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Computational intelligence Router of machine and Artificial intelligence Learning for the Expansion of Agriculture's Manufacturing Sector 3

Computational Intelligence Router of Machine and Artificial ...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raman, Ramakrishnan Alanya-Beltran, Joel Akram, Shaik Vaseem Shah, Heli Sanjay, P.C. Ponnusamy, R. Symbiosis Institute of Business Management Pune Maharashtra Pune India Universidad Tecnológica Del Perú Peru Uttaranchal University Uttaranchal Institute of Technology India Parul University Mechatronics & Robotics Pit India India Chennai Institute of Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350399264

The economic sector includes agriculture as a major player. Around the world, the primary issue and hot topic is agriculture automation. As a result of the population boom, there is a huge increase in the need for both jobs and food. Farmers employed traditional techniques, which included insufficient to meet these needs. This led to the introduction of new automated techniques. These innovative techniques supplied the world's food needs while giving billions of people access to jobs. An agricultural revolution has been sparked by artificial intelligence. With the use of this technology, crop yields are now more resistant to many conditions like population increase, climate change, job troubles, and food security concerns. The primary goal of this study is to evaluate the numerous ways artificial intelligence is being used in agriculture, including the use of sensors and other tools built into drones and robots for weeding, spraying, and irrigation. The excessive use of pesticides, herbicides, water, and other resources is reduced by these technologies, and the soil fertility is maintained. They also aid in the effective use of labor, which raises productivity and quality. In order to provide a concise summary of the current state of agricultural automation, including the use of drones and robots for weeding, this study analyses the research of several researchers. Along with two automated weeding strategies, the various ways of soil water sensing are described. This study discusses the use of drones as well as the numerous spraying and crop monitoring techniques that may be utilized with them. © 2023 IEEE.

关键词： Drones

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SweetSight: A Deep Convolutional Neural Network Approach for Automatic Categorization of Bengal Sweets 3rd

SweetSight: A Deep Convolutional Neural Network Approach for...

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3rd international conference on Artificial intelligence and Knowledge Processing (AIKP)

作者： Supriya, Soummo Rimi, Iffat Firozy Islam, Md Moinul Rahman, Md Sadekur Nawshin, Samia Habib, Md Tarek Daffodil Int Univ Dept Comp Sci & Engn Dhaka Bangladesh Independent Univ Bangladesh Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9783031686160;9783031686177

The manufacture of a wide variety of sweets is on the rise in the entire Bengal (both Bangladesh and West Bengal). As a consequence, the sweet's name escapes the vast majority of individuals in our country. Computer vision advancements have made object recognition from photos easier in recent years. Using computer vision to automatically categorize sweets is still a challenge because of the similarity between various sorts and characteristics such as their placement or lighting conditions. Classifying sweets may be useful in a variety of domains, including autonomous economic robots and the creation of mobile apps for identifying certain sweets on the market. In this article, we employed deep convolutional neural network (DCCN) methods to evaluate five alternative models for sweet detection. The endemic Bengali delicacies we used to train my model included Inception-v3, ResNet-50, VGG15, AlexNet, and CNN. This model was efficient. Our dataset comprised images of confections from thirteen distinct sweet categories. Two portions of the dataset were separated: 80% for training and 20% for testing. The training dataset was enhanced and increased to make preparation simpler. Using the Inception-v3 model, we were able to attain a 100% accuracy rate with our dataset.

关键词： Bengal Sweets Expert System DCNN Performance Classification Inception-v3 Precision

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022) 1

1st International Virtual Conference on Visual Pattern Extra...

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Amelio, Alessia Montelpare, Sergio Ursino, Domenico InGeo University "G. d’Annunzio" Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy DII Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy

This document introduces the 1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022), a premier forum for presenting the state-of-the-art, new research, ongoing work, academic and project reports in advanced statistics and machine learning, 3D modelling and simulation, knowledge representation, intelligent systems, information retrieval and software engineering, for visual pattern extraction, analysis and recognition to preserve the cultural heritage. © 2022 Copyright for this paper by its authors.

关键词： 3D modeling

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AFHO-DL: Enhancing Energy Efficiency through Resource Allocation AI-enabled WSNs and IoT Integration 3

AFHO-DL: Enhancing Energy Efficiency through Resource Alloca...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Kalaiselvan, S.A. Manoranjini, J. Hemalatha, S. Kumar, M. Lenin Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Vignan Institute of Technology and Sciences Department of Electronics Communication and Engineering Telangana India

