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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kumar, Rahul Moolchandani, Jhankar Shukla, Anshita Sahu, Santosh Thada, Vikas Chole, Vikrant Amity School of Engineering and Technology Amity University Madhya Pradesh Department of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350380583

In furtherance to this, this research work focuses on the role of applying machine learning techniques in predicting the price of Gold using price data along with three macroeconomic variables. Thirteen models were tested in order to determine which of the three models: They also established that among the seven algorithms, namely linear regression, random forest regressor, and gradient boosting regressor, is the most useful in predicting gold price. Gradient Boosting Regressor was the optimized of all to tremendous accuracy with the help of the used algorithm that let it learn complex and non-linear features in the data inputs. They proved that the recent month and year price data, long term moving average and US Dollar Index (DXY) are the variables which affect the gold prices. The results confirm the necessity to include a broad set of economic variables in the model and the information can be reputable and valuable to investors and policy makers. Nevertheless, it could be proposed that future work would try to incorporate more features and even try to apply deep learning as an effort to improve the accuracy of the envisioned predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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Speech to Action for Assisting Deaf Communities 8th

Speech to Action for Assisting Deaf Communities

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Krishna, Vunnam Vamsi Naini, Dheeraj Reddy Reddy, Vaka Satwik Sakri, Aditya Rajesh Radha, D. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9789819766802

In response to the changing world we live in, this groundbreaking project aims to redefine how people interact within the deaf and hard of hearing communities. The main goal is to develop a system that can understand and translate sign language while also preserving the aspect of emotion. By leveraging the power of technologies like speech recognition, emotional analysis, and deep learning techniques like CNN, LSTM an extensive system has been designed to adapt and respond in real time. This ensures that translated sign language maintains its subtleties for an enriching communication experience. Extensive testing of the system has been conducted as a part of the feedback loop by gathering information using the application. The results have confirmed its accuracy in translating languages and capturing emotions leading to empathetic interactions within the community. This work represents an advancement in how the hearing impaired interact within the communities, highlighting the importance of inclusivity, in advancements. It signifies a milestone toward envisioning a future where the captivating essence of communication effortlessly transcends through expressive sign language ultimately improving user experiences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Emotion Recognition from Speech using Telugu Language and Deep Learning Models 1

Emotion Recognition from Speech using Telugu Language and De...

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1st IEEE International Conference on Communications and computer science, InCCCS 2024

作者： Aditya, S. Satwik, Vaka R. Krishna, Vamsi V. Vimal, C. Palaniswamy, Suja Pati, Peeta Basa Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350358858

This research involves the use of deep learning techniques to detect emotions, in Telugu speech signals, a research domain that has received little attention. Amrita speech emotion dataset-1 (ASED-1) consisting of 550 signals representing five categories. Surprise, anger, happiness, neutrality, and sadness. We explore how well various deep learning models can recognize emotions from speech. Our approach involves training models such as Convolutional Neural Networks (CNNs) Long Short-Term Memory (LSTM) networks and Multi-Layer Perceptron (MLPs) on a portion of the data and evaluating their performance on a test set. The MLP model achieved an accuracy rate of 84% although this was constrained by the size of the dataset. The CNN model showed results with decreasing trends in both training and validation loss indicating learning and generalization abilities. Similarly, the AlexNet architecture demonstrated a decline in both training and validation loss suggesting a fit without overfitting. Additionally, we included a machine learning classifier at the layer. Observed different accuracies across various models. The RNN model achieved an encouraging accuracy rate on the test data showing potential for emotion classification tasks but also room for improvement, through complex architectures or larger datasets. This research paper adds to the knowledge, in the field of emotion recognition, in Telugu speech by emphasizing the difficulties and possibilities of using deep learning techniques in this domain. © 2024 IEEE.

关键词： Emotion Recognition

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Shape-Guided Industrial Anomaly Detection

Shape-Guided Industrial Anomaly Detection

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2024 Jiangsu Annual Conference on Automation, JACA 2024

作者： Chen, Hao Yan, Yaping School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China

ISBN: (纸本)9781837242689

Current industrial anomaly detection models mainly employ either convolutional neural networks (CNNs) or Transformer networks in isolation, often overlooking their unique strengths and limitations in anomaly detection. This limits the capability of model to effectively handle diverse types of anomaly patterns. To this end, we propose a novel progressive framework for anomaly detection by strategically integrating these two architectures to leverage their respective advantages. First, we utilize the global receptive field and low-frequency information processing capability of the Transformer to restore the shape of input image. This restored shape information is then serves as auxiliary input to a CNN-based reconstructive module, enabling the subnetwork to perform a more refined reconstruction by relying on the structural context. Finally, by comparing the fully reconstructed image to the test image, we can accurately localize anomalous regions. Our approach achieves state-of-the-art performance on industrial anomaly detection datasets, demonstrating its effectiveness in enhancing anomaly detection across a variety of scenarios. © The Institution of engineering & Technology 2024.

