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YOLOCSP-PEST for Crops Pest Localization and Classification

computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

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computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

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A hybrid system to predict brain stroke using a combined feature selection and classifier

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Intelligent Medicine 2024年第2期4卷 75-82页

作者： Priyanka Bathla Rajneesh Kumar Department of Computer Science&Engineering Maharishi Markandeshwar UniversityMullanaAmbalaHaryanaIndia

Background Brain stroke is a serious health issue that requires timely and accurate prediction for effective treatment and *** study described a hybrid system that used the best feature selection method and classifier to predict brain *** The Stroke Prediction Dataset from Kaggle was used for this *** minority over-sampling technique(SMOTE)analysis was used to accomplish class ***,sensitivity,specificity,precision,and the F-Measure were the main performance parameters considered for *** determine the best combination for predicting brain stroke,the performance of five classifiers,Naïve Bayes(NB),support vector machine(SVM),random forest(RF),adaptive boosting(Adaboost),and extreme gradient boosting(XGBoost),was compared along with three feature selection techniques,mutual information(MI),Pearson correlation(PC),and feature importance(FI).The performance parameters were assessed using k-fold *** The hybrid system proposed in this study identified a reduced set of features that were able to effectively predict brain *** provided a feature reduction ratio of 36.3%.The most successful hybrid system for predicting brain stroke used FI as the feature selection technique and RF as the classifier,achieving an accuracy of 97.17%.Conclusion The proposed system predicted brain stroke with high *** findings could be used to inform the early detection and prevention of brain stroke,allowing healthcare professionals to provide timely and targeted care to at-risk patients.

关键词： Machine learning Naive Bayes Extreme gradient boosting Support vector machine Adaptive boosting Random forest

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Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

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computers, Materials & Continua 2023年第1期74卷 2195-2209页

作者： S.Rama Sree Inderjeet Kaur Alexey Tikhonov E.Laxmi Lydia Ahmed A.Thabit Zahraa H.Kareem Yousif Kerrar Yousif Ahmed Alkhayyat Department of Computer Science&Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Computer Science&Engineering Ajay Kumar Garg Engineering CollegeGhaziabadUttar PradeshIndia Department of Human Resource Management Moscow Aviation InstituteMoscowRussia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Computer Communication Engineering Al-Rafidain University CollegeBaghdadIraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Technical Engineering Al-Hadba University CollegeMosulIraq College of Technical Engineering The Islamic UniversityNajafIraq

Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and *** ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with *** of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were ***,healthcare and machine learning(ML)industries are combined for determining the existence of various *** article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)*** goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical *** proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform *** addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature ***,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ***,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU *** experimental assessment of the JSODL-ASDDC model is investigated against distinct *** experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.

关键词： Autism spectral disorder biomedical data deep learning feature selection hyperparameter optimization data classification machine learning

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LDformer:a parallel neural network model for long-term power forecasting

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Frontiers of Information Technology & Electronic engineering 2023年第9期24卷 1287-1301页

作者： Ran TIAN Xinmei LI Zhongyu MA Yanxing LIU Jingxia WANG Chu WANG College of Computer Science&Engineering Northwest Normal UniversityLanzhou 730070China

Accurate long-term power forecasting is important in the decision-making operation of the power grid and power consumption management of customers to ensure the power system’s reliable power supply and the grid economy’s reliable ***,most time-series forecasting models do not perform well in dealing with long-time-series prediction tasks with a large amount of *** address this challenge,we propose a parallel time-series prediction model called ***,we combine Informer with long short-term memory(LSTM)to obtain deep representation abilities in the time ***,we propose a parallel encoder module to improve the robustness of the model and combine convolutional layers with an attention mechanism to avoid value redundancy in the attention ***,we propose a probabilistic sparse(ProbSparse)self-attention mechanism combined with UniDrop to reduce the computational overhead and mitigate the risk of losing some key connections in the *** results on five datasets show that LDformer outperforms the state-of-the-art methods for most of the cases when handling the different long-time-series prediction tasks.

关键词： Long-term power forecasting Long short-term memory(LSTM) UniDrop Self-attention mechanism

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A Concise Survey On Predicting Stock Prices 9

A Concise Survey On Predicting Stock Prices

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Kondiparthy, Obed Nimmagadda, Sandeep Venkata, Girish Kumar Mannepalli Gera, Joseph Rahul Burugari, Vijay Kumar Kailasam, Swathi Koneru Lakshmaiah Education Foundation Department of Computer Science&Engineering Guntur522502 India

ISBN: (纸本)9798350397376

Stocks price prediction is a current highly profitable area of research where future stock value predictions are made utilising different deep learning models and various machine learning algorithms. Due to the erratic price changes, market participants have exhibited interest in the subject of stock prediction. Since stock prices are effected by various factors, it becomes difficult to predict the trend in which the direction of price moves. This forecasting of prices helps investors reduce risk and help them to take informed decision. This papers reviews previously proposed model ranging since 2015 to 2022. All papers are classified into different segments that are then compared and analyzed, this research also reviews the data sets and the models proposed in this paper. This research compares hybrid machine models and through this survey the authors conclude that machine learning techniques are rapidly used for financial modeling. © 2023 IEEE.

