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Enhancing Arecanut Farming Profits through Technological Advancements: A CNN-based Approach for Efficient Grading and Sorting 5

Enhancing Arecanut Farming Profits through Technological Adv...

引用

5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Dhanush Ghate, D. Pallavi, K.N. Poojari, Asmita NMAM Institute of Technology Dept. of Computer Science and Engineering Karnataka Nitte574110 India

ISBN: (纸本)9798350395235

Technological advancements can have powerful impact on the economic growth of agriculture in India. Areca nut farming is one such terrain of agriculture where investment in technology and automation can elevate the profit from farming by bringing down production cost. Areca nut is the major crop of Karnataka having roots in fifteen different districts. The grading and sorting of areca nuts is an important step in the production of areca nuts and is traditionally done by man power. People are trained to identify good and bad areca nuts, but the time taken for sorting areca nuts is more and it is not feasible for a large quantity of areca nuts. In order to minimize the time consumed, this method is introduced which classify the areca nuts into four different grades based on their quality. © 2024 IEEE.

关键词： Sorting

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Event Management System Using Generative AI 7

Event Management System Using Generative AI

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thummala, Sruthi Thammishetti, Saketh Varkol, Sharanya Thirunahari, Amarthya Lakshmi Kanthey, V.V.S. B V Raju Institute of Technology Dept. Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

This project depicts how Artificial intelligence is used in developing a "User-Engaging Event Management System". The developed system will spontaneously record all the events and online contests registered by an individual. Motivates the individual to participate the particular event by providing sufficient resources. It implements a persistent alarm or remainder system that operates continuously. The implementation of the system starts with choosing appropriate technologies (Deep learning Libraries, Natural Language Processor, DBMS, Web Development Framework and so on) focusing on generative AI followed by data collection (from existing event data, social media, crowdsourcing and so on) and preprocessing the data to ensure the quality for AI model training which integrates with app architecture. This study evaluates the effectiveness of the generative AI model in Event Management App by employing key numerical metrices. The accuracy metric gauges the model's overall correctness in generating event-related content, while precision focuses on the accuracy of positive predictions. It incorporates a robust feedback mechanism ,allowing users to provide input and receive corresponding responses, enhancing the overall user experience. The system will provide security for user information, access controls, and authentication protocols © 2024 IEEE.

关键词： Access control

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Robotics Process Automation Technologies for Data Collection and Analysis 15

Robotics Process Automation Technologies for Data Collection...

引用

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

作者： Usman, K. Hayath, T.M. Shafiulla, Mohammed Dadapeer Ballari Institute of Technology and Management Dept. of Computer Science and Engineering Karnataka Ballari India

ISBN: (纸本)9798350370249

Data collection and analysis are critical aspects of various business processes. However, these tasks can be time-consuming, prone to errors, and delays employee productivity when done manually, especially when we have to deal with huge data. Robotic Process Automation (RPA) technologies offer a promising solution in form of a BOT, by automating tedious tasks related with data handling. A survey by The Economist corporation believe that automation kick launches digital transformation, robotic process automation (RPA) has evolved from an emerging technology to a must-have digital transformation tool for enterprises to compete globally. An increasing number of companies are now using RPA to automate normal business processes in a non-intrusive and cost-effective way. On the other hand, decision-making, pattern recognition, and many other business-related tasks benefit greatly from the use of big data and analytics. This paper explores the application of RPA in data collection and analysis, examining its functionalities, benefits, and limitations. In practical, Bot produces error-free work and takes 94.44% less time for data collection compared to a manual process, which encourages all firms to adopt to RPA technologies. © 2024 IEEE.

关键词： Automation Data collection Process Robotics RPA

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Investigating the Efficacy of Diverse Machine Learning Classifiers for Parkinson's Disease Detection 15

Investigating the Efficacy of Diverse Machine Learning Class...

