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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Nunna, Bhargava Satya Tiwari, Ashutosh Department of Computer Science and Systems Engineering Andhra University Visakhapatnam India Geosciences Virginia Tech Blacksburg United States

ISBN: (纸本)9789819950140

Diabetic Retinopathy (DR) is an eye condition, caused by the complications of diabetes mellitus and this could even lead to extent of vision loss. DR is an irreversible process, early diagnosis and proper treatment could help to sustain vision from any further loss. It is diagnosed through colored fundus images, which require an ophthalmologist to identify the presence of lesions in images and detect the severity of the disease. As with the exponential growth of the population, people suffering with DR were also increasing rapidly and in this case, a manual diagnosis process by clinicians would be time-intensive, tedious, and cost-consuming. An effective automated DR detection using image analysis can prevent misdiagnosis, reduce time, cost, and effort. In this work, we present a novel deep learning architecture that comprises a state-of-the-art Convolutional Neural Networks (CNN) integrated with a self-attention module recently originated from a transformers network. A self-attention mechanism was applied to the image feature maps extracted from the two state-of-art architectures ResNet and DenseNet, such that the system learns from the features extracted from both the models and with respect to each other. The proposed method has obtained a propitious performance with an accuracy of 89.09%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Generating Fashion Sketches Using Generative Adversarial Networks 14

Generating Fashion Sketches Using Generative Adversarial Net...

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14th International Conference on Information and Communication systems, ICICS 2023

作者： Al-Samawi, Amira Mallak, Shatha Duwairi, Rehab Jordan University of Science and Technology Computer Engineering Department Irbid Jordan Jordan University of Science and Technology Computer Information Systems Department Irbid Jordan

ISBN: (纸本)9798350307870

Generative Adversarial Networks (GANs) are an active area of research in deep learning and computer vision. They are concerned with generating and manipulating photorealistic synthetic images as well as manipulating real ones. This work used two types of GANs to generate new fashion sketches. The first type was Deep Convolutional GAN (DCGAN), which is one of the earliest GAN models. The results were acceptable compared with the size of the data and the basic types of networks used. For the second model, fashion sketches were generated using StyleGAN-ADA, a powerful model developed mainly for limited datasets. The results were much better visually, with an Fréchet Inception Distance (FID) score of 15.69 and a Kernel Inception Distance (KID) score of 3.2e-3 for 50k generated images against the entire dataset. Besides its ability to be used for data augmentation in projects that use fashion sketches, our research can be a source of inspiration for fashion designers. © 2023 IEEE.

关键词： Generative adversarial networks

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A NOTE ON THE QUALITATIVE BEHAVIOUR OF AN ODE MODEL RELATED TO ENDOTOXIN TOLERANCE

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第4期86卷 127-130页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

关键词： Differential equations

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Predicting software vulnerability based on software metrics: a deep learning approach

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Iran Journal of computer science 2024年第4期7卷 801-812页

作者： Agbenyegah, Francis Kwadzo Asante, Micheal Chen, Jinfu Akpaku, Ernest Department of Computer Science Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Information Technology Faculty of Computing and Information Systems Ghana Communication Technology University Accra Ghana

The security of IT systems is the topmost priority of software developers. Software vulnerabilities undermine the security of computer systems. Lately, there have been a lot of reported issues of software vulnerabilities recorded in individuals and corporate systems. Software metrics-based techniques have been used and found to be effective due to low false positives and false negatives. However, there are software metrics that have not been investigated and could have an impact on the performance of the predictive model. In this study, we developed an ensemble deep learning algorithm made of LSTM, CNN, and MLP models with the non-investigated metrics as a feature to learn the latent representation of code metrics to predict anomalies in source code effectively. We trained the proposed model on the SySeVR datasets and compared the model's performance against five deep learning algorithms and four state-of-the-art SVPs. The proposed model performs better than the other deep learning with a classification accuracy of 96.17%, and a precision of 97.72%. The model's classification accuracy is 4.98% higher than that of the five deep learning models and 1.71% lower in precision than the state-of-the-art SVP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Deep learning Machine learning Software metrics Software security Software vulnerabilities

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Application of Machine Learning for Ensuring Water Quality in Aquaculture Ponds

