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deep neural network-based tools for hotel marketing activities of online travel agents

Multimedia Tools and Applications 2025年 1-24页

作者： Keles, Ali Keles, Ayturk Keles, Mustafa Berk Mutlu, Behçet Department of Computer Education and Instructional Technology Faculty of Education Agri Ibrahim Cecen University Agri04100 Turkey Machine Learning Engineer *** Istanbul Turkey Director of Technology R&D Center *** Istanbul Turkey Department of Computer Engineering Gebze Technical University Kocaeli Turkey

Vacationers always want to choose the hotel that best meets their needs and expectations. For this, they typically use online travel agents (OTAs). OTAs must analyze millions of images from thousands of hotels quickly and classify them accurately to best meet demands. This is a highly complex and challenging task. OTAs also have to choose a default image to represent best way each hotel in order to best market hotels to vacationers and encourage click behavior. This is another challenge to overcome. The biggest problem in classifying millions of images from different areas of hotels is that these areas often have similar objects and environments. For this reason, fewer categories have been used in deep learning (dL) studies conducted so far. In this study, two deep neural networks (dNN) have been developed to accomplish these challenging tasks. The first network is the "hotel image classification network" (HICNet), which classifies images of different areas of hotels into 11 categories. In a multi-class classification task that includes closely related categories such as bar, food, and lobby, the HICNet network achieved very good results, reaching an average of 79% precision, sensitivity, and F1-score. The second network is the "hotel default image network" (HdINet), which enables the determination of a default image for each hotel on the OTA page. HdINet can accurately identify the default image of hotels with an average of 92% precision, sensitivity, and F1-score. These networks have been integrated into the OTA system named *** and are currently in active use. © The Author(s) 2025.

关键词： Hotels

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A distributive Hybrid Cell Balancing Technique for Series-Connected Lithium-Ion Cells 3

A Distributive Hybrid Cell Balancing Technique for Series-Co...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Imran, Syed Ali Kazmi, Syed Najeeb Ali Sakandar, Huzaifa Ulasyar, Abasin Khalid, Alishba Electrical Engineering Department University of Engineering & Technology Taxila Pakistan School of Engineering & Built Environment Griffith University Gold Coast Australia Drivr Team Washing Machine-R&D Center Beko Istanbul Turkey

ISBN: (纸本)9798331504861

As the demand for high energy density batteries has surged due to their use in transportation electrification and energy storage systems (ESS), the role of battery management systems (BMS) has become even more critical. Individual cells in a battery pack can develop imbalances in their state of charge (SOC) during charge/discharge operations, which in turn can diminish battery pack’s ability to operate at its rated capacity. Therefore, cell balancing that ensures optimal performance of a battery pack becomes a critical function of the BMS. In this study, a distributive hybrid cell balancing technique is presented which incorporates both passive and active balancing strategies for optimal cell balancing based on the difference in SOC among the cells. The proposed method is designed to address imbalance in SOC more effectively and in a shorter duration compared to conventional inductor based active balancing and resistors based passive balancing. Simulations were run to validate the performance of the proposed technique during three different modes of battery operation: idle, charging, and discharging. Furthermore, a hardware prototype was also developed for practical demonstration of the proposed hybrid technique. ©2024 IEEE.

关键词： Battery Pack

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Vehicle Prediction for BUS Identification Output from our route Testing real Time Algorithm 5

Vehicle Prediction for BUS Identification Output from our Ro...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Misba, M. ramya, r. dickson, Joel Sharmila, L. Kavitha, J. Udayakumar, K. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu India St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India Bethlehem Institute of Engineering Tamil Nadu Karungal India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331530013

Traffic generation in Indian cities is of a mixed nature, comprising over a dozen types of vehicles that can be broadly categorized into slow-moving and fast-moving vehicles. This study highlights the challenges associated with the inefficient operation of the existing bus system. GPS and mobile networks form integral components of the current system, enabling data storage and management using PostgreSQL. The system provides robust and reliable data to fulfill requests from other software applications. Operating on cloud platforms, it is compatible with all major web browsers. The proposed algorithm continuously tracks the real-time location of buses and their corresponding WMS (Web Map Service) location, alerting users through marker notifications. This paper also explores measures to evaluate the efficiency and effectiveness of bus services. recognizing that an inadequate public transport system impedes economic growth and development, this study emphasizes the need to enhance the overall quality and accessibility of public transportation. The findings aim to improve the operational efficiency of bus services, contributing to a more effective and sustainable transport system. © 2024 IEEE.

