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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Chen, Jiayin Jia, Ya-Hui Bi, Ying Chen, Wei-Neng School of Future Technology South China University of Technology Guangzhou China School of Electrical and Information Engineering Zhengzhou University Zhengzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350308365

Genetic programming (GP) hyper-heuristic method has been extensively studied to solve multiple dynamic job shop scheduling tasks by generating an effective heuristic for each task simultaneously. However, a fundamental question has not been answered. Do we need to customize a specific heuristic for each task? To fill this research gap, we propose to generate a single heuristic for handling multiple tasks. Without designing complex evolution mechanisms, only during the evaluation process of GP, the fitness of a heuristic is evaluated by multiple tasks. Since there are multiple tasks, a heuristic has multiple objective values. A rank aggregation (RA) fitness evaluation strategy is designed to convert multiple objective values of multiple tasks into a fitness value for a single heuristic. To validate the effectiveness of the generated solution and the proposed RA strategy, we design multitask scenarios that encompass tasks with diverse objectives, utilization levels, and maximum operation times. The results demonstrate that the performance of the single heuristic generated in multitask scenarios is comparable to solutions generated by GP using the single-task learning paradigm, meaning that with an appropriate training method, GP can generate a heuristic with good generality. © 2024 IEEE.

关键词： Genetic programming

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Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

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An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

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International Journal of information technology (Singapore) 2024年第8期16卷 5033-5040页

作者： William, P. Patil, Jaikumar M. Panda, Sunita Venugopal, Anita Vidyullatha, Pellakuri Kumar, Nellore Manoj Jandwani, Aman Department of Information Technology Sanjivani College of Engineering SPPU Department of Computer Science and Engineering Shri Sant Gajanan Maharaj College of Engineering SGBAU Department of Electrical Electronics and Communication Engineering GITAM School of Technology IT Unit Dhofar University Department: Computer Science and Engineering Koneru Lakshmaiah Education Foundation Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Research Task

Nation’s rapid urbanization growth and economic development, waste generation has significantly increased. An important environmental concern on a global scale is waste management. In a majority of the world’s nations, including India’s metropolitan centers, organic waste management is a major problem. Therefore, it is necessary to create a productive system that will either eliminate or significantly decrease this issue. It will assist us in effectively maintaining a hygienic environment, and natural atmosphere. The implementation of smart cities requires an effective garbage collection system. In the given paper, we propose a smart waste collection and management (SWCM) system centered on the Internet of Things (IoT) architecture. A deep learning (DL) approach for classifying waste is being used. The proposed framework includes the use of smart bins for the collection of garbage, the categorization of that waste into several groups, and the management of smart waste. For this study, data from smart garbage cans were gathered. The data was normalized during preprocessing. The efficacy of the proposed system is assessed and compared with conventional as well as existing recent studies based on performance metrics such as classification accuracy (%) and power consumption (%). The results of the study reveal that the proposed approach is impactful in the smart collection and management of waste. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： And hygienic environment Deep learning Internet of things (IoT) Smart cities Smart waste

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Leveraging Deep Learning Methodologies in Data science Practices: A Systematic analysis 15

Leveraging Deep Learning Methodologies in Data Science Pract...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gupta, Eesha Senthilkumar, K.K. Gaikwad, Vidya S. Nagpal, Manish Singh, Akhilendra Pratap Sharma, Sonal Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

This paper proposes a systematic analysis of using deep mastering methodologies in statistics technology practices for the cause of leveraging the strength of deep mastering algorithms. It presents an overview of the exceptional types of deep gaining knowledge of methodologies and architectures in addition to their application within the various areas of records technology. Similarly, it critiques the prevailing literature and famous several useful findings regarding the usage of deep mastering in facts science and also the potential demanding situations and possibilities that could come in conjunction with it. It details the coding and implementation of numerous deep mastering fashions such as convolutional neural networks, deep belief networks, recurrent neural networks, and lengthy quick term memory (LSTM). Additionally, it presents an in-intensity analysis of the professionals and cons of the usage of deep gaining knowledge of for information technology. Eventually, it gives guidelines on a way to effectively use deep studying in facts technological know-how. © 2024 IEEE.

