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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Suneetha, K. Sumit Akshatha, Y. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Hyperspectral imagery is a faraway sensing fact that gives one-of-a-kind spectral reflectance measurements across the electromagnetic spectrum. This form of imagery has various uses for monitoring the environment and tracking flora, wooded area cowl, and urban increase. However, successful analysis of hyperspectral imagery requires correct item-orientated classification, which may be challenging and time-consuming to acquire with traditional methods. This paper affords a choice tree-based method to the object-oriented type of hyperspectral imagery. This technique uses statistical class strategies and, blended with professional information, picks out the numerous objects described in the imagery. The proposed approach has been tested on hyperspectral imagery from exceptional environments, and the consequences reveal high accuracy and occasional processing times. This approach can offer a more accurate object-oriented class of hyperspectral imagery than conventional strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hyperspectral imaging

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Parameters Estimation of Modified Triple Diode Model of PSCs Considering Charge Accumulations and Electric Field Effects Using Puma Optimizer

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computer Modeling in engineering & Sciences 2025年第4期143卷 723-745页

作者： Amlak Abaza Ragab A.El-Sehiemy Mona Gafar Ahmed Bayoumi Electrical Engineering Department Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam Bin Abdulaziz UniversityKharj16278Saudi Arabia Machine Learning and Information Retrieval Department Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Electrical Department Faculty of Engineering and TechnologyBadr University in CairoBadr11829Egypt Physics and Mathematics Department Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh33516Egypt College of Engineering Al-Ayen UniversityThi-Qar64001Iraq

Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution *** study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on *** models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs *** PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis *** suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its *** results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the *** statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing *** convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive ***,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.

关键词： Dynamic model of PSCs puma optimizer parameter estimation triple diode model

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A Model for the Optimal Ordering of Weighted Vertices on a Graph for Local Integrated Energy Systems

A Model for the Optimal Ordering of Weighted Vertices on a G...

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2023 International Russian Smart Industry Conference, SmartIndustryCon 2023

作者： Knyazeva, Margarita Tselykh, Alexey Kotov, Eduard Southern Federal University Institute of Computer Technology and Information Security Engineering and Technological Academy Taganrog Russia

ISBN: (纸本)9781665464291

Locally integrated energy systems and networks presuppose locally and regionally available energy sources, infrastructure, production and consumption characteristics as well as consumer structures from different sectors, including the transportation system. Scheduling the optimal commissioning process and further repair of such energy stations are among actual problems for locally integrated energy systems. Equipment downtime and commissioning time of both entire regions and individual settlements are important parameters for decision making process. The paper introduces the problem of optimal scheduling of commissioning of energy stations or hubs with respect to temporal variables, downtimes and fines. The proposed model focuses on finding a quasi-Hamiltonian path for a weighted graph, on whose arcs temporal weights and estimates are given while minimizing the total fine or loss under downtimes. © 2023 IEEE.

关键词： Decision making

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Deep Learning-Based Image Analysis for Detecting Nutrient Deficiencies of Tomato Plants 5

Deep Learning-Based Image Analysis for Detecting Nutrient De...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Nirasha Jayasiri, K.C. Chandrasiri, Sanjeevi Rupasinghe, Shalini Karunanayake, K.O. Rivindu Uthayachandran, Abineshh Zihara, M.I. Fathima Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9798350358131

Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, which can be challenging for inexperienced farmers and home gardeners to work accurately. There is a growing need for a quick, reliable, and accurate nutrient deficiency identification system to address this issue. The proposed method is based on image processing and deep learning technologies that are highly effective for image classification tasks. Two models were trained using a dataset collected from tomato plants in Sri Lanka and evaluated using Mask Region-based Convolutional Neural Network (Mask R-CNN) and, You Only Look Once (YOLO) for deficiency classification, and results obtained 92% and 98% accuracy, respectively. Deficiency dispersion level expressed as a percentage using Mask R-CNN and followed by image processing techniques. Overall, this proposed system offers a convenient and accessible tool for farmers and home gardeners to monitor and maintain the health of their tomato plants, enabling them to achieve optimal yields and ensure profitable returns. © 2023 IEEE.

