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Statistical Data Mining with Slime Mould Optimization for intelligent Rainfall Classification

computer Systems science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMiRPSMODL)*** the presented SDMiRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMiRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMiRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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Mitigating Crop Losses: Ai-enabled Disease Detection in Tomato Plants 20

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

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20th international Conference on Frontiers of information Technology, FiT 2023

作者： Ashfaq, Hassan Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (Ai) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of Ai in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model's performance. To address this, we propose using regularization and data augmentation techniques to enhance the model's generalizability on unseen data. Additionally, we delve into inherent challenges that plague Ai-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. in identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of Ai technologies in agriculture. © 2023 iEEE.

关键词： Crops

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Leveraging Ai and NLP in Chatbot Development: An Experimental Study 20

Leveraging AI and NLP in Chatbot Development: An Experimenta...

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20th international Conference on Frontiers of information Technology, FiT 2023

作者： Paracha, Abdul Wahab Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

in the current era of chatbots, this research delves into the advancements in Ai chatbots, drawing on artificial intelligence (Ai) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model's performance indicates a considerable dependence on the dataset's size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems. © 2023 iEEE.

关键词： Natural language processing systems

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Research on Supply Planning and Transshipment Scheme Based on Genetic Algorithm 10

Research on Supply Planning and Transshipment Scheme Based o...

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10th international Conference on information Systems and Computing Technology, iSCTech 2022

作者： Zhong, Zhenkun Ren, Junyu Li, Heng Peng, Hui School of Software Engineering Xinjiang University Xinjiang830008 China School of Mathematics and Applied Mathematics Xinjiang University Xinjiang830017 China School of Computer Science and Engineering Central South University Changsha410087 China

ISBN: (纸本)9798350332933

Aiming at the optimal cost planning problem in the process of raw material ordering and transshipment, this pa-per studies and develops a supply chain planning manage-ment model based on genetic algorithm by constructing ob-jective function and constraints. On the basis of analyzing the raw material demand of production enterprises, combined with the supplier's supply capacity and the transshipment loss rate of the transshipment carrier, the mathematical model of the optimal ordering scheme and the transshipment scheme is constructed, and the optimal supply planning model that can meet the actual supply chain planning needs is proposed, and the planning model is improved by using genetic algorithms, which greatly improves the operation speed and has good applicability. © 2022 iEEE.

关键词： Genetic algorithms

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Analyses of Parametric Relation in Ant Colony Approach for Robot Path Planning Problem

Analyses of Parametric Relation in Ant Colony Approach for R...

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iEEE Workshop on Advanced Robotics and its Social impacts, ARSO

作者： Sudeep Sharan Anh Tong Ngoc Minh Juan José Domínguez-Jiménez Peter Nauth Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cádiz Spain

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

One of the most challenging areas of robotics research is the mobile robot vehicle in a complicated obstacle environment. Recently, algorithms for autonomous mobile vehicles have largely been influenced by nature-based algorithms. The swarm behavior of many different organisms, such as ants, fireflies, honey bees, and foxes, is simulated by several algorithms in mobile robot research, and the results are highly interesting and inspiring. in this paper, we took the inspired algorithm as an ant colony which is used to solve the path planning problems for any mobile vehicle/robots. While studying and using the ant colony approaches to solve the problem, we found that the parameter selections are one of the important expects to make the results of any application worse or better. Therefore, we decided to perform the study on the parametric relation in an ant colony approach. The importance of utilization of the Ant Colony optimization (ACO) approach is to control the parameters. in this paper, we analysed/investigated the relationship between the different control parameters of the ACO algorithm and their performance in the resolution of path planning problem. Simulation is performed with different parameter values and by the result analysis, a selection of parameter is presented.

关键词： Ant colony optimization Path planning Organisms Mobile robots

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Accurate Real-World Co-ordinate Finding and the Object Classification for the Robot Manipulator

Accurate Real-World Co-ordinate Finding and the Object Class...

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Embedded Systems and Artificial intelligence (ESAi), international Conference on

作者： Sudeep Sharan Hung Do Thanh Peter Nauth Juan José Domínguez-Jiménez Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cádiz Spain

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

This paper presents an object detection and classification of the objects using Deep Learning (DL). The integration of object detection algorithms and depth camera developed is capable of providing robots, such as self developed assistive intelligent robot called “ROSWiTHA” (RObot System WiTH Autonomy) with the ability to detect an object and interpret their surrounding environment. it also provides the real-world coordinates/position of the object in that environment, which helps the robot to perform some applications, such as grabbing and serving, etc. You Only Look Once (YOLO) is one of the most popular object detection algorithms which is treated as a single regression problem and is processed by a single neural network. in this work, the YOLOv5 (YOLO version 5) network is implemented on a depth camera “RealSense” for processing object detection and estimating the position of the objects. The model is trained on reduced/filtered application-required objects (e.g. bottle and cup) from the available COCO dataset, and the training outcome alongside the performance of the overall system was evaluated. The results exhibited a satisfactory performance of the model in accurately detecting and classifying the objects with a high confidence score ranging from the least to $\mathbf{7 2 \%}$ to the highest of $\mathbf{9 0 \%}$. The confidence score could also be adjusted by the user on the developed GUi to attain the most certain predictions.

