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Smart Crop Recommender System Using Machine Learning 2

Smart Crop Recommender System Using Machine Learning

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Nokshangthemba Kumar, Saurabh Soloman, Shyam Sunder Jannu Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797 103 India

ISBN: (纸本)9798350366570

Agriculture is key to any country's economic growth. However, meeting food requirements of the present population due to the ever-rising human population, changing climatic conditions, and limited natural resources becomes a difficult problem. Smart agriculture is an innovative way of supporting farming by recommending the best crops to use and solving the current problems in maintaining farming sustainable. The classification machine learning algorithm is used to build the recommendation model. This paper's concept is a method of choosing appropriate crops that results in several favorable outcomes for agriculture and farmers. Since the crop yield can be increased, it will contribute to boosting our country's economy. Crops recommendation system for agriculture is based on various attributes such as crop type, soil composition, weather condition, soil pH and rainfall. The data have taken from the historic data which was collected from soil and water conservation and district agriculture office Nagaland and use supervised machine learning approaches to apply different ML algorithms. The effected of algorithm are measure on a various scale including Accuracy, Precision, Recall and F1-Score. The random forest outperformed in specifying the suitable crop with an impressive accuracy of 92.59%. © 2024 IEEE.

关键词： Smart agriculture

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Biomedical Data Management and Analytics in IOMT 8th

Biomedical Data Management and Analytics in IOMT

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8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Niha, K. Surendiran, B. Amutha, S. School of Computer Science and Engineering VIT Vellore Campus Tamil Nadu Vellore India Department of Computer Science and Engineering National Institute of Technology Puducherry India School of Computer Science and Engineering VIT Chennai Campus Chennai India

ISBN: (纸本)9789819732449

The IoMT is a device that works with different healthcare systems to help develop networking technologies. It reduces the unnecessary burden of visiting the hospital. Instead, it enables the transfer of medical related data with physicians through a secured network. Smart devices, such as wearable and medical monitors are used for healthcare use in the IoMT network. Telehealth and other services are adapted in homes and hospitals in other real-time locations. Approximately in the globe, 80% of healthcare have implemented IoT (Internet of Things) technologies and 20% are expected to do the same in the future. This paper focuses on four topics: (1) recent advances on Internet of medical things, (2) highlights the emerging data fusion process for data preparation, (3) discusses the life cycle of sensor devices and their protocols, and (4) processing data with different techniques and theories. In a nutshell, this paper gives a comprehensive overview of IoMT research advancement, application, data management, and analytics techniques along with the current research challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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Influential Node Analysis in Covid-19 Spread 1

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Adithyan, R. Punitha, D. Rahul, P. Vithun, K.S. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chennai600026 India

ISBN: (数字)9789819765812

ISBN: (纸本)9789819765805

Social Network Analysis, is an approach for investigating social structures which is referred to as structural analysis. A network is a group of individuals or entities that are related to each other. It may be represented as a graph. Each element represents the infected cases traveling from one place to another and is visualized in the form of a graph with nodes. The project proposes to carry out a visual inspection of the various stages of the spread of the Indian subcontinent and find the most influential node in the network using social network algorithms. The whole time period is divided into several parts to visualize during which the virus spreads throughout the country. The model aims to find the most influenced state (influential node analysis) that is responsible for the influence of the virus. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Social networking (online)

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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Low Dose CT Image Reconstruction Using Deep Convolutional Residual Learning Network

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SN computer Science 2023年第6期4卷 1-14页

作者： Ramanathan, Shalini Ramasundaram, Mohan Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Trichy India

