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Advances in Transportation Studies 2023年第Special issue期2卷 75-86页

作者： Wu, R.J. Patnaik, S. Henan Vocational College of Information and Statistics Henan Zhengzhou450000 China Department of Computer Science and Engineering SOA University Bhubaneswar751030 India

In the emergency logistics system, the planning of transportation routes becomes a key link in emergency rescue and plays an important role in the entire rescue process. In order to shorten the transportation time of emergency materials and improve transportation efficiency, this paper proposes an optimal route planning method for emergency transportation logistics based on time constraints. Firstly, analyze the characteristics of emergency transportation logistics transportation, and then calculate the impact weights of supply and demand points, analyzing the factors that affect route planning. Secondly, calculate the penalty coefficients for emergency transportation logistics transportation in hard and soft time windows under time constraints, and construct an objective function for the optimal route planning of emergency transportation logistics transportation. Finally, the optimal route planning result is obtained by solving the constructed objective function. The experimental results show that the proposed method has the highest efficiency of emergency transportation logistics transportation of about 99%, effectively improving the efficiency of emergency transportation logistics transportation. © 2023, Aracne Editrice. All rights reserved.

关键词： Transportation routes

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Optimized Resource Allocation for IoT-D2D Communication Using WSN

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computer Systems science & engineering 2023年第5期45卷 1789-1804页

作者： S.Arun Mozhi Selvi Roobaea Alroobaea Saeed Rubaiee Abdulkader S.Hanbazazah Department of Computer Science and Engineering Holy Cross Engineering CollegeVagaikulam628851Tamil NaduIndia Department of Computer Sceience College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21974Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringUniversity of JeddahJeddah21959Saudi Arabia

The need for a strong system to access radio resources demands a change in operating frequency in wireless networks as a part of Radio Resource Management(RRM).In the fifth-generation(5G)wireless networks,the capacity of the system is expected to be enhanced by Device-to-Device(D2D)*** cooperation and Resources Allocation(RA)in the development of Internet of Things(IoT)enabled 5G wireless networks are investigated in this *** radio RA methods for D2D communication while not affecting any Mobile Users’(MU)communication is the main challenge of this *** performance goals such as practising equality in the rates of user data,increasing Network Throughput(NT),and reducing End-to-End Delay(EED)are achieved by *** study undertaken on optimising performance for various wireless networks is focused on in this research *** a polynomialtime Proportional Fair Resource Allocation Method(PFRAM),which considers the MU’s rate requirements,is the prime objective of this *** Resource Allocation Method(RAM)can be used by the proposed method for MU,and the time and differing location channel conditions are the factors to be adapted *** more than one resource block is allowed by our PFRAM to a D2D *** automatic maintenance of battery-less IoT wireless devices’energy level is done potentially using an Extensible Energy Management System(EEMS).Finally,the device’s Node Transmission Power(NTP)can be managed using an Energy-Saving Algorithm(ESA)designed in this work for Node Uplink Data Transmission(NUDT).The trade-off between the Packet Loss Rate(PLR)and NTP is balanced by the *** cost of NUDT’s average Energy Consumption(EC)is reduced by locating the optical *** order to free much bandwidth for wireless information,NUDT conserves the harvested energy for minimising Radio Frequency(RF)Energy Transmission(ET).MATLAB simulations are used to assess the proposed *** IoT device’s NTP is managed using

关键词： IoT energy transmission WSN mobility resource allocation

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A Novel Biased Probability Neural Network (BPNN) and Regularized Extreme Learning Machine (RELM) based Hearing Loss Prediction System

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第2期4卷 56-71页

作者： Selvaganesh, N. Shanthi, D. Rajesh Pandian, N. Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Sriperumbudur602117 India Department of Computer Science and Engineering PSNA College of Engineering and Technology Kothandaraman Nagar Tamil Nadu Dindigul624622 India Faculty of Engineering and Technology Karnataka Bengaluru560069 India

Hearing loss or hearing impairment is one of the most leading cause highly affecting the people around the world in present days. Additionally, this disease primarily affects children and adults, which has an impact on their daily lives, careers, education, and other aspects of their lives. Therefore, it has to be correctly identified and diagnosed so that early therapies can be given to save people's lives. The many kinds of automated hearing loss prediction/detection systems are created for this aim in conventional works. The majority of the current research focuses on applying prediction techniques based on machine learning and deep learning to diagnose diseases. Its shortcomings include challenging computational procedures, longer training and testing times, greater mis-prediction outcomes, and incorrect outputs. Therefore, the proposed work objects to develop a Human Age - Hearing Impairment & Level (HAHIL) prediction system by using the machine learning methodologies. In this study, we describe a computer-aided strategy to forecast hearing loss and then prevent it. Here, three distinct prediction models are deployed for age prediction, hearing loss detection, and its severity level prediction. The Biased Probability Neural Network (BPNN) technique is utilized to predict the age based on simulated human acoustical signals. Then, the Regularized Extreme Learning Machine (RELM) mechanism is deployed for predicting the hearing impairment by constructing the weight and target matrices. During evaluation, the performance of the proposed HAHIL prediction system is validated and tested by using various evaluation indicators. The effectiveness of our computer-aided hearing loss prediction methods has been proven to be very high and they can be applied to the actual application system. Also, it provides an increased accuracy up to 99%, which is highly superior than the existing prediction models. © 2023 Multidiscip. Sci. J. All rights reserved.

