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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Amondkar, Madhavi Bhoite, Sachin Vidyalankar School of Information Technology Department of Information Technology & Data Science Mumbai India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science & Applications Pune India

ISBN: (纸本)9798331300579

Maharashtra is a remarkable state in India that is a major producer of onions, accounting for a sizeable portion of the country's total production. An ideal climate for onion growth is provided in areas like Nashik, Ahmednagar, and Pune Despite its importance, growing onions is filled with difficulties, the most significant of which being the frequency of illnesses that damage onion crops. Serious risks to onion production include diseases which are downy mildew, purple blotch, bacterial blight, white rot & basal rot which result in output losses and financial hardship for farmers. Machine learning models have become extremely effective tools for managing and forecasting agricultural crop diseases such as tomato, corn, grape etc. While nearly all crop leaf diseases may be predicted by these algorithms, very little or none has been done for onions in Maharashtra. There is almost no research done focusing on the onion disease found in different parts of Maharashtra. The purpose of the study is to assist farmers in Maharashtra in recognizing various diseases during the cultivation of onions by utilizing a machine learning model that combines CNN with image classification. Using a dataset of plant leaf diseases, this model ran with 95% accuracy. Additionally, given that the suggested model may be trained on any kind of leaf disease picture dataset and assist in disease type prediction, it can be suggested to work on an onion disease dataset as well. © Grenze Scientific Society, 2024.

关键词： basal rot CNN leaf disease machine learning Maharashtra onion disease purple blotch

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Sparse Channel Estimation in Massive MIMO Systems Using Compressed Sensing and Neural Networks

Sparse Channel Estimation in Massive MIMO Systems Using Comp...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala 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 ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this paper, based on compressed sensing and neural networks, we present an intelligent sparse channel estimation technique for Massive MIMO systems. We show that our approach improves considerably the estimation accuracy in terms of Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) with respect to classical methods. We demonstrate [Figure: ] that our approach is computationally more efficient, requires less time and resources (memory/disk) compared to the existing literature but for a robustness of performance across distal or low Signal-to-Noise Ratios (SNRs). We also confirm our method exhibits fast convergence and efficiently accommodates a variety of sparsity levels. By visualizing it using heatmaps and ROC curves we show that this method is able to effectively differentiate between signal and noise, asserting our approach as a powerful tool for the contemporary set of problems present in wireless communications. © 2024 IEEE.

关键词： Signal to noise ratio

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Development a Novel Hybrid Deep Learning-Model for Brain Tumor Classification and Automated Diagnosis

Development a Novel Hybrid Deep Learning-Model for Brain Tum...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Gupta, Shubhi Bansla, Vandna Kumar, Sandeep Singh, Gurwinder Srivastav, Akanksha Jain, Arpit Amity University Department Of Computer Science And Engineering Uttar Pradesh Greater Noida India Shivalik Engineering College Department Of Computer Science And Engineering Uttarakhand Dehradun India Tula's Institute Department Of Computer Science And Engineering Uttarakhand Dehradun India Chandigarh University Department Of Aitcse Punjab Mohali India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering AP Vaddeswaram India

ISBN: (纸本)9798350366846

Tumors of the brain are among the deadliest diseases of the century. Artificial intelligence (AI) and neuroscience are used to identify, identify, and categorize brain tumors. A large amount of MRI scans must be manually segmented and categorized. An effective computer-aided diagnosis (CAD) system is essential for the timely analysis of brain tumors. Magnetic resonance imaging (MRI) is used to segment, extract, and classify brain tumors. All systems have achieved a superior level of performance. We used deep learning-based models to predict brain tumor disease with 99.10% accuracy. By analyzing huge amounts of data and extracting significant features accurately and efficiently, these models can offer improved patient outcomes and timely treatment in the healthcare sector in a short timeframe compared to existing approaches. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Secure and Reliable Routing in the Internet of Vehicles Network:AODV-RL with BHA Attack Defense

