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Deploying DenseNet for Cotton Leaf Disease Detection on Deep Learning 3rd

Deploying DenseNet for Cotton Leaf Disease Detection on De...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Bappi, Md. Basitur Rahman Swapno, S. M. Masfequier Rahman Rabbi, M. M. Fazle Computer Science and Engineering Bangladesh University of Business and Technology Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

Cotton trees are tropical and subtropical trees that flourish in warm climates. It is a popular, costly as well as a cash crop. Farmers face difficulties selling their products when their production is decreased due to diseases harming cotton plants. It is critical to manage any dangerous illnesses as soon as feasible in order to increase quality and production. This problem prompted the development of novel technologies for detecting and diagnosing cotton plant diseases, as well as expert systems for disease prevention. The use of computer vision in agriculture has grown in popularity as a result of its ability to give critical information in real time. To achieve our goal, we built a dataset that included 4 classes of diseased and leaf images. We have applied InceptionV3, DenseNet, and MobileNetV2 to our dataset. By using DenseNet, we achieve the best accuracy of 99.24%. Our system with the capability of identifying leaf diseases of cotton has been built. As a result, we believe that our initiative will help the user by allowing us to make more items, which will impact our economy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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A Review on Emotion Detection from Text: Opportunities and Challenges 3rd

A Review on Emotion Detection from Text: Opportunities and C...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Mahmud, Anisur Rahman Mubtasim Fuad, Md. Jahid Hasan, Md. Minhazur Rafid, Md. Eusuf Khan, Md. Fazle Rabbi, M.M. Department of Computer Science and Engineering Bangladesh University of Business and Technology Road 9 Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

In this digital era, almost everyone can share their thoughts over the internet through texts in messages, posts or blogs, and people often convey emotions of different sorts as well. Sometimes it is about mental health, review about a bad product or simply opinions on different aspects of the modern world. Because of its massive popularity, emotion detection from text has become a burning topic for researchers. This review paper tries to provide understanding of different levels in various emotion models, different works that have been done in emotion identification from text and how it works along with introduction to various available datasets. Also how different models like LSTM, CNN, BERT and so on are utilized by researchers to provide better results in text-based emotion detection is also discussed with their advantages and disadvantages. Finally this paper also points out different obstacles and challenges that lie in the study of emotion extraction from text. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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A Comprehensive Sentiment Analysis on Beauty Product Usage Among Bangladeshi Comsumers 3rd

A Comprehensive Sentiment Analysis on Beauty Product Usage ...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Jahan, Ishrat Jahan Mim, Sabiha Shahidur Rahman, Mohammad Computer Science and Engineering Shahjalal University of Science and Technology Sylhet3114 Bangladesh

ISBN: (纸本)9789819739363

Beauty products are an integral part of daily routines for many women. The beauty industry continually evolves, introducing innovative formulations and trends that cater to diverse preferences and needs. Sentiment analysis of beauty products is crucial as it allows companies to understand customer opinions, preferences, and experiences. In this study, we explore sentiment analysis in the context of beauty products. We collected data from various platforms like ***, ***, and ***, resulting in a dataset of 4431 reviews. Employing this unique dataset, we conducted a comprehensive analysis, employing a trio of neural network architectures: the foundational simple neural network, the robust 1D convolutional neural network (1D CNN), and the intricate long short-term memory (LSTM). Upon completing data preprocessing, we incorporated word embeddings derived from the GloVe 100D file, resulting in the attainment of an accuracy level of 78.78%. By combining data collection, model experimentation, and embedding techniques, we gain a multifaceted view, offering deeper insights into beauty product sentiment. In addition to advancing sentiment analysis methodologies, our study signifies a positive step toward empowering beauty industry stakeholders with actionable intelligence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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IoT-Based Health Monitoring System for Post-Covid with Diabetes 3rd

IoT-Based Health Monitoring System for Post-Covid with Diabe...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Pankaja Lakshmi, P. Sivagami, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu Chennai600127 India

