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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Saniya Saratkar Meher Langote Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The availability of computing resources and services through the Internet is a revolutionary approach to delivering computer resources. It is considered by many managers and experts as capable of making changes in the state and quality of health care services research, and health information technology. technology has been described as a revolution in the health sector in which cloud computing is a major development that offers hitherto opportunities for creativity, efficiency, and cooperation. In this article, the author exposes the possible changes brought by cloud computing in the context of healthcare services. It analyses the several ways cloud computing could potentially revolutionize the delivery of healthcare services concerning enhanced data storage and processing, modularity, and open access. Cloud computing is a modern approach to delivering resources and services that focus on computing. It is thought that many managers and experts have the potential to enhance healthcare services and research and change health information technology. Cloud computing is among the most revolutionary technical innovations applied in the healthcare sector, creating unprecedented creativity, value, and synergism. The following article aims to investigate the enabling role of cloud computing in the UK healthcare services industry. They look at the different approaches that cloud computing is likely to deliver for the provision of healthcare in terms of accessibility, scalability, data management, and even analysis.

关键词： Cloud computing Technological innovation Open Access Scalability Medical services Transforms Regulation Stakeholders Information technology Creativity

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Smart Wheelchair Based on Voice Recognition for Physically Disabled People 4

Smart Wheelchair Based on Voice Recognition for Physically D...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Deekan, S. Manjula, S. Pown, M. Velu, C.M. Aadarsh, K.A.S. Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai India Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India Chennai India

ISBN: (纸本)9798331508845

An advanced automated system for wheelchairs has been developed to improve the mobility and independence of individuals who have physical disabilities, including those who have been affected by paralysis, polio, or limb loss. The project has been designed to provide a user-friendly experience for people who have limited hand movements, and it incorporates voice recognition technology. The wheelchair system allows for smooth navigation in different directions based on voice commands, and it comes with interactive features to enhance functionality. The main aim of the proposed system is to promote voice recognition and to enable great communication between the user and the device. This means that users can easily access the wheelchair where they want to go within a specific location, such as a bedroom, and it will autonomously navigate to the designated area. The wheelchair system also integrates with the environment by allowing users to operate household fixtures such as fans and lights through voice commands. This interactive functionality is particularly useful for individuals with physical challenges, as it enhances their overall experience by adding a new level of convenience and accessibility. Overall, this innovative wheelchair system describes an advancement in assistive technology, providing a comprehensive solution to address the transportability needs of individuals with physical disabilities. The project makes use of an ESP32 Microcontroller, Voice recognition module, wheelchair mechanism, and Relay board, and it has been successfully implemented to benefit physically disabled individuals who are using the wheelchair. © 2024 IEEE.

关键词： Wheelchairs

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Cross-Modality Comprehensive Feature Aggregation Network for Road Scene Segmentation 8

Cross-Modality Comprehensive Feature Aggregation Network for...

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8th International Symposium on Computer science and Intelligent Control, ISCSIC 2024

作者： Yang, Yale Zhang, Zihao Hou, Huifang Zhang, Wenwen Zhuang, Chunsheng Henan University of Technology College of Information Science and Engineering Zhengzhou China Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control School of Artificial Intelligence and Big Data Zhengzhou China Data Henan University of Technology School of Artificial Intelligence and Big Zhengzhou China Henan Academy of Sciences Zhengzhou China

ISBN: (纸本)9798350380286

RGB-thermal semantic segmentation is a potential solution for reliable semantic scene understanding under adverse weather and complex lighting conditions. However, past studies have primarily focused on the design of fusion paradigms. Unfortunately, these methods have not fully considered the modality differences caused by different imaging mechanisms, thereby reducing the performance of pixel-level semantic analysis. In response to this issue, this paper proposes an innovative Cross-Modality Comprehensive Feature Aggregation Network (CFANet). This method first designs a Cross-Modality Feature Rectification Module (CFRM) to align modality differences;then, it constructs a Global-Local Fusion Module (GLF) to sharpen object edges and refine the segmentation map. Results on two urban scene understanding datasets demonstrate that CFANet can effectively aggregate multi-layer deep features and outperform existing advanced deep learning methods in scene understanding performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Exploring Deep Neural Networks for Accurate Diabetic Retinopathy Prediction