关键词： Forecasting

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D-scheduler:A scheduler in time-triggered distributed system through decoupling dependencies between tasks and messages

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science China(Technological sciences) 2024年第1期67卷 183-196页

作者： YANG TingTing ZHANG YuQi YUE FengLai WUNIRI QiQiGe TONG Chao School of Computer Science&Engineering Beihang UniversityBeijing 100191China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China National Innovation Center of Intelligent and Connected Vehicles Beijing 100176China

Time-triggered architecture,as a mainstream design of the distributed real-time system,has been successfully applied in the aerospace,automotive and mechanical ***,time-triggered scheduling is a challenging NP-hard *** are few studies that could quickly solve the scheduling problem of large distributed time-triggered *** solve this problem,a communication affinity parameter is defined in this paper to describe the degree of bias of the shaper task towards sending or receiving *** on this,an innovative task-message decoupling model named D-scheduler is built to reduce the computation complexity of the scheduling problem in large-scale ***,we provide mathematical proof that our model is a convex optimization that is easy to solve with existing computational *** experiments substantiate the efficacy of the *** dramatically reduces the scheduling complexity of large-scale real-time systems with a small loss of solving space compared to the federal scheduler.

关键词： time-triggered architecture time-triggered scheduling communication affinity parameter task-message decoupling model

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Framework for Personalized Chronic Pain Management:Harnessing AI and Personality Insights for Effective Care

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Journal of Artificial Intelligence and Technology 2024年第2期4卷 132-144页

作者： Akshi Kumar Rahul Seewal Dipika Jain Ravleen Kaur Department of Computing Goldsmiths University of LondonLondonUK Chelsea and Westminster Hospital and East Kent Hospital NHS Find Trust UK MyPain■ UK Department of Computer Science&Engineering Delhi Technological UniversityNew DelhiIndia Department of Computer Science&Engineering Netaji Subhas University of TechnologyNew DelhiIndia

This paper introduces a cutting-edge framework for personalized chronic pain management,leveraging the power of artificial intelligence(AI)and personality *** explores the intricate relationship between personality traits and pain perception,expression,and management,identifying key correlations that influence an individual’s experience of *** integrating personality psychology with AI-driven personality assessment,this framework offers a novel approach to tailoring chronic pain management strategies for each patient’s unique personality *** highlights the relevance of well-established personality theories such as the Big Five and the Myers-Briggs Type Indicator(MBTI)in shaping personalized pain management ***,the paper introduces multimodal AI-driven personality assessment,emphasizing the ethical considerations and data collection processes necessary for its *** illustrative case studies,the paper exemplifies how this framework can lead to more effective and patient-centered pain relief,ultimately enhancing overall *** conclusion,the paper positions the need of an“AI-Powered Holistic Pain Management Initiative”which has the potential to transform chronic pain management by providing personalized,data-driven solutions and create a multifaceted research impact influencing clinical practice,patient outcomes,healthcare policy,and the broader scientific community’s understanding of personalized medicine and AI-driven interventions.

关键词： artificial intelligence health informatics pain personality traits

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VCNet:A generative model for volume completion

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Visual Informatics 2022年第2期6卷 62-73页

作者： Jun Han Chaoli Wang Department of Computer Science&Engineering University of Notre DameNotre DameIN 46556United States

We present VCNet,a new deep learning approach for volume completion by synthesizing missing *** solution leverages a generative adversarial network(GAN)that learns to complete volumes using the adversarial and volumetric *** core design of VCNet features a dilated residual block and long-term *** training,VCNet first randomly masks basic subvolumes(e.g.,cuboids,slices)from complete volumes and learns to recover ***,we design a two-stage algorithm for stabilizing and accelerating network *** trained,VCNet takes an incomplete volume as input and automatically identifies and fills in the missing subvolumes with high *** quantitatively and qualitatively test VCNet with volumetric data sets of various characteristics to demonstrate its *** also compare VCNet against a diffusion-based solution and two GAN-based solutions.

关键词： Volume visualization Generative adversarial network Data completion

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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