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AI Method for Improving Crop Yield Prediction Accuracy Using ANN

computer systems science & engineering 2023年第10期47卷 153-170页

作者： T.sivaranjani s.P.vimal Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical CampusCoimbatore642109India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering CollegeCoimbatore641022India

Crop Yield Prediction(CYP)is critical to world food *** safety is a top priority for *** rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural *** Yield(CY)is a complex variable influenced by multiple factors,including genotype,environment,and their *** is a significant agrarian ***,CYP is the main task due to many composite factors,such as climatic conditions and soil *** Learning(ML)is a powerful tool for supporting CYP decisions,including decision support on which crops to grow in a specific ***,Artificial Neural Networks(ANN)are usually used to predict the behaviour of complex non-linear *** a result,this research paper attempts to determine the correlations between climatic variables,soil nutrients,and CYwith the available ***,threemethods,Levenberg-Marquardt(LM),bayesian regularisation(bR),and scaled conjugate gradient(sCG),are used to train the neural network(NN)model and then compared to determine prediction *** performance measures of the training,as declared above,such as Mean squared Error(MsE)and correlation coefficient(R),were determined to assess the ANN models that had been *** experimental study proves that LM training algorithms are better,while bR and sCG have minimal performance.

关键词： Crop prediction accuracy ANN precision agriculture crop yield

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Enhancing Relational Database Interaction through Open AI and stanford Core NLP-based on Natural Language Interface 5

Enhancing Relational Database Interaction through Open AI an...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCsT 2024

作者： Kumar, surjeet Alam, Md shamsher Khursheed, Zeeshan bashar, shadan Kalam, Neha Government Engineering College Computer Science & Engineering Bihar Sheikhpura India R.V.S College of Engineering & Technology Computer Science and Engineering Jamshedpur India Jio Platform Limited DGM Mumbai India R.V.S College of Engineering & Technology Master of Computer Application Jamshedpur India R.V.S College of Engineering & Technology Computer Science and Engineering Student Jamshedpur India

ISBN: (纸本)9798350351378

In the contemporary digital era, data has evolved into the cornerstone of myriad sectors, encompassing academia, commerce, and medical research. Yet, the acquisition of insights from this expansive reservoir of information has historically demanded a level of proficiency in formal languages such as sQL, thereby imposing a substantial barrier for individuals lacking technical expertise. In response to this pervasive challenge, the advent of Natural Language Interface to Databases (NLIDb) has emerged as a promising and transformative solution. NLIDb offers users the capability to engage with databases through the utilization of their natural language, whether expressed in textual or verbal form. This paradigm shift empowers both seasoned experts and neophytes to effortlessly extract valuable information from databases without the prerequisite of specialized technical skills. In this research paper, we propose an innovative system that capitalizes on an intermediary sQL generation process, facilitating the seamless bridge between natural language input and subsequent database retrieval. This novel approach eliminates the need for users to possess awareness of the intricate underlying complexities of the database management process. by simplifying the interaction between users and databases, our system endeavours to democratize data access and promote the universal utilization of this invaluable resource across diverse domains. © 2024 IEEE.

关键词： Natural language processing systems

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Towards Improved speech Enhancement:Parameter Optimization in the scalart Wiener Filter Method 2

Towards Improved Speech Enhancement:Parameter Optimization i...

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

作者： Puthran, Prathvi P samhitha, D. Prahlad, siri Udupa, spoorthi sooda, Kavitha B.M.S College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350366570

Communication in noisy environments demands efficient noise suppression techniques due to reduced intelligibility and listener fatigue. This research optimizes the Decision-Directed Wiener filter for speech enhancement by meticulously adjusting the smoothing factor (alpha) and noise updating parameter (NoiseLength), crucial for adaptive performance across diverse acoustic settings. Using the Noizeus dataset, a thorough evaluation was conducted to assess how varying these parameters impacts speech enhancement quality. The proposed adjustments were rigorously evaluated using objective metrics such as Perpetual Evaluation of speech Quality(PEsQ), Normalized Cross-correlation Metric (NCM), and signal-to-Noise Ratio (sNR), offering a comprehensive assessment of speech enhancement quality. These metrics collectively offer insights into speech enhancement quality. Findings show that fine-tuning alpha and NoiseLength parameters significantly improve speech quality, underscoring their critical role in optimizing the Decision-Directed Wiener filter. With the optimal settings, the enhancements led to an average increase of 19.72% in Perpetual Evaluation of speech Quality (PEsQ), 17.68% in Normalized Cross-correlation Metric (NCM), and 59.35% in signal-to-Noise Ratio (sNR), demonstrating the effectiveness of the parameter adjustments. © 2024 IEEE.