引用

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

作者： Roy, Sourabarna Debbarma, Tina Pal, Tannistha Duraisamy, Prakash National Institute of Technology Agartala Dept. of Computer Science and Engineering Agartala India University of Wisconsin- Green Bay Dept. of Computer Science and Engineering WI54311 United States

ISBN: (纸本)9798350370249

To detect Parkinson's disease, we compare the effectiveness of K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) algorithms. Utilizing a dataset with clinical and biomedical features, we preprocess the data to handle missing values and standardize the features. Subsequently, we train each algorithm with the preprocessed data and evaluate their performance using metrics like accuracy, precision, recall, and F1-score. Our results indicate that all four algorithms achieve excellent accuracy in diagnosing Parkinson's disease, with KNN slightly outperforming the others. However, the selection of the algorithm may depend on specific needs such as interpretability and computational efficiency. Additionally, we conduct a feature importance analysis to identify the most relevant features for Parkinson's disease identification, offering insights that can aid in early diagnosis and disease management. © 2024 IEEE.

关键词： Logistic regression

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A Transformer-Based Model for Image Caption Generation with Memory Enhancement

A Transformer-Based Model for Image Caption Generation with ...

引用

2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Ome, Shadika Afroze Azhar, Tanvir Asaduzzaman East Delta University Dept. of Computer Science & Engineering Chattogram Bangladesh Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798331535476

This study proposes a convolution-free transformer-based method for generating accurate descriptions of images. A Vision Transformer is utilized as the primary encoder, replacing traditional CNN, and a Meshed Memory Transformer is integrated for generating captions. The Vision Transformer splits images into fixed-size patches for extracting visual features, which are further processed by a memory-augmented encoder to integrate prior knowledge. It ensures key features are preserved and reused and the meshed decoder combines the text embedding with the outputs from each encoding layer to generate more contextually rich and accurate captions compared to conventional approaches. The model's performance is evaluated on the Flickr30K and the MSCOCO datasets, demonstrating that the Meshed Memory architecture in our approach improves both the image encoding and caption generation step. Our model outperforms the conventional 'CNN-LSTM' models and 'CNN-Transformer' methods, achieving a 1.02% improvement in METEOR and 1.31% in SPICE on the MSCOCO dataset. Additionally, on the Flickr30k dataset, our approach exhibits superior performance across most metrics, notably a 6.37% increase in the METEOR score. © 2024 IEEE.

关键词： Encoding (symbols)

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Software Effort Estimation using ANN (Back Propagation) 7

Software Effort Estimation using ANN (Back Propagation)

引用

7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ravi Kumar, B.N. Suresh, Yeresime Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India Dept. of Computer Science and Engineering Ballari Institute of Technology and Management Ballari India

ISBN: (纸本)9781665464086

Project managers often use Effort Estimating strategies to manage the human resources of current or upcoming software projects. Prior to project implementation, cost, time, and personnel estimation are basically necessary. For every project of software, getting an accuracy in Effort Estimation has always been difficult. In case of this study, estimating the labor involved in developing software determined using a Back Propagation Model. This model's goal is to investigate the capabilities and potential uses of utilizing Artificial Neural Networks (ANN) as a tool for forecasting the effort required for software development. In order to estimate the software work, we are attempting to implement a machine learning technique in this research. Out of all machine learning methods, we are applying an algorithm based on Artificial Neural Networks that is Back propagation. The Desharnais dataset, a well-known publicly available dataset for estimation of software effort, is used to test the approach. The performance and accuracy of the tested model have been evaluated using three metrics: MMRE, MRE, and Pred (0.25). In the sections below that follows, I explain the algorithm and its results. © 2023 IEEE.

