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An Efficient Model for Early Detection of Maize Leaf Disease using Deep Learning Approaches 4

An Efficient Model for Early Detection of Maize Leaf Disease...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. K.L.E.Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India K.L.E.Institute of Technology Dept. of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350354218

Maize plays a fundamental role as a staple food for millions worldwide. However, the significant threat posed by various diseases to maize cultivation has led to substantial yield losses on a global scale. Early and accurate detection of these diseases is essential for prompt intervention and effective treatment strategies. In this regard, the development of advanced detection methods employing deep learning and image processing has gained attention, offering the potential to revolutionize disease identification and management practices. Accordingly, this research introduces a Deep Convolutional Neural Network model combined with Transfer Learning techniques, reducing the training time for the AI model. Furthermore, Data Augmentation practices are employed to improve accuracy by addressing dataset variability. The projected model achieves an impressive accuracy of 98.6%, crucial for mitigating current crop wastage resulting from undetected diseases. Enhancing accuracy further is essential for improving current crop yield conditions impacted by undetected diseases. Moreover, a GUI-based application model is developed for early detection of maize diseases, aiding farmers in their fields. This accuracy can be further increased by incorporating additional parameters. Traditional image recognition and Deep Learning approaches are progressively making their way into the field of plant disease recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Recognition of Fingerprint Images using CNN for Cybercrime Detection System 2

Recognition of Fingerprint Images using CNN for Cybercrime D...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Kavitha, T. Patil, Bhimaraya Saraswathi, S. Rajarajeswari, S. Patil, Anita New Horizon College of engineering Dept. of Computer Engineering Bangalore India Sri Sivasubramaniya Nadar College of Engineering Dept. of Computer Science Engineering Chennai India Vellore Institute of Technology School of computer science and engineering Vellore India City Engineering College Dept. of Information Science Bengaluru India

ISBN: (纸本)9798350372892

Fingerprint identification plays a crucial role in crime detection, but manual analysis is time-consuming and error-prone. This research aims to develop an automated system for accurate and efficient fingerprint matching using machine learning. The proposed approach involves image acquisition, preprocessing, feature extraction, and classification. Fingerprint images are enhanced, binarized, and segmented. Minutiae (ridge endings and bifurcations) are extracted as features. A convolutional neural network (CNN) is trained on a dataset of fingerprint images for classification. By comparing algorithms, Random Forest achieved over 8 1 % accuracy, outperforming Decision Trees (nearly 75% accuracy). The trained CNN model can accurately match fingerprints from crime scenes with potential suspects, providing reliable evidence. The developed system offers law enforcement agencies an automated and accurate fingerprint identification tool, improving crime-solving efficiency and reducing manual effort. The proposed approach showcases the potential of machine learning in forensic applications. © 2024 IEEE.

关键词： Random forests

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Resilient Fog-Based Adaptive Traffic Control System 14

Resilient Fog-Based Adaptive Traffic Control System

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Matange, Ashwini Taneja, Varun Chaumal, Aarya Buwa, Pallavi Abraham, Jibi Coep Tech Dept. of Computer Engineering and Information Technology Pune India

ISBN: (纸本)9798350335095

Traffic congestion is a growing problem in India, largely caused by the increasing number of vehicles. To address this, creating an adaptive traffic control system has become essential. To present a solution, this paper aims to create a resilient, adaptive traffic control system that dynamically adjusts signal timings based on the current traffic density. The paper proposes introducing a Fog layer at each traffic junction to capture real-time video feeds and process them locally, with the final decision about dynamic signal control being taken in the Cloud. The validity of the solution is tested through simulations in SUMO. A Machine Learning model is trained in the Cloud to predict expected vehicle numbers to set adaptive signal times for contingency situations. The paper aims to demonstrate the resilience and performance of the system with the adaptive traffic signal control algorithm, potentially offering a solution to traffic congestion in India. © 2023 IEEE.

关键词： Traffic congestion

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Exploring the Impact of Univariate Feature Selection Method on Machine Learning Algorithms for Heart Disease Prediction

Exploring the Impact of Univariate Feature Selection Method ...

引用

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

作者： Islam, Mainul Islam, Rafiqul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

This study compares various machine learning techniques such as logistic regression, support vector machine (SVM), random forest, Naïve Bayes, and decision tree. Later, a feature selection approach that considers the Univariate Feature Selection (UFS) method was applied to select the best features based on statistical testing. In this research, we employed the ANOVA F-test to pick the most significant features, and we used a cutting-edge dataset for experimental analysis. The ANOVA F-test was used to exploit the target variable's dependency on the continuous variables. The results show that employing the UFS approach for picking features improves the performance of the algorithms. According to the experimental results, using UFS with the logistic regression technique yields 93.3% accuracy, whereas the standalone Naïve Bayes algorithm achieved the best accuracy. © 2023 IEEE.

