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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Poornimathi, K. Deepak Kumar, A. Srinithi, A. Rajalakshmi Engineering College Department of Computer Science and Business Systems India

ISBN: (纸本)9798350353068

Revolutionizing the urban commuting by introducing a ground-breaking AI-powered carpooling portal. This comprehensive platform provides an ultimate solution combining all the latest technologies for transportation challenges. The system ensures effortless scalability and real-time synchronization of user profiles, tip histories and route information across devices. Using the integration of emerging technologies such as Artificial intelligence and Machine learning algorithms, commuters are given a unique advantage of having a seamless travel and an optimized cost for the travel using AI and ML models and they are matched based on their preferences, schedules and historical data that makes the travel more convenient. The system also provides enhanced route security by analyzing the geospatial information to suggest the safest and most efficient routes for carpooling groups. It also streamlines the billing process by introducing Robotic process automation (RPA), ensuring accurate and tamper-free billing process for the carpooling groups. This combination of different technologies makes carpooling into a sophisticated, seamless and efficient mode of sustainable transportation that also is secure while delivering unparalleled convenience and peace of mind to its users. © 2024 IEEE.

关键词： Robotics

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Revitalizing Paddy Yields with computer Vision 2

Revitalizing Paddy Yields with Computer Vision

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Siva M.e, K.P. Vibin, K.C. Kaarnika, A. Ramkumar, T.R. Priyanka, D. SriEshwarCollege of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350327533

A groundbreaking innovation in the field of plant science revolutionizes the precise identification of nutrient deficiencies in paddy plants through meticulous leaf analysis. The methodology commences by breaking down input leaf images into manageable blocks, subsequently subjecting them to rigorous evaluation using specialized convolutional neural networks (CNNs), each meticulously trained to discern specific nutrient deficiencies. The advanced network architecture systematically scrutinizes these blocks for characteristic deficiency symptoms. To achieve an all-encompassing evaluation of the leaf's condition, the responses generated by all CNNs are aggregated, and a multi-layer perceptron synthesizes them into a singular block response. The proposed approach results in a comprehensive assessment of the overall leaf health. In a comprehensive study encompassing seven different deficiency types - including Nitrogen (N), Phosphorus (P), Potassium (K), and Zinc (Zn) deficiencies - a vast dataset comprising 2,750 carefully collected leaf images was employed. The results demonstrate the proposed method's superiority, surpassing the accuracy of expert human assessments in identifying various nutrient deficiencies. The proposed method harnesses the power of CNNs, effectively merging the image analysis, machine learning, and Convolutional Neural Networks (CNNs) to propel nutrient deficiency analysis in paddy plants to unprecedented heights. © 2024 IEEE.

关键词： Convolutional Neural Networks Image Segmentation Leaf Analysis Machine Learning Nutrient Deficiency

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Blockchain based Single Path and Multi-Path Routing in Payment Channel Network using Artificial Intelligence Techniques 2

Blockchain based Single Path and Multi-Path Routing in Payme...

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2nd IEEE International Conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Swathi, Pothurajula Rao, Kasukurthi Venkata Department of Computer Science & Systems Engineering Andhra University College of Engineering Visakhapatnam India

ISBN: (纸本)9798331504960

Blockchain-based Payment Channel Network (PCN) routing for payment transactions includes the transfer of funds between multiple parties through a network of interconnected payment channels. These channels permit off-chain transactions, balance payment channels, enhance speed, and reduce the transaction costs. However, finding the best route for transactions between parties is challenging due to the dynamic nature of the network. The routing concept for PCN is divided as single-path routing and multi-path routing to enhance transactions based on the requirements of the users/organizations. Channel-based strategy, off-chain transactions, channel rebalancing techniques, and payment execution methods are considered essential in PCN transactions for an efficient routing. This study analyses the following methodologies: Deep Neural Network (DNN), Deep Reinforcement Learning (DRL), Graph NN (GNN), as well as genetic optimization algorithm utilized for efficient routing. Currently, distance-based methods like Markov chain-based routing are also being employed in PCN for finding an optimal path for payments. This survey examines the strengths and challenges of these Deep Reinforcement learning-based routing approaches. © 2024 IEEE.

关键词： Markov chains

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Convolutional Neural Network (CNN) Classifiers Used in Land Use/Land Cover Monitoring and Classification: A Review 23th

Convolutional Neural Network (CNN) Classifiers Used in Land ...

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Prabukumar, M. Agilandeeswari, L. Prabhavalkar, Sushant Prashant School of Computer Science Engineering and Information Systems VIT Vellore India

ISBN: (纸本)9783031647758

Land Use/Land Cover Analysis is conducted to perform continuous monitoring and assessment of land resources in order to manage the Land Usage and Land Utilization and to take the right decisions to protect the environment. This article discusses various techniques/algorithms used to build robust models to perform Land Use/Land Cover Classification. The article includes various architecture models including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Residual Networks (RN), etc. It also includes various Accuracy Measurement Metrics, proposed models, and corresponding issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Recurrent neural networks

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Vertebrae Landmark Detection and Scoliosis Assessment Using Deep Learning

Vertebrae Landmark Detection and Scoliosis Assessment Using ...

