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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Shweta Bhattacharjee Porna Md Firoz Kabir Md Imran Chowdhury Rana Md Saiful Islam Sajol Tonmoy Roy Mohammad Aman Ullah Khan Md Al Adnan Girigula Durga Bhavani Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Master’s in Information Technology University of the Cumberlands USA Bachelor of Business Administration International American university Los Angeles California USA Department of Computer Science and Engineering Louisiana State University Louisiana USA Data Analytics & Information Systems Utah State University Logan USA Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Bsc in software engineering Noakhali Science And Technology University School of Computer Science & Engineering (SCOPE) VIT-AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

The classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. This paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. These included 4,000 training samples and 3,524 testing samples. The images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. The suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model’s performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of $100 \%$ on the test set. These findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture.

关键词： Deep learning Training Precision agriculture Accuracy Robustness Convolutional neural networks Tuning Optimization Diseases Testing

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RETRACTED ARTICLE: Obstacles Uncovering System for Slender Pathways Using Unmanned Aerial Vehicles with Automatic Image Localization Technique

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International Journal of Computational Intelligence systems 2023年第1期16卷 1-14页

作者： Selvarajan, Shitharth Manoharan, Hariprasath Khadidos, Alaa O. Shankar, Achyut Khadidos, Adil O. Onyema, Edeh Michael Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University Leeds UK Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK School of Computer Science Engineering Lovely Professional University Phagwara India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Mathematics and Computer Science Coal City University Enugu Nigeria

In this study, unidentified flying machines are built with real-time monitoring in mid-course settings for obstacle avoidance in mind. The majority of the currently available methods are implemented as comprehensive monitoring systems, with significant success in monitored applications like bridges, railways, etc. So, the predicted model is developed exclusively for specific monitoring settings, as opposed to the broad conditions that are used by the current approaches. Also, in the design model, the first steps are taken by limiting the procedure to specific heights, and the input thrust that is provided for take up operation is kept to a minimum. Due to the improved altitudes, the velocity and acceleration units have been cranked up on purpose, making it possible to sidestep intact objects. In addition, Advanced Image Mapping Localization (AIML) is used to carry out the implementation process, which identifies stable sites at the correct rotation angle. Besides, Cyphal protocol integration improves the security of the data-gathering process by transmitting information gathered from sensing devices. The suggested system is put to the test across five different case studies, where the designed Unmanned aerial vehicle can able to detect 25 obstacles in the narrow paths in considered routs but existing approach can able to identify only 14 obstacle in the same routes.

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Prediction of Paddy Plant Height with Vermicompost Fertilizer Treatment on Tidal Land using ANFIS Method 6

Prediction of Paddy Plant Height with Vermicompost Fertilize...

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6th International Conference on Informatics and Computing, ICIC 2021

作者： Rahman, Abdul Ermatita Budianta, Dedik Abdiansah Multi Data Palembang University Department of Informatics Palembang Indonesia Sriwijaya University Department of Information Systems Palembang Indonesia Sriwijaya University Department of Soil Science Palembang Indonesia Sriwijaya University Department of Informatics Engineering Palembang Indonesia

ISBN: (纸本)9781665421553

The main problem in tidal land is high soil acidity, and the availability of nutrients in the soil is relatively low. Utilization of local resource vermicompost is used to improve soil conditions in tidal lands in order to increase crop yields. The parameter of paddy plant height has a very high correlation with paddy yields. This study aims to implement the ANFIS method to predict paddy plant height based on the treatment of vermicompost organic fertilizer. The dataset used for ANFIS training was taken directly from the observation data on the height of the paddy plant and the results of soil laboratory tests. The ANFIS process consists of 5 inputs consisting of fertilizer treatment, pH, N, P, K, and one output, namely paddy plant height. The results obtained from the training data process are that there are 486 rules and the error rate using MAPE is 3.53%, or the accuracy level of the prediction results is 96.47%. © 2021 IEEE.

关键词： Forecasting

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

arXiv

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arXiv 2024年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore

The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 2024, CC BY.

关键词： Generative adversarial networks

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Evaluating Deep Learning Models for Customer Lifetime Value Forecasting Based on Hybrid DT and Naive Bayes Model

Evaluating Deep Learning Models for Customer Lifetime Value ...

