In this digital era, social media is one of the key platforms for collecting customer feedback and reflecting their views on various aspects, including products, services, brands, events, and other topics of interest....
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In this digital era, social media is one of the key platforms for collecting customer feedback and reflecting their views on various aspects, including products, services, brands, events, and other topics of interest. However, there is a rise of sarcastic memes on social media, which often convey contrary meaning to the implied sentiments and challenge traditional machine learning identification techniques. The memes, blending text and visuals on social media, are difficult to discern solely from the captions or images, as their humor often relies on subtle contextual cues requiring a nuanced understanding for accurate interpretation. Our study introduces Offensive images and Sarcastic Memes Detection to address this problem. Our model employs various techniques to identify sarcastic memes and offensive images. The model uses Optical Character Recognition (OCR) and bidirectional long-short term memory (Bi-LSTM) for sarcastic meme detection. For offensive image detection, the model employs Autoencoder LSTM, deep learning models such as Densenet and mobilenet, and computer vision techniques like Feature Fusion Process (FFP) based on Transfer Learning (TL) with image Augmentation. The study showcases the effectiveness of the proposed methods in achieving high accuracy in detecting offensive content across different modalities, such as text, memes, and images. Based on tests conducted on real-world datasets, our model has demonstrated an accuracy rate of 92% on the Hateful Memes Challenge dataset. The proposed methodology has also achieved a Testing Accuracy (TA) of 95.7% for Densenet with transfer learning on the NPDI dataset and 95.12% on the Pornography dataset. Moreover, implementing Transfer Learning with a Feature Fusion Process (FFP) has resulted in a TA of 99.45% for the NPDI dataset and 98.5% for the Pornography dataset.
The problem of producing a natural language description of an image for describing the visual content has gained more attention in natural language processing(NLP)and computer vision(CV).It can be driven by applicatio...
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The problem of producing a natural language description of an image for describing the visual content has gained more attention in natural language processing(NLP)and computer vision(CV).It can be driven by applications like image retrieval or indexing,virtual assistants,image understanding,and support of visually impaired people(VIP).Though the VIP uses other senses,touch and hearing,for recognizing objects and events,the quality of life of those persons is lower than the standard *** image captioning generates captions that will be read loudly to the VIP,thereby realizing matters happening around *** article introduces a Red Deer Optimization with Artificial Intelligence Enabled image Captioning System(RDOAI-ICS)for Visually Impaired *** presented RDOAI-ICS technique aids in generating image captions for *** presented RDOAiiCS technique utilizes a neural architectural search network(NASNet)model to produce image ***,the RDOAI-ICS technique uses the radial basis function neural network(RBFNN)method to generate a textual *** enhance the performance of the RDOAI-ICS method,the parameter optimization process takes place using the RDO algorithm for NasNet and the butterfly optimization algorithm(BOA)for the RBFNN model,showing the novelty of the *** experimental evaluation of the RDOAI-ICS method can be tested using a benchmark *** outcomes show the enhancements of the RDOAI-ICS method over other recent image captioning approaches.
This article proposes a model that combines the issues related to autonomous vehicles into seven groups. The groups are included in mutual iterations between the user, the autonomous vehicle and the environment. They ...
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As everyone knows that in today's time Artificial Intelligence, machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we a...
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As everyone knows that in today's time Artificial Intelligence, machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we are also seeing that the second wave of corona has wreaked havoc in India. More than 4 lakh cases are coming in 24 h. In the meantime, news came that a new deadly fungus has come, which doctors have named Mucormycosis (Black fungus). This fungus also spread rapidly in many states, due to which states have declared this disease as an epidemic. It has become very important to find a cure for this life-threatening fungus by taking the help of our today's devices and technology such as artificial intelligence, data learning. It was found that the CT-Scan has much more adequate information and delivers greater evaluation validity than the chest X-Ray. After that the steps of imageprocessing such as pre-processing, segmentation, all these were surveyed in which it was found that accuracy score for the deep features retrieved from the ResNet50 model and SVM classifier using the Linear kernel function was 94.7%, which was the highest of all the findings. Also studied about Deep Belief Network (DBN) that how easy it can be to diagnose a life-threatening infection like fungus. Then a survey explained how computer vision helped in the corona era, in the same way it would help in epidemics like Mucormycosis.
