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CloudyPages: A Secure and Scalable Serverless Web-Based Blogging Application 1

2nd International Conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Prakash, Vijay Jaiswal, Shrestha Purohit, Saumya Singh, Pardeep Garg, Lalit School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MsidaMSD 2080 Malta

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

The CloudyPages app is a precise and trustworthy platform for bloggers to create and effectively manage blogs. The CloudyPages application provides bloggers a platform for creating and managing their blogs. The application can achieve remarkable accuracy, precision, and review rates by utilizing AWS services, assuring minimal errors and enhancing the discoverability and accessibility of bloggers’ content. Collectively, these metrics contribute to an optimized user experience. AWS WAF (Amazon Web Services Web Application Firewall) provides robust protection against common web application vulnerabilities such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF) attacks. By implementing this security layer, the application effectively protects the data of bloggers and readers from potential attacks. The AWS Web Application Firewall (WAF) protects against known attack patterns and continuously monitors and detects harmful activities to prevent unauthorized access. The application's serverless architecture lets bloggers focus on content creation and administration instead of server maintenance. The application's affordable price makes it an appealing, adaptable, and inclusive solution. The application's serverless design and AWS WAF and AWS Open make it flexible, affordable, and accessible for bloggers of all sizes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blogs

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A Practical Embedded System Utilizing Artificial Intelligence Technology 4

A Practical Embedded System Utilizing Artificial Intelligenc...

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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Molapo, Makhabane Tu, Chunling Du Plessis, Deao Du, Shengzhi Tshwane University of Technology Computer Systems Engineering Pretoria South Africa Tshwane University of Technology Computer Science Department Pretoria South Africa

ISBN: (纸本)9798350387988

Thereis a clear and present lethal clash between South Africa's wildlife and its expanding population. Some of its main effects are harm to people and property. Fatalities, crop damage, livestock threads, and habitat devastation. We are inspired by the need for data-driven approaches in artificial intelligence in today's technologies. We have built an embedded system called 'SmartFarmApp' by leveraging datasets for object detection. This system capably serves as the backbone of computer vision models by focusing on detecting and classifying livestock according to their classes using the Florence 2 AI (Artificial Intelligence) model. We have livestock of interest and wild animals as a threat to our livestock and if wild animals are around this class label the message for security concern must be raised. This system utilizes the Florence 2 AI vision-language model developed by Microsoft that excels in various vision tasks, including object detection where we use object detection Application Programmable Interfaces (APIs) to do detection for this system in a realistic outdoor environment. However, Florence 2 as vision tasks it does not support Object tracking, so we improved Florence 2 model to work by allowing tracking. Florence 2 model feeds our system, while built-in Raspberry Pi devices equipped with cameras feed this system in real-time using the mobile application to remotely view daily activities in our farms. © 2024 IEEE.

关键词： Livestock

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Unveiling Interpretability: Analyzing Transfer Learning in Deep Learning Models for Traffic Sign Recognition

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SN computer science 2024年第6期5卷 682页

作者： Waziry, Sadaf Rasheed, Jawad Ghabban, Fahad Mahmoud Alsubai, Shtwai Elkiran, Harun Alqahtani, Abdullah Department of Software Engineering Istanbul Aydin University Isanbul 34295 Turkey Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul 34303 Turkey Faculty of Computer Science and Engineering Taibah University Medina Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al Kharj 11942 Saudi Arabia

Since the advent of automobiles and driver assistance technologies, traffic sign recognition has been of the utmost importance for Industry 4.0. In the driving system, good data pre-processing is critical. For such objectives, sophisticated transformations or fundamentally computational image processing approaches are out of the question. Convolutional Neural Networks (CNN) have been used to perform more object identification challenges, thus, improving most computer vision applications, both existing and new, because of their excellent recognition rate and rapid execution. This study introduces a method for recognizing traffic signs by utilizing a CNN-based model and the transfer learning concept. The TensorFlow library is used for training the underlying neural network model. The offered approach makes use of the German Traffic Sign Recognition Benchmark (GTSRB) and images from the Traffic Sign Images from Turkey (TSIT) databases. These datasets are dependable and vibrant, and they have been used to train many algorithms. Furthermore, after training the model, the proposed scheme acquired a testing accuracy is 99.44%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CNN German traffic sign Image processing Transfer learning