关键词： Feature Selection

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NeuroNet: An Attention-Driven Lightweight Deep Learning Model for Improved Brain Cancer Diagnosis

NeuroNet: An Attention-Driven Lightweight Deep Learning Mode...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Istyak Nabil, Hadiur Rahman Abir, Golam Rabbani Hossain, Tazdik Das, Aritra Mridha, M.F. American International University-Bangladesh Dept. of Computer Science Dhaka Bangladesh Bangladesh University of Business and Technology Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350369106

This paper introduces NeuroNet architecture, a lightweight deep-learning framework designed for brain tumor identification, integrating a spatial attention-driven convolutional neural network (CNN) architecture. NeuroNet aims to enhance the classification accuracy of brain tumors, specifically targeting three types: gliomas, meningiomas, and general brain tumors, using Magnetic resonance imaging (MRI) data. The framework is estimated on the Bangladesh Brain Cancer MRI Dataset, which consists of 6,056 MRI images divided into three balanced classes. Unlike traditional machine learning techniques that rely on handcrafted features, NeuroNet's architecture leverages CNN layers with a spatial attention mechanism, enabling the model to focus on critical tumor regions. This approach reduces manual intervention and enhances feature representation. The proposed model performed a high classification accuracy of 99.04%, with a weighted F1-score of 99.02%, outperforming conventional models. These results indicate NeuroNet's potential to assist medical professionals by providing an accurate and reliable diagnostic tool, thus improving brain tumor treatment outcomes. This research underscores the promise of attention mechanisms within deep learning frameworks for advancing cancer diagnosis and medical imaging. © 2024 IEEE.

关键词： Diseases

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Employing Convolutional Neural Network for Real-time Detection and Localization of Physical Contact in a Redundant Manipulator Body 12

Employing Convolutional Neural Network for Real-time Detecti...

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12th RSI International Conference on Robotics and Mechatronics, ICRoM 2024

作者： Jafari, Amirhossein Tafti, Ali Abolhasani Karami, Abbas Isfahan University of Technology Dept. of Electrical and Computer Engineering Isfahan Iran Isfahan University of Technology Dept. of Mechanical Engineering Isfahan Iran Shiraz University of Technology Dept. of Mechanical Engineering Shiraz Iran

ISBN: (纸本)9798331529734

In the developing field of human-robot collaboration, robots increasingly share their workspace with humans, which becomes necessary to achieve seamless and safe interactions for manual guidance of robots in physical cooperation scenarios. The present study proposes a new method based on a convolutional neural network (CNN) for safe human-robot cooperation in the presence of intentional physical interactions, which is able to detect the occurrence and localization of contact on the robot arm in real-time. In this approach, contact detection and its localization are done only using the online external torque vector obtained by the residual-based observer of the robot, which eliminates any need to use additional force and vision sensors. Unlike traditional methods that depend on predefined thresholds or model-based techniques, our presented architecture with one-dimensional CNN can automatically learn the patterns from collected raw torque data and, by analyzing them, can properly predict the contact position. The performance of the proposed approach during manipulation has been validated through multiple online experiments in static and dynamic states on a KUKA LBR iiwa robot. © 2024 IEEE.

关键词： Redundant manipulators

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Identification of Genomic Associations Between Parkinson's and Neurodegenerative Diseases Using Bioinformatics Models 3

Identification of Genomic Associations Between Parkinson's a...

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3rd International Conference on Electrical, computer and Communication Engineering, ECCE 2023

作者： Rana, Md. Shohel Podder, Nitun Kumar Rana, Humayan Kabir Hasan, Md. Imam Azam, Md. Shafiul Rahim, Md Abdur Hasan Sazzad Iqbal, S.M. Saha, Subir Pabna University of Science and Technology Dept. of Computer Science and Engineering Pabna Bangladesh Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350345360

Neurodegenerative diseases (NDGs) can slowly damage the brain as well as the central and peripheral nervous systems. Parkinson's disease (PD) is a sensitive sensory disease that causes a malfunction of dynamic equilibrium. PD causes certain brain neuronal cells to gradually split or die. There are certain significant characteristics of PD that may increase the risk of acquiring deferent NDGs including Alzheimer's disease (AD), Amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Multiple sclerosis disease (MSL). We conducted a transcriptomic analysis to find connections between PD and NDGs. To explore how PD may affect the advancement of NDGs, we employed a quantitative framework to analyze the gene expression microarray dataset. To obtain genomic associations between PD and NDGs, we performed gene expression profiling by identifying dysregulated pathways, gene ontologies, PPIs, and phylogenetic analysis. We found 15, 11, 28, and 24 significant genes are shared between PD and AD, ALS, HD, and MSL respectively. We also identified a significant number of functional and ontological pathways, hub proteins and phylogenetic associations that indicate PD may have a link to develop NDGs (AD, ALS, HD, and MSL). We validate our findings through gold benchmark databases. This systematic approach is highly beneficial in understanding the causes and progression of NDGs owing to the effect of PD. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Ink Detection from Carbonized Herculaneum Papyri using Deep Learning 26

