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

作者： Dasari, Chetan Aditya Kumar, Solasa Manikanta Arpitalaxmi, Arpitalaxmi Thushara, M.G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350376685

Manual university course timetable scheduling is a time-consuming and error-prone process, often resulting in conflicts and inefficiencies. An optimized timetable is crucial for smooth academic operations, ensuring efficient use of resources and fostering a positive learning environment for students and faculty. We address the challenge of university course scheduling by proposing an optimized time-table scheduling algorithm. This paper will identify suitable approaches to automate timetable generation, considering factors like lecturer availability, student groups, room capacity, and course requirements. Keeping track of all the different factors that affect things like course requirements, teacher availability, room capacity, and fair task distribution is hard. By working on slot-based scheduling methods, we hope to find ways to make the schedule work best for both students and teachers, which will make everyone happier and help them learn more. © 2024 IEEE.

关键词： Students

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An Iterative PRISMA Review of GAN Models for Image Processing, Medical Diagnosis, and Network Security

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computers, Materials & Continua 2025年第2期82卷 1757-1810页

作者： Uddagiri Sirisha Chanumolu Kiran Kumar Sujatha Canavoy Narahari Parvathaneni Naga Srinivasu Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522302India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad501301India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravati522503India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Inti International University Nilai71800Malaysia

The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some

关键词： GAN CGAN WGAN DCGAN image analysis

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Development of Weather Forecast Application Using API 1

Development of Weather Forecast Application Using API

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1st International Conference on computing, sciences and Communications, ICCSC 2024

作者： Chadha, Shikha Ahmad, Salman Khursheed Sharma, Dolly Singh, Manu Srivastavaline, Anubhava Sharda University Department of Computer Science Greater Noida India School Of Computer Science & Engineering Galgotias University Department Of Computer Science Engineering Greater Noida India Graphic Era Hill University School of Computing Department Haldwani India ABES Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

This research paper presents the development of a weather forecasting model that incorporates real-time data through Application Programming Interfaces. This model utilises simple algorithms to analyse meteorological parameters from diverse API data sources, aiming to improve forecast accuracy compared to traditional methods. Extensive datasets are used for training and validation, demonstrating the model's effectiveness in providing more precise predictions. The study highlights the significance of API integration in advancing weather forecasting methodologies and fostering innovation in the field. The model created has yielded highly reliable results with minimal error. © 2024 IEEE.

关键词： Prediction models

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A survey for light field super-resolution

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High-Confidence computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Novel multi‐domain attention for abstractive summarisation

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 796-806页

作者： Chunxia Qu Ling Lu Aijuan Wang Wu Yang Yinong Chen College of Computer Science and Engineering Chongqing University of TechnologyChongqingChina School of Computing and Augmented Intelligence Arizona State UniversityTempeArizonaUSA

The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary,and the summary generated by models lacks the cover of the subject of source document due to models'small *** order to make up these disadvantages,a multi‐domain attention pointer(MDA‐Pointer)abstractive summarisation model is proposed in this ***,the model uses bidirectional long short‐term memory to encode,respectively,the word and sentence sequence of source document for obtaining the semantic representations at word and sentence ***,the multi‐domain attention mechanism between the semantic representations and the summary word is established,and the proposed model can generate summary words under the proposed attention mechanism based on the words and ***,the words are extracted from the vocabulary or the original word sequences through the pointer network to form the summary,and the coverage mechanism is introduced,respectively,into word and sentence level to reduce the redundancy of sum-mary ***,experiment validation is conducted on CNN/Daily Mail *** evaluation indexes of the model without and with the coverage mechanism are improved respectively,and the results verify the validation of model proposed by this paper.

关键词： abstractive summarisation attention mechanism Bi‐LSTM coverage mechanism pointer network

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An ensemble deep learning framework for foetal plane identification

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1377-1386页

作者： Thomas, Seena Harikumar, Sandhya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam 690525 India

Antenatal (prenatal) care stipulates periodic monitoring of the foetus in alleviating risk factors and improving pregnancy outcomes. Foetal images generated from the ultrasound during prenatal care can be classified into different planes, such as the brain, abdomen, femur, etc., and can be further used for the abnormality detection, disease diagnosis, and foetal growth estimation of the foetus. Therefore, recognizing the foetal plane is one of the initial steps to automate antenatal care. This paper proposes an Ensemble Convolution Neural Network (ECNN) model that combines the base models to classify the foetal planes by using an open-access database containing 12,400 images with six foetal planes. Prior studies on this database involved state-of-the-art CNN methods, and Densenet-169 pre-trained model gave an accuracy of 93.6%. Three pre-trained CNN models (base learners) are trained using the transfer learning approach in the proposed method. The predicted features derived from the base learners are then used to train a Deep Neural Network (DNN) based meta-learner to achieve high classification rates. The stacked ensemble model resulted in an accuracy of 96 % , which is better than the accuracy of the individual pre-trained models. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Antenatal care Classification Deep neural network Ensembled convolution neural network Foetal planes

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Explore Statistical Properties of Undirected Unweighted Networks from Ensemble Models 27th

