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Soil Fertility Assessment and Crop Recommendation for Sustainable Farming using Machine Learning and Deep Learning 4

Soil Fertility Assessment and Crop Recommendation for Sustai...

引用

4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Vandana, W.M. Kavya, B. Dayananda Sagar University Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350393354

'Soil Fertility Assessment and Crop Recommendation for Sustainable Farming using Machine Learning and Deep Learning' represents a pioneering endeavor at the intersection of agriculture and data science. It harnesses a diverse dataset encompassing soil properties, historical rainfall data, and temperature records, meticulously collected from reliable sources. This project employs advanced machine learning algorithms to predict soil fertility levels accurately and offer precise crop recommendations based on the unique conditions of each agricultural region. By bridging the gap between traditional farming practices and cutting-edge technology, the initiative addresses key challenges faced by modern agriculture. It facilitates enhanced crop yields through optimized resource allocation and reduced environmental impact by tailoring planting schedules and nutrient management. Moreover, it fosters sustainability by promoting responsible land usage, which is crucial in the face of evolving climate patterns and the increasing global demand for food security. In essence, Precision Agriculture emerges as a transformative solution that empowers farmers with actionable insights, ushering in an era of data-driven farming practices. By combining the strengths of data science, environmental science, and agriculture, this project contributes significantly to the broader goals of sustainable farming, economic viability, and ecological *** dataset consists of 2200 instances and 8 features. We train several machine models and also implement deep learning models to check for its performance. It was observed that the Naive Bayes shows 99.5 percent accuracy after hypertuning and with CNN showing the least at 87.1 percent accuracy. These conclusions help in building better recommendation systems and farming techniques. © 2024 IEEE.

关键词： Crops

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Revisiting Under-Represented Knowledge of Latin American Literature in Large Language Models 27

Revisiting Under-Represented Knowledge of Latin American Lit...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Kim, Jinsung Koo, Seonmin Lim, Heuiseok Korea University Department of Computer Science and Engineering Korea Republic of

ISBN: (纸本)9781643685489

With the advent of large language models (LLMs), concerns about knowledge bias have recently increased. Previously, prevalent research has focused on detecting the bias of model knowledge by providing explicit social terms, such as race, gender, and age, into inputs. However, revealing the subtle and implicit bias of the model knowledge requires verification utilizing language expressed in a more implied form, such as literary works. This is because literature implicitly contains subjective filters of individuals and their living regional culture. Accordingly, this study aims to probe a research question of whether LLMs have a knowledge under-representation problem between two different regions using the same language, Spain and Spanish-speaking countries in Latin America. To this end, we design an under-representation verification task, REGion and Literary Author prediction (REGLA) and dataset based on Spanish-written literary works. Inspired by the knowledge shortcut concept from a previous study, REGLA consists of two tasks to figure out meta-information of poems, i.e., region and author. Moreover, we explore various prompting methods that can unleash the knowledge observed to be under-represented within the verification process. According to the verification and prompt engineering results, knowledge about the literary works of Latin American countries appears to be more under-represented compared to those of Spain in LLMs. It is also observed that the task decomposition prompting method effectively lets under-represented knowledge be generated. © 2024 The Authors.

关键词： Knowledge representation

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Analysing Lossless Image Compression Techniques for IoT Devices 3

Analysing Lossless Image Compression Techniques for IoT Devi...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Challapalli, Kausik Kothamasu, Vivek Garikapati, Hasha Charan, Roop Sai Thota, ManiSankar Anamalamudi, Satish Srm University Ap Computer Science and Engineering Mangalagiri India

ISBN: (纸本)9798350369083

Lossless image compression is the technique now that allows to keep the quality high as long as the file size is being minimized to meet the needs of uploading or archiving [1]. The other category consists of Deflate whose shortcomings are fast speed and lower efficiency [2]. By considering this, smaller files size might never be the case but it is the best alternative for quality images and this forms part of formats like PNG and ZIP [3]. On the other hand, the BROTLI admirably shines with its extremely high compression ratios, when dealing with repeated pattern of information [4]. On the contrast, it pertains to the extremely resource intensive, which may eventually need more computing power, as the ratio is in favor of the compression [5]. Regardless of the chosen technique, the outcome remains consistent: for a compressed file whose data original size has been truncated, having a size that ranges from pixels to a file which can work on them without giving a distorted image, targeting those who need high-end images but small file sizes [6]. © 2024 IEEE.

