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A Deep Learning Approach for Classification and Segmentation of Leafy Vegetables and Diseases

A Deep Learning Approach for Classification and Segmentation...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Islam, Ashraful Sama Raisa, Syeda Rafiatus Khan, Nahiyan Habib Rifat, Ariful Islam Sylhet Engineering College Dept. of Computer Science and Engineering Sylhet Bangladesh American International University-Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Bangladesh's economy is heavily dependent on agriculture, making the region an exceedingly crucial sector that requires great vigilance. In comparison to other vegetables, leafy greens have yet to garner as much attention as other crops that this study's large-scale analysis comes up with, particularly when categorizing readily available leafy vegetables and identifying the affected area and whether they are affected or not. Plant diseases can cause substantial losses in agricultural productivity, and many researchers are working to address the difficulties farmers encounter in identifying and treating these conditions while diagnosing their diseases. The purpose of this study is to develop a dependable and effective approach for classifying seven varieties of leafy vegetables and detecting seven types of plant diseases precisely in Bangladesh using machine learning and deep learning CNN models. To this end, this paper analyzed and provided solutions using a total of eight models: five classification models (VGG16, VGG19, ResNet50, YOLOv5, and YOLOv8) and three instance segmentation models (YOLOv5, YOLOv7, and YOLOv8). Two distinct datasets, 'LeafyVclassify7BD' with 3306 images and 'LeafyVdisease7BD' with 4493 images, were developed to classify leaves, segment the diseased area, and classify and detect damaged leaves. The research outputs will help strengthen the region's ability to control plant diseases more effectively while also cutting diagnostic costs and enhancing disease detection accuracy in the country's agricultural surroundings. © 2023 IEEE.

关键词： Classification (of information)

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Utilization of MFCC in Conjuction with Elaborated LSTM Architechtures for the Amplification of Lexical Descrimination in Acoustic Milieus Characterized by Pronounced Nocturnal Disturbance 2

Utilization of MFCC in Conjuction with Elaborated LSTM Archi...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Upadhyay, Deepak Malhotra, Sonal Rawat, Ramesh Singh Gupta, Mridul Mishra, Somesh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350371055

In this pioneering research endeavor, we delved into the intricate realm of speech recognition technology, aiming to surmount the formidable challenges posed by acoustically complex environments, notably those marred by pronounced nocturnal disturbance. Through the synergistic amalgamation of Mel-frequency cepstral coefficients (MFCC) and meticulously crafted Long Short-Term Memory (LSTM) architectures, we charted a transformative path toward the amplification of lexical discrimination. Our multifaceted approach not only enhanced the discernment of subtle linguistic nuances but also showcased unparalleled efficacy in the face of disruptive nocturnal noise. The meticulously calibrated fusion of advanced signal processing techniques and neural network architectures culminated in a novel paradigm, revolutionizing the landscape of speech recognition technology. By pushing the boundaries of computational linguistics, this study not only advances scientific understanding but also paves the way for real-world applications in domains reliant on precise and resilient speech recognition systems. © 2024 IEEE.

关键词： AI LSTM MFCC Transfer Learning

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Optimizing Job Search: A Multi-Model Approach with DRNN-BES, Collaborative Filtering, and LLM-NER Resume Analysis 3

Optimizing Job Search: A Multi-Model Approach with DRNN-BES,...

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3rd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Jayakumar, Naveenkumar Patil, Netra Pokal, Roshankumar S. Dev, Rudra Pratap Nair, Sidharth Vellore Institute of Technology School of Computer Science and Engineering Vellore India College of Engineering Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

This research introduces an innovative job recommendation platform designed to optimize the job search process through advanced technology integration. The platform features a sophisticated Resume Analyzer leveraging NLP and ML to meticulously extract candidate information. Coupled with a Recommendation system that employs mixture models such as DRNN-BES and collaborative filtering, it delivers personalized job suggestions tailored to individual profiles. Additionally, the system utilizes state-of-the-art web scraping technologies to continuously update its database with diverse and current job listings from various online sources, ensuring a wide range of opportunities are accessible. This integration of technologies not only refines job-matching precision but also significantly improves the efficiency of connecting job seekers with appropriate opportunities. Initial experimental results underscore the enhanced effectiveness and accuracy of our recommendation system, particularly when incorporating user behavioral data into the collaborative filtering algorithm. This paper demonstrates the transformative potential of integrating AI into job seeking, offering a scalable, unbiased, and time-efficient approach to modern employment challenges. © 2024 IEEE.

关键词： Collaborative filtering

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EEG-driven GAN for Alpha Rhythm Generation in Passive BCI 3

EEG-driven GAN for Alpha Rhythm Generation in Passive BCI

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Cinquetti, Ettore Zanni, Guglielmo Menegaz, Gloria Storti, Silvia F. University of Verona Dept. of Computer Science Italy University of Verona Dept. of Engineering for Innovation Medicine Italy

ISBN: (纸本)9798350378009

Passive brain-computer interface (BCI) based on electroencephalographic (EEG) signals show promise for monitoring human vigilance in critical settings that continuously strain human attention. However, the lack of extensive public datasets limits progress in artificial intelligence research. To overcome this problem, we implemented generative adversarial networks (GANs) to augment existing EEG datasets. We focused on two datasets: a publicly available resting-state dataset and a custom one simulating industrial activities. After extracting time-variant alpha indices using continuous wavelet transform, we compared the generated data with real data using the L2 distance metric and autocorrelation function. We additionally assessed two forecasting models trained on original and augmented datasets, comparing their predictions. The integration of synthetic data led to an improvement in signal behavior prediction, as evidenced by a 32% reduction in mean absolute error for the resting-state dataset. Furthermore, a metric inspired by the Frechét Inception Distance was computed using the forecasting model to discern the distributions of embeddings for the real and generated data, with results showing a strong resemblance between the two. This study challenges the limitation of acquiring domain-specific EEG data and aims to develop a robust signal generation framework. © 2024 IEEE.

