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Advancements in Environmental data Analysis for Climate-Resilient Agriculture Using Remote Sensing and deep learning

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 670-680页

作者： Nirmal, Khyati R. Reddy, Chilukala Mahender P, Jose Tulasi, Rajesh Maheswari, G. Uma Sapthami, I. SNJB’S Late Sau KBJ CoE Maharashtra Nashik India Department of Artificial Intelligence and Data Science GITAM Deemed to Be University Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai Avadi India Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation A.P Vaddeswaram Guntur Dist. India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Pudhuvoyal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Weather significantly influences agricultural productivity. Plant biotic and abiotic stressors are primarily induced by climate change, resulting in a detrimental effect on worldwide agricultural productivity. These two components are closely interconnected. This paper presents an innovative approach in smart agriculture for climate change combining remote sensing and a deep learning algorithm. The input is obtained as a multispectral environmental image and then subjected to noise reduction and normalisation processing. The image has been retrieved using a primary convolutional component with a stacked encoder model. The retrieved features are identified using ResNet graph reinforcement neural networks. The resulting classification output displays environmental imagery depicting climate variations. The agriculture sector has been analysed based on the classified climate analysis. The experimental results have been conducted on diverse farm datasets related to climate change, evaluating the detection accuracy, recall, mean average precision, normalized correlation, and F-1 score. The proposed method achieved a detection accuracy of 98%, a normalised correlation of 95%, a mean average precision of 92%, a recall of 97%, and an F-MEASURE of 94%. Machine learning can assist in monitoring and predicting the impact of climate change on food security, as indicated by the findings. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change deep learning model Remote sensing Smart agriculture Stacked encoder model

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Proactive Risk Management for Handling Flammable liquids Safely IoT Integration with Random Forest Algorithm 8

Proactive Risk Management for Handling Flammable Liquids Saf...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Bharath, A. Muthukumaran, d. Varadarajan, Mageshkumar Naarayanasamy Sivanantham, E. Mahalakshmi, R. Srinivasan, C. Cvr College of Engineering Department of Computer Science and Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Capital One Glen AllenVA United States Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

Preventing accidents and ensuring worker safety in industrial settings requires the careful handling of flammable substances. In this research, it provides a proactive strategy for risk management that makes use of the Random Forest algorithm in combination with the Internet of Things (IoT). Integrating the IoT with Random Forest for proactive risk management and dependable monitoring is the objective to increase safety in the handling of hazardous liquids. It collects detailed information about the environment around storage and handling locations for flammable liquids by using IoT sensors to track variables like humidity, temperature, and pressure in real-time. After that, the Random Forest algorithm is used to the data to find patterns and make predictions about possible accident risk factors. Our strategy employs IoT and machine learning to achieve continuous monitoring and early anomaly identification. This method is for prompt action to reduce risks and improve safety measures. It provides experimental data that shows how our suggested strategy improves the safety of handling flammable liquids, which in turn reduces the danger of accidents and protects both people and property. © 2024 IEEE.

关键词： Risk management

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Cryptography Innovations for Securing data in the Quantum Computing Era: Integrating Machine learning for Enhanced Security

Cryptography Innovations for Securing Data in the Quantum Co...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Temara, Sheetal Bhagyalakshmi, l. Suman, Sanjay Kumar Shakunthala, M. Chitra, N. Thulasi Golla, Kishor University of the Cumberlands Department of Computer and Information Sciences United States Rajalakshhmi Engineering College Dept. of ECE TN Chennai India St. Martin's Engineering College Dept. of Ece and Dean R&d Telangana Secunderabad India R.M.D. Engineering College Dept. of ECE TN Thiruvallur India Mlr Institute of Technology Dept. of CSE Hyderabad India St. Martin's Engineering College Dept. of CSE Telangana Secunderabad India