关键词： Data compression ratio

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Adaptive Multi-Scale Attentional Network for Semantic Segmentation of Remote Sensing Images 14

Adaptive Multi-Scale Attentional Network for Semantic Segmen...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Zare, Melika Hashemi, Sattar Computer Science and Engineering Department Shiraz University Shiraz Iran

ISBN: (纸本)9798331511272

High-resolution remote sensing image segmentation presents challenges due to the complexity and diversity of ground objects, variations in scale, and the presence of small, hard-to-detect targets. Traditional methods often fail to capture multi-scale contextual information and accurately segmenting small objects in cluttered backgrounds. In this paper, we propose the Adaptive Multi-Scale Attentional Network (AMSANet), which addresses these challenges by leveraging a Hierarchical Multi-Scale Dilated Convolution Module (HMSDCM) and an Efficient Residual Atrous Spatial Pyramid Pooling (ER-ASPP) module to enhance feature extraction and refinement. The AMSANet architecture integrates these advanced modules to effectively capture and merge features at multiple scales, employing coordinate attention mechanisms for precise final refinement. Validated on the ISPRS Vaihingen and Potsdam datasets, AMSANet achieves a 2.1% improvement in mean intersection over union (mIoU), a 1.44% increase in overall accuracy (OA), and a 1.54% boost in mean F1 score over state-of-the-art methods. These results underscore AMSANet’s efficiency in improving segmentation accuracy and robustness, particularly in managing high-resolution remote sensing images with complex details and heterogeneous landscapes. ©2024 IEEE.

关键词： Semantic Segmentation

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Finger Vein Extraction and Authentication for Security Purpose Based on CNN

Finger Vein Extraction and Authentication for Security Purpo...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Nathan, R. Sakthivel, S. Selvi Rajendran, P. Hindustan Institute Of Technology And Science Department Of Computer Science &engineering Chennai India

ISBN: (纸本)9798350364828

Finger Vein ID is a biometric verification toolthat compares a person's finger vascular pattern to previously gathered data. Numerous applications, such as automated teller machines, computer and network authentication, end point security, and fingerprint recognition-one of the numerous biometrics used to identify people-are now being developed or deployed for this technology. Individuals and validate their identities that uses images of the vein patterns in human fingers that lie beneath the skin's surface employeetime and attendance tracking, credit card authentication, and vehicle security. With the advancement of electronics, there has been a steady rise in the need for easy-to-use, convenient, and highly secure authentication solutions to safeguard personal data held in mobile devices. One of the most effective biometric identification methods for user authentication is the finger vein recognition technique. due to its security and singularity. Since the dawn of technology, created for the detection of finger veins;however, the majority of these techniques relied on manually constructed characteristics, which have limitations with regard to image quality, finger location, etc. Since neural networks initially appeared, numerous identity verification techniques created. This research identifies the variables that may impact the outcome and surveys many finger vein verification methods utilizing CNN. The experimental examination of these methods uses both user- designed datasets with different qualities and publicly available datasets from finger veins. Biometrics, a method of protecting personal information, employs physiological or behavioral traits of humans to uniquely identify them. We send over SMS using AVR technology to deliver the SMS to a verified individual. © 2024 IEEE.

关键词： Biometrics

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PANDA: Persona Attributes Navigation for Detecting and Alleviating Overuse Problem in Large Language Models

PANDA: Persona Attributes Navigation for Detecting and Allev...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Kim, Jinsung Koo, Seonmin Lim, Heuiseok Department of Computer Science and Engineering Korea University Korea Republic of

ISBN: (纸本)9798891761643

In the persona-grounded dialogue (PGD) task, it is required not only to respond fluently, but also to ground the attributes according to the current conversation topic properly. However, due to their tendency to overly ground given attributes, LLMs often generate unnatural responses provoked by using attributes that deviate from the flow of the conversation or by exploiting too many attributes at once. We term this phenomenon the overuse problem of LLMs. Unfortunately, research devising precise criteria and frameworks to quantitatively verify LLMs' overuse problem is obviously insufficient. To address this issue, we propose Persona Attributes Navigation for Detecting and Alleviating the overuse problem (PANDA) framework. PANDA is the first study to quantify the persona overuse problem of LLMs by establishing clear standards of the problem and verifying various LLMs based on them. Moreover, this framework navigates us into understanding persona attributes by introducing diverse and detailed dialogue topics that consider practical conversation situations. We provide insights related to LLMs' persona attribute overuse problem through comprehensive verification and analysis with PANDA in the PGD task. Our code and resources can be found at http://***/jin62304/PANDA. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Potato Leaf Disease Detection Using Dense Net-CNN 2

Potato Leaf Disease Detection Using Dense Net-CNN

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Srivastava, Animesh Saini, Parveen Kumar Kumar, Kamal Tiwari, Shweta Garg, Navin Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Chandigarh University Department of Computer Science and Engineering Mohali Punjab India University Institute of Engineering Chandigarh University Department Computer Science and Engineering Department Mohali Punjab India Uttarakhand Dehradun India

ISBN: (纸本)9798350327533

There needs to be accurate detection and identification of crop diseases because they hurt crop yield. Using DenseNet-CNN and smart farming methods might make it possible for infected crops to be found accurately. This research study discusses about the DenseNet-CNN architectures that are effective in finding leaf diseases. A potato leaf database is utilized in the training and testing phases. The proposed DenseNet-CNN model was employed to extract disease-specific features from the images present in the training dataset, which were subsequently utilized for performing disease classification. The proposed model has achieved a remarkable accuracy of 99.6% based on the results obtained from training, testing, and experimentation. © 2024 IEEE.