Application of Machine Learning for Ensuring Water Quality i...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Bharathi, S.V.Shri Sai, Paidipati Lakshman Ayyappa, Paidipati Charan Ram Reddy, Kakuru Jaya Praveen Reddy, Gangireddy Sai Preetham Rajesh, Katuri Madhava SRMIST- Ktr Department Of Data Science And Business Systems Tamil Nadu Chennai India Vit Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350352689

Aquaculture plays a pivotal role in meeting the growing global demand for seafood. However, ensuring optimal water quality within aquaculture ponds is a pressing challenge. This project proposes a paradigm shift by integrating machine learning to enhance water quality assurance. The system leverages real-time monitoring, predictive analytics, and adaptable algorithms, contributing to sustainability and responsible aquaculture practices. © 2024 IEEE.

关键词： Water quality

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A new method for human activity identification using convolutional neural networks

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International Journal of Cloud Computing 2023年第2-4期12卷 191-200页

作者： Prakash, P.S. Balakrishnan, S. Venkatachalam, K. Balasubramanian, Saravana Balaji Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tiruchengode637205 India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Department of Applied Cybernetics Faculty of Science University of Hradec Králové Hradec Králové50003 Czech Republic Department of Information Technology Lebanese French University Erbil Iraq

Body fitness monitoring applications are using mobile sensors to identify human activities. Human activity identification is a challenging task because of the wide availability of human activities. This paper proposes a novel technique that extracts the discriminative dimensions for human activity identification. Particularly, a novel technique with convolutional neural networks (CNN) is used for catching dependency. A deep convolutional neural network (DCNN) consists of two different types of layers, convolutional and pooling are used. The depth of each filter increases from left to right in the network. Three activities like walking, running, remaining still are collected from smart mobile sensors. The axis like x, y, and z information was transferred with column vector magnitude information and utilised for studying or training CNN. Experimental results show that CNN-based method achieves 93.67% accuracy than the baseline random forest approach's 89.20%. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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GAN-Enhanced Brain Slice Images for Detecting Alzheimer's Disease 11

GAN-Enhanced Brain Slice Images for Detecting Alzheimer's Di...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Vincent, Albin Ancy, C.A. Rajagiri School of Engineering & Technology Department of Information Technology Kakkanad India Rajagiri School of Engineering & Technology Department of Computer Science and Business Systems Kakkanad India

ISBN: (纸本)9798350379136

Alzheimer's Disease (AD) is a progressive neurological disorder marked by cognitive decline, memory loss, and impaired reasoning, affecting millions globally. Early and accurate diagnosis of AD is crucial for managing the disease, yet traditional diagnostic methods such as clinical assessments and Magnetic Resonance Imaging (MRI) face significant challenges. This research proposes a novel approach to enhance the diagnostic process by using Generative Adversarial Networks (GANs) to improve brain MRI image quality, thereby aiding automated detection of AD. It involves preprocessing brain MRI scans, designing a GAN for image enhancement, and utilizing these enhanced images in a Convolutional Neural Network (CNN) for detecting AD. The effectiveness of method is evaluated using key metrics such as accuracy, sensitivity, and specificity, comparing its performance to baseline methods using non-enhanced images. Cross-validation and testing with external datasets are conducted to ensure the model's robustness and generalizability. The integrated GAN-CNN framework demonstrated a remarkable accuracy of 99.9 % in predicting AD using MRI images from the OASIS dataset. © 2024 IEEE.

关键词： Generative adversarial networks

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Intelligent Chatbot for Hospital Recommendation System 2

Intelligent Chatbot for Hospital Recommendation System

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rajkumar, K. Ragupathi, T. Karthikeyan, S. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Department of Computer Science and Engineering Bangalore562112 India