关键词： YOLO Training Accuracy Robot kinematics Current measurement Robot vision systems Cameras Manipulators Distance measurement Robots

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ETHACA - A Container-Based Cyber Range for Research and Education 29

ETHACA - A Container-Based Cyber Range for Research and Educ...

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29th iEEE international Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Chouliaras, Nestoras Kantzavelou, ioanna Pantziou, Grammati Maglaras, Leandros Chouliara-Sidera, Panagiota Adamakos, Antonis Chalatsakos, Panagiotis University of West Attica Department of Informatics and Computer Engineering Athens Greece School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom University of Thessaly Department of Computer Science and Biomedical Infromatics Lamia Greece Senior Software Engineer at *** Athens Greece Open University of Cyprus Faculty of Pure and Applied Sciences Nicosia Cyprus

ISBN: (纸本)9798350377644

The advent of industry 4.0 and the industrial internet of Things (iioT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies such as ioT devices and Ai. However, this increased digitization has also led to heightened cybersecurity risks, with a notable rise in the frequency and complexity of cyber-attacks. Cyber Ranges emerges as a critical solution in addressing these challenges, offering a controlled environment for simulating cyber-attack scenarios, training cybersecurity teams, and developing robust defense strategies. The escalating demand for cybersecurity professionals has highlighted the crucial need for effective training solutions. This article presents an open-source Cyber Range constructed on container-based architecture, meticulously engineered for efficient operation with minimal resource consumption. in response to the scarcity of cybersecurity experts, this platform prioritizes extensive scalability and robust isolation between exercises, ensuring secure learning environments, and also integrates a complex developed exercise series aimed at strengthening cybersecurity learning experiences. These exercises encompass a variety of scenarios and challenges, catering to professionals at different skill levels. Each exercise replicates real-world cyber threats, offering practical, hands-on learning opportunities. Additionally, a comprehensive questionnaire accompanies these exercises, designed to gather feedback on participants' experiences. © 2024 iEEE.

关键词： Cyber attacks

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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COViD-19 on Chest Radiographs

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computer Systems science & engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COViD-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COViD-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COViD-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(iFFO-DLEF)model for COViD-19 detection and *** major intention of the iFFO-DLEF model is to investigate the presence or absence of *** do so,the presented iFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the iFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the iFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the iFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented iFFO-DLEF model over recent approaches.

关键词： COViD-19 computer vision deep learning image classification fusion model

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A Preliminary Result of Food Object Detection using Swin Transformer 22

A Preliminary Result of Food Object Detection using Swin Tra...

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8th international Conference on computer Technology Applications, iCCTA 2022

作者： Jung, Daeil Shim, Simon Choo, Charles Hwang, Doosung Nah, Yunmook Oh, Sejong Dept. of Computer Science Dankook University Korea Republic of Dept. of Computer Engineering San Jose State University United States Dept. of Electrical Engineering San Jose State University United States Dept. of Software Science Dankook University Korea Republic of Dept. of Applied Computer Engineering Dankook University Korea Republic of

ISBN: (纸本)9781450396226

An inappropriate diet is one of the main causes of poor health. However, it is difficult to sustain a quantitative diet assessment in the general living environment. Food object detection is a key method for solving this problem;still, it is difficult to find studies that apply recent object detection techniques. in addition, the currently used high-performance food object detection models have a special architecture that combines two deep learning models-food localization and food classification-in series to achieve high accuracy. The disadvantage of this architecture is that it is difficult to predict the scalability of a model. in this study, we built an end-to-end food object detection model using the Swin Transformer, which is one of the latest backbone models. The experiment was conducted to compare the performance of the UECFOOD dataset with other food object detection studies. For the UECFOOD-100 dataset, a mAP(mean Average Precision) of 0.522 was obtained;also, a mAP of 0.52 was obtained for the UECFOOD-256 dataset. The findings show that the proposed model that uses only end-to-end object detection produces better performance than previous studies using a combination of food localization and food classification. © 2022 ACM.

关键词： Object detection

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Classifying Psychiatric Patients Using Machine Learning 25

Classifying Psychiatric Patients Using Machine Learning

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25th international Arab Conference on information Technology, ACiT 2024

作者： Alrjoob, Hmmam M. Alazaidah, Raed Batyha, Radwan Khafajeh, Hayel Elsoud, Esraa Abu Saeb Al-Sherideh, Ala'a Elrashidi, Ali Alqaraleh, Muhyeeddin Amer, Waleed Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyper Security Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa Jordan Zarqa University Faculty on Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798331540012

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. it is beneficial to detect the illness in its early stages to increase the likelihood of recovery. in this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. it can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCi Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-Data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both iBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection. © 2024 iEEE.

关键词： Contrastive Learning

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