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Smart Water Management in Agriculture: Enhancing Crop Productivity with Restricted Boltzmann Machine 1

Smart Water Management in Agriculture: Enhancing Crop Produc...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Gore, K.B. Chidambararaj, N. Kartheesan, l. Ghamande, Manasi Vyankatesh Sarode, Ghanasham C. Kalra, Gourav SNJB's Ls K. B. Jain College of Engineering Department of Mechanical Engineering Nashik India St. Joseph's College of Engineering Department of Electrical and Electronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Technology Department of Engineering Science and Humanities Pune India Dr. D.Y. Patil Institute of Technology Department of Civil Engineering Pune India Department of Mechanical Engineering Ambala India

ISBN: (纸本)9798350352931

Effective water management in agriculture is crucial for sustainable food production in the face of water scarcity and climate change. The focus of this literature is on new methods and technology that aim to maximize water usage in agricultural activities and improve irrigation efficiency. Efficient water management practices are critical for increasing agricultural yields while lowering water wastage. By implementing water-saving measures and utilizing efficient irrigation systems, farmers can enhance yields while reducing pressure on scarce freshwater resources. Preprocessing, feature selection, and model training are all part of the suggested method. during the preprocessing phase, an Adaptive Inverse Normalization layer is employed. The RFE methodology relies on a ranking algorithm that sorts the dataset from best to worst according to the attributes that are considered most important. Throughout training, the proposed approach utilized the RBM model without interruption. With a 93.21% average accuracy, this cutting-edge approach surpasses both dBN and KNN. © 2024 IEEE.

关键词： Irrigation

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deep learning based Recognition of Auto Covariance Signatures through Protein Interaction Model for Amino Acid Sequence 8

Deep Learning based Recognition of Auto Covariance Signature...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Balamurugan, K.S. Geetha Rani, K. Sadish Sendil, M. Kumutha, d. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Department of Computer Science and Engineering Bangalore560069 India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Vel Nagar Avadi Chennai India Jeppiaar Institute of Technology Sriperumudur Department of Electronics and Communication Engg Tamilnadu India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798350367904

The detection of protein interaction partners using amino acid sequences is a challenging task in bioinformatics and molecular biology. This study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to improve the accuracy of protein interaction prediction. Auto-covariance signatures are employed to capture the positional relationships between amino acids and protein sequences, facilitating the extraction of structural and functional features. CNNs are used to automatically extract both short-and long-range dependencies, while RNNs handle temporal patterns. The model incorporates self-supervised learning to handle unlabeled data, improving generalization. With a benchmark dataset, the system achieved an impressive accuracy of 98.9%, outperforming existing methods in protein interaction prediction. The approach also highlights the importance of optimization techniques in managing protein sequence structures and functions effectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Forecasting North Indian Ocean Tropical Cyclone Intensity 2

Forecasting North Indian Ocean Tropical Cyclone Intensity

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2nd International Conference on Smart Technologies, Communication and Robotics, STCR 2022

作者： Mahajan, Akshath daru, deap Thaker, Aditya Narvekar, Meera Mukhopadhyay, debajyoti Computer Engineering D. J. Sanghvi College of Engineering Mumbai India D. J. Sanghvi College of Engineering Mumbai India WIDiCoReL Research Lab Mumbai India

ISBN: (纸本)9781665460477

India is prone to tropical cyclones annually, originating from the North Indian Ocean basin. Tropical cyclones are destructive and sudden natural occurrences that annually wreak havoc by taking a huge toll on human lives and property. This engenders a need for accurately forecasting the scale of such mass-destructive events, to provide us with enough time to take precautionary measures that can reduce the death toll and minimize costs. Using the CyINSAT dataset, which gives a multimodal and temporal resolution for TCs occurred from 2014 to 2022, this paper employs and compares multiple techniques to solve the wind speed forecasting issue. All models involve recurrent networks along with image feature extractors, which are used together to predict the next wind speeds from a sequence of images. The architectural differences between these models mainly focus on the nuances involved in handling the current wind speed. The proposed architecture gives higher importance to the currently recorded wind speeds and performs significantly better than the baseline models. It successfully obtained an RMSE of 6.31, MAE of 0.093 and MAPE of 4.53. © 2022 IEEE.

