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2023 International Conference on Intelligent systems for Communication, IoT and Security, ICISCoIS 2023

作者： Preethi, C. Saran, V.S. Shree Meikannan, M. Hammed, S.Shahul Haripriya, K. Kumar, B.Arun PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798350335835

E-commerce, also known as electronic commerce or internet commerce, refers to the exchange of money and data for the purpose of business operations through the internet. The term 'ecommerce' is commonly used to refer to the online sale of tangible goods, but it may also refer to any type of commercial transaction made possible via the internet. It is nowadays one of the most important components of the internet. Electronic commerce is the process of conducting business using computer networks. An individual sitting in front of a computer may use all of the Internet's resources to purchase or sell things. E-commerce, which began in the early 1990s, has made enormous strides in the world of computers. B2B e-commerce is used to increase the usage of e-commerce in developing nations by enhancing access to global markets for enterprises in developing countries. Regardless of the rapid growth of technology, e-commerce has reached its apex. This article proposes a novel application concept. It describes the public's needs for M-Commerce, as well as the analysis and literacy survey of essential components of mobile devices that use such apps. The design and security of the application are both carefully studied. This study examines the characteristics and possibilities of a mobile E-App for selling and purchasing fresh vegetables. The outcomes demonstrate how the application has impacted the public, employment, and long-Term growth. © 2023 IEEE.

关键词： Developing countries

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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey 4

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Ilayaraja, V. Rajamohana, S.P. Jagadeesan, S. Aishwarya, V. Rakesh, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632 014 India School of Engineering & Technology Pondicherry University Department of Computer Science Puducherry Karaikal India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Tamil Nadu Vellore632 014 India

ISBN: (纸本)9798331528171

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability. © 2024 IEEE.

关键词： Tropical cyclone

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Crime Prediction using Machine Learning Algorithms 15

Crime Prediction using Machine Learning Algorithms

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chitteti, Chengamma Kunapareddy, Pranathi Dharani, M. HimaKeerthi, Bapathi Sravani, Chintapatla Chilukuri, Geethika Tirupati India Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Technology improvements have changed how crimes are solved, which has led to more collaborative study into how criminals act. "Prophet," an additive model-based method for predicting complicated, nonlinear time series data, such as crime actions, is presented. It does a good job of capturing time trends like holiday and seasonal affects. Using Prophet along with machine learning methods like Bayesian Regressor, Logistic Regression, Decision Tree, Gradient Boosting, Random Forest, Voting Regressor, KNN, LSTM, and Neural Network makes it more accurate at predicting crimes. With these tools, police can proactively decide how to use their resources and stop crime, which means that neighborhoods will be better. This essay shows how new technologies and models that are based on data are changing the way crimes are solved, starting a new era of preventing crimes and keeping communities safe. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Sports Events Recognition Using Multi Features and Deep Belief Network

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computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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An Efficient Methodology for Identifying the Similarity Between Languages with Levenshtein Distance 1

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Janardhana Rao, Peluru Nageswara Rao, Kunjam Gokuruboyina, Sitaratnam Neeraja, Kondamudi Naga Department of Computer Science and Systems Engineering Andhra University College of Engineering Andhra University Visakhapatnam India Visakhapatnam India

ISBN: (数字)9789819971374

ISBN: (纸本)9789819971367

This article compares the multilingual texts that are used for bilingual lexicon extraction and plagiarism detection. A collection of related sentences and sentences that are translations of one another, a parallel corpus, is used to compare multilingual content. In order to determine the similarity between the sentences and words in a multilingual content, Levenshtein Distance based String Similarity technique is used along with three other techniques such as Sequence matcher, Fuzzy-Wuzzy (Ratio), and Spacy Similarity techniques in the literature. The Comparative study is presented in this article to compare similar kind of work implemented with proposed techniques. The Levenshtein Distance based string similarity technique out performs in terms of Accuracy compared to Sequence matcher, Fuzzy-Wuzzy and Spacy similarity techniques for the identification of similarity between the languages. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intellectual property

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End-to-End Verifiable Decentralized Federated Learning 6

End-to-End Verifiable Decentralized Federated Learning

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6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Lee, Chaehyeon Heiss, Jonathan Tai, Stefan Hong, James Won-Ki Postech Department of Computer Science and Engineering Pohang Korea Republic of Tu Berlin Information Systems Engineering Berlin Germany

ISBN: (纸本)9798350316742

Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions. © 2024 IEEE.

