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A Comprehensive Crop Recommendation System Integrating Machine Learning and Deep Learning Models 1

A Comprehensive Crop Recommendation System Integrating Machi...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Ghosh, Asit Mohapatra, Sunil Kumar Pattanaik, Pritam Dash, Prasant Kumar Chakravarty, Sujata Centurion University of Technology and Management Department of Computer Science & Engineering Bhubaneswar India CV Raman Global University Department of Computer Science & Engineering Bhubaneswar India

ISBN: (纸本)9798350361780

A crucial development in recent agriculture is the emergence of crop recommendation systems, which provide a unique method for optimizing crop yields and resource use. In this work, we have proposed an advanced 'Crop Recommendation System' to help farmers choose the right crop according to different soil and environmental parameters. Our system is trying to improve crop yields and agricultural productivity using machine learning (ML) and deep learning (DL) models. The data used for training and evaluation was collected from the Kaggle dataset repository. It includes the eight most important features such as nitrogen (N), phosphorus (P), potassium (K), rainfall, pH, temperature, humidity, and crop labels representing 22 different crops. This work employs machine learning and deep learning models such as Decision Trees, Random Forests, XGBoost, Support Vector Machine, K-nearest neighbors, Naive Bayes, Artificial Neural Networks, Deep Neural Networks, and Temporal Convolutional Networks. Based on performance evaluation, Random Forest and TCN are the best models, showing 99.2% and 99.9% accuracy, respectively. The performance of other models was also excellent, with accuracy levels ranging from 93.8% to 98.7%. SVM was a little behind, but still performed well with 93.4% accuracy. The project also includes parameter optimization for XGBoost and TCN. The TCN model gives a better result than XGBoost using parameter optimization. The outcome of this research shows that machine learning and deep learning models perform well in crop recommendation systems and the TCN model demonstrates its commitment to accurate and efficient crop recommendations. This research work supports precision agriculture by providing a web interface to the farmers with reliable parameters to improve crop selection based on environment and soil conditions. © 2024 IEEE.

关键词： Crops

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Soft-Touch or Hard-Touch: Quantifying Touches through A New System Focusing on A Pool Touchpad for Measuring Timing of Swimmers 23

Soft-Touch or Hard-Touch: Quantifying Touches through A New ...

引用

10th International Conference on Networking Systems and Security, NSysS 2023

作者： Quaium, Adnan Islam, Md. Ashraful Rahman, M. Sohel Islam, A. B. M. Alim Al Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh and Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798400708787

The swimming turn or finishing presents an integral part of competitive swimming, and the time it takes by a swimmer to push off the wall is a significant factor in their ratings. Therefore, it is essential to have a reliable way to measure the timing of touch, i.e., wall-contact, over the finishing or turning points. To do so, swimming pool touchpads are widely used in swimming competitions to overcome human error in measurements by enabling touch-sensing systems at the finishing or turning points. The touch-sensing devices use their sensitive touch sensors to record time with millisecond accuracy, providing athletes with the most precise measurements possible. However, these devices might need a minimal force applied from the swimmers to sense the touch, and thus, to record the timing of the touch. As such, insufficient force applied on the touchpad during swimming could cause skipping the time log. As a result, it is critical to have a trustworthy method for measuring the extent of touch or wall-contact exerted over the touch-sensing systems. This study, for the first time in the literature to the best of our knowledge, proposes such a method for measuring the extent of touch or wall-contact for swimming pool touchpads. In the process of proposing the method, we design and develop a bi-modal sensing module to distinguish the soft-touch and hard-touch exerted over a real pool touchpad used in a swimming pool organizing national and international competitions. Our experiments show that the pool touchpad under experimentation is not always accurate in registering touches. It sometimes fails to register low-force touches while generally registering touches with high force. © 2023 ACM.

