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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Logapriya, E. Surendran, R. Poornima, D. Kartheesan, L. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

This study presents a deep learning architecture designed to monitor a woman's menstrual cycle and provide personalized dietary suggestions to improve health. The system employs complex models, including CNNs, RNNs, Transformers, T-GNNs, VAEs, and GANs, to predict the intensity of symptoms, determine the phases of the menstrual cycle, and offer personalized nutritional advice. By analysing data from menstrual diaries, food consumption, and symptom tracking, the method establishes a strong association between hormonal fluctuations, symptoms, and nutritional needs. Model accuracy is enhanced through data preprocessing techniques, including imputation, normalization, and augmentation, which provide high-quality training inputs. Research shows that Transformer models outperform other approaches in phase recognition (96% accuracy) and phase-aware nutritional recommendation accuracy. This research highlights how deep learning can transform women's healthcare by providing personalized nutritional recommendations based on data that align with the phases of a woman's menstrual cycle, ultimately contributing to improved health for women. © 2024 IEEE.

关键词： Generative adversarial networks

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BuddyBot: AI Powered Chatbot for Enhancing English Language Learning 2

BuddyBot: AI Powered Chatbot for Enhancing English Language ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Dhivvya, J.P. Karnati, Sai Bhargav Amrita Vishwa Vidyapeetham Amrita School of Computing Center for Wireless Networks and Applications Department of Computer Science and Engineering Kollam Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Kollam Amritapuri India

ISBN: (纸本)9798350360523

Today, many students are having a hard time learning to speak the English language and the problem is even worse for those in rural areas, where both teachers and resources are rare. We are trying to break this disparity and provide better resources for those in need utilizing Artificial Intelligence and Natural Language Processing (NLP) to contribute to the SDG4 Quality and Inclusive Education. In this paper, we present the idea of how we developed Chatbot named BuddyBot Utilizing OpenAI Generative Pre-Trained Transformer 2 (GPT-2) and Google Text-to-Text Transfer Transformer (FLAN-T5) models. This system incorporates diverse datasets for fine-tuning and specialized tasks in English language learning. BuddyBot follows a five-phase architecture - User interface, Pre-processing, Natural Language Processing (NLP) stack, language generation engine and adaptive learning personalization. The E-Learn web-based interface offers interactive courses with BuddyBot assistance, emphasizing adaptive learning personalization. Dataset preprocessing includes tokenization, sentiment analysis, and specialized formatting. The NLP stack integrates GPT-2 and FLAN-T5 models into the framework, maintaining dialogue structure and personalization. Validation tests reveal model effectiveness, with reduced training loss (0.778 to 0.414 by epoch 8) and increased accuracy (0.810 training, 0.792 validation). Overall, BuddyBot's robust methodology and validation results showcase its efficacy in providing personalized English language learning experiences. © 2024 IEEE.

关键词： Sentiment analysis

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Maximizing Coverage over a Surveillance Region Using a Specific Number of Cameras 27th

Maximizing Coverage over a Surveillance Region Using a Speci...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Sumi Suresh, M.S. Menon, Vivek Setlur, Srirangaraj Govindaraju, Venu Department of Computer Science and Engineering University at Buffalo BuffaloNY14260 United States Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam690525 India

ISBN: (纸本)9783031783111

An efficient approach to camera deployment enhances the cost-effectiveness and functionality of a multi-camera surveillance network. Traditional camera placement strategies involve maximizing total coverage over a surveillance region with predefined camera locations by optimizing the camera orientations. However, some modern approaches allow users to specify the desired total coverage over the surveillance region, and the algorithms subsequently determine the optimal number of cameras, as well as their exact locations and orientations, to meet this specified coverage. The Reward Penalty Score (RPS) algorithm and Extended Greedy Grid Voting (EGGV) algorithm are two innovative algorithms designed to attain the coverage constraint by proposing an optimal number of cameras and their locations and orientations. In certain scenarios, the number of cameras available for designing the surveillance network is limited, which should be treated as an input constraint rather than a specified coverage requirement. Under such conditions, the primary objective becomes maximizing total coverage with the given number of cameras by determining their optimal locations and orientations. Currently, there are no widely recognized algorithms specifically designed to handle this particular scenario. In this paper, we effectively modify the RPS and EGGV algorithms (m-EGGV and m-RPS), adapting them to optimally deploy the specified number of cameras over the entire surveillance region in an efficient manner to maximize coverage. Additionally, by employing the m-EGGV and m-RPS algorithms, we address the scenario of coverage loss resulting from the failure of one or more cameras. These modified algorithms facilitate the relocation of a subset of potential cameras which can alleviate this loss in coverage caused by the failure of cameras. The m-EGGV and m-RPS algorithms demonstrate a robust performance through extensive testing in diverse simulation environments. © The Author(s), under exclusive