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Communication & Computing (WCONF), World Conference on

作者： Nawab Akram Shobana M Harsh R Kathiriya Prasanthi Gottumukkala S. Rathika Rajasree R Magadh Professional Institute Patna Bihar India Department of Computer Science and Business Systems MKumarasamy College of Engineering Karur India Data Scientist at Big Analytixs Atlanta USA Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Science (Artificial Intelligence & Data Science) Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Customer lifetime value, a core concept in direct response marketing for quite some time, is now receiving increasing attention from management at Boston University's School of Management. However, the majority of published works on the topic have mainly concentrated on lauding its usefulness as a decision-making metric, offering only one numerical example of how it is calculated or determined, incorporating it into broader discussions of profitability, and examining its function in decisions regarding customer acquisition and the tensions between acquiring and retaining customers. There are three steps to the suggested method: preprocessing, feature extraction, and model training. After archiving duplicate and missing data, data preparation is employed to eradicate missing values. The feature selection process incorporates K-means, principal component analysis (PCA), and customer lifetime value measurements. An increase in the model training procedure's accuracy was achieved by utilizing the Hybrid DT and naive bayes. The recommended approach achieves a higher accuracy rate of 96.03 % compared to its rivals, which includes naive bayes and Hybrid DT.

关键词： Training Accuracy Profitability Computational modeling Predictive models Feature extraction Bayes methods Numerical models Forecasting Principal component analysis

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An Investigation of Smart Detection for Small Lung Tumor with Tumor Pattern Recognition Algorithm

An Investigation of Smart Detection for Small Lung Tumor wit...

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2023 International Conference on Artificial Intelligence and knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Hariharan, G. Prasanth, P. Devarani, P. Arthi Sajana, T. Indhumathi, C. Kumar, Ashok Malla Reddy University Deportment of Artificial Intelligence and Machine Learning Telangana Hyderabad500100 India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamil Nadu Chennai600062 India R. M K College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Thiruvallur601206 India KoneruLakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram522502 India R.M. K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai601206 India BanasthaliVidyapith Department of Computer Science Rajasthan304022 India

ISBN: (纸本)9798350334357

The Small-cell lung tumor is the prime public concern, resulting in increased mortality. Various therapeutic approaches have made progress in the handling of small-cell lung tumor. It's considered the backbone of cytotoxic chemotherapy treatment. However, clinical research shows that because small-cell lung tumor is chemically approachable, high response rates among small-cell lung tumorinfecters are generally not translated into cure. Therefore, it is high time to make progress in small-cell lung tumor research because every success in clinical trials is fought, and every failure is a lesson. A recent study of small-cell lung tumor has improved the prognosis of patients. Some notable research areas have provided new options for patients by conducting multiple clinical trials. In this paper smart edge detection based small lung tumor identification method was proposed. This model operates with the pattern recognition algorithm model. This algorithm smartly finds the edges of the tumor cells and provides the edge results. This will helpful for the doctors to identify the tumor part exactly. © 2023 IEEE.

关键词： Tumors

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AI-DRIVEN DAY-TO-DAY ROUTE CHOICE

arXiv

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arXiv 2024年

作者： Wang, Leizhen Duan, Peibo He, Zhengbing Lyu, Cheng Chen, Xin Zheng, Nan Yao, Li Ma, Zhenliang Department of Data Science and Artificial Intelligence Monash University Melbourne Australia Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge United States Chair of Transportation Systems Engineering Technical University of Munich Munich Germany School of Civil Engineering The University of Queensland Brisbane Australia Department of Civil Engineering Monash University Melbourne Australia School of Computer Science and Engineering Southeast University Nanjing China Department of Civil and Architectural Engineering KTH Royal Institute of Technology Stockholm Sweden

Understanding travelers’ route choices can help policymakers devise optimal operational and planning strategies for both normal and abnormal circumstances. However, existing choice modeling methods often rely on predefined assumptions and struggle to capture the dynamic and adaptive nature of travel behavior. Recently, Large Language Models (LLMs) have emerged as a promising alternative, demonstrating remarkable ability to replicate human-like behaviors across various fields. Despite this potential, their capacity to accurately simulate human route choice behavior in transportation contexts remains doubtful. To satisfy this curiosity, this paper investigates the potential of LLMs for route choice modeling by introducing an LLM-empowered agent, "LLMTraveler." This agent integrates an LLM as its core, equipped with a memory system that learns from past experiences and makes decisions by balancing retrieved data and personality traits. The study systematically evaluates the LLMTraveler’s ability to replicate human-like decision-making through two stages of day-to-day (DTD) congestion games: (1) analyzing its route-switching behavior in single origin-destination (OD) pair scenarios, where it demonstrates patterns that align with laboratory data but cannot be fully explained by traditional models, and (2) testing its capacity to model adaptive learning behaviors in multi-OD scenarios on the Ortuzar and Willumsen (OW) network, producing results comparable to Multinomial Logit (MNL) and Reinforcement Learning (RL) models. Additionally, the study assesses lightweight, open-source LLMs, highlighting their effectiveness in route choice simulation and their potential as cost-effective alternatives to more advanced closed-source models. These experiments demonstrate that the framework can partially replicate human-like decision-making in route choice while providing natural language explanations for its decisions. This capability offers valuable insights for transportation poli

