Chatbot is a solution to the problems citizens face in accessing government healthcare services. Combining technology with a user-oriented interface, the project allows even those with technical knowledge to easily ac...
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ISBN:
(数字)9798331536695
ISBN:
(纸本)9798331536701
Chatbot is a solution to the problems citizens face in accessing government healthcare services. Combining technology with a user-oriented interface, the project allows even those with technical knowledge to easily access the system. Chatbot’s personalized recommendations are based on user demographics, allowing health plans and services to be tailored to each individual’s unique needs. This goal has a positive impact on connecting citizens with the resources they need, saving time and effort while promoting inclusivity. improving its accuracy and adaptability over time. The system continues to update and improve its ability to solve questions and provide suggestions by analyzing user interactions and feedback. The current development ensures that the chatbot remains up-to-date and efficient even when new medical services and policies are introduced. With real-time updates, users can stay up-to-date with the latest developments in the state’s healthcare services, making the system more reliable and inefficient. Chatbots encourage citizens to participate in healthcare management by providing useful information in a conversational and accessible manner. Furthermore, the integration of strong security systems protects users’ sensitive information, increasing trust and confidence in the system. Ultimately, the chatbot aims to revolutionize the way citizens in Tamil Nadu access healthcare, help improve public health, and empower communities.
Internet of Vehicle (IoV) is a combined form of Internet of Things (IoT) and Vehicular Ad-hoc Network (VANET). Its fundamental goal is to increase the service quality of Intelligent Transportation System (ITS). The dy...
Internet of Vehicle (IoV) is a combined form of Internet of Things (IoT) and Vehicular Ad-hoc Network (VANET). Its fundamental goal is to increase the service quality of Intelligent Transportation System (ITS). The dynamic nature of IoV such as high dynamic topology, high mobility of vehicles, etc. are some of the factors that causes more congestion thus reducing the efficiency of routing. Many algorithms were designed to obtain the shortest path from the source to the destination. Choosing of Ant Colony Optimization (ACO) algorithm is one of the best ways to obtain the shortest path. In this paper, an Enhanced Ant Colony Optimization with Dynamic Evaporation rate (EACODE) algorithm is proposed to obtain the congestion-free optimized path which reduces the travel time, travel cost and traffic problems. Instead of fixed evaporation rate, it dynamic predicts the pheromone evaporation rate with the help of run time metrics to avoid the congested paths thus improving the efficiency of the travel. The simulation results show that the proposed EACO-DE algorithm improves the efficiency of the routing compared with primitive Ant Colony Optimization (ACO) algorithm and Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) under various performance metrics.
Recent advancements in Artificial Intelligence, particularly Convolutional Neural Networks (CNNs), have significantly enhanced computer vision tasks, including plant disease detection. India is a global leader in pome...
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ISBN:
(数字)9798350350593
ISBN:
(纸本)9798350350609
Recent advancements in Artificial Intelligence, particularly Convolutional Neural Networks (CNNs), have significantly enhanced computer vision tasks, including plant disease detection. India is a global leader in pomegranate cultivation and production. Pomegranate is a vital horticultural commodity with significant export potential but is highly susceptible to various diseases, leading to substantial economic losses. Existing research on automatic pomegranate disease detection has limitations, as it typically analyzes either fruits or leaves in isolation and uses datasets with plain backgrounds that do not reflect real-world complexities such as lighting variations and overlapping foliage. This study proposes a novel hybrid deep learning approach that addresses these limitations. We utilized a hybrid CNN model specifically developed using the pomegranate dataset provided by the ICAR-National Research Centre on Pomegranate. This comprehensive collection includes 1,632 high-resolution images captured from orchards across India, categorized into three classes: healthy, bacterial diseases, and fungal diseases. Our model achieves an impressive accuracy rate of 98.46%, demonstrating its potential for real-world application in pomegranate disease detection and improved agricultural outcomes.
Plant diseases identification is frequently used by physical examination or laboratory investigation which creates delays that, by the time identification is finished, results in yield loss. Diseases may affect differ...