关键词： Federated learning

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Optimizing task scheduling in cloud environments: a hybrid golden search whale optimization algorithm approach

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Neural Computing and Applications 2025年 1-23页

作者： Acharya, Biswaranjan Panda, Sucheta Das, Satyabrata Majhi, Santosh Kumar Gerogiannis, Vassilis C. Kanavos, Andreas Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla India Bilaspur India Department of Digital Systems University of Thessaly Larissa Greece Department of Informatics Ionian University Corfu Greece

Managing fluctuating workloads and optimizing resource utilization in cloud environments pose significant challenges, particularly in fields requiring real-time data processing, such as healthcare. This paper introduces a novel hybrid metaheuristic technique, the Golden Search Whale Optimization Algorithm (GSWOA), specifically designed for scheduling independent dynamic biomedical data. GSWOA merges the strengths of Golden Search Optimization (GSO) and Whale Optimization Algorithm (WOA), optimizing numerical function optimization and achieving a balance between exploration and exploitation. The algorithm’s effectiveness was assessed using MATLAB by applying standard benchmark functions and further evaluated on a real-world biomedical dataset within the CloudSim environment. The performance evaluations demonstrate that GSWOA significantly outperforms existing metaheuristic and traditional scheduling techniques, achieving a 42.71% increase in resource utilization and a 14.17% reduction in makespan compared to conventional methods. These results highlight GSWOA’s potential to enhance scheduling efficiency substantially in cloud computing infrastructures, suggesting it is a powerful tool for complex task allocations. Future research will explore the scalability of GSWOA and its applicability across other data-intensive sectors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Scheduling algorithms

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Fault-tolerant visual servo control for a robotic arm with actuator faults

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Neural Computing and Applications 2024年第25期36卷 15815-15828页

作者： Li, Jiashuai Peng, Xiuyan Li, Bing Sreeram, Victor Wu, Jiawei College of Intelligent Systems Science and Engineering Harbin Engineering University Heilongjiang Nantong Harbin150001 China Department of Electrical Electronic and Computer Engineering The University of Western Australia Stirling Highway PerthWA6009 Australia

The study targets uncertain coupling faults in robotic arm actuators and proposes a new fault-tolerant visual servo control strategy. Specifically, it considers both multiplicative and additive actuator faults within the dynamic of the robotic arm, treating the coupling faults and time-varying disturbances as an aggregate of concentrated uncertainties. A radial basis function neural network-based state observer is introduced to online approximate these concentrated uncertainties, which include fault information, eliminating the need for prior knowledge of faults. Furthermore, a fault-tolerant controller based on a non-singular fast terminal sliding mode is proposed, which separately decouples the nominal quantities and concentrated uncertainties and develops individual adaptive control laws for each. This effectively reduces the detrimental impact of coupled faults and disturbances on the system’s performance, facilitating image feature trajectory tracking control with minimal jitter, high precision, and strong transient response capabilities. The stability of the state observer and the fault-tolerant controller has been substantiated through Lyapunov’s theory. Lastly, numerical simulations validate the efficacy and robustness of the proposed fault-tolerant visual servo control approach. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Robotic arms

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A novel deep learning approach for automated grading of knee osteoarthritis severity

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Multimedia Tools and Applications 2024年 1-20页

作者： Kaur, PrabSimran Kohli, Guneet Singh Bedi, Jatin Wasly, Saud Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Electrical and Computer Engineering Department King Abdulaziz University Jeddah22254 Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdulaziz University Jeddah22254 Saudi Arabia

Knee Osteoarthritis (OA) is a prevalent musculoskeletal disorder that affects the knee joint that causes pain, stiffness, and reduced mobility. It is also known as "Degenerative Joint Disease" and is caused by the degeneration of cartilage in the knee joint, leading to bone-on-bone contact and further damage. Knee OA is prevalent in the population, affecting around 22% to 39% of people in India, and there is currently no treatment available that can halt the progression of the disease. Therefore, early diagnosis and management of symptoms are essential to reduce its impact on an individual’s quality of life. To address this issue, have introduced a framework that leverages ConvNeXt architecture, a modernization of ResNets (ResNet-50) architecture towards Hierarchical Transformers (Swin Transformers), to provide accurate identification and classification of knee osteoarthritis. The classification of knee osteoarthritis was done using the Kellgren and Lawrence (KL) graded X-ray images. These images of the damaged knees are preprocessed and augmented, creating a scaled, enhanced, and varied version of the features, thus making the data fitter and more significant for classification. The performance estimation of the proposed strategy is conducted on the Osteoarthritis Initiative (OAI), a research project focused on knee osteoarthritis that works in partnership with NIH and other private industries to develop a public domain dataset that can facilitate research and evaluation. It involves training the prepared data using various hyper-tuned versions of ConvNeXt. The different fine-tuned results of the ConvNeXt models on each KL Grade are evaluated against the other state-of-the-art models and vision transformers. The comparative assessment of widely used performance measures shows that the proposed approach outperforms the conventional models by generating the highest score for all the KL grades. Lastly, an approach is employed to statistically confirm the validity of t

关键词： Joints (anatomy)

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