关键词： Forecasting

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deep learning Based Cancer detection in Bone Marrow using Histopathological Images

Deep Learning Based Cancer Detection in Bone Marrow using Hi...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Rani B, Swaroopa Geetha, B. Shivaprasad Yadav, S.G. Shivakanth, Gandla Manjula, B.M. Hyderabad India H. K. E. Society's S. L. N. College of Engineering Department of Computer Science & Engineering Raichur India Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bangalore India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

The cancerous proliferation of abnormal white blood cells (WBC) in the bone marrow and blood is characteristic of leukemia, which has its origins in these blood-forming tissues. Blasts are immature aberrant cells that inhibit the creation of new, lineage-normal blood cells. Acute lymphoblastic leukemia (All), Acute Myeloid leukemia (AMl), Chronic lymphocytic leukemia (Cll), and Chronic Myelomonocytic leukemia (CMl) are the four Chronic lymphocytic leukemia (Cll), Chronic myeloid leukemia (CMl), and Acute myeloid leukemia (AMl) (CMl). Patients with leukemia are often diagnosed after their symptoms have been evaluated, a complete blood count (CBC) has been run, and a peripheral blood smear has been examined under a microscope by a pathologist. As part of the process to confirm and classify leukemia, a bone marrow examination and other sophisticated laboratory testing are performed. Traditional blood and bone marrow smear examination using light microscopy is plagued by intra- and inter-observer variability. Overcoming these individual biases of doctors is possible with the use of image processing-based approaches that can automatically analyse pictures of blood and bone marrow smears to detect abnormal cells. Using an American Society of Hematology dataset, the researchers classified cases of All into B-cell and T-cell subtypes, as defined by the World Health Organization (ASH). The research improves classification accuracy to 94.12% and compares the effectiveness of three distinct training strategies. The research also includes subtyping AMl into M3, M4, and M5 using a ResNet-50 pretrained network and traditional machine learning based classifiers based on the CNN's characteristics. Using the features of ResNet-50, we were able to get a classification accuracy of 96.43 percent, and we were able to get a hundred percent accuracy using just a small number of traditional classifiers. Using the properties of Element and an ASH dataset, the researchers also sought to clas

关键词： Extraction

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Air-Writing Recognition System 1

Air-Writing Recognition System

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Ramya, S.Thanga Sakthi, R. Rohitha, B. Praveena, d. R.M.K Engineering College Computer Science and Design Chennai India R.M.K Engineering College Computer Science and Engineering Chennai India R.M.D Engineering College Information Technology Chennai India

ISBN: (纸本)9781665456876

Air-Writing is the use of an object or finger movements to create linguistic characters or words in free space. Writing in the air with fingers/using an object is practically a metaphor of pen-based writing. detecting intended writing among extraneous finger/object movements entirely irrelevant to letters or words poses a challenge which should be addressed in the common pattern recognition methods. We propose a system that writes the exact mean of the motion that is drawn in front of the sensor using Hidden Markov Algorithm and OpenCV. This system draws the exact motion and is also used for virtual key generation. The output is shown like 2d trajectory and we will recognize the word error of the exact drawing. We will also analyze the 6-dOF (degree Of Freedom) motion of the recognition. The findings of the experiments indicate that the average rate of recognition of digits and numbers is 98.3% accurate. © 2022 IEEE.

关键词： degrees of freedom (mechanics)

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A Novel Routing Protocol for UWSNs Based on Integrated Simulated Annealing and Sine Cosine Algorithm 2

A Novel Routing Protocol for UWSNs Based on Integrated Simul...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Agarwal, Rohit Saravanan, T. Mohamed Ali, Hassan Premalatha, B. Samyuktha, P. Ramesh, P.S. GLA University Department of Computer Engineering & Applications Mathura India New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq CMR College of Engineering & Technology Department of ECE India GRIET Department of AIML India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (纸本)9798350368109

This paper proposes a new SA-SCA based routing protocol for UWSNs with the objective of costing the better routing paths. The presented protocol has been designed to consider the nature of UWSNs which include energy limitation, dynamic network structure and high end to end delay. Simulation outcomes revealed that the proposed SA-SCA protocol saves energy consumption up to 13% and extend the network lifetime up to 9% as well as the delivery ratio is up to 5% more than the conventional SA based protocols. The outcomes established show the ability of the protocol in improving the throughput and dependability of UWSNs. © 2024 IEEE.

关键词： Energy utilization

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Performance Evaluation of AE-RBM Based Pedestrian detectors in Autonomous Vehicles: Substantial Issues and Challenges 1

Performance Evaluation of AE-RBM Based Pedestrian Detectors ...

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

作者： Reddy, Abhilash Reddy Pabbath lal, Madan Boobalan, M. Sapkal, Narayan Pandurang Swetha Reddy, A. Kumar, A.Saran Infobuilders inc Department of IT Georgia University College of Engineering and Technology Department of Mechanical Engineering Bikaner India K S R Institute For Engineering And Technology Department of CSE Tamilnadu Tiruchengode India Dr. D.Y. Patil Institute of Technology Mechanical Engineering Dr. D. Y. Patil Unitech Society's Maharashtra Pune India R.M.K. College of Engineering and Technology Electronics and Communication Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India

ISBN: (纸本)9798350371802

Autonomous vehicles' rising profile is a reflection of the widespread interest in them. Improved dependability, reduced fuel consumption, cost savings, and enhanced passenger convenience are all benefits of these cars. However, autonomous vehicles must first have their functionality problems resolved before they can be extensively used on major routes. Pedestrian detection is a crucial component in autonomous vehicles that helps to avoid accidents. Preprocessing, feature extraction, and model training all rely on proper sequencing. A number of filters, such as the Median, Histogram, dilation and Erosion, Gaussian, mean, and others, can be used to smooth out images before lane marking recognition. Haar wavelet and lBP are the components that make up feature extraction. In order to train for AE-RBM, one must have precise control over the attributes. This method looks so much more modern than the RBM and AE algorithms of today. The results demonstrated a notable enhancement in accuracy, reaching 95.84%. © 2024 IEEE.