ISBN: (纸本)9789819726707

Internet of Things (IoT) devices are proliferating in the modern world, and this has a significant impact on healthcare. It can lower the price of medical care and offer early diagnosis of health issues. A patient that needs to be watched over constantly needs a healthcare monitoring system. In order to save their lives in dire circumstances, elderly patients, in particular, should periodically have their health state checked and informed to the doctor. Quiet people suffer the negative effects of true medical issues because of a lack of adequate health monitoring. Many IoT devices are available today to autonomously monitor patient health in order to address this problem. As of now, there is no other significant and adequate framework, to predict the impact of Covid-19 infection in diabetes patients. The literature has shown a separate ML/DL model for Covid-19 prediction and diabetes severity analysis. This is mainly because of the large gap in the database model. This paper has a detailed study of the machine learning (ML) model combined with IoT devices and the proposed framework would have been used for diabetic patients who were affected with Covid-19 and other symptoms for early prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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AI-Based Precision Farming for Sustainable Agriculture in Bangladesh 3rd

AI-Based Precision Farming for Sustainable Agriculture in ...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Chowdhury, Rup Islam, Md. Nazmul Das, Prapti Narin Nur, Fernaz Saiful Islam, A.H.M. Computer Science and Engineering Notre Dame University Bangladesh GPO Box 7 2/A Arambagh Motijheel Dhaka1000 Bangladesh

ISBN: (纸本)9789819739363

Agriculture, as an indispensable facet of human civilization and a vital economic contributor, plays a pivotal role in sustaining societies. Bangladesh, a nation heavily reliant on agriculture, currently employs traditional methods, resulting in a significant impact on its economic stability. Improvements in agricultural technology and practices have the potential to increase productivity, reduce waste, and improve the quality of agricultural products. As the global population swells and food demand rises, sustainable agricultural methods become increasingly crucial. To address this challenge, we have developed an AI-based precision farming system employing machine learning algorithm, including CNN networks, to analyze and predict crop health and potential yield. Additionally, we propose an AI-based decision-making module that forecasts crop yields, pest detection, and disease detection. Our system, rigorously evaluated using real-world data, has demonstrated substantial improvements in crop yield, fostering sustainability and contributing to global food security while minimizing environmental impact. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Material Selection and Performance Analysis of Organic MEMS Cantilever Force Sensors with Integrated Piezoresistive Readout 3rd

Material Selection and Performance Analysis of Organic MEMS ...

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3rd international conference on Signal and Data Processing, ICSDP 2023

作者： Choudhary, Sadanand Singh Mathew, Ribu School of Electrical & Electronics Engineering (SEEE) VIT Bhopal University Bhopal 466114 India Manipal School of Information Sciences (MSIS) Manipal Academy of Higher Education (MAHE) Manipal 576104 India

ISBN: (纸本)9789819795772

In this work, we investigate the performance of micro-electro-mechanical systems (MEMS) technology based organic cantilever force sensors with integrated piezoresistive readout. The study has been carried out in two phases: Phase-I involves material selection of both the organic cantilever platform and the piezoresistor to maximize the electrical sensitivity of the sensor. Here, the piezoresistor Gauge factor (G) to Young’s modulus (Y) of the cantilever platform material ratio (G/E) is considered as the performance evaluation metric. Subsequently, in phase-II, for the chosen material set, numerical investigation is carried out. Finite element method (FEM) based computer aided design (CAD) software is utilized for numerical simulations. Here, we compare two different organic materials (paper and Polydimethylsiloxane- PDMS) based sensors using simulation software. For comparison of cantilever force sensor performance realized with paper and PDMS, for both cases, we have considered the same cantilever shape, i.e. triangular with constant area (1200 µm²). Simulations were carried out, to investigate the electro-mechanical response of the sensors. From simulation results, it is found that for the same area and external applied force, the combination of Graphite piezoresistor and PDMS substrate depicts relatively higher mechanical sensitivity and electrical sensitivity than its counterpart sensor. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cantilever FEM Organic Paper PDMS Piezoresistive Sensor SU-8

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NAS for Automated ML Deployment on Extreme Edge Devices 3rd

NAS for Automated ML Deployment on Extreme Edge Devices

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3rd international conference on Signal and Data Processing, ICSDP 2023

作者： Mahendran, Gayathri Thirumaran, Gokhulnath Ali, Syed Imam Jadon, Vishwas Raj SSN College of Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Amity School of Engineering and Technology Noida India