关键词： Forecasting

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Quadtree-based Free-space Cell-decomposition for Pathplanning with RRT* Implementation

Quadtree-based Free-space Cell-decomposition for Pathplannin...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Otte, Noah Satpute, Sumeet Gajanan Banerjee, Avijit Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a novel, computationally efficient, random-search based path-planning algorithm specifically designed to enhance the dexterous accessibility of autonomous lunar rovers operating on highly cluttered obstacle-dense planetary surfaces. The proposed pathplanning framework employs a Quadtree-based cell decomposition to enhance the performance of sampling-based path-planning algorithms, specifically those in the category of Rapidlyexploring Random Trees (RRT) and its optimized variant, RRT*. In the process of generating the path through an occupancy grid map, the obstacle-free cells are utilized as discrete sampling points for the RRT* algorithm. A unique non-uniform sampling approach is employed to favour larger cells with a higher degree of safety, thereby enabling seamless navigation through the obstacle-dense map. Additionally, the non-uniform sampling approach enhances the overall computational efficiency of the framework, making it suitable for fast, lightweight onboard implementation. The efficacy of the proposed planning framework is demonstrated through numerous simulation scenarios. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Decision trees

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Robust Watermarking Scheme for NIfTI Medical Images

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computers, Materials & Continua 2022年第5期71卷 3107-3125页

作者： Abhishek Kumar Kamred Udham Singh Visvam Devadoss Ambeth Kumar Tapan Kant Abdul Khader Jilani Saudagar Abdullah Al Tameem Mohammed Al Khathami Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Khalid Mahmood Malik School of Computer Science and IT JAIN(Deemed to be University)Bangalore560069India Department of Computer Science and Information Engineering National Cheng Kung UniversityTainan701Taiwan Department of Computer Science&Engineering Panimalar Engineering CollegeAnna UniversityChennai600123India Patna Women’s College Patna800001India Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science and Engineering Oakland UniversityRochester48309USA

Computed Tomography(CT)scan and Magnetic Resonance Imaging(MRI)technologies are widely used in medical *** the last few months,due to the increased use of CT scans,millions of patients have had their CT scans ***,as a result,images showing the Corona Virus for diagnostic purposes were digitally transmitted over the *** major problem for the world health care system is a multitude of attacks that affect copyright protection and other ethical issues as images are transmitted over the *** a result,it is important to apply a robust and secure watermarking technique to these ***,watermarking schemes have been developed for various image formats,***,.bmp,***,but their impact on NIfTI(Neuroimaging Informatics Technology Initiative)images is not noteworthy.A watermarking scheme based on the Lifting Wavelet Transform(LWT)and QR factorization is presented in this *** LWT and QR are combined,the NIfTI image maintains its inherent sensitivity and mitigates the watermarking scheme’s *** watermarks are added to the host image in this *** the performance of the graphics card is done by using PSNR,SSIM,Q(a formula which measures image quality),SNR,and Normalized *** watermarking scheme withstands a variety of noise attacks and conversions,including image compression and decompression.

关键词： Watermarking NIfTI LWT QR image security

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Sustainable Algorithms using Artificial Intelligence and Various Stages for Precision Agricultural Cultivation 2

Sustainable Algorithms using Artificial Intelligence and Var...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Kalaiselvi, V.K.G. Gopalakrishnan, Jagadeesh Anand, Sahana Shri Hariharan, Shanmugasundaram Saravanan, Sivasankaran Annamalai, Hemalatha Sri Sairam Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350325799

India, the largest exporter of farm products, faces low agricultural productivity, resulting in reduced income for farmers. To boost revenue, performance improvement is crucial. Irrigation plays a vital role in farming, necessitating careful management of soil moisture levels. However, traditional irrigation methods often lead to water wastage. Efficient utilization of land and resources is essential in addressing this issue. Pest attacks pose a significant threat to crop yield and quality, making pest identification critical. Farmers and agronomists now utilize sensors and IoT technology to remotely monitor crops and manage them in controlled environments. However, crop productivity is still affected by weather conditions and disease variations. Precise detection of plant deficiencies and accurate fertilizer deployment is crucial for efficient farming. Modern computer vision techniques can be leveraged for positive financial and environmental outcomes in these tasks. Good crop selection significantly impacts yield, especially in developing countries. Additionally, growing vegetables in greenhouses or hydroponics offers a promising solution to discontinuous and costly supply chains. This abstract highlights the importance of addressing productivity challenges and implementing innovative farming methods to enhance agricultural outcomes. © 2023 IEEE.

