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Logistics Shipping Based Blockchain Using Smart Contracts 1

2nd EAI International Conference on Pervasive knowledge and collective intelligence on Web and Social Media, PerSOM 2023

作者： Prashanth, Mallellu Sai Karnati, Ramesh Velpuru, Muni Sekhar Venkateshwara Reddy, H. Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9783031660443

ISBN: (纸本)9783031660436

The logistics industry is a vital component of the global economy, but it still faces several challenges, including inefficient processes, lack of transparency, and high costs due to intermediaries. Blockchain technology has the potential to address these challenges by providing a decentralized and transparent platform that can automate and streamline logistics processes through smart contracts. This research paper presents a study on Logistics shipping based on blockchain using smart contracts in Solidity language. The paper discusses the challenges in the logistics industry and the potential of blockchain technology to solve them. It then presents a Smart Contract for Logistics shipping, including the required variables, a struct for shipments, a mapping to hold the shipments, events for shipment creation and delivery, and functions to create a new shipment, mark a shipment as delivered, and get the shipment details. The paper also discusses the benefits and limitations of the proposed solution and compares it with existing logistics systems. It concludes that the proposed solution can provide a secure, transparent, and efficient platform for logistics shipping that can eliminate intermediaries, reduce costs, and improve the overall logistics ecosystem. Overall, this research paper provides insights into the potential of blockchain technology for logistics shipping and presents a practical solution that can be implemented in real-world scenarios. It also highlights the need for further research and development to optimize the solution and address any potential challenges in its implementation. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Smart contract

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Comparative Analysis of Unified Payment Interface (UPI) in Tamil Nadu Using Machine Learning Techniques

Comparative Analysis of Unified Payment Interface (UPI) in T...

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

作者： Vanitha, C.N. Sathyamoorthy, Malathy Dhanaraj, Rajesh Kumar Sivakumar, P. Kongu Engineering College Department Of Computer Science And Engineering Erode India Kpr Institute Of Engineering And Technology Department Of It Coimbatore India Pune India Kongu Engineering College Department Of Computer Technology Erode India

ISBN: (纸本)9798350366846

Nowadays, the process of transferring money in physical mode is eradicated and moved to the next level based on online UPI payments. The proposed system will integrate machine learning techniques to identify the impact on UPI transactions. The transaction details are classified based on the patterns by utilizing advanced machine learning techniques and produce the accurate output. The user behavior, timestamp of the transaction and the size of the transaction are considered as important factors in the proposed system. The proposed approach deploys an effective framework for classification which will help the stakeholders to respond based on the constantly changing market needs. This approach is effectively used for predicting the implications of UPI transactions based on the changes in the market needs in an effective way. © 2024 IEEE.

关键词： E-learning

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Deep Learning-Driven Brain Tumor Segmentation from MRI Scans 3

Deep Learning-Driven Brain Tumor Segmentation from MRI Scans

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Dharun, S. Bhuvaneshwaran, P.S. Dineshkumar, R. Kaviarasu, K. K.S.Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798331509675

Brain tumors present significant challenges for medical imaging and diagnostics. Brain tumors from Magnetic Resonance Imaging (MRI) pictures need to be precisely segregated for treatment planning and monitoring. This paper proposes a novel hybrid design that combines the VGG19 and U-Net models to increase the accuracy of brain tumor segmentation. VGG19 is employed for efficient feature extraction, whereas U-Net uses skip connections to help retain spatial information. The hybrid design aims to generate more accurate segmentation results by improving both geographical context and feature representation. © 2025 IEEE.

关键词： Deep learning

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Hybrid Algorithm for Secure Routing in MANET Using Cryptographic Applications

Hybrid Algorithm for Secure Routing in MANET Using Cryptogra...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure Routing framework (CMNDSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%. © 2024 IEEE.

关键词： Encryption algorithms

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Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

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computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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Deep Belief Network Optimization Using PSOGAA Algorithm for Efficient Crop Recommendation 5th

Deep Belief Network Optimization Using PSOGAA Algorithm for ...

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5th International Conference on Cognitive Computing and Information Processing, CCIP 2023

作者： Madhuri, J. Indiramma, M. Nagarathna, N. Department of Computer Science and Engineering Bangalore Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9783031607240

The agricultural sector plays a crucial role in the development of an emerging economy, providing employment to a significant portion of the population. Selecting the most suitable crops based on specific weather and soil conditions is vital to maximize yield and ensure profitability for farmers, contributing to global food security. In this context, this paper introduces a crop recommendation model utilizing Deep Belief Networks (DBN), fine-tuned by a hybrid optimization approach combining Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Adam optimization. To enhance the efficiency of DBN-based crop recommendation model, this paper proposes the PSOGAA algorithm, a novel hybrid optimization algorithm designed to optimize the DBN’s network structure. Subsequently, genetic operators featuring self-adjusting crossover and mutation probabilities are applied to further refine the PSO and explore a global optimization solution. Ultimately, this global optimization solution is employed to construct the network structure of the crop recommendation model. Experimental results demonstrate the superior performance of the proposed algorithm compared to other DBN optimization techniques, showcasing an average classification accuracy improvement of at least 2.3%. This emphasizes the effectiveness of the PSOGAA algorithm as an efficient DBN optimization technique. Focusing on four major crops in India—rice, sugarcane, maize, and finger millet—the crop recommendation model’s outcomes are compared with those of the DBN model optimized using alternative algorithms. The experimental findings highlight that the proposed hybrid optimization algorithm surpasses other optimizers, exhibiting enhanced accuracy in crop recommendations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Crops