关键词： Fashions Guidelines Mastering Networks Technological

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Enhancing UAV Coverage Path Planning via Deep Reinforcement learning with Intrinsic Curiosity 10

Enhancing UAV Coverage Path Planning via Deep Reinforcement ...

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10th IEEE Smart World Congress, SWC 2024

作者： Zhou, Yinxin Chen, Yanxi Wang, Renyu Huang, Haiping Liu, Shenghao Deng, Xianjun Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798331520861

Unmanned Aerial Vehicles (UAVs) are widely used in various fields due to their agility and versatility, but their limited energy supply necessitates efficient Coverage Path Planning (CPP). Traditional CPP methods are effective in structured environments but often fall short in more complex scenarios. This paper proposes a novel approach integrating advanced reinforcement learning techniques to enhance UAV path planning. Our method combines Double Deep Q-Networks (DDQN) with Long Short-Term Memory (LSTM) networks to improve decision-making and temporal information processing capabilities. Additionally, we incorporate an Intrinsic Curiosity Module (ICM) to drive exploration and Prioritized Experience Replay (PER) to focus on high-value experiences, accelerating learning and enhancing performance. The proposed framework demonstrates superior coverage efficiency and adaptability in simulations, achieving higher coverage ratios and reduced redundant paths compared to traditional methods. These innovations provide a robust and flexible solution for autonomous UAV navigation, offering a solid foundation for future research in CPP optimization. © 2024 IEEE.

关键词： Deep reinforcement learning

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Improving students' cognitive abilities in online environment based on neurofeedback 14

Improving students' cognitive abilities in online environmen...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Jamil, Nuraini Lakas, Abderrahmane Belkacem, Abdelkader Nasreddine College of Information Technology United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates College of Information Technology United Arab Emirates University Department of Computer and Network Engineering United Arab Emirates

ISBN: (纸本)9798350399431

The brain-computer interface (BCI) and eye-tracking technologies can potentially improve the learning environment in education. Cognitive BCIs can give a deep knowledge of brain functioning, enabling the creation of more effective learning approaches and improving brain-based abilities. This study proposes a neurofeedback strategy based on BCI and eye-tracking to collect factual data (monitoring students' brainwaves and eye movement) and analyze their cognitive capacities during online learning. This study aims to create patterns regarding students' learning behavior based on brain and eye movement responses to learning activities as part of the learning environment. As a result, teachers may adapt to new pedagogical ideas and a more flexible delivery style. © 2023 IEEE.

关键词： Brain

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Predicting Schizophrenia from fMRI Using Deep Learning 1

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3rd International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Kardani, Shail Sharma, Raghav Sharma, Abhishek Deparment of Computer Science Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India Deparment of Electronics and Communication Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India

ISBN: (数字)9789819926022

ISBN: (纸本)9789819926015

Schizophrenia is a complex and multifaceted disorder that is still not fully understood. However, many processes are known to drive its onset, relapse, symptomatology, and therapy. One possible explanation for Schizophrenia is that the person has difficulty using proper analytic methods to deal with the complexity and variety of the condition. Many AI algorithms have been designed and developed over time specifically for Neuroinformatics. These algorithms have been a go-to for many researchers and scientists, even for those in the medical field. Because of these AI-powered algorithms, Scientists are able to detect various diseases, whether they are related to brain activity or any other organs. Deep Learning is yet another field of Machine Learning which is made by the inspiration of how neural networks are connected inside our brain. These are more flexible than the Scikit-Learn-based Machine Learning models as these Deep Neural Network models don’t often require data preprocessing at initial stages. Not only this fact, but also they are used in various places, for example, processing of unstructured data, including texts and images, feature extraction, etc. In this study, we have attempted to detect Schizophrenia from resting-state functional magnetic resonance images using trained Deep Neural Networks as well as Scikit-Learn-based state-of-the-art Machine Learning models. For the dataset, we have utilized the public COBRE dataset available in the nilearn library, along with some public datasets from OpenfMRI. The Deep Learning models detected Schizophrenia with a reliable performance compared to Scikit-Learn-based Machine Learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Magnetic resonance

