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ReIoT-Powered Smart Agriculture in Controlled Environments

ReIoT-Powered Smart Agriculture in Controlled Environments

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rao, Phuke Rakesh Sandhu, Ramandeep Islam, Sardar M N Lovely Professional University School of Computer Application Phagwara Punjab Jalandhar India Lovely Professional University School of Computer Science and Engineering Phagwara Punjab Jalandhar India Institute for Sustainable Industries & Liveable Cities Victoria University Australia

ISBN: (纸本)9798331508685

The Internet of Things (IoT) is crucial in improving the agricultural sector. To achieve sustainability and sustained production an innovative approach should be planned, tested, implemented, and proposed to achieve the targeted goals parameters such as pollution, lack of water, and drastic climate changes should be analyzed. This study provides information on how to alleviate these problems, and as a result attain the proposed controlled environment with good-quality air, water, and climate is needed. If these goals cannot be achieved globally, alternate solutions must be set to match current climatic conditions and which crops can sustain unaltered weather patterns and disasters. An in-depth analysis is needed to fine-tune these areas. One application of IoT is the development of a system called "Plants for Survival"(P4S) which aims to solve outdoor problems through detailed studies in a controlled environment. This case study helps predict climatic changes and develops appropriate solutions in a prototype setup. The Data-Driven Development Methodology for IoT-based Systems guides P4S, with quality checks and surveys to ensure efficiency and effectiveness Technology and Parameters that influence weather conditions should be considered while testing in a simulated environment. © 2025 IEEE.

关键词： Smart agriculture

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AI for Disaster Management: Predictive Models and Response Strategies

AI for Disaster Management: Predictive Models and Response S...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Shameem, A. Saroja, Devupalli Hotkar, Damodar S. Nayak, Bijaya Bijeta Pandey, Anand Tripathi, Sachin Amet University Amet Business School India Department of Computer Science and Engineering Visakhapatnam India R.T.E. Society's Rural Engineering College Department of Computer Science and Engineering Karnataka Hulkoti582205 India Kiit Deemed to Be University School of Mechanical Engineering Odisha Bhubaneswar India Sharda University Computer Science and Application U.P Greater Noida India Wathoda Symbiosis Law School Nagpur Maharashtra Nagpur India

ISBN: (纸本)9798331508685

The application of artificial intelligence (AI) in managing severe weather events has become more necessary in recent years owing to their increased frequency. Disaster management is required to control and avoid such occurrences. To successfully aid in better and quicker reactions to catastrophes, AI is widely employed in prediction and disaster preparation, damage mitigation and minimization, and rescue phases. To determine how AI technology can be used to lessen the damaging impacts of different disasters, this article looks into the potential for connecting artificial intelligence methods based on technological innovation. The study potentially contributes in designing an effective framework for disaster management based on the predictive accuracy and response time. © 2025 IEEE.

关键词： Disaster prevention

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Real-Time Connectivity with the Industrial Internet of Things 15

Real-Time Connectivity with the Industrial Internet of Thing...

引用

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

作者： Jayakumar, Sujayaraj Samuel Kumar, Ashwini Kharbas, Vikas Kumar School of Sciences Department of Forensic Science Karnataka Bangalore India Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

The economic net of things (Eliot) is an emerging era permitting the relationship and optimization of devices, machines, and belongings in industrial settings via statistics switch and exchange. Real-time connectivity with the Iota has revolutionized how business systems perform and has opened the door for full-size upgrades in productiveness, performance, and protection. With actual-time connectivity, commercial operations were capable of advantage near real-time perception into gadget performance, surroundings conditions, and the behavior of machines and property. It interprets into improved situational cognizance, higher decision-making main to optimized operational processes, and stepped forward usual operational visibility. Furthermore, actual-time connectivity with the Eliot is likewise allowing superior information analytics capabilities, taking into account the improvement of predictive analytics fashions, optimization of plant operations, and the gathering of critical information factors. All of those traits have improved the efficiency, scalability, and reliability of business operations and networks, permitting companies to higher-utilize their assets and become greater efficient. © 2024 IEEE.