Advances in multimodal machine learning help artificial intelligence to resemble human intellect more closely, which perceives the world from multiple modalities. We surveyed state-of-the-art research on the modalitie...
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Advances in multimodal machine learning help artificial intelligence to resemble human intellect more closely, which perceives the world from multiple modalities. We surveyed state-of-the-art research on the modalities of bidirectional machine learning translation of image and natural language processing (NLP), which address a considerable proportion of human life. Recently, with the advances in deep learning model architectures and learning methods in the fields of image and NLP, considerable progress has been made in multimodal machine learning translations that can be built by integrating image and NLP. Our goal is to explore and summarize state-of-the-art research on multimodal machine learning translation and present a taxonomy for the multimodal bidirectional machine learning translation of image and NLP. Furthermore, we reviewed the evaluation metrics and compared state-of-the-art approaches that influences this field. We believe that this survey will become a cornerstone of future research by discussing the challenges in multimodal machine learning translation and direction of future research based on understanding state-of-the-art research in the field.
Given the rise of multimedia content, human translators increasingly focus on culturally adapting not only words but also other modalities such as images to convey the same meaning. While several applications stand to...
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In this paper we have addressed the implementation of the accumulation and projection of high-resolution event data stream (HD - 1280×720 pixels) onto the image plane in FPGA devices. The results confirm the feas...
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Modern day computer visionapplications are frequently implemented using machine learning approaches. While these implementations can perform very well, the performance is heavily dependent on sufficient and accurate ...
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Smart measurements are widely deployed in many applications due to the technology advancement. For various industrial applications, automated inspection and analysis based on the image is provided by machinevision. F...
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Smart measurements are widely deployed in many applications due to the technology advancement. For various industrial applications, automated inspection and analysis based on the image is provided by machinevision. For the measurements in these applications, sensors must be connected. machinevision tries to creatively combine already existing technology and use them to address current issues. The term "measurement" is frequently used to refer to many tasks and is the cornerstone of industrial automation and security deployment. This Special Issue of Instrumentation & Measurement Magazine addresses some novel achievements in the measurement and instrumentation science and technology fields. It advances machinevision concerning production, application of smart materials, measurement and estimation techniques, etc. The variety of selected papers reflects the efforts made by the authors to focus either on methodological aspects or technical issues. In particular, three papers have been accepted for publication, reflecting several aspects of the abovementioned fields by covering machinevision and imageprocessing technology.
Agriculture has been the most primary source of the livelihood of man for thousands of years. Even today, it provides subsistence to about 50% of the world population. Plant diseases are the serious cause of big losse...
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Agriculture has been the most primary source of the livelihood of man for thousands of years. Even today, it provides subsistence to about 50% of the world population. Plant diseases are the serious cause of big losses to crop production every year worldwide. It is necessary to keep the plants healthy at various stages of their growth/development to deal with the financial losses from plant diseases. Symptoms of infections are visible mainly at plant leaves;thus leaves are commonly used to detect and identify the diseases. Detecting the disease through visual observation is itself a challenging task and requires a lot of human expertise. imageprocessing techniques along with computational intelligence or soft computing techniques can be used to provide a better assistance for disease detection to the farmers. A disease in plants can be detected based on its symptoms extracted in the form of features. Feature extraction techniques thus play a vital role in such systems. The paper emphasizes on the review of hand-crafted and deep learning based feature extraction with their merits and demerits. It provides a comprehensive discussion on a variety of image features such as color, texture, and shape for various disorders in different cultures.
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