ISBN: (纸本)9798331513894

The rise of quantum computing introduces substantial risks to traditional cryptographic protocols, which are vulnerable to quantum decryption methods such as Shor's algorithm. To address these emerging threats, this paper proposes an innovative cryptographic framework that integrates machine learning (Ml) techniques to enhance data security in the quantum computing era. Our approach leverages Ml-driven anomaly detection, adaptive key management, and predictive analytics to create a flexible and resilient cryptographic defense. The system's anomaly detection module utilizes neural networks to identify potential quantum-based decryption attempts, while reinforcement learning optimizes key generation and distribution in response to detected threats. Experimental results demonstrate that the proposed Ml-augmented framework significantly improves anomaly detection accuracy and reduces vulnerability to quantum decryption attempts by dynamically adjusting cryptographic parameters. These findings underscore the potential of machine learning to strengthen cryptographic systems, making them adaptable to the advanced threats posed by quantum computing. © 2025 IEEE.

关键词： Predictive analytics

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Mental Health and Suicide Risk among college Students: A detailed Exploration of Contributing Factors 15

Mental Health and Suicide Risk among College Students: A Det...

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

作者： Meena, R. Gunasundari, B. Omana, J. Sripriya, N. Yamini, G. Revathi, K.P. Easwari Engineering College Department of AI&DS Chennai India Prathyusha Engineering College Department of CSE Chennai India VIT University School of Computer Science and Engineering Chennai India Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of English Chennai India

ISBN: (纸本)9798350370249

This study aims to identify critical predictors of suicide and inform targeted interventions using data from 27,014 college students. It examines a comprehensive range of mental health indicators, including psychiatric symptoms, suicidal intentions, impulsivity, sleep disturbances, internet addiction, aggression, self-injury behaviors, and eating problems, alongside demographic and socioeconomic variables. Analytical techniques, including exploratory data analysis, cross-tabulation, and correlation analysis, uncover the complex interplay between these factors and suicide risk. The study evaluates several machine learning algorithms. The weighted Random Forest uses feature weighting based on point-biserial correlation coefficients to enhance prediction accuracy and prioritize relevant features. The findings reveal significant associations between higher scores in mental health indicators and increased suicidal behavior, emphasizing the need for comprehensive mental health support systems. By identifying key risk factors through machine learning models, the study provides actionable insights for developing targeted interventions aimed at reducing suicide risk among college students. © 2024 IEEE.

关键词： Feature weighting Fuzzy evaluation method Machine learning models Point-biserial correlation coefficients

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FAMMNPB: design of an Enhanced Forensic Analysis Model with Multidomain Feature Extraction and Advanced Neural Architecture for Permissioned Blockchains

FAMMNPB: Design of an Enhanced Forensic Analysis Model with ...

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International Conference for Convergence of Technology (I2CT)

作者： Ashwini Y. Mate Kiran S. Khandare Mohammad Hassan Kiran Nikesh Bode Nikesh S. Bode Karishma K. Missal Ashwini Y. Mate Department of Computer Science and Engineering (AIML) Ramdeobaba College of Engineering and Management Nagpur Department of Computer Science and Engineering (AIML) Ramdeobaba College of Engineering and Management Nagpur Department of Electronics and Telecommunication J D College of Engineering and Technology Nagpur Department of Computer Science and Engineering J D College of Engineering and Technology Nagpur Department of Computer Science and Engineering Suryodaya College of Engineering and Technology Nagpur Department of Information Technology J D College of Engineering and Technology Nagpur

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

This work introduces a novel approach that leverages multidomain feature extraction coupled with a 1d Convolutional Neural Network (CNN) for robust anomaly detection. To address the challenge of transaction delay predictions, we employ a Bi-directional Gated Recurrent Unit (BiGRU) RNN, for its capability to capture temporal sequences with high precision. For the task of chain splitting, the Teaching-learning-based Optimization (TlbO) technique is used to ensure the efficient creation of sidechains. Further, we integrated deepSHAP, in tandem with 1d CNN and BiGRU, to enhance the performance of our models, fostering better trust in the results. Our proposed methodology yields notable improvements over existing techniques. As we observed enhancements in the precision of anomaly detection by 4.9%, accuracy in detecting anomalous events by 3.5%, Recall by 4.5% and AUC by 3.9%. These advancements fortify the security posture of permissioned blockchains and optimize their operational efficacy, thus marking a significant improvement in blockchain forensics.

