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A multimodal approach for enhanced disease management in cauliflower crops:integration of spectral sensors, machine learning models and targeted spraying technology

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Frontiers of Agricultural science and engineering 2025年第2期12卷 261-273页

作者： Rohit ANAND Roaf Ahmad PARRAY Indra MANI Tapan Kumar KHURA Harilal KUSHWAHA Brij Bihari SHARMA Susheel SARKAR Samarth GODARA Shideh MOJERLOU Hasan MIRZAKHANINAFCHI Division of Agricultural Engineering ICAR-IARI Vasantrao Naik Marathwada Krishi Vidyapeeth Central Arid Zone Research Institute Division of Vegetable Sciences ICAR-IARI Division of Design of Experiments ICAR-IASRI Division of Computer Applications ICAR-IASRI Department of Agroecology-Entomology and Plant Pathology Aarhus University Electrical Engineering and Computer Science Daktronics Engineering Hall South Dakota State University

This research explored a novel multimodal approach for disease management in cauliflower crops. With the rising challenges in sustainable agriculture, the research focused on a patch spraying method to control disease and reduce crop losses and environmental impact. For non-destructive disease assessment, a spectral sensor was used to collect spectral information from diseased and healthy cauliflower parts. The spectral data sets were analyzed using decision tree and support vector machine（SVM） algorithms to identify the most accurate model for distinguishing diseased and healthy plants. The chosen model was integrated with a low-volume sprayer(50-150 L·ha-1),equipped with an electronic control unit for targeted spraying based on sensor-detected regions. The decision tree model achieved 89.9% testing accuracy, while the SVM model achieved 96.7% accuracy using hyperparameters: cost of 10.0 and tolerance of 0.001. The research successfully demonstrated the integration of spectral sensors, machine learning, and targeted spraying technology for precise input ***, the optimized sprayer achieved a 72.5% reduction in chemical usage and a significant time-saving of 21.0% compared to a standard sprayer for black rot-infested crops. These findings highlight the potential efficiency and resource conservation benefits of innovative sprayer technology in precision agriculture and disease management.

关键词： Disease management site-specific sprayer spectral sensor machine learning models cauliflower crop black-rot disease

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Improved AO based Classification Framework for Eye Diseases

Improved AO based Classification Framework for Eye Diseases

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Binju Saju V Asha Jincy C Mathew Sengcheng Sangma Shouvik Ghosh Sivaguru K Department of Computer Science - AI Engineering Adi Shankara Institute of Engineering & Technology Kalady Kerala Master of Computer Applications New Horizon College of Engineering Bengaluru India

A sensory organ that reacts to visible light, the human eye is a component of the sensory nervous system. A new approach is needed since, although physical detection of the eye condition is still a possibility, it is challenging and time-consuming. The study recommends a hybrid paradigm to classify various eye illnesses. Prior to further processing, pictures are first reduced in size, made grayscale, and noise-free using a combination of the Weiner and Median filters. Histogram equalization based on a Gaussian Mixture is also used to enhance pictures. Picture features are recovered with the aid of a thick convolutional neural network. An improved Aquila optimized ResNet101 model is used for classification. The effectiveness of the models is contrasted with that of related studies in the literature. A F1 score of 98.18%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 98.91%, and precision of 98% were attained by the suggested model.

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Complex Event Processing in Web Streams with Ontology-Based Abstraction Layers for Smart City Frameworks 2

Complex Event Processing in Web Streams with Ontology-Based ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Krishna, M Hari Manjunatha Kumar, Rakesh Singh, Navdeep Kumar, Ashwani Ftaiet, Adnan Allawi Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

