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Optimizing Artificial Neural Network for Demography Based Crop Recommendation: An Ocean Water Current Inspired Approach in Precision Agriculture

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International Journal of Information technology (Singapore) 2024年 1-18页

作者： Mishra, Aishwarya Goel, Lavika Department of Computer Science and Engineering Malaviya National Institute of Technology (NIT) Jaipur Rajasthan Jaipur India

The challenge of optimal crop selection becomes more complex by the dynamic nature of the ever-changing features of land surface type, soil type, climate, water requirements, phosphorus content, Leaf area index (LAI), and other phenological parameters. In this research, Landsat 8 satellite imagery is used to create an optimized crop recommendation system, extending an Artificial Neural Network (ANN) model with an Ocean water current optimizer for prediction. This research work aims to provide farmers with effective crop recommendations while addressing a wide range of environmental and operational factors. The approach relies on a classifier and an optimizer to choose the best crop to grow in a given region, taking into account all of the features of a specific demography. The training time of the ANN model is also reduced by using OWCO optimizer as compared to other metaheuristic models. The various computing techniques such as Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Nature inspired Optimization Algorithms (NIOAs) for smart farming are also analyzed. The proposed approach complements the ANN by also handling dynamic and mutating factors in the crop recommendation system. The ANN model consistently shows better training accuracy as the number of epochs approaches 60. The accuracy of OWCO with ANN is 94.12% which is better than other metaheuristic models. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Artificial neural network (ANN) Machine learning Metaheuristic algorithms Nature inspired algorithms Ocean water current optimization (OWCO)

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Comparative Analysis of Decision Tree with Existing Approaches for Improving Prediction Performance 15

Comparative Analysis of Decision Tree with Existing Approach...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pathan, Shabana Sharma, Sanjeev Kumar Department of Computer Science & Engineering Oriental University M.P Indore India Department of Computer Science and Engineering Oriental Institute of Science and Technology M.P Bhopal India

ISBN: (纸本)9798331300579

Data mining has applications for a wide range of enterprises, including those in the banking, telecommunications, energy, medical, marketing, and finance industries. The real-world situations involve noisy, unpredictable, ambiguous, and untrustworthy data, which further muddies the waters. During the information-gathering process, uncertainty arises in various application domains and is comprised of measurement errors, outdated information (data staleness), and repeated entries of the same type. This could lead to the prediction model performing poorly. The suggested method uses Deep Learning to control the uncertainty in the dataset before building Decision Tree models for categorization. This study compares two proposed approaches for decision tree optimization with the state-of-the-art approaches based on F1 score, accuracy, precision, recall, and evaluation parameters. © Grenze Scientific Society, 2024.

关键词： Prediction models

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Unraveling the Intersection of HCI and Neurodiversity in Bangladesh: Exploring ADHD, technology-Based Interventions, and Cultural Perspectives 26

Unraveling the Intersection of HCI and Neurodiversity in Ban...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Alam, Dewan Mahinur Jibon, Md. Mehedi Hasan Chowdhury, Farida Rabbi, Md Forhad Shahjalal University of Science and Technology Department of Computer Science and Engineering Sylhet Bangladesh BRAC University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition worldwide, including in Bangladesh. Children with ADHD encounter difficulties in sustaining attention, impaired executive functioning, and struggles in academic and social settings. These problems become very prevalent in the schooling context. In many cases, children are sent to special education schools. However, either there are not enough special schools in Bangladesh or the existing special schools lack resources. On the flip side, the traditional Bangladeshi schooling system requires more attention and focus, which ADHD children find difficult to adjust to. In this regard, we hypothesize that e-learning-based intervention can address both of these problems. Besides, there is a notable absence of research on e-learning-based interventions for ADHD children by breaking cultural barriers. This study explored Bangladeshi parental attitudes, beliefs, and social and cultural perceptions towards ADHD and its technology-based interventions. We conducted a semi-structured interview with parents and caregivers of children with ADHD. In this paper, after a thorough analysis, we have presented some design challenges of implementing an effective e-learning-based intervention for Bangladeshi children with ADHD. Our results reveal that using gamification, and reward systems in the intervention will provide a sense of achievement in children, and the inclusion of practical learning tasks along with animated learning content will foster their sustained engagement in the learning process. © 2023 IEEE.

