E. Sandeep, Varada Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Tami Nadu Chennai India S.A Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India St Peter's Institute of Higher Education and Research Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

With the static nature of recommender systems, it is crucial to take into account the intricate details of customer behaviour to better improve operating efficiencies. This paper is about original methods to augment recommendation frameworks by employing spectral clustering methods, to be more specific. Limited segmentation techniques now cannot catch up with the growing complexity and variety of customer conduct. To address this need, we are proposing a new way in which the spectral clustering technique, a supervised learning technique, can be applied. Revealing in-depth relationships inside customer data will enable an upgrade in the segmentation approach beyond the existing norms. Spectral clustering stands out as a really amazing tool for accomplishing extraordinarily precise customer segments, which help understand the tastes of users on a deeper level. Through discovering separate customer groups, we are aiming for a meaningful contribution in the field of recommendation systems. This project combines the effectiveness of spectral clustering with the convenience of a recommendation system that aims to provide a holistic, meaningful, and user-oriented approach to enhancing recommendation systems in the area of personalised recommendation. Our aim is to create recommendation systems that are not only attuned to the heterogeneity of customer preferences but also adopt new trends in user experience design beforehand. The segmentation is done with K-Means along with the proposed algorithm and the clusters are hence compared and Implementation results shows that spectral clustering is much more efficient than the existing K-Means algorithm © 2024 IEEE.

关键词： Sales

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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

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computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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Designing an Energy Tracker and Smart Home Solutions Using Design Thinking Methodology 10

Designing an Energy Tracker and Smart Home Solutions Using D...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Hiererra, Siti Elda Chandra, Victor Ivana, Jane Wibowo, Jessica Lynn Asadi, Faisal School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Business Analytics Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

As energy consumption rises, finding ways to reduce energy usage in homes is crucial. This paper presents a case study using design thinking to create a daily energy tracker that helps users reduce energy consumption. We conduct user research, define the problem, brainstorm solutions (mobile app, smart thermostat, smart plug, physical tracker), and test prototypes. A mobile app that tracks energy usage in real-time and offers personalized tips emerges as the most promising solution. We develop and launch the app, partnering with an energy company. This case study showcases the effectiveness of design thinking in creating a user-centered solution for reducing energy consumption. © 2023 IEEE.

关键词： Energy utilization

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IoT Device Security for Smart Card Fraud Detection for Credit Cards 2

IoT Device Security for Smart Card Fraud Detection for Credi...

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Devi, R. Aiyshwariya Raffik, R. Sabitha, B. Akila, K. Computer Science and Business Systems Psna College of Engineering and Technology Dindigul India Mechatronics Engineering Kumaraguru College of Technolgy Coimbatore India

ISBN: (纸本)9798350306811

Credit card theft is on the upswing as technology advances. Also, it may be appealing that as global communication advances, economic fraud is sharply rising. Millions of dollars are lost every day due to these frauds, according to records. master card fraud states to the somatic forfeiture of the master card or defeat of profound master card information. For detection, a variety of machine learning methods are frequently utilized. This study presents a few of the algorithms that will be used to catalogue connections as faithful or fraudulent credit cards. The study used a dataset for fraud detection. Sorting and searching were utilized for oversampling because the dataset was quite unbalanced. NFC technology is decisive for user re-authentication as integrates the Internet of Things for double transaction verification to reduce fraudulent transactions, Random Forest, Bayesian Network, and Multilayer Perception were the strategies adopted in the experiment, including feature selection and dataset division into training and test data. Results show that every technique is regularly and reliably used to spot master card fraud. © 2023 IEEE.

关键词： Bayesian networks

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Removing/Minimising Gaussian noise from real time CCTV footages using GAN 1

Removing/Minimising Gaussian noise from real time CCTV foota...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Prashanth, B.S. Puneeth, B.H. Manoj Kumar, M.V. Sreenivasa, B.R. Suraj, Adyapadi Hegde, Akshay Prashant Pranava Aithal, K.S. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India Bapuji Institute of Engineering and Technology Department of Computer Science and Design Karnataka Davanagere577004 India

ISBN: (纸本)9798350376517

This paper describes a system that uses generative adversarial networks (GANs) to eliminate gaussian noise from CCTV of footages. Unwanted noise like gaussian noise frequently degrades the quality of images and makes them more difficult to interpret. In our approach, a discriminator network is used to direct the training of a generator network, whose job it is to produce denoised images from noisy inputs. This framework includes operations like picture enhancement, noise reduction, and evaluation with metrics like the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The resultant denoised images show enhanced visual quality and have potential uses in image analysis and computer vision. © 2024 IEEE.

关键词： Generative adversarial networks

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Emergency Vehicle Detection Using Deep ConvNet2D and computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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A Framework Using Federated Learning for IoT-Based Forest Fire Prediction

A Framework Using Federated Learning for IoT-Based Forest Fi...