关键词： local binary pattern

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Edge Computing in Smart Cities: Enhancing Real-Time data Processing 2

Edge Computing in Smart Cities: Enhancing Real-Time Data Pro...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Rajakumari, S. Natarajan, C. Alone, Vinod N. Senthilkumar, P. Gayathri, R. John, Godwin J. Department of Mathematics R.M.D. Engineering College Chennai India Department of CSE P. S. R Engineering College Tamilnadu Sivakasi India Computer Engineering Department VPPCOE &VA Sion Maharashtra Mumbai India School of Architecture Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of ECE Rajalakshmi Engineering College Tamilnadu Chennai India Department of Mechanical Engineering Rajalakshmi Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331541217

One of the most crucial issues that smart cities have to keep addressing as they grow is urban mobility improvement. Maximizing the efficiency of transportation, so lowering congestion, and so optimizing traffic flow can help to solve the growing urbanization and increasing vehicle numbers. This research aims to develop and implement a deep learning method based on denseNet in order to reach the target of raising the effectiveness of urban traffic management. The unique selling proposition that distinguishes this sector from others is the introduction of modern technologies into a sector that has always depended on conventional methods. The main goal of the work was to apply denseNet architecture to analyze traffic patterns. Considering the two other strategies, the proposed option has a lot of possibilities. From evaluation criteria-which comprised recall, precision, and F1-score, the proposed solution outperformed the approaches regarded to be state-of- the-art. © 2024 IEEE.

关键词： deep learning denseNet Smart Cities Traffic Management

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Emergency Medical Service System with Integrated Private Access Server 4

Emergency Medical Service System with Integrated Private Acc...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Ayub Khan, A. Malarvizhi, E. Sivakumar, T. Gayathri Priya, S. Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India R. M. D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

during the Covid-19 epidemic, almost every hospital in India was full, and many issues led to the total collapse of the health system. The ambulance drivers who take patients to the hospital had to wait outside for a long time since there weren't enough beds, ventilators, medical Intensive care units (ICUs), or oxygen. In this research study, the microcontroller board will use all the biomedical parameters from the sensors and check with the networked medical system availability utilizing the Internet of Things (IoT) to identify which categories the patient fits into, such as oxygen bed support, ventilator support, and emergency care support. It may be challenging for a whole family to monitor a critically ill patient, who passes out to determine which hospital he/she is being transported to in an ambulance. Consequently, the microcontroller board will save details about the ambulance, hospital, and patient. It will then send the information to a community channel on Telegram for the nearby hospital and family members. © 2023 IEEE.

关键词： Internet of things

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IoT-driven Agricultural Monitoring Architecture with Novel light Machine learning for Enhanced Precision Crop Health Analysis 3rd

IoT-Driven Agricultural Monitoring Architecture with Novel L...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Andi, Hari Krishnan Ramasamy, lokeshkumar Nramban Kannan, Senthil Kumar Murugesan, Sasikumar Benarji, Bejjam S. N. Manogaran, Umamaheswari Sengan, Sudhakar Centre for Postgraduate Studies Asia Metropolitan University Johor81750 Malaysia School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Deemed to Be University Vel Tech Rangarajan Dr Sagunthala R&H12D Institute of Science and Technology Tamil Nadu Chennai600062 India C. Abdul Hakeem College of Engineering & Technology Tamil Nadu Melvisharam Ranipet632509 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

In the age of digital transformation, agriculture is harnessing the potential of advanced technologies to address global challenges related to food security and environmental sustainability. While Smart Agriculture and Precision Agriculture lay the foundation, there remains a pressing need for real-time, granular data processing to realize their full potential. Edge computing, coupled with Machine Intelligence (MI), promises a solution, yet existing models often lack the adaptability and real-time efficiency demanded by diverse agricultural scenarios. Addressing these challenges, this paper proposes the Precision Intensive Crop Health Observer (PICHO). PICHO is an innovative three-tier architecture comprising the Field data Interface Unit (FdIU) for seamless sensor data collection, the Edge Analytics Processor (EAP) for real-time edge-based analytics, and the Cloud Coordination Hub (CCH) for data storage and in-depth analysis. Central to the EAP's efficacy is the lightweight learning model lENet, which is designed with dual pipelines for processing sensor and image data. Specifically crafted to be trained on resource-constrained edge devices, lENet ensures that PICHO remains adaptable, efficient, and poised to redefine the future of precision agriculture. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2025.

关键词： Smart agriculture

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