关键词： Security systems

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Speech-Based Visual Aid for the Blind Eye Using Deep Convolution Neural Network 4

Speech-Based Visual Aid for the Blind Eye Using Deep Convolu...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Nandwani, Prasidhi Jain, Garima Jain, Sanat Phulre, Ajay Kumar Joshi, Suneet Swaroop, Rupayali School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Kothri-Kalan Sehore India School of Computer Science and Engineering VIT Bhopal University Kothri-Kalan Sehore India Computer Science and Engineering JECRC University Rajasthan Jaipur India

ISBN: (纸本)9798350353266

To enhance the navigation experience for visually impaired individuals, this work proposes a novel assistive system for visually impaired individuals that leverages object detection, distance estimation, and voice feedback functionalities. The system utilizes a camera to capture real-time surroundings. Advanced object detection algorithms, like SSD or YOLO, will be employed to identify objects within the field of view. Subsequently, distance estimation techniques will be implemented to determine the approximate distance between the user and the detected object. Finally, a text-to-speech engine will convert this information into clear audio messages, providing the user with crucial environmental awareness. Voice commands will be integrated to allow users to interact with the system, initiating object detection or requesting specific information about their surroundings. This comprehensive approach aims to empower blind users by enhancing their spatial understanding and fostering independent navigation in everyday environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated Classification and Segmentation of Brain Tumor Images using Backboned ResUNet Framework 6

Automated Classification and Segmentation of Brain Tumor Ima...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Prasanalakshmi, B. Anitha, Cuddapah Balamanigandan, R. Mahaveerakannan, R. Gade, N.V. Ratnakishor Adjunct Faculty Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Computer Science and Engineering School of Computing Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

One of the most challenging issues in computer imaging is the automated segmentation of brain tumors using Magnetic Resonance Images (MRI). Several approaches are explored using Deep Neural Networks in image segmentation since they perform well in automated tumor segmentation using tumor scans. Due to the complexity, training deeper neural networks usually takes a long time and many processing resources. An automated approach for classifying and segmenting images of Brain tumor MRI is proposed. As a hybrid transfer learning approach, modified ResNet50 identifies the class of MRI images as Benign or malignant. After identifying the Benign images, the tumor’s location is segmented using a hybrid network involving ResUNet with the Xception network as Backbone. This combination approach is chosen after its comparative analysis with other backbones. Various combination approaches are carried out, and performance metrics like Jaccard, Dice, and Tversky coefficient are done to prove the ResUNet backboned with Xception plays a better role concerning accuracy and speed. The proposed approach was tested on the dataset Cancer Imaging Archive to validate its efficiency. The proposed model has an average dice similarity coefficient score of 0.94, outperforming the conventional transfer learning training method with all the layers. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Automatic Skull Shape Completion of Defective Skulls Using Transformers for Cranial Implant Design 2

Automatic Skull Shape Completion of Defective Skulls Using T...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Resmi, S. Singh, Rimjhimpadam Palaniappan, Kannappan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electrical Engineering and Computer Science University of Missouri MO United States

Cranioplasty is a surgical method that restores the aesthetic and protecting function of a damaged skull by implanting material into the damaged *** and accurate design of patient specific cranial implants is very much required in the process of *** time consumption for designing and manufacturing of patient specific cranial implant has become an obstruction for cranioplasty procedures. Hence, a fully automatic and fast design of cranial implant becomes very important. The cranial implant design processmainly comprises of two steps. The former step concentrates on the automatic skull shape completion of defective skulls to fill the gaps and the cracks created in the skull. While the second step computes the difference between defective input and the completed skull for generating the implant. Currently computer aided design is used for the skull shape completion task which is a time consuming process. The application of deep learning techniques may result to faster and accurate skull shape completionwhich can be used for the design of patient specific cranial implants. This work proposes a novel approach combining 3D U-Net with Transformers for the automatic skull shape completion *** are using a vision transformer in the encoder section of the 3D U-Net architecture to consider the volumetric skull reconstruction as sequence- to-sequence prediction problem and to efficaciously grasp the global contextual information. The work also compares its performance with the famous variants of 3D U-Net deep network model namely, 3D U-Net and 3D U-Net with attention. From the values resulted for the dice coefficient metric it is clear that the proposed 3D U-Net with transformer approach performs better than the other two models on test images. © 2024 Elsevier B.V.. All rights reserved.