关键词： Predictive analytics

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Pancreatic Cancer Diagnosis from CT Scan Images Using Machine Learning Methods 7

Pancreatic Cancer Diagnosis from CT Scan Images Using Machin...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kashikar, Arya Maurya, Sudhanshu Likhar, Tirthak Mirza, Kashish Yadav, Ajay Kumar Asudani, Deepak Suresh Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University Department of Computer Science & Engineering Bhimtal Campus Uttarakhand India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798350350067

This research focuses its objectives on the use of machine learning techniques in the detection of images of a CT scan to diagnose Pancreatic cancer. The work used an extensive database containing diverse CT images, more specifically, concerning pancreatic cancer patients. Two primary methodologies were implemented in this study. The first was the Support Vector Machine (SVM) classifier, one of the most used classifier methods in classification problems. The second component is the Convolution Neural Network (CNN), which has ResNet 50 at its base. The architecture of this model is commendably profound, and it can perceive the details of the images. The SVM classifier marked a precision of 83.07% for detecting cancer;such a level of accuracy can give clues, though it is still possible to define additional possibilities for improving the classification of complicated cases. On the other hand, the CNN model based on the ResNet50 learned a more accurate model than the training accuracy of 90. 01%. Such a performance increase is explained quite well by deep learning methodologies' capabilities, especially in medical image analysis. These studies draw attention to the possibility of utilizing more sophisticated CNN models to promote the accuracy and reliability of pancreatic cancer diagnosis. The increased accuracy also has ramifications for the diagnostic phase of pancreatic cancer, but also it emphasizes the necessity of using complex algorithms for early identification of this disease. © 2024 IEEE.

关键词： computerized tomography

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Hybrid Transformer-CNN Model for Automated Diagnosis of Pomegranate Fruit Diseases 10

Hybrid Transformer-CNN Model for Automated Diagnosis of Pome...

引用

10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Tiwari, Raj Gaurang Misra, Alok Maheshwari, Himani Agarwal, Ambuj Kumar Sharma, Manish Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University School of Computer Application Punjab Phagwara India Graphic Era Hill University School of Computing Uttrakhand Dehradun India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350353778

Improving crop quality and output relies on early and precise disease diagnosis in pomegranate fruits. This study presents a new Feature Fusion Network for disease classification in pomegranate fruits, which combines Transformer and CNN modules. The proposed model classifies pomegranate fruit into five categories that are Healthy, Bacterial Blight, Anthracnose, Cercospora Fruit Spot, and Alternaria Fruit Spot. While the CNN module is great at collecting local visual aspects, the Transformer module is used to gather global contextual information. The model uses both global and local information for robust disease categorization by combining two complimentary feature representations. Adam, Adadelta, Gradient Descent, and RMSProp optimization algorithms are used to fine-tune the suggested Feature Fusion Network. After extensive training with Adam Optimizer, the suggested method attained a maximum accuracy of 96.45% in disease classification of pomegranate fruits. In addition, when the performance of the proposed model is compared with other popular deep learning architectures like ResNet, Inception, VGG, and MobileNet, it outperforms. © 2024 IEEE.

关键词： Fruits

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Fault Diagnosis and Region Detection of CCD Image in Double Delay Three-Step Autocorrelator 4

Fault Diagnosis and Region Detection of CCD Image in Double ...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Li, Suicheng He, Zizhou Shen, Wenwen Xia, Yanwen Ni, Zhigao Zhou, Xiaowei Institute of Computer Application CAEP Department of Computer Engineering and Science Sichuan Province China Research Center of Laser Fusion CAEP Department of Optical Instrument Sichuan Province China

ISBN: (纸本)9798350324303

With the development of national optical experimental facilities, countries worldwide have invested significant amounts of funding into constructing various facilities. Among them, the double delay three-step autocorrelator is an important instrument for measuring the ultrashort pulse laser waveform and is frequently used in laser experiments. Because electronic instruments are too slow to measure directly, we can only use CCD images to reconstruct the laser waveform. Therefore, CCD, in particular, plays a vital role in the closed-loop control system of double delay three-step autocorrelator, and CCD image quality and processing can directly affect the measurement results of the pulse laser. In this paper, we propose an image feature-based CCD fault diagnosis and effective region detection method for the double delay three-step autocorrelator. This fault diagnosis method first calculates the pixel sum of each row and analyzes the primary pixel distribution of each frame which could represents the main features of CCD image. Next, we design a fault diagnosis model to classify those features and pick out the suitable image. Finally, we analyze and process the above image and design a boundary detection algorithm to detect the effective region for further waveform recovery algorithm. In addition, to verify performance of the proposed method, we conduct a significant number of experiments. Through qualitative and quantitative analyses of the experimental results, we verify the effectiveness and robustness of the proposed method for CCD fault diagnosis. © 2023 IEEE.

关键词： Failure analysis

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The development of a new edge computing device based on deep learning YOLOv8 algorithm for diabetic retinopathy Detection 5

The development of a new edge computing device based on deep...