ISBN: (纸本)9789819795772

Machine Learning (ML) and Artificial Intelligence are omnipresent topics nowadays. Especially since small personal devices, such as smartphones, are capable of running computation intensive tasks on their own, they can improve our lives in many different ways, e.g. computer vision or speech detection. Deploying neural network models on tiny and therefore performance, memory and power-constrained devices, such as microcontrollers, is currently an important research topic. For mobile and other embedded target devices, Neural Architecture Search, or NAS for short, is a novel technique to find appropriate network models without requiring manual error-prone adjustments by engineers, which allows to automate the workflow from the definition of a real work problem to a deployable solution. While there are a lot of comprehensive surveys on NAS methods in general, none of them considers the application of those ideas on edge devices like microcontrollers (MCUs). This work provides an overview of the recent developments and compares different approaches, which appeared over the last few years, against one another and with well-known reference applications intended for the usage on mobile phones. In the end some opportunities for future work on this topic are discussed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： AutoML MCUNet Once-for-All SpArSe The MicroNet models

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An Improved Encryption Technique for IPv6 Networks 3rd

An Improved Encryption Technique for IPv6 Networks

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3rd international conference on Signal and Data Processing, ICSDP 2023

作者： Cherukuru, Pavani Hanji, Bhagyashri R. Information Science of Engineering Dayananda Sagar Academy of Technology and Management Bengaluru India Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bengaluru India

ISBN: (纸本)9789819795772

Encryption method for IPv6 networks provides new features and security concepts with a lot of profoundness likely Authentication Header and Encapsulating Security Payload Header (ESP). This paper shows the method of preventing certain types of network attacks over the internet and open issues of IPv6 security. It mainly deals with the Authentication and Encryption methods such as RSA and DES that are being used for years, and their drawbacks in IPv6 environment to enhance the multilevel security. There are three levels of encryption for security enhancement. The first level includes the conversion of a string of characters into corresponding ASCII values and then encrypt these ASCII values into random prime factors so that uniqueness is maintained. It has repeated patterns that is removed in the second level, where it uses two private keys which are prime for even and odd numbers and perform the addition with the first level results and convert into a binary form. In the third level, 2’s complement is applied on binary form and sent it through ESP packet of IPv6. On the other side, all the levels are repeated in reverse to get the original message. The experimental result shows the higher level of security for IPV6. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： ASCII value Authentication header Encryption IPv6 Multilevel encryption Private key

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Aircraft Fuel Flow Modeling and Performance Optimization Using Machine Learning 3rd

Aircraft Fuel Flow Modeling and Performance Optimization Usi...

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3rd international conference on Signal and Data Processing, ICSDP 2023

作者： Dharrao, Deepak Misal, Mrinank Rai, Naivedya Joshi, Nirgoon Rajagopalan, Siddharth Pangavhane, Madhuri Bongale, Anupkumar Department of Computer Science and Engineering Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Department of Information Technology Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India

ISBN: (纸本)9789819795772

The modeling of fuel flow in aircraft is a critical element in evaluating their performance. However, many of the existing approaches rely on highly aggregated data, which may not provide the necessary accuracy required by the aviation industry. To address this challenge, a new data-driven approach is proposed in this study. This method leverages machine-learning techniques and uses full-flight data from aircraft sensors to develop fuel flow models. The approach focuses on identifying the features that impact fuel flow rates during different flight phases, using a unique deep-learning approach for modeling. The results demonstrate a significant improvement over traditional practices, providing a more comprehensive and accurate alternative for performance evaluations. As a result, airlines and maintenance providers can optimize their operations and reduce fuel costs while meeting environmental targets. The proposed method achieved a 99.25% adjusted R2 score, highlighting the potential of machine learning tools in developing accurate and detailed fuel flow models based on full-flight data, providing significant benefits for the aviation industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fuel consumption and emissions Fuel flow models Full-flight data Multi-layer perceptron (MLP) Optimization of airline operations

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3rd international conference on electrical, computer and Communication engineering, ECCE 2023

3rd International Conference on Electrical, Computer and Com...

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3rd international conference on electrical, computer and Communication engineering, ECCE 2023

ISBN: (纸本)9798350345360

The proceedings contain 137 papers. The topics discussed include: sexual harassment detection using machine learning and deep learning techniques for Bangla text;optimization strategies for micro-grid energy management and scheduling systems by sine cosine algorithm;efficient hardware and software co-design for EEG signal classification based on extreme learning machine;measurement and modeling of GaAs based nano-pHEMT: small signal to large signal analysis;Alzheimer’s disease classification from 2D MRI brain scans using convolutional neural networks;faulty classes prediction in object-oriented programming using composed Dagging technique;interpretable multi labeled Bengali toxic comments classification using deep learning;protein structure prediction in structural genomics without alignment using support vector machine with fuzzy logic;and impact of transmission line capacity expansion on electricity operation cost and selling price.

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