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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Bharadwaj, Shambhu Prabhu, A. Solanki, Vipin Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Applied Science Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

Weed detection is an essential assignment in agricultural settings. Negative environmental results, crop yield loss, and mechanical weeding hard work prices related to weed management have necessitated the improvement of automation solutions to discover and treat weeds. Hyperspectral imaging (HSI) is a promising method that can produce abundant statistics about the spectral residences of flowers. This generation has been used in weed detection packages to classify weeds from crops, and these days deep mastering has been used to provide high accuracy prices in this discipline. In this abstract, we explore the utility of HSI for weed detection. We define current demanding situations that require further research earlier than automatic weed detection structures using HSI grow to be widely available. In particular, the presently available algorithms lack robustness and scalability, and further improvements in gadget-gaining knowledge of algorithms and techniques are wished to conquer those constraints, in addition to advancing the computational capabilities of these structures. Moreover, we discuss the potential of HSI as a weed detection answer in various contexts, including agroforestry and precision farming. In conclusion, we advise that the software of HSI for automatic weed detection has the massive capability to reduce labor fees related to weed manipulation, improve farming performance, and in the end, boom crop yields. Weed detection is a first-rate challenge inside the agricultural enterprise, as guide weed control is costly, time-consuming, and the correct identity of weeds is rigid. Hyper Spectral photo evaluation (HSIA) offers an alternative to guide weed detection, considering the rapid and effective mapping of weed-infested land without the need for guide labor. HSIA may be used to routinely detect the spectral signature of weed species, allowing for correct identity and brief remedy. This method uses hyperspectral scanners to gather spectral records, whic

关键词： accuracy crops harm measures promising

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A Severity Grading Framework for Diabetic Retinopathy Detection using Transfer Learning

A Severity Grading Framework for Diabetic Retinopathy Detect...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Akhtar, Samia Aftab, Shabib Kousar, Shagufta Rehman, Ambreen Ahmad, Munir Saeed, Ali Q Virtual University of Pakistan Department of Computer Science Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan College of Dentistry University of Science and Technology of Fujairah Fujairah United Arab Emirates College of Informatics Korea University Seoul02841 Korea Republic of NCBA&E Lahore54000 Pakistan Computer Centre Northern Technical University Ninevah Iraq

ISBN: (纸本)9798350369106

Diabetic retinopathy is one of the primary reasons of blindness among individuals. It is a threatening visual disorder that affects delicate blood vessels present within the retina. Early identification of this condition is beneficial for preserving vision and managing it effectively. Detecting diabetic retinopathy manually takes great amount of time and is susceptible to errors. This research aims to propose an automated and efficient framework for grading the severity of diabetic retinopathy with higher accuracy using transfer learning techniques. For this purpose, we have used 3 distinct diabetic retinopathy datasets;Messidor-1, Messidor-2 and IDRiD, containing fundus images. Preprocessing techniques including cropping, denoising, CLAHE, and image resizing are applied to each dataset. Data augmentation is used to tackle class imbalance issues in order to avoid biased results. The dataset was split into three subsets for training, validation, and testing, using a split of 70:10:20. Pre-trained model EfficientNetB5 is fine-tuned and hyper-parameters are adjusted. This model is used to train and test each dataset separately. Results show that our model attained test accuracies of 98.43% for Messidor-1, 97.36% for Messidor-2, and 97.67% for the IDRiD dataset. On comparing the best results of this research with previous state of the art techniques, EfficientNetB5 outperformed those existing methods. © 2024 IEEE.

关键词： Transfer learning

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Decentralized brokered enabled ecosystem for data marketplace in smart cities towards a data sharing economy

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Environment Systems and Decisions 2023年第3期43卷 453-471页

作者： Anthony, Bokolo Department of Applied Data Science Institute for Energy Technology Halden 1777 Norway Department of Computer Science and Communication Østfold University College BRA Veien 4 Halden 1757 Norway

Presently data are indispensably important as cities consider data as a commodity which can be traded to earn revenues. In urban environment, data generated from internet of things devices, smart meters, smart sensors, etc. can provide a new source of income for citizens and enterprises who are data owners. These data can be traded as digital assets. To support such trading digital data marketplaces have emerged. Data marketplaces promote a data sharing economy which is crucial for provision of available data useful for cities which aims to develop data driven services. But currently existing data marketplaces are mostly inadequate due to several issues such as security, efficiency, and adherence to privacy regulations. Likewise, there is no consolidated understanding of how to achieve trust and fairness among data owners and data sellers when trading data. Therefore, this study presents the design of an ecosystem which comprises of a distributed ledger technology data marketplace enabled by message queueing telemetry transport (MQTT) to facilitate trust and fairness among data owners and data sellers. The designed ecosystem for data marketplaces is powered by IOTA technology and MQTT broker to support the trading of sdata sources by automating trade agreements, negotiations and payment settlement between data producers/sellers and data consumers/buyers. Overall, findings from this article discuss the issues associated in developing a decentralized data marketplace for smart cities suggesting recommendations to enhance the deployment of decentralized and distributed data marketplaces. © 2023, The Author(s).