关键词： Object detection

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Optimising Business Processes in Project Tracking using CRM 2

Optimising Business Processes in Project Tracking using CRM

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Ramprasath, J. Saranya, N. Arun Kumar, M. Chandru Vignesh, C. Ramprakash, P. Krishnaraj, N. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of AI & DS Coimbatore India

ISBN: (纸本)9798331534950

Customer relationship management, called CRM, is essential for businesses looking to sustain profitable growth in today's fiercely competitive business environment. This essay examines cutting-edge methods to improve clients in the digital era and thoroughly explains CRM practices. The research examines best-serving customers through various touchpoints by integrating data analytics, AI, and personalized marketing strategies. In addition, it explores the role of organizational alignment and a customer-centric culture in developing enduring customer relationships. The study uses theoretical frameworks and practical data to highlight the main obstacles and changes associated with implementing efficient CRM procedures. As a result, synthesizing pertained research and case studies provides insights into new trends and industry best practices for building long-lasting relationships with customers. The results highlight the significance of proactive involvement, ongoing innovation, and ethicizing deliberations in fostering trust and allegiance across various consumer categories. This paper adds to the current conversation on CRM by providing practice advice that companies can use to adjust to and prosper in a constantly changing market. © 2024 IEEE.

关键词： Sales

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DEFAEK: Domain Effective Fast Adaptive Network for Face Anti-Spoofing

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Neural Networks 2023年 161卷 83-92页

作者： Lin, Jiun-Da Han, Yue-Hua Huang, Po-Han Tan, Julianne Chen, Jun-Cheng Tanveer, M. Hua, Kai-Lung Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115201 Taiwan Department of Mathematics Indian Institute of Technology Indore Simrol453552 India

Existing deep learning based face anti-spoofing (FAS) or deepfake detection approaches usually rely on large-scale datasets and powerful networks with significant amount of parameters to achieve satisfactory performance. However, these make them resource-heavy and unsuitable for handheld devices. Moreover, they are limited by the types of spoof in the dataset they train on and require considerable training time. To produce a robust FAS model, they need large datasets covering the widest variety of predefined presentation attacks possible. Testing on new or unseen attacks or environments generally results in poor performance. Ideally, the FAS model should learn discriminative features that can generalize well even on unseen spoof types. In this paper, we propose a fast learning approach called Domain Effective Fast Adaptive nEt-worK (DEFAEK), a face anti-spoofing approach based on the optimization-based meta-learning paradigm that effectively and quickly adapts to new tasks. DEFAEK treats differences in an environment as domains and simulates multiple domain shifts during training. To further improve the effectiveness and efficiency of meta-learning, we adopt the metric learning in the inner loop update with careful sample selection. With extensive experiments on the challenging CelebA-Spoof and FaceForensics++ datasets, the evaluation results show that DEFAEK can learn cues independent of the environment with good generalization capability. In addition, the resulting model is lightweight following the design principle of modern lightweight network architecture and still generalizes well on unseen classes. In addition, we also demonstrate our model's capabilities by comparing the numbers of parameters, FLOPS, and model performance with other state-of-the-art methods. © 2023 Elsevier Ltd

关键词： Large dataset

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Evaluating Neighboring Site Group Queries with Constraint of Budget Range 23

Evaluating Neighboring Site Group Queries with Constraint of...