ISBN: (纸本)9798350369083

In the realm of AI-enabled Wireless Sensor Networks (WSNs) and Internet of Things (IoT) integration, efficient resource allocation is paramount for enhancing energy efficiency and optimizing data utilization. The dynamic nature of environments poses challenges to achieving optimal performance measures, necessitating sophisticated solutions. To address this, we propose the AFHO-DL model, which combines the Fire Hawk Optimizer (FHO), UNet architecture, and Multi-objective Jaya algorithm. The FHO algorithm, inspired by the foraging behavior of Fire Hawks, efficiently optimizes resource allocation in WSNs by iteratively updating node positions based on objective functions. Leveraging FHO, we fine-tune the parameters of the UNet architecture, a deep learning model widely used in image processing applications. The UNet architecture, consisting of encoder and decoder paths, extracts features and reconstructs images, enhancing adaptability to various data types. Furthermore, the Multi-objective Jaya algorithm is employed to refine solutions by striking a balance between exploration and exploitation in the solution space, further improving resource allocation strategies. Our experimental results demonstrate the effectiveness of the proposed AFHO-DL model in enhancing energy efficiency and optimizing data utilization in dynamic environments. Through post-processing and visualization, we evaluate the performance of the optimization algorithms, generating Pareto-optimal solutions that represent the best trade-offs between competing goals. The AFHO-DL model facilitates intelligent decision-making and resource allocation strategy optimization, ensuring smooth data transfer and improving network efficiency in AI-enabled WSNs coupled with IoT platforms. © 2024 IEEE.

关键词： Deep learning

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Random Forests for Predicting Diabetes Progression and Complications 3

Random Forests for Predicting Diabetes Progression and Compl...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Ranganathan, Chitra Sabapathy Nandhini, R. Nisha, K. Sumathi, G. Sudhakar, G. Srinivasan, C. Mphasis Corporation Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

The main objective of the presented work in this paper is to use Random Forests as an alternative biomarker for predicting the development of diabetes. Identifying high-risk patients is crucial for improving patient outcomes and reducing healthcare expenses. The proposed system improves diabetes treatment via the use of predictive analytics, which promotes preventative and early intervention-focused healthcare practices. It helps manage the risks associated with diabetes by using machine learning, which is part of a larger movement towards precision medicine. This method shows promise to enhance long-term health outcomes, optimize resource allocation, and provide personalized treatment to patients. Findings from the Pima Indians Diabetes Database quantify the risks of diabetes and patient characteristics. Blood pressure, glucose, insulin, and body mass index are all taken from each patient. The dangers of retinopathy, neuropathy, and nephropathy are also numerically encoded, with 0 indicating minimal risk, 1 medium risk, and 2 high risks. HbA1c is also included. The blood pressure ranges from 115/78 to 130/85mmHgA1c levels, the body mass index (BMI) from 26 to 32 mm, and the glucose level from 110 to 140 mm Hg. © 2024 IEEE.

关键词： Blood pressure

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Fault Diagnosis in Micro Grid Using Wavelet Transform in Combination with machine Learning 3

Fault Diagnosis in Micro Grid Using Wavelet Transform in Com...

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3rd Odisha international conference on Electrical Power Engineering, Communication and Computing Technology, ODICON 2024

作者： Saiprakash, Chidurala Kumar Joga, S Ramana Sowmya, Bera Krishna, Teki Vamsee Narayana, V. Lakshmi Manoj, Vasupalli Sr University Department of Electrical and Electronics Engineering Warangal India Dadi Institute of Engineering and Technology Eee Department Anakapalle India Department of EEE Tadepalligudem India Dadi Institute of Engineering and Technology Bs&h Department Anakapalle India Gmrit Eee Department Andhra Pradesh Rajam India

ISBN: (纸本)9798350354379

Microgrids, which are defined by their decentralized methods of generating and distributing electricity, have a vital role in improving the resilience and sustainability of energy systems. Ensuring the stability and efficiency of micro grids relies heavily on the identification of faults. Conventional fault detection techniques often lack the necessary sophistication to effectively manage the intricacy and variety of contemporary micro grid systems. This paper focuses on the issue of fault identification in micro grids. It presents a new method that combines wavelet transform and data mining approaches to tackle this difficulty. The goal is to provide a reliable approach that can accurately detect and categorize defects, resulting in reduced downtime and enhanced system performance. The wavelet transform allows for the analysis of signals by breaking them down into several frequency components, thereby offering a comprehensive depiction of the dynamics of a system. The suggested technique provides various benefits over existing fault detection methods, which often depend on unsophisticated threshold-based approaches. As a consequence, there is an enhancement in precision and dependability, which allows for prompt detection and resolution of errors, while reducing interruptions in the provision of energy. Moreover, the use of sophisticated data analytics improves the capability to distinguish between typical operational circumstances and distinct fault situations, hence minimizing inaccurate alerts and enhancing the efficiency of the system. The effectiveness of the suggested technique in improving the accuracy of fault detection and assuring the dependable functioning of micro grids is confirmed via simulation results. © 2024 IEEE.

关键词： Artificial intelligence

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Investigation of Local and Global Methods of Explainable Artificial intelligence. Metrics for Evaluating Explanation Methods 3

Investigation of Local and Global Methods of Explainable Art...

