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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Majeed, Asad Faridoon, Fatima Irfan, Muhammad Rashid, Ahmar Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi Pakistan Department of Electrical Engineering Sukkur IBA University Sukkur Pakistan

ISBN: (纸本)9798331504465

Weed infestation in cotton fields significantly challenges agricultural productivity by competing for essential nutrients and water resources. This study presents a comprehensive comparative analysis of two deep learning-based object detection models - YOLO and Faster R-CNN - for automated weed detection in cotton fields. We developed and annotated a custom dataset comprising field images captured under various environmental conditions, labelled in both COCO and YOLO formats using Label Studio. Performance evaluation revealed the YOLO model’s superior capabilities, achieving a mAP50 of 0.775, precision scores of 0.859 for cotton and 0.656 for weed detection, and recall rates of 0.849 and 0.543 for cotton and weed respectively on the test dataset. In comparison, Faster R-CNN showed lower performance with AP scores of 0.708 for cotton and 0.269 for weed detection, particularly struggling with objects of varying sizes. The YOLO model maintained consistent performance across both validation and test datasets, with validation metrics showing mAP50 of 0.982 and mAP50-95 of 0.829. These results establish YOLO’s effectiveness as a reliable tool for automated weed detection in cotton fields, offering a practical solution for precision agriculture applications. The developed model demonstrates the potential for integration into automated crop management systems, contributing to more efficient and targeted weed control strategies. © 2024 IEEE.

关键词： Cotton

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Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1126-1171页

作者： Zong-Shan Wang Shi-Jin Li Hong-Wei Ding Gaurav Dhiman Peng Hou Ai-Shan Li Peng Hu Zhi-Jun Yang Jie Wang School of Information Science and Engineering Yunnan UniversityKunmingChina Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhenChina Academic Affairs Office Yunnan University of Finance and EconomicsKunmingChina Faculty of Management and Economics Kunming University of TechnologyKunmingChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirutLebanon Department of Computer Science and Engineering University Centre for Research and DevelopmentChandigarh UniversityChennaiIndia Department of Computer Science and Engineering Graphic Era Deemed to Be UniversityDehradunIndia School of Computer Science Fudan UniversityShanghaiChina Rackham Graduate School University of MichiganAnn ArborMIUSA Research and Development Department Youbei Technology Co.LTDKunmingChina School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhouChina

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation *** the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO *** alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is ***,the adaptive elite-guided search mechanism enhances the balance between exploration and *** modified mutualism phase reinforces the information interaction among particles and local optima *** cooperation of these two mechanisms boosts the overall performance of the basic *** AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test *** results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and *** addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)*** experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

关键词： ANT COLONY optimization CLOUD COMPUTING GENETIC ALGORITHMS SWARM intelligence

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COVID19 Outbreak:A Hierarchical Framework for User Sentiment Analysis

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computers, Materials & Continua 2022年第2期70卷 2507-2524页

作者： Ahmed F.Ibrahim M.Hassaballah Abdelmgeid A.Ali Yunyoung Nam Ibrahim A.Ibrahim Department of Computer Science Faculty of Computer ScienceNahda UniversityBanisuefEgypt Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt Faculty of Computers and Information Minia UniversityAlMiniaEgypt Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Social networking sites in the most modernized world are flooded with large data *** the sentiment polarity of important aspects is necessary;as it helps to determine people’s opinions through what they *** Coronavirus pandemic has invaded the world and been given a mention in the social media on a large *** a very short period of time,tweets indicate unpredicted increase of *** reflect people’s opinions and thoughts with regard to coronavirus and its impact on *** research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa;due to their shortness and *** this paper,a hierarchical twitter sentiment model(HTSM)is proposed to show people’s opinions in short *** proposed HTSM has two main features as follows:constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width,as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner(VADER)sentiment *** tweets for each extracted important aspect can be categorized as follows:strongly positive,positive,neutral,strongly negative,or *** quality of the proposed model is validated by applying it to a popular product and a widespread *** results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.