关键词： Convolutional neural networks

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Efficient Airlift and Enhanced Care: Unmanned Aerial Vehicles and Droid Solutions for Battlefield Casualty Evacuation 5

Efficient Airlift and Enhanced Care: Unmanned Aerial Vehicle...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： George, Angelina Ashik, F. Jose, Alphonsa Niranjan, D.K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350385649

In modern military operations, timely and effective medical evacuation is crucial for reducing soldier casualties and enhancing combat effectiveness. This research presents an investigation into the use of Unmanned Aerial Vehicles (UAVs) and Droids as innovative solutions for battlefield casualty evacuation. The study explores the potential of UAVs and Droids in swiftly and accurately evacuating injured soldiers from the battlefield, providing enhanced medical care, thereby reducing casualties. By leveraging these unmanned systems, this research aims to optimize the response time and medical care quality in combat scenarios. This study has conducted a detailed assessment of the technology and potential challenges, considering various battlefield conditions and scenarios. The accuracy obtained for this study was 81.81%, achieved through a Deep Learning model based on the MobileNetV2 architecture, a powerful Convolutional Neural Network (CNN) pre-trained on ImageNet. The results of this investigation promise to significantly impact the efficiency and safety of military medical evacuation, ultimately improving the survival rates and well-being of soldiers in the heat of battle. © 2024 IEEE.

关键词： Deep learning

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MMDFD- A Multimodal Custom Dataset for Deepfake Detection 2023

MMDFD- A Multimodal Custom Dataset for Deepfake Detection

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15th International Conference on Contemporary Computing, IC3 2023

作者： Asha, S. Vinod, P. Menon, Varun G. Department of Computer Science & Engineering SCMS School of Engineering & Technology APJ Abdul Kalam Technological University Trivandrum India Department of Computer Applications Cochin University of Science & Technology Cochin India Department of Computer Science & Engineering SCMS School of Engineering & Technology Ernakulam India

ISBN: (纸本)9798400700224

A multi-modal deepfake dataset is relevant in addressing the growing concern of deepfake misuse, which poses a significant security and privacy threat. Deepfakes are becoming increasingly sophisticated, and their potential to deceive individuals and organizations is a significant issue. The ability to generate synthesized human voices using deep learning models and inserting fake subtitles has added to this problem, making it more challenging to detect deepfakes accurately. A superior quality dataset is essential to developing a competent deepfake detector. However, existing datasets are limited and often biased, making it difficult to detect deepfakes accurately. A multi-modal deepfake dataset, such as the proposed multi-modal Audio-Video-Text Deepfake dataset (MMDFD) addresses this gap by providing a more realistic and unbiased dataset. Such a dataset helps develop more accurate and effective deepfake detection methods, which can detect audio, video, and textual deepfakes simultaneously. The proposed dataset is more reflective of situations in the real world since it contains actual YouTube recordings of celebrities from four different racial origins. This helps to avoid the creation of deepfake detectors that are biased toward certain racial or ethnic groups. Overall, a multi-modal deepfake dataset is essential in addressing the growing concerns of deepfake misuse and developing effective detection methods that can detect deepfakes accurately, regardless of the medium. © 2023 ACM.

关键词： Deep neural networks

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Deep Learning Image Classification Abiotic Plant Disease Detection Classification Using MobileNet 4

Deep Learning Image Classification Abiotic Plant Disease Det...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Aggarwal, Ritu Aggarwal, Eshaan Jain, Anurag Aluvalu, Rajanikanth Maheswari, Uma Choudhury, Tanupriya Department of Computer Science and Engineering India School of Computer Science and Engineering Iilm University Haryana Gurugram India Symbiosis Institute of Technology Hyderabad Campus Hyderabad India Symbiosis International University Pune India Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India School of Computer Sciences University of Petroleum and Energy Studies Upes Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350387490

In the field of agriculture sector the key role of feature food which has a major role in the emergent population with their economy. In the food production for plant disease, it may cause significant loss for the era of diversity in species and the crop production rate in the plant disease detection and diagnosis the accurate detecting techniques could increase the superiority in food productivity and reduce the economic loss for the image categorization and object recognition. As per computed results and analysis of the various techniques, deep learning is as best approach to get the accuracy means and classify the images. In this proposed work to tune the parameters required to train load balancing overload forecasting load balancing overload forecasting the models using CNN. The model performance is evaluated by confusion matrix parameters such as accolade balancing overload forecasting accuracy, precision, Recall, sensitivity, specificity, f-measure, etc. In the plants, the stress will destroy the crop's growth, and yields are affected negatively due to the water supply's suboptimal quantity, soil erection, and abnormal temperature due to other physiological conditions and changes in biochemicals. In this paper, the current model Mobile, which is used for image detection and classification in disease, achieves an accuracy of 99.8%. © 2024 IEEE.