关键词： Sports

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A Review on the Applications of Machine Learning and Deep Learning Algorithms for Image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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Maximizing Weighted Dominance in the Plane 21st

Maximizing Weighted Dominance in the Plane

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21st International Colloquium on Theoretical Aspects of Computing, ICTAC 2024

作者： Akram, Waseem Saxena, Sanjeev Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur208016 India

ISBN: (纸本)9783031770180

Let P be a set of n weighted points, Q be a set of m unweighted points in the plane, and k a non-negative integer. We study the k-maxDominance problem, where the objective is to find a subset Q′⊆Q with size at most k such that the sum of the weights of the points in P dominated by at least one point in Q′ is maximized. A point q in the plane dominates another point p if and only if x(q)≥x(p) and y(q)≥y(p), and at least one inequality is strict. An algorithm for the k-maxDominance problem can be used to solve problems previously examined in scheduling, graph optimization, and database systems. We present a simple algorithm solving the problem in O(km2+nlogm) time and O(n+m) space. The algorithm is faster than the existing one when m=o(n). Additionally, we provide another solution to the problem with O(kmin{n+m,nk+m2}logm) time and O(n+m) space. These bounds are the same or better than those of the existing solution (the bounds are better when m=o(n)). The expected number of skyline points is θ(logn), a term much less than n. To obtain the second solution, we design a general technique that can be used to reduce the number of points n in P to min{n,m2} in any algorithm for the k-maxDominance problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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Temporal Harmony: Bridging Gaps in Multivariate Time Series Data with GAN-Transformer Integration 2

Temporal Harmony: Bridging Gaps in Multivariate Time Series ...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Saravana, M.K. Roopa, M.S. Arunalatha, J.S. Venugopal, K.R. Bangalore University Department of Computer Science and Engineering Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350360523

Multivariate Time Series (MTS) data imputation plays a pivotal role in enhancing the robustness of temporal data analyses across diverse domains. In this paper, we propose a novel hybrid model that combines the generative ability of Generative Adversarial Networks (GANs) with the sequence modeling capabilities of Transformers for MTS data imputation. The generator of the GAN synthesizes realistic MTS data, while the discriminator ensures authenticity by creating an adversarial training dynamic. Simultaneously, a Transformer processes both observed and synthetic MTS data, leveraging its attention mechanisms to capture intricate temporal dependencies and generate imputations for missing values. The joint optimization of the GAN and Transformer components fosters a synergistic model, proficient in generating contextually accurate missing values. We detail the architecture, training process, and hyperparameters of the proposed model, evaluating its performance on diverse MTS datasets. Our model aims to advance the state-of-the-art in MTS data imputation, addressing challenges practically through the fusion of GANs and Transformers. Experimental results showcase promising imputation accuracy and underscore the potential of the hybrid approach in temporal data analytics. © 2024 IEEE.

关键词： Generative adversarial networks

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USRD: User-Searchable and Revocable Data Sharing Scheme in Multi-Fog-IoVs 6

USRD: User-Searchable and Revocable Data Sharing Scheme in M...

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6th International Conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Jiang, LuYao Li, Yongkang Chen, DongDong Gu, Ke Zuo, YiMing School of Computer and Communication Engineering Changsha University of Science & Technology ChangSha China

ISBN: (纸本)9798350388374

Aiming at the security of data sharing in the Internet of vehicles environment, we propose a user-searchable and revocable data-sharing scheme in the Multi-Fog-IoVs. The scheme utilizes a fog node to alleviate the computing burden on users and enhance the computing efficiency of the system. The Garbled Bloom filter and inverted index are used to create a bidirectional index for efficient multi-keyword search and document retrieval. Furthermore, a flexible and efficient user revocation mechanism is constructed while realizing the dynamic updates of data security. Finally, the safety analysis and performance evaluation of the proposed scheme are conducted, and the results indicate that the scheme meets both forward and backward safety requirements while demonstrating outstanding retrieval efficiency. © 2024 IEEE.

关键词： Fog computing

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Assessing the Readability and Coherence in Gemini's Triple Draft Generation: A Multi-metric Approach 15

Assessing the Readability and Coherence in Gemini's Triple D...

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

作者： Afsal, C.P. Kuppusamy, K.S. Pondicherry University School of Engineering and Technology Department of Computer Science Puducherry India

ISBN: (纸本)9798350370249

Large Language Models (LLMs) play a crucial role in modern society by enabling natural language understanding and generation, which enhances communication, automation, and information processing across diverse sectors, fostering innovation and efficiency. The response generated by an LLM for a given prompt is crucial as it reflects the model's ability to understand context, convey relevant information, and maintain coherence, thereby shaping user perception and interaction. This study analyzes the coherence and readability of drafts generated by the Gemini LLM across 20 prompts. Coherence scores, assessed through semantic and cosine similarity, reveal variability across drafts, with Draft-1 generally excelling in both semantic and cosine similarity across the prompts. Readability analysis using Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) metrics indicates that Drafts 2 and 3 consistently offer higher readability compared to Draft 1. However, user interaction with Gemini's interface reveals the potential overlook of alternative drafts, highlighting the importance of exploring all options for optimal content selection. This study underscores the significance of considering coherence and readability metrics collectively for informed decision-making in content-generation tasks. © 2024 IEEE.

