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1st International Conference for Women in computing, InCoWoCo 2024

作者： Priya, S. Baghavathi Sriram, S. Surya Santhosh, V.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518943

As a dynamic sport, Cricket provides various complex shots which are extremely precise to execute and hard to classify. Shot identification or classification is based on the manual method which prone to error and time consuming. For improving the performance and coaching the cricket players, this paper purpose a system which can detect the shots for different games. In this research, by using advanced algorithm properties for analyzing the player movements and shot patter, the authors propose an automated shot detection and classifying model that provide a data driven way for highlighting the cricket performance. Moreover, it responds to the perpetual challenges in terms of the lack of access to high-quality data and elevated costs that current systems present. Such a solution can be scaled and utilized at various levels of the sport. The system's real-time classification feature that can be linked to broadcasts has the potential of creating a more in-depth understanding of players' performance, which may attract more spectators to the sport, as they would have a chance to differentiate between different types of shots. This research responds to the increasing interest in sports analytics and writes a chapter in the field by offering a new approach to automatic shot detection in cricket. © 2024 IEEE.

关键词： Cricket (Sports)

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Diabetic Retinopathy Detection using EfficientNet-based Framework with Segmentation 3

Diabetic Retinopathy Detection using EfficientNet-based Fram...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Anitha, J. Sangapu, Sreenivasa Chakravarthi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350354171

Diabetic retinopathy(DR) has become a major issue among ophthalmologists worldwide, with a majority of type 2 diabetes patients suffering from this disease. Currently, medical diagnosis is primarily performed through manual examination using an ophthalmoscope, which requires trained doctors, or other imaging devices. The scarcity of experts and the large population affected by chronic diabetes highlight the need for automated diagnostic tools - software that can provide accurate diagnostics and work effectively even with limited data. The study leveraged a pre-trained EfficientNetB0 model, which was fine-tuned with DR-specific data to address the global issue of DR. Due to a limited dataset, advanced data augmentation techniques were implemented to enhance the model's robustness. Additionally, a customized dense layer was integrated for the precise classification of DR from levels 0 to 4. The use of Canny edge-based detection accurately segmented retinal vascular blood vessels, resulting in significantly improved classification accuracy. The methodology achieved an impressive accuracy of 87.73% across DR stages and an outstanding 95.77% precision for the 'No-DR' class, highlighting high recall metrics for each category. This work effectively integrated state-of-the-art data augmentation and edge detection techniques into a unified system, providing a superior approach for diagnosing eye diseases such as DR that may surpass current automation standards. © 2024 IEEE.

关键词： Image segmentation

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Convolutional Neural Network Based Age Estimation using Diverse Facial Datasets 15

Convolutional Neural Network Based Age Estimation using Dive...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Chandana, Sidda Siri Vithyatharshana, N. Ghnana Prasoona, Y. Thrinayani, Yedhoti Jyotsna, C. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Determining an individual's age accurately is important in many contexts, including forensic investigations, medical diagnostics, and security applications. Traditional age estimation techniques rely on manual evaluation, which can be laborious and error prone. Convolutional neural networks (CNN) have been utilized more and more for image-based age estimation since the development of deep learning. The Objective is to create a CNN-based age estimation algorithm that can precisely estimate a person's age from facial photos. The proposed study used the UTKFace dataset and Facial Image dataset, which included 30,000+ facial images of people from various ethnic backgrounds, aged 0 to 116 and Facial pictures, a public dataset, which is a compilation of digital portraits of people. Five convolutional layers, five pooling layers, and two fully linked layers make up the deep CNN model that has been developed and trained in this project. The study's findings show how well CNN-based deep learning models may be used to determine an individual's age from face photos. For the model's training, the UTKFace dataset and Facial Images dataset supplied a wide-ranging and sizable combined dataset. The model performed better than more established age estimation techniques. © 2024 IEEE.

关键词： Age detection Convolutional Neural Networks Deep Learning Facial Images Image Analysis UTKFace

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Enhanced Aerial Vehicle Detection Using a Customized YOLOv8 Model for Real-World Applications 9

Enhanced Aerial Vehicle Detection Using a Customized YOLOv8 ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Thalapilly, Maanav Agasti, Ketone Nambiar, Niranjan S. Suresh, Ananthu Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350377972

Aerial vehicle detection is a critical function in surveillance, disaster response, and many defense systems. In this paper, a customized model, which extends the YOLOv8 model to detect aerial vehicles with exact precision and efficiency with four classes-categorized as helicopters, airplanes, drones, and UAVs-is developed. The dataset comprises 5,050 labeled images of various sources, augmented for robustness, and formatted in the YOLO-compatible structure for smooth training. The proposed approach integrates optimized depth and width multipliers for reducing complexity of computation at a preserved high detection accuracy. Its efficiency reflects in an mAP of 98.7%, which is superior to YOLOv8 (97.1%) and YOLOv5 (95.8%). Comparison in the two regimes indicates that the work offers superior precision and computational efficiency, hence suitable for real-world scenarios where resources are constrained. Results confirm the proposed approach is efficient in dealing with the cases of small objects, overlapping entities, and complex backgrounds, and its potential could be for further development of aerial surveillance technologies. © 2024 IEEE.