关键词： Convolutional neural networks DenseNet Leaf Disease Potato

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Real-time Face-based Gender Identification System Using Pelican Support Vector Machine 2

Real-time Face-based Gender Identification System Using Peli...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ojo, Olufemi S. Oyediran, Mayowa Oyedepo Awodoye, Olufemi O. Ajagbe, Sunday Adeola Awotunde, Joseph Bamidele Bandyopadhyay, Anjan Adigun, Matthew O. Department of Computer Science Ajayi Crowther University Oyo Nigeria Department of Computer Engineering Ajayi Crowther University Oyo Nigeria Department of Computer Engineering First Technical University Ibadan200255 Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Computer Science University of Zululand Kwadlangezwa3886 South Africa

Gender identification from video is an emerging research field that aims to automatically classify the gender of individuals based on video data. Due to the numerous applications for this task, it has received a lot of attention, including surveillance, human-computer interaction, and targeted marketing. In this study, we propose a gender identification system that utilizes the Pelican optimizer algorithm in combination with a Support Vector Machine (SVM) classifier. The Pelican optimizer is a metaheuristic algorithm inspired by the hunting behaviour of pelicans and has shown promising results in solving optimization problems. Pelican optimizer algorithm (POA) is applied to optimize the SVM parameter selection process such as kernel function. The POA algorithm searches for an optimal subset of parameters that maximizes the classification performance of the SVM model after the application of preprocessing and feature extraction techniques such as Local Binary Pattern (LBP). Finally, the selected optimized parameters otherwise known as POA-SVM classifier learns a decision boundary based on the labeled training data. The POA-SVM model is trained to distinguish between male and female samples and generalize the classification to unseen video data. Experimental evaluations are conducted using a benchmark dataset consisting of video samples with labelled gender information. The effectiveness of the suggested system is contrasted with other cutting-edge gender identification techniques. The results demonstrate the effectiveness of the Pelican Optimization Algorithm-SVM system, showing improved accuracy of 95%, and sensitivity of 94.4% at a faster recognition rate in gender classification from video data. © 2024 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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Evaluating Human Emotions Using Image Processing and Brain Waves 3

Evaluating Human Emotions Using Image Processing and Brain W...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Abirami, R. Privadharshini, S. Aroul Canessane, R. Jany Shabu, S.L. Refonaa, J. Gayathri, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Human sentiments are analyzed based on two prominent technologies, electroencephalography and image processing. Electroencephalography helps in detecting the brains activity by attaching the electrodes to the scalp. The neurons are charged with ions and constantly emit them. When charged ions repel and propagate this repulsion, a brain wave is produced at a unique frequency depending on the state of mind. Image processing helps in determining the characteristics of facial expression using mathematical operations and functions. This includes capturing image or more appropriately 20 signals. The output of image processing yields the characteristics of the image. The combined output of both Electroencephalography device and image processing are mapped together to genuinely analyze the human emotions. © 2024 IEEE.

关键词： Electroencephalography

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Classification of Soil Moisture Content with the Application of Deep Learning

Classification of Soil Moisture Content with the Application...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Ansari, Mohammad Shaharyar Gautam, Prateek Raj Centre for Advanced Studies AKTU Department of Computer Science and Engineering Lucknow India School of Computer Science UPES Uttarakhand Dehradun India

ISBN: (纸本)9798350364088

India is an agricultural-oriented nation where soil moisture plays a vital role in the growth of plants and crops. Measuring soil moisture and maintaining a balance in the soil moisture content help sustain water resources. The traditional method for calculating the soil moisture content is to calculate the weight of moisture soil and dry soil differences. With time passage, different techniques are introduced based on different sensors. Recently, machine learning and deep learning have emerged as powerful tools and can be used for the classification of different levels of soil moisture. Convolution Neural Network is a technique used in the field of deep learning for the classification of images. This algorithm is used to analyze the images on various factors like texture, color, hue, and shapes. In this research, images have been collected from the internet image search and stored in separate folders according to their class. CNN is applied to the created dataset and after tuning the hyperparameters model reaches an accuracy of 73%. © 2024 IEEE.

关键词： Soil moisture

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