Explore Statistical Properties of Undirected Unweighted Netw...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Xunda Wu, Xing Wang, Jianjia School of Computer Engineering and Science Shanghai University Shanghai China Engineering and Science Shanghai Institute for Advanced Communication and Data Science School of Computer Shanghai University Shanghai China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9783031783975

Complex network theory has been widely demonstrated as a powerful tool in modeling and characterizing various complex systems. In the past, complex network theory has focused on the behaviors as well as the characteristics of the network nodes and edges. However, with the continuous evolution of society, traditional graph theory faces challenges due to the emergence of extermely large network structures. Recently, complex network method based statistics has attracted much attention. The new approach effectively manages very large networks and uncovers their intrinsic properties. In this paper, we present a complex network analysis model for undirected, unweighted networks based on a statistical analysis approach. This model is inspired by the ensemble model in thermostatistical physics. Based on the established mathematical model, we derive physical measures that reflect the intrinsic properties of the network, including Entropy, Free Energy, Temperature, and so on. In the experimental part, we first explored the mathematical characterization of these metrics. Then, we observed the performance of various network categories under the same metric. Finally, we applied these measures to the field of graph classification. Extensive experiments demonstrate the effectiveness and superiority of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Undirected graphs

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2D Wound Image Segmentation Using Deep Learning Algorithms 2

2D Wound Image Segmentation Using Deep Learning Algorithms

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Sivakumar, Soubraylu Abhiram, V.S.V. Rama, P. Kallakrinda, Vikram Kumar, T. Rajesh Saravanan, P. Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331541217

The purpose of wound segmentation is to accurately identify and delineate wound areas in images, enabling precise analysis of metrics like size, shape, and spread. This aids healthcare professionals in assessing wound severity and monitoring treatment progress. This research focuses on developing an automated wound image segmentation tool using the Deep learning Algorithms. The objective is to enhance wound assessment accuracy by precisely identifying and segmenting wound areas in digital images. Three models are employed to find the effectiveness in segmenting the wound which includes U- Net, DeepLabV3+ and Mask-CNN. The deep learning models are trained on a massive dataset of wound pictures and masks;they then provide segmented outputs, which are examined for wound characteristics and severity. The U-Net model has obtained better performance in segmenting the wound with an accuracy of 92.3%. This work seeks to incorporate advanced capabilities into a user-friendly interface for practical use in clinical settings. Initial results demonstrate promising segmentation accuracy, with plans for further enhancements in future. © 2024 IEEE.

关键词： Image segmentation

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Vertex Partitions Based Approach to Solve the Graph Colouring Problem

Vertex Partitions Based Approach to Solve the Graph Colourin...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Shukla, Ajay Kumar Pandey, Neeraj Maji, Srabanti Kumar, Ashok Shamoon, Roosha Computer Science & Engineering Graphic Era Hill University Dehradun India Computer Science & Engineering Graphic Era Deemed To Be University Dehradun India School Of Computing Dit University Dehradun India Computer Science & Engineering Uttaranchal University Dehradun India

ISBN: (纸本)9798350366846

The graph colouring problem is a NP- class problem explored in the literature due to its numerous applications in real world engineering problems. Graph colouring problem involves to assign colours in such a fashion that neighbouring nodes have distinct colours. In this paper our objective is to find the number of distinct colours that can be used to colour the vertices in the graph. During the colouring process the objective is to use the least number of colours corresponding to considered graph. In this work we devise a new colouring algorithm using greedy technique by exploring the adjacent vertices which is simple and effective and can be used to compute the graph colouring problem in more enhanced way. The algorithm starts with the calculation of links of each vertex in the graph and then unexplored highest degree vertex will be selected for computation of adjacent nodes for colouring. Finally, we examined the presented algorithm on standard DIMACS graph colouring instances to evaluate the success of the devised algorithm. The result obtained after running the proposed algorithm for several categories of graphs like myciel, mugg, mulsol, david, huck, jean in terms of number of colours is equal to the chromatic number of the graph instances. © 2024 IEEE.

关键词： Color

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Enhancing Unmanned Aerial Vechicle Surveillance: Real Time Drone Detection with Yolov7 Deep Learning 3

Enhancing Unmanned Aerial Vechicle Surveillance: Real Time D...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Priyadharshini, M. Srinidhi, K. Murugesh, V. Department of Computer Science and Engineering Hyderabad501 203 India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India

ISBN: (纸本)9798350369083

This is an advanced object detection system with YOLOv7 model, implemented for UAV surveillance as outlined in the article. By integrating the system with Roboflow serving as its database manager and data augmentation software, it benefits from a broad array of reputable datasets which are improving training and inferences. In the blog, we cover all steps from model cloning (on TensorFlow) with one/two script line conversion to preprocessing of datasets and work automation for training or evaluation. The optimizations in the implementation of YOLOv7 dog led to scalability, allow more object categories and deploy security for real time surveillance with autonomous navigation. Extensive testing demonstrates that our system exceeds state-of-the-art methods in terms of detection performance as well as efficiency, demonstrating the significant benefits for UAV hotspots gained from a deployment of advanced deep learning techniques. © 2024 IEEE.

关键词： Deep learning

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