Image reconstruction from computed tomography measurement is formulated as a thought-provoking statistical inverse problem. Deep learning algorithms are best for ill-posed statistical inverse problems that presently achieve state-of-art reconstruction results. The challenging task is to lower the potentially harmful radiation a patient is exposed to during the CT scan. In recently available CT Scanners, Low-Dose CT (LDCT) reconstruction is presented with a post-processing approach, which uses deep learning-based medical image reconstruction methods to reduce the dose level without compromising the image quality. Therefore, this paper proposes a deep learning-based post-processing method called Deep Convolutional Neural Network with Residual Learning (DCNN-RL). The method trains the network on a newly available low-dose CT benchmark dataset (LoDoPaB-CT). It also enables to compare with other benchmark CT datasets such as AAPM LDCT and COVIDx-CT. The proposed architecture optimizes the filtering part to minimize the error function. It learns the parameters of the residual network via numerous training to maximize the efficiency of production. This paper compares noise methods on DCNN-RL using various LDCT datasets of the same domain (human being's chest CT scan) to analyze the image quality. The experiment findings suggest that the Adagrad optimizer is the best for LDCT images. Gaussian noise with a minor variance outperforms the medical image reconstruction task. Here, it has been demonstrated that this approach with these benchmark datasets drastically improves the medical CT image quality, shown through qualitative and quantitative outcomes. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Computed tomography Deep learning Image reconstruction Low-dose CT Quantitative comparison Residual learning

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A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning 25

A Case Study on Context-Aware Neural Machine Translation wit...

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25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna India Wipro AI Lab45 London United Kingdom School of AI and Data Science Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781068690709

In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies (Li et al., 2020) have shown that the context encoder generates noise and makes the model robust to the choice of context. This paper further investigates this observation by explicitly modelling context encoding through multi-task learning (MTL) to make the model sensitive to the choice of context. We conduct experiments on cascade MTL architecture, which consists of one encoder and two decoders. Generation of the source from the context is considered an auxiliary task, and generation of the target from the source is the main task. We experimented with German-English language pairs on News, TED, and Europarl corpora. Evaluation results show that the proposed MTL approach performs better than concatenation-based and multi-encoder DocNMT models in low-resource settings and is sensitive to the choice of context. However, we observe that the MTL models are failing to generate the source from the context. These observations align with the previous studies, and this might suggest that the available document-level parallel corpora are not context-aware, and a robust sentence-level model can outperform the context-aware models. © 2024 The authors, © 2024 European Association for Machine Translation.

关键词： Neural machine translation

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Water Quality Index Prediction Using Modified LSTM-GAN Model Towards Sustainable Urban Development 27

Water Quality Index Prediction Using Modified LSTM-GAN Model...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Narayan, Arpita Das, Ayan Kumar Birla Institute of Technology Mesra off campus Patna Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350392319

Rivers are major source of fresh water. However, urbanization is severely polluting fresh water bodies, posing serious risks to life. To handle this challenge, the proposed model predicts Water Quality Index (WQI), which is an amalgamation of GAN (Generative Adversarial Network) and modified LSTM (Long Short Term Memory) with connected memory. The GAN model is used in the pre-processing stage for intensive missing value imputation and data scaling is performed for error minimization. Further, in the prediction stage preprocessed dataset passed to LSTM with connected memory that merges input and forget gate to reduce complexity. The memory cell directly connected to the gates of modified model with added intelligence. The proposed model demonstrates superior performance over compatible existing baseline models achieving higher efficiency in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 0.0998 and 0.0977 respectively. Moreover, the R-square value shows slight increase of 0.998. © 2024 IEEE.

关键词： Generative adversarial networks

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Sign Language Translation and Voice Impairment Support System using Deep Learning 5

Sign Language Translation and Voice Impairment Support Syste...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Shajeena, J. Shiny, R.M. Mary Vespa, M. Kavitha, R. Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