Ink Detection from Carbonized Herculaneum Papyri using Deep ...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Mou, Farzana Sharmin Ahmed, Tanvir University of Birmingham Dept. of Computer Science Birmingham United Kingdom Dhaka University of Engineering & Technology Gazipur Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

A Roman villa in the town of Herculaneum, next to Pompeii, housed a library with thousands of ancient Greek and Roman scrolls. The eruption of Mount Vesuvius buried this villa approximately 2000 years old ago which has been discovered. The scrolls can no longer be opened without breaking them because they were carbonized by the heat of the volcano. Since Herculaneum ink is carbon-based, it affords no X-ray contrast against the underlying carbon-based papyrus. Some detached papyrus fragments that can be read under infrared light can be used as ground truth data for a machine learning model that could identify otherwise undetectable ink from X-rays. The process of ink detection involves locating the inked portions of the papyrus using data from a 3D X-ray scan of the papyrus surface. The model has been trained by selecting a pixel from the binary label picture and sampling a subvolume from the surface volume at the same coordinates. In order to update the model weights, then backpropagate the known label data. Once trained, the model can be used to predict the appearance of a label picture from diverse input data. Three distinct models, namely U-Net, Attention U-Net, and a Pretrained Autoencoder with Attention U-Net, have been applied. Then, utilizing those three models, ensemble approaches have been employed (average, voting, and weighted average) to choose the optimum outcome. The best dice score of 0.766 has been achieved by the average ensembling technique. This project shows how machine learning can detect ink in 3D scans and decipher carbonized parchment contents. © 2023 IEEE.

关键词： attention mechanism computer vision ensemble technique ink detection pretrained autoencoder unet

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Student Writing Segmentation Using Various Classification Techniques 3

Student Writing Segmentation Using Various Classification Te...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Goyal, Charu Bisht, Vansh Singh Narang, Simar Jyot Singh Kaur, Manpreet Magoo, Chandni Manav Rachna University Dept. of Computer Science and Technology Faridabad India

ISBN: (纸本)9798350338607

Writing is a crucial aspect of learning. It encourages you to clarify and sort out concepts and analyse textual evidence, and practice conclusions, which improves understanding and aids the cognitive development. This current study proposes an automated framework model that can assess and offer feedback on a student's argumentative writing skills by segmenting the texts and classifying them. As a result, it will contribute to giving individuals more opportunities to develop their writing. Most of the 15601 text files (144294 sentences) used in the evaluation were written by students in grades 6 to 12. Deep learning models were developed based on a random collection of 80% of the phrases, and then evaluated on the other 20%, in order to assess the automated analysis of evaluation in writing. To compare the outcomes, we have employed classification machine learning methods like Passive Aggressive Classifier and Random Forest with deep learning techniques including Recurrent Neural Network i.e., RNN and Long short-term memory i.e., LSTM. Using these methods, student's essays were segregated based on lead, position, claim, counterclaim, rebuttal, and conclusion. The accuracy of our suggested BiLSTM based model is 74.38% and 81.23% ,LSTM-based model is 76.2% and 88.49% based on different hyperparameters, 62.4% for Random Forest-based models and 61.5% accuracy for Passive Aggressive Classifier (PAC). We can infer from the accuracy that LSTM performs better than BiLSTM, RF and PAC in terms of performance. As a result, this study aids in essay segmentation and generates a solution for text segmentation that is fairly accurate. © 2023 IEEE.

关键词： Students

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Study of Digital Image Compression Techniques and Framework for Suitability Selection based on Similarity Measuring Metrics 15

Study of Digital Image Compression Techniques and Framework ...

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

作者： Mishra, Joynath Kumar, Vipin New Delhi India Dept. of Computer Science and Information Technology Mahatma Gandhi Central University Bihar Motihari India

ISBN: (纸本)9798350370249

In recent years, capturing and transmitting color images through the network to convey visual information in a short time has become a crucial task. Choosing the appropriate compression technique is a challenging endeavor due to technological advancements with inccrement of the memory usage. Various lossless and lossy image compression techniques have been developed over the years. However, the studies pertains to a lack of extensive assessment across various compression algorithms and metrics, necessitating a practical comparative analysis of existing methodologies for color image optimization in graphical representation and storage. Hence, a comprehensive comparative study is conducted on six types of lossless compression techniques (Huffman, RLE, LZW encoding, JPEG2KLeGall53, PNG, WebP) and six types of lossy compression techniques (JPEG-DCT, SVD, K-means, JPEG2K-CDF97, PNG, WebP), evaluating their performance based on input parameters for compresion such as quality factor, selection of singular values, and number of clusters. The outputs are analzed followed by the extracted parameters such as compression ratio (CR), time, RMSE, PSNR, SSIM, FSIM, and bpp to determine the best case of compression. Furthermore, a weighted Friedman ranking test is conducted to determine the most effective compression technique. It is concluded that JPEG2K-CDF97 is optimal for lossy compression, while WebP is considerable for lossless compression. © 2024 IEEE.

关键词： Image compression

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