关键词： Measurement Analytical models Forensics Biological system modeling Predictive models Feature extraction Blockchains

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A Review of Recent Advancements in Infant Brain MRI Segmentation Using deep learning Approaches 7th

A Review of Recent Advancements in Infant Brain MRI Segmenta...

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7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Ahir, Param Parikh, Mehul Gujarat Technological University Gujarat Ahmedabad382424 India L.D. College of Engineering Gujarat Ahmedabad380015 India

ISBN: (纸本)9789819907687

In this paper, a critical analysis of recent trends and techniques for tissue segmentation of an pediatric brain Magnetic Resonance Imaging (MRI) is performed. A significant amount of research has recently been conducted in the field of medical image analysis. In medical image analysis, MRI is regarded as one of the primary imaging modalities. MRI is capable of providing a clear view of various organs in depth. Tissue segmentation is an important process in brain research, especially when the subjects are infants and defining normalcy is difficult. Our paper focuses on recent developments in this field. deep learning technological advancements are assisting this field in moving forward with dataset availability via challenges such as iseg-2017 and iseg-2019. This paper compares methodology, dataset, and results obtained by various methods. The paper also discusses the limitations of all available approaches as well as possible future directions in the field. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Magnetic resonance imaging

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Transfer Function Based Feature Selection Using Harris Hawk Optimization for Biomedical Applications

Transfer Function Based Feature Selection Using Harris Hawk ...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Jayachitra, S. Prasanth, A. Prasannakiruba, G.S. Abbineni, Srichandana PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331528126

Feature Selection plays key role to truncate the high dimensionality in the datasets through eradicating the unwanted and noisy attributes to improve the performance of classification. The Meta-heuristic technique is one of the most optimistic search techniques to travel the feature space to locate finest feature subset. The Harris Hawk Optimization (HHO) excited from hunting approach of hawk in nature. The cost effectiveness and simplicity formulates the algorithm prominent and superior in solving optimization problems. The binary version of the optimization is accountable for delineating continuous searching space to discrete feature space. Here, 6 transfer functions with S-Shaped and V-Shaped families were interposed and analyzed. Twelve benchmark datasets from UCI repository has been introduced to examine the transfer functions and choose the best feature pertaining to mitigate local minima and high speed of convergence. To analyze the efficacy of transfer function, the proposed method was compared over exiting algorithms and the proposed results attain higher classification accuracy in many of the datasets. © 2024 IEEE.

关键词： Heuristic methods

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PUF-Based lightweight Authentication Protocol for IoT devices

PUF-Based Lightweight Authentication Protocol for IoT Device...

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Intelligent Computing and Networking - Proceedings of IC-ICN 2023

作者： Shah, Amita Pandya, Hetal Soni, Mukesh Karimov, Akramjon Maaliw, Renato R. Keshta, Ismail Department of Computer Engineering L D College of Engineering Gujarat Ahmedabad India Department of CSE University Centre for Research and Development Chandigarh University Punjab Mohali140413 India Department of Corporate Finance and Securities Tashkent Institute of Finance Tashkent Uzbekistan College of Engineering Southern Luzon State University Quezon Lucban Philippines Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia

ISBN: (纸本)9789819931767

The Internet of Things (IoTs) carries the secure transmission and storage of a large amount of sensitive information. This paper uses the anti-tampering and anti-cloning features of the hardware physical unclonable function (PUF) to generate a shared key, combined with security primitives such as MASK algorithm and Hash function and proposes a lightweight Anonymous key-sharing security authentication protocol. Through the security analysis and verification of Ban logic and the proper tool ProVerif. It is proved that the protocol can defend against man-in-the-middle attacks, desynchronization attacks, impersonation attacks, modeling attacks, etc. By comparing other protocols, it is verified that the protocol has the advantages of low computing cost, small communication overhead and storage capacity, and high-security performance;it is suitable for secure communication transmission of resource-constrained devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Authentication

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The Application of Machine learning Algorithms to the Classification of EEG Signals

The Application of Machine Learning Algorithms to the Classi...