关键词： Crops

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Novel Functional Community Detection in Networked Smart Grid Systems-Based Improved Louvain Algorithm

Novel Functional Community Detection in Networked Smart Grid...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar

ISBN: (纸本)9781665490719

Despite using Community Detection Algorithms (CDA) in various network partitioning real-world applications, these algorithms tend to fail to partition complex weighted functional networks such as power grids. When contingency events occur, appropriate power grid partitioning enables an effective distributed control strategy to detect system faults and cyberattacks and accelerate electrical assets recovery through fast remedial control. This paper addresses the CDA by considering the functional structure of the power grid. When comparing the topological and functional structures of the power grid network, electrical Coupling Strength (ECS) reflects both direct and indirect power flows, unlike a conventional adjacency matrix, which only considers an unweighted direct connection between buses. The proposed Improved Louvain Algorithm (ILA) employs the ECS patterns to represent the network connection. Here, the quality function is modified as electrical modularity Qe to assess the goodness of the produced clusters. Large Qe indicates that the power transmission within the partition is dense compared to power transmission with other partitions. The algorithm has been tested on IEEE 39-bus and 118-bus systems. Using different case scenarios, the simulation results demonstrate the high effectiveness of the proposed ILA to the traditional Louvain algorithm in partitioning the complex networked electrical grid into adequate subsystems. © 2023 IEEE.

关键词： Intrusion detection

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Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey 4

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Ilayaraja, V. Rajamohana, S.P. Jagadeesan, S. Aishwarya, V. Rakesh, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632 014 India School of Engineering & Technology Pondicherry University Department of Computer Science Puducherry Karaikal India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Tamil Nadu Vellore632 014 India

ISBN: (纸本)9798331528171

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability. © 2024 IEEE.

关键词： Tropical cyclone

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A Clustering-Based 3D Localization in Wireless Sensor Networks Using RSSI and AoA

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SN computer science 2023年第5期4卷 1-23页

作者： Wajgi, Dipak W. Tembhurne, Jitendra V. Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur 441108 India Department of Computer Engineering St. Vincent Pallotti College of Engineering and Technology Maharashtra Nagpur 441108 India

Localization in wireless sensor network (WSN) is an important issue since it helps to find the origin of the event. Many localization algorithms have been proposed and are efficient up to certain extent. Energy efficient and low cost 3D localization algorithm in wireless sensor networks is still a big challenge. Accuracy of location estimation with minimum computational complexity is the main objective of the localization algorithm. In this paper, we propose the clustering-based localization algorithm for 3D environment which is energy efficient and has less computational complexity. The sensor nodes are grouped in a cluster which is based on the received signal strength at the respective anchor nodes. The nearest neighbor clustering algorithm is formulated for forming the clusters. The anchor nodes act as the cluster heads. The distance information from received signal strength indicator (RSSI) along with the angle of arrival (AoA) information is combined to estimate the location of the cluster members creating the local map. The energy dissipation in the proposed approach can be reduced by adopting the density control strategy. The simulation results show that the proposed approach performs better as compared to the PSO, BBO, and FA in terms of localization error. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Angle of arrival Clustering Density control Energy efficiency Localization RSSI WSN

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Utilization of Machine Learning in Breast Cancer Detection 25

Utilization of Machine Learning in Breast Cancer Detection

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25th International Arab Conference on information Technology, ACIT 2024

作者： Momani, Mutaz Yousef, Nidal Aljawazneh, Qotadeh Issa, A.T. Abuowaida, Suhaila Alazaidah, Raed Khouj, Mohammed Talat Batyha, Radwan Elsoud, Esraa Abu Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Al-Albayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan Zarqa University Department of Data Science and Ai Faculty of Information Technology Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Private University Faculty of It Applied Science Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Breast cancer is the most prevalent cancer among women and can be deadly, necessitating early detection to enhance patient outcomes and treatment effectiveness. Recently, Machine Learning (ML) techniques have shown potential in medical diagnostics, especially in breast cancer screening. ML algorithms examine extensive medical databases, including patient records, to identify subtle patterns and anomalies indicative of cancer. These algorithms excel at recognizing intricate patterns that may be missed by human analysis. Thereby, increasing the precision of breast cancer diagnoses. By evaluating and analyzing medical data, ML algorithms can detect minute anomalies, facilitating early identification of potential cancer cases. A significant advantage of ML-based breast cancer detection is its capacity for continuous learning and adaptation. In this study, we utilized 34 classifiers and five feature selection methods on three datasets. The first dataset comprises 286 instances with 10 integer-type features. The second dataset includes 699 instances with 10 integer-type features, and the third dataset consists of 286 instances with 13 integer-type features. For Dataset 1, Filtered Classifier and J-48 yielded the best results, while the Bayesnet classifier performed best on Dataset 2. In Dataset 3, SimpleLogistic and LMT achieved the best outcomes. Regarding feature selection, the study showed that the GainRatioAttributeEval, InfoGainAttributeEval, and CorrelationAttributeEval techniques are the best among the feature selection methods tested in the study. © 2024 IEEE.

关键词： Lung cancer

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