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Enhanced Growth Optimizer and Its Application to Multispectral Image Fusion

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computers, Materials & Continua 2024年第11期81卷 3033-3062页

作者： Jeng-Shyang Pan Wenda Li Shu-Chuan Chu Xiao Sui Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The growth optimizer(GO)is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social ***,the original GO algorithm is constrained by two significant limitations:slow convergence and high mem-ory *** restricts its application to large-scale and complex *** address these problems,this paper proposes an innovative enhanced growth optimizer(eGO).In contrast to conventional population-based optimization algorithms,the eGO algorithm utilizes a probabilistic model,designated as the virtual population,which is capable of accurately replicating the behavior of actual populations while simultaneously reducing memory ***,this paper introduces the Lévy flight mechanism,which enhances the diversity and flexibility of the search process,thus further improving the algorithm’s global search capability and convergence *** verify the effectiveness of the eGO algorithm,a series of experiments were conducted using the CEC2014 and CEC2017 test *** results demonstrate that the eGO algorithm outperforms the original GO algorithm and other compact algorithms regarding memory usage and convergence speed,thus exhibiting powerful optimization ***,the eGO algorithm was applied to image *** a comparative analysis with the existing PSO and GO algorithms and other compact algorithms,the eGO algorithm demonstrates superior performance in image fusion.

关键词： Growth optimizer probabilistic model Lévy flight image fusion

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C3BAM-XAI: Convolutional Block Attention Module Enhanced Explainable Artificial Intelligence-Based Parkinson’s Disease Stage Classification

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Cognitive Computation 2025年第3期17卷 1-19页

作者： Abbas, Muhammad John Khan, Muhammad Attique Hamza, Ameer Alsenan, Shrooq Rehman, Aleesha Baili, Jamel Zhang, Yudong Department of Computer Science HITEC University Taxila47080 Pakistan Center of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas51368 Lithuania Information Systems Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Mang Pakistan Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia Department of Informatics University of Leicester Leicester United Kingdom

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes significant impairment in neurons, physiological structures, and behavior of people. However, these changes are very subtle in the early stages of PD, making diagnosis and treatment challenging. To overcome such challenges, we proposed a novel convolutional neural network (CNN) architecture called C3BAM-XAI that is based on the convolutional block attention module (CBAM) enhanced CNN and explainable artificial intelligence (XAI). In the proposed architecture, the data imbalance issue was resolved at the initial stage by employing augmentation techniques for the training data. After that, the proposed CNN architecture is designed with CBAM. The CBAM architecture is based on two sub-modules—the channel attention module and the spatial attention module. The input of the initial convolutional layer is passed to the channel attention module, which is further passed to the spatial attention module for more critical information extraction. For smooth training, hyperparameters are selected using Nadam Optimization (NO) instead of manual assignment. In the testing phase, features are extracted from the dense layer and passed to several neural network classifiers. In addition to this, the designed model is interpreted using the explainable AI technique to see the predicted label and insight decision features. The experimental process was performed on the publically available PD Kaggle dataset and obtained an improved accuracy of 93.33%. In addition, several ablation studies were conducted, and it is concluded that the CBAM not only improved accuracy but also made this model a reliable tool for clinical diagnosis, both in terms of accuracy and interpretability. The results thus open new avenues toward applying deep learning models practically for applications in the medical domain and early-stage detection of Parkinson’s disease, thereby helping to improve patient outcomes. © The Author(s), under

关键词： Convolutional neural networks

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Integrating Software-Defined Networking and Fog Computing for a Novel Routing Protocol in Vehicular Ad-Hoc Networks 7

Integrating Software-Defined Networking and Fog Computing fo...