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computer Modeling in Engineering & sciences 2024年第4期139卷 633-659页

作者： Nadeem Ahmed Khalid Mohammadani Ali Kashif Bashir Marwan Omar Angel Jones Fayaz Hassan School of Electronic Science Beijing University of Post and TelecommunicationBeijing100086China Department of Computer Science Huanggang Normal UniversityHuanggangChina Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabad502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Information Technology and Management Illinois Institute of TechnologyChicagoUSA College of Computing Capital Technology UniversityLaurelUSA

Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes *** the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against *** additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network *** testing of our solution has revealed its remarkable ability to enhance communication in ***,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and *** results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.

关键词： Black hole attack IoV vehicular ad hoc network AODV routing protocol

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Smart Transportation System with Collision Detection and Caution Notification 4th

Smart Transportation System with Collision Detection and Cau...

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4th International Conference on data, Engineering, and Applications, IDEA 2022

作者： Rao, Manjula Gururaj Pawar, Sumathi Reddy, K. Hemant Kumar Priyanka, H. NMAM Institute of Technology Nitte Karkala India Department of Computer Science and Engineering VIT AP University Amaravati India Computer Science and Engineering PESU Bengaluru India

ISBN: (纸本)9789819724505

One of the most vital components of a person's life is their transportation system. Today's transportation is increasingly automated. The development of an automated intelligent transportation system (AITS/ITS) is the subject of numerous studies. Because it must take action in relation to the ITS’s visualization and quick action. Developing the ITS is one of the more difficult tasks. Detecting moving and non-moving objects is one of the major components of ITS visualization. Vehicle collision detection and responding appropriately to that is one of the tasks in the visualization of moving things in the ITS. This essay focuses on developing a low-cost forward collision detection warning system that offers a level of safety that may be used during driving of the automobiles. The system measures the distance among moving vehicles that are traveling in a unidirectional and exactly equal path using an ultrasonic sensor and an Arduino controller. It then alerts the driver if they are getting too close to a risky area. Using an audio-visual and ultrasonic warning that measures the distance, the driver and/or any passengers are adequately warned. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ultrasonic sensors

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ABES: attention bi-directional ensemble SVM for early detection of brain tumors

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Neural Computing and Applications 2024年第26期36卷 16179-16193页

作者： Subramaniam, Erana Veerappa Dinesh Krishnasamy, Valarmathi Department of Computer Science and Business Systems Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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A Variational Optical Flow Model for Accurate Motion Estimation from Rotational Image Sequences

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SN computer science 2024年第4期5卷 359页

作者： Doshi, Hirak Kiran, N. Uday Department of Mathematics and Computer Science Sri Sathya Sai Institute of Higher Learning Andhra Pradesh Puttaparthi India

There are several optical flow models which aim to capture spatial characteristics of the flow such as divergence and curl. However accurate estimation is often a key challenge. In this context, we propose a variational optical flow model motivated by the harmonic-constraint based regularization for improving the estimation of rotation in motion. Our model is the standard Horn and Schunck model with an additional constraint penalizing the curl of the flow with an anisotropic weight term. Our implementation scheme is based on the robust Chambolle-Pock primal-dual algorithm. Further, to refine the flow-edges, we use the heuristic of weighted median filtering in our algorithm as a post-processing step. The results of our model indicate that it is comparable to, and in some cases, outperform some of the top-performing variational methods across multiple datasets in both angular and end-point errors. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： 35A15 49K20 49N15 Accuracy Curl Optical flow Primal-dual Rotation

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Generation of Historical Artwork Using GAN 7th

Generation of Historical Artwork Using GAN

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7th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Soumya, A. Rao, Karthik S. Aradhya, Sumalatha Department of Computer Science and Engineering RV College of Engineering Bangalore India Department of Computer Science and Engineering Siddaganga Institute of Technology Bangalore India