关键词： Climate change Crop disease production Drought Heat stress Plant responses Stress management

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Deep learning for diabetic retinopathy detection and classification Using Long Short-Term Memory (LSTM) 4

Deep learning for diabetic retinopathy detection and classif...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Prema Kumar, M. Srinivas Goud, D. Sankar, S. Sharath Kumar, S. Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India Vignana Bharathi Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Kavaraipettai Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350393354

Macular edema, diabetic retinopathy, and other apparent microvascular abnormalities in the human retina are common consequences of diabetes. The primary cause of blindness in the past ten years, diabetic retinopathy (DR), is the subject of this study. Preserving blood glucose control and preventing certain diabetic reactions require immediate or early therapy. Due of its intricacy and depth, DR is difficult to diagnose by laborious manual diagnostic procedures. This paper uses a novel methodology. Instead of manually creating features, we used the Long Short-Term Memory (LSTM) model to extract features for the Region-based Convolutional Neural Network (R-CNN) system. It is an R-CNN-LSTM model with two parts. The proposed R-CNN-LSTM system adds certain ground truth labels to training samples, which enables it to first identify the most informative patches and images before learning basic to complicated retinal properties. Using two independent public datasets, the suggested method investigates the retina vasculature and hard-exudate symptoms of diabetic retinopathy. The recommended illumination normalisation methodology improved the deep learning model and excelled other cutting-edge techniques in terms of accuracy, recall, and precision, according to extensive experimental results. © 2024 IEEE.

关键词： Long short-term memory

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Multi-Fault Diagnosis, Quantitative Analysis, and Warning Strategy for Lithium-Ion Battery System: When LSTM Meets the Fuzzy Logic Theory

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8224-8235页

作者： Chen, Hui Tian, Engang Wang, Licheng Zheng, Yuejiu University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Shanghai University of Electric Power College of Automation Engineering Shanghai200090 China University of Shanghai for Science and Technology College of Mechanical Engineering Shanghai200093 China

This article is concerned with the multilevel secure warning problem for lithium-ion battery systems (LBSs) subject to multi-fault scenarios. First, the long short-term memory (LSTM) network is employed to design a series of cell voltage estimators. In comparison with the estimation of the cell voltage and its measurement, the estimation residual is constructed in terms of the Euclidean distance (ED), which plays an essential index for the multi-fault detection issue. Furthermore, in combination with the voltage envelope relationship, a specific type of fault is subsequently identified. Then, for the subsequent fault warning purpose, a novel fault quantitative analysis method is proposed to further determine not only the fault amplitude but also its duration. In addition, on the basis of specific characteristics of the fault such as the type, count, duration, and amplitude, a fuzzy-logic-based multilevel secure warning strategy is put forward. Finally, the effectiveness of the developed multilevel fault warning approach is substantiated through comprehensive experimental validation. © 2015 IEEE.

关键词： Fuzzy logic

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Enhanced Art Style Classification with VGG16 and Machine Learning Techniques 5

Enhanced Art Style Classification with VGG16 and Machine Lea...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Karthik, Garikipati Muppala, Tejaswi Vinitha Chowdary, A. Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Art is an expression of human creativity and culture that has greatly impacted history. Classifying art styles is an important process in art and culture to preserve cultural heritage and facilitate educational and research purposes. The proposed work presents a comprehensive study that focuses on classifying 13 different art styles using deep learning techniques. The VGG16 convolutional neural network was employed for feature extraction using its convolutional and pooling layers up to the 'fc1' layer, generating 512 and 4096-dimensional feature vectors. To evaluate the classification performance, seven machine learning classifiers were utilized. The classification accuracy is evaluated before and after applying Principal Component Analysis (PCA), to reduce the dimensionality of the feature vectors. Additionally, the paper explored the direct classification by the VGG16 model. Results prove that deep feature learning with the combination of traditional classifiers of machine learning is efficient. © 2024 IEEE.

关键词： Convolutional neural networks

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