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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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Deep learning-based classification,detection,and segmentation of tomato leaf diseases:A state-of-the-art review

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2025年第2期15卷 192-220页

作者： Aritra Das Fahad Pathan Jamin Rahman Jim Md Mohsin Kabir M.F.Mridha Department of Computer Science and Engineering American International University-BangladeshDhaka 1229Bangladesh Institute of Information and Communication Technology Shahjalal University of Science and TechnologySylhet 3114Bangladesh Advanced Machine Intelligence Research Lab Dhaka 1207Bangladesh Bangladesh University of Business and Technology DhakaBangladesh

The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and *** by the farmers poses the risk of inadequate treatments,harming both tomato plants and *** of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early *** regions are ideal for tomato plants,but there are inherent concerns,such as weather-related *** diseases largely cause financial losses in crop *** slow detection periods of conventional approaches are insufficient for the timely detection of tomato *** learning has emerged as a promising avenue for early disease *** study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and *** study delves into various diagnostic procedures,including image pre-processing,localization and *** conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.

关键词： Deep learning Tomato leaf disease Classification Detection Segmentation Imaging

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Abnormal Behavior Prediction in Elderly Persons Using Deep Learning

Abnormal Behavior Prediction in Elderly Persons Using Deep L...

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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Ramana, Attili Venkata Bhoga, Ujwala Dhulipalla, Ramya Krishna Kiran, Ajmeera Chary, B Deepthi Chandra Shaker Reddy, Pundru Telanagana Hyderabad India Anurag University Telangana Hyderabad India Vnr Vignana Jyothi Institute of Engineering and Technology Computer Science and Engineering Telangana Hyderabad India Mlr Institute of Technology Computer Science and Engineering Telangana Hyderabad India Malla Reddy Institute of Engineering and Technology Computer Science and Engineering Hyderabad India Geethanjali College of Engineering and Technology Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350338003

A "smart home"is one in which conveniences like remote monitoring and helpful services are installed to help their residents maintain their sense of autonomy and privacy. When helping an elderly person, it's important to be able to distinguish between the person's typical and atypical actions. Abnormal behaviours noticed while performing ADLs are an excellent predictor of the presence of health and behavioural issues requiring care and support. In this paper, we offer a method for automatically detecting aberrant behaviours in the elderly using long short-term memory recurrent neural networks (LSTM). Our technique can be used to simulate the time-dependent information contained in longitudinal data sets. The purpose of this research is to assess how well LSTM can detect and foresee inappropriate behaviour in the elderly within connected homes. We conducted extensive experiments on a dataset to show that the suggested strategy is effective in predicting abnormal behaviours with a high degree of precision. We also showed that our method is far better than the current gold standard. © 2023 IEEE.

关键词： Long short-term memory

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Federated and Split Learning for Enhanced Breast Cancer Diagnosis

Federated and Split Learning for Enhanced Breast Cancer Diag...

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engineering Informatics, IEEE

作者： Hemanth Karnati Baiju B V School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350338522

ISBN: (纸本)9798350338539

Protecting patient data is very important in healthcare sector, With the rise of digital applications and the increasing volume of patient data being generated, there is a pressing need to develop models that can handle this data responsibly while still delivering accurate results. This research explores the distributed machine learning techniques of Federated Learning (FL) and Split Learning (SL). By applying these methods to the renowned Breast Cancer Wisconsin (Diagnostic) dataset (WDBC), we observed remarkable results: 98% accuracy with FL and 99% with SL. These figures not only demonstrate the effectiveness of these models but also their edge over traditional centralized approaches. Importantly, while delivering this level of accuracy, FL and SL ensure that data remains decentralized, bolstering patient data security. Our research underscores the viability of these approaches in modern healthcare scenarios, where the balance between data confidentiality and diagnostic precision is crucial.

关键词： Solid modeling Data privacy Federated learning Data security Medical services Pressing Breast cancer

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Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

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computer Systems science & engineering 2023年第6期45卷 2447-2460页

作者： Ahmed Zohair Ibrahim P.Prakash V.Sakthivel P.Prabu Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhP.O.Box 8442811671KSA School of Computer Scence and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India

In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is *** has been considered in earlier times with the support of traditional *** learning process has also been widely considered in these genomics data processing *** this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional ***,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)*** suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage *** DBNJZZ gives better performance than previously available methods.

关键词： Deep belief networks zig zag deep learning mean absolute percentage error mean absolute error root mean square error DNA genomics

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An Offloading Scheme for Diverse Inter-Dependent Tasks in Vehicle Edge Computing 10

An Offloading Scheme for Diverse Inter-Dependent Tasks in Ve...

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10th IEEE Smart World Congress, SWC 2024

作者： Cao, Dun Cai, Dan Peng, Bo Wang, Jin Changsha University of Science and Technology School of Computer and Communication Engineering China Sanya Institute of Hunan University of Science and Technology China

ISBN: (纸本)9798331520861

Vehicle Edge Computing (VEC) is a promising paradigm for efficiently processing massive amounts of data from sensors and stable responses to diverse applications from vehicles. In the usual scenario, the data needs to be transmitted between different nodes and the applications can be represented as inter-dependent tasks. Moreover, the subtasks in each task have diverse interdependencies and each task has a different deadline. Therefore, in order to make the task offloading decision, two questions must be answered: when and where each subtask should be offloaded according to its dependencies. This paper formulates the execution sequence of subtasks by modeling the relationships between subtasks with Directed Acyclic Graph (DAG). Meanwhile, to satisfy the deadline of applications, the computing resources of idle vehicles are sufficiently utilized, and the computing resources on the VEC server are efficiently allocated to tasks. To optimize the overall performance of the system, this paper constructs an optimization problem of minimizing average task completion delay, and proposes the Optimal Offloading Strategy and Resource Allocation for Multi-Dependent Tasks (OSRA-MDT) scheme to solve this optimization problem. Simulation results show that our proposed OSRA-MDT scheme has achieved up to 24 % improvement in reducing the average task completion latency of the system compared to the baseline scheme. © 2024 IEEE.