关键词： Reinforcement learning

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SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Nfissi, Alaa Bouachir, Wassim Bouguila, Nizar Mishara, Brian Data Science Laboratory Montréal Canada Concordia University Concordia Institute for Information Systems Engineering Montréal Canada Centre for Research Intervention on Suicide Ethical Issues End-of-Life Practices Montréal Canada University of Québec at Montréal Psychology Department Montréal Canada

In the field of human-computer interaction and psychological assessment, speech emotion recognition (SER) plays an important role in deciphering emotional states from speech signals. Despite advancements, challenges persist due to system complexity, feature distinctiveness issues, and noise interference. This paper introduces a new end-to-end (E2E) deep learning multi-resolution framework for SER, addressing these limitations by extracting meaningful representations directly from raw waveform speech signals. By leveraging the properties of the fast discrete wavelet transform (FDWT), including the cascade algorithm, conjugate quadrature filter, and coefficient denoising, our approach introduces a learnable model for both wavelet bases and denoising through deep learning techniques. The framework incorporates an activation function for learnable asymmetric hard thresholding of wavelet coefficients. Our approach exploits the capabilities of wavelets for effective localization in both time and frequency domains. We then combine one-dimensional dilated convolutional neural networks (1D dilated CNN) with a spatial attention layer and bidirectional gated recurrent units (Bi-GRU) with a temporal attention layer to efficiently capture the nuanced spatial and temporal characteristics of emotional features. By handling variable-length speech without segmentation and eliminating the need for pre or post-processing, the proposed model outperformed state-of-the-art methods on IEMOCAP and EMO-DB datasets. The source code of this paper is shared on the Github repository: https://***/alaaNfissi/SigWavNet-Learning-Multiresolution-Signal-Wavelet-Network-for-Speech-Emotion-Recognition. © 2010-2012 IEEE.

关键词： Discrete wavelet transforms

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Trans-Bifurcation Prediction of Dynamics in terms of Extreme Learning Machines with Control Inputs

arXiv

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arXiv 2024年

作者： Tadokoro, Satoru Yamaguchi, Akihiro Namiki, Takao Tsuda, Ichiro Education and Research Center for Mathematical and Data Science Hokkaido University Sapporo Japan Department of Information and Systems Engineering Fukuoka Institute of Technology Fukuoka Japan Department of Mathematics Faculty of Science Hokkaido University Sapporo Japan AIT Center Sapporo City University Sapporo Japan

By extending the extreme learning machine by additional control inputs, we achieved almost complete reproduction of bifurcation structures of dynamical systems. The learning ability of the proposed neural network system is striking in that the entire structure of the bifurcations of a target one-parameter family of dynamical systems can be nearly reproduced by training on transient dynamics using only a few parameter values. Moreover, we propose a mechanism to explain this remarkable learning ability and discuss the relationship between the present results and similar results obtained by Kim et al. Predicting the behaviors of dynamical systems is critical in various scientific fields. The recent advancements in machine learning have enabled the prediction of dynamical system trajectories for fixed parameter values. However, the accuracy of predicting behavioral changes due to changes in parameter values remains insufficient. In this study, we extend the extreme learning machine, a neural network-based technique, to enhance its capabilities for predicting behavioral changes in response to changes in parameter values. We subsequently evaluate the predictive performance of this extended model for a variety of dynamical systems, thereby elucidating its underlying mechanisms. © 2024, CC BY.

关键词： Adversarial machine learning

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Unsupervised Domain Adaptation via Domain-Adaptive Diffusion

arXiv

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arXiv 2023年

作者： Peng, Duo Ke, Qiuhong Lei, Yinjie Liu, Jun The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore The Department of Data Science and AI Monash University ParkvilleVIC3010 Australia The College of Electronics and Information Engineering Sichuan University Chengdu610065 China

Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by diffusion models which have strong capability to gradually convert data distributions across a large gap, we consider to explore the diffusion technique to handle the challenging UDA task. However, using diffusion models to convert data distribution across different domains is a non-trivial problem as the standard diffusion models generally perform conversion from the Gaussian distribution instead of from a specific domain distribution. Besides, during the conversion, the semantics of the source-domain data needs to be preserved for classification in the target domain. To tackle these problems, we propose a novel Domain-Adaptive Diffusion (DAD) module accompanied by a Mutual Learning Strategy (MLS), which can gradually convert data distribution from the source domain to the target domain while enabling the classification model to learn along the domain transition process. Consequently, our method successfully eases the challenge of UDA by decomposing the large domain gap into small ones and gradually enhancing the capacity of classification model to finally adapt to the target domain. Our method outperforms the current state-of-the-arts by a large margin on three widely used UDA datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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