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Market resilience in turbulent times: a proactive approach to predicting stock market responses during geopolitical tensions

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Journal of Capital Markets Studies 2024年第2期8卷 173-194页

作者： Maddodi, Srivatsa Kunte, Srinivasa Rao Department of Computer Science Srinivas University Mangaluru India Department of Data Engineering United Airlines Inc Bengaluru India

Purpose: The Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions. Design/methodology/approach: Hybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty. Findings: Our study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events. Originality/value: To the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making. © 2024, Srivatsa Maddodi and Srinivasa Rao Kunte.

关键词： Geopolitical tension LSTM Momentum indicator Sentiment analysis Stock market prediction

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Digitalizing the Dress-up Experience: An Exploration of Virtual Try-On for Traditional Chinese Costume 18

Digitalizing the Dress-up Experience: An Exploration of Virt...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Lu, Yifan Zhang, Maochun Huang, Wenxuan Guan, Shouqin Ningbotech University College of Computer Science and Data Engineering Ningbo China

ISBN: (纸本)9798350312201

With the recent rapid developments in artificial intelligence (AI) technology, virtual try-on technology empowered by generative approaches has reached a level of maturity that is ripe for commercialization. However, the application of virtual try-on technology to traditional cultural costumes has not received adequate attention from research institutions and enterprises. To address this research gap, we present a novel virtual try-on system for traditional Chinese costume that incorporates diverse AI technologies, including generative models, pose estimation, human parsing, and image processing algorithms, to provide an accurate and immersive virtual try-on experience. In particular, we leverage five distinct techniques Human Keypoint Detection, Human Parsing, Densepose Parts Segmentation, Cloth Mask Extraction, and Clothing-Agnostic Representation - to realize a web service that can effectively handle the specific challenges of traditional Chinese costume, such as the need to preserve cultural authenticity and ensure accurate fit and drape. Finally, we conduct various experiments to evaluate the system's performance and have received positive feedback from users, indicating that it provides a realistic and engaging virtual try-on experience. Our work contributes to the growing body of research on virtual try-on technology and has the potential to impact the way traditional cultural costumes are designed, marketed, and worn. © 2023 IEEE.

关键词： Web services

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Encrypt Decrypt ReLU Activated UNet Prototype based Prediction of Leaf Disease Segmentation

Encrypt Decrypt ReLU Activated UNet Prototype based Predicti...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Shyamala Devi, M. Eswar, R. Hibban Rashid, M.D. Jai Harshan, J.H. Hari, P. Hemanth, S. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798331540685

The issues of sustainable agriculture and climate change are intimately linked to the problem of effective leaf disease prevention. In practice, the primary method for identifying and detecting plant diseases is expert observation with the unaided eye. However, this necessitates for the ongoing supervision of specialists, which can be too costly for big farms. The automatic identification of leaf diseases is a significant area of research because it might be useful for monitoring vast agricultural fields and identify diseases based only on symptoms that occur on the leaves of the plants. This paper recommends ReLU Activated UNet (RAA-UNet) that categorizes the leaf diseases with high precision. The KAGGLE Leaf disease dataset that contains 2094 leaf images were used for implementation. The dataset consists of 1047 leaf image and 1047 respective leaf images that have been segmented. The proposed ReLU Activated UNet initiates by creating masked leaf images by masking the original image with a segmented leaf image. To classify the leaf disease, the masked leaf images are assigned to ReLU Activated UNet with contracting and expansion which was activated using Relu Activation function. The proposed ReLU Activated UNet and conventional CNN models are fitted to the masked leaf images. As demonstrated by the experiments, the proposed ReLU Activated UNet model works better with a high accuracy of 98.85% in the classification of leaf disease like leaf spot, blight and powdery mildew. © 2024 IEEE.