关键词： Neural networks

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Face Generation and Recognition in Forensic science 11

Face Generation and Recognition in Forensic Science

引用

11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Ravi, Gayathri Joy, Heynes Jitto, Jeffin Joshy, Jocelyn Jose, Jisha Mary Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350379136

Suspect identification can be challenging for forensic investigations since standard procedures are time-consuming and prone to mistakes. This calls for the creation of novel approaches utilizing developments in machine learning (ML) and artificial intelligence (AI). In order to overcome these obstacles, the proposed Face Generation and Recognition in Forensic science will make use of sophisticated recognition algorithms and AI-based face generation models. Fully trained Stable Diffusion model is applied to generate high-quality face images from textual descriptions. Image Generation, Text Guided Image Manipulation using Denoising Diffusion Probabilistic Models (DDPMs), and Dataset Matching are the three primary components of the process. Using a stable diffusion model, Image Generation quickly creates high-resolution images from word prompts by combining an autoencoder (VAE), U-Net, and text encoder. With the introduction of an alternate noise space for DDPMs, Text Guided picture Manipulation makes it possible to do meaningful picture altering tasks in response to text prompts. VGG-16 , a convolutional neural network architecture is used in dataset matching to extract features and calculate similarity, which makes dataset alignment and comparison easier. The suggested methodology gives law enforcement authorities effective tools for identifying suspects, which represents a substantial development in forensic investigations. The project intends to increase the efficiency of criminal investigations, accelerate the matching process with large datasets, and enhance the accuracy of facial sketches by utilizing AI and ML approaches. The approach's ability to produce coherent and contextually relevant face images is validated by experimental results, which also show the approach's potential for speeding up the conclusion of criminal cases, particularly unsolved cold cases. All things considered, Face Generation and Recognition in Forensic science is a promising step in st

关键词： Convolutional neural networks

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

引用

2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Furusawa, Sota Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350378214

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into 'learned subject (class)' and 'unlearned subjects (classes)'. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments. © 2024 IEEE.

关键词： Generative adversarial networks

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Identification of Affective States from Malayalam Audios Based on Valence Arousal Prediction

Identification of Affective States from Malayalam Audios Bas...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： George, Talit Sara George, Jini Pournami, P.N. National Institute of Technology Dept. of Computer Science and Engg. Calicut India Providence College of Engineering Dept. of Computer Science and Engg. Chengannur India

ISBN: (纸本)9798331540067

Affective state plays a key role in emotion recognition, influencing various applications from mental health evaluation to human-machine interaction. These states can be described using two primary aspects: valence, which indicates the pleasantness of the speech, and arousal, which relates to the intensity of the emotion. This study aims to automate the prediction of valence and arousal values from speech data, addressing the limitations of manual annotation and human error. The IIITSAINT-EMOMDB dataset, created from Malayalam films, uses the Circumplex Model of Affect to map emotions such as happy, sad, angry, and calm. The approach leverages machine learning models, including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), for prediction. Experimental results demonstrate that the traditional models like SVR and ANN are outperformed by the CNN model, which achieved a mean squared error (MSE) of 0.3 for valence and 0.1 for arousal. These findings highlight the power of automated emotion recognition in large-scale audio datasets, offering promising applications in affective computing and psychological analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization of Biomedical Research Articles using Seq2Seq Transformers 23

HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization...

引用

23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Malik, Hemang Pradeep, Gaurav Seth, Pratinav Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Data Science & Computer Applications India Dept. of Computer Science & Engineering India

ISBN: (纸本)9798891761308

Lay summarization aims to generate summaries of technical articles for non-experts, enabling easy comprehension for a general audience. The technical language used in research often hinders effective communication of scientific knowledge, making it difficult for non-experts to understand. Automatic lay summarization can enhance access to scientific literature, promoting interdisciplinary knowledge sharing and public understanding. This has become especially important for biomedical articles, given the current global need for clear medical information. Large Language Models (LLMs) (Yao et al., 2024), with their remarkable language understanding capabilities, are ideal for abstractive summarization, helping to make complex information accessible to the public. This paper details our submissions to the BioLaySumm 2024 Shared Task: Lay Summarization of Biomedical Research Articles (Goldsack et al., 2024). We fine-tune and evaluate sequence-to-sequence models like T5 across various training dataset settings and optimization methods such as LoRA (Hu et al., 2021b) for lay summarization. Our submission achieved the 53rd position overall.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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