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Networking and Communications (ICNWC), International Conference on

作者： Karan Devanand Patra Arya Adesh Bhosle Deepali Mukesh Kothari Tatwadarshi P. Nagarhalli Dept. Artificial Intelligence and Data Science Vidyavardhini’s College of Engineering and Technology Mumbai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

In agriculture, the sector grapples with pressing challenges arising from climate change, water shortages, and pest infestations. Particularly affecting farmers in developing nations, these challenges amplify issues of food insecurity and poverty, necessitating innovative solutions for sustainable global agricultural progress. Addressing the complexities of climate variability, water scarcity, and pest outbreaks requires comprehensive tools and information. The absence of predictive insights intensifies crop losses and hampers overall profitability. In response, this research introduces DigiFarmer, an innovative solution employing advanced machine learning models. DigiFarmer adopts a holistic approach, encompassing crop disease detection, weed classification using Convolutional Neural Networks (CNNs), and crop quality analysis with a Light Gradient Boosting Machine (LGBM) classifier. Yield prediction is facilitated by the Random Forest algorithm, with real-time weather data integration enhancing efficacy. DigiFarmer stands out for empowering farmers with tools for sustainable and resilient agriculture. Leveraging cutting-edge technologies, DigiFarmer not only addresses immediate challenges but equips farmers with foresight to mitigate future risks. The ultimate goal is to contribute to the welfare of global farming communities by enabling informed decisions that enhance productivity, profitability, and sustainability in agriculture.

关键词： Productivity Adaptation models Machine learning algorithms Profitability Crops Boosting Prediction algorithms

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CAM-Based High Efficient Weakly Supervised Multi-Object Multi-Class Detection Method

CAM-Based High Efficient Weakly Supervised Multi-Object Mult...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Xiu-Zhi Chen Hsiao-Wei Huang Yen-Lin Chen Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Two approaches are available for WSOD: MIL and CAM. MIL-based methods are capable of addressing multiple targets and multiple class problems. However, these methods require the input of region proposals. In contrast, CAM-based methods are straightforward and more efficient, yet they are constrained by limited localization accuracy and are likely to be applicable only to single target classes. This study proposes a CAM-based WSOD method that is capable of addressing multi-object, multi-class tasks with high efficiency, which could prove beneficial for the development of specific consumer electronic applications, such as smart cameras and smart home devices. The proposed method was evaluated on two public datasets, demonstrating that it is significantly more efficient than the baseline method.

关键词： Location awareness Electric potential Accuracy Smart cameras Object detection Smart homes Computational efficiency Proposals Consumer electronics Automotive engineering

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Ink Detection from Carbonized Herculaneum Papyri using Deep Learning

Ink Detection from Carbonized Herculaneum Papyri using Deep ...

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International Conference on computer and Information Technology (ICCIT)

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

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.

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Real-Time Face Mask Detector 3

Real-Time Face Mask Detector

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3rd IEEE International Conference on Technology, engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Patel, Vishwajeet Guru Prasad, M.S. Aditya Pai, H. Kumar, A Suresh Praveen Gujjar, J. Naveen Kumar, H.N. Dept. of Computer Science and Engg. Dehradun India JAIN [Deemed-to-be University Faculty of Management Studies Bengaluru India Vidyavardhaka College of Engineering Dept. of Electronics and Communication Engg. Karnataka Mysuru India

ISBN: (纸本)9798350397819

When the COVID-19 pandemic was present we all have faced various challenges in our everyday lives. Pandemic created worldwide emergency which led to the lockdown in most of the countries resulting into disruption in world trade and movement. To reduce the covid cases WHO recommended to wear mask and maintain social distancing and then these steps became mandatory for some places, such as restaurants, hospitals, grocery stores etc., The purpose of limiting interaction with individuals as much as possible in such situations is simply defeated by having a staff personally check each person to make sure their mask is on. To avoid this dilemma, it became necessary to build a project named 'Real-time face mask detector', This reveals whether a mask-wearing individual is there. This project needed the use of some python libraries like TensorFlow, Keras, OpenCV. Using this project, we will be able to Detect mask-on faces in live video clip with accuracy of 97-98 %. © 2023 IEEE.