关键词： Buses

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detection of Medicinal Plants Using machine Learning 12th

Detection of Medicinal Plants Using Machine Learning

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12th International Conference on recent Trends in Computing, ICrTC 2024

作者： ravikumar, S. Eugene Berna, I. Babu, r. Arockia raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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Ensemble Learning: Importance, Applications, Challenges, and Future Work

Ensemble Learning: Importance, Applications, Challenges, and...

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2024 International Conference on Integration of Emerging Technologies for the digital World, ICIETdW 2024

作者： Manjunathan, N. Shanmugapriya, d. Girija, P. Vasudevan, M. Murugesan, S. Siva Subramanian, r. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India IFET College of Engineering Department of Artificial Intelligence and Machine Learning Tamilnadu Villupuram India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept of CSE India Andhra Pradesh Madanapalle517325 India R.M.D Engineering College Department of Computer Science and Engineering Kavaraipettai India RMK College of Engg and Tech Department of Computer Science and Engineering India

ISBN: (纸本)9798331541170

Ensemble learning has become one of the most used approaches in machine learning that helps to increase predictive capabilities and model stability. Thus, this survey paper offers an extensive analysis of ensemble learning, stressing its significance, potential uses, inherent difficulties, and future research prospects. In the first step, we explain the basic ideas and the major ensemble approaches, including bagging, boosting, and stacking. We then discuss the flexibility of ensemble learning in classification, regression, and outlier detection problems and its implications in health informatics, finances, e-business, and self-driving systems. However, there are challenges associated with ensemble learning, for example, computational cost, the quality of data used, and the fact that the models themselves cannot be explained. These are the issues that can hinder the performance of ensemble models and the ideas to help to improve the efficiency of this method. In addition, we outline potential future research avenues such as novel ensemble learning methods, optimization of computational complexity, interpretability of models as well as new use cases in emerging fields like IoT, quantum computing, and edge computing. We also highlight the need to consider the ethical issues towards attaining fairness and accountability in ensemble learning. The results of this survey should contribute to raising the base of knowledge about ensemble learning and stimulate further research and development in this actively evolving area. © 2024 IEEE.

关键词： Federated learning

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Adaptive Observer Based Output Voltage-Tracking Control of dC-dC Boost Converters 21

Adaptive Observer Based Output Voltage-Tracking Control of D...

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21st International Bhurban Conference on Applied Sciences and technology, IBCAST 2024

作者： Zad, Haris Sheh Ulasyar, Abasin Zohaib, Adil Pak-Austria Fachhochschule: Institute of Applied Sciences & Technology Department of Mechanical & Manufacturing Engineering Haripur Pakistan Beko Corporate Drive Team Washing Machine-R&D Center Istanbul Turkey Riphah International University Department of Electrical & Computer Engineering Islamabad Pakistan

ISBN: (纸本)9798331516680

In this paper, the voltage regulation of dC-dC boost converters is investigated in the existence of unmodeled system dynamics and load perturbations. An adaptive observer based sliding mode controller (SMC) is proposed based on the radial basis function (rBF) neural networks for estimating the uncertain system function. The parameters of the rBF are updated online based on the adaptive law. The adaptive control law is developed based on the Lyapunov synthesis for the rBF model. The Lyapunov theory is utilized for proving the stability of the overall system. The chattering phenomenon is eliminated, and the robust action of the controller is ensured. For checking the effectiveness of the designed robust and adaptive controller, comparative simulations are carried out. Simulation results show better dynamic regulation of the boost converter compared to the conventional control in the presence of uncertainties and unmodeled dynamics. © 2024 IEEE.

关键词： radial basis function networks

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Evaluation of machine Learning Models for Kidney Stone Classification Using HOG Features on CT Images

Evaluation of Machine Learning Models for Kidney Stone Class...

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2024 International Conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： ravikumar, S. Chandralekha, E. Vijay, K. Sundar, Koushik Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Citibank NA Jersey CityNJ United States

ISBN: (纸本)9798350390049

Kidney stones, or renal calculi, represent hard mineral and salt deposits which are formed in the kidneys and may cause intense pain, infections of the urinary tracts, and other complications. machine learning techniques are commonly applied to the problem of kidney stone classification, as this task is critical in medical imaging and contributes to correct diagnosis and treatment. This work aims to evaluate the performance of Gradient Boost, XGBoost, AdaBoost, and CatBoost models when processing a set of kidney CT images organized in Cyst, Normal, Stone, and Tumor classes. The initial dataset, available on Kaggle, is placed in the public domain and includes grayscale images resized to 128×128 pixels: these images are further processed using Histograms of Oriented Gradients. It was found that although AdaBoost achieved high precision for Cysts, lower overall accuracy (0.71) and variable performance, particularly in Stone classification, were observed. Conversely, XGBoost, Gradient Boosting, and CatBoost demonstrated near-perfect accuracy (0.99) and rOC values of 1.00 across all categories, with CatBoost showing especially consistent performance. The superior classification capabilities of XGBoost, Gradient Boosting, and CatBoost for kidney stone identification were highlighted, emphasizing the importance of selecting ML models based on detailed performance metrics. Future research may focus on optimizing these models or enhancing feature extraction methods. © 2024 IEEE.