关键词： computer aided design

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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction

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Neural computing and Applications 2024年第27期36卷 16911-16940页

作者： Abu Talib, Manar Bou Nassif, Ali Azzeh, Mohammad Alesh, Yaser Afadar, Yaman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics Sharjah United Arab Emirates King Hussein School for Computing Sciences Princess Sumaya University for Technology Amman Jordan

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l

关键词： Forecasting

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Exploring Video Event Classification: Leveraging Two-Stage Neural Networks and Customized CNN Models with UCF-101 and CCV Datasets 18

Exploring Video Event Classification: Leveraging Two-Stage N...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Sachdeva, Kumud Sandhu, Jasminder Kaur Sahu, Rakesh Chandigarh University Department of Computer Science and Engineering Punjab Gharuan India Galgotias University School of Computing Science & Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9789380544519

Rapid technological advances have significantly improved our ability to analyse video data. This comprehensive review examines machine learning (ML) models applied to video event detection and classification, including CNNs, deep neural networks (DNNs), and RNNs. When evaluated on benchmark datasets for accuracy, these approaches demonstrate their relative strengths and weaknesses. Researchers have encountered numerous challenges in video event detection, which are addressed throughout the review. However, achieving high detection precision remains challenging due to diverse event types, video quality issues, model over fitting risks, and lack of large labeled training datasets. Background scenes, lighting, and object occlusion further complicate accurate identification. As datasets and computational power grow, video event detection stands to gain significantly. This review assessed action recognition models trained on the UCF-101 and CCV databases. On CCV, a 2-stage neural network achieved 75% accuracy;while a multi-stream deep learning (DL) system obtained 77.5%. For the larger UCF101, 2-stream and RNN architectures realized 92% and 89% accuracy using video-level prediction. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： CCV datasets Convolutional Neural Network (CNN) Deep Neural Network (DNN) Recurrent Neural Network (RNN) UCF-101 Video Event Detection (VED)

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Implementation of Basic Mathematical Operations on Openpower-ISA of Libresoc 12th

Implementation of Basic Mathematical Operations on Openpower...

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12th International Conference on Soft computing for Problem Solving, SocProS 2023

作者： Bilal, Maliha Simeen Chickerur, Satyadhyan Giraddi, Shantala School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India Centre of High-performance Computing School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India

ISBN: (纸本)9789819731794

In this era, it is observed that electronic system gadgets are developed every day with the most upcoming features which are beneficial and convenient for the user. SoCs, Softcores, and Softprocessors play a vital role in developing such systems. Softcore is the digital circuitry that is implemented using logic synthesis. One such Softcore used by authors here is Libresoc. This Softcore precisely focuses on designing a SoC which contains all the functionalities of 3D GPU, CPU, and VPU. The main reason for choosing Libresoc as the base softcore is that it uses Openpower-ISA 3.0. The main objective of the proposed solution is to implement basic mathematical operations such as Addition, Subtraction, Multiplication, and Division on the decoder test cases of Openpower-ISA. This solution can act as the base for the further development of SoCs with high-end features. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： System-on-chip

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Development of a Comprehensive Learning Management System using Full Stack Technology: A Modern Approach to Educational Platforms 5

Development of a Comprehensive Learning Management System us...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Supriya, M. Rathinavel, Keshav Radhakrishnan, Divya Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

Using the advantages of modern web technologies, this project utilizes powerful data modelling characteristics of Prisma and dynamic front-end capacities of *** to design a Learning Management System (LMS). The aim of this project is to create a comprehensive platform that would effectively facilitate the organization of educational resources and assignments, as well as student cooperation, ensuring scalability and performance at the same time. Many software engineering practices are utilized during this project for code maintainability, security, and performance. The full cycle of software development consists of testing and iterative adaptation of the developed systems based on the opinions of end-users. Ultimately, the LMS is improved through thorough testing and feedback from the users in an iterative and progressive process. The findings of this research are pertinent to the future developments of educational technology that will seek to implement modern, full-featured, and user-friendly LMS. © 2024 IEEE.

关键词： Software testing

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