引用

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Singh, Devendra Mohan, Lalit Pant, Janmejay Mittal, Amit Dobhal, Dinesh C. Graphic Era Deemed To Be University Department Of Computer Science And Engineering Dehradun India Graphic Era Hill University Department Of Computer Science And Engineering Bhimtal India Graphic Era Hill University Department Of Allied Science Bhimtal India Graphic Era Deemed To Be University Department Of Computer Science Application Dehradun India

ISBN: (纸本)9798350330861

Diabetic retinopathy is a prevalent eye disorder that occurs in individuals with diabetes. The condition is caused by damage to the retinal blood vessels caused by elevated blood sugar levels. The retina is the tissue behind the eye that is sensitive to light. Among the leading causes of blindness worldwide is diabetic retinopathy. However, early detection and prompt treatment can effectively prevent or minimize vision loss. Deep learning algorithms, such as 'You Only Look Once' (YOLO) Convolutional Neural Networks (CNNs), have demonstrated state-of-the-art performance on benchmark datasets and computer vision competitions. YOLO is a single end-to-end model that enables real-time object identification with high accuracy. Our proposed YOLOv8-based model integrated with Jetson Nano Developer Kit with CSI camera module shows the highest top1 accuracy of 93.77 on the training dataset and validation dataset with all given classes its measured accuracy top1 accuracy is 72.6 with 2.6ms inference time. © 2023 IEEE.

关键词： Learning algorithms

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Enabling Clinical Decision-Making in Smart Healthcare through AI/ML Algorithms 15

Enabling Clinical Decision-Making in Smart Healthcare throug...

引用

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

作者： Kharbas, Vikas Kumar Meena, Y.R. Thakur, Praveen Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Arka Jain University Department of Law Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

Using AI/ML algorithms in clever healthcare has emerged as a promising improvement to facilitate green and effective medical choice-making. This novel era uses gadgets mastering and predictive analytics to extract deep insights from numerous assets of healthcare statistics, together with clinical statistics, development, imaging, and laboratory results. through leveraging huge datasets and effective algorithms, AI/ML-primarily based structures are capable of extra as it should be and suddenly become aware of correlations between elements that contribute to the development of diverse sicknesses. it can permit the implementation of more evidence-based interventions and customized treatments tailored to an individual-affected character's health profile. moreover, AI/ML algorithms can also robotize the scientific workflow, contemplating the automated detection and prognosis of sure illnesses and medical conditions. As a result, AI/ML structures can seriously lessen healthcare prices and enhance patient consequences. in the end, using AI/ML algorithms in smart healthcare can empower clinical vendors to make more informed decisions approximately the analysis and treatment of their patients. © 2024 IEEE.

关键词： Consisting Customized Healthcare Implementation Improvement Interventions Resources Structures

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Correlation Between 3D Magnetic Resonance Imaging and Clinical Outcomes in Traumatic Brain Injury 15

Correlation Between 3D Magnetic Resonance Imaging and Clinic...

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

作者： Srikantha, H. Joseph, Ginu Anie Yadav, Surendra School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Arka Jain University Department of Nursing Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

This article identifies a correlation between the 3D Magnetic Resonance Imaging (MR) of patients with worrying brain harm (TBI) and the patient's medical results. The research team compares 3-D MR imaging outcomes with the patient's observe-up go-to facts, including neurologic assessments, Glasgow Coma Scale rating, and other parameters. The effects confirmed that patients who had a better degree of injury discovered from three-D MR scans had worse scientific effects in comparison to sufferers who had milder harm. This correlation became great for acute and subacute TBI patients. The authors endorse that 3-D MR scans can be used to predict the clinical results of TBI patients and can have the potential to inform treatment selections. © 2024 IEEE.

关键词： Article Correlation Glasgow Identifies Magnetic Resonance

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Improving Cancer Prediction in Recommender Systems through Gradient Boosting 15

Improving Cancer Prediction in Recommender Systems through G...

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

作者： Sengupta, Pompi Das Kharbas, Vikas Kumar Adarsha, H. Arka Jain University Department of Management Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

The analysis of various styles of most cancers is frequently reliant on accurate prediction models. Recommender systems are powerful equipment that allows the prediction of cancer development and recurrence based totally on information accumulated from affected person information. But, standard feed-forward neural networks (FFNNs) utilized in current cancer prediction fashions are limited via their potential to capture subtle nonlinear relationships among affected person variables. Gradient boosting is a device gaining knowledge of set of rules that makes use of an ensemble of weak selection bushes to produce an effective predictive version. This have a look at examines the capability for boosting algorithms to enhance the predictive ability of the FFNN fashions currently used in recommender structures for predicting most cancers progression and recurrence. The outcomes of our test reveal that boosting algorithms are able to boom model accuracy over FFNNs whilst additionally lowering training time. Moreover, nonlinear relationships between affected person variables are captured extra successfully by means of boosting than with FFNNs. those findings keep crucial implications for the development of powerful predictive models for cancer progression and recurrence. © 2024 IEEE.

关键词： Cancer Implications Networks Neural Predictive Produce Recurrence Relationships

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