关键词： Resource allocation

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DeepPCPS: Deep Personalized Care Through Patient Similarity 11

DeepPCPS: Deep Personalized Care Through Patient Similarity

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11th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2023

作者： Sreenivasan, M. Dhar, Sayan Chacko, Anu National Institute of Technology Computer Science and Engineering Calicut India

ISBN: (纸本)9798350326147

The shift from paper-based health records to Electronic Health Records (EHR) resulted in a vast volume of digital patient information. The knowledge derived from this data can be used for better decision-making and im-proving Personalized Care. It is very challenging to analyze EHR due to its high dimensional and heterogeneous nature. Deep Learning techniques can be used in this scenario. This work proposes a personalized care framework using Patient Similarity (DeepPCPS). DeepPCPS incorporates autoencoders for dimensionality reduction and uses various techniques for deducing patient similarity to find clusters o f similar patients. Experiments were conducted with the Jaccard index and the Sorensen Dice similarity index technique to calculate simi-larity scores. Based on similarity score, K-means, DBSCAN and agglomerative clustering algorithms were applied to form patient cohorts. The result suggests that the Sorensen Dice similarity with K-means has better results than the Jaccard index similarity. A quantitative analysis is conducted to support the claim. Qualitative analysis was conducted on the clusters formed from Sorensen similarity to show how the cohorts were used for customized treatment. The results obtained were promising, and further investigation is needed by incorporating additional treatment and timeline features for personalized care. © 2023 IEEE.

关键词： Decision making

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Mobility Stick with Haptic Feedback for People with Vision Impairments

Mobility Stick with Haptic Feedback for People with Vision I...

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

作者： Raghul, J. Durga Devi, K. Sreya, M. Maithreye, S. SRM Institute of Science and Technology Department of Electronic and Computer Engineering Chennai Ramapuram India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Ramapuram India

ISBN: (纸本)9798331540685

In this research, haptic motor, Node MCU, ultrasonic sensors are used to address the problem associated with the current walking stick for visually disabled people. These problems include disturbing their main sense of survival which is hearing. Hence, a system containing ultrasonic sensors, haptic motor and Internet of Things has been recommended in this study. This study illustrates that the implementation of haptic motors and ultrasonic sensors can successfully address the major issues that current alternatives face and it has a positive ad significant impact on visually disabled people. Using this method, one can monitor the person's movement and whether they fell down. This system makes use of IoT it integrate the data from sensors and update it onto the website for other person monitoring. © 2024 IEEE.

关键词： Ultrasonic sensors

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Bi-RNN and Bi-LSTM Based Text Classification for Amazon Reviews 1

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4th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2022

作者： Kashid, Shamal Kumar, Krishan Saini, Parul Dhiman, Abhishek Negi, Alok Computer Science and Engineering National Institute of Technology Uttarakhand India

ISBN: (数字)9783031303968

ISBN: (纸本)9783031303951

Electronic Commerce (E-Commerce) enables the effective implementation of product-based online business transactions. In this paper, we categorize the dataset consisting of Amazon reviews into positive and negative. Consumers are able to choose a product solely on these binary text categorizations, neglecting the manual rating provided in the reviews. Recently, Deep Learning (DL) approaches started gaining popularity in E-Commerce applications. DL Techniques offers a powerful and effective approach for data analysis through filtering humongous E-Commerce data and finding hidden patterns and valuable details. Here, the research focuses on investing DL techniques for categorizing the text into positive and negative reviews. In this paper, we implemented two different DL approaches and found the accuracy of both approaches. DL models used here are bidirectional recurrent neural networks (Bi-RNN), and bidirectional long-short-term memory (Bi-LSTM). The accuracy obtained with Bi-RNN and Bi-LSTM is 89.60% and 92.80% respectively. The result shows that Bi-LSTM performs best on the Amazon review dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Sample-based relationship for assisting diagnosis of pneumonia in medical care

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Multimedia Tools and Applications 2025年第1期84卷 267-288页

作者： Chen, Hongkang Lu, Huijuan Zhu, Wenjie Zhou, Ye Yao, Yudong Wang, Renfeng College of Information Engineering China Jiliang University Zhejiang Hangzhou China Drore Information and Technology Co. LTD Zhejiang Hangzhou China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ United States College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China

Deep neural networks have succeeded in learning balanced and imbalanced data in the field of pneumonia diagnosis. However, both require separate model designs in their respective domains. The pneumonia recognition methods have poor transfer performance between the two types of image distribution data. This paper proposes BeFormer, which focuses on sample relationships. BeFormer introduces two stages, feature extraction and establishing sample relationships, to solve the problem. Specifically, in the feature extraction stage, a new backbone network called PANet based on Multi-Scale Frequency Domain Attention (FPSA) is proposed. In establishing sample relationships, Deformable Transformer is used in parallel to model the relationships between partial regions of interest among the same and different samples in the pneumonia dataset. These two modules are connected in series to form BeFormer. Without making any changes to the model, this paper conducted experiments using BeFormer on both balanced and imbalanced pneumonia datasets. As compared to traditional pneumonia recognition models, the proposed method achieved significant improvements in the two different data distributions, demonstrating outstanding transfer performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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