关键词： Wiener filtering

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Group Person Identification and Communication Using Deep Learning

Group Person Identification and Communication Using Deep Lea...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCs 2024

作者： verma, vijaya Acharya, Atharva shahi, sakshi Kumar, vishnu Patil, seema B.M.S College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372502

This innovative project introduces a pioneering solution for group person identification and communication by using the power of Multi-Task Cascaded Convolutional Neural Networks (MT-CNNs). The system is designed to seamlessly identify individuals within a group photo and facilitate direct communication with them through the integrated platform, enhancing user interactions in various contexts. The project's core objective revolves around the evolution of a strong MT-CNN-based model for precise person identification within group photographs. Leveraging state-of-the-art deep learning techniques, our model is trained on a diverse dataset, encompassing a large set of group sizes and compositions. Extensive preprocessing ensures accurate facial detection and alignment, setting the foundation for reliable person recognition. Upon successful identification, the system initiates direct communication channels with the recognized individuals. The user interface is designed for simplicity, enabling users to effortlessly upload photos, initiate person identification, and engage in immediate communication. This communication module is integrated into the framework to enable users to establish immediate contact with recognized individuals, fostering efficient and meaningful interactions. This project's innovative nature lies in its ability to lessen the distance between digital and physical worlds, offering a transformative solution for enhanced social interactions. © 2024 IEEE.

关键词： Face recognition

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breast Cancer Detection with MobileNetv2 and Explainable AI 4

Breast Cancer Detection with MobileNetV2 and Explainable AI

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4th International Conference on sustainable Expert systems, ICsEs 2024

作者： sumaiyya, sara shyamala, G. Latha, N.R. Pallavi, G.b. B.M.S College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

To save lives, early identification of breast cancer is essential. While medical imaging benefits greatly from the use of models such as AlexNet, vGGNet and ResNet their high computing demands make their adoption difficult in locations with limited resources. This study presents an innovative approach to breast cancer detection by leveraging MobileNetv2, a model designed for efficiency in low-resource environments. While the standard MobileNetv2 achieved an accuracy level between 85% and 87%, our implementation of a custom classification head significantly enhanced performance, achieving an impressive accuracy of 98.8%. The model also demonstrated a precision of 97.5%, recall of 98.2%, and an Fl score of 97.8%. To further improve transparency, we applied Local Interpretable Model-agnostic Explanations (LIME), which gave us significant understanding into the model's decision-making process. This integration of efficiency and interpretability offers a viable answer to affordable breast cancer detection, with substantial potential for application in resource-limited settings. © 2024 IEEE.

关键词： Accuracy Head Explainable AI Computational modeling Decision making breast cancer Expert systems standards biomedical imaging

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An Algorithmic Approach to 4-way Autonomous Intersection Management 2

An Algorithmic Approach to 4-way Autonomous Intersection Man...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： savanur, Niranjan Chandru, Nithin Asha, G.R. Ravi, Kusum Meda B.M.S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350383287

The objective of this research is to present an approach focused on optimizing intersection management, a subset of end-to-end autonomous driving. The proposed method relies on vehicle-to-vehicle (v2v) communication and an intersection signal amplifier to enhance intersection handling. This approach prioritizes improving intersection management by using v2v communication in place of conventional traffic signal intersections and also increases intersection safety while streamlining resource allocation and time efficiency. Evaluations are conducted using the microscopic traffic simulator sUMO (simulation of Urban MObility), and the results indicate a noticeable reduction in congestion. The intended goal here is to reduce the average time taken for travel and provide travel convenience by aiming for constant speed. Therefore provides an efficient algorithm for implementing and testing autonomous intersection management strategies with autonomous driving systems, contributing to developing more efficient and safer autonomous transportation solutions. © 2024 IEEE.

关键词： Autonomous vehicles

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A Comparative Analysis of Traditional Machine Learning, Deep Learning and boosting Algorithms on Phishing URL Detection

A Comparative Analysis of Traditional Machine Learning, Deep...

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2024 IEEE International Conference on computer vision and Machine Intelligence, CvMI 2024

作者： Ashok, Arvind Rathis, Dhiksha Raghavendra, Richa Umadevi, v. B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350376876

Phishing attacks pose a significant threat to user's data security by using counterfeit Uniform Resource Locators (URLs) that are hard to distinguish from legitimate ones. Machine learning can help identify phishing URLs by analyzing their features. This research work compares phishing detection mechanisms using Machine Learning, Deep Learning, and boosting techniques, evaluating their accuracy across five datasets. The datasets are: Dataset-1 (Kaggle, 2 class), Dataset-2 (UCI, 2 class), Dataset-3 (Mendeley, 2 class), Dataset-4 (UNb, 5 class) and Dataset-5 (Kaggle, 4 class). The analysis shows that boosting techniques outperform traditional Machine Learning and Deep Learning, achieving detection accuracies of 9 7 %, 9 6 %, 9 8 %, 9 8 % and 92% across the five datasets respectively. © 2024 IEEE.