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ISBN:
(数字)9798350389692
ISBN:
(纸本)9798350389708
Plant diseases identification is frequently used by physical examination or laboratory investigation which creates delays that, by the time identification is finished, results in yield loss. Diseases may affect different parts of plant, specifically the leaf. Different Research have made significant contributions to this sector and used computer vision technologies and machine learning effectively. Consequently, having a thorough understanding of the most recent advancements in machine learning technology and its applications for the identification of leaf diseases. In this paper, a technique is proposed by employing texture features with SVM, XGBoost and CNN to identify leaf disease of five types of plants: Alstonia, Gauva, Jamun, Lemon, and Mango. We have used Local Binary Pattern to extract texture features from leaf because the diseases can affect the texture of plant, such as hairs, ridges, and waxy coatings. We have used challenging dataset which taken under different conditions, such as humidity, light, nutrition, water, and temperature which affect the texture, shape, colour and size of leaves. The findings show that the CNN based LBP achieved high accuracy in comparing with others models.
We consider an online channel scheduling problem for a single transmitter-receiver pair equipped with N arbitrarily varying wireless channels. The transmission rates of the channels might be non-stationary and could b...
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Induction motors can be operated as induction generators when additional capacitors are added to the stator terminals. Capacitors connected to induction generators can generate voltage and can provide reactive power. ...
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Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that output polarity and emotional intensity when given a piece of text as input. Like other AIs, SASs are also known to have unst...
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Living in the age of social media, it is a daily routine for individuals to post videos, audio, pictures, and text online. In addition, the proliferation of Artificial Intelligence (AI) technology allows customizing m...
Living in the age of social media, it is a daily routine for individuals to post videos, audio, pictures, and text online. In addition, the proliferation of Artificial Intelligence (AI) technology allows customizing multimedia content to meet personal demands. However, the popularity of AI-based text-to-image generators like DeepAI also opens the door to generating images for social media platforms that impersonate unsuspecting users without their permission. While people enjoy high creativity, such “fake” images could enable the propagation of deceptive information that negatively impacts an individual's personal life and potentially cause public unrest. Therefore, reliable methods to facilitate image authentication are vital to identify and flag them. In this paper, we present AUSOME-2, an upgraded version of our system that AUthenticates SOcial MEdia images (AUSOME) using frequency analysis technologies and machine learning (ML) algorithms. Images from several text-to-image platforms, such as Dall-E 2 and Google Deep Dream, are distinguished from genuine images. Spectral analysis techniques are used to obtain features and fingerprints in the frequency domain. These features enable the ML model to classify AI -generated social media images from genuine ones. The experimental results, on top of a proof-of-concept prototype, showed that the AUSOME-2 system is a promising approach to authenticate images with decent detection accuracy.
We study the problem of finding the Nash equilibrium in a two-player zero-sum Markov game. Due to its formulation as a minimax optimization program, a natural approach to solve the problem is to perform gradient desce...
ISBN:
(纸本)9781713871088
We study the problem of finding the Nash equilibrium in a two-player zero-sum Markov game. Due to its formulation as a minimax optimization program, a natural approach to solve the problem is to perform gradient descent/ascent with respect to each player in an alternating fashion. However, due to the non-convexity/non-concavity of the underlying objective function, theoretical understandings of this method are limited. In our paper, we consider solving an entropy-regularized variant of the Markov game. The regularization introduces structure into the optimization landscape that make the solutions more identifiable and allow the problem to be solved more efficiently. Our main contribution is to show that under proper choices of the regularization parameter, the gradient descent ascent algorithm converges to the Nash equilibrium of the original unregularized problem. We explicitly characterize the finite-time performance of the last iterate of our algorithm, which vastly improves over the existing convergence bound of the gradient descent ascent algorithm without regularization. Finally, we complement the analysis with numerical simulations that illustrate the accelerated convergence of the algorithm.
This study uses cryptography to tackle the important problem of data security in cloud computing. Two keys are used in the Dual Key Encryption (DKE) method for both encryption and decryption. DKE employs a public key ...
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ISBN:
(纸本)9798400708268
This study uses cryptography to tackle the important problem of data security in cloud computing. Two keys are used in the Dual Key Encryption (DKE) method for both encryption and decryption. DKE employs a public key for the first encryption round before cloud upload and a user-only private key for the second round of encryption. Decryption operates oppositely. When tested and simulated with different file sizes on a CloudAnalyst simulator, DKE operates faster and more efficiently than cryptographic methods such as Triple DES (3DES), AES, RSA, and DES. Thanks to developments in information technology, cloud computing has made several services available online. Data security assurance is still very difficult to achieve, nevertheless. In this context, cryptography is essential and has led to the creation of the novel DKE technology. This technology is not only far more efficient than well-known cryptographic techniques such as 3DES, but it also improves data security.
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