关键词： Semantics

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A CNN Model Based Approach for Disease Detection in Mango Plant Leaves 12th

A CNN Model Based Approach for Disease Detection in Mango Pl...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Singh, Pankaj Pratap Kumar, Devanshu Srivastava, Aakriti Basumatary, Madhusmita Prasad, Shitala Department of Computer Science and Engineering Central Institute of Technology Kokrajhar Assam India School of Mathematics and Computer Science Indian Institute of Technology Goa Goa India

ISBN: (纸本)9789819732913

In this study, we propose a machine learning (ML) based method for the early detection of plant leaf diseases. Plant diseases are a major concern in agriculture, impacting crop yield, and food security. Early and accurate identification of these diseases is vital for effective disease management. Our approach employs deep learning algorithms and a dataset of annotated images containing both healthy and diseased plant leaves. Through training a deep neural network, we enable automated disease detection by extracting relevant features from leaf images and classifying them as healthy or diseased. By reducing reliance on human expertise, our approach enables timely detection, facilitating prompt implementation of disease management strategies. This ML-based method has the potential to revolutionize the field of plant pathology, offering valuable insights for the development of precision agriculture techniques aimed at ensuring sustainable crop production. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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Analysis of Maternity and Child Health Care System Integrated with IoT and ML 10

Analysis of Maternity and Child Health Care System Integrate...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Monika, S. Battana, Hari Sangeetha, M. Shaik, Meeravali Jawahar, M. Malla Reddy University Computer Science and Engineering Hyderabad India K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798350384369

Recent report states that in 2020, approximately 800 women died every day from pregnancy and child birth. The causes for all these deaths are because of low-resource settings. Diverse complications might happen beforehand in pregnancy but they get worse all over pregnancy, predominantly if it is not managed properly. Hence, essential efforts must be taken from giving quality and timely health assistance that will lead to healthy child birth. This paper aims to provide a solution by developing an innovative IoT -based Maternal and Fetal Health Monitoring System, leveraging cutting-edge technology to enhance prenatal care. The system gathers health metrics of a pregnant woman including blood pressure level, heart pulse rate, SPO2, temperature and the count of fetal kicks. The data can be collected from the sensors and transferred to the THINGSPEAK CLOUD along with the date and time by means of IoT setup with the aid of ARDUINO UNO. Later, the data can be analyzed by means of various algorithms in Machine Learning like Decision Tree classifier, Random Forest algorithm, Support Vector Machine (SVM), Logistic Regression and KNN to predict the accuracy of risks associated during the pregnancy period. © 2024 IEEE.

关键词： Obstetrics

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Modified Osprey-Optimized DM-CNN Model for Human Activity Recognition 6th

Modified Osprey-Optimized DM-CNN Model for Human Activity Re...

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

作者： Tyagi, Anshuman Singh, Pawan Dev, Harsh Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

ISBN: (纸本)9789819778614

Human Activity Recognition (HAR) is an AI-based technique aimed at identifying and labeling human activities. It gathers activity data from sources like wearable sensors and smart devices. Essentially, it categorizes human activities based on interactions, movements, and actions. Currently, it finds widespread application across various domains, leading to continuous research efforts for practical improvements. The present study revolves around HAR, a hybrid DM-CNN model, where the activities of humans are recognized by four phases including preprocessing, segmentation, feature extraction, and recognition. Here, the hybrid DM-CNN model uses a specific set of extracted features for enhanced training and they are MTH, IGBP and hierarchy of skeleton. Most importantly, the hybrid DM-CNN model provides excellent results on human activity recognition through the development of the Modified Osprey Optimization (MOO) algorithm for fine-tuning the DM and CNN models. The specificity of the proposed model is 95%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Human form models

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