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11th International Conference on computer and Communications Management, ICCCM 2023

作者： Huang, Yuan-Ko Lin, Lien-Fa Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan

ISBN: (纸本)9798400707735

In recent years, many of the location-based queries have been proposed that consider the proximity of spatial sites to provide users with information. However, most studies focus on a single type of sites and do not consider the cost of visiting the sites. As a result, in this paper we present a novel type of location-based queries, namely the Budget-Range-based neighboring site group query (or BR-based NSGQ for short), to find the groups of sites that are within a distance d of each other and have a total cost not exceeding a budget range [bgtm, bgtM]. The site groups are termed the neighboring site groups within budget range (abbreviated as NSGs-BR). In order to efficiently answer the BR-based NSGQs, we modify the R-tree structure to present the RcC-tree, and then design four checking rules to prune the non-qualifying site groups. We propose a query processing algorithm, namely the BR-based NSGQ algorithm, to find all the NSGs-BR by combing the traversal of the RcC-tree and the four checking rules. Finally, numerous experiments are evaluated to demonstrate the efficiency of the BR-based NSGQ algorithm in terms of the CPU time and the number of node accesses of the RcC-tree. © 2023 ACM.

关键词： Budget control

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Exploring the Benefits of Deep Learning for Data Science Practices 15

Exploring the Benefits of Deep Learning for Data Science Pra...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Mohit Kumar Kumar, Ritesh Sinha, Deepak Kumar Senthilkumar, K.K. Dhabliya, Dharmesh Ahluwalia, Gunveen Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

With the growing occurrence of records technology and analytics, Deep studying has become an effective device within the records technology field. Deep mastering is a subset of artificial Intelligence that uses algorithms to construct complex fashions for huge-scale records analysis. The models created through Deep studying are often extra correct than traditional analytic techniques, as they use a wide style of capabilities and labels to discover records. Deep studying models can remedy many facts and technological know-how problems, from herbal language processing to photo class to regression modeling. Deep mastering models are compelling in conditions in which a high degree of accuracy is needed and where the inputs are complicated and varied. In brief, the blessings of Deep studying for data technological know-how consist of improved accuracy, faster processing, and automated feature extraction. © 2024 IEEE.

关键词： Economic and social effects

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Research on Optimization of Resource Scheduling Algorithm Based on Responsive Web Front-End 6th

Research on Optimization of Resource Scheduling Algorithm Ba...

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6th International Conference on Communication, Devices and Networking, ICCDN 2022

作者： Liyan, Chen Department of Computer Information Engineering Nanchang Institute of Technology Jiangxi Province Nanchang City330033 China

ISBN: (纸本)9789819919826

Research on optimization of resource scheduling algorithm based on responsive web front-end. The purpose of this study is to design a resource scheduling algorithm, which can provide the best performance for applications in terms of response time and page loading time, while maintaining an acceptable quality of service level. The solution consists of two parts: (1) a new method to determine the optimal number of instances, and (2) an adaptive mechanism to allocate instances according to their priority. A new method was developed to determine the number of instances that should be scheduled for each request type. This study is very useful for those who use large-scale applications with high traffic and need to pay attention to their time to market. The proposed solution helps us achieve better performance, scalability, and reliability by using prediction algorithms that can predict future load or traffic patterns. It also provides a mechanism to monitor the time required for each request to complete. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scheduling algorithms

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Utilizing Remote Sensing to Monitor Health and Wellbeing in Sustainable Medical Environments 5th

Utilizing Remote Sensing to Monitor Health and Wellbeing in ...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Bhatnagar, Gunjan Bengalur, Megha D. Agarwal, Trapty Prabhu, A. Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The challenge is focused on developing and validating techniques for making use of far flung sensors along with drones and unmanned aerial motors (UAVs) to acquire health-related information in relation to the environment of sustainable clinical facilities along with hospitals, clinics, and long-term care facilities. Moreover, the mission targets to evaluate the health and health of sufferers within those facilities with the aid of tracking the facts accumulated for related tendencies and styles. The challenge collaborates with a variety of partners to ensure the information is accurate and timely, and also involves the development of new algorithms to make use of the information to better apprehend how healthcare experts can fine serve their patients. Ultimately, the mission seeks to reduce value and effort related to healthcare delivery while offering a more green, safe, and sustainable medical environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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