The Sign Language Translation and Voice Impairment Support System (SLT-VISS) represents a groundbreaking application of deep learning methodology aimed at facilitating communication for individuals with hearing impairments or speech disabilities. By utilizing Convolutional Neural Network (CNN), OpenCV for image processing, and Pyttsx3 for text-to-voice conversion, SLT-VISS offers real-time translation of sign language gestures into both text and speech output. Using a camera as input, the CNN algorithm accurately identifies and classifies sign language gestures displayed in front of it. OpenCV complements this process by preprocessing the input images for gesture segmentation and feature extraction, enhancing the accuracy of gesture recognition. Once a gesture is recognized, Pyttsx3 converts the translated text into synthesized speech, providing immediate auditory feedback to the user. SLT-VISS is designed to bridge the communication gap faced by individuals with communication disabilities, enabling them to express themselves effectively in various social and professional settings. By seamlessly translating sign language into both text and speech, this system promotes inclusivity and accessibility, empowering users to communicate with confidence and independence. The integration of CNN, OpenCV, and Pyttsx3 technologies within SLT-VISS signifies a significant advancement in assistive technology. As technology continues to evolve, further enhancements to SLT-VISS holds the potential to revolutionize communication accessibility and enable greater inclusivity in society. © 2024 IEEE.

关键词： Convolutional neural networks

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Facial Recognition-Based Automatic Attendance Management System Using Deep Learning 2nd

Facial Recognition-Based Automatic Attendance Management Sys...

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2nd International Conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Nath, Saranga Pani Borah, Manditjyoti Das, Debojit Kalita, Nilam Kumar Hussain, Zakir Borah, Malaya Dutta Department of Computer Science and Engineering National Institute of Technology Silchar Cachar Assam Silchar788010 India

ISBN: (纸本)9789819934805

Nowadays, computer-based face recognition, along with other biometric methods, is a mature and reliable mechanism that is widely used in many access control scenarios. Face recognition is divided into two parts: Face Verification and Face Identification. Face Verification evaluates whether two photographs are connected to one person or not by comparing them, whereas Face Identification must identify a specific face among a group of available faces in the database. Angle, illumination, position, facial expression, noise, resolution, occlusion, and the small amount of one-class samples with which to work are all obstacles in face identification. In this study, we use transfer learning in a siamese network, which comprises two comparable CNNs, to do facial recognition. A pair of two face photographs is supplied to the network as input, after which the network extracts the features of the pair of images, and eventually, using a similarity criterion, it assesses if the pair of images belongs to one person or not. The results suggest that the proposed model can compete with advanced models trained on datasets with a large number of samples. Furthermore, it enhances facial recognition accuracy when compared to algorithms that are trained using datasets with a small number of samples. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Detection of Alzheimer's Disease using Machine Learning Classification 9

Detection of Alzheimer's Disease using Machine Learning Clas...

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Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Sharma, Ravi Kumar Mughalu Soloman, Shyam Sunder Jannu Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797 103 India

ISBN: (纸本)9798350318241

Alzheimer's disease is a progressive and and irreversible brain disorder that basically affects memory, thinking, and behavior. Overtime, people with this disease experience confusion, difficulty with language and changes in personality. Detecting Alzheimer's is a complex and time-consuming task that requires brain imaging reports and human expertise. The main aim of the proposed work is to develop a smart and reliable way to diagnose disease at an early stage. Early diagnosis enables timely intervention and treatments which results in potentially slowing down progress of the disease. Identifying individuals with MCI (Mild Cognitive Impairment) who are about to get migrated into Alzheimer's disease is important in both clinical trials and practices. Combining MRI (Magnetic Resonance Imaging) data and neuro-psychological test results as inputs to a multi-dimensional space for the classification of disease is a promising approach. This approach can lead to several benefits and ultimately resulting in better decisions and significant savings. In the proposed work, Multiple datasets Alzheimer's disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS) are used. A variety of supervised machine learning algorithms have been applied for automatic Alzheimer's disease detection. The performance metrics with respect to the accuracy values have been analyzed and from the results, it is observed that the minimum accuracy of 67.75% is achieved in AdaBoost and a maximum accuracy of 80.22% has been achieved using Gradient Boosting Classifier. © 2024 IEEE.

关键词： Supervised learning

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