关键词： Wavelet transforms

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End-to-End SNR Capacity and throughput enhancement Using Non-Orthogonal Multiple access and Massive MIMO in 5G 4

End-to-End SNR Capacity and throughput enhancement Using Non...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350349900

This study provides a detailed analysis of the potential improvements in 5G network performance due to the adoption of NOMA and Massive MIMO technologies. Obtaining higher data rates, reduced latency, and increased overall network capacities remain a primary objective, and the research provides a comparison based on simulated data. Simulation models are designed to compare the proposed model against the conventional implementation of 5G networks based on a set of primary indicators, namely, throughput and latency. An abstract Wave Efficiency indicator is also presented to showcase a new metric for determining the qualitative improvements in signal processing and spectrum utilization. As the results of the research have demonstrated, the incorporation of NOMA and Massive MIMO increases overall network throughput while reducing latency. This demonstrates the potential contributions of these technologies to making wireless communications systems more efficient and reliable. The introduction of the Wave Efficiency is offered as a measurable variable for displaying the qualitative improvement in the wireless communications technologies, facilitating further implementation of NOMA and Massive MIMO in 5G networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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A BrainNet (BrN) based New Approach to Classify Brain Stroke from CT Scan Images

A BrainNet (BrN) based New Approach to Classify Brain Stroke...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Tripura, Dhonita Haque, Imdadul Dutta, Mithun Dev, Shaikat Jahan, Tanjila Ghosh, Shomitro Kumar Islam, Md. Ashiqul Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350396485

Worldwide, brain stroke is known as the 2nd leading cause of death, and based on Indian history, three people have suffered every minute. There are mainly two different types of brain stroke: ischemic stroke and Hemorrhagic stroke used to train the proposed models. Ischemic stroke is the most common and it contributes mostly to 80% of the brain stroke and Hemorrhagic stroke contributes mostly to 20% of the brain stroke. In the proposed model, there has been used a hybrid model called BrainNet (BrN) as CNN(Convolutional Neural Network) and SVM(Support Vector Machine)to classify brain stroke disease. After applying the required proposed model, it has produced a smart score of 91.91% accuracy, and compared to the existing model it performs pretty well. The BrainNet (BrN) model is mainly designed based on a deep neural network with dataset collection, preprocessing, and feature extraction with the desired model and make the classification concerning SVM. With compare to the existing model, it is an acceptable performance that belongs to the collected dataset designed with Ischemic stroke and Hemorrhagic stroke disease within the total number of 2515 data. © 2023 IEEE.

关键词： Support vector machines

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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...

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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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An Ensemble ML Model to Predict the Wastage of Food: Towards Achieving the Food Sustainability

An Ensemble ML Model to Predict the Wastage of Food: Towards...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Masuk Shopnil, Md Masrur Husna, Asmaul Sultana, Shaheena Islam, Muhammad Nazrul Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh NotreDame University Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Food waste is a major issue in students' hostel or dormitories, where uneaten food often ends up in the garbage, resulting in economic losses as well as reducing the food sus-tainability. Although Machine Leaning (ML) has emerged as a promising tool for predicting future events in several files, but a very few has been focused to generate accurate predictions and reducing food waste by analyzing read data for a particular context or a country. Therefore, the objective of this research is to explore the factors of affecting food waste in students' hostel or dormitories and to develop an ensemble ML-based food waste prediction system for the student hostel or dormitories. In order to attain these objectives, this study firstly find out the 18 key features that ensure a comprehensive understanding of food waste patterns;then collected the data against these features over the last six months from various mess records of a students' hostel of an engineering university situated in Dhaka, Bangladesh. Secondly, six classification models were developed and analyzed their results and found that Random Forest and Decision Tree performed best accurate prediction (75.41%). Thirdly, the all possible combinations of the studies ML models were explored to find out the best possible ensemble combination;as such, the proposed ensemble ML model showed the highest F1-score of 86.88% that considers the Random Forest and AdaBoost;while all other possible combinations showed around 80-82% F1-score. This research thus contributes to the broader goal of reducing food waste and promoting sustainability in students' mess and other food service settings;as well as may assist the management team in making data-driven decisions about food production, purchasing, and resource allocation. © 2023 IEEE.

关键词： Forecasting

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Involutional Neural Network Based Approach for the Detection of Melanoma Cancer

Involutional Neural Network Based Approach for the Detection...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350388534

This paper presents an extraordinary method to detected melanoma, the most lethal form of skin cancer, using Involutional Neural Networks (INNs). Melanoma's early detection is crucial for effecacius treatment, yet existing diagnostic methods often lack the requiring precision and scalability. INNs offer a solve through their advanced capability to adaptively process spatial information in images, a huge different from traditional convolutional techniques. Our reaserch applies INNs to melanoma detectionair, focusing on their unique involution operations that significant enhance image analysis for accurately classification. The performance of our model, assessed across different metrics including accurate, precision, recall, and F1-scores, demonstrates its superior diagnostic capability. This investigation not only underscores the potency of INNs in medical imagining but also opens up new avenues for their application across diverse analytical needs in healthcare. By advancing melanoma detection methods, we contribute to the broader goal of improving early cancer diagnose and treatment outcomes! © 2024 IEEE.

关键词： Lung cancer

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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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