关键词： Data ecosystem Data sharing economy Decentralized data marketplace Emerging technologies IOTA technology MQTT broker Smart cities Sustainable society

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Performance Analysis of Training Optimizers on Diabetic Retinopathy Detection Using SP-Net CNN

Performance Analysis of Training Optimizers on Diabetic Reti...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

ISBN: (纸本)9798350369106

Diabetic retinopathy is an ocular disease linked to long-term diabetes mellitus and it can lead to permanent loss of vision if not addressed promptly. Timely detection of this disease is crucial as it can significantly reduce the risk of severe complications. Our aim is to identify the most effective optimization algorithm for training our deep learning (CNN) model. Our goal is to achieve high accuracy and efficiency in grading diabetic retinopathy into 5 different classes. In this research, we conducted a comparative evaluation of three optimizers namely Adam, SGD and RMSProp. For classification, we developed a convolutional neural network called SP-Net. An image based dataset named APTOS 2019 is taken from Kaggle and various pre-processing techniques are applied on it such as cropping, denoising, histogram equalization, and resizing. We used data augmentation techniques to tackle the class imbalance issue. Then the dataset was divided into three subsets (training, validation, and testing) using a split of 70:10:20. Our model SP-Net incorporates several layers including convolutional, dropout, max pooling, and fully connected layers. The results demonstrate that SP-Net achieved a highest test accuracy of 97.76% when it was trained using the SGD optimizer. Moreover, this model trained with the best optimizer outperformed the existing state-of-the-art techniques on comparison. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT) 1

Exploring Sensor-Based Smart Farming Technologies in the Int...

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling

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computers, Materials & Continua 2022年第7期72卷 1995-2014页

作者： Mutasem K.Alsmadi Ghaith M.Jaradat Malek Alzaqebah Ibrahim A.Lmarashdeh Fahad A.Alghamdi Rami Mustafa A.Mohammad Nahier Aldhafferi Abdullah Alqahtani Department of MIS College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Department of Computer Science Faculty of Computer Science and InformaticsAmman Arab UniversityAmmanJordan Department of Mathematics College of ScienceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Basic and Applied Scientific Research CenterImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Computer Information Systems Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

Timetabling problem is among the most difficult operational tasks and is an important step in raising industrial productivity,capability,and *** tasks are usually tackled using metaheuristics techniques that provide an intelligent way of suggesting solutions or *** intelligence techniques including Particle Swarm Optimization(PSO)have proved to be effective *** recent experiments showed that the PSO algorithm is reliable for timetabling in many applications such as educational and personnel timetabling,machine scheduling,***,having an optimal solution is extremely challenging but having a sub-optimal solution using heuristics or metaheuristics is *** research paper seeks the enhancement of the PSO algorithm for an efficient timetabling *** algorithm aims at generating a feasible timetable within a reasonable *** enhanced version is a hybrid dynamic adaptive PSO algorithm that is tested on a round-robin tournament known as ITC2021 which is dedicated to sports *** competition includes several soft and hard constraints to be satisfied in order to build a feasible or sub-optimal *** consists of three categories of complexities,namely early,test,and middle *** showed that the proposed dynamic adaptive PSO has obtained feasible timetables for almost all of the *** feasibility is measured by minimizing the violation of hard constraints to *** performance of the dynamic adaptive PSO is evaluated by the consumed computational time to produce a solution of feasible timetable,consistency,and *** dynamic adaptive PSO showed a robust and consistent performance in producing a diversity of timetables in a reasonable computational time.

关键词： Sports timetabling particle swarm optimization ITC2021 roundrobin tournament dynamic adaptive

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Asynchronous Complete Visibility Algorithm for Luminous Robots on Grid 11

Asynchronous Complete Visibility Algorithm for Luminous Robo...