ISBN: (纸本)9789819940394

Preserving cultural history and heritage improves the sense of belongingness and cooperation, which leads to a healthy and prosperous economy. Museums housing historically famous artworks are one of the key contributing factors to cultural preservation. However, reports suggest that museums’ footfall and general impression are declining and hence, acts as a catalyst towards cultural extinctions. It is imperative to explore and design new ways for people to discover, create and learn with historically famous art collections. The creation of art inspired by famous works leads to an innate level of connection and engagement that preserves cultural heritage and enriches it. This paper details the mechanisms and techniques involved in building an experience that allows users to instantly generate new artworks inspired by historically famous ones using an interface powered by a Generative Adversarial Network (GAN). It also covers a follow-up experience where users can visually search their newly generated artwork to find its closest visual matches within the complete collection and discover new areas of interest. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Anthracnose Detection and Classification in Chilli Leaves Using Mobile Net: UAV Images 9

Anthracnose Detection and Classification in Chilli Leaves Us...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bandaru, Prudhvi Narayana Anuradha, G. Bora, Himavarshini Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence And Data Science Andra Pradesh India Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science And Engineering Andra Pradesh India

ISBN: (纸本)9798350394474

The health of a plant has remained an indispensable factor for sustainable and enriching crop production to fulfill the Nation's food demands. A degradation in the health of the crop leads to low crop yields. Out of a wide range of factors affecting the health of the crop the most concerning factor remains the plant diseases and pests. More than 50% of India's population is engaged in agricultural practices. In the agricultural sector, India is among the top 3 exporters of Dried Chilli. However, Chilli exports have faced a descent in recent years owing to crop yield loss due to diseases caused by fungi or pests. Anthracnose is the most prevalent disease in Chilli plants. The process of anthracnose disease detection in Chilli crops is a tedious process due to a lack of laboratory facilities and expertise. Such a situation demands for development of an 'automated detection of anthracnose disease in Chilli plant' system which puts the process of early and precise detection of plant disease at ease. computer Vision advancements make it possible for automatic disease detection using visible symptoms on the crop. Keeping this in view, this paper proposes an automated model for the detection of anthracnose in the Chilli plant using a Mobile Net-based convolutional neural network (CNN). Over the last few years researchers such as Boulent et al. stated in their article that the integration of aerial image data and AI-based methods holds promise for offering an effective, high-throughput technique for detecting plant diseases in real-world conditions. Hence the dataset utilized for our model is aerial images using UAV (unnamed aerial vehicle). The proposed system for anthracnose detection in Chilli developed using Mobile Net achieved an accuracy of 99.6%. © 2024 IEEE.

关键词： Crops

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Faster Sampling Algorithms for Polytopes with Small Treewidth

Faster Sampling Algorithms for Polytopes with Small Treewidt...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Ke, Yekun Li, Xiaoyu Song, Zhao Zhou, Tianyi University of Washington Department of Computer Science SeattleWA United States Stevens Institute of Technology Department of Computer Science HobokenNJ United States The Simons Institute for the Theory of Computing University of California Berkeley BerkeleyCA United States University of Southern California Department of Computer Science Los AngelesCA United States

ISBN: (纸本)9798350362480

Sampling is a fundamental problem in optimization, machine learning and theoretical computer science. A common region of interest for sampling is the polytope, which is defined by a set of linear inequalities. The algorithm that is sampling from polytopes usually requires heavy matrix algebra, including matrix multiplication, matrix inversion and matrix determinant. In this work, we show how to implement the heavy matrix algebra in the area of sampling in nearly linear time for the polytope that has small treewidth. In particular, given a polytope defined by a matrix A ∈ n×d with treewidth τ, we improve the running time of each iteration for three typical sampling algorithms for polytopes such as Dikin Walk, Soft-Threshold Dikin Walk and Vaidya Walk from O(nd2) to O(nτ2) by exploiting the small treewidth structures of the matrices. © 2024 IEEE.

关键词： Trees (mathematics)

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