关键词： Attention-Deficit/Hyperactivity Disorder Cultural Factors E-Learning System Human-computer Interaction Interaction Design Semi-Structured Interview Technological Intervention

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Regeneration Sites Selection and Regenerator Assignment Strategies for Translucent Elastic Optical Networks: Survey and Analysis

Regeneration Sites Selection and Regenerator Assignment Stra...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Bala, Shibsankar Chatterjee, Monish Sarkar, Pradyut Asansol Engineering College Department of Computer Science & Engineering West Bengal Asansol India Maulana Abul Kalam Azad University of Technology Department of Computer Science & Engineering West Bengal India

ISBN: (纸本)9798350390759

Elastic Optical Networks (EONs) form the backbone of the present day Internet. In Translucent EONs (T-EONs), optical signals are propagated beyond their transparent reach and so in order to maintain the desired Quality of Transmission (QoT) level, the signals have to undergo 3R (re-shaping, re-timing and re-amplification) regeneration at some nodes designated as regeneration sites, also called regenerators. So identification of regenerators is an important problem and is known as Regeneration Site Selection (RSS) problem. Once the regenerators are identified, an important problem is the assignment of optical signals to these regenerators to meet various objectives of the network operator such as minimizing total used spectrum, total number of regenerations etc. and is known as the Regenerator Assignment (RA) problem. In this paper we extend two RSS algorithms originally designed for traditional translucent Wavelength Division Multiplexed (WDM) optical networks to make them applicable for Orthogonal Frequency Division Multiplexing (OFDM) based T-EONs. We further study and analyses two RA algorithms. In addition, we present a survey of the various RSS and RA strategies for T-EONs available in the literature and thereby identify the future scope of research. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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A Group-Based Many-Task Collaborative Optimization Framework for Evolutionary Robots Design

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3492-3505页

作者： Hou, Yaqing Yu, Zhaoping Guo, Zheng Pei, Wenbin Wu, Yaoxin Ge, Hongwei Xue, Bing Zhang, Mengjie Dalian University of Technology College of Computer Science and Technology Dalian116024 China Avic Xi'an Aeronautics Computing Technique Research Institute 18th Research Department Xi'an710068 China Eindhoven University of Technology Department of Industrial Engineering and Innovation Sciences Eindhoven5612 AZ Netherlands Victoria University of Wellington School of Engineering and Computer Science Wellington6140 New Zealand

In evolutionary robotics (ER), the evolution of a robot's morphology (i.e., physical structure) or controller (i.e., control algorithm or instruction sequence) often entails tackling an extensive number of tasks. The use of evolutionary multitasking (EMT) in ER, which optimizes multiple tasks simultaneously by reusing potentially useful knowledge across diverse tasks, could improve the performance of problem-solving to each task. However, existing EMT methods do not fully use intertask correlations, limiting knowledge sharing. In view of this, this study introduces a novel framework, termed adaptive group-based collaborative optimization, tailored for handling optimization problems involving a large number of tasks within the ER domain simultaneously. The proposed framework divides tasks into groups according to their similarity and then proceeds through two principal stages, namely, intergroup knowledge separation and intragroup knowledge reunion. During intergroup knowledge separation stage, an adaptive method for selecting crossover operators enables source tasks to share useful knowledge to the target task across groups. During intragroup knowledge reunion stage, an adaptive knowledge combination strategy facilitates the target task in assimilating knowledge from multiple sources intragroup. We validated the efficacy of the proposed framework in both planar manipulators and hexapod robot experiments. The results indicate that our method outperforms existing state-of-the-art algorithms (i.e., MME, MMKT) on several metrics (e.g., mean fitness and quality diversity metrics). The proposed method can effectively improve the effectiveness and diversity of solutions in solving ER problems with a large number of tasks (e.g., 5 000 or 10 000), and has broad potential in practical ER applications. © 2025 IEEE.

关键词： Collaborative robots

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Block Verification Mechanism Based on Zero-Knowledge Proof in Blockchain

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computer Systems science & engineering 2023年第5期45卷 1805-1819页

作者： Jin Wang Wei Ou Osama Alfarraj Amr Tolba Gwang-Jun Kim Yongjun Ren School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea School of Computer Science Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China

Since transactions in blockchain are based on public ledger verification,this raises security concerns about privacy *** it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification,when the whole transaction on the chain is *** order to improve the efficiency and privacy protection of block data verification,this paper proposes an efficient block verification mechanism with privacy protection based on zeroknowledge proof(ZKP),which not only protects the privacy of users but also improves the speed of data block *** is no need to put the whole transaction on the chain when verifying block *** just needs to generate the ZKP and root hash with the transaction information,then save them to the smart contract for ***,the ZKP verification in smart contract is carried out to realize the privacy protection of the transaction and efficient verification of the *** the data is validated,the buffer accepts the complete transaction,updates the transaction status in the cloud database,and packages up the ***,the ZKP strengthens the privacy protection ability of blockchain,and the smart contracts save the time cost of block verification.