关键词： COVID-19

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A Factor Based Multiple Imputation Approach to Handle Class Imbalance

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Procedia computer science 2023年 218卷 103-112页

作者： Pranita Baro Malaya Dutta Borah Dept. of Computer Science and Engineering National Institute of Technology Silchar Silchar-788010 India

Class imbalance and incompleteness are the two most serious problems faced in data science and machine learning when working on real-life datasets. Both of these cases have severe implications on the ability of classification algorithms to make accurate predictions. When a dataset used for training classifiers is both imbalanced as well as incomplete, the traditional approach is to address the missing data first and then handle class imbalance but it could lead to some issues such as overfitting as well as amplification of some errors due to random duplication. In this paper, an alternate factor-based multiple imputation oversampling method (FB-MIO) is proposed to handle class imbalance as well as missing values in the training dataset at the same time. First, a new factor is presented to evaluate the density of missing values belonging to the majority class with respect to the minority class in a particular region. With the help of this factor, an oscillator is developed to guide how imputation based oversampling should be carried out. Then the training set is divided into multiple smaller subsets and used the oscillator to determine whether missing values for the majority class belonging to that subsets should be imputed or not. This would help in preventing exaggerated duplication when not needed. Experiments were carried out on 27 imbalanced datasets after random addition of missing value and the F1 and AUROC scores of FB-MIO were compared to other dataset level resampling methods such as SMOTE, ADASYN, B-SMOTE etc. The effectiveness of the proposed method has been validated after experiments on benchmark datasets and the comparative results are presented in the form of average rank and number of wins.

关键词： class imbalance overfitting oversampling imputation missing value

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Simple Lightweight Cryptographic Algorithm to Secure Imbedded IoT Devices

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第20期16卷 98-113页

作者： Mhaibes, Hakeem Imad Abood, May Hattim Farhan, Alaa Kadhim Technical Institute of Kut Middle Technical University Baghdad Iraq College of Engineering Al Iraqia University Baghdad Iraq Computer Science Dept. University of Technology Baghdad Iraq

Improving the voting system has become a widely discussed issue. Paper-based elections are not safe because of the possibility of changing and adding ballots. Consequently, many countries use e-voting systems to ensure security, authenticity and time efficiency. Blockchain e-voting systems can be adopted to reduce fraud and increase voting access from home, especially in pandemics. This paper suggests a blockchain e-voting system that tackles two security and authentication issues. The security has been ensured using hybrid publickey cryptography;the voter information is encrypted using the regional election office elliptic public key, while the homomorphic public supreme election authority encrypts the vote. Using homomorphic encryption for voice enables the calculations of results as the authority encrypts it without revealing the vote itself. Authentication has been improved for home voting by a robust login system. This login system consists of two steps. In the first step, the voter enters the site using his unique QR code number scanned by webcam;in the second step, the system checks the voter's face using a face recognition system by web camera to be routed to the voting page. Voting public keys are also authenticated using a digital certificate schema. The system has been tested to show its efficiency and suitability in block establishment time and the encryption and key generator randomness using NIST tests. The internet of things (IoT) revolution has been sparked by the exponential increase in connected devices caused by recent advances in wireless technology. These embedded devices gather, analyze, and send vast data via the network. data transmission security is a primary problem in IoT networks. Several lightweight encryption/decryption algorithms have been developed for such resource-constraint devices. One of most effective and fast lightweight encryption algorithms for IoT applications is the Tiny Encryption Algorithm (TEA). TEA has few lines source

关键词： Internet of things

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DeepCensored: Deep-Learning Based Probabilistic Forecasting Framework for Censored data

DeepCensored: Deep-Learning Based Probabilistic Forecasting ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jiahao Tian Michael D. Porter School of Data Science University of Virginia Charlottesville VA USA Dept. of Systems and Information Engineering School of Data Science University of Virginia Charlottesville VA USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Accurate time series forecasting is critical in various fields, including resource allocation and crime prevention. While traditional approaches often focus on continuous data, count data forecasting, especially with partially observed (censored) data, remains challenging. This paper introduces DeepCensored, a novel deep learning-based framework that combines the Expectation-Maximization (EM) algorithm with deep neural networks to deliver robust probabilistic forecasts. DeepCensored naturally handles interval-censored event data, where exact event times are unknown but fall within a specific interval. Through extensive simulations and real-world crime data analysis, our method significantly outperforms traditional forecasting approaches, reducing the Mean Absolute Error (MAE) by 50% and better detection of emerging crime trends. These results highlight DeepCensored's potential to provide actionable insights for law enforcement and public safety by predicting crime intensity and enabling resource-efficient policing strategies.

