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Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

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computer Systems science & Engineering 2023年第7期46卷 835-851页

作者： R.Surendran Youseef Alotaibi Ahmad F.Subahi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,*** utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and ***-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor ***,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the *** this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster *** major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster *** achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence *** addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management *** experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

关键词： Disaster management real-time applications wireless sensor networks clustering bioinspired algorithms

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A Hybrid Recommendation Model for Tourist Using Evolutionary Algorithm Combined with Local Search Algorithm for Trip Planning

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SN computer science 2024年第6期5卷 1-13页

作者： Lakshmi, J.V.N. Pallavi, M.O. School of Computer Science and Application Reva University Department of MCA Acharya Institute of Technology

Recommender systems play a crucial role in assisting tourists with travel recommendations considering the customized demands is a necessary practice for improving the tourism, business, and aids significantly in decision-making process. The study proposes using a evolutionary genetic algorithm (GA) to efficiently determine the shortest tourist route within a constrained area, allowing for quick visits to multiple destinations. The goal is to find the most efficient route with minimal physical exertion. By exploring all possible routes, the GA can identify the quickest path from the starting point to the destination. The study first provides a brief overview of the enhanced GA, followed by a detailed analysis of its construction and solution. The paper introduces a novel approach to recommendation systems in the tourism industry, combining the Genetic algorithm with 2-Opt algorithm for local search adaptation. The hybrid model is used to optimize the system by considering multiple criteria. data for this study were collected through online google map resources defining a positive ideal distance matrix for 11 locations with 121 edges. Finally, the GA is further optimized by pipeline the 2-opt algorithm, and a simulation is conducted to examine the average path convergence for each individual route by customizing based on the preferences. Finally proposed model is compared against the traditional algorithms such as Heuristic and Graph network algorithms. Subsequently, the hybrid algorithm searches among destinations to recommend the best tourist trip plan to users. Selecting the shortest trip route plan will reduce the travel cost and time. This model enhances the practical value for researchers and for practical application. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2-opt algorithm Evolutionary algorithm Genetic algorithm Hybrid recommendation Optimization Travel planning

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Retrainable Hand Gesture Recognition Model Driven by Edge AI 11

Retrainable Hand Gesture Recognition Model Driven by Edge AI

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11th International Conference on Dependable Systems and Their Applications, DSA 2024

作者： Hsu, Tse-Chuan Chang, Dong-Meau Soochow University Department of Computer Science & Information Management Taipei Taiwan Lingnan Normal University School of Computer Science and Intelligence Education China

ISBN: (纸本)9798331532390

Based on computer vision and machine learning, accurate hand behavior and gesture recognition are crucial, especially for missing or incomplete hand models. Finger joint point prediction technology is of great value for applications such as virtual reality and human posture recognition. However, hand joint point prediction models, such as Google's MediaPipe, perform well in full-finger models but are ineffective in missing-finger situations. Missing fingers are familiar in clinical medicine, rehabilitation facilities, and unique scenarios such as hand injuries or congenital hand deformities. Accurate prediction of hand joints is critical for hand rehabilitation, prosthetic design, and human motion analysis and inspires the development of related technologies. Since traditional prediction models rely on complete hand data, and missing fingers can make the hand structure irregular, the complexity of prediction increases. This study proposes an improved prediction model focusing on hand joint point prediction in the case of missing fingers, hoping to improve prediction accuracy and provide valuable reference for future applications and research. Among the research results, we successfully integrated the new training image model with MediaPipe, significantly improving missing fingers' prediction accuracy. © 2024 IEEE.

关键词： Prediction models

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Irrigation Control System for Seedlings Based on the Internet of Things 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： de Carvalho, André Kusuma, Gede Putra Handoyo, Alif Tri Department of Computer Science BINUS Graduate Program—Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

The agricultural activities in the Vocational school of Agricultural Engineering (ESTVA) in Timor-Leste requires large quantities of water with high availability for the production and cultivation of plants. However, the crop results sometimes decrease due to challenges problem faced by the management of the water resources. This study aims to design an Internet of Things (IoT) based automatic control system to control water supply and maintain soil moisture conditions effectively. The evaluation of manual and automatic irrigation systems during the 21 days trial shows that the automatic system provides stable utilization of water with watering frequency of 274 occurs only in 12 days, with an amount of total watering of 35.3 L. Meanwhile, the manual system uses up to 49.8 L of water, which is 14.5 L more than the water spent by the automatic system. The results of the evaluation also indicate that automatic system obtains the average moisture value of 509 with a standard deviation of 63, which is smaller than the manual system that obtains an average moisture value of 559 and a significant standard deviation of 126. From these results, the automatic system is more effective in maintaining the soil moisture level and yet requires less water compared to the manual watering system. Furthermore, the quality and productivity of the growth of mustard seedlings with automatic system prove that the seeds are more stable and feasible to continue transplanting since the automatic system produces 12 more mustard trees compared to the manual system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Soil moisture