关键词： Accuracy Activation CNN ReLU Segmentation UNet

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Calligraphy Alphabet Perception Using Artificial Intelligence 9

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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An Efficient Image to Speech Generation System for Visually Impaired Individuals Using Deep Learning Techniques 10

An Efficient Image to Speech Generation System for Visually ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Priyanka, G. Nivaashini, M. Shree Akshaya, B. Subashini, M. Ritish, G. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

As the world is becoming more visually oriented, visually impaired people are struggling to access and understand visual content. This paper aims to represent the model created to assist blind and visually impaired people. This method leverages deep learning techniques to convert images into meaningful audio outputs. It involves capturing input images and processing them using advanced deep-learning algorithms. The extracted features are translated into speech. The resulting auditory output provides an alternative sensory experience for individuals with visual impairments, enabling them to perceive and interpret visual information through sound. This study implements four deep learning models namely CNN-LSTM, Resnet34, YOLOv5 and Vision Transformer to process sequential visual data capturing intricate patterns and dependencies within images. By integrating these models in this work, the Image to audio converter not only achieves superior accuracy in image recognition but also enhances the richness and depth of the auditory output, creating a nuanced and immersive sensory experience for users. The performance of the proposed models was evaluated using several metrics and it was noted that vision transformer outperformed the remaining models. This seamless integration of deep learning models amplifies the study's effectiveness in translating diverse visual inputs into detailed and coherent auditory output, thereby enhancing accessibility and inclusivity for individuals with visual impairments. © 2024 IEEE.

关键词： Image enhancement

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Corner Key Point Extracted Deep Learning Framework Based Birds Species Classification 8

Corner Key Point Extracted Deep Learning Framework Based Bir...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Devi, M. Shyamala Jamal Mohammed, R. Hemanth, S. Harish Raj, B. Gajapathy, R. Kamalesh, P. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798331505462

Climate change and habitat destruction have caused the extinction of many bird species. Classifying bird species is a crucial aspect of ornithology, as it aids in the comprehension of species distribution, habitat needs, and environmental changes that impact bird populations. Ornithologists can use variations in bird species distributions to measure the health of a particular environment. Expert ornithologists have relied heavily on visual inspection, which can be tedious, time-consuming, and unreliable when classifying bird species. The development of technology can be used to categorize the bird species. This paper proposes Corner Key Filtered VGG16 (CKP-VGG16) that categorizes eight bird species with high accuracy. The execution was carried out using the 4000 bird images from the KAGGLE Birds species dataset. First, the CKP-VGG16 model separates the images of birds, and then it labels each image. After data augmentation on the labeled images, 84,000 bird images are produced. The augmented images are transformed into grayscale versions, which are then used in filtering techniques to produce Corner Key Point Filtered (CKP) bird image. The performance is analyzed by applying the CKP filtered bird images to the current CNN models. The application demonstrates that the suggested CKP-VGG16 model performs significantly well in classifying bird species, with a high accuracy of 99.7%. © 2024 IEEE.

关键词： Birds

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Watch Product Categorization through Conv2D Sequential Covolutional Neural Network with Truncated Normal Kernel Initializer 2

Watch Product Categorization through Conv2D Sequential Covol...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Harivishnu, K.C. Jamal Mohammed, R. Jagan, S. Gurucharan, G. Mohamed Arshad, M. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

For the sake of customer satisfaction and high profitable revenue, the products are designed in the Ecommerce application along with the product category. Based on the product category, prices may differ in a broad range. So, the application must be able to categorize the products before releasing the price voucher to the customers. The absence of effective product categorization, made it challenging to provide deep learning algorithm that results in the usage of modern tools towards product classification. This paper proposes Truncated Normal Kernel Initializer Conv2D (TNKIC) Sequential model for categorizing the watch product. The suggested TNKIC model includes the Watche Image dataset from KAGGLE which consists of 2000 pictures divided into 10 groups. To classify watch goods, pictures are preprocessed and fitted using the TNKIC model. Three convolutional layers, a max pooling layer, and three dense layers that are weight initialized using a Truncated Normal Kernel initializer are preprocessed on the watch pictures in order to classify the watch products. The watch images are splitted into training and testing images. The TNKIC model is examined to determine how well the suggested model works in the watch product categorization than the current conV2D models after being trained with watch images. When compared to current conV2D models, implementation observations using Python on a Gtx Geforce Tesla V100 Nvidia GPU workstation utilizing 64 and 250 training intervals show that the suggested TNKIC model classifies the watch product category with the accuracy of 97.88%. © 2024 IEEE.