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

作者： Maharasi, M. Senthilnayaki, N. Snehaprabha, K. Computer Science And Business Systems Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798350352689

Deep learning is transforming a number of industries including finance, healthcare, and autonomous cars. It has shown extraordinary success in many areas. We propose a novel deep-learning approach for vertebrae landmark detection and scoliosis assessment. Scoliosis, a spinal deformity which is characterized by the deformation of the spine structure resulting in 'S' or 'C' shaped curves. The U-Net is employed to determine whether the given image exhibits scoliosis or not. Additionally, the U-Net method determines the cobb angle by identifying the apex, bottom, and top vertebrae. By considering the measured angle values from the U-Net output, we can determine the scoliosis stage using OpenCV. The suggested technique uses an artificial deep convolution neural network to identify spinal landmarks in X-ray images. Trained on a large dataset of annotated images, the network learns complex patterns and relationships between different vertebrae. The detected landmarks are then used to compute various clinical parameters related to scoliosis, such as the Cobb angle and vertebral rotation. Scoliosis is classified into three stages: Mild scoliosis (45). © 2024 IEEE.

关键词： Large datasets

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A Mathematical and Dynamical Model for Analyzing H-Shaped PM Vernier Motor for Electric Motorcycle Mid-Drive Applications

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IEEE Transactions on Energy Conversion 2025年第1期40卷 529-543页

作者： Ghods, Mehrage Faiz, Jawad Bazrafshan, Mohammad Amin Gorginpour, Hamed Toulabi, Mohammad Sedigh University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran Persian Gulf University Department of Intelligent Systems Engineering and Data Science Bushehr*** Iran Oakland University Department of Electrical and Computer Engineering RochesterMI48309 United States

The permanent magnet (PM) Vernier machines enhance torque density and decrease cogging torque compared to conventional permanent magnet synchronous motor. This paper presents a novel fractional-slot H-shaped PM Vernier machines, which incorporates unique design for the PM housing and rotor core construction. These designs are specifically optimized for the small-scale electric vehicles application. The choice of the PM arrangement is based on the combination of V-shaped and spoke-array PM topologies. The aim is to achieve a higher torque density than V-shaped PM machines, while reducing cogging torque compared to spoke-array PM machines. By using this methodology, leakage fluxes in the terminal region of the PMs reduce, resulting in an augmentation of both flux-linkage and power factor. A prototype 680 W motor was fabricated with 12 slots and 8 poles to verify the accuracy and effectiveness of the proposed model. Mathematical model and dynamical analysis are proposed for the analytical prediction of machine performance. Due to the unique shape feature of the machine, the conformal mapping approach is applied to design the rotor core. To validate the model, the predicted performance of the motor was compared with those obtained by finite element analysis and experiments. © 1986-2012 IEEE.

关键词： Permanent magnets

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Automatic Text Summarization for Medical Dataset-An Analysis 23th

Automatic Text Summarization for Medical Dataset-An Analysis

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Agilandeeswari, L. Dagar, Akash Deepthi, A. Arangasakthivel, R. School of Computer Science Engineering and Information Systems VIT Vellore India

ISBN: (纸本)9783031648120

This review paper explores the application of deep learning techniques in abstractive text summarization, with a focus on their relevance to medical datasets. Abstractive text summarization is a crucial area of natural language processing, with the potential to significantly impact the field of healthcare and medical research. The paper provides a concise and crisp examination of the various deep learning models, methods, and algorithms employed in generating abstractive summaries from medical texts. It also highlights the challenges, trends, and potential future directions in this domain. The primary objective of this review is to offer insights into the current state-of-the-art methods for abstractive text summarization in the medical domain and their implications for healthcare professionals, researchers, and data scientists. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Information management

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Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive Algorithm and Logistic Regression Classifier

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computers, Materials & Continua 2023年第11期77卷 1515-1534页

作者： Shabana R.Ziyad Liyakathunisa Eman Aljohani I.A.Saeed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah41411Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia

Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in *** can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five *** research study aims to develop an automated tool for diagnosing autism in *** computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification *** most deterministic features are selected from the self-acquired dataset by novel feature selection methods before *** Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this *** performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research *** experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired *** ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different *** Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.

关键词： Autism spectrum disorder feature selection imperialist competitive algorithm LASSO logistic regression random forest

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Data Secure Storage Mechanism for IIoT Based on Blockchain

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computers, Materials & Continua 2024年第3期78卷 4029-4048页

作者： Jin Wang Guoshu Huang R.Simon Sherratt Ding Huang Jia Ni School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Nanjing University of Information Science and TechnologyNanjing210044China

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of *** technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the *** the traditional blockchain,data is stored in a Merkle *** data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based ***,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of *** solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC ***,this paper uses PVC instead of the Merkle tree to store big data generated by *** can improve the efficiency of traditional VC in the process of commitment and ***,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of *** mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

关键词： Blockchain IIoT data storage cryptographic commitment

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