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Towards optimal path planning for sustainable data collection in real-time wireless sensor networks

International Journal of computers and Applications 2024年第5期46卷 301-309页

作者： Ranjan, Rajeev Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Patna India Department of Computer Science and Engineering Bakhtiyarpur College of Engineering Bakhtiyarpur India

data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojourning at rendezvous points (RPs) and gathering data there from nearby sensor nodes (SNs). However, determining the optimal number and location of RPs is a hard problem. Metaheuristic algorithms are well suited to solve such kind of problems. In this paper, we propose a tour planning scheme for the MS by a combined approach that uses K-means clustering and Particle Swarm Optimization algorithm. The proposed scheme first determines a set of near-optimal RPs and then uses them to schedule the tour of the MS. Herein, the energy consumption of the overall scheme is reduced due to the low computational cost of the proposed algorithm. At last, the proposed scheme is shown to outperform the comparative algorithms through simulation. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Motion planning

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Suitable Crop Prediction based on affecting parameters using Naïve Bayes Classification Machine Learning Technique

Suitable Crop Prediction based on affecting parameters using...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Banda, Latha Rai, Aarushi Kansal, Ankit Vashisth, Animesh Kumar Abes Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350323887

Agriculture is one of the most important occupations for the majority of people in the world's second largest populated country, India. However, due to a lack of education, accurate information, and India's rapid climate change, farmers frequently produce the same crops or the incorrect crops, regardless of whether they are appropriate given the soil, climate, and other elements in that particular place or not. This has caused an impact negatively on agricultural crop efficiency and output over the past few decades. Predicting the absolutely correct crops to grow based on the most important parameters for crop production would be of good help to farmers in choosing the right crops, improving crop quality, production and yield. In order to tackle the above problem, we have worked on a project using Naive Bayes Classification Machine Learning algorithm and Web Scraping. Our project consists of a friendly interactive chatbot with which the farmers can easily interact. The chatbot would make the farmer to provide some of the important parameters for crop production and would also fetch real time data through Web Scraping. The results of the crop prediction would be available to the farmer through that chatbot itself. By analyzing the parameters such as current weather conditions, location, soil, season and many more, our crop prediction system will be able to predict the right crops for the farmers to grow. This project will help to bridge the digital gap between farmers and right information and will help them to make smart choices about their crops to reduce the chances of crop failures. © 2023 IEEE.

关键词： Crops

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Deep Learning-Based Diabetes Detection and Real-Time Exercise Monitoring for Enhanced Healthcare 5

Deep Learning-Based Diabetes Detection and Real-Time Exercis...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sharma, Daksh Gaur, Madhvi Computer Science and Engineering Abes Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350383522

Diabetes is a major global health issue that requires early detection and appropriate *** it can't be treated we can make measure and address this issue by merging real-time fitness tracking and deep learning for diabetes detection. We create a robust diabetes detection system with high accuracy by carefully identifying features, using machine learning, and preprocessing data using the explainable boosting model. In parallel, we use Mediapipe to develop pose detection models for real-time evaluation of workout forms, allowing users to receive fast feedback. Exercise selection is also done keeping the injuries patient may have in mind. By integrating these elements, we hope to encourage a healthy lifestyle, empower people to control their health care, and advance early diabetes detection. © 2024 IEEE.

关键词： 3D pose detection Angle Heuristics Diabetes detection Explainable Boosting Machine Learning Mediapipe OpenCV Python Real-time

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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Integration of data science with the intelligent IoT(IIoT): Current challenges and future perspectives

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Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon University University of Electronic Science and Technology of China College of Information Science and Engineering Hohai University Department of Computer Science University of Salerno Graduate School of Informatics and Engineering The University of Electro-Communications

The Intelligent Internet of Things(IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence(AI) and machine learning, to make accurate decisions. data science is the science of dealing with data and its relationships through intelligent approaches. Most state-of-the-art research focuses independently on either data science or IIoT, rather than exploring their integration. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions.

关键词： data science Internet of things (IoT) Big data Communication systems Networks Security data science analytics

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Loan Status Prediction in the Banking Sector using Machine Learning 1

Loan Status Prediction in the Banking Sector using Machine L...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Bhatt, Kusumlata Sharma, Pranchal Verma, Megha Agarwal, Kadambri Abes Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350338027

There have been tremendous advancements in technology that have led to an increase in the needs of people. This, in turn, has led to more loan approval requests in the banking sector. Some attributes are considered to check loan status while selecting an applicant for loan approval. Banks are facing a serious challenge when it comes to evaluating loan applications and mitigating the risks associated with borrowers potentially defaulting on their loans. This process is particularly laborious for banks, as they must thoroughly verify each individual's loan eligibility. This paper proposes utilizing machine learning models with Ensemble Learning Techniques to determine the viability of granting individual loan requests. By using this approach, it is possible to enhance the accuracy with which suitable candidates are selected from an existing list. Therefore, this process can be used to address the aforementioned concerns surrounding loan approval processes. The model is helpful to both bank staff and applicants as it drastically reduces the time taken to sanction the loan. © 2023 IEEE.

关键词： Decision trees

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A computer Vision Based Framework for Posture Analysis and Performance Prediction in Athletes

A Computer Vision Based Framework for Posture Analysis and P...