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Diagnosing Phytophthora infestans infestations on Solanum tuberosum leaves using Machine Learning Classifiers

Diagnosing Phytophthora infestans infestations on Solanum tu...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Pijush Kanti Kumar, Indrajeet Kumar, Sanjeev Kumar, Padmanavan Harshith, John Dutta, Anurag Government College of Engineering and Textile Technology Department of Information Technology Serampore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India G L Bajaj Institute of Technology and Management Department of Master of Computer Applications Greater Noida India Kalinga Institute of Industrial Technology Department of Computer Science and Engineering Bhubaneswar India Vellore Institute of Technology Department of Computer Science and Engineering Vellore Campus Vellore India Government College of Engineering and Textile Technology Department of Computer Science and Engineering Serampore India

ISBN: (纸本)9798350323917

In reaction to recent technology breakthroughs, modern computational fields such as the IoT, Data science, Machine Learning, AI, and several more have arisen. Humans benefit greatly from these fields. In this study, we would employ parts of machine learning to forecast plant illnesses. Particularly amongst the potato leaves, severe Late Blight Disease was discovered. The potato plant, also known as Solanum tuberosum, is a significant crop that is planted all over the world and yields copious amounts of nutritious tubers. The potato is a frequent staple food and has several health benefits. When ingested in moderation, the fluid extracted from tubers can cure stomach ulcers and reduce inflammation and acidity. Both Early and Late Blight are dangerous and widespread potato diseases. We would concentrate on Late Blight in this effort. A fatal fungus that harms potatoes is called Late Blight. It is accessible everywhere in the world. It can be found in areas of the world where potatoes are grown. Winter is the primary growing season for potatoes in India. It is followed by sweltering summer months in the lowlands. Heat and dryness kill the fungus that lives in the soil. The fungus Phytophthora Infestans, the most prevalent agricultural disease that causes the Late blight of potatoes, can quickly result in crop failure if appropriate control measures are not adopted. Potato crop declines in epidemic years might be as high as 80%. We acquired a collection of roughly one thousand samples of Late Blight-affected leaves of potatoes for this investigation. With the help of conventional classifier algorithms, we have developed a machine-learning classification paradigm that may be able to forecast the onset of Late Blight disease. Medical science has developed significantly. Plant pathology could be able to prevent the threat from happening if we could perhaps predict the illness in its early stages. © 2023 IEEE.

关键词： Diseases

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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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Predicting Heart Failure Survival: A Machine Learning Approach with Explainable AI

Predicting Heart Failure Survival: A Machine Learning Approa...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Chowdhury, Aritra Dey, Sourav Hossain, Sohrab Hasan, Mehdi Chowdhury, Sunzida East Delta University Department of Computer Science and Engineering Chittagong Bangladesh Chittagong University of Engineering and Technology Department of Electrical and Electronic Engineering Chittagong Bangladesh East Delta University School of Science Engineering and Technology Chittagong Bangladesh Yeshiva University Graduate School of Computer Science and Engineering New York United States University of Cumberlands Department of Information Science New York United States

ISBN: (纸本)9798331529765

Heart failure is one of the global health issues that is needed for precise prognostic instruments. In order to predict survival and time-to-event outcomes in patients with heart failure, this study assesses machine learning techniques. Most effective prediction models are determined for heart failure prognosis by combining clinical information and evaluating model performance. According to the results, the LightGBM classifier had the lowest accuracy at 77%, while the SVM classifier had the highest accuracy at 88%. These findings suggest that machine learning can greatly improve clinical judgement in cardiovascular medicine and advance patient-focused treatment. © 2024 IEEE.

关键词： Patient treatment

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