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2023 IEEE International Conference on Internet of Things and Intelligence systems, IoTaIS 2023

作者： Purcell, Richard Naik, Kshirasagar Lung, Chung-Horng Zaman, Marzia Sampalli, Srinivas Mutakabbir, Abdul Dalhousie University Faculty of Computer Science HalifaxNS Canada Electrical and Computer Engineering University of Waterloo WaterlooON Canada Systems and Computer Engineering Carleton University OttawaON Canada Research and Development Cistel Technology OttawaON Canada

ISBN: (纸本)9798350319040

Forest fires are a growing threat to human commu-nities. The Canadian Wildland Fire Information System gives realtime information to fire management agencies and the public. However, machine learning use for forest fire ignition classification prediction within the platform and ones like it, is yet to be fully realized. We propose a novel framework that uses federated machine learning combined with Internet of Things technologies, for forest fire ignition classification prediction. The framework incorporates distributed IoT weather stations deployed in an area prone to forest fires. We find comparable prediction accuracy between a federated machine learning system and a central server machine learning system. Our federated system, trained on an imbalanced dataset comprising 5,008,365 non-ignition cases and 45,411 ignition instances, has shown encouraging outcomes. It attained an Accuracy of around 0.76 and a ROC-AUC of about 0.80. The performance is on par with other systems, indicating that our approach is effective in classifying forest fire ignitions with a spatial resolution markedly superior to that of centralized systems. © 2023 IEEE.

关键词： Deforestation

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Brain Image Denoising and Segmentation Using Advanced Mixed Thresholding and Edge Preservation Analysis Technique

Brain Image Denoising and Segmentation Using Advanced Mixed ...

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2024 International Conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Symbiosis Institute of Business Management Nagpur India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the realm of medical imaging, particularly brain imaging, the clarity and accuracy of images are paramount for effective diagnosis and treatment planning. However, the inherent presence of noise in imaging modalities such as MRI often degrades the quality of these crucial images, complicating the segmentation process and potentially leading to inaccurate diagnoses. Addressing this challenge, our research introduces an innovative approach that synergizes advanced mixed thresholding with edge preservation analysis to significantly enhance brain image denoising and segmentation. This technique leverages a novel mixed thresholding method that dynamically adjusts to the specific noise characteristics of brain images, effectively reducing noise while retaining critical image details. Concurrently, our edge preservation analysis ensures that essential structural information is maintained, preventing the loss of vital features during the denoising process. The combination of these methods provides a robust solution to the persistent issue of noise in brain imaging, promising to improve the accuracy of segmentation and, by extension, the reliability of medical diagnoses derived from these images. By integrating advanced algorithms and mathematical models, our approach not only outperforms existing denoising and segmentation techniques but also establishes a new benchmark for image processing in medical diagnostics. © 2024 IEEE.

关键词： Image denoising

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A Machine Learning Model Marketplace to Overcome Low Price Elasticity of Sales Forecasting 17

A Machine Learning Model Marketplace to Overcome Low Price E...

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17th International Conference on Wirtschaftsinformatik, WI 2022

作者： Hütsch, Marek University of Duisburg-Essen Institute for Computer Science and Business Information Systems Essen Germany

Machine learning (ML) sales forecasting applications occupy a special position in industry and retail, as used in numerous optimization activities, such as price optimization. While optimizing prices, sales forecasting tools require elasticity regarding the optimization objective. When ML methods are used for sales forecasting, there must be sufficient data to learn the proper elasticity requirements. To overcome the problem of low price elasticity of sales forecasts, when historical price-change data are scarce, we present a sales forecasting model marketplace in which trained ML models are shared across companies. Evaluations of a design science project with 43 retail stores show that a model marketplace with neural networks and dropout profiles achieves good forecasting accuracy and high price elasticity. With these results, companies can implement automated optimization algorithms while avoiding extant cumbersome stovepiped methods. © 2022 17th International Conference on Wirtschaftsinformatik, WI 2022. All rights reserved.

关键词： Wine

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A* Search Algorithm for an Optimal Investment Problem in Vehicle-Sharing systems 12th

A* Search Algorithm for an Optimal Investment Problem in ...

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12th International Conference on Computational Data and Social Networks, CSoNet 2023

作者： Le, Ba Luat Martin, Layla Demir, Emrah Vu, Duc Minh ORLab and Faculty of Computer Science Phenikaa University Hanoi Viet Nam Department of Industrial Engineering and Eindhoven AI Systems Institute Eindhoven University of Technology Eindhoven Netherlands Cardiff Business School Cardiff University Cardiff United Kingdom

ISBN: (纸本)9789819706686

We study an optimal investment problem that arises in the context of the vehicle-sharing system. Given a set of locations to build stations, we need to determine i) the sequence of stations to be built and the number of vehicles to acquire in order to obtain the target state where all stations are built, and ii) the number of vehicles to acquire and their allocation in order to maximize the total profit returned by operating the system when some or all stations are open. The profitability associated with operating open stations, measured over a specific time period, is represented as a linear optimization problem applied to a collection of open stations. With operating capital, the owner of the system can open new stations. This property introduces a set-dependent aspect to the duration required for opening a new station, and the optimal investment problem can be viewed as a variant of the Traveling Salesman Problem (TSP) with set-dependent cost. We propose an A* search algorithm to address this particular variant of the TSP. Computational experiments highlight the benefits of the proposed algorithm in comparison to the widely recognized Dijkstra algorithm and propose future research to explore new possibilities and applications for both exact and approximate A* algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Traveling salesman problem

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