关键词： Medical imaging

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Hybrid Fuzzy Assisted rNN Algorithm for diabetic retinopathy Identification

Hybrid Fuzzy Assisted RNN Algorithm for Diabetic Retinopathy...

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2023 International Conference on Energy, Materials and Communication Engineering, ICEMCE 2023

作者： Sivaprakash, P. Gifta Jerith, G. Packiaraj, H. Manikandan, r.A. Thanish Kumar, T. Ashok, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu India Malla Reddy University School of Engineering Department of Artificial Intelligence & Machine Learning Hyderabad India

ISBN: (纸本)9798350393378

The most common cause of blindness in diabetics is diabetes retinopathy. The importance of early detection in preventing the dr cannot be overstated. However, because eye checkups are so expensive, many people let dr worsen and eventually lead to blindness. To identify persons with dr, the current study employs a color fundus image and a Fuzzy rNN algorithm. Although the purpose of this study is to save money, it could be a game changer for people with dr who cannot afford a medical diagnosis. Prior processing methods include green band extraction, histogram equalization, filtration, optic disc removal with organizational components on architecturally procedures, and illumination changes. The data acquired by GLCM contains contrast, correlation, energy, and homogeneity, and it is utilised to extract characteristics from the preprocessing results. This paper describes a threshold segmentation-based dr detection system. As a result of this study, a fuzzy-rNN methodology for identifying diabetic retinopathy has been developed. Threshold-based categorization is used to determine the diabetic zone. GLCM (Grey Level Co-occurrence Matrix) is the feature extraction technique applied in this case. These characteristics are utilised for categorization. For classification, an rNN classifier is utilised. To simulate the image processing using Phython in diabetic retinopathy is used to validate the final outcome. © 2023 IEEE.

关键词： recurrent neural networks

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Enhancing Solar Energy Harvesting Efficiency: A dual-Axis Solar Tracking System 4

Enhancing Solar Energy Harvesting Efficiency: A Dual-Axis So...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： ravikumar, S. Eugene Berna, I. Vijay, K. Pradeep, r. Babu, r. R&d Institute of Science and Technology Vel Tech Rangarajan Dr.Sagunthala Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of College of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307757

The photovoltaic effect has permanently transformed the landscape of renewable sources by making it possible to convert solar energy into useable power. This concept is based on solar photovoltaic (PV) cells, but current PV technology limits us to capturing just a small percentage of the sun's energy at any given time. However, productivity can be greatly increased by using solar monitoring equipment. Creating and validating a low-priced active dual-axis solar tracking system is a breeze if you follow the steps outlined in this article. The best orientation for solar panels is perpendicular to the sun because their production is directly related to the amount of light they receive. Conventional solar panels have trouble keeping up with the ever-changing ideal angle for sunlight absorption and energy gathering. By tracing the sun's movement across the sky, solar trackers can triple the effectiveness of solar panels. This technological advancement is expected to lead to an improvement in energy generation and overall efficiency. This state-of-the-art solar tracker is powered by LEds and an Arduino Uno. These systems work together to follow the sun's motion and maintain the solar panels facing the appropriate way. To ensure that the solar panels receive the most amount of sunshine possible, the system will automatically adjust their angle. data processing and analysis in LabVIEW demonstrated the improved efficiency of the novel architecture. researchers discovered a remarkable 13.44% boost in energy production compared to conventional fixed solar PV systems. © 2024 IEEE.

关键词： Solar panels

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Question Answering FAQ Bot Using Natural Language Processing 11th

Question Answering FAQ Bot Using Natural Language Processing

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

作者： Vijay, K. Sowmia, K.r. ravikumar, S. Arockia raj, Y. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9789819717231

The BOT retorts with the precise results that help the user for the query and also requests them to rate the response to provide better results. The [frequently answered questions] BOT is a program developed through machine learning and natural language processing. It classifies the queries and results as key-value pairs and maps the response to the user query. It extricates itself by providing additional capabilities such as NLU processes and reinforcement learning. NLU helps the bot to make it convenient for an array of languages to meet the diverse expectations. reinforcement learning retrieves user rating to provide better performance and search results. The project was developed due to a concern about fetching meticulous responses for specific service-related queries. The overall satisfaction rate of users in getting responses to the questions they post is devastating. The root cause for the proposed project has aimed at providing an environment for a people to find their answers at better accuracy and also respond to their satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： reinforcement learning

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