关键词： Blockchain privacy protection zero-knowledge proof smart contract

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Paired-Spacing-Constrained Package Routing with Net Ordering Optimization 25

Paired-Spacing-Constrained Package Routing with Net Ordering...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Ciou, Yi-Sian Jiang, Ying-Jie Liu, Yi-Yu Fang, Shao-Yun Liu, Wen-Hao Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Nvidia Hsinchu Taiwan

ISBN: (纸本)9798400706356

Package design has become increasingly complex with the evolution of technology nodes and heterogeneous integration. To optimize timing performance and signal integrity, it is essential to separate different pairs of geometrically adjacent nets with distinct spacing values, which is referred to as the paired-spacing constraint. This paper presents the first free-assignment package routing algorithm flow considering the paired-spacing constraint. To minimize the routing resource demand and overall wirelength, we propose a dynamic programming-based net ordering method to maximize the number of nets with the same/similar spacing rules positioned next to each other. In addition, the free-assignment routing problem is elegantly solved with a minimum-cost maximum-flow problem on a delicately designed graph model. Experimental results show that the proposed flow can achieve 100% routability for the adopted industrial-modified benchmarks. In contrast, even with modifications to superficially consider paired spacings, a classic model experiences significant routability degradation. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Dynamic programming

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Deep Reinforcement Learning for Optimizing Multi-hop Distributed Collaborative Task Offloading in R2X

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9533-9548页

作者： Zhao, Wei Weng, Tangjie Cheng, Yu Liu, Zhi Kato, Nei Anhui University of Technology School of Computer Science and Technology Anhui Ma'anshan243032 China University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Tohoku University Graduate School of Information Sciences Sendai Japan

Roadside units (RSUs) with strong sensing abilities enhance the viability of the RSU-to-Everything (R2X) paradigm, offering crucial infrastructure support for Mobile Edge Computing (MEC) that enables real-time data processing and reduced delay. Since RSUs collect a large volume of data but have limited computing capability, data analysis tasks are usually offloaded to other network nodes, such as the cloud, other RSUs, or even vehicles. The multi-hop distributed collaborative task offloading scheme is expected to achieve high resource utilization efficiency and low task delay in this scenario, despite increasing energy consumption in data transmission. However, the highly dynamic nature of the R2X network topology makes it challenging for a node to independently select the next hop and collaboratively allocate tasks to neighbors in a multi-hop transmission path. Specifically, offloading decisions made by an individual node are influenced not only by its immediate neighbors but also by other nodes along the multi-hop path, referred to in this paper as the effect value. Additionally, the heterogeneity in computing resources and link delays among network nodes further increases the difficulty. To address these challenges, we first apply a Long Short-Term Memory (LSTM) model to predict and update the neighbors for each node while considering effect values, allowing them to independently adapt to environmental changes. Then, we design a two-layer Deep Reinforcement Learning (DRL) algorithm for network nodes to make decisions. The first-layer DRL algorithm is implemented by RSUs to determine task offloading modes. When an RSU decides to offload tasks to multiple vehicles for collaborative computing, the second-layer DRL algorithm is used by a vehicle to select its next hop vehicle and allocate tasks. Simulation results show that our proposed approach effectively adapts to topology changes in complex and highly dynamic network environments. Compared with existing methods,

关键词： Deep reinforcement learning

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Comprehensive Overview and Analytical Study on Automatic Bird Repellent Laser System for Crop Protection

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Journal of Harbin Institute of technology(New Series) 2024年第1期31卷 38-53页

作者： Sireesha Abotula Srinivas Gorla Prasad Reddy PVGD Mohankrishna S Department of Computer Science and Engineering GITAM Deemed to be UniversityVisakhapatnam 530045India Department of Information Technology Andhra UniversityVisakhapatnam 530003India Department of Computer Science and Systems Engineering Andhra UniversityVisakhapatnam 530003India

Birds are a huge hazard to agriculture all around the world,causing harm to profitable field *** use a variety of techniques to keep them away,including visual,auditory,tactile,and olfactory deterrents. This study presents a comprehensive overview of current bird repellant approaches used in agricultural contexts,as well as potential new ways. The bird repellent techniques include Internet of Things technology,Deep Learning,Convolutional Neural Network,Unmanned Aerial Vehicles,Wireless Sensor Networks and Laser biotechnology. This study’s goal is to find and review about previous approach towards repellent of birds in the crop fields using various technologies.

关键词： Bird repellent crop protection IoT UAV Deep Learning

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Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

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computer Systems science & engineering 2023年第5期45卷 1545-1559页

作者： P.Sumathi V.V.Karthikeyan M.S.Kavitha S.Karthik Department of Information Technology SNS College of EngineeringCoimbatoreTamilnaduIndia Department of Electrical and Electronics Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all ***,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper *** present decade,the application of deep learning models in many fields of research has created greater *** increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil *** that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil ***,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)***,soil nutrient data has been given as second input to the DNNR *** utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been *** training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the *** results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification *** results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.

关键词： Soil quality classification accuracy deep learning neural network soil features training and testing

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