关键词： Phishing

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Leveraging Online social Content for Early Detection of suicidal Ideation: A Multi-Modal Deep Learning Approach

Leveraging Online Social Content for Early Detection of Suic...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCs 2024

作者： Toliya, Nikita Paras Nagarathna, N. B.M.S. College of Engineering Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9798350372502

The contemporary society is grappling with the alarming rise in suicide rates, exacerbated by factors such as prolonged exposure to negative emotions and challenging life events. Detecting early signs of suicidal ideation holds significant promise in addressing this crisis. In the digital age, online communication platforms have become outlets for individuals to express distress, presenting an opportunity for timely intervention. This study aims to explore online social content for the early identification of suicidal ideation, with a focus on user-generated text that contains valuable insights into people's mental states. The research begins with a thorough content analysis to extract knowledge from suicide-related text, establishing a benchmark for binary classification of suicidal ideation using various classifiers, including feature-based and deep neural networks. The urgency of early detection for prevention underscores the importance of this research. by integrating content analysis, feature engineering, and advanced deep learning methodologies, such as deep neural networks and attentive relation networks, this study aims to contribute to suicide prevention efforts. This paper advocates for the application of multi-modal deep learning techniques to identify early signs of suicidal ideation from online social content. The integration of machine learning and mental health intervention is crucial in addressing the escalating concern of suicide rates, especially among vulnerable populations like youths. Through our research, we aim to provide a valuable tool for promoting mental well-being and ultimately saving lives. © 2024 IEEE.

关键词： Deep neural networks

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GAN based Model for virtual Try on of Clothes

GAN Based Model for Virtual Try on of Clothes

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCs 2024

作者： Toliya, Nikita Paras Chadaga, Neha b. Harshitha, R.M. Dhruva, M. Nagarathna, N. B.M.S College of Engineering Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9798350372502

E-commerce platforms focused on fashion, imagebased virtual try-on solutions have shown considerable potential for enhancing the user experience and increasing customer satisfaction. The 2 D solutions that exist fail to provide a complete satisfactory experience as they lack the capability to take into consideration customer-specific postures and modifications. They rely majorly on generative models due to which reconstructionof high-quality images of the clothing is difficult. The clothing in 3 D v T solutions is created as 3 D models, which are thendraped on 3 D human avatars with various body types. A huge catalog of digitized 3 D garment and clothing models is a crucial requirement for a 3 D vT system, but this requirement requires expensive multiview capture/scan equipment and cannot scaleup for big catalog volumes, especially in the current fast-fashion settings. Hence, we propose a video virtual Try-On model which is based on fully learnable characteristics preserving virtual try on network C P vTON [3]. It first forms a TsP for transforming clothes to fit the target body shape via Geometric Matching *** use a GAN based model for pose *** accuracy resulting from such a model could help in decreasing the waiting time in traditional stores, and also help in decreasing sanitizing costs. © 2024 IEEE.

关键词： Customer satisfaction

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Implementation of Land Registry system Using Ethereum blockchain and IPFs 8

Implementation of Land Registry System Using Ethereum Blockc...

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8th IEEE International Conference on Computational system and Information Technology for sustainable solutions, CsITss 2024

作者： Chandru, Nithin savanur, Niranjan Asha, G.R. vijayeshjeevan, Matam B.M.S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Frequent changes in property ownership can lead to complex and lengthy records, which may allow fraudulent or incomplete records to go unnoticed. The primary issues stem from incomplete and damaged records caused by different departments storing their own copies, resulting in a lack of comprehensive verification and an increased risk of document forgery. This system proposes a solution using a blockchain-based approach, which creates immutable and transparent records of land ownership by creating an entity to represent land in the blockchain network, making it difficult for unlawful users to forge or modify records. It employs cryptographic methods, consensus protocols, and hashing algorithms to preserve the integrity of the transaction chain. This system proposes an implementation model using Ethereum, and benchmarking shows that the system has a low transaction processing time, making it suitable for practical use. Additionally, the use of the Interplanetary File system (IPFs) for storing all land and user-related documents provides additional security. Cost and performance analysis are carried out based on experiments conducted, demonstrating the system's efficiency and effectiveness. This could streamline the process of land ownership transfer, promote environmental sustainability by minimizing the need for manual paperwork, and reduce the likelihood of disputes and fraud. © 2024 IEEE.

关键词： Ethereum

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