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Courier Delivery Optimization in Supply Chain via Group Multirole Assignment

Courier Delivery Optimization in Supply Chain via Group Mult...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lin, Xuewei Ke, Xintong Zhu, Haibin Liu, Dongning School of Computer Science and Technology Guangdong University of Technology Guangzhou China Nipissing University Department of Computer Science and Mathematics North Bay Canada

ISBN: (纸本)9781665410205

Courier delivery is the end of the supply chain and affects the final delivery of products. Due to the promulgation of new courier delivery regulations, home delivery services have become the main choice for consumers, which has resulted in a greater workload. How to Reasonably Allocate Couriers for LEan Management (RACLEM) in supply chain optimization poses a challenge to traditional logistics. By extending the Group Multirole Assignment with Efficiency Degradation (GMRAED), this paper formalizes the problem. Moreover, we propose a quantitative calculation method of efficiency degradation based on Amdahl's law, taking region similarity and the number of tasks as important criteria, and compare the performance of different personnel arrangements. Additionally, compared with the brute force algorithm, we combine GMRAED and Genetic Algorithm (GA) to create a practical solution. Large-scale simulation experiments demonstrate that the genetic algorithm significantly shortens the solution time, and the lowest experimental accuracy is 99.407%. By using the above method, decision-makers are able to assist companies optimize personnel resource allocation, minimize personnel waste, and improve the performance of courier delivery within a shorter timeframe. © 2024 IEEE.

关键词： Efficiency

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An Evaluation Of Hospital Accreditation From The Survey With Text Vector Analysis Techniques 20

An Evaluation Of Hospital Accreditation From The Survey With...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Prapunwattana, Dittaphong Imsombut, Aurawan Suwannahitatorn, Picha Saethang, Thammakorn Kasetsart University Biomedical Data Science Master's Program Faculty of Science Bangkok Thailand Kasetsart University Faculty of Science Department of Computer Science Bangkok Thailand Bangkok Thailand

ISBN: (纸本)9798350300505

The Healthcare Accreditation Institute has an assessment and certification process for hospitals applying for Healthcare Accreditation. The assessment process requires a large number of text-based reports. The purpose of this research was to study the text analysis of the self-assessment reports of healthcare facilities and surveyor reports on issues related to the pharmaceutical system to evaluate and rate the accreditation of medical facilities. The natural language text vector analysis technique, together with the Universal Sentence Encoder (USE) was compared to Learning Lightweight Language-agnostic Sentence Embeddings (LEALLA) for encoding data into a high-dimensional format. Next the sentence encoding feature was fed through a machine learning procedure, including artificial neural networks, logistic regression, and support vector machines to classify nursing facility accreditation ratings. The experimental results showed that the USE embedding yielded better performance than the LEALLA embedding across all models with a precision of 0.70 but took slightly longer to encode feature sentences. This research could improve the performance of the analysis and scoring. © 2023 IEEE.

关键词： Embeddings

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DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment 38

DFA-GNN: Forward Learning of Graph Neural Networks by Direct...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Gongpei Wang, Tao Lang, Congyan Jin, Yi Li, Yidong Ling, Haibin Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education China School of Computer Science & Technology Beijing Jiaotong University China Department of Computer Science Stony Brook University United States