ISBN: (纸本)9798350371055

The main objective of this project is to create a user-friendly intelligent chatbot for Medical guidance and support. This application has been classified into two different modes based on user preference, those are Online and Offline modes of interaction. In the Online mode of interaction, there is no need for any automation, there will be a real person at the other end answering user queries. Users can even ask for untrained data and will receive instant replies. This application is similar to a normal chat application, but the main disadvantage is that a real person is needed at the other end at all times. In the Offline mode of conversation which is entirely automated the user will be performing an interaction with the chatbot itself. The chatbot must consist of data sets and trained data collection, in this process there is no need for any real person, the chatbot itself will reply instantly based on the trained data. The chatbot will be more accurate when a large number of data is trained. This application is compatible with working in various browsers and varied networks. The Admin of the application plays a significant role. Admin can access all the given functions and is responsible for data training. Data training plays a vital role in this application, the trained data will be used by the chatbot while answering user queries i.e. Offline mode. This chatbot application can be accessed by several users at the same time, where IP address plays the main role. The main function of this application is that user can post their corresponding queries and the application will be providing relevant information based on queries. The Latest Modern chatbots are used in applications where simple interaction is needed, for example, service and marketing technology to provide answers such as products, services, and consultancy. If the customer's queries don't match with the trained data sets, the customer will be escalated to manual interaction i.e. Online mode. © 2024 IE

关键词： Automated Intelligent Chat IP Address Offline mode Online mode Trained Data

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Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

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Intelligent Automation & Soft Computing 2023年第5期36卷 1767-1782页

作者： M.P.Thiruvenkatasuresh Surbhi Bhatia Shakila Basheer Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl HasaSaudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia Corresponding

The study aimed to apply to Machine Learning(ML)researchers working in image processing and biomedical analysis who play an extensive role in compre-hending and performing on complex medical data,eventually improving patient *** a novel ML algorithm speciﬁc to Diabetic Retinopathy(DR)is a chal-lenge and need of the *** images include several challenges,including relevant feature selection,class variations,and robust classiﬁ*** the cur-rent research in DR has yielded favourable results,several research issues need to be *** is a requirement to look at novel pre-processing methods to discard irrelevant features,balance the obtained relevant features,and obtain a robust classi-ﬁ*** is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classiﬁer(SKPD-PSC)*** novelty of this method relies on the appropriate non-linear classiﬁcation of exclusive image processing models in har-mony with the Platt Scale Classiﬁer(PSC)to improve the accuracy of DR ***,a Steerable Filter Kernel Pre-processing(SFKP)model is applied to the Retinal Images(RI)to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians(DDG).Next,the Partial Derivative Image Localization(PDIL)model is applied to the extracted fea-tures to localize candidate features and suppress the background ***,a Platt Scale Classiﬁer(PSC)is applied to the localized features for robust classiﬁ*** the experiments,we used the publicly available DR detection database provided by Standard Diabetic Retinopathy(SDR),called DIARETDB0.A database of 130 image samples has been collected to train and test the ML-based classiﬁ*** results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accu-racy rate with minimum time and complexity compared to

关键词： Diabetic retinopathy retinal images machine learning image localization Platt Scale classiﬁer accuracy

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Accelerating Gradient Tracking with Periodic Global Averaging 63

Accelerating Gradient Tracking with Periodic Global Averagin...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Feng, Shujing Jiang, Xin Lehigh University Department of Computer Science and Engineering BethlehemPA United States Lehigh Univeristy Department of Industrial and Systems Engineering BethlehemPA United States

ISBN: (纸本)9798350316339

Decentralized optimization algorithms have recently attracted increasing attention due to its wide applications in all areas of science and engineering. In these algorithms, a collection of agents collaborate to minimize the average of a set of heterogeneous cost functions in a decentralized manner. State-of-the-art decentralized algorithms like Gradient Tracking (GT) and Exact Diffusion (ED) involve communication at each iteration. Yet, communication between agents is often expensive, resource intensive, and can be very slow. To this end, several strategies have been developed to balance between communication overhead and convergence rate of decentralized methods. In this paper, we introduce GT-PGA, which incorporates GT with periodic global averaging. With the additional PGA, the influence of poor network connectivity in the GT algorithm can be compensated or controlled by a careful selection of the global averaging period. Under the stochastic, nonconvex setup, our analysis quantifies the crucial trade-off between the connectivity of network topology and the PGA period. Thus, with a suitable design of the PGA period, GT-PGA improves the convergence rate of vanilla GT. Numerical experiments are conducted to support our theory, and simulation results reveal that the proposed GT-PGA accelerates practical convergence, especially when the network is sparse. © 2024 IEEE.

关键词： Cost functions

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