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

作者： Bose, Divyansh Arora, Bhumika Srivastava, Akhilesh Kumar Garg, Puneet Computer Science and Engineering ABES Engineering College Ghaziabad India Department of CSE SAITM Gurugram India

ISBN: (纸本)9798350366846

In the previous several decades, there have been tremendous improvements in the analysis of human movement in the field of sports biomechanics and rehabilitation. Driven by the demand for faster and more complex ways to capture movement across a variety of situations, ranging from clinical gait analysis to video game animation, motion analysis techniques have undergone significant development. This research explores the previous work done on the sports performance analytics systems based on computer vision and how they affect the development of athletic performance but the prior work lacks in accuracy and versatility. Therefore, in this research, a framework has been proposed which uses 3D pose estimation technique and parameters like distance offset and inclination between neck and torso to analyse athletes' postures and motions during any sporting activity in order to predict the correctness of an athlete's posture. In this work, an analysis has been carried out with the help of machine learning models on the sports videos having a frame rate of 25 fps. To improve the robustness of the model, variations in blur, illumination, and clarity are incorporated into the dataset. This dataset has been trained on various machine learning models like Random Forest, MLP, and SVM, and procured the accuracy rates of 85.03\%, 71.42% , and 63.26%, and F1-scores of 84.99%, 71.57%, and 61.80%, respectively. Our results show how well these technologies work to provide coaches and athletes with performance information and real-time feedback. © 2024 IEEE.

关键词： Support vector machines

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Fictitious Merchandise Detection Using Blockchain 1

Fictitious Merchandise Detection Using Blockchain

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Gupta, Amogh Trivedi, Shivani Abes Engineering College Department of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350338027

Nowadays we are seeing that the merchandise (product) we are buying can have (20-25) % chances that it can be fictitious merchandise. And this also affects the sales of the company. This paper discusses an application called "Fictitious Merchandise Detection Using Blockchain"to avoid the situation of Fake Products. Blockchain is a decentralized distributed ledger, any updating cannot be a part of this ledger. Blockchain consists of blocks that have their own hash value produced by an algorithm known as SHA-512 or SHA-256. This is an independent ledger I.e., it does not depend upon third parties. So here we are using a QR code scanner that will scan the QR code provided by the user then, it will match that QR code with the blockchain database. If it is matched, a popup message will be generated "Concrete Merchandise"otherwise "Fictitious Merchandise"will be generated as a popup message. © 2023 IEEE.

关键词： Blockchain

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An Analysis of Deployment Challenges for Kubernetes: A NextGen Virtualization 1

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Jaglan, Nisha Upadhyay, Divya Department of Computer Science & Engineering ABES Engineering College Ghaziabad India

ISBN: (数字)9789819959747

ISBN: (纸本)9789819959730

Virtualization is a technology that allows us to create numerous simulated environments, which means instead of using an actual version of something a virtual copy is created. It makes things easier to manage. It was also a choice for optimizing server capacity. But with the introduction of Kubernetes, we get a highly efficient way to do the different tasks using containers. Containers came up as a less-weight replacement for Virtual Machines (VM) and have adequate microservice architecture backing such as the Internet of Things (IoT), and big data in various clouds. Though a lot of efforts have already been made to explore Kubernetes, a methodical unification of the current publications for evaluating its role in face-lifting Cloud is still not readily available. This paper analyzes the Kubernetes and its requirements to be deployed on any cloud environment. It was observed that the deploying Kubernetes on private cloud is not as simple as it is for public clouds. The performance measures are highly dependent on monotony that can greatly reduce the downtime. It was also examined that Kubernetes detects the failure as soon as services are live, and it doesn’t depend on the restoration time of the defective unit. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Virtual machine

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DeepGAN: Utilizing generative adversarial networks for improved deep learning

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International Journal of Knowledge-Based and Intelligent engineering Systems 2024年第4期28卷 732-748页

作者： V, Edward Naveen A, Jenefa T.M, Thiyagu A, Lincy Taurshia, Antony Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Department of Computer Science and Engineering National Engineering College India

In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper addresses the existing challenges in synthesizing high-quality data and harnessing the capabilities of GANs for improved deep learning outcomes. Unlike traditional approaches that heavily rely on manually engineered data augmentation techniques, our work introduces a novel framework that leverages DeepGANs to autonomously generate diverse and high-fidelity data. Our experiments encompass a diverse spectrum of datasets, including images, text, and time series data. In the context of image classification tasks, we conduct experiments on the widely recognized CIFAR-10 dataset, which consists of 50,000 image samples. Our results demonstrate the remarkable efficacy of DeepGANs in enhancing model performance across various data domains. Notably, in image classification using the CIFAR-10 dataset, our innovative approach achieves an impressive accuracy of 97.2%. This represents a substantial advancement beyond conventional CNN models, underscoring the profound impact of DeepGANs in the realm of deep learning. In summary, this research sheds light on DeepGANs as a fundamental component in the pursuit of enhanced deep learning performance. Our framework not only overcomes existing limitations but also heralds a new era of data augmentation, with generative adversarial networks leading the way. The attainment of an accuracy rate of 97.2% on CIFAR-10 serves as a compelling testament to the transformative potential of DeepGANs, solidifying their pivotal role in the future of deep learning. This promises the development of more robust, adaptive, and accurate models across a myriad of applications, marking a significant contribution to the field. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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