Exploring Deep Neural Networks for Accurate Diabetic Retinop...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Kujani, T. Arivubrakan, P. Raju, K. Sankar, P. Mani Reddy, B. Surekha Bhavana, E. Computer Science And Engineering Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India Artificial Intelligence And Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India Artificial Intelligence And Data Science Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Chennai India

ISBN: (纸本)9798350352689

Diabetic Retinopathy (DR) emerges as a serious complication associated with diabetes mellitus, causing damage to retinal blood vessels, and resulting in vision loss. The critical need for early detection and effective management to curb the progression towards vision impairment and potential blindness underscores the significance of this issue. To address these challenges, have propose a novel deep learning system utilizing a Densely connected Convolutional Network (DenseNet-201) for the efficient detection of DR. This model is specifically trained to classify fundus images into six distinct categories: no DR, mild, moderate, and severe, proliferative DR, and hard exudates. The datasets utilized in this study include the publicly available Diabetic Retinopathy Detection 2015 and Aptos 2019 blindness detection datasets from Kaggle. The proposed system comprises multiple stages, including data Collection, Preprocessing, Augmentation, and Modeling. During the preprocessing phase, normalization techniques are to eliminate biases or distortions present in the fundus images. Furthermore, the augmentation of data is employed to enhance the diversity and quantity of the training dataset. In the modeling stage, have designed and implemented the deep learning model, with the robust and high-performing DenseNet-201 architecture selected as the model's backbone. The model exhibits a significant accuracy of 87.7% in its classification task. Additionally, have introduced a Deep Neural Network (DNN) model, achieving an impressive accuracy rate of 95.8%. These results underscore the effectiveness of our proposed system in accurately and efficiently detecting DR. The primary objective of this research is to develop a reliable and automated system for Diabetic Retinopathy detection, with the overarching goal of improving the quality of life for individuals affected by diabetes. © 2024 IEEE.

关键词： Deep neural networks

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Deep Learning for Disease Identification in Cassava Plants Using RGB Imaging 6th

Deep Learning for Disease Identification in Cassava Plants U...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Sutar, Shivam Mohite, Snehal Kamble, Tushar Kolhar, Shrikrishna Jagtap, Jayant Shastri, Rajveer Joshi, Shubham Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology Maharashtra Baramati India Maharashtra Pune India Marik Institute of Computing Artificial Intelligence Robotics and Cybernetics NIMS University Rajasthan Jaipur303121 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819778614

Agriculture is the most critical sector in the world economy. Food security and consistent food supply are the key challenges due to rapid growth in the world population and crop diseases. Crop yield gets significantly hampered due to diseases attacking large crop fields. The precise classification of plant diseases is essential to provide appropriate pesticide supplements. This study applies deep learning (DL) models to identify diseases in cassava plants using red–green–blue (RGB) images. Three models, namely Xception-net, MobileNetV2, and EfficientNetB0, are trained and evaluated on the cassava disease dataset publicly available on Kaggle. Since the dataset is imbalanced, five-fold cross-validation is used to check the consistency of the results. Transfer learning and random weight initialization were used for model training. The Xception-net is the clear winner among the three models, with an accuracy of 87.8% using transfer learning, 81.33% using random weight initialization, and 85% average accuracy using five-fold cross-validation. Further, this work is extended to develop web and mobile applications with DL models deployed at the backend to predict plant diseases from input images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Plant diseases

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Multi-Agent Reinforcement Learning for Crop Planning Model with Interactive Multi-Stakeholder

Multi-Agent Reinforcement Learning for Crop Planning Model w...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Mengmeng Wang, Haoyu Wang, Xiujuan Hua, Jing Kang, Mengzhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350368604