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Multi-Convolutional Neural Network Based Crowd Counting Using VGG-19 Architecture

Multi-Convolutional Neural Network Based Crowd Counting Usin...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： John Pradeep, J. Raja Arumugham, M. Santhi, S. Kalaiselvi, S. Department of Information Technology National Engineering College Kovilpatti India Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9789819746491

Crowd counting has raised a major issue in computer vision with its extensive range of applications, like crowd management, public protection, and city development. The proposed model involves developing a method that can precisely determine the total number of crowds from a raw photograph captured from a random viewpoint and crowd density. A density map-based estimation that uses Multi-convolution Neural Network that is built on the VGG-19 architecture is developed that are trained to categorize the visual inputs with varying densities. The deeper architecture allows VGG-19 to learn more complex features from the input image, which is especially beneficial in crowd counting tasks where the input image contains a huge number of objects. The proposed crowd counting technique combines the VGG-19 architecture with the MCNN model to achieve accurate results. By using filters of different sizes, the MCNN model can handle input images of varying sizes or resolutions, while also accounting for variations in individuals or head sizes caused by viewpoint effects. This allows to accurately estimate crowd density without needing to identify the perspective map of the input image, making it highly versatile and operative for crowd counting tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Decentralized Fog Architecture for Video Preprocessing in Cloud-based Video Surveillance as a Service 1

A Decentralized Fog Architecture for Video Preprocessing in ...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Divya, G. Swetha, K. Siva Shruthika, S. Guru Santhosh, P. Santhi, S. Kalaiselvi, S. National Engineering College Department of Information Technology Kovilpatti India National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

With the increasing demand for Cloud-based Video Surveillance as a Service (VSaaS), the efficient processing of vast amounts of video data poses significant challenges. The framework leverages Fog computing at the network edge, enabling real-time video analytics, object detection, and event recognition to be performed closer to the data source. By offloading intensive computational tasks from the central cloud to edge fog nodes, the framework reduces bandwidth consumption and minimizes processing latency. By integrating the state-of-the-art machine learning algorithms, automated video preprocessing with higher accuracy can be achieved. Moreover, the decentralized fog architecture enhances system responsiveness and ensures data privacy by processing sensitive information locally. The primary objective of this preprocessing is to reduce the volume of data transmitted to the cloud while preserving critical information for subsequent analysis. By leveraging fog computing, background subtraction, and intelligent video compression, a substantial reduction is achieved in data transmission, leading to more cost-effective and responsive video surveillance systems. © 2024 IEEE.

关键词： Network architecture

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EnsemBEP: A Robust Ensemble Model for Enhanced B-cell Epitope Prediction 8

EnsemBEP: A Robust Ensemble Model for Enhanced B-cell Epitop...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Karthiga, M. Shiv Navin, V.B. Cibin Kumar, R.M. Sathiya, B. Satheesh Kumar, A. Priyanga, M.A. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India Nandha Engineering College Department of Computer Science and Engineering Tamilnadu Erode India

ISBN: (纸本)9798350367904

B-cell epitope prediction has found its use in understanding B-cell recognition spots on a number of antigens that is vital in vaccine design and immunotherapy. These fields urgently need powerful prediction models, but the existing methods often fail to satisfy the requirement of both high sensitivity and high specificity. In response, this manuscript presents an ensemble model named EnsemBEP that combines Random Forest, Gradient Boosting, and XGBoost for improving the performance of B-cell epitope prediction. New model for epitope prediction, named EnsemBEP shows better performance in comparison with other models including EpiDope and BepiPred-2.0. The proposed EnsembEP model demonstrates superior performance compared to existing models such as EpiDope and BepiPred-2.0. Through rigorous evaluation, EnsemBEP achieved an AUC of 0.945, accuracy of 0.885, sensitivity of 0.823, specificity of 0.926, and an MCC of 0.743. All these findings point out that EnsemBEP yield better results than competing models thereby confirming that it is more accurate and reliable in predicting B-cell epitopes. The results provide evidence for the significance of ensemble learning methodologies in attaining higher predictive performance in immunoinformatic. © 2024 IEEE.

关键词： Prediction models

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Fabric Defect Detection Using Deep Learning 23th

Fabric Defect Detection Using Deep Learning

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

作者： Natesan, P. Thamilselvan, R. Gothai, E. Harini, M. Kasthuri, Chitrasena Nehru Deepankumar, G. Department of Information Technology Kongu Engineering College Perundurai India Department of Computer Science and Engineering Kongu Engineering College Perundurai India

ISBN: (纸本)9783031648359

The identification of fabric imperfections presents a considerable obstacle in the fabric manufacturing sector, owing to the complex configurations and diverse array of defects that may exist. Traditionally, visual inspections by human operators have been employed to identify such flaws, but this approach is both slow and prone to inaccuracies. In order to tackle these problems, this study investigates the benefits of employing Faster R-CNN and a range of YOLOv5 algorithms to accurately identify the fabric flaws. The deep learning object detection algorithms like YOLOv5s and Faster R-CNN have undergone rapid development and have been successfully applied in various industries, demonstrating strong performance. This paper proposes the use of the YOLOv5s algorithm, which exhibits superior accuracy in detecting fabric defects. Experimental results indicate that, compared to both YOLOv5s and Faster R-CNN, the YOLOv5s algorithm achieves higher accuracy in terms of mean Average Precision (mAP) and faster performance in terms of Frames Per Second (FPS). The FPS of YOLOv5s and faster RCNN is calculated and compared. From the results, it is inferred that YOLOv5s algorithm can accurately and rapidly identify the location of defects, making it suitable for real-time applications in the defect detection industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Defects

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