关键词： COVID19 COVID data sentiment analysis hierarchical clustering sentiment tree

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An Intelligent Data Analytics Approach for Crisis Management in Healthcare Systems

An Intelligent Data Analytics Approach for Crisis Management...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Madian, Fatma A. Ebrahim, Gamal A. Salah, Mostafa M. Abdelaal, Marwa A. Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Future University in Egypt Electrical Engineering Department Cairo Egypt Faculty of Computers and Information Sadat Academy for Management Sciences Computer Science Department Cairo Egypt

ISBN: (纸本)9798350367560

Crisis management is preparing for and managing possible crises that may impact organizations and individuals at different levels. It involves effective communication, quick decision-making, and strategic planning to minimize the negative impact of a crisis and ensure swift recovery. It plays a vital role in healthcare systems, especially in virus outbreaks. Its role is to monitor and manage the spread of viruses. Moreover, it can use Machine Learning (ML) techniques to achieve this goal. This paper discusses how to apply ML approaches to daily reports of infection cases. The adopted ML approaches are Auto Regressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model. Furthermore, it uses Root Mean Square Error (RMSE) as a performance measure to evaluate the applied models. Simulation results show that ARIMA model performs better as compared to other models, which can provide a prediction accuracy of more than 99%. © 2024 IEEE.

关键词： Electronic health record

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Assessing and prioritizing crucial drivers for CloudIoT-based healthcare adoption: an analytic hierarchy process approach

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

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare settings remains limited. There is a lack of comprehensive understanding of the factors that ensure successful implementation. To address this issue, we have used the Analytic Hierarchy Process to prioritize 26 factors that could potentially influence the adoption of CloudIoT-based healthcare. These factors were derived from existing literature and categorized into four main groups: personal-specific, technology-specific, environment-specific, and organization-specific factors. Our findings revealed that performance expectancy, trust, facilitating conditions, task complexity, and perceived security are the top five critical factors influencing CloudIoT-based healthcare adoption. Interestingly, our results indicated that environmental factors were less influential than those in the other categories, with personal, technological, and organizational factors dominating the top ten significant factors. The insights from this study can help policymakers, CloudIoT service providers, healthcare providers, designers, and developers better understand the factors contributing to the successful implementation of CloudIoT-based healthcare. This understanding can guide decision-making and inform the development of strategies to promote the broader adoption of this innovative technology. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Analytic hierarchy process CloudIoT Factors Healthcare industry

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Textual Emotion Recognition: Advancements and Insights 6

Textual Emotion Recognition: Advancements and Insights

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Esmail, Ahmed A. Abdallah, Sayed M. Elsayed, Mohamed A. Ghaith, Amr E. Elbanhawy, Magy Al-Zoghby, Aya M. Moawad, Ibrahim F. Artificial Intelligence Science New Mansoura University Faculty of Computer Science and Engineering Dakhlia Egypt Mansoura University Faculty of Computers and Information Dakhlia Egypt New Mansoura University Faculty of Computer Science and Engineering Dakhlia Egypt Portsaid University Faculty of Engineering Egypt Damietta University Faculty of Computers and Artificial Intelligence Computer Science Department Damietta Egypt Ain Shams University Information Systems Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

In recent years, there has been a significant increase in attention toward emotion detection in text analysis, driven by its broad applications across marketing, political science, psychology, human-computer interaction, and artificial intelligence. This growing interest is primarily due to the critical role of textual expression as a repository of human emotions and sentiments. The development of sophisticated natural language processing (NLP) techniques has emphasized the importance of exploring emotion detection and recognition within textual data. By utilizing a wide range of sources, including social media content, microblogs, news articles, and customer feedback, text mining aims to reveal the underlying emotional currents within the text. However, existing models often struggle to capture the complicated emotional nuances woven into words. Addressing this challenge, the innovative semantic emotion neural network (SENN) architecture has been introduced. The SENN model marks a significant advancement, featuring two synergistic sub-networks: a bidirectional long short-term memory (BiLSTM) network that extracts contextual information and a convolutional neural network (CNN) that analyzes and extracts emotional features, highlighting the text's intrinsic emotional connections. The SENN model's performance has been thoroughly evaluated on widely used real-world datasets, benchmarked against Ekman's six fundamental emotions. Results demonstrated its superiority, showing that the SENN model excels in emotion recognition accuracy and quality in conjunction with additional techniques. It also holds potential for enhancement by incorporating more comprehensive emotional word embedding, suggesting a promising future for text-based emotion analysis. The proposed paper presents goals for detecting sentiment in text data and introduces a novel architecture that effectively captures the complexity of emotional nuances. We create an abstract model and compare three types of m

关键词： Emotion Recognition

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Comparative Analysis of Edge Detection Algorithms with Particle Swarm Optimization for Enhanced Image Processing 11