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Kim, Yonghwan Katayama, Yoshiaki Wada, Koichi Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan Hosei University Dept. of Applied Informatics Tokyo Japan

ISBN: (纸本)9798350306941

An autonomous mobile robot system is a distributed system consisting of mobile computational entities (called robots) that autonomously and repeatedly perform three operations: Look, Compute, and Move. Various problems related to autonomous mobile robots, such as gathering, pattern formation, or flocking, have been extensively studied to understand the relation between each robot's capabilities and the solvability of these problems. In this study, we focus on the complete visibility problem, which involves relocating all the robots on an infinite grid plane so that each robot is visible to every other robot. We assume that each robot is a luminous robot (i.e., has a light with a constant number of colors) and opaque (not transparent). In this paper, we propose an algorithm to achieve complete visibility (i.e., every robot can observe all the other robots) when a set of robots is given. The algorithm ensures that complete visibility is achieved even when robots operate asynchronously and have no knowledge of the total number of robots on the grid plane using only two colors. © 2023 IEEE.

关键词： Visibility

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Software Defined Network Based Next Generation Mobile Communication Network Architecture

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第23期18卷 138-148页

作者： Kumar, Sumit Ben Sujin, B. Bhavani, K. Srilatha, Doddi Anand, Ketan Dept. of CSE Haridwar University Uttarakhand Roorkee India University of Technology and Applied Sciences Nizwa Oman Department of Computer Science and Business Systems Panimalar Engineering College Tamil Nadu Chennai India Department of CSE Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Sreenidhi Institute of Science and Technology Telangana Hyderabad India

As mobile networks and network speeds become more prevalent, the demand for marketing strategies increases. The operators are thinking about this, and the development of 5G communication networks is one of their main concerns. As the need for higher transmission speeds increases, 5G networks face challenges of scalability and adaptability. The next-generation mobile network (NMCN-SDN) architecture proposed in this study is based on Software-Defined Networks (SDN). A new network model called computer-defined networking allows for dynamic network definition and programming. A network simulator is created to examine the efficiency of the built infrastructure under different network conditions, including throughput, latency, and resource consumption. In this paper, the comparison of end-toend latency between the standard communication architecture and the proposed NMCN-SDN architecture is done. The results show that the proposed architecture has less space in various conditions compared to existing communication architectures in various conditions. © 2024 by the authors of this article.

关键词： 5G end-to-end latency mobile communication resource utilization Software-Defined Networks (SDN) throughput

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Optimizing Connection Weights in Neural Networks Using Ali Baba and the Forty Thieves Algorithm 24

Optimizing Connection Weights in Neural Networks Using Ali B...

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24th International Arab Conference on Information technology, ACIT 2023

作者： Al-Hiary, Heba Braik, Malik Al-Betar, Mohammed Azmi Al-Balqa Applied University Department of Computer Information Systems Al-salt Jordan Al-Balqa Applied University Department of Computer Science Al-salt Jordan Department of Information Technology Ajman United Arab Emirates Al-Balqa Applied University Irbid Jordan

ISBN: (纸本)9798350384307

Learning neural networks (NNs) is one of the most challenging problems in machine learning and has lately drawn the attention of many academics. The nonlinear structure of NNs and the unknown optimal set of key governing parameters (biases and weights) make training NNs particularly challenging. The primary drawbacks of conventional training methods are delayed convergence and local optima stagnation. Due to this, stochastic optimization techniques are dependable substitutes for addressing these problems. This study presents a new training method on the basis of the newly announced ali Baba and the forty thieves (AFT) algorithm. It has been shown that this algorithm can tackle a variety of optimization problems and can outperform many existing algorithms. Our efforts to measure its performance were prompted by this when training NNs. The proposed AFT-based trainer was tested using a bunch of 15 datasets that vary in intricacy. A comparison of AFT-based training NNs with other meta-heuristics presented for the same purpose was conducted to explore the efficiency of this training method. In most datasets, the training method-based AFT converged faster and was more adept at avoiding local optima than other rival methods. © 2023 IEEE.

关键词： AFT algorithm Classification Multilayer perceptron Neural networks

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A Prototype for Machine Learning Model Deployment in Cloud Environment

A Prototype for Machine Learning Model Deployment in Cloud E...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Ramalakshmi, K. Agarwal, Aditya Gunasekaran, Hemalatha Khan, Sameer Ali Mandal, Nilesh Kumar Alliance University Computer Science & Engineering Bengaluru India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman

ISBN: (纸本)9798350317060

Training and developing a Machine Learning (ML) model is a difficult task, and then after successfully creating a working model, deploying and distribution is an added feature. In most instances, those models are never deployed. To help with this, we present a prototype, a SaaS platform to allow users to dynamically deploy their machine learning models to the cloud and host them so that the user has complete control over the visibility and *** delivery and deployment model provides lower upfront cost, timely updates, and a dedicated work/host environment. The platform's sole purpose revolves around the idea of a sharable deployable and ready to use Machine Learning Model. It takes advantage of the Continuous Integration and Continuous Delivery archetype facilitated by Kubernetes to dynamically provide custom and updated with the latest libraries docker environment. © 2023 IEEE.

关键词： Machine learning

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