Current crop planning models aim to optimize planting schemes for farmers to maximize their social and ecological benefits within specified constraints, or to optimize planting structure schemes for related departments. However, farmers’ planting strategy is closely tied to national planting subsidy policies, and governmental optimization and adjustment of planting structures require farmers’ compliance. Focusing solely on the planting plans of either farmers or relative departments may not effectively address real-world scenarios. We propose an Interactive Multi-Stakeholder Crop Planning Model (IMSCPM) based on multi-agent reinforcement learning approaches. This model aims to provide specialized crop planning recommendations to individual farmers while simultaneously offering guiding policy suggestions to relevant regional departments. Stakeholders involved in the crop planning process are represented as multiple intelligent agents with specific objectives, allowing them to interact with each other. To address challenges posed by the proliferation of network input dimensions, the problem of strategy pursuit between two types of agents and the instability of the multiagent environment caused by the excessive number of agents in the Regional-Farmer planting planning model, we introduce the methods of "Integrated observation", "Asynchronous training" and "Planting consultant". We constructed a multi-stakeholder interactive crop planning environment, and obtained strategies that are stable and consistent with the values pursued by each agent. The results demonstrate that our crop planning model based on this novel framework is effective for agricultural system with multi-stakeholders. © 2024 IEEE.

关键词： Multi-Agent Multi-Stakeholder Regional-Farmer Crop Planning Model Reinforcement Learning

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Comparative Analysis of Customised Neural Network Model and ML Classifiers for Brain Stroke Prediction

Comparative Analysis of Customised Neural Network Model and ...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Tejashree, S. Varshitha, S. Kodipalli, Ashwini Rao, Trupthi Kumarswamy, S. Global Academy of Technology Department of Cse Karnataka Bangalore India Global Academy of Technology Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

An abrupt interruption of the brain's blood supply leads to stroke, which causes severe damage to the brain. Due to this condition, there are people disabled and thus leading to death. In this work, the dataset contains ten features where environmental tests are done for the stroke which is utilized to evaluate the efficiency of Machine Learning classifiers and Deep Learning artificial Neural Network. The purpose of this paper is to provide a forecast of the likelihood of a brain stroke occurring at an early stage using Machine Learning and Deep Learning techniques. The accuracy and efficiency of various types of Machine Learning models and customized Deep Learning models have been compared based on this impression. The custom Deep Learning model here used is an artificial Neural Network with dropouts having the highest accuracy of 97.06% using RMSprop optimizer and a Decision Tree Machine Learning Classifier, achieved the highest accuracy of 95.98%. © 2023 IEEE.

关键词： Statistical tests

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Learning Approach 12

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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Metaheuristic Optimization for Path Planning in UAV Networks for Long-Distance Inspection Tasks 13

Metaheuristic Optimization for Path Planning in UAV Networks...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Akshya, J. Manochitra, M. Indu Poornima, R. Jeevitha, T. Saravanakumar, M. Sundarrajan, M. Choudhry, Mani Deepak Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Coimbatore India Institute of Technology Department of Information Technology KGiSL Coimbatore India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Deportment of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

With broad applicability in surveillance, delivery, and environmental monitoring, multi-drone path planning has received a great deal of attention recently. So far, wide applicability has been explored by various applications of Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, which have already established reliable solutions to navigation and coverage through drone path optimization. However, in dynamic, obstacle-dense environments, these algorithms fail to adapt in real-time with energy efficiency. Traditional GA and PSO approaches can be effective for generating an initial path but fail to respond to collision avoidance and efficient resource management. In complex and unpredictable conditions, ACO fails to converge rapidly, which leads to suboptimal paths and increased energy consumption. To overcome such barriers, we present here an HGARL for enhanced multi-drone path optimization. The proposed algorithm thus combines the population-based search capability of GA with RL's adaptive learning ability, therefore providing an algorithm that in addition to finding optimal paths adjusts the paths according to changing environmental situations. HGARL presents better performance compared to existing methods. In path convergence, it reaches up to 15%, and in energy consumption, it reduces up to 20% with safe collision-free navigation. The strong potential of HGARL for robust, efficient, and adaptive solutions of real-time multi-drone operation in complex and dynamic environments has set a new benchmark for UAV path planning. © 2024 IEEE.

关键词： Genetic algorithms

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