Smart cities embody the integration of information technology, urban infrastructure, and citizen engagement to optimize city functions and drive economic growth. A critical component in achieving this integration is the ability to process complex events in real-time, particularly from diverse web streams. This paper introduces a novel framework for Complex Event Processing (CEP) in web streams by employing ontology-based abstraction layers, aiming to enhance the interpretability and interoperability of heterogeneous data sources within smart city frameworks. The proposed approach uses a layered architecture where the bottom layer deals with raw data streams from various sources, including IoT devices, social media, and sensor networks. The middle layer employs an ontology-based model to abstract and enrich the raw data, providing a unified and semantic representation. This representation facilitates the identification and correlation of complex events, which are pertinent to city management and planning. The top layer involves the application of advanced data mining techniques to predict, detect, and respond to urban events in a timely and efficient manner. Through this methodology, the framework addresses the challenges of scalability, semantic heterogeneity, and real-time processing needs inherent in smart city applications. Empirical evaluations demonstrate the efficacy of the approach in various urban scenarios, showing promising improvements over existing methods. This work lays a foundational architecture for future research and development in smart city technologies, aiming to make urban areas more livable, sustainable, and efficient. © 2024 IEEE.

关键词： Complex Event Processing Data Mining Ontology-Based Abstraction Smart Cities Web Streams

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Delay Minimization using Cooperative Localization to Avoid Multiple Vehicle Collision in Vehicular Adhoc Networks

Delay Minimization using Cooperative Localization to Avoid M...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Kalist, V. Bhargavi, V. Sharmila Nirmaladevi, G. Jayakumar, T. Srithar, V. Sathyabama Institute of Science and Technology Department of ECE Chennai India R.M.K. Engineering College Department of CSE Chennai India Sri Sairam Institute of Technology Department of CSE Chennai India KCG College of Technology Department of Mechatronics Engineering Chennai India PeriyarManiammai Institute of Science and Technology Department of Computer Science and Applications Vallam India

ISBN: (纸本)9798350319200

Vehicular Ad Hoc Networks (VANETs) are currently evolved in an effective manner and it occupies huge number of applications in intelligent transmission system. In general vehicles in this technology consist of certain characteristics like high speed with random mobility and unpredictable topological changes which cause high collision and delay among the vehicles at the time of data transmission. In order to attends table communication it becomes essential to reduce the vehicle collision and delay occurrences. For that purpose in this article delay minimization using cooperative localization and collision avoidance (DMCL-VANETs) is concentrated. The core process of this model is effective system model construction, delay minimization process and cooperative localization based routing model. Through this process the delay and network collision is greatly reduced and that provide the way to increase the network overall performance and stability. The implementation of DMCL-VANETs is performed in NS2 and from the output analysis it is prone that the developed model is performed better than terms of delivery ratio on delay when contrasted with the previous baseline methodologies like RMJM-VANETs and NSSC-VANETs. © 2023 IEEE.

关键词： Collision Avoidance and Effective System Model Construction Delay Minimization Using Cooperative Localization Vehicular Ad Hoc Networks (VANETs)

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Anomaly Detection in Conventional Meters and Electricity Consumption Using Optimized Extreme Gradient Boosting 3

Anomaly Detection in Conventional Meters and Electricity Con...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Alzubaidi, Laith H. Sathyavani, B. Mamatha Bai, B.G. Dutta, Papiya Saranya, N. Naga College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Cse Karnataka Bengaluru India Bharat Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Hyderabad India Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

With the progress of innovative information technologies, smart energy meters have designed a substantial quantity of real-time electricity consumption. Energy consumption is based on the various factors such as humidity, building area, temperature, habitation and so on and these factors are cooperatively describing the energy meter background. The smart meter supports to capable to identify data abnormalities for efficient monitoring as well as investigation. The anomaly detection supports for better decision making for minimizing the energy consumption. In this research, Machine Learning (ML) approach of Optimized Extreme Gradient Boosting (OXGBoosting) for the detection and classification of anomaly for energy consumption in conventional meters. This research collects the large-scale anomaly dataset for estimating the effectiveness. The proposed method is estimated by various performance metrices and it achieves an accuracy of 0.95, precision of 0.92, recall of 0.92, F1-score of 0.91 and AUC of 0.95 respectively when compared to the previous studies such as Extreme Gradient Boosting (XGB), Two-Class Boosted Decision Tree and NGBoost. © 2024 IEEE.