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

作者： Gurumoorthy, Sasikumar Avikkal, Saritha Rani, M. Radha, K. Rajesh, N. J.J.College of Engineering and Technology Department of Computer Science and Engineering Trichy India Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Electronics and Communication Engineering Telangana 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

In order to study cognitive states and detect deficiencies in thinking, planning, and judgement, automated cognitive assessment systems have been developed. Having access to the opportunity to collaborate with experts in their working environment is another benefit of these platforms, particularly in services where decisions have direct effects on people's lives. This dissertation assesses the brain's reaction to mental tasks including motor imagery, attention, working memory, mental fatigue, and attention deficit hyperactivity disorder (AdHd). Electroencephalography (EEG), which encodes neuron activity as action potentials, is a popular method for analyzing these cognitive functions. These EEG data are processed using state-of-the-art machine learning algorithms, automating cognitive ability testing in the process. Recent state-of-the-art approaches have shown problems with high dimensionality, irrelevant channels, subject and task variability, generalizability, and interpretability. Because of this, classifications of intelligence are often inaccurate. The use of meta-heuristics to the solution of both continuous and binary optimization problems was found to be very fruitful in this scenario. despite increased performance in EEG signal processing for other applications, these methods have seen very little exploration in the field of cognitive evaluation. This encourages more research into these meta-heuristics approaches to intelligence testing. It is important to look for the most appropriate optimization strategy for a certain task, since all of them will not work in every circumstance. In order to address problems in cognitive testing, this research looks on parameter-free optimization algorithms that are tailored to each kind of test. One of the main problems that hinders the effectiveness of Ml models is its dimensionality. Another is its lack of generalizability. The overarching goal of this proposed work is to build and construct an effective channel select

关键词： Electroencephalography

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Understanding the Implications of SIM Card Swap Fraud in India: A Comprehensive Study

Understanding the Implications of SIM Card Swap Fraud in Ind...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Bhavana, S.U. doreen Hephzibah Miriam, d. Robin, C.R. Rene S.D.N.B Vaishnav College For Women Pg Department Of Computer Science Chennai India Chennai India Sri Sairam Engineering College Department of CSE Chennai India

ISBN: (纸本)9798350352689

SIM swapping is a type of cyber attack where an attacker persuades a mobile service provider to transfer a victim's phone number to an attacker-controlled SIM card. This allows the attacker to access the victim's internet accounts, including banking and social media, by changing passwords associated with the victim's phone number. Recent high-profile incidents, such as a Californian man stealing $5.3 million in cryptocurrencies and a Massachusetts man stealing $550,000 worth of cryptocurrencies through SIM swapping attacks, underscore the severity of the crime and the urgent need for enhanced security measures to prevent such attacks. SIM swapping poses a significant security risk with potentially catastrophic consequences for victims. It is crucial for individuals to be aware of the dangers of SIM swapping and take precautions to protect themselves, such as adopting two-factor authentication and using secure passwords. Additionally, mobile service providers must implement measures to ensure the security of client accounts against SIM swapping attacks. despite experiencing numerous intrusions, many people in India remain unaware of effective cybersecurity solutions to safeguard their businesses, leading to significant losses. This article discusses the process of SIM card swapping, its benefits and drawbacks, real-life instances of SIM swapping attacks, and its impact on cybersecurity. It also provides several preventive measures to mitigate the risk of similar attacks. © 2024 IEEE.

关键词： Cybersecurity

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