关键词： Time series analysis Predictive models Probabilistic logic Market research data models Time measurement Public security Resource management Forecasting Synthetic data

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Enhancements to ODXT SSE Scheme: Algorithm Optimization and Leakage Suppression

Enhancements to ODXT SSE Scheme: Algorithm Optimization and ...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Prathit Aswar Syed Taqi Ali Dept. of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

A Dynamic SSE (Searchable symmetric encryption) scheme should support updates and keyword searches with outsourced symmetrically encrypted data while minimizing the amount of data revealed to the untrusted server. For this purpose, literature in this field has identified two critical security properties, i.e. forward and backward *** this work, we build upon the previously developed "Oblivious Dynamic Cross Tags" (ODXT) scheme for conjunctive keyword searches, featuring forward and backward privacy. The objective is to improve flexibility in the update operation of the given scheme, solve the open problem of s-term leakage, and perform leakage analysis of the improved scheme. We propose flexODXT, to reduce the time required for individual keyword update operations to a constant instead of linear complexity, and we propose suppODXT, to reduce the s-term leakage, an open problem found in ODXT.

关键词： Privacy Heuristic algorithms Keyword search Distributed databases computer networks Encryption Servers computer security Secure storage Optimization

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Prediction of Alzheimer's Disease using Deep Learning Algorithms

Prediction of Alzheimer's Disease using Deep Learning Algori...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： P Praveen Koleti Srilatha Masabathini Sathvika Edulapuram Nishitha Madduri Nikhil Dept. of Computer Science and Artificial Intelligence SR University Warangal India Dept. of Computer Science Engineering SR Engineering College Warangal India

Throughout the world, at least five crore individuals are thought to have the disease of Alzheimer. This Alzheimer's disease is the most well-known and prevalent kind of dementia (AD). Since the modest but significant advancements in Alzheimer's disease (AD) treatment, the diagnostic emphasis has progressively switched to the precise identification of the disease's earliest stage. In numerous attempts at clinical classification, the difficulty of differentiating pre-clinical AD from changes associated with normal aging or developed AD has been recognized. Alzheimer's disease is a neurological and brain disorder that eventually makes it difficult to do even the most fundamental tasks. It gradually destroys memory and thinking skills. It is a neurological degenerative disorder that causes both brain cell shrinkage and degeneration. The most typical reason of dementia is a gradual loss of cognitive ability, behavioral, and social abilities, limiting their capacity to operate independently. It causes both brain cell shrinkage and degeneration. As people live longer, there are more concerns about aging. Damage to brain cells can be avoided with an early diagnosis of this disease. Early detection can considerably slow or stop the growth of this disease because there is no cure for it. To stop the progression of Alzheimer's disease, early detection is essential. As a result, experts can initiate preventive care as soon as possible. They call for prompt and precise Alzheimer's disease detection in its initial and most elusive stages. The only accurate approach for diagnosis is magnetic resonance imaging (MRI) brain imaging, although exams like the Mini-Mental State Examination (Fol stein 1975), or MMSE, are routinely used for earlier diagnosis. The main objective of this research work is to devise a technique for accurately identifying and staging diseases in magnetic resonance imaging (MRI). Deep learning excels at analyzing images and predicting diseases. A growing body o

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Deep Neural Network-Based Approach to Identify the Paddy Leaf Disease using ResNet50-V2

Deep Neural Network-Based Approach to Identify the Paddy Lea...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Md. Ashiqul Islam Md. Mizanur Rahman Abdul Alim Shohan Rishad Amin Pulok Sanzida Akter Md. Tanvir Ahmed Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Dept. of Computer Science and Engineering East-West University Dhaka Bangladesh

South Asian countries especially Bangladesh, India, and Srilanka are very involved in paddy cultivation. In Bangladesh and India, one of the key producing crops is paddy. Worldwide more than 40 percent of the world's population derives most of its calories from rice and traditionally farmers are directly involved production of rice. But every year lot of farmers have to face a tremendous amount of loss due to paddy leaf disease. Major of the farmers do not know how to identify perfectly in the primary stage that’s why they are facing loss. Due to the increase in population, it is very important to make paddy cultivation scientific and produce more. There are more than 30 types of paddy leaf disease and from there in Bangladesh, approximately 10 types of disease are quite similar. If it is possible to ensure the leaf disease in the very primary stage within a very short time, then every farmer can reduce their loss. Nowadays many researchers are interested in identifying paddy leaf disease using modern science. In this paper, we worked on paddy leaf disease detection and it has worked on 4 types of disease and 1 healthy and without any disease leaf class of paddy. This paper through an automated detection approach using deep learning techniques achieved the highest accuracy instead of the previous applicable working model and traditional manual disease detection process. In this paper, we have analyzed it using 4 different types of models. ResNet50-V2, Inception-V3, MobileNet-V2, and DensNet-201 algorithm models have been applied here and the finding of the research is, that all the models achieved more than 95% accuracy from these ResNet50-V2 has achieved the best accuracy and it is 99.06%. We hope that these deep learning models and techniques will detect the disease with high accuracy in the very beginning time and it will help reduce damage and loss of paddy cultivation.

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