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Darabkh, Khalid A. Al-Mistarihi, Mamoun F. Odat, Bayan Abdallah The University of Jordan School of Engineering Computer Engineering Department Amman11942 Jordan Jordan University of Science and Technology Faculty of Engineering Electrical Engineering Department P.O. Box 3030 Irbid22110 Jordan

ISBN: (纸本)9798350367027

This study presents an intelligent strategy designed to enhance the efficacy of routing in VANETs. In particular, this work presents a new clustering approach for Cluster Head (CH) selection based on a weighted formula. Our protocol's objectives are to create clusters and allocate C H to each of them. In our protocol, three significant parameters are involved in C H selection process, including vehicle speed, average vehicle distance among other vehicles, and vehicle LifeTime (LT). By considering vehicle speed, the protocol enhances route stability and dependability while the average distance of a vehicle among other vehicles mitigates long-distance communications. Interestingly, the vehicle LT greatly minimizes the control overhead. Furthermore, for improved programmability and scalability, our approach incorporates novel technologies like Software Defined Network (SDN) and Fog Computing (FC) for location awareness. Proper network simulations are performed to test the performance of our proposed protocol, and considerable increases in terms of throughput are achieved over those of our protocol's counterparts. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Detection of Diabetic Retinopathy Severity by Image Classification Using a Hybrid Model and Attention Mechanism 7th

Detection of Diabetic Retinopathy Severity by Image Classi...

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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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Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

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computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf Abdullah Almujally Saud S.Alotaibi Abdulwahab Alazeb Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information System Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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Traffic Density Estimation by Distributed Proxy Model Learning for Internet of Vehicle

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IEEE Internet of Things Journal 2025年第12期12卷 19972-19980页

作者： Li, Qilei Cheng, Jing-An Gao, Mingliang Chen, Jinyong Jeon, Gwanggil Shandong University of Technology School of Electrical and Electronic Engineering Zibo255000 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

Autonomous driving has been significantly advanced in today’s society, which revolutionized daily routines and facilitated the development of the Internet of Vehicles (IoV). A crucial aspect of this system is understanding traffic density to enable intelligent traffic management. With the rapid improvement in deep neural networks (DNNs), the accuracy of density estimation has markedly improved. However, there are two main issues that remain unsolved. First, current DNN-based models are excessively heavy, characterized by an overwhelming number of training parameters (millions or even billions) and substantial computational complexity, indicated by a high number of FLOPs. These requirements for storage and computation severely limit the practical application of these models, especially on edge devices with limited capacity and computational power. Second, despite the superior performance of DNN models, their effectiveness largely depends on the availability of large-scale data for training. Growing privacy concerns have made individuals increasingly hesitant to allow their data to be publicly used for model training, particularly in vehicle-related applications that might reveal personal movements, which leads to data isolation issues. In this article, we address these two problems at once with a systematic framework. Specifically, we introduce the proxy model distributed learning (PMDL) model for traffic density estimation. PMDL model is composed of two main components. First, we introduce a proxy model learning strategy that transfers fine-grained knowledge from a larger master model to a lightweight proxy model, i.e., a proxy model. Second, we design a distributed learning strategy that trains multiple proxy models with privacy-aware local data and seamlessly aggregates these models via a global parameter server. This ensures privacy protection while significantly improving estimation performance compared to training models with limited, isolated data. We tested t

关键词： Differential privacy

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So Far Yet So Near: Time Series Data Augmentation with Exploring non-Semantic Boundaries based on Reinforcement Learning

So Far Yet So Near: Time Series Data Augmentation with Explo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Haoran Zhang, Zhibo Kang, Jiarong Jiang, Xun Gong, Xiaoli Zhang, Jin Sun, Zhe Cichocki, Andrzej College of Computer Science Nankai University Tianjin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine Faculty of Health Data Science Juntendo University Japan Systems Research Institute Polish Academy of Science Warsaw Poland

ISBN: (纸本)9798350368741

Data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augmented and original time series data, causing label noise and thereby degrading downstream task performance. We argue that data augmentation should preserve time series semantic consistency and expand the non-semantic information space. In this paper, we reformulate data augmentation as a semantic path planning problem between original data and augmented data, modeled as a Markov Decision Process (MDP). We propose a reinforcement learning-based algorithm (RL) named FreqSYN, where the action space is defined by a set of learnable Gaussian kernels that perturbs the frequency domain of the original data to generate augmented samples. The confidence coefficients of augmented data in semantically relevant classification tasks are used as a reward to iteratively refine the FreqSYN. Our method is validated across four datasets, achieving state-of-the-art performance, with a 2% improvement in F1 score over the SimPSI method. The code and models are available at https://***/NKU-EmbeddedSystem/FreqSYN. © 2025 IEEE.

关键词： Data argumentation frequency domain augmentation Gaussian kernel prediction time series

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