Comparative Analysis of Edge Detection Algorithms with Parti...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Rahayu, Sri Nurtantio Andono, Pulung Pujiono Zainul Fanani, Ahmad Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia Institut Teknologi Garut Computer Science Department Garut Indonesia

ISBN: (纸本)9798350389654

Edge detection plays an important role in various fields by identifying object boundaries and supporting advanced image analysis, such as segmentation, recognition, and tracking. Many edge detection algorithms, such as Canny, Laplacian, and Prewitt, have been developed to address issues such as noise sensitivity, computational complexity, and detection accuracy. This research compares the optimized algorithms using Particle Swarm Optimization (PSO). The results of this study show that the optimized algorithm provides better performance based on the evaluation conducted using entropy, Mean Squared Error (MSE), and computation time, and has practical implications. This research also determines the most effective edge detection technique in various image processing scenarios, thus contributing to the optimization of image analysis workflows in real-world contexts. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Challenges in Securing UAV IoT Framework: Future Research Perspective

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2607-2629页

作者： Aljumah, Abdullah Ahanger, Tariq Ahamed Ullah, Imdad Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Saudi Arabia CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia The University of Sydney School of Computer Science Faculty of Engineering SydneyNSW2006 Australia

Unmanned Aerial Vehicles (UAVs) offer the immense capability for allowing novel applications in a variety of domains including security, military, surveillance, medicine, and traffic monitoring. The prevalence of UAV systems is due to the collaboration and accomplishment of tasks efficiently and effectively. UAVs embedded with camcorders, GPS receivers, and wireless sensors propose enormous promise in realizing the Internet of Things (IoT) service delivery in vast domains. It results in establishing an airborne field of the IoT when empowered with communication protocols of LTE, 4G, and 5G/6G networks. However, numerous difficulties must be addressed before UAVs may be used effectively namely privacy, security, and administration. Conspicuously, in the current article, novel UAV-specific domains enabled by IoT and 5G/6G technology are explored. Moreover, the presented technique assesses sensor requirements and provides an overview of fleet management systems that address aerial networking, privacy, and security concerns. Furthermore, a framework based on the IoT-5G/6G aspect is proposed which can be deployed over UAVs. Finally, in a heterogeneous computational platform, the proposed framework provides a complete IoT architecture that enables secure UAVs. © 2004-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Identification and Classification of Crowd Activities

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computers, Materials & Continua 2022年第7期72卷 815-832页

作者： Manar Elshahawy Ahmed O.Aseeri Shaker El-Sappagh Hassan Soliman Mohammed Elmogy Mervat Abu-Elkheir Department of Information Technology Faculty of Computers and InformationMansoura UniversityEgypt Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems Faculty of Computers and Artificial IntelligenceBenha UniversityEgypt Faculty of Computer Science and Engineering Galala University435611SuezEgypt Department of Computer Science Faculty of Media Engineering and TechnologyGerman UniversityEgypt

The identification and classification of collective people’s activities are gaining momentum as significant themes in machine learning,with many potential applications *** need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd *** paper investigates the capability of deep neural network(DNN)algorithms to achieve our carefully engineered pipeline for crowd *** includes three principal stages that cover crowd analysis ***,individual’s detection is represented using the You Only Look Once(YOLO)model for human detection and Kalman filter for multiple human tracking;Second,the density map and crowd counting of a certain location are generated using bounding boxes from a human detector;and Finally,in order to classify normal or abnormal crowds,individual activities are identified with pose *** proposed system successfully achieves designing an effective collective representation of the crowd given the individuals in addition to introducing a significant change of crowd in terms of activities *** results onMOT20 and SDHA datasets demonstrate that the proposed system is robust and *** framework achieves an improved performance of recognition and detection peoplewith a mean average precision of 99.0%,a real-time speed of 0.6ms non-maximumsuppression(NMS)per image for the SDHAdataset,and 95.3%mean average precision for MOT20 with 1.5ms NMS per image.

关键词： Crowd analysis individual detection You Only Look Once(YOLO) multiple object tracking kalman filter pose estimation

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Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Models 2

Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Mo...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ravikumar, S. Raj, Arockia Babu, R. Vijay, K. Ramani, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai600062 India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Computational Intelligence Faculty of College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Chennai603203 India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamilnadu Chennai602117 India Department of Computer Science and Engineering P.S.R. Engineering College Tamilnadu Sivakasi626140 India

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications. © 2024 Elsevier B.V.. All rights reserved.

关键词： Network architecture

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