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3rd international conference on Neural Networks and Neurotechnologies, NeuroNT 2022

作者： Shevskaya, Natalya V. Shishkin, Ivan V. Stepanov, Vladislav D. Oblizanov, Alexander D. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9781665467766

Artificial intelligence systems in meaning of machine learning face not only technical (collecting enough data, efficient hardware implementation), but also social difficulties in implementation. The reason for this is distrust due to a lack of understanding by end users how such systems work and why they make certain decisions. Therefore, this paper discusses the methods of explainable artificial intelligence aimed at solving the problem of misunderstanding, also considers existing approaches to the evaluation of these methods and determines the requirements for the evaluation of AI system and the metrics that can be used in it. It will also answer the question that runs through this work - what is the role of hybrid intelligence in the tasks of explaining models? © 2022 IEEE.

关键词： Lime

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Cloud-Based Road Safety for Real-Time Vehicle Rash Driving Alerts with Random Forest Algorithm 3

Cloud-Based Road Safety for Real-Time Vehicle Rash Driving A...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Presitha Aarthi, M. Reddy, Chilukala Mahender Anbarasi, A. Mohankumar, N. Ishwarya, M.V. Murugan, S. Shree Sathyam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Salem India Gitam University Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

The cloud-based road safety technology introduced in this research improves real-time vehicle behavior monitoring and warns for rash driving. It uses cloud computing to gather and interpret data from onboard car sensors and other traffic monitoring equipment. Powerful machine learning technology the Random Forest (RF) algorithm analyses this data intelligently. The RF algorithm is trained on a varied array of driving metrics to detect rash driving tendencies. Real-time data streams may be processed efficiently and scalable on the cloud, enabling speedy decision-making for rash driving detection. A network sends alerts to authorities and nearby vehicles. The system is adaptable to different road conditions, resistant to sensor data noise, and able to enhance rash driving detection accuracy via model updates. Cloud architecture makes the system available worldwide. With its focus on real-time notifications utilizing the RF algorithm, this cloud-based road safety system may reduce the hazards of reckless driving and avoid accidents. © 2024 IEEE.

关键词： Cloud computing architecture

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Elevated Learning based Secured Phishing Website Identification Methodology using Artificial intelligence Assistance 5

Elevated Learning based Secured Phishing Website Identificat...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Ramkumar, G. Saveetha University Department of Ece Chennai India

ISBN: (纸本)9798350379945

Phishing attacks pose a huge threat for online security and therefore need advanced detecting techniques in order to mitigate the damages it causes. This paper introduces a sophisticated model for the detection of phishing websites, named the Blended ResNet-EfficientNet Model (BREM), which unifies the advantages of the ResNet and EfficientNet architectures. To address this challenge, BREM uses the rich hierarchical pattern recognition ability of ResNet-50 and the practical feature extraction capability of EfficientNet-B3 to achieve classification performance in phishing detection. On overall assessment BREM outperforms both traditional machine learning models and standalone deep learning model with an accuracy of 96%, precision at 94%, recall of 95% and FI score of 94.5%. These same authors validate the high specificity (97 %), negative predictive value (95 %) and Matthews correlation coefficient of 0.92 further underlining the robustness and reliability of BREM. This approach does not only improve the accuracy of detection, but also provides much better security against phishing campaigns. In the near future research directions such as real-time deployment, more experiments on different feature sets, adversarial robustness, transfer learning from heterogeneous datasets, model learning models and pattern recognition. They are able to draw from an immense amount of data and address minor deviances, which allows AI's better discrimination of real versus false websites, therefore increasing user cyber protections and preventable breaches [3] [4]. At the heart of securing phishing websites identification is making use of powerful AI capabilities to scan and recognize phishing convincingly. This AI utilizes different methods - natural language processing (NLP), image recognition and behavioral analysis - to keep an eye on things happening to web content and its design. Behavior analysis tracks user-interactions and web behavior to detect for abnormal operations, which co

关键词： Phishing

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SimpleFusion: 3D Object Detection by Fusing RGB Images and Point Clouds

SimpleFusion: 3D Object Detection by Fusing RGB Images and P...

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2023 international conference on pattern recognition, machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Zhang, Yongchang Guo, Yue Niu, Hanbing Zhang, Bo Cao, Yun He, Wenhao University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Chinese Academy of Sciences Institute of Automation Beijing China University of Electronic Science and Technology of China Chengdu China Chinese Institute of Coal Science China Coal Technology & Engineering Group Co. Ltd. Intelligent Mining Research Academy Beijing China

ISBN: (纸本)9798350346596

Achieving robust 3D object detection by fusing images and point clouds remains challenging. In this paper, we propose a novel 3D object detector (SimpleFusion) that enables simple and efficient multi-sensor fusion. Our main motivation is to boost feature extraction from a single modality and fuse them into a unified space. Specifically, we build a new visual 3D object detector in the camera stream that leverages point cloud supervision for more accurate depth prediction;in the lidar stream, we introduce a robust 3D object detector that utilizes multi-view and multi-scale features to overcome the sparsity of point clouds. Finally, we propose a dynamic fusion module to focus on more confident features and achieve accurate 3D object detection based on dynamic weights. Our method has been evaluated on the nuScenes dataset, and the experimental results indicate that it outperforms other state-of-the-art methods by a significant margin. © 2023 IEEE.

关键词： Object detection

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