关键词： Aircraft detection

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Hyperspectral Patterns With Deep Learning For Classification For Indian Pines 15

Hyperspectral Patterns With Deep Learning For Classification...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vishwanath, Vaishnav Sreekanth, K. Prakash, Jishnu Rajendran, Adithya Gopakumar, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

The Indian Pines test site, located primarily in north-western Indiana, is renowned for its Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) scene, which has been extensively studied over the years. The hyperspectral nature of this image introduces challenges related to pixel mixture complexity, creating difficulties for supervised subpixel detection and mixed-pixel classification. Despite the prevalent use of supervised approaches for this scene due to the complexity of forming an appropriate sample pool for supervised classification, there is limited research on supervised classification for this image. The study leverages cutting-edge deep learning models and transfer learning techniques, including ResNet50 (99.68% accuracy), InceptionV3 (99.55% accuracy), DenseNet121 (99.10% accuracy), and MobileNetV3 (99.94% accuracy). These models are employed for predictive analysis, highlighting their effectiveness in handling the complexities of the Indian Pines scene and showcasing their high accuracy rates in supervised image classification. This research contributes valuable insights into the application of deep learning and transfer learning methodologies, offering a promising approach to addressing the challenges presented by complex hyperspectral imagery. © 2024 IEEE.

关键词： Self-supervised learning

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Voice driven Visual Question Answering 5

Voice driven Visual Question Answering

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

作者： Santhosh, Abhijith Rohith, K.P. Ananthakrishnan, S. Rajesh, Aravind Bhaskar, Jasmine Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350376685

Visual Question Answering is a multi-modal task to find an answer to any question asked about a given image. In this paper, we propose a model of dual CNNs that provide the correct answer to a spoken question based on a given image in audio or text form. Mirroring real-world scenarios like helping the visually impaired, the questions and answers are considered in spoken mode. Visual questions selectively address different areas of an image, including underlying context and background information. In this paper, we introduce a dual-CNN model - a conjunction of two convolutional neural networks concerning Visual Question Answering. This first CNN model is used to select the significant features from the image that contribute to the Answer, and the second CNN to extract relevant textual features from the question. The proposed framework has been tested with ***. In terms of overall accuracy and execution time, the experimental findings demonstrated very good results. © 2024 IEEE.

关键词： Convolutional neural networks

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Decentralized User Wallet: Transforming Digital Banking with Blockchain 8

Decentralized User Wallet: Transforming Digital Banking with...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Asmitha, M. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

Digital payment applications are now a crucial aspect of our everyday routines, completely changing how to handle money transactions. The use of blockchain technology is becoming more common in online payment systems, providing various advantages such as decentralization and trust, enhanced security, reduced costs, global accessibility, smart contracts that add to the importance of these applications in everyday living. These apps are accessible anytime and anywhere, offering users the flexibility to manage their finances at their convenience, regardless of banking hours. Blockchain data is stored in a series of blocks that are connected in order to apply guaranteed and secure records. This solution also involves the linking of each block represented as a hash, which is a digital signature of the block as well as the block that precedes it. Solving the analytical problem, it is feasible to notice that hashing, guaranteeing the formation of permanent fingerprints for each block, plays a very important role in blockchain. SHA-256 hashing algorithm is widely adopted in many blockchain applications due to the nature of the security in collision measurements. Hashing and blockchain designs help the enterprises to create reliable solutions. Since the web interface is applied to allow full access to the blockchain technology, the healthcare, supply chain management, and finance industries may now build confident systems. © 2024 IEEE.

关键词： Decentralized finance

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Deep Learning for Automated Air Quality Classification Using Image Data: A Comparative Study of GoogleNet and MobileNet 8

Deep Learning for Automated Air Quality Classification Using...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Thalapilly, Maanav Agasti, Ketone Kisor, G. Anjali, T. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350367904

The following study contributes to the improvement of environmental monitoring by providing an approach for the estimation of air quality in real-time based on deep learning models GoogleNet and MobileNet. With either compromised realtime processing capability or spatial resolution in conventional approaches, these two models can serve as practical and scalable alternatives. Empirical results demonstrate that both GoogleNet and MobileNet can classify AQI levels effectively and transfer learning significantly improves the effectiveness and generalization of comparison for tiny datasets. Class labels include 'AQI-Good' to 'AQI-Hazardous/Severe' Pollution information collected at sites in cities of India and Nepal along with location, time, pollutant concentration. A detailed data collection enhances the capability of models to generate accurate predictions over a range of scenarios and furthers our knowledge of temporal and spatial variability of air quality. Evaluation criteria like recall, accuracy, precision, and F1score portray the stability and reliability of the method. These measures assure their steady performance in a variety of contexts and thus guaranteeing their applicability in real life. Hence, GoogleNet and MobileNet are a few significant developments in the real-time air quality monitoring process, which provides information on would be crucial in dealing with the problems associated with air pollution. © 2024 IEEE.

关键词： Data quality

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RIP based Knowledge Graph construction for Prescription Reasoning 15

RIP based Knowledge Graph construction for Prescription Reas...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Padmavathi, A. Thillai Manjari, T. Kumar, Dharan Balaji, V. Ramanathan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

A piece of processed information is interpreted as knowledge, and the human brain connects this knowledge in multiple ways, which helps us understand and assimilate the real-world objects we see around the world. A similar method is a knowledge graph, where the information or data is stored in a graph database and visualized in a graph data structure, which stores data in the form of nodes and relationships instead of tables, without any constraints in the way they can be interpreted. A knowledge graph utilizes the graph-data structure, which contains the data in nodes and forms connections between these nodes based on the semantics or context. A Medical Knowledge Graph (MKG) is the application of Knowledge Graphs specifically for applications in Medicine. Knowledge Graph is used in many places, especially in search engines like Google, Yahoo, Microsoft Edge, etc. Existing methodologies use type-constrained negative sampling or random negative sampling techniques, which generate a lot of negative predictions. The proposed methodology uses a reasoning implementation path (RIP) over MKG, which uses medical prescription in reasoning the diagnosis, which focuses on the finer granularity of features with lower computational cost and improves the overall performance and interpretability of the model. © 2024 IEEE.

关键词： Knowledge graph

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