关键词： Deep learning

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Emotion Recognition Using Focal Network and Resnet

Emotion Recognition Using Focal Network and Resnet

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Meena, T. Balasai, Sigireddy Anandh Duggireddy Kumar, P. Phaneendhra Velagapudi Ramakrishna Siddartha Engineering College Department of Computer Science and Engineering India Velagapudi Ramakrishna Siddartha Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350382693

Image emotion recognition involves finding the emotions from visual data, usually done through convolutional neural networks (CNN) or deep neural networks (DNN). The existing methodologies are often high complex or time complex and it is not giving the good considerable results and computationally expensive due to high number of parameters. In this paper we try to accomplish a better output by using focal network which stood among other transformers architecture due to the unique property of the dynamic attention Recent studies have shown the efficacy of transformer architectures in various deep learning tasks and challenged the convolution neural networks on various tasks. but, It has a tradeoff between reduced parameter complexity and the need for extensive training data. Drawing inspiration from recent publications, the study adopts a focal network for training, demonstrating its effectiveness in enhancing the model's attention and optimizing emotional feature extraction. The proposed methodology aims to do the process of image emotion recognition by using the advantages of transformers while mitigating the computational problem associated with the deep neural networks. The incorporation of a focal network addresses attention-related challenges, and it contributes to the development of more efficient emotion recognition systems. This research offers insights into the evolving landscape of image emotion recognition methodologies and underscores the potential of transformer architectures in advancing this field. © 2024 IEEE.

关键词： Convolution

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Early Diagnosis of Schizophrenia in Patients Using Deep Learning Techniques 10

Early Diagnosis of Schizophrenia in Patients Using Deep Lear...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Priyanka, G. Nivaashini, M. Pavithra, S. Srimaha, T.S. Kanishgaa, P.R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

While Brain, emotion, and behaviours abnormalities are hallmarks of schizophrenia, a complex and long-lasting mental illness. Its symptoms are divided into three categories: positive, negative, and cognitive. Positive symptoms include delusions, disordered thinking, abnormal gait, and hallucinations. Diminished ability to express emotions, alogia, anhedonia, avolition, and social disengagement are detrimental symptoms. Deficiencies in working memory, problems paying attention, and trouble making decisions are examples of cognitive symptoms. Environmental variables, genetic predisposition, and neurochemical imbalances interact intricately in an etiology of schizophrenia. Typically, antipsychotic drugs, psychotherapy, and social support are used in conjunction for treatment. The management of symptoms and enhancement of the general health of persons diagnosed with schizophrenia need early intervention and customized treatment strategies. The CNN, BiLSTM, GRU, and Transformer deep learning models are implemented in this study, and their respective performances are assessed. The transformer and GRU models outperform the other models when hyperparameter adjustment is carried out to increase the models' efficacy. © 2024 IEEE.

关键词： Diseases

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ResNet Blended Five Dense Layered Deep Fully Connected Neural Network based Road Damage Classification 2

ResNet Blended Five Dense Layered Deep Fully Connected Neura...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Keerthivasan, R. Jai Harshan, H. Kamalesh, P. Harishraj, B. Hibban Rashid, M.D. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

Operating a vehicle on a deteriorated roadway causes an enormous threat to safety, particularly for self-driving automobiles. Deep learning-based image processing technology has made it possible to study models that assess roadway conditions. Disparity of roadway damage and complicated backgrounds in roadway shots makes it an extremely difficult challenge classifying the road damage. To address this problem, this paper recommends ResNet Blended Five Dense Layered Deep Fully Connected Neural Network (RB-5DFCNN) that classifies the road damage with high efficiency. The KAGGLE Road Damage dataset, which consists of 2074 road snapshots, is used by the proposed RB-5DFCNN model. In order to classify road damage, the RB-5DFCNN model first preprocesses the images before fitting ResNet CNN, which consists of five steps of convolutional block and identity block processing. After the ResNet model completed its task, five dense layers were applied to the DFCNN in order to classify the state of road damage. The classification of road damage condition and the determination of the necessary number of dense layers to identify road damage with high accuracy are the innovative aspects of the proposed RB-5DFCNN. To test the effectiveness of the road damage categorization, the Road Damage dataset was applied to both the proposed RB-5DFCNN model and the current CNN approaches. Python execution on a GTX Geforce Tesla V100 Nvidia graphics card server over 64 and 150 training intervals shows that the suggested RB-5DFCNN model projects an accuracy of 98.77% in comparison to the state-of-the-art CNN models. © 2024 IEEE.

关键词： Statistical tests

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