Graph neural networks (GNNs) are recognized for their strong performance across various applications, with the backpropagation (BP) algorithm playing a central role in the development of most GNN models. However, despite its effectiveness, BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. While several non-backpropagation (non-BP) training algorithms, such as the direct feedback alignment (DFA), have been successfully applied to fully-connected and convolutional network components for handling Euclidean data, directly adapting these non-BP frameworks to manage non-Euclidean graph data in GNN models presents significant challenges. These challenges primarily arise from the violation of the independent and identically distributed (i.i.d.) assumption in graph data and the difficulty in accessing prediction errors for all samples (nodes) within the graph. To overcome these obstacles, in this paper we propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning. The proposed method breaks the limitations of BP by using a dedicated forward training mechanism. Specifically, DFA-GNN extends the principles of DFA to adapt to graph data and unique architecture of GNNs, which incorporates the information of graph topology into the feedback links to accommodate the non-Euclidean characteristics of graph data. Additionally, for semi-supervised graph learning tasks, we developed a pseudo error generator that spreads residual errors from training data to create a pseudo error for each unlabeled node. These pseudo errors are then utilized to train GNNs using DFA. Extensive experiments on 10 public benchmarks reveal that our learning framework outperforms not only previous non-BP methods but also the standard BP methods, and it exhibits excellent robustness against various types of noise and attacks. © 2024 Neura

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An Object Oriented Floating Fuzzy Decision Model for Evaluating the Student Performance 6

An Object Oriented Floating Fuzzy Decision Model for Evaluat...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Utama, Ditdit Nugeraha Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350358346

The concept of object-oriented (OO) serves is a fundamental approach in the development of models. The stages associated with this method contribute significantly to ensuring that the resulting model is both lucid and transparent. The primary objective of the study is to create a decision model for evaluating student performance. Floating fuzzy logic (FFL) is employed as a technique to handle fluctuating data within the model. Moreover, OO conception plays a central role in analyzing, designing, and constructing the model through the utilization of four distinct types of Unified Modeling Language (UML) diagrams: object, activity, state-machine, and sequence diagrams. The model itself is crafted using the Python programming language and executed in the Google Colab platform. Additionally, this model has the capability to simulate changes in students' performance on a semester-by -semester basis, exhibiting a variance of 15 % when compared to the conventional fuzzy logic model. © 2023 IEEE.

关键词： Fuzzy logic

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Towards More Robust and Scalable Deep Learning Systems for Medical Image Analysis

Towards More Robust and Scalable Deep Learning Systems for M...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Yenumala, Akshaj Zhang, Xinyue Lo, Dan School of Computer Science Georgia Institute of Technology Atlanta United States Kennesaw State University Department of Computer Science Kennesaw United States

ISBN: (纸本)9798350362480

Deep learning (DL) has attracted interest in healthcare for disease diagnosis systems in medical imaging analysis (MedIA) and is especially applicable in Big data environments like federated learning (FL) and edge computing. However, there is little research into mitigating the vulnerabilities and robustness of such systems against adversarial attacks, which can force DL models to misclassify, leading to concerns about diagnosis accuracy. This paper aims to evaluate the robustness and scalability of DL models for MedIA applications against adversarial attacks while ensuring their applicability in FL settings with Big data. We fine-tune three state-of-the-art transfer learning models, DenseNet121, MobileNet-V2, and ResNet50, on several MedIA datasets of varying sizes and show that they are effective at disease diagnosis. We then apply the Fast Gradient Sign Method (FGSM) to attack the models and utilize adversarial training (AT) and knowledge distillation to defend them. We provide a performance comparison of the original transfer learning models and the defended models on the clean and perturbed data. The experimental results show that the defensive techniques can improve the robustness of the models to the FGSM attack and be scaled for Big data as well as utilized for edge computing environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Classification of Health Insurance Fraud Risk with Machine Learning

Classification of Health Insurance Fraud Risk with Machine L...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Muhammad Kent Al-Ghazi Ryan Bertrand Muhammad Dzul Qarrnayn Destra Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

Health insurance is a useful service that can help its users gain lifesaving medical aid when they are in need. However, health insurance is also exploitable to insurance fraud through the falsification of information to increase the amount of reimbursement and cause massive loss of funds to the insurance provider. We propose the usage of machine learning to accurately determine potential health insurance fraud. The objective of conducting this research is to determine which features are the most important to determine healthcare insurance fraud. This research used a dataset provided in Kaggle titled Healthcare Provider Fraud Detection Analysis using Random Forest Classifier and Logistic Regression. The best-performing model in this test, the Logistic Regression, is then used to which features are the most important for the classification. Our research shows that the most important feature in detecting health insurance fraud is the amount of money reimbursed associated with a provider. The Logistic Regression model achieved an accuracy of 0.90, precision of 0.93, recall of 0.91, and an F1 Score of 0.90, outperforming the Random Forest model in comparative analysis.

关键词： Logistic regression Analytical models Technological innovation Accuracy Insurance Medical services Feature extraction Fraud Information technology Random forests

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