关键词： Smart meters

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Detection of Eye Disease Using Deep Learning Algorithms

Detection of Eye Disease Using Deep Learning Algorithms

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Sustainable Expert Systems (ICSES), International Conference on

作者： Omkumar J. Patel Kshitij Verma Chirayu Vyas Trilok Nath Pandey Bibhuti Bhusan Dash Sudhansu Shekhar Patra School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The human eye is a complex organ that can perceive a wide variety of visual inputs. However, its functioning can be severely impaired by diseases such as cataract and diabetic retinopathy which have the potential to cause permanent blindness which not only disrupts the productivity and mobility of patients but also causes a socio-economic impact that decreases the quality of life. This paper reviews the recent advancements in deep learning for diagnosing retinal diseases such as diabetic retinopathy (DR), cataract and glaucoma. The paper presents an extensive comparative analysis of 4 definite deep learning models - Vision Transformers (ViT), ResNet, GoogLeNet and Convolutional Neural Network (CNN). The results from the conducted experiments show that ViT has the highest accuracy followed by CNN. The outcomes highlights the potential of deep learning models in revolutionizing eye disease detection technology.

关键词： Deep learning Cataracts Diabetic retinopathy computer vision Visualization Accuracy Blindness Transformers Convolutional neural networks Socioeconomics

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Tutorial on laser-based visible light communications[Invited]

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Chinese Optics Letters 2024年第9期22卷 126-136页

作者： Yuqi Hou Yue Wang Zengxin Li Meixin Liu Shulan Yi Xiaoqian Wang Liang Xia Guangyi Liu Jianyang Shi Ziwei Li Junwen Zhang Nan Chi Tien Khee Ng Boon S.Ooi Chao Shen Key Laboratory for Information Science of Electromagnetic Waves Ministry of EducationSchool of Information Science and TechnologyFudan UniversityShanghai 200433China ZGC Institute of Ubiquitous-X Innovation and Applications Beijing 100876China Photonics Laboratory Electrical and Computer EngineeringDivision of ComputerElectrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia China Mobile Research Institute Beijing 100876China

Facing escalating demands for high-speed,large-bandwidth,and low-latency wireless data links,laser communication technology has emerged as a promising *** free-space optical communication conventionally utilizes nearinfrared light sources,there has been growing interest in exploring new spectral resources,including visible ***,laser-based white light has been demonstrated in visible light communication(VLC),with a unique capability to seamlessly integrate with illumination and display *** review summarizes the key devices and system technologies in semiconductor-laser-based white light for VLC-related *** recent advances and many emerging applications in the evolution of lighting,display,and communication are discussed.

关键词： visible light communication LiFi laser lighting GaN laser semiconductor laser

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Image Processing Based Early Breast Cancer Detection in Mammography Images Using GRU and XGBoost Approach 1

Image Processing Based Early Breast Cancer Detection in Mamm...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Pandey, Abhishek Ramesh, V. Mohan, Ravi Kaliappan, S. Swetha Reddy, A. Gurunathan, K. SCSVMV University Department of Computer Applications Kanchipuram India Shri Ram Institute of Technology Department of ECE Madhya Pradesh Jabalpur India Lovely Professional University Division of Research and Development Punjab India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India Mount Zion College of Engineering and Technology Department of Computer Science and Engineering Pudukkottai India

ISBN: (纸本)9798350371802

One out of every eight women will be affected by breast cancer at some point in their lives, making it one of the most common malignancies in females. The most reliable method for finding breast cancer in its early stages is mammography. Due to the low visibility and weak contrast of mammographic photos, early identification of breast cancer is crucial for effective therapy. As a result of several computer-aided detection approaches, radiologists are now able to make more precise diagnoses. Operating Sequencing is crucial for several tasks, including morphology, preprocessing, segmentation, feature selection, and training models. The suggested method makes use of preprocessing technologies that can enhance mammography, remove noise, and identify pectoral muscles. Using image processing techniques, a digital image can be 'segmented' into many layers. Acquiring a precise image of the mass is the goal of the morphological process. Geometric algorithms are employed by the feature selection technique. Training a GRU-XGBoost model requires careful consideration of which features to use. The proposed method outperforms the current gold standard algorithms, GRU and XGBoost, in every respect. The results showed a remarkable improvement, with an accuracy of 95.63%. © 2024 IEEE.

关键词： Diseases

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An Approach against Vampire Attack for Successful Transmission in Wireless Sensor Network 3

An Approach against Vampire Attack for Successful Transmissi...

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3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Juneja, Vikas Kumar Dinkar, Shail Uttarakhand Technical University Dehradun India Seth Jai Parkash Mukand Lal Institute of Engineering & Technology Department of Information Technology Kurukshetra Radaur India Govind Ballabh Pant Institute of Engineering and Technology Department of Computer Science and Applications Uttarakhand Pauri Garhwal India

ISBN: (纸本)9781665453578

In wireless sensor network, various factors of sensor node are used for ensured delivery of packets like battery or energy, node's status, neighboring nodes etc. which are essential for successful transmission between source and destination as well as among the intermediate nodes. Open communication and lack of energy make networks vulnerable to several security attacks. During data transmission, data packets move from source to destination through many intermediate nodes that may not be trusted. In this paper, a probabilistic approach is proposed to calculate the trust of the nodes. A constant factor of energy is needed to transfer the packet from one node to another. Trust value and estimated energy consumption at every node is determined. If the calculated energy consumption of a node when compared with divided constant factor of energy using fuzzy logic is said to be the trusted node. If energy consumption matches with the estimated energy value approximately then delivery of data packet is assumed successful otherwise it is considered as Vampire Node. In this paper, an algorithm is proposed to detect Vampire Attack to save the network from extra consumption of energy consumed by a particular node. The performance of the proposed algorithm is measured and compared using various parameters such as energy and traffic load. © 2023 IEEE.

关键词： Energy utilization

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Utilizing Blockchain and Deep Learning for Decentralized Discovery of Deceptive Practices in Healthcare Insurance 3

Utilizing Blockchain and Deep Learning for Decentralized Dis...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Shaik, Nazeer Kar, Nihar Ranjan Thankachan, Blessy Pathak, Amit Kumar Singh, Jagendra Gupta, Shelly SRIT-Autonomous Department of Computer Science Engineering Anantapur India School of Pharmacy University of Technology and Management Gopalpur India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India Ghaziabad India

ISBN: (纸本)9798350342338

To address the complex issues of detecting fraudulent practices in healthcare insurance, this research employs sophisticated machine learning, especially the Long Short-Term Memory (LSTM) model, to provide a complete framework for reliable fraud detection. The study meticulously examines performance metrics such as accuracy, precision, recall, and the F1 score by employing and evaluating the LSTM model across two distinct datasets-Dataset A (198810 samples) and Dataset B (434319 samples)-illuminating the model's capacity to detect fraudulent activities while minimizing misclassifications. The evaluation process, as shown by confusion matrices displayed as percentages, reveals the model's strengths and points up areas for improvement. This study makes an important contribution to the field of fraud detection by providing practical insights to strengthen healthcare insurance against misleading practices. This research proclaimers a paradigm shift by combining innovative methodologies, extensive dataset curation, and stringent evaluation, ushering in increased security, transparency, and efficacy in healthcare insurance fraud detection, ultimately fostering a future of resilient and precise fraud detection mechanisms